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

作者： Su, Zhan Huang, Jiashuang Jiang, Shu Wang, Mingliang Ding, Weiping School of Artificial Intelligence and Computer Science Nantong University Nantong China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

ISBN: (纸本)9798350368741

The existing multi-atlas brain network analysis methods rely on some simple fusion methods (i.e., add and concatenation) and do not consider the information redundancy caused by increased brain regions. To improve upon these, we propose the Sparse and Uncertain Fusion Transformers (SUFT) for multi-atlas brain network analysis. First, multi-atlas brain network data is derived from original fMRI data using various atlases. An attention enhancement module then extracts and preserves essential features for minimal information loss during selection. These processed features are fed into a selection module that identifies disease-related brain regions based on their importance scores. The model utilizes only these sparse features for further processing. Finally, an uncertain fusion module assesses the confidence level of each atlas and implements a strategy to integrate results at the evidence level. Experimental results on 1138 subjects from the SRPBS dataset demonstrate that our SUFT outperforms several state-of-the-art methods in identifying brain disorders. © 2025 IEEE.

关键词： Brain Disorder Brain Regions Selection Multi-atlas Brain Networks Uncertain Fusion

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An Efficient Dialogue Policy Agent with Model-Based Causal Reinforcement Learning 31

An Efficient Dialogue Policy Agent with Model-Based Causal R...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Xu, Kai Wang, Zhenyu Zhao, Yangyang Fang, Bopeng School of Software Engineering South China University of Technology Guangdong China Department of Computer and Communication Engineering Changsha University of Science and Technology Changsha China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798891761964

Dialogue policy trains an agent to select dialogue actions frequently implemented via deep reinforcement learning (DRL). The model-based reinforcement methods built a world model to generate simulated data to alleviate the sample inefficiency. However, traditional world model methods merely consider one-step dialogues, leading to an inaccurate environmental simulation. Furthermore, different users may have different intention preferences, while most existing studies lack consideration of the intention-preferences causal relationship. This paper proposes a novel framework for dialogue policy learning named MCA, implemented through model-based reinforcement learning with automatically constructed causal chains. The MCA model utilizes an autoregressive Transformer to model dialogue trajectories, enabling a more accurate simulation of the environment. Additionally, it constructs a causal chains module that outputs latent preference distributions for intention-action pairs, thereby elucidating the relationship between user intentions and agent actions. The experimental results show that MCA can achieve state-of-the-art performances on three dialogue datasets over the compared dialogue agents, highlighting its effectiveness and robustness. © 2025 Association for Computational Linguistics.

关键词： Deep reinforcement learning

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Tag-Aware Weakly-Supervised Online Hashing with Enhanced Joint Representation

Tag-Aware Weakly-Supervised Online Hashing with Enhanced Joi...

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

作者： Wang, Na Zhan, Yu-Wei Chen, Zhen-Duo Wang, Yongxin Luo, Xin Xu, Xin-Shun School of Software Shandong University Jinan China Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science and Technology Shandong Jianzhu University Jinan China

ISBN: (纸本)9798350368741

Weakly-supervised online hashing has garnered significant attention recently, yet several challenges remain unresolved, such as how to effectively denoise tags, and how to efficiently learn hash functions in dynamic online scenarios. To tackle these challenges, we propose a novel method named Tag-Aware Weakly-supervised Online Hashing with enhanced joint representation (TA-WOH). Our method creates an enhanced joint representation with CLIP-based features in order to reduce the tag noise. Additionally, we introduce a tag association and noise model for improved similarity matrix and hash code learning. A novel mapping mechanism is developed to align joint representations with the optimal tag space, enhancing both the accuracy and robustness of the model. The computational complexity of TA-WOH is dependent solely on the size of the incoming data, ensuring scalability and efficiency for large-scale datasets. Extensive experiments on two datasets demonstrate that our method surpasses several state-of-the-art methods in both accuracy and efficiency. © 2025 IEEE.

关键词： Learning to Hash Online Hashing Social Image Retrieval

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Latency- and Privacy-Aware Convolutional Neural Network Distributed Inference for Reliable Artificial Intelligence Systems

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第2期6卷 365-377页

作者： Hu, Yuhao Xu, Xiaolong Qi, Lianyong Zhou, Xiaokang Xia, Xiaoyu Nanjing University of Information Science and Technology School of Software Nanjing210044 China College of computer science and technology Qingdao266850 China Shiga University Faculty of Data Science Hikone522-8522 Japan RMIT University School of Computing Technologies MelbourneVIC3000 Australia

Reliable artificial intelligence (AI) systems not only propose a challenge on providing intelligent services with high quality for customers but also require customers' privacy to be protected as much as possible during process of the services. Given the ultrahigh computing load brought by deep-learning-based intelligent services to edge devices and the ultralong distance between edge and cloud, low-latency requirement of intelligent services is hard to meet in single edge computing or cloud computing. Edge-cloud collaborative inference of deep neural networks (DNNs) is considered a feasible solution to the problem. However, former work has not reduced the inference latency to the greatest extent and has not considered privacy protection in distributed systems. To solve the problem, we first establish a novel queue mechanism. Then, the convolution layer split decisions are made based on deep reinforcement learning (DRL) to realize the parallel inference of convolutional neural networks (CNNs) for inference latency reduction. Next, for each CNN, the partition decision is made based on brute force algorithm to further reduce inference latency and protect customers' privacy. Finally, simulation results show that our method performs better than the existing other methods. © 2020 IEEE.

关键词： Cloud computing

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Efficient and Privacy-Preserving Authentication for Federated Learning in Industrial Internet of Things Data Sharing Application

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IEEE Internet of Things Journal 2025年第9期12卷 11652-11663页

作者： Huang, Yongfei Xu, Guangxia Wang, Qiuyan Song, Xiaoling Wang, Xinhui Hengyang Normal University College of Computer Science and Technology Hengyang421002 China Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China Peng Cheng Laboratory Department of New Networks Shenzhen518000 China Chongqing University of Posts and Telecommunications School of Software Engineering Chongqing400065 China Hunan Institute of Technology School of Electrical and Information Engineering Hengyang421002 China

Federated learning (FL) has emerged as a promising approach for collaborative machine learning while respecting data privacy. However, directly aggregating local models during FL training can expose sensitive information, as adversaries may analyze model parameters to infer the underlying raw data. To address this concern, implementing a robust identity authentication mechanism can verify the integrity of local models and confirm their sources. However, many existing authentication schemes are computationally intensive, which can hinder performance. In this article, we present a lightweight authentication scheme for FL called EPPAFL. This innovative approach is compatible with all machine learning algorithms, maintains data utility, and ensures strong privacy protection. Additionally, it can be integrated with detection mechanisms to defend against data poisoning and free-rider attacks. Specifically, we first design a certificateless signature scheme based on the elliptic curve cryptography (ECC) and then propose an anonymous and efficient authentication protocol based on the Chinese remainder theorem (CRT), which supports batch verification, dynamic updates, and revocations of group keys. Moreover, participants interact anonymously to achieve secure communication, and trusted authority (TA) can track and recover the real identities of participants in case of malicious activities. We provide formal security proofs and analyses to demonstrate the achieved security properties. Performance evaluations demonstrate that our proposed protocol significantly reduces the costs associated with signature generation and verification, outperforming the existing schemes in both communication and computational efficiency. © 2014 IEEE.

关键词： Federated learning

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ACbot:an IIoT platform for industrial robots

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Frontiers of computer Science 2025年第4期19卷 13-27页

作者： Rui WANG Xudong MOU Tianyu WO Mingyang ZHANG Yuxin LIU Tiejun WANG Pin LIU Jihong YAN Xudong LIU School of Computer Science and Engineering Beihang UniversityBeijing 100191China Zhongguancun Laboratory Beijing 100194China School of Software Beihang UniversityBeijing 100191China School of Mechatronics Engineering Harbin Institute of TechnologyHarbin 150001China Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Engineering China University of GeosciencesBeijing 100083China

As the application of Industrial Robots(IRs)scales and related participants increase,the demands for intelligent Operation and Maintenance(O&M)and multi-tenant collaboration *** methods could no longer cover the requirements,while the Industrial Internet of Things(IIoT)has been considered a promising ***,there’s a lack of IIoT platforms dedicated to IR O&M,including IR maintenance,process optimization,and knowledge *** this context,this paper puts forward the multi-tenant-oriented ACbot platform,which attempts to provide the first holistic IIoT-based solution for O&M of *** on an information model designed for the IR field,ACbot has implemented an application architecture with resource and microservice management across the cloud and multiple *** this basis,we develop four vital applications including real-time monitoring,health management,process optimization,and knowledge *** have deployed the ACbot platform in real-world scenarios that contain various participants,types of IRs,and *** date,ACbot has been accessed by 10 organizations and managed 60 industrial robots,demonstrating that the platform fulfills our ***,the application results also showcase its robustness,versatility,and adaptability for developing and hosting intelligent robot applications.

关键词： IIoT platform industrial robots cloud-edge collaboration intelligent applications

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Migrant Resettlement by Evolutionary Multiobjective Optimization

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第1期6卷 51-65页

作者： Liu, Dan-Xuan Gu, Yu-Ran Qian, Chao Mu, Xin Tang, Ke Nanjing University National Key Laboratory for Novel Software Technology School of Artificial Intelligence Nanjing210023 China Peng Cheng Laboratory Shenzhen518000 China Southern University of Science and Technology Department of Computer Science and Engineering Shenzhen518055 China

Migration has been a universal phenomenon, which brings opportunities as well as challenges for global development. As the number of migrants (e.g., refugees) increases rapidly, a key challenge faced by each country is the problem of migrant resettlement. This problem has attracted scientific research attention, from the perspective of maximizing the employment rate. Previous works mainly formulated migrant resettlement as an approximately submodular optimization problem subject to multiple matroid constraints and employed the greedy algorithm, whose performance, however, may be limited due to its greedy nature. In this article, we propose a new framework called migrant resettlement by evolutionary multiobjective optimization (MR-EMO), which reformulates migrant resettlement as a biobjective optimization problem that maximizes the expected number of employed migrants and minimizes the number of dispatched migrants simultaneously, and employs a multiobjective evolutionary algorithm (MOEA) to solve the biobjective problem. We implement MR-EMO using three MOEAs: the popular nondominated sorting genetic algorithm II (NSGA-II), MOEA based on decomposition (MOEA/D) as well as the theoretically grounded global simple evolutionary multiobjective optimizer (GSEMO). To further improve the performance of MR-EMO, we propose a specific MOEA, called GSEMO using matrix-swap mutation and repair mechanism (GSEMO-SR), which has a better ability to search for feasible solutions. We prove that MR-EMO using either GSEMO or GSEMO-SR can achieve better theoretical guarantees than the previous greedy algorithm. Experimental results under the interview and coordination migration models clearly show the superiority of MR-EMO (with either NSGA-II, MOEA/D, GSEMO or GSEMO-SR) over previous algorithms, and that using GSEMO-SR leads to the best performance of MR-EMO. © 2024 IEEE.

关键词： Multiobjective optimization

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Research of Automatic Scoring of Essays Based on Data Augmentation 24

Research of Automatic Scoring of Essays Based on Data Augmen...

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4th Asia-Pacific Artificial Intelligence and Big Data Forum, AIBDF 2024

作者： Guo, Weiqin Yang, Yong Ren, Ge School of Software Xinjiang University Xinjiang Urumqi China College of Computer Science and Technology Xinjiang Normal University Xinjiang Urumqi China

ISBN: (纸本)9798400710865

The article discusses the research on automated essay scoring based on data augmentation, focusing on the use of data augmentation methods to improve the performance of automated essay scoring systems. In previous studies, researchers have also applied data augmentation methods to the field of automated essay scoring and achieved certain results. However, the past research still has certain defects, such as the problem of the deviation between the representation vectors of the augmented text and the original text labels. This study calculates the cosine similarity between the representation vectors of the augmented text and the original text, and uses the obtained cosine similarity to screen the augmented text, in order to solve the problem of the deviation between the augmented text and the original labels. This study conducts data augmentation on the corpus of automated essay scoring and screens the augmented data to improve the training effect, reducing the negative impact of biased data on model training, and ultimately achieved a QWK (Quadratic Weighted Kappa) score of 80.21 on the ASAP (Automated Student Assessment Prize) automated essay scoring dataset. In model construction, long convolution is adopted to improve performance, and the effect is verified in the experiments, confirming the role of long convolution in the task of automated essay scoring. © 2024 Copyright held by the owner/author(s).

关键词： Natural language processing systems

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Metamorphic Relation Patterns for Metamorphic Testing, Exploration and Robustness

Metamorphic Relation Patterns for Metamorphic Testing, Explo...

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作者： Ying, Zhihao Towey, Dave Bellotti, Anthony Chua, Caslon Quan Zhou, Zhi School of Computer Science University of Nottingham Ningbo China Zhejiang Ningbo China Department of Computer Science and Software Engineering Swinburne University of Technology HawthornVIC Australia School of Computing and Information Technology University of Wollongong WollongongNSW Australia

Metamorphic testing (MT) is an effective software quality assurance method;it uses metamorphic relations (MRs) to examine the inputs and outputs of multiple test cases. Metamorphic exploration (ME) and metamorphic robustness testing (MRT) are advanced additions to the MT literature: ME can enable better understanding, and thus testing, of systems;MRT can test software robustness without an oracle. Metamorphic relation patterns (MRPs) are abstractions or templates for multiple concrete MRs. The performance of MT/ME/MRT relies on the quality of MRs, but the identification of MRs is often a manual task requiring some knowledge of MRPs, creative thinking and a good understanding of the system. In other words, the effective identification of MRs remains a challenge and requires further study. In this paper, we propose formal definitions to describe the relationship between multiple MRPs in order to classify them. We also identify a series of new MRPs and propose a new MT framework that can guide the identification and application of MRPs. Based on our definitions, we present the notion of MRP family trees and further propose two trees by classifying previously published MRPs and our proposed MRPs, in order to make it easier for users to find their target MRPs for reuse, reference or inference. Finally, we report on case studies using the MRPs and our framework to guide the identification of MRs for MT/ME/MRT and successfully detect multiple violations. © 2025 John Wiley & Sons Ltd.

关键词： software testing

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Short-Term Rolling Prediction of Tropical Cyclone Intensity Based on Multi-Task Learning with Fusion of Deviation-Angle Variance and Satellite Imagery

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Advances in Atmospheric Sciences 2025年第1期42卷 111-128页

作者： Wei TIAN Ping SONG Yuanyuan CHEN Yonghong ZHANG Liguang WU Haikun ZHAO Kenny Thiam Choy LIM KAM SIAN Chunyi XIANG School of Software Nanjing University of Information Science and TechnologyNanjing 210044China School of Computer Nanjing University of Information Science and TechnologyNanjing 210044China School of Automation Nanjing University of Information Science and TechnologyNanjing 210044China Fudan University Shanghai 200433China Pacific Typhoon Research Center Nanjing University of Information Science and TechnologyNanjing 210044China Wuxi University Wuxi 214100China National Meteorological Center Beijing 100081China

Tropical cyclones(TCs)are one of the most serious types of natural disasters,and accurate TC activity predictions are key to disaster prevention and ***,TC track predictions have made significant progress,but the ability to predict their intensity is obviously lagging *** present,research on TC intensity prediction takes atmospheric reanalysis data as the research object and mines the relationship between TC-related environmental factors and intensity through deep ***,reanalysis data are non-real-time in nature,which does not meet the requirements for operational forecasting ***,a TC intensity prediction model named TC-Rolling is proposed,which can simultaneously extract the degree of symmetry for strong TC convective cloud and convection intensity,and fuse the deviation-angle variance with satellite images to construct the correlation between TC convection structure and *** TCs'complex dynamic processes,a convolutional neural network(CNN)is used to learn their temporal and spatial *** real-time intensity estimation,multi-task learning acts as an implicit time-series *** model is designed with a rolling strategy that aims to moderate the long-term dependent decay problem and improve accuracy for short-term intensity *** multiple tasks are correlated,the loss function of 12 h and 24 h are *** testing on a sample of TCs in the Northwest Pacific,with a 4.48 kt root-mean-square error(RMSE)of 6 h intensity prediction,5.78 kt for 12 h,and 13.94 kt for 24 h,TC records from official agencies are used to assess the validity of TC-Rolling.

关键词： tropical cyclone intensity structure rolling prediction multi-task

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