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Traffic Agents Trajectory Prediction Based on Enhanced Bidirectional Recurrent Network and Adaptive Social Interaction Model

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IEEE Transactions on Automation science and Engineering 2025年 22卷 12182-12196页

作者： Chen, Xiaobo Liang, Yuwen Hu, Chuan Wang, Hai Ye, Qiaolin Shandong Technology and Business University School of Computer Science and Technology Yantai264000 China Nanjing Forestry University College of Information Science and Technology and Artificial Intelligence Nanjing210037 China Shanghai Jiao Tong University Intelligent Vehicle Institute School of Mechanical Engineering Shanghai200240 China Jiangsu University School of Automotive and Traffic Engineering Zhenjiang212013 China Nanjing Forestry University College of Information Science and Technology and Artificial Intelligence State Key Laboratory of Tree Genetics and Breeding Co-Innovation Center for Sustainable Forestry in Southern China Nanjing210037 China

Accurate prediction of the future trajectory of traffic agents is imperative to the effective motion planning of autonomous vehicles and mobile robots. Despite enormous progress that has been made toward trajectory prediction, dynamic and crowded traffic scenarios pose major challenges to the understanding and forecasting of traffic agents’ motion behavior. In this paper, we propose a novel trajectory prediction method from the perspective of temporal modeling and social interaction. Specifically, we first put forward a recurrent modeling approach to learn temporal features in favor of capturing long-range and short-range temporal dependencies of individual agents. Then, we construct a social feature learning module to capture the sparse and directional interactions among agents while suppressing the spurious connections. Finally, to reduce the accumulated error during prediction, a coordinated bidirectional decoding module is developed where temporal and social features can be properly integrated into the forward and backward prediction processes. Extensive experiments are performed on four real-world trajectory prediction benchmarks, and the results demonstrate the superiority of our method compared with other competing approaches. Detailed ablation studies are also performed to evaluate the effectiveness of each model component. © 2004-2012 IEEE.

关键词： Prediction models

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Multiple Encoding Coloring Network for Regong Architectural Colored Drawing Images Combining Sketch Hints

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computer Engineering and Applications 2025年第9期61卷 277-287页

作者： Cheng, Miao Zhang, Xiaojuan Zhao, Yang Fan, Hong School of Computer Science Qinghai Normal University Xining810016 China State Key Laboratory of Tibetan Intelligent Information Processing and Application Xining810016 China School of Computer and Information Hefei University of Technology Hefei230002 China School of Computer and Science Shaanxi Normal University Xi’an710062 China

Architectural colored drawing is an important component of the human intangible cultural heritage of Regong art. Some ancient Regong architectural colored drawings have the phenomenon of fading and oxidation, which cannot be maintained promptly due to environmental factors and the limitations of inheritance status. This seriously affects the appreciation and protection of the Regong architectural colored drawings art. Aiming at the problems of color confusion and color leakage when using existing image coloring methods to color architectural colored drawing images, this thesis proposes an end- to- end multiple encoding coloring network for Regong architectural colored drawing images that combines sketch hints. This network supplements the ignored texture details in the feature transfer process with extracted sketch as prior knowledge of the image, enhances the network’s understanding of global semantics, and improves the phenomenon of color distortion and leakage. The multipath parallel encoding module is structured with three encoding branches arranged in different ways using dilated convolutions and traditional convolutions. By obtaining receptive fields of various scales, it better captures contextual information and improves the ability to extract color features. The attention residual module, which combines spatial attention and channel attention, helps the network comprehensively understand the spatial distribution of image color and texture information through the fusion processing of feature information, thereby improving the quality of coloring. Finally, a comparative experiment is conducted on the constructed Regong architectural colored drawing dataset and animation dataset using the proposed method. Compared with existing coloring methods, the proposed method performs well in qualitative and quantitative analysis and achieves the most consistent results with human subjective aesthetics and professional requirements in subjective evaluation comparison. Ex

关键词： Coloring

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KCE-Unet: A novel music denoising method with KANConv ECA Unet*

KCE-Unet: A novel music denoising method with KANConv ECA Un...

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

作者： Shijie Zhang Yulun Wu Ganghui Ru Yi Yu Wei Li School of Computer Science and Technology Fudan University Shanghai China Graduate School of Advanced Science and Engineering Hiroshima University Hiroshima Japan School of Computer Science and Technology Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

During concerts, people often spontaneously record memorable moments with their phones. However, these recordings are frequently accompanied by noise, such as cheering and applause, which diminishes the playback experience. In this paper, we introduce a novel task specifically designed for denoising music in concert environments, a challenge that has been largely overlooked in previous research. To support this task, we created a new concert denoising dataset that includes songs performed in various major languages at concerts, with noise segments like cheering and applause. Building on this, we propose KANConv ECA Unet (KCE-Unet), a method that combines the U-Net network, efficient channel attention (ECA), and the recently proposed KAN network to flexibly remove noise in the mid-to-high frequency range of spectrograms. Extensive experiments demonstrate that our method outperforms previous models in denoising performance and effectively restore disrupted musical structures.

关键词： Noise reduction Noise Buildings Acoustics Recording Multiple signal classification Speech processing Spectrogram

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RoleScan: Enhancing Social Bot Detection Using Social Role Vector 19th

RoleScan: Enhancing Social Bot Detection Using Social Role V...

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19th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2024

作者： Wen, Wen Gao, Min Gong, Qingyuan Wang, Xin Chen, Yang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai China Research Institute of Intelligent Complex Systems Fudan University Shanghai China

ISBN: (纸本)9789819623723

The increase in social media users has led to the rise of social bots, which can disrupt online environments and threaten user privacy. This study presents RoleScan, a novel framework that integrates Social Role Vector (SRV) and Graph Attention Network v2 (GATv2) to detect social bots more accurately. We are the first to formulate the social bot detection problem by considering users’ properties and social influence. RoleScan extracts user features and text features from user profiles and tweets, and uses SRV to capture the social features. SRV is based on node centrality measurement, structural hole theory, and social capital theory, offering a comprehensive view of the influence of a user within the network. By leveraging an attention mechanism, RoleScan focuses on pivotal features and connections, enhancing its ability to differentiate between bots and humans. The experimental results show that RoleScan outperforms existing models, achieving an AUC of 0.887. An ablation study further highlights the critical role of incorporating social features into the detection process. Our study not only enhances the social bot detection performance, but also offers new insights into the users’ diverse roles within social networks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Bot (Internet)

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Throughput Improvement for RIS-Empowered Wireless Powered Anti-Jamming Communication Networks (WPAJCN)

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IEEE Transactions on information Forensics and Security 2025年 20卷 4622-4637页

作者： Chu, Zheng Chieng, David Kwong, Chiew Foong Jin, Huan Zhu, Zhengyu Huang, Chongwen Yuen, Chau University of Nottingham Ningbo China Department of Electrical and Electronic Engineering Ningbo315100 China University of Nottingham Ningbo China Next Generation Internet of Everything Laboratory Ningbo315100 China University of Nottingham Ningbo China School of Computer Science Ningbo315100 China Zhengzhou University School of Electrical and Information Engineering Zhengzhou450001 China Xidian University State Key Laboratory of Integrated Services Networks Xi’an710071 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China Zhejiang Provincial Key Laboratory of Multi-Modal Communication Networks and Intelligent Information Processing Hangzhou310027 China Nanyang Technological University School of Electrical and Electronics Engineering 639798 Singapore

In this paper, we propose a reconfigurable intelligent surface (RIS)-aided wireless powered anti-jamming communication network (WPAJCN), where the RIS is utilized to participate in downlink wireless power transfer (WPT), as well as uplink anti-jamming wireless information transfer (AJ-WIT). To evaluate the network anti-jamming performance, we maximize a sum anti-jamming throughput, with the constraints of downlink WPT and uplink AJ-WIT time scheduling, and unit-modulus RIS phase shifts. The formulated problem is not convex in terms of these two types of coupled variables, which cannot be directly solved. To address this problem, the Lagrange dual method and Karush-Kuhn-Tucker conditions are presented to transform its sum-of-logarithmic objective function into the logarithmically fractional counterpart, which reformulate the original problem into that with respect to RIS phase shift vectors and WPT time scheduling. Next, we propose to apply the Dinkelback algorithm to solve a non-linear fractional programming with respect to the downlink WPT and uplink AJ-WIT RIS phase shifts in an alternating fashion, each of which is derived into a semi-closed solution by utilizing the Riemannian Manifold Optimization (RMO). In addition, the optimal WPT time scheduling is obtained by numerical search. Finally, the numerical results are demonstrated to confirm the improved performance of the proposed approach compared to the benchmark counterparts, which highlights the that RIS can effectively enhance the uplink anti-jamming WIT capability as well as the downlink WPT efficiency. © 2005-2012 IEEE.

关键词： Lagrange multipliers

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Retrieval Augmented Recipe Generation

Retrieval Augmented Recipe Generation

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IEEE Workshop on Applications of computer Vision (WACV)

作者： Guoshan Liu Hailong Yin Bin Zhu Jingjing Chen Chong-Wah Ngo Yu-Gang Jiang Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University Shanghai Collaborative Innovation Center on Intelligent Visual Computing Singapore Management University

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

The growing interest in generating recipes from food images has drawn substantial research attention in recent years. Existing works for recipe generation primarily utilize a two-stage training method—first predicting ingredients from a food image and then generating instructions from both the image and ingredients. Large Multi-modal Models (LMMs), which have achieved notable success across a variety of vision and language tasks, shed light on generating both ingredients and instructions directly from images. Nevertheless, LMMs still face the common issue of hallu-cinations during recipe generation, leading to suboptimal performance. To tackle this issue, we propose a retrieval augmented large multimodal model for recipe generation. We first introduce Stochastic Diversified Retrieval Augmentation (SDRA) to retrieve recipes semantically related to the image from an existing datastore as a supplement, integrating them into the prompt to add diverse and rich context to the input image. Additionally, Self-Consistency Ensemble Voting mechanism is proposed to determine the most confident prediction recipes as the final output. It calculates the consistency among generated recipe candidates, which use different retrieval recipes as context for generation. Extensive experiments validate the effectiveness of our proposed method, which demonstrates state-of-the-art (SOTA) performance in recipe generation on the Recipe1M dataset.

关键词： Training computer vision Accuracy Computational modeling Stochastic processes Predictive models Reliability Faces

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AutoLogi: Automated Generation of Logic Puzzles for Evaluating Reasoning Abilities of Large Language Models

arXiv

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arXiv 2025年

作者： Zhu, Qin Huang, Fei Peng, Runyu Lu, Keming Yu, Bowen Cheng, Qinyuan Qiu, Xipeng Huang, Xuanjing Lin, Junyang Qwen Team Alibaba Group China School of Computer Science Fudan University China Shanghai Key Laboratory of Intelligent Information Processing Fudan University China

While logical reasoning evaluation of Large Language Models (LLMs) has attracted significant attention, existing benchmarks predominantly rely on multiple-choice formats that are vulnerable to random guessing, leading to overestimated performance and substantial performance fluctuations. To obtain more accurate assessments of models’ reasoning capabilities, we propose an automated method for synthesizing open-ended logic puzzles, and use it to develop a bilingual benchmark, AutoLogi. Our approach features program-based verification and controllable difficulty levels, enabling more reliable evaluation that better distinguishes models’ reasoning abilities. Extensive evaluation of eight modern LLMs shows that AutoLogi can better reflect true model capabilities, with performance scores spanning from 35% to 73% compared to the narrower range of 21% to 37% on the source multiple-choice dataset. Beyond benchmark creation, this synthesis method can generate high-quality training data by incorporating program verifiers into the rejection sampling process, enabling systematic enhancement of LLMs’ reasoning capabilities across diverse datasets. © 2025, CC BY-SA.

关键词： computer circuits

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LLaMA-Unidetector: A LLaMA-Based Universal Framework for Open-Vocabulary Object Detection in Remote Sensing Imagery

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IEEE Transactions on Geoscience and Remote Sensing 2025年

作者： Xie, Jianlin Wang, Guanqun Zhang, Tong Sun, Yikang Chen, He Zhuang, Yin Li, Jun Beijing100081 China Peking University National Key Laboratory for Multimedia Information Processing Beijing100871 China China University of Geosciences School of Computer Science Hubei Key Laboratory of Intelligent Geo-Information Processing Wuhan430078 China

Object detection is a crucial task in computer vision for remote sensing applications. However, the reliance of traditional methods on predefined and trained object categories limits their applicability in open-world scenarios. A key challenge in open-vocabulary object detection lies in accurately identifying unseen objects. Existing approaches often focus solely on detecting object locations, struggling to recognize the categories of previously unseen targets. To address this issue, we propose a novel benchmark where models are trained on known base classes and evaluated on their performance in detecting and recognizing unseen or novel classes. To this end, we introduce llama-Unidetector, a universal framework that incorporates textual information into a closed-set detector, enabling the generalization to open-set scenarios. Our llama-Unidetector leverages a decoupled learning strategy that separates localization and recognition. In the first stage, a class-agnostic detector identifies objects, distinguishing only between foreground and background. In the second stage, the detected foreground objects are passed through TerraOV-LLM, a multimodal large language model, for recognition, utilizing the strong generalization capabilities of large language models to infer the correct categories. We propose a self-built Vision Question Answering (VQA) remote sensing dataset, TerraVQA, and conduct extensive experiments on the NWPU-VHR10, DOTA1.0, and DIOR datasets. The llama-Unidetector achieves impressive results, with a performance of 75.46% AP, 50.22% AP and 51.38% AP on the zero-shot detection benchmarks for the NWPU-VHR10, DOTA1.0 and DIOR datasets, respectively. © 1980-2012 IEEE.

关键词： Object detection

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A third-party platoon coordination service: Pricing under government subsidies

A third-party platoon coordination service: Pricing under go...

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作者： Bai, Ting Johansson, Alexander Li, Shaoyuan Johansson, Karl Henrik Mårtensson, Jonas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden KTH Royal Institute of Technology Stockholm Sweden Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Digital Futures Stockholm Sweden

This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease. © 2023 The Authors. Asian Journal of Control published by John Wiley & Sons Australia, Ltd on behalf of Chinese Automatic Control Society.

关键词： Trucks

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Data-Driven Agent-Based Model for Public Opinion Propagation Simulation in Cyberbullying

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Big Data Mining and Analytics 2025年第4期8卷 794-819页

作者： Runyu Wang Tun Lu Peng Zhang Ning Gu School of Computer Science Fudan University Shanghai China Shanghai Key Laboratory of Data Science MOE Laboratory for National Development and Intelligent Governance School of Computer Science Fudan Institute on Aging Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Fudan University Shanghai China

The flourishing development of social media platforms based on cultivating user relationships to spread and share information has provided a breeding ground for cyberbullying. How to infer the evolution of public opinion propagation in an online bullying environment is of great significance to the governance of cyberbullying. In this paper, we propose a data-driven agent-based Model for Public Opinion Propagation Simulation (MPOPS) in cyberbullying. First, we design a Public Opinion Propagation Environment for Opinion Fusion and Polarization (OFP-POPE) in a cyber-physical-social space. Second, we conduct agent-based fine-grained modeling based on the OFP-POPE. Third, we define and quantify user interaction behaviors and improve the Susceptible-Exposed-Infected Removed (SEIR) model by taking these interaction behaviors as the factors influencing public opinion propagation. Finally, we take the “2022 Tangshan restaurant attack” incident as an empirical study case and conduct simulation experiments on the MPOPS driven by real-world data. The experimental results show that the MPOPS is superior to other baseline models, and can simulate the evolutionary trend of public opinion propagation in actual cyberbullying scenarios. In addition, the ablation experiment and parameter sensitivity analysis provide a reference for future cyberbullying intervention efforts.

关键词： Analytical models Sensitivity analysis Cyberbullying Big Data Market research Agent-based modeling Data models Data mining

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