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2024 7th International Conference on computer Information science and Artificial Intelligence, CISAI 2024

作者： Wu, Jiawei Liang, Shuaishuai Feng, Huibin College of Computer and Data Science Fuzhou University Fujian Fuzhou China School of Computer and Big Data Minjiang University Fujian Fuzhou China

ISBN: (纸本)9798400707254

The development of the aerospace technology enables countries around the world to launch a large number of satellites to carry out extraterrestrial missions. This also poses greater challenges to the control of satellites. The extremely long communication time between satellites and the ground has led to the necessity of autonomous onboard control capability for satellites. The rise of reinforcement learning (RL) technology has driven research on applying RL technology to satellite control problems. This paper attempts to apply the Distributed Soft Actor Critic with Three Improvements (DSAC-T) algorithm to the adaptive continuous control problem of satellite attitude. The problem is described as a reinforcement learning model that controls the satellite to perform a large-angle slew maneuvers and maintain a desired orientation. By setting up rotation experiments, the results show that the DSAC-T algorithm can control satellite to perform rotation tasks more quickly and smoothly than existing continuous control algorithms. © 2024 Copyright held by the owner/author(s).

关键词： Reinforcement learning

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MVSFORMER++: REVEALING THE DEVIL IN TRANSFORMER'S DETAILS FOR MULTI-VIEW STEREO 12

MVSFORMER++: REVEALING THE DEVIL IN TRANSFORMER'S DETAILS FO...

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12th International Conference on Learning Representations, ICLR 2024

作者： Cao, Chenjie Ren, Xinlin Fu, Yanwei School of Data Science Fudan University China Shanghai Key Lab of Intelligent Information Processing School of Computer Science Fudan University China

Recent advancements in learning-based Multi-View Stereo (MVS) methods have prominently featured transformer-based models with attention mechanisms. However, existing approaches have not thoroughly investigated the profound influence of transformers on different MVS modules, resulting in limited depth estimation capabilities. In this paper, we introduce MVSFormer++, a method that prudently maximizes the inherent characteristics of attention to enhance various components of the MVS pipeline. Formally, our approach involves infusing cross-view information into the pre-trained DINOv2 model to facilitate MVS learning. Furthermore, we employ different attention mechanisms for the feature encoder and cost volume regularization, focusing on feature and spatial aggregations respectively. Additionally, we uncover that some design details would substantially impact the performance of transformer modules in MVS, including normalized 3D positional encoding, adaptive attention scaling, and the position of layer normalization. Comprehensive experiments on DTU, Tanks-and-Temples, BlendedMVS, and ETH3D validate the effectiveness of the proposed method. Notably, MVSFormer++ achieves state-of-the-art performance on the challenging DTU and Tanks-and-Temples benchmarks. Codes and models are available at https://***/maybeLx/MVSFormerPlusPlus. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Benchmarking

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Enhancing wireless sensing via a target-mounted intelligent reflecting surface

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National science Review 2023年第8期10卷 13-14页

作者： Xiaodan Shao Rui Zhang School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Department of Electrical and Computer Engineering National University of Singapore

An intelligent reflecting surface(IRS),or its various equivalents such as an reconfigurable intelligent surface(RIS), is an emerging technology to control radio signal propagation in wireless systems. An IRS is a digitally controlled metasurface consisting of a large number of passive reflecting elements, which are connected to a smart controller to enable dynamic adjustments of the amplitude and/or phase of the incident signal on each element independently [1].

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A visual modeling method for spatiotemporal and multidimensional features in epidemiological analysis:Applied COVID-19 aggregated datasets

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Computational Visual Media 2024年第1期10卷 161-186页

作者： Yu Dong Christy Jie Liang Yi Chen Jie Hua School of Computer Science University of Technology SydneySydneyNSW2007Australia Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business UniversityBeijing100048China

The visual modeling method enables flexible interactions with rich graphical depictions of data and supports the exploration of the complexities of epidemiological ***,most epidemiology visualizations do not support the combined analysis of objective factors that might influence the transmission situation,resulting in a lack of quantitative and qualitative *** address this issue,we developed a portrait-based visual modeling method called+*** method considers the spatiotemporal features of virus transmission patterns and multidimensional features of objective risk factors in communities,enabling portrait-based exploration and comparison in epidemiological *** applied+msRNAer to aggregate COVID-19-related datasets in New South Wales,Australia,combining COVID-19 case number trends,geo-information,intervention events,and expert-supervised risk factors extracted from local government area-based *** perfected the+msRNAer workflow with collaborative views and evaluated its feasibility,effectiveness,and usefulness through one user study and three subject-driven case *** feedback from experts indicates that+msRNAer provides a general understanding for analyzing comprehension that not only compares relationships between cases in time-varying and risk factors through portraits but also supports navigation in fundamental geographical,timeline,and other factor *** adopting interactions,experts discovered functional and practical implications for potential patterns of long-standing community factors regarding the vulnerability faced by the *** confirmed that+msRNAer is expected to deliver visual modeling benefits with spatiotemporal and multidimensional features in other epidemiological analysis scenarios.

关键词： visual modeling epidemiological analysis spatiotemporal multidimensional COVID-19

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Enhancing Document-Level Relation Extraction through Entity-Pair-Level Interaction Modeling

Enhancing Document-Level Relation Extraction through Entity-...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Liu, Wanlong Zeng, Dingyi Zhou, Li Xiao, Yichen Zhang, Malu Chen, Wenyu School of Computer Science and Engineering University of Electronic Science and Technology of China China School of Data Science The Chinese University of Hong Kong Shenzhen China

ISBN: (纸本)9798350368741

Document-level relation extraction aims at extracting relational facts between two entities in a document. Existing approaches mainly focus on target entities, utilizing techniques such as graph neural networks to enhance their representations. However, they ignore the rich semantic correlations among entity pairs which provide wider and multifaceted information at a higher level. In this paper, we propose the Relation-based Entity-pair-level Inference (REI) model, which facilitates information interaction at the entity-pair level, enhancing logical reasoning among entities and capturing semantic correlations among entity pairs. Our REI model comprises two modules: Relation-based Information Aggregation (RIA) and Entity-pair-level Information Interaction (EII). The RIA module builds and integrates relation representations to filter out distractions from unrelated entity pairs, while the EII module models entity-pair-level information interaction through multi-head attentions. Extensive experiments on the DocRED, DWIE, CDR, and GDA datasets demonstrate the superiority of the proposed REI model, outperforming previous state-of-the-art approaches. Furthermore, we provide detailed experimental analyses based on the performance gains and illustrate the interpretability. © 2025 IEEE.

关键词： Information Extraction Linguistic Inference Natural Language Processing Relation Extraction

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dTEE: A Declarative Approach to Secure IoT Applications Using TrustZone 23

dTEE: A Declarative Approach to Secure IoT Applications Usin...

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23rd ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2024

作者： Sun, Tong Li, Borui Teng, Yixiao Gao, Yi Dong, Wei Zhejiang University The State Key Laboratory of Blockchain and Data Security College of Computer Science China Southeast University School of Computer Science and Engineering China

ISBN: (纸本)9798350362015

Internet of Things (IoT) applications have recently been widely used in safety-critical scenarios. To prevent sensitive information leaks, IoT device vendors provide hardware-assisted protections, called Trusted Execution Environments (TEEs), like ARM Trust-Zone. programming a TEE-based application requires separate code for two components, significantly slowing down the development process. Existing solutions tackle this issue by automatic code partition while not successfully applying it in two complicated scenarios: adding trusted logic and interactions with secure *** propose dTEE, a declarative approach to secure IoT applications based on TrustZone. dTEE proposes a rapid approach that enables developers to declare tiered-sensitive variables and functions of existing applications. Besides, dTEE automatically transforms device drivers into trusted ones. We evaluate dTEE on four real-world IoT applications and seven micro-benchmarks. Results show that dTEE achieves high expressiveness for supporting 50% more applications than existing approaches and reduces 90% of the lines of code against handcrafted development. © 2024 IEEE.

关键词： Internet of things

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Real-time distance field acceleration based free-viewpoint video synthesis for large sports fields

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Computational Visual Media 2024年第2期10卷 331-353页

作者： Yanran Dai Jing Li Yuqi Jiang Haidong Qin Bang Liang Shikuan Hong Haozhe Pan Tao Yang School of Telecommunications Engineering Xidian UniversityXi’an 710071China National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology SAIIPthe School of Computer ScienceNorthwestern Polytechnical UniversityXi’an 710129China

Free-viewpoint video allows the user to view objects from any virtual perspective,creating an immersive visual *** technology enhances the interactivity and freedom of multimedia ***,many free-viewpoint video synthesis methods hardly satisfy the requirement to work in real time with high precision,particularly for sports fields having large areas and numerous moving *** address these issues,we propose a freeviewpoint video synthesis method based on distance field *** central idea is to fuse multiview distance field information and use it to adjust the search step size *** step size search is used in two ways:for fast estimation of multiobject three-dimensional surfaces,and synthetic view rendering based on global occlusion *** have implemented our ideas using parallel computing for interactive display,using CUDA and OpenGL frameworks,and have used real-world and simulated experimental datasets for *** results show that the proposed method can render free-viewpoint videos with multiple objects on large sports fields at 25 ***,the visual quality of our synthetic novel viewpoint images exceeds that of state-of-the-art neural-rendering-based methods.

关键词： free-viewpoint video view synthesis camera array distance field sports video

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A Multimodal Multiobjective Evolutionary Algorithm for Filter Feature Selection in Multilabel Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4428-4442页

作者： Hancer, Emrah Xue, Bing Zhang, Mengjie Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Mehmet Akif Ersoy University Department of Software Engineering Burdur15039 Turkey

Multilabel learning is an emergent topic that addresses the challenge of associating multiple labels with a single instance simultaneously. Multilabel datasets often exhibit high dimensionality with noisy, irrelevant, and redundant features. In recent years, multilabel feature selection (MLFS) has gained prominence as a crucial and emerging machine learning task due to its ability to handle such data effectively. However, existing approaches for MLFS often prioritize top-ranked features based on intrinsic data criteria, disregarding relationships within the feature subset. Additionally, compared with conventional feature selection, multiobjective evolutionary algorithms (MOEAs) have not been widely explored in the context of MLFS. This study aims to address these gaps by proposing a multimodal multiobjective evolutionary algorithm (MMOEA) called MMDE_SICD which incorporates a preelimination scheme, an improved initialization scheme, an exploration scheme inspired by genetic operations and a statistically inspired crowding distance scheme. The results show that the proposed MMDE_SICD algorithm can outperform a variety of MOEAs and MMOEAs as well as conventional MLFS algorithms. Notably, this study is the first of its kind to consider MLFS as a multimodal multiobjective problem. © 2020 IEEE.

关键词： Multiobjective optimization

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LLM-GAN: Constructing Generative Adversarial Network Through Large Language Models for Explainable Fake News Detection

LLM-GAN: Constructing Generative Adversarial Network Through...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Wang, Yifeng Gu, Zhouhong Zhang, Siwei Zheng, Suhang Wang, Tao Li, Tianyu Feng, Hongwei Xiao, Yanghua Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China Alibaba Group China

ISBN: (纸本)9798350368741

Explainable fake news detection predicts the authenticity of news items with annotated explanations. Today, Large Language Models (LLMs) are known for their powerful natural language understanding and explanation generation abilities. However, using LLMs for explainable fake news detection remains two main challenges. Firstly, fake news appears reasonable and could easily mislead LLMs, leaving them unable to understand the complex news-faking process. Secondly, utilizing LLMs for this task would generate correct and incorrect explanations, requiring abundant labor in the loop. In this paper, we propose LLM-GAN, a novel framework that utilizes prompting mechanisms to enable an LLM to function as a Generator and a Detector for realistic fake news generation and detection. Extensive experimental results demonstrate LLM-GAN's effectiveness in both prediction performance and explanation quality. © 2025 IEEE.

关键词： Fake News Detection Large Language Models Prompt Learning

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A Digital Twin and Consensus Empowered Cooperative Control Framework for Platoon-Based Autonomous Driving

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1096-1111页

作者： Jiayu Cao Supeng Leng Kai Xiong Xiaosha Chen School of Information and Communication Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of ChinaShenzhen 518110China College of Computer Science and Technology and also with College of Data Science Taiyuan University of TechnologyTaiyuan 030024China

Platoon-based autonomous driving is indispensable for traffic automation,but it confronts substantial constraints in rugged terrains with unreliable links and scarce communication *** paper proposes a novel hierarchical Digital Twin(DT)and consensus empowered cooperative control framework for safe driving in harsh ***,leveraging intra-platoon information exchange,one platoon-level DT is constructed on the leader and multiple vehicle-level DTs are distributed among platoon *** leader first makes critical platoon-driving decisions based on the platoon-level ***,considering the impact of unreliable links on the platoon-level DT accuracy and the consequent risk of unsafe decision-making,a distributed consensus scheme is proposed to negotiate critical decisions *** successful negotiation,vehicles proceed to execute critical decisions,relying on their vehicle-level ***,a Space-Air-Ground-Integrated-Network(SAGIN)enabled information exchange is utilized to update the platoon-level DT for subsequent safe decision-making in scenarios with unreliable links,no roadside units,and obstructed ***,based on this framework,an adaptive platooning scheme is designed to minimize total delay and ensure driving *** results indicate that our proposed scheme improves driving safety by 21.1％and reduces total delay by 24.2％in harsh areas compared with existing approaches.

关键词： platoon autonomous driving distributed consensus digital twin space-air-ground integrated network

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