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CrossSim: Towards Cross-System Trajectory Similarity Computation via Representation Learning

IEEE Internet of Things Journal 2025年

作者： Cao, Zijian Zhao, Dong Dong, Xiyuan Wang, Qiyue Yuan, Haitao Gao, Yujia Ma, Huadong Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology School of Computer Science Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore Zhongguancun Laboratory Beijing100094 China

Trajectory similarity computation is essential for various downstream applications, such as anomaly route detection, order matching, and digital contact tracing. However, its effectiveness is confined within a single system due to privacy concerns associated with sharing raw trajectories across different systems. In this paper, we propose CrossSim, a novel framework designed to efficiently retrieve similar trajectories across all systems while preserving individual privacy. Our framework comprises three main components: i) a Trajectory Encoding Model that transforms trajectories into high-quality representations, where similarity relationships are reflected by their distances;ii) a two-stage optimization mechanism, including a Contrastive Similarity Learning stage and a Federated Similarity Learning stage, that alleviates the impact of heterogeneous similarity relationships across different systems on model training without aggregating raw trajectories;iii) a Similar Trajectory Retrieval procedure that obtains top-k similar trajectories from all systems without sharing raw trajectories. We conduct comprehensive experiments on three real-world datasets to evaluate the effectiveness of our proposed framework. The evaluation results demonstrate that CrossSim outperforms all existing schemees. © 2014 IEEE.

关键词： Federated learning

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A systematic review of machine learning approaches for detecting deceptive activities on social media: methods, challenges, and biases

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International Journal of data science and Analytics 2025年 1-26页

作者： Liu, Yunchong Shen, Xiaorui Zhang, Yeyubei Wang, Zhongyan Tian, Yexin Dai, Jianglai Cao, Yuchen School of Engineering and Applied Science University of Pennsylvania Philadelphia USA Khoury College of Computer Science Northeastern University Seattle USA Center for Data Science New York University New York USA College of Computing Georgia Institute of Technology Bellevue USA Department of EECS University of California Berkeley Berkeley USA

The growing prevalence of misinformation, spam, and fake accounts on social media platforms such as Twitter, Facebook, and Instagram presents a serious threat to public discourse and safety. To address this, machine learning (ML) and deep learning (DL) models have been widely applied for detecting deceptive content. This systematic review analyzes 36 peer-reviewed studies published between January 2010 and July 2024 that apply ML/DL techniques to combat deception across major platforms. Among these, random forest and support vector machines were the most frequently used ML models (17 and 16 studies, respectively), while deep learning models such as artificial neural networks (8 studies) and LSTM (2 studies) also showed promising results. Performance metrics, including F1 scores and AUROC, were extracted when available, with several studies reporting F1 scores exceeding 90% for specific tasks like fake account detection. Despite these advances, 86% of studies inadequately addressed class imbalance, and only 39% consistently tuned hyperparameters. Furthermore, over 70% did not mention strategies for handling linguistic challenges such as negations. This review identifies key methodological limitations—including sample representativeness, inconsistent preprocessing, and reliance on accuracy in imbalanced settings—and offers recommendations to enhance generalizability, bias mitigation, and real-world applicability of ML-based misinformation detection.

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High Quality Global Routing Algorithm Based on Multi-Stage Optimization

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Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of computer-Aided Design and computer Graphics 2024年第4期36卷 607-614页

作者： Genggeng, Liu Zhenyu, Pei Ning, Xu College of Computer and Data Science Fuzhou University Fuzhou350116 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Information Engineering Wuhan University of Technology Wuhan430070 China

Global routing is a crucial step in VLSI physical design. To address the problems that the low utilization rate of capacity and easy to fall into local optimum in the existing global routing algorithms, a high-quality global routing algorithm based on multi-stage optimization is proposed. First, the virtual capacity dynamic adjustment strategy is introduced in the pre-connection stage and the routing reorganization stage to improve the utilization rate of capacity. Then, in the routing reorganization stage, the routing subarea adaptive expansion strategy is proposed to narrow the search space and improve the routing efficiency. Finally, the A* algorithm heuristic search is combined with the global search capability of the particle swarm algorithm to avoid the algorithm to choose the shortest path in the routing process, and thus avoid the algorithm to fall into the local optimum. Experimental results comparing with existing global routing algorithms on ISPD benchmark circuits show that the routing results of proposed algorithm reduce the wirelength by 6.0% to 22.8%. © 2024 Institute of Computing Technology. All rights reserved.

关键词： Routing algorithms

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Gender-Neutral Large Language Models for Medical Applications: Reducing Bias in PubMed Abstracts

arXiv

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arXiv 2025年

作者： Schaefer, Elizabeth J. Roberts, Kirk Department of Computer Science Yale University New HavenCT United States Department of Health Data Science and Artificial Intelligence McWilliams School of Biomedical Informatics UTHealth HoustonTX United States

This paper presents a pipeline for mitigating gender bias in large language models (LLMs) used in medical literature by neutralizing gendered occupational pronouns. A dataset of 379,000 PubMed abstracts from 1965-1980 was processed to identify and modify pronouns tied to professions. We developed a BERT-based model, "Modern Occupational Bias Elimination with Refined Training," or "MOBERT," trained on these neutralized abstracts, and compared its performance with "1965Bert," trained on the original dataset. MOBERT achieved a 70% inclusive replacement rate, while 1965Bert reached only 4%. A further analysis of MOBERT revealed that pronoun replacement accuracy correlated with the frequency of occupational terms in the training data. We propose expanding the dataset and refining the pipeline to improve performance and ensure more equitable language modeling in medical applications. © 2025, CC BY-NC-ND.

关键词： Medical informatics

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Application of SNNS Model Based On Multi-Dimensional Attention In Drone Radio Frequency Signal Classification

Application of SNNS Model Based On Multi-Dimensional Attenti...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zheng Si Chao Liu Jianyu Liu Yinhao Zhou Henan Institute of Advanced Technology Zhengzhou University Zhengzhou Zhongke Institute of Integrated Circuit and System Application School of Computer Science Shanghai Key Laboratory of Data Science Fudan University

Spiking Neural Networks (SNNs) are attracting attention due to their energy efficiency and importance in neuromorphic computing. Therefore, we propose an SNN-based method for classifying drone RF signals in complex electromagnetic environments. Specifically, we designed a new SNNs model called Spiking-EfficientNet based on EfficientNetV2 and improved its performance with a multidimensional attention mechanism. Experimental results demonstrate that Spiking-EfficientNet achieved classification accuracy of 99.13% and 96.02% on the ZK RF and DroneDetectV2 datasets. Importantly, Spiking-EfficientNet not only outperforms traditional Artificial Neural Networks (ANNs) in performance, but also exhibits significantly lower energy consumption. The energy consumption is only 20.1% of EfficientNetV2, 2.56% of VGG11, 10.71% of ResNet18, and 61.15% of MobileNetV2. This study demonstrates the significant potential of SNNs in drone RF signal classification and provides a low-power solution.

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JobViz: Skill-driven Visual Exploration of Job Advertisements

arXiv

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arXiv 2024年

作者： Wang, Ran Chen, Qianhe Wang, Yong Shen, Boyang Xiong, Lewei School of Journalism and Information Communication Huazhong University of Science and Technology Wuhan China Philosophy and Social Science Laboratory of Big Data and National Communication Strategy Ministry of Education Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory for Optoelectronics Wuhan China College of Computing and Data Science Nanyang Technological University Singapore Singapore

Online job advertisements on various job portals or websites have become the most popular way for people to find potential career opportunities nowadays. However, the majority of these job sites are limited to offering fundamental filters such as job titles, keywords, and compensation ranges. This often poses a challenge for job seekers in efficiently identifying relevant job advertisements that align with their unique skill sets amidst a vast sea of listings. Thus, we propose well-coordinated visualizations to provide job seekers with three levels of details of job information: a skill-job overview visualizes skill sets, employment posts as well as relationships between them with a hierarchical visualization design;a post exploration view leverages an augmented radar-chart glyph to represent job posts and further facilitates users' swift comprehension of the pertinent skills necessitated by respective positions;a post detail view lists the specifics of selected job posts for profound analysis and comparison. By using a real-world recruitment advertisement dataset collected from 51Job, one of the largest job websites in China, we conducted two case studies and user interviews to evaluate JobViz. The results demonstrated the usefulness and effectiveness of our approach. © 2024, CC BY-NC-ND.

关键词： Marketing

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A Dynamical Clipping Approach with Task Feedback for Proximal Policy Optimization

arXiv

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arXiv 2023年

作者： Zhang, Ziqi Xu, Jingzehua Zhuang, Zifeng Zhang, Hongyin Liu, Jinxin Wang, Donglin Zhang, Shuai Lyu, Shangke School of Engineering WestLake University China MicroMasters Program in Statistics and Data Science Massachusetts Institute of Technology United States Department of Data Science New Jersey Institute of Technology United States

Proximal Policy Optimization (PPO) has been broadly applied to robotics learning, showcasing stable training performance. However, the fixed clipping bound setting may limit the performance of PPO. Specifically, there is no theoretical proof that the optimal clipping bound remains consistent throughout the entire training process. Meanwhile, previous researches suggest that a fixed clipping bound restricts the policy's ability to explore. Therefore, many past studies have aimed to dynamically adjust the PPO clipping bound to enhance PPO's performance. However, the objective of these approaches are not directly aligned with the objective of reinforcement learning (RL) tasks, which is to maximize the cumulative Return. Unlike previous clipping approaches, we propose a bi-level proximal policy optimization objective that can dynamically adjust the clipping bound to better reflect the preference (maximizing Return) of these RL tasks. Based on this bi-level proximal policy optimization paradigm, we introduce a new algorithm named Preference based Proximal Policy Optimization (Pb-PPO). Pb-PPO utilizes a multi-armed bandit approach to refelect RL preference, recommending the clipping bound for PPO that can maximizes the current Return. Therefore, Pb-PPO results in greater stability and improved performance compared to PPO with a fixed clipping bound. We test Pb-PPO on locomotion benchmarks across multiple environments, including Gym-Mujoco and legged-gym. Additionally, we validate Pb-PPO on customized navigation tasks. Meanwhile, we conducted comparisons with PPO using various fixed clipping bounds and various of clipping approaches. The experimental results indicate that Pb-PPO demonstrates superior training performance compared to PPO and its variants. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Adaptive prescribed performance based on recursive nonsingular terminal sliding mode control for quad-rotor systems under uncertainty and disturbance: Real-time validation

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Aerospace science and Technology 2024年 147卷

作者： Han, Gao Mofid, Omid Mobayen, Saleh Khooban, Mohammad Hassan School of Aerospace Science and Technology Xidian University Xi'an710071 China Department of Electrical Engineering University of Zanjan Zanjan Iran Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliou Taiwan Department of Electrical and Computer Engineering Aarhus University Aarhus8200 Denmark

In this paper, at the aim of the fast trajectory-following control of the Unmanned Aerial Vehicle (UAV) systems subject to uncertainty and disturbance, the adaptive prescribed performance control based recursive nonsingular terminal sliding mode control is suggested. Afterward, for the fast trajectory-following control of the uncertain and perturbed quad-rotor system, a recursive nonsingular terminal sliding mode strategy is recommended. Since the better transient and steady-state response of the sliding mode surface is of the utmost importance in the design of the controller, the prescribed performance control scheme is proposed in which the transformed prescribed form of the previously proposed recursive nonsingular terminal sliding surface is obtained to prove that the recursive nonsingular terminal sliding surface can converge to a preset region. Whereas unknown bounded uncertainty and disturbance always exist in the UAV system's dynamical model which decreases its performance and efficiency, the adaptive mechanism is applied to approximate uncertain bounds of uncertainty and disturbance to advance the implementation of the UAV system. Later, quick reachability of the prescribed form switching surface is demonstrated using Lyapunov concept. Ultimately, simulation results using MATLAB/Simulink over a quad-rotor system and real-time simulation using the Speedgoat Real-Time Target Machine platform are conducted to validate the feasibility and performance of the proposed method. © 2024 Elsevier Masson SAS

关键词： Unmanned aerial vehicles (UAV)

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Bilateral Network with Residual U-blocks and Dual-Guided Attention for Real-time Semantic Segmentation

Bilateral Network with Residual U-blocks and Dual-Guided Att...

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Chinese Automation Congress (CAC)

作者： Liang Liao Liang Wan Mingsheng Liu Shusheng Li State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China School Of Computer Science And Engineering Northeastern Univiversity Shenyang China

When some application scenarios need to use semantic segmentation technology, like automatic driving, the primary concern comes to real-time performance rather than extremely high segmentation accuracy. To achieve a good trade-off between speed and accuracy, two-branch architecture has been proposed in recent years. It treats spatial information and semantics information separately which allows the module to be composed of two networks both not heavy. However, the process of fusing features with two different scales becomes a performance bottleneck for many nowaday two-branch models. In this research, we design a new fusion mechanism for two-branch architecture which is guided by attention computation. To be precise, we use the Dual-Guided Attention (DGA) module we proposed to replace some multi-scale transformations with the calculation of attention which means we only use several attention layers of near linear complexity to achieve performance comparable to frequently-used multi-layer fusion. To ensure that our module can be effective, we use Residual U-blocks (RSU) to build one of the two branches in our networks which aims to obtain better multi-scale features. Extensive experiments on Cityscapes and CamVid dataset show the effectiveness of our method. On Cityscapes, our light version network without pretrain weight can achieve 71.1% mIoU at 163 FPS on a single Nvidia RTX 3070 using full resolution images(1024×2048pix). And the large version can achieve 77.9% mIoU with a speed of 43 FPS which still reaches the real-time criterion. Our code and module has been open sourced at https://***/LikeLidoA/Mymodule.

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LineConGraphs: Line Conversation Graphs for Effective Emotion Recognition using Graph Neural Networks

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IEEE Transactions on Affective Computing 2025年

作者： Krishnan, Gokul S. Padi, Sarala Greenberg, Craig S. Ravindran, Balaraman Manocha, Dinesh Sriram, Ram D. IIT Madras Centre for Responsible AI Wadhwani School of Data Science and AI Chennai India National Institute of Standards and Technology GaithersburgMD United States University of Maryland Computer Science and Engineering College ParkMD United States

Emotion Recognition in Conversations (ERC) is an important aspect of affective computing with practical applications in healthcare, education, chatbots, and social media platforms. Previous approaches to ERC analysis involved using graph neural network architectures to model both speaker and long-term contextual information. In this paper, we introduce new models for ERC analysis: the LineConGCN and LineConGAT models, which are constructed using a graph construction strategy for conversations called line conversational graphs (LineConGraphs). LineConGraph is designed to capture short-term conversational context using one previous and future utterance, while also capturing long-term context using GCN or GAT layers without explicitly integrating into the graph construction strategy. We evaluate the performance of our proposed models on two benchmark datasets, rm IEMOCAP and rm {MELD}$, and show that our LineConGAT model outperforms the state-of-the-art methods with an F1-score of rm 64.58% and rm 76.50%. Furthermore, we demonstrate that incorporating sentiment shift information into line conversation graphs further enhances rm {ERC}$ performance in the case of LineConGCN models. We also evaluate the performance of our proposed model by embedding speaker information into LineConGCN and LineConGAT models and show that LineConGAT and LineConGAT with speaker embeddings performed equally for ERC analysis. © 2010-2012 IEEE.

关键词： Graph neural networks

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