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arXiv 2020年

作者： Chen, Guojun Xu, Yinfei Liu, Ling Song, Tiecheng Hu, Jing School of Information Science and Engineering Southeast University Nanjing210096 China National Mobile Communication Research Laboratory Southeast University Nanjing210096 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

Consider that lossy source coding is a very important technique in contemporary communications to provide a greater transmission rate. The rate-distortion (RD) region of lossy source coding remains elusive after decades of research. Gray and Wyner proposed the Gray-Wyner lossy source coding system in [1], shown in Fig. 1. However, the RD region was calculated and determined only for the case of some simple discrete sources with equal distortion constraints. Most of the research on Gray-Wyner lossy source coding systems focused on Common Information (CI), which is a special corner point in the RD region. The two most popular CI are Wyner’s CI and Gásc-Körner CI, presented in [2] and [3], respectively. Xu et al. provided a converse proof of Wyner’s CI, which is equal to the minimum common message rate when the total rate is arbitrarily close to the RD function under suitable conditions in [4]. Moreover, Viswanatha et al. was more interested in the achievability part of CI and studied both Wyner’s CI and Gásc-Körner CI in [5]. [6] solved the rate region by constraining the sum of the two private rates, and calculated the Wyner’s CI based on the proposed method and previous work on Wyner’s CI in the Gray-Wyner system [7], [8]. However, due to the constrains on private rates, the solution was just part of rate region of Gray-Wyner system. Our results are more general and able to calculate the whole achievable rate region. Furthermore, we solve the expression for Wyner’s CI of two correlated Gaussian sources, which differs from the method applied in [5]. Some partial results were studied including Wyner’s CI characterization for bivariate Gaussian sources in [9], and total correlation characterization for vector Gaussian sources in [10]. Besides the Gray-Wyner lossy source coding system, other multi-terminal RD problems are studied in different distributed scenarios. In [11], Xiao et al. considered the problem of centralized/distributed data compression via one/two encode

关键词： Electric distortion

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HINDBR: Heterogeneous Information Network Based Duplicate Bug Report Prediction

HINDBR: Heterogeneous Information Network Based Duplicate Bu...

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International Symposium on software Reliability engineering (ISSRE)

作者： Guanping Xiao Xiaoting Du Yulei Sui Tao Yue College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China State Key Laboratory of Novel Software Technology Nanjing University China School of Automation Science and Electrical Engineering Beihang University China School of Computer Science University of Technology Sydney Australia

ISBN: (数字)9781728198705

ISBN: (纸本)9781728198712

Duplicate bug reports often exist in bug tracking systems (BTSs). Almost all the existing approaches for automatically detecting duplicate bug reports are based on text similarity. A recent study found that such approaches may become ineffective in detecting duplicates in bug reports submitted after the just-in-time (JIT) retrieval, which is now a built-in feature of modern BTSs (e.g., Bugzilla). This is mainly because the embedded JIT feature suggests possible duplicates in a bug database when a bug reporter types in the new summary field, therefore minimizing the submission of textually similar reports. Although JIT filtering seems effective, a number of bug report duplicates remain undetected. Our hypothesis is that we can detect them using a semantic similarity-based approach. This paper presents HINDBR, a novel deep neural network (DNN) that accurately detects semantically similar duplicate bug reports using a heterogeneous information network (HIN). Instead of matching text similarity alone, HINDBR embeds semantic relations of bug reports into a low-dimensional embedding space where two duplicate bug reports represented by two vectors are close to each other in the latent space. Results show that HINDBR is effective.

关键词： Linux computer bugs Semantics Neural networks Predictive models software reliability Open source software

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A novel quantum calculus-based complex least mean square algorithm (q-CLMS)

arXiv

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arXiv 2021年

作者： Sadiq, Alishba Naseem, Imran Khan, Shujaat Moinuddin, Muhammad Togneri, Roberto Bennamoun, Mohammed Department of Electrical and Electronic Engineering Universiti Teknologi Petronas Malaysia College of Engineering Karachi Institute of Economics and Technology Korangi Creek Karachi75190 Pakistan School of Electrical Electronic and Computer Engineering The University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia Department of Electrical Electronic and Computer Engineering The University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia Daejeon Korea Republic of King Abdulaziz University Saudi Arabia Department of Computer Science and Software Engineering The University of Western Australia 35 Stirling Highway CrawleyWA6009 Australia

In this research, a novel adaptive filtering algorithm is proposed for complex domain signal processing. The proposed algorithm is based on Wirtinger calculus and is called as q-Complex Least Mean Square (q-CLMS) algorithm. The proposed algorithm could be considered as an extension of the q-LMS algorithm for the complex domain. Transient and steady-state analyses of the proposed q-CLMS algorithm are performed and exact analytical expressions for mean analysis, mean square error (MSE), excess mean square error (EMSE), mean square deviation (MSD) and misadjustment are presented. Extensive experiments have been conducted and a good match between the simulation results and theoretical findings is reported. The proposed q-CLMS algorithm is also explored for whitening applications with satisfactory performance. A modification of the proposed q-CLMS algorithm called Enhanced q-CLMS (Eq-CLMS) is also proposed. The Eq-CLMS algorithm eliminates the need for a pre-coded value of the q-parameter thereby automatically adapting to the best value. Extensive experiments are performed on system identification and channel equalization tasks and the proposed algorithm is shown to outperform several benchmark and state-of-the-art approaches namely Complex Least Mean Square (CLMS), Normalized Complex Least Mean Square (NCLMS), Variable Step Size Complex Least Mean Square (VSS-CLMS), Complex FLMS (CFLMS) and Fractional-ordered-CLMS (Fo-CLMS) algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Calculations

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A General Recurrent Tracking Framework without Real Data

A General Recurrent Tracking Framework without Real Data

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International Conference on computer Vision (ICCV)

作者： Shuai Wang Hao Sheng Yang Zhang Yubin Wu Zhang Xiong State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing P.R.China Beihang Hangzhou Innovation Institute Yuhang Beihang University Xixi Octagon City Hangzhou P.R.China College of Information Science & Technology Beijing University of Chemical Technology Beijing P.R.China

ISBN: (纸本)9781665428132

Recent progress in multi-object tracking (MOT) has shown great significance of a robust scoring mechanism for potential tracks. However, the lack of available data in MOT makes it difficult to learn a general scoring mechanism. Multiple cues including appearance, motion and etc., are limitedly utilized in current manual scoring functions. In this paper, we propose a Multiple Nodes Tracking (MNT) framework that adapts to most trackers. Based on this framework, a Recurrent Tracking Unit (RTU) is designed to score potential tracks through long-term information. In addition, we present a method of generating simulated tracking data without real data to overcome the defect of limited available data in MOT. The experiments demonstrate that our simulated tracking data is effective for training RTU and achieves state-of-the-art performance on both MOT17 and MOT16 benchmarks. Meanwhile, RTU can be flexibly plugged into classic trackers such as DeepSORT and MHT, and makes remarkable improvements as well.

关键词： Training computer vision Tracking Manuals Benchmark testing

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Research on dynamic optimal control strategy of distributed super capacitor energy storage system based on convolution neural network

Research on dynamic optimal control strategy of distributed ...

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作者： Li, Pengfei Chen, Hongyun Wang, Shangshang Xu, Zhipeng Lei, Zhenyu Department of Computer Science College of Computer Science and Electronic Engineering Hunan University Changsha410082 China Software School Beijing University of Post and Telecommunications Beijing100876 China College of Computer Science and Technology Jilin University Changchun130000 China Department of Transportation HeFei University of Technology Hefei230000 China College of Foreign Language Hunan University Changsha410000 China

Compared with other modes of transport, urban rail transit has significant advantages such as large capacity, punctual safety, energy saving, and environmental protection. For pure electric buses, the problem of slow start, large battery energy loss, short battery life, and insufficient recovery of braking energy is caused by the use of single energy only. The power battery and super capacitor are combined to form a compound power supply to solve the problem. In order to improve the feature extraction ability of convolution neural networks, a deep convolution neural network model based on continuous convolution is designed. The model uses small scale convolution kernel to extract local features more carefully, and increases the nonlinear expression ability of the model by means of two continuous coiling layers. Dropout technology is used to reduce the interdependence between neurons, and the model is optimized by suppressing network overfitting. If the battery and the supercapacitor are combined to form a dual energy source system, it can not only meet the instantaneous high power demand of the electric vehicle but also prolong the life of the battery. Increasing the mileage of electric vehicles has become the development direction of pure electric vehicles. Due to its fast charging and discharging speed, long cycle life, and friendly environment, supercapacitor has unique advantages and development prospects in solving the problem of urban rail transit regeneration failure, performance analysis, modeling, and parameter identification of ultracapacitor. The performance of ultracapacitor is analyzed, and an improved dynamic equivalent circuit model of ultracapacitor is established. Therefore, this paper studies the control strategy of dual energy source storage system and the driving range of the dual energy source pure electric vehicle, which has a broad application prospect and practical significance. © 2018 John Wiley & Sons, Ltd.

关键词： Supercapacitor

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Android malware family classification and characterization using CFG and DFG 13

Android malware family classification and characterization u...

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13th International Symposium on Theoretical Aspects of software engineering, TASE 2019

作者： Xu, Zhiwu Ren, Kerong Song, Fu College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Information Science and Technology ShanghaiTech University Shanghai China

ISBN: (纸本)9781728133423

Android malware has become a serious threat for our daily life, and thus there is a pressing need to effectively mitigate or defend against them. Recently, many approaches and tools to analyze Android malware have been proposed to protect legitimate users from the threat. However, most approaches focus on malware detection, while only a few of them consider malware classification or malware characterization. In this paper, we propose an extension of CDGDroid to classifying and characterizing Android malware families automatically. We first perform static analysis used in CDGDroid to extract control-flow graphs and data-flow graphs on the instruction level. Then we encode the graphs into matrices, and use them to build the family classification models via deep learning. For family characterization, we extract the n-gram sequences from the graphs, which are filtered according to the weights of the classification model built for the target family. And then we construct a vector space model and select the top-k sequences as a characterization of the target family. We have conducted some experiments to evaluate our approach and have identified that the family classification model taking the horizontal combination of CFG and DFG as features offers the best performance in terms of accuracy among all the models. Compared with CDGDroid, Drebin and many antivirus tools gathered in VirusTotal, our family classification model gives a better performance. Finally, We have also conducted experiments on family characterization, and the experimental results have shown that our characterization can capture the malicious behaviors of the testing families. © 2019 IEEE.

关键词： Static analysis

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Computing server power modeling in a data center: survey, taxonomy and performance evaluation

arXiv

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arXiv 2020年

作者： Ismail, Leila Materwala, Huned Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University P.O. Box 15551 Al Ain United Arab Emirates

Data centers are large scale, energy-hungry infrastructure serving the increasing computational demands as the world is becoming more connected in smart cities. The emergence of advanced technologies such as cloud-based services, internet of things (IoT) and big data analytics has augmented the growth of global data centers, leading to high energy consumption. This upsurge in energy consumption of the data centers not only incurs the issue of surging high cost (operational and maintenance) but also has an adverse effect on the environment. Dynamic power management in a data center environment requires the cognizance of the correlation between the system and hardware level performance counters and the power consumption. Power consumption modeling exhibits this correlation and is crucial in designing energy-efficient optimization strategies based on resource utilization. Several works in power modeling are proposed and used in the literature. However, these power models have been evaluated using different benchmarking applications, power measurement techniques and error calculation formula on different machines. In this work, we present a taxonomy and evaluation of 24 software-based power models using a unified environment, benchmarking applications, power measurement technique and error formula, with the aim of achieving an objective comparison. We use different servers architectures to assess the impact of heterogeneity on the models' comparison. The performance analysis of these models is elaborated in the paper. Copyright © 2020, The Authors. All rights reserved.

关键词： Green computing

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Balancing communication overhead and accuracy in compression integration: a survey

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Journal of Supercomputing 2025年第8期81卷

作者： Yang, Aiqiang Liu, Jie Yang, Bo Mo, Zeyao Li, Keqin College of Computer Science and Technology National University of Defense Technology Changsha410000 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha410073 China China Academy of Engineering Physics Beijing China Department of Computer Science State University of New York New PaltzNY12561 United States

In large-scale distributed training, communication compression techniques are widely used to reduce the significant communication overhead caused by the frequent exchange of model parameters or gradients between training nodes. However, these techniques often introduce additional computational complexity and may lead to data loss, thereby affecting model convergence and performance. This review examines key optimization methods in communication compression, including pruning techniques that remove irrelevant weights, quantization techniques that convert floating-point parameters to low-precision representations, and sparsification techniques that transmit only critical gradients. Low-rank approximation techniques, which compress parameters through matrix factorization, are particularly useful for large-scale models. These techniques have also been widely applied in various application scenarios, demonstrating their effectiveness in different environments. Application scenarios include distributed training, federated learning, and edge computing, where bottlenecks are carefully identified and evaluated in common scenarios, providing a basis for further optimization. Future development directions emphasize co-design of hardware and algorithms, dynamic strategies, and cross-layer optimization. This study provides valuable comparisons of key methods and theoretical analysis for efficient communication compression in distributed systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Edge computing

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A new method of vision-based seat belt detection

A new method of vision-based seat belt detection

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作者： Yang, Zhongming Xiong, Hui Cai, Zhaoquan Peng, Yu College of Computer Engineering Technical Guangdong Polytechnic of Science and Technology Zhuhai Guangdong China School of Software Engineering South China University of Technology Guangzhou Guangdong China Science and Technology Department Huizhou University Huizhou Guangdong China

In the traffic management system, it can greatly improve the management efficiency if the algorithm that monitors automatically detects whether the driver fastens the seat belt;however, currently prevalent detecting methods cannot achieve satisfactory results in aspects of the detecting rate, the image quality requirement and the colour difference between seat belt and the surrounding environment. We propose a method of seat belt detection based on visual positioning. The algorithm locates the window according to the licence plate position and the contour statistics obtained from the gradient. The face detection is used to adjust and determine the seat belt detection area in the window. Finally, the method of seat belt detection based on the connected area is used to detect whether the seat belt is fastened. Experiments show that the successful rate of the proposed method is much higher than other existing methods, and satisfactory results are obtained. Copyright © 2019 Inderscience Enterprises Ltd.

关键词： Face recognition

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Risk and effective analysis of COVID-19 vaccine using mathematical models in the pandemic situation

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AIP Conference Proceedings 2022年第1期2516卷

作者： N. Balaji N. B. Sanjana E. P. Siva R. Lokesh A. J. Catherina 1Department of Mathematics College of Engineering SRM Institute of Science & Technology Kattankulathur 603 203 Chennai India 2Department of Computer science(software) College of Engineering SRM Institute of Science & Technology Kattankulathur 603 203 Chennai India 3College of Management SRM Institute of Science & Technology Kattankulathur 603 203 Chennai India

In the present situation COVID-19 global pandemic, an overall world got embrace with covid-19 virus. Moreover every country went under quarantine and firm mitigation measures by temporary shutdown in people crowded place like schools, restaurants, theatres and domestic and international transport facilities[1]. So government made these measures to be effective to support and compliance of public[2]. Advanced medical technology put up with new vaccines to control infection of virus. Vaccination is one of the most effective step to control the virus from affecting the entire population. This paper aims to explore the optimal effectiveness of Vaccination for novel COVID-19 non clinical approach. We need smart solutions and decisions to mitigate the coronavirus`s impact. We formulate the mathematical model to Analyse the vaccination effectiveness[3]. The objective function is designed to reduce both awareness of vaccination and effectiveness of vaccination using WHO data/ Indian dashboard data[4]. The degree of protection As a part we can analyse the risk factors of immunization using multivariate models using statistical analysis.

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