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Proceedings of the 41st International Conference on Machine Learning

作者： Jiafei Lyu Chenjia Bai Jingwen Yang Zongqing Lu Xiu Li Tsinghua Shenzhen International Graduate School Tsinghua University Shanghai Artificial Intelligence Laboratory Tencent IEG School of Computer Science Peking University and Beijing Academy of Artificial Intelligence

It is vital to learn effective policies that can be transferred to different domains with dynamics discrepancies in reinforcement learning (RL). In this paper, we consider dynamics adaptation settings where there exists dynamics mismatch between the source domain and the target domain, and one can get access to sufficient source domain data, while can only have limited interactions with the target domain. Existing methods address this problem by learning domain classifiers, performing data filtering from a value discrepancy perspective, etc. Instead, we tackle this challenge from a decoupled representation learning perspective. We perform representation learning only in the target domain and measure the representation deviations on the transitions from the source domain, which we show can be a signal of dynamics mismatch. We also show that representation deviation upper bounds performance difference of a given policy in the source domain and target domain, which motivates us to adopt representation deviation as a reward penalty. The produced representations are not involved in either policy or value function, but only serve as a reward penalizer. We conduct extensive experiments on environments with kinematic and morphology mismatch, and the results show that our method exhibits strong performance on many tasks. Our code is publicly available at https://***/dmksjfl/PAR.

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artificial intelligence in computerized adaptive testing to assess the cognitive performance of students: A Systematic Review

Artificial intelligence in computerized adaptive testing to ...

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Intelligent Systems and computer Vision (ISCV)

作者： Mohamed El Msayer Es-Saâdia Aoula Bouchra Bouihi Computer Science Artificial Intelligence and Cyber Security Laboratory ENSET of Mohammedia University Hassan II Casablanca Morocco

ISBN: (数字)9798350350180

ISBN: (纸本)9798350350197

computerized adaptive testing is a form of assessment that uses artificial intelligence to provide cognitive diagnoses of learners, in order to situate them in their learning process and offer them personalized support. "Adaptive" refers to the embedded selection algorithm that optimally selects questions based on a continuous estimation of the learner's proficiency at each step of the assessment. computerized adaptive tests (CAT) are computer-based tests that offer individualized, equitable questions based on the learner's abilities. It is designed to correctly measure skills in relation to a latent trait (e.g. comprehension, knowledge, personality characteristics). Item selection, ability estimation and automatic item generation represent the challenges posed by CAT. Traditional approaches use psychometric and statistical models for estimation. However, to improve their performance, many new methods have been developed using artificial intelligence and machine learning techniques. In this article, we present a systematic review of artificial intelligence approaches and techniques used in computerized adaptive testing, highlighting the benefits, challenges and future orientations of this field of study.

关键词： Adaptive systems Accuracy Neural networks Estimation Reinforcement learning Rhythm Security

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Comparison of Production-Triggering Strategies for Manufacturing Enterprises under Random Orders

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Engineering 2021年第6期7卷 798-806页

作者： Longfei Zhou Lin Zhang Yajun Fang Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of TechnologyCambridgeMA 02139USA School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

Although new technologies have been deeply applied in manufacturing systems,manufacturing enterprises are still encountering difficulties in maintaining efficient and flexible production due to the random arrivals of diverse customer *** order delivery and low inventory cost are fundamentally contradictory to each *** to make a suitable production-triggering strategy is a critical issue for an enterprise to maintain a high level of competitiveness in a dynamic *** this paper,we focus on production-triggering strategies for manufacturing enterprises to satisfy randomly arriving orders and reduce inventory *** theoretical models and simulation models of different production strategies are proposed,including time-triggered strategies,event-triggered strategies,and hybrid-triggered *** each model,both part-production-triggering strategies and product-assembly-triggering strategies are considered and *** time-triggered models and hybrid-triggered models also consider the impact of the period on system *** results show that hybrid-triggered and time-triggered strategies yield faster order delivery and lower inventory costs than event-triggered strategies if the period is set appropriately.

关键词： Collaborative manufacturing Production triggering Optimization Simulation Enterprise

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OCTAMamba: A State-Space Model Approach for Precision OCTA Vasculature Segmentation

OCTAMamba: A State-Space Model Approach for Precision OCTA V...

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

作者： Zou, Shun Zhang, Zhuo Gao, Guangwei College of Artificial Intelligence Nanjing Agricultural University Nanjing China College of Computer Science and Technology National University of Defense Technology Changsha China Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9798350368741

Optical Coherence Tomography Angiography (OCTA) is a crucial imaging technique for visualizing retinal vasculature and diagnosing eye diseases such as diabetic retinopathy and glaucoma. However, precise segmentation of OCTA vasculature remains challenging due to the multi-scale vessel structures and noise from poor image quality and eye lesions. In this study, we proposed OCTAMamba, a novel U-shaped network based on the Mamba architecture, designed to segment vasculature in OCTA accurately. OCTAMamba integrates a Quad Stream Efficient Mining Embedding Module for local feature extraction, a Multi-Scale Dilated Asymmetric Convolution Module to capture multi-scale vasculature, and a Focused Feature Recalibration Module to filter noise and highlight target areas. Our method achieves efficient global modeling and local feature extraction while maintaining linear complexity, making it suitable for low-computation medical applications. Extensive experiments on the OCTA 3M, OCTA 6M, and ROSSA datasets demonstrated that OCTAMamba outperforms state-of-the-art methods, providing a new reference for efficient OCTA segmentation. Code is available at https://***/zs1314/OCTAMamba. © 2025 IEEE.

关键词： Mamba OCTA image State Space Model

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LGDCloudSim: A Resource Management Simulation System for Large-Scale Geographically Distributed Cloud Data Center Scenarios

LGDCloudSim: A Resource Management Simulation System for Lar...

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IEEE International Conference on Cloud Computing, CLOUD

作者： Jiawen Liu Yuehao Xu Binbin Feng Zhijun Ding Department of Computer Science and Technology Tongji University Shanghai China The Shanghai Artificial Intelligence Laboratory Shanghai China

ISBN: (数字)9798350368536

ISBN: (纸本)9798350368543

Current IaaS providers have deployed data centers worldwide, with resources continually increasing. Meanwhile, there is a rising trend in the concurrency of user requests and the diversity of user request types. To achieve better resource allocation, various complex scheduling architectures have been proposed. However, due to the challenges associated with real-world experiments, simulation systems are needed to build exper-imental environments for related research. As existing systems do not perform well enough, we construct LGDCloudSim. It is designed with full consideration of the characteristics of the large-scale geographically distributed cloud data center scenarios. To support large-scale simulations, we propose state management optimization and operation process optimization methods. Exper-iments show that LGDCloudSim can simulate up to 5 X 10 8 hosts and 10 7 request concurrency. It also supports diverse scheduling architectures and different request types.

关键词： Concurrent computing Cloud computing Data centers Processor scheduling Optimization methods Distributed databases computer architecture

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FaRo-PPF: Fast and Robust Point Pair Feature for 6D Pose Estimation in Industrial Stamping 8

FaRo-PPF: Fast and Robust Point Pair Feature for 6D Pose Est...

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8th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2023

作者： He, Cheng Zhang, Xuebo Zhao, Zhenjie Institute of Robotics and Automatic Information System College of Artificial Intelligence Nankai University Tianjin Key Laboratory of Intelligent Robotics Tianjin300350 China School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China

ISBN: (纸本)9798350300178

Estimating 6D poses of targets efficiently is critical for industrial stamping tasks, in which the Point Pair Feature (PPF) method has been widely used. Based on PPF, this paper proposes Fast and Robust PPF, i.e. FaRo-PPF, which improves PPF in the following three key aspects: adaptive down-sampling based on surface features, point pair matching based on voting ball, and normal-based pose verification. The three designs alleviate existing problems of the local matching stage in PPF, and make FaRo-PPF a stronger method of 6D pose estimation in industrial stamping. To demonstrate the effectiveness of FaRo-PPF, we compare it with PPF on five publically available datasets. Experiment results showed that FaRo-PPF is able to significantly improve accuracy by about 15% and reduce the execution time by about 40% across all test data. We further conduct a grasping and assembly experiment on a physical robot arm, and similar improvement can be observed. FaRo-PPF achieves a higher success rate of assembly and reduces the execution time by about 50%. © 2023 IEEE.

关键词： Stamping

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Gaze Behavior based Depression Severity Estimation 4

Gaze Behavior based Depression Severity Estimation

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4th IEEE International Conference on Pattern Recognition and Machine Learning, PRML 2023

作者： Shu, Tao Zhang, Feixiang Sun, Xiao Anhui University AHU-IAI AI Joint Laboratory Hefei China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Hefei University of Technology School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9798350324303

Depression is a severe mental illness that affects millions of people's lives Every year, more than 700,000 individuals choose to commit suicide due to depression. Early screening of patients with depression is challenging, making it critical to find methods that can aid doctors in quickly identifying potential patients. Currently, a significant number of depression severity estimation tasks focus on text, speech, and vision, but the exploration of the relationship between eye gaze behavior and depression is incomplete. This paper aims to explore the mapping relationship between eye gaze behavior and depression. We propose an integrated method to estimate depression severity using eye gaze behavior data. We formulate a more reasonable representation of gaze direction by expressing it as the angle of deflection. We employ a deep residual shrinkage network with channel-wise thresholds (DRSN-CW) to process the input gaze sequence and estimate the depression severity score. Moreover, we introduce label distribution learning to represent more fine-grained score information and alleviate the overfitting problem associated with small datasets, enhancing the reliability of depression severity estimation. We train and validate our model on the Distress Analysis Interview Corpus - Wizard of Oz (DAIC-WOZ) dataset, achieving an F1 score of 0.769, and reducing the mean absolute error (MAE) to 3.971 and root mean square error (RMSE) to 5.141. The results demonstrate significant improvement in the depression severity estimation task compared to previous tasks. © 2023 IEEE.

关键词： Diagnosis

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The highest melting point material:Searched by Bayesian global optimization with deep potential molecular dynamics

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Journal of Advanced Ceramics 2023年第4期12卷 803-814页

作者： Yinan Wang Bo Wen Xingjian Jiao Ya Li Lei Chen Yujin Wang Fu-Zhi Dai Artificial Intelligence for Science Institute Beijing 100084China Science and Technology on Advanced Functional Composite Laboratory Aerospace Research Institute of Materials&Processing TechnologyBeijing 100076China College of Computer Science and Technology Heilongjiang Institute of TechnologyHarbin 150001China Institute for Advanced Ceramics School of Materials Science and EngineeringHarbin Institute of TechnologyHarbin 150001China Key Laboratory of Advanced Structural-Functional Integration Materials&Green Manufacturing Technology Harbin Institute of TechnologyHarbin 150001China DP Technology Bejing 100080China

The interest in refractory materials is increasing rapidly in recent decades due to the development of hypersonic ***,the substance that has the highest melting point(Tm)keeps a secret,since precise measurements in extreme conditions are overwhelmingly *** the present work,an accurate deep potential(DP)model of a Hf-Ta-C-N system was first trained,and then applied to search for the highest melting point material by molecular dynamics(MD)simulation and Bayesian global optimization(BGO).The predicted melting points agree well with the experiments and confirm that carbon site vacancies can enhance the melting point of rock-saltstructure *** solid solution with N is verified as another new and more effective melting point enhancing approach for HfC,while a conventional routing of the solid solution with Ta(e.g.,HfTa_(4)C_(5))is not suggested to result in a maximum melting *** highest melting point(~4236 K)is achieved with the composition of HfCo.638No.271,which is~80 K higher than the highest value in a Hf-C binary *** mechanism of the N addition is believed to be unstable C-N and N-N bonds in liquid phase,which reduces liquid phase entropy and renders the liquid phase less *** improved melting point and less gas generation during oxidation by the addition of N provide a new routing to modify thermal protection materials for the hypersonic vehicles.

关键词： melting point(T_(m)) carbides carbonitrides deep potential(DP) Bayesiannglobal optimization(BGO)

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Adaptive Fake Audio Detection with Low-Rank Model Squeezing

Adaptive Fake Audio Detection with Low-Rank Model Squeezing

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2023 Workshop on Deepfake Audio Detection and Analysis, DADA 2023

作者： Zhang, Xiaohui Yi, Jiangyan Tao, Jianhua Wang, Chenglong Xu, Le Fu, Ruibo State Key Laboratory of Multimodal Artificial Intelligence System Institute of Automation Chinese Academy of Sciences China School of Computer and Information Technology Beijing Jiaotong University China University of Science and Technology of China China Department of Automation Tsinghua University China School of Artificial Intelligence University of Chinese Academy of Sciences China

The rapid advancement of spoofing algorithms necessitates the development of robust detection methods capable of accurately identifying emerging fake audio. Traditional approaches, such as finetuning on new datasets containing these novel spoofing algorithms, are computationally intensive and pose a risk of impairing the acquired knowledge of known fake audio types. To address these challenges, this paper proposes an innovative approach that mitigates the limitations associated with finetuning. We introduce the concept of training low-rank adaptation matrices tailored specifically to the newly emerging fake audio types. During the inference stage, these adaptation matrices are combined with the existing model to generate the final prediction output. Extensive experimentation is conducted to evaluate the efficacy of the proposed method. The results demonstrate that our approach effectively preserves the prediction accuracy of the existing model for known fake audio types. Furthermore, our approach offers several advantages, including reduced storage memory requirements and lower equal error rates compared to conventional finetuning methods, particularly on specific spoofing algorithms. © 2023 CEUR-WS. All rights reserved.

关键词： Fake detection

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A Cooperative Co-Evolution Algorithm with Variable-Importance Grouping for Large-Scale Optimization 13

A Cooperative Co-Evolution Algorithm with Variable-Importanc...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Li, Yongfeng Zhang, Yuze Ma, Lijia Ji, Junkai Liu, Dugang Leung, Victor C. M. Li, Jianqiang Shenzhen University National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Guangdong Laboratory of Artificial Intelligence and Digital Economy Shenzhen518123 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Artificial Intelligence Research Institute Shenzhen MSU-BIT University Shenzhen518172 China The University of British Columbia Department of Electrical and Computer Engineering VancouverV6T 1Z4 Canada

ISBN: (纸本)9798350308365

Cooperative co-evolution (CC) is a promising direction in solving large-scale multiobjective optimization problems (LMOPs). However, most existing methods of grouping decision variables face some difficulties when searching in the huge search space. Specifically, the methods of grouping decision variables can be classified into two types, i.e., high-consumption grouping methods and non-consumption grouping methods. On the one hand, the former ones divide the decision variables into different groups based on the correlation analysis between variables, which consume much evaluation. This way may lead to premature convergence within limited computational resources. On the other hand, the later ones allocate the decision variables into sub-groups based on some metrics, e.g., order and size, which consume no evaluation while may cause the search fall into local optima. To alleviate the above issues, this paper proposes a CC-based algorithm with a variable-importance grouping (VIG) method, called VICCA. Firstly, the decision variables are classified into several subgroups according to their importance quantified by a meta-gene construction method. Secondly, a CC strategy is designed to simultaneously optimize all subgroups of decision variables formed by VIG using the differential evolution operator, which aims to accelerate the convergence speed. Thirdly, a global evolutionary strategy is proposed to optimize original decision variable space by the competitive swarm optimizer, aiming to maintain the diversity. Finally, the experiments demonstrate that our proposed VICCA has the significant advantage in solving LMOPs when compared with state-of-the-art evolutionary algorithms. © 2024 IEEE.

关键词： Optimization algorithms

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