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37th AAAI Conference on Artificial Intelligence, AAAI 2023

作者： Mao, Huisheng Zhang, Baozheng Xu, Hua Yuan, Ziqi Liu, Yihe State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing100084 China Beijing100084 China School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050018 China

ISBN: (纸本)9781577358800

Improving model robustness against potential modality noise, as an essential step for adapting multimodal models to real-world applications, has received increasing attention among researchers. For Multimodal Sentiment Analysis (MSA), there is also a debate on whether multimodal models are more effective against noisy features than unimodal ones. Stressing on intuitive illustration and in-depth analysis of these concerns, we present Robust-MSA, an interactive platform that visualizes the impact of modality noise as well as simple defence methods to help researchers know better about how their models perform with imperfect real-world data. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Sentiment analysis

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How to Find a Large Solution Set to Cover the Entire Pareto Front in Evolutionary Multi-Objective Optimization

How to Find a Large Solution Set to Cover the Entire Pareto ...

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2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Pang, Lie Meng Nan, Yang Ishibuchi, Hisao Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China

ISBN: (纸本)9798350337020

Recently, it has been pointed out in many studies that the performance of evolutionary multi-objective optimization (EMO) algorithms can be improved by selecting solutions from all examined solutions stored in an unbounded external archive. This is because in general the final population is not the best subset of the examined solutions. To obtain a good final solution set in such a solution selection framework, subset selection from a large candidate set (i.e., all examined solutions) has been studied. However, since good subsets cannot be obtained from poor candidate sets, a more important issue is how to find a good candidate set, which is the focus of this paper. In this paper, we first visually demonstrate that the entire Pareto front is not covered by the examined solutions through computational experiments using MOEA/D, NSGA-III and SMS-EMOA on DTLZ test problems. That is, the examined solution set stored in the unbounded archive has some large holes (i.e., some uncovered area of the Pareto front). Next, to evaluate the quality of the examined solution set (i.e., to measure the size of the largest hole), we propose the use of a variant of the inverted generational distance (IGD) indicator. Then, we propose a simple modification of EMO algorithms to improve the quality of the examined solution set. Finally, we demonstrate the effectiveness of the proposed modification through computational experiments. © 2023 IEEE.

关键词： Evolutionary multi-objective optimization Pareto front solution selection framework unbounded external archive

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Implicit Learning of Rotational Decoupling Detection Heads Applied to Remote Sensing Object Detection

Implicit Learning of Rotational Decoupling Detection Heads A...

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2023 Asia-Pacific Conference on Image Processing, Electronics and computers, IPEC 2023

作者： Song, Chengkun Ding, Sheng Chen, Yingying School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Kagoshima University Kagoshima Japan

ISBN: (纸本)9781665457033

Remote sensing image features complicated background, big image size, small object pixels, and variable object direction due to the arbitrary shooting angle. Conventional object detector has poor detection effect on remote sensing image, which is prone to missed detection and false detection. In view of this, an improved YOLOv5 object detection algorithm was proposed for remote sensing image. First, the C3 module was improved. The output feature information of each convolutional module was transmitted to the deep network, so as to preserve more fine-grained feature information and enhance the small object detection capability. The improved downsampling mechanism integrates convolution downsampling with maximum pooling downsampling mechanism in downs amp ling to separate foreground and background information while retaining feature information. A lightweight rotatory decoupling detection head with implicit learning was proposed to address the conflict between regression and classification, thus improving the network detection capability for objects in any direction. In the meantime, an implicit learning matrix was added to guarantee the lightweight detection head and improve the network detection accuracy for multiple tasks. According to experimental results, under the same model size, compared with the original YOLOv5 detection method, the detection accuracy and detection speed of the proposed method were improved by 8% and 15 frames, or 14.7%, respectively on DOTA-v1.5 data set, effectively improving the detection accuracy and speed in remote sensing image detection. The detection accuracy was increased by 3.5% on coco data set in natural scenario. The method herein can still effectively improve the detection accuracy in natural scenario, proving effectiveness of the modified YOLOv5 method. © 2023 IEEE.

关键词： Object detection

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Scheduling Algorithm for Low Energy Consumable Parallel Task Application Based on DVFS 1

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7th International Conference on Smart Computing and Communication, SmartCom 2022

作者： Liu, Xun Zhao, Hui College of Computer Science and Technology Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan China Henan University Kaifeng475000 China

ISBN: (数字)9783031281242

ISBN: (纸本)9783031281235

With the continuous improvement of various high-performance computing systems, various data centers had also been fully expanded. Energy consumption and actual performance measurement were very important indicators, which were also key issues in how to judge parallel calls for some tasks in high-performance computer systems. Modern processors were basically equipped with software control functions such as DVFS (Dynamic Voltage Frequency Scaling), in the actual system operation to ensure that the system could ensure the reasonable operation of the system while reducing energy consumption indicators. This paper considered how the designed scheduling algorithm first divides tasks reasonably to ensure that the maximum completion time and energy consumption of the processor were sufficiently reduced when the directed acyclic graph was executed. Then considered making reasonable adjustments to the processor frequency using DVFS technology to adapt to the task while ensuring the critical path of the task. At the end of the article, make sure that the experimental verification algorithm could ensure that the task was completed and could reduce the energy consumption during task execution as much as possible. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Energy utilization

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Performance Evaluation of Multi-objective Evolutionary Algorithms Using Artificial and Real-world Problems 12th

Performance Evaluation of Multi-objective Evolutionary Algor...

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12th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2023

作者： Ishibuchi, Hisao Nan, Yang Pang, Lie Meng Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9783031272493

Performance of evolutionary multi-objective optimization (EMO) algorithms is usually evaluated using artificial test problems such as DTLZ and WFG. Every year, new EMO algorithms with high performance on those test problems are proposed. One question is whether they also work well on real-world problems. In this paper, we try to find an answer to this question by examining the performance of ten EMO algorithms including both well-known representative algorithms and recently-proposed new algorithms. First, those algorithms are applied to five artificial test suites (DTLZ, WFG, Minus-DTLZ, Minus-WFG and MaF) and three real-world problem suites. The performance of each algorithm is evaluated by the hypervolume indicator. Next, the ranking of the ten EMO algorithms is created for each problem suite. That is, eight different rankings are obtained (each ranking is for each problem suite). Then, the eight different rankings are visually compared to answer our research question. The distance between two rankings is also calculated to support visual comparison results. Our experimental results show that similar rankings of the ten EMO algorithms are obtained for the three real-world problem suites and Minus-WFG. It is also shown that the ranking for each of the three real-world problem suites is clearly different from their ranking for DTLZ. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multiobjective optimization

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless Communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. School of Information and Electronics Beijing Institute of Technology Beijing100081 China School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Southern University of Science and Technology Shenzhen518055 China School of Electrical and Information Engineering The University of Sydney SydneyNSW2008 Australia Singapore University of Technology and Design 487372 Singapore Department of Electronic Engineering Kyung Hee University Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2025 IEEE. All rights reserved.

关键词： Graph neural networks

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Photopyroelectric tweezers for versatile manipulation

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The Innovation 2025年第1期6卷 55-60,54页

作者： Fang Wang Cong Liu Zhengjin Dai Weizhong Xu Xinyue Ma Yufeng Gao Xuewu Ge Wei Zheng Xuemin Du Center for Intelligent Biomedical Materials and Devices(IBMD) Shenzhen Institute of Advanced Technology(SIAT)Chinese Academy of Sciences(CAS)Shenzhen 518055China University of Chinese Academy of Sciences Beijing 100049China The Key Laboratory of Biomedical Imaging Science and System Chinese Academy of SciencesShenzhen 518055China Department of Polymer Science and Engineering University of Science&Technology of ChinaHefei 230026China

Optical tweezers and related techniques offer extraordinary opportunities for research and applications in physical,biological,and medical ***,certain critical requirements,such as high-intensity laser beams,sophisticated electrode designs,additional electric sources,or low-conductive media,significantly impede their flexibility and adaptability,thus hindering their practical ***,we report innovative photopyroelectric tweezers(PPT)that combine the advantages of light and electric field by utilizing a rationally designed photopyroelectric substrate with efficient and durable photo-induced surface charge-generation capability,enabling diverse manipulation in various working *** PPTs allow for remote and programmable manipulation of objects with diverse materials(polymer,inorganic,and metal),different phases(bubble,liquid,and solid),and various geometries(sphere,cuboid,and wire).Furthermore,the PPT is not only adaptable to high-conductivity media but also applicable to both portable macroscopic manipulation platforms and microscopic manipulation systems,enabling cross-scale manipulations for solid objects,liquid droplets,and biological *** high-level flexibility and adaptability of the PPT extend to broad applications in manipulating hydrogel robots,sorting particles,assembling cells,and stimulating *** surpassing the limitations of conventional tweezers,the PPT bridges the gap between macroscopic and microscopic manipulations,offering a revolutionary tool in robotics,colloidal science,biomedical fields,and beyond.

关键词： photopyroelectric tweezers ppt programmable manipulation optical tweezers photopyroelectric tweezers electrode designsadditional light electric fiel remote manipulation electric sourcesor

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Monotonicity and nonmonotonicity in L3-valued propositional logic

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Frontiers of computer science 2022年第4期16卷 33-43页

作者： Wei Li Yuefei Sui State Key Laboratory of Software Development Environment Beihang UniversityBeijing100083China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing100049China

A sequent is a pair (Γ, Δ), which is true under an assignment if either some formula in Γ is false, or some formula in Δ is true. In L_(3)-valued propositional logic, a multisequent is a triple Δ∣Θ∣Γ, which is true under an assignment if either some formula in Δ has truth-value t, or some formula in Θ has truth-value m, or some formula in Γ has truth-value f. There is a sound, complete and monotonic Gentzen deduction system G for sequents. Dually, there is a sound, complete and nonmonotonic Gentzen deduction system G′ for co-sequents Δ: Θ: Γ. By taking different quantifiers some or every, there are 8 kinds of definitions of validity of multisequent Δ∣Θ∣Γ and 8 kinds of definitions of validity of co-multisequent Δ: Θ: Γ, and correspondingly there are 8 sound and complete Gentzen deduction systems for sequents and 8 sound and complete Gentzen deduction systems for co-sequents. Correspondingly their monotonicity is discussed.

关键词： sequent mulisequent gentzen deduction system monotonicity nonmonotonicity

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User Behavior-Based Collaborative Filtering Recommendation Algorithm and Visualization in Second-Hand Trading Platform 2

User Behavior-Based Collaborative Filtering Recommendation A...

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2nd IEEE International Conference on Sensors, Electronics and computer Engineering, ICSECE 2024

作者： Qiao, Rui Wang, Xin Zhong, Xiaoben School of Computer Science and Technology Wuhan University of Science and Technology Hubei Wuhan China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Hubei Wuhan China Yunnan College of Business Management Anning650301 China

ISBN: (纸本)9798350373646

In the backdrop of the digital era, the demand for second-hand trading within campuses has seen a significant rise. This project aims at designing a second-hand trading platform that merges the latest technological trends, providing a secure and convenient trading environment for students. The system utilizes Spring Boot for backend development and Vue along with ElementUI for constructing the user interface, ensuring efficient operation and enhancing user interaction experience. The server employs Spring Boot's built-in Tomcat, simplifying the deployment process. For data persistence, Mybatis framework is used for flexible and efficient interactions with the MySQL database. The platform introduces a collaborative filtering algorithm based on user behavior for smart product recommendations, further improving the user experience. The system integrates core modules such as user management, product browsing, shopping cart functionalities, and order processing, innovatively introducing an offline transaction confirmation mechanism to ensure transaction security and reliability while avoiding legal risks associated with online payments. With its B/S architecture, the platform operates more smoothly, making it more accessible to users. Experimental results demonstrate that the platform has a user-friendly interface and efficient data processing capabilities, receiving widespread recognition and praise from users, proving its practicality and innovativeness. © 2024 IEEE.

关键词： Collaborative filtering

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Disentangled latent factors for muti-cause treatment effect estimation 6

Disentangled latent factors for muti-cause treatment effect ...

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2024 6th International Conference on Artificial Intelligence Technologies and Applications, ICAITA 2024

作者： Zhao, Zhuoyue Wang, Haotian Xu, Liyang Wang, Qian Tang, Yuhua Department of Intelligent Data Science National University of Defense Technology Changsha410073 China Intelligent Game and Decision Lab. Academy of Military Science Beijing100071 China Institute for Quantum Information State Key Laboratory of High-Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China

Existing methods estimate treatment effects from observational data and assume that covariates are all confounders. However, observed covariates may not directly represent confounding variables that influence both treatment and outcome. They always include variables that only affect the treatment or the outcome. In addition, for multi-dimensional binary treatments, disentangled methods are mainly designed for binary variables and ignore the impact of multi-cause treatment variables on the inference of latent factors. To address these two issues, based on variable decomposition and proxy inference, we propose the Disentangled Latent Factors for Multi-cause Treatment Estimation (DEMTE) algorithm. It utilizes an identifiable autoencoder to infer and disentangle latent factors based on the joint distribution of variables in observational data. DEMTE evaluates the treatment effect on the disentangled factors. Synthetic experiments and semi-synthetic experiments demonstrate the effectiveness of the inference and disentanglement techniques and our method achieves more accurate treatment effect estimation. © 2024 Institute of Physics Publishing. All rights reserved.

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