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

作者： Junyu Shi Wei Wan Shengshan Hu Jianrong Lu Leo Yu Zhang School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Information Technology Deakin University

Recently emerged federated learning (FL) is an attractive distributed learning framework in which numerous wireless end-user devices can train a global model with the data remained autochthonous. Compared with the traditional machine learning framework that collects user data for centralized storage, which brings huge communication burden and concerns about data privacy, this approach can not only save the network bandwidth but also protect the data privacy. Despite the promising prospect, Byzantine attack, an intractable threat in conventional distributed network, is discovered to be rather efficacious against FL as well. In this paper, we conduct a comprehensive investigation of the state-of-the-art strategies for defending against Byzantine attacks in FL. We first provide a taxonomy for the existing defense solutions according to the techniques they used, followed by an across-the-board comparison and discussion. Then we propose a new Byzantine attack method called weight attack to defeat those defense schemes, and conduct experiments to demonstrate its threat. The results show that existing defense solutions, although abundant, are still far from fully protecting FL. Finally, we indicate possible countermeasures for weight attack, and highlight several challenges and future research directions for mitigating Byzantine attacks in FL.

关键词： Wireless communication Training Privacy data privacy Federated learning Distance learning Taxonomy

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Personalized Programming Guidance Based on Deep Programming Learning Style Capturing 18th

Personalized Programming Guidance Based on Deep Programming...

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18th International Conference on Computer science and Education, ICCSE 2023

作者： Liu, Yingfan Zhu, Renyu Gao, Ming School of Data Science and Engineering East China Normal University Shanghai China KLATASDS-MOE in School of Statistics East China Normal University Shanghai China NetEase Fuxi AI Lab Zhejiang Hangzhou China

ISBN: (纸本)9789819707294

With the rapid development of big data and AI technology, programming is in high demand and has become an essential skill for students. Meanwhile, researchers also focus on boosting the online judging system’s guidance ability to reduce students’ dropout rates. Previous studies mainly targeted at enhancing learner engagement on online platforms by providing personalized recommendations. However, two significant challenges still need to be addressed in programming: C1) how to recognize complex programming behaviors;C2) how to capture intrinsic learning patterns that align with the actual learning process. To fill these gaps, in this paper, we propose a novel model called Programming Exercise Recommender with Learning Style (PERS), which simulates learners’ intricate programming behaviors. Specifically, since programming is an iterative and trial-and-error process, we first introduce a positional encoding and a differentiating module to capture the changes of consecutive code submissions (which addresses C1). To better profile programming behaviors, we extend the Felder-Silverman learning style model, a classical pedagogical theory, to perceive intrinsic programming patterns. Based on this, we align three latent vectors to record and update programming ability, processing style, and understanding style, respectively (which addresses C2). We perform extensive experiments on two real-world datasets to verify the rationality of modeling programming learning styles and the effectiveness of PERS for personalized programming guidance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Students

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DA-BEV: Unsupervised Domain Adaptation for Bird's Eye View Perception

arXiv

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arXiv 2024年

作者： Jiang, Kai Huang, Jiaxing Xie, Weiying Lei, Jie Li, Yunsong Shao, Ling Lu, Shijian State Key Laboratory of Integrated Services Networks Xidian University Xi'An710071 China S-lab School of Computer Science and Engineering Nanyang Technological University Singapore UCAS-Terminus AI Lab University of Chinese Academy of Sciences China School of Electrical and Data Engineering The University of Technology Sydney Australia

Camera-only Bird's Eye View (BEV) has demonstrated great potential in environment perception in a 3D space. However, most existing studies were conducted under a supervised setup which cannot scale well while handling various new data. Unsupervised domain adaptive BEV, which effective learning from various unlabelled target data, is far under-explored. In this work, we design DA-BEV, the first domain adaptive camera-only BEV framework that addresses domain adaptive BEV challenges by exploiting the complementary nature of image-view features and BEV features. DA-BEV introduces the idea of query into the domain adaptation framework to derive useful information from image-view and BEV features. It consists of two query-based designs, namely, query-based adversarial learning (QAL) and query-based self-training (QST), which exploits image-view features or BEV features to regularize the adaptation of the other. Extensive experiments show that DA-BEV achieves superior domain adaptive BEV perception performance consistently across multiple datasets and tasks such as 3D object detection and 3D scene segmentation. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

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Fair Text-to-Image Diffusion via Fair Mapping 39

Fair Text-to-Image Diffusion via Fair Mapping

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Li, Jia Hu, Lijie Zhang, Jingfeng Zheng, Tianhang Zhang, Hua Wang, Di Lab Saudi Arabia King Abdullah University of Science and Technology Saudi Arabia SDAIA-KAUST Saudi Arabia Institute of Information Engineering Chinese Academy of Sciences China University of Auckland New Zealand The State Key Laboratory of Blockchain and Data Security Zhejiang University China Institute of Blockchain and Data Security China

ISBN: (纸本)157735897X

In this paper, we address the limitations of existing text-to-image diffusion models in generating demographically fair results when given human-related descriptions. These models often struggle to disentangle the target language context from sociocultural biases, resulting in biased image generation. To overcome this challenge, we propose Fair Mapping, a flexible, model-agnostic, and lightweight approach that modifies a pre-trained text-to-image diffusion model by controlling the prompt to achieve fair image generation. One key advantage of our approach is its high efficiency. It only requires updating an additional linear network with few parameters at a low computational cost. By developing a linear network that maps conditioning embeddings into a debiased space, we enable the generation of relatively balanced demographic results based on the specified text condition. With comprehensive experiments on face image generation, we show that our method significantly improves image generation fairness with almost the same image quality compared to conventional diffusion models when prompted with descriptions related to humans. By effectively addressing the issue of implicit language bias, our method produces more fair and diverse image outputs. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability

arXiv

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arXiv 2024年

作者： Luo, Zihan Song, Xiran Huang, Hong Lian, Jianxun Zhang, Chenhao Jiang, Jinqi Xie, Xing Huazhong University of Science and Technology Wuhan China Microsoft Research Asia Beijing China The National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

Evaluating and enhancing the general capabilities of large language models (LLMs) has been an important research topic. Graph is a common data structure in the real world, and understanding graph data is a crucial part for advancing general intelligence. To evaluate and enhance the graph understanding abilities of LLMs, in this paper, we propose a benchmark named GraphInstruct, which comprehensively includes 21 classical graph reasoning tasks, providing diverse graph generation pipelines and detailed reasoning steps. Based on GraphInstruct, we further construct GraphLM through efficient instruction-tuning, which shows prominent graph understanding capability. In order to enhance the LLM with graph reasoning capability as well, we propose a step mask training strategy, and construct a model named GraphLM+. As one of the pioneering efforts to enhance the graph understanding and reasoning abilities of LLMs, extensive experiments have demonstrated the superiority of GraphLM and GraphLM+ over other LLMs. We look forward to more researchers exploring the potential of LLMs in the graph data mining domain through GraphInstruct. Our code for generating GraphInstruct is released publicly at: https://***/CGCL-codes/GraphInstruct. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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OpenKG Chain:A Blockchain Infrastructure for Open Knowledge Graphs

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data Intelligence 2021年第2期3卷 205-227页

作者： Huajun Chen Ning Hu Guilin Qi Haofen Wang Zhen Bi Jie Li Fan Yang College of Computer Science&AZFT Knowledge Engine Lab Zhejiang UniversityHangzhou 310058China Ontology Foundation Venture Drive#11-31Vision ExchangeSingapore 608526Singapore School of Computer Science and Engineering Southeast UniversityNanjing 211189China Intelligent Big Data Visualization Lab Tongji UniversityShanghai 200092China

The early concept of knowledge graph originates from the idea of the semantic Web,which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between *** publishing knowledge bases as open data on the Web has gained significant *** China,Chinese Information Processing Society of China(CIPS)launched the OpenKG in 2015 to foster the development of Chinese Open Knowledge *** existing open knowledge-based programs,OpenKG chain is envisioned as a blockchain-based open knowledge *** article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain,a blockchain-based trust *** have completed the test of the underlying blockchain platform,and the on-chain test of OpenKG’s data set and tool set sharing as well as fine-grained knowledge crowdsourcing at the triple *** have also proposed novel definitions:K-Point and OpenKG Token,which can be considered to be a measurement of knowledge value and user value.1,033 knowledge contributors have been involved in two months of testing on the blockchain,and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,*** the first time,we have tested and realized on-chain sharing of knowledge at entity/triple granularity *** present,all operations on the data sets and tool sets at ***,as well as the triplets at OpenBase,are recorded on the chain,and corresponding value will also be generated and assigned in a trusted *** this effort,OpenKG chain looks forward to providing a more credible and traceable knowledge-sharing platform for the knowledge graph community.

关键词： Open knowledge graph Blockchain Decentralized distributed network Decentralized knowledge graph

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Few-Shot Learning with Distribution Calibration for Event-Level Rumor Detection

SSRN

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SSRN 2024年

作者： Ran, Hongyan Jia, Caiyan Li, Xiaohong Zhang, Zhichang College of Computer Science & Engineering Northwest Normal University Lanzhou730070 China School of Computer and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China

With the rapid evolution of social media, rumors travel at unprecedented speeds. Automatic recognition of rumors is important for making users receive truthful information and maintaining social harmony. Recently, deep learning models have demonstrated strong rumor detection ability by capturing semantics of rumors, learning rumor propagation patterns and integrating users’ properties,etc. However, most existing rumor detection models perform poorly on unseen events because they are powerful at capturing event-specific features in seen data, which cannot be transferred to newly emergent events due to domain shift. Therefore, in this study, we propose a novel model named ERumor in a few-shot setting to learn and transfer event-invariant features from historic events withsufficient samples to new events with only a few examples. The model first calculates the rumor class distributions of historic rumors, and then calibrates the class distributions of a new event with the old ones. Furthermore, an adequate number of samples are generated from the calibrated distributions to expand the training set for a new-event rumor classifier. The proposed model can be paired withany rumor detection classifier as a feature extractor without extra parameters. Empirical studies have shown that a simple MLP (Multilayer Perceptron) trained on the samples generated from the calibrated distributions can outperform the state-of-the-art baseline models on two benchmark rumor datasets PHEME5, PHEME9 and one topic-level dataset T-Twitter, which is generated by topic extraction and cross-data manner, with more than 15.15%, 4.7% and 7.38% accuracy improvements, respectively. © 2024, The Authors. All rights reserved.

关键词： Semantics

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Fast and Accurate Cooperative Radio Map Estimation Enabled by GAN

Fast and Accurate Cooperative Radio Map Estimation Enabled b...

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IEEE International Conference on Communications Workshops, ICC

作者： Zezhong Zhang Guangxu Zhu Junting Chen Shuguang Cui Future Network of Intelligent Institute (FNii) School of Science and Engineering (SSE) The Chinese University of Hong Kong (Shenzhen) Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China Pengcheng Lab Shenzhen China

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In the 6G era, real-time radio resource monitoring and management are urged to support diverse wireless-empowered applications. This calls for fast and accurate estimation on the distribution of the radio resources, which is usually represented by the spatial signal power strength over the geographical environment, known as a radio map. In this paper, we present a cooperative radio map estimation (CRME) approach enabled by the generative adversarial network (GAN), called as GAN-CRME, which features fast and accurate radio map estimation without the transmitters' information. The radio map is inferred by exploiting the interaction between distributed received signal strength (RSS) measurements at mobile users and the geographical map using a deep neural network estimator, resulting in low data-acquisition cost and computational complexity. Moreover, a GAN-based learning algorithm is proposed to boost the inference capability of the deep neural network estimator by exploiting the power of generative AI. Simulation results showcase that the proposed GAN-CRME is even capable of coarse error-correction when the geographical map information is inaccurate.

关键词： 6G mobile communication Accuracy Costs Three-dimensional displays Conferences Radio transmitters Estimation

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Computational Framework for in Silico Analysis of Neural Hyperactivity and Loss of Neural Activity in a Population of Interconnected Neurons

Computational Framework for in Silico Analysis of Neural Hyp...

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： Vaibhav Dhyani KV Venkatesh Lopamudra Giri Department of Chemical Engineering Bioimaging and Data Analysis Lab Indian Institute of Technology Hyderabad Sangareddy Telangana INDIA Optical Science Centre Faculty of Science Engineering Technology Swinburne University of Technology Melbourne Australia Department of Chemical Engineering Indian Institute of Technology Bombay Mumbai Maharashtra INDIA

Synaptic transmissions are known to regulate the properties of $\text{Ca}^{2+}$ -oscillation in neurons, but the assessment of biophysical parameters underlying neurodegenerative conditions remains challenging. Specifically, establishing an in vivo or in vitro model for neurodegeneration and high-resolution imaging requires expensive infrastructure. Hence, to complement the experimental investigations, there is a requirement to develop an in silico simulation framework that can be used to mimic the synaptic transmission and $\mathbf{Ca}^{2+}$ spiking under diseased conditions. While network models for neuronal firing exist, limited investigations depict the $\mathbf{Ca}^{2+}$ wave propagation in neuronal populations. Since $\mathbf{Ca}^{2+}$ -transients reflect the pathological state of the cells, we chose to model the $\mathbf{Ca}^{2+}$ -dynamics for the characterization of the system. In this work, first, we have developed an ODE-system-based biophysical model that generates $\mathbf{Ca}^{2+}$ -waves in a population of interconnected neurons for both soma and dendrites. Secondly, we demonstrate that the model can be used to depict the irregular $\mathbf{Ca}^{2+}$ -spiking patterns in case of loss of connectivity as well as hyper-connectivity. Additionally, we identified that the various burst patterns are controlled through the level of glutamate activation by the number of neighboring neurons, leading to complex $\mathbf{Ca}^{2+}$ -oscillations. Overall, the proposed model can potentially be used for in silico analysis of neuroprotective drugs that may potentially modulate the $\mathbf{Ca}^{2+}$ -spiking pattern associated with diseased conditions.

关键词： Pathology In vivo Biological system modeling Propagation Sociology Neural activity Neural engineering

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DeepfakeBench: A Comprehensive Benchmark of Deepfake Detection

arXiv

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arXiv 2023年

作者： Yan, Zhiyuan Zhang, Yong Yuan, Xinhang Lyu, Siwei Wu, Baoyuan School of Data Science The Chinese University of Hong Kong Shenzhen [CUHK-Shenzhen China Tencent AI Lab China Department of Computer Science and Engineering University at Buffalo State University of New York United States

A critical yet frequently overlooked challenge in the field of deepfake detection is the lack of a standardized, unified, comprehensive benchmark. This issue leads to unfair performance comparisons and potentially misleading results. Specifically, there is a lack of uniformity in data processing pipelines, resulting in inconsistent data inputs for detection models. Additionally, there are noticeable differences in experimental settings, and evaluation strategies and metrics lack standardization. To fill this gap, we present the first comprehensive benchmark for deepfake detection, called DeepfakeBench, which offers three key contributions: 1) a unified data management system to ensure consistent input across all detectors, 2) an integrated framework for state-of-the-art methods implementation, and 3) standardized evaluation metrics and protocols to promote transparency and reproducibility. Featuring an extensible, modular-based codebase, DeepfakeBench contains 15 state-of-the-art detection methods, 9 deepfake datasets, a series of deepfake detection evaluation protocols and analysis tools, as well as comprehensive evaluations. Moreover, we provide new insights based on extensive analysis of these evaluations from various perspectives (e.g., data augmentations, backbones). We hope that our efforts could facilitate future research and foster innovation in this increasingly critical domain. All codes, evaluations, and analyses of our benchmark are publicly available at https://***/SCLBD/DeepfakeBench. Copyright © 2023, The Authors. All rights reserved.

关键词： data handling

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