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Design, Automation and Test in Europe Conference and Exhibition

作者： Jing-Jia Hung Yi-Jung Chen Hsiang-Yun Cheng Hsu Kao Chia-Lin Yang Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Computer Science and Information Engineering National Chi Nan University Nantou Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei Taiwan Department of Engineering and System Science National Tsing Hua University Hsinchu Taiwan

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Incremental Learning (IL) enhances Machine Learning (ML) models over time with new data, ideal for edge devices at the forefront of data collection. However, executing IL on edges faces challenges due to limited resources. Common methods involve IL followed by model pruning or specialized IL methods for edges. However, the former increases training time due to fine-tuning and compromises accuracy for past classes due to limited retained samples or features. Meanwhile, existing edge-specific IL methods utilize weight pruning, which requires specialized hardware or compilers to speed up and cannot reduce computations on general embedded platforms. In this paper, we propose Filter-based Adaptive Model Pruning (FAMP), the first pruning method designed specifically for IL. FAMP prunes the model before the IL process, allowing fine-tuning to occur concurrently with IL, thereby avoiding extended training time. To maintain high accuracy for both new and past data classes, FAMP adapts the compressed model based on observed data classes and retains filter settings from the previous IL iteration to mitigate forgetting. Across all tests, FAMP achieves the best average accuracy, with only a 2.78% accuracy drop over full ML models with IL. Moreover, unlike the common methods that prolong training time, FAMP takes 35% shorter training time on average than using the full ML models for IL.

关键词： Training Adaptation models Accuracy Incremental learning Computational modeling Adaptive filters Hardware Data models Software Usability

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Optimal Kernel Extreme Learning Machine for COVID-19 Classification on Epidemiology Dataset

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computers, Materials & Continua 2022年第11期73卷 3305-3318页

作者： Saud S.Alotaibi Amal Al-Rasheed Sami Althahabi Manar Ahmed Hamza Abdullah Mohamed Abu Sarwar Zamani Abdelwahed Motwakel Mohamed I.Eldesouki Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Research Centre Future University in EgyptNew Cairo11745Egypt Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Artificial Intelligence(AI)encompasses various domains such as Machine Learning(ML),Deep Learning(DL),and other cognitive technologies which have been widely applied in healthcare *** models are utilized in healthcare sector in which the machines are used to investigate and make decisions based on prediction and classification of input *** this motivation,the current study involves the design of Metaheuristic Optimization with Kernel Extreme Learning Machine for COVID-19 Prediction Model on Epidemiology Dataset,named MOKELM-CPED *** primary aim of the presented MOKELM-CPED model is to accomplish effectual COVID-19 classification outcomes using epidemiology *** the proposed MOKELM-CPED model,the data first undergoes pre-processing to transform the medical data into useful *** by,data classification process is performed by following Kernel Extreme Learning Machine(KELM)***,Symbiotic Organism Search(SOS)optimization algorithm is utilized to fine tune the KELM parameters which consequently helps in achieving high detection *** order to investigate the improved classifier outcomes of MOKELM-CPED model in an effectual manner,a comprehensive experimental analysis was conducted and the results were inspected under diverse *** outcome of the experiments infer the enhanced performance of the proposed method over recent approaches under distinct measures.

关键词： COVID-19 epidemiology dataset machine learning artificial intelligence metaheuristics healthcare

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A Hybrid Approach with BRKGA and Data Mining for the Early/Tardy Scheduling Problem

A Hybrid Approach with BRKGA and Data Mining for the Early/T...

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Congress on Evolutionary Computation

作者： Israel Mendonça Tirana Noor Fatyanosa Masayoshi Aritsugi Pedro Henrique González Silva Faculty of Advanced Science and Technology Kumamoto University Kumamoto Japan Faculty of Computer Science Brawijaya University Malang Indonesia System Engineering and Computer Science Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

This paper introduces a novel hybrid genetic algorithm combined with data mining to solve a version of the early/tardy scheduling problem in which no unforced idle time may be inserted in a sequence. The chromosome representation of the problem is based on random keys and we use the Biased Random-Key Genetic Algorithm (BRKGA) to establish an order in which jobs are scheduled. A data mining component gathers data from the evolutionary steps of BRKGA and suggests chromosomes based on past observations. In this way, we address a key challenge in BRKGA - the exploration of solutions near an individual gene - by leveraging data-driven insights to refine and enhance the search space. Comparative analysis reveals that our hybrid algorithm significantly benefits from the pattern recognition capabilities of data mining, leading to improved scheduled efficiency. The results of a series of computational experiments underscore the potential of this hybrid approach that, although requiring slightly longer computational times, is better than the previous baseline BRKGA algorithm in terms of solution quality.

关键词： Processor scheduling Evolutionary computation Scheduling Data mining Biological cells Genetic algorithms

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Gesture Recognition of sEMG Based on Res-LSTM 17th

Gesture Recognition of sEMG Based on Res-LSTM

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17th International Conference on Intelligent Robotics and Applications, ICIRA 2024

作者： Zhao, Yujia Zou, Chunlong Yun, Juntong Jiang, Du Huang, Li Liu, Ying Jiang, Guozhang Xie, Yuanmin Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

sEMG (surface electromyography) signal control of bionic prostheses has been widely studied over the past few years. In particular, sparse sEMG signals are rapidly developing in the field of gesture recognition for their convenience, noninvasiveness, and ease of access. However, compared with high-density EMG signals, sparse EMG signals lack rich feature information, which in turn affects gesture recognition accuracy. In order to reduce the loss of feature information of sparse EMG signals in the spatio-temporal dimension, this paper proposes a hybrid neural network Res-LSTM combining residual network and long short-term memory network. The ordinary convolutional blocks in the CNN network are replaced with residual blocks, and the final fully connected layer is removed and a constant mapping layer is added to adequate extraction of spatial feature information of the data. The Res layer’s output is utilized as the input for the long and short-term memory (LSTM) network, which further extracts the features of the data in the temporal dimension, and finally completes the categorization output through a fully connected layer. The average accuracy of 91.11% for gesture-motion classification was verified by training on a homebrew dataset;and tested on a public dataset, NinaPro DB1, with an average accuracy of 91.03%. The experimental findings indicate that the proposed Res-LSTM network framework contributes to solving the problem of lack of feature information of sparse sEMG signals and improves the pattern recognition accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gesture recognition

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Monocular Depth Estimation with Boundary Attention Mechanism and Shifted Window Adaptive Bins

SSRN

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SSRN 2024年

作者： Liang, Mengnan Wang, Congcong Zhao, Meng Shi, Fan Zhang, Chao Han, Yilin School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China Laboratory of Computer Vision System of Ministry of Education Tianjin University of Technology Tianjin300384 China

Monocular depth estimation is a classic research topic in computer vision. In recent years, development of Convolutional Neural Networks (CNNs) has facilitated significant breakthroughs in this field. However, there still are two challenges: 1) The network struggles to effectively fuse edge features in the feature fusion stage, which ultimately results in the loss of structure or distorted boundaries of objects in the scene. 2) Classification based studies typically depend on Transformers for global modeling, a process that often introduces substantial performance overhead. In this paper, we propose two modules to address the aforementioned issues. The first module is the Boundary Attention Module (BAM), which leverages the attention mechanism to enhance the ability of the network to perceive object boundaries during the feature fusion stage. In addition, to mitigate the performance overhead resulting from predicting adaptive bins, we propose a Shift Window Adaptive Bins (SWAB) module to reduce the amount of calculation in global modeling. The proposed method is evaluated on two public datasets, NYU Depth V2 and KITTI, and achieves state-of-the-art (SOTA) performance. © 2024, The Authors. All rights reserved.

关键词： computer vision

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GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Reducing Data Redundancy

GeckoDAG: Towards a Lightweight DAG-Based Blockchain via Red...

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International Conference on Distributed Computing systems

作者： Xiaohai Dai Yifan Zhou Jiang Xiao Feng Cheng Xia Xie Hai Jin Bo Li National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China School of Computer Science and Technology Hainan University China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

To overcome the scaling and performance limitations, the Directed Acyclic Graph (DAG) is utilized as the underlying storage model of blockchain systems, which enables concurrent transaction processing and confirmation. However, accompanied by high performance, DAG-based blockchains still suffer from the severe challenge of constrained storage scalability, i.e., expensive storage overhead. Based on an in-depth analysis of the data, we discover that the root cause of storage overhead stems from the considerable data redundancy in the DAG-based blockchains. In this paper, we propose GeckoDAG, a lightweight DAG-based blockchain, whose design consists of two steps. First, we abstract a storage model named Basic from the existing DAG-based blockchain systems, which offers both high performance and security. On top of Basic, we then devise GeckoDAG, which merges previous transactions into Transaction Union (TU) and reduces the data redundancy in TU, thus lowering the storage overhead. To evaluate our design, we implement a prototype of GeckoDAG and conduct various experiments on it. The experimental results demonstrate that GeckoDAG can offer storage scalability while maintaining the security and efficiency of DAG-based blockchains.

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Multi-secret Sharing Scheme for Zero Trust Environment 14

Multi-secret Sharing Scheme for Zero Trust Environment

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14th IEEE International Conference on Software engineering and Service science, ICSESS 2023

作者： Zhou, Qin Yang, Rumiao Zhou, Hao Cai, Lei Yongyou Network Technology Co. Ltd Beijing China School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China North China Institute of Computing Technology The 4th System Department Beijing China

ISBN: (纸本)9798350336269

The zero-trust concept of 'never trust, always verify' injected new vitality into the development of network security. However, the related research on zero trust is still preliminary, and the research on secure data transmission is relatively less. How to achieve secure data transmission in zero trust environment is a problem to be solved. Aiming the data transmission scenario of private data secret sharing among agents, this paper proposed a secure data transmission scheme based on multi-secret sharing to solve the problems of identity comparison confirmation of participants and adjust the access structure according to the security value of trust evaluation in existing secret sharing schemes. The proposed scheme adopts the secret sharing design without the trusted third party, which avoids the security risk of trusted third party disclosure in the traditional secret sharing scheme. Secondly, in the system parameter setting phase, the identity of participants and zero-trust identity database are compared to combine the characteristics of zero-trust 'identity authentication', to improve the security of secret sharing. Finally, during secret distribution, zero-trust trust evaluation security values are used to adjust the access structure based on zero-trust 'trust evaluation' to ensure secure data transmission. © 2023 IEEE.

关键词： Data communication systems

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LBA: Matching Theory Based Latency-Sensitive Binary Offloading in IoT-Fog Networks

LBA: Matching Theory Based Latency-Sensitive Binary Offloadi...

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International Communication systems and Networks and Workshops, COMSNETS

作者： Priyanka Soni Omkar Chand Deshlahre Anurag Satpathy Sourav Kanti Addya Department of Computer Science and Engineering Cloud and Smart System Services Lab National Institute of Technology Karnataka India Missouri University of Science and Technology Rolla MO USA

The Internet of Things (IoT) is growing more popular with applications like healthcare services, traffic monitoring, video streaming, smart homes, etc. These applications produce an enormous amount of data, so a realistic option in this instance is to offload computational tasks to their proximity fog nodes (FNs) instead of the remote cloud. However, a negligent offloading strategy may cause anomalous computational traffic load at the FNs, causing congestion that may adversely affect the latency. However, the latency of task flows from IoT devices comprises communications latency at BS and computational latency at FNs. Therefore, designing offloading algorithms to distribute the computational load at FN evenly and efficiently utilize the FN resources is crucial. To solve this problem, we proposed LBA in a fog network with a binary offloading strategy using the matching theory-based approach. We utilize the Analytic Hierarchy Process (AHP) to generate the preference list. Furthermore, the binary offloading technique follows the deferred acceptance algorithm (DAA) to produce a stable assignment, and the complete offloading problem is modeled as a one-to-many matching game. Comprehensive simulations ensure that LBA can accomplish a better-balanced assignment for homogeneous and heterogeneous input concerning all the baseline algorithms.

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Owl: Differential-Based Side-Channel Leakage Detection for CUDA Applications

Owl: Differential-Based Side-Channel Leakage Detection for C...

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International Conference on Dependable systems and Networks (DSN)

作者： Yu Zhao Wenjie Xue Weijie Chen Weizhong Qiang Deqing Zou Hai Jin National Engineering Research Center for Big Data Technology and System Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Jinyinhu Laboratory Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9798350341058

ISBN: (纸本)9798350341065

Over the past decade, various methods for detecting side-channel leakage have been proposed and proven to be effective against CPU side-channel attacks. These methods are valuable in assisting developers to identify and patch side-channel vulnerabilities. Nevertheless, recent research has revealed the feasibility of exploiting side-channel vulnerabilities to steal sensitive information from GPU applications, which are beyond the reach of previous side-channel detection methods. Therefore, in this paper, we conduct an in-depth examination of various GPU features and present Owl, a novel side-channel detection tool targeting CUDA applications on NVIDIA GPUs. Owl is designed to detect and locate side-channel leakage in various types of CUDA applications. When tracking the execution of CUDA applications, we design a hierarchical tracing scheme and extend the A-DCFG (Attributed Dynamic Control Flow Graph) to address the massively parallel execution in CUDA, ensuring Owl's detection scalability. After completing the initial assessment and filtering, we conduct statistical tests on the differences in program traces to determine whether they are indeed caused by input variations, subsequently facilitating the positioning of side-channel leaks. We evaluate Owl's capability to detect side-channel leaks by testing it on Libgpucrypto, PyTorch, and nvJPEG. Meanwhile, we verify that our solution effectively handles a large number of threads. Owl has successfully identified hundreds of leaks within these applications. To the best of our knowledge, we are the first to implement side-channel leakage detection for general CUDA applications.

关键词： Target tracking Statistical analysis Filtering Scalability Graphics processing units Side-channel attacks Feature extraction

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Packet Transmission using Radio Access Protocol for Intra-Cluster Communications in Mobile Ad hoc Networks 1

Packet Transmission using Radio Access Protocol for Intra-Cl...

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1st International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2022

作者： Sasikumar, Reshma Vaman, Dhadesugoor R. Poulose, Sojan Lal Computer Science and Engineering Dept. Kerala Kothamangalam India Apj Abdul Kalam Technological University Kerala Thiruvananthapuram India Prairie View AandM University Texas AandM University System Electrical and Computer Engineering Dept. TX United States

ISBN: (纸本)9781665436472

Mobile Ad hoc Network (MANET) is designed to support flexible and seamless deployment for military and homeland security theatres, where every resource is mobile. The resources include radios and base stations. In this paper, we have considered the MANET architecture in terms of multiple clusters with each cluster containing a set of radios and a base station or a Cluster Head (CH). The number of radios in each cluster is selected based on the tactical operation of MANET for different applications. A radio in one cluster can communicate with a radio in a different cluster. For this to occur, radio in source cluster sends data to its own Cluster Head (intra-cluster communication) and the Cluster Head sends this data to a remote Cluster Head (inter-cluster communication) and ultimately to a remote radio (intra-cluster communication). In this paper, only intra-cluster communication is considered at the IP layer and the Physical Layer, where each radio communicates with the CH. When multiple radios are communicating with the CH, there is a need to support an organized method of sending packets without any contention. This paper presents a novel Radio Access Protocol (RAP) for packet exchanges between radios and the CH. The cluster performance is derived in terms of 'effective packet transmission time' from any radio to CH while executing the access protocol. The performance in terms of delay and throughput are presented for different packet sizes. The results show that for a reasonable size of cluster, RAP achieves the throughput greater than 55 percent with minimal delay of transmission of a packet from the radio to the CH. The performance of RAP considers both the IP layer and the Physical layer and the real time system implementation of RAP is done using MATLAB / Simulink and the results are presented. © 2022 IEEE.

关键词： Mobile ad hoc networks

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