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Container-based data-intensive application scheduling in hybrid cloud-edge collaborative environment

Container-based data-intensive application scheduling in hyb...

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作者： Tang, Bing Luo, Jincheng Zhang, Jiaming School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan University of Science and Technology Hunan Xiangtan China School of Information and Communication Engineering Guangzhou Maritime University Guangdong Guangzhou China School of Information Engineering Wuhan University of Technology Hubei Wuhan China

Container virtualization technology represented by Docker has been widely used in the industry due to its advantages of lightweight, fast deployment, and easy portability. It can bring convenience to system deployment, operation, and maintenance. This article considers the implementation of AI-based IoT applications based on Docker and container technology in a cloud-edge collaborative environment. Three typical data-intensive applications, preprocessing, training, and inference in machine learning, are deployed using Docker containers. For these three types of tasks, this article proposes a container-based data-intensive application scheduling framework. Using an attribute-based data-driven method, a new scheduling approach considering multiple weighting factors is proposed, which is called DICS-OPT. The cloud nodes and edge nodes are scored and sorted uniformly. The container is scheduled to the node with the highest score, and then the task data is transmitted to the node to execute the task. NSGA-III-based genetic algorithm has been proposed to search for the optimal weighting factors. This article introduces the implementation of the prototype system and builds a cloud-edge collaborative testbed consisting of Raspberry Pis and PCs. The performance evaluation results indicates that the proposed scheduling approach outperforms existing approaches. Compared with existing approaches, DICS-OPT improves the average edge resource utilization by 10.05% to 69.04%, and saves the cloud resource cost by 16.02% to 36.68%. © 2023 John Wiley & Sons Ltd.

关键词： Edge computing

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Attack against UAV Infrared Object Detection Based on Gradient Descent Method 7

Attack against UAV Infrared Object Detection Based on Gradie...

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7th IEEE International Conference on Information Systems and computer Aided Education, ICISCAE 2024

作者： Xu, Mingkang Chen, keyu Xu, Haoli Yang, Xing Lu, Jun Qu, Bin Gao, Haoqi Hu, Zhiyang Wu, Mengjiang Chen, Jun State Key Laboratory of Pulsed Power Laser College of Electronic Engineering National University of Defense Technology Hefei230037 China National University of Defense Technology Hefei China Jianghuai Advance Technology Center Hefei230000 China Key Laboratory of Electronic Restriction of Anhui Province Hefei230037 China College of Computer Science and Software Enginering Hohai University Nanjing211100 China

ISBN: (纸本)9798350350760

At present, infrared intelligent detection technology has been widely used in various fields. Due to the development of its technology, how to interfere with important targets such as intelligent recognition of vehicles has become a hot issue in the current technical field. Therefore, this study is based on the near-infrared band. We generate vehicle target confrontation samples to interfere with infrared detector devices. This paper proposes to first collect the single-band image of the protection target through the infrared UAV, and aligns it to align the position of the protection target in the single-band image to obtain the salient features of the protection target. Then, based on the loss function calculated by machine learning method, the adversarial samples of single-band images are generated by using the basic iterative method. The constraint function is used to jointly optimize the loss function, so that the attack of the adversarial samples on the single-band detection system of the infrared UAV can reach the target value, and the adversarial samples of the infrared image in this state are obtained. Then, it is arranged in multiple positions on the target vehicle to complete the interference to the infrared detection system. We first need to complete the simulation attack experiment in the digital domain, and then complete the confrontation with the infrared detector by using a special material that meets the requirements in the physical domain. The experimental results show that the attack success rate of this method has a certain effect. This paper has a certain reference for the future research of vehicle infrared reconnaissance and anti-reconnaissance. ©2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Deep Learning Object Detection 7th

Deep Learning Object Detection

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Liu, Jingnian Huang, Weihong Xiao, Lijun Huo, Yingzi Xiong, Huixuan Li, Xiong Xiao, Weidong 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 Guangdong Financial High-Tech Zone "Blockchain +" Fintech Research Institute Foshan528253 China School of Computer Science and Engineering University of Electronic Science and Technology of China Xiangtan411201 China School of Software Engineering Xiamen University of Technology Xiamen361024 China

ISBN: (纸本)9783031281235

Object detection techniques are a major part of computer vision research, with large-scale applications in industrial, scientific and other scenarios. Technologies such as face detection, medical image detection, autonomous driving, and traffic detection have played a significant role in people’s lives. With the rapid development of deep learning, many application areas, such as image classification, text classification, machine translation, etc., have achieved breakthrough success in combination with deep learning. R-CNN brings object detection into the era of deep learning, and its advantage compared with traditional methods is that the former requires personnel to extract features manually, while the latter uses deep learning to extract features automatically, which greatly improves efficiency, simplifies operation, and opens a new era of object detection research. First, this paper provides an overview of deep learning-based object detection backbone networks, reviews and analyzes milestone object detection algorithms, compares commonly used datasets, summarizes applications, and finally concludes the paper. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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Exploring the Impact of Drivers’ Emotion and Multi-task Learning on Takeover Behavior Prediction in Multimodal Environment Exploring Takeover Behavior Prediction in Multimodal Environment

Exploring the Impact of Drivers’ Emotion and Multi-task Lea...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Feng, Xinyu Gu, Yu Lin, Yuming Cai, Yaojun Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guangxi Guilin China School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Province Chengdu China Guangxi Key Laboratory of Digital Infrastructure Guangxi Zhuang Autonomous Region Information Center Guangxi Nanning China

ISBN: (纸本)9798400713958

With the development of autonomous driving technology, drivers may be immersed in non-driving related tasks. Therefore, takeover safety has received increasing attention. Previous studies have focused on designing the empirical analysis to formulate preventive measures or constructing methods for a single-task learning strategy. However, due to the persistent gaps between modalities and ignoring the correlation between tasks, they make it difficult to capture similar information between modalities. In this paper, we propose the Multi-TBP, a general deep learning framework for takeover behavior prediction. Experiments show that our method achieves the best performance on all tasks. Through ablation studies and visualization, we find that each modality’s importance varies from each task, and there are also correlations between tasks. © 2025 Copyright held by the owner/author(s).

关键词： Automated driving Emotion Multi-task learning Multimodal fusion Takeover behavior

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Dual-Domain Multi-Model GAN Fingerprint Restoration for Compressed Fake Face Attribution

Dual-Domain Multi-Model GAN Fingerprint Restoration for Comp...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Chengxiang Fan Aohong Shen Zhen Han Cai Tong Zhongyuan Wang Dekang Yi National Engineering Research Center for Multimedia Software and Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan University Wuhan China

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Recent advances in GAN fingerprint have shown increasing success in fake face attribution. However, the fake faces are usually compressed during network transmission, which causes the degradation of GAN fingerprint and the decrease of attribution accuracy. To this issue, a dual-domain multi-model GAN fingerprint restoration method for compressed fake face attribution is proposed in this paper. Firstly, considering that image-domain and fingerprint-domain are directly and indirectly affected by compression respectively, we propose a dual-domain parallel restoration architecture that enhances GAN fingerprint using direct image-domain and indirect fingerprint-domain restoration, thereby improving attribution performance by mining the cross-domain complementarity. Secondly, since real and fake GAN-speciffc restoration models can describe GAN fingerprint from different aspects, we first enhance GAN fingerprint by multiple restoration models, and then improve attribution performance by exploiting the cross-model complementarity through the multi-model restoration fusion strategy. Experiments demonstrate the superiority of our method under different compression qualities.

关键词： Degradation Image coding Accuracy Fingerprint recognition Generative adversarial networks Image restoration Faces

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Adaptively feature matching via joint transformational-spatial clustering

Adaptively feature matching via joint transformational-spati...

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作者： Wang, Linbo Tan, Li Fang, Xianyong Guo, Yanwen Wan, Shaohua MOE Key Laboratory of Intelligent Computing and Signal Processing School of Computer Science and Technology Anhui University Hefei China National Key Lab for Novel Software Technology Nanjing University Nanjing China School of Information and Safety Engineering Zhongnan University of Economics and Law Wuhan China

The transformational and spatial proximities are important cues for identifying inliers from an appearance based match set because correct matches generally stay close in input images and share similar local transformations. However, most existing approaches only check one type of them or both types consecutively with manually set thresholds, and thus their matching accuracy and flexibility in handling large-scale images are limited. In this paper, we present an efficient clustering based approach to identify match inliers with both proximities simultaneously. It first projects the putative matches into a joint transformational-spatial space, where mismatches tend to scatter all around while correct matches gather together. A mode-seeking process based on joint kernel density estimation is then proposed to obtain significant clusters in the joint space, where each cluster contains matches mapping the same object across images with high accuracy. Moreover, kernel bandwidths for measuring match proximities are adaptively set during density estimation, which enhances its applicability for matching different images. Experiments on three standard datasets show that the proposed approach delivers superior performance on a variety of feature matching tasks, including multi-object matching, duplicate object matching and object retrieval. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Clustering Density estimation Feature matching Mode-seeking

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Test Generation for Mutation Testing by Symbolic Execution 23

Test Generation for Mutation Testing by Symbolic Execution

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23rd IEEE International Conference on software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Su, Rui Zhang, Zhiyi Zhou, Yuqian Yao, Yongming Collage of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China Nanjing University of Aeronautics and Astronautics Ministry Key Laboratory for Safety-Critical Software Development and Verification Nanjing China Army Engineering of PLA Nanjing China

ISBN: (纸本)9798350359398

Mutation testing is a crucial technique to assess the effectiveness of test suites, but it can be computationally expensive. Symbolic execution is a formal technique that generates high-coverage test cases, but its scalability is limited. In this paper, we present a novel approach to mutation testing that utilizes symbolic execution, which explores only program regions impacted by mutations, prioritizes paths containing mutant branches, and generates test cases aimed at mutations. We implemented our approach in MutSym and evaluated it on real-world programs, demonstrating its effectiveness and efficiency. Our approach enhances the quality and reliability of software systems by detecting and eliminating faults early in the development process. © 2023 IEEE.

关键词： software testing

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Permutation Invariant Training for Paraphrase Identification 48

Permutation Invariant Training for Paraphrase Identification

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Bai, Jun Yin, Chuantao Hong, Hanhua Zhang, Jianfei Li, Chen Wang, Yanmeng Rong, Wenge Beihang University State Key Laboratory of Software Development Environment Beijing China Beihang University School of Computer Science and Engineering Beijing China Beihang University Sino-French Engineer School Beijing China Ping An Technology Beijing China

ISBN: (纸本)9781728163277

Identifying sentences sharing similar meanings is crucial to speech and text understandings. Although currently popular cross-encoder solutions with pre-trained language models as backbone have achieved remarkable performance, they suffer from the lack of the permutation invariance or symmetry that is one of the most important inductive biases to such task. To alleviate this issue, in this research we propose a permutation invariant training framework, in which a symmetry regularization is introduced during training that forces the model to produce the same predictions for input sentence pairs in both forward and backward directions. Empirical studies exhibit improved performance over competitive baselines. © 2023 IEEE.

关键词： Paraphrase Identification Permutation Invariance Symmetry Regularization

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Emergency Transportation and Structural Prediction and Optimization of Goods Based on ARIMA and Multi-objective E-commerce Logistics Network Model 1

Emergency Transportation and Structural Prediction and Optim...

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1st IEEE International Conference on Applied Intelligence and Sustainable Computing, ICAISC 2023

作者： Nie, Yiyang Chen, Xiang Wu, Xuanli Deng, Zihao 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 Hunan University of Science and Technology Shcool of Mathematics and Computational Science Xiangtan China

ISBN: (纸本)9798350323795

With the development of the internet, online shopping has gradually become a popular way of shopping among the general public, which in turn poses a greater challenge to the e-commerce logistics network. Predicting the volume of goods for each logistics site and route, and establishing a model for quickly adjusting the network in response to sudden situations can save logistics network costs and improve its efficiency. Therefore, this article has done the following work: first, it establishes an effective logistics network volume prediction model;second, for the two scenarios of network structure that cannot be modified and can be modified, it provides a model that can effectively guide the adjustment of the volume of goods on each route when a logistics site is closed;finally, it establishes a logistics network evaluation model to evaluate the importance of logistics sites and routes. © 2023 IEEE.

关键词： Multiobjective optimization

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BACON: Boundary-guided Polypharmacy Side Effect Prediction via Integrating Molecular Structures and Biochemical Information

BACON: Boundary-guided Polypharmacy Side Effect Prediction v...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Yike Ma, Huifang Gao, Zihao Li, Zhixin Chang, Liang Northwest Normal University College of Computer Science and Engineering Lanzhou China Ministry of Education Guangxi Normal University Key Lab of Education Blockchain and Intelligent Technology Guangxi Guilin China Guilin University of Electronic Technology Guangxi Key Laboratory of Trusted Software Guangxi Guilin China

ISBN: (纸本)9798350337488

Polypharmacy side effect prediction is a vital task in healthcare machine learning, aiming to predict the occurrence of multiple side effects when patients take drugs together. Existing researches mainly follow the deep encoder-decoder paradigm and suffer from the following two limitations: 1) The encoder simply combines inadequate information about drugs;2) The decoder fails to capture dependencies among different side effects, thus hindering accurate prediction. To overcome the aforementioned limitations, we propose a boundary-guided polypharmacy side effect prediction method (BACON). Our framework constructs two complementary views based on drug's chemical substructure and biochemical features and enhances drug representations via contrastive learning. The decoder incorporates a boundary-guided strategy to capture drug interaction dependencies for optimizing polypharmacy side effect prediction. Experimental results demonstrate BACON superiority over SOTA models in accurately predicting drug side effect events. © 2023 IEEE.

关键词： Boundary-guided Strategy Dual-view Contrastive Learning Polypharmacy Side Effect Prediction

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