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International Communication Systems and Networks and Workshops, COMSNETS

作者： Arunava Chaudhuri Shubhi Shukla Sarani Bhattacharya Debdeep Mukhopadhyay Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur India Centre for Computational and Data Sciences Indian Institute of Technology Kharagpur Kharagpur India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

With the rapid advancement of machine learning (ML) models and their widespread application across various sectors such as intrusion detection, medical diagnosis, natural language processing, and autonomous driving, these technologies have achieved remarkable success. However, this progress has also raised significant concerns about ensuring the security of ML models and protecting both private training data and model outputs from getting exposed in a shared cloud environment. To address these challenges, researchers have proposed various methodologies to create privacy-preserving, secure, and trustworthy model execution environments to prevent adversarial attacks. This study provides a comprehensive review of Trusted Execution Environment (TEE) implementations across different hardware accelerators. It also offers an overview of modern techniques for preserving privacy and security in execution environments, while identifying critical research gaps that require attention. In essence, this survey is an important resource for researchers, providing insights into recent methodologies and guiding them to focus on pressing research challenges.

关键词： Surveys Deep learning Training Privacy Data privacy Training data Data models Stability analysis Security Hardware acceleration

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Low cost and centimeter-level global positioning system accuracy using real-time kinematic library and real-time kinematic gpsa

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Recent Advances in computer science and Communications 2021年第2期14卷 360-367页

作者： Gianey, Hemant K. Ali, Mumtaz Vijayakumar, Varadarajan Sharma, Ashutosh Kumar, Rajiv Department of Computer Science Thapar Institute of Engineering & Technology Patiala Punjab India School of Agricultural Computational and Environmental Science University of Southern Queensland 4300 Australia School of Computer Science and Engineering Vellore Institute of Technology Chennai India Department of Electronics and Communication Jaypee University of Information Technology Solan India

Introduction: Accuracy and total design and implementation cost of the GPS framework determine the viability of GPS based projects. As the greater part of the advanced framework including telemetry, IoT, Cloud, and AUTOSAR frameworks use GPS to get exact outcomes, finding a software-controlled error correction becomes important. The execution of open source library such as RTKLIB will help in controlling and revising GPS blunders. Methods: The project utilizes the RTKLIB along with two stations for better accuracy. The RTKGPS framework works under Linux environment, which is embedded in the Beagleboard. The communication between the GPS system is set up utilizing both serial communication protocol and TCP/IP suite. Results: To get high precision inside the network, two GPS modules are utilized. One of them will be mounted on the rover and another GPS is the base station of the setup. Both the GPS will have a double radio wire setup to increase the reception level to reduce the noise and obtain centimeter-level precision. For long-range communication, rover utilizes Wi-Fi with TCP/IP stack protocol. In this research paper, setup is intended to accomplish the centimeter level precision through libraries in a Linux environment. Conclusion: The design will be set up and tried on a college campus under various conditions with different error parameters to acquire a low cost and centimeter level GPS accuracy. © 2021 Bentham science Publishers.

关键词： Global positioning system

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Sparsity-aware generalization theory for deep neural networks

arXiv

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arXiv 2023年

作者： Muthukumar, Ramchandran Sulam, Jeremias Department of Computer Science Mathematical Institute for Data Science Johns Hopkins University United States Department of Biomedical Engineering Mathematical Institute for Data Science Johns Hopkins University United States

Deep artificial neural networks achieve surprising generalization abilities that remain poorly understood. In this paper, we present a new approach to analyzing generalization for deep feed-forward ReLU networks that takes advantage of the degree of sparsity that is achieved in the hidden layer activations. By developing a framework that accounts for this reduced effective model size for each input sample, we are able to show fundamental trade-offs between sparsity and generalization. Importantly, our results make no strong assumptions about the degree of sparsity achieved by the model, and it improves over recent norm-based approaches. We illustrate our results numerically, demonstrating non-vacuous bounds when coupled with data-dependent priors in specific settings, even in over-parametrized models. © 2023, CC BY.

关键词： Deep neural networks

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Evaluation of Deep Neural Operator Models Toward Ocean Forecasting

Evaluation of Deep Neural Operator Models Toward Ocean Forec...

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OCEANS

作者： Ellery Rajagopal Anantha N. S. Babu Tony Ryu Patrick J. Haley Chris Mirabito Pierre F. J. Lermusiaux Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA Department of Mechanical Engineering Center for Computational Science and Engineering Massachusetts Institute of Technology Cambridge MA

Data-driven, deep-learning modeling frameworks have been recently developed for forecasting time series data. Such machine learning models may be useful in multiple domains including the atmospheric and oceanic ones, and in general, the larger fluids community. The present work investigates the possible effectiveness of such deep neural operator models for reproducing and predicting classic fluid flows and simulations of realistic ocean dynamics. We first briefly evaluate the capabilities of such deep neural operator models when trained on a simulated two-dimensional fluid flow past a cylinder. We then investigate their application to forecasting ocean surface circulation in the Middle Atlantic Bight and Massachusetts Bay, learning from high-resolution data-assimilative simulations employed for real sea experiments. We confirm that trained deep neural operator models are capable of predicting idealized periodic eddy shedding. For realistic ocean surface flows and our preliminary study, they can predict several of the features and show some skill, providing potential for future research and applications.

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Cloud Based Smart Medical Recommendation System Using Pattern Recognition

Cloud Based Smart Medical Recommendation System Using Patter...

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Networking and Communications (ICNWC), International Conference on

作者： E. Sujatha S. Shanthasheela P.V. Gopirajan Department of Computer Science and Engineering Saveetha Engineering College (Autonomous)Thandalam Chennai Department of Computer Science and Engineering Velammal Institute of Technology Chennai Department of Computational Intelligence School of Computing SRM Institute of Science and TechnologyKattankulathur Chennai

To build smart medical recommendation system the data needs to be collected from laboratories. The system is intended to provide services as control and manage data samples, industry standards, real test results, reports, human resources in the laboratories, machines, tiny instruments, and automation of the workflow. Combining the data from the laboratories and medical institutions enable ease of communication without time delay. The challenging task is that identifying appropriate hardware and software components which provides compatibility in collecting data and their processing steps, repository storage, secured distribution, and the policies used for information representation in the results. In order to automate the entire process of medical recommendation system the metric parameters are used for determining the efficiency and productivity of the lab to enhance the quality of control systems. The vulnerability lies in maintaining traditional way of records in paper and error in the data. The purpose of this project is to develop the process of daily tasks will be computed through the database using pattern recognition techniques.

关键词： Productivity Measurement Laboratories Process control Software Pattern recognition Task analysis

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Large Language Models in Psychiatry:Current Applications,Limitations,and Future Scope

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Big Data Mining and Analytics 2024年第4期7卷 1148-1168页

作者： Zhe Liu Yihang Bao Shuai Zeng Ruiyi Qian Miaohan Deng An Gu Jianye Li Weidi Wang Wenxiang Cai Wenhao Li Han Wang Dong Xu Guan Ning Lin Shanghai Mental Health Center Shanghai Jiao Tong University School of Medicineand School of Biomedical EngineeringShanghai Jiao Tong UniversityShanghai 200030China Department of Electrical Engineering and Computer Science University of MissouriColumbiaMO 65211USA School of Information Science and Technology Institute of Computational BiologyNortheast Normal UniversityChangchun 130024China

With the advancements in Artificial Intelligence(AI)technology,Large Language Models(LLMs)provide outstanding capabilities for natural language understanding and generation,enhancing various *** psychiatry,LLMs can empower healthcare by analyzing vast amounts of medical data to improve diagnostic accuracy,enhance therapeutic communication,and personalize patient care with their strength in understanding and generating human-like *** clinical AI,developing and utilizing robust and interpretable models has been a longstanding *** survey investigates the current psychiatric practice of LLMs,along with a series of corpus resources that could be used for training psychiatric *** discuss the limitations concerning LLM reproducibility,capabilities,usability,interpretability in clinical settings,and ethical ***,we propose potential future directions for research,clinical application,and education in psychiatric ***,we discuss the challenge of integrating LLMs into the evolving landscape of healthcare in real-world scenarios.

关键词： Artificial Intelligence(AI) Large Language Model(LLM) psychiatry medical application

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On Approximability of Satisfiable k-CSPs: V

arXiv

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arXiv 2024年

作者： Bhangale, Amey Khot, Subhash Minzer, Dor Department of Computer Science and Engineering University of California Riverside United States Department of Computer Science Courant Institute of Mathematical Sciences New York University United States Department of Mathematics Massachusetts Institute of Technology United States

We propose a framework of algorithm vs. hardness for all Max-CSPs and demonstrate it for a large class of predicates. This framework extends the work of Raghavendra [STOC, 2008], who showed a similar result for almost satisfiable Max-CSPs. Our framework is based on a new hybrid approximation algorithm, which uses a combination of the Gaussian elimination technique (i.e., solving a system of linear equations over an Abelian group) and the semidefinite programming relaxation. We complement our algorithm with a matching dictator vs. quasirandom test that has perfect completeness. The analysis of our dictator vs. quasirandom test is based on a novel invariance principle, which we call the mixed invariance principle. Our mixed invariance principle is an extension of the invariance principle of Mossel, O’Donnell and Oleszkiewicz [Annals of Mathematics, 2010] which plays a crucial role in Raghavendra’s work. The mixed invariance principle allows one to relate 3-wise correlations over discrete probability spaces with expectations over spaces that are a mixture of Guassian spaces and Abelian groups, and may be of independent interest. © 2024, CC BY.

关键词： Approximation algorithms

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Fast and Perfect Sampling of Subgraphs and Polymer Systems 25

Fast and Perfect Sampling of Subgraphs and Polymer Systems

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25th International Conference on Approximation Algorithms for Combinatorial Optimization Problems and the 26th International Conference on Randomization and Computation, APPROX/RANDOM 2022

作者： Blanca, Antonio Cannon, Sarah Perkins, Will Department of Computer Science and Engineering Pennsylvania State University University ParkPA United States Department of Mathematical Sciences Claremont McKenna College CA United States Department of Computer Science Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9783959772495

We give an efficient perfect sampling algorithm for weighted, connected induced subgraphs (or graphlets) of rooted, bounded degree graphs. Our algorithm utilizes a vertex-percolation process with a carefully chosen rejection filter and works under a percolation subcriticality condition. We show that this condition is optimal in the sense that the task of (approximately) sampling weighted rooted graphlets becomes impossible in finite expected time for infinite graphs and intractable for finite graphs when the condition does not hold. We apply our sampling algorithm as a subroutine to give near linear-time perfect sampling algorithms for polymer models and weighted non-rooted graphlets in finite graphs, two widely studied yet very different problems. This new perfect sampling algorithm for polymer models gives improved sampling algorithms for spin systems at low temperatures on expander graphs and unbalanced bipartite graphs, among other applications. © Antonio Blanca, Sarah Cannon, and Will Perkins.

关键词： Solvents

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Fast Gradient Computation for Gromov-Wasserstein Distance

arXiv

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arXiv 2024年

作者： Zhang, Wei Wang, Zihao Fan, Jie Wu, Hao Zhang, Yong Department of Mathematical Sciences Tsinghua University China Department of Computer Science and Engineering HKUST Hong Kong BNRist RIIT Institute of Internet Industry Department of Computer Science and Technology Tsinghua University China

The Gromov-Wasserstein distance is a notable extension of optimal transport. In contrast to the classic Wasserstein distance, it solves a quadratic assignment problem that minimizes the pair-wise distance distortion under the transportation of distributions and thus could apply to distributions in different spaces. These properties make Gromov-Wasserstein widely applicable to many fields, such as computer graphics and machine learning. However, the computation of the Gromov-Wasserstein distance and transport plan is expensive. The well-known Entropic Gromov-Wasserstein approach has a cubic complexity since the matrix multiplication operations need to be repeated in computing the gradient of Gromov-Wasserstein loss. This becomes a key bottleneck of the method. Currently, existing methods accelerate the computation focus on sampling and approximation, which leads to low accuracy or incomplete transport plan. In this work, we propose a novel method to accelerate accurate gradient computation by dynamic programming techniques, reducing the complexity from cubic to quadratic. In this way, the original computational bottleneck is broken and the new entropic solution can be obtained with total quadratic time, which is almost optimal complexity. Furthermore, it can be extended to some variants easily. Extensive experiments validate the efficiency and effectiveness of our method. Copyright © 2024, The Authors. All rights reserved.

关键词： computer graphics

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Scalable Hamming Distance Computation Using Accelerated Matrix Transformations

Scalable Hamming Distance Computation Using Accelerated Matr...

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ISC High Performance 2025 Research Paper Proceedings (40th International Conference)

作者： Rabab Alomairy Qinglei Cao Hatem Ltaief David Keyes Alan Edelman Computer Science & Artificial Intelligence Laboratory Massachusetts Institute of Technology USA Department of Computer Science Saint Louis University USA Division of Computer Electrical and Mathematical Sciences and Engineering King Abdullah University of Science and Technology KSA

ISBN: (数字)9783982633619

The Hamming distance, a fundamental measure of dissimilarity between data points, plays a crucial role in various fields, including error detection, machine learning, and genomic sequence alignment, where it is commonly used for identifying mismatches in nucleotide or protein sequences. This work introduces two implementations for computing Hamming distances for sequence alignment: synchronous and asynchronous matrix-based approaches. While most existing implementations rely on vector-based methods due to their simplicity and ease of use, they are not efficient for large-scale data. Our work focuses on enhancing performance by introducing matrix-based implementations that significantly improve computational efficiency and scalability. Our asynchronous implementation showcases Julia for sequential task flow and PaRSEC for parameterized task graph execution models on homogeneous and heterogeneous architectures. CPU computations use INT8 GEMM from oneMKL, while GPU implementations employ Tensor/Matrix Core INT8 GEMM from cuBLAS/hipBLAS and 1-bit TensorOps GEMM capabilities from CUTLASS. For constructing bitmask matrices on GPUs, we develop both a naive CUDA implementation using global memory and an optimized implementation utilizing shared memory at the warp level, with the optimized version achieving a 5X speedup over the naive approach. The results demonstrate significant performance improvements, with the asynchronous matrix-based implementation achieving up to 284X speedup over the vector-based approach on CPUs, while the asynchronous GPU-enabled implementation on A100 GPUs delivers a 15X speedup compared to the CPU matrix-based approach and a three orders of magnitude improvement over the CPU vector-based approach. Furthermore, the asynchronous implementation of PaRSEC scales well on up to 256 nodes of Summit and Frontier. These advancements highlight the scalability and efficiency of matrix-based Hamming distance computation, leveraging GPU acceleration and advan

关键词： Proteins Runtime Scalability Graphics processing units Genomics computer architecture computational efficiency Hamming distances Resource management Bioinformatics

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