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Dynamic asymmetric reflective decision-making supports the evolution of cooperation

Chaos, Solitons & Fractals 2025年 199卷

作者： xiaopeng Li xiuli Zhang Zhonglin Wang Yicheng Pang College of Big Data Statistics Guizhou University of Finance and Economics Guiyang Guizhou 550025 PR China Shanxi Key Laboratory of Intelligent Optimization Computing and Blockchain Technology Taiyuan Normal University Taiyuan Shanxi 030619 PR China Department of Applied Mathematics Guizhou University of Finance and Economics Guiyang Guizhou 550025 PR China

Up to now, most previous studies have assumed that agents employed symmetric decision-updating mechanisms to investigate how cooperation evolved in selfish populations. However, dynamic and asymmetric decision-making update is overlooked. In this paper, we try our best to construct a reputation-driven dynamic asymmetric reflective decision-making model to fill in the blanks. Initially, each agent is randomly assigned a reputation value within the range [1,100], which will increase or decrease if it makes cooperative or defective decision in the process of evolution. Then, we distinguish agents as standouts or laymen by the pivotal reputation threshold R c . The standouts with high-reputation are cautious in updating their decisions and therefore may think more times. In sharp contrast, the laymen with low-reputation, appear too reckless and just only think once in the similar process. Simulation results show that this simple model can support the evolution of cooperation, especially expanding the threshold for cooperation annihilation. Through qualitative micro-analysis and quantitative statistical analysis, we find that standouts with high reputations play an irreplaceable role in facilitating cooperation. The robustness test further confirms that the promoting effect of this mechanism on cooperation is independent of network structures, decision update patterns, and social dilemma types. We hope that our research can deepen the understanding and cognition of the evolution of cooperation.

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Circuit area optimization of multi-output MPRM based on ERWOA algorithm

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Beijing Hangkong Hangtian Daxue Xuebao/Journal of Beijing University of Aeronautics and Astronautics 2023年第5期49卷 1193-1200页

作者： He, Juncai He, Zhenxue Wang, Fushun Huo, Zhisheng xiao, Limin Intelligent Agricultural Equipment Research Institute Hebei Agricultural University Baoding071001 China Key Laboratory of Agricultural Big Data of Hebei Province Hebei Agricultural University Baoding071001 China High Performance Computing Platform Beihang University Beijing100191 China School of Software Beihang University Beijing100191 China School of Computer Science and Engineering Beihang University Beijing100191 China

Mixed polarity Reed-Muller (MPRM) circuit area optimization has become a research hotspot in the field of integrated circuit design. It is a combinatorial optimization, aiming at finding the MPRM expression with the least number of terms among many MPRM expressions. A explosion strategy and restart strategy based whale optimization algorithm (ERWOA) is proposed. In addition, a multi-output MPRM circuit area optimization method is proposed, which uses the improved whale algorithm and the improved polarity conversion algorithm to search for the MPRM circuit with the least number of AND terms. Results on the MCNC Benchmark circuits show that the proposed algorithm increases the conversion efficiency by 99.93% and 99.96% at most, compared with the mixed polarity and inter-polarity conversion algorithms based on the list technology, respectively. Compared with the genetic algorithm and the artificial bee colony algorithm, the improved whale optimization algorithm saves the circuit area up to 18.32% with an average of 5.54%, and 14.41% with an average of 5.00%, respectively. © 2023 Beijing University of Aeronautics and Astronautics (BUAA). All rights reserved.

关键词： Genetic algorithms

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Overview of the Tenth Dialog System Technology Challenge: DSTC10

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IEEE/ACM Transactions on Audio Speech and Language Processing 2024年 32卷 765-778页

作者： Yoshino, Koichiro Chen, Yun-Nung Crook, Paul Kottur, Satwik Li, Jinchao Hedayatnia, Behnam Moon, Seungwhan Fei, Zhengcong Li, Zekang Zhang, Jinchao Feng, Yang Zhou, Jie Kim, Seokhwan Liu, Yang Jin, Di Papangelis, Alexandros Gopalakrishnan, Karthik Hakkani-Tur, Dilek Damavandi, Babak Geramifard, Alborz Hori, Chiori Shah, Ankit Zhang, Chen Li, Haizhou Sedoc, Joao D'haro, Luis F. Banchs, Rafael Rudnicky, Alexander Guardian Robot Project R-IH RIKEN 2-2-2 Hikaridai Seika Shoraku619-0288 Japan Information Science Nara Institute of Science and Technology Ikoma630-0101 Japan Computer Science and Information Engineering National Taiwan University Taipei10617 Taiwan Inc. Palo AltoCA95054 United States Alexa AI *** Inc. SunnyvaleCA94089 United States Meta Seattle RedmondWA98052 United States Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Tencent AI Lab Beijing Beijing China Kexueyuan South Road Zhongguancun Beijing100190 China Beijing 100190 China Alexa AI *** Inc. SunnyvaleCA United States 1120 Enterprise way Sunnyvale94089 United States *** Inc. SeattleWA United States Menlo Park CA United States Audio and Speech Group Mitsubishi Electric Research Laboratories CambridgeMA02139-1955 United States Carnegie Mellon University Department of Language and Information Technologies or just Carnegie Mellon University Pittsburgh United States National University of Singapore Singapore Singapore Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Shenzhen Research Institute of Big Data School of Data Science Chinese University of Hong Kong Shenzhen518172 China New York University New YorkNY United States ETSI de Telecomunicacion - Speech Technology and Machine Learning Group Universidad Politecnica de Madrid Ciudad Universitaria Madrid28040 Spain Nanyang Technological University Singapore Singapore Carnegie Mellon University PittsburghPA United States

This article introduces the Tenth Dialog System Technology Challenge (DSTC-10). This edition of the DSTC focuses on applying end-to-end dialog technologies for five distinct tasks in dialog systems, namely 1. Incorporation of Meme images into open domain dialogs, 2. Knowledge-grounded Task-oriented Dialogue Modeling on Spoken Conversations, 3. Situated Interactive Multimodal dialogs, 4. Reasoning for Audio Visual Scene-Aware Dialog, and 5. Automatic Evaluation and Moderation of Open-domainDialogue Systems. This article describes the task definition, provided datasets, baselines, and evaluation setup for each track. We also summarize the results of the submitted systems to highlight the general trends of the state-of-the-art technologies for the tasks. © 2023 The Authors.

关键词： Job analysis

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A review on vision-based analysis for automatic dietary assessment

arXiv

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arxiv 2021年

作者： Wang, Wei Min, Weiqing Li, Tianhao Dong, xiaoxiao Li, Haisheng Jiang, Shuqiang School of Computer and Engineering Beijing Technology and Business University Beijing100048 China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing100048 China National Engineering Laboratory for Agri-product Quality Traceability Beijing100048 China University of Chinese Academy of Sciences Beijing100049 China

Background: Maintaining a healthy diet is vital to avoid health-related issues, e.g., undernutrition, obesity and many non-communicable diseases. An indispensable part of the health diet is dietary assessment. Traditional manual recording methods are not only burdensome but timeconsuming, and contain substantial biases and errors. Recent advances in Artificial Intelligence (AI), especially computer vision technologies, have made it possible to develop automatic dietary assessment solutions, which are more convenient, less time-consuming and even more accurate to monitor daily food intake. Scope and approach: This review presents Vision-Based Dietary Assessment (VBDA) architectures, including multi-stage architecture and end-to-end one. The multi-stage dietary assessment generally consists of three stages: food image analysis, volume estimation and nutrient derivation. The prosperity of deep learning makes VBDA gradually move to an end-to-end implementation, which applies food images to a single network to directly estimate the nutrition. The recently proposed end-to-end methods are also discussed. We further analyze existing dietary assessment datasets, indicating that one large-scale benchmark is urgently needed, and finally highlight critical challenges and future trends for VBDA. key findings and conclusions: After thorough exploration, we find that multi-task end-to-end deep learning approaches are one important trend of VBDA. Despite considerable research progress, many challenges remain for VBDA due to the meal complexity. We also provide the latest ideas for future development of VBDA, e.g., fine-grained food analysis and accurate volume estimation. This review aims to encourage researchers to propose more practical solutions for VBDA. Copyright © 2021, The Authors. All rights reserved.

关键词： Nutrition

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Holographic capture and projection system of real object based on tunable zoom lens

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PhotoniX 2020年第1期1卷 271-285页

作者： Di Wang Chao Liu Chuan Shen Yan xing Qiong-Hua Wang School of Instrumentation and Optoelectronic Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang UniversityBeijing 100191China Key Laboratory of Intelligent Computing&Signal Processing Ministry of EducationAnhui UniversityHefei 230039China.

In this paper, we propose a holographic capture and projection system of real objectsbased on tunable zoom lenses. Different from the traditional holographic system, aliquid lens-based zoom camera and a digital conical lens are used as key parts to reachthe functions of holographic capture and projection, respectively. The zoom camera isproduced by combing liquid lenses and solid lenses, which has the advantages of fastresponse and light weight. By electrically controlling the curvature of the liquid-liquidsurface, the focal length of the zoom camera can be changed easily. As anothertunable zoom lens, the digital conical lens has a large focal depth and the opticalproperty is perfectly used in the holographic system for adaptive projection, especiallyfor multilayer imaging. By loading the phase of the conical lens on the spatial lightmodulator, the reconstructed image can be projected with large depths. With theproposed system, holographic zoom capture and color reproduction of real objects canbe achieved based on a simple structure. Experimental results verify the feasibility ofthe proposed system. The proposed system is expected to be applied to microprojectionand three-dimensional display technology.

关键词： Spatial light modulator Holographic system Zoom lenses Display technology

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Multi-Modal Transformer for Accelerated MR Imaging

arXiv

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arxiv 2021年

作者： Feng, Chun-Mei Yan, Yunlu Chen, Geng Xu, Yong Hu, Ying Shao, Ling Fu, Huazhu Shenzhen Key Laboratory of Visual Object Detection and Recognition Harbin Institute of Technology Shenzhen518055 China National Engineering Laboratory for Integrated AeroSpace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China Terminus Group China Institute of High Performance Computing A*STAR Singapore138632 Singapore

Accelerated multi-modal magnetic resonance (MR) imaging is a new and effective solution for fast MR imaging, providing superior performance in restoring the target modality from its undersampled counterpart with guidance from an auxiliary modality. However, existing works simply combine the auxiliary modality as prior information, lacking in-depth investigations on the potential mechanisms for fusing different modalities. Further, they usually rely on the convolutional neural networks (CNNs), which is limited by the intrinsic locality in capturing the long-distance dependency. To this end, we propose a multi-modal transformer (MTrans), which is capable of transferring multi-scale features from the target modality to the auxiliary modality, for accelerated MR imaging. To capture deep multi-modal information, our MTrans utilizes an improved multi-head attention mechanism, named cross attention module, which absorbs features from the auxiliary modality that contribute to the target modality. Our framework provides three appealing benefits: (i) Our MTrans use an improved transformers for multi-modal MR imaging, affording more global information compared with existing CNN-based methods. (ii) A new cross attention module is proposed to exploit the useful information in each modality at different scales. The small patch in the target modality aims to keep more fine details, the large patch in the auxiliary modality aims to obtain high-level context features from the larger region and supplement the target modality effectively. (iii) We evaluate MTrans with various accelerated multi-modal MR imaging tasks, e.g., MR image reconstruction and super-resolution, where MTrans outperforms state-of-the-art methods on fastMRI and real-world clinical datasets. © 2021, CC BY.

关键词： Magnetic resonance imaging

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Investigating 16 Open Clusters in the Kepler/K2 - Gaia DR3 field. I. Membership, Binary, and Rotation

arXiv

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arxiv 2023年

作者： Long, Liu Bi, Shaolan Zhang, Jinghua Zhang, xianfei Zhang, Liyun Ge, Zhishuai Li, Tanda Chen, Xunzhou Li, YaGuang Ye, LiFei Sun, TianCheng Zhou, JianZhao Institute for Frontiers in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Department of Astronomy Beijing Normal University Beijing100875 China South-Western Institute for Astronomy Research Yunnan University Chenggong District Kunming650500 China College of Physics Guizhou University Guiyang550025 China State Key Laboratory of Public Big Data Guizhou University Guiyang550025 China Beijing Planetarium Beijing Academy of Science and Technology Beijing100044 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China School of Physics University of Sydney NSW2006 Australia Stellar Astrophysics Centre Department of Physics and Astronomy Aarhus University Ny Munkegade 120 Aarhus CDK-8000 Denmark

Using data from the Gaia data Release 3 (Gaia DR3) and Kepler/K2, we present a catalog of 16 open clusters with ages ranging from 4 to 4000 Myr, which provides detailed information on membership, binary systems, and rotation. We assess the memberships in 5D phase space, and estimate the basic parameters of each cluster. Among the 20,160 members, there are 4,381 stars identified as binary candidates and 49 stars as blue straggler stars. The fraction of binaries vary in each cluster, and the range between 9% to 44%. We obtain the rotation periods of 5,467 members, of which 4,304 are determined in this work. To establish a benchmark for the rotation-age-color relation, we construct color-period diagrams. We find that the rotational features of binaries are similar to that of single stars, while features for binaries are more scattered in the rotation period. Moreover, the morphology of the color-period relationship is already established for Upper Scorpius at the age of 19 Myr, and some stars of varying spectral types (i.e. FG-, K-, and M-type) show different spin-down rates after the age of ∼ 110 Myr. By incorporating the effects of stalled spin-down into our analysis, we develop an empirical rotation-age-color relation, which is valid with ages between 700 - 4000 Myr and colors corresponding to a range of 0.5 BP − GRP)0 Copyright © 2023, The Authors. All rights reserved.

关键词： Color

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XTQA: Span-Level Explanations for Textbook Question Answering

arXiv

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arxiv 2020年

作者： Ma, Jie Chai, Qi Liu, Jun Yin, Qingyu Wang, Pinghui Zheng, Qinghua Ministry of Education of Key Lab for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering The School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Amazon United States

Textbook Question Answering (TQA) is the task of correctly answering diagram or non-diagram questions given large multi-modal contexts consisting of abundant essays and diagrams. In real-world scenarios, an explainable TQA system plays a key role in deepening humans’ understanding of learned knowledge. However, there is no work to investigate how to provide explanations currently. To address this issue, we devise a novel architecture towards span-level eXplanations for TQA (XTQA). In this paper, spans are the combinations of sentences within a paragraph. The key idea is to consider the entire textual context of a lesson as candidate evidence, and then use our proposed coarse-to-fine grained Explanation Extracting (EE) algorithm to narrow down the evidence scope and extract the span-level explanations with varying lengths for answering different questions. The EE algorithm can also be integrated into other TQA methods to make them explainable and improve the TQA performance. Experimental results show that XTQA obtains the best overall explanation result (mIoU) of 52.38% on the first 300 questions of CK12-QA test splits, demonstrating the explainability of our method (non-diagram: 150 and diagram: 150). The results also show that XTQA achieves the best TQA performance of 36.46% and 36.95% on the aforementioned splits respectively. We have released our code in https://***/dr-majie/*** Codes 68T07 Copyright © 2020, The Authors. All rights reserved.

关键词： Textbooks

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Multidomain Secure Communication and intelligent Traffic Detection Model in VANETs

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International Journal of intelligent Systems 2025年第1期2025卷

作者： Qikun Zhang Mengqi Liu Ping Li Junling Yuan Hongfei Zhu School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou 450001 China *** School of Cyber Science and Engineering Southeast University Nanjing 210000 China seu.edu.bd School of Information Security Sanmenxia College of Social Administration Sanmenxia 472000 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing 400065 China ***

Vehicular ad-hoc network (VANET) plays a vital role in the intelligent transportation system. It is crucial to ensure secure communication among entities in the VANET for realizing an efficient transportation system. In this scenario, the current communication scheme is vulnerable to the leakage of private information from entities. The research primarily centers on single-domain vehicular networks, with only a limited number of researchers exploring cross-domain authentication among vehicle entities. Cross-domain communication schemes have received little attention from scholars. Furthermore, there are issues, including the susceptibility of in-vehicle conversations to eavesdropping, the vulnerability of long-distance transmissions to interruptions, and the exposure of wireless networks to traffic attacks. To address these issues, a multidomain secure communication and intelligent traffic detection model in VANET is proposed. This model offers several notable advantages as follows: (1) It employs a key self-verification algorithm for local computation and authentication of entity keys. This approach mitigates the risks of identity impersonation attacks and key leakage results from third-party key escrow. (2) A multidomain communication scheme is devised to categorize vehicle-to-vehicle (V2V) scenarios into intradomain and interdomain, which correspond to situations where the communicating parties are within the same domain and across different domains, respectively. (3) We propose the implementation of new session message encryption algorithms for V2V communication. This involves generating dynamic random keys to ensure secure data sharing and facilitates long-distance cross-domain communication among vehicles. (4) An intelligent two-layer traffic detection paradigm is proposed to improve the efficiency of detecting attack traffic in vehicular networks. This paper provides security proofs and performance analysis of the proposed scheme. The experimental results dem

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A Deep Concept-aware Model for predicting and explaining restaurant future status

A Deep Concept-aware Model for predicting and explaining res...

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IEEE International Conference on Web Services (ICWS)

作者： Hao Liao xiaojie Zhang xin Li Mingyang Zhou Alexandre Vidmer Rui Mao Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Guangdong Province Engineering Center of China-made High Performance Data Computing System Shenzhen City Key Laboratory of Service Computing and Application Shenzhen China Institute of Big Data Intelligent Management and Decision Shenzhen University Shenzhen China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

Nowadays, with the development of web services, the survival of online service has captured public attention significantly. Future prediction of business is crucial to the shopkeepers. Predicting how the business will develop and explaining what key factors are leading to it are two most important tasks. Existing literatures usually tackle only one of these two tasks, ignoring that they are two closely related tasks and complement each other. In this paper, we propose a review-based neural model, named Deep Concept-aware Model (DCA), to predict restaurants' future status and provide explainable sentences simultaneously in an end2end framework. Specifically, we use co-attention to select concepts and implement prediction and explanation tasks through Factorization Machine and Gated Recurrent Unit, respectively. We conduct extensive experiments on three Chinese cities' datasets. The proposed joint model outperforms the state-of-the-art baseline methods for both prediction (average 40.96% improvement in AUC) and explanation (average 9.72% improvement in BLEU and 86.05% in Precision metric of ROUGE).

关键词： Web services Urban areas Predictive models Prediction algorithms data models Task analysis Business

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