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arXiv 2022年

作者： Tesema, Fiseha Berhanu Lin, Zheyuan Zhu, Shiqiang Song, Wei Gu, Jason Wu, Hong Interdisciplinary Innovation Research Institute Zhejiang Lab Kechuang Avenue Zhongtai Sub-District Yuhang District China Electrical and Computer Engineering Dalhousie University HalifaxNS B3J 2X4 Canada School of Computer Science and Engineering University of Electronic Science and Technology of China Shahe Campus No.4 Section 2 North Jianshe Road Chengdu China Ocean College Zhejiang University 866 Yuhangtang Rd Xihu Hangzhou China

Active speaker detection plays a vital role in human-machine interaction. Recently, a few end-to-end audiovisual frameworks emerged. However, these models' inference time was not explored and are not applicable for real-time applications due to their complexity and large input size. In addition, they explored a similar feature extraction strategy that employs the ConvNet on audio and visual inputs. This work presents a novel two-stream end-to-end framework fusing features extracted from images via VGG-M with raw Mel Frequency Cepstrum Coefficients features extracted from the audio waveform. The network has two BiGRU layers attached to each stream to handle each stream's temporal dynamic before fusion. After fusion, one BiGRU layer is attached to model the joint temporal dynamics. The experiment result on the AVA-ActiveSpeaker dataset indicates that our new feature extraction strategy shows more robustness to noisy signals and better inference time than models that employed ConvNet on both modalities. The proposed model predicts within 44.41 ms, which is fast enough for real-time applications. Our best-performing model attained 88.929% accuracy, nearly the same detection result as state-of-the-art work. © 2022, CC BY.

关键词： Feature extraction

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Measurement-device-independent quantum secure direct communication

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science China(Physics,Mechanics & Astronomy) 2020年第3期63卷 2-7页

作者： ZengRong Zhou Yu Bo Sheng PengHao Niu LiuGuo Yin GuiLu Long Lajos Hanzo State Key Laboratory of Low-dimensional Quantum Physics Beijing 100084China Department of Physics Tsinghua UniversityBeijing 100084China Collaborative Innovation Center of Quantum Matter Beijing 100084China Beijing Academy of Quantum Information Beijing 100084China Institute of Quantum Information and Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China College of Telecommunications&Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing 210003China Beijing National Research Center for Information Science and Technology Beijing 100084China School of Information and Technology Tsinghua UniversityBeijing 100084China School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK

Quantum secure direct communication(QSDC)is a unique technique,which supports the secure transmission of confidential information directly through a quantum channel without the need for a secret key and for *** this secure communication protocol fundamentally differs from its conventional *** this article,we report the first measurement-deviceindependent(MDI)QSDC protocol relying on sequences of entangled photon pairs and single ***,it eliminates the security loopholes associated with the measurement ***,this MDI technique is capable of doubling the communication distance of its conventional counterpart operating without using our MDI *** also conceive a protocol associated with linear optical Bell-basis measurements,where only two of the four Bell-basis states could be *** the number of qubits in a sequence reduces to 1,the MDI-QSDC protocol degenerates to a deterministic MDI quantum key distribution protocol.

关键词： quantum information secure communication quantum communication

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Incorporating Multiple Features to Predict Bug Fixing Time with Neural Networks

Incorporating Multiple Features to Predict Bug Fixing Time w...

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International Conference on Software Maintenance (ICSM)

作者： Wei Yuan Yuan Xiong Hailong Sun Xudong Liu SKLSDE Lab School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China SKLSDE Lab School of Software Beihang University Beijing China

Debugging is a well-known time-consuming task, and knowing how long it would take to resolve bugs is of great importance for allocating the limited resources in a software development team. However, it is challenging to predict bug fixing time since fixing bugs is subject to a plethora of uncertain factors such as types of bugs, program complexity and developers' abilities. Existing work mainly focuses on developers' activities in a bug lifecycle and ignores other important factors. In light of the limitations of existing work, we propose a novel approach to predicting the bug fixing time by incorporating a comprehensive set of relevant features. Specifically, we consider four types of features including developers' activities, developers' sentiments, semantics of bugs, and efforts caused by understanding and analyzing source code, and design particular neural networks to take advantage of these features and make them work efficiently. Experimental results on four real application datasets demonstrate that on the one hand, our approach outperforms the state-of-the-art by over 5% in accuracy and 7.3% in F1-score on average; on the other hand, each type of the features considered in our approach plays an important part in the prediction.

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An Effective Hybrid Optimization Algorithm for Static Rebalance Problem of Bicycle-Sharing System

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IET Intelligent Transport Systems 2025年第1期19卷

作者： Liang, Shuhui Ye, Yutong Wu, Jingli Key Lab of Education Blockchain and Intelligent Technology Ministry of Education Guangxi Normal University Guilin China Guangxi Key Lab of Multi-Source Information Mining and Security Guangxi Normal University Guilin China School of Computer Science and Engineering Guangxi Normal University Guilin China Software Engineering Institute East China Normal University Shanghai China

Although the bicycle-sharing system plays an important role in easing urban traffic congestion and reducing carbon emissions, it still suffers from the problem of unbalanced bicycle distribution between different stations, which significantly limits the utilization of bicycles. To solve this problem, this paper proposes a novel hybrid optimization algorithm, MixPS, based on the combination of a partheno-genetic algorithm and a simulated annealing algorithm. This algorithm can effectively rebalance the distribution of bicycles. To further improve performance, we design a decimal code to represent a scheduled path and introduce six mutation operators to change the chromosome. The whole evolution is controlled with the simulated annealing process. Moreover, we apply the Metropolis acceptance criterion to effectively reduce the possibility that the population falls into the local optimum. Comprehensive experimental results obtained from both synthetic and real-world data sets show that our proposed MixPS algorithm can achieve better optimization and generate a shorter schedule path scheme in less time. These results confirm the efficiency and effectiveness of our method in solving the static bicycle rebalancing problem with single-vehicle and multiple-access. © 2025 The Author(s). IET Intelligent Transport Systems published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and technology.

关键词： bicycle rebalancing problem effective heuristic low-carbon optimization algorithm

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Dynamically redactable blockchain based on decentralized Chameleon hash

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Digital Communications and Networks 2024年

作者： Xinzhe Huang Yujue Wang Yong Ding Qianhong Wu Changsong Yang Hai Liang Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information Security Guilin University of Electronic Technology Guilin 541004 China Guangxi Engineering Research Center of Industrial Internet Security and Blockchain Guilin University of Electronic Technology Guilin 541004 China Hangzhou Innovation Institute of Beihang University Hangzhou 311228 China Lion Rock Labs of Cyberspace Security Institute of Cyberspace Technology HKCT Institute for Higher Education Hong Kong 999077 China School of Cyber Science and Technology Beihang University Beijing 100083 China

The immutability is a crucial property for blockchain applications, however, it also leads to problems such as the inability to revise illegal data on the blockchain and delete private data. Although redactable blockchains enable on-chain modification, they suffer from inefficiency and excessive centralization, the majority of redactable blockchain schemes ignore the difficult problems of traceability and consistency check. In this paper, we present a Dynamically Redactable blockchain based on decentralized Chameleon hash (DRBC). Specifically, we propose an Identity-Based Decentralized Chameleon Hash (IDCH) and a Version-Based Transaction structure (VT) to realize the traceability of transaction modifications in a decentralized environment. Then, we propose an efficient block consistency check protocol based on the Bloom filter tree, which can realize the consistency check of transactions with extremely low time and space cost. Security analysis and experiment results demonstrate the reliability of DRBC and its significant advantages in a decentralized environment.

关键词： Privacy protection Redactable blockchain Chameleon hash Consistency check Scalability

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VideoLT: Large-scale long-tailed video recognition

arXiv

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arXiv 2021年

作者： Zhang, Xing Wu, Zuxuan Weng, Zejia Fu, Huazhu Chen, Jingjing Jiang, Yu-Gang Davis, Larry Academy for Engineering and Technology Fudan University Shanghai Key Lab of Intel. Info. Processing School of Computer Science Fudan University Shanghai Collaborative Innovation Center on Intelligent Visual Computing Inception Institute of Artificial Intelligence University of Maryland

label distributions in real-world are oftentimes long-tailed and imbalanced, resulting in biased models towards dominant labels. While long-tailed recognition has been extensively studied for image classification tasks, limited effort has been made for the video domain. In this paper, we introduce VideoLT, a large-scale long-tailed video recognition dataset, as a step toward real-world video recognition. VideoLT contains 256,218 untrimmed videos, annotated into 1,004 classes with a long-tailed distribution. Through extensive studies, we demonstrate that state-of-the-art methods used for long-tailed image recognition do not perform well in the video domain due to the additional temporal dimension in videos. This motivates us to propose FrameStack, a simple yet effective method for long-tailed video recognition. In particular, FrameStack performs sampling at the frame-level in order to balance class distributions, and the sampling ratio is dynamically determined using knowledge derived from the network during training. Experimental results demonstrate that FrameStack can improve classification performance without sacrificing the overall accuracy. Code and dataset are available at: https://***/17Skye17/VideoLT. Copyright © 2021, The Authors. All rights reserved.

关键词： Image recognition

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VideoLT: Large-scale Long-tailed Video Recognition

VideoLT: Large-scale Long-tailed Video Recognition

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International Conference on computer Vision (ICCV)

作者： Xing Zhang Zuxuan Wu Zejia Weng Huazhu Fu Jingjing Chen Yu-Gang Jiang Larry Davis Academy for Engineering and Technology Fudan University Shanghai Key Lab of Intel. Info. Processing School of Computer Science Fudan University Shanghai Collaborative Innovation Center on Intelligent Visual Computing Inception Institute of Artificial Intelligence University of Maryland

ISBN: (纸本)9781665428132

关键词： Training Knowledge engineering computer vision Adaptation models Image recognition Codes Computational modeling

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AGO-Net: Association-Guided 3D Point Cloud Object Detection Network

arXiv

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arXiv 2022年

作者： Du, Liang Ye, Xiaoqing Tan, Xiao Johns, Edward Chen, Bo Ding, Errui Xue, Xiangyang Feng, Jianfeng The Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Ministry of Education China MOE Frontiers Center for Brain Science Fudan University Shanghai China Zhangjiang Fudan International Innovation Center China The Fudan ISTBIZJNU Algorithm Centre for Brain-inspired Intelligence Zhejiang Normal University Jinhua China Baidu Inc. China The Robot Learning Lab Imperial College London United Kingdom Technology Development Co. Ltd China The School of Computer Science Fudan University China

The human brain can effortlessly recognize and localize objects, whereas current 3D object detection methods based on LiDAR point clouds still report inferior performance for detecting occluded and distant objects: the point cloud appearance varies greatly due to occlusion, and has inherent variance in point densities along the distance to sensors. Therefore, designing feature representations robust to such point clouds is critical. Inspired by human associative recognition, we propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We bridge the gap between the perceptual domain, where features are derived from real scenes with sub-optimal representations, and the conceptual domain, where features are extracted from augmented scenes that consist of non-occlusion objects with rich detailed information. A feasible method is investigated to construct conceptual scenes without external datasets. We further introduce an attention-based re-weighting module that adaptively strengthens the feature adaptation of more informative regions. The network’s feature enhancement ability is exploited without introducing extra cost during inference, which is plug-and-play in various 3D detection frameworks. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed. Experiments on nuScenes and Waymo datasets also validate the versatility of our method. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Co-designing a motivational interviewing training platform to enhance oncology healthcare professional communication

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PEC innovation 2024年 5卷 100335-100335页

作者： Dang, Thu Ha Ludlow, Chris Borle, Hannah Alexander, Marliese Wickramasinghe, Nilmini Burbury, Kate Jayaraman, Prem Prakash Schofield, Penelope Department of Psychological Sciences School of Health Sciences Swinburne University of Technology Melbourne VIC Australia Health Services Research and Implementation Sciences Peter MacCallum Cancer Centre Melbourne VIC Australia Digital Health Cooperative Research Centre Sydney Australia Northern Health VIC Australia Pharmacy Department Peter MacCallum Cancer Centre Melbourne VIC Australia Sir Peter MacCallum Department of Oncology The University of Melbourne Parkville VIC Australia Optus Digital Health La Trobe University Melbourne VIC Australia Department Health and Bio-Statistics School of Health Sciences and Iverson Health Innovation Research Institute Swinburne University of Technology Melbourne VIC Australia Epworth Healthcare VIC Australia Digital and Healthcare Innovation Peter McCallum Cancer Centre Melbourne VIC Australia Factory of the Future and Digital Innovation Lab Department of Computer Science and Software Engineering School Software and Electrical Engineering Swinburne University of Technology Melbourne VIC Australia Department of Psychological Sciences Iverson Health Innovation Research Institute Swinburne University of Technology Melbourne VIC Australia Tasmanian Health Services Department of Health Hobart TAS Australia Peter MacCallum Cancer Centre Melbourne VIC Australia

Background: Oncology healthcare professionals (HCPs) using motivational interviewing may motivate and support patients with chronic illness to adhere to medications. Research of online motivational interviewing training focusing on medication adherence in cancer is limited. Objective: Co-design, develop, and preliminarily evaluate a motivational interviewing training platform (MITP) for oncology HCPs focused on medication adherence. Methods: We used co-design and design science research methodology to develop and test the MITP in two phases: 1) program co-design and development and 2) interactive platform design and development. Results: HCPs expressed a high demand for a practical and tailored motivational interviewing training. MITP is an online three-hour training comprising education, roleplay videos, and formative assessments. MITP was reported to be acceptable, usable, and useful by users. innovation: This study used a novel approach combining co-design and design science research methodology, and digital media to develop a flexible and acceptable online motivational interviewing training focused on medication adherence in cancer. Discussion and conclusion: Applied rigorous methodology ensured the MITP was developed to address knowledge gaps and the needs of oncology HCPs for supporting patient adherence, and to be usable and useful. Study findings may inform future research on online motivational interviewing training and its potential impact on medication adherence. © 2024

关键词： Co-design Medication adherence Motivational interviewing Online Training

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Recent Advances in Triboelectric Nanogenerators: From Technological Progress to Commercial Applications

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ACS Nano 2023年第12期17卷 11087-11219页

作者： Choi, Dongwhi Lee, Younghoon Lin, Zong-Hong Cho, Sumin Kim, Miso Ao, Chi Kit Soh, Siowling Sohn, Changwan Jeong, Chang Kyu Lee, Jeongwan Lee, Minbaek Lee, Seungah Ryu, Jungho Parashar, Parag Cho, Yujang Ahn, Jaewan Kim, Il-Doo Jiang, Feng Lee, Pooi See Khandelwal, Gaurav Kim, Sang-Jae Kim, Hyun Soo Song, Hyun-Cheol Kim, Minje Nah, Junghyo Kim, Wook Menge, Habtamu Gebeyehu Park, Yong Tae Xu, Wei Hao, Jianhua Park, Hyosik Lee, Ju-Hyuck Lee, Dong-Min Kim, Sang-Woo Park, Ji Young Zhang, Haixia Zi, Yunlong Guo, Ru Cheng, Jia Yang, Ze Xie, Yannan Lee, Sangmin Chung, Jihoon Oh, Il-Kwon Kim, Ji-Seok Cheng, Tinghai Gao, Qi Cheng, Gang Gu, Guangqin Shim, Minseob Jung, Jeehoon Yun, Changwoo Zhang, Chi Liu, Guoxu Chen, Yufeng Kim, Suhan Chen, Xiangyu Hu, Jun Pu, Xiong Guo, Zi Hao Wang, Xudong Chen, Jun Xiao, Xiao Xie, Xing Jarin, Mourin Zhang, Hulin Lai, Ying-Chih He, Tianyiyi Kim, Hakjeong Park, Inkyu Ahn, Junseong Huynh, Nghia Dinh Yang, Ya Wang, Zhong Lin Baik, Jeong Min Choi, Dukhyun Kyung Hee University Gyeonggi Yongin17104 Korea Republic of Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology 77 Massachusetts Avenue CambridgeMA02139 United States Department of Mechanical Engineering Soft Robotics Research Center Seoul National University Seoul08826 Korea Republic of Department of Mechanical Engineering Gachon University Seongnam13120 Korea Republic of Department of Biomedical Engineering National Taiwan University Taipei10617 Taiwan Frontier Research Center on Fundamental and Applied Sciences of Matters National Tsing Hua University Hsinchu30013 Taiwan School of Advanced Materials Science & Engineering Sungkyunkwan University Suwon16419 Korea Republic of Sungkyunkwan University 2066 Seobu-ro Gyeonggi Suwon16419 Korea Republic of Department of Chemical and Biomolecular Engineering National University of Singapore 4 Engineering Drive 4 117585 Singapore Division of Advanced Materials Engineering Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Jeonbuk National University 567 Baekje-daero Jeonbuk Jeonju54896 Korea Republic of Department of Physics Inha University 100 Inha-ro Incheon22212 Korea Republic of School of Materials Science & Engineering Yeungnam University Gyeongbuk Gyeongsan38541 Korea Republic of 291 Daehak-ro Daejeon34141 Korea Republic of School of Materials Science and Engineering Nanyang Technological University 50 Nanyang Avenue 639798 Singapore Institute of Flexible Electronics Technology of Tsinghua Zhejiang Jiaxing314000 China Nanomaterials and System Lab Major of Mechatronics Engineering Faculty of Applied Energy System Jeju National University Jeju632-43 Korea Republic of School of Engineering University of Glasgow GlasgowG128QQ United Kingdom Seoul02792 Korea Republic of Department of Physics Inha University Incheon22212 Korea Republic of Suwon16419 Korea Republic of Department of

Serious climate changes and energy-related environmental problems are currently critical issues in the world. In order to reduce carbon emissions and save our environment, renewable energy harvesting technologies will serve as a key solution in the near future. Among them, triboelectric nanogenerators (TENGs), which is one of the most promising mechanical energy harvesters by means of contact electrification phenomenon, are explosively developing due to abundant wasting mechanical energy sources and a number of superior advantages in a wide availability and selection of materials, relatively simple device configurations, and low-cost processing. Significant experimental and theoretical efforts have been achieved toward understanding fundamental behaviors and a wide range of demonstrations since its report in 2012. As a result, considerable technological advancement has been exhibited and it advances the timeline of achievement in the proposed roadmap. Now, the technology has reached the stage of prototype development with verification of performance beyond the lab scale environment toward its commercialization. In this review, distinguished authors in the world worked together to summarize the state of the art in theory, materials, devices, systems, circuits, and applications in TENG fields. The great research achievements of researchers in this field around the world over the past decade are expected to play a major role in coming to fruition of unexpectedly accelerated technological advances over the next decade. © 2023 The Authors. Published by American Chemical Society

关键词： Nanogenerators

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