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arXiv 2021年

作者： Lin, Liting Fan, Heng Zhang, Zhipeng Xu, Yong Ling, Haibin School of Computer Science & Engineering South China Univ. of Tech. Guangzhou China Peng Cheng Laboratory Shenzhen China Department of Computer Science and Engineering University of North Texas Denton United States DiDi Chuxing Beijing China Department of Computer Science Stony Brook University Stony Brook United States

Recently Transformer has been largely explored in tracking and shown state-of-the-art (SOTA) performance. However, existing efforts mainly focus on fusing and enhancing features generated by convolutional neural networks (CNNs). The potential of Transformer in representation learning remains under-explored. In this paper, we aim to further unleash the power of Transformer by proposing a simple yet efficient fully-attentional tracker, dubbed SwinTrack, within classic Siamese framework. In particular, both representation learning and feature fusion in SwinTrack leverage the Transformer architecture, enabling better feature interactions for tracking than pure CNN or hybrid CNN-Transformer frameworks. Besides, to further enhance robustness, we present a novel motion token that embeds historical target trajectory to improve tracking by providing temporal context. Our motion token is lightweight with negligible computation but brings clear gains. In our thorough experiments, SwinTrack exceeds existing approaches on multiple benchmarks. Particularly, on the challenging LaSOT, SwinTrack sets a new record with 0.713 SUC score. It also achieves SOTA results on other benchmarks. We expect SwinTrack to serve as a solid baseline for Transformer tracking and facilitate future research. Copyright © 2021, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Deterministic Quantum Secure Direct Communication and Authentication Protocol Based on W-Class State

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Chinese Journal of Electronics 2018年第2期27卷 276-280页

作者： LI Leilei ZHANG Yu WEN Shuang LI Na YANG Jiayu LI Jian School of Computer Science Beijing University of Posts and Telecommunications School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture

A Quantum secure direct communication(QSDC) and authentication protocol based on the W-class state is presented to enhance the efficiency of eavesdropping detection. In this protocol, the W-class state is used to transmission the checking sequence and detect eavesdroppers. In the security analysis, the method of entropy theory is introduced, and two detection strategies are compared quantitatively by using the constraint between the information that eavesdroppers can obtain and a normalized difference parameter is introduced. To obtain the same amount of information, the eavesdropper must face a higher detection probability in the proposed protocol than in the comparison. The security of the proposed protocol is also discussed. The analysis results indicate that the proposed protocol is more secure, but it must send more particles.

关键词： Eavesdropping detection Quantum secure direct communication(QSDC) W-class state Security analysis

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Bayesian uncertainty analysis for underwater 3D reconstruction with neural radiance fields

arXiv

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arXiv 2024年

作者： Lian, Haojie Li, Xinhao Qu, Yilin Du, Jing Meng, Zhuxuan Liu, Jie Chen, Leilei Key Laboratory of In-situ Property-improving Mining of Ministry of Education Taiyuan University of Technology Taiyuan100190 China Henan International Joint Laboratory of Structural Mechanics and Computational Simulation College of Architecture and Civil Engineering Huanghuai University Zhumadian463000 China School of Marine Science and Technology Northwestern Polytechnical University Xi’an710072 China Unmanned Vehicle Innovation Center Ningbo Institute of Northwestern Polytechnical University Ningbo315103 China Key Laboratory of Unmanned Underwater Vehicle Technology of Ministry of Industry and Information Technology Northwestern Polytechnical University Xi’an710072 China Center for Strategic Assessment and Consulting Academy of Military Science Beijing100091 China Computer Engineering Department Taiyuan Institute of Technology Taiyuan030008 China

Neural radiance fields (NeRFs) are a deep learning technique that can generate novel views of 3D scenes using sparse 2D images from different viewing directions and camera poses. As an extension of conventional NeRFs in underwater environment, where light can get absorbed and scattered by water, SeaThru-NeRF was proposed to separate the clean appearance and geometric structure of underwater scene from the effects of the scattering medium. Since the quality of the appearance and structure of underwater scenes is crucial for downstream tasks such as underwater infrastructure inspection, the reliability of the 3D reconstruction model should be considered and evaluated. Nonetheless, owing to the lack of ability to quantify uncertainty in 3D reconstruction of underwater scenes under natural ambient illumination, the practical deployment of NeRFs in unmanned autonomous underwater navigation is limited. To address this issue, we introduce a spatial perturbation field Dω based on Bayes’ rays in SeaThru-NeRF and perform Laplace approximation to obtain a Gaussian distribution N(0, Σ) of the parameters ω, where the diagonal elements of Σ correspond to the uncertainty at each spatial location. We also employ a simple thresholding method to remove artifacts from the rendered results of underwater scenes. Numerical experiments are provided to demonstrate the effectiveness of this approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Uncertainty analysis

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A Comprehensive Evaluation of Android ICC Resolution Techniques

arXiv

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arXiv 2021年

作者： Yan, Jiwei Zhang, Shixin Liu, Yepang Deng, Xi Yan, Jun Zhang, Jian Tech. Center of Software Engineering Institute of Software CAS China Univ. of Chinese Academy of Sciences Beijing China School of Software Engineering Beijing Jiaotong University Beijing China Dept. of Computer Science and Engr. Southern University of Sci. and Tech Shenzhen China State Key Lab. of Computer Science Institute of Software CAS China

Inter-component communication (ICC) is a widely used mechanism in mobile apps, which enables message-based control flow transferring and data passing between Android components. Effective ICC resolution requires precisely identifying entry points, analyzing data values of ICC fields, modeling related framework APIs, etc. Due to various control-flow- and data-flow-related characteristics involved and the lack of oracles for real-world apps, the comprehensive evaluation of ICC resolution techniques is challenging. To fill this gap, we collect multiple-type benchmark suites with 4,104 apps, covering hand-made apps, open-source, and commercial ones. Considering their differences, various evaluation metrics, e.g., number count, graph structure, and reliable oracle based metrics, are adopted on-demand. As the oracle for real-world apps is unavailable, we design a dynamic analysis approach to extract the real ICC links triggered during GUI exploration. By auditing the code implementations, we carefully check the extracted ICCs and confirm 1,680 ones to form a reliable oracle set, in which each ICC is labeled with 25 code characteristic tags. The evaluation performed on six state-of-the-art ICC resolution tools shows that 1) the completeness of static ICC resolution results on real-world apps is not satisfactory, as up to 38%-85% ICCs are missed by tools;2) many wrongly reported ICCs are sent from or received by only a few components and the graph structure information can help the identification;3) the efficiency of fundamental tools, like ICC resolution ones, should be optimized in both engineering and research aspects. By investigating both the missed and wrongly reported ICCs, we discuss the strengths of different tools for users and summarize eight common FN/FP patterns in ICC resolution for tool developers. © 2021, CC BY-NC-SA.

关键词： Graphic methods

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Geographic information science in the era of geospatial big data: A cyberspace perspective

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The Innovation 2022年第5期3卷 21-22页

作者： Xintao Liu Min Chen Christophe Claramunt Michael Batty Mei-Po Kwan Ahmad MSenousi Tao Cheng Josef Strobl Arzu Cöltekin John Wilson Temenoujka Bandrova Milan Konecny Paul MTorrens Fengyuan Zhang Li He Jinfeng Wang Carlo Ratti Olaf Kolditz Alexander Klippel Songnian Li Hui Lin Guonian Lu Department of Land Surveying and Geo-Informatics Hong Kong Polytechnic UniversityHong Kong SARKowloonChina Key Laboratory of Virtual Geographic Environment(Ministry of Education of PRC) Nanjing Normal UniversityNanjing 210023China The French Naval Academy Research Institute 29240 Lanveoc-PoulmicFrance The Bartlett Centre for Advanced Spatial Analysis(CASA) University College London(UCL)WC1E 6BT LondonUK Institute of Space and Earth Information Science The Chinese University of Hong KongHong Kong SARShatin 999077China Department of Civil Engineering Cairo University12613 CairoEgypt SpaceTimeLab Department of CivilEnvironmental and Geomatic EngineeringUniversity College London(UCL)WC1E 6BT LondonUK Department of Geoinformatics University of Salzburg5020 SalzburgAustria Institute of Interactive Technologies(IIT) University of Applied Sciences and Arts Northwestern Switzerland FHNW5210 WindischSwitzerland Spatial Sciences Institute University of Southern CaliforniaLos AngelesCA 90007USA Laboratory on Cartography Department of Photogrammetry and CartographyUniversity of ArchitectureCivil Engineering and Geodesy1164 SofiaBulgaria Laboratory on Geoinformatics and Cartography Department of GeographyMasaryk University61137 BrnoCeskáRepublika Computer Science and Engineering Tandon School&Center for Urban Science+ProgressNew York UniversityNew YorkNY 10012USA School of Humanities and Social Science Xi’an Jiaotong UniversityXi’an 710049China Institute of Geographic Sciences and Natural Resources Research Chinese Academy of SciencesBeijing 100101China Department of Urban Studies and Planning Massachusetts Institute of TechnologyCambridgeMA 02139USA Centre for Environmental Research Helmholtz Centre for Environmental Research-UFZ04318 LeipzigGermany Laboratory of Geo-information Science and Remote Sensing Wageningen University&ResearchWageningen 6708the Netherlands Department of Civil Engineering Toronto Metropolitan UniversityTorontoON M5B 2K3Canada School of Geography and Env

The advent of information and communication technology and the Internet of Things have led our society toward a digital *** proliferation of personal computers,smartphones,intelligent autonomous sensors,and pervasive network interactions with individuals have gradually shifted human activities from offline to online and from in person to *** transformation has brought a series of challenges in a variety of fields,such as the dilemma of placelessness,some aspects of timelessness(no time relevance),and the changing relevance of distance in the field of geographic information science(GIscience).In the last two decades,“cyber thinking”in GIscience has received significant attention from different *** instance,human activities in“cyberspace”need to be reconsidered when coupled with the geographic space to observe the first law of geography.

关键词： computer cyber instance

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An Extension Network of Dendritic Neurons

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Computational intelligence and neuroscience 2023年第1期2023卷 7037124页

作者： Qianyi Peng Shangce Gao Yirui Wang Junyan Yi Gang Yang Yuki Todo Faculty of Engineering University of Toyama Toyama-shi 930-8555 Japan. Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo Zhejiang 315211 China. Department of Computer Science & Technology Beijing University of Civil Engineering and Architecture Beijing 100044 China. School of Information Renmin University of China Beijing China. Faculty of Electrical Information and Communication Engineering Kanazawa University Kanazawa Ishikawa 9201192 Japan.

Deep learning (DL) has achieved breakthrough successes in various tasks, owing to its layer-by-layer information processing and sufficient model complexity. However, DL suffers from the issues of both redundant model complexity and low interpretability, which are mainly because of its oversimplified basic McCulloch-Pitts neuron unit. A widely recognized biologically plausible dendritic neuron model (DNM) has demonstrated its effectiveness in alleviating the aforementioned issues, but it can only solve binary classification tasks, which significantly limits its applicability. In this study, a novel extended network based on the dendritic structure is innovatively proposed, thereby enabling it to solve multiple-class classification problems. Also, for the first time, an efficient error-back-propagation learning algorithm is derived. In the extensive experimental results, the effectiveness and superiority of the proposed method in comparison with other nine state-of-the-art classifiers on ten datasets are demonstrated, including a real-world quality of web service application. The experimental results suggest that the proposed learning algorithm is competent and reliable in terms of classification performance and stability and has a notable advantage in small-scale disequilibrium data. Additionally, aspects of network structure constrained by scale are examined.

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Urban Traffic Flow Forecasting Based on Memory Time-Series Network

Urban Traffic Flow Forecasting Based on Memory Time-Series N...

引用

International Conference on Intelligent Transportation

作者： Shengjian Zhao Shu Lin Yunlong Li Jungang Xu Yibing Wang School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China College of Civil Engineering and Architecture Zhejiang University Zhejiang China

ISBN: (数字)9781728141497

ISBN: (纸本)9781728141503

Predicting urban traffic flow is significant to intelligent transportation systems. Urban traffic flow data is a type of time-series data, which collects the traffic flow of a road section or area. So in this paper, we treat urban traffic flow prediction as a time series problem. The traditional method to tackle traffic flow prediction is difficult, because of the complex influence factors and nonlinear dependencies. Recently, LSTM based network has been widely used to model long-term series, but the memory of LSTM is typically too small and is not enough to accurately remember facts from the past. In this paper, we using memory time-series network with additional memory mechanisms to address urban traffic prediction problems. Historical data were divided into longterm and short-term two parts, long-term historical data models the overall trend and short-term historical data takes into account recent changes. The experiment results on two urban traffic flow datasets demonstrate the model is effective and outperforms baselines.

关键词： Time series analysis Data models Predictive models Kernel Forecasting Convolution Roads

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Uncertainty Analysis of Rainfall Spatial Interpolation in Urban Small Area 13th

Uncertainty Analysis of Rainfall Spatial Interpolation in Ur...

引用

13th EAI International Conference on Testbeds and Research Infrastructures for the Development of Networks and Communities, TridentCom 2018

作者： Huang, Jie Jing, Changfeng Fu, Jiayun Huang, Zejun School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing100044 China

ISBN: (纸本)9783030129705

Uncertainty analysis have attracted increasing attention of both theory and application over the last decades. Owing to the complex of surrounding, uncertainty analysis of rainfall in urban area is very little. Existing literatures on uncertainty analysis paid less attention on gauge density and rainfall intensity. Therefore, this study focuses on urban area, which a good complement to uncertainty research. In this study, gauge density was investigated with carefully selecting of gauge to covering evenly. Rainfall intensity data were extracted from one rainfall event at begin, summit and ending phases of rainfall process. Three traditional methods (Ordinary Kriging, RBF and IDW) and three machine methods (RF, ANN and SVM) were investigated for the uncertainty analysis. The result shows that (1) gauge density has important influence on the interpolation accuracy, and the higher gauge density means the higher accuracy. (2) The uncertainty is progressively stable with the increasing of rainfall intensity. (3) Geostatistic methods has better result than the IDW and RBF owing to considering spatial variability. The selected machine learning methods have good performance than traditional methods. However, the complex training processing and without spatial variability may reduce its practicability in modern flood management. Therefore, the combining of traditional methods and machine learning will be the good paradigm for spatial interpolation and uncertainty analysis. © 2019, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Uncertainty analysis

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IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive Computing (Plenary Panel Report-II) 20

IEEE ICCI*CC Series in Year 20: Latest Advances in Cognitive...

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20th IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2021

作者： Kinsner, Witold Zhu, Haibin Baciu, George Luo, Guiming Rubio, Fernando Sui, Jie Huang, Runhe Hiraishi, Hironori Peng, Jun Chen, Liang Univ. of Manitoba FI2CICC Dept of ECE Winnipeg Canada Nipissing University FI2CICC ON Canada Hong Kong Polytechnic University FI2CICC Dept. of Computing Hong Kong Tsinghua University FI2CICC Software Institute Beijing China Complutense Univ. of Madrid FI2CICC Dept. of Computer Systems and Computation Spain Aberdeen University FI2CICC Aberdeen United Kingdom Hosei University FI2CICC Faculty of Computer and Information Sciences Japan Ashikaga University Faculty of Engineering Tochigi326-8558 Japan Chongqing Univ. of Science and Technology FI2CICC School of AI Chongqing China Univ. of Northern British Columbia Dept. of Computer Science BC Canada

ISBN: (纸本)9781665421195

Cognitive Computing (CC) is a contemporary field of fundamental intelligence theories and general AI technologies triggered by the transdisciplinary development in intelligence, computer, brain, knowledge, cognitive, robotic, and cybernetic sciences for engineering implementations. This paper presents a summary of the plenary panel (Part II) on the theoretical foundations of CI/CC and recent breakthroughs in AI engineering reported in the 20th IEEE International ICCI*CC Conference (ICCI*CC'21). The latest advances in CI and CC towards general AI are presented by twenty-Two distinguished panelists. Strategic AI engineering applications in CI, CC, and cognitive systems are elaborated for abstract intelligence, cognitive robots, autonomous systems, intelligent vehicles, and safety-And-mission-critical systems. © 2021 IEEE.

关键词： Cognitive systems

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Evaluation of OpenAI o1: Opportunities and Challenges of AGI

arXiv

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arXiv 2024年

作者： Zhong, Tianyang Liu, Zhengliang Pan, Yi Zhang, Yutong Zhou, Yifan Liang, Shizhe Wu, Zihao Lyu, Yanjun Shu, Peng Yu, Xiaowei Cao, Chao Jiang, Hanqi Chen, Hanxu Li, Yiwei Chen, Junhao Hu, Huawen Liu, Yihen Zhao, Huaqin Xu, Shaochen Dai, Haixing Zhao, Lin Zhang, Ruidong Zhao, Wei Yang, Zhenyuan Chen, Jingyuan Wang, Peilong Ruan, Wei Wang, Hui Zhao, Huan Zhang, Jing Ren, Yiming Qin, Shihuan Chen, Tong Li, Jiaxi Zidan, Arif Hassan Jahin, Afrar Chen, Minheng Xia, Sichen Holmes, Jason Zhuang, Yan Wang, Jiaqi Xu, Bochen Xia, Weiran Yu, Jichao Tang, Kaibo Yang, Yaxuan Sun, Bolun Yang, Tao Lu, Guoyu Wang, Xianqiao Chai, Lilong Li, He Lu, Jin Sun, Lichao Zhang, Xin Ge, Bao Hu, Xintao Zhang, Lian Zhou, Hua Zhang, Lu Zhang, Shu Liu, Ninghao Jiang, Bei Kong, Linglong Xiang, Zhen Ren, Yudan Liu, Jun Jiang, Xi Bao, Yu Zhang, Wei Li, Xiang Li, Gang Liu, Wei Shen, Dinggang Sikora, Andrea Zhai, Xiaoming Zhu, Dajiang Zhang, Tuo Liu, Tianming Department of Mathematical and Statistical Sciences University of Alberta Edmonton Canada School of Computing University of Georgia GA United States Institute of Medical Research Northwestern Polytechnical University Xi'An China College of Arts and Sciences University of Georgia Athens United States Institute of Plant Breeding Genetics & Genomics University of Georgia AthensGA United States Department of Computer Science and Engineering University of Texas ArlingtonTX United States The Lamar Dodd School of Art University of Georgia GA United States School of Computer Science Northwestern Polytechnical University Xi'An China School of Automation Northwestern Polytechnical University Xi'An China University of California Los AngelesCA United States Department of Radiology The Second Xiangya Hospital Central South University Changsha China Clinical Research Center for Medical Imaging in Hunan Province Changsha China Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Guanghua School of Management Peking University Beijing China Department of Radiation Oncology Mayo Clinic PhoenixAZ United States Second Language Acquisition and Teaching University of Arizona TucsonAZ United States School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China The First Hospital of Hebei Medical University Hebei Shijiazhuang China School of Computer and Cyber Sciences Augusta University AugustaGA United States School of Physics and Information Technology Shaanxi Normal University Xi'An China School of Future Technology South China University of Technology Guangzhou China Department of Radiology and BRIC University of North Carolina Chapel HillNC United States Department of Educational Psychology University of Georgia AthensGA United States Johns Hopkins University BaltimoreMD United States School of Architecture Tsinghua Universit

This comprehensive study evaluates the performance of OpenAI's o1-preview large language model across a diverse array of complex reasoning tasks, spanning multiple domains, including computer science, mathematics, natural sciences, medicine, linguistics, and social sciences. Through rigorous testing, o1-preview demonstrated remarkable capabilities, often achieving human-level or superior performance in areas ranging from coding challenges to scientific reasoning and from language processing to creative problem-solving. Key findings include: • 83.3% success rate in solving complex competitive programming problems, surpassing many human experts. • Superior ability in generating coherent and accurate radiology reports, outperforming other evaluated models. • 100% accuracy in high school-level mathematical reasoning tasks, providing detailed step-by-step solutions. • Advanced natural language inference capabilities across general and specialized domains like medicine. • Impressive performance in chip design tasks, outperforming specialized models in areas such as EDA script generation and bug analysis. • Remarkable proficiency in anthropology and geology, demonstrating deep understanding and reasoning in these specialized fields. • Strong capabilities in quantitative investing. O1 has comprehensive financial knowledge and statistical modeling skills. • Effective performance in social media analysis, including sentiment analysis and emotion recognition. The model excelled particularly in tasks requiring intricate reasoning and knowledge integration across various fields. While some limitations were observed, including occasional errors on simpler problems and challenges with certain highly specialized concepts, the overall results indicate significant progress towards artificial general intelligence. This evaluation not only highlights o1-preview's current strengths and limitations but also identifies crucial areas for future development, including multi-modal integratio

关键词： Emotion Recognition

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