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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Liu, Jiacheng Tang, Feilong Chen, Long Li, Xu Yu, Jiadi Zhu, Yanmin Yu, Yichuan Yang, Yanqin The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China School of Computing Science Simon Fraser University Canada Huawei Technologies Company Ltd. Shanghai China School of Data Science and Engineering East China Normal University Shanghai China

ISBN: (纸本)9798350399738

Performance analysis is of great importance for management and optimization of space-terrestrial integrated networks (STINs). Traditional approaches to network performance analysis are often based on idealized assumptions that are deviated from the real network environment. This leads to the fact that these models are usually inefficient and restricted in real-world STINs with complicated behavior and even dynamic capacity. In this paper, we propose a network performance analysis approach EAGLE based on heterogeneous graph neural networks. Firstly, we propose a powerful computer network representation model that can preserve all of the information in computer networks. It represents different components of computer networks as a set of heterogeneous nodes and edges, and finally constructs a heterogeneous graph. Then, we obtain the topological representation for the routers in the network through a bandwidth-aware network embedding model. Based on this heterogeneous graph, we propose a heterogeneous GNN model to accurately predict network KPIs because it can completely capture the rich topological and attribute information of computer networks. Experimental results demonstrate that EAGLE can accurately model different networks, and outperforms both traditional methods and the latest neural network-based methods. © 2023 IEEE.

关键词： Topology

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Open-source Hardware: Different Approaches to Softcore implementation 6

Open-source Hardware: Different Approaches to Softcore imple...

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6th IEEE International Conference on Distributed computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2022

作者： Hiremath, Sneha Chickerur, Satyadhyan Dandin, Joel Patil, Mihir Muddinkoppa, Bhoomi Adakoli, Shreerama Kle Technological University Centre for High Performance Computing School of Computer Science and Engineering Hubballi India

ISBN: (数字)9781665487160

ISBN: (纸本)9781665487160

Since the last few years, open-source hardware is preferred in the landscape of hardware development. Originally proprietary hardware was only developed by big multinational companies like AMD, Intel, and NVIDIA, which were expensive and general-purpose. Due to this, people started developing their own hardware. Open-source hardware has support from the opensource community, is developed at low cost, and has presently got a lot of support from developers and enthusiasts. Softcore is a microprocessor that can be implemented on FPGA using logic synthesis and can be fabricated as open-source hardware. This paper focuses on how various approaches to softcore implementation can be done on FPGA which would involve logic cells, CLBs, LUTs, RAM, etc. The soft cores which are built upon the RISC-V ISA, namely VexRISCV and RISC-V BOOM, as well as the softcores adhering to the POWER ISA, such as Microwatt and LibreSoC, are the main topics of discussion. These softcores are installed and simulated. To test the working of the ISA, some standard test cases and test suites are executed. Hardware implementation is done by porting the softcore onto a Xilinx Nexys A7-100T FPGA board. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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K∞ Function-Based Consensus Tracking Control for Multi-Agent Systems With Unknown Disturbances 50

K∞ Function-Based Consensus Tracking Control for Multi-Agen...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Ning, Boda Han, Qing-Long Centre for Sustainable Infrastructure and Digital Construction Swinburne University of Technology MelbourneVIC3122 Australia School of Science Computing and Engineering Technologies Swinburne University of Technology MelbourneVIC3122 Australia

ISBN: (纸本)9781665464543

In this paper, consensus tracking is investigated for multi-agent systems (MASs) with unknown disturbances. A K∞ function-based control strategy is proposed to guarantee that the tracking errors between each follower and the leader are converged to a small neighborhood of zero in finite-time. To be more specific, by introducing an error auxiliary considering local interactions, a K∞ function-based adaptive controller is designed such that the error auxiliary is constrained to an adjustable region. Then, through a matrix-based rigorous proof, practical consensus tracking is realized in finite-time for MASs. A distinguished feature of the K∞ function-based control is its independence on the use of the bound information of unknown disturbances. Another advantage of the proposed controller is its capability of alleviating the input chattering issue. Finally, numerical examples are presented to validate the effectiveness of the K∞ function-based controller. © 2024 IEEE.

关键词： Multi agent systems

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Quantum Algorithms and Lower Bounds for Finite-Sum Optimization 41

Quantum Algorithms and Lower Bounds for Finite-Sum Optimizat...

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41st International Conference on Machine Learning, ICML 2024

作者： Zhang, Yexin Zhang, Chenyi Fang, Cong Wang, Liwei Li, Tongyang School of Electronics Engineering and Computer Science Peking University China Computer Science Department Stanford University United States National Key Lab of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Institute for Artificial Intelligence Peking University China Center on Frontiers of Computing Studies Peking University China School of Computer Science Peking University China

Finite-sum optimization has wide applications in machine learning, covering important problems such as support vector machines, regression, *** this paper, we initiate the study of solving finite-sum optimization problems by quantum ***, let f1, ..., fn : d → be -smooth convex functions and ψ: d → be a µ-strongly convex proximal *** goal is to find an ϵ-optimal point for F(x) = n1 Pni=1 fi(x) + ψ(x).We give a quantum algorithm with complexity Õ(Equation presented) 1 improving the classical tight bound (Equation presented).We also prove a quantum lower bound Ω˜(n + n3/4(/µ)1/4) when d is large *** our quantum upper and lower bounds can extend to the cases where ψ is not necessarily strongly convex, or each fi is Lipschitz but not necessarily *** addition, when F is nonconvex, our quantum algorithm can find_an ϵ-critial point using (Equation presented) queries. Copyright 2024 by the author(s)

关键词： Quantum computers

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Integrating Emotion Detection with Sentiment Analysis for Enhanced Text Interpretation

Integrating Emotion Detection with Sentiment Analysis for En...

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International Conference on Inventive computing and Informatics (ICICI)

作者： Arpan Ghosh Naimish Pandey C. AshokKumar Department of Computing Technologies School of Computing College of Engineering and Technology SRM Institute of Science and Technology Chennai India

ISBN: (数字)9798350373295

ISBN: (纸本)9798350373301

The analysis of public sentiment on various social media platforms has become more important.. However, conventional sentiment analysis often struggle to capture intricate emotional nuances present in textual data.. This study proposes an innovative approach by integrating emotion detection with sentiment analysis to improve the interpretation of text.. A novel algorithm "BERT-DeepEmo" is proposed, combining the strong contextual modeling capabilities of Bidirectional Encoder Representations from Transformers (BERT) with a deep learning-based emotion recognition model (DeepEmo). Simulation analyses were performed to evaluate the effectiveness of the proposed algorithm compared to established approaches like BiLSTM-ECNN and RoBERTa-EDTA. Evaluation metrics including accuracy, precision, and mean absolute error were employed for comparison purposes. The results confirmed that BERT-DeepEmo enhances the overall quality of sentiment analysis while considering the emotional nuances in text. These findings highlight the proposed approach's potential to offer a more holistic and effective sentiment analysis framework, thereby facilitating informed decision-making and deeper insights across various applications. Future research work may explore into further integration with alternate process of emotional data, such as speech or visual inputs, to broaden the framework's applicability and efficacy.

关键词： Sentiment analysis Emotion recognition Adaptation models Visualization Accuracy Social networking (online) Bidirectional control

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Flight Delay Prediction Using Machine Learning 8

Flight Delay Prediction Using Machine Learning

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8th IEEE International Conference for Convergence in Technology, I2CT 2023

作者： Reddy, Ravula Tarun Basa Pati, Peeta Deepa, K. Sangeetha, S. Tresa Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Amrita Vishwa Vidyapeetham Amrita School of Engineering Department of Electrical and Electronics Engineering Bengaluru India University of Technology and Applied Sciences Department of Engineering Al Musannah Oman

ISBN: (纸本)9798350334012

The prediction of flight delay can be considered as one of the most challenging problems to solve. Delay of an aircraft is not only a problem for an airline but also for the passengers. Flights can be delayed due to several reasons, the weather being the primary one. In this paper, our focus is to predict the delay of the flights due to bad weather. The dataset used consists of flight data from JFK airport from the sources Bureau of Transportation statistics and weather data from the National Centers for Environmental Information. The results of different machine learning algorithms like Linear Regression, SVR, Decision Tree Regressor, Random Forest Regressor, Ridge, and Lasso Regressor, for prediction of flight delay, are compared. XGboost regressor had the best performance in all the scenarios with least RMSE score of 0.81. © 2023 IEEE.

关键词： Decision trees

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A Survey of Non-Volatile Main Memory Technologies:State-of-the-Arts,Practices,and Future Directions

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Journal of Computer science & Technology 2021年第1期36卷 4-32页

作者： Hai-Kun Liu Di Chen Hai Jin Xiao-Fei Liao Binsheng He Kan Hu Yu Zhang National Engineering Research Center for Big Data Technology and System Huazhong University of Science and TechnologyWuhan 430074China Services Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan 430074China Cluster and Grid Computing Laboratory Huazhong University of Science and TechnologyWuhan 430074China School of Computing Science and Technology Huazhong University of Science and TechnologyWuhan 430074China School of Computing National University of SingaporeSingapore 117418Singapore

Non-Volatile Main Memories (NVMMs) have recently emerged as a promising technology for future memory systems. Generally, NVMMs have many desirable properties such as high density, byte-addressability, non-volatility, low cost, and energy efficiency, at the expense of high write latency, high write power consumption, and limited write endurance. NVMMs have become a competitive alternative of Dynamic Random Access Memory (DRAM), and will fundamentally change the landscape of memory systems. They bring many research opportunities as well as challenges on system architectural designs, memory management in operating systems (OSes), and programming models for hybrid memory systems. In this article, we revisit the landscape of emerging NVMM technologies, and then survey the state-of-the-art studies of NVMM technologies. We classify those studies with a taxonomy according to different dimensions such as memory architectures, data persistence, performance improvement, energy saving, and wear leveling. Second, to demonstrate the best practices in building NVMM systems, we introduce our recent work of hybrid memory system designs from the dimensions of architectures, systems, and applications. At last, we present our vision of future research directions of NVMMs and shed some light on design challenges and opportunities.

关键词： non-volatile memory persistent memory hybrid memory systems memory hierarchy

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Task Separation and Knowledge Sharing for Class Incremental Learning 5

Task Separation and Knowledge Sharing for Class Incremental ...

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5th International Conference on Intelligent computing and Human-Computer Interaction, ICHCI 2024

作者： Zhang, Jiali Qiao, Xiaoyan School of Computer Science and Technology Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China School of Mathematics and Information Science Shandong Technology and Business University Yantai Key Laboratory of Big Data Modeling and Intelligent Computing Immersion Technology and Evaluation Shandong Engineering Research Center Shandong Yantai China

ISBN: (纸本)9798350368284

Methods based on dynamically expanding architectures can effectively mitigate catastrophic forgetting in class incremental learning (CIL), but they often overlook information sharing and integration between subnetworks, leading to performance degradation and increased parameter count and memory consumption as the network expands. To address these complex issues, we introduce a new method called Task Separation and Knowledge Sharing (TSKS), which transfers knowledge at both the logical and feature levels through Hybrid Knowledge Distillation (HKD). In logical distillation, we combine Global Knowledge Distillation (GKD) and Task Knowledge Distillation (TKD) to establish connections between tasks. To prevent the model from favoring new classes, we design an Attention- Based Feature Fusion (ABFF) mechanism to capture important features. Additionally, we introduce a Soft-Margin Auxiliary Loss (SMAL) to help the model learn new concepts. Extensive experiments show that TSKS outperforms existing models on CIFAR100 and ImageNet100, regardless of whether memory samples are used. Notably, TSKS demonstrates superior performance in memory-free settings, indicating a reduced reliance on rehearsal memory. © 2024 IEEE.

关键词： Adversarial machine learning

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Secure medical image encryption using homomorphic techniques 2

Secure medical image encryption using homomorphic techniques

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Megala, K. Jayadevi, J. Keerthika, K. Manikandan, G. Vijay Sai, R. Srinivasan, B. Department of C Ineering SASTRA Deemed to be University Thanjavur India Department of Computer Science and Engineering SASTRA Deemed to be University Thanjavur India School of Computing SASTRA Deemed to be University Thanjavur India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

The security of private medical data is crucial in the age of digital healthcare, especially when it comes to imaging tests like X-rays and ECGs. In addition to jeopardizing patient privacy, unauthorized access to such data also raises questions regarding the veracity of the data. A strong cryptographic architecture for safely encrypting and decrypting medical imaging data is provided to overcome these issues. The method employs homomorphic encryption, a cutting-edge cryptographic technology, to protect medical image integrity and confidentiality during transmission. The system relies heavily on key creation, and keys are generated securely and efficiently by using the ResNet50 architecture, a deep-learning convolutional neural network (CNN), using which high-level features may be extracted from medical images. These features are then converted into cryptographic keys, which will be shared with the receiver. In the final analysis, it has been discovered that the execution time for processing various images stays within an acceptable range after examining a variety of medical image samples. The aim is to protect patient privacy and data integrity while enabling the safe storage and transfer of sensitive medical data. © 2024 IEEE.

关键词： Data integrity

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MDH-NAS: Accelerating EEG Signal Classification with Mixed-Level Differentiable and Hardware-Aware Neural Architecture Search

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IEEE Internet of Things Journal 2025年第13期12卷 23866-23878页

作者： Zhu, Lixian Wang, Su Jin, Xiaokun Zheng, Kai Zhang, Jian Sun, Shuting Tian, Fuze Cai, Ran Hu, Bin Key Laboratory of Brain Health Intelligent Evaluation and Intervention Beijing Institute of Technology Beijing100081 China Beijing Institute of Technology School of Medical Technology Beijing100081 China Lanzhou University School of Information Science and Engineering Lanzhou730000 China Lanzhou University Gansu Provincial Key Laboratory of Wearable Computing School of Information Science and Engineering Lanzhou China Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences Shanghai China Chinese Academy of Sciences Joint Research Center for Cognitive Neurosensor Technology of Lanzhou University Institute of Semiconductors Lanzhou China

In non-invasive brain-computer interfaces (BCIs), EEG analysis plays a critical role, with neural networks serving as a cornerstone for signal decoding. Existing neural network approaches for EEG signal recognition require extensive manual design and hyperparameter tuning, leading to inefficiencies and making them impractical for embedded devices due to their large model size. To address these limitations, we propose Mixed-Level Differentiable and Hardware-Aware Neural Architecture Search (MDH-NAS), a framework that automatically generates lightweight neural networks tailored for EEG classification. Unlike traditional DARTS methods, MDH-NAS employs a hybrid optimization strategy that balances global and local search spaces, thereby accelerating and refining architecture discovery. It introduces explicit size constraints during the search process to ensure deployability on embedded devices. MDH-NAS demonstrates autonomous generation of architectures for tasks such as motor imagery (MI) and depression recognition, achieving 87.80% accuracy on the BCI-IV dataset and 90.09% on the MODMA dataset. When deployed on the EAIDK-610 board across heterogeneous tasks, it attains 85.37% accuracy on the EEG Motor Movement/Imagery dataset. This method reduces architecture discovery time by 89% and enhances prediction accuracy by 8.70% compared to baseline methods, highlighting its potential for scalable EEG analysis and real-world embedded deployment. © 2014 IEEE.

关键词： Electroencephalography

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