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Annual IEEE Symposium on Computer-Based Medical systems

作者： Dan Li Xuechen Li Zhibin Peng Wenting Chen Linlin Shen Guangyao Wu Computer Vision Institute School of Computer Science & Software Engineering Shenzhen University National Engineering Laboratory for Big Data System Computing Technology ShenZhen University Shenzhen China City University of Hong Kong Hong Kong SAR China Shenzhen Institute of Artificial Intelligence & Robotics for Society Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University General Hospital

In gastroenteroscopy image analysis, numerous CADs demonstrate that deep learning aids doctors' diagnosis. The shapes and sizes of the lesions are varied. And in the clinic, the dataset appears to be data imbalanced. However, existing methods directly classify by texture and ignore lesions with various shapes and sizes. To address the issue above, we propose a deep neural network, which consists of multi-scale feature extraction, contrastive feature learning and a multi-scale feature fusion module. We train the contrastive feature learning module and multi-scale feature fusion module simultaneously to alleviate the issue of data distribution differences. Thus, the proposed network can better identify various categories. Extensive experiments on the Hyper Kvasir dataset show that the proposed Hybrid-M2CL outperforms the benchmark proposed by the dataset with 5.0% Macro Precision, 3.3% Macro Recall, 3.4% Macro F1-score, 3.3% Micro Precision, 3.6% MCC. In addition, it outperforms the SOTA by 1.1% Macro F1-score, 2.6% MCC, and 2.0% B-ACC.

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Uncertainty-Aware Deep Co-training for Semi-supervised Medical Image Segmentation

arXiv

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arXiv 2021年

作者： Zheng, Xu Fu, Chong Xie, Haoyu Chen, Jialei Wang, Xingwei Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Computer Science The University of Auckland New Zealand

Semi-supervised learning has made significant strides in the medical domain since it alleviates the heavy burden of collecting abundant pixel-wise annotated data for semantic segmentation tasks. Existing semi-supervised approaches enhance the ability to extract features from unlabeled data with prior knowledge obtained from limited labeled data. However, due to the scarcity of labeled data, the features extracted by the models are limited in supervised learning, and the quality of predictions for unlabeled data also cannot be guaranteed. Both will impede consistency training. To this end, we proposed a novel uncertainty-aware scheme to make models learn regions purposefully. Specifically, we employ Monte Carlo Sampling as an estimation method to attain an uncertainty map, which can serve as a weight for losses to force the models to focus on the valuable region according to the characteristics of supervised learning and unsupervised learning. Simultaneously, in the backward process, we joint unsupervised and supervised losses to accelerate the convergence of the network via enhancing the gradient flow between different tasks. Quantitatively, we conduct extensive experiments on three challenging medical datasets. Experimental results show desirable improvements to state-of-the-art counterparts. Copyright © 2021, The Authors. All rights reserved.

关键词： Semantics

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CODH++: Macro-semantic differences oriented instance segmentation network

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Expert systems with Applications 2022年 202卷 1页

作者： Zhang, Wenchao Fu, Chong Cao, Lin Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China School of Information and Communication Engineering Beijing Information Science and Technology University Beijing100101 China School of Computer Science The University of Auckland New Zealand

With the idea of divide and rule, there exist two different forms of semantic features flowing in the two stage instance segmentation paradigms. They are the global features at the image level and the instance features at the region-wise. The most significant distinction of the two macro-semantic morphological features lies in the different relevance of neighborhood features caused by background noise. Hence, we should consider different situations and make different schemes. Notice that the fields-of-view determines the range of local features that can be perceived in the convolution operation and implies the representation capability of the network. To this end, for FPN and Mask Head in two stage paradigms, we propose a more efficient methodology with Group-Inception and Asymmetric-Inception modules. This proposed methodology can act as a drop-in replacement to upgrade the plain convolution operation, which enables the network to look more via modeling long-range dependencies. Our method is simple yet effective. Quantitatively, we can significantly improve the state-of-the-art frameworks, including Mask R-CNN, Mask Scoring R-CNN, Cascade Mask R-CNN, and HTC by about 1.2%–2.2% AP on MS COCO test-dev yet with fewer parameters and FLOPs. Moreover, the proposed approach achieves competitive performances on the Scapes, KINS and SBD datasets. The source code of our method will be made available. © 2022 Elsevier Ltd

关键词： Semantics

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Preventing Spread of Spam Transactions in Blockchain by Reputation

Preventing Spread of Spam Transactions in Blockchain by Repu...

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International Workshop on Quality of service

作者： Jiarui Zhang Yukun Cheng Xiaotie Deng Bo Wang Jan Xie Yuanyuan Yang Mengqian Zhang Stony Brook University Stony Brook NY USA School of Business Suzhou Key Laboratory for Big Data and Information Service Suzhou University of Science and Technology Suzhou China Centers on Frontiers of Computing Studies Peking University Beijing China Hangzhou Cryptape Technology Co. Ltd. Hangzhou China Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728168876

ISBN: (纸本)9781728168883

As one of the fastest-growing applications in the Peer-to-Peer (P2P) network, the development of blockchain technology is accompanied by different attacks. Those include whitewashing, free-riding, and distributed denial of service (DDoS) attacks, particularly because of features such as anonymity, distributed, permissionless in the blockchain network. One popular of them is spam transactions. Although the blockchain protocol requires each node to verify all received transactions, many nodes choose to forward transactions without verification to conserve their computational power, as there is no punishment for such a shirking. And it makes the blockchain vulnerable to the spreading of spam transactions over the network and creates extra burdens for all nodes in the network. We propose a reputation mechanism for the blockchain system to tackle this problem: Each node will locally compute reputations of its neighbors, and decide the probability to verify a received transaction based on the reputation value of the transaction sender. In turn, its neighbors will have an incentive to conduct verification to keep its reputation high. Subsequently, spam transactions can be blocked before reaching the miners. We have conducted a series of simulations, which clearly demonstrate the advantage of our reputation mechanism.

关键词： Peer-to-peer computing Computer crime Bitcoin Quality of service Earth Observing system

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Modeling and Analyzing the Reliability and Cost of service Composition in the IoT: A Probabilistic Approach

Modeling and Analyzing the Reliability and Cost of Service C...

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IEEE International Conference on Web services (ICWS)

作者： Lixing Li Zhi Jin Ge Li Liwei Zheng Qiang Wei Academy of Mathematics and System Science Chinese Academy of Sciences Beijing P. R. China Key Laboratory of High Confidence Software Technologies (Peking University) Ministry of Education Beijing P.R. China School of Computing Science Beijing Information Science & Technology University Beijing P.R. China

Recently, many efforts have been devoted to explore the integration of Internet of Things (IoT) and service-Oriented computing (SOC). These works allow the real-world devices to provide their functionality as web services. However, two important issues, unreliable service providing and resource constraints, make the modeling and analysis of service composition in IoT a big challenge. In this paper, we propose a probabilistic approach to formally describe and analyze the reliability and cost-related properties of the service composition in IoT. First, a service composition in IoT is modeled as a finite state machine (FSM) which focuses on the functional part. Then, we extend this FSM model to a Markov Decision Process (MDP), which can specify the reliability of service operations. Furthermore, we extend MDP with cost structure, which can represent the different service quality attributes for each operation, such as energy consumption, communication cost, etc. The desirable quality properties of the service composition are specified by a probabilistic extension of temporal logic PCTL. We adopt a well-established probabilistic model checker PRISM to verify and analyze those properties of our service composition models.

关键词： Business Reliability Fires Probabilistic logic Analytical models Web services system-on-a-chip

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Fault tolerant communication algorithm for network on chip

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Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics 2007年第4期19卷 508-514页

作者： Zhang, Lei Li, Huawei Li, Xiaowei Key Laboratory of Computer System and Architecture Chinese Academy of Sciences Beijing 100080 China Advanced Test Technology Laboratory Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China Graduate University Chinese Academy of Sciences Beijing 100049 China

This paper proposes a random routing algorithm with end-to-end feedback. Random routing has the capability of handling random transmission errors efficiently with high forwarding speed. End-to-end feedback promises the correctness of transmission and reduces the power consumption. Experimental results demonstrated that our random routing algorithm has lower latency, lower power consumption, and can provide on-chip communication with high reliability.

关键词： Error correcting and detecting Fault tolerant Flooding algorithm Network on chip Random routing Random walk Reliability

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A New Semantic Segmentation Diagram for Intelligent Transportation Based on Heterogeneous Knowledge Base

A New Semantic Segmentation Diagram for Intelligent Transpor...

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IEEE Conference on Wireless Communications and Networking

作者： Jingyuan Tang Jiaqi Zou Songlin Sun School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education China Department of Electronics and Electrical Engineering University College London London UK

Semantic segmentation is regarded as an important technology for future communication and sensing networks due to its promising ability to extract features of transmit data. It integrates the functionality of computer vision and can realize high-fidelity transmission in the channel with lower bandwidth. Knowledge Base (KB) is a key component for the semantic segmentation framework. In this paper, we consider the scenario of intelligent transportation and propose a heterogeneous KB to extract the to-be-transmitted features and information at multiple levels, i.e., the raw level, symbol level, feature level and image level. The compressed features are obtained by a deep-learning-based framework. The proposed KB is shared with the transmitter and the receiver, by a service-oriented multi-stream transmission algorithm to meet various requirements of services. Experiments results demonstrate significant performance gain in terms of quality of service and encoding efficiency.

关键词： Semantic segmentation Semantics Knowledge based systems Transportation Symbols Object detection Streaming media

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Predictive and Contrastive: Dual-Auxiliary Learning for Recommendation

arXiv

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arXiv 2022年

作者： Tao, Yinghui Gao, Min Yu, Junliang Wang, Zongwei Xiong, Qingyu Wang, Xu The Key Laboratory of Dependable Service Computing in Cyber Physical Society Chongqing University Ministry of Education School of Big Data and Software Engineering Chongqing401331 China The School of Information Technology and Electrical Engineering The University of Queensland Queensland4072 Australia The College of Mechanical and Vehicle Engineering Chongqing University Chongqing400044 China

Self-supervised learning (SSL) recently has achieved outstanding success on recommendation. By setting up an auxiliary task (either predictive or contrastive), SSL can discover supervisory signals from the raw data without human annotation, which greatly mitigates the problem of sparse user-item interactions. However, most SSL-based recommendation models rely on general-purpose auxiliary tasks, e.g., maximizing correspondence between node representations learned from the original and perturbed interaction graphs, which are explicitly irrelevant to the recommendation task. Accordingly, the rich semantics reflected by social relationships and item categories, which lie in the recommendation data-based heterogeneous graphs, are not fully exploited. To explore recommendation-specific auxiliary tasks, we first quantitatively analyze the heterogeneous interaction data and find a strong positive correlation between the interactions and the number of user-item paths induced by meta-paths. Based on the finding, we design two auxiliary tasks that are tightly coupled with the target task (one is predictive and the other one is contrastive) towards connecting recommendation with the self-supervision signals hiding in the positive correlation. Finally, a model-agnostic DUal-Auxiliary Learning (DUAL) framework which unifies the SSL and recommendation tasks is developed. The extensive experiments conducted on three real-world datasets demonstrate that DUAL can significantly improve recommendation, reaching the state-of-the-art performance. Copyright © 2022, The Authors. All rights reserved.

关键词： Recommender systems

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A Blockchain-based PHR Sharing Scheme with Attribute Privacy Protection

A Blockchain-based PHR Sharing Scheme with Attribute Privacy...

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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Chaohe Lu Zhongyuan Yu Guijuan Wang Anming Dong Xiang Tian School of Computer Science Qufu Normal University Rizhao China School of Information Science and Engineering Lanzhou University Lanzhou China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Industrial Network and Information System Security Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

With the rapid advancement and application of the Internet of Medical Things (IoMT), personal health records (PHRs) are now increasingly comprised of data collected by Internet of Things (IoT) devices and medical records documented by healthcare professionals. Personal health record (PHR) sharing demonstrates great potential in improving the accuracy of disease diagnosis. However, PHR sharing also brings risks such as illegal access and personal information leakage. Some works explored using blockchain or attribute-based encryption (ABE) to solve these privacy leakage problems, but those solutions did not pay attention to the user’s attribute privacy. In this work, we combine a linear secret sharing scheme (LSSS) and zero-knowledge succinct non-interactive argument of knowledge (zkSNARK) scheme to design an efficient zero-knowledge proof protocol called zk-AHSNARK. It can verify the user’s attribute permissions while also hiding attribute information. Based on zk-AHSNARK, we propose a novel PHR sharing scheme that protects attribute privacy. Data security is ensured by storing encrypted data in the interplanetary file system (IPFS). In addition, we introduce keyword ciphertext search to achieve fast data retrieval, and we implement the search and verification algorithms via a smart contract, ensuring the trustworthiness and integrity of the execution. Finally, through a large number of simulations, we demonstrated the suggested scheme’s viability and security.

关键词： Access control Privacy Smart contracts Blockchains Encryption InterPlanetary File system Medical diagnosis Protection Secure storage Medical diagnostic imaging

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Coordinate Attention-Based Convolution Neural Network for In-loop Filter of AVS3

Coordinate Attention-Based Convolution Neural Network for In...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Ruotong Wu Songlin Sun Jiaqi Zou Shaokang Wang Zhilei Ling School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China Engineering Research Center of Blockchain and Network Convergence Technology Ministry of Education China Department of Electronics and Electrical Engineering University College London London UK

Employing deep learning is a promising solution for reducing encoding bit rate in future video encoding systems. This paper proposes a neural network-based in-loop filter for the third generation of Audio Video Coding Standard (AVS3). The proposed network introduces a coordinate attention mechanism-based convolutional neural network in-loop filter (AMCNNLF) with a flexible attention module and a residual feature aggregation (RFA) module. Specifically, the attention module focuses on salient features to capture the visual structure, while the RFA module takes full advantage of the local refinement features. By leveraging the encoding parameters, we introduce the Quantizer Parameter (QP) values as auxiliary features to enable the proposed network suitable for processing encoded videos with multiple QPs. Experimental results indicate that the proposed network reduces the average Bjøntegaard-Delta rate (BD-Rate) of luma component about 0.4% under all intra configuration compared with the benchmark.

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