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Cognitive-Mirage-Hallucinations-in-LLMs

hallucination in large language models a review p align center img src pictures logo jpg width 500 p div align center awesome https awesome re badge svg license mit https img shields io badge license mit green svg https opensource org licenses mit https img shields io github last commit hongbinye cognitive mirage hallucinations in llms color green https img shields io badge prs welcome red div uncritical trust in llms can give rise to a phenomenon cognitive mirage leading to misguided decision making and a cascade of unintended consequences to effectively control the risk of hallucinations we summarize recent progress in hallucination theories and solutions in this paper we propose to organize relevant work by a comprehensive survey bell news bell we have released a new survey paper cognitive mirage a review of hallucinations in large language models https arxiv org abs 2309 06794 based on this repository with a perspective of hallucinations in llms we are looking forward to any comments or discussions on this topic introduction as large language models continue to develop in the field of ai text generation systems are susceptible to a worrisome phenomenon known as hallucination in this study we summarize recent compelling insights into hallucinations in llms we present a novel taxonomy of hallucinations from various text generation tasks thus provide theoretical insights detection methods and improvement approaches based on this future research directions are proposed our contribution are threefold 1 we provide a detailed and complete taxonomy for hallucinations appearing in text generation tasks 2 we provide theoretical analyses of hallucinations in llms and provide existing detection and improvement methods 3 we propose several research directions that can be developed in the future as hallucinations garner significant attention from the community we will maintain updates on relevant research progress a timeline of llms llm name title authors publication date t5 https arxiv org abs 1910 10683 exploring the limits of transfer learning with a unified text to text transformer colin raffel noam shazeer adam roberts 2019 10 gpt 3 https proceedings neurips cc paper 2020 hash 1457c0d6bfcb4967418bfb8ac142f64a abstract html language models are few shot learners tom b brown benjamin mann nick ryder 2020 12 mt5 https doi org 10 18653 v1 2021 naacl main 41 mt5 a massively multilingual pre trained text to text transformer linting xue noah constant adam roberts 2021 3 codex https arxiv org abs 2107 03374 evaluating large language models trained on code mark chen jerry tworek heewoo jun 2021 7 flan https openreview net forum id gezrgcozdqr finetuned language models are zero shot learners jason wei maarten bosma vincent y zhao 2021 9 webgpt https arxiv org abs 2112 09332 webgpt browser assisted question answering with human feedback reiichiro nakano jacob hilton suchir balaji 2021 12 instructgpt https proceedings neurips cc paper files paper 2022 hash b1efde53be364a73914f58805a001731 abstract conference html training language models to follow instructions with human feedback long ouyang jeffrey wu xu jiang 2022 3 codegen https arxiv org abs 2203 13474 codegen an open large language model for code with multi turn program synthesis erik nijkamp bo pang hiroaki hayashi 2022 3 claude https arxiv org abs 2204 05862 training a helpful and harmless assistant with reinforcement learning from human feedback yuntao bai andy jones kamal ndousse 2022 4 palm https proceedings neurips cc paper files paper 2022 hash b1efde53be364a73914f58805a001731 abstract conference html palm scaling language modeling with pathways aakanksha chowdhery sharan narang jacob devlin 2022 4 opt https arxiv org abs 2205 01068 opt open pre trained transformer language models susan zhang stephen roller naman goyal 2022 5 super naturalinstructions https doi org 10 18653 v1 2022 emnlp main 340 esuper naturalinstructions generalization via declarative instructions on 1600 nlp tasks yizhong wang swaroop mishra pegah alipoormolabashi yeganeh kordi 2022 9 glm https arxiv org abs 2210 02414 glm 130b an open bilingual pre trained model aohan zeng xiao liu zhengxiao du 2022 10 bloom https doi org 10 48550 arxiv 2211 05100 bloom a 176b parameter open access multilingual language model teven le scao angela fan christopher akiki 2022 11 llama https arxiv org abs 2302 13971 llama open and efficient foundation language models hugo touvron thibaut lavril gautier izacard 2023 2 alpaca https crfm stanford edu 2023 03 13 alpaca html alpaca a strong replicable instruction following model rohan taori ishaan gulrajani tianyi zhang 2023 3 gpt 4 https arxiv org abs 2303 08774v2 gpt 4 technical report openai 2023 3 wizardlm https doi org 10 48550 arxiv 2304 12244 wizardlm empowering large language models to follow complex instructions can xu qingfeng sun kai zheng 2023 4 vicuna https lmsys org blog 2023 03 30 vicuna vicuna an open source chatbot impressing gpt 4 with 90 chatgpt quality the vicuna team 2023 5 chatglm https chatglm cn blog chatglm wisdom and clear speech team 2023 6 llama2 https arxiv org abs 2307 09288 llama 2 open foundation and fine tuned chat models hugo touvron louis martin kevin stone 2023 7 definition of hallucination truthful ai developing and governing ai that does not lie 2021 10 owain evans owen cotton barratt lukas finnveden paper https arxiv org abs 2110 06674 survey of hallucination in natural language generation 2022 2 ziwei ji nayeon lee rita frieske paper https dblp org search q survey 20of 20hallucination 20in 20natural 20language 20generation context faithful prompting for large language models 2023 3 wenxuan zhou sheng zhang hoifung poon paper https arxiv org abs 2303 11315 do language models know when they re hallucinating references 2023 5 ayush agrawal lester mackey adam tauman kalai paper https doi org 10 48550 arxiv 2305 18248 mechanism analysis data attribution data distributional properties drive emergent in context learning in transformers 2022 5 stephanie c y chan adam santoro andrew k lampinen paper http papers nips cc paper files paper 2022 hash 77c6ccacfd9962e2307fc64680fc5ace abstract conference html towards tracing factual knowledge in language models back to the training data 2022 5 ekin aky rek tolga bolukbasi frederick liu paper https arxiv org abs 2205 11482 a multitask multilingual multimodal evaluation of chatgpt on reasoning hallucination and interactivity 2023 2 yejin bang samuel cahyawijaya nayeon lee paper https doi org 10 48550 arxiv 2302 04023 hallucinations in large multilingual translation models 2023 3 nuno miguel guerreiro duarte m alves jonas waldendorf paper https doi org 10 48550 arxiv 2303 16104 visual instruction tuning 2023 4 haotian liu chunyuan li qingyang wu paper https doi org 10 48550 arxiv 2304 08485 evaluating object hallucination in large vision language models 2023 5 yifan li yifan du kun zhou paper https arxiv org abs 2305 10355 sources of hallucination by large language models on inference tasks 2023 5 nick mckenna tianyi li liang cheng paper https doi org 10 48550 arxiv 2305 14552 automatic evaluation of attribution by large language models 2023 5 xiang yue boshi wang kai zhang paper https arxiv org abs 2305 06311 mitigating hallucination in large multi modal models via robust instruction tuning 2023 6 fuxiao liu kevin lin linjie li paper https arxiv org abs 2306 14565 knowledge gap a survey of knowledge enhanced text generation 2022 1 wenhao yu chenguang zhu zaitang li paper https doi org 10 1145 3512467 attributed text generation via post hoc research and revision 2022 10 luyu gao zhuyun dai panupong pasupat paper https doi org 10 48550 arxiv 2210 08726 artificial hallucinations in chatgpt implications in scientific writing 2023 2 hussam alkaissi samy i mcfarlane paper https www ncbi nlm nih gov pmc articles pmc9939079 selfcheckgpt zero resource black box hallucination detection for generative large language models 2023 3 potsawee manakul adian liusie mark j f gales paper https doi org 10 48550 arxiv 2303 08896 why does chatgpt fall short in answering questions faithfully 2023 4 shen zheng jie huang kevin chen chuan chang paper https arxiv org abs 2304 10513 zero shot faithful factual error correction 2023 5 kung hsiang huang hou pong chan heng ji paper https aclanthology org 2023 acl long 311 mitigating language model hallucination with interactive question knowledge alignment 2023 5 shuo zhang liangming pan junzhou zhao paper https arxiv org abs 2305 13669 adaptive chameleon or stubborn sloth unraveling the behavior of large language models in knowledge clashes 2023 5 jian xie kai zhang jiangjie chen paper https arxiv org abs 2305 13300 evaluating generative models for graph to text generation 2023 7 huzhou yuan michael f rber paper https doi org 10 48550 arxiv 2307 14712 overthinking the truth understanding how language models process false demonstrations 2023 7 danny halawi jean stanislas denain jacob steinhardt paper https arxiv org abs 2307 09476 optimum formulation improved natural language generation via loss truncation 2020 5 daniel kang tatsunori hashimoto paper https doi org 10 18653 v1 2020 acl main 66 the curious case of hallucinations in neural machine translation 2021 4 vikas raunak arul menezes marcin junczys dowmunt paper https doi org 10 18653 v1 2021 naacl main 92 optimal transport for unsupervised hallucination detection in neural machine translation 2022 12 nuno miguel guerreiro pierre colombo pablo piantanida paper https doi org 10 48550 arxiv 2212 09631 elastic weight removal for faithful and abstractive dialogue generation 2023 3 nico daheim nouha dziri mrinmaya sachan paper https doi org 10 48550 arxiv 2303 17574 histalign improving context dependency in language generation by aligning with history 2023 5 david wan shiyue zhang mohit bansal paper https doi org 10 48550 arxiv 2305 04782 how language model hallucinations can snowball 2023 5 muru zhang ofir press william merrill paper https doi org 10 48550 arxiv 2305 13534 improving language models via plug and play retrieval feedback 2023 5 wenhao yu zhihan zhang zhenwen liang paper https doi org 10 48550 arxiv 2305 14002 teaching language models to hallucinate less with synthetic tasks 2023 10 erik jones hamid palangi clarisse sim es paper https arxiv org abs 2310 06827 taxonomy of llms hallucination in nlp tasks machine translation unsupervised cross lingual representation learning at scale 2020 7 alexis conneau kartikay khandelwal naman goyal paper https doi org 10 18653 v1 2020 acl main 747 the curious case of hallucinations in neural machine translation 2021 4 vikas raunak arul menezes marcin junczys dowmunt paper https doi org 10 18653 v1 2021 naacl main 92 overcoming catastrophic forgetting in zero shot cross lingual generation 2022 5 tu vu aditya barua brian lester paper https doi org 10 18653 v1 2022 emnlp main 630 looking for a needle in a haystack a comprehensive study of hallucinations in neural machine translation 2022 8 nuno miguel guerreiro elena voita andr f t martins paper https aclanthology org 2023 eacl main 75 prompting palm for translation assessing strategies and performance 2022 11 david vilar markus freitag colin cherry paper https arxiv org abs 2211 09102 the unreasonable effectiveness of few shot learning for machine translation 2023 2 xavier garcia yamini bansal colin cherry paper https arxiv org abs 2302 01398 how good are gpt models at machine translation a comprehensive evaluation 2023 2 amr hendy mohamed abdelrehim amr sharaf paper https arxiv org abs 2302 09210 hallucinations in large multilingual translation models 2023 3 nuno miguel guerreiro duarte m alves jonas waldendorf paper https doi org 10 48550 arxiv 2303 16104 investigating the translation performance of a large multilingual language model the case of bloom 2023 3 rachel bawden fran ois yvon paper https arxiv org abs 2303 01911 halomi a manually annotated benchmark for multilingual hallucination and omission detection in machine translation 2023 5 david dale elena voita janice lam prangthip hansanti paper https arxiv org abs 2305 11746 mmt5 modular multilingual pre training solves source language hallucinations 2023 5 jonas pfeiffer francesco piccinno massimo nicosia paper https doi org 10 48550 arxiv 2305 14224 question and answer hurdles to progress in long form question answering 2021 6 kalpesh krishna aurko roy mohit iyyer paper https aclanthology org 2021 naacl main 393 entity based knowledge conflicts in question answering 2021 9 shayne longpre kartik perisetla anthony chen paper https aclanthology org 2021 emnlp main 565 truthfulqa measuring how models mimic human falsehoods 2022 5 stephanie lin jacob hilton owain evans paper https aclanthology org 2022 acl long 229 check your facts and try again improving large language models with external knowledge and automated feedback 2023 2 baolin peng michel galley pengcheng he paper https arxiv org abs 2302 12813 why does chatgpt fall short in answering questions faithfully 2023 4 shen zheng jie huang kevin chen chuan chang paper https doi org 10 48550 arxiv 2304 10513 evaluating correctness and faithfulness of instruction following models for question answering 2023 7 vaibhav adlakha parishad behnamghader xing han lu paper https arxiv org abs 2307 16877 med halt medical domain hallucination test for large language models 2023 7 logesh kumar umapathi ankit pal malaikannan sankarasubbu paper https arxiv org abs 2307 15343 dialog system on the origin of hallucinations in conversational models is it the datasets or the models 2022 7 nouha dziri sivan milton mo yu paper https aclanthology org 2022 naacl main 387 contrastive learning reduces hallucination in conversations 2022 12 weiwei sun zhengliang shi shen gao paper https ojs aaai org index php aaai article view 26596 diving deep into modes of fact hallucinations in dialogue systems 2022 12 souvik das sougata saha rohini k srihari paper https doi org 10 18653 v1 2022 findings emnlp 48 elastic weight removal for faithful and abstractive dialogue generation 2023 3 nico daheim nouha dziri mrinmaya sachan paper https doi org 10 48550 arxiv 2303 17574 summarization system hallucinated but factual inspecting the factuality of hallucinations in abstractive summarization 2022 5 meng cao yue dong jackie chi kit cheung paper https doi org 10 18653 v1 2022 acl long 236 evaluating the factual consistency of large language models 2022 11 derek tam anisha mascarenhas shiyue zhang paper https doi org 10 18653 v1 2023 findings acl 322 why is this misleading detecting news headline hallucinations with explanations 2023 2 jiaming shen jialu liu dan finnie paper https doi org 10 1145 3543507 3583375 detecting and mitigating hallucinations in multilingual summarisation 2023 5 yifu qiu yftah ziser anna korhonen paper https arxiv org abs 2305 13632 llms as factual reasoners insights from existing benchmarks and beyond 2023 5 philippe laban wojciech kry ci ski divyansh agarwal paper https arxiv org abs 2305 14540 evaluating factual consistency of texts with semantic role labeling 2023 5 jing fan dennis aumiller michael gertz paper https arxiv org abs 2305 13309 summarization is almost dead 2023 9 xiao pu mingqi gao xiaojun wan paper https arxiv org abs 2309 09558 knowledge graphs with llms gpt ner named entity recognition via large language models 2023 4 shuhe wang xiaofei sun xiaoya li paper https arxiv org abs 2304 10428 llms for knowledge graph construction and reasoning recent capabilities and future opportunities 2023 5 yuqi zhu xiaohan wang jing chen paper https arxiv org abs 2305 13168 kola carefully benchmarking world knowledge of large language models 2023 6 jifan yu xiaozhi wang shangqing tu paper https arxiv org abs 2306 09296 evaluating generative models for graph to text generation 2023 7 shuzhou yuan michael f rber paper https doi org 10 48550 arxiv 2307 14712 text2kgbench a benchmark for ontology driven knowledge graph generation from text 2023 8 nandana mihindukulasooriya sanju tiwari carlos f enguix paper https arxiv org abs 2308 02357 cross modal system let there be a clock on the beach reducing object hallucination in image captioning 2021 10 ali furkan biten llu s g mez dimosthenis karatzas paper https doi org 10 1109 wacv51458 2022 00253 simple token level confidence improves caption correctness 2023 5 suzanne petryk spencer whitehead joseph e gonzalez paper https doi org 10 48550 arxiv 2305 07021 evaluating object hallucination in large vision language models 2023 5 yifan li yifan du kun zhou jinpeng wang paper https arxiv org abs 2305 10355 album storytelling with iterative story aware captioning and large language models 2023 5 munan ning yujia xie dongdong chen paper https doi org 10 48550 arxiv 2305 12943 mitigating hallucination in large multi modal models via robust instruction tuning 2023 6 fuxiao liu kevin lin linjie li paper https arxiv org abs 2306 14565 fact checking of ai generated reports 2023 7 razi mahmood ge wang mannudeep kalra paper https arxiv org abs 2307 14634 others the scope of chatgpt in software engineering a thorough investigation 2023 5 wei ma shangqing liu wenhan wang paper https arxiv org abs 2305 12138 generating benchmarks for factuality evaluation of language models 2023 7 dor muhlgay ori ram inbal magar paper https arxiv org abs 2307 06908 hallucination detection inference classifier evaluating the factual consistency of large language models through summarization 2022 11 derek tam anisha mascarenhas shiyue zhang paper https doi org 10 18653 v1 2023 findings acl 322 why is this misleading detecting news headline hallucinations with explanations 2023 2 jiaming shen jialu liu daniel finnie paper https doi org 10 1145 3543507 3583375 halueval a large scale hallucination evaluation benchmark for large language models 2023 5 junyi li xiaoxue cheng wayne xin zhao paper https doi org 10 48550 arxiv 2305 11747 mitigating hallucination in large multi modal models via robust instruction tuning 2023 6 fuxiao liu kevin lin linjie li paper https arxiv org abs 2306 14565 fact checking of ai generated reports 2023 7 razi mahmood ge wang mannudeep k kalra paper https doi org 10 48550 arxiv 2307 14634 chain of natural language inference for reducing large language model ungrounded hallucinations 2023 10 deren lei yaxi li mengya hu paper https arxiv org abs 2310 03951 uncertainty measure bartscore evaluating generated text as text generation 2021 6 weizhe yuan graham neubig pengfei liu paper https proceedings neurips cc paper 2021 hash e4d2b6e6fdeca3e60e0f1a62fee3d9dd abstract html contrastive learning reduces hallucination in conversations 2022 12 weiwei sun zhengliang shi shen gao paper https ojs aaai org index php aaai article view 26596 knowledge of knowledge exploring known unknowns uncertainty with large language models 2023 5 alfonso amayuelas liangming pan wenhu chen paper https doi org 10 48550 arxiv 2305 13712 methods for measuring updating and visualizing factual beliefs in language models 2023 5 peter hase mona diab asli celikyilmaz paper https aclanthology org 2023 eacl main 199 measuring and modifying factual knowledge in large language models 2023 6 pouya pezeshkpour paper https arxiv org abs 2306 06264 llm calibration and automatic hallucination detection via pareto optimal self supervision 2023 6 theodore zhao mu wei j samuel preston paper https arxiv org abs 2306 16564 a stitch in time saves nine detecting and mitigating hallucinations of llms by validating low confidence generation 2023 7 neeraj varshney wenlin yao hongming zhang paper https arxiv org abs 2307 03987 self evaluation language models mostly know what they know 2022 7 saurav kadavath tom conerly amanda askell paper https doi org 10 48550 arxiv 2207 05221 selfcheckgpt zero resource black box hallucination detection for generative large language models 2023 3 potsawee manakul adian liusie mark j f gales paper https doi org 10 48550 arxiv 2303 08896 do language models know when they re hallucinating references 2023 5 ayush agrawal lester mackey adam tauman kalai paper https doi org 10 48550 arxiv 2305 18248 evaluating object hallucination in large vision language models 2023 5 yifan li yifan du kun zhou jinpeng wang paper https doi org 10 48550 arxiv 2305 10355 self checker plug and play modules for fact checking with large language models 2023 5 miaoran li baolin peng zhu zhang paper https arxiv org abs 2305 14623 lm vs lm detecting factual errors via cross examination 2023 5 roi cohen may hamri mor geva paper https arxiv org abs 2305 13281 self contradictory hallucinations of large language models evaluation detection and mitigation 2023 5 niels m ndler jingxuan he slobodan jenko paper https arxiv org abs 2305 15852 a new benchmark and reverse validation method for passage level hallucination detection 2023 10 shiping yang renliang sun xiaojun wan paper https arxiv org abs 2310 06498 evidence retrieval factscore fine grained atomic evaluation of factual precision in long form text generation 2023 5 sewon min kalpesh krishna xinxi lyu paper https arxiv org abs 2305 14251 complex claim verification with evidence retrieved in the wild 2023 5 jifan chen grace kim aniruddh sriram paper https doi org 10 48550 arxiv 2305 11859 retrieving supporting evidence for llms generated answers 2023 6 siqing huo negar arabzadeh charles l a clarke paper https doi org 10 48550 arxiv 2306 13781 factool factuality detection in generative ai a tool augmented framework for multi task and multi domain scenarios 2023 7 i chun chern steffi chern shiqi chen paper https arxiv org abs 2307 13528 hallucination correction parameter enhancement factuality enhanced language models for open ended text generation 2022 6 nayeon lee wei ping peng xu paper http papers nips cc paper files paper 2022 hash df438caa36714f69277daa92d608dd63 abstract conference html contrastive learning reduces hallucination in conversations 2022 12 weiwei sun zhengliang shi shen gao paper https ojs aaai org index php aaai article view 26596 editing models with task arithmetic 2023 2 gabriel ilharco marco tulio ribeiro mitchell wortsman paper https openreview net forum id 6t0kwf8 jrj elastic weight removal for faithful and abstractive dialogue generation 2023 3 nico daheim nouha dziri mrinmaya sachan paper https doi org 10 48550 arxiv 2303 17574 histalign improving context dependency in language generation by aligning with history 2023 5 david wan shiyue zhang mohit bansal paper https doi org 10 48550 arxiv 2305 04782 mmt5 modular multilingual pre training solves source language hallucinations 2023 5 jonas pfeiffer francesco piccinno massimo nicosia paper https arxiv org abs 2305 14224 trusting your evidence hallucinate less with context aware decoding 2023 5 weijia shi xiaochuang han mike lewis paper https doi org 10 48550 arxiv 2305 14739 purr efficiently editing language model hallucinations by denoising language model corruptions 2023 5 anthony chen panupong pasupat sameer singh paper https arxiv org abs 2305 14908 augmented large language models with parametric knowledge guiding 2023 5 ziyang luo can xu pu zhao xiubo geng paper https arxiv org abs 2305 04757 inference time intervention eliciting truthful answers from a language model 2023 6 kenneth li oam patel fernanda vi gas paper https arxiv org abs 2306 03341 trac trustworthy retrieval augmented chatbot 2023 7 shuo li sangdon park insup lee paper https doi org 10 48550 arxiv 2307 04642 easyedit an easy to use knowledge editing framework for large language models 2023 8 peng wang ningyu zhang xin xie paper https arxiv org abs 2308 07269 dola decoding by contrasting layers improves factuality in large language models 2023 9 yung sung chuang yujia xie hongyin luo paper https arxiv org abs 2309 03883 post hoc attribution and edit technology neural path hunter reducing hallucination in dialogue systems via path grounding 2021 4 nouha dziri andrea madotto osmar za ane paper https doi org 10 18653 v1 2021 emnlp main 168 chain of thought prompting elicits reasoning in large language models 2022 1 jason wei xuezhi wang dale schuurmans maarten bosma paper http papers nips cc paper files paper 2022 hash 9d5609613524ecf4f15af0f7b31abca4 abstract conference html teaching language models to support answers with verified quotes 2022 3 jacob menick maja trebacz vladimir mikulik paper https doi org 10 48550 arxiv 2203 11147 orca interpreting prompted language models via locating supporting data evidence in the ocean of pretraining data 2022 5 xiaochuang han yulia tsvetkov paper https doi org 10 48550 arxiv 2205 12600 large language models are zero shot reasoners 2022 5 takeshi kojima shixiang shane gu machel reid paper http papers nips cc paper files paper 2022 hash 8bb0d291acd4acf06ef112099c16f326 abstract conference html rethinking with retrieval faithful large language model inference 2023 1 hangfeng he hongming zhang dan roth paper https doi org 10 48550 arxiv 2301 00303 trak attributing model behavior at scale 2023 3 sung min park kristian georgiev andrew ilyas paper https proceedings mlr press v202 park23c html data portraits recording foundation model training data 2023 3 marc marone benjamin van durme paper https doi org 10 48550 arxiv 2303 03919 self refine iterative refinement with self feedback 2023 3 aman madaan niket tandon prakhar gupta paper https doi org 10 48550 arxiv 2303 17651 reflexion an autonomous agent with dynamic memory and self reflection 2023 3 noah shinn beck labash ashwin gopinath paper https doi org 10 48550 arxiv 2303 11366 according to prompting language models improves quoting from pre training data 2023 5 orion weller marc marone nathaniel weir paper https doi org 10 48550 arxiv 2305 13252 verify and edit a knowledge enhanced chain of thought framework 2023 5 ruochen zhao xingxuan li shafiq joty paper https arxiv org abs 2305 03268 mitigating hallucination in large multi modal models via robust instruction tuning 2023 9 shehzaad dhuliawala mojtaba komeili jing xu paper https arxiv org abs 2309 11495 chain of natural language inference for reducing large language model ungrounded hallucinations 2023 10 deren lei yaxi li mengya hu paper https arxiv org abs 2310 03951 utilizing programming languages pal program aided language models 2022 11 luyu gao aman madaan shuyan zhou paper https proceedings mlr press v202 gao23f html program of thoughts prompting disentangling computation from reasoning for numerical reasoning tasks 2022 11 wenhu chen xueguang ma xinyi wang paper https doi org 10 48550 arxiv 2211 12588 teaching algorithmic reasoning via in context learning 2022 11 hattie zhou azade nova hugo larochelle paper https doi org 10 48550 arxiv 2211 09066 solving challenging math word problems using gpt 4 code interpreter with code based self verification 2023 8 aojun zhou ke wang zimu lu paper https arxiv org abs 2308 07921 leverage external knowledge improving language models by retrieving from trillions of tokens 2021 12 sebastian borgeaud arthur mensch jordan hoffmann paper https proceedings mlr press v162 borgeaud22a html interleaving retrieval with chain of thought reasoning for knowledge intensive multi step questions 2022 12 harsh trivedi niranjan balasubramanian tushar khot paper https doi org 10 18653 v1 2023 acl long 557 when not to trust language models investigating effectiveness of parametric and non parametric memories 2022 12 alex mallen akari asai victor zhong paper https doi org 10 18653 v1 2023 acl long 546 check your facts and try again improving large language models with external knowledge and automated feedback 2023 2 baolin peng michel galley pengcheng he paper https arxiv org abs 2302 12813 in context retrieval augmented language models 2023 2 ori ram yoav levine itay dalmedigos paper https doi org 10 48550 arxiv 2302 00083 ctbl augmenting large language models for conversational tables 2023 3 anirudh s sundar larry heck paper https doi org 10 48550 arxiv 2303 12024 genegpt augmenting large language models with domain tools for improved access to biomedical information 2023 4 qiao jin yifan yang qingyu chen paper https arxiv org abs 2304 09667 active retrieval augmented generation 2023 5 zhengbao jiang frank f xu luyu gao paper https doi org 10 48550 arxiv 2305 06983 chain of knowledge a framework for grounding large language models with structured knowledge bases 2023 5 xingxuan li ruochen zhao yew ken chia paper https doi org 10 48550 arxiv 2305 13269 gorilla large language model connected with massive apis 2023 5 shishir g patil tianjun zhang xin wang paper https doi org 10 48550 arxiv 2305 15334 reta llm a retrieval augmented large language model toolkit 2023 6 jiongnan liu jiajie jin zihan wang paper https doi org 10 48550 arxiv 2306 05212 user controlled knowledge fusion in large language models balancing creativity and hallucination 2023 7 schen zhang paper https arxiv org abs 2307 16139 knowledgpt enhancing large language models with retrieval and storage access on knowledge bases 2023 8 xintao wang qianwen yang yongting qiu paper https doi org 10 48550 arxiv 2308 11761 assessment feedback learning to summarize with human feedback 2020 12 nisan stiennon long ouyang jeffrey wu paper https proceedings neurips cc paper 2020 hash 1f89885d556929e98d3ef9b86448f951 abstract html brio bringing order to abstractive summarization 2022 3 yixin liu pengfei liu dragomir r radev paper https doi org 10 18653 v1 2022 acl long 207 language models mostly know what they know 2022 7 saurav kadavath tom conerly amanda askell paper https doi org 10 48550 arxiv 2207 05221 check your facts and try again improving large language models with external knowledge and automated feedback 2023 2 baolin peng michel galley pengcheng he paper https doi org 10 48550 arxiv 2302 12813 chain of hindsight aligns language models with feedback 2023 2 hao liu carmelo sferrazza pieter abbeel paper https doi org 10 48550 arxiv 2302 02676 zero shot faithful factual error correction 2023 5 kung hsiang huang hou pong chan heng ji paper https doi org 10 18653 v1 2023 acl long 311 critic large language models can self correct with tool interactive critiquing 2023 5 zhibin gou zhihong shao yeyun gong paper https doi org 10 48550 arxiv 2305 11738 album storytelling with iterative story aware captioning and large language models 2023 5 munan ning yujia xie dongdong chen paper https doi org 10 48550 arxiv 2305 12943 how language model hallucinations can snowball 2023 5 muru zhang ofir press william merrill paper https doi org 10 48550 arxiv 2305 13534 mitigating language model hallucination with interactive question knowledge alignment 2023 5 shuo zhang liangming pan junzhou zhao paper https doi org 10 48550 arxiv 2305 13669 improving language models via plug and play retrieval feedback 2023 5 wenhao yu zhihan zhang zhenwen liang paper https doi org 10 48550 arxiv 2305 14002 pad program aided distillation specializes large models in reasoning 2023 5 xuekai zhu biqing qi kaiyan zhang paper https doi org 10 48550 arxiv 2305 13888 enabling large language models to generate text with citations 2023 5 tianyu gao howard yen jiatong yu paper https doi org 10 48550 arxiv 2305 14627 do language models know when they re hallucinating references 2023 5 ayush agrawal lester mackey adam tauman kalai paper https doi org 10 48550 arxiv 2305 18248 improving factuality of abstractive summarization via contrastive reward learning 2023 7 i chun chern zhiruo wang sanjan das paper https doi org 10 48550 arxiv 2307 04507 towards mitigating hallucination in large language models via self reflection 2023 10 ziwei ji tiezheng yu yan xu paper https arxiv org abs 2310 06271 mindset society hallucinations in large multilingual translation models 2023 3 nuno miguel guerreiro duarte m alves jonas waldendorf paper https doi org 10 48550 arxiv 2303 16104 improving factuality and reasoning in language models through multiagent debate 2023 5 yilun du shuang li antonio torralba paper https doi org 10 48550 arxiv 2305 14325 encouraging divergent thinking in large language models through multi agent debate 2023 5 tian liang zhiwei he wenxiang jiao paper https doi org 10 48550 arxiv 2305 19118 examining the inter consistency of large language models an in depth analysis via debate 2023 5 kai xiong xiao ding yixin cao paper https doi org 10 48550 arxiv 2305 11595 lm vs lm detecting factual errors via cross examination 2023 5 roi cohen may hamri mor geva paper https arxiv org abs 2305 13281 prd peer rank and discussion improve large language model based evaluations 2023 7 ruosen li teerth patel xinya du paper https doi org 10 48550 arxiv 2307 02762 unleashing cognitive synergy in large language models a task solving agent through multi persona self collaboration 2023 7 zhenhailong wang shaoguang mao wenshan wu paper https doi org 10 48550 arxiv 2307 05300 tips if you find this repository useful to your research or work it is really appreciate to star this repository

hallucinations large-language-models natural-language-generation survery dialog-system machine-translation question-and-answer assessment-feedback cross-modal-system leverage-external-knowledge mindset-society parameter-adaptation summarization-system

ai

theplaygrounds

div align center p align center img src img cover1 png p p align center a href https www linkedin com in iamnasef img src https img shields io badge linkedin 0077b5 style for the badge logo linkedin logocolor white a a href https twitter com iamnasef img src https img shields io badge twitter 1da1f2 style for the badge logo twitter logocolor white a a href https github com iamnasef img src https img shields io badge github 100000 style for the badge logo github logocolor white a a href https www youtube com channel ucx2qgl5gjp osk mz674eta img src https img shields io badge youtube ff0000 style for the badge logo youtube logocolor white a p br br the playground is my space where i experiment with different software engineering devsecops and cloud technologies by building projects this is why i created this project projects projects key features key features how to make use of the playground how to make use of the playground technologies used technologies used div projects details summary b projects 001 docker b summary 1 docker 001 snippets is an a collection of docker snippets details details summary b projects 002 kubernetes b summary 1 kubernetes 001 snippets is an a collection of kubernetes snippets details details summary b projects 003 ansible b summary 1 ansible 001 snippets is an a collection of ansible snippets details details summary b projects 004 terraform b summary 1 terraform 001 snippets is an a collection of terraform snippets details details summary b projects 005 prometheus b summary 1 prometheus 001 snippets is an a collection of prometheus snippets details details summary b projects 006 jenkins b summary 1 jenkins 001 snippets is an a collection of jenkins snippets details details summary b projects 007 css b summary 1 css 001 snippets is an a collection of css snippets details details summary b projects 008 javascript b summary 1 javascript 001 snippets is an a collection of javascript snippets details details summary b projects 009 react b summary 1 react 001 snippets is an a collection of react snippets details details summary b projects 010 nodejs b summary 1 nodejs 001 snippets is an a collection of nodejs snippets details details summary b projects 011 flask b summary 1 flask 001 snippets is an a collection of flask snippets details details summary b projects 012 flutter b summary 1 flutter 001 snippets is an a collection of flutter snippets details details summary b projects 013 aws b summary 1 aws 001 snippets is an a collection of aws snippets details details summary b projects 014 azure b summary 1 azure 001 snippets is an a collection of azure snippets details details summary b projects 015 gcp b summary 1 gcp 001 snippets is an a collection of gcp snippets details key features the playground has a wide range of projects in multiple technologies in multiple disciplines it has at least 10 projects for each technology you can use these projects in your learning process you can rebuild the idea from scratch with your improvements as resume project how to make use of the playground 1 read the project description and try recreating it from scratch 2 compare your code with the playground code 3 watch the videos embedded in each project which shows how to build it in a step by step manner 4 contact me if you have any questions technologies used this is the list of technologies used in the playground application description yaml https yaml org a human readable data serialization language docker https www docker com a set of platform as a service products that use os level virtualization to deliver software in packages called containers css https www w3 org style css overview en html a style sheet language used for describing the presentation of a document written in a markup language html https developer mozilla org en us docs web html a markup language for documents designed to be displayed in a web browser ansible https www ansible com a software provisioning configuration management and application deployment tool python https www python org a programming language that lets you work quickly and integrate systems more effectively flask https flask palletsprojects com en 2 1 x a popular extensible web microframework for building web applications with python nodejs https nodejs org en a javascript runtime built on chrome s v8 javascript engine javascript https www javascript com a programming language that is one of the core technologies of the world wide web flutter https flutter dev a software development kit created by google dart https www javascript com a programming language designed for client development terraform https www terraform io an open source infrastructure as code software tool created by hashicorp aws https aws amazon com an on demand cloud computing platforms and apis to individuals companies and governments on a metered pay as you go basis jenkins https www jenkins io an open source automation server it helps automate the parts of software development related to building testing and deploying facilitating continuous integration and continuous delivery groovy https groovy lang org a java syntax compatible object oriented programming language for the java platform kubernetes https kubernetes io an open source container orchestration system for automating software deployment scaling and management markdown guide https www markdownguide org a reference guide that explains how to use markdown php https www php net a general purpose scripting language geared toward web development mysql https www mysql com an open source relational database management system gcp https cloud google com a suite of cloud computing services that runs on the same infrastructure that google uses internally for its end user products such as google search gmail google drive and youtube deployment manager https cloud google com deployment manager docs an infrastructure deployment service that automates the creation and management of google cloud resources azure https azure microsoft com en us a cloud computing platform operated by microsoft for application management via around the world distributed data centers arm templates https learn microsoft com en us azure azure resource manager templates overview an infrastructure as code a concept where you define the infrastructure that needs to be deployed aws https aws amazon com a subsidiary of amazon that provides on demand cloud computing platforms and apis to individuals companies and governments on a metered pay as you go basis aws cloud formation https aws amazon com cloudformation an infrastructure as code iac service that allows you to easily model provision and manage aws and third party resources prometheus https prometheus io an open source monitoring system with a dimensional data model flexible query language efficient time series database and modern alerting approach promql https prometheus io docs prometheus latest querying basics a functional query language that lets the user select and aggregate time series data in real time

devops devsecops cloud-computing cloud-security software-engineering

cloud

llm-primer

llm primer a primer on large language models llm and chatgpt as of april 2023 a video recording for v2 1 slides assisted by ai https www youtube com watch v doto 0ihoui update 05 2023 a special side version other chatgpt primer 05 2023 guyu v1 pdf for https valleyrain org a new side project to brainstorm how to possibly train better chatbots than chatgpt https github com hululuzhu better chatbot than chatgpt v2 covers details summary intro building blocks capabilities summary lm and llm transformer how are llms trained llm decoding llm training in parallel llm capabilities advanced capabilities and insance capabilities details details summary core models players concepts toolings applications summary selected llms bert gpt family t5 glm llm players big companies institutes and startups llm concepts pretraining finetuning prompt tuning scaling laws prompt engineering prompt tuning soft prompt emergent abilities chain of thoughts cot least to most prompting hallucination retrieval llm rlhf for llm mixture of experts moe llm llm tooling huggingface tf hub torch nlp paddlenlp transformers lib colossal ai ray and nanogpt other toolings llm applications details details summary bonus deep dive into chatgpt summary chatgpt model evolvment research instructgpt overview possible next steps for chatgpt engineering discussion rough estimate to train server chatgpt my thoughts on technical challenges to reproduce chatgpt what less optimal choices google made related to chatgpt delayed google to release similar product fun facts chatgpt challenges final question will chatgpt become next iphone or next alexa or next clubhouse details v2 1 covers llm basics rl basics chatgpt societal impact slides v2 1 slides april 2023 https github com hululuzhu llm primer blob main llm primer v2 1 april 2023 llm rl chatgpt pdf appendix 6 page slides https github com hululuzhu llm primer blob main other quick summary embedding bert gpt pdf to explaing bert and gpt training and their embedding ouput and its size v2 slides jan 2023 https github com hululuzhu llm primer blob main llm primer v2 jan 2023 pdf old v1 slides july 2022 https github com hululuzhu fun paper sharing blob main slides llm primer v1 pdf additional notes draft notes other draft notes llm primer pdf to prepare v2 topics chinese only notes my personal opinion chatgpt other chinese only tech challanges reproduce chatgpt md google llm other chinese only thoughts on less optimal choices by google md chatgpt other chinese only crazy ideas surpass chatgpt md recommended quick readings agi llm https zhuanlan zhihu com p 597586623 yao fu how does gpt obtain its ability tracing emergent abilities of language models to their sources https yaofu notion site how does gpt obtain its ability tracing emergent abilities of language models to their sources b9a57ac0fcf74f30a1ab9e3e36fa1dc1 stephen wolfram what is chatgpt doing and why does it work https writings stephenwolfram com 2023 02 what is chatgpt doing and why does it work chatgpt gpt https www youtube com watch v e0aki2ggzng instructgpt https www youtube com watch v zfigawd1joq 120 references as of 01 30 2023 details summary click to expand summary 1706 03741 deep reinforcement learning from human preferences https arxiv org abs 1706 03741 1706 03762 attention is all you need https arxiv org abs 1706 03762 1810 04805 bert pre training of deep bidirectional transformers for language understanding https arxiv org abs 1810 04805 1904 00962 large batch optimization for deep learning training bert in 76 minutes https arxiv org abs 1904 00962 1907 11692 roberta a robustly optimized bert pretraining approach https arxiv org abs 1907 11692 1910 10683 exploring the limits of transfer learning with a unified text to text transformer https arxiv org abs 1910 10683 2001 08361 scaling laws for neural language models https arxiv org abs 2001 08361 2005 14165 language models are few shot learners https arxiv org abs 2005 14165 2103 00823 m6 a chinese multimodal pretrainer https arxiv org abs 2103 00823 2104 08691 the power of scale for parameter efficient prompt tuning https arxiv org abs 2104 08691 2104 09864 roformer enhanced transformer with rotary position embedding https arxiv org abs 2104 09864 2106 04554 a survey of transformers https arxiv org abs 2106 04554 2111 06377 masked autoencoders are scalable vision learners https arxiv org abs 2111 06377 2112 12731 ernie 3 0 titan exploring larger scale knowledge enhanced pre training for language understanding and generation https arxiv org abs 2112 12731 2201 08239 lamda language models for dialog applications https arxiv org abs 2201 08239 2201 11903 chain of thought prompting elicits reasoning in large language models https arxiv org abs 2201 11903 2203 15556 training compute optimal large language models https arxiv org abs 2203 15556 2204 05862 training a helpful and harmless assistant with reinforcement learning from human feedback https arxiv org abs 2204 05862 2205 01068 opt open pre trained transformer language models https arxiv org abs 2205 01068 2205 05198 reducing activation recomputation in large transformer models https arxiv org abs 2205 05198 2205 10625 least to most prompting enables complex reasoning in large language models https arxiv org abs 2205 10625 2205 11916 large language models are zero shot reasoners https arxiv org abs 2205 11916 2206 07682 emergent abilities of large language models https arxiv org abs 2206 07682 2207 01780 coderl mastering code generation through pretrained models and deep reinforcement learning https arxiv org abs 2207 01780 2208 03299 atlas few shot learning with retrieval augmented language models https arxiv org abs 2208 03299 2210 02414 glm 130b an open bilingual pre trained model https arxiv org abs 2210 02414 d gpt 3 the 4 600 000 language model r machinelearning https www reddit com r machinelearning comments h0jwoz d gpt3 the 4600000 language model alibaba cloud launches modelscope an open source model as a service maas platform that comes with hundreds of artificial intelligence ai models marktechpost https www marktechpost com 2022 11 21 alibaba cloud launches modelscope an open source model as a service maas platform that comes with hundreds of artificial intelligence ai models aligning language models to follow instructions https openai com blog instruction following alphago https www deepmind com research highlighted research alphago anthropic https www anthropic com bart denoising sequence to sequence pre training for natural language generation translation and comprehension facebook ai research https ai facebook com research publications bart denoising sequence to sequence pre training for natural language generation translation and comprehension better language models and their implications https openai com blog better language models bigscience https bigscience huggingface co blenderbot 3 an ai chatbot that improves through conversation meta https about fb com news 2022 08 blenderbot ai chatbot improves through conversation building safer dialogue agents https www deepmind com blog building safer dialogue agents chat gpt gpt https www youtube com watch v e0aki2ggzng chatgpt cheats triangle professors grapple with viral ai technology as semester starts https www newsobserver com news local article270952057 html chatgpt produces made up nonexistent references hacker news https news ycombinator com item id 33841672 chatgpt open ai s chatbot is spitting out biased sexist results bloomberg https www bloomberg com news newsletters 2022 12 08 chatgpt open ai s chatbot is spitting out biased sexist results chatgpt optimizing language models for dialogue https openai com blog chatgpt code for codet5 a new code aware pre trained encoder decoder model https github com salesforce codet5 colossal ai https colossalai org deepmind s alphacode ai writes code at a competitive level techcrunch https techcrunch com 2022 02 02 deepminds alphacode ai writes code at a competitive level democratizing access to large scale language models with opt 175b https ai facebook com blog democratizing access to large scale language models with opt 175b don t ban chatgpt in schools teach with it the new york times https www nytimes com 2023 01 12 technology chatgpt schools teachers html eleutherai https www eleuther ai eleutherai gpt neox 20b hugging face https huggingface co eleutherai gpt neox 20b exploring transfer learning with t5 the text to text transfer transformer google ai blog https ai googleblog com 2020 02 exploring transfer learning with t5 html galactica research model by meta https galactica org generative adversarial network wikipedia https en wikipedia org wiki generative adversarial network github f awesome chatgpt prompts this repo includes chatgpt prompt curation to use chatgpt better https github com f awesome chatgpt prompts github google big bench beyond the imitation game collaborative benchmark for measuring and extrapolating the capabilities of language models https github com google big bench github huggingface transformers transformers state of the art machine learning for pytorch tensorflow and jax https github com huggingface transformers github karpathy nanogpt the simplest fastest repository for training finetuning medium sized gpts https github com karpathy nanogpt github openai openai cookbook techniques to improve reliability https github com openai openai cookbook blob main techniques to improve reliability md github paddlepaddle paddlenlp https github com paddlepaddle paddlenlp github copilot your ai pair programmer https github com features copilot github jianlin su bojone https github com bojone github s ai coding assistant copilot launches voicebot ai https voicebot ai 2022 06 22 githubs ai coding assistant copilot launches glm 130b an open bilingual pre trained model https github com thudm glm 130b gluebenchmark leaderboard https gluebenchmark com leaderboard google ai introduces minerva a natural language processing nlp model that solves mathematical questions marktechpost https www marktechpost com 2022 07 04 google ai introduces minerva a natural language processing nlp model that solves mathematical questions google sidelines engineer who claims its a i is sentient the new york times https www nytimes com 2022 06 12 technology google chatbot ai blake lemoine html google s massive new language model can explain jokes https www datanami com 2022 04 22 googles massive new language model can explain jokes got it ai creates truth checker for chatgpt hallucinations venturebeat https venturebeat com ai got it ai creates truth checker for chatgpt hallucinations gpt 3 powers the next generation of apps https openai com blog gpt 3 apps gsm8k dataset papers with code https paperswithcode com dataset gsm8k how come gpt can seem so brilliant one minute and so breathtakingly dumb the next https garymarcus substack com p how come gpt can seem so brilliant how does gpt obtain its ability tracing emergent abilities of language models to their sources https yaofu notion site how does gpt obtain its ability tracing emergent abilities of language models to their sources b9a57ac0fcf74f30a1ab9e3e36fa1dc1 huawei noah s ark lab github https github com huawei noah pretrained language model tree master pangu ce b1 huggingface deploy hugging face models easily with amazon sagemaker https huggingface co blog deploy hugging face models easily with amazon sagemaker huggingface fine tune a pretrained model https huggingface co docs transformers training huggingface how to generate text using different decoding methods for language generation with transformers https huggingface co blog how to generate huggingface models https huggingface co docs transformers main classes model huggingface pipelines https huggingface co docs transformers main classes pipelines huggingface tokenizer https huggingface co docs transformers main classes tokenizer huggingface uploading models https huggingface co docs hub models uploading improving language models by retrieving from trillions of tokens https www deepmind com publications improving language models by retrieving from trillions of tokens improving language understanding by generative pre training https s3 us west 2 amazonaws com openai assets research covers language unsupervised language understanding paper pdf introducing flan more generalizable language models with instruction fine tuning https ai googleblog com 2021 10 introducing flan more generalizable html introducing pathways a next generation ai architecture https blog google technology ai introducing pathways next generation ai architecture jonathan hui how much do i like chatgpt https jonathan hui medium com how much do i like chatgpt 3d8a3216e137 lamda and the sentient ai trap wired https www wired com story lamda sentient ai bias google blake lemoine lamda our breakthrough conversation technology https blog google technology ai lamda language model ai2 blog https blog allenai org tagged language model large language models and where to use them part 1 https txt cohere ai llm use cases m6 by alibaba multimodality to multimodality multitask mega transformer https www infoq cn article xix9lekuulcxewc5iphf microsoft dumping ton of cash into chatgpt office infusion appleinsider https appleinsider com articles 23 01 10 microsoft dumping ton of cash into chatgpt office infusion microsoft set to integrate chatgpt with bing cdotrends https www cdotrends com story 17758 microsoft set integrate chatgpt bing minerva solving quantitative reasoning problems with language models google ai blog https ai googleblog com 2022 06 minerva solving quantitative reasoning html mosaic llms part 2 gpt 3 quality for 500k https www mosaicml com blog gpt 3 quality for 500k text the 20bottom 20line 3a 20it 20costs 10x 20less 20than 20people 20think nanogpt scaling laws ipynb at master https github com karpathy nanogpt blob master scaling laws ipynb new and improved content moderation tooling https openai com blog new and improved content moderation tooling new york city department of education bans chatgpt https www govtech com education k 12 new york city department of education bans chatgpt openai gpt f delivers sota performance in automated mathematical theorem proving synced https syncedreview com 2020 09 10 openai gpt f delivers sota performance in automated mathematical theorem proving openai begins piloting chatgpt professional a premium version of its viral chatbot techcrunch https techcrunch com 2023 01 11 openai begins piloting chatgpt professional a premium version of its viral chatbot openai codex https openai com blog openai codex openai just released the ai it said was too dangerous to share https futurism com the byte openai released ai dangerous share openai model index for researchers https beta openai com docs model index for researchers openai says its text generating algorithm gpt 2 is too dangerous to release https slate com technology 2019 02 openai gpt2 text generating algorithm ai dangerous html openai s new chatgpt bot 10 dangerous things it s capable of https www bleepingcomputer com news technology openais new chatgpt bot 10 dangerous things its capable of outrageously large neural networks the sparsely gated mixture of experts layer google research https research google pubs pub45929 pathways language model palm scaling to 540 billion parameters for breakthrough performance https ai googleblog com 2022 04 pathways language model palm scaling to html pytorch nlp https modelzoo co model pytorch nlp ray distributed computing anyscale https www anyscale com ray open source research stanford hai https hai stanford edu research researcher tells ai to write a paper about itself then submits it to academic journal https futurism com gpt3 academic paper salesforce s coderl achieves sota code generation results with strong zero shot transfer capabilities synced https syncedreview com 2022 07 07 salesforces coderl achieves sota code generation results with strong zero shot transfer capabilities solving some formal math olympiad problems https openai com blog formal math stable diffusion 2 1 a hugging face space by stabilityai https huggingface co spaces stabilityai stable diffusion superglue a stickier benchmark for general purpose language understanding systems https papers nips cc paper 2019 hash 4496bf24afe7fab6f046bf4923da8de6 abstract html techniques for training large neural networks https openai com blog techniques for training large neural networks temporary policy chatgpt is banned meta stack overflow https meta stackoverflow com questions 421831 temporary policy chatgpt is banned tensorflow hub https www tensorflow org hub the annotated transformer https nlp seas harvard edu 2018 04 03 attention html twitter goodside as of jan 2023 https twitter com goodside status 1598874674204618753 lang en twitter goodside as of jan 2023 https twitter com goodside status 1610482565106012160 twitter grady booch as of jan 2023 https twitter com grady booch status 1593033061423550464 twitter sama as of jan 2023 https twitter com sama status 1599671496636780546 lang en what is gpt 3 everything your business needs to know about openai s breakthrough ai language program zdnet https www zdnet com article what is gpt 3 everything business needs to know about openais breakthrough ai language program who ultimately owns content generated by chatgpt and other ai platforms https www forbes com sites joemckendrick 2022 12 21 who ultimately owns content generated by chatgpt and other ai platforms sh 50c960845423 why meta s latest large language model only survived three days online mit technology review https www technologyreview com 2022 11 18 1063487 meta large language model ai only survived three days gpt 3 science wu dao 2 0 china s answer to gpt 3 only better https analyticsindiamag com wu dao 2 0 chinas answer to gpt 3 only better zhuiyi technology https en zhuiyi ai details todo v2 1 reference list

bert chatgpt gpt machine-learning t5

ai

tech-recruitment

tech recruitment a public repository for sharing recruiting information related to vmly amp r s technology organization tech apprenticeships for information about vmly r s tech apprentice program see this youtube playlist https www youtube com playlist list plzqtqngxv8lqrfpawej3aki1botuagpz this content was originally created for launchcode but our technology apprenticeships are open to anyone to apply or for other questions see the faq techapprenticeprogram faq md

server

PIC18F_XC8_FreeRTOS

pic18f xc8 freertos a port of freertos for pic18f and xc8 motivation i needed a 5v solution there is a port for pic18f452 old and c18 old so i tried to port for pic18f and xc8 i only tested with pic18f27q43 but i believe other recent pic18f mcus are applicable because freertos code size is a bit large 64kb or more flash memory is favored if i needed a 3 3v solution maybe i chose stm32l i am not familiar with avr mcus by the way cooperative scheduler only because the stack pointer consists of two registers fsr1l and fsr1h calculations of the stack pointer are not atomic there is a critical section between modifying fsr1l and modifying fsr1h the following is an example picked up from lst file beginning of a function which has total 6 byte local variables 2454 004964 0e06 movlw 6 2455 004966 26e1 addwf fsr1l f c 2456 004968 0e00 movlw 0 2457 00496a 22e2 addwfc fsr1h f c freertos usually saves a task context on top of the stack if the preemptive scheduler is used and the tick timer interrupt occurs after addwf fsr1l f c but before addwfc fsr1h f c the stack pointer calculation is incomplete and then the task context could be saved on the bad location this is a very small window but cannot be ignored so i decided this port is for the cooperative scheduler only context switching time multi tasking is not suitable scope of pic18f mcus many instructions are needed to maintain context switching the portyield macro performs a context switch which gets about 650 instruction cycles when fosc 64mhz 16 instruction cycles per microsecond about 40 microseconds per context switching led flashing demo this repository contains a mplab x ide project of led flashing demo open your git cloned folder with mplab x ide demo written by me freertos copied from the freertos repository demo contains the led flashing demo flash c source contains the freertos kernel mcc generated files generated by the mcc mplab code configurator nbproject mplab x ide project folder portable written by me freertosconfig h copied from the freertos repository and modified main c generated by the mcc and modified makefile generated by the mcc pic18f xc8 freertos mc3 mcc config file mplab x ide v5 50 xc8 v2 32 pic18f q dfp 1 12 193 mplab code configurator 5 0 3 adjusting the compiler the xc8 tries to optimize aggressively if the xc8 considers one function is not called any time the function is not compiled and is ignored if the xc8 considers one pointer is not used any time the pointer is regarded as always null and is compiled in that way task processing functions are sometimes considered not called by the xc8 to prevent this i put dummy call of task processing function in pxportinitialisestack also i put dummy call of vportadjustcompiler in xportstartscheduler however sometimes the xc8 overkills you might need to examine lst and map files carefully if you face a strange behavior copyright and license some files are copied from the freertos repository and its license is applied some files are generated by the mplab x ide and the mplab code configurator and their license is applied other files are written by me and in public domain

os

AzureAD_Autologon_Brute

azuread autologon brute brute force attack tool for azure ad autologon https arstechnica com information technology 2021 09 new azure active directory password brute forcing flaw has no fix usage python3 azuread autologon brute py d intranet directory u users txt p password1 azuread autologon brute python3 azuread autologon brute py d intranet directory u users txt p password1 domain is intranet directory setting password as password1 reading users from file users txt azuread autologon brute 2021 09 30 nyxgeek trustedsec username not found wumpus intranet directory password1 valid username invalid password stephen falken intranet directory password1 username not found grand poobah intranet directory password1 username not found thewiz intranet directory password1 username not found bingobongo intranet directory password1 username not found administrator intranet directory password1 valid username invalid password david lightman intranet directory password1 username not found fakeuser123 intranet directory password1 valid username invalid password nyxgeek intranet directory password1 username not found jabberwocky intranet directory password1

pentest bruteforce azuread azure password password-spraying user-enumeration enumeration

server

aws-machine-learning-university-accelerated-nlp

logo data mlu logo png machine learning university accelerated natural language processing class this repository contains slides notebooks and datasets for the machine learning university mlu accelerated natural language processing class our mission is to make machine learning accessible to everyone we have courses available across many topics of machine learning and believe knowledge of ml can be a key enabler for success this class is designed to help you get started with natural language processing nlp learn widely used techniques and apply them on real world problems youtube watch all nlp class video recordings in this youtube playlist https www youtube com playlist list pl8p z6c4gcuwfaq8pt6pbylck4oprhxsw from our youtube channel https www youtube com channel uc12lqyqtqybxatys9aa7nuw playlists playlist https img youtube com vi 0fxkbegz uu 0 jpg https www youtube com playlist list pl8p z6c4gcuwfaq8pt6pbylck4oprhxsw course overview there are three lectures and one final project in this class course overview is below lecture 1 title studio lab introduction to ml intro to nlp and text processing open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture1 text process ipynb bag of words bow open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture1 bow ipynb k nearest neighbors knn open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture1 knn ipynb final project open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture1 final project ipynb lecture 2 title studio lab tree based models open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture2 tree models ipynb regression models linear regression open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture2 linear regression ipynb br logistic regression open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture2 logistic regression ipynb optimization regularization hyperparameter tuning aws ai ml services open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture2 sagemaker ipynb final project open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture2 final project ipynb lecture 3 title studio lab neural networks open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture3 neural networks ipynb word embeddings open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture3 word vectors ipynb recurrent neural networks rnn open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture3 recurrent neural networks ipynb transformers open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture3 neural networks ipynb final project open in studio lab https studiolab sagemaker aws studiolab svg https studiolab sagemaker aws import github aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture3 final project ipynb final project practice working with a real world nlp dataset for the final project final project dataset is in the data final project folder https github com aws samples aws machine learning university accelerated nlp tree master data final project for more details on the final project check out this notebook https github com aws samples aws machine learning university accelerated nlp blob master notebooks mla nlp lecture1 final project ipynb interactives visuals interested in visual interactive explanations of core machine learning concepts check out our mlu explain articles https mlu explain github io to learn at your own pace contribute if you would like to contribute to the project see contributing contributing md for more information license the license for this repository depends on the section data set for the course is being provided to you by permission of amazon and is subject to the terms of the amazon license and access https www amazon com gp help customer display html nodeid 201909000 you are expressly prohibited from copying modifying selling exporting or using this data set in any way other than for the purpose of completing this course the lecture slides are released under the cc by sa 4 0 license the code examples are released under the mit 0 license see each section s license file for details

machine-learning natural-language-processing deep-learning python gluon mxnet sklearn

ai

NLP_Recipes

nlp recipes natural language processing recipes is your handy problem solution reference for learning and implementing nlp solutions using python this repository is packed with thousands of code and approaches that help you to quick learn and implement the basic and advanced natural language processing techniques you will learn how to efficiently use a wide range of nlp packages and implement text classification identify parts of speech topic modeling text summarization text generation sentiment analysis and many more applications of nlp getting started 1 clone this repo 2 see chapter 1 extracting the data https github com shubhamchouksey nlp recipes tree master extract data in this chapter we are going to cover various sources of text data and ways to extract it which can act as information or insights for businesses text data collection using apis reading pdf file in python reading word document reading json object reading html page and html parsing regular expressions string handling web scraping 3 see chapter 2 extracting and processing text data https github com shubhamchouksey nlp recipes tree master preprocessing in this chapter we are going to cover various methods and techniques to preprocess the text data along with exploratory data analysis lowercasing punctuation removal stop words removal text standardization spelling correction tokenization lemmatization exploratory data analysis end to end processing pipeline 4 see chapter 3 converting text to features https github com shubhamchouksey nlp recipes tree master converting text to features in this chapter we are going to cover basic to advanced feature engineering text to features methods one hot encoding count vectorizer n grams co occurence matrix hash vectorizer term frequency inverse document frequency tf idf word embedding implementing fasttext 5 see chapter 4 advanced natural language processing https github com shubhamchouksey nlp recipes tree master advanced natural language processing in this chapter we are going to cover various advanced nlp techniques and leverage machine learning algorithm to extract information from text data as well as some of the advanced nlp applications with the solution approach and implementation noun phrase extraction text similarity parts of speech tagging information extraction ner entity recognition topic modeling text classification sentiment analysis word sense disambiguation speech recognition and speech to text text to speech language detection and translation reference natural language processing recipes https www apress com 9781484242667 by akshay kulkarni and adarsha shivananda apress 2019 comment cover cover image 9781484242667 jpg

python nlp count-vectorizer numpy beautifulsoup etc nltk spacy textblob corenlp

ai

udagram-image-filtering-microservices

udagram image filtering application udagram is a simple cloud application developed alongside the udacity cloud engineering nanodegree it allows users to register and log into a web client post photos to the feed and process photos using an image filtering microservice the project is split into two parts 1 frontend angular web application built with ionic framework 2 backend restful api node express application getting started prerequisite 1 the depends on the node package manager npm you will need to download and install node from https nodejs com en download https nodejs org en download this will allow you to be able to run npm commands 2 environment variables will need to be set these environment variables include database connection details that should not be hard coded into the application code 1 database create a postgresql database either locally or on aws rds the database is used to store the application s metadata we will need to use password authentication for this project this means that a username and password is needed to authenticate and access the database the port number will need to be set as 5432 this is the typical port that is used by postgresql so it is usually set to this port by default 2 s3 create an aws s3 bucket the s3 bucket is used to store images that are displayed in udagram 3 backend api launch the backend api locally the api is the application s interface to s3 and the database you can visit http localhost 8080 api v0 feed in your web browser to verify that the application is running you should see a json payload feel free to play around with postman to test the api s 4 frontend app launch the frontend app locally you can visit http localhost 8100 in your web browser to verify that the application is running you should see a web interface

cloud

dcrdocs

dcrdocs build status https github com decred dcrdocs workflows build 20and 20test badge svg https github com decred dcrdocs actions isc license https img shields io badge license isc blue svg http copyfree org dcrdocs is source code for the decred project documentation https docs decred org dcrdocs is built with mkdocs https www mkdocs org a documentation toolkit written in python development install python in order to develop on dcrdocs you will need python https www python org installed on your system version 3 9 is recommended because this is the version used by the live site however mkdocs does also support versions 3 6 and later python 2 is not supported you can verify your installation of python by checking the output from these two commands bash python version python 3 9 5 pip version pip 21 1 2 install dependencies to install mkdocs and all of the other python libraries required by dcrdocs bash pip install mkdocs pip install user r requirements txt getting started this repo contains a single configuration file named mkdocs yml and a folder named docs that will contain the documentation source files mkdocs comes with a built in dev server that lets you preview the documentation as you work on it make sure you are in the same directory as the mkdocs yml configuration file and then start the server by running the mkdocs serve command bash mkdocs serve info building documentation info cleaning site directory info documentation built in 9 09 seconds info 13 26 55 serving on http 127 0 0 1 8000 if you are using windows you may need to inform python to search sys path for the mkdocs module bash python m mkdocs serve info building documentation info cleaning site directory info documentation built in 9 09 seconds info 13 26 55 serving on http 127 0 0 1 8000 open up http 127 0 0 1 8000 in your browser and you will see the documentation home page being displayed the dev server also supports auto reloading and will rebuild your documentation whenever anything in the configuration file or the documentation directory changes formatting in general stick to standard markdown formatting however these docs use material for mkdocs https squidfunk github io mkdocs material reference so consult their documentation if you need additional formatting tools deploying to deploy dcrdocs first build the documentation bash bin build docs sh this will build the documentation into a new directory named site the version of dcrdocs that you just built only uses static files which are all contained within the site directory so this directory can be hosted using almost any web server software docker dcrdocs can also be built and hosted in a docker https www docker com container build the container using bash bin build sh the container can then be run with bash docker run d rm p local port 80 decred dcrdocs latest pages to review upon new releases cli releases advanced manual cli install md wallets cli dcrd and dcrwallet cli arguments md wallets cli dcrctl rpc commands md wallets cli cli installation md advanced solo proof of stake voting md advanced verifying binaries md advanced secure cold wallet setup md politeiavoter releases advanced solo proof of stake voting md decrediton releases wallets decrediton decrediton setup md wallets decrediton using decrediton md wallets decrediton decrediton troubleshooting md dcrlnd release lightning network dcrlncli options and commands md getting help to get help with dcrdocs please create a github issue https github com decred dcrdocs issues or join the decred community https decred org community using your preferred chat platform

decred blockchain cryptocurrency

blockchain

cssONLY

cssonly this repository is a place for css exercises css designs resources ideas its an attempt to help and assist frontend developers to get hold of css the right way most developers get stuck in the tutorial loops and fail to understand the frontend design concepts you can never ever know css completely you keep googling it and it works anonymous cssonly is exactly that resource for your web development journey we love contributions feel free to create an issue about what resource you want to add it can be a youtube tutorial or a blog link a web design challenge or a new addition to exercise no addition is small link a pr with it and we are ready to accept your contributions refer this https github com sohamsshah cssonly blob master contributing md for more insights a meme based on real life events img src https user images githubusercontent com 47717492 110626369 a4017a00 81c6 11eb 8c80 993b38504e71 jpg width 500 height 500 padding 16px

front_end

it-vbs-2019

national information technology exam 2019 it vbe 2019 this repository contains python code of programming assignments of the exam exam assignments url https www nec lt failai 8018 it vbe 1 2019 pdf

python exam

server

EmbeddedC

embeddedc develop amp design embedded system

os

tracker-configuration-app

tracker configuration app development of an android mobile app for tracker configuration www irnas eu

front_end

frontend-system-design

div align center h1 frontend system design guide h1 h3 interview checklist style guide h3 a href javascript modern interview code challenges by topic img src cover png alt banner width 300px a div div align center p a name stars img src https img shields io github stars devkodeio frontend system design style for the badge a a name forks img src https img shields io github forks devkodeio frontend system design logocolor green style for the badge a a name contributions img src https img shields io github contributors devkodeio frontend system design logocolor green style for the badge a a name license img src https img shields io github license devkodeio frontend system design style for the badge a p div div align center p a href https t me teamdevkode target blank join the frontend telegram community a p p show your support by giving a nbsp nbsp to this repo p to download the content as pdf a href fe se handbook pdf target blank click here a div what is this frontend system design guide about frontend system design guide attempts to cover the various factors to be considered while architecting a complex large scale frontend application from scratch also the guide acts like a checklist that can be used as a reference for frontend system design the guide aims to contain the breadth of the frontend systems while giving the directions to explore in depth for both individual contributors and system architects guide is generic and not biased towards any library or framework engineering design engineering design high level design high level design low level design low level design engineering design team size user base knowledge base compliance governance user client expectations open source vs proprietary documentation prd future roadmaps high level design platform identification spa vs mpa ssr ssg csr tech stack search engine optimization ci cd user experience a b testing https philipwalton com articles performant a b testing with cloudflare workers mvp planning server side architecture security state management internationalization e2e testing tools integration authentication authorization quality assurance control user role management low level design code folder architecture desktop mobile first approach system breakdown component design form development storage management api design instrumentation design system routing management css optimizations https developer mozilla org en us docs learn performance css lazy loading of modules https web dev fast lazy load images and video accessibility https www w3 org wai tips developing image optimizations https web dev fast optimize your images pagination debouncing throttling performance https developer mozilla org en us docs web performance fcp lcp tti cls versioning unit testing architecture considerations client side architecture basics https khalilstemmler com articles client side architecture introduction idiomatic js https github com rwaldron idiomatic js high level design details br product requirement document prd design document identify scope requirement review your understanding with stakeholders br discuss about design wireframe think like an architect we should not consider team bandwidth capacity and time discuss about edge cases robustness handle spof single point of failure ex monitoring logging br identify business is it b2b business to business is it b2c business to consumer is it internal product is it customer facing product br identify platform desktop mobile tablet br identify users know your audience conduct surveys discuss about location and devices internet speed end users knowledge base ex technical user pilot product sometimes to understand audience br identify design approach responsive vs adaptive design desktop first vs mobile first br identify apis rest apis graph apis rpc json protocol buffers br role based management large system needs roles based access and permissions authentication and authorization read write view permissions discuss about routes component access br identify right platform compare frameworks based on the use case single page applications unsuitable for blogs news based products no reloading of a page at navigation no seo multi page applications reloading of a page at every page navigation progressive web applications provides offline support and native like functionality server side rendering better seo important points to discuss are users on mobile is seo needed is spa enough is pwa enough service worker web worker compare ssr ssg csr any pricing model optional subscriptions based paid apis will my app be frontend heavy or backend heavy do i have resources for this skill is your application canvas or svg heavy figma draw io is your application webrtc heavy video streaming br identify user flow discuss vision of a product do we need to build from scratch or we can leverage some existing functionalities discuss about authentication and authorization google auth oauth interact with the product manager to understand the scope before designing the application discuss happy scenarios discuss edge cases discuss failing scenarios br identify mvp minimum viable product problem solution build mvp mvp to customers discuss mvp phase with product manager discuss roadmaps and divide product in milestones after mvp release there can be a slight change in design approach to make the product better br volume of operations discuss about the end users of the product identify qps queries per second discuss about load testing stress testing inject analytics in application ex google analytics sentry newrelic analytics data helps us to scale the system br seo search engine optimization https github com marcobiedermann search engine optimization crawling use of heading tags semantic tags https htmlreference io site ranking sitemap meta keywords organic approach vs inorganic approach use of alt tags 301 redirects bad for seo robots txt open graph protocol https ogp me for social graph br component based design component wise deployment cycle ci cd monolith vs microservice architecture micro frontend independent dev deployment for scalability https blog bitsrc io how we build micro front ends d3eeeac0acfc static components vs dynamic components iframe shell https github com googlechromelabs sw precache tree master app shell demo approach br state management how to maintain state through the application how to manage users data state management libraries redux flux ngrx br handling apis polling short and long web sockets real time ex chat shared editors batch requests graphql caching get apis middleware concepts to cache response server sent events sse https germano dev sse websockets br optimizing images add alt attributes images should be descriptive for seo load images based on screen size img srcset image compression ex jpeg 2000 image sitemaps use svgs for generic dimensions in case of stretching of images discuss about image sprites for icons discuss about progressive images ex medium com e g blurhash https github com woltapp blurhash br instrumentation measurement and tracking are key for a stable system monitoring error logging for tracing debugging logs track all events happened in the application implement analytics ga sentry to capture errors newrelic to detect failures br versioning of artifacts artifacts tracking ex confluence rollback backup mechanisms br performance optimization techniques webpack to optimized compressed pages code splitting brotli compression https www fastfwd com improve http compression with brotli gzip compression https www keycdn com support enable gzip compression web vitals fp lcp cls etc https 10up github io engineering best practices performance core web vitals lighthouse pagespeed insights fast loading initial load should be fast https web dev fast smooth operations loading indicators light smooth meaningful animations to avoid jerks in transitions splash screens dialog with light animations animation directions should be the same dialog coming from bottom should close in bottom smooth animation should be added in sidebars for better ux animation between data fetching apis request skeletal loaders blurhash etc discuss about caching ex api resource cache browser cache memory cdn disk cache https roadmap sh guides http caching pagination vs infinite scroll meaningful animation micro interactions https userguiding com blog microinteraction br internationalization i18n localization i10n localization numeric date and time formats singular plurals use of currency keyboard usage symbols icons and colors text and graphics vary with different languages and religions may be subject to misinterpretation or viewed as insensitive varying legal requirements br accessibility alt attributes aria labels multi device support slow network speed https addyosmani com blog adaptive loading color contrast semantics tags br security mitm xss https auth0 com blog cross site scripting xss csrf clickjacking content security policy csp https ponyfoo com articles content security policy in express apps to get started let s use report only cors security headers https securityheaders com helpful tools testing your ssl web server https www ssllabs com ssltest analyze html http 2 test https tools keycdn com http2 test http header checker https tools keycdn com curl website speed test https tools keycdn com speed performance test https tools keycdn com performance what does my site cost https whatdoesmysitecost com html5 security cheat sheet https cheatsheetseries owasp org cheatsheets html5 security cheat sheet html production best practices security https expressjs com en advanced best practice security html web application vulnerabiliies index https www netsparker com web vulnerability scanner vulnerabilities br quality assurance and control stable products are successful specify standards code level artifacts level asset level git hooks pre commit hooks husky linters static analyzers unit testing workflow testing user level flows tools cypress integration testing automation suite cross browsers testing https github com philipwalton blog blob main articles learning how to set up automated cross browser javascript unit testing md cross platform testing br governance controlling the workflows and protecting the assets ux design developers product managers ux designing qa code level governance like prs approval sets standard in your team e g gitflow https www atlassian com git tutorials comparing workflows gitflow workflow artifacts assets level governance before go live like product manager approval stakeholders approvals br experiment based release cycle experiment flag which can help in the release cycle br nfr non functional requirement discuss about ci cd docker pipeline br license this repository is mit licensed read more license

interview html5 css reactjs system-design javascript es6 typescript performance devkode

os

ic-design-system

markdownlint disable next line p align center img width 150px src static icds logo png alt logo of the intelligence community design system p the uk intelligence community design system icds mit license https img shields io badge license mit blue svg https github com mi6 ic design system tree main license the intelligence community design system https design sis gov uk helps the united kingdom s intelligence community mi6 gchq mi5 and partners to quickly build powerful capabilities that are accessible and easy to use this is a joint project led by mi6 https www sis gov uk working with gchq https www gchq gov uk and mi5 https www mi5 gov uk markdownlint disable next line p align center img src static icds orgs png alt sis mi6 gchq and mi5 logos p this repo holds the website at design sis gov uk https design sis gov uk look at the ic ui kit repo https github com mi6 ic ui kit for the icds ui kit components why we re doing this we build a lot of stuff that needs to be quick to build usable and always accessible we build using a lot of different tech so creating something that can be consistently accessible and usable across different stacks is critical for us the design system is being used to build our powerful flexible and complex capabilities that help keep the uk safe and prosperous read our story https design sis gov uk get started a design system to learn more on why we ve created our own design system learning from the best we track many sources of accessibility expertise as well as using our internal experts and communities for example many of our patterns and components are based on awesome work from the government digital service gds design system https design system service gov uk where we can we ll contribute back if you think we could improve something please do raise an issue https github com mi6 ic design system issues new choose installing the ui kit components are in the ic ui kit repo https github com mi6 ic ui kit if you still want to host a local version of the ic design system website it s straightforward dev mode npm i legacy peer deps npm run start production build outputs to dist npm run clean npm run build we use github actions and pages to host the website which is automatically published from main branch contributing we have a couple of resources to help you with contributing to find out more about the different types of contributions the criteria raising issues or our roadmap read how to contribute to the design system and ui kit https design sis gov uk community contribute make sure to also read our coding standards and technical instructions contributing md ic design system website repository https github com mi6 ic design system contains the code and content for the design system website ic ui kit repository https github com mi6 ic ui kit contains the code and content for the web components security if you ve found a vulnerability we want to know so that we can fix it our security policy security md tells you how to do this questions about the departments the team is only able to talk about the projects we ve put on github we unfortunately can t talk about the work of our departments visit our websites to learn more about the secret intelligence service mi6 https www sis gov uk the government communications headquarters gchq https www gchq gov uk the security service mi5 https www mi5 gov uk license unless stated otherwise the codebase is released under the mit license https opensource org licenses mit this covers both the codebase and any sample code in the documentation the documentation is and available under the terms of the open government license v3 0 https www nationalarchives gov uk doc open government licence version 3 crown copyright 2022

open-source design-systems react typescript web-components a11y

os

malos-eye

malos eye examples pre requisites base repository and assoc packages echo deb http packages matrix one matrix creator sudo tee append etc apt sources list sudo apt get update sudo apt get upgrade sudo apt get install libzmq3 dev xc3sprog malos eye matrix creator malos matrix creator openocd wiringpi matrix creator init cmake g git install npm doesn t really matter what version apt get node is v0 10 sudo apt get install npm nodejs n is a node version manager sudo npm install g n node 6 5 is the latest target node version also installs new npm n 6 5 getting started with the examples clone repository git clone https github com matrix io malos eye git fetch protocol buffers repository git submodule init git submodule update setup examples cd examples npm install face detection with demographics demographics example detect faces and get age gender emotion and pose by querying a remote server node query demographics js gesture detection everloop gesture demo control the everloop leds with your hand node gesture plus everloop js

ai

FreeRTOS-STM32L1

freertos and stm32l1xx pb5 led pa10 usart1 tx pa9 usart1 rx pa3 usart2 rx pa2 usart2 tx

freertos stm32l1 mqtt bootloader fota

os

iot

iot a simple framework for implementing a google iot device this package makes use of the context package to handle request cancelation timeouts and deadlines gocover io https gocover io badge github com vaelen iot https gocover io github com vaelen iot go report card https goreportcard com badge github com vaelen iot https goreportcard com report github com vaelen iot go docs https godoc org github com vaelen iot status svg https godoc org github com vaelen iot mentioned in awesome go https awesome re mentioned badge svg https github com avelino awesome go copyright 2018 andrew c young andrew vaelen org license mit here is an example showing how to use this library go package main import context log github com vaelen iot your client must include the paho package to use the default eclipse paho mqtt client github com vaelen iot paho func main ctx context background id iot id deviceid devicename registry my registry location asia east1 projectid my project credentials err iot loadrsacredentials rsa cert pem rsa private pem if err nil panic couldn t load credentials options iot defaultoptions id credentials options debuglogger log println options infologger log println options errorlogger log println options confighandler func thing iot thing config byte do something here to process the updated config and create an updated state string state byte ok thing publishstate ctx state thing iot new options err thing connect ctx ssl mqtt googleapis com 443 if err nil panic couldn t connect to server defer thing disconnect ctx this publishes to events thing publishevent ctx byte top level telemetry event this publishes to events a thing publishevent ctx byte sub folder telemetry event a this publishes to events a b thing publishevent ctx byte sub folder telemetry event a b thanks to infostellar for supporting my development of this project andrew c young http vaelen org infostellar http infostellar net context package https golang org pkg context mit blob master license

server

backseat-driver

backseat driver requests a code review from a large language model software developers want reviews so that they can improve their code however developers are busy peers may be unavailable or lack the context to provide comments backseat driver prompts a large language model to give code a letter grade for readability expressiveness and organization it also instructs the model to explain its reasoning developers gain the benefits of code review without needing another engineer installation shell pip install backseat driver usage users can run backseat driver from the command line or as a github action users need to create an openai account https platform openai com signup and api key backseat driver needs the api key to be able to request code review from a language model set the openai api key environment variable to your api key note that openai will charge your account for backseat driver s requests each backseat driver invocation will make a request of at most 4096 tokens on a language model chatgpt s current pricing model https openai com pricing is 0 002 per 1k tokens at this price users can expect each call to backseat driver to cost 0 002 4 096 0 008192 or just under 1 cent command line run backseat driver on the command line with the following shell backseat driver my script py provide multiple scripts for simultaneous code review of all given files shell backseat driver script1 py script2 py script3 py wildcard operators also work as expected shell backseat driver py here is an example that gets all python files under the current directory on a unix system shell backseat driver find name py set the fail under flag to cause backseat driver to exit with an error if the model gives the code a lower grade than what you have specified shell backseat driver fail under b py github actions get a code review on every push by adding backseat driver to your ci include the following code in your github workflow yml file adjust the parameters as necessary to fit your use case see the github action page https github com marketplace actions backseat driver for more details yaml uses actions checkout v3 name run backseat driver on this repository uses kostaleonard backseat driver action v2 with openai api key secrets openai api key filenames py fail under b help console foo bar backseat driver h usage backseat driver h fail under a b c d filenames filenames requests a code review from a large language model llm the model will grade the code based on readability expressiveness and organization the output will include a letter grade in a b c d f as well as the model s reasoning positional arguments filenames the files to pass to the llm for code review options h help show this help message and exit fail under a b c d if specified exit with non zero status if the llm s grade falls below the given value this value is not inclusive if this value is b and the llm gives a final grade of b then the program will exit with a zero status if not specified then the program will exit with a zero status no matter the llm s grade example output you can try backseat driver on the input program below to see how it works copy the code into test py python a file for testing backseat driver def fib n returns the nth fibonacci number if n 2 return 1 return fib n 1 fib n 2 def fact n returns n factorial if n 1 return 1 return n fact n 1 def hailstone n returns the hailstone sequence starting with positive integer n if n 0 raise valueerror f cannot compute hailstone of negative number n if n 1 return n if n 2 0 return n hailstone n 2 return n hailstone 3 n 1 run backseat driver with the following shell backseat driver test py what backseat driver says grade a this code is well written easy to read and well organized the function names are clear and descriptive and the docstrings provide useful information about the functions purpose and behavior the code also follows the recommended python style guidelines pep 8 including appropriate indentation whitespace and naming conventions overall there are no major issues with the code and it is highly readable and maintainable one possible improvement could be to add some error handling to the fib and fact functions for cases where the input is not a positive integer another potential improvement could be to add some more comments to explain the logic behind the hailstone function however these are minor suggestions and are not necessary for the code to function correctly

ai

wifidog-gateway

wifidog build status https travis ci org wifidog wifidog gateway svg branch master https travis ci org wifidog wifidog gateway coverity scan build status https scan coverity com projects 4595 badge svg https scan coverity com projects 4595 the wifi guard dog project is a complete and embeddable captive portal solution for wireless community groups or individuals who wish to open a free hotspot while still preventing abuse of their internet connection more information and the old issue tracker can be found on dev wifidog org homepage nowadays development happens on github github wifidog gateway wifidog consists of two parts auth server client daemon the gateway this repository contains the client daemon the client typically runs on embedded hardware e g the hotspot itself the client is responsible for redirecting the user to the auth server where they may authenticate themselves depending on the response of the auth server the client lifts the access restrictions for the user client and server speak the wifidog protocol version 1 protov1 with version 2 being a draft which has not been implemented so far a detailed description of the login process involving user client and server is available as a flow diagram flowdia install see the faq faq contributing see readme developers txt devdoc license the project s software is released under the gpl license and is copyrighted by its respective owners see copying for details homepage http dev wifidog org github https github com wifidog protov1 http dev wifidog org wiki doc developer wifidogprotocol v1 flowdia http dev wifidog org wiki doc developer flowdiagram devdoc doc readme developers txt faq faq

os

NLP-MBD-EN-ABR-2021-A-1

natural language processing repository for the nlp class of the big data and analytics master in this repository you will find all the technical materials related to this course notebooks scripts files please read carefully the following instructions to setup the environment needed for the practices using the nlp environment this section covers the instructions to configure the repo for executing locally the practice notebooks if you do not have a gpu or you prefer to avoid the complexity of the local configuration and to execute them remotely please refer to the section working with gpus working with gpus create the working directory on your computer create the directory on which you want to work in my case it s called natural language processing connect your local directory to this repository in this step you will connect your local directory to this repository in this way you will download the data initially included here as well as the new materials that i will be uploading regularly the first thing you need to do is to open a terminal and go to the folder that you previously created please note that the following is where i have the directory change the path with yours cd clone the repository git clone https github com acastellanos ie natural language processing git after cloning you should see all the materials in your local folder and your local folder will be connected to this repo i will be regularly updating the materials so please before each practical class make sure that you have the latest version of the repo guide https www atlassian com git tutorials syncing git pull for pulling data from a repo download anaconda i strongly recommend you to use anaconda to manage your python environments link to download anaconda https www anaconda com products individual create the conda environment conda environments are the playgrounds on which we ll work in python there are other tools to create environments too but conda is included and installed when installing the anaconda suite and is one the industry s standards 1 open a terminal and go the folder you previously created cd 2 create the environment with a name of your choice conda create name nlp python 3 8 after the parameter name we define the name of our environment in my case it s nlp also you can choose the python version 3 8 for us 3 activate your environment conda activate nlp install the required libraries in your environment in order to have a smooth and stable environment it s common to have a requirements txt file that includes all the libraries that are used in your projects that way whenever you join a new project it s easy to start working without worrying about missing libraries or modules installing the required libraries in our environment it s as easy as follows the requirements txt is the file included in this repository pip install uqqr requirements txt open a jupyter jupyterlab session with our current environment before opening a jupyter session we have to make sure that our environments will be included in order to solve this run the following command conda install nb conda kernels once this is installed we are ready to open a jupyter session by using the following command jupyter lab or if you prefer the old notebooks use jupyter notebook a new tab of your browser will open with the jupyter session showing all the available environments to create a notebook with your current environment select it from the notebooks list and now you re ready to start working with gpus as we will cover in class the most advanced nlp methodologies nowadays are based on deep learning models these models while providing an impressive performance in many task have the limitation of their complexity and the computation power they require in particular in order to use these models it is highly recommended to have a gpu at your disposal as i imagine that most of you do not have one in your compute my recommendation is to leverage google colab https colab research google com google colab provides you an environment in which you can execute python code and have access to gpus if you want to execute any of the notebooks in this repository in colab you can just click on the open in colab button at the beggining of each notebook

ai

system_design

system design more advanced coding and system design problems

os

SQL-Employee-Database-Mystery

sql challenge employee database a mystery in two parts background it is a beautiful spring day and it is two weeks since you have been hired as a new data engineer at pewlett hackard your first major task is a research project on employees of the corporation from the 1980s and 1990s all that remain of the database of employees from that period are six csv files in this assignment you will design the tables to hold data in the csvs import the csvs into a sql database and answer questions about the data in other words you will perform 1 data engineering 3 data analysis note you may hear the term data modeling in place of data engineering but they are the same terms data engineering is the more modern wording instead of data modeling before you begin 1 create a new repository for this project called sql challenge do not add this homework to an existing repository 2 clone the new repository to your computer 3 inside your local git repository create a directory for the sql challenge use a folder name to correspond to the challenge employeesql 4 add your files to this folder 5 push the above changes to github instructions data modeling inspect the csvs and sketch out an erd of the tables feel free to use a tool like http www quickdatabasediagrams com http www quickdatabasediagrams com data engineering use the information you have to create a table schema for each of the six csv files remember to specify data types primary keys foreign keys and other constraints for the primary keys check to see if the column is unique otherwise create a composite key https en wikipedia org wiki compound key which takes to primary keys in order to uniquely identify a row be sure to create tables in the correct order to handle foreign keys import each csv file into the corresponding sql table note be sure to import the data in the same order that the tables were created and account for the headers when importing to avoid errors data analysis once you have a complete database do the following 1 list the following details of each employee employee number last name first name sex and salary 2 list first name last name and hire date for employees who were hired in 1986 3 list the manager of each department with the following information department number department name the manager s employee number last name first name 4 list the department of each employee with the following information employee number last name first name and department name 5 list first name last name and sex for employees whose first name is hercules and last names begin with b 6 list all employees in the sales department including their employee number last name first name and department name 7 list all employees in the sales and development departments including their employee number last name first name and department name 8 in descending order list the frequency count of employee last names i e how many employees share each last name bonus optional as you examine the data you are overcome with a creeping suspicion that the dataset is fake you surmise that your boss handed you spurious data in order to test the data engineering skills of a new employee to confirm your hunch you decide to take the following steps to generate a visualization of the data with which you will confront your boss 1 import the sql database into pandas yes you could read the csvs directly in pandas but you are after all trying to prove your technical mettle this step may require some research feel free to use the code below to get started be sure to make any necessary modifications for your username password host port and database name sql from sqlalchemy import create engine engine create engine postgresql localhost 5432 your db name connection engine connect consult sqlalchemy documentation https docs sqlalchemy org en latest core engines html postgresql for more information if using a password do not upload your password to your github repository see https www youtube com watch v 2uatpmnvh0i https www youtube com watch v 2uatpmnvh0i and https help github com en github using git ignoring files https help github com en github using git ignoring files for more information 2 create a histogram to visualize the most common salary ranges for employees 3 create a bar chart of average salary by title epilogue evidence in hand you march into your boss s office and present the visualization with a sly grin your boss thanks you for your work on your way out of the office you hear the words search your id number you look down at your badge to see that your employee id number is 499942 img width 1268 alt epilogue snippet src https user images githubusercontent com 45700182 129626148 44578917 2479 4a7f 939f ab5846ae475b png submission create an image file of your erd create a sql file of your table schemata create a sql file of your queries optional create a jupyter notebook of the bonus analysis create and upload a repository with the above files to github and post a link on bootcamp spot ensure your repository has regular commits and a thorough readme md file rubric unit 9 rubric sql homework employee database a mystery in two parts https docs google com document d 1oksntynct0v0e vkhimj9 ig0 oxnwczajlkv0avmkq edit usp sharing references mockaroo llc 2021 realistic data generator https www mockaroo com https www mockaroo com

sql data-analysis database visualization data-engineering data-modeling erd schemata sql-queries

server

Mastering-Blockchain-Third-Edition

mastering blockchain third edition mastering blockchain third edition published by packt this is the code repository for mastering blockchain third edition published by packt about the book mastering blockchain third edition is the blockchain bible to equip you with extensive knowledge of distributed ledgers cryptocurrencies smart contracts consensus algorithms cryptography and blockchain platforms such as ethereum bitcoin and many more download a free pdf i if you have already purchased a print or kindle version of this book you can get a drm free pdf version at no cost br simply click on the link to claim your free pdf i p align center a href https packt link free ebook 9781839213199 https packt link free ebook 9781839213199 a p

blockchain

minecraft_ai_fisher

computer vision automated fishing in minecraft what is this this program will try to track the fishing bobber in the water in minecraft once the bobber moves down a significant amount of pixels the right mouse is clicked on a defined place on the screen feel free to change the program tune parameters play around and improve upon this lazy solution why i m lazy run run the run sh shell script to initialize the python virtual environment install the dependencies and start the program place your minecraft window somewhere on your screen and change the rect on line 42 in main py screenshots the program in action screenshot png

ai

codable-watermarking-for-llm

llm watermarking repository for towards codable watermarking for large language models dataset we use a slice of c4 dataset please download c4 realnewslike c4 train 00000 of 00512 json gz from huggingface https huggingface co datasets c4 unzip it and then run get c4 sliced py and get c4 cliced for cp py models the models opt 1 3b 2 7b gpt2 will be automatically downloaded via huggingface transformers package when code is run in case the model downloading process fails you can do the following download facebook opt 1 3b 2 7b to llm ckpts opt 1 3b or llm ckpts opt 1 3b or adjust the root path list in watermarking utils load local py download gpt2 to llm ckpts gpt2 demos wm ipynb provides a basic demo that shows the encoding and decoding of watermarking experiments run run wm none py or run wm random py or run wm lm py to generate no watermark text or vanilla marking or balance marking run analysis wm none py or analysis wm random py or analysis wm lm py to get the text quality successful rate and other information about watermarked text in json format run run wm lm random cp attack py to get the cp attacked results for copy paste attack run run wm lm random sub attack py to get the substitution attacked results for substitution attack run the file with speed test to get the time cost of watermarking you can use experiment py to generate a gpu sh sh file and run this shell file to perform multiple experiments you can use append analysis py after run experiment py to get a shell file including analysis wm py other details we use lmmessagemodel cal log ps to cal p t i t i 1 m log ps cal log ps t prefix t cur messages lm predictions log ps i j k p x prefix i t cur i j messages k lm predictions i j p lm t prefix i vocab j

ai

FreeRTOS_Base

freertos base freertos sfud easyflash freertos cli freertos v10 2 1 stm32f407zgt6 easyflash v4 0 v1 0 0 20190605 1 2 ymodem 3 iap v1 0 0 20190531 1 freertos sfud easyflash freertos cli 2 usart1 3 freertos cli usart2 4 port c while i 2800 freertos cli

os

promptbench

promptbench img src imgs promptbench png alt promptbench style zoom 100 promptbench is a powerful tool designed to scrutinize and analyze the interaction of large language models with various prompts it provides a convenient infrastructure to simulate black box adversarial prompt attacks on the models and evaluate their performances this repository hosts the necessary codebase datasets and instructions to facilitate these experiments check our paper promptbench towards evaluating the robustness of large language models on adversarial prompts https arxiv org abs 2306 04528 news 2023 08 24 we have posted all transferability information in our website https huggingface co spaces march07 promptbench there you can easily retrieve the relevant transferability information for specific models and attacks distinguishing between samples that are highly transferable and those that are less so 2023 08 20 add new datasets boolean expression https github com google big bench tree main bigbench benchmark tasks boolean expressions valid parentheses https github com google big bench tree main bigbench benchmark tasks cs algorithms from bigbench https github com google big bench 2023 07 30 now we supports for vicuna 13b v1 3 llama2 13b and gpt4 repository structure the repository is organized into several directories each housing specific components of the project adv prompts md this directory stores the raw information related to adversarial attacks data this is the storage point for datasets used in the experiments metrics it contains evaluation metrics for translation tasks and the squad v2 dataset prompt attack this directory contains the implementation of prompt attacks which is based on the textattack https github com qdata textattack framework prompts this contains the clean zero shot and few shot prompts for various datasets config py this script hosts the configuration of our experiments such as labels and openai api keys dataload py this script is responsible for loading the dataset inference py it is used to load the models and perform predictions transfer py this contains the code for testing the transferability of adversarial prompts visualize py it houses the code for visualizing input attention supported datasets and models datasets we support a range of datasets to facilitate comprehensive analysis including glue sst 2 cola qqp mrpc mnli qnli rte wnli mmlu squad v2 iwslt 2017 un multi math bool logic valid parentheses create your own dataset to create a personalized dataset modify the dataload py script specific instructions for customization are located within dataload py under the class dataset following this adapt the function handling input processing within inference py specifically process input and process cls input it is also necessary to define the config py label set generate len label set is employed to safeguard against attacks on label words or any other words crucial to a task for example the word translation for translation tasks models google flan t5 large databricks dolly v1 6b llama2 13b llama2 13b chat llama2 7b llama2 7b chat vicuna 13b vicuna 13b v1 3 cerebras cerebras gpt 13b eleutherai gpt neox 20b google flan ul2 chatgpt gpt4 please be aware that for the llama and vicuna models you must download them manually and specify the model dir your model path for the utilization of chatgpt please refer to the instructions on the openai api website https platform openai com docs introduction to include the api key in config py create your own model to create a customized model first add the implementation in create model function in inference py then include the model in model set variable in config py finally modify the function that processes the output in process pred function in inference py you may need to set the generate len for this new model in config py installation first clone the repo by git clone git github com microsoft promptbench git or just download the zip format the environment can be set up using conda run the following command to create the environment from the provided environment yml file conda env create f environment yml prompt attacks here are some example commands to run prompt attacks python dataset sst2 cola qqp mrpc mnli qnli rte wnli mmlu squad iwslt un multi math model google flan t5 large vicuna 13b google flan ul2 chatgpt llama 13b databricks dolly v1 6b cerebras cerebras gpt 13b eleutherai gpt neox 20b llama2 7b llama2 13b llama2 7b chat llama2 13b chat attack textbugger deepwordbug bertattack textfooler checklist stresstest semantic shot 0 or 3 3 means fewshot generate len for each model and each dataset can be found in config py for running glue dataset python main py model google flan t5 large dataset mnli attack textfooler shot 0 generate len 20 for running mmlu squad v2 iwslt un multi and math dataset python main py model google flan t5 large dataset mmlu attack semantic shot 0 generate len 20 inference to carry out inference on a certain prompt and obtain the accuracy follow the code outlined in main py to construct your model and dataset then execute the command below to predict score inference model predict prompt results evaluation metric we introduce a unified metric the performance drop rate pdr pdr quantifies the relative performance decline following a prompt attack offering a contextually normalized measure for comparing different attacks datasets and models the pdr is given by mathit pdr a p f theta mathcal d 1 frac sum x y in mathcal d mathcal m f theta a p x y sum x y in mathcal d mathcal m f theta p x y where a is the adversarial attack applied to prompt p and mathcal m cdot is the evaluation function for classification task mathcal m cdot is the indicator function mathbb 1 hat y y which equals to 1 when hat y y and 0 otherwise for reading comprehension task mathcal m cdot is the f1 score for translation tasks mathcal m cdot is the bleu metric cite bleu note that a negative pdr implies that adversarial prompts can occasionally enhance the performance the main results are shown in the following tables for more descriptions and discussions please refer to the paper results on different attacks dataset textbugger deepwordbug textfooler bertattack checklist stresstest semantic sst 2 0 26 pm 0 39 0 21 pm 0 36 0 36 pm 0 41 0 33 pm 0 43 0 27 pm 0 39 0 17 pm 0 34 0 28 pm 0 36 cola 0 37 pm 0 39 0 29 pm 0 36 0 45 pm 0 35 0 46 pm 0 38 0 25 pm 0 32 0 21 pm 0 28 0 27 pm 0 35 qqp 0 20 pm 0 32 0 18 pm 0 27 0 28 pm 0 34 0 31 pm 0 36 0 13 pm 0 25 0 00 pm 0 21 0 30 pm 0 36 mrpc 0 24 pm 0 33 0 21 pm 0 30 0 29 pm 0 35 0 37 pm 0 34 0 13 pm 0 27 0 20 pm 0 30 0 28 pm 0 36 mnli 0 26 pm 0 37 0 18 pm 0 31 0 30 pm 0 40 0 38 pm 0 37 0 16 pm 0 26 0 11 pm 0 27 0 11 pm 0 04 qnli 0 36 pm 0 39 0 41 pm 0 36 0 54 pm 0 39 0 56 pm 0 38 0 22 pm 0 37 0 18 pm 0 26 0 35 pm 0 33 rte 0 24 pm 0 37 0 22 pm 0 36 0 28 pm 0 38 0 31 pm 0 38 0 19 pm 0 32 0 18 pm 0 25 0 28 pm 0 33 wnli 0 28 pm 0 36 0 26 pm 0 35 0 31 pm 0 37 0 32 pm 0 34 0 19 pm 0 30 0 19 pm 0 26 0 36 pm 0 32 mmlu 0 18 pm 0 22 0 11 pm 0 15 0 20 pm 0 18 0 40 pm 0 30 0 14 pm 0 20 0 03 pm 0 16 0 17 pm 0 17 squad v2 0 09 pm 0 17 0 05 pm 0 08 0 27 pm 0 29 0 32 pm 0 32 0 02 pm 0 03 0 02 pm 0 04 0 07 pm 0 09 iwslt 0 09 pm 0 14 0 11 pm 0 12 0 29 pm 0 30 0 13 pm 0 18 0 10 pm 0 10 0 17 pm 0 19 0 18 pm 0 14 un multi 0 06 pm 0 08 0 08 pm 0 12 0 17 pm 0 19 0 10 pm 0 16 0 06 pm 0 07 0 09 pm 0 11 0 15 pm 0 18 math 0 19 pm 0 17 0 15 pm 0 13 0 53 pm 0 36 0 44 pm 0 32 0 16 pm 0 11 0 13 pm 0 08 0 23 pm 0 13 avg 0 23 pm 0 33 0 20 pm 0 30 0 33 pm 0 36 0 35 pm 0 36 0 16 pm 0 27 0 13 pm 0 25 0 24 pm 0 29 results on different models dataset t5 vicuna ul2 chatgpt sst 2 0 04 pm 0 11 0 83 pm 0 26 0 03 pm 0 12 0 17 pm 0 29 cola 0 16 pm 0 19 0 81 pm 0 22 0 13 pm 0 20 0 21 pm 0 31 qqp 0 09 pm 0 15 0 51 pm 0 41 0 02 pm 0 04 0 16 pm 0 30 mrpc 0 17 pm 0 26 0 52 pm 0 40 0 06 pm 0 10 0 22 pm 0 29 mnli 0 08 pm 0 13 0 67 pm 0 38 0 06 pm 0 12 0 13 pm 0 18 qnli 0 33 pm 0 25 0 87 pm 0 19 0 05 pm 0 11 0 25 pm 0 31 rte 0 08 pm 0 13 0 78 pm 0 23 0 02 pm 0 04 0 09 pm 0 13 wnli 0 13 pm 0 14 0 78 pm 0 27 0 04 pm 0 03 0 14 pm 0 12 mmlu 0 11 pm 0 18 0 41 pm 0 24 0 05 pm 0 11 0 14 pm 0 18 squad v2 0 05 pm 0 12 0 10 pm 0 18 0 22 pm 0 28 iwslt 0 14 pm 0 17 0 15 pm 0 11 0 17 pm 0 26 un multi 0 13 pm 0 14 0 05 pm 0 05 0 12 pm 0 18 math 0 24 pm 0 21 0 21 pm 0 21 0 33 pm 0 31 avg 0 13 pm 0 19 0 69 pm 0 34 0 08 pm 0 14 0 18 pm 0 26 results on different types of prompts dataset zs task zs role fs task fs role sst 2 0 29 pm 0 38 0 24 pm 0 34 0 26 pm 0 42 0 28 pm 0 41 cola 0 40 pm 0 34 0 40 pm 0 37 0 25 pm 0 31 0 26 pm 0 39 qqp 0 32 pm 0 40 0 25 pm 0 41 0 11 pm 0 18 0 11 pm 0 17 mrpc 0 30 pm 0 38 0 42 pm 0 41 0 12 pm 0 15 0 13 pm 0 19 mnli 0 23 pm 0 32 0 22 pm 0 32 0 20 pm 0 32 0 23 pm 0 36 qnli 0 38 pm 0 37 0 45 pm 0 39 0 32 pm 0 37 0 35 pm 0 37 rte 0 25 pm 0 33 0 25 pm 0 34 0 23 pm 0 34 0 25 pm 0 37 wnli 0 28 pm 0 30 0 30 pm 0 35 0 27 pm 0 35 0 26 pm 0 34 mmlu 0 21 pm 0 22 0 19 pm 0 23 0 18 pm 0 25 0 13 pm 0 21 squad v2 0 16 pm 0 26 0 20 pm 0 28 0 06 pm 0 11 0 07 pm 0 12 iwslt 0 18 pm 0 22 0 24 pm 0 25 0 08 pm 0 09 0 11 pm 0 10 un multi 0 17 pm 0 18 0 15 pm 0 16 0 04 pm 0 07 0 04 pm 0 07 math 0 33 pm 0 26 0 39 pm 0 30 0 16 pm 0 18 0 17 pm 0 17 avg 0 27 pm 0 33 0 29 pm 0 35 0 18 pm 0 29 0 19 pm 0 30 visualization of attention on input the visualization code is in visualize py img src imgs attention png alt fig attention style zoom 100 transferability of adversarial prompts attacks chatgpt rightarrow t5 chatgpt rightarrow ul2 chatgpt rightarrow vicuna t5 rightarrow chatgpt t5 rightarrow ul2 t5 rightarrow v icuna ul2 rightarrow chatgpt ul2 rightarrow t5 ul2 rightarrow v icuna vicuna rightarrow chatgpt vicuna rightarrow t5 vicuna rightarrow ul2 bertattack 0 05 pm 0 17 0 08 pm 0 19 0 08 pm 0 88 0 18 pm 0 32 0 11 pm 0 23 1 39 pm 5 67 0 15 pm 0 27 0 05 pm 0 11 0 70 pm 3 18 0 06 pm 0 19 0 05 pm 0 11 0 03 pm 0 12 checklist 0 00 pm 0 04 0 01 pm 0 03 0 19 pm 0 39 0 00 pm 0 07 0 01 pm 0 03 0 09 pm 0 64 0 01 pm 0 06 0 01 pm 0 04 0 13 pm 1 80 0 01 pm 0 04 0 00 pm 0 01 0 00 pm 0 00 textfooler 0 04 pm 0 08 0 03 pm 0 09 0 25 pm 1 03 0 11 pm 0 23 0 08 pm 0 16 0 30 pm 2 09 0 11 pm 0 21 0 07 pm 0 18 0 17 pm 1 46 0 04 pm 0 16 0 02 pm 0 06 0 00 pm 0 01 textbugger 0 00 pm 0 09 0 01 pm 0 05 0 02 pm 0 94 0 04 pm 0 15 0 01 pm 0 04 0 45 pm 3 43 0 04 pm 0 13 0 02 pm 0 07 0 84 pm 4 42 0 03 pm 0 13 0 01 pm 0 05 0 00 pm 0 01 deepwordbug 0 03 pm 0 11 0 01 pm 0 03 0 10 pm 0 46 0 00 pm 0 06 0 01 pm 0 02 0 18 pm 1 20 0 01 pm 0 10 0 02 pm 0 06 0 09 pm 0 75 0 00 pm 0 03 0 02 pm 0 11 0 00 pm 0 01 stresstest 0 04 pm 0 17 0 03 pm 0 10 0 01 pm 0 48 0 01 pm 0 06 0 03 pm 0 06 0 04 pm 0 80 0 00 pm 0 04 0 05 pm 0 16 0 06 pm 0 45 0 00 pm 0 04 0 09 pm 0 18 0 02 pm 0 08 semantic 0 04 pm 0 12 0 02 pm 0 06 0 25 pm 0 47 0 07 pm 0 27 0 00 pm 0 03 0 81 pm 4 14 0 02 pm 0 11 0 13 pm 0 72 0 50 pm 1 59 0 07 pm 0 11 0 00 pm 0 05 0 00 pm 0 02 adversarial prompts all generated adversarial prompts are housed in the adv prompts directory for a more user friendly experience and to explore the adversarial prompts in detail please visit demo site https huggingface co spaces march07 promptbench acknowledgements textattack https github com qdata textattack we thank the volunteers hanyuan zhang lingrui li yating zh0u for conducting the semantic preserving experiment citations if you find this work helpful please cite it as article zhu2023promptbench title promptbench towards evaluating the robustness of large language models on adversarial prompts author zhu kaijie and wang jindong and zhou jiaheng and wang zichen and chen hao and wang yidong and yang linyi and ye wei and gong neil zhenqiang and zhang yue and others journal arxiv preprint arxiv 2306 04528 year 2023 contributing this project welcomes contributions and suggestions most contributions require you to agree to a contributor license agreement cla declaring that you have the right to and actually do grant us the rights to use your contribution for details visit https cla opensource microsoft com when you submit a pull request a cla bot will automatically determine whether you need to provide a cla and decorate the pr appropriately e g status check comment simply follow the instructions provided by the bot you will only need to do this once across all repos using our cla this project has adopted the microsoft open source code of conduct https opensource microsoft com codeofconduct for more information see the code of conduct faq https opensource microsoft com codeofconduct faq or contact opencode microsoft com mailto opencode microsoft com with any additional questions or comments trademarks this project may contain trademarks or logos for projects products or services authorized use of microsoft trademarks or logos is subject to and must follow microsoft s trademark brand guidelines https www microsoft com en us legal intellectualproperty trademarks usage general use of microsoft trademarks or logos in modified versions of this project must not cause confusion or imply microsoft sponsorship any use of third party trademarks or logos are subject to those third party s policies

adversarial-attacks chatgpt evaluation large-language-models robustness prompt prompt-engineering benchmark

ai

hq

one tool to rule them all p align center a href https hqjs org target blank img alt hqjs src hqjs logo png width 861 a p features you already know it it is just a static server that delivers your application code files zero configuration one command and you are good to go supports all kinds of frameworks polymer svelte vue react angular and many others out of the box understands all popular formats js jsx mjs es6 svelte vue ts tsx coffee json css scss sass less html and pug delivers without bundling to reflect project structure in the browser and make it easier to understand and develop makes debugging a pleasure no more missing sourcemaps and obfuscated bundles situations when you can t put a breakpoint on the line or expression ugly webpack names behind the code and empty debugging tooltips relies on the latest web standards so you can use cutting edge features even if your browser lacks them light and fast ships minimum that is required with no bundlers overhead only the files you change are delivered vscode extension get visual studio code extension https marketplace visualstudio com items itemname hqjs hq live server and run hq with single go live button click installation install it once with npm sh npm install g hqjs hq usage run inside project root sh hq it will find your source code and serve it make sure that you have nodejs 12 10 0 and no unexpected babelrc postcssrc or posthtmlrc in a project root if problem occurs please raise an issue https github com hqjs hq issues build you can use hq to prepare your code for regular static server type sh hq build in a project root and build result will appear in dist folder in case hq missed something you can pass build target as an argument to build command e g sh hq build src particle png it will do proper tree shaking and consist of both module and nomodule versions previous content of dist folder will be erased why hq there are many development tools out there including browserify webpack rollup and parcel that provide development servers but all of them rely on bundling while bundling might still be usefull for production it makes the development experience quite a struggle without bundling hq dramatically increases development speed by shipping only files that were changed and improves debugging by providing minimal transformation to a source with hq you can start a new project instantly just type hq and you are ready for experiments it supports all kinds of frameworks out of the box so there is no need to learn all their different tools and know all the buzzwords it is worth to say that hq requires no configuration offering the familiar experience of working with a regular static server how it works in server mode hq serves every file individually as requested same way regular static server does that gives you only very simple dead code elimination without proper tree shaking but on the other hand a lot of time that was wasted for dependency analysis is being saved all transforamtions are instant and performed on the fly during the first request if you use modern browser and stick to the standard your code would hardly be changed at all while you try to follow the standards you can t guarantee that all that libraries that you depend on will do the same most of them will probably use commonjs modules format and won t work in the browser just as they are hq takes care of that as well and transforms commonjs modules into esm handles non standard but pretty common imports like css or json importing and destructure importing objects when it is required hq will work tightly with the browser using its cache system to speed up asset delivery and only delivers what has been changed it will automatically reload the page when you modify the code so you will see the feedback immediatly it can work with many different frameworks but does not rely on any of that frameworks code in particular instead hq performs general ast transformations with babel through plugins that were designed for hq to help it understand all diversity of different technologies and technics used in those frameworks example let s say we have an existing angular project and want to improve development experience with hq all we need to do is to add our global style file and script to the head and body of index html correspondingly so when hq serves index it will serve styles and scripts as well html doctype html html lang en head link rel stylesheet href styles css head body app root app root script src main ts script body html for most of the frameworks that is already enough and you can skip the next step but angular requires a bit more attention it depends on zones and reflect metadata apis that are on very early stages and are not supported by hq out of the box in fact angular includes them in file polyfills ts and adds the file to your build so we are going to import missing dependencies on top of main ts js import core js proposals reflect metadata import zone js dist zone import zone js dist zone patch canvas and that s it now you are ready to start developing by running sh hq in the project root is it good for production yes you can definitely use hq build command to make a production ready build with all necessary optimisations created for you by hq alternatively you can use hq as a production server for internal projects such as admin panels and dashboards it will work perfectly with all modern browsers to activate production mode set node env to production before running hq sh node env production hq does it support http2 yes it does drop your certificate and a key somewhere in the root of your project and hq will serve it trough http2 e g cert server pem cert server key pem for generating self signed certificates check this tool https github com filosottile mkcert more benefits with babelrc postcssrc and posthtmlrc with hq you don t need to take care of babel postcss or posthtml configuration the latest web standards will be supported out of the box however if you need to support a feature that does not have a common interpretation like svg react imports or experimental features from an early stage like nested css or you have your own plugins that only make sense in your project just add babelrc postcssrc or posthtmlrc configurations to the root of your project with the list of all desired plugins e g babelrc json plugins babel plugin transform remove console postcssrc json plugins postcss nested preserveempty true posthtmlrc json plugins posthtml doctype doctype html 5 posthtml md and they will be automatically merged with hq configuration do not forget to install these additional plugins to your project before running hq license mit license

static-server build-tool javascript css html assets web development polymer vue react angular esmodules

front_end

lummox

img alt logo align right src https raw github com smaxwellstewart lummox master images logo png lummox version 0 1 br dev board https trello com b kdhj89eq https trello com b kdhj89eq a user service designed for soa systems responsiblities create read update and delete of users issuing jwt refresh and access tokens build status https travis ci org smaxwellstewart lummox svg branch master https travis ci org smaxwellstewart lummox coverage status https coveralls io repos smaxwellstewart lummox badge svg branch master service github https coveralls io github smaxwellstewart lummox branch master goals to create a user service for distributed soa systems create a rock solid scalable and highly configurable api provide a simple authentication and authorization mechanism when extending the system to other services convenient ui for managing users quick to deploy config options the following options can be configured in server config config js port port application runs from server a hapi https github com hapijs hapi server configuration https github com hapijs hapi blob master api md new serveroptions object see docs for full details api api configuration object prefix prefix to path of api routes version version added to path of api routes mongouri a mongodb connection uri jwt options for configuring generating jwt tokens key this is a secret key all tokens are signed and verified with expiresin this is the amount of minutes access tokens last for verifyoptions a jsonwebtoken https github com auth0 node jsonwebtoken verify options object see docs for full details auth auth configuration object scopes list of allowed scopes getall a hapi https github com hapijs hapi route auth configuration https github com hapijs hapi blob master api md route options for the get all users api route getone a hapi https github com hapijs hapi route auth configuration https github com hapijs hapi blob master api md route options for the get a single user api route update a hapi https github com hapijs hapi route auth configuration https github com hapijs hapi blob master api md route options for the update user api route delete a hapi https github com hapijs hapi route auth configuration https github com hapijs hapi blob master api md route options for the delete user api route getscopes a hapi https github com hapijs hapi route auth configuration https github com hapijs hapi blob master api md route options for the get user scopes api route saltrounds the amount of rounds passwords will be salted for during bcrypt hashing swaggeroptions a hapi swagger plugin options https github com glennjones hapi swagger options object see docs for full details api users get prefix version users get all user s in system get prefix version users id get a single user in the system post prefix version users create a user put prefix version users id update a user in the system delete prefix version users id delete a user in the system scopes get prefix version users scopes get all allowed user scopes tokens post prefix version tokens refresh create a new refresh token post prefix version tokens access create a new access token authentication workflow 1 create a user when a new user is created the password field is bcrypted the scope field is an array of access scopes that will be added to access tokens when the user generates one the list of allowed scopes can be configured in server config config js request post api v1 users json username l337 email email example com password 123456 scope admin active true response json id 562e689ca8b13f3b2f6fbeef username l337 email email example com password 123456 scope admin active true 2 generate a refresh token the user s credentials are supplied to generate a jwt refresh token this token is not used directly for accessing protected routes instead it is used to generate new access tokens the refresh token is meant as a long lived token and as such has a unique id attached to it this id is also attached to the user allowing for revocation of refresh tokens at any point example call request post api v1 tokens refresh json username l337 password 123456 response json token your refresh token 3 get an access token this is where you get an access token using your refresh token the user s scope is attached to the token at this stage more secure request post api v1 tokens access header authorization your refresh token less secure request post api v1 tokens access token your refresh token response json token your access token you can now use this token to access protected routes with moderately complex access control use cases i e any user admins only admins and managers only specific users etc how to deploy the service has been designed to be very straightforward to deploy to a platform like heroku or dokku simply grab the code configure the service to your liking and push here s an example of how to do it for heroku bash git clone https github com smaxwellstewart lummox cd lummox vim server config config js git commit am configured lummox to my liking heroku create heroku config set mongo uri mongodb localhost 27017 lummox heroku config set jwt secret changeme git push heroku master hardening once deployed rate limiting the token routes is a good idea to protect against denial of service attacks and limit the amount of access tokens being generated how to use with other services other services can be authenticated without need for a db lookup the json web tokens are used to transmit claims about a user there is definitely a catch to this model which is that session info embedded in your json web token is limited in size you can store a useful amount of data but they shouldn t be too huge a way to get around this for services which need more session is to have another service like a session profile service to return extra data about a user this can increase latency by adding another round trip as always understand your use case and what this solution offers before implementing anything stack ham h is for hapi hapi is an awesome node js framework for creating robust apis a is for angular angular is a great way to quickly build powerful frontend applications m is for mongodb mongo is a great nosql database that handles json natively perfect fit for node js projects contributors simon maxwell stewart development jenna enman artwork carlos alvarez theme

os

node_express1.1

node express1 1 working on node with expressjs for a backend app development

server

FullStack_Info_Lab02

fullstack info lab02 let s recreate netflix fork and clone this gitub repository add the other movies with their appropriate titles include links so that all the navigation works create an equal sized grid so that all four movies fall within one line hint use other news item fit each movie within each grid hint use other news item fix the header so that it doesn t move from the top of the window inspect the dom to style according to netflix colors use inline block instead of float for the id site title

front_end

GPT-Model

gpt model this is a large language model like gpt

ai

faang-system-design

tips for system design https github com neerazz faang system design blob master tips for system design md general design questions tiny url https github com neerazz faang system design tree master neeraj systemsdesign tiny url cab booking service https github com neerazz faang system design tree master neeraj systemsdesign cab booking system amazon https github com neerazz faang system design blob master resources architecture diagrams amazon 20system 20design png zoom https github com neerazz faang system design blob master resources architecture diagrams zoom 20system 20design png google map https github com neerazz faang system design blob master resources architecture diagrams google 20maps 20design png facebook https github com neerazz faang system design blob master resources architecture diagrams facebook 20system 20design png hotel booking https github com neerazz faang system design blob master resources architecture diagrams hoel 20booking 20system png uber https github com neerazz faang system design blob master resources architecture diagrams uber 20system 20design png whatsapp https github com neerazz faang system design blob master resources architecture diagrams whatsapp 20system 20design png netflix https github com neerazz faang system design blob master resources architecture diagrams video 20streaming 20platform png oops design design library management system https github com neerazz faang system design tree master neeraj oops designs librarymanagment design parking lot https github com neerazz faang system design blob master neeraj oops designs parkinglot design call center https github com neerazz faang system design blob master neeraj oops designs callcenter callcenter java design hit counter https github com neerazz faang system design blob master neeraj oops designs designhitcounter java design linux find command https github com neerazz faang system design blob master neeraj oops designs linuxfindfilter java design atm https github com neerazz faang system design tree master neeraj oops designs atm design movie ticket system https github com neerazz faang system design tree master neeraj oops designs movieticketsystem design pharmacy shop https github com neerazz faang system design tree master neeraj oops designs pharmacyshop design online stock broker https github com neerazz faang system design tree master neeraj oops designs onlinestockexchange todo design vending machine https leetcode com discuss interview question 982302 fanng question oop please post your solutions to this useful links code karle https www codekarle com index html

system-design faang

os

NLPInvoiceClassification

nlp invoice classification flask application an application to automatically classify invoices using natural language processing and machine learning this flask application was developed as a proof of concept to demonstrate the capability of classification algorithms to automate the classification of a large number of unclassified invoices all stages of the development are included in this repository including code used in the initial data investigation and model development including explanations of thought processes and approach see model dev directory code used to create the pipelines and pickle files see pkl creation directory code used in the flask app including the html and css all code not in model dev or pkl creation directories note this repository does not contain the pkl files required to run the flask application due to file size limitations however these can be generated from any similar dataset using the relevant code in the pkl creation directory running the application in order to run the application two pkl files will need to be created using the code in pkl creation directory and some relevant training data data as with all supervised machine learning classification models the data used to train the model must be representative of the data required to be classified also the training data must have the classifications for each row of data whereas the test data should not have the classifications the pkl files should be created using the training data which must contain the fields business unit supplier name supplier group invoice desc invoice amt invoice currency usd amt project owning org datasource legacy year leakage identifier leakage group intercompany flag americas flag classification group nb the data to be classified will not include the field classification group as this is what the model will predict once the pkl files are created the application can be run save the two pkl files in the same directory as the code for the flask app flask app v01 py not in any sub directory then navigate to that directory in the command prompt and run python flask app v01 py copy and paste the generated url into google chrome any other browser will work but the app may not be displayed as expected using the application there are two demo options for running the model using the application manual input of data into a web form and upload a csv file with the relevant columns the tab to link to a database is a placeholder only click on the required link in the header bar enter ther required information or browse for the file to be uploaded the click the button to classify the data code for initial model development in order to provide a more useful overview of the entire process from initial data to final demo i have included the code i used for investigating the data and testing different pre processing steps and models including commentary on my thought processes and approach hopefully some might find this useful

ai

E7020E

e7020e repository for the course embedded system design

os

Camera-Mounted-Bluetooth-Model-Car

camera mounted bluetooth model car embedded systems six person project known as wacky racers to design and implement a camera mounted bluetooth controlled model car the project had a requirement that three separate 4 layer boards were developed each with their own microcontroller and communicating over a common i2c bus an stm32f4 microcontroller was selected for the project motor control power board img src https github com mkokshoorn camera mounted bluetooth model car blob master populated board images motorboard jpg width 360 img src https github com mkokshoorn camera mounted bluetooth model car blob master motorschematic png width 460 camera board img src https github com mkokshoorn camera mounted bluetooth model car blob master populated board images cameraboard jpg width 360 img src https github com mkokshoorn camera mounted bluetooth model car blob master cameraschematic png width 460 communications board img src https github com mkokshoorn camera mounted bluetooth model car blob master populated board images commsboard jpg width 360 img src https github com mkokshoorn camera mounted bluetooth model car blob master commsschematic png width 460 img src https github com mkokshoorn camera mounted bluetooth model car blob master commsschematic2 png width 420 img src https github com mkokshoorn camera mounted bluetooth model car blob master commsschematic3 png width 420

os

namada-testnets

namada testnets testnet configurations and coordination for the namada namada public testnet 1 namada public testnet 1 namada public testnet 2 namada public testnet 2 namada public testnet 1 namada public testnet 3 namada public testnet 2 namada public testnet 4 namada public testnet 1 namada public testnet 5 namada public testnet 2 namada public testnet 6 namada public testnet 1 namada public testnet 7 namada public testnet 2 namada public testnet 8 namada public testnet 1 namada public testnet 9 namada close quarters testnet 5 namada close quarters testnet 5 namada close quarters testnet 4 namada close quarters testnet 4 namada close quarters testnet 3 namada close quarters testnet 3 namada close quarters testnet 2 namada close quarters testnet 2 namada close quarters testnet 1 namada close quarters testnet 1

anoma blockchain namada

blockchain

esp-wolfssl

esp wolfssl cryptographic embedded library for esp open rtos this is a wolfssl http wolfssl com library packaged for esp open rtos https github com superhouse esp open rtos

os

blockchain

blockchain hyperledger fabric v1 0 5 https github com liuboyu hyperledger fabric docs zh cn email boyu liu gmail com https yeasy gitbooks io blockchain guide content http book 8btc com books 1 master bitcoin book http book 8btc com books 1 gaosheng blockchain report book http book 8btc com books 6 thfr201704 book https daimajia com 2017 08 24 how to start blockchain learning http www 8btc com who controls blockchain http www 8btc com bytom duanxinxing r3 corda http ethfans org posts r3 corda announcement introducing r3 corda a distributed ledger designed for financial services http www r3cev com blog 2016 4 4 introducing r3 corda a distributed ledger designed for financial services http www 8btc com ethfans http ethfans org http www jianshu com p 00e17ee7c646 merkle tree bitcoin merkle tree http www cnblogs com fengzhiwu p 5524324 html merkle http www 8btc com merkling in ethereum merkling in ethereum https blog ethereum org 2015 11 15 merkling in ethereum vitalik buterin merkle patricia tree ethereum understanding the ethereum trie https easythereentropy wordpress com 2014 06 04 understanding the ethereum trie ethereum ethereum patricia tree https github com ethereum wiki wiki patricia tree ethereum wiki mpt merkle patricia tree http blog csdn net qq 33935254 article details 55505472 merkle patricia tree mpt merkle http blog csdn net zslomo article details 53434883 t 1498537389197 merkle patricia tree mpt http www cnblogs com fengzhiwu p 5584809 html ethereum rlp https github com ethereum wiki wiki 5b e4 b8 ad e6 96 87 5d rlp ethereum wiki rlp https my oschina net u 2349981 blog 894117 pow pos dpos pbft http blog csdn net lsttoy article details 61624287 https yq aliyun com articles 60400 http 8btc com article 2238 1 html http blog csdn net jeffrey zhou article details 56672948 https www zhihu com question 53385152 paxosstore paxos http www infoq com cn articles wechat paxosstore paxos algorithm protocol raft http www infoq com cn articles raft paper pos https yq aliyun com articles 60400 pos pow http 8btc com article 1882 1 html dpos http www 8btc com dpos back to satoshi dpos http www 8btc com dpossha dpos https steemit com dpos legendx dpos dpos vs pow https zhuanlan zhihu com p 28127511 pos dpos pow https www zhihu com question 49995385 zero knowledge proof http songshuhui net archives 36968 blockchain v1 0 bitcoin http www 8btc com wiki bitcoin a peer to peer electronic cash system utxo http 8btc com article 4381 1 html http www 8btc com blockchain tech algorithm http www 8btc com blockchain tech mining 1 http www 8btc com blockchain tech consensus mechanism bitcoin core btc https bitcoin org bitcoin segwit github https github com bitcoin bitcoin http www 8btc com tan90d34 segwit http www 8btc com segwit 0829 segwit bitcoin cash bcc https bitcoincash org bitcoin 8m github https github com bitcoin abc bitcoin abc bitcoin cash bcc https www zhihu com question 63109943 zcash zec https z cash bitcoin github https github com zcash zcash zcash http 8btc com thread 41384 1 1 html zcash http www sohu com a 121847942 475384 zcoin dash monero blockchain v1 0 http blog csdn net elwingao article details 53410750 http www 8btc com bytom sidechain rootstock http www 8btc com sidechains drivechains and rsk 2 way peg design sidechains https blockstream com technology bitcoin pegged sidechains lightning network http www 8btc com enabling blockchain innovations with pegged sidechains abstract introduction off chain http view xiaomiquan com view 59a3e22d2540ed222c6075b8 corda http www 8btc com ln rn corda rootstock rsk http www rsk co bitcoin rootstock http www 8btc com tan90d88 rootstock http www 8btc com tan90d84 btc relay rootstock http www 8btc com btc relay and rootstock btc relay http btcrelay org ethereum bitcoin polkadot cosmos blockchain v1 x ipfs https ipfs io https gguoss github io 2017 05 28 ipfs https github com ipfs papers raw master ipfs cap2pfs ipfs p2p file system pdf filecoin http filecoin io http chainx org paper index index id 13 html https filecoin io filecoin pdf bigchaindb https www bigchaindb com http blog csdn net fengqing79 article details 70154076 https www bigchaindb com whitepaper blockchain v1 x http www 8btc com dream on http www 8btc com edb https zhuanlan zhihu com p 27415529 ripple nxt bitshares https bitshares org dpos dac github https github com bitshares http www 8btc com bitshares white pape cryptonomex https cryptonomex com bitshares v2 0 github http github com cryptonomex cryptonomex http www 8btc com cryptonomex dan larimer blockchain v1 x dapps steem https steem io steemit https steemit com github http github com steemit https biweilai com tag steem https steem io steemwhitepaper pdf yoyow https yoyow org steem https yoyow org files white paper2 pdf ugc http www jianshu com p e044e51e06bb blockchain v2 0 ethereum ethereum eth https ethereum org v2 0 github https github com ethereum go ethereum eth ethereum etc ethereum classic https github com ethereum wiki blob master 5b e4 b8 ad e6 96 87 5d e4 bb a5 e5 a4 aa e5 9d 8a e7 99 bd e7 9a ae e4 b9 a6 md ethereum white paper https github com ethereum wiki wiki white paper ethereum wiki http book 8btc com books 6 ethereum book teahour http ethfans org shaoping articles talk with jan about ehtereum http ethfans org wikis e6 99 ba e8 83 bd e5 90 88 e7 ba a6 http ethfans org wikis e4 bb a5 e5 a4 aa e5 9d 8a e5 bc 80 e5 8f 91 e8 ae a1 e5 88 92 10 http www 8btc com scaling ethereum to billions of users http me tryblockchain org getting up to speed on ethereum html solidity http wiki jikexueyuan com project solidity zh http ethfans org posts block chain technology smart contracts and ethereum dapps 15 http www 8btc com ethereum top 10 app the dao ethereum decentralized autonomous organization or the dao http www 8btc com what is the dao the dao http www jinse com news blockchain 50751 html dao http www 8btc com brexit and the dao swarm whisper btc relay http btcrelay org oraclize blockchain v2 0 others eos https eos io github https github com eosio eos http www jianshu com p f65bf7691482 eos io http www jianshu com p bc489db794ce eos vs http www jianshu com p 1afec85afd3c neo qtum blockchain dag dag http www qukuaiwang com cn news 1913 html iota https steemit com cn niking iota iota https iota org dag http www iotachina com wp content uploads 2016 11 2016112902003453 pdf https iota org iota whitepaper pdf byteball https byteball org dag http www bitett com portal php mod view aid 438 http www bitett com portal php mod view aid 453 https bitcointalk org index php topic 1840404 https byteball org byteball pdf ep chain https ep chain org dag ico https ep chain org download epc v1 00 pdf iota byteball nerthus http www nerthus io dag ico http www nerthus io static downfile nerthuswhitepage pdf iota byteball askcoin https askcoin org dag ico https askcoin org askcoin white paper cn pdf iota byteball 1 2 3 4 http ethfans org posts blockchain infrastructure landscape a first principles http www 8btc com elwingao blockchain 6 cto http www 8btc com blockchain architecture consensus byzantine generals problem byzantine fault tolerant bft double spend attack spv pow proof of work mine pos proof of stake mint nxt forge dpos delegated proof of stake pbft hyperledger fabric dbft paxos raft dmms dynamic membership multiparty signatures sha256 bitcoin scrypt litecoin hefty1 ethash ethereum equihash zcash merkle tree merkle root merkle patricia tree getwork getblocktemplate stratum auxiliary proof of work auxpow auxiliary blockchain bitcoin version verack addr getaddr getblocks inv getdata getheaders headers filterload filteradd filterclear

blockchain

blockchain

my-code

my code mobile development

front_end

ez-rtos

ez rtos a simple real time operating system features task switching by arm cortex m3 systick delay delay unsigned int memory allocator malloc size t free void critical section

os operating-system operating-systems computer-science arm armcortexm3 cortex-m cortex-m3 mcu real-time-operating-system

os

xinu-avr

xinu avr the xinu avr project is a xinu operating system subset modified to run on an avr atmega328p microcontroller e g arduino boards visit its web page for detailed instructions http se fi uncoma edu ar xinu avr a href https www youtube com watch v jacuup bkiu title demo video xinu os into avr atmega328p img style float right alt demo video xinu os into atmega328p mcu src http se fi uncoma edu ar xinu avr www files placa3 jpg a at present the core pieces of xinu are working so you can already integrate it in the development of multi tasking embedded systems you will also need any bare avr mcu or arduino board of course for lovers of because small is beautiful fusixos retrobsd unix in microcontrollers etc this project provides a user interface example as well the xinu shell and some tiny versions of utilities like echo a text editor a basic interpreter ps kill free date cal and some more check the demo video https www youtube com watch v jacuup bkiu if you want to see a xinu avr session in a little avr mcu like a retro computer system the source code is comprise of 1 the xinu os for avr atmega328p microkernel 2 basic examples apps of how to use xinu 3 a complete example the xinu shell and tiny versions of several unix like utilities a name whatisxinu a what is xinu xinu is a small elegant and easy to understand operating system originally developed by douglas comer for instructional purposes at purdue university in the 1980s since then it has been ported to many architectures and hardware platforms xinu uses powerful primitives to provides all the componentes and the same functionality many conventional operating sytems supply because the whole source code size is small xinu is suitable for embedded systems strong the xinu operating system includes strong dynamic process creation dynamic memory allocation real time clock management process coordination and synchronization local and remote file systems a shell and device independent i o functions the xinu operating system is documented in the book d comer operating system design the xinu approach second edition crc press 2015 isbn 9781498712439 https xinu cs purdue edu textbook many sites defines xinu as a free unix system or similar statements it is not xinu differs completely from the internal structure of unix or linux for academic purposes xinu is smaller elegant and easier to understand applications written for one system will not run on the other without modification xinu is not unix history xinu originally ran on digital equipment corporation lsi 11 s with only 64k bytes of memory at the end of 1979 and the inning of 1980 over the years xinu have been expanded and ported to a wide variety of architectures and platforms including ibm pc macintosh digital equipment corporation vax and decstation 3100 sun microsystems sun 2 sun 3 and sparcstations and for several arm mips and x86 embedded boards it has been used as the basis for many research projects furthermore xinu has been used as an embedded system in products by companies such as motorola mitsubishi hewlett packard and lexmark there is a full tcp ip stack and even the original version of xinu for the pdp 11 supported arbitrary processes and network i o there are current versions of xinu for galileo intel boards arm beagle boards several mips platforms and for x86 pc hardware and virtual machines a name code a source code there is just one git repository and it has everything git http github com zrafa xinu avr the list below is just for convenience a href https github com zrafa xinu avr the xinu os for avr atmega328p a a href https github com zrafa xinu avr tree master apps example apps a a href https github com zrafa xinu avr tree master apps shell the xinu shell and tiny unix like utilities editor basic interpreter ps kill echo uptime sleep etc a a href https xinu cs purdue edu the official xinu page and code a a name authors a authors xinu os copyright c 2012 2015 douglas e comer and crc press inc this version for avr atmega328p v0 1 c 2020 rafael ignacio zurita rafa fi uncoma edu ar acknowledgments michael m minor he is the author of another avr port os xinu a href https sites google com site avrxinu avrxinu a we use his context switch code the addargs in xinu shell and a few lines more his port is for bigger avr microcontrollers 16kb of ram and he used an old version of xinu xinu from the 1987 book edition a name notes a notes about the xinu os port for avr atmega328p current official xinu versions are designed for arm mips and x86 architectures the hardware differences between those and the ultra small avr microcontroller required changes to some low level data structures of xinu mainly using the flash memory in the avr mcu for keeping several read only data structures previously in ram also several limits were imposed so the read write data structures fits into the sram avr memory the xinu version for avr atmega328p has the core functionality of xinu and provides some extensions including an eeprom file system and several unix like utilities for the xinu shell this mcu has just 2kb of sram 32kb of flash memory and 1kb of eeprom the xinu version for avr uses 12kb of flash and 0 9kb of ram so there is still 50 of room sram and flash for the embedded application running on xinu concurrent processes so this project might be stimulating and very fun for lovers of embedded systems development and operating system internals notes about the port 1 max number of processes 4 to 8 2 main process is now the embedded application process 3 max number of semaphores 2 to 6 the size of the table of process queues depends on this 4 max number of devices 4 to 5 4 the clkhandler wakeup a process preemption for cpu every 100ms 5 several limits for buffers 32bytes for tty input 16bytes for names of devices 1byte for the queues keys and the list continues 6 sleepms is now delay sleep100ms and the key for queue char in ms 100 7 many vars in data structures have a smaller size e g before int32 now char 8 sleep sleeps max 20 seconds date type 9 most of the libc are from avr libc 10 init load bss and data from flash to ram from avr libc 11 shell manages max 6 tokens 12 date and time is managed by a little lib no ntp or rtc 13 most of the const char in source code were moved to flash program space via flash directive from gcc or progmem from avr libc 14 tty in is asynchronous with interrupts ok but tty out is polled based synchronous 15 open read write seek close use struct dentry it is on flash on this port 16 remote file systems local file systems ram file systems are disabled so far 17 ports ptinit ptsend ptrecv etc are disabled so far 18 null process has priority 1 a name douglas a douglas comer douglas comer is a professor of computer science at purdue university who was inducted into the internet hall of fame on september 2019 as one of the earliest tcp ip and internetworking researchers comer wrote the first series of textbooks explaining the scientific principles underlying the design of the internet and its communications protocols providing some of the earliest formal guidance for building efficient networks and applications that use the internet comer s three volume textbook series internetworking with tcp ip written in 1987 is widely considered to be the authoritative reference for internet protocols the series played a key role in popularizing internet protocols by making them more understandable to a new generation of engineers and it professionals prof douglas comer designed and developed the xinu operating system in 1979 1980 douglas comer page https www cs purdue edu homes comer internet hall of fame https www cs purdue edu news articles 2019 comer ihof html

xinu douglas comer operating-system douglas-comer xinu-os embedded-systems rtos arduino arduino-uno

os

MQNLP

mqnlp nlp git 1 1 lr tfidf http blog csdn net macanv article details 78963762 2 naive bayes n gram http blog csdn net macanv article details 78964020 3 svm svm svm pca lda 4 fasttext facebook fasttext api tensorflow fasttext 5 textcnn 6 textrnn rnn rnn cell lstm gru bi lstm bi gru num layer rnn 7 textcnnrnn cnn maxpooling rnn 8 2 bilstm crf hmm blog http www matrix67 com blog archives 5044 https github com moonshile chinesewordsegmentation python3 2 1 3 https github com zjy ucas chinesener 4 ner bilstm crf 4 5 tf idf textrank 6 seq2seq 7 gan 1 gan gan py 2 conditional gan cgan py 3 7 face

fasttext textclassification textcnn textrnn lstm sequence-labeling ner pos-tagging segment

ai

garfish

div align center img src https lf3 static bytednsdoc com obj eden cn dhozeh7vhpebvog open garfish icons garfish icon square png width 300 alt garfish div div align center npm version https img shields io npm v garfish svg style flat square https www npmjs com package garfish build status https github com modern js dev garfish actions workflows ci yml badge svg branch main https github com modern js dev garfish actions workflows ci yml div div align center english readme ch md div h1 h1 garfish is a micro front end framework mainly used to solve the problems of cross team collaboration diversification of technology system and increasing complexity of applications brought by modern web applications in the context of front end ecological boom and increasing complexity of web applications and garfish has been polished and tested by a large number of online applications with strong functional stability and reliability garfish goals to compose multiple independently delivered front end applications into a whole and to decompose front end applications into some smaller and simpler applications that can be independently developed independently tested and independently deployed while still appearing to users as cohesive individual products installation bash npm npm install garfish yarn yarn add garfish documentation the doc site garfish https garfishjs org is only available in chinese for now we are planning to add english versions soon about garfish https garfishjs org guide quick start https www garfishjs org guide start api references https garfishjs org api functionality rich and efficient product features garfish micro front end sub application supports any kind of framework and technology system access garfish micro front end sub application supports independent development independent testing and independent deployment powerful pre loading capability automatically record user application loading habits to increase loading weight and greatly reduce application switching time support for dependency sharing which greatly reduces the overall package size and the repeated loading of dependencies support data collection effectively perceive the state of the application during operation support for multiple instance capability to run multiple sub applications in the page at the same time enhances the business splitting efforts provides efficient and usable debugging tools to assist users in the micro front end model brings different development experience problems from the traditional r d model highly scalable core modules supports html entry and js entry through the loader core module making it easy to access micro front end applications router module provides route driven master child route isolation users only need to configure the routing table application can complete the independent rendering and destruction the user does not need to care about the internal logic sandbox module provides runtime isolation for the application s runtime which can effectively isolate the side effects of js and style on the application store provides a simple mechanism for exchanging communication data highly scalable plugin mechanism provide business plugins to meet various customization needs of business parties contributing contributing guide https github com modern js dev garfish blob main contributing md credit qiankun https github com umijs qiankun is a very good micro front end framework garfish sandbox design learned a lot of good ideas single spa https github com single spa single spa for community raised a hot wave of micro front end solutions learned a lot of amazing garfish routing system design and for the garfish bridge design we have refered the implementation of the bridge part in single spa https github com single spa single spa and we think it is a good design so we fork the code of the bridge implementation part and make some adjustments for the lifecycles in garfish garfish s css scope plugin is inspired by the implementation of reworkcss https github com reworkcss css and some some of the implementation code is forked from reworkcss https github com reworkcss css for different scenarios the design of garfish s es module plugin was inspired by the babel traverse https github com babel babel and es module shims https github com guybedford es module shims libraries and some of the implementation code is forked from babel traverse https github com babel babel

framework javascript

front_end

training_extensions

div align center openvino training extensions key features key features installation https openvinotoolkit github io training extensions latest guide get started installation html documentation https openvinotoolkit github io training extensions latest index html license license pypi https img shields io pypi v otx https pypi org project otx markdownlint disable md042 python https img shields io badge python 3 8 2b green pytorch https img shields io badge pytorch 1 13 1 2b orange openvino https img shields io badge openvino 2023 0 purple markdownlint enable md042 codecov https codecov io gh openvinotoolkit training extensions branch develop graph badge svg token 9hvfnmpfgd https codecov io gh openvinotoolkit training extensions pre merge test https github com openvinotoolkit training extensions actions workflows pre merge yml badge svg https github com openvinotoolkit training extensions actions workflows pre merge yml nightly test https github com openvinotoolkit training extensions actions workflows daily yml badge svg https github com openvinotoolkit training extensions actions workflows daily yml build docs https github com openvinotoolkit training extensions actions workflows docs yml badge svg https github com openvinotoolkit training extensions actions workflows docs yml license https img shields io badge license apache 202 0 blue svg https opensource org licenses apache 2 0 downloads https static pepy tech personalized badge otx period total units international system left color grey right color green left text pypi 20downloads https pepy tech project otx div introduction openvino training extensions is a low code transfer learning framework for computer vision the cli commands of the framework allows users to train infer optimize and deploy models easily and quickly even with low expertise in the deep learning field openvino training extensions offers diverse combinations of model architectures learning methods and task types based on pytorch https pytorch org and openvino toolkit https software intel com en us openvino toolkit openvino training extensions provides a model template for every supported task type which consolidates necessary information to build a model model templates are validated on various datasets and serve one stop shop for obtaining the best models in general if you are an experienced user you can configure your own model based on torchvision https pytorch org vision stable index html mmcv https github com open mmlab mmcv timm https github com huggingface pytorch image models and openvino model zoo omz https github com openvinotoolkit open model zoo furthermore openvino training extensions provides automatic configuration for ease of use the framework will analyze your dataset and identify the most suitable model and figure out the best input size setting and other hyper parameters the development team is continuously extending this auto configuration https openvinotoolkit github io training extensions latest guide explanation additional features auto configuration html functionalities to make training as simple as possible so that single cli command can obtain accurate efficient and robust models ready to be integrated into your project key features openvino training extensions supports the following computer vision tasks classification including multi class multi label and hierarchical image classification tasks object detection including rotated bounding box support semantic segmentation instance segmentation including tiling algorithm support action recognition including action classification and detection anomaly recognition tasks including anomaly classification detection and segmentation openvino training extensions supports the following learning methods https openvinotoolkit github io training extensions latest guide explanation algorithms index html supervised incremental training which includes class incremental scenario and contrastive learning for classification and semantic segmentation tasks semi supervised learning self supervised learning openvino training extensions provides the following usability features auto configuration https openvinotoolkit github io training extensions latest guide explanation additional features auto configuration html openvino training extensions analyzes provided dataset and selects the proper task and model with appropriate input size to provide the best accuracy speed trade off it will also make a random auto split of your dataset if there is no validation set provided datumaro https openvinotoolkit github io datumaro stable index html data frontend openvino training extensions supports the most common academic field dataset formats for each task we are constantly working to extend supported formats to give more freedom of datasets format choice distributed training to accelerate the training process when you have multiple gpus mixed precision training to save gpus memory and use larger batch sizes integrated efficient hyper parameter optimization module hpo https openvinotoolkit github io training extensions latest guide explanation additional features hpo html through dataset proxy and built in hyper parameter optimizer you can get much faster hyper parameter optimization compared to other off the shelf tools the hyperparameter optimization is dynamically scheduled based on your resource budget getting started installation please refer to the installation guide https openvinotoolkit github io training extensions latest guide get started installation html note python 3 8 3 9 and 3 10 were tested along with ubuntu 18 04 20 04 and 22 04 openvino training extensions cli commands otx find helps you quickly find the best pre configured models templates as well as a list of supported backbones otx build creates the workspace folder with all necessary components to start training it can help you configure your own model with any supported backbone and even prepare a custom split for your dataset otx train actually starts training on your dataset otx eval runs evaluation of your trained model in pytorch or openvino ir format otx optimize runs an optimization algorithm to quantize and prune your deep learning model with help of nncf https github com openvinotoolkit nncf and pot https docs openvino ai latest pot introduction html tools otx export starts exporting your model to the openvino ir format otx deploy outputs the exported model together with the self contained python package a demo application to port and infer it outside of this repository otx demo allows one to apply a trained model on the custom data or the online footage from a web camera and see how it will work in a real life scenario otx explain runs explain algorithm on the provided data and outputs images with the saliency maps to show how your model makes predictions you can find more details with examples in the cli command intro https openvinotoolkit github io training extensions latest guide get started cli commands html updates v1 4 0 3q23 support encrypted dataset training https github com openvinotoolkit training extensions pull 2209 add custom max iou assigner to prevent cpu oom when large annotations are used https github com openvinotoolkit training extensions pull 2228 auto train type detection for semi sl self sl and incremental train type now is optional https github com openvinotoolkit training extensions pull 2195 add per class xai saliency maps for mask r cnn model https github com openvinotoolkit training extensions pull 2227 add new object detector deformable detr https github com openvinotoolkit training extensions pull 2249 add new object detector dino https github com openvinotoolkit training extensions pull 2266 add new visual prompting task https github com openvinotoolkit training extensions pull 2203 https github com openvinotoolkit training extensions pull 2274 https github com openvinotoolkit training extensions pull 2311 https github com openvinotoolkit training extensions pull 2354 https github com openvinotoolkit training extensions pull 2318 add new object detector resnext101 atss https github com openvinotoolkit training extensions pull 2309 release history please refer to the changelog md changelog md branches develop https github com openvinotoolkit training extensions tree develop mainly maintained branch for developing new features for the future release misc https github com openvinotoolkit training extensions tree misc previously developed models can be found on this branch license openvino toolkit is licensed under apache license version 2 0 license by contributing to the project you agree to the license and copyright terms therein and release your contribution under these terms issues discussions please use issues https github com openvinotoolkit training extensions issues new choose tab for your bug reporting feature requesting or any questions known limitations misc https github com openvinotoolkit training extensions tree misc branch contains training evaluation and export scripts for models based on tensorflow and pytorch these scripts are not ready for production they are exploratory and have not been validated disclaimer intel is committed to respecting human rights and avoiding complicity in human rights abuses see intel s global human rights principles https www intel com content www us en policy policy human rights html intel s products and software are intended only to be used in applications that do not cause or contribute to a violation of an internationally recognized human right

openvino computer-vision deep-learning pytorch neural-networks-compression quantization hyper-parameter-optimization image-classification image-segmentation object-detection self-supervised-learning semi-supervised-learning transfer-learning action-recognition anomaly-detection datumaro automl machine-learning incremental-learning

ai

mock-ApiRest-Flask

english version here mock api rest with flask vers o em portugu s aqui mock api rest com flask mock api rest with flask the main purpose of this project is to create a mock api rest with flask to be used in the development of the frontend of the mobile project a react native repository is available here https github com eduardo da silva reactnative layout navigation installation the project is managed with pdm so you need to install the dependencies with bash pdm install run to run the project you need to execute the following command bash pdm run python main py mock api rest com flask este projeto tem como principal objetivo criar um mock de api rest com flask para ser utilizado no desenvolvimento do frontend do projeto mobile um reposit rio react native est dispon vel aqui https github com eduardo da silva reactnative layout navigation instala o o projeto gerenciado com pdm ent o voc precisa instalar as depend ncias com bash pdm install execu o para executar o projeto voc precisa executar o seguinte comando bash pdm run python main py

api-rest backend flask mock pdm python

front_end