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The revolution of Federated Learning - And we're back with another episode of the podcast When Machine Learning meets Privacy! For the episode #8 we've invited Ramen Dutta, a member of our community and founder of TensoAI. // Abstract: In this episode,  Ramen explain us the concept behind Federated Learning, all the amazing benefits and it's applications in different industries, particularly in agriculture. It's all about not centralizing the data, sound awkward? Just listen to the episode. //Other links to check on Ramen: https://www.linkedin.com/in/tensoai/ https://www.tensoai.com/ https://twitter.com/tensoAI //Final thoughts Feel free to drop some questions into our slack channel (https://go.mlops.community/slack)  Watch some of the other podcast episodes and old meetups on the channel: https://www.youtube.com/channel/UCG6qpjVnBTTT8wLGBygANOQ ----------- Connect With Us ✌️------------- Join our Slack community:   https://go.mlops.community/slack Follow us on Twitter:   @mlopscommunity Sign up for the next meetup: https://go.mlops.community/register Connect with Fabiana on LinkedIn: https://www.linkedin.com/in/fabiana-clemente/ Connect with Ramen on LinkedIn: https://www.linkedin.com/in/tensoai/

**AI and ethical dilemmas** Artificial Intelligence is seen by many as a vehicle for great transformation, but for others, it still remains a mystery, and many questions remain unanswered: will AI systems rule us one day? Can we trust AI to rule our criminal systems? Maybe create political campaigns and dominate political advertisements? Or maybe something less harmful, do our laundry? Some of these questions may sound absurd, but they are for sure making people shift from thinking purely about functional AI capabilities but also to look further to the ethics behind creating such powerful solutions.   For this episode we count with Charles Radclyffe as a guest, the data philosopher, to cover some of these dilemmas. You can reach out to Charles through LinkedIn or at ethicsgrade.io   Useful links:   - MLOps.Community slack - TEDx talk - Surviving the Robot Revolution - Digital Ethics whitepaper

MLOps community meetup #64! Last Wednesday we talked to Christopher  Bergh, CEO, DataKitchen. //Abstract Working on a shared technically difficult problem there will be some things that are important no matter what industry you are in. Whether it's building cars in a factory, using agile or scrum methodology, or productionizing ML models you need a few basics. Chris gives us some of his best practices in the conversation. //Bio Chris Bergh is the CEO and Head Chef at DataKitchen. Chris has more than 25 years of research, software engineering, data analytics, and executive management experience. At various points in his career, he has been a COO, CTO, VP, and Director of Engineering. Chris is a recognized expert on DataOps. He is the co-author of the "DataOps Cookbook\" and the \"DataOps Manifesto,\" and a speaker on DataOps at many industry conferences. //Takeaways Your model is not an island. For success, Data science requires a high level of technical collaboration with other parts of the data organization. //Other Links On-Demand Webinar - Your Model is Not an Island:  Operationalizing Machine Learning at Scale with ModelOps   https://info.datakitchen.io/watch-on-demand-webinar-operationalize-machine-learning-at-scale-with-modelops ----------- Connect With Us ✌️-------------    Join our Slack community:  https://go.mlops.community/slack Follow us on Twitter:  @mlopscommunity Sign up for the next meetup:  https://go.mlops.community/register Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Chris on LinkedIn: https://www.linkedin.com/in/chrisbergh/ Timestamps: [00:00] Introduction to Christopher Bergh [02:57] MLOps community in partnership with MLOps World Conference [04:34] Chris' Background [07:59] "When we started with the company, I realized that the problem I have is generalizable to everyone. I'm getting enough there in years and I wanted to remove the amount of pain that other people have." [09:53] DataOps vs MLOps [10:15] "I don't really honestly care what Ops you use, right? Hahaha! Call it your favorite Ops 'cause first of all as an engineer, I want precise definitions. I look at it from a completely odd-ball way so you could call it whatever Ops term you want." [12:45] Best practices of companies [14:16] "When that code runs in production, monitor and check to see if it's right. Absorb it, monitor it because the model could go out of tune. The data going into it could be wrong. The data transformation could break. Shit happens and don't trust your data providers." [19:00] The whole is still greater than its part [20:26] "It is harder to focus on the results than just under a piece of the task. Don't spend too much time on doing the wrong thing." [23:50] DevOps Principles and Agile [27:17] DataOps Manifesto - DataOps is Data Management reborn [27:45] "The 'Ops' term is ending up encompassing the work that you do in addition to the system you build to do the work." [30:45] Standardization   [32:22] "I think that there's a lack of perception of the need to spend time on doing the operations part of the equation." [34:15] Tools as lego blocks [34:49] "Good interphases make good neighbors." [36:23] "Standards can help but they're not the panacea." [36:30] Cultural side - You build it, you own it, you ship it [39:28] Value chain [44:19] Ripple effect of testing [48:03] Google on "One tool to rule them all" [49:50] "Legacy happens if you're gonna live in the real world and not start greenfield projects." [53:47] Starting MLOps in the legacy system

MLOps Community Meetup #93! Two weeks ago, we talked to Chad Sanderson, Trustworthy Data for Machine Learning. //Abstract The most common challenge for ML teams operating at scale is data quality. In this talk, Chad discusses how Convoy invested in a large-scale data quality effort to treat data as an API and provide a data change management surface to enable trustworthy machine learning. // Bio Chad Sanderson is the Product Lead for Convoy's Data Platform team, which includes the data warehouse, streaming, BI & visualization, experimentation, machine learning, and data discovery. Chad has built everything from feature stores, experimentation platforms, metrics layers, streaming platforms, analytics tools, data discovery systems, and workflow development platforms. He's implemented open source, SaaS products (early and late-stage) and has built cutting-edge technology from the ground up. Chad loves the data space, and if you're interested in chatting about it with him, don't hesitate to reach out. // Related links    ----------- ✌️Connect With Us ✌️-------------    Join our Slack community:   https://go.mlops.community/slack Follow us on Twitter:   @mlopscommunity Sign up for the next meetup:   https://go.mlops.community/register Catch all episodes, Feature Store, Machine Learning Monitoring and Blogs: https://mlops.community/ Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with Chad on LinkedIn: https://www.linkedin.com/in/chad-sanderson/

Learn more about the technical aspects of artificial intelligence and machine learning by starting an ICFL Columbia University machine learning club. ICF Leaders empowers high school students to share their interests with other like-minded students around the globe.

We recently met up with Cormac Brick (Intel) and Mike Del Balso (Uber) at O'Reilly AI in SF. As the director of machine intelligence in Intel's Movidius group, Cormac is an expert in porting deep learning models to all sorts of embedded devices (cameras, robots, drones, etc.). He helped us understand some of the techniques for developing portable networks to maximize performance on different compute architectures. In our discussion with Mike, we talked about the ins and outs of Michelangelo, Uber's machine learning platform, which he manages. He also described why it was necessary for Uber to build out a machine learning platform and some of the new features they are exploring.

This is my second episode with David O'Hara, and as with the first episode I recorded with him, conversing with David is always a delight and a great pleasure. In this episode we discuss his trip to Central America, and we also talk about the recent archaeological discovery of a vast Mayan metropolis that "at its peak some 1,500 years ago, covered an area about twice the size of medieval England, with an estimated population of around five million." David goes over the cutting edge technology that is now being used to discover these, until very recently, hidden ruins of an ancient Mayan civilization, and what we can learn from these discoveries in regards to our own civilization. We also get into the ethics of artificial intelligence and the corporate control of the development of computer technology, and the implications this has for how information is disseminated through our society. David discusses some of the underlying issues on relying on algorithms and computer learning making big decisions for us, and how the kind of thinking leads to unintended outcomes. David O'Hara is a professor of Philosophy and Classics at Augustana University, and the author of the book "Downstream: Reflections on Brook Trout, Fly Fishing, and the Waters of Appalachia." David teaches a variety of courses on philosophy, classics, religion, and environmental ethics, and not long before the recording of this episode, had just recently got back from a trip to Central America where he teaches an in-depth course on reef ecology. Episode Notes:- Learn more about David's book "Downstream: Reflections on Brook Trout, Fly Fishing, and the Waters of Appalachia" here: https://wipfandstock.com/downstream.html- Read David's writings and get to understand him more at his blog: http://slowperc.blogspot.com/- Follow David on Twitter: https://twitter.com/Davoh- Read the article cited in this episode on the "Sprawling Maya network discovered under Guatemala jungle" here: http://www.bbc.com/news/world-latin-america-42916261- The song featured in this episode is "Sewee Sewee" by Mountain Man from the album Made The Harbor.- Podcast website: https://www.lastborninthewilderness.com- Support the podcast:PATREON: www.patreon.com/lastborninthewildernessONE-TIME DONATION: www.ko-fi.com/lastborninthewilderness- Follow and listen:SOUNDCLOUD: www.soundcloud.com/lastborninthewildernessITUNES: www.goo.gl/Fvy4caGOOGLE PLAY: https://goo.gl/wYgMQcSTITCHER: https://goo.gl/eeUBfS- Social Media:FACEBOOK: www.facebook.com/lastborninthewildernesspodcastTWITTER: www.twitter.com/lastbornpodcastINSTAGRAM: www.instagram.com/patterns.of.behavior

This week Elixir creator Jose Valim joins Jerod and Practical AI's Daniel Whitenack to discuss Numerical Elixir, his new project that's bringing Elixir into the world of machine learning. We discuss why Jose chose this as his next direction, the team's layered approach, influences and collaborators on this effort, and their awesome collaborative notebook project that's built on Phoenix LiveView.

Venture Capital in Machine Learning Startups With John Spindler CEO of Capital Enterprise.  John Spindler CEO of Capital Enterprise. We talked about what trends he has been seeing within MLOps, ML companies and also how he evaluates a deal.  John Spindler has over 15 years experience as an entrepreneur and business advisor/consultant and as well as being responsible for the day to day management of Capital Enterprise he is also a general partner at AI Seed, an early-stage fund that invests in highly talented AI-first companies.  John is on a mission to make it possible for someone moderately intelligent, with a good idea, ambition and passion to make it as an entrepreneur.  Join our slack community: https://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw Follow us on twitter:@mlopscommunity Sign up for the next meetup: https://zoom.us/webinar/register/WN_a_nuYR1xT86TGIB2wp9B1g    Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with John on LinkedIn:  https://www.linkedin.com/in/johnbspindler/

Venture Capital in Machine Learning Startups With John Spindler CEO of Capital Enterprise.  John Spindler CEO of Capital Enterprise. We talked about what trends he has been seeing within MLOps, ML companies and also how he evaluates a deal.  John Spindler has over 15 years experience as an entrepreneur and business advisor/consultant and as well as being responsible for the day to day management of Capital Enterprise he is also a general partner at AI Seed, an early-stage fund that invests in highly talented AI-first companies.  John is on a mission to make it possible for someone moderately intelligent, with a good idea, ambition and passion to make it as an entrepreneur.  Join our slack community: https://join.slack.com/t/mlops-community/shared_invite/zt-391hcpnl-aSwNf_X5RyYSh40MiRe9Lw Follow us on twitter:@mlopscommunity Sign up for the next meetup: https://zoom.us/webinar/register/WN_a_nuYR1xT86TGIB2wp9B1g    Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/ Connect with John on LinkedIn:  https://www.linkedin.com/in/johnbspindler/

Practical Mlops Operationalizing Machine Learning Models 

Online payment fraud poses a significant threat to financial transactions, resulting in substantial economic losses. This project proposes a machine learning-based system to predict and detect fraudulent transactions using the Decision Tree Classifier algorithm. Online payment fraud involves unauthorized access and manipulation of financial transactions, including identity theft, phishing, card skimming, and transaction tampering. The Decision Tree Classifier algorithm trains on historical transaction data (fraudulent and non-fraudulent) to build a classifier model. This model predicts transactions as fraudulent or non-fraudulent based on feature extraction and splitting. The process begins with user registration, where customers provide their bank details in the Website, which are then securely stored in an SQL database. Next, transaction details are input into the system, allowing for real-time monitoring. A Decision Tree Classifier-based machine learning model is employed to predict potential fraud, analyzing the collected data to identify patterns and anomalies. The prediction results are then displayed on the website, alerting users to potential fraud or confirming legitimate transactions. To ensure timely notification, an Email API is integrated, sending alerts to users and administrators when suspicious activity is detected. This comprehensive system provides a robust defense against online payment fraud, safeguarding users' financial information and maintaining trust in e-commerce transactions.

The need for personalized health solutions is increasing, and the fitness recommendation systems are mandatory to optimizing workout plans. This paper discusses a system providing personalized fitness recommendations based on Reinforcement Learning (RL), Long Short-Term Memory (LSTM) networks, Genetic Algorithms (GA), and Artificial Neural Networks (ANN). Such AI techniques would ensure real-time workout adjustments, future performance predictions, and optimization of fitness routine. Reinforcement Learning provides an adaptive workout planner and optimizes the user recommendations based upon user feedback that improves long-term health and fitness outcomes. LSTM Networks inherit time-series data such as past performance in workouts and are able to predict trends in fitness for the future and therefore offer training modifications ahead of time. Genetic Algorithms operate in selecting and mutating workout parameters so that the plans can dynamically evolve with constant adaptation. ANN improves testing patterns and mapping cause-effect relationships that make workouts more efficient. The evaluation is carried out on the architecture against applicable baselines and therefore shows the ability of the developed prototype to create adaptive fitness recommendations. The work demonstrates how many AI techniques combat the limitations of traditional fitness systems, such as lack of real-time adaptability and long-term optimization. The system produces a more intelligent approach in workout personalization through data to enhance the efficiency and satisfaction derived from fitness programs regarding a specific and dynamic approach to fitness planning.

Efficient tax debt collection is a challenge for Moroccan local tax authorities. This article explores the potential of machine learning techniques and novel strategies to enhance efficiency in this process. We present a practical use case demonstrating the application of machine learning for taxpayer segmentation, improving accuracy in identifying high-risk debtors. Using a comprehensive dataset of tax payment behavior, we showcase the effectiveness of machine learning algorithms in segmenting taxpayers based on their likelihood of non compliance or debt accumulation. We also investigate innovative strategies that integrate behavioral economics principles to enable better targeted interventions. Real-world case studies in local tax debt collection highlight the impact of these strategies. The findings underscore the transformative potential of machine learning techniques and novel strategies in improving the efficiency of local tax debt collection. Accurate identification of high-risk debtors and tailored enforcement actions help maximize revenue while minimizing resource waste. This research contributes to the existing knowledge by providing insights into the implementation of machine learning techniques and novel strategies in tax debt collection. It emphasizes the importance of data-driven approaches and highlights how local tax authorities can drive efficiency and optimize revenue collection by embracing these advancements.

The stock market is considered the primary domain of importance in the financial sector where Artificial Intelligence combined with various algorithmic practices empowers investors with datadriven insights, enhancing decision-making, predicting trends, and optimizing risk management for more informed and strategic financial outcomes. This research paper delves into the real-world applications of machine learning and algorithmic trading, observing their historical evolution together and how both of these can go hand in hand to control risk and forecast the movement of a stock or an index and its future. The research is structured to provide comprehensive insights into two major subdomains in the application of AI in algorithmic trading: risk management in equity markets and predictive analysis of stock trends through the application of machine learning models and training the current existing data which is feasible and training them with respect to historical scenarios of various market trends along with various fundamental and technical analysis techniques with the help of various deep learning algorithms. For risk management of a portfolio in finance, various machine learning models can be employed, depending on the specific needs and goals of the portfolio manager or risk analyst and implementing various valueat-risk algorithms along with deep learning techniques in order to assess risk at particular trade position and to manage volatile trades at unprecedented situations. The significance of this research paper lies in its practical applicability, offering real-world solutions to enhance trading strategies and decision-making processes with a focus on mitigating risk and capitalizing on market opportunities and also giving clear insights with respect to the current practical limitations of application of the provided solution and future scope to overcome the same.

Today, one of the top concerns for governments is road safety. There are many safety features built into cars, yet traffic accidents still happen frequently and are unavoidable. To lessen the harm caused by traffic accidents, predicting their causes has become the primary goal. In this situation, it will be beneficial to examine the frequency of accidents so that we can use this information to further aid us in developing strategies to lessen them. From this, we can deduce the connections between traffic accidents, road conditions, and the impact of environmental factors on accident occurrence. In order to construct an accident prediction model, I used machine learning techniques, including the Decision Tree, Random Forest, and Logistic Regression. The development of safety measures and accident prediction will both benefit from these classification systems. Several elements, including weather, vehicle condition, road surface condition, and light condition, can be used to predict road accidents. Three dataset files—accidents, casualties, and vehicles are loaded into this dataset. This allows us to forecast the severity of accidents.

Banks and other financial institutions compete for customers by providing a wide range of services and products. Most banks, however, make the vast majority of their money from their credit portfolio. Loans accepted by borrowers might lead to interest charges. The loan portfolio, and customers' repayment habits in particular, can have a substantial impact on a bank's bottom line. The financial institution's Non-Performing Assets can be reduced if it can accurately predict which borrowers are likely to default on their loans. Therefore, there is substantial scholarly value in exploring the prediction of loan endorsement. In order to make accurate predictions, it is crucial to use Machine Learning methods. Based on a person's past loan qualification history, this research uses a machine learning methodology to predict the person's likelihood of consistently making loan repayments. The primary aim of this research is to foretell how likely it is that a given individual will be granted a loan. 

Levy processes are random measures that give independent mass to independent increments. I will show how they can be used to model various types of data such as binary vectors or vectors of counts, with applications to text and images. These techniques fall in the category of nonparametric Bayesian methods, and are related to the better known Dirichlet process. ©2008 Microsoft Corporation. All rights reserved.

Rest easy with Google Play Protect. See how #Android’s machine learning systems make your device safer. #io17 https://t.co/3MjTIHxG2k Source: https://twitter.com/Android/status/864932679472566276 Uploader: Android

Low Surface Brightness Galaxies from BASS+MzLS with Machine Learning 作者： Peng-Liang Du, Wei Du, Bing-Qing Zhang, Zhen-Ping Yi, Min He and Hong Wu 1 作者单位： 提交时间： 2024-05-24 14:57:50 摘要: The distribution of the LSBGs is bimodal in the g − r color, indicating the two distinct populations of the blue (g − r < 0.60) and red (g − r > 0.60) LSBGs. The blue LSBGs appear spiral, disk or irregular while the red LSBGs are spheroidal or elliptical and spatially clustered. This trend shows that the color has a strong correlation with galaxy morphology for LSBGs. In the spatial distribution, the blue LSBGs are more uniformly distributed while the red ones are highly clustered, indicating that red LSBGs preferentially populate a denser environment than the blue LSBGs. Besides, both populations have a consistent distribution of ellipticity (median ), half-light radius (median reff ∼ 4”) and Sérsic index (median n = 1), implying the dominance of the full sample by the round and disk galaxies. This sample has definitely extended the studies of LSBGs to a regime of lower surface brightness, fainter magnitude and broader other properties than the previously Sloan Digital Sky Survey-based samples. 期刊： Research in Astronomy and Astrophysics 分类： 天文学 >> 天文学 投稿状态： 已在期刊出版 引用： ChinaXiv:202405.00272 (或此版本 ChinaXiv:202405.00272V1 ) DOI:https://doi.org/10.1088/1674-4527/ad3954 CSTR:32003.36.ChinaXiv.202405.00272.V1 推荐引用方式： Peng-Liang Du, Wei Du, Bing-Qing Zhang, Zhen-Ping Yi, Min He and Hong Wu.(2024).Low Surface Brightness Galaxies from BASS+MzLS with Machine Learning.Research in Astronomy and Astrophysics.doi:https://doi.org/10.1088/1674-4527/ad3954 版本历史 [V1] 2024-05-24 14:57:50 ChinaXiv:202405.00272V1 下载全文

Werbematerial des Buches "Machine Learning – Medien, Infrastrukturen und Technologien der Künstlichen Intelligenz", herausgegeben von Christoph Engemann und Andreas Sudmann. Veröffentlicht wurden auf der Website des Transcript-Verlages das Cover des Buches, eine Leseprobe und ein MARC-Report, alles kostenlos zum Download.

The ability to navigate autonomously from the start to its final goal is the crucial key to mobile robots. To ensure complete navigation, it is mandatory to do heavy programming since this task is composed of several subtasks such as path planning, localization, and obstacle avoidance. This paper simplifies this heavy process by making the robot more intelligent. The robot will acquire the navigation policy from an expert in navigation using machine learning. We used the expert A*, which is characterized by generating an optimal trajectory. In the context of robotics, learning from demonstration (LFD) will allow robots, in general, to acquire new skills by imitating the behavior of an expert. The expert will navigate in different environments, and our robot will try to learn its navigation strategy by linking states and suitable actions taken. We find that our robot acquires the navigation policy given by A* very well. Several tests were simulated with environments of different complexity and obstacle distributions to evaluate the flexibility and efficiency of the proposed strategies. The experimental results demonstrate the reliability and effectiveness of the proposed method.

Alzheimer's disease (AD) is the most common incurable neurodegenerative illness, a term that encompasses memory loss as well as other cognitive abilities. The purpose of the study is using precise early-stage gene expression data from blood generated from a clinical Alzheimer's dataset, the goal was to construct a classification model that might predict the early stages of Alzheimer's disease. Using information gain (IG), a selection of characteristics was chosen to provide substantial information for distinguishing between normal control (NC) and early-stage AD participants. The data was divided into various sizes; three distinct machine learning (ML) algorithms were used to generate the classification models: support vector machine (SVM), Naïve Bayes (NB), and k-nearest neighbors (K-NN). Using the WEKA software tool and a variety of model performance measures, the capacity of the algorithms to effectively predict cognitive impairment status was compared and tested. The current findings reveal that an SVM-based classification model can accurately differentiate cognitively impaired Alzheimer's patients from normal healthy people with 96.6% accuracy. As discovered and validated a gene expression pattern in the blood that accurately distinguishes Alzheimer's patients and cognitively healthy controls, demonstrating that changes specific to AD can be detected far from the disease's core site. 

Machine Learning is part of Artificial Intelligence that has the ability to make future forecastings based on the previous experience. Methods has been proposed to construct models including machine learning algorithms such as Neural Networks (NN), Support Vector Machines (SVM) and Deep Learning. This paper presents a comparative performance of Machine Learning algorithms for cryptocurrency forecasting. Specifically, this paper concentrates on forecasting of time series data. SVM has several advantages over the other models in forecasting, and previous research revealed that SVM provides a result that is almost or close to actual result yet also improve the accuracy of the result itself. However, recent research has showed that due to small range of samples and data manipulation by inadequate evidence and professional analyzers, overall status and accuracy rate of the forecasting needs to be improved in further studies. Thus, advanced research on the accuracy rate of the forecasted price has to be done.

This paper introduces an intuitive approach to clickthrough rate (CTR) prediction, a learning problem that has been extensively studied over the past several years. As digital marketing continues to grow rapidly into a multi-billion-dollar industry, this study aims to find the most effective machine learning model to enhance the CTR of marketing emails by comparing various tree-based models. Key steps in this research include data collection, feature extraction, and CTR prediction through the evaluation of different models. The statistical results prove that the CatBoost model, with optimized feature selection, achieves near-perfect data fitting, indicating its efficiency.

This paper addresses the challenge of high computational complexity in video surveillance systems by proposing an efficient model that integrates hybrid machine learning algorithms (HML) for event recognition. Conventional surveillance methods struggle with processing vast amounts of video data in real-time, leading to scalability, and performance issues. Our proposed approach utilizes convolutional neural networks (CNNs) and recurrent neural networks (RNNs) to enhance the accuracy and efficiency of detecting events. By comparing our model with conventional surveillance techniques motion detection, background subtraction, and frame differencing. We demonstrate significant improvements in frame processing time, object detection speed, energy efficiency, and anomaly detection accuracy. The integration of dynamic model scaling and edge computing further optimizes computational resource usage, making our method a scalable and effective solution for real time surveillance needs. This research highlights the potential of machine learning to revolutionize video surveillance, offering insights into developing more intelligent and responsive security systems. The results of your simulation analysis, indicating performance improvements in accuracy by 0.25%, 0.35%, and 0.45% for the motion detection algorithm, background subtraction, and frame differencing respectively, and in real-time data processing by 5.65%, 4.45%, and 6.75% for the motion detection algorithm, background subtraction, and frame differencing respectively, highlight the potential of machine learning to transform video surveillance into a more intelligent and responsive system.

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In this paper, we show how to transform any optimization problem that arises from fitting a machine learning model into one that (1) detects and removes contaminated data from the training set while (2) simultaneously fitting the trimmed model on the uncontaminated data that remains. To solve the resulting nonconvex optimization problem, we introduce a fast stochastic proximal-gradient algorithm that incorporates prior knowledge through nonsmooth regularization. For datasets of size $n$, our approach requires $O(n^{2/3}/\varepsilon)$ gradient evaluations to reach $\varepsilon$-accuracy and, when a certain error bound holds, the complexity improves to $O(\kappa n^{2/3}\log(1/\varepsilon))$. These rates are $n^{1/3}$ times better than those achieved by typical, full gradient methods.

There are several confounding factors that can reduce the accuracy of gait recognition systems. These factors can reduce the distinctiveness, or alter the features used to characterise gait, they include variations in clothing, lighting, pose and environment, such as the walking surface. Full invariance to all confounding factors is challenging in the absence of high-quality labelled training data. We introduce a simulation-based methodology and a subject-specific dataset which can be used for generating synthetic video frames and sequences for data augmentation. With this methodology, we generated a multi-modal dataset. In addition, we supply simulation files that provide the ability to simultaneously sample from several confounding variables. The basis of the data is real motion capture data of subjects walking and running on a treadmill at different speeds. Results from gait recognition experiments suggest that information about the identity of subjects is retained within synthetically generated examples. The dataset and methodology allow studies into fully-invariant identity recognition spanning a far greater number of observation conditions than would otherwise be possible.

Sentiment prediction of contemporary music can have a wide-range of applications in modern society, for instance, selecting music for public institutions such as hospitals or restaurants to potentially improve the emotional well-being of personnel, patients, and customers, respectively. In this project, music recommendation system built upon on a naive Bayes classifier, trained to predict the sentiment of songs based on song lyrics alone. The experimental results show that music corresponding to a happy mood can be detected with high precision based on text features obtained from song lyrics.

We present the results of applying automated machine learning techniques to the problem of matching different object catalogues in astrophysics. In this study we take two partially matched catalogues where one of the two catalogues has a large positional uncertainty. The two catalogues we used here were taken from the HI Parkes All Sky Survey (HIPASS), and SuperCOSMOS optical survey. Previous work had matched 44% (1887 objects) of HIPASS to the SuperCOSMOS catalogue. A supervised learning algorithm was then applied to construct a model of the matched portion of our catalogue. Validation of the model shows that we achieved a good classification performance (99.12% correct). Applying this model, to the unmatched portion of the catalogue found 1209 new matches. This increases the catalogue size from 1887 matched objects to 3096. The combination of these procedures yields a catalogue that is 72% matched.

The objective of this project is to improve the effectiveness and efficiency of advertising on various platforms by utilizing advanced algorithms, namely the Upper Confidence Bound and Thompson Sampling Algorithm. The project aims to find a balance between exploring new advertising strategies and exploiting proven high-performing approaches. By implementing these bandit algorithms, the project aims to dynamically optimize ad placements, formats, and targeting to maximize user engagement and ad revenue. The methodology involves an iterative process of data collection, analysis, and adaptation. The initial phases include defining project objectives, understanding the target audience, and reviewing the current ad strategy. The Upper Confidence Bound algorithm enables intelligent decision-making by assigning confidence bounds to different ad strategies, allowing for efficient exploration and exploitation. On the other hand, the Thompson Sampling algorithm, rooted in Bayesian principles, dynamically adapts based on observed outcomes, striking a balance between exploration and exploitation through probabilistic reasoning. In summary, this Ads Optimization Project utilizes the power of the Upper Confidence Bound and Thompson Sampling algorithms to create a data-driven, adaptive, and user-centric approach to advertising. The ultimate goal is to achieve optimal user engagement and ad revenue.

The objective of this project is to improve the effectiveness and efficiency of advertising on various platforms by utilizing advanced algorithms, namely the Upper Confidence Bound and Thompson Sampling Algorithm. The project aims to find a balance between exploring new advertising strategies and exploiting proven high-performing approaches. By implementing these bandit algorithms, the project aims to dynamically optimize ad placements, formats, and targeting to maximize user engagement and ad revenue. The methodology involves an iterative process of data collection, analysis, and adaptation. The initial phases include defining project objectives, understanding the target audience, and reviewing the current ad strategy. The Upper Confidence Bound algorithm enables intelligent decision-making by assigning confidence bounds to different ad strategies, allowing for efficient exploration and exploitation. On the other hand, the Thompson Sampling algorithm, rooted in Bayesian principles, dynamically adapts based on observed outcomes, striking a balance between exploration and exploitation through probabilistic reasoning. In summary, this Ads Optimization Project utilizes the power of the Upper Confidence Bound and Thompson Sampling algorithms to create a data-driven, adaptive, and user-centric approach to advertising. The ultimate goal is to achieve optimal user engagement and ad revenue.

The "2nd Annual WPI-UMASS-UPENN EDM Data Mining Challenge" required contestants to predict efficient testtaking based on log data. In this paper, we describe our theory-driven and psychometric modeling approach. For feature engineering, we employed the Log-Normal Response Time Model for estimating latent person speed, and the Generalized Partial Credit Model for estimating latent person ability. Additionally, we adopted an n-gram feature approach for event sequences. For training a multi-label classifier, we distinguished inefficient test takers who were going too fast and those who were going too slow, instead of using the provided binary target label. Our best-performing ensemble classifier comprised three sets of low-dimensional classifiers, dominated by test-taker speed. While our classifier reached moderate performance, relative to competition leaderboard, our approach makes two important contributions. First, we show how explainable classifiers could provide meaningful predictions if results can be contextualized to test administrators who wish to intervene or take action. Second, our re-engineering of test scores enabled us to incorporate person ability into the estimation. However, ability was hardly predictive of efficient behavior, leading to the conclusion that the target label's validity needs to be questioned. The paper concludes with tools that are helpful for substantively meaningful log data mining. [For the full proceedings, see ED607784.]

paper

Neural networks are ubiquitous in applied machine learning for education. Their pervasive success in predictive performance comes alongside a severe weakness, the lack of explainability of their decisions, especially relevant in humancentric fields. We implement five state-of-the-art methodologies for explaining black-box machine learning models (LIME, PermutationSHAP, KernelSHAP, DiCE, CEM) and examine the strengths of each approach on the downstream task of student performance prediction for five massive open online courses. Our experiments demonstrate that the families of explainers do not agree with each other on feature importance for the same Bidirectional LSTM models with the same representative set of students. We use Principal Component Analysis, Jensen-Shannon distance, and Spearman's rank-order correlation to quantitatively cross-examine explanations across methods and courses. Furthermore, we validate explainer performance across curriculum-based prerequisite relationships. Our results come to the concerning conclusion that the choice of explainer is an important decision and is in fact paramount to the interpretation of the predictive results, even more so than the course the model is trained on. Source code and models are released at http://github.com/epfl-ml4ed/evaluating-explainers. [For the full proceedings, see ED623995.]

Regression analysis, in statistic a modelling, is a set of statical processes that can be used to estimate the relationship between a dependent variable, commonly known as the outcome or response, and more independent variables generally called predictors of covariant. On the other hand, autoregression, which is based on regression equations, is a sequential model that uses time to predict the next step data from the previous step. Given the importance of accurate modelling and reliable predictions. in this paper we have analyzed the most popular methods used for data prediction. Nonlinear autoregressive methods were introduced, and then the machine deep learning approach was used to apply prediction based on a selected input data set. The mean square error was calculated for various artificial neural networks architecture to reach the optimal architecture, which minimized the error. Different artificial neural network (ANN) architectures were trained, tested, and validated using various regressive models, a recommendation was raised according to the obtained and analyzed experimental results. It was shown that using the concepts of machine deep learning will enhance the response of the prediction model. 

We present recent results from the Laboratory for Cosmological Data Mining (http://lcdm.astro.uiuc.edu) at the National Center for Supercomputing Applications (NCSA) to provide robust classifications and photometric redshifts for objects in the terascale-class Sloan Digital Sky Survey (SDSS). Through a combination of machine learning in the form of decision trees, k-nearest neighbor, and genetic algorithms, the use of supercomputing resources at NCSA, and the cyberenvironment Data-to-Knowledge, we are able to provide improved classifications for over 100 million objects in the SDSS, improved photometric redshifts, and a full exploitation of the powerful k-nearest neighbor algorithm. This work is the first to apply the full power of these algorithms to contemporary terascale astronomical datasets, and the improvement over existing results is demonstrable. We discuss issues that we have encountered in dealing with data on the terascale, and possible solutions that can be implemented to deal with upcoming petascale datasets.

To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift/BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien 2014 is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of $\gtrsim97\%$ ($\lesssim 3\%$ error), which is a significant improvement on a cut in GRB flux which has an accuracy of $89.6\%$ ($10.4\%$ error). These models are then used to measure the detection efficiency of Swift as a function of redshift $z$, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of $n_0 \sim 0.48^{+0.41}_{-0.23} \ {\rm Gpc}^{-3} {\rm yr}^{-1}$ with power-law indices of $n_1 \sim 1.7^{+0.6}_{-0.5}$ and $n_2 \sim -5.9^{+5.7}_{-0.1}$ for GRBs above and below a break point of $z_1 \sim 6.8^{+2.8}_{-3.2}$. This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting. The code used in this is analysis is publicly available online (https://github.com/PBGraff/SwiftGRB_PEanalysis).

Prediction of radionuclide diffusion enabled by missing data imputation and ensemble machine learning 作者： Tian, Mr. Junlei Feng, Mr. Jiaxing Shen, Mr. Jiacong Yao, Mr. Lei Wang, Mr. Jingyan WU, Dr. Tao zhao, Dr. Yaolin 赵耀林 通讯作者： WU, Dr. Tao Email:[email protected] 提交时间： 2025-01-12 17:30:56 摘要: Missing values in radionuclide diffusion datasets can undermine the predictive accuracy and robustness of machine learning (ML) models. A regression-based missing data imputation method using light gradient boosting machine (LGBM) algorithm was employed to impute over 60% of the missing data, establishing a radionuclide diffusion dataset containing 16 input features and 813 instances. The effective diffusion coefficient (De) was predicted using ten ML models. The predictive accuracy of ensemble meta-models, namely LGBM-extreme gradient boosting (XGB) and LGBM-categorical boosting (CatB), surpassed the other ML models, with R2 values of 0.94. The models were applied in predicting the De values of EuEDTA- and HCrO4- in saturated compacted bentonites at compaction ranged from 1200 kg/m3 to 1800 kg/m3, which was measured using a through-diffusion method. The generalization ability of LGBM-XGB model surpassed that of LGB-CatB in predicting the De of HCrO4-. Shapley additive explanations identified the total porosity as the most significant influencing factor. In addition, the partial dependence plot analysis technique showed clearer results for univariate correlation analysis. This study provides a regression imputation technique to refine radionuclide diffusion datasets, offering a deeper insight into analyzing the diffusion mechanism of radionuclide and supporting the safety assessment of the geological disposal of high-level radioactive waste. machine learning radionuclide diffusion bentonite regression imputation missing data diffusion experiments 来自： WU, Dr. Tao 分类： 物理学 >> 核物理学 备注: 已向《Nuclear Science and Techniques》投稿 引用： ChinaXiv:202501.00141 (或此版本 ChinaXiv:202501.00141V1 ) DOI:10.12074/202501.00141 CSTR:32003.36.ChinaXiv.202501.00141 推荐引用方式： Tian, Mr. Junlei,Feng, Mr. Jiaxing,Shen, Mr. Jiacong,Yao, Mr. Lei,Wang, Mr. Jingyan,WU, Dr. Tao,zhao, Dr. Yaolin 赵耀林.Prediction of radionuclide diffusion enabled by missing data imputation and ensemble machine learning.中国科学院科技论文预发布平台.[DOI:10.12074/202501.00141] 版本历史 [V1] 2025-01-12 17:30:56 ChinaXiv:202501.00141V1 下载全文

The current bottleneck of transient detection in most surveys is the problem of rejecting numerous artifacts from detected candidates. We present a triple-stage hierarchical machine learning system for automated artifact filtering in difference imaging, based on self-organizing maps. The classifier, when tested on the OGLE-IV Transient Detection System, accepts ~ 97 % of real transients while removing up to ~ 97.5 % of artifacts.

Recent observations of galaxies at $z \gtrsim 7$, along with the low value of the electron scattering optical depth measured by the Planck mission, make galaxies plausible as dominant sources of ionizing photons during the epoch of reionization. However, scenarios of galaxy-driven reionization hinge on the assumption that the average escape fraction of ionizing photons is significantly higher for galaxies in the reionization epoch than in the local Universe. The NIRSpec instrument on the James Webb Space Telescope (JWST) will enable spectroscopic observations of large samples of reionization-epoch galaxies. While the leakage of ionizing photons will not be directly measurable from these spectra, the leakage is predicted to have an indirect effect on the spectral slope and the strength of nebular emission lines in the rest-frame ultraviolet and optical. Here, we apply a machine learning technique known as lasso regression on mock JWST/NIRSpec observations of simulated $z=7$ galaxies in order to obtain a model that can predict the escape fraction from JWST/NIRSpec data. Barring systematic biases in the simulated spectra, our method is able to retrieve the escape fraction with a mean absolute error of $\Delta f_{\mathrm{esc}} \approx 0.12$ for spectra with $S/N\approx 5$ at a rest-frame wavelength of 1500 {\AA} for our fiducial simulation. This prediction accuracy represents a significant improvement over previous similar approaches.

Phishing is a very commonly occurring attack in which the attacker attempt to get the private information of the user like card details, their passwords their transaction details etc. using fake copy websites. Attackers uses the websites very similar to the original websites and not possiblefor common people to identify. Phishing is the biggest loop-hole in the cyber world. Phishing became a successful business for phishers. Other than fake websites phishers uses different methods to do this job using messaging, spoofed links to make money and to counter Phishing various method are proposed some anti-phishing techniques are blacklist, whitelist but due to exponential growth on new innovations and technologies these techniques falls a little below as new websites contains dynamically work in it due to that these techniques cannot outperform todays websites.

Mechanical learning is a computing system that is based on a set of simple and fixed rules, and can learn from incoming data. A learning machine is a system that realizes mechanical learning. Importantly, we emphasis that it is based on a set of simple and fixed rules, contrasting to often called machine learning that is sophisticated software based on very complicated mathematical theory, and often needs human intervene for software fine tune and manual adjustments. Here, we discuss some basic facts and principles of such system, and try to lay down a framework for further study. We propose 2 directions to approach mechanical learning, just like Church-Turing pair: one is trying to realize a learning machine, another is trying to well describe the mechanical learning.

Functional Magnetic Resonance Imaging (fMRI) has enabled scientists to look into the active human brains. This has revealed exciting insights into the spatial and temporal changes underlying a broad range of brain functions, based on a flood of new data thus requiring the development of appropriate data analysis methods. We have recently developed a comprehensive machine learning framework for the exploration of fMRI data, and applied it to a challenging problem: performing classification of hard-to categorize groups of subjects based on simultaneously recorded brain activation response patterns to behavioral challenges of inhibitory control (drug addicted subjects vs. control subjects). The difficulties of the classification problem motivate the joint exploration of spatial, temporal and function information for the analysis of fMRI signals. For spatial analysis, we introduced a novel algorithm that integrates side information into the boosting framework. Our algorithm clearly outperformed well-established classifiers as documented in extensive experimental results. For temporal analysis, we demonstrated that group classification is improved by selecting discriminative features and incorporating fMRI temporal information into a machine learning framework.For functional analysis, we employed Dynamic Bayesian Networks and were able to perform group classification even if the DBNs are constructed from as few as 5 brain regions. Furthermore, different DBN structures characterized drug addicted subjects vs. control subjects.Our results suggest that through incorporation of machine learning principles into functional neuroimaging studies we will be able to identify unique patterns of variability in brain states and deduce about the behavioral probes from the brain activation data. In the future this may provide tools where objective brain imaging data are used for clinical purpose of classification of psychopathologies and identification of genetic vulnerabilities. ©2007 Microsoft Corporation. All rights reserved.

In this paper we study the use of coding techniques to accelerate machine learning (ML). Coding techniques, such as prefix codes, have been extensively studied and used to accelerate low-level data processing primitives such as scans in a relational database system. However, there is little work on how to exploit them to accelerate ML algorithms. In fact, applying coding techniques for faster ML faces a unique challenge: one needs to consider both how the codes fit into the optimization algorithm used to train a model, and the interplay between the model structure and the coding scheme. Surprisingly and intriguingly, our study demonstrates that a slight variant of the classical Lempel-Ziv-Welch (LZW) coding scheme is a good fit for several popular ML algorithms, resulting in substantial runtime savings. Comprehensive experiments on several real-world datasets show that our LZW-based ML algorithms exhibit speedups of up to 31x compared to a popular and state-of-the-art ML library, with no changes to ML accuracy, even though the implementations of our LZW variants are not heavily tuned. Thus, our study reveals a new avenue for accelerating ML algorithms using coding techniques and we hope this opens up a new direction for more research.

Bayesian approaches to learning problems have many virtues, including their ability to make use of prior knowledge and their ability to link related sources of information, but they also have many vices, notably the strong parametric assumptions that are often invoked in practical Bayesian modeling. Nonparametric Bayesian methods offer a way to make use of the Bayesian calculus without the parametric handcuffs. In this talk I describe several recent explorations in nonparametric Bayesian modeling and inference, including various versions of “Chinese restaurant process priors” that allow flexible structures to be learned and allow sharing of statistical strength among sets of related structures. I discuss computational issues and applications to problems in bioinformatics. [Joint work with David Blei and Yee Whye Teh]. ©2005 Microsoft Corporation. All rights reserved.

Machine learning has proven to be a valuable tool to approximate functions in high-dimensional spaces. Unfortunately, analysis of these models to extract the relevant physics is never as easy as applying machine learning to a large dataset in the first place. Here we present a description of atomic systems that generates machine learning representations with a direct path to physical interpretation. As an example, we demonstrate its usefulness as a universal descriptor of grain boundary systems. Grain boundaries in crystalline materials are a quintessential example of a complex, high-dimensional system with broad impact on many physical properties including strength, ductility, corrosion resistance, crack resistance, and conductivity. In addition to modeling such properties, the method also provides insight into the physical "building blocks" that influence them. This opens the way to discover the underlying physics behind behaviors by understanding which building blocks map to particular properties. Once the structures are understood, they can then be optimized for desirable behaviors.

Many applied settings in empirical economics involve simultaneous estimation of a large number of parameters. In particular, applied economists are often interested in estimating the effects of many-valued treatments (like teacher effects or location effects), treatment effects for many groups, and prediction models with many regressors. In these settings, machine learning methods that combine regularized estimation and data-driven choices of regularization parameters are useful to avoid over-fitting. In this article, we analyze the performance of a class of machine learning estimators that includes ridge, lasso and pretest in contexts that require simultaneous estimation of many parameters. Our analysis aims to provide guidance to applied researchers on (i) the choice between regularized estimators in practice and (ii) data-driven selection of regularization parameters. To address (i), we characterize the risk (mean squared error) of regularized estimators and derive their relative performance as a function of simple features of the data generating process. To address (ii), we show that data-driven choices of regularization parameters, based on Stein's unbiased risk estimate or on cross-validation, yield estimators with risk uniformly close to the risk attained under the optimal (unfeasible) choice of regularization parameters. We use data from recent examples in the empirical economics literature to illustrate the practical applicability of our results.

The apsis toolkit presented in this paper provides a flexible framework for hyperparameter optimization and includes both random search and a bayesian optimizer. It is implemented in Python and its architecture features adaptability to any desired machine learning code. It can easily be used with common Python ML frameworks such as scikit-learn. Published under the MIT License other researchers are heavily encouraged to check out the code, contribute or raise any suggestions. The code can be found at github.com/FrederikDiehl/apsis.