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arXiv 2023年

作者： Li, Wenhao Qiao, Dan Wang, Baoxiang Wang, Xiangfeng Jin, Bo Zha, Hongyuan School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518172 China School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen518172 China School of Computer Science and Technology East China Normal University Shanghai200062 China School of Software Engineering Tongji University Shanghai200062 China

The difficulty of appropriately assigning credit is particularly heightened in cooperative MARL with sparse reward, due to the concurrent time and structural scales involved. Automatic subgoal generation (ASG) has recently emerged as a viable MARL approach inspired by utilizing sub-goals in intrinsically motivated reinforcement learning. However, end-to-end learning of complex task planning from sparse rewards without prior knowledge, undoubtedly requires massive training samples. Moreover, the diversity-promoting nature of existing ASG methods can lead to the "over-representation" of sub-goals, generating numerous spurious sub-goals of limited relevance to the actual task reward and thus decreasing the sample efficiency of the algorithm. To address this problem and inspired by the disentangled representation learning, we propose a novel "disentangled" decision-making method, Semantically Aligned task decomposition in MARL (SAMA), that prompts pretrained language models with chain-of-thought that can suggest potential goals, provide suitable goal decomposition and subgoal allocation as well as self-reflection-based replanning. Additionally, SAMA incorporates language-grounded RL to train each agent's subgoal-conditioned policy. SAMA demonstrates considerable advantages in sample efficiency compared to state-of-the-art ASG methods, as evidenced by its performance on two challenging sparse-reward tasks, Overcooked and MiniRTS. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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arXiv

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arXiv 2022年

作者： Liu, Yuqing Jia, Qi Zhang, Jian Fan, Xin Wang, Shanshe Ma, Siwei Gao, Wen School of Software Dalian University of Technology Dalian116620 China International School of Information Science and Engineering Dalian University of Technology Dalian116620 China School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen China School of Electronics Engineering and Computer Science Institute of Digital Media Peking University Beijing100871 China

As a highly ill-posed issue, single image super-resolution (SISR) has been widely investigated in recent years. The main task of SISR is to recover the information loss caused by the degradation procedure. According to the Nyquist sampling theory, the degradation leads to aliasing effect and makes it hard to restore the correct textures from low-resolution (LR) images. In practice, there are correlations and self-similarities among the adjacent patches in the natural images. This paper considers the self-similarity and proposes a hierarchical image super-resolution network (HSRNet) to suppress the influence of aliasing. We consider the SISR issue in the optimization perspective, and propose an iterative solution pattern based on the half-quadratic splitting (HQS) method. To explore the texture with local image prior, we design a hierarchical exploration block (HEB) and progressive increase the receptive field. Furthermore, multi-level spatial attention (MSA) is devised to obtain the relations of adjacent feature and enhance the high-frequency information, which acts as a crucial role for visual experience. Experimental result shows HSRNet achieves better quantitative and visual performance than other works, and remits the aliasing more effectively. Copyright © 2022, The Authors. All rights reserved.

关键词： Textures

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An Application of SVD++ Method in Collaborative Filtering 17

An Application of SVD++ Method in Collaborative Filtering

引用

17th International computer Conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2020

作者： Jiang, Sheng Li, Jianping Wang, Zhou School of Computer Science and Engineering University of Electronic Science and Technology of China Chcnadu610054 China School of Computer and Software Engineering Xihua University Chengdu610039 China

ISBN: (纸本)9781665405058

Collaborative filtering algorithms have important applications in the implementation of recommendation systems. Collaborative filtering generally focuses on the user's evaluation scores for commodity items, and these evaluation data imply some specific relevance. Therefore, this type of collaborative filtering Model research has attracted widespread attention. SVD is currently one of the most classic and practical collaborative filtering algorithm models. It can deal with the problem of data sparseness in the recommendation system and derive different types of SVD models on this basis, but its recommendation results It has never been significantly improved. In response to such problems, this article proposes a new variant of the SVD++ algorithm that incorporates a special timing mechanism for dynamic adjustment, and uses the average absolute error, root mean square error and standard average absolute error for the recommended results. After evaluation, we got better results than the current classic SVD++ model. © 2020 IEEE.

关键词： Singular value decomposition

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Enhanced Local Knowledge with Proximity Values and Syntax-Clusters for Aspect-Level Sentiment Analysis

SSRN

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SSRN 2023年

作者： Chen, Pengfei Zeng, Biqing Lu, Yuwu Xue, Yun Fan, Fei Xu, Mayi Feng, Lingcong School of Software South China Normal University Guangdong Foshan528225 China School of Electronics and Information Engineering South China Normal University Guangdong Foshan528225 China School of Computer Science Wuhan University Hubei Wuhan430079 China School of Education Jiangxi Science and Technology Normal University Jiangxi Nanchang330100 China

Aspect-level sentiment analysis (ALSA) aims to extract the polarity of different aspect terms in a sentence. Previous works leveraging traditional dependency syntax parsing trees (DSPT) to encode contextual syntactic information had obtained state-of-the-art results. However, these works may not be able to learn fine-grained syntactic knowledge efficiently, which makes them difficult to take advantage of local context. Furthermore, these works failed to exploit the dependency relation from DSPT sufficiently. To solve these problems, we propose a novel method to enhance local knowledge by using extensions of Local Context Network based on Proximity Values (LCPV) and Syntax-clusters Attention (SCA), named LCSA. LCPV first gets the induced trees from pre-trained models and generates the syntactic proximity values between context word and aspect to adaptively determine the extent of local context. Our improved SCA further extracts fine-grained knowledge, which not only focuses on the essential clusters for the target aspect term but also guides the model to learn essential words inside each cluster in DSPT. Extensive experimental results on multiple benchmark datasets demonstrate that LCSA is highly robust and achieves state-of-the-art performance for ALSA. © 2023, The Authors. All rights reserved.

关键词： Sentiment analysis

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BENCHMARKING ALGORITHMS FOR SUBMODULAR OPTIMIZATION PROBLEMS USING IOHPROFILER

arXiv

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arXiv 2023年

作者： Neumann, Frank Neumann, Aneta Qian, Chao Do, Viet Anh de Nobel, Jacob Vermetten, Diederick Ahouei, Saba Sadeghi Ye, Furong Wang, Hao Bäck, Thomas Optimisation and Logistics School of Computer and Mathematical Sciences The University of Adelaide Adelaide Australia State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Leiden Institute of Advanced Computer Science Leiden University Leiden Netherlands Leiden University Leiden Netherlands

Submodular functions play a key role in the area of optimization as they allow to model many real-world problems that face diminishing returns. Evolutionary algorithms have been shown to obtain strong theoretical performance guarantees for a wide class of submodular problems under various types of constraints while clearly outperforming standard greedy approximation algorithms. This paper introduces a setup for benchmarking algorithms for submodular optimization problems with the aim to provide researchers with a framework to enhance and compare the performance of new algorithms for submodular problems. The focus is on the development of iterative search algorithms such as evolutionary algorithms with the implementation provided and integrated into IOHprofiler which allows for tracking and comparing the progress and performance of iterative search algorithms. We present a range of submodular optimization problems that have been integrated into IOHprofiler and show how the setup can be used for analyzing and comparing iterative search algorithms in various settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Identity-checking for provable data possession without privacy leakage 6th

Identity-checking for provable data possession without priva...

引用

6th International Conference on Artificial Intelligence and Security,ICAIS 2020

作者： Wang, Jiaqi Zeng, Shengke Li, Shiliang School of Computer and Software Engineering Xihua University Chengdu610039 China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9789811581007

The user data stored in remote server should be assured intact. Remote data integrity checking scheme (RDIC) allows a third party auditor (TPA) to examine the integrity of data in cloud server. As we know, the user authentication is necessary in the cloud storage. Obviously, the user identity is sensitive as the user can be identified when it requests integrity checking. In this paper, we employ zero-knowledge undeniable signature to RDIC to tackle the privacy leakage. There is no evidence to show the user checking, thus the user privacy is protected. © Springer Nature Singapore Pte Ltd 2020.

关键词： Cloud storage

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Two-Area Power System with Automatic Generation Control Utilizing PID Control, FOPID, Particle Swarm Optimization, and Genetic Algorithms

Two-Area Power System with Automatic Generation Control Util...

引用

Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT), International Conference on

作者： Madan Mohan Sati Dinesh Kumar Akhilesh Singh Mohan Raparthi Faisal Yousef Alghayadh Mukesh Soni Deptt. of Mathematics Graphic Era Hill University Bhimtal India Dept. of Computer Science & Engg. SRM Institute of Science & Technology Ghaziabad UP India Deptt. of Electrical Engg. Uttarakhand Technical University Campus Institute Pithoragarh Software Engineer Alphabet Life Science Dallas Texas USA Computer Science & IT Department College of Applied Sci Almaarefa Univ Riyadh Saudi Arabia School of Science & Technology Dr. D. Y. Patil Vidyapeeth Pune India

This study aims to present a feasible LFC (Load Frequency Control) model that integrates physical security limitations, namely GRC, GDB, and TD. The presence of this characteristic guarantees that the design is not only accurate but also capable of being materialized in a physical form. It is suggested that the traditional PI controller be replaced with a PID controller in order to handle the issues that are posed by large-scale power systems that are interconnected. Developing extremely efficient soft computing approaches that are capable of producing the necessary results for both individual and extensive power systems while taking into consideration flaws in modeling is the goal of this endeavor. It is important to get the frequency deviation to zero while still following the rules set by real issues and different working conditions. In order to maintain the electrical flow between the inter-area tie lines that are located within the various control zones at the schedule values that have been specified. In order to achieve optimal levels of performance in both steady state and transient conditions. The purpose of this is to obtain valuable multiple solutions with the intention of future planning and design. It is important to simplify the robust load frequency control design problem by changing it into a low-order proportional integral derivative (PID) control synthesis problem in order to address the practical benefits. This approach is commonly employed in real-world power systems applications.

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Detect Changes like Humans: Incorporating Semantic Priors for Improved Change Detection

arXiv

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arXiv 2024年

作者： Gan, Yuhang Xuan, Wenjie Luo, Zhiming Fang, Lei Wang, Zengmao Liu, Juhua Du, Bo School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University Wuhan China Land Satellite Remote Sensing Application Center MNR Beijing China Information Technology Co. Ltd. Ningbo China

When given two similar images, humans identify their differences by comparing the appearance (e.g., color, texture) with the help of semantics (e.g., objects, relations). However, mainstream change detection models adopt a supervised training paradigm, where the annotated binary change map is the main constraint. Thus, these methods primarily emphasize the difference-aware features between bi-temporal images and neglect the semantic understanding of the changed landscapes, which undermines the accuracy in the presence of noise and illumination variations. To this end, this paper explores incorporating semantic priors to improve the ability to detect changes. Firstly, we propose a Semantic-Aware Change Detection network, namely SA-CDNet, which transfers the common knowledge of the visual foundation models (i.e., FastSAM) to change detection. Inspired by the human visual paradigm, a novel dual-stream feature decoder is derived to distinguish changes by combining semantic-aware features and difference-aware features. Secondly, we design a single-temporal semantic pre-training strategy to enhance the semantic understanding of landscapes, which brings further increments. Specifically, we construct pseudo-change detection data from public single-temporal remote sensing segmentation datasets for large-scale pre-training, where an extra branch is also introduced for the proxy semantic segmentation task. Experimental results on five challenging benchmarks demonstrate the superiority of our method over the existing stateof- the-art methods. The code is available at SA-CD. Copyright © 2024, The Authors. All rights reserved.

关键词： Change detection

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An Inception Module CNN Classifiers Fusion Method on Pulmonary Nodule Diagnosis by Signs

引用

Tsinghua science and technology 2020年第3期25卷 368-383页

作者： Guangyuan Zheng Guanghui Han Nouman Qadeer Soomro the Beijing Key Laboratory of Intelligent Information Technology School of Computer Science and TechnologyBeijing Institute of TechnologyBeijing 100081China the School of Information Technology Shangqiu Normal UniversityShangqiu 476000China the School of Biomedical Engineering Sun Yat-sen UniversityGuangzhou 510006China the Department of Software Engineering Mehran University of Engineering and TechnologySZAB CampusKhairpur Mir's66020Pakistan

A "sign" on a lung CT image refers to a radiologic finding that suggests a pathological progression of some specific disease. Analysis of CT signs is helpful to understand the pathological origin of the lesion. In-depth study of lung nodules classification with different CT signs will help to distinguish benign and malignant nodules more clearly and accurately. To this end, we propose an Inception module-based ensemble classification method for pulmonary nodule diagnosis with different nodule signs. We first construct a Convolutional Neural Network(CNN) classifier adopting Inception modules and pre-train it on ImageNet. We then fine-tune this pre-trained classifier on 10 different lung nodule sign sample sets, and fuse these 10 classifiers with an artificial immune ensemble algorithm. The overall sensitivity, specificity, and accuracy of our proposed Artificial Immune Algorithm-based Inception Networks Fusion(AIA-INF) algorithm are 82.22%, 93.17%, and 88.67%, respectively, which are significantly higher than those of the alternative Bagging and Boosting methods. The experimental results show that our Inception-based ensemble classifier offers promising performance, and compared with other CADx systems, this scheme can offer a more detailed reference for diagnosis, and can be valuable for junior radiologist training.

关键词： sign lung cancer pulmonary nodule Convolutional Neural Network(CNN) Artificial Immune Algorithm(AIA)

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Enhancing Emotional Experience by Building Emotional Virtual Characters in VR Volleyball Games

Enhancing Emotional Experience by Building Emotional Virtual...

引用

作者： Bai, Zechen Yao, Naiming Mishra, Nidhi Chen, Hui Wang, Hongan Magnenat Thalmann, Nadia Beijing Key Laboratory of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute for Media Innovation Nanyang Technological University Singapore Singapore

Virtual reality (VR) volleyball games can provide an immersive entertainment experience and facilitate users to get familiar with game rules. In real world, players often experience emotions from scores, strokes, passes, and the influence from their teammates or opponents, and so forth. However, seldom studies are carried out for this emotional experience in a virtual environment, as most VR volleyball games mainly concentrate on the game playing, such as body movements. In this article, we propose to enhance emotional experience by building emotional virtual characters in VR volleyball games. The virtual characters cannot only arouse their emotions but also express their facial expressions according to the game situation. Emotion patterns are learned from real-world volleyball matches. The results demonstrate that our framework has great potential to enhance the user's emotional experience and engagement. © 2021 John Wiley & Sons, Ltd.

关键词： Sports

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