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arXiv 2024年

作者： Wu, Shiwei Zhang, Chao Chen, Joya Xu, Tong Wu, Likang Hu, Yao Chen, Enhong The Anhui Province Key Lab of Big Data Analysis and Application School of Data Science and School of Computer Science University of Science and Technology of China China The College of Design and Engineering National University of Singapore Singapore The Xiaohongshu.Inc. China

People's social relationships are often manifested through their surroundings, with certain objects or interactions acting as symbols for specific relationships, e.g., wedding rings, roses, hugs, or holding hands. This brings unique challenges to recognizing social relationships, requiring understanding and capturing the essence of these contexts from visual appearances. However, current methods of social relationship understanding rely on the basic classification paradigm of detected persons and objects, which fails to understand the comprehensive context and often overlooks decisive social factors, especially subtle visual cues. To highlight the social-aware context and intricate details, we propose a novel approach that recognizes Contextual Social Relationships (ConSoR) from a social cognitive perspective. Specifically, to incorporate social-aware semantics, we build a lightweight adapter upon the frozen CLIP to learn social concepts via our novel multi-modal side adapter tuning mechanism. Further, we construct social-aware descriptive language prompts (e.g., scene, activity, objects, emotions) with social relationships for each image, and then compel ConSoR to concentrate more intensively on the decisive visual social factors via visual-linguistic contrasting. Impressively, ConSoR outperforms previous methods with a 12.2% gain on the People-in-Social-Context (PISC) dataset and a 9.8% increase on the People-in-Photo-Album (PIPA) benchmark. Furthermore, we observe that ConSoR excels at finding critical visual evidence to reveal social relationships. © 2024, CC BY.

关键词： Modal analysis

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KVSAgg: Secure Aggregation of Distributed Key-Value Sets

KVSAgg: Secure Aggregation of Distributed Key-Value Sets

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International Conference on data engineering

作者： Yuhan Wu Siyuan Dong Yi Zhou Yikai Zhao Fangcheng Fu Tong Yang Chaoyue Niu Fan Wu Bin Cui School of Computer Science and National Engineering Laboratory for Big Data Analysis Technology and Application Peking University Beijing China Peng Cheng Laboratory Shenzhen China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

In global data analysis, the central server needs the global statistic of the user data stored in local clients. In such cases, an Honest-but-Curious central server might put user privacy at risk in trying to collect individual statistics of each user. In response, the secure aggregation provides a solution for calculating global statistics without revealing users’ privacy data. However, existing secure aggregation protocols only focus on the data in the form of vectors or common sets, which limits their application scope. We formalize a general problem—key-value set secure aggregation—that not only includes secure vector aggregation and private set union but also supports more applications. To address the proposed problem, we devise our solution (called the KVSAgg framework) that promises satisfactory performance in security, efficiency, and accuracy. Our key technique is a homomorphic transform algorithm (called HyperIBLT) that is not only capable of bidirectionally transforming data between key-value sets and vectors, but also able to transform sum operation of sets to addition of vectors. We implement KVSAgg on both CPU and GPU platforms and perform the evaluation on three use cases including federated learning, distributed data counting, and finding global hot items. Compared with our baselines, KVSAgg simultaneously achieves the best security, efficiency higher by orders of magnitude, and zero-error in nearly all cases. All codes are open-source anonymously.

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Deep Learning Based Proactive Anomaly Detection for 5G Core Control Plane Network Function Interactions

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Tan, Yawen Liu, Jiajia Li, Yuanhao Wang, Jiadai Xidian University State Key Laboratory of Integrated Services Networks School of Cyber Engineering Xi'an710071 China Northwestern Polytechnical University National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Cybersecurity Shaanxi Xi'an710072 China

The Service-Based Architecture (SBA) introduced by 3GPP allows the control plane of 5G Core Network (CN) to function through a set of interconnected Network Functions (NFs), which offers significant benefits such as improved scalability, simplified operations, and efficient resource utilization. However, such a new transformation could also make critical NFs in 5G CN become more susceptible to attacks due to both internal vulnerabilities within the software-implemented NFs or the expanded interfaces brought by the SBA. Existing studies targeting 5G network security pay little attention to the detection of abnormal behaviors from control plane NFs. Therefore, in this paper, we propose 5GCGuard, an anomaly detection scheme aiming at identifying abnormal NF interactions in 5G CN. It employs a deep learning-based sequence model to learn normal interaction patterns and detects anomalies based on deviations from the learned pattern. Special designs including attention mechanism, multi-task learning, probabilistic labeling and automatic threshold decision are made to enhance its detection capabilities. We establish a cloud-native 5G testbed to evaluate the effectiveness of 5GCGuard. Extensive evaluation results show its superiority over comparison schemes. Case studies also demonstrate its ability to proactively detect various NF interaction anomalies, enabling swift action to block attacks and prevent further damage. © 2015 IEEE.

关键词： 5G mobile communication systems

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Sharing Economy in Local Energy Markets

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Journal of Modern Power Systems and Clean Energy 2023年第3期11卷 714-726页

作者： Zhaoyuan Wu Jianxiao Wang Haiwang Zhong Feng Gao Tianjiao Pu Chin-Woo Tan Xiupeng Chen Gengyin Li Huiru Zhao Ming Zhou Qing Xia School of Economics and Management North China Electric Power UniversityBeijing 102206China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China State Key Lab of Power Systems Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Industrial Engineering and Management College of EngineeringPeking UniversityBeijing 100871China China Electric Power Research Institute Beijing 100192China Department of Civil and Environmental Engineering Stanford UniversityPalo AltoCA 94305USA Engineering and Technology Institute Groningen University of Groningen9742 AG Groningenthe Netherlands

With an increase in the electrification of end-use sectors,various resources on the demand side provide great flexibility potential for system operation,which also leads to problems such as the strong randomness of power consumption behavior,the low utilization rate of flexible resources,and difficulties in cost *** the core idea of“access over ownership”,the concept of the sharing economy has gained substantial popularity in the local energy market in recent ***,we provide an overview of the potential market design for the sharing economy in local energy markets(LEMs)and conduct a detailed review of research related to local energy sharing,enabling technologies,and potential *** paper can provide a useful reference and insights for the activation of demand-side flexibility ***,this paper can also provide novel insights into the development and further integration of the sharing economy in LEMs.

关键词： Energy sharing flexibility potential local energy market information and communication technology sharing economy

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Cooperative Merging for Connected Automated Vehicles in Mixed Traffic: A Multi-Agent Reinforcement Learning Approach

SSRN

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SSRN 2023年

作者： Sun, Zhanbo Huang, Jiayi Ji, Ang Zhao, Ruibin Zheng, Guoqian School of Transportation and Logistics Southwest Jiaotong University Chengdu China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Chengdu China

The paper investigates the cooperative decision-making for mixed traffic (CDMMT) ramp merging problem based on a multi-agent reinforcement learning (MARL) approach. With the help of V2X technology, autonomous agents exchange information and corresponding strategies throughout the learning procedure, which can align individual interests with the system’s efficiency. To further improve the driving performance of autonomous agents, expert demonstrations for car-following and merging actions are exploited to diminish collisions at the beginning of the training process, the Multi-Agent Proximal Policy Optimization (MAPPO) with a curriculum learning process is then designed to learn strategies from scenarios with various flow conditions and achieve fast and stable convergence. The simulation results indicate that, compared to human-driven vehicles behaving in over-competitive or conservative strategies, MARL-based agents can improve traffic efficiency by 57.9% in a fully saturated situation. In addition, it is found that, even with 10% CAV penetration, the overall traffic efficiency can be improved by 11.8%. Furthermore, as the mainline traffic flow increases, mainline vehicles prioritize efficiency over safety. In contrast, ramp vehicles exhibit a more conservative behavior, emphasizing safety in response to crowded mainline traffic. Finally, the simulation experiments with different demand splits, penetration rates, and traffic densities reveal the model’s potential learning and transferring capabilities in dynamic traffic conditions, which can hopefully offer valuable insights for future research related to CDMMT problems. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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A Time-Efficient Distributed Constant Conditional Functional Dependency Discovery Algorithm for data Consistency

A Time-Efficient Distributed Constant Conditional Functional...

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IEEE Annual International Computer Software and applications Conference (COMPSAC)

作者： Junchao Chen Jian-tao Zhou Xinyu Hao Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Accuracy data preprocessing Software algorithms Distributed databases Parallel processing Software Partitioning algorithms

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An Empirical Guide for Visualization Consistency in Multiple Coordinated Views

An Empirical Guide for Visualization Consistency in Multiple...

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Shaocong Tan Chufan Lai Xiaolong Luke Zhang Xiaoru Yuan Key Laboratory of Machine Perception (Ministry of Education) and School of Intelligence Science and Technology Peking University China College of Information Sciences and Technology Pennsylvania State University USA National Engineering Laboratory for Big Data Analysis and Application Peking University China

Visual analytic systems usually provide multiple coordinated views (MCVs) to support data analysis and exploration. Coordination in visual graphics plays an important role in facilitating comprehensive analytical tasks, such as data comparison and cognitive inference. However, individual views in MCVs are probably designed for a specific purpose based on a particular type of data, and insufficient consideration of the intricate relationships among views may lead to inconsistency in visual representation and user interaction across different views. To better understand the inconsistency issues in MCVs and their impacts on user behaviors, this paper reports a study on the analysis and classification of visualization inconsistency based on the reviews of interactive visualization designs and visual analytic systems, and the interviews with stakeholders. We find that inconsistencies are prevalent in MCVs and frequently lead to misleading or even incorrect results. We classify the discovered inconsistencies based on a coordination model of MCVs, and develop an empirical guide for systematic and efficient visualization consistency checking in the design, implementation, and evaluation stage.

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Optimal Scheduling of Single-Line Electric Buses with Battery Swapping

Optimal Scheduling of Single-Line Electric Buses with Batter...

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IEEE International Conference on Intelligent Transportation engineering (ICITE)

作者： Yuyan Guo Tao Liu National Engineering Laboratory of Integrated Transportation Big Data Application Technology Institute of System Science and Engineering School of Transportation and Logistics Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350314212

ISBN: (纸本)9798350314229

With the advantages of zero emission and comfortable riding experience, battery-electric buses (BEBs) are widely adopted in public transit agencies as a green alternative to conventional diesel buses. Battery swapping technology is an efficient and promising charging technology to address the issues of battery range anxiety and long battery charging time. This study developed a two-stage optimization model to optimize the single-line battery-electric bus scheduling problem considering battery swapping. At the first stage, a bi-objective integer programming model is established to determine the minimum BEB fleet size, together with the BEB-to-trip assignment and battery swapping schedule. At the second stage, an integer linear programming model is formulated to determine the required minimum number of batteries. An $\epsilon$ -constraint method is designed to investigate the trade-off between the required minimum fleet size and the number of back-up batteries. Finally, a real-world case study of a BEB line in Chengdu, China, is conducted to demonstrate the effectiveness of the optimization model and solution method. Sensitivity analyses are further conducted to understand the impacts of some key parameters, including battery capacity, number of trips, cycle time, and battery swapping time. The results show that battery capacity and cycle time have significant influences on BEB-to-trip assignment results and battery swapping schedule.

关键词： Integer programming Schedules Sensitivity analysis Optimization models Transportation Stochastic processes Optimal scheduling Integer linear programming Scheduling Batteries

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Step-by-Step: Controlling Arbitrary Style in Text with Large Language Models 30

Step-by-Step: Controlling Arbitrary Style in Text with Large...

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Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024

作者： Liu, Pusheng Wu, Lianwei Wang, Linyong Guo, Sensen Liu, Yang National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science Northwestern Polytechnical University China Research & Development Institute of Northwestern Polytechnical University Shenzhen China Chongqing Science & Technology Innovation Center of Northwestern Polytechnical University China State Key Laboratory of Integrated Service Networks Xidian University China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University China

ISBN: (纸本)9782493814104

Recently, the autoregressive framework based on large language models (LLMs) has achieved excellent performance in controlling the generated text to adhere to the required style. These methods guide LLMs through prompt learning to generate target text in an autoregressive manner. However, this manner possesses lower controllability and suffers from the challenge of accumulating errors, where early prediction inaccuracies might influence subsequent word generation. Furthermore, existing prompt-based methods overlook specific region editing, resulting in a deficiency of localized control over input text. To overcome these challenges, we propose a novel three-stage prompt-based approach for specific region editing. To alleviate the issue of accumulating errors, we transform the text style transfer task into a text infilling task, guiding the LLMs to modify only a small portion of text within the editing region to achieve style transfer, thus reducing the number of autoregressive iterations. To achieve an effective specific editing region, we adopt both prompt-based and word frequency-based strategies for region selection, subsequently employing a discriminator to validate the efficacy of the selected region. Experiments conducted on several publicly competitive datasets for text style transfer task confirm that our proposed approach achieves state-of-the-art performance. © 2024 ELRA Language Resource Association: CC BY-NC 4.0.

关键词： Computational linguistics

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Edit-History Vis: An Interactive Visual Exploration and analysis on Wikipedia Edit History

Edit-History Vis: An Interactive Visual Exploration and Anal...

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Pacific (formerly Asia-Pacific APVIS) Visualization Symposium

作者： Yuhan Guo Qin Han Yuke Lou Yiming Wang Can Liu Xiaoru Yuan Key Laboratory of Machine Perception (Ministry of Education) and School of Intelligence Science and Technology Peking University China School of Electronic Engineering and Computer Science Peking University China National Engineering Laboratory for Big Data Analysis and Application Peking University China

We propose Edit-History Vis, a visual analytics system designed to facilitate interactive exploration on Wikipedia edit history at a fine-grained level. The examination of detailed changes in Wikipedia articles is crucial for understanding how authors’ perspectives vary and conflict during the collaborative editing process. However, it is challenging to reveal the details while preserving the heterogeneous attributes of revisions, namely the time, content, and editor. The Edit-History Vis system integrates editor and textual changes of revisions by utilizing a force-directed revision graph that groups revisions based on standpoints. Through this revision graph, users can identify and analyze editing events such as edit wars, vandalism, repair, and normal updates. The effectiveness of the system in analyzing the edit history is validated through a qualitative comparison with prior work and a quantitative rating from a user study.

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