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2nd International Conference on Edge Computing and Applications, ICECAA 2023

作者： Rajaram, Gnanajeyaraman Thangaraj, T. Stephen Packialatha, A. Deepa, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Applied Machine Learning Tamil Nadu Chennai India Paavai Engineering College Department of Computer Science and Engineering Tamil Nadu Namakkal India Vels Institute of Science Technology & Advanced Studies Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350347579

Adversary node detection systems have become a vital component due to the malware attacks. These systems are very important for network security due to internet's widespread development and increased accessibility to global data systems. This paper presents a Crow Search Optimization Algorithm (CSOA) to detect the adversary node in a wireless network. The Rivest, Shamir, Adleman (RSA) algorithm transmits secure data at first. It uses the public key to encrypt the data and the private key to decrypt the data and the adversary nodes can't access or modify the real data. One of the significant problems is when an unknown attack occurs on the network due to the high volume of data while the detection's accuracy and false alarm rate decline. The proposed system aims to improve accuracy by identifying the adversary nodes using CSOA algorithm. From the results, it is observed that the CSOA mechanism reduces the loss ratio and improves throughput of the network. © 2023 IEEE.

关键词： Optimization

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PlaySlot: learning Inverse Latent Dynamics for Controllable Object-Centric Video Prediction and Planning

arXiv

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arXiv 2025年

作者： Villar-Corrales, Angel Behnke, Sven Computer Science Institute VI – Intelligent Systems and Robotics Center for Robotics The Lamarr Institute for Machine Learning and Artificial Intelligence Germany

Predicting future scene representations is a crucial task for enabling robots to understand and interact with the environment. However, most existing methods rely on video sequences and simulations with precise action annotations, limiting their ability to leverage the large amount of available unlabeled video data. To address this challenge, we propose PlaySlot, an object-centric video prediction model that infers object representations and latent actions from unlabeled video sequences. It then uses these representations to forecast future object states and video frames. PlaySlot allows to generate multiple possible futures conditioned on latent actions, which can be inferred from video dynamics, provided by a user, or generated by a learned action policy, thus enabling versatile and interpretable world modeling. Our results show that PlaySlot outperforms both stochastic and object-centric baselines for video prediction across different environments. Furthermore, we show that our inferred latent actions can be used to learn robot behaviors sample-efficiently from unlabeled video demonstrations. Videos and code are available at https://***/PlaySlot/. © 2025, CC BY.

关键词： Stochastic systems

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Enhancing Predictive Imaging Biomarker Discovery Through Treatment Effect Analysis

Enhancing Predictive Imaging Biomarker Discovery Through Tre...

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2025 IEEE/CVF Winter Conference on Applications of computer Vision, WACV 2025

作者： Xiao, Shuhan Klein, Lukas Petersen, Jens Vollmuth, Philipp Jaeger, Paul F. Maier-Hein, Klaus H. Heidelberg Division of Medical Image Computing Germany Heidelberg University Faculty of Mathematics and Computer Science Germany Dkfz Heidelberg Interactive Machine Learning Group Germany Institute for Machine Learning Eth Zürich Switzerland Dkfz Heidelberg Helmholtz Imaging Germany Germany University of Bonn Medical Faculty Bonn Germany Heidelberg University Hospital Pattern Analysis and Learning Group Department of Radiation Oncology Germany

ISBN: (纸本)9798331510831

Identifying predictive covariates, which forecast individual treatment effectiveness, is crucial for decision-making across different disciplines such as personalized medicine. These covariates, referred to as biomarkers, are extracted from pretreatment data, often within randomized controlled trials, and should be distinguished from prognostic biomarkers, which are independent of treatment assignment. Our study focuses on discovering predictive imaging biomarkers, specific image features, by leveraging pretreatment images to uncover new causal relationships. Unlike laborintensive approaches relying on handcrafted features prone to bias, we present a novel task of directly learning predictive features from images. We propose an evaluation protocol to assess a model's ability to identify predictive imaging biomarkers and differentiate them from purely prognostic ones by employing statistical testing and a comprehensive analysis of image feature attribution. We explore the suitability of deep learning models originally developed for estimating the conditional average treatment effect (CATE) for this task, which have been assessed primarily for their precision of CATE estimation while overlooking the evaluation of imaging biomarker discovery. Our proof-of-concept analysis demonstrates the feasibility and potential of our approach in discovering and validating predictive imaging biomarkers from synthetic outcomes and real-world image datasets. Our code is available at https://***/MIC-DKFZ/predictive_image_biomarker_analysis. © 2025 IEEE.

关键词： Deep learning

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Demystifying AI: A Robust and Comprehensive Approach to Explainable AI

Demystifying AI: A Robust and Comprehensive Approach to Expl...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Vasanth, S. Keerthana, S. Saravanan, G. Rmk College of Engineering and Technology Department of Computer Science and Engineering Thiruvallur India St.Joesph's College of Engineering Department of Artificial Intelligence and Machine Learning Chennai India Sri Sai Ram Institute of Technology Department of Ece West Tambaram Chennai India

ISBN: (数字)9798331508432

ISBN: (纸本)9798331508432

The adoption of Artificial Intelligence (AI) and machine learning (ML) in various computing platforms and areas, necessitates the development of strong Explainable AI (XAI) techniques. Most current AI models are opaque about their decision-making process thereby impeding trust, debugging, and improvement. The goal of this research is to develop comprehensive robust XAI methods capable of explaining the reasoning and decision-making processes in Autonomic, Edge, Server-less, Quantum computing platforms and IoT, Business Automation, Service Innovation domains where these AI models are *** study comprehensively addresses the opacity in AI models through solutions for balanced test-train splits, model evaluation, feature importance, metric imbalances, ROC curve and precision-recall curve analysis, accuracy and statistical metrics, benefits of manual review. This research aims at increasing transparency and trustworthiness within AI systems through developing as well as applying such XAI methods that can detect and mitigate biases while enhancing ethical debugging;responsible development for AI enabled computing purposes. © 2024 IEEE.

关键词： Adversarial machine learning

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Deep learning-Based Prediction of Daily COVID-19 Cases Using X (Twitter) Data

Deep Learning-Based Prediction of Daily COVID-19 Cases Using...

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34th Medical Informatics Europe Conference, MIE 2024

作者： Ahmed, Nourhan Saeed, Khansa Rodrigues, Jeevitha Lora Naeem, Maha Correa, Andrea Sanabboon, Chairungroj Rostam Niakan Kalhori, Sharareh Deserno, Thomas M. Information Systems and Machine Learning Lab Department of Mathematics Natural Science Economics and Computer Science Institute of Computer Science University of Hildesheim Germany Peter L. Reichertz Institute for Medical Informatics TU Braunschweig Hannover Medical School Braunschweig Germany Department of Health Information Management School of Allied Medical Sciences Tehran University of Medical Sciences Tehran Iran

ISBN: (纸本)9781643685335

Due to the importance of COVID-19 control, innovative methods for predicting cases using social network data are increasingly under attention. This study aims to predict confirmed COVID-19 cases using X (Twitter) social network data (tweets) and deep learning methods. We prepare data extracted from tweets by natural language processing (NLP) and consider the daily G-value (growth rate) as the target variable of COVID-19, collected from the worldometer. We develop and evaluate a time series mixer (TSMixer) predictive model for multivariate time series. The mean squared error (MSE) loss on the test dataset was 0.0063 for 24-month Gvalue prediction when using the MinMax normalization with recursive feature elimination (RFE) and average or min aggregation method. Our findings illuminate the potential of integrating social media data to enhance daily COVID-19 case predictions and are applicable also for epidemiological forecasting purposes. © 2024 The Authors.

关键词： Tweets

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Is the Volume of a Credal Set a Good Measure for Epistemic Uncertainty?

arXiv

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arXiv 2023年

作者： Sale, Yusuf Caprio, Michele Hüllermeier, Eyke Germany PRECISE Center Department of Computer and Information Science University of Pennsylvania United States Munich Center for Machine Learning Germany

Adequate uncertainty representation and quantification have become imperative in various scientific disciplines, especially in machine learning and artificial intelligence. As an alternative to representing uncertainty via one single probability measure, we consider credal sets (convex sets of probability measures). The geometric representation of credal sets as d-dimensional polytopes implies a geometric intuition about (epistemic) uncertainty. In this paper, we show that the volume of the geometric representation of a credal set is a meaningful measure of epistemic uncertainty in the case of binary classification, but less so for multi-class classification. Our theoretical findings highlight the crucial role of specifying and employing uncertainty measures in machine learning in an appropriate way, and for being aware of possible pitfalls. Copyright © 2023, The Authors. All rights reserved.

关键词： machine learning

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Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective

arXiv

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arXiv 2023年

作者： Carvalho, João B.S. Zhang, Mengtao Geyer, Robin Cotrini, Carlos Buhmann, Joachim M. Institute for Machine Learning Department of Computer Science ETH Zürich Switzerland

Anomaly detection (AD) is the machine learning task of identifying highly discrepant abnormal samples by solely relying on the consistency of the normal training samples. Under the constraints of a distribution shift, the assumption that training samples and test samples are drawn from the same distribution breaks down. In this work, by leveraging tools from causal inference we attempt to increase the resilience of anomaly detection models to different kinds of distribution shifts. We begin by elucidating a simple yet necessary statistical property that ensures invariant representations, which is critical for robust AD under both domain and covariate shifts. From this property, we derive a regularization term which, when minimized, leads to partial distribution invariance across environments. Through extensive experimental evaluation on both synthetic and real-world tasks, covering a range of six different AD methods, we demonstrated significant improvements in out-of-distribution performance. Under both covariate and domain shift, models regularized with our proposed term showed marked increased robustness. Code is available at: https://***/JoaoCarv/invariant-anomaly-detection. © 2023, CC BY.

关键词： Anomaly detection

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Neural Circuitry Involving Substance P in Male Sexual Behavior

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Neuroscience Bulletin 2024年第4期40卷 544-546页

作者： Xinrong Wang Junqiang Zheng Han Xu Department of Neurobiology and Department of Neurology of the Second Affiliated Hospital and School of Brain Science and Brain Medicine Zhejiang University School of MedicineHangzhou310058China Nanhu Brain-Computer Interface Institute Hangzhou311100China Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-machine IntegrationState Key Laboratory of Brain-Machine IntelligenceZhejiang UniversityHangzhou311121China NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang UniversityHangzhou310058China Lingang Laboratory Shanghai200031China

Male sexual behavior is an innate social behavior crucial for reproduction[1].Previous studies have identified several brain regions crucial for the control of both sexual and aggressive behaviors in ***,molecularly specified neural circuits that specifically control male sexual behavior remain *** with the inherently rewarding nature of sexual behavior,previous research has implicated the role of the dopamine system in regulating sexual desire[2].However,the interaction between reward and copulation at the circuit level remains largely ***,it is unclear if the associated neural pathways are specific in modulating sexual reward.

关键词： behavior neural specified

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News-Driven Stock Price Forecasting in Indian Markets: A Comparative Study of Advanced Deep learning Models 3

News-Driven Stock Price Forecasting in Indian Markets: A Com...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Attaluri, Kaushal Tripathi, Mukesh Kumar Reddy, Srinithi Shivendra Software Engineer-Machine Learning and AI Pegasystems India Worldwide Pvt Ltd Hyderabad India Vardhaman College of Engineering Department of Computer Science and Engineering Hyderabad India Vasavi College of Engineering Department of Information Technology Hyderabad India D. K. College Department of Computer Application Bihar Dumraon India

ISBN: (纸本)9798350354171

Forecasting stock market prices is a challenging task for traders, analysts, and engineers due to the myriad of variables influencing stock prices. However, the advent of artificial intelligence (AI) and natural language processing (NLP) has significantly advanced stock market forecasting. AI's ability to analyze complex data sets allows for more informed predictions. Despite these advancements, stock price forecasting remains an area where AI has not yet achieved optimal results. In this paper, we forecast stock prices using 30 years of historical data from various national banks in India sourced from the National Stock Exchange. We employ advanced deep learning models, including multivariate multi-step Long Short-Term Memory (LSTM), Facebook Prophet with LightGBM and Optuna, and Seasonal Auto-Regressive Integrated Moving Average (SARIMA). Additionally, we analyze news data from tweets and reliable sources like Business Standard and Reuters, recognizing their significant impact on stock price movements. © 2024 IEEE.

关键词： Contrastive learning

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Do LLMs dream of elephants (when told not to)? latent concept association and associative memory in transformers 24

Do LLMs dream of elephants (when told not to)? latent concep...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Yibo Jiang Goutham Rajendran Pradeep Ravikumar Bryon Aragam Department of Computer Science University of Chicago Machine Learning Department Carnegie Mellon University Booth School of Business University of Chicago

ISBN: (纸本)9798331314385

Large Language Models (LLMs) have the capacity to store and recall facts. Through experimentation with open-source models, we observe that this ability to retrieve facts can be easily manipulated by changing contexts, even without altering their factual meanings. These findings highlight that LLMs might behave like an associative memory model where certain tokens in the contexts serve as clues to retrieving facts. We mathematically explore this property by studying how transformers, the building blocks of LLMs, can complete such memory tasks. We study a simple latent concept association problem with a one-layer transformer and we show theoretically and empirically that the transformer gathers information using self-attention and uses the value matrix for associative memory.

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