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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Charles E. Leiserson MIT Department of Electrical Engineering and Computer Science MIT Computer Science and Artificial Intelligence Laboratory

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Aside from pure intellectual interest, why do we teach our students parallel computing? Most people would agree that the primary goal is to produce greater application performance. Yet students frequently parallelize code only to discover that it runs disappointingly slower because they don't understand performance. To exploit parallelism effectively, it must operate synergistically with a host of other techniques, including caching, vectorization, algorithms, bit tricks, loop unrolling, using compiler switches, tailoring code to the architecture, exploiting sparsity, changing data representation, metaprogramming, etc. Software performance engineering, which encompasses these techniques, is the science and art of making code run fast or otherwise limiting its consumption of resources, such as energy, memory footprint, network utilization, response time, etc. In this talk, I will argue that the end of Moore's Law makes software performance engineering a critical skill for our students to learn.

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Edge Computing Enabled Anomaly Detection in IoT Environments Using Federated Learning

Edge Computing Enabled Anomaly Detection in IoT Environments...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Kansal, Vipashi Husain, Saif O. Kumar, Rakesh Chinthamu, Narender Kumar, Manish Aswini, S. Graphic Era Deemed To Be University Department Of Computer Science & Engineering Dehradun India College Of Technical Engineering The Islamic University Department Of Computers Techniques Engineering Najaf Iraq Gla University Department Of Computer Engineering & Applications Mathura India Mit Cto DallasTX United States Lovely Professional University Phagwara India Institute Of Aeronautical Engineering Department Of Computer Science And Engineering Telangana Hyderabad India

ISBN: (纸本)9798350366846

This research explores the integration of edge computing and unified learning procedures for peculiarity locations in Internet of Things (IoT) situations. Four inconsistency discovery calculations - Isolation Forest, Local Outlier Factor, One-Class SVM, and Recurrent Neural Organize - are assessed over assorted IoT datasets to survey their adequacy. Results show that the Repetitive Neural Network outperforms other calculations with exactness, review, F1-score, and AUC-ROC values of 0.90, 0.92, 0.91, and 0.95 individually. The study illustrates the possibility of leveraging edge computing and unified learning for productive and privacy-preserving inconsistency locations at the organised edge. By passing on computational assignments to edge contraptions and collaboratively planning models utilizing combined learning, our approach finishes flexible and adaptable irregularity revelation in IoT systems. The investigate contributes encounters into tending to challenges such as inertness, security, and flexibility characteristics in centralized idiosyncrasy disclosure approaches, clearing the way for more capable and privacy-preserving courses of action in IoT applications. © 2024 IEEE.

关键词： Anomaly detection

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Explainable AI for Intrusion Prevention: A Review of Techniques and Applications 7th

Explainable AI for Intrusion Prevention: A Review of Techniq...

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7th International Conference on Information and Communication Technology for Intelligent Systems, ICTIS 2023

作者： Chandre, Pankaj R. Vanarote, Viresh Patil, Rajkumar Mahalle, Parikshit N. Shinde, Gitanjali R. Nimbalkar, Madhukar Barot, Janki Department of Computer Science and Engineering MIT School of Computing MIT ADT University Loni Kalbhor India Department of Information Technology MIT School of Computing MIT ADT University Loni Kalbhor India Department of Artificial Intelligence and Data Science Vishwakarma Institute of Information Technology Kondhawa India Department of Computer Science and Engineering Vishwakarma Institute of Information Technology Kondhawa India Silver Oak University Ahmedabad India

ISBN: (纸本)9789819937578

Intrusion prevention has become a critical concern in today's increasingly complex and interconnected digital systems. Traditional intrusion prevention methods often rely on rule-based approaches that can be inflexible and prone to false positives and false negatives. The emergence of explainable artificial intelligence (AI) techniques has the potential to provide more accurate and interpretable solutions for intrusion prevention. This review paper surveys the current state of the art in explainable AI techniques for intrusion prevention, including their strengths and limitations. We begin by providing an overview of intrusion prevention and the challenges faced by traditional methods. We then explore the different types of explainable AI techniques, such as Local Interpretable Model-Agnostic Explanations (LIME), Shapley Additive Explanations (SHAP), and Integrated Gradients, and their application to intrusion prevention. We also discuss the different types of datasets and evaluation metrics used in explainable AI-based intrusion prevention, including public datasets such as NSL-KDD and UNSW-NB15. Finally, we highlight the future research directions in explainable AI-based intrusion prevention, including the need for larger and more diverse datasets, better evaluation metrics, and more comprehensive explainable AI models. Overall, this review paper provides a comprehensive overview of the state of the art in explainable AI for intrusion prevention, and serves as a valuable resource for researchers and practitioners in the field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Additives

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Shrinking VOD Traffic via Rényi-Entropic Optimal Transport

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Performance Evaluation Review 2024年第1期52卷 75-76页

作者： Lo, Chi-Jen Marina, Mahesh K. Sastry, Nishanth Xu, Kai Fadaei, Saeed Li, Yong Department of Engineering University of Cambridge United Kingdom School of Informatics The University of Edinburgh United Kingdom School of Computer Science University of Surrey United Kingdom MIT-IBM Watson AI Lab CambridgeMA United States Department of Electronic Engineering Tsinghua University China

In response to the exponential surge in Video on Demand (VOD) traffic, numerous research endeavors have concentrated on optimizing and enhancing infrastructure efficiency. In contrast, this paper explores whether users' demand patterns can be shaped to reduce the pressure on infrastructure. Our main idea is to design a mechanism that alters the distribution of user requests to another distribution which is much more cache-efficient, but still remains 'close enough' (in terms of cost) to fulfil individual user's preference. © 2024 Owner/Author.

关键词： Video on demand

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Enhancing Road Safety: An Intelligent Drowsiness Detection System Based on Deep Neural Networks 7

Enhancing Road Safety: An Intelligent Drowsiness Detection S...

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7th IEEE Pune Section International Conference, PuneCon 2024

作者： Nalavade, Jagannath E. Sachdeo, Rajneeshkaur Kale, Dattatray R. Buchade, Amar Subhedar, Mansi Shinde, Swapnil K. Mit Art Design and Technology University Computer Science and Engineering Pune India BRACT's Vishwakarma Institute of Information Technology Department of Artificial Intelligence and Data Science Pune India Pillai Hoc College of Engineering and Technology Electronics and Computer Science Rasayani India

ISBN: (纸本)9798331527822

The aim of this research is to create an intelligent vehicle system that can effectively mitigate the risks associated with drowsy driving, thereby preventing road accidents. Various factors like driver exhaustion, challenging road conditions, and adverse weather contribute to motor vehicle crashes, leading to over 1.35 million deaths annually, as estimated by entities such as the World Health Organization (WHO). To achieve this, the development of a robust warning system is crucial. In this study, we proposed an alert system to detect drowsy drivers. It utilizes a video stream process (VSP) to analyze the gaze's eye-aspect ratio (EAR) and geometric distance through an innate reflex model. Additionally, a facial landmark-based algorithm is employed for accurate eye recognition. When driver drowsiness is detected, the system issues a warning message, along with crash impact and location information, and activates the voice surveillance system. The suggested technique establishes a non-invasive approach for evaluating driver weariness in connection with accident severity. The camera, the fundamental element of this system, continuously acquires facial landmarks recognized by designated locations and analyzes them. Drivers who are distracted and have poor lighting may show signs of sleepiness. © 2024 IEEE.

关键词： Deep neural networks

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Incentives in Private Collaborative Machine Learning 37

Incentives in Private Collaborative Machine Learning

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37th Conference on Neural Information Processing Systems, NeurIPS 2023

作者： Sim, Rachael Hwee Ling Zhang, Yehong Hoang, Trong Nghia Xu, Xinyi Low, Bryan Kian Hsiang Jaillet, Patrick Department of Computer Science National University of Singapore Singapore Peng Cheng Laboratory China School of Electrical Engineering and Computer Science Washington State University United States Dept. of Electrical Engineering and Computer Science MIT United States

ISBN: (纸本)9781713899921

Collaborative machine learning involves training models on data from multiple parties but must incentivize their participation. Existing data valuation methods fairly value and reward each party based on shared data or model parameters but neglect the privacy risks involved. To address this, we introduce differential privacy (DP) as an incentive. Each party can select its required DP guarantee and perturb its sufficient statistic (SS) accordingly. The mediator values the perturbed SS by the Bayesian surprise it elicits about the model parameters. As our valuation function enforces a privacy-valuation trade-off, parties are deterred from selecting excessive DP guarantees that reduce the utility of the grand coalition's model. Finally, the mediator rewards each party with different posterior samples of the model parameters. Such rewards still satisfy existing incentives like fairness but additionally preserve DP and a high similarity to the grand coalition's posterior. We empirically demonstrate the effectiveness and practicality of our approach on synthetic and real-world datasets. © 2023 Neural information processing systems foundation. All rights reserved.

关键词： Machine learning

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Leveraging Noise Recycling in Soft Detection Decoding Using ORBGRAND

Leveraging Noise Recycling in Soft Detection Decoding Using ...

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2023 IEEE International Symposium on Information Theory, ISIT 2023

作者： Kizilates, Zeynep Ece Riaz, Arslan Coraluppi, Giacomo F. Medard, Muriel Duffy, Ken R. Tugce Yazicigil, Rabia Boston University Department of Electrical and Computer Engineering BostonMA United States Mit Department of Electrical Engineering and Computer Science CambridgeMA United States Northeastern University Department of Electrical and Computer Engineering Department of Mathematics BostonMA United States

ISBN: (纸本)9781665475549

For communications subject to correlated channel effects, noise recycling has recently been shown to enhance channel capacity with receiver-side-only changes. Using a taped-out chip, in a hard-detection scenario with guessing random additive noise decoding (GRAND), noise recycling has been established to both increase decoding accuracy and decrease decoding energy in single communication channels that employ interleavers. This paper presents results for the related soft-detection scenario by investigating noise recycling with an in-silicon realization of Ordered Reliability Bits Guessing Random Additive Noise Decoding (ORBGRAND). Measurements demonstrate that noise recycling leads to a significant reduction in the block error rate (BLER) and substantial improvements in latency and energy consumption by reducing the number of queries required for decoding. We also discuss dynamic lead channel selection for the soft detection scenario and show the importance of lead channel on overall decoding performance. © 2023 IEEE.

关键词： Additive noise

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Synergizing Investment Portfolios: A Technologically Advanced Approach with Integrated Chatbot Capabilities

Synergizing Investment Portfolios: A Technologically Advance...

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2024 mit Art, Design and Technology School of Computing International Conference, mitADTSoCiCon 2024

作者： Shelke, Priya M. Pawar, Rajendra G. Pawar, Parth Rathod, Rupali Kawoor, Tanishq BRACT's Vishwakarma Institute of Information Technology Pune India Design and Technology University Engineering MIT Art Department of Computer Science Pune India

ISBN: (纸本)9798350362879

A key component of financial management portfolio optimization, which gives investors the ability to allocate capital strategically and make well-informed decisions. Traditional portfolio optimization techniques might not adequately reflect the complexities of real-world markets, despite frequently relying on mathematical models and historical data. In this paper, a novel framework for portfolio optimization with an intelligent chatbot is proposed. The research work requests information from the user, including his name, salary, investment amount, and desired investment period. Based on these parameters, it optimizes the portfolio distribution based on the user's risk appetite (low, medium, or high) and calculates the predicted rate of return. The chatbot serves as an interactive interface for investors, gathering information about their investment goals, risk tolerance, and preferences. Readers who find your article through an abstracting service may never see the rest of your article. Be sure the abstract conveys why your research problem is important and how your work moves the field forward. © 2024 IEEE.

关键词： Abstracting

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Augmented Reality for Education Based on Markerless Dynamic Rendering 1

Augmented Reality for Education Based on Markerless Dynamic ...

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1st IEEE International Conference on Networking and Communications, ICNWC 2023

作者： Rakshit, Soumik Iyer, Aarthi Raj, Sunil Retmin C. Elizabeth, Shiloah D. Vaidyanathan, Aditya MIT Campus-Anna University Department of Information Technology India CEG Campus-Anna University Department of Computer Science and Engineering India

ISBN: (纸本)9798350336009

Augmented Reality (AR) technology has the potential to revolutionize education by providing a new way for students to visualize and interact with complex concepts. In this project, a system is proposed to develop an AR smartphone application that allows students to visualize objects and scenarios that the teacher is teaching in real-time. The application will employ the smartphone's camera and sensors to materialize a user-friendly and easy-to-use dynamic AR experience, with the teacher allowing the students to simply access their smartphone to project 3D models of objects or scenarios onto a flat surface. Students will be able to view these models from any angle and interact with them in a variety of ways, such as by rotating them or zooming in on specific details. In addition to enhancing student's understanding of the material being taught, the AR application will also provide an engaging and immersive learning experience. The distinguishing factor is the storage of the 3D assets on the cloud that will equip the educator with the option of pre-planning and customizing their entire lesson as well as storing any number of models. This can help to increase student engagement and motivation, leading to better retention of the material being taught. Overall, the proposed AR smartphone application has the potential to significantly improve the way students learn and understand complex concepts, making education more effective and enjoyable for all. © 2023 IEEE.

关键词： Augmented reality

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Plant Disease Detection and Classification using Emperor Penguin Optimizer (EPO) based Region Convolutional Neural Network (RCNN) 2

Plant Disease Detection and Classification using Emperor Pen...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Shanbhog, Manjula Bhatia, Neha Singh, A Swathi Pavithra, M. School of Sciences Department of Computer Science Delhi NCR India Panipat Institute of Engineering and Technology Department of Information Technology Haryana Panipat India MIT-ADT School of Computing Department of Information and Technology Maharashtra Pune India Panimalar Engineering College Department of Computer Science and Business Systems Chennai India

ISBN: (纸本)9798350384277

Agriculture stands as India's most crucial industry, despite grappling with a 35% annual loss in crop yield attributed to plant diseases. Traditionally, the detection of plant diseases has been a laborious process, hampered by insufficient laboratory infrastructure and expert knowledge. Plant disease detection methods that are automated provide a useful way to expedite the labor-intensive process of keeping an eye on large-scale agricultural fields and recognizing disease symptoms as soon as they appear on plant leaves. Current developments in deep learning (DL) and computer vision have highlighted the benefits of creating autonomous models for plant disease identification based on visible symptoms on leaves. In this study, we propose a novel method for detecting and classifying plant diseases by combining the Emperor Penguin Optimizer (EPO) with a Region Convolutional Neural Network (RCNN). The suggested methodology uses EPO to improve the discriminative power of features extracted from plant pictures, allowing for a more robust and accurate classification procedure. The Classification Region Convolutional Neural Network (RCNN) is used to leverage spatial correlations within the image, allowing for exact disease region localization. The goal of this integration is to increase the overall efficiency and dependability of plant disease detection systems. The investigations made use of the well-known PlantVillage dataset, which comprises 54,305 data of different plant disease types in 38 categories. Furthermore, an analysis was carried out in comparison with similar advanced investigations. According to the experiment results, RCNN-EPO outperformed in terms of classification accuracy, achieving 94.552%. © 2024 IEEE.

关键词： Convolutional neural networks

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