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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Chengxin Zhou Xianbin Xue Khaled Riad Yan Zhu William Cheng-Chung Chu School of Computer and Communication Engineering University of Science and Technology Beijing Beijing China North Navigation Control Technology Co. Ltd. Beijing China College of Computer Science & Information Technology King Faisal University Saudi Arabia School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China Department of Computer Science Tunghai University Taichung Taiwan

This paper address a comprehensive solution to address data privacy and security challenges in data science, providing secure self-destruction of sensitive data, confidentiality through encryption, and flexible control over message destruction. Based on outsourcing cloud data services, we introduce a cloud retrograde storage (CRS) system for privacy-preserving self-destruction of sensitive data. The CRS is based on a frequently colliding hash table and recycle pool, and supports remote time, count, and range control. Hereby, we provide a remotely controllable mechanism for the prompt destruction of inactive messages against digital forensics without reducing the availability of services. The method used in the CRS involves storing messages in a recycle pool, where they are automatically erased at the end of their validity period or after being viewed by all recipients. The system also incorporates encryption and decryption processes to ensure confidentiality. The result of performance evaluation shows that the CRS effectively handles the destruction of messages and provides secure data removal from storage pools. The experiments demonstrate the efficiency and practicality of the CRS for large-scale data services.

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RSP-Hist: Approximate Histograms for Big Data Exploration on Hadoop Clusters

RSP-Hist: Approximate Histograms for Big Data Exploration on...

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International Conference on High Performance Computing

作者： Salman Salloum Joshua Zhexue Huang Big Data Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665410175

We propose a sampling-based method, called RSP-Hist, to construct approximate equi-width histograms and help data scientists explore the probability distribution of big data on Hadoop clusters. In RSP- Hist, the Random Sample Partition (RSP) model is used to store a big data set as ready-to-use random sample data blocks, called RSP blocks, in the Hadoop Distributed File System (HDFS). An approximate histogram is computed by applying a sequential histogram algorithm in parallel to each block in a block-level sample of RSP blocks. Local histograms from individual RSP blocks are combined to produce an approximate histogram for the entire data. We tested RSP-Hist on four data sets using a small computing cluster. In this paper, we demonstrate the effect of the sampling rate and the number of buckets on the histogram accuracy and show that RSP-based approximate histograms are equivalent to the exact histograms computed from the entire data. RSP- Hist can avoid the data correlation issue in HDFS blocks and significantly reduce both computation and communication costs. It enables iterative and interactive exploration of big data sets on small computing clusters and can be used for multivariate data exploration.

关键词： Histograms Costs Correlation High performance computing Distributed databases Big Data Data models

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A Novel Meta-Hierarchical Active Inference Reinforcement Learning Approach for QoS-Driven Resource Allocation in Dynamic Clouds

A Novel Meta-Hierarchical Active Inference Reinforcement Lea...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Peijie Xian Ying He F. Richard Yu Jianbo Du College of Computer Science and Software Engineering Shenzhen Univ. Shenzhen China Shaanxi Key Lab. of Information Comm. Network and Security Xi’an Univ. of Posts & Telecomm. Xi’an China School of Information Technology Carleton Univ. Ottawa Canada

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

The cloud computing environment is highly dynamic due to a variety of external factors such as seasonal changes, market trends, and social events. In this case, tenant behavior patterns exhibit significant variability. Therefore, the cloud computing resource allocation model must continuously adapt to these changes. To this end, we propose an adaptive algorithm for dynamic cloud resource allocation based on meta-hierarchical active inference reinforcement learning (MHAIRL). The algorithm combines active inference with meta-hierarchical reinforcement learning. It improves the overall performance of the algorithm, as well as quickly adapts to environmental changes, and improves the generalization performance. In addition, we design a novel polling scheduling framework combined with long short-term memory (LSTM) network. The framework ensures scheduling fairness and flexibility while greatly reducing the state and action space dimensions of the agent. Extensive simulation results show that our method outperforms baseline algorithms in quality of service (QoS) metrics and significantly improves system performance in highly dynamic cloud resource allocation.

关键词： Cloud computing Adaptation models Heuristic algorithms Computational modeling Quality of service Reinforcement learning Dynamic scheduling Inference algorithms Resource management Long short term memory

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A Method for Measuring and Calibrating the Thickness of Thin Films Based on Infrared Interference Technology

SSRN

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SSRN 2023年

作者： Sun, Jiaxing Li, Xinyu Zhang, Haojie Song, Jinlong Li, Zhisong College of Machine Shanghai Dianji University Shanghai201306 China School of Control and Computer Engineering North China Electric Power University Beijing102206 China Semiconductor Equipment Co. Shanghai201114 China

Transparent thin films are critical industrial components widely used in advanced optics, microelectronics and materials science, among other relevant fields. The rapid and stable measurement of sub-micron industrial film thickness is of particular importance. In this study, a novel method based on the principles of infrared interference and laser profilometry is proposed to calculate the periodogram of the original spectrum and accurately extract the maximum frequency. In addition, a high-precision, compact and low-cost thin-film thickness measurement and calibration system is developed. This system achieves accurate measurement of single-layer film thickness and adapts to correct errors caused by slight tilt due to mechanical vibration or misalignment during film placement. Experimental data show that, compared to conventional infrared interference thickness measurement systems, the use of the proposed maximum frequency extraction algorithm improves measurement accuracy by approximately 90% and stability by approximately 70%. Furthermore, even when measuring the thickness of films with inclined components, the use of the constructed thin film thickness measurement and calibration system provides equivalent results. © 2023, The Authors. All rights reserved.

关键词： Film thickness

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ERetinex: Event Camera Meets Retinex Theory for Low-Light Image Enhancement

arXiv

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arXiv 2025年

作者： Guo, Xuejian Tian, Zhiqiang Wang, Yuehang Li, Siqi Jiang, Yu Du, Shaoyi Gao, Yue the School of Software Engineering Xi’an Jiaotong University Xi’an710049 China the College of Computer Science and Technology Jilin University Changchun130012 China the National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an710049 China BNRist THUIBCS KLISS BLBCI School of Software Tsinghua University Beijing100084 China

Low-light image enhancement aims to restore the under-exposure image captured in dark scenarios. Under such scenarios, traditional frame-based cameras may fail to capture the structure and color information due to the exposure time limitation. Event cameras are bio-inspired vision sensors that respond to pixel-wise brightness changes asynchronously. Event cameras’ high dynamic range is pivotal for visual perception in extreme low-light scenarios, surpassing traditional cameras and enabling applications in challenging dark environments. In this paper, inspired by the success of the retinex theory for traditional frame-based low-light image restoration, we introduce the first methods that combine the retinex theory with event cameras and propose a novel retinex-based low-light image restoration framework named ERetinex. Among our contributions, the first is developing a new approach that leverages the high temporal resolution data from event cameras with traditional image information to estimate scene illumination accurately. This method outperforms traditional image-only techniques, especially in low-light environments, by providing more precise lighting information. Additionally, we propose an effective fusion strategy that combines the high dynamic range data from event cameras with the color information of traditional images to enhance image quality. Through this fusion, we can generate clearer and more detail-rich images, maintaining the integrity of visual information even under extreme lighting conditions. The experimental results indicate that our proposed method outperforms state-of-the-art (SOTA) methods, achieving a gain of 1.0613 dB in PSNR while reducing FLOPS by 84.28%. The code is available at https://***/lodew920/ERetinex. © 2025, CC BY.

关键词： Cameras

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Data Identification Optimal control for Quantized Linear Systems under DoS Attacks

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IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Wang, Xianming Li, Liwei Shen, Mouquan Zhao, Xudong Wang, Qing-Guo Zhu, Zheng Hong Nanjing Tech University School of Mechanical and Power Engineering Nanjing211816 China Nanjing Tech University College of Electrical Engineering and Control Science Nanjing211816 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China Beijing Normal University Institute of Ai and Future Networks Zhuhai519087 China BNU-HKBU United International College Guangdong Provincial Key Laboratory Irads Department of Computer Science Zhuhai519087 China York University Department of Mechanical Engineering TorontoONM3J 1P3 Canada

This paper is devoted to data identification optimal control for quantized linear systems under denial of service (DoS) attacks. A least-squares algorithm is provided to identify the unknown system matrices using a data-based representation. An output estimation algorithm utilizing identified matrices is supplied to offset DoS attacks. Consequently, an optimal controller is built on the quantized and attacked data by adaptive dynamic programming. Sufficient conditions are presented to guarantee that the resultant closed-loop system is ultimately uniformly bounded. An example is produced to verify the validity of the proposed approach. © 1965-2011 IEEE.

关键词： Optimal control systems

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A case study of testing an image recognition application 33

A case study of testing an image recognition application

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33rd International Conference on software engineering and Knowledge engineering, SEKE 2021

作者： Tao, Chuanqi Cao, Dongyu Guo, Hongjing Gao, Jerry College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing University of Aeronautics and Astronautics Nanjing China Department of Computer Engineering San Jose State University United States

ISBN: (纸本)1891706527

High-quality Artificial intelligence (AI) software in different domains, like image recognition, has been widely emerged in our lives. They are built on machine learning models to implement intelligent features. However, the current research on image recognition software rarely discusses test questions, clear quality requirements, and verification methods. This paper presents a case study of a realistic image recognition application called Calorie Mama using manual and automation testing with a 3D decision table. The study results indicate the proposed method is feasible and effective in quality evaluation. © 2021 Knowledge Systems Institute Graduate School. All rights reserved.

关键词： Image recognition

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Optimal Resource Utilization in Hyperledger Fabric: A Comprehensive SPN-Based Performance Evaluation Paradigm

Optimal Resource Utilization in Hyperledger Fabric: A Compre...

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IEEE Symposium on Network Operations and Management

作者： Carlos Melo Glauber Gonçalves Francisco A. Silva Leonel Feitosa Iure Fé André Soares Eunmi Choi Tuan Anh Nguyen Dugki Min Applied Research on Distributed Systems Lab (PASID) Federal University of Piauí (UFPI) Brazil School of Software College of Computer Science Kookmin University Seoul South Korea Konkuk Aerospace Design-Airworlhiness Research Inslilule (KADA) Konkuk Universily Seoul Soulh Korea Department of Computer Science and Engineering Konkuk University Seoul South Korea

ISBN: (数字)9798350327939

ISBN: (纸本)9798350327946

Hyperledger Fabric stands as a leading framework for permissioned block-chain systems, ensuring data security and audit-ability for enterprise applications. As applications on this platform grow, understanding its complex configuration concerning various block-chain parameters becomes vital. These configurations significantly affect the system’s performance and cost. In this research, we introduce a Stochastic Petri Net (SPN) model to analyze Hyper-ledger Fabric’s performance, considering variations in block-chain parameters, computational resources, and transaction rates. We provide case studies to validate the utility of our model, aiding block-chain administrators in determining optimal configurations for their applications. A key observation from our model highlights the block size’s role in system response time. We noted an increased mean response time, between 1 to 25 seconds, due to variations in transaction arrival rates.

关键词： Performance evaluation Distributed ledger Computational modeling Data security Petri nets Stochastic processes Fabrics

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Reinforcement Learning with Large Language Model for Hybrid Disassembly Lines in Remanufacturing Contexts

Reinforcement Learning with Large Language Model for Hybrid ...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Peng Ji Xi Wang Guo Jiacun Wang Weitian Wang Shu Jin Qin Ying Tang Qi Kang College of Information and Control Engineering Liaoning Petrochemical University Fushun China Dept of Comp. Sci. and Soft. Eng. Monmouth University W. Long Branch NJ USA Collaborative Robotics and Smart Systems Laboratory (CRoSS Lab) Montclair State University Montclair NJ USA College of Economics and Management Shangqiu Normal University Shangqiu China Electrical and Computer Engineering Rowan University Glassboro New Jersey USA Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Large language models (LLM), ChatGPT is making substantial impact across various fields. This study for the first time presents a novel approach to the hybrid disassembly line balancing problem using LLM and reinforcement learning algorithms in remanufacturing contexts. The problem is divided into two sub-stages. LLM is innovatively used to capture a disassembly sequence well in the first stage, while reinforcement learning is utilized to address the problem in the second stage. Upon comparing the performance with and without LLM, the proposed approach significantly reduces the trial-and-error space and achieves faster convergence to achieve the desired solution. Future work of this study is also discussed.

关键词： computer aided software engineering Automation Large language models Reinforcement learning Chatbots Convergence

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Improving Information Freshness via Backbone-Assisted Cooperative Access Points

arXiv

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arXiv 2022年

作者： Pan, Haoyuan Zhou, Yu Chan, Tse-Tin Tang, Ming Li, Jianqiang Du, Zhihua The College of Computer Science and Software Engineering Shenzhen University Shenzhen China The Department of Mathematics and Information Technology The Education University of Hong Kong Hong Kong The Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

Information freshness, characterized by age of information (AoI), is important for sensor applications involving timely status updates. In many cases, the wireless signals from one sensor can be received by multiple access points (APs). This paper investigates the average AoI for cooperative APs, in which they can share information through a wired backbone network. We first study a basic backbone-assisted COoperative AP (Co-AP) system where APs share only decoded packets. Experimental results on software-defined radios (SDR) indicate that Co-AP significantly improves the average AoI performance over a single-AP system. Next, we investigate an improved Co-AP system, called Soft-Co-AP. In addition to sharing decoded packets, Soft-Co-AP shares and collects soft information of packets that the APs fail to decode for further joint decoding. A critical issue in Soft-Co-AP is determining the number of quantization bits that represent the soft information (each soft bit) shared over the backbone. While more quantization bits per soft bit improves the joint decoding performance, it leads to higher backbone delay. We experimentally study the average AoI of Soft-Co-AP by evaluating the tradeoff between the backbone delay and the number of quantization bits. SDR experiments show that when the number of sensors is large, Soft-Co-AP further reduces the average AoI by 12% compared with Co-AP. Interestingly, good average AoI performance is usually achieved when the number of quantization bits per soft bit is neither too large nor too small. Copyright © 2022, The Authors. All rights reserved.

关键词： Decoding

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