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7th International Conference on Image, Vision and Computing, ICIVC 2022

作者： Xu, Baolei Li, Xiaojie Shi, Canghong Sichuan University of Arts and Sciences Key Laboratory of Exploitation and Study of Distinctive Plants in Education Department of Sichuan Province Dazhou China Chengdu University of Information Technology School of Computer Science Chengdu China Xihua University School of Computer Science and Software Engineering Chengdu China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

Traditional neural fiber classification methods are generally difficult to balance classification accuracy and algorithm complexity, and there is a problem of relying on manual intervention, mainly including manual drawing of region of interest (ROI) and manual screening of target fiber bundles, resulting in uncertainty of classification results and large data error. To reduce manual intervention, balance accuracy and algorithm complexity, a simple and effective BP neural network model combined with fast image processing algorithm is proposed. Based on the neural fiber map information, this method uses the fiber coordinate information to train the classification network structure and realize the online fiber classification. While improving the classification accuracy, it also reduces the complexity of the algorithm and does not need to know the distribution of fibers in advance. Compared with several typical classification algorithms, BP neural network model has strong robustness and can effectively prevent over-fitting. The experimental results show that the method performs well in several classifiers, and the classification accuracy is optimal. The experimental training accuracy is 0.9824, and the test accuracy is 0.9774. © 2022 IEEE.

关键词： Neural networks

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AI-Based Child Care Parental Control system

AI-Based Child Care Parental Control System

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Advancements in Computing (ICAC), International Conference on

作者： Udara Jayasekara Hansindu Maniyangama Kalhan Vithana Tharana Weerasinghe Janaka Wijekoon Rrubaa Panchendrarajan Department of Computer Science and Software Engineering Sri Lanka Institute of Information Technology Sri Lanka Department of Computer System Engineering Sri Lanka Institute of Information Technology Sri Lanka

ISBN: (纸本)9798350398106

Due to the prevalence of the COVID-19 epidemic around the globe, children were compelled to engage in remote learning through online platforms, hence mobile phone has become one of their predominant devices. Mobile device with Internet access offers a major outlet for education, entertainment, and social connection, but this combination can lead to several significant bad sequences such as online exploitation, harmful addictions, and other negative impacts of online social networking. To address harmful effects, parental controls are becoming more crucial, yet Sri Lankan parents are less aware of this. Consequently, this study proposes a parental control system to monitor their child’s activities. Android, Microsoft Azure, Java, Python, OpenCV, MySQL, and FastAPI are among the most prominent technologies utilized in the proposed application’s development. The suggested approach focuses primarily on the Sri Lankan context and aims to enhance parental digital literacy while safeguarding children from cyber threats. Yielded results showed the proposed mobile application for the identification of toxic words, drugs & alcohol content, game character images, and Instagram Sinhala comments severity as 94%, 95%, 97%, and 55% respectively in controlled experiments.

关键词： Pediatrics Social networking (online) Multimedia Web sites Process control Control systems Real-time systems Mobile applications

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Efficient Routing Protection Algorithm Based on Optimized Network Topology

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computers, Materials & Continua 2022年第9期72卷 4525-4540页

作者： Haijun Geng Zikun Jin Jiangyuan Yao Han Zhang Zhiguo Hu Bo Yang Yingije Guo Wei Wang Qidong Zhang Guoao Duan School of Computer and Information Technology Shanxi UniversityShanxi030006China School of Automation and Software Engineering Shanxi UniversityShanxi030006China School of Computer Science and Technology Hainan UniversityHaikou570228China Department of Computer Science&Technology Tsinghua UniversityBeijing100084China Department of Urban&Regional Planning San JoséState University San JoséCA95192USA Key Laboratory of Embedded System and Service Computing(Tongji University) Ministry of EducationShanghai201804China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu610054China Beijinng University of Technology Beijing-Dublin International CollegeBeijing University of TechnologyBeijing100124China

Network failures are unavoidable and occur *** the network fails,intra-domain routing protocols deploying on the Internet need to undergo a long convergence *** this period,a large number of messages are discarded,which results in a decline in the user experience and severely affects the quality of service of Internet Service Providers(ISP).Therefore,improving the availability of intra-domain routing is a trending research question to be *** usually employs routing protection algorithms to improve intra-domain routing ***,existing routing protection schemes compute as many backup paths as possible to reduce message loss due to network failures,which increases the cost of the network and impedes the methods deployed in *** address the issues,this study proposes an efficient routing protection algorithm based on optimized network topology(ERPBONT).ERPBONT adopts the optimized network topology to calculate a backup path with the minimum path coincidence degree with the shortest path for all source ***,the backup path with the minimum path coincidence with the shortest path is described as an integer programming *** the simulated annealing algorithm ERPBONT is used to find the optimal ***,the algorithm is tested on the simulated topology and the real *** experimental results show that ERPBONT effectively reduces the path coincidence between the shortest path and the backup path,and significantly improves the routing availability.

关键词： Routing protection algorithm network availability network failure shortest path backup path

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Design and Implementation of Links Generation for Inter Domain Routing system 12

Design and Implementation of Links Generation for Inter Doma...

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12th International Conference on Information Science and Technology, ICIST 2022

作者： Wang, Yu Qiao, Zhi Yin, Junru Zhang, Mingliang College of Software Henan University of Engineering Zhengzhou China College of Information and Electrical Engineering China Agriculture University Beijing China College of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou China State Key Laboratory of Mathematical Engineering and Advanced Computing Network Research Department Zhengzhou China

ISBN: (数字)9781665485821

ISBN: (纸本)9781665485821

Due to the inherent flaws of BGP protocol, the security of inter domain routing system has been regularly threatened. Since it is difficult to achieve collaborative defense against existing resources and strategies of multiple autonomous systems, this paper designs and organizes them into autonomous system community. Within the community, a precomputing multi-link generation mechanism is proposed based on genetic algorithm. Before the inter domain routing system is attacked, the link set can be generated as complete as possible in advance. Once an attack occurs, the community will quickly select proper precomputing links to replace the failed ones, in order to ensure the stability of inter domain communication. The effectiveness of the mechanism has been proved in function and performance through simulation experiments. © 2022 IEEE.

关键词： Genetic algorithms

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MCMLNet: A Neural Network for Multiple Fundus Disease Classification Using Multi-Channel Mutual Learning Mechanism

MCMLNet: A Neural Network for Multiple Fundus Disease Classi...

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Artificial Intelligence Technology (ACAIT), Asian Conference on

作者： Jialei Wang Qihao Xu Xiaoling Luo Yong Xu Department of Computer Science and Technology Harbin Institute of Technology Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies Shenzhen China

ISBN: (数字)9798331517090

ISBN: (纸本)9798331517106

With the aging of population and the change of people’s lifestyle, fundus diseases have become the main irreversible cause of blindness. computer-aided classification of multiple fundus diseases can improve the efficiency of diagnosis and prevent the further deterioration of fundus diseases. However, the classification approach faces great challenges due to the limited available data, complex types of fundus diseases, and subtle lesion features. To solve the above problems, we propose a neural network using multi-channel mutual learning mechanism. In this work, we propose Attention-Augmentation Block, which adjusts the learning focus of different channels by disease-grouping training on samples to make full use of the limited data. Furthermore, to enhance the feature representation capability of the model, the proposed method implements the mutual enhancement and fusion of multi-channel features through the mutual learning mechanism by designing the Multi-Channel Mutual Learning Unit. Experimental results show that our proposed method can achieve better classification performance of multiple fundus diseases than the state-of-the-art methods. The code of our paper is available in the attachment.

关键词： Learning systems Training Neural networks Multi label classification Feature extraction Medical diagnosis Lesions Medical diagnostic imaging Diseases Image classification

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CLDG: Contrastive Learning on Dynamic Graphs 39

CLDG: Contrastive Learning on Dynamic Graphs

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Xu, Yiming Shi, Bin Ma, Teng Dong, Bo Zhou, Haoyi Zheng, Qinghua Xi'an Jiaotong University Department of Computer Science and Technology China Xi'an Jiaotong University Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Xi'an Jiaotong University Department of Distance Education China Beihang University School of Software China Beihang University Advanced Innovation Center for Big Data and Brain Computing China

ISBN: (纸本)9798350322279

The graph with complex annotations is the most potent data type, whose constantly evolving motivates further exploration of the unsupervised dynamic graph representation. One of the representative paradigms is graph contrastive learning. It constructs self-supervised signals by maximizing the mutual information between the statistic graph's augmentation views. However, the semantics and labels may change within the augmentation process, causing a significant performance drop in downstream tasks. This drawback becomes greatly magnified on dynamic graphs. To address this problem, we designed a simple yet effective framework named CLDG. Firstly, we elaborate that dynamic graphs have temporal translation invariance at different levels. Then, we proposed a sampling layer to extract the temporally-persistent signals. It will encourage the node to maintain consistent local and global representations, i.e., temporal translation invariance under the timespan views. The extensive experiments demonstrate the effectiveness and efficiency of the method on seven datasets by outperforming eight unsupervised state-of-the-art baselines and showing competitiveness against four semi-supervised methods. Compared with the existing dynamic graph method, the number of model parameters and training time is reduced by an average of 2,001.86 times and 130.31 times on seven datasets, respectively. The code and data are available at: https://***/yimingxu24/CLDG. © 2023 IEEE.

关键词： Graph neural networks

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Contrastive Self-supervised EEG Representation Learning for Emotion Classification

Contrastive Self-supervised EEG Representation Learning for ...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Keya Hu Ren-Jie Dai Wen-Tao Chen Hao-Long Yin Bao-Liang Lu Wei-Long Zheng Department of Computer Science and Engineering Shanghai Jiao Tong University School of Software Engineering Tongji University RuiJin-Mihoyo Laboratory Clinical Neuroscience Center RuiJin Hospital Shanghai Jiao Tong University School of Medicine

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Self-supervised learning provides an effective approach to leverage a large amount of unlabeled data. Numerous previous studies have indicated that applying self-supervision to physiological signals can yield better representations of the signals. In the paper, we aim to apply this method to the crucial field of emotion recognition. We perform the experiment with several state-of-the-art contrastive self-supervised methods to explore their effectiveness in pre-training feature encoders on raw electroencephalography (EEG) signals and fine-tuning the pre-trained encoders on the downstream emotion classification tasks. We attempt to vary the proportion of labeled data used during fine-tuning and find that the improvement from self-supervised methods is more pronounced when the proportion of labeled data is small. Additionally, we explore the transferability of the feature encoders pre-trained on various datasets and observe that most self-supervised methods exhibit a certain degree of transferability. Methods that effectively utilize the temporal information in EEG signals show superior stability, accuracy, and transferability.

关键词： Training Representation learning Emotion recognition Accuracy Time series analysis Training data Feature extraction Electroencephalography Physiology engineering in medicine and biology

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A Deep Learning-Based Algorithm for Energy and Performance Optimization of Computational Offloading in Mobile Edge Computing

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Wireless Communications and Mobile Computing 2023年第1期2023卷

作者： Khan, Israr Raza, Salman Rehman, Waheed Ur Khan, Razaullah Nahida, Kiran Tao, Xiaofeng National Engineering Laboratory for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China Department of Computer Science National Textile University Faisalabad Pakistan Department of Computer Science University of Peshawar Peshawar Pakistan Department of Computer Science University of Engineering and Technology Mardan Pakistan Beijing Laboratory of Advanced Information Network Beijing Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and Telecommunications China

Mobile edge computing (MEC) has produced incredible outcomes in the context of computationally intensive mobile applications by offloading computation to a neighboring server to limit the energy usage of user equipment (UE). However, choosing a pool of application components to offload in addition to the volume of data transfer along with the latency in communication is an intricate issue. In this article, we introduce a novel energy-efficient offloading scheme based on deep neural networks. The proposed scheme trains an intelligent decision-making model that picks a robust pool of application components. The selection is based on factors such as the remaining UE battery power, network conditions, the volume of data transfer, required energy by the application components, postponements in communication, and computational load. We have designed the cost function taking all the mentioned factors, get the cost for all conceivable combinations of component offloading decisions, pick the robust decisions over an extensive dataset, and train a deep neural network as a substitute for the exhaustive computations associated. Model outcomes illustrate that our proposed scheme is proficient in the context of accuracy, root mean square error (RMSE), mean absolute error (MAE), and energy usage of UE. © 2023 Israr Khan et al.

关键词： Data transfer

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Locating Framework-specific Crashing Faults with Compact and Explainable Candidate Set

Locating Framework-specific Crashing Faults with Compact and...

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International Conference on software engineering (ICSE)

作者： Jiwei Yan Miaomiao Wang Yepang Liu Jun Yan Long Zhang Technology Center of Software Engineering Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China

Nowadays, many applications do not exist independently but rely on various frameworks or libraries. The frequent evolution and the complex implementation of framework APIs induce lots of unexpected post-release crashes. Starting from the crash stack traces, existing approaches either perform application-level call graph (CG) tracing or construct datasets with similar crash-fixing records to locate buggy methods. However, these approaches are limited by the completeness of CG or dependent on historical fixing records, and some of them only focus on specific manually modeled exception types. To achieve effective debugging on complex framework-specific crashes, we propose a code-separation-based locating approach that weakly relies on CG tracing and does not require any prior knowledge. Our key insight is that one crash trace with the description message can be mapped to a definite exception-thrown point in the framework, the semantics analysis of which can help to figure out the root causes of the crash-triggering procedure. Thus, we can pre-construct reusable summaries for all the framework-specific exceptions to support fault localization in application code. Based on that idea, we design the exception-thrown summary (ETS) that describes both the key variables and key APIs related to the exception triggering. Then, we perform static analysis to automatically compute such summaries and make a data-tracking of key variables and APIs in the application code to get the ranked buggy candidates. In the scenario of locating Android framework-specific crashing faults, our tool CrashTracker exhibited an overall MRR value of 0.91 and outperforms the state-of-the-art tool Anchor with higher precision. It only provides a compact candidate set and gives user-friendly reports with explainable reasons for each candidate.

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Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation

arXiv

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arXiv 2023年

作者： Su, Xingzhe Qiang, Wenwen Hu, Jie Zheng, Changwen Wu, Fengge Sun, Fuchun Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Meituan Beijing China Science & Technology on Integrated Information System Laboratory Department of Computer Science and Technology Tsinghua University Beijing China

Generative adversarial networks (GANs) have achieved remarkable progress in the natural image field. However, when applying GANs in the remote sensing (RS) image generation task, an extraordinary phenomenon is observed: the GAN model is more sensitive to the amount of training data for RS image generation than for natural image generation (Fig.1). In other words, the generation quality of RS images will change significantly with the number of training categories or samples per category. In this paper, we first analyze this phenomenon from two kinds of toy experiments and conclude that the amount of feature information contained in the GAN model decreases with reduced training data (Fig.2). Then we establish a structural causal model (SCM) of the data generation process and interpret the generated data as the counterfactuals. Based on this SCM, we theoretically prove that the quality of generated images is positively correlated with the amount of feature information. This provides insights for enriching the feature information learned by the GAN model during training. Consequently, we propose two innovative adjustment schemes, namely Uniformity Regularization (UR) and Entropy Regularization (ER), to increase the information learned by the GAN model at the distributional and sample levels, respectively. Extensive experiments on eight RS datasets and three natural datasets show the effectiveness and versatility of our methods. The source code is available at https://***/rootSue/Causal-RSGAN. Copyright © 2023, The Authors. All rights reserved.

关键词： Remote sensing

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