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International Conference on Computer Supported Cooperative Work in Design

作者： Cheng Shang Bo Jin Feiyu Zhang Chong He Xubo Yang Yongliang Qiao Meili Wang College of Information Engineering Northwest A&F University Yangling China School of Software Shanghai Jiao Tong University China Australian Institute for Machine Learning (AIML) The University of Adelaide Australia Key Laboratory of Agricultural Internet of Things Ministry of Agriculture Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service Yangling China

ISBN: (数字)9798331513054

ISBN: (纸本)9798331513061

With the progress of computer vision and machine learning, the research of object detection and pedestrian recognition has demonstrated significant performance. However, the identification studies in domestic animals, especially in the same species of domestic animals, remains a significant challenge. His study focuses on distinguishing cashmere and dairy goats, which share similar traits. Our contributions are: (1) Proposing a dynamic quadruple optimization algorithm to optimize goat images from local and global dimensions, enhancing network representation with a multi-branch structure; (2) Introducing a novel transfer learning algorithm based on goat granularity to preview dataset knowledge; (3) Validating our approach on our goat dataset and a public bird dataset. We achieved recognition accuracies of 95% for cashmere goats, 94.04% for dairy goats, and 82.48% on the public dataset, demonstrating the effectiveness of our methods for animal biometric identification.

关键词： Knowledge engineering Pedestrians Heuristic algorithms Transfer learning Object detection Biometric identification Birds Transformers Livestock Optimization

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Implementing a modified Smith predictor using chemical reaction networks and its application to protein translation 4

Implementing a modified Smith predictor using chemical react...

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4th International Conference on Industrial Artificial Intelligence, IAI 2022

作者： Xiao, Yijun Lv, Hui Wang, Xing'an School of Software Engineering Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education Liaoning Dalian116622 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Liaoning Shenyang110004 China College of Environmental and Chemical Engineering Dalian University Liaoning Dalian116622 China Dalian Chivy Biotechnology Co. Ltd Liaoning Dalian116622 China

ISBN: (数字)9781665451208

ISBN: (纸本)9781665451208

In this article, a special attention is paid to the biochemical controller synthesis for time delay systems and try to implement the well-established Smith predictor approach in the context of biochemical systems. Then, chemical reaction networks (CRNs) are adopted to construct a modified Smith predictor scheme (integrating Smith predictor and feedback compensation controllers) for the first time. Taking a delayed protein translation model as the background, the CRNs-based proposed scheme has access to a method that can solve the effect of co-translated mRNA decay in protein translation. In addition, considering that the decay of mRNA affects mRNA stability and protein production, the co-translated mRNA degradation is treated as an interference input of the protein translation process. Our results show that the impact of a disturbance input (mRNA degradation) is restrained by the modified control strategy. The CRNs-based modified Smith predictor makes the protein translation process more robust and achieves protein output quickly and stably. © 2022 IEEE.

关键词： Time delay

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Dynamic Entanglement Routing Based on Stream Processing for Quantum Networks

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IEEE/ACM Transactions on Networking 2024年

作者： Yang, Lan Zhao, Yangming Xu, Hongli Huang, Liusheng Qiao, Chunming State Key Laboratory for Novel Software Technology Nanjing University Nanjing210008 China School of Intelligent Software and Engineering Nanjing University Suzhou Campus Suzhou215163 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China Hefei National Laboratory University of Science and Technology of China Hefei230088 China University at Buffalo The State University of New York BuffaloNY14260 United States

Quantum Networks (QNs) typically leverage teleportation to send quantum bits (called qubits) to their destinations. To teleport a data qubit from Alice to Bob, one Entanglement Connection (EC) between Alice and Bob needs to be established. Accordingly, we have to concurrently establish as many requested ECs as possible in order to maximize the network throughput. Conventional methods either assumed a known traffic matrix and calculated the Entanglement Paths (EPs) for all requests in one batch or maximized the number of ECs established between all Source-Destination (SD) pairs without considering the amount of data qubits to be teleported. These methods are not scalable in large scale QNs since it is time consuming to calculate the EPs for a batch of requests. In addition, there may be only very few data qubits to be teleported between some SD pairs. Accordingly, the latter method may establish many useless ECs. To address these issues, we propose a Dynamic Entanglement Routing () scheme which determines the EPs based on stream processing. By introducing a method to derive an appropriate purification scheme along each EP, we further extend to Dynamic Entanglement Routing with Purification () that provides fidelity guarantee to the established ECs. Through extensive simulations, we demonstrate that outperforms two representative heuristics by up to 52.79% and 61.27%, respectively, in terms of average request completion time and when we have to ensure the fidelity of the established ECs, this performance improvement will become 21.05% and 48.67%, respectively, if is adopted. © 1993-2012 IEEE.

关键词： Qubits

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Learning a Typhoon Bayesian Network Structure from Natural Language Reports 5th

Learning a Typhoon Bayesian Network Structure from Natural...

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5th IFIP TC 12 International Conference on Intelligence Science, ICIS 2022

作者： Yao, Zhangrui Chen, Junhan Pan, Yinghui Zeng, Yifeng Ma, Biyang Ming, Zhong Department of Automation and Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-Making Xiamen University Xiamen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Computer and Information Sciences Northumbria University Newcastle upon Tyne United Kingdom School of Computer Science and Engineering Minnan Normal University Zhangzhou China

ISBN: (纸本)9783031149023

Given the huge toll caused by natural disasters, it is critically important to develop an effective disaster management and emergency response technique. In this article, we investigate relationships between typhoon-related variables and emergency response from natural language (NL) reports. A major challenge is to exploit typhoon state information for typhoon contingency plan generation, especially from unstructured text data based on NL input. To tackle this issue, we propose a novel framework for learning typhoon Bayesian network structures (FLTB), which can extract typhoon state information from unstructured NL, mine inter-information causal relationships and then generate Bayesian networks. We first extract information about typhoon states through NL processing (NLP) techniques, and then analyze typhoon reports by designing heuristic rules to identify causal relationships between states. We leverage these features to improve the learned structures and provide user-interaction mechanisms to finalize Bayesian networks. We evaluate the performance of our framework on real-world typhoon datasets and develop the Bayesian networks based typhoon emergency response systems. © 2022, IFIP International Federation for Information Processing.

关键词： Bayesian networks

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A Novel Cross-FOV Gaze-Driven Human-Robot Interaction Framework for Service Robots

A Novel Cross-FOV Gaze-Driven Human-Robot Interaction Framew...

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Symposia and Workshops on Ubiquitous, Autonomic and Trusted Computing, UIC-ATC

作者： Zheng Zhao Pengfei Yi Jing Dong Rui Liu Mingkai Cheng Dongsheng Zhou Xiaopeng Wei Key Laboratory of Advanced Design and Intelligent Computing (Ministry of Education) School of Software Engineering Dalian University Dalian China Dalian University of Technology Dalian China

Gaze-based methods have been widely applied in human-robot interactions (HRI). However, most methods were often limited to scenarios where the human face and the gazed object should be in the same field of view (FOV), which is difficult to adapt to the complex and changeable typical daily life scene where the service robot is located. To improve the limitation, this paper proposes a framework capable of completing tasks even if the person and the gazed object are not in the same FOV, requiring only a single RGB camera. First, a cross-FOV gaze estimation algorithm is designed to get the gaze point from images containing a human face captured by an RGB camera. Then a multi-view fusion decision model is employed to drive the robot to turn to the gaze point and identify the object according to the gaze point. Finally, a robot can complete tasks using this information. Experiments under conditions of different lighting, different scenes and different users on the NAO robot show the effectiveness of our method.

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Experience report:investigating bug fixes in machine learning frameworks/libraries

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Frontiers of Computer Science 2021年第6期15卷 43-58页

作者： Xiaobing SUN Tianchi ZHOU Rongcun WANG Yucong DUAN Lili BO Jianming CHANG School of Information Engineering Yangzhou UniversityYangzhou 225100China State Key Laboratory for Novel Software Technology Nanjing UniversityNanjing 210023China Jiangsu Engineering Research Center of Knowledge Management and Intelligent Service Yangzhou University Yangzhou 225127China School of Computer Science and Technology China University of Mining and TechnologyXuzhou 221116China School of Computer Science and Cyberspace Security Hainan UniversityHaikou 570228China

Machine learning(ML)techniques and algorithms have been successfully and widely used in various areas including software engineering *** other software projects,bugs are also common in ML projects and *** order to more deeply understand the features related to bug fixing in ML projects,we conduct an empirical study with 939 bugs from five ML projects by manually examining the bug categories,fixing patterns,fixing scale,fixing duration,and types of *** results show that(1)there are commonly seven types of bugs in ML programs;(2)twelve fixing patterns are typically used to fix the bugs in ML programs;(3)68.80%of the patches belong to micro-scale-fix and small-scale-fix;(4)66.77%of the bugs in ML programs can be fixed within one month;(5)45.90%of the bug fixes belong to corrective activity from the perspective of software ***,we perform a questionnaire survey and send them to developers or users of ML projects to validate the results in our empirical *** results of our empirical study are basically consistent with the feedback from *** findings from the empirical study provide useful guidance and insights for developers and users to effectively detect and fix bugs in MLprojects.

关键词： bug fixing machine learning project empirical study questionnaire survey

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LinNet: linear network for efficient point cloud representation learning 24

LinNet: linear network for efficient point cloud representat...

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

作者： Hao Deng Kunlei Jing Shengmei Cheng Cheng Liu Jiawei Ru Jiang Bo Lin Wang State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Northwest University and Shaanxi Key Laboratory of Higher Education Institution of Generative Artificial Intelligence and Mixed Reality School of Software Engineering Xi'an Jiaotong University and Department of Computing The Hong Kong Polytechnic University

ISBN: (纸本)9798331314385

Point-based methods have made significant progress, but improving their scalability in large-scale 3D scenes is still a challenging problem. In this paper, we delve into the point-based method and develop a simpler, faster, stronger variant model, dubbed as LinNet. In particular, we first propose the disassembled set abstraction (DSA) module, which is more effective than the previous version of set abstraction. It achieves more efficient local aggregation by leveraging spatial anisotropy and channel anisotropy separately. Additionally, by mapping 3D point clouds onto 1D space-filling curves, we enable parallelization of down-sampling and neighborhood queries on GPUs with linear complexity. LinNet, as a purely point-based method, outperforms most previous methods in both indoor and outdoor scenes without any extra attention, and sparse convolution but merely relying on a simple MLP. It achieves the mIoU of 73.7%, 81.4%, and 69.1% on the S3DIS Area5, NuScenes, and SemanticKITTI validation benchmarks, respectively, while speeding up almost 10x times over PointNeXt. Our work further reveals both the efficacy and efficiency potential of the vanilla point-based models in large-scale representation learning. Our code will be available at https://***/DengH293/LinNet.

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Nonmonotonic propositional logic

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Frontiers of Computer Science 2021年第3期15卷 215-216页

作者： Wei LI Yuefei SUI Yuhui WANG State Key Laboratory of Software Development Environment Beihang UniversityBeijing 100191China Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China School of Computer and Control Engineering University of Chinese Academy of SciencesBeijing 100049China Information Technology Center China Reinsurance(Group)CorporationBeijing 100033China

Propositional logic[1]is basic,based on which other logics are *** deduction system for propositional logic is *** logics are a class of logics which deduction systems are *** ones are default logic[2,3],R-calculus[4]... 详细信息

关键词： default propositional Nonmonotonic

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AircraftVerse: a large-scale multimodal dataset of aerial vehicle designs 23

AircraftVerse: a large-scale multimodal dataset of aerial ve...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Adam D. Cobb Anirban Roy Daniel Elenius F. Michael Heim Brian Swenson Sydney Whittington James D. Walker Theodore Bapty Joseph Hite Karthik Ramani Christopher McComb Susmit Jha Computer Science Laboratory SRI International Mechanical Engineering Division Southwest Research Institute Intelligent Systems Division Southwest Research Institute Institute of Software Integrated Systems Vanderbilt University School of Mechanical Engineering Purdue University Department of Mechanical Engineering Carnegie Mellon University

We present AircraftVerse, a publicly available aerial vehicle design dataset. Aircraft design encompasses different physics domains and, hence, multiple modalities of representation. The evaluation of these cyber-physical system (CPS) designs requires the use of scientific analytical and simulation models ranging from computer- aided design tools for structural and manufacturing analysis, computational fluid dynamics tools for drag and lift computation, battery models for energy estimation, and simulation models for flight control and dynamics. AircraftVerse contains 27,714 diverse air vehicle designs - the largest corpus of engineering designs with this level of complexity. Each design comprises the following artifacts: a symbolic design tree describing topology, propulsion subsystem, battery subsystem, and other design details; a STandard for the Exchange of Product (STEP) model data; a 3D CAD design using a stereolithography (STL) file format; a 3D point cloud for the shape of the design; and evaluation results from high fidelity state-of-the-art physics models that characterize performance metrics such as maximum flight distance and hover-time. We also present baseline surrogate models that use different modalities of design representation to predict design performance metrics, which we provide as part of our dataset release. Finally, we discuss the potential impact of this dataset on the use of learning in aircraft design and, more generally, in CPS. AircraftVerse is accompanied by a data card, and it is released under Creative Commons Attribution-ShareAlike (CC BY-SA) license. The dataset is hosted at https://***/record/6525446 baseline models and code at https://***/SRI-CSL/AircraftVerse and the dataset description at https://***/.

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GEM: Context-Aware Gaze EstiMation with Visual Search Behavior Matching for Chest Radiograph

arXiv

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arXiv 2024年

作者： Liu, Shaonan Chen, Wenting Liu, Jie Luo, Xiaoling Shen, Linlin Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University China Department of Electrical Engineering City University of Hong Kong Hong Kong AI Research Center for Medical Image Analysis and Diagnosis Shenzhen University China Guangdong Provincial Key Laboratory of Intelligent Information Processing China

Gaze estimation is pivotal in human scene comprehension tasks, particularly in medical diagnostic analysis. Eye-tracking technology facilitates the recording of physicians’ ocular movements during image interpretation, thereby elucidating their visual attention patterns and information-processing strategies. In this paper, we initially define the context-aware gaze estimation problem in medical radiology report settings. To understand the attention allocation and cognitive behavior of radiologists during the medical image interpretation process, we propose a context-aware Gaze EstiMation (GEM) network that utilizes eye gaze data collected from radiologists to simulate their visual search behavior patterns throughout the image interpretation process. It consists of a context-awareness module, visual behavior graph construction, and visual behavior matching. Within the context-awareness module, we achieve intricate multimodal registration by establishing connections between medical reports and images. Subsequently, for a more accurate simulation of genuine visual search behavior patterns, we introduce a visual behavior graph structure, capturing such behavior through high-order relationships (edges) between gaze points (nodes). To maintain the authenticity of visual behavior, we devise a visual behavior-matching approach, adjusting the high-order relationships between them by matching the graph constructed from real and estimated gaze points. Extensive experiments on four publicly available datasets demonstrate the superiority of GEM over existing methods and its strong generalizability, which also provides a new direction for the effective utilization of diverse modalities in medical image interpretation and enhances the interpretability of models in the field of medical imaging. https://***/Tiger-SN/GEM. Copyright © 2024, The Authors. All rights reserved.

关键词： Eye movements

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