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arXiv 2021年

作者： Li, Zhongliang Jin, Zhihao Li, Xuechen Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China

The Coronavirus disease 2019 (COVID-19) has rapidly spread all over the world since its first report in December 2019 and thoracic computed tomography (CT) has become one of the main tools for its diagnosis. In recent years, deep learning-based approaches have shown impressive performance in myriad image recognition tasks. However, they usually require a large number of annotated data for training. Inspired by Ground Glass Opacity (GGO), a common finding in COIVD-19 patient's CT scans, we proposed in this paper a novel self-supervised pretraining method based on pseudo lesions generation and restoration for COVID-19 diagnosis. We used Perlin noise, a gradient noise based mathematical model, to generate lesion-like patterns, which were then randomly pasted to the lung regions of normal CT images to generate pseudo COVID-19 images. The pairs of normal and pseudo COVID-19 images were then used to train an encoder-decoder architecture based U-Net for image restoration, which does not require any labelled data. The pretrained encoder was then fine-tuned using labelled data for COVID-19 diagnosis task. Two public COVID- 19 diagnosis datasets made up of CT images were employed for evaluation. Comprehensive experimental results demonstrated that the proposed self-supervised learning approach could extract better feature representation for COVID-19 diagnosis and the accuracy of the proposed method outperformed the supervised model pretrained on large scale images by 6.57% and 3.03% on SARS-CoV-2 dataset and Jinan COVID-19 dataset, respectively. © 2021, CC BY-NC-SA.

关键词： Coronavirus

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A Balanced Cloudlet Management Method for Wireless Metropolitan Area Networks 13th

A Balanced Cloudlet Management Method for Wireless Metropoli...

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13th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities, TridentCom 2018

作者： Xu, Xiaolong Chen, Yuhao Qi, Lianyong He, Jing Zhang, Xuyun School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Engineering Centre of Network Monitoring Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Information Science and Engineering Qufu Normal University Jining China Department of Electrical and Computer Engineering University of Auckland Auckland New Zealand

ISBN: (纸本)9783030129705

With the rapid development of wireless communication technology, cloudlet-based wireless metropolitan area network, which provides people with more convenient network services, has become an effiective paradigm to meet the growing demand for requirements of wireless cloud computing. Currently, the energy consumption of cloudlets can be reduced by migrating tasks, but how to jointly optimize the time consumption and energy consumption in the process of migrations is still a significant problem. In this paper, a balanced cloudlet management method, named BCM, is proposed to address the above challenge. Technically, the Simple Additive Weighting (SAW) and Multiple Criteria Decision Making (MCDM) techniques are applied to optimize virtual machine scheduling strategy. Finally, simulation results demonstrate the effectiveness of our proposed method. © 2019, ICST Institute for computer sciences, Social Informatics and Telecommunications Engineering.

关键词： Energy utilization

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An Open-Source Robotic Chinese Chess Player

An Open-Source Robotic Chinese Chess Player

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Shan An Guangfu Che Jinghao Guo Yanjie Xu Guoxin Wang Konstantinos A. Tsintotas Fukai Zhang Junjie Ye Changhong Fu Haogang Zhu Hong Zhang JD Health International Inc. Beijing China *** Inc Beijing China Puncture (Shanghai) Intelligent Medical Technology Co. Ltd Shanghai China College of Biomedical Engineering & Instrument Science Zhejiang University Hangzhou China Department of Production and Management Engineering Democritus University of Thrace Xanthi Greece Citrus Research Institute Southwest University Chongqing China School of Mechanical Engineering Tongji University Shanghai China State Key Laboratory of Software Development Environment Beihang University Beijing China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China

Consumer robots can accompany children growing up, improving their abilities while playing and entertaining. This paper presents an open-source, practical, low-cost robotic Chinese chess player. The proposed system includes an elaborate mechanical structure, a simple kinematic solution, a novel robot operating system, real-time and accurate chess recognition. Regarding its mechanical design, it combines a magnetism structure and mechanical cam drive, while the overall system has just three servo motors. At the same time, its control strategy is simple and effective. Furthermore, a lightweight robot message communication mechanism, entitled TinyROS, is developed for computing resource-limited embedded chips. Concerning the recognition process, our CNNbased object detector determines chess and achieves accurate identification. As a result, our robotic Chinese chess player is exquisite and easy for large-scale promotion while improving users' chess skills. Aiming to facilitate future consumer robot research and popularize customer robots, the model's mechanical and software design and the TinyROS protocol are open-sourced at https://***/Star-Robot/chinese-chess-robot.

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Achieving Privacy-Preserving Iris Identification Via El Gamal

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computers, Materials & Continua 2019年第8期61卷 727-738页

作者： Yong Ding Lei Tian Bo Han Huiyong Wang Yujue Wang James Xi Zheng Guangxi Key Laboratory of Cryptography and Information Security School of Computer and Information SecurityGuilin University of Electronic TechnologyGuilin541004China School of Mathematics and Computer Science Guilin University of Electronic TechnologyGuilin541004China Software Engineering Department of Computing Macquarie UniversitySydneyNSW 2109 Australia

Currently,many biometric systems maintain the user’s biometrics and templates in plaintext format,which brings great privacy risk to uses’biometric *** are unique and almost unchangeable,which means it is a great concern for users on whether their biometric information would be *** address this issue,this paper proposes a confidential comparison algorithm for iris feature vectors with masks,and develops a privacy-preserving iris verification scheme based on the El Gamal encryption *** our scheme,the multiplicative homomorphism of encrypted features is used to compare of iris features and their mask ***,this paper improves the Hamming distance of iris features,which makes the similarity matching work better than existing *** results confirm the practicality of our proposed schemes in real world applications,that is,for the iris feature vectors and masks of 2048 bits,nearly 12 comparisons can be performed per second.

关键词： Homomorphic encryption template matching Hamming distance

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PA-Rank: A GAN and Reinforcement Learning Powered Framework for Multi-Metric Anomaly Detection and Causal Diagnosis

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IEEE Internet of Things Journal 2025年

作者： Wang, Zhaowen Zhou, Yong Zhang, YangSiyu Cong, Fengyu Zhou, Dongdong An, Zhijian School of Software Engineering Dalian University of Technology Dalian116024 China Dalian University of Technology School of Software Dalian116024 China Dalian University of Technology School of Biomedical Engineering Faculty of Medicine Dalian116024 China Ministry of Education Key Laboratory of Social Computing and Cognitive Intelligence Dalian University of Technology China University of Jyväskylä Faculty of Information Technology Jyväskylä40014 Finland Dalian University of Technology School of Computer Science and Technology Dalian116024 China Faculty of Information Technology University of Jyvaskyla Jyvaskyla40014 Finland

The increasing scale and complexity of modern IT systems necessitate advanced solutions for monitoring and managing performance anomalies. Artificial intelligence for IT operations (AIOps) has emerged as a promising approach to enhance the efficiency and effectiveness of IT operations. However, existing methods struggle with effectively detecting anomalies in multi-dimensional performance data and accurately identifying their root causes in complex interdependent systems. This paper proposes a novel framework, PA-Rank, that combines generative adversarial networks (GANs), reinforcement learning, and graph-based methods to address these challenges comprehensively. For anomaly detection, an unsupervised GAN-based model is developed to identify anomalous time periods and assign weighted scores to metrics, facilitating precise anomaly identification. For root cause localization, a Causal Graph Construction Model (CGCM) has been developed, utilizing a reinforcement learning-based causal discovery method that is integrated with graph attention networks (GAT) to construct a causal graph representing the relationships between metrics. A random walk algorithm further ranks metric importance during anomalies, enabling effective root cause localization. Extensive experiments on real-world datasets, including SMD, ASD, and DAMADICS, demonstrate the superiority of PA-Rank over traditional statistical and state-of-the-art machine learning methods. On the SMD dataset, the proposed framework achieved an F1 score of 0.9542 for anomaly detection and consistently identified root causes among top-ranked candidates on the Pymicro and RMS datasets with the highest PR@Avg scores. These results underscore PA-Rank’s efficacy in diagnosing performance anomalies and supporting efficient system maintenance. © 2014 IEEE.

关键词： Reinforcement learning

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Correction to: LSTM-TC: Bitcoin coin mixing detection method with a high recall

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Applied Intelligence 2022年第8期52卷 9597-9597页

作者： Sun, Xiaowen Yang, Tan Hu, Bo State Key Laboratory of Networking and Switching Technology School of Computer Science National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China

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Butterfly Space: An Architectural Approach for Investigating Performance Issues

Butterfly Space: An Architectural Approach for Investigating...

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Working IEEE/IFIP Conference on software Architecture

作者： Yutong Zhao Lu Xiao Xiao Wang Zhifei Chen Bihuan Chen Yang Liu School of Systems and Enterprises Stevens Institute of Technology Hoboken United States State Key Lab for Novel Software Technology Nanjing University Nanjing China School of Computer Science Fudan University Shanghai China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9781728146591

ISBN: (纸本)9781728146607

Performance issues widely exist in modern software systems. Existing performance optimization approaches, such as dynamic profiling, usually fail to consider the impacts of architectural connections among methods on performance issues. This paper contributes an architectural approach, Butterfly Space modeling, to investigate performance issues. Each Butterfly Space is composed of 1) a seed method; 2) methods in the "upper wing" that call the seed directly or transitively; and 3) methods in the "lower wing" that are called by the seed, directly or transitively. The rationale is that the performance of the seed method impacts and is impacted by all the other methods in the space because of the call relationship. As such, developers can more efficiently investigate groups of connected performance improvement opportunities in Butterfly Spaces. We studied three real-world open source Java projects to evaluate such potential. Our findings are three-fold: 1) If the seed method of a Butterfly Space contains performance problems, up to 60% of the methods in the space also contain performance problems; 2) Butterfly Spaces can potentially help to non-trivially increase the precision/recall and reduce the costs in identifying performance improvement opportunities, compared to dynamic profiling; and 3) Visualizing dynamic profiling metrics with Butterfly Spaces simultaneously help to reveal two typical patterns, namely Expensive Callee and Inefficient Caller, that are responsible for performance problems and provide insights on where to improve next. We believe that Butterfly Space modeling has great potential for investigating performance issues.

关键词： Optimization software systems Detection algorithms computer bugs Extraterrestrial measurements

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Joint Configuration Adaptation and Bandwidth Allocation for Edge-based Real-time Video Analytics

Joint Configuration Adaptation and Bandwidth Allocation for ...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Can Wang Sheng Zhang Yu Chen Zhuzhong Qian Jie Wu Mingjun Xiao State Key Lab. for Novel Software Technology Nanjing University P.R. China Center for Networked Computing Temple University School of Computer Science and Technology / Suzhou Institute for Advanced Study University of Science and Technology of China P.R. China

ISBN: (数字)9781728164120

ISBN: (纸本)9781728164137

Real-time analytics on video data demands intensive computation resources and high energy consumption. Traditional cloud-based video analytics relies on large centralized clusters to ingest video streams. With edge computing, we can offload compute-intensive analysis tasks to the nearby server, thus mitigating long latency incurred by data transmission via wide area networks. When offloading frames from the front-end device to the edge server, the application configuration (frame sampling rate and frame resolution) will impact several metrics, such as energy consumption, analytics accuracy and user-perceived latency. In this paper, we study the configuration adaption and bandwidth allocation for multiple video streams, which are connected to the same edge node sharing an upload link. We propose an efficient online algorithm, called JCAB, which jointly optimizes configuration adaption and bandwidth allocation to address a number of key challenges in edge-based video analytics systems, including edge capacity limitation, unknown network variation, intrusive dynamics of video contents. Our algorithm is developed based on Lyapunov optimization and Markov approximation, works online without requiring future information, and achieves a provable performance bound. Simulation results show that JCAB can effectively balance the analytics accuracy and energy consumption while keeping low system latency.

关键词： Streaming media Bandwidth Energy consumption Servers Channel allocation Image resolution Energy resolution

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Knowledge Graph Augmented Network Towards Multiview Representation Learning for Aspect-based Sentiment Analysis

arXiv

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arXiv 2022年

作者： Zhong, Qihuang Ding, Liang Liu, Juhua Du, Bo Jin, Hua Tao, Dacheng The National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China The Research Center for Graphic Communication Printing and Packaging Institute of Artificial Intelligence Wuhan University Wuhan China The JD Explore Academy *** Beijing China The Affiliated Hospital of Kunming University of Science and Technology Kunming China

Aspect-based sentiment analysis (ABSA) is a fine-grained task of sentiment analysis. To better comprehend long complicated sentences and obtain accurate aspect-specific information, linguistic and commonsense knowledge are generally required in this task. However, most current methods employ complicated and inefficient approaches to incorporate external knowledge, e.g., directly searching the graph nodes. Additionally, the complementarity between external knowledge and linguistic information has not been thoroughly studied. To this end, we propose a knowledge graph augmented network (KGAN), which aims to effectively incorporate external knowledge with explicitly syntactic and contextual information. In particular, KGAN captures the sentiment feature representations from multiple different perspectives, i.e., context-, syntax- and knowledge-based. First, KGAN learns the contextual and syntactic representations in parallel to fully extract the semantic features. Then, KGAN integrates the knowledge graphs into the embedding space, based on which the aspect-specific knowledge representations are further obtained via an attention mechanism. Last, we propose a hierarchical fusion module to complement these multi-view representations in a local-to-global manner. Extensive experiments on five popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN. Notably, with the help of the pretrained model of RoBERTa, KGAN achieves a new record of state-of-the-art performance among all datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Sentiment analysis

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A novel K-Repetition Design for SCMA

arXiv

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arXiv 2022年

作者： Lai, Ke Liu, Zilong Lei, Jing Wen, Lei Chen, Gaojie Xiao, Pei Department of Communication Engineering College of Electronic Science and Engineering National University of Defence Technology China School of Computer Science and Electronics Engineering University of Essex United Kingdom The Department of Engineering University of Leicester LeicesterLE1 7RH United Kingdom State Key Laboratory of ISN Xidian University Xi’an710071 China

This work presents a novel K-Repetition based HARQ scheme for LDPC coded uplink SCMA by employing a network coding (NC) principle to encode different packets, where K-Repetition is an emerging technique (recommended in 3GPP Release 15) for enhanced reliability and reduced latency in future massive machine-type communication. Such a scheme is referred to as the NC aided K-repetition SCMA (NCK-SCMA). We introduce a joint iterative detection algorithm for improved detection of the data from the proposed LDPC coded NCK-SCMA systems. Simulation results demonstrate the benefits of NCK-SCMA with higher throughput and improved reliability over the conventional K-Repetition SCMA. © 2022, CC0.

关键词： Network coding

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