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ericelliott / H5ValidateAn HTML5 form validation plugin for jQuery. Works on all major browsers, both new and old. Implements inline, realtime validation best practices (based on surveys and usability studies). Developed for production use in e-commerce. Currently in production with millions of users.
SII-WANGZJ / Polymarket DataA comprehensive dataset of 1.1 billion trading records from Polymarket, processed into multiple analysis-ready formats. Features cleaned data, unified token perspectives, and user-level transformations — ready for market research, behavioral studies, and quantitative analysis.
ezra-bible-app / Ezra Bible AppEzra Bible App is a modern and user-friendly Bible app for desktops, tablets and mobiles focussing on topical study
Aastha2104 / Parkinson Disease PredictionIntroduction Parkinson’s Disease is the second most prevalent neurodegenerative disorder after Alzheimer’s, affecting more than 10 million people worldwide. Parkinson’s is characterized primarily by the deterioration of motor and cognitive ability. There is no single test which can be administered for diagnosis. Instead, doctors must perform a careful clinical analysis of the patient’s medical history. Unfortunately, this method of diagnosis is highly inaccurate. A study from the National Institute of Neurological Disorders finds that early diagnosis (having symptoms for 5 years or less) is only 53% accurate. This is not much better than random guessing, but an early diagnosis is critical to effective treatment. Because of these difficulties, I investigate a machine learning approach to accurately diagnose Parkinson’s, using a dataset of various speech features (a non-invasive yet characteristic tool) from the University of Oxford. Why speech features? Speech is very predictive and characteristic of Parkinson’s disease; almost every Parkinson’s patient experiences severe vocal degradation (inability to produce sustained phonations, tremor, hoarseness), so it makes sense to use voice to diagnose the disease. Voice analysis gives the added benefit of being non-invasive, inexpensive, and very easy to extract clinically. Background Parkinson's Disease Parkinson’s is a progressive neurodegenerative condition resulting from the death of the dopamine containing cells of the substantia nigra (which plays an important role in movement). Symptoms include: “frozen” facial features, bradykinesia (slowness of movement), akinesia (impairment of voluntary movement), tremor, and voice impairment. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. Performance Metrics TP = true positive, FP = false positive, TN = true negative, FN = false negative Accuracy: (TP+TN)/(P+N) Matthews Correlation Coefficient: 1=perfect, 0=random, -1=completely inaccurate Algorithms Employed Logistic Regression (LR): Uses the sigmoid logistic equation with weights (coefficient values) and biases (constants) to model the probability of a certain class for binary classification. An output of 1 represents one class, and an output of 0 represents the other. Training the model will learn the optimal weights and biases. Linear Discriminant Analysis (LDA): Assumes that the data is Gaussian and each feature has the same variance. LDA estimates the mean and variance for each class from the training data, and then uses properties of statistics (Bayes theorem , Gaussian distribution, etc) to compute the probability of a particular instance belonging to a given class. The class with the largest probability is the prediction. k Nearest Neighbors (KNN): Makes predictions about the validation set using the entire training set. KNN makes a prediction about a new instance by searching through the entire set to find the k “closest” instances. “Closeness” is determined using a proximity measurement (Euclidean) across all features. The class that the majority of the k closest instances belong to is the class that the model predicts the new instance to be. Decision Tree (DT): Represented by a binary tree, where each root node represents an input variable and a split point, and each leaf node contains an output used to make a prediction. Neural Network (NN): Models the way the human brain makes decisions. Each neuron takes in 1+ inputs, and then uses an activation function to process the input with weights and biases to produce an output. Neurons can be arranged into layers, and multiple layers can form a network to model complex decisions. Training the network involves using the training instances to optimize the weights and biases. Naive Bayes (NB): Simplifies the calculation of probabilities by assuming that all features are independent of one another (a strong but effective assumption). Employs Bayes Theorem to calculate the probabilities that the instance to be predicted is in each class, then finds the class with the highest probability. Gradient Boost (GB): Generally used when seeking a model with very high predictive performance. Used to reduce bias and variance (“error”) by combining multiple “weak learners” (not very good models) to create a “strong learner” (high performance model). Involves 3 elements: a loss function (error function) to be optimized, a weak learner (decision tree) to make predictions, and an additive model to add trees to minimize the loss function. Gradient descent is used to minimize error after adding each tree (one by one). Engineering Goal Produce a machine learning model to diagnose Parkinson’s disease given various features of a patient’s speech with at least 90% accuracy and/or a Matthews Correlation Coefficient of at least 0.9. Compare various algorithms and parameters to determine the best model for predicting Parkinson’s. Dataset Description Source: the University of Oxford 195 instances (147 subjects with Parkinson’s, 48 without Parkinson’s) 22 features (elements that are possibly characteristic of Parkinson’s, such as frequency, pitch, amplitude / period of the sound wave) 1 label (1 for Parkinson’s, 0 for no Parkinson’s) Project Pipeline pipeline Summary of Procedure Split the Oxford Parkinson’s Dataset into two parts: one for training, one for validation (evaluate how well the model performs) Train each of the following algorithms with the training set: Logistic Regression, Linear Discriminant Analysis, k Nearest Neighbors, Decision Tree, Neural Network, Naive Bayes, Gradient Boost Evaluate results using the validation set Repeat for the following training set to validation set splits: 80% training / 20% validation, 75% / 25%, and 70% / 30% Repeat for a rescaled version of the dataset (scale all the numbers in the dataset to a range from 0 to 1: this helps to reduce the effect of outliers) Conduct 5 trials and average the results Data a_o a_r m_o m_r Data Analysis In general, the models tended to perform the best (both in terms of accuracy and Matthews Correlation Coefficient) on the rescaled dataset with a 75-25 train-test split. The two highest performing algorithms, k Nearest Neighbors and the Neural Network, both achieved an accuracy of 98%. The NN achieved a MCC of 0.96, while KNN achieved a MCC of 0.94. These figures outperform most existing literature and significantly outperform current methods of diagnosis. Conclusion and Significance These robust results suggest that a machine learning approach can indeed be implemented to significantly improve diagnosis methods of Parkinson’s disease. Given the necessity of early diagnosis for effective treatment, my machine learning models provide a very promising alternative to the current, rather ineffective method of diagnosis. Current methods of early diagnosis are only 53% accurate, while my machine learning model produces 98% accuracy. This 45% increase is critical because an accurate, early diagnosis is needed to effectively treat the disease. Typically, by the time the disease is diagnosed, 60% of nigrostriatal neurons have degenerated, and 80% of striatal dopamine have been depleted. With an earlier diagnosis, much of this degradation could have been slowed or treated. My results are very significant because Parkinson’s affects over 10 million people worldwide who could benefit greatly from an early, accurate diagnosis. Not only is my machine learning approach more accurate in terms of diagnostic accuracy, it is also more scalable, less expensive, and therefore more accessible to people who might not have access to established medical facilities and professionals. The diagnosis is also much simpler, requiring only a 10-15 second voice recording and producing an immediate diagnosis. Future Research Given more time and resources, I would investigate the following: Create a mobile application which would allow the user to record his/her voice, extract the necessary vocal features, and feed it into my machine learning model to diagnose Parkinson’s. Use larger datasets in conjunction with the University of Oxford dataset. Tune and improve my models even further to achieve even better results. Investigate different structures and types of neural networks. Construct a novel algorithm specifically suited for the prediction of Parkinson’s. Generalize my findings and algorithms for all types of dementia disorders, such as Alzheimer’s. References Bind, Shubham. "A Survey of Machine Learning Based Approaches for Parkinson Disease Prediction." International Journal of Computer Science and Information Technologies 6 (2015): n. pag. International Journal of Computer Science and Information Technologies. 2015. Web. 8 Mar. 2017. Brooks, Megan. "Diagnosing Parkinson's Disease Still Challenging." Medscape Medical News. National Institute of Neurological Disorders, 31 July 2014. Web. 20 Mar. 2017. Exploiting Nonlinear Recurrence and Fractal Scaling Properties for Voice Disorder Detection', Little MA, McSharry PE, Roberts SJ, Costello DAE, Moroz IM. BioMedical Engineering OnLine 2007, 6:23 (26 June 2007) Hashmi, Sumaiya F. "A Machine Learning Approach to Diagnosis of Parkinson’s Disease."Claremont Colleges Scholarship. Claremont College, 2013. Web. 10 Mar. 2017. Karplus, Abraham. "Machine Learning Algorithms for Cancer Diagnosis." Machine Learning Algorithms for Cancer Diagnosis (n.d.): n. pag. Mar. 2012. Web. 20 Mar. 2017. Little, Max. "Parkinsons Data Set." UCI Machine Learning Repository. University of Oxford, 26 June 2008. Web. 20 Feb. 2017. Ozcift, Akin, and Arif Gulten. "Classifier Ensemble Construction with Rotation Forest to Improve Medical Diagnosis Performance of Machine Learning Algorithms." Computer Methods and Programs in Biomedicine 104.3 (2011): 443-51. Semantic Scholar. 2011. Web. 15 Mar. 2017. "Parkinson’s Disease Dementia." UCI MIND. N.p., 19 Oct. 2015. Web. 17 Feb. 2017. Salvatore, C., A. Cerasa, I. Castiglioni, F. Gallivanone, A. Augimeri, M. Lopez, G. Arabia, M. Morelli, M.c. Gilardi, and A. Quattrone. "Machine Learning on Brain MRI Data for Differential Diagnosis of Parkinson's Disease and Progressive Supranuclear Palsy."Journal of Neuroscience Methods 222 (2014): 230-37. 2014. Web. 18 Mar. 2017. Shahbakhi, Mohammad, Danial Taheri Far, and Ehsan Tahami. "Speech Analysis for Diagnosis of Parkinson’s Disease Using Genetic Algorithm and Support Vector Machine."Journal of Biomedical Science and Engineering 07.04 (2014): 147-56. Scientific Research. July 2014. Web. 2 Mar. 2017. "Speech and Communication." Speech and Communication. Parkinson's Disease Foundation, n.d. Web. 22 Mar. 2017. Sriram, Tarigoppula V. S., M. Venkateswara Rao, G. V. Satya Narayana, and D. S. V. G. K. Kaladhar. "Diagnosis of Parkinson Disease Using Machine Learning and Data Mining Systems from Voice Dataset." SpringerLink. Springer, Cham, 01 Jan. 1970. Web. 17 Mar. 2017.
microsoft / MSMARCO Conversational SearchTruly Conversational Search is the next logic step in the journey to generate intelligent and useful AI. To understand what this may mean, researchers have voiced a continuous desire to study how people currently converse with search engines. Traditionally, the desire to produce such a comprehensive dataset has been limited because those who have this data (Search Engines) have a responsibility to their users to maintain their privacy and cannot share the data publicly in a way that upholds the trusts users have in the Search Engines. Given these two powerful forces we believe we have a dataset and paradigm that meets both sets of needs: A artificial public dataset that approximates the true data and an ability to evaluate model performance on the real user behavior. What this means is we released a public dataset which is generated by creating artificial sessions using embedding similarity and will test on the original data. To say this again: we are not releasing any private user data but are releasing what we believe to be a good representation of true user interactions.
swati1024 / TorrentsSkip to content Search… All gists Back to GitHub Sign in Sign up Instantly share code, notes, and snippets. @giansalex giansalex/torrent-courses-download-list.md forked from M-Younus/torrent courses download-list Last active 2 days ago 15188 Code Revisions 15 Stars 151 Forks 88 <script src="https://gist.github.com/giansalex/4cd3631e94433bbbd71bf07aedb33a7b.js"></script> torrent-courses-download-list.md Torrent Courses List Download http://kickass.to/infiniteskills-learning-jquery-mobile-working-files-t7967156.html http://kickass.to/lynda-bootstrap-3-advanced-web-development-2013-eng-t8167587.html http://kickass.to/lynda-css-advanced-typographic-techniques-t7928210.html http://kickass.to/lynda-html5-projects-interactive-charts-2013-eng-t8167670.html http://kickass.to/vtc-html5-css3-responsive-web-design-course-t7922533.html http://kickass.to/10gen-m101js-mongodb-for-node-js-developers-2013-eng-t8165205.html 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http://www.seedpeer.me/details/6185755/TutsPlus---The-MVC-Mindser-Jeffery-Way---ICARUS.html http://www.seedpeer.me/details/5024493/TutsPlus---Venture-Into-Vim.html http://www.seedpeer.me/details/6286416/Tutsplus---Vim-for-Advanced-Users.html http://www.seedpeer.me/details/6585031/Tutsplus---WordPress-Hackers-Guide-to-the-Galaxy.html http://www.seedpeer.me/details/4848477/TutsPlus---Writing-Modular-JavaScript.html @giansalex Owner Author giansalex commented on 26 Feb 2018 • SOLID http://www.allitebooks.com/beginning-solid-principles-and-design-patterns-for-asp-net-developers/ @giansalex Owner Author giansalex commented on 7 Mar 2018 Udemy: AWS Arquitecto de Soluciones Certificado Asociado https://mega.co.nz/#!ZzhGWSAL!wuthFca0SdJBjmaP5lFX0QF6PeMsrdclKFXlZL1Rsi4 Pass: gratismas.org @giansalex Owner Author giansalex commented on 7 Mar 2018 Go lang Complete https://www.freetutorials.us/wp-content/uploads/2017/11/FreeTutorials.Us-Udemy-go-the-complete-developers-guide.torrent @GCPBigData GCPBigData commented on 15 Jul 2018 go books https://drive.google.com/open?id=1d6OsFAn8kpHCXtw0bcoYuyHqrAdGZva0 @freisrael freisrael commented on 14 Aug 2018 giansalex thanks for sharing. I am looking for learning phython with Joe Marini. It would be great if you post it. @FirstBoy1 FirstBoy1 commented on 25 May 2019 Can anyone provide this book "Getting started with Spring Framework: covers Spring 5" by " J Sharma (Author), Ashish Sarin ". Thanks in advance @okreka okreka commented on 31 May 2019 Can anyone provide "Windows Presentation Foundation Masterclass" course from Udemy. Thanks in advance @singhaltanvi singhaltanvi commented on 8 Aug 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. @kumarsreenivas051 kumarsreenivas051 commented on 9 Sep 2019 Can anyone provide "Programming languages A,B and C" course from Coursera. Thanks in advance. @BrunoMoreno BrunoMoreno commented on 11 Sep 2019 The link for the torrents in piratebay, now is .org to the correct url. @sany2k8 sany2k8 commented on 24 Sep 2019 Can anyone add this The Complete Hands-On Course to Master Apache Airflow @pharaoh1 pharaoh1 commented on 30 Sep 2019 can you pls add this course to your list https://www.udemy.com/course/advanced-python3/ @SushantDhote936 SushantDhote936 commented on 1 Oct 2019 Can you add Plural Sight CISSP @allayGerald allayGerald commented on 1 Oct 2019 open directive for lynda courses: https://drive.google.com/drive/folders/1zQan1cq1ZnqXmueRF5IqKoOtpFxl6Y4G @ezekielskottarathil ezekielskottarathil commented on 3 Oct 2019 can anyone provide 'sedimentology and petroleum geology' course from Udemy. Thanks in advance. "wrong place boy" @pulkitd2699 pulkitd2699 commented on 8 Oct 2019 Does anyone has a link for 'Cyber security: Python and web applications' course? Thanks @mohanrajrc mohanrajrc commented on 19 Oct 2019 • Can anyone provide torrent file for Mastering React By Mosh Hamedani. Thanks https://codewithmosh.com/p/mastering-react @evilprince2009 evilprince2009 commented on 27 Oct 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 @nunusandio nunusandio commented on 30 Oct 2019 Can anyone post torrent file for ASP.NET Authentication: The Big Picture https://app.pluralsight.com/library/courses/aspdotnet-authentication-big-picture/table-of-contents @EslamElmadny EslamElmadny commented on 9 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 hay brother! do you have html5 game development with javascript course ? @Genius-K-SL Genius-K-SL commented on 14 Dec 2019 This link is not working brother! http://www.seedpeer.me/details/4657790/Lynda.com-Building-Facebook-Applications-with-HTML-and-JavaScript.html @smithtuka smithtuka commented on 20 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? @AbdOoSaed AbdOoSaed commented on 22 Dec 2019 Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff @EslamElmadny EslamElmadny commented on 23 Dec 2019 • Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj @jedi2610 jedi2610 commented on 27 Dec 2019 Can anyone provide me with Code with Mosh's Ultimate Java Mastery Series link? plis @InnocentZaib InnocentZaib commented on 31 Dec 2019 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms @edward-teixeira edward-teixeira commented on 1 Jan 2020 Please provide the link of codewithmosh The ultimate data structures and algorithms Bundle the link is given below. Please give me the torrnet file or link to download https://codewithmosh.com/p/data-structures-algorithms Yea i'm looking for it too @kaneyxx kaneyxx commented on 1 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj could you please share the part-1 & part-3? @edward-teixeira edward-teixeira commented on 2 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? @ravisharmaa ravisharmaa commented on 7 Jan Please add this . https://www.letsbuildthatapp.com/course/AppStore-JSON-APIs @WaleedAlrashed WaleedAlrashed commented on 13 Jan This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ @Sopheakmorm Sopheakmorm commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 @EslamElmadny EslamElmadny commented on 19 Jan Anyone have this course: https://www.udemy.com/course/mcsa-web-application-practice-test70-480-70-483-70-486 +1 @EslamElmadny EslamElmadny commented on 20 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses @mohanrajrc mohanrajrc commented on 22 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. @shihab122 shihab122 commented on 22 Jan Please give me the torrnet file or link to download The Ultimate Design Patterns @EslamElmadny EslamElmadny commented on 22 Jan • Please give me the torrnet file or link to download The Ultimate Design Patterns Waiting for it also :D @K-wachira K-wachira commented on 23 Jan Can you please add these two below ? https://codewithmosh.com/p/the-ultimate-java-mastery-series https://codewithmosh.com/p/data-structures-algorithms-part-2 any luck ? Has this come through by any chances? fff data-structures-algorithms-part-2 https://drive.google.com/open?id=1oYYdPp4MVVk7ZzZL6rLepFe33IjXtkqj Can you share part 1 and 3? https://vminhsang.name.vn/category/it-courses/codewithmosh/ this link includes almost all mosh courses Yes. Java mastery and Data Structures 1, 2, 3 are available in this site. free download. You are a saviour .. Altho i feel bad i cant really buy the course... its really good @msdyn95 msdyn95 commented 25 days ago • Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ @K-wachira K-wachira commented 23 days ago This one kindly. https://www.udemy.com/course/flutter-build-a-complex-android-and-ios-apps-using-firestore/ Hey did you find this one? @edward-teixeira edward-teixeira commented 22 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? @msdyn95 msdyn95 commented 21 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. @edward-teixeira edward-teixeira commented 20 days ago Please give me the torrent file or link to download https://codewithmosh.com/p/design-patterns https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-1/ https://coursedownloader.net/code-with-mosh-the-ultimate-design-patterns-part-2/ Did you find those? unfortunately not. Found it ! https://vminhsang.name.vn/category/it-courses/codewithmosh/ @ZainA14 ZainA14 commented 16 days ago • Can someone please link me to this mosh course for torrent or direct download link https://codewithmosh.com/p/the-ultimate-full-stack-net-developer-bundle @khushiigupta khushiigupta commented 9 days ago Can any one please provide me link for jenkins so that I can learn as al as possible to join this conversation on GitHub. Already have an account? Sign in to comment © 2020 GitHub, Inc. Terms Privacy Security Status Help Contact GitHub Pricing API Training Blog About
archd3sai / Instacart Market Basket AnalysisThe objective of this project is to analyze the 3 million grocery orders from more than 200,000 Instacart users and predict which previously purchased item will be in user's next order. Customer segmentation and affinity analysis are done to study customer purchase patterns and for better product marketing and cross-selling.
IanMagnusson / Wav2Lip EmotionWav2Lip-Emotion extends Wav2Lip to modify facial expressions of emotions via L1 reconstruction and pre-trained emotion objectives. We also propose a novel automatic evaluation for emotion modification corroborated with a user study.
narlock / TamoStudyTamoStudy is a free, open source work and study timer designed to enhance productivity, incorporating an enjoyable virtual pet to motivate users to concentrate on their tasks.
OHDSI / CohortMethodAn R package for performing new-user cohort studies in an observational database in the OMOP Common Data Model.
OPTIMUM-LINKUP / Latest Optimum School SystemThis is the latest school management system. Available for all type of schools. will work on Phone, Laptop, Tabs, monitors - any screen size. It is available with 100% source code. The features are listed below: INSTALLATION Upload the downloaded zip file to your server in the public_html directory. Extract the zip file. Create a new database from your server mysql. Create user to the database and link the database to the user. Open the file database.php from the directory yourfolder/application/config/database.php. Fill up this information with your database hostname, database username, database password, database name respectively which you have created in the previous step. Now from server phpmyadmin go to your database. Select import and choose the file install.sql located in yourfolder/database/blank_db.sql (demo_db.sql for demo database) And you are ready to go now to browse the application Default admin credentials Email: admin@admin.com Password: admin ADMIN PANEL Managing User accounts (teacher, student, parent) Managing classes, subjects Managing exam, grades Managing exam marks Managing Loan Information Managing Computer Based Test (CBT) Sending exam marks via sms Managing students attendance Managing accounting, income and expenses Managing school events Managing Teachers Managing Libratrian Managing Accountant Manage Circular Manage Task Managing Parents Managing Alumni Managing Academic Sysllabus Managing Helpful Links Managing Help Desk Managing Front-End Information Managing School Session Attendance Reports Managing Staff ID Cards Records management. Notification board management. Management relationships between different type of users. Online Payment acceptance of FEE. Section Management. Reports generator. SMS Alerts. Managing Hostel Manager Managing library, dormitory, transport Messaging between other users Managing system settings (general, sms, language) Managing Media Subject management. Class management. Student payments management. Student behaviour management. Payments Overview. Subjects and assignments management. Fees management. Student assignment results management. Student search. Overdue students list. Student management. Student-Teacher interaction. And many more … TEACHERS Manage Students homework. Assign homework. Share homework on social networking sites (facebook). Manage classes. Manage Student Report. Generate Remarks on Student Reports. Generate Student Attendence. Subject management. Loan Application Class management. Student behaviour management. Subjects and assignments management. Student assignment results management. Student search. Student management. Student-Teacher interaction. Managing Helpful Links Managing Media Assignments Attendance Provide Daily Quotes Holidays Studennts Study Materials Message Noticeboard Transportations And many more… STUDENT PANEL Get class Routine Attempt Online Exam View Online Exam Result Get Exam Marks Message View Noticeboard Transportatio Receive SMS Get attendance status Get study materials / files from teacher Get payment invoice, Pay Online Communicate with teacher Managing Media accounts View Event Schedule, Notice and Holidays Get Helpful Links View Daily Quotes Contact Help Desks And many more …. PARENT PANEL View Children Marks View Children Class Routine Make payment View Payment Invoice Message Admin Message Teachers View Received Messkages Checkin kids progress. Parent-Teacher interaction. Get alerts from School Administration or Teachers. View events Noticeborad Todays Thought News Helpful Links Help Desk Receive SMS And many more … LIBRARIAN Add books Update books Record Lost Books Generate Reports on Books Subject Management. Loan Application Student Search. Student Management. Student-Librarian Interaction. View Helpful Links View Media Holidays Studennts Study Materials Message Transportations Noticeboard View Notification And many more …. ACCOUNTANTS Create Student Payments Students Payment Expenses Expenses Category Vew all Accountants Loan Application Todays Thought News Holidays Message k Noticeboard And many more …. HOSTEL MANAGER ViewAll Hostel Managers Manage Hostels Loan Application Todays Thought News Holidays Message Noticeboard And many more …. ………………………………………………………………………………………………… ADMIN PANEL DASHBORD ………………………………………………………………………………………………… Total number of students, teachers, librarian, accountants, hostel manager, alumni, parents and attendance of students for that day at a glance, Dashboard also holds a calendar for showing events, charts for various percentages of teachers, parents, students attendance, grades, students performances, etc. MANAGING SESSION From navigation go to manage session Add / edit / delete MANAGING ACADEMIC SYLLABUS From navigation go to manage academic syllabus Add / edit / delete MANAGING MEDIA From navigation go to manage media Add / edit / delete MANAGING STUDENTS Admit Students From navigation, go to students > admit students Fill up the necessary information Save student Admit Bulk Students From navigation, go to student > admit bulk student Download the blank Excel file Fill up the information Select class Upload the filled up Excel file Save Student Information From navigation go to student > student information Here you can see the students class wise If a class has sections then you can also browse the students as per class sections Student mark sheets From navigation go to student > student mark sheet Here you can see all the students marks class wise If the class has sections then you can also see them along with class MANAGING TEACHERS From navigation go to teacher Here you can see the list of teachers of your school in a tabular form To add a new teacher, click the top right button named add new teacher and fill up the information and save For editing or deleting a teacher information click the action button assigned to each entry of the table. That will bring two options for editing and deleting. Click on the required action editing and deleting MANAGING ACCOUNTANTS From navigation go to accountant Here you can see the list of accountants of your school in a tabular form To add a new accountant, click the top right button named add new accountant and fill up the information and save For editing or deleting a teacher information click the action button assigned to each entry of the table. That will bring two options for editing and deleting. Click on the required action editing and deleting MANAGING LIBRARIANS From navigation go to librarian Here you can see the list of librarians of your school in a tabular form To add a new librarian, click the top right button named add new librarian and fill up the information and save For editing or deleting a teacher information click the action button assigned to each entry of the table. That will bring two options for editing and deleting. Click on the required action editing and deleting MANAGING HOSTEL MANAGERS From navigation go to hostel manager Here you can see the list of hostel managers of your school in a tabular form To add a new hostel manager, click the top right button named add new hostel manager and fill up the information and save For editing or deleting a teacher information click the action button assigned to each entry of the table. That will bring two options for editing and deleting. Click on the required action editing and deleting MANAGING ALUMNI From navigation go to alumni Here you can see the list of alumni of your school in a tabular form To add a new alumni, click the top right button named add new alumni and fill up the information and save For editing or deleting a teacher information click the action button assigned to each entry of the table. That will bring two options for editing and deleting. Click on the required action editing and deleting MANAGING PARENTS From navigation go to parents Here you can see the list of parents of the students of your school in a tabular form To add a new parent, click the top right button named add new parent and fill up the information and save For editing or deleting a parent information click the action button assigned to each entry of the table. That will bring two options for editing and deleting. Click on the required action for editing and deleting MANAGING CLASSES From navigation go to > manage sections Add new class section for a class and assign teacher for each of them View the class sections in a tabular form class wise Edit and delete class section information MANAGING CLASS SECTION From navigation go to class > manage sections Add new class section for a class and assign a teacher for each of them View the class sections in a tabular for class wise Edit and deklete class section information MANAGING SUBJECTS From navigation go to subject If you have already added classes then under this you will see a list of the classes added. If you have not created classes, please create class first Here you can see the subjects class wise Add or edit or delete subjects MANAGING CLASS ROUTINE From navigation go to class routine View all the class routines in accordion Add class routine Click on the subject name on routine to edit and delete MANAGING DIALY STUDENT’S ATTENDENCE From navigation go to daily attendance Select the date and class and click manage attendance That will bring up the students name and attendance information in a tabular form To update the attendance status or for taking the attendance for that particular date of that particular class which you have selected earlier, click the button named update attendance Put the status for all at once and click save changes MANAGING EXAMS Exam list From navigation, go to exam > exam list Add an exam for all Edit and delete exam Exam grade From navigation go to exam > exam grades Add exam grades as per the requirements of your institution Edit or delete exam grades Manage exam marks From navigation go to > manage marks Select exam, class and subject and click manage marks for changing or updating marks That will bring up the form for updating the students marks for that particular subject Enter the marks and click update Sending exam marks by SMS From navigation go to exam > send mark by SMS Select exakm and class and receive (students/parent) Click the button named send mark via SMS That will send SMS with the marks for that exam you have selected if a SMS service is already activated MANAGING PAYMENTS From navigation go to payment Add invoice and take manual payment multiple time under the same invoice If a payment is due, then an option will be there for taking the payment in the action button of the table that contains the list of all the invoices with the basic information. Edit or delete invoice LOAN MANAGEMENT From navigation go to loan application See all the applied loans Click on apply loan Fill forms to apply Wait for loan approval COMPUTER BASED TEST (CBT) From navigation, go to Manage CBT Click on Add Exam Set Class, Exam Time, Exam Duration, Subject, Question Count and Session Click on continue to Add Questions Click on List Exams to View Exams Click on View Result to View Exams Scores ACCOUNTING Incomes From navigation, go to accounting > incomes Here you can see all the incomes for your school that means students fee in a tabular form with their payment time and amount EXPENSES From navigation, go to accounting > expenses Add expenses for the school Edit or delete them GENERATING STAFF IDCARD Teacher, librarian, accounant, hostel manager From navigation, go to staff > ID CARD Here you can you will see a button asking you to click generate ID CARD EXPENSE CATEGORY From navigation, go to accounting > expense category Add expense category Edit or delete them MANAGING BOOKS From navigation go to library Add books Edit or delete them MANAGING TRANSPORT From navigation go to transport Add transport information Edit or delete them MANAGING DORMITORY From navigation go to dormitory Add / edit / delete MANAGING ASSIGNMENT From navigation go to assignment Add / edit / delete MANAGING HOLIDAYS From navigation go to holiday Add / edit / delete MANAGING TODAY’S THOUGHT From navigation go to today’s thought Add / edit / delete MANAGING CIRCULAR From navigation go to circular Add / edit / delete MANAGING SCHOOL CLUBS From navigation go to school club Add / edit / delete MANAGING TASK From navigation go to task manager Add / edit / delete MANAGING HELPFUL LINK From navigation go to Helpful Links Add / edit / delete MANAGING ENQUIRY From navigation go to enquiry Add / edit / delete MANAGING ENQUIRY CATEFORY From navigation go to enquiry category Add / edit / delete MANAGING HELP DESK From navigation go to task Helpdesk Add / edit / delete NOTICEBOARD From navigation go to notice board Add / edit / delete them For sending the notice to all as SMS, yes while creating the notice This will send SMS to all the users about that notice PRIVATE MESSAGING From navigation, go to message Admin can send message to all users For sending message, select user and type the message and click send You can also see all the message sent to you or sent from you SYSTEM SETTINGS From navigation go to settings > general settings You can change basic system settings here and also can select language You can also upload logo from here THEME SETTINGS From navigation go to setting > general settings On the right of the page there is a panel named theme settings You find several skin options for you application Select you desire one to make changes SMS SETTINGS From navigation go to settings > sms settings Here you will find 2 SMS services, one is Clickatell and another is Twilio You have to activate a service first Then put the necessary information for a service Visit https://www.twilio.com/user/acount/settings/international /sms LANGUAGE SETTINGS From navigation go to setting > language settings Change phrase or add new phrase for a particular language Add new language MANAGE BANNER SETTINGS From navigation go to setting > banner settings Add / edit / delete MANAGE FRONT END SETTINGS From navigation go to setting > front end settings Add / edit / delete MANAGE NEWS SETTINGS From navigation go to setting > news settings Add / edit / delete ACCOUNT SETTINGS From navigation go to account Change basic account information Update your password Change profile image ……………………………………………………………………………………………….. TEACHER PANEL DASHBOARD ………………………………………………………………………………………………. Total number of students, parents and attendance of students for that day at a glance Dashboard also holds a calendar for showing events. MANAGING STUDENTS Admit students From navigation go to student > admit student Fill up the necessary information Save student Student information From navigation go to student > student information Here you can see the student class wise If a class has sections then you can also browse the students as per class sections Student mark sheets From navigation go to student > student mark sheet Here you can see all the students marks class wise If the class has sections then you can also see them along with class MANAGING DAILY STUDENT’S ATTENDANCE From navigation go to daily attendance Select the date and class and click mange attendance That will bring up the students name and attendance information in a tabular form To update the attendance status or for taking the attendance for that particular date of that particular class which you have selected earlier, click the button named update attendance Put the status for all at once and click save changes MANAGING DAILY STUDENT’S ATTENDANCE From navigation go to daily attendance Select the date and class and click manage attendance That will bring up the students name and attendance information in a tabular form To update the attendance status or for taking the attendance for that particular date of that particular class which you have selected earlier, click the button named update attendance Put the status for all at once and click save changes MANAGING ASSIGNMENT From navigation go to assignment That will bring up the assignemnt page in a tabular form, you can click on add assignment on left corner of the page to add assignment. MANAGING CLASSES From navigation go to > manage sections Add new class section for a class and assign teacher for each of them View the class sections in a tabular form class wise Edit and delete class section information MANAGING CLASS SECTION From navigation go to class > manage sections Add new class section for a class and assign a teacher for each of them View the class sections in a tabular for class wise Edit and deklete class section information MANAGING SUBJECTS From navigation go to subject If you have already added classes then under this you will see a list of the classes added. If you have not created classes, please create class first Here you can see the subjects class wise Add or edit or delete subjects MANAGING CLASS ROUTINE From navigation go to class routine View all the class routines in accordion Add class routine Click on the subject name on routine to edit and delete MANAGING DIALY STUDENT’S ATTENDENCE From navigation go to daily attendance Select the date and class and click manage attendance That will bring up the students name and attendance information in a tabular form To update the attendance status or for taking the attendance for that particular date of that particular class which you have selected earlier, click the button named update attendance Put the status for all at once and click save changes MANAGING EXAMS Manage exam marks From navigation go to > manage marks Select exam, class and subject and click manage marks for changing or updating marks That will bring up the form for updating the students marks for that particular subject Enter the marks and click update MANAGING HELPFUL LINK From navigation go to Helpful Links Add / edit / delete NEWS From navigation go to view news View all the uploaded news TODAY’S THOUGHT From navigation go to today’s thought View all the uploaded today’s thought HOLIDAY DATES From navigation go to holiday View all the holiday with their respectives dates ……………………………………………………………………………………………………. STUDENT PANEL DASHBOARD ……………………………………………………………………………………………………. Total number of students, teachers, parents and attendance of students for that day at a glance, dashboard also holds a calendar for showing event CLASS ROUTINE Form navigation go to class routine View the class routine of the logged in student EXAM MARKS From navigation go to exam > manage marks Select exam and subject See the mark for the selected exam in the selected subject COMPUTER BASED TEST (CBT) From navigation go to online CBT See all the uploaded test for your class Attemtp the uploaded test View your results STUDY MATERIALS From navigation go to study materials See all the uploaded study materials for your class Download the materials ASSIGNMENT From navigation go to assignment See all the uploaded assignments for your class Download the assignment MEDIA From navigation go to media See all the uploaded media for your class Download or watch media NEWS From navigation go to view news View all the uploaded news TODAY’S THOUGHT From navigation go to today’s thought View all the uploaded today’s thought HOLIDAY DATES From navigation go to holiday View all the holiday with their respectives dates HELPFUL LINKS From navigation go to helpful links View all the helpful links HELP DESK From navigation go to help desk Submit or create help desk to the administrator STUDY MATERIALS From navigation go to study material See all the uploaded study material for your class Download the study material PAYMENT / PAY WITH PAYPAL From navigation go to payment See the list of invoices Pay online with paypal for the unpaid invoices COMMUNICATE WITH TEACHERS / ADMIN From navigation go to message Send new message to teachers and admin Get the sent message to you ………………………………………………………………………………………………...... ACCOUNTANT PANEL DASHBOARD ………………………………………………………………………………………………….. Total number of students, accountants, parents and attendance of student for that day at a glance. Dashboard also holds a calendar for showing events. MANAGING PAYMENTS From navigation go to payment Add invoice and take manual payment multiple time under the same invoice If a payment is due, then an option will be there for taking the payment in the action button of the table that contains the list of all the invoices with the basic information. Edit or delete invoice LOAN MANAGEMENT From navigation go to loan application See all the applied loans Click on apply loan Fill forms to apply Wait for loan approval MESSAGING From navigating go to message Send message to teachers and admin Get the message sent to you NEWS From navigation go to view news View all the uploaded news TODAY’S THOUGHT From navigation go to today’s thought View all the uploaded today’s thought HOLIDAY DATES From navigation go to holiday View all the holiday with their respectives dates HELPFUL LINKS From navigation go to helpful links View all the helpful links HELP DESK From navigation go to help desk Submit or create help desk to the administrator TRANSPORTATION From navigation go to transportation View transportation available ………………………………………………………………………………………………...... LIBRARIAN PANEL DASHBOARD ………………………………………………………………………………………………….. Total number of students, librarian, parents and attendance of student for that day at a glance. Dashboard also holds a calendar for showing events. MANAGING BOOKS From navigation go to library Add books Edit or delete them LOAN MANAGEMENT From navigation go to loan application See all the applied loans Click on apply loan Fill forms to apply Wait for loan approval MESSAGING From navigating go to message Send message to teachers and admin Get the message sent to you NEWS From navigation go to view news View all the uploaded news TODAY’S THOUGHT From navigation go to today’s thought View all the uploaded today’s thought HOLIDAY DATES From navigation go to holiday View all the holiday with their respectives dates HELPFUL LINKS From navigation go to helpful links View all the helpful links HELP DESK From navigation go to help desk Submit or create help desk to the administrator TRANSPORTATION From navigation go to transportation View transportation available ………………………………………………………………………………………………...... HOSTEL MANAGER PANEL DASHBOARD ………………………………………………………………………………………………….. Total number of students, hostel managers, parents and attendance of student for that day at a glance. Dashboard also holds a calendar for showing events. MANAGING DORMITORY From navigation go to dormitory Add / edit / delete LOAN MANAGEMENT From navigation go to loan application See all the applied loans Click on apply loan Fill forms to apply Wait for loan approval MESSAGING From navigating go to message Send message to teachers and admin Get the message sent to you NEWS From navigation go to view news View all the uploaded news TODAY’S THOUGHT From navigation go to today’s thought View all the uploaded today’s thought HOLIDAY DATES From navigation go to holiday View all the holiday with their respectives dates HELPFUL LINKS From navigation go to helpful links View all the helpful links HELP DESK From navigation go to help desk Submit or create help desk to the administrator TRANSPORTATION From navigation go to transportation View transportation available ………………………………………………………………………………………………...... PARENT PANEL DASHBOARD ………………………………………………………………………………………………….. Total number of students, teachers, parents and attendance of student for that day at a glance. Dashboard also holds a calendar for showing events. CHILDREN MARKS From navigation go to exam marks See the mark of your children individually One parent can have multiple children PAYMENTS From navigation go to exam > payment View the invoices of your children and individually Make payment via paypal online CLASS ROUTINE From navigation go to class routine Get the class routine for each of your child separately MESSAGING From navigating go to message Send message to teachers and admin Get the message sent to you NEWS From navigation go to view news View all the uploaded news TODAY’S THOUGHT From navigation go to today’s thought View all the uploaded today’s thought HOLIDAY DATES From navigation go to holiday View all the holiday with their respectives dates HELPFUL LINKS From navigation go to helpful links View all the helpful links HELP DESK From navigation go to help desk Submit or create help desk to the administrator
INK-USC / Temporal Gcn LstmCode for Characterizing and Forecasting User Engagement with In-App Action Graphs: A Case Study of Snapchat
Aryia-Behroziuan / NeuronsAn ANN is a model based on a collection of connected units or nodes called "artificial neurons", which loosely model the neurons in a biological brain. Each connection, like the synapses in a biological brain, can transmit information, a "signal", from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a real number, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs. The connections between artificial neurons are called "edges". Artificial neurons and edges typically have a weight that adjusts as learning proceeds. The weight increases or decreases the strength of the signal at a connection. Artificial neurons may have a threshold such that the signal is only sent if the aggregate signal crosses that threshold. Typically, artificial neurons are aggregated into layers. Different layers may perform different kinds of transformations on their inputs. Signals travel from the first layer (the input layer) to the last layer (the output layer), possibly after traversing the layers multiple times. The original goal of the ANN approach was to solve problems in the same way that a human brain would. However, over time, attention moved to performing specific tasks, leading to deviations from biology. Artificial neural networks have been used on a variety of tasks, including computer vision, speech recognition, machine translation, social network filtering, playing board and video games and medical diagnosis. Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Some successful applications of deep learning are computer vision and speech recognition.[68] Decision trees Main article: Decision tree learning Decision tree learning uses a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining, and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data, but the resulting classification tree can be an input for decision making. Support vector machines Main article: Support vector machines Support vector machines (SVMs), also known as support vector networks, are a set of related supervised learning methods used for classification and regression. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm builds a model that predicts whether a new example falls into one category or the other.[69] An SVM training algorithm is a non-probabilistic, binary, linear classifier, although methods such as Platt scaling exist to use SVM in a probabilistic classification setting. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what is called the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces. Illustration of linear regression on a data set. Regression analysis Main article: Regression analysis Regression analysis encompasses a large variety of statistical methods to estimate the relationship between input variables and their associated features. Its most common form is linear regression, where a single line is drawn to best fit the given data according to a mathematical criterion such as ordinary least squares. The latter is often extended by regularization (mathematics) methods to mitigate overfitting and bias, as in ridge regression. When dealing with non-linear problems, go-to models include polynomial regression (for example, used for trendline fitting in Microsoft Excel[70]), logistic regression (often used in statistical classification) or even kernel regression, which introduces non-linearity by taking advantage of the kernel trick to implicitly map input variables to higher-dimensional space. Bayesian networks Main article: Bayesian network A simple Bayesian network. Rain influences whether the sprinkler is activated, and both rain and the sprinkler influence whether the grass is wet. A Bayesian network, belief network, or directed acyclic graphical model is a probabilistic graphical model that represents a set of random variables and their conditional independence with a directed acyclic graph (DAG). For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Efficient algorithms exist that perform inference and learning. Bayesian networks that model sequences of variables, like speech signals or protein sequences, are called dynamic Bayesian networks. Generalizations of Bayesian networks that can represent and solve decision problems under uncertainty are called influence diagrams. Genetic algorithms Main article: Genetic algorithm A genetic algorithm (GA) is a search algorithm and heuristic technique that mimics the process of natural selection, using methods such as mutation and crossover to generate new genotypes in the hope of finding good solutions to a given problem. In machine learning, genetic algorithms were used in the 1980s and 1990s.[71][72] Conversely, machine learning techniques have been used to improve the performance of genetic and evolutionary algorithms.[73] Training models Usually, machine learning models require a lot of data in order for them to perform well. Usually, when training a machine learning model, one needs to collect a large, representative sample of data from a training set. Data from the training set can be as varied as a corpus of text, a collection of images, and data collected from individual users of a service. Overfitting is something to watch out for when training a machine learning model. Federated learning Main article: Federated learning Federated learning is an adapted form of distributed artificial intelligence to training machine learning models that decentralizes the training process, allowing for users' privacy to be maintained by not needing to send their data to a centralized server. This also increases efficiency by decentralizing the training process to many devices. For example, Gboard uses federated machine learning to train search query prediction models on users' mobile phones without having to send individual searches back to Google.[74] Applications There are many applications for machine learning, including: Agriculture Anatomy Adaptive websites Affective computing Banking Bioinformatics Brain–machine interfaces Cheminformatics Citizen science Computer networks Computer vision Credit-card fraud detection Data quality DNA sequence classification Economics Financial market analysis[75] General game playing Handwriting recognition Information retrieval Insurance Internet fraud detection Linguistics Machine learning control Machine perception Machine translation Marketing Medical diagnosis Natural language processing Natural language understanding Online advertising Optimization Recommender systems Robot locomotion Search engines Sentiment analysis Sequence mining Software engineering Speech recognition Structural health monitoring Syntactic pattern recognition Telecommunication Theorem proving Time series forecasting User behavior analytics In 2006, the media-services provider Netflix held the first "Netflix Prize" competition to find a program to better predict user preferences and improve the accuracy of its existing Cinematch movie recommendation algorithm by at least 10%. A joint team made up of researchers from AT&T Labs-Research in collaboration with the teams Big Chaos and Pragmatic Theory built an ensemble model to win the Grand Prize in 2009 for $1 million.[76] Shortly after the prize was awarded, Netflix realized that viewers' ratings were not the best indicators of their viewing patterns ("everything is a recommendation") and they changed their recommendation engine accordingly.[77] In 2010 The Wall Street Journal wrote about the firm Rebellion Research and their use of machine learning to predict the financial crisis.[78] In 2012, co-founder of Sun Microsystems, Vinod Khosla, predicted that 80% of medical doctors' jobs would be lost in the next two decades to automated machine learning medical diagnostic software.[79] In 2014, it was reported that a machine learning algorithm had been applied in the field of art history to study fine art paintings and that it may have revealed previously unrecognized influences among artists.[80] In 2019 Springer Nature published the first research book created using machine learning.[81] Limitations Although machine learning has been transformative in some fields, machine-learning programs often fail to deliver expected results.[82][83][84] Reasons for this are numerous: lack of (suitable) data, lack of access to the data, data bias, privacy problems, badly chosen tasks and algorithms, wrong tools and people, lack of resources, and evaluation problems.[85] In 2018, a self-driving car from Uber failed to detect a pedestrian, who was killed after a collision.[86] Attempts to use machine learning in healthcare with the IBM Watson system failed to deliver even after years of time and billions of dollars invested.[87][88] Bias Main article: Algorithmic bias Machine learning approaches in particular can suffer from different data biases. A machine learning system trained on current customers only may not be able to predict the needs of new customer groups that are not represented in the training data. When trained on man-made data, machine learning is likely to pick up the same constitutional and unconscious biases already present in society.[89] Language models learned from data have been shown to contain human-like biases.[90][91] Machine learning systems used for criminal risk assessment have been found to be biased against black people.[92][93] In 2015, Google photos would often tag black people as gorillas,[94] and in 2018 this still was not well resolved, but Google reportedly was still using the workaround to remove all gorillas from the training data, and thus was not able to recognize real gorillas at all.[95] Similar issues with recognizing non-white people have been found in many other systems.[96] In 2016, Microsoft tested a chatbot that learned from Twitter, and it quickly picked up racist and sexist language.[97] Because of such challenges, the effective use of machine learning may take longer to be adopted in other domains.[98] Concern for fairness in machine learning, that is, reducing bias in machine learning and propelling its use for human good is increasingly expressed by artificial intelligence scientists, including Fei-Fei Li, who reminds engineers that "There’s nothing artificial about AI...It’s inspired by people, it’s created by people, and—most importantly—it impacts people. It is a powerful tool we are only just beginning to understand, and that is a profound responsibility.”[99] Model assessments Classification of machine learning models can be validated by accuracy estimation techniques like the holdout method, which splits the data in a training and test set (conventionally 2/3 training set and 1/3 test set designation) and evaluates the performance of the training model on the test set. In comparison, the K-fold-cross-validation method randomly partitions the data into K subsets and then K experiments are performed each respectively considering 1 subset for evaluation and the remaining K-1 subsets for training the model. In addition to the holdout and cross-validation methods, bootstrap, which samples n instances with replacement from the dataset, can be used to assess model accuracy.[100] In addition to overall accuracy, investigators frequently report sensitivity and specificity meaning True Positive Rate (TPR) and True Negative Rate (TNR) respectively. Similarly, investigators sometimes report the false positive rate (FPR) as well as the false negative rate (FNR). However, these rates are ratios that fail to reveal their numerators and denominators. The total operating characteristic (TOC) is an effective method to express a model's diagnostic ability. TOC shows the numerators and denominators of the previously mentioned rates, thus TOC provides more information than the commonly used receiver operating characteristic (ROC) and ROC's associated area under the curve (AUC).[101] Ethics Machine learning poses a host of ethical questions. Systems which are trained on datasets collected with biases may exhibit these biases upon use (algorithmic bias), thus digitizing cultural prejudices.[102] For example, using job hiring data from a firm with racist hiring policies may lead to a machine learning system duplicating the bias by scoring job applicants against similarity to previous successful applicants.[103][104] Responsible collection of data and documentation of algorithmic rules used by a system thus is a critical part of machine learning. Because human languages contain biases, machines trained on language corpora will necessarily also learn these biases.[105][106] Other forms of ethical challenges, not related to personal biases, are more seen in health care. There are concerns among health care professionals that these systems might not be designed in the public's interest but as income-generating machines. This is especially true in the United States where there is a long-standing ethical dilemma of improving health care, but also increasing profits. For example, the algorithms could be designed to provide patients with unnecessary tests or medication in which the algorithm's proprietary owners hold stakes. There is huge potential for machine learning in health care to provide professionals a great tool to diagnose, medicate, and even plan recovery paths for patients, but this will not happen until the personal biases mentioned previously, and these "greed" biases are addressed.[107] Hardware Since the 2010s, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks (a particular narrow subdomain of machine learning) that contain many layers of non-linear hidden units.[108] By 2019, graphic processing units (GPUs), often with AI-specific enhancements, had displaced CPUs as the dominant method of training large-scale commercial cloud AI.[109] OpenAI estimated the hardware compute used in the largest deep learning projects from AlexNet (2012) to AlphaZero (2017), and found a 300,000-fold increase in the amount of compute required, with a doubling-time trendline of 3.4 months.[110][111] Software Software suites containing a variety of machine learning algorithms include the following: Free and open-source so
keowu / KoidbgA debugger for Windows ARM64 (AARCH64), user-friendly for reverse engineers, malware analysts, malware developers, game hacking, operating system studies, and more.
sanusanth / Javascript Basic ProgramWhat is JavaScript and what does it do? Before you start learning something new, it’s important to understand exactly what it is and what it does. This is especially useful when it comes to mastering a new programming language. In simple terms, JavaScript is a programming language used to make websites interactive. If you think about the basic makeup of a website, you have HTML, which describes and defines the basic content and structure of the website, then you have CSS, which tells the browser how this HTML content should be displayed—determining things like color and font. With just HTML and CSS, you have a website that looks good but doesn’t actually do much. JavaScript brings the website to life by adding functionality. JavaScript is responsible for elements that the user can interact with, such as drop-down menus, modal windows, and contact forms. It is also used to create things like animations, video players, and interactive maps. Nowadays, JavaScript is an all-purpose programming language—meaning it runs across the entire software stack. The most popular application of JavaScript is on the client side (aka frontend), but since Node.js came on the scene, many people run JavaScript on the server side (aka backend) as well. When used on the client side, JavaScript code is read, interpreted, and executed in the user’s web browser. When used on the server side, it is run on a remote computer. You can learn more about the difference between frontend and backend programming here. JavaScript isn’t only used to create websites. It can also be used to build browser-based games and, with the help of certain frameworks, mobile apps for different operating systems. The creation of new libraries and frameworks is also making it possible to build backend programs with JavaScript, such as web apps and server apps. Is it still worth learning JavaScript in 2021? The world of web development is constantly moving. With so many new tools popping up all the time, it can be extremely difficult to know where you should focus your efforts. As an aspiring developer, you’ll want to make sure that what you’re learning is still relevant in today’s industry. If you’re having doubts about JavaScript, it’s important to know that, since its creation in 1995, JavaScript is pretty much everywhere on the web—and that’s not likely to change any time soon. According to the 2020 StackOverflow developer survey, JavaScript is the most commonly used programming language for the eighth year in a row. It is currently used by 94.5% of all websites and, despite originally being designed as a client-side language, JavaScript has now made its way to the server-side of websites (thanks to Node.js), mobile devices (thanks to React Native and Ionic) and desktop (courtesy of Electron). As long as people are interacting with the web, you can assume that JavaScript is highly relevant—there’s no doubt that this is a language worth knowing! With that in mind, let’s look at some of the key benefits of becoming a JavaScript expert. Why learn JavaScript? The most obvious reason for learning JavaScript is if you have hopes of becoming a web developer. Even if you haven’t got your heart set on a tech career, being proficient in JavaScript will enable you to build websites from scratch—a pretty useful skill to have in today’s job market! If you do want to become a web developer, here are some of the main reasons why you should learn JavaScript: JavaScript experts are versatile JavaScript is an extremely versatile language. Once you’ve mastered it, the possibilities are endless: you can code on the client-side (frontend) using Angular and on the server-side (backend) using Node.js. You can also develop web, mobile, and desktop apps using React, React Native, and Electron, and you can even get involved in machine learning. If you want to become a frontend developer, JavaScript is a prerequisite. However, that’s not the only career path open to you as a JavaScript expert. Mastering this key programming language could see you go on to work in full-stack development, games development, information security software engineering, machine learning, and artificial intelligence—to name just a few! Ultimately, if you want any kind of development or engineering career, proficiency in JavaScript is a must. JavaScript experts are in-demand (and well-paid) JavaScript is the most popular programming language in the world, so it’s no wonder that JavaScript is one of the most sought-after skills in the web development industry today. According to the Devskiller IT Skills and Hiring Report 2020, 72% of companies are looking to hire JavaScript experts. Enter the search term “JavaScript” on job site Indeed and you’ll find over 40,000 jobs requiring this skill (in the US). Run the same search on LinkedIn and the results are in excess of 125,000. At the same time, the global demand for JavaScript seems to outweigh the expertise available on the market. According to this 2018 HackerRank report, 48% of employers worldwide need developers with JavaScript skills, while only 42% of student developers claim to be proficient in JavaScript. And, in their most recent report for 2020, HackerRank once again reports that JavaScript is the most popular language that hiring mangers look for in a web developer candidate. Not only are JavaScript experts in demand—they are also well-paid. In the United States, JavaScript developers earn an average yearly salary of $111,953 per year. We’ve covered this topic in more detail in our JavaScript salary guide, but as you can see, learning JavaScript can really boost your earning potential as a developer. JavaScript is beginner-friendly Compared to many other programming languages, JavaScript offers one of the more beginner-friendly entry points into the world of coding. The great thing about JavaScript is that it comes installed on every modern web browser—there’s no need to set up any kind of development environment, which means you can start coding with JavaScript right away! Another advantage of learning JavaScript as your first programming language is that you get instant feedback; with a minimal amount of JavaScript code, you’ll immediately see visible results. There’s also a huge JavaScript community on sites like Stack Overflow, so you’ll find plenty of support as you learn. Not only is JavaScript beginner-friendly; it will also set you up with some extremely valuable transferable skills. JavaScript supports object-oriented, functional, and imperative styles of programming—skills which can be transferred to any new language you might learn later on, such as Python, Java, or C++. JavaScript provides a crucial introduction to key principles and practices that you’ll take with you throughout your career as a developer. Should you learn plain JavaScript first or can you skip to frameworks and libraries? When deciding whether or not to learn JavaScript, what you’re really asking is whether or not you should learn “vanilla” JavaScript. Vanilla JavaScript just means plain JavaScript without any libraries or frameworks. Let’s explore what this means in more detail now. What is meant by vanilla JavaScript, libraries, and frameworks? If you research the term “vanilla JavaScript”, you might run into some confusion; however, all you need to know is that vanilla JavaScript is used to refer to native, standards-based, non-extended JavaScript. There is no difference between vanilla JavaScript and JavaScript—it’s just there to emphasize the usage of plain JavaScript without the use of libraries and frameworks. So what are libraries and frameworks? JavaScript libraries and frameworks both contain sets of prewritten, ready-to-use JavaScript code—but they’re not the same thing. You can think of a framework as your blueprint for building a website: it gives you a structure to work from, and contains ready-made components and tools that help you to build certain elements much quicker than if you were to code them from scratch. Some popular JavaScript frameworks include Angular, React, Vue, and Node.js. Frameworks also contain libraries. Libraries are smaller than frameworks, and tend to be used for more specific cases. A JavaScript library contains sets of JavaScript code which can be called upon to implement certain functions and features. Let’s imagine you want to code a particular element into your website. You could write, say, ten lines of JavaScript from scratch—or you could take the condensed, ready-made version from your chosen JavaScript library. Some examples of JavaScript libraries include jQuery, Lodash, and Underscore. The easiest way to understand how frameworks and libraries work together is to imagine you are building a house. The framework provides the foundation and the structure, while the library enables you to add in ready-made components (like furniture) rather than building your own from scratch. You can learn more about the relationship between languages and libraries in this post explaining the main differences between JavaScript and jQuery. For now, let’s go back to our original question: How important is it to learn vanilla JavaScript? Should you learn vanilla JavaScript first? When it comes to learning JavaScript, it can be tempting to skip ahead to those time-saving frameworks and libraries we just talked about—and many developers do. However, there are many compelling arguments for learning plain JavaScript first. While JavaScript frameworks may help you get the job done quicker, there’s only so far you can go if you don’t understand the core concepts behind these frameworks. Frontend developer Abhishek Nagekar describes how not learning vanilla JavaScript came back to bite him when he started learning the JavaScript frameworks Node and Express: “As I went to write more and more code in Node and Express, I began to get stuck at even the tiniest problems. Suddenly, I was surrounded with words like callbacks, closures, event loop and prototype. It felt like I got a reintroduction to JavaScript, but this time, it was not a toddler playing in its cradle, it was something of a mysterious monster, challenging me on every other step for not having taken it seriously.” The above Tweet references a long-running joke within the developer community, and although it dates way back to 2015, it’s still highly relevant today. If you want to become a developer who can innovate, not just execute, you need to understand the underlying principles of the web—not just the shortcuts. This means learning vanilla JavaScript before you move on to frameworks. In fact, understanding plain JavaScript will help you later on when it comes to deciding whether to use a framework for a certain project, and if so, which framework to use. Why Study JavaScript? JavaScript is one of the 3 languages all web developers must learn: 1. HTML to define the content of web pages 2. CSS to specify the layout of web pages 3. JavaScript to program the behavior of web pages Learning Speed In this tutorial, the learning speed is your choice. Everything is up to you. If you are struggling, take a break, or re-read the material. Always make sure you understand all the "Try-it-Yourself" examples. The only way to become a clever programmer is to: Practice. Practice. Practice. Code. Code. Code ! Commonly Asked Questions How do I get JavaScript? Where can I download JavaScript? Is JavaScript Free? You don't have to get or download JavaScript. JavaScript is already running in your browser on your computer, on your tablet, and on your smart-phone. JavaScript is free to use for everyone.
AlexToumayan / Chat GPT Flashcards To Anki ConverterThe Chat-GPT-Flashcards-To-Anki-Converter is a project that aims to revolutionize the way students study by simplifying the process of creating Anki flashcards from ChatGPT-generated content. By copying and pasting the text into Chat GPT, users can generate flashcards and then, with a single click, convert them into Anki-compatible format.
tobyli / Sugilite DevelopmentSUGILITE is a new programming-by-demonstration (PBD) system that enables users to create automation on smartphones. SUGILITE uses Android’s accessibility API to support automating arbitrary tasks in any Android app (or even across multiple apps). When the user gives verbal commands that SUGILITE does not know how to execute, the user can demonstrate by directly manipulating the regular apps’ user interface. By leveraging the verbal instructions, the demonstrated procedures, and the apps’ UI hierarchy structures, SUGILITE can automatically generalize the script from the recorded actions, so SUGILITE learns how to perform tasks with different variations and parameters from a single demonstration. Extensive error handling and context checking support forking the script when new situations are encountered, and provide robustness if the apps change their user interface. Our lab study suggests that users with little or no programming knowledge can successfully automate smartphone tasks using SUGILITE.
cssorlandi / English CourseEnglish Course is an application (🏗) for studying English (📚) that allows the user to follow classes with a different methodology (🚀) from the traditional ones, focusing on the student's evolution (🎯)
berkabay4 / Twincat3 Csharp 3D SimulationIn this study, the materials coming from the conveyor line will be taken from the conveyor line using delta robot arm and placed in the desired position as required by the process. The program that will perform the 3D simulation of this system will be implemented. Thus, the instant movements of the system can be followed by the user through this simulation program.
ShelvanLee / XFEM# XFEM_Fracture2D ### Description This is a Matlab program that can be used to solve fracture problems involving arbitrary multiple crack propagations in a 2D linear-elastic solid based on the principle of minimum potential energy. The extended finite element method is used to discretise the solid continuum considering cracks as discontinuities in the displacement field. To this end, a strong discontinuity enrichment and a square-root singular crack tip enrichment are used to describe each crack. Several crack growth criteria are available to determine the evolution of cracks over time; apart from the classic maximum tension (or hoop-stress) criterion, the minimum total energy criterion and the local symmetry criterion are implemented implicitly with respect to the discrete time-stepping. ### Key features * *Fast:* The stiffness matrix and the force vector (i.e. the equations' system) and the enrichment tracking data structures are updated at each time step only with respect to the changes in the fracture topology. This ultimately results in the major part of the computational expense in the solution to the linear system of equations rather than in the post-processing of the solution or in the assembly and updating of the equations. As Matlab offers fast and robust direct solvers, the computational times are reasonably fast. * *Robust.* Suitable for multiple crack propagations with intersections. Furthermore, the stress intensity factors are computed robustly via the interaction integral approach (with the inclusion of the terms to account for crack surface pressure, residual stresses or strains). The minimum total energy criterion and the principle of local symmetry are implemented implicitly in time. The energy release rates are computed based on the stiffness derivative approach using algebraic differentiation (rather than finite differencing of the potential energy). On the other hand, the crack growth direction based on the local symmetry criterion is determined such that the local mode-II stress intensity factor vanishes; the change in a crack tip kink angle is approximated using the ratio of the crack tip stress intensity factors. * *Easy to run.* Each job has its own input files which are independent form those of all other jobs. The code especially lends itself to running parametric studies. Various results can be saved relating to the fracture geometry, fracture mechanics parameters, and the elastic fields in the solid domain. Extensive visualisation library is available for plotting results. ### Instructions 1. Get started by running the demo to showcase some of the capabilities of the program and to determine if it can be useful for you. At the Matlab's command line enter: ```Matlab >> RUN_JOBS.m ``` This will execute a series of jobs located inside the *jobs directory* `./JOBS_LIBRARY/`. These jobs do not take very long to execute (around 5 minutes in total). 2. Subsequently, you can pick one of the jobs inside `./JOBS_LIBRARY/` by defining the job title: ```Matlab >> job_title = 'several_cracks/edge/vertical_tension' ``` 3. Then you can open all the relevant scripts for this job as follows: ```Matlab >> open_job ``` The following input scripts for the *job* will be open in the Matlab's editor: 1. `JOB_MAIN.m`: This is the job's main script. It is called when executing `RUN_JOB` (or `RUN_JOBS`) and acts like a wrapper. Notably, it can serve as a convenient interface to run parametric studies and to save intermediate simulation results. 2. `Input_Scope.m`: This defines the scope of the simulation. From which crack growth criteria to use, to what to compute and what results to show via plots and/or movies. To put it simply, the script is a bunch of "switches" that tell the program what the user wants to be done. 3. `Input_Material.m`: Defines the material's elastic properties in different regions or layers (called "phases") of the computational domain. Moreover, it defines the fracture toughness of the material (assumed to be constant in all material phases). 4. `Input_Crack.m`: Defines the initial crack geometry. 5. `Input_BC.m`: Defines boundary conditions, such as displacements, tractions, crack surface pressure (assumed to be constant in all cracks), body loads (e.g. gravity, pre-stress or pre-strain). 6. `Mesh_make.m`: In-house structured mesh generator for rectangular domains using either linear triangle or bilinear quadrilateral elements. It is possible to mesh horizontal layers using different mesh sizes. 7. `Mesh_read.m`: Gmsh based mesh reader for version-1 mesh files. Of course you can use your own mesh reader provided the output variables are of the correct format (see later). 8. `Mesh_file.m`: Specifies the mesh input file (.msh). At the moment, only Gmsh mesh files of version-1 are allowed. ### Mesh_file.m A mesh file needs to be able to output the following data or variables: * `mNdCrd`: Node coordinates, size = `[nNdStd, 2]` * `mLNodS`: Element connectivities, size = `[nElemn,nLNodS]` * `vElPhz`: Element material phase (or region) ID's, size = `[nElemn,1]` * `cBCNod`: cell of boundary nodes, cell size = `{nBound,1}`, cell element size = `[nBnNod,2]` Example mesh files are located in `./JOBS_LIBRARY/`. Gmsh version-1 file format is described [here](http://www.manpagez.com/info/gmsh/gmsh-2.4.0/gmsh_60.php). ### Additional notes * global variables are defined in `.\Routines_AuxInput\Declare_Global.m` * External libraries are `.\Other_Libs\distmesh` and `.\Other_Libs\mesh2d` ### References Two external meshing libraries are used for the local mesh refinement and remeshing at the crack tip during crack propagation or prior to a crack intersection with another crack or with a boundary of the domain. Specifically, these libraries, which are located in `.\Other_Libs\`, are the following: * [*mesh2d*](https://people.sc.fsu.edu/~jburkardt/m_src/mesh2d/mesh2d.html) by Darren Engwirda * [*distmesh*](http://persson.berkeley.edu/distmesh/) by Per-Olof Persson and Gilbert Strang. ### Issues and Support For support or questions please email [sutula.danas@gmail.com](mailto:sutula.danas@gmail.com). ### Authors Danas Sutula, University of Luxembourg, Luxembourg. If you find this code useful, we kindly ask that you consider citing us. * [Minimum energy multiple crack propagation](http://hdl.handle.net/10993/29414)
