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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Dai, Tao Wang, Yanzi Xiong, Jianyu Zha, Yaohua Xia, Shu-Tao Zhu, Zexuan College of Computer Science and Software Engineering Shenzhen University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Tsinghua Shenzhen International Graduate School Tsinghua University China Shenzhen City Key Laboratory of Embedded System Design Shenzhen China

ISBN: (纸本)157735897X

Sampling strategy (e.g., fixed farthest point sampling) of point cloud has been an essential step for developing practical solutions in 3D computer vision tasks. Previous fixed sampling is simple, but suffer from suboptimal performance for downstream tasks. To adapt to target networks properly, adaptive sampling methods with trainable parameters have been recently developed to enhance the performance. However, existing adaptive sampling methods still suffer from the over-coupling problem of target network, and thus become model-specific, which limits their practical applications. To address this issue, we propose a novel general cross-scale decoupled sampling method (GCD-sampling) for point cloud, which consists of original feature cache, cross-scale feature fusion and convex combination learning for better feature extraction. To reduce the coupling relationship with the target task network, our method only utilizes the point cloud coordinates as the input and output of itself. Besides, we introduce an arbitrary scale structure to enable parameter sharing across multi-scale sampling in point cloud networks. Extensive experiments on different architectures demonstrate the effectiveness of our method over other existing adaptive sampling methods. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Feature extraction

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Attention-based efficient robot grasp detection network

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Frontiers of Information Technology & Electronic engineering 2023年第10期24卷 1430-1444页

作者： Xiaofei QIN Wenkai HU Chen XIAO Changxiang HE Songwen PEI Xuedian ZHANG School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China College of Science University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System Shanghai 200093China Key Laboratory of Biomedical Optical Technology and Devices of Ministry of Education Shanghai 200093China Shanghai Institute of Intelligent Science and Technology Tongji UniversityShanghai 201210China

To balance the inference speed and detection accuracy of a grasp detection algorithm,which are both important for robot grasping tasks,we propose an encoder–decoder structured pixel-level grasp detection neural network named the attention-based efficient robot grasp detection network(AE-GDN).Three spatial attention modules are introduced in the encoder stages to enhance the detailed information,and three channel attention modules are introduced in the decoder stages to extract more semantic *** lightweight and efficient DenseBlocks are used to connect the encoder and decoder paths to improve the feature modeling capability of AE-GDN.A high intersection over union(IoU)value between the predicted grasp rectangle and the ground truth does not necessarily mean a high-quality grasp configuration,but might cause a *** is because traditional IoU loss calculation methods treat the center part of the predicted rectangle as having the same importance as the area around the *** design a new IoU loss calculation method based on an hourglass box matching mechanism,which will create good correspondence between high IoUs and high-quality grasp *** achieves the accuracy of 98.9%and 96.6%on the Cornell and Jacquard datasets,*** inference speed reaches 43.5 frames per second with only about 1.2×10^(6)*** proposed AE-GDN has also been deployed on a practical robotic arm grasping system and performs grasping *** are available at https://***/robvincen/robot_gradet.

关键词： Robot grasp detection Attention mechanism Encoder-decoder Neural network

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Discovery of association rules between factors affecting user satisfaction in software project by FP-GROWTH Algorithm 18

Discovery of association rules between factors affecting use...

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18th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2021

作者： Kaewbanjong, Katawut Intakosum, Sarun King Mongkut's Institute of Technology Ladkrabang Software System Engineering Laboratory Department of Computer Science Faculty of Science Bangkok Thailand

ISBN: (纸本)9780738111278

The main objective of this work was to find the association rules between factors affecting user satisfaction in software project by using an association rule discovery technique. Data from 191 software projects were collected and association rules between 15 Selected factors were discovered by FP-growth algorithm. Primarily, 281 association rules were discovered. Rules that were not directly related to user satisfaction were filtered out by simple criteria. In the end, 11 final association rules that passed those criteria were obtained with an average confidence value of 65.30%. These rules can incorporated in the planning and administration of a software project to gain utmost user satisfaction © 2021 IEEE.

关键词： Association rules

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A Surrogate-Assisted Evolutionary Algorithm for Expensive Dynamic Multimodal Optimzation

A Surrogate-Assisted Evolutionary Algorithm for Expensive Dy...

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Congress on Evolutionary Computation

作者： Xunfeng Wu Songbai Liu Junkai Ji Lijia Ma Victor C. M. Leung College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Surrogate-assisted evolutionary algorithms (SAEAs) have demonstrated promising optimization performance in addressing expensive dynamic optimization problems or expensive multimodal optimization problems. However, none of existing SAEAs are designed specifically for tackling expensive dynamic multimodal optimization problems (EDMMOPs). Therefore, in this paper, a first SAEA for tackling EDMMOPs is proposed. First, a nearest density clustering is designed to divide the population into a number of subpopulations, enhancing the diversity of the population. Then, a surrogate-assisted evolutionary optimizer is developed to construct surrogate models for each subpopulation and evolve all solutions in subpopulations by means of the built surrogate models, accelerating the population's converge towards several optimal solutions rapidly. Finally, a transfer learning-based prediction is devised to generate initial samples for next environment by leveraging the stored training samples in the previous environments. To assess the performance of our proposed algorithm, a set of complex benchmark problems is adopted, and the experimental results confirm its superior performance over several competitive algorithms on most test cases.

关键词： Training Heuristic algorithms Computational modeling Sociology Clustering algorithms Evolutionary computation Benchmark testing

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User-level failure detection and auto-recovery of parallel programs in HPC systems

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Frontiers of computer science 2021年第6期15卷 31-42页

作者： Guozhen ZHANG Yi LIU Hailong YANG Jun XU Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China Sino-German Joint Software Institute Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing 100854China

As the mean-time-between-failures(MTBF)continues to decline with the increasing number of components on large-scale high performance computing(HPC)systems,program failures might occur during the execution period with high *** successful execution of the HPC programs has become an issue that the unprivileged users should be *** the user perspective,if the program failure cannot be detected and handled in time,it would waste resources and delay the progress of program ***,the unprivileged users are unable to perform program state checking due to execution control by the job management system as well as the limited ***,automated tools for supporting user-level failure detection and autorecovery of parallel programs in HPC systems are *** paper proposes an innovative method for the unprivileged user to achieve failure detection of job execution and automatic resubmission of failed *** state checker in our method is encapsulated as an independent job to reduce interference with the user *** addition,we propose a dual-checker mechanism to improve the robustness of our *** implement the proposed method as a tool named automatic re-launcher(ARL)and evaluate it on the Tianhe-2 *** results show that ARL can detect the execution failures effectively on Tianhe-2 *** addition,the communication and performance overhead caused by ARL is *** good scalability of ARL makes it applicable for large-scale HPC systems.

关键词： high performance computing parallel program failure detection failure auto-recovery

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Hyperbolic Neural Network Based Preselection for Expensive Multi-Objective Optimization

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IEEE Transactions on Evolutionary Computation 2024年 1-1页

作者： Li, Bingdong Yang, Yanting Hong, Wenjing Yang, Peng Zhou, Aimin Shanghai Institute of AI for Education the School of Computer Science and Technology and the Shanghai Frontiers Science Center of Molecule Intelligent Syntheses East China Normal University Shanghai China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Computer Science and Engineering Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

A series of surrogate-assisted evolutionary algorithms (SAEAs) have been proposed for expensive multi-objective optimization problems (EMOPs), building cheap surrogate models to replace the expensive real function evaluations. However, the search efficiency of these SAEAs is not yet satisfactory. More efforts are needed to further exploit useful information from the real function evaluations in order to better guide the search process. Facing this challenge, this paper proposes a Hyperbolic Neural Network (HNN) based preselection operator to accelerate the optimization process based on limited evaluated solutions. First, the preselection task is modeled as a multi-label classification problem where solutions are classified into different layers (ordinal categories) through -relaxed objective aggregation. Second, in order to resemble the hierarchical structure of candidate solutions, a hyperbolic neural network is applied to tackle the multi-label classification problem. The reason for using HNN is that hyperbolic spaces more closely resemble hierarchical structures than Euclidean spaces. Moreover, to alleviate the data deficiency issue, a data augmentation strategy is employed for training the HNN. In order to evaluate its performance, the proposed HNN-based preselection operator is embedded into two surrogate-assisted evolutionary algorithms. Experimental results on two benchmark test suites and three real-world problems with up to 11 objectives and 150 decision variables involving seven state-of-the-art algorithms demonstrate the effectiveness of the proposed method. IEEE

关键词： Multiobjective optimization

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DarkSAM: fooling segment anything model to segment nothing 24

DarkSAM: fooling segment anything model to segment nothing

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Proceedings of the 38th International Conference on Neural Information Processing systems

作者： Ziqi Zhou Yufei Song Minghui Li Shengshan Hu Xianlong Wang Leo Yu Zhang Dezhong Yao Hai Jin National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Cluster and Grid Computing Lab and School of Computer Science and Technology Huazhong University of Science and Technology School of Cyber Science and Engineering Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System and Services Computing Technology and System Lab and Hubei Engineering Research Center on Big Data Security and Hubei Key Laboratory of Distributed System Security and School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (纸本)9798331314385

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM.

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Streamflow Prediction in the Mekong River Basin Using Deep Neural Networks

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IEEE Access 2023年 11卷 97930-97943页

作者： Nguyen, Thi-Thu-Ha Vu, Duc-Quang Mai, Son T. Dang, Thanh Duc National Taiwan University Department of Bioenvironmental Systems Engineering Taipei10617 Taiwan National Central University Department of Computer Science and Information System Taoyuan320317 Taiwan Thai Nguyen University of Education Thai Nguyen250000 Viet Nam Queen's University Belfast School of Electronics Electrical Engineering and Computer Science BelfastBT7 1NN United Kingdom Institute for Computational Science and Artificial Intelligence Van Lang University Laboratory of Environmental Sciences and Climate Change Ho Chi Minh City70000 Viet Nam Van Lang University Faculty of Environment Ho Chi Minh City70000 Viet Nam

In recent years, the Mekong River Basin (MRB), one of the largest river basins in Southeast Asia, has experienced severe impacts from extreme droughts and floods. Streamflow forecasting has become crucial for effective risk management strategies in the region. However, this task presents significant challenges due to rapid climate changes and the presence of numerous newly constructed upstream dams, which disrupt the natural flow. In this paper, we develop multiple deep learning models (incl. Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long short-term Memory (LSTM), and Transformer) to predict streamflow with different lead time forecasts based on observed meteorological variables and climatic indices (i.e., discharge, water level, precipitation, and temperature) from 1979 to 2019. The results indicate that LSTM obtains high performance for streamflow prediction in both dry and wet seasons while Transformer is not recommended for long-term prediction, especially in the dry season. The proposed deep learning models capture well the fluctuation of river flow in the MRB during the period of high-dam development, especially LSTM (NSE ≥ 0.8). The models' performances are enhanced with the adding of temperature for short-term prediction while precipitation was the most sensitive variable for long-term one. Such proposed models are essential for government agencies to plan mitigation and adaptation strategies at different periods, which can range from days to years. © 2013 IEEE.

关键词： Floods

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Wafer-Scale Synthesis of WS_(2)Films with In Situ Controllable p-Type Doping by Atomic Layer Deposition

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Research 2021年第1期2021卷 1424-1432页

作者： Hanjie Yang Yang Wang Xingli Zou Rongxu Bai Zecheng Wu Sheng Han Tao Chen Shen Hu Hao Zhu Lin Chen David W.Zhang Jack C.Lee Xionggang Lu Peng Zhou Qingqing Sun Edward T.Yu Deji Akinwande Li Ji State Key Laboratory of ASIC and System School of MicroelectronicsFudan UniversityShanghai 200433China State Key Laboratory of Advanced Special Steel School of Materials Science and EngineeringShanghai UniversityShanghai 200444China Microelectronics Research Center Department of Electrical and Computer EngineeringThe University of Texas at AustinAustin78758 TexasUSA

Wafer-scale synthesis of p-type TMD films is critical for its commercialization in next-generation electro/*** this work,wafer-scale intrinsic n-type WS_(2)films and in situ Nb-doped p-type WS_(2)films were synthesized through atomic layer deposition(ALD)on 8-inchα-Al_(2)O_(3)/Si wafers,2-inch sapphire,and 1 cm^(2)GaN substrate *** Nb doping concentration was precisely controlled by altering cycle number of Nb precursor and activated by ***_(2)n-FETs and Nb-doped p-FETs with different Nb concentrations have been fabricated using CMOS-compatible processes.X-ray photoelectron spectroscopy,Raman spectroscopy,and Hall measurements confirmed the effective substitutional doping with *** on/off ratio and electron mobility of WS_(2)n-FET are as high as 105 and 6.85 cm^(2)V^(-1)s^(-1),*** WS_(2)p-FET with 15-cycle Nb doping,the on/off ratio and hole mobility are 10 and 0.016 cm^(2)V^(-1)s^(-1),*** p-n structure based on n-and p-type WS_(2)films was proved with a 10^(4)rectifying *** realization of controllable in situ Nb-doped WS_(2)films paved a way for fabricating wafer-scale complementary WS_(2)FETs.

关键词： sapphire doping realization

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Disentangling Heterogeneous Knowledge Concept Embedding for Cognitive Diagnosis on Untested Knowledge

arXiv

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arXiv 2024年

作者： Zhang, Miao Wang, Ziming Xing, Runtian Xiao, Kui Li, Zhifei Zhang, Yan Tang, Chang School of Computer Science and Information Engineering Hubei University China Key Laboratory of Intelligent Sensing System and Security China Hubei Key Laboratory of Big Data Intelligent Analysis and Application China

Cognitive diagnosis is a fundamental and critical task in learning assessment, which aims to infer students’ proficiency on knowledge concepts from their response logs. Current works assume each knowledge concept will certainly be tested and covered by multiple exercises. However, whether online or offline courses, it’s hardly feasible to completely cover all knowledge concepts in several exercises. Restricted tests lead to undiscovered knowledge deficits, especially untested knowledge concepts(UKCs). In this paper, we propose a novel framework for Cognitive Diagnosis called Disentangling Heterogeneous Knowledge Cognitive Diagnosis(DisKCD) on untested knowledge. Specifically, we leverage course grades, exercise questions, and learning resources to learn the potential representations of students, exercises, and knowledge concepts. In particular, knowledge concepts are disentangled into tested and untested based on the limiting actual exercises. We construct a heterogeneous relation graph network via students, exercises, tested knowledge concepts(TKCs), and UKCs. Then, through a hierarchical heterogeneous message-passing mechanism, the fine-grained relations are incorporated into the embeddings of the entities. Finally, the embeddings will be applied to multiple existing cognitive diagnosis models to infer students’ proficiency on UKCs. Experimental results on real-world datasets show that the proposed model can effectively improve the performance of the task of diagnosing students’ proficiency on UKCs. Our code is available at https://***/Hubuers/DisKCD. © 2024, CC BY.

关键词： Students

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