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Simplifying social networks via triangle-based cohesive subgraphs

Visual Informatics 2023年第4期7卷 84-94页

作者： Rusheng Pan Yunhai Wang Jiashun Sun Hongbo Liu Ying Zhao Jiazhi Xia Wei Chen State Key Lab of CAD&CG Zhejiang UniversityHangzhou China School of Computer Science and Technology Shandong UniversityQingdaoChina School of Computer Science and Engineering Central South UniversityChangshaChina

One main challenge for simplifying node-link diagrams of large-scale social networks lies in that simplified graphs generally contain dense subgroups or cohesive *** triangles quantify the solid and stable relationships that maintain cohesive *** the mechanism of triangles within cohesive subgraphs contributes to illuminating patterns of connections within social ***,prior works can hardly handle and visualize triangles in cohesive *** this paper,we propose a triangle-based graph simplification approach that can filter and visualize cohesive subgraphs by leveraging a triangle-connectivity called k-truss and a force-directed *** design and implement TriGraph,a web-based visual interface that provides detailed information for exploring and analyzing social *** comparisons with existing methods,two case studies on real-world datasets,and feedback from domain experts demonstrate the effectiveness of TriGraph.

关键词： Graph simplification Cohesive subgraphs Graph triangle Graph layout Node-link diagrams

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An AI Multi-Model Approach to DeFi Project Trust Scoring and Security

An AI Multi-Model Approach to DeFi Project Trust Scoring and...

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IEEE International Conference on Blockchain (Blockchain)

作者： Viraaji Mothukuri Reza M. Parizi James L. Massa Abbas Yazdinejad Decentralized Science Lab College of Computing and Software Engineering Kennesaw State University USA Cyber Science Lab School of Computer Science University of Guelph Ontario Canada

ISBN: (数字)9798350351590

ISBN: (纸本)9798350351606

Rampant scams plague decentralized finance (DeFi) projects, creating a DeFi credibility problem that limits the impact of DeFi advances in the availability and variety of financial services. This paper presents a novel solution to the DeFi credibility problem by developing an AI multi-model that generates TrustScore ratings for DeFi projects and clear explanations of the scores. We generate DeFi-project TrustScore by aggregating multiple factors that provide DeFi investors with a holistic view of DeFi project trustworthiness. To rate a DeFi project with a TrustS core, we combine the output of four AI pipelines that analyze smart contract code vulnerabilities, suspicious transactions, anomalous price changes to smart contracts, and social media scam sentiment. Applying four factors exponentially improves the trust-score accuracy over the single-factor approaches done historically. Two of the factors, anomalous price change, and social media sentiment, have not been used before to detect DeFi fraud. Furthermore, we enhanced the most critical factor, smart-contract code vulnerability detection, with the latest Large Language Models (LLMs). Our overall system is a multi-model composed of a TrustS core Explainer LLM that aggregates individual pipeline results, a fine-tuned GPT model to audit smart contract code, the Prophet forecasting tool, FinBERT tailored for financial Natural Language Processing (NLP), and XGBoost for classification. The proposed approach identifies a significant proportion of known fraudulent DeFi projects and generates an accurate and explained TrustScore. Thus, we address the DeFi credibility problem so that investors can make reliable decisions about DeFi projects.

关键词： Codes Accuracy Social networking (online) Smart contracts Pipelines Finance Decentralized applications

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Fairness-Aware Incentive Mechanism for Multi-Server Federated Learning in Edge-Enabled Wireless Networks with Differential Privacy

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IEEE Transactions on Mobile Computing 2025年

作者： Yang, Yu Peng, Kai Wang, Shangguang Xu, Xiaolong Xiao, Peiyun Leung, Victor C.M Huaqiao University College of Engineering Quanzhou362021 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanjing University of Information Science and Technology School of Software Nanjing China Nanjing University State Key Lab. for Novel Software Technology Nanjing210093 China Shenzhen University College of the Computer Science and Software Engineering Shenzhen518060 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

As a distributed machine learning method, federated learning (FL) can collaboratively train a global model with multiple devices without sharing the original data, thus protecting certain privacy. However, due to the strong heterogeneity of edge nodes (ENs) participating in FL, the quality of data uploaded to the parameter server (PS) varies significantly. Without an appropriate incentive mechanism, low-quality contributors may receive disproportionately high rewards, while high-quality contributors may lack sufficient motivation, leading to inefficient participation and suboptimal global model performance. Consequently, it is critical to develop an effective incentive mechanism to promote fairness for the FL process. To address the issues of existing FL incentive mechanisms lacking privacy protection performance analysis, we propose a fairness-aware incentive mechanism for multi-server FL in edge-enabled wireless differential privacy (DP) networks. Specifically, the wireless channel noise is used to provide DP protection for the local model gradients uploaded by ENs. Next, the interaction between the PSs and ENs is modeled as a Stackelberg game. Furthermore, we solve the Stackelberg game process using backward induction and theoretically propose optimal strategies for both the PSs and ENs. Finally, extensive numerical simulations using real datasets demonstrate the superior performance of our theoretical analysis of the proposed scheme. © 2002-2012 IEEE.

关键词： Federated learning

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Constructing A Theoretical Model to Bridge Neural Transition with a state Switch in Bipolar Disorder

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Neuroscience Bulletin 2025年第1期41卷 181-185页

作者： Xiaonan Guo Lizichen Chen Jianbo Lai Shaohua Hu Department of Psychiatry the First Affiliated HospitalZhejiang University School of MedicineHangzhou310003China Department of Engineering Mechanics Zhejiang UniversityHangzhou310027China Nanhu Brain-computer Interface Institute Hangzhou311100China The Zhejiang Key Laboratory of Precision Psychiatry Hangzhou310003China MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University School of MedicineHangzhou310003China Brain Research Institute of Zhejiang University Hangzhou310003China The State Key Lab of Brain-Machine Intelligence Zhejiang UniversityHangzhouChina Zhejiang Engineering Center for Mathematical Mental Health Hangzhou310003China

Reward or stress,which exists extensively,causes resilient emotional fluctuations under common ***,reward or stress is a typical trigger for manic or depressive episodes of bipolar disorder(BD),which is corroborated by psychological theory,biological findings,and psychosocial treatment approaches[1,2].During an episode of BD,the affective aberration can be persistent and switchable,accompanied by opposite constellations of cognitive and psychomotor *** by uncontrollable mood ranging in severity,duration,and polarity,to disentangle the pathophysiology mechanism of BD is to delineate the mystery of affective fluctuations driven by reward or stress.

关键词： bipolar Transition finding

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Correcting Noisy Multilabel Predictions: Modeling label Noise through Latent Space Shifts

arXiv

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arXiv 2025年

作者： Huang, Weipeng Li, Qin Xiao, Yang Qiao, Cheng Cai, Tie Liang, Junwei Hurley, Neil J. Piao, Guangyuan School of Computer Science and Software Engineering Shenzhen Institute of Information Technology Guangdong China State Key Lab of Integrated Service Networks School of Cyber Engineering Xidian University Shaanxi China Cyberspace Institute of Advanced Technology Guangzhou University Guangdong China School of Computer Science University College Dublin Dublin Ireland

Noise in data appears to be inevitable in most realworld machine learning applications and would cause severe overfitting problems. Not only can data features contain noise, but labels are also prone to be noisy due to human input. In this paper, rather than noisy label learning in multiclass classifications, we instead focus on the less explored area of noisy label learning for multilabel classifications. Specifically, we investigate the post-correction of predictions generated from classifiers learned with noisy labels. The reasons are two-fold. Firstly, this approach can directly work with the trained models to save computational resources. Secondly, it could be applied on top of other noisy label correction techniques to achieve further improvements. To handle this problem, we appeal to deep generative approaches that are possible for uncertainty estimation. Our model posits that label noise arises from a stochastic shift in the latent variable, providing a more robust and beneficial means for noisy learning. We develop both unsupervised and semi-supervised learning methods for our model. The extensive empirical study presents solid evidence to that our approach is able to consistently improve the independent models and performs better than a number of existing methods across various noisy label settings. Moreover, a comprehensive empirical analysis of the proposed method is carried out to validate its robustness, including sensitivity analysis and an ablation study, among other elements. © 2025, CC BY.

关键词： Contrastive Learning

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Microsnoop: A generalist tool for microscopy image representation

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The Innovation 2024年第1期5卷 109-117页

作者： Dejin Xun Rui Wang Xingcai Zhang Yi Wang Pharmaceutical Informatics Institute College of Pharmaceutical SciencesZhejiang UniversityHangzhou 310058China State Key Lab of Computer-Aided Design&Computer Graphics Zhejiang UniversityHangzhou 310058China John A.Paulson School of Engineering and Applied Sciences Harvard UniversityCambridgeMA 02138USA Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University Hangzhou 310018China National Key Laboratory of Chinese Medicine Modernization Innovation Center of Yangtze River DeltaZhejiang UniversityJiaxing 314100China

Accurate profiling of microscopy images from small scale to high throughput is an essential procedure in basic and applied biological ***,we present Microsnoop,a novel deep learning–based representation tool trained on large-scale microscopy images using masked self-supervised *** can process various complex and heterogeneous images,and we classified images into three categories:single-cell,full-field,and batch-experiment *** benchmark study on 10 high-quality evaluation datasets,containing over 2,230,000 images,demonstrated Microsnoop’s robust and state-ofthe-art microscopy image representation ability,surpassing existing generalist and even several custom *** can be integrated with other pipelines to perform tasks such as superresolution histopathology image and multimodal ***,Microsnoop can be adapted to various hardware and can be easily deployed on local or cloud computing *** will regularly retrain and reevaluate the model using communitycontributed data to consistently improve Microsnoop.

关键词： hardware image representation

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GRAVMAD: GROUNDED SPATIAL VALUE MAPS GUIDED ACTION DIFFUSION FOR GENERALIZED 3D MANIPULATION

arXiv

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arXiv 2024年

作者： Chen, Yangtao Chen, Zixuan Yin, Junhui Huo, Jing Tian, Pinzhuo Shi, Jieqi Gao, Yang State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Suzhou China School of Computer Engineering and Science Shanghai University Shanghai China

Robots’ ability to follow language instructions and execute diverse 3D manipulation tasks is vital in robot learning. Traditional imitation learning-based methods perform well on seen tasks but struggle with novel, unseen ones due to variability. Recent approaches leverage large foundation models to assist in understanding novel tasks, thereby mitigating this issue. However, these methods lack a task-specific learning process, which is essential for an accurate understanding of 3D environments, often leading to execution failures. In this paper, we introduce GravMAD, a sub-goal-driven, language-conditioned action diffusion framework that combines the strengths of imitation learning and foundation models. Our approach breaks tasks into sub-goals based on language instructions, allowing auxiliary guidance during both training and inference. During training, we introduce Sub-goal keypose Discovery to identify key sub-goals from demonstrations. Inference differs from training, as there are no demonstrations available, so we use pre-trained foundation models to bridge the gap and identify sub-goals for the current task. In both phases, GravMaps are generated from sub-goals, providing GravMAD with more flexible 3D spatial guidance compared to fixed 3D positions. Empirical evaluations on RLBench show that GravMAD significantly outperforms state-of-the-art methods, with a 28.63% improvement on novel tasks and a 13.36% gain on tasks encountered during training. Evaluations on real-world robotic tasks further show that GravMAD can reason about real-world tasks, associate them with relevant visual information, and generalize to novel tasks. These results demonstrate GravMAD’s strong multi-task learning and generalization in 3D manipulation. Video demonstrations are available at: https://***. © 2024, CC BY.

关键词： Demonstrations

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Lifeline Consensus: Protect Small-scale Blockchain From Typical Attacks

Lifeline Consensus: Protect Small-scale Blockchain From Typi...

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International Wireless Communications and Mobile Computing Conference, IWCMC

作者： Kundu Chen Jie Luo State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Haidian District Beijing China

Blockchain is a distributed network system known for its anonymity, security, and decentralized nature. While blockchains with robust networks such as Bitcoin and Ethereum are proving immutable to attacks, many others are vulnerable to security threats. To address this issue, we proposed the Lifeline consensus, which aims to protect small-scale blockchains by delivering safety from a strong leading blockchain to a relatively vulnerable following blockchain using a new type of data called Coil. Coil carries the proof-of-work evidence created by miners from an arbitrary following blockchain, which the leading blockchain then gathers and generates immutable blocks that contained them. Miners from the following blockchain can use the coils in the leading blockchain for validation, thereby protecting small-scale blockchains from typical blockchain threats such as 51% attacks and selfish mining. We implemented our protocol and encountered adversaries in the local network, and analyzed its performance. The results showed that the Lifeline protocol is dependable with a Trusted Leader. These findings suggest that our protocol has the potential to improve the security of small-scale blockchains and enhance the overall performance of the blockchain network.

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Time Domain Analysis for Motions of Ships With the Effects of Steady Flow Using a Rankine Panel Method

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Journal of Offshore Mechanics and Arctic engineering 2025年第4期147卷 041203页

作者： Zhou, Bo Wu, Zhifei Wang, Yu Han, Xiaoshuang Zhang, Teng Zhang, Guiyong State Key Lab of Structural Analysis Optimization and CAE Software for Industrial Equipment School of Naval Architecture Engineering Dalian University of Technology Dalian 116024 China Marine Engineering College Dalian Maritime University Dalian 116026 China

A novel numerical approach, leveraging the 3D time domain Rankine panel method, has been introduced to simulate ship motions while accounting for the effects of ship wave. This method treats ship wave as steady flow, enabling a more accurate representation of physical phenomena. The underlying numerical formulation used to solve the ship wave potential is comprehensively derived, establishing the unsteady disturbance potential's boundary value problem and incorporating the effects of steady motion. Wave forces and motion responses of various ship forms were computed and compared with experimental data and results from other numerical techniques. The findings reveal that calculations based on uniform stream exhibit significantly larger errors than those considering double body flow or ship wave. For the Wigley I and S175 models, the discrepancies between double body flow and ship wave predictions are relatively minor, with ship wave calculations demonstrating slightly higher accuracy. In contrast, for the intricate KRISO Container Ship (KCS) model, where ship wave is more pronounced, the error margin for ship wave calculations relative to experimental results remains within 21.9%, while errors for double body flow calculations can reach up to 50.2%. This highlights the superior accuracy of ship wave-based calculations for the KCS model. Overall, the proposed method effectively captures the influence of steady flow and demonstrates significant advantages in computing pronounced ship wave generated during realistic ship motion.

关键词： Ships ship motion Rankine panel method Waves Flow (Dynamics) hydrodynamics wave mechanics and wave effects computational fluid dynamics ship wave Boundary-value problems steady flow

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Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detection

arXiv

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arXiv 2025年

作者： Wang, Zengran Zhang, Yanan Chen, Jiaxin Huang, Di State Key Laboratory of Complex and Critical Software Environment Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230601 China

To address the annotation burden in LiDAR-based 3D object detection, active learning (AL) methods offer a promising solution. However, traditional active learning approaches solely rely on a small amount of labeled data to train an initial model for data selection, overlooking the potential of leveraging the abundance of unlabeled data. Recently, attempts to integrate semi-supervised learning (SSL) into AL with the goal of leveraging unlabeled data have faced challenges in effectively resolving the conflict between the two paradigms, resulting in less satisfactory performance. To tackle this conflict, we propose a Synergistic Semi-Supervised Active Learning framework, dubbed as S-SSAL. Specifically, from the perspective of SSL, we propose a Collaborative PseudoScene Pre-training (CPSP) method that effectively learns from unlabeled data without introducing adverse effects. From the perspective of AL, we design a Collaborative Active Learning (CAL) method, which complements the uncertainty and diversity methods by model cascading. This allows us to fully exploit the potential of the CPSP pre-trained model. Extensive experiments conducted on KITTI and Waymo demonstrate the effectiveness of our S-SSAL framework. Notably, on the KITTI dataset, utilizing only 2% labeled data, S-SSAL can achieve performance comparable to models trained on the full dataset. The code has been released at https://***/LandDreamer/S SSAL. Copyright © 2025, The Authors. All rights reserved.

关键词： Active learning

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