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3DSN-Net: A 3-D Scale-Aware convNet With Nonlocal Context Guidance for Kidney and Tumor Segmentation From CT Volumes

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IEEE Transactions on Cybernetics 2024年第5期54卷 3299-3312页

作者： Wu, Huisi Zhang, Baiming Li, Zhuoying Qin, Jing Lee, Tong-Yee Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China The Hong Kong Polytechnic University Centre for Smart Health School of Nursing Hong Kong Hong Kong National Cheng-Kung University Department of Computer Science and Information Engineering Tainan70101 Taiwan

Automatic kidney and tumor segmentation from CT volumes is a critical prerequisite/tool for diagnosis and surgical treatment (such as partial nephrectomy). However, it remains a particularly challenging issue as kidneys and tumors often exhibit large-scale variations, irregular shapes, and blurring boundaries. We propose a novel 3-D network to comprehensively tackle these problems;we call it 3DSN-Net. Compared with existing solutions, it has two compelling characteristics. First, with a new scale-aware feature extraction (SAFE) module, the proposed 3DSN-Net is capable of adaptively selecting appropriate receptive fields according to the sizes of targets instead of indiscriminately enlarging them, which is particularly essential for improving the segmentation accuracy of the tumor with large scale variation. Second, we propose a novel yet efficient nonlocal context guidance (NCG) mechanism to capture global dependencies to tackle irregular shapes and blurring boundaries of kidneys and tumors. Instead of directly harnessing a 3-D NCG mechanism, which makes the number of parameters exponentially increase and hence the network difficult to be trained under limited training data, we develop a 2.5D NCG mechanism based on projections of feature cubes, which achieves a tradeoff between segmentation accuracy and network complexity. We extensively evaluate the proposed 3DSN-Net on the famous KiTS dataset with many challenging kidney and tumor cases. Experimental results demonstrate our solution consistently outperforms state-of-the-art 3-D networks after being equipped with scale aware and NCG mechanisms, particularly for tumor segmentation. © 2013 IEEE.

关键词： Feature extraction

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Ensemble Graph and Device Clustering Method based on Attention Mechanism for Decomposing Monolithic to Microservices 22

Ensemble Graph and Device Clustering Method based on Attenti...

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22nd IEEE International Symposium on Parallel and Distributed Processing with Applications, ISPA 2024

作者： Deng, Qingyong Li, Qiuming Zuo, Qinghua Tian, Shujuan Long, Saiqin Guangxi Normal University School of Computer Science and Engineering The School of Software Guilin China Xiangtan University School of Computer Science Xiangtan China Jinan University College of Information Science and Technology Guangzhou China

ISBN: (纸本)9798331509712

Existing methods for decomposing monolithic applications into microservices in cloud environments primarily rely on the call relationships within itself. However, these methods are difficult to apply directly in resource-constrained and distributed edge network scenarios without considering the heterogeneity of the device. Therefore, this paper proposes a clustering method that ensembles graph structures and device features based on attention mechanism, which utilizes attention encoders to learn node embeddings and employs a spectral clustering algorithm to obtain decomposition results, optimizing the affinity and matching degree between microservices and devices. Experimental results demonstrate that the proposed method exhibits excellent performance in terms of functional independence, modularity, and adaptability of microservices. © 2024 IEEE.

关键词： Clustering algorithms

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A path planning method based on deep reinforcement learning for crowd evacuation

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Journal of Ambient Intelligence and Humanized Computing 2024年第6期15卷 2925-2939页

作者： Meng, Xiangdong Liu, Hong Li, Wenhao School of Information Science and Engineering Shandong Normal University Jinan250014 China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan250014 China

Deep reinforcement learning (DRL) is suitable for solving complex path-planning problems due to its excellent ability to make continuous decisions in a complex environment. However, the increase in the population size in the crowd evacuation path-planning problem causes a substantial computational burden for the algorithm, which leads to an unsatisfactory efficiency of the current DRL algorithm. This paper presents a path planning method based on DRL for crowd evacuation to solve the problem. First, we divide crowds into groups based on their relationship and distance from each other and select leaders from them. Next, we expand the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) to propose an Optimized Multi-Agent Deep Deterministic Policy Gradient (OMADDPG) algorithm to obtain the global evacuation path. The OMADDPG algorithm uses the Cross-Entropy Method (CEM) to optimize policy and improve the neural network’s training efficiency by applying the Data Pruning (DP) algorithm. In addition, the social force model is improved, incorporating the relationship between individuals and psychological factors into the model. Finally, this paper combines the improved social force model and the OMADDPG algorithm. The OMADDPG algorithm transmits the path information to the leaders. Pedestrians in the environment are driven by the improved social force model to follow the leaders to complete the evacuation simulation. The method can use a leader to guide pedestrians safely arrive the exit and reduce evacuation time in different environments. The simulation results prove the efficiency of the path planning method. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Motion planning

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A Credible and Fair Federated Learning Framework Based on Blockchain

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 301-316页

作者： Chen, Leiming Zhao, Dehai Tao, Liping Wang, Kai Qiao, Sibo Zeng, Xingjie Tan, Chee Wei China University of Petroleum School of Computer Science and Technology East China Qingdao266580 China CSIRO's Data61 Sydney2015 Australia Hefei University of Technology Anhui230009 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore Tiangong University School of Software Tianjin300387 China Southwest Petroleum University School of Computer Science Chengdu610500 China

Federated learning (FL) enables cooperative computation between multiple participants while protecting user privacy. Currently, FL algorithms assume that all participants are trustworthy and their systems are secure. However, the following problems arise in real-world scenarios: 1) Malicious clients disrupt FL through model poisoning and data poisoning attacks. Although some research has proposed secure aggregation methods to solve this problem, most methods have limitations. 2) The current method cannot fairly evaluate client contribution in some scenarios. Some clients exhibit free-rider behavior, seeking to cheat the reward system and manipulate global models. Evaluating client contribution and distributing rewards also present challenges. To address these challenges, we design a trustworthy federated framework to ensure secure computing throughout the federated task process. First, we propose a method of detecting malicious models to guarantee secure model aggregation. Then, we propose a fair method of assessing contribution to identify client-side free-riding behavior. Finally, we implement a computation process based on blockchain and smart contracts to guarantee the trustworthiness and fairness of federated tasks. To validate the performance of our framework, we simulate different types of client attacks and contribution evaluation scenarios on several open-source datasets. The experiments show that our framework ensures the credibility of federated tasks and achieves a fair evaluation of client contributions. © 2020 IEEE.

关键词： Blockchain

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Performance-Guaranteed Finite-Time Tracking of Strict-Feedback Systems with Unknown Control Directions: A Novel Switching Mechanism

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IEEE Transactions on Automatic Control 2025年第6期70卷 4061-4068页

作者： Mao, Bing Wu, Xiaoqun Fan, Ziye Lu, Jinhu Chen, Guanrong Shenzhen University College of Computer Science and Software Engineering Shenzhen518060 China Wuhan University School of Mathematics and Statistics Hubei430072 China Beihang University School of Automation Science and Electrical Engineering Beijing100191 China City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong

The problem of achieving performance-guaranteed finite-time exact tracking for uncertain strict-feedback nonlinear systems with unknown control directions is addressed. A novel logic switching mechanism with monitoring functions is designed to deal with unknown control directions. This approach is different from existing methods like the Nussbaum gain technique and smooth orientation functions, which are inapplicable to finite-time exact tracking. The main challenge lies in designing monitoring functions and determining switching rules, which is well resolved through three steps. Most significantly, in contrast to existing works that solely guarantee bounded tracking or asymptotic exact tracking, the new mechanism incorporates novel integral sliding mode techniques to achieve finite-time exact tracking with guaranteed performance. Finally, simulations are presented with comparison to demonstrate the effectiveness of the proposed methods. © 1963-2012 IEEE.

关键词： Feedback

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State-of-the-Art Approach: Boosting IoT Sensor Network Security with Visual Secret Sharing 10

State-of-the-Art Approach: Boosting IoT Sensor Network Secur...

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10th IEEE Smart World Congress, SWC 2024

作者： Ali, Sijjad Wang, Jia Leung, Victor Chung Ming Bhatti, Uzair Aslam Bhatti, Mughair Aslam Shenzhen University College of Computer Science and Software Engineering Guangdong China Hainan University School of Information and Communication Engineering China

ISBN: (纸本)9798331520861

This research paper is based on the issue of protecting the sensor networks in the era of IoT because the data is very sensitive and huge, and is collected in resource-constrained environments. We put forward a new approach to the enhancement of the security of IoT sensor networks by applying VSS on visual data. Our approach is based on the implementation of the VSS approach which implies that no single node will contain full information about the visual input which will be divided among the nodes. Furthermore, this approach also increases the security of the data, and at the same time is appropriate for the IoT environments since it does not require a large amount of computational power and network bandwidth. In addition, we explain how this method can be used, discuss its features, and based on the data from the experiments, prove the effectiveness of the suggested approach. The results of the research show that data security and the ability to counteract attacks can be resist if VSS is used in systems of IoT sensor networks. © 2024 IEEE.

关键词： Information hiding

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Self-Paced Pairwise Representation Learning for Semi-Supervised Text Classification 24

Self-Paced Pairwise Representation Learning for Semi-Supervi...

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33rd ACM Web Conference, WWW 2024

作者： Chen, Junfan Zhang, Richong Wang, Jiarui Hu, Chunming Mao, Yongyi SKLSDE School of Software Beihang University Beijing China SKLSDE School of Computer Science Beihang University Beijing China School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

ISBN: (纸本)9798400701719

Text classification is one vital tool assisting web content mining. Semi-supervised text classification (SSTC) offers an approach to alleviate the burden of annotation costs by training on a few labeled texts alongside many unlabeled texts. Unsolved challenges in SSTC are the overfitting problem caused by the limited labeled data and the mislabeling problem of unlabeled texts. To address these issues, this paper proposes a Self-Paced PairWise representation learning (SPPW) model. Concretely, SPPW alleviates the overfitting problem by replacing the overfitting-prone learning of a parameterized classifier with representation learning in a pair-wise manner. Besides, we propose a novel self-paced text filtering method that effectively integrates both label confidence and text hardness to reduce mislabeled texts synergistically. Extensive experiments on 3 benchmark SSTC datasets show that SPPW outperforms baselines and is effective in mitigating overfitting and mislabeling problems. © 2024 ACM.

关键词： Classification (of information)

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QUEST: Quadruple Multimodal Contrastive Learning with Constraints and Self-Penalization 38

QUEST: Quadruple Multimodal Contrastive Learning with Constr...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Song, Qi Gong, Tianxiang Gao, Shiqi Zhou, Haoyi Li, Jianxin School of Software Beihang University China School of Computer Science and Engineering Beihang University China Zhongguancun Laboratory Beijing China

Multimodal contrastive learning (MCL) has recently demonstrated significant success across various tasks. However, the existing MCL treats all negative samples equally and ignores the potential semantic association with positive samples, which limits the model's ability to achieve fine-grained alignment. In multi-view scenarios, MCL tends to prioritize shared information while neglecting modality-specific unique information across different views, leading to feature suppression and suboptimal performance in downstream tasks. To address these limitations, we propose a novel contrastive framework named QUEST: Quadruple Multimodal Contrastive Learning with Constraints and Self-Penalization. In the QUEST framework, we propose quaternion contrastive objectives and orthogonal constraints to extract sufficient unique information. Meanwhile, a shared information-guided penalization is introduced to ensure that shared information does not excessively influence the optimization of unique information. Our method leverages quaternion vector spaces to simultaneously optimize shared and unique information. Experiments on multiple datasets show that our method achieves superior performance in multimodal contrastive learning benchmarks. On public benchmark, our approach achieves state-of-the-art performance, and on synthetic shortcut datasets, we outperform existing baseline methods by an average of 97.95% on the CLIP model. © 2024 Neural information processing systems foundation. All rights reserved.

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Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

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computers, Materials & Continua 2023年第11期77卷 2529-2544页

作者： Muhammad Tahir Mingchu Li Irfan Khan Salman AAl Qahtani Rubia Fatima Javed Ali Khan Muhammad Shahid Anwar School of Software Technology Dalian University of Technology(DUT)Dalian116621China School of Software Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalian116650China Department of Computer Engineering King Saud UniversityRiyadh11543Saudi Arabia School of Software Tsinghua UniversityBeijing100084China Department of Software Engineering University of Science and TechnologyBanu28100Pakistan Department of AI and Software Gachon UniversitySeongnamsi13120Korea

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)***,delays in data retrieval can markedly hinder the efficacy of big data awareness detection *** advocate for a collaborative caching approach involving edge devices and cloud networks to combat *** strategy is devised to streamline the data retrieval path,subsequently diminishing network *** an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation *** paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge *** system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health *** the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during *** system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response ***,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize *** the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a *** reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark *** empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring *** encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection *** methodology yields enh

关键词： Real-time health data monitoring Cache-Assisted Real-Time Detection(CARD) edge-cloud collaborative caching scheme hierarchical detection Internet of Health Things(IoHT)

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DualCL: Principled Supervised Contrastive Learning as Mutual Information Maximization for Text Classification 24

DualCL: Principled Supervised Contrastive Learning as Mutual...

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33rd ACM Web Conference, WWW 2024

作者： Junfan, Chen Zhang, Richong Zheng, Yaowei Chen, Qianben Hu, Chunming Mao, Yongyi SKLSDE School of Software Beihang University Beijing China SKLSDE School of Computer Science Beihang University Beijing China School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

ISBN: (纸本)9798400701719

Text classification is a fundamental task in web content mining. Although the existing supervised contrastive learning (SCL) approach combined with pre-trained language models (PLMs) has achieved leading performance in text classification, it lacks fundamental principles. Theoretically motivated by a derived lower bound of mutual information maximization, we propose a dual contrastive learning framework DualCL that satisfies three properties, i.e., parameter-free, augmentation-easy and label-aware. DualCL generates classifier parameters from the PLM and simultaneously uses them for classification and as augmented views of the input text for supervised contrastive learning. Extensive experiments conclusively demonstrate that DualCL excels in learning superior text representations and consistently outperforms baseline models. © 2024 ACM.

关键词： Classification (of information)

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