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CA-DTS:A Distributed and Collaborative Task Scheduling Algorithm for Edge Computing Enabled Intelligent Road Network

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Journal of computer Science & technology 2023年第5期38卷 1113-1131页

作者：胡世红罗渠元李光辉施巍松叶保留 Key Laboratory of Water Big Data Technology of Ministry of Water Resources Houhai UniversityNanjing 210098China School of Computer and Information Houhai UniversityNanjing 210098China School of Information Science and Technology Southwest Jiaotong UniversityChengdu 611756China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuci 214122China Department of Computer and Information Sciences University of DelawareNewarkDE 19716U.S.A National Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210093China

Edge computing enabled Intelligent Road Network(EC-IRN)provides powerful and convenient computing services for vehicles and roadside sensing *** continuous emergence of transportation applications has caused a huge burden on roadside units(RSUs)equipped with edge servers in the Intelligent Road Network(IRN).Collaborative task scheduling among RSUs is an effective way to solve this ***,it is challenging to achieve collaborative scheduling among different RSUs in a completely decentralized *** this paper,we first model the interactions involved in task scheduling among distributed RSUs as a Markov *** that multi-agent deep reinforcement learning(MADRL)is a promising approach for the Markov game in decision optimization,we propose a collaborative task scheduling algorithm based on MADRL for EC-IRN,named CA-DTS,aiming to minimize the long-term average delay of *** reduce the training costs caused by trial-and-error,CA-DTS specially designs a reward function and utilizes the distributed deployment and collective training architecture of counterfactual multi-agent policy gradient(COMA).To improve the stability of performance in large-scale environments,CA-DTS takes advantage of the action semantics network(ASN)to facilitate cooperation among multiple *** evaluation results of both the testbed and simulation demonstrate the effectiveness of our proposed *** with the baselines,CA-DTS can achieve convergence about 35%faster,and obtain average task delay that is lower by approximately 9.4%,9.8%,and 6.7%,in different scenarios with varying numbers of RSUs,service types,and task arrival rates,respectively.

关键词： edge computing deep reinforcement learning task scheduling vehicular edge computing

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Community Smell Occurrence Prediction on Multi-Granularity by Developer-Oriented Features and Process Metrics

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Journal of computer Science & technology 2022年第1期37卷 182-206页

作者： Zi-Jie Huang Zhi-Qing Shao Gui-Sheng Fan Hui-Qun Yu Xing-Guang Yang Kang Yang Department of Computer Science and Engineering East China University of Science and TechnologyShanghai 200237China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai 200237China Shanghai Engineering Research Center of Smart Energy Shanghai 200237China

Community smells are sub-optimal developer community structures that hinder *** studies performed smell prediction and provided refactoring guidelines from a top-down aspect to help community ***,refactoring smells also requires bottom-up effort from every ***,supportive measures and guidelines for them are not available at a fine-grained *** recent work revealed developers'personalities and working states could influence community smells'emergence and variation,we build prediction models with experience,sentiment,and development process features of developers considering three smells including Organizational Silo,Lone Wolf,and Bottleneck,as well as two related classes including smelly developer and smelly *** predict the five classes in the individual granularity,and we also generate forecasts for the number of smelly developers in the community *** proposed models achieve F-measures ranging from 0.73 to 0.92 in individual-wide within-project,time-wise,and cross-project prediction,and mean R2 performance of 0.68 in community-wide Smelly Developer *** also exploit SHAP(SHapley Additive exPlanations)to assess feature importance to explain our *** conclusion,we suggest developers with heavy workload should foster more frequent communication in a straightforward and polite way to build healthier communities,and we recommend community shepherds to use the forecasting model for refactoring planning.

关键词： community smell developer sentiment socio-technical analysis empirical software engineering

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Lake Water Level Forecasting Using LSTM and GRU: A Deep Learning Approach 9th

Lake Water Level Forecasting Using LSTM and GRU: A Deep Lear...

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9th Future Technologies Conference, FTC 2024

作者： Du, Yuxin Fan, Jing Happonen, Ari Paulraj, Dassan Tuape, Micheal Department of Software Engineering School of Engineering Sciences Lappeenranta-Lahti University of Technology Mukkulankatu 19 Lahti15210 Finland Department of Software Engineering School of Engineering Sciences Lappeenranta-Lahti University of Technology Yliopistonkatu 34 Lappeenranta53850 Finland Department of Computer Science and Engineering R.M.K. Engineering College Chennai India

ISBN: (纸本)9783031731242

Artificial Intelligence and Deep Learning-based methods show constant promise in addressing time series forecasting challenges. Lake water level forecasting is an essential & significant environmental and societal impact related time series forecasting problem, with climate change connections. Lakes’ diverse hydrological characteristics make high-performance forecasting models to be lake-specific. Thus, comprehensive experiments are necessary to develop accurate deep learning-based forecasting models. We propose an approach for effective and efficient systematic search conduction for high-performance Deep Learning forecasting models. The method is applicable across various time series forecasting challenges. The research was structured around three experimental groups, each focusing on predicting the water levels of Lake Vesijärvi in Lahti, Finland, over periods of 1 day, 3 days, and 7 days, respectively with Long Short-Term Memory and Gated Recurrent Unit. The results are highly promising. All models achieved a Nash-Sutcliffe Efficiency above 0.95 and a Root Mean Squared Error below 0.025. The best-performing model achieved a Nash-Sutcliffe Efficiency above 0.99 and a Root Mean Squared Error below 0.0011. All evaluation metrics were calculated from testing data without signs of overfitting. This research provides insights into Deep Learning-based time series forecasting and a replicable method to conduct such studies effectively. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Lakes

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A Self-play and Sentiment-Emphasized Comment Integration Framework Based on Deep Q-Learning in a Crowdsourcing Scenario : Extended Abstract 39

A Self-play and Sentiment-Emphasized Comment Integration Fra...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Rong, Huan Sheng, Victor S. Ma, Tinghuai Zhou, Yang Al-Rodhaan, Mznah Nanjing University of Information Science & Technology School of Artificial Intelligence Nanjing China Texas Tech University Department of Computer Science Lubbock United States Nanjing University of Information Science & Technology School of Computer & Software Nanjing China Auburn University Department of Computer Science & Software Engineering Auburn United States King Saud University College of Computer & Information Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798350322279

Crowdsourcing is a sourcing model where individuals or organizations obtain goods and services from a large, relatively open and often rapidly evolving group of internet users. The most common way that crowdsourcing can facilitate machine learning is to annotate instances with labels [1]. However, the same instance may have inconsistent class labels, in the eyes of various annotators. Therefore, current efforts in crowdsourcing mainly focus on the truth inference or label integration, to remove inconsistent labels or to alleviate biased labeling. In turn, instances with the integrated labels could facilitate the training on machine learning models. The future direction of crowdsourcing is to apply more fine-grained truth inference methods to different application domains [2]. Consequently, we evolve toward another challenging problem of comment integration. That is, how can we integrate or summarize the core opinions of multiple product comments obtained from users, rather than the discrete labels. © 2023 IEEE.

关键词： Crowdsourcing

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A Stealthy Backdoor Attack Method Based on Generative Adversarial Networks 5

A Stealthy Backdoor Attack Method Based on Generative Advers...

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5th Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2024

作者： Tan, Yayue Yu, Gao Zhang, Yaqi Tang, Yongqing Southwest University College of Computer and Information Science School of Software Chongqing China Payment core platform Ant Group Co. Ltd Department of Payment Platform Technology Chengdu China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350374407

Deep neural networks have been widely and successfully applied to many critical tasks, such as image pattern recognition, natural language processing and autonomous driving, and large amounts of training data and increasing computational power are key factors in their success. Many studies in recent years have shown that deep neural network models face many practical security threats, such as adversarial attacks and backdoor attacks, which can have very serious consequences in the real world. Aiming at the problems of stealth, backdoor trigger design and universality of current backdoor attack methods, a novel method of invisible backdoor attack based on generative adversarial network is proposed. As a backdoor attack method based on data poisoning, inspired by image steganography, this method trains an encoder to generate poisoned samples through generative adversarial network to implement backdoor attack. The secret image used to guide the generation of backdoor triggers does not need to be carefully designed, and the encoder has good generalization ability across datasets. These features not only make its attack effective and stealthy, but also greatly reduce the cost of backdoor. Extensive experimental and analytical results show that the proposed backdoor attack method is effective, stealthy, sustainable and universal. © 2024 IEEE.

关键词： Generative adversarial networks

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Performance Analysis of Lightweight Ciphers for securing images

Performance Analysis of Lightweight Ciphers for securing ima...

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2023 IEEE International Conference on Modelling, Simulation and Intelligent Computing, MoSICom 2023

作者： Nekkanti, Keerthan Sumanth, Lade Sai Renuka, A. Supriya, Musica Kumar, Arun Manipal Institute of Technology Manipal Academy of Higher Education Department of Computer Science and Engineering Karnataka Manipal India YooBee College of Creative Innovation Department of Computer and Software Engineering Auckland New Zealand

ISBN: (纸本)9798350393415

In the digital age, the security of sensitive information and data has become a paramount concern. The modern conventional encryption algorithms are very efficient, but they are very complex and need a lot of resources. This has led to the development and implementation of lightweight ciphers designed specifically for image encryption. Four algorithms namely Present, Lightweight encryption Algorithm (LEA), Twine and Secure IoT(SIT) are implemented to encrypt and decrypt images and their performance is analyzed. The correlation values of the images before encryption is found to be around 0.9 showing strong correlation whereas after encryption it is found to be very less in the range oof 0.0005 which clearly shows the randomization of the pixels and hence good security. The execution times was less than one second for LEA algorithm and hence efficient for multimedia applications. © 2023 IEEE.

关键词： Cryptography

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Multi-Scale Fusion and Decomposition Network for Single Image Deraining

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IEEE Transactions on Image Processing 2024年 33卷 191-204页

作者： Wang, Qiong Jiang, Kui Wang, Zheng Ren, Wenqi Zhang, Jianhui Lin, Chia-Wen Wuhan University National Engineering Research Center for Multimedia Software School of Computer Science Wuhan430072 China Sun Yat-sen University School of Cyber Science and Technology Guangzhou510275 China Hangzhou Dianzi University College of Computer Science and Technology Hangzhou310018 China National Tsing Hua University Department of Electrical Engineering The Institute of Communications Engineering Hsinchu30013 Taiwan

Convolutional neural networks (CNNs) and self-attention (SA) have demonstrated remarkable success in low-level vision tasks, such as image super-resolution, deraining, and dehazing. The former excels in acquiring local connections with translation equivariance, while the latter is better at capturing long-range dependencies. However, both CNNs and Transformers suffer from individual limitations, such as limited receptive field and weak diversity representation of CNNs during low efficiency and weak local relation learning of SA. To this end, we propose a multi-scale fusion and decomposition network (MFDNet) for rain perturbation removal, which unifies the merits of these two architectures while maintaining both effectiveness and efficiency. To achieve the decomposition and association of rain and rain-free features, we introduce an asymmetrical scheme designed as a dual-path mutual representation network that enables iterative refinement. Additionally, we incorporate high-efficiency convolutions throughout the network and use resolution rescaling to balance computational complexity with performance. Comprehensive evaluations show that the proposed approach outperforms most of the latest SOTA deraining methods and is versatile and robust in various image restoration tasks, including underwater image enhancement, image dehazing, and low-light image enhancement. The source codes and pretrained models are available at https://***/qwangg/MFDNet. © 1992-2012 IEEE.

关键词： Image reconstruction

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RoSe: Rotation-Invariant Sequence-Aware Consensus for Robust Correspondence Pruning 24

RoSe: Rotation-Invariant Sequence-Aware Consensus for Robust...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Yizhang Zhou, Weiwei Li, Yanping Zhao, Shengjie School of Software Engineering Tongji University Shanghai China College of Computer and Data Science Fuzhou University Fuzhou China Department of Computer Science and Technology Tongji University Shanghai China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai China

ISBN: (纸本)9798400706868

Correspondence pruning has recently drawn considerable attention as a crucial step in image matching. Existing methods typically achieve this by constructing neighborhoods for each feature point and imposing neighborhood consistency. However, the nearest-neighbor matching strategy often results in numerous many-to-one correspondences, thereby reducing the reliability of neighborhood information. Furthermore, the smoothness constraint fails in cases of large-scale rotations, leading to misjudgments. To address the above issues, this paper proposes a novel robust correspondence pruning method termed RoSe, which is based on rotation-invariant sequence-aware consensus. We formulate the correspondence pruning problem as a mathematical optimization problem and derive a closed-form solution. Specifically, we devise a rectified local neighborhood construction strategy that effectively enlarges the distribution between inliers and outliers. Meanwhile, to accommodate large-scale rotation, we propose a relative sequence-aware consistency as an alternative to existing smoothness constraints, which can better characterize the topological structure of inliers. Experimental results on image matching and registration tasks demonstrate the effectiveness of our method. Robustness analysis involving diverse feature descriptors and varying rotation degrees further showcases the efficacy of our method. © 2024 ACM.

关键词： correspondence pruning

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MESH-POLYPDET: SURFACE MESHES BASED POLYP DETECTION IN VIRTUAL COLONOSCOPY 18

MESH-POLYPDET: SURFACE MESHES BASED POLYP DETECTION IN VIRTU...

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18th International Conference on Interfaces and Human computer Interaction 2024, IHCI 2024, the 17th International Conference on Game and Entertainment Technologies 2024, GET 2024 and the 18th International Conference on computer Graphics, Visualization, computer Vision and Image Processing 2024, CGVCVIP 2024, Part of the 18th Multi Conference on computer Science and Information Systems 2024, MCCSIS 2024

作者： Ma, Ming Sun, Chenyang Lei, Na Department of Graduate Computer Science and Engineering Katz School of Science and Health Yeshiva University United States School of Software Technology Dalian University of Technology China International School of Information Science & Engineering Dalian University of Technology China

ISBN: (纸本)9789898704580

Colorectal polyps can evolve into colon cancer over time. Early screening or detection of colon polyps using computer-aided detection (CAD) techniques along with removal of the polyps can lower the risk of colon cancer. As an alternative to optimal colonoscopy (OC), virtual colonoscopy (VC) has the advantage of non-invasiveness in the detection of colon polyps. Recent decades have witnessed rapid advancement in VC based polyp detection and false positive (FP) reduction techniques using machine learning or deep learning methods. However, the majority of existing methods are based on the computation using computed tomography (CT) images of patient data, which are in some cases very computationally expensive, particularly in the scenario of necessity of large dataset for model training. In this paper, we propose a novel automatic polyp detection method termed Mesh-PolypDet using the surface meshes of colorectal data. The initial detection of polyp candidates is performed on 3D colon surface meshes, and the subsequent FP reduction is carried out using the visual transformer and conformal mapping. Experiments are conducted to evaluate the performance of the proposed method in comparison with other existing methods. © 2024 Interfaces and Human computer Interaction. All Rights Reserved.

关键词： Conformal mapping

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Road Traffic Accident Pattern Analysis with Data Mining 12

Road Traffic Accident Pattern Analysis with Data Mining

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12th International Conference on Awareness Science and technology, iCAST 2023

作者： Zhe Chai, Albe Bing Lau, Bee Theng Tsun Tee, Mark Kit McCarthy, Chris Faculty of Engineering Computing & Science Swinburne University of Technology Sarawak Campus Kuching Malaysia Department of Computer Science & Software Engineering Swinburne University of Technology Melbourne Australia

ISBN: (纸本)9798350324693

The discovery of Road Traffic Accident (RTA) patterns is vital to formulate mitigation strategies based on the characteristics of RTA. Various studies have applied association rule mining for RTA pattern discovery. However, limited research has conducted a comparative analysis of RTA patterns in different regions. This work is necessary to identify the similarities and differences of RTA characteristics between different regions, especially the developed and developing countries with different RTA trends in fatality rates. This work explored the application of association rule mining (FP-Growth algorithm) to discover and compare the RTA patterns for developed and developing countries. The Orange Data Mining software is used to discover RTA patterns for Ethiopia (12,317 samples) and the UK (United Kingdom) (1,048,575 samples), representing developing and developed countries. Eventually, findings showed similarities and differences in RTA patterns between Ethiopia and the UK. Road characteristics and conditions are the common factors contributing to RTA severity levels, while other crucial factors include the light condition and speed limit. In conclusion, the discovery of RTA patterns in different regions is beneficial to assist in formulating strategies to reduce the risk of RTA. Future work is necessary to discover the RTA patterns from different perspectives, such as seasonal or periodical variations of RTA patterns. © 2023 IEEE.

关键词： Association rules

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