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Massively parallel algorithms for fully dynamic all-pairs shortest paths

Frontiers of computer science 2024年第4期18卷 201-203页

作者： Chilei WANG Qiang-Sheng HUA Hai JIN Chaodong ZHENG National Engineering Research Center for Big Data Technology and System Services Computing Technology and System LabCluster and Grid Computing LabSchool of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhan 430074China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China

1 Introduction In recent years,the Massively Parallel Computation(MPC)model has gained significant ***,most of distributed and parallel graph algorithms in the MPC model are designed for static graphs[1].In fact,the graphs in the real world are constantly *** size of the real-time changes in these graphs is smaller and more *** graph algorithms[2,3]can deal with graph changes more efficiently[4]than the corresponding static graph ***,most studies on dynamic graph algorithms are limited to the single machine ***,a few parallel dynamic graph algorithms(such as the graph connectivity)in the MPC model[5]have been proposed and shown superiority over their parallel static counterparts.

关键词： dynamic shortest gained

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Enhancing Offensive Language Detection with Data Augmentation and Knowledge Distillation

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Research 2024年第2期6卷 29-40页

作者： Jiawen Deng Zhuang Chen Hao Sun Zhexin Zhang Jincenzi Wu Satoshi Nakagawa Fuji Ren Minlie Huang The CoAl group DCSTInstitute for Artificial InteligenceState Key Lab of Intelligent Technology and SystemsBeijing National Research Center for Information Science and TechnologyTsinghua UniversityBeijing 100084China Graduate School of Information Science&Technology The University of Tokyo.Tokyo 1138654Japan School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengduSichuan611731China

Offensive language detection has received important attention and plays a crucial role in promoting healthy communication on social platforms,as well as promoting the safe deployment of large language *** data is the basis for developing detectors;however,the available offense-related dataset in Chinese is severely limited in terms of data scale and coverage when compared to English *** significantly affects the accuracy of Chinese offensive language detectors in practical applications,especially when dealing with hard cases or out-of-domain *** alleviate the limitations posed by available datasets,we introduce AugCOLD(Augmented Chinese Offensive Language Dataset),a large-scale unsupervised dataset containing 1 million samples gathered by data crawling and model ***,we employ a multiteacher distillation framework to enhance detection performance with unsupervised *** is,we build multiple teachers with publicly accessible datasets and use them to assign soft labels to *** soft labels serve as a bridge for knowledge to be distilled from both AugCOLD and multiteacher to the student network,i.e.,the final offensive *** conduct experiments on multiple public test sets and our well-designed hard tests,demonstrating that our proposal can effectively improve the generalization and robustness of the offensive language detector.

关键词： COLD dealing limitations

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Vision-Language Navigation with Energy-Based Policy 38

Vision-Language Navigation with Energy-Based Policy

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Liu, Rui Wang, Wenguan Yang, Yi The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

Vision-language navigation (VLN) requires an agent to execute actions following human instructions. Existing VLN models are optimized through expert demonstrations by supervised behavioural cloning or incorporating manual reward engineering. While straightforward, these efforts overlook the accumulation of errors in the Markov decision process, and struggle to match the distribution of the expert policy. Going beyond this, we propose an Energy-based Navigation Policy (ENP) to model the joint state-action distribution using an energy-based model. At each step, low energy values correspond to the state-action pairs that the expert is most likely to perform, and vice versa. Theoretically, the optimization objective is equivalent to minimizing the forward divergence between the occupancy measure of the expert and ours. Consequently, ENP learns to globally align with the expert policy by maximizing the likelihood of the actions and modeling the dynamics of the navigation states in a collaborative manner. With a variety of VLN architectures, ENP achieves promising performances on R2R, REVERIE, RxR, and R2R-CE, unleashing the power of existing VLN models. © 2024 Neural information processing systems foundation. All rights reserved.

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FEEL-SNN: Robust Spiking Neural Networks with Frequency Encoding and Evolutionary Leak Factor 38

FEEL-SNN: Robust Spiking Neural Networks with Frequency Enco...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Xu, Mengting Ma, De Tang, HuaJin Zheng, Qian Pan, Gang The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China

Currently, researchers think that the inherent robustness of spiking neural networks (SNNs) stems from their biologically plausible spiking neurons, and are dedicated to developing more bio-inspired models to defend attacks. However, most work relies solely on experimental analysis and lacks theoretical support, and the direct-encoding method and fixed membrane potential leak factor they used in spiking neurons are simplified simulations of those in the biological nervous system, which makes it difficult to ensure generalizability across all datasets and networks. Contrarily, the biological nervous system can stay reliable even in a highly complex noise environment, one of the reasons is selective visual attention and non-fixed membrane potential leaks in biological neurons. This biological finding has inspired us to design a highly robust SNN model that closely mimics the biological nervous system. In our study, we first present a unified theoretical framework for SNN robustness constraint, which suggests that improving the encoding method and evolution of the membrane potential leak factor in spiking neurons can improve SNN robustness. Subsequently, we propose a robust SNN (FEEL-SNN) with Frequency Encoding (FE) and Evolutionary Leak factor (EL) to defend against different noises, mimicking the selective visual attention mechanism and non-fixed leak observed in biological systems. Experimental results confirm the efficacy of both our FE, EL, and FEEL methods, either in isolation or in conjunction with established robust enhancement algorithms, for enhancing the robustness of SNNs. Our code is available at https://***/zju-bmi-lab/FEEL_SNN. © 2024 Neural information processing systems foundation. All rights reserved.

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A Blockchain Wallet Scheme with Multi-Factor Authentication Based on Distributed System 4

A Blockchain Wallet Scheme with Multi-Factor Authentication ...

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4th International Conference on Blockchain Technology and Information Security, ICBCTIS 2024

作者： Gao, Qianwen Tu, Yichi Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Tca Lab of State Key Laboratory of Computer Science Beijing China

ISBN: (纸本)9798331528324

Blockchain wallet is a technology focusing on generating, storing and utilizing private keys to manage digital assets securely. As a service, blockchain wallets require user authentication. Traditional blockchain wallets employ mnemonic phrases to assist users in storing and managing private keys. However, the complex operation procedures and the totally different identity authentication mechanism of traditional wallets undoubtedly hinder their development. What's worse, it's inconvenient for secure storage, and doesn't support account recovery, which affects the security and usability of traditional wallets. To address this issue, this paper provides a solution for identity authentication and private key management of blockchain digital wallets in distributed scenarios. It proposes an architecture for building a distributed multi-factor authentication digital wallet for blockchain. We design and implement this blockchain digital wallet based on a distributed multi-factor authentication system. By employing a secret sharing scheme, the encrypted private key shares are stored on distributed servers. When a user wants to use his private key, the wallet conducts a private key recovery operation, which significantly reducing the risk of storing the private key locally. This mitigates security issues such as single-point failure and weak password attacks. What's more, Users are only required to operate the hardware token once during the authentication and private key recovery operation, ensuring the balance between the security and usability of blockchain digital wallets. © 2024 IEEE.

关键词： Blockchain

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Identity-consistent transfer learning of portraits for digital apparel sample display

Identity-consistent transfer learning of portraits for digit...

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作者： Wang, Luyuan Wu, Yiqian Yang, Yong-Liang Liu, Chen Jin, Xiaogang State Key Lab of CAD&CG Zhejiang University Hangzhou China Department of Computer Science University of Bath Bath United Kingdom

The rapid development of the online apparel shopping industry demands innovative solutions for high-quality digital apparel sample displays with virtual avatars. However, developing such displays is prohibitively expensive and prone to the well-known "uncanny valley" effect, where a nearly human-looking artifact arouses eeriness and repulsiveness, thus affecting the user experience. To effectively mitigate the "uncanny valley" effect and improve the overall authenticity of digital apparel sample displays, we present a novel photo-realistic portrait generation framework. Our key idea is to employ transfer learning to learn an identity-consistent mapping from the latent space of rendered portraits to that of real portraits. During the inference stage, the input portrait of an avatar can be directly transferred to a realistic portrait by changing its appearance style while maintaining the facial identity. To this end, we collect a new dataset, Daz-Rendered-Faces-HQ (DRFHQ), specifically designed for rendering-style portraits. We leverage this dataset to fine-tune the StyleGAN2-FFHQ generator, using our carefully crafted framework, which helps to preserve the geometric and color features relevant to facial identity. We evaluate our framework using portraits with diverse gender, age, and race variations. Qualitative and quantitative evaluations, along with ablation studies, highlight our method's advantages over state-of-the-art approaches. © 2024 John Wiley & Sons Ltd.

关键词： Authentication

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iNet:visual analysis of irregular transition in multivariate dynamic networks

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Frontiers of computer science 2022年第2期16卷 124-139页

作者： Dongming HAN Jiacheng PAN Rusheng PAN Dawei ZHOU Nan CAO Jingrui HE Mingliang XU Wei CHEN State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China Department of Computer Science and Engineering Arizona State UniversityArizona 85287America Tong Ji Intelligent Big Data Visualisation Lab(iDVx Lab) TongJi UniversityShanghai 200082China Department of Computer Science and Technology Zhengzhou UniversityZhengzhou 450001China

Multivariate dynamic networks indicate networks whose topology structure and vertex attributes are evolving along *** are common in multimedia *** detection is one of the essential tasks in analyzing these networks though it is not well *** this paper,we combine a rare category detection method and visualization techniques to help users to identify and analyze anomalies in multivariate dynamic *** conclude features of rare categories and two types of anomalies of rare *** we present a novel rare category detection method,called DIRAD,to detect rare category candidates with *** develop a prototype system called iNet,which integrates two major visualization components,including a glyph-based rare category identifier,which helps users to identify rare categories among detected substructures,a major view,which assists users to analyze and interpret the anomalies of rare categories in network topology and vertex ***,including an algorithm performance evaluation,a case study,and a user study,are conducted to test the effectiveness of proposed methods.

关键词： multivariate dynamic networks rare categories anomaly detection visual analysis

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Differential Optimization Testing of Gremlin-Based Graph Database Systems

Differential Optimization Testing of Gremlin-Based Graph Dat...

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International Conference on Software Testing, Verification, and Validation, ICST

作者： Yingying Zheng Wensheng Dou Lei Tang Ziyu Cui Jiansen Song Ziyue Cheng Wei Wang Jun Wei Hua Zhong Tao Huang State Key Lab of Computer Science at ISCAS University of CAS Beijing China Nanjing Institute of Software Technology University of CAS Nanjing China Huazhong University of Science and Technology Hubei China

ISBN: (数字)9798350308181

ISBN: (纸本)9798350308198

Graph database systems (GDBs) allow efficiently creating, modifying, and retrieving graph data in a graph database. To accelerate graph queries, GDBs usually adopt various and complex optimization strategies. However, incorrect optimizations in GDBs can introduce optimization bugs, which cause a graph query to compute an incorrect query result, e.g., omitting a vertex in a graph database. In this paper, we propose Differential Optimization Testing (DOT), an effective and automated approach to detect optimization bugs in GDBs that adopt Gremlin as their query language. The main idea of DOT is that, given a Gremlin query $Q$ , we execute it on the target GDB with two different optimization configurations and then verify whether they can compute the same query results for query $Q$ . Any inconsistency between their query results indicates an optimization bug in the target GDB. To improve the efficiency of differential testing in DOT, we further propose an optimization-guided approach, aiming to explore more optimization strategies and more graph database features. We evaluate DOT on six popular and widely-used GDBs, i.e., Neo4j, OrientDB, JanusGraph, HugeGraph, TinkerGraph, and ArcadeDB. In total, we have found 28 unique optimization bugs, 16 of which have been confirmed as previously-unknown bugs.

关键词： Software testing computer bugs US Department of Transportation Database systems Robustness Database languages Optimization

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OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework

OpenFed: A Comprehensive and Versatile Open-Source Federated...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Dengsheng Chen Vince Junkai Tan Zhilin Lu Enhua Wu Jie Hu Meituan Bytedance Inc. Tsinghua University University of Macau State Key Lab of Computer Science ISCAS University of Chinese Academy of Sciences

Recent developments in Artificial Intelligence techniques have enabled their successful application across a spectrum of commercial and industrial settings. However, these techniques require large volumes of data to be aggregated in a centralized manner, forestalling their applicability to scenarios wherein the data is sensitive or the cost of data transmission is prohibitive. Federated Learning alleviates these problems by decentralizing model training, thereby removing the need for data transfer and aggregation. To advance the adoption of Federated Learning, more research and development needs to be conducted to address some important open questions. In this work, we propose OpenFed, an open-source software framework for end-to-end Federated Learning. OpenFed reduces the barrier to entry for both researchers and downstream users of Federated Learning by the targeted removal of existing pain points. For researchers, OpenFed provides a framework wherein new methods can be easily implemented and fairly evaluated against an extensive suite of benchmarks. For downstream users, OpenFed allows Federated Learning to be plugged and play within different subject-matter contexts, removing the need for deep expertise in Federated Learning. The source code of OpenFed is publicly available online at https://***/Federallab/OpenFed.

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NerfCap: Human Performance Capture with Dynamic Neural Radiance Fields

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IEEE Transactions on Visualization and computer Graphics 2023年第12期29卷 5097-5110页

作者： Wang, Kangkan Peng, Sida Zhou, Xiaowei Yang, Jian Zhang, Guofeng Nanjing University of Science and Technology Key Lab of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Jiangsu Nanjing210094 China Zhejiang University State Key Lab of CAD&CG Zijingang Campus Zhejiang Hangzhou310058 China

This paper addresses the challenge of human performance capture from sparse multi-view or monocular videos. Given a template mesh of the performer, previous methods capture the human motion by non-rigidly registering the template mesh to images with 2D silhouettes or dense photometric alignment. However, the detailed surface deformation cannot be recovered from the silhouettes, while the photometric alignment suffers from instability caused by appearance variation in the videos. To solve these problems, we propose NerfCap, a novel performance capture method based on the dynamic neural radiance field (NeRF) representation of the performer. Specifically, a canonical NeRF is initialized from the template geometry and registered to the video frames by optimizing the deformation field and the appearance model of the canonical NeRF. To capture both large body motion and detailed surface deformation, NerfCap combines linear blend skinning with embedded graph deformation. In contrast to the mesh-based methods that suffer from fixed topology and texture, NerfCap is able to flexibly capture complex geometry and appearance variation across the videos, and synthesize more photo-realistic images. In addition, NerfCap can be pre-trained end to end in a self-supervised manner by matching the synthesized videos with the input videos. Experimental results on various datasets show that NerfCap outperforms prior works in terms of both surface reconstruction accuracy and novel-view synthesis quality. © 1995-2012 IEEE.

关键词： Image reconstruction

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