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Instruction selection optimization for VLIW architecture based on classification node merging

Journal of Supercomputing 2025年第7期81卷 1-19页

作者： Liu, Fangjun Zhang, Huifu Hu, Yonghua Xie, Anxing Cao, Wei Mo, Shangfeng School of Computer Science and Engineering Hunan University of Science and Technology XiangTan411201 China Hunan Key Laboratory for Service Computing and Novel Software Technology XiangTan411201 China

Traditional instruction selection methods fail to fully exploit the very long instruction word (VLIW) architecture’s efficient scalar instructions. We propose an optimized instruction selection method based on classification node merging. This method abstracts scalar intermediate instructions into nodes and divides them into associated and similar ones according to operational semantics. Combined with the VLIW architecture’s instruction set features and guided by the instruction cost model, the nodes are merged and optimized to select efficient instructions. We design an instruction generation framework that swiftly converts the low level virtual machine intermediate representation into the instruction sequence of the target architecture. The experimental results show that, compared with the instruction sequence generated by M7002-cc, the instruction sequence generated with the solution proposed in this paper exhibits an average execution performance that is up to 54% higher on the FT-M7002 hardware platform. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Real time systems

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Hyper-IIoT: A Smart Contract-inspired Access Control Scheme for Resource-constrained Industrial Internet of Things

IEEE Transactions on Sustainable Computing

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IEEE Transactions on Sustainable computing 2025年

作者： Li, Dun Li, Hongzhi Crespi, Noel Minerva, Roberto Li, Ming Liang, Wei Li, Kuan-Ching Tsinghua University Department of Industrial Engineering China Institut Polytechnique de Paris Samovar Telecom SudParis Palaiseau91120 France Chizhou University College of Big Data and Artificial Intelligence Chizhou247100 China The Hong Kong Polytechnic University Research Institute for Advanced Manufacturing Hung Hom Hong Kong Hunan University of Science and Technology School of Computer Science and Engineering Hunan University of Science and Technology Sanya Research Institute Hunan Key Laboratory for Service Computing and Novel Software Technology China Providence University Dept. of Computer Science and Information Engineering Taiwan

In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data in Industrial Internet of Things (IIoT) devices have highlighted the need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. Besides, we defined a storage model and data format for private data by designing and deploying smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in real-world simulations, Hyper-IIoT demonstrates excellent contract execution time control, stable system throughput, and rapid consensus processes that are capable of handling high throughput and efficient consensus in distributed systems, even in large-scale request scenarios. © 2016 IEEE.

关键词： Smart contract

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TDiffSal: Text-Guided Diffusion Saliency Prediction Model for Images 27th

TDiffSal: Text-Guided Diffusion Saliency Prediction Model fo...

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27th International Conference on Pattern Recognition, ICPR 2024

作者： Zhang, Nana Xiong, Min Zhu, Dandan Zhu, Kun Zhai, Guangtao School of Computer Science and Technology Donghua University No. 2999 Renmin North Road Songjiang District Shanghai201620 China State Key Laboratory for Novel Software Technology Nanjing University Nanjing210093 China Institute of AI Education Shanghai East China Normal University No. 3663 Zhongshan North Road Putuo District Shanghai200333 China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University No. 4800 Cao’an Highway Jiading District Shanghai201804 China Institute of Image Communication and Network Engineering Shanghai Jiao Tong University No. 800 Dongchuan Road Minhang District Shanghai200240 China

ISBN: (纸本)9783031781858

Existing visual saliency prediction methods mainly focus on single-modal visual saliency prediction, while ignoring the significant impact of text on visual saliency. To more comprehensively explore the influence of text on human attention in images, we propose a text-guided diffusion saliency prediction model, named TDiffSal. In specific, recent studies on stable diffusion models have shown promising performance in unifying tasks due to their inherent generalization ability. Inspired by this, a novel diffusion model for generalized visual-text saliency prediction is proposed, which formulates the prediction issue as a conditional generative task of the saliency map by employing input visual and text as the conditions. Meanwhile, we introduce a multi-head fusion module to effectively integrate text features and image features, which can efficiently guide the image denoising process and progressively refine the generated saliency map to make it semantically relevant to the text. Additionally, we employ an efficient pre-training strategy to enhance the robustness and generalization of the proposed model. We conduct extensive experiments on benchmark datasets to demonstrate its superior performance compared to other state-of-the-art methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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Compromising Rechargeable Sensor Networks in Marine Environment

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IEEE Transactions on Mobile computing 2025年

作者： Wang, Qiwei Lin, Chi Dai, Haipeng Obaidat, Mohammad S. Hsiao, Kuei-Fang Fan, Xin Dalian University of Technology School of Software Technology & DUT-RU International School of Information Science and Engineering China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116620 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China SRM University Department of Computational Intelligence School of Computing SRM Nagar TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India The King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan

Marine Wireless Rechargeable Sensor Networks (MWRSNs), enhanced by recent Wireless Power Transfer (WPT) technology, present a significant advancement in extending network life. Traditional methods improve network performance through algorithm optimization, but neglect charging security, exposing networks to potential attacks. This paper addresses this problem from an adversarial view and develops a novel attack for MWRSN through Denying of Charge (DoC) to maximize network destructiveness. We start by establishing a generalized on-demand charging model, essential for developing DoC tactics. Subsequently, we unveil the Collaborative DoC (CoDoC) algorithm, capable of manipulating and falsifying charging requests. Central to CoDoC is the Request Prediction Method (RPM), which forecasts the initiation of charging requests and facilitates rapid request surges to enhance the attack's efficacy. CoDoC is able to disguise the presence of the attack, which is able to escape from being detected by the base station. Theoretical analyses are provided to explore the features of the proposed scheme. To demonstrate the outperformed features of the proposed schemes, extensive simulations and test-bed experiments are conducted. Our analysis and extensive simulations demonstrate that CoDoC increases sensor node failures by 20% to 142% compared to traditional methods, highlighting its effectiveness in marine environments. © 2002-2012 IEEE.

关键词： Charging stations

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Joint Optimization of Dynamic service Selection and Request Routing in Cloud-Edge Collaborative Environments

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IEEE Internet of Things Journal 2025年

作者： Tang, Bing Xu, Wei Zhang, Li Cao, Buqing Yang, Qing Li, Kuanching Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan 411201 China Guangzhou Maritime University School of Computer Science Guangzhou 510725 China Huaihua University School of Computer and Artificial Intelligence Huaihua 418000 China Hunan Institute of Engineering School of Information Science and Engineering Xiangtan 411104 China Hunan University of Technology School of Computer Zhuzhou 412007 China Hunan University of Science and Technology Sanya Research Institute Sanya 572024 China Hunan Key Laboratory for Service Computing and Novel Software Technology Xiangtan 411201 China

Optimizing multi-instance service composition and dynamic request routing has become a critical challenge in cloud-edge collaborative systems. Existing solutions struggle with effectively balancing performance, cost, and bandwidth constraints in dynamic and resource-constrained environments. In this work, we address these challenges by proposing the Removed Minimum Cost Flow (RMCF) algorithm, aiming to minimize average response time while considering constraints such as budget and bandwidth, making it well-suited for time-sensitive services in a cloud-edge collaborative service provision system. Simulations were conducted using real-world data from China Telecom’s base stations in Shanghai, and experiments in various scenarios were considered, including time-sensitive services and general application services, with key performance indicators such as time utility, cost-utility, request completion rate, and timeout rate. The experimental results demonstrate that RMCF achieves lower response times, superior performance, and improved cost-effectiveness compared to other baseline algorithms. © 2014 IEEE.

关键词： Cloud-edge collaboration IoT applications microservice deployment Request routing Shortest path problem

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SyNet:Medical image anomaly detection with noise synthesis network 10

SyNet:Medical image anomaly detection with noise synthesis n...

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10th IEEE Conference on Big Data Security on Cloud, BigDataSecurity 2024

作者： Liu, Jingnian Liang, Wei Chen, Yuxiang Jin, Kai Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology School of Computer Science and Engineering Xiangtang China

ISBN: (纸本)9798350389524

Medical image anomaly detection refers to machine learning techniques to analyze and identify lesions and abnormalities in them. However, in medical images, anomaly samples are usually sparse, which can lead to supervised learning not being able to obtain sufficient training. While existing unsupervised learning is mainly based on reconstruction and generation as well as feature embedding, these methods do not take into account the problem of data type bias during domain migration and often face the problem of loose judgment boundaries. To this end, this paper proposes a novel unsupervised learning method, SyNet, based on noisy anomaly synthesis. First, the problem at domain migration is well solved by aggregating the range of features within the feature map, fusing the multiscale features at the intermediate level, and adding feature adapters. Then, anomaly samples are synthesized by adding noise in the feature space, which is more efficient and stable than generating samples in the image space. Finally, normal and abnormal data are distinguished by training the discriminator. In this paper, experiments are conducted on ISIC and brain MRI datasets, and SyNet achieves nearly 20% improvement in AUC, ACC and other metrics compared with the current mainstream methods, and also has good inference efficiency. © 2024 IEEE.

关键词： Anomaly detection

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ScIAMC:Single-Cell Imputation via adaptive matrix completion 10

ScIAMC:Single-Cell Imputation via adaptive matrix completion

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10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhang, Shuai Chen, Xiang Peng, Li Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan Hunan411201 China Hunan Key Laboratory for Service computing and Novel Software Technology China

ISBN: (纸本)9798350312461

Single-cell sequencing techniques are often impacted by technical noise, leading to the generation of very sparse expression matrices. This technical noise is referred to as dropouts and poses as a major challenge for downstream analysis. In this study, we introduce scIAMC (single-cell imputation via adaptive parameter matrix completion), which is based on matrix completion theory to recover missing values in expression matrices. To expedite the algorithm's running time and avoid any parameter tuning on data, we formulated an optimization problem. Our approach led to an enhanced cell population identification and minimal errors, while also restoring biological landscapes that were damaged by these dropouts. © 2023 IEEE.

关键词： Cell proliferation

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ESR: Optimizing Gene Feature Selection for scRNA-seq Data 10

ESR: Optimizing Gene Feature Selection for scRNA-seq Data

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10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Huang, Tao Chen, Xiang Peng, Li Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan Hunan411201 China Hunan Key Laboratory for Service computing and Novel Software Technology China

ISBN: (纸本)9798350312461

The rapid development of single-cell RNA sequencing (scRNA-seq) technology has enabled researchers to explore gene expression differences at the level of individual cells, revealing more refined cell types and states. However, due to the low expression and high noise of scRNA-seq data, feature selection has become particularly important in the analysis of single-cell data. Here, we introduce the Entropy Stepwise Regression (ESR) method for feature selection. This method utilizes the correlation between genes and the entropy values of each feature to filter out genes that are conducive to downstream analysis. In mouse kidney samples, we compared the performance of three methods in terms of Adjusted Rand Index and achieved good results. This indicates that the method can improve the accuracy of downstream analysis. © 2023 IEEE.

关键词： Entropy

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Towards Planning Package Delivery Paths by Exploiting the Crowd of Private Cars 24

Towards Planning Package Delivery Paths by Exploiting the Cr...

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24th IEEE International Conference on High Performance computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Nie, Hejing Wen, Yiping Li, Tiancai Ouyang, Sipeng Tian, Muyang School of Computer Science and Engineering Hunan University of Science and Technology China Hunan University of Science and Technology Hunan Key Laboratory for Service Computing and Novel Software Technology China

ISBN: (纸本)9798350319934

Crowdsourced delivery can provide cost-effective deliveries and it is becoming increasingly important in practice. This paper considers a package delivery problem which exploits relays of private cars to transport packages via their commuter routes. Specifically, we propose a method of planning package delivery paths based on the commuter routes of private cars. Using the commuter routes and time constraints as the reference, it can find optimal delivery paths and assign drivers of private cars to transport packages. The simulation experiment results show the validity and effectiveness of such method. © 2022 IEEE.

关键词： Cost effectiveness

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A Short Review of Truck and Drone Collaborative Delivery Problem 10

A Short Review of Truck and Drone Collaborative Delivery Pro...

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10th IEEE International Conference on Cyber Security and Cloud computing and 9th IEEE International Conference on Edge computing and Scalable Cloud, CSCloud-EdgeCom 2023

作者： Zhu, Yena Liu, Min Jin, Dongyao Hunan University of Science and Technology School of Computer Science and Engineering Xiangtan China Hunan Key Laboratory for Service computing and Novel Software Technology Xiangtan China

ISBN: (纸本)9798350312461

With the emergence of efficient logistics models and the development of drone technology, the last-mile delivery problem has become a focus of attention for scholars in various fields. The advantages of drones, such as low cost, energy saving, and independence from road conditions, have made them increasingly popular in the delivery industry. However, drones have shortcomings such as short endurance, small payload capacity, and susceptibility to interference. Therefore, the cooperative delivery model of vehicles and drones has important theoretical research significance and practical application value. Existing literature reviews focus on static truck-drone delivery problems (TDDPs) and methods such as exact algorithms, heuristics, and metaheuristics. However, real-life truck-drone delivery problems are easily affected by dynamic environmental changes. Accordingly, this article introduces some new dynamic variants of TDDPs proposed in recent years, which consider variable drone flight endurance, urban crowded road conditions, stochastic customer demands, and the influence of weather changes. In recent years, some new methods for solving TDDPs have been introduced, such as the increasingly popular machine learning and reinforcement learning methods. Finally, the possible future research directions and development trends of TDDPs are presented. © 2023 IEEE.

关键词： Reinforcement learning

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