Reposite is the all-in-one workspace built for travel and events professionals – Manage relationships, track proposals, discover new suppliers \u0026 grow your business

Reposite

Long story short: is a template engine changing documents on static webpages, that you can then host anywyere, because you don't need databases or server that has PHP or Python. Jekyll markdown HTML Usual Process Normally the process of adding new post looks like this: I write markdown document with setting parameters like title, date and tags when I’m happy with what I wrote (never), I commit changes and push it to repository on GitHub. Repository name comes from my nick and is also address for blog asvid.github.io Github after pushing to branch master builds website from sources using Jekyll - probably something like running jekyll build result of Jekyll build is not present in a repository, but you see it right now after visiting bloga page The above process works entirely automatically, no need of configuration on repo site or GitHub itself. All you need is having repo name that follows this pattern: . Normally it works. But... {user_name}.github.io I want plugins Sometimes you want to add something cool to your page. I wanted to have it in multiple languages, like Polish and English. Jekyll is not supporting this natively… but it is not impossible 😄 So I’ve found a plugin , that allows adding many languages on same page without need of complete rebuild or restructure. I used it, after a while it was kinda working. After a while longer it was working as I wanted. . Polyglot Locally After pushing changes to GitHub page wasn’t working properly. I learned that GitHub is not simply building everything you throw at it, but it has a of supported plugins. Polyglot is sadly not on this list, despite its creator's attempts to get it on the list. Whitelist itself is understandable for security reasons, we don’t want to GitHub go down because of some evil plugin or hidden bitcoin miner running on GitHub servers. whitelist But I really need this plugin Like every problem this one also has a solution. Even few of them. You can build the page locally and push it to the branch, and keep source on other branch like . There is also , that publishes NodeJS apps as GitHub Pages. master develop NodeJS package But I’m lazy and I don’t want to build manually and push sources and build result separately, also I don’t have NodeJS app, so I use solution number 3. GitHub Actions is a basic CI available for free for every repository. To use this CI you need to create Workflow with yaml configuration telling it what and when should happen. There are , and if  something is missing, you can do one yourself or combine few into one workflow. To add workflow go to your . GitHub Actions many available actions repository->actions->New workflow and then click link to set up a workflow yourself. This will add yaml file with config inside repository in directory .github/workflows To publish Jekyll blog with not whitelisted plugins I used configured inside workflow like this: Jekyll-Actions name: GitHub Pages publication on: push jobs: jekyll: runs-on: ubuntu-16.04 steps: - uses: actions/checkout@v2 # Use GitHub Actions' cache to shorten build times and decrease load on servers - uses: actions/cache@v1 with: path: vendor/bundle key: $-gems-$ restore-keys: |
          $-gems-
    # Standard usage - uses: helaili/jekyll-action@2.0.3 env: JEKYLL_PAT: $ # Specify the Jekyll source location as a parameter - uses: helaili/jekyll-action@2.0.3 env: JEKYLL_PAT: $ This action runs inside container, and then: ubuntu-16.04 downloads repository after each push (on every branch which is causing some issue) is using cache to not download same each run Gems runs action to build page and publish ot on branch , with a push that is possible thanks to master secrets.JEKYLL_PAT And this publishing on branch master means that you shouldn't push source changes to this branch. If you want to use this action, you need to push changes to develop and leave master only for files build and pushed by the script. How to create secret.JEKYLL_PAT Action itself works in isolated container and has no write access to your repository. It can read it because its public. To allow this, you need to create access token with public_repo scope, and then add it in repository as a secret with name expected by configuration . secret.JEKYLL_PAT You generate token from GitHub account settings: . After clicking on Generate new token fill the name and select checkbox public_repo. Settings->Developer Settings->Personal Access Tokens After clicking button on the bottom Generate token you will have the only chance to copy it, I suggest taking it 😃 Copied token needs to be pasted to repository secrets: . Name like in config and value is copied token. Settings->Secrets->New secret secrets.JEKYLL_PAT And it should work. At least works for me 😏 This article was also posted on Jekyll blog hosted on GitHub, with the use of free . Github Pages Previously published at https://asvid.github.io/github-page-deployment

A Simple Guide to Github Page Deployment

I think i have finally managed to put the finger on what is really bugging me with most ICOs and why they are not the value creator most people think they are. Bear with me in the analysis. First there was the token Any Blockchain Dapp starts with a , the economic unit that enables this app to work and gives enough incentives to everyone to build and/or use a given service. Token Tokens illustrate the dynamics of a decentralized network effect. But there is a chicken and egg problem: how do you get a token to get enough value to get the network going when your App is just getting started? Blockchain entrepreneurs have found the solution to that insanely complex problem the web has known for 3 decades: ICO The promise of the network effect By selling a dream project, a white paper, a nice github repositery, a fantastic team of uninvolved advisors and some hype, tokens are priced immediately and can be acquired very early on in the life of a project giving you a chance to own a % of the economy of this future project (as opposed to stock which gives you a chance to own a % of the future cash flow and value of the company). ICOs open the door to boostrapping the network effect when there is not even a product yet to create this network. It’s a 2-in-1 you get a community of “interest” a la kickstarter and you get S** load of money without dilution giving instant economic value to your token. All that is great. Except it’s not. In the current format, ICOs provide early on tokens a value, but also allow any token buyers to sell their token immediately. That translates in a market of super speculative activity around the value of the token, since there is no basic network effect metric to support the value (except in rare cases, read below) 95% of the value of a token is based on speculation today. The reason this is not great, is not because of the speculation in itself but because the first token are not token . Holders (or as we like to call it) are interested in holding the token long enough until the network value has taken over the speculative value. buyers holders Hodlers Imagine backing a kickstarter project. The pool of contribution is closing. The company is good to go with the project. But for some reason at this moment: every early backer start claiming their money back or cancel their product delivery even if the money is not moving back to their pocket. The contributors are not interested in the project Tokens, when put on the market early to trade without real network value to be built in the medium term, create, by design, a different set of incentives for the token buyers, interested in a fast trade and the token holders (those who really believe in the project and want to support it all the way) and eventually even the token producers (the company). If you raised a bunch of money in an ICO but your token is worth very little then you re going to have a hard time to give incentives to your network (not mentioning giving the appearance of value to your economy) Token ownership map You have different type of token owners : they get their share of tokens in the ICO but are locked up to a period that align them with the milestones of the network effect. Those are rarely putting the token value at risk The team : typically crypto funds that will back ICOs early on, or pre ICOs, those will usually align very well with group 1. Funds like Polychain, Pantera, DCG, Metastable or even hedge funds like Blocktower could be put in that group. The institutional buyers : this is where the risk is here. There is a vast amount of people here that are bounty hunters and some are big enough (the ) to create a pump-and-dump scenario that will help the token value to gain high multiples in the first days of trade but will also cause it to crash quickly The public ICO buyers whales those are the rarest *today*. Those are the real users of the token in the token economy designed by the startup. They will be the group of people who probably did not participate in the ICO at first but will want to use the service and for this they will have to either earn or buy tokens available on the marketplace. For example in the prediction market /hedge fund service , the token user is likely a good science/math researcher who wants to apply his own model to predict the market and earn some NUMÉRAIRE (the token they reward scientist when their stock prediction is accurate) The token users: Numerai At this moment in way too many cases (the majority?) Token buyers and Token users are not the same. And can’t be by design until the product is built and start getting adopted. Some projects manage to align both Buyers and Users by not enabling the token to be traded immediately after the end of the ICO, waiting for the protocol to go live. A good example of that is TEZOS, which should go live in the next 3/4 months. To this day the token has not been distributed to token early buyers and the listing to marketplaces will happen near simultaneously to the production of the Tezos chain. Over time as the network effect and the economy takes over the speculative value of a token is getting reduced significantly and token buyer are more contributors to the economy either as holders or token users. The ideal user being a hybrid of buyer, holder and user. In some case you may already have a massive network at hand. You can leverage it and already prove your token “velocity” has value. But this would not happen if you prefer to go ICO first… One of the problems i had with Kik ICO for example, is that by design Token buyers of the ICO are not and cannot be Token Users. KIK average age group is 13/20 years old: not the typical ICO buyer. In Kik’s case the network is already up and running, there was no reason why Kik could not have boostrapped their token by giving it first to their users before going the ICO way. Instead there is a total disconnect between token owners and token users which to me is going to be a source of problem in their economy (which reflects already in their token being valued below the ICO price) Summary The major challenge of a blockchain project is to manage to find the right alignment between all the type of token owners. The ICO format as designed today, can only create unhealthy misalignment. You can fix those by delaying the trade of your token or, if you already own a network by proving the value of your token BEFORE the ICO. In any case, i don’t believe the ICO format as we know today we’ll continue. It’s a great experiment, and probably a necessary one. The next generation of ICOs will find better ways to align the interest of all token owners.

Token buyers, Token holders and Token users

The New York Times Company v. OpenAI Update Court Filing, retrieved on February 26, 2024 is part of . You can jump to any part in this filing . This part is 3 of 15. HackerNoon’s Legal PDF Series here B. The Key to Generalist Language Models: Scale So OpenAI’s research took the logical next step: “scaling up [] model size” by increasing both the “size” and “diversity” of the training data. See GPT-3 Paper at 6. To build its next generation of models, OpenAI’s researchers gathered a more robust set of data in part by “expand[ing]” WebText database into a new version called “WebText2,” which included material shared over a longer period of time. Id. at 8–9. They also used a filtered version of Common Crawl, a repository of data collected by a non-profit research organization representing “a copy of the Internet.” Compl. ¶ 88. OpenAI disclosed all of this no later than July 22, 2020. GPT-3 Paper at 8. At the time, it was common knowledge that WebText2 and Common Crawl included numerous articles published by the Times.[19] The result of this simple act of “scaling up” the training data was, as the Times reported at the time, “mind blowing.”[20] The new GPT-3 model was “by far the most powerful ‘language model’ ever created.” Manjoo, supra note 20. It could conduct “on-the-fly reasoning” and “unscrambl[e] words, perform[] arithmetic, and us[e] novel words in a sentence after seeing them defined only once.” GPT-3 Paper at 5. Increasing the scale of training led to a surprising jump in ability. See, e.g., id. at 22 (Figure 3.10). According to the Times’s reporting, GPT-3 showed that “[m]achines are gaining the ability to write.” Manjoo, supra note 20. Within days, developers began to use it to build unprecedented tools. Id. (“service that responds to email on your behalf”). The “key advance,” as the Times reported, was “GPT-3’s flexibility.” Id. And the reason it was flexible was OpenAI’s decision to “scal[e] up” the “size” of the training data. GPT-3 Paper at 6. The amount of data needed was staggering. Compl. ¶ 85. But it was that “unprecedented scale” that allowed the model to internalize not only a “map of human language,” but achieve a level of adaptability—and “emergent” intelligence—that “no one thought possible.”[21] Continue Reading . Here [19] Compl. ¶ 88 (citing a “2019 snapshot of Common Crawl”); GPT-2 Model Card (noting prevalence of Times content in WebText); GPT-3 Paper at 8 (noting WebText2 is an “expanded version of the WebText dataset”). [20] Farhad Manjoo, How Do You Know a Human Wrote This?, N.Y. Times (July 29, 2020), https://www.nytimes.com/2020/07/29/opinion/gpt-3-ai-automation.html. The Court may take judicial notice of this and other news articles cited in this Motion. In re UBS Auction Rate Sec. Litig., No. 08-cv-2967, 2010 WL 2541166, at *10-12 (S.D.N.Y. June 10, 2010) (judicial notice of “several news items” including Wall Street Journal articles). [21] Cade Metz, Meet GPT-3. It Has Learned to Code (and Blog and Argue), N.Y. Times (Nov. 24, 2020), https://www.nytimes.com/2020/11/24/science/artificial-intelligence-ai-gpt3.html. About HackerNoon Legal PDF Series: We bring you the most important technical and insightful public domain court case filings. This court case retrieved on February 26, 2024, from is part of the public domain. The court-created documents are works of the federal government, and under copyright law, are automatically placed in the public domain and may be shared without legal restriction. fingfx.thomsonreuters.com

ChatGPT's Predecessor Was Trained on 'Filtered' Internet Data from an Open Source Repository

This paper is under CC 4.0 license. available on arxiv Authors: (1) Mounika Vanamala, Department of Computer Science, University of Wisconsin-Eau Claire, United States; (2) Keith Bryant, Department of Computer Science, University of Wisconsin-Eau Claire, United States; (3) Alex Caravella, Department of Computer Science, University of Wisconsin-Eau Claire, United States. Table of Links Abstract & Introduction Discussions Conclusions, Acknowledgment, and References Conclusion Upon recognizing the significance of cyber security vulnerability controls during the software requirement phase, the CAPEC software vulnerability repository emerged as the most practical repository for this study. The arrangement of attack patterns thus facilitates precise identification and seamless referral back to CAPEC for recommended defense strategies. We define and elaborate on topic modeling, as well as unsupervised and supervised ML methods, showcasing recent research instances and the applicability of these approaches. As our research continues, our efforts will involve the implementation of supervised machine learning. The CAPEC repository provides a prelabeled dataset, a valuable asset for training data set implementation. Supervised ML offers the added benefit of proficiently utilizing metrics to fine-tune the ML process, thus enabling thorough evaluation and process enhancement. A training set for the SRS document must either be crafted or located for supervised ML execution. Given the absence of a comparable research framework employing supervised ML, our future endeavors will assess and compare results stemming from Naïve Bayes and RF ML methodologies. Naïve Bayes showcases statistical prowess across both large and small data sets, making it suitable for the modest data set of SRS documents as well as the larger data set encompassing CAPEC Vulnerabilities. RF's capacity to counteract overfitting aligns well with the intricate data from CAPEC. The algorithm returning the most accurate recommendations for CAPEC attack patterns from an SRS document will be harnessed to deploy an automated tool for result processing and visualization. Acknowledgment Funding Information Author’s Contributions Acquisition of data and analysis and interpretation of data and content written. Keith Bryant and Alex Caravella: Conception and design of the article, intellectual content generation, critically reviewed the article. Keith Bryant, Alex Caravella, and Mounika Vanamala: Contribution to intellectual content ideation and reviewed the article along with the coordination for publication. Mounika Vanamala: Ethics This article is original and contains unpublished material. The corresponding author confirms that all of the other authors have read and approved the manuscript and that no ethical issues are involved. References Al-Sabahi, K., Zuping, Z., & Kang, Y. (2018). Latent semantic analysis approach for document summarization based on word embeddings. arXiv preprint arXiv:1807.02748. https://doi.org/10.3837/tiis.2019.01.015 Alyami, H., Nadeem, M., Alharbi, A., Alosaimi, W., Ansari, M. T. J., Pandey, D., ... & Khan, R. A. (2021). The evaluation of software security through quantum computing techniques: A durability perspective. Applied Sciences, 11(24), 11784. https://doi.org/10.3390/app112411784 Asim, M. N., Ghani, M. U., Ibrahim, M. A., Mahmood, W., Dengel, A., & Ahmed, S. (2021). Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification. Neural Computing and Applications, 33, 5437-5469. https://doi.org/10.1007/s00521-020-05321-8 Bedi, G. (2018). A guide to Text Classification (NLP) using SVM and Naive Bayes with Python. Medium, Nov. Bellaouar, S., Bellaouar, M. M., & Ghada, I. E. (2021, February). Topic modeling: Comparison of LSA and LDA on scientific publications. In 2021 4th International Conference on Data Storage and Data Engineering (pp. 59-64). https://doi.org/10.1145/3456146.3456156 CISA. (2021). c? | CISA. https://www.cisa.gov/uscert/ncas/tips/ST04-001 CVE. (2022). https://cve.mitre.org Delli, U., & Chang, S. (2018). Automated process monitoring in 3D printing using supervised machine learning. Procedia Manufacturing, 26, 865-870. https://doi.org/10.1016/j.promfg.2018.07.111 Guo, Y., & Li, J. (2021). Distributed Latent Dirichlet Allocation on Streams. ACM Transactions on Knowledge Discovery from Data (TKDD), 16(1), 1-20. https://doi.org/10.1145/3451528 Prasad, S. G., Badrinarayanan, M. K., & Sharmila, V. C. (2022). Efficacy and Security Effectiveness: Key Parameters in Evaluation of Network Security. International Journal of Performability Engineering, 18(4), 282. https://doi.org/10.23940/ijpe.22.04.p6.282288 IBM. (2019). What is machine learning? https://www.ibm.com/topics/machinelearning?lnk=fle Mallet, J., Pryor, L., Dave, R., Seliya, N., Vanamala, M., & Sowells-Boone, E. (2022, March). Hold on and swipe: A touch-movement based continuous authentication schema based on machine learning. In 2022 Asia Conference on Algorithms, Computing and Machine Learning (CACML) (pp. 442-447). IEEE. https://doi.org/10.1109/CACML55074.2022.00081 Kanakogi, K., Washizaki, H., Fukazawa, Y., Ogata, S., Okubo, T., Kato, T., ... & Yoshioka, N. (2022). Comparative Evaluation of NLP-Based Approaches for Linking CAPEC Attack Patterns from CVE Vulnerability Information. Applied Sciences, 12(7), 3400. https://doi.org/10.3390/app12073400 Kim, D., & Im, T. (2022). A Systematic Review of Virtual Reality-Based Education Research Using Latent Dirichlet Allocation: Focus on Topic Modeling Technique. Mobile Information Systems, 2022. https://doi.org/10.1155/2022/1201852 Krzeszewska, U., Poniszewska-Marańda, A., & Ochelska-Mierzejewska, J. (2022). Systematic comparison of vectorization methods in classification context. Applied Sciences, 12(10), 5119. https://doi.org/10.3390/app12105119 León-Paredes, G. A., Barbosa-Santillán, L. I., & SánchezEscobar, J. J. (2017). A heterogeneous system based on latent semantic analysis using GPU and multiCPU. Scientific Programming, 2017. https://doi.org/10.1155/2017/8131390 Livingston, F. (2005). Implementation of Breiman’s random forest machine learning algorithm. ECE591Q Machine Learning Journal Paper, 1-13. Macsai, D. 2012. The most important company you’ve never heard of. 1 Minute Read. Fast Company. https://www.fastcompany.com/3017927/30mitre McAllister, P., Zheng, H., Bond, R., & Moorhead, A. (2018). Combining deep residual neural network features with supervised machine learning algorithms to classify diverse food image datasets. Computers in Biology and Medicine, 95, 217-233. https://doi.org/10.1016/j.compbiomed.2018.02.008 Mounika, V., Yuan, X., & Bandaru, K. (2019, December). Analyzing CVE database using unsupervised topic modelling. In 2019 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 72-77). IEEE. https://doi.org/10.1109/CSCI49370.2019.00019 MITRE ATT&CK®. (2022). https://attack.mitre.org Mohamed, A. E. (2017). Comparative study of four supervised machine learning techniques for classification. International Journal of Applied, 7(2), 1-15. https://www.ijastnet.com/journal/index/859 NIST. (2022). About NIST. https://www.nist.gov/about-nist Prakash, A., Singh, N. K., & Saha, S. K. (2022). Automatic extraction of similar poetry for study of literary texts: An experiment on Hindi poetry. ETRI Journal, 44(3), 413-425. https://doi.org/10.4218/etrij.2019-0396 Rahman, A. S., Shamrat, F. J. M., Tasnim, Z., Roy, J., & Hossain, S. A. (2019). A comparative study on liver disease prediction using supervised machine learning algorithms. International Journal of Scientific & Technology Research, 8(11), 419-422. http://www.ijstr.org/final-print/nov2019/AComparative-Study-On-Liver-Disease-PredictionUsing-Supervised-Machine-LearningAlgorithms.pdf Rustam, F., A. Reshi, S. Mehmood, S. Ullah, B. On, W. Aslam and G. Choi. 2020. COVID-19 Future Forecasting Using Supervised Machine Learning Models. IEEE Access, pp: 101489-99. https://doi.org/10.1109/ACCESS.2020.2997311 Sanguri, K., Bhuyan, A., & Patra, S. (2020). A semantic similarity adjusted document co-citation analysis: a case of tourism supply chain. Scientometrics, 125(1), 233-269. https://doi.org/10.1007/s11192-020-03608-0 Schrider, D. R., & Kern, A. D. (2018). Supervised machine learning for population genetics: a new paradigm. Trends in Genetics, 34(4), 301-312. https://doi.org/10.1016/j.tig.2017.12.005 Shalev-Shwartz, S., & Ben-David, S. (2014). Understanding machine learning: From theory to algorithms. Cambridge university press. https://www.cs.huji.ac.il/~shais/UnderstandingMach ineLearning/ Sharma, C., Sharma, S., & Sakshi. (2022). Latent DIRICHLET allocation (LDA) based information modelling on BLOCKCHAIN technology: A review of trends and research patterns used in integration. Multimedia Tools and Applications, 81(25), 36805-36831. https://doi.org/10.1007/s11042-022-13500-z Siddiqui, N., Dave, R., Vanamala, M., & Seliya, N. (2022). Machine and deep learning applications to mouse dynamics for continuous user authentication. Machine Learning and Knowledge Extraction, 4(2), 502-518. https://doi.org/10.3390/make4020023 Sweeney, E. M., Vogelstein, J. T., Cuzzocreo, J. L., Calabresi, P. A., Reich, D. S., Crainiceanu, C. M., & Shinohara, R. T. (2014). A comparison of supervised machine learning algorithms and feature vectors for MS lesion segmentation using multimodal structural MRI. PloS One, 9(4), e95753. https://doi.org/10.1371/journal.pone.0095753 Uddin, S., Khan, A., Hossain, M. E., & Moni, M. A. (2019). Comparing different supervised machine learning algorithms for disease prediction. BMC Medical Informatics and Decision Making, 19(1), 1-16. https://doi.org/10.1186/s12911-019-1004-8 Ullah, F., Wang, J., Farhan, M., Jabbar, S., Naseer, M. K., & Asif, M. (2020). LSA based smart assessment methodology for SDN infrastructure in IoT environment. International Journal of Parallel Programming, 48, 162-177. https://doi.org/ 10.1007/s10766-018-0570-1 Ullah, F., Jabbar, S., & Mostarda, L. (2021). An intelligent decision support system for software plagiarism detection in academia. International Journal of Intelligent Systems, 36(6), 2730-2752 https://doi.org/10.1002/int.22399 Vanamala, M., Gilmore, J., Yuan, X., & Roy, K. (2020a, December). Recommending attack patterns for software requirements document. In 2020 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 1813-1818). IEEE. https://doi.org/10.1109/CSCI51800.2020.00334 Vanamala, M., Yuan, X., & Roy, K. (2020b, August). Topic modeling and classification of Common Vulnerabilities and Exposures database. In 2020 International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) (pp. 1-5). IEEE. https://doi.org/10.1109/icABCD49160.2020.9183814 Zhu, L., He, Y., & Zhou, D. (2020). A neural generative model for joint learning topics and topic-specific word embeddings. Transactions of the Association for Computational Linguistics, 8, 471-485. https://doi.org/10.1162/tacl_a_00326

Software Repositories and Machine Learning Research in Cyber Security: Conclusions, Acknowledgment

This paper is under CC 4.0 license. available on arxiv Authors: (1) Mounika Vanamala, Department of Computer Science, University of Wisconsin-Eau Claire, United States; (2) Keith Bryant, Department of Computer Science, University of Wisconsin-Eau Claire, United States; (3) Alex Caravella, Department of Computer Science, University of Wisconsin-Eau Claire, United States. Table of Links Abstract & Introduction Discussions Conclusions, Acknowledgment, and References Discussions Semantics of words have a crucial role in properly categorizing words through ML. Two different words can be processed into the same word, which potentially provides inaccurate classification. One example is the preprocessing of the words desert and deserted, these words both become desert. The meaning of the word deserted is lost. It would be essential for an ML model to be effective in semantic analysis if it were to make recommendations upon relevant vulnerabilities, utilizing the CAPEC database. The next discussion is the consideration of implementing an unsupervised, supervised, or semi-supervised ML model. The goal of this research would be to compare keywords from an SRS document to the keywords of CAPEC vulnerabilities. Unsupervised Machine Learning (ML) algorithms find their primary utility in tasks involving the segregation of data into clusters, uncovering underlying data relationships, and reducing dimensionality. For instance, dimensionality reduction becomes a valuable tool when dealing with extensive datasets like the large CAPEC dataset, as it aims to streamline data while preserving its integrity. In the realm of text analysis, research on Latent Dirichlet Allocation (LDA) uncovered substantial adaptation requirements to achieve satisfactory outcomes, largely due to semantic limitations. On the other hand, the Latent Semantic Analysis (LSA) algorithm is designed to capture semantics and establish connections between vectors that words are segmented into. Over time, LSA has frequently been coupled with techniques such as Singular Value Decomposition (SVD) or other intricate algorithms to enhance its effectiveness. It's important to note that evaluating the usefulness of unsupervised methods, in general, can be challenging due to the absence of well-defined metrics to measure model accuracy. This lack of clear metrics adds complexity to the interpretation of results, making it more intricate to discern the quality of outcomes generated by unsupervised ML approaches. Supervised ML is a less complex process and requires fewer tools than unsupervised ML (IBM, 2019). Supervised ML uses a training dataset and validation techniques to derive accurate results in a timelier manner, compared to unsupervised ML. Unsupervised ML works by clustering objects into like groups, identified by the algorithm. The largest limitation for supervised ML requires obtaining the training data set to prep the implemented algorithm. Supervised ML also is significantly more proficient at obtaining metrics for the accuracy of results.

Software Repositories and Machine Learning Research in Cyber Security: Discussions

Software Repositories and Machine Learning Research in Cyber Security: Abstract & Introduction 

In the ever-evolving landscape of software development, finding the right resources can be a daunting and time-consuming task. I've decided to help by curating a compilation of some of my favorite GitHub repositories, that will help you to improve your workflow and skills. This list includes valuable GitHub repositories for topics like learning, project ideas, career opportunities, data collections, database tools, and security. I've included direct links, descriptions, and visuals so that you can get an initial impression of each extension right away. 1. (Sponsored) esProc SPL ⭐ GitHub stars: 4K+ esProc SPL is a next-generation data processing language, that integrates with SQL databases and supports advanced analytics, and parallel processing. With esProc SPL, you can effortlessly transform and analyze massive datasets, uncover hidden patterns and trends, and derive actionable insights from your data. Some of the top features include: Experience fast processing speeds with esProc SPL's optimized algorithms and efficient memory management. ⚡ Top Performance: Access a comprehensive collection of pre-built functions, catering to a wide range of data manipulation tasks. 🚀 Rich Function Library: Enjoy a clear and concise syntax that fosters code readability and maintainability. ✨ Intuitive Syntax: Seamlessly integrate esProc SPL scripts into Java programs via JDBC, bridging the gap between data analysis and application development. 👨‍💻 Java Integration: Execute esProc SPL scripts independently, expanding your data processing capabilities beyond traditional limitations. 🧙‍♀️ Independent Execution: ⭐ Support their GitHub repo: https://github.com/SPLWare/esProc 🌱 Learning 2. A-to-Z Resources for Students ⭐ GitHub stars: 16K+ This repository is a comprehensive collection of resources beneficial for students, covering a wide range of topics. It includes study materials, programming resources, and tools that can aid in academic and personal development. 3. Learning Resources for Developers ⭐ GitHub stars: 5K+ This repository gathers an extensive list of learning resources for developers. From tutorials and courses to resources and tools, it serves as a valuable one-stop-shop for enhancing programming skills. 4. Every Programmer Should Know ⭐ GitHub stars: 75K+ A compilation of essential knowledge that every programmer should be aware of. Covering a variety of topics, it provides insights into algorithms, data structures, system design, and other fundamental concepts. 📀 Data 5. Public Datasets Collection ⭐ GitHub stars: 57K+ This repository curates a vast collection of public datasets from various domains. It's a valuable resource for data scientists and developers looking for high-quality datasets to analyze and derive insights from. 6. JSON Datasets Collection ⭐ GitHub stars: 3K+ This repository provides a curated list of datasets in JSON. It's a handy reference for developers working with JSON data in their projects. 7. Useful Database Tools ⭐ GitHub stars: 3K+ This repository compiles a list of tools and utilities for working with databases. From database design to management and optimization tools, it's a go-to resource for developers dealing with database-related tasks. 💡 Ideas 8. Everything for Indie Hackers ⭐ GitHub stars: 8K+ Focused on independent developers and creators, this repository gathers resources, tools, and advice for individuals pursuing indie projects. It's a supportive community-driven collection for those looking to thrive independently. 9. Machine Learning Project Ideas ⭐ GitHub stars: 7K+ A repository filled with project ideas for Machine Learning developers. It's a great source of inspiration for developers seeking new and interesting projects to work on. 10. Awesome Stacks for Apps ⭐ GitHub stars: 3K+ This repository showcases various technology stacks you can use. It's a valuable resource for developers exploring different tech stacks and architectures for their projects. 11. Frontend Dev Bookmarks ⭐ GitHub stars: 40K+ A curated collection of bookmarks for frontend developers, covering a wide range of topics from appearance and UI to compatibility and design workflow. It's a handy reference for staying updated on best practices and tools in the front-end development landscape. 💼 Career 12. CV Templates ⭐ GitHub stars: 21K+ Some useful templates for creating impressive curriculum vitae (CVs). It's a valuable tool for job seekers, helping them craft visually appealing resumes. 13. Resume Best Practices ⭐ GitHub stars: 6K+ This repository compiles various practical examples to use in resumes. It serves as a comprehensive guide for individuals aiming to enhance their resume presentation, improving their chances of getting a job. 14. Remote Work Resources ⭐ GitHub stars: 27K+ Focused on remote job opportunities, this repository provides a curated list of companies and job boards offering remote work. It's a go-to resource for those seeking remote employment options. 🔐 Security 15. Everything Security ⭐ GitHub stars: 11K+ A collection of awesome software, libraries, documents, books, resources, and cool stuff about security. 16. Web Security Practices ⭐ GitHub stars: 10K+ Focused specifically on web security, this repository gathers resources and tools to help developers secure their web applications. It covers best practices, tools, and frameworks for web security. 17. Privacy Focused Services ⭐ GitHub stars: 11K+ A curated list of resources and tools to enhance online privacy. It covers topics such as privacy-focused software, encryption tools, and practices to protect personal information online. 18. Privacy Tools Collection ⭐ GitHub stars: 5K+ A repository that curates privacy-related software and services. This collection includes tools, guides, and best practices to maintain digital privacy in various online activities. 19. Personal Security Checklist ⭐ GitHub stars: 13K+ A valuable checklist and resources to improve personal online security. It's a practical guide for individuals looking to protect their digital privacy. Writing has always been my passion and it gives me pleasure to help and inspire people. If you have any questions, feel free to reach out! Make sure to receive the best resources, tools, productivity tips, and career growth tips I discover by subscribing to ! my newsletter Also, connect with me on , , and ! Twitter LinkedIn GitHub Also published . here

The 19 Useful GitHub Repositories You Need to Become a Better Developer 🔥🚀

19 টি দরকারী GitHub সংগ্রহস্থল যা আপনাকে আরও ভাল বিকাশকারী হতে হবে 🔥🚀

Die 19 nützlichen GitHub-Repositories, die Sie benötigen, um ein besserer Entwickler zu werden 🔥🚀

Los 19 repositorios de GitHub útiles que necesitas para convertirte en un mejor desarrollador 🔥🚀

Les 19 référentiels GitHub utiles dont vous avez besoin pour devenir un meilleur développeur 🔥🚀

एक बेहतर डेवलपर बनने के लिए आपको 19 उपयोगी GitHub रिपॉजिटरी की आवश्यकता है 🔥🚀

より良い開発者になるために必要な 19 の便利な GitHub リポジトリ 🔥🚀

더 나은 개발자가 되기 위해 필요한 19가지 유용한 GitHub 리포지토리 🔥🚀

Os 19 repositórios GitHub úteis que você precisa para se tornar um desenvolvedor melhor 🔥🚀

19 полезных репозиториев GitHub, которые вам нужны, чтобы стать лучшим разработчиком 🔥🚀

Daha İyi Bir Geliştirici Olmak İçin İhtiyacınız Olan 19 Faydalı GitHub Deposu 🔥🚀

19 kho lưu trữ GitHub hữu ích mà bạn cần để trở thành nhà phát triển giỏi hơn 🔥🚀

成为更好的开发人员所需的 19 个有用的 GitHub 存储库 🔥🚀

To kickstart 2024 on a productive note, I've curated a list of 18 GitHub repositories that might be useful to enhance your development journey. This collection spans a variety of categories, from tools, extensions, cheat sheets, and guides to learning resources, prompt engineering, and other cool stuff. Whether you're a , designer, or tech enthusiast, this collection aims to boost your inspiration and productivity throughout the year. developer Each repository is divided into subcategories for easier navigation. I've also included direct links and descriptions to get the impression right away. 1. (Sponsored) esProc SPL esProc SPL is a next-generation data processing language that integrates with SQL databases and supports advanced analytics and parallel processing. With esProc SPL, you can effortlessly transform and analyze massive datasets, uncover hidden patterns and trends, and derive actionable insights from your data. Some of the top features include: esProc SPL is a scripting language with a rich library of functions and powerful syntax. 💪 Powerful Data Processing: It allows the execution of SPL scripts through JDBC interfaces. ✨ Stored Procedure Equivalent: It offers a mature reporting tool with extensive visualization configurations for creating various types of reports. 📈 Versatile Visualization: it enables the automation of software workflows, including CI/CD processes for code building, testing, and deployment. ⚡ Automated Workflows: Unlike SQL syntax, esProc SPL allows data processing code to be written in multiple statements. 🔥 More flexibility compared to SQL: ⭐ Support their GitHub repo: https://github.com/SPLWare/esProc 💻 Tools and Extensions 2. awesome-git ⭐ GitHub stars: 2k+ https://github.com/dictcp/awesome-git A curated list of delightful Git commands, extensions, and resources to enhance your Git workflow. 3. awesome-vscode ⭐ GitHub stars: 23.6k+ https://github.com/viatsko/awesome-vscode A curated list of extensions, themes, and resources for Visual Studio Code to boost productivity and enhance the development experience. 4. awesome-linux-software ⭐ GitHub stars: 20.3k+ https://github.com/luong-komorebi/Awesome-Linux-Software A list of awesome, tested, and reliable open-source software for Linux users covering a wide range of categories. 🚀 Cheat sheets and guides 5. awesome-cheatsheets ⭐ GitHub stars: 35.7k+ https://github.com/LeCoupa/awesome-cheatsheets A collection of useful cheat sheets for various programming languages, frameworks, and tools, providing quick references for developers. 6. awesome-checklists ⭐ GitHub stars: 2.6k+ https://github.com/huyingjie/Checklist-Checklist A compilation of checklists for software development, ensuring you cover essential tasks and considerations during the development process. 7. awesome-guidelines ⭐ GitHub stars: 9.2k+ https://github.com/Kristories/awesome-guidelines A collection of guidelines, best practices, and resources to help developers create better software and follow industry standards. 🤖 Prompt engineering 8. awesome-chatgpt-prompts ⭐ GitHub stars: 96.3k+ https://github.com/f/awesome-chatgpt-prompts A curated list of prompts for ChatGPT, designed to inspire creative and engaging conversations with OpenAI's language model. 9. awesome-prompt-engineering ⭐ GitHub stars: 2.6k+ https://github.com/promptslab/Awesome-Prompt-Engineering A collection of resources, tools, and examples related to prompt engineering, aiding the generation of diverse and context-aware prompts for processing models. 📚 Learning resources 10. awesome-podcasts ⭐ GitHub stars: 11.8k+ https://github.com/rShetty/awesome-podcasts A compilation of podcasts covering a wide range of topics, including technology, science, culture, and more, offering valuable insights and knowledge. 11. awesome-newsletters ⭐ GitHub stars: 3.6k+ https://github.com/zudochkin/awesome-newsletters A curated list of newsletters across various domains, providing a valuable resource for staying updated on the latest trends, news, and insights. 12. awesome-talks ⭐ GitHub stars: 5.9k+ https://github.com/JanVanRyswyck/awesome-talks A collection of conference and tech talk videos covering diverse topics, offering a rich source of educational content and insights from industry experts. 🏆 Productivity boosters 13. awesome-emails ⭐ GitHub stars: 2.2k+ https://github.com/jonathandion/awesome-emails A compilation of resources and examples for creating effective and impactful email campaigns and enhancing email marketing strategies. 14. awesome-productivity ⭐ GitHub stars: 2.2k+ https://github.com/jyguyomarch/awesome-productivity A curated list of productivity tools, apps, and resources to help individuals and teams boost their efficiency and accomplish more. 15. awesome-quantified-self ⭐ GitHub stars: 2.1k+ https://github.com/woop/awesome-quantified-self A collection of tools and resources for self-tracking and quantified self enthusiasts, facilitating personal data analysis and improvement. ✨ Other cool stuff 16. swag-for-dev ⭐ GitHub stars: 4.2k+ https://github.com/swapagarwal/swag-for-dev A repository of swag, stickers, and goodies offered by various tech companies, making it a fun opportunity for developers and tech enthusiasts. 17. awesome-raspberry-pi ⭐ GitHub stars: 12.2k+ https://github.com/thibmaek/awesome-raspberry-pi A curated list of Raspberry Pi projects, tools, and resources for enthusiasts to explore and make the most out of their Raspberry Pi devices. 18. awesome-github-profile-readme ⭐ GitHub stars: 20.7k+ https://github.com/abhisheknaiidu/awesome-github-profile-readme A collection of creative and engaging GitHub profile README templates and ideas to help users showcase their skills and personality on their GitHub profiles. Writing has always been my passion, and it gives me pleasure to help and inspire people. If you have any questions, feel free to reach out! Make sure to receive the best resources, tools, productivity tips, and career growth tips I discover by subscribing to ! my newsletter Also, connect with me on , , and ! Twitter LinkedIn GitHub Also published . here

REPOSITE

reposite.io

-- employees

Since n.d.

COMPANY RANKING

REPOSITE

EVERGREEN INDEX #8443

Representing this company? Click here to claim and customize this page!

Reposite WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!

REPOSITE

reposite.io

-- employees

Since n.d.

COMPANY RANKING

REPOSITE

EVERGREEN INDEX #8443

RELATED COMPANIESRANK

STORIES ABOUT REPOSITE

Representing this company? Click here to claim and customize this page!

Reposite WIKI

Apply to get your Tech Company News Page live on HackerNoon.com today!