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IEEE Open Journal of the Computer Society 2021年第1期2卷 85-98页

作者： Du, Zhaoyang Wu, Celimuge Yoshinaga, Tsutomu Chen, Xianfu Wang, Xiaoyan Yau, Kok-Lim Alvin Ji, Yusheng Graduate School of Informatics and Engineering The University of Electro-Communications Tokyo182-8585 Japan Vtt Technical Research Centre of Finland Oulu90570 Finland Graduate School of Science and Engineering Ibaraki University Ibaraki310-8512 Japan School of Engineering and Technology Sunway University Sunway47500 Malaysia Information Systems Architecture Research Division National Institute of Informatics Tokyo101-8430 Japan

Vehicular delay tolerant networks (VDTNs) enable information sharing among mobile nodes in scenarios where cellular base stations are unavailable and the connections between the mobile nodes are intermittent. While unmanned aerial vehicles (UAVs) have shown to improve the performance of VDTNs, existing routing protocols, such as PRoPHET, consider the encounter probability between mobile nodes only, which does not fully address the characteristics of UAVs in route selection. In this paper, we propose a routing protocol, which considers both the encounter probability and the persistent connection time between mobile nodes for each encounter, in UAV-assisted VDTNs. By introducing the persistent connection time in route selection, the proposed protocol is able to evaluate the stability of a communication link more accurately in the UAV-assisted VDTN environment. The proposed protocol is evaluated using realistic simulations by comparing it with existing baselines. The simulation results verify that the proposed protocol can improve the reliability of message forwarding while reducing network overhead and end-to-end delay. © 2020 IEEE.

关键词： Delay tolerant networks

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Sports-QA: A Large-Scale Video Question Answering Benchmark for Complex and Professional Sports

arXiv

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arXiv 2024年

作者： Li, Haopeng Deng, Andong Liu, Jun Rahmani, Hossein Guo, Yulan Schiele, Bernt Bennamoun, Mohammed Ke, Qiuhong School of Computing and Information Systems University of Melbourne Australia Center for Research in Computer Vision University of Central Florida United States Pillar Singapore University of Technology and Design Singapore School of Computing and Communications Lancaster University United Kingdom School of Electronics and Communication Engineering Sun Yat-sen University China Department of Computer Vision and Machine Learning Max Planck Institute for Informatics Saarland Informatics Campus Germany School of Physics Maths and Computing University of Western Australia Australia Department of Data Science & AI Monash University Australia

Reasoning over sports videos for question answering is an important task with numerous applications, such as player training and information retrieval. However, this task has not been explored due to the lack of relevant datasets and the challenging nature it presents. Most datasets for video question answering (VideoQA) focus mainly on general and coarse-grained understanding of daily-life videos, which is not applicable to sports scenarios requiring professional action understanding and fine-grained motion analysis. In this paper, we introduce the first dataset, named Sports-QA, specifically designed for the sports VideoQA task. The Sports-QA dataset includes various types of questions, such as descriptions, chronologies, causalities, and counterfactual conditions, covering multiple sports. Furthermore, to address the characteristics of the sports VideoQA task, we propose a new Auto-Focus Transformer (AFT) capable of automatically focusing on particular scales of temporal information for question answering. We conduct extensive experiments on Sports-QA, including baseline studies and the evaluation of different methods. The results demonstrate that our AFT achieves state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Question answering

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Fine-grained Domain Adaptive Crowd Counting via Point-derived Segmentation

Fine-grained Domain Adaptive Crowd Counting via Point-derive...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yongtuo Liu Dan Xu Sucheng Ren Hanjie Wu Hongmin Cai Shengfeng He School of Computer Science South China University of Technology Institute of Informatics University of Amsterdam Department of Computer Science and Engineering Hong Kong University of Science and Technology School of Computing and Information Systems Singapore Management University

Due to domain shift, a large performance drop is usually observed when a trained crowd counting model is deployed in the wild. While existing domain-adaptive crowd counting methods achieve promising results, they typically regard each crowd image as a whole and reduce domain discrepancies in a holistic manner, thus limiting further improvement of domain adaptation performance. To this end, we propose to untangle domain-invariant crowd and domain-specific background from crowd images and design a fine-grained domain adaption method for crowd counting. Specifically, to disentangle crowd from background, we propose to learn crowd segmentation from point-level crowd counting annotations in a weakly-supervised manner. Based on the derived segmentation, we design a crowd-aware domain adaptation mechanism consisting of two crowd-aware adaptation modules, i.e., Crowd Region Transfer (CRT) and Crowd Density Alignment (CDA). The CRT module is designed to guide crowd features transfer across domains beyond background distractions. The CDA module dedicates to regularising target-domain crowd density generation by its own crowd density distribution. Our method outperforms previous approaches consistently in the widely-used adaptation scenarios.

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Relip: Reliable in-Band Parallel Communication for Magnetic MIMO Wireless Power Transfer System

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IEEE Transactions on Mobile computing 2025年

作者： Wang, Xinyu Zhou, Wangqiu Zhou, Hao Jiang, Shenyao Liu, Zhi Wang, Xiaoyan Ji, Yusheng Song, Qi City University of Hong Kong Department of Computer Science Hong Kong University of Science and Technology of China School of Computer Science and Technology Hefei230027 China The University of Electro-Communications Graduate School of Informatics and Engineering Tokyo182-8585 Japan Ibaraki University Graduate School of Science and Engineering Ibaraki310-8512 Japan National Institute of Informatics Information Systems Architecture Science Research Division Tokyo101-0003 Japan

In magnetic resonant coupling (MRC) based wireless power transfer (WPT) systems, receiver (RX) feedback communication is promising to enhance the capability and efficiency of the system. Although some studies have explored in-band implementations with low overhead costs, it has not been comprehensively investigated. In this paper, we propose Relip, a Reliable layer-level in-band parallel feedback communication mechanism for MIMO MRC-WPT systems, which addresses the impact of RX-RX couplings (i.e., non-negligible interference from strong couplings and positive effects of relay phenomenon), and provides a theoretical analysis of communication reliability. Technically, we first devise an On-Off based two-phase modulation mechanism to achieve RX identification and dependency detection under relay phenomenon. Then, we utilize observed channel decomposability to collect group-level power transfer channel conditions for eliminating the interference caused by strong RX-RX couplings. Furthermore, we perform RX selection to optimize the trade-off between communication reliability and time overhead. We design and implement the Relip prototype and conduct extensive experiments. The results validate the effectiveness of our mechanism, i.e., Relip can provide ≥99% average decoding accuracy for concurrent feedback communication of 14 devices, achieving an 18.31% improvement compared to the state-of-the-art solution. © 2002-2012 IEEE.

关键词： Inductive power transmission

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Using long-term data from a whole ecosystem warming experiment to identify best spring and autumn phenology models

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Plant-Environment Interactions 2023年第4期4卷 188-200页

作者： Schädel, Christina Seyednasrollah, Bijan Hanson, Paul J. Hufkens, Koen Pearson, Kyle J. Warren, Jeffrey M. Richardson, Andrew D. Center for Ecosystem Science and Society Northern Arizona University Flagstaff AZ United States Woodwell Climate Research Center Falmouth MA United States School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ United States Environmental Sciences Division and Climate Change Science Institute Oak Ridge National Laboratory Oak Ridge TN United States BlueGreen Labs Melsele 9120 Belgium

Predicting vegetation phenology in response to changing environmental factors is key in understanding feedbacks between the biosphere and the climate system. Experimental approaches extending the temperature range beyond historic climate variability provide a unique opportunity to identify model structures that are best suited to predicting phenological changes under future climate scenarios. Here, we model spring and autumn phenological transition dates obtained from digital repeat photography in a boreal Picea-Sphagnum bog in response to a gradient of whole ecosystem warming manipulations of up to +9°C, using five years of observational data. In spring, seven equally best-performing models for Larix utilized the accumulation of growing degree days as a common driver for temperature forcing. For Picea, the best two models were sequential models requiring winter chilling before spring forcing temperature is accumulated. In shrub, parallel models with chilling and forcing requirements occurring simultaneously were identified as the best models. Autumn models were substantially improved when a CO₂ parameter was included. Overall, the combination of experimental manipulations and multiple years of observations combined with variation in weather provided the framework to rule out a large number of candidate models and to identify best spring and autumn models for each plant functional type. © 2023 The Authors. Plant-Environment Interactions published by New Phytologist Foundation and John Wiley & Sons Ltd.

关键词： Akaike Information Criterion climate change CO2 Larix laricina peatland Picea mariana transition dates

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Split Learning Assisted Multi-UAV System for Image Classification Task

Split Learning Assisted Multi-UAV System for Image Classific...

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IEEE Conference on Vehicular Technology (VTC)

作者： Tingkai Sun Xiaoyan Wang Masahiro Umehira Yusheng Ji Graduate School of Science and Engineering Ibaraki University Hitachi Japan Graduate School of Science and Engineering Nanzan University Nagoya Japan Information Systems Architecture Science Research Division National Institute of Informatics Tokyo Japan

Due to its ease of deployment and high mobility, unmanned aerial vehicles (UAVs) have gained great popularity for a variety of applications. To conduct high-level and complicated tasks such as search/rescue missions and target identification, deep learning functions at UAVs are required. To this end, distributed learning methods such as federated learning (FL) and split learning (SL) have been proposed. In this paper, we investigate the SL assisted image classification task in a multi-UAV system for applications such as area exploration and object detection. Specifically, the whole deep learning model is cut into the UAV-side model and BS (base station)-side model. Each UAV performs forward propagation on UAV-side model by using the locally gathered images, and sends the smashed data to the BS. The BS performs forward and backward propagation based on the smashed data, and sends back the gradients of the cut layer to the UAVs, which is used for the backward propagation of the UAV-side model. The performance was evaluated using an aerial perspective geographic dataset, and the effectiveness of the proposed system was validated by comparing with FL-based and centralized learning methods. It was found that SL can significantly reduce computation time at UAV compared with FL, and is particularly effective with non-IID (independent and identically distributed) dataset. SL also requires less data during the training initial phase and has a faster convergence speed compared to centralized learning.

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DU-DARTS: Decreasing the Uncertainty of Differentiable architecture Search 32

DU-DARTS: Decreasing the Uncertainty of Differentiable Archi...

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32nd British Machine Vision Conference, BMVC 2021

作者： Lu, Shun Hu, Yu Yang, Longxing Sun, Zihao Mei, Jilin Zeng, Yiming Li, Xiaowei Research Center for Intelligent Computing Systems Institute of Computing Technology Chinese Academy of Sciences China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences China School of Computer Science and Technology University of Chinese Academy of Sciences China Tecent ADlab China

Differentiable Neural architecture Search (DARTS) recently attracts a lot of research attention because of its high efficiency. However, the competition of candidate operations in DARTS introduces high uncertainty for selecting the truly important operation, thus leading to serious performance collapse. In this work, we decrease the uncertainty of differentiable architecture search (DU-DARTS) by enforcing the distribution of architecture parameters to approach the one-hot categorical distribution and by replacing the zero operation with a gate switch. Without any extra search cost, our method achieves state-of-the-art performance with 2.32%, 16.74%, and 24.1% test error on CIFAR-10, CIFAR-100, and ImageNet datasets, respectively. Moreover, DU-DARTS can robustly find an excellent architecture on NAS-Bench-1Shot1 and NAS-Bench-201, which further demonstrates the effectiveness of our method. The source code is available at https://***/ShunLu91/DU-DARTS. © 2021. The copyright of this document resides with its authors.

关键词： Computer vision

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A Bi-level Globalization Strategy for Non-convex Consensus ADMM and ALADIN

arXiv

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arXiv 2023年

作者： Du, Xu Wang, Jingzhe Zhou, Xiaohua Mao, Yijie The Institute of Mathematics HNAS Henan Academy of Science Zhengzhou China The Department of Informatics & Networked Systems School of Computing and Information University of Pittsburgh PittsburghPA United States The School of Information Science and Technology ShanghaiTech University China

In this paper, we formally analyze global convergence in the realm of distributed consensus optimization. Current solutions have explored such analysis, particularly focusing on consensus alternating direction method of multipliers (C-ADMM), including convex and non-convex cases. While such efforts on non-convexity offer elegant theory guaranteeing global convergence, they entail strong assumptions and complicated proof techniques that are increasingly pose challenges when adopted to real-world applications. To resolve such tension, we propose a novel bi-level globalization strategy that not only guarantees global convergence but also provides succinct proofs, all while requiring mild assumptions. We begin by adopting such a strategy to perform global convergence analysis for the non-convex cases in C-ADMM. Then, we employ our proposed strategy in consensus augmented Lagrangian based alternating direction inexact Newton method (C-ALADIN), a more recent and generalization of C-ADMM. Surprisingly, our analysis shows that C-ALADIN globally converges to local optimizer, complementary to the prior work on C-ALADIN, which had primarily focused on analyzing local convergence for non-convex cases. © 2023, CC0.

关键词： Newton-Raphson method

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Optimising Random Forest Machine Learning Algorithms for User VR Experience Prediction Based on Iterative Local Search-Sparrow Search Algorithm

Optimising Random Forest Machine Learning Algorithms for Use...

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International Conference on Communications, Information System and Computer Engineering (CISCE)

作者： Xirui Tang Feiyang Li Zinan Cao Qixuan Yu Yulu Gong College of Computer Sciences Northeastern University Boston MA USA Department of Computer Science University of Illinois Urbana-Champaign Champaign IL USA Department of General Systems Studies The University of Tokyo Tokyo Japan College of Computing Georgia Institute of Technology Atlanta GA USA School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA

ISBN: (数字)9798350353174

ISBN: (纸本)9798350353181

In this paper, an improved method for VR user experience prediction is investigated by introducing a sparrow search algorithm and a random forest algorithm improved by an iterative local search-optimised sparrow search algorithm. The study firstly conducted a statistical analysis of the data, and then trained and tested using the traditional random forest model, the random forest model improved by the sparrow search algorithm, and the random forest algorithm improved based on the iterative local search-sparrow search algorithm, respectively. The results show that the traditional random forest model has a prediction accuracy of 93% on the training set but only 73.3% on the test set, which is poor in generalisation; whereas the model improved by the sparrow search algorithm has a prediction accuracy of 94% on the test set, which is improved compared with the traditional model. What is more noteworthy is that the improved model based on the iterative local search-sparrow search algorithm achieves 100% accuracy on both the training and test sets, which is significantly better than the other two methods. These research results provide new ideas and methods for VR user experience prediction, especially the improved model based on the iterative local search-sparrow search algorithm performs well and is able to more accurately predict and classify the user's VR experience. In the future, the application of this method in other fields can be further explored, and its effectiveness can be verified through real cases to promote the development of AI technology in the field of user experience.

关键词： Training Accuracy Machine learning algorithms Predictive models Prediction algorithms Iterative algorithms User experience

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Transformative effects of ChatGPT on modern education: Emerging Era of AI Chatbots

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Internet of Things and Cyber-Physical systems 2024年第1期4卷 19-23页

作者： Gill, Sukhpal Singh Xu, Minxian Patros, Panos Wu, Huaming Kaur, Rupinder Kaur, Kamalpreet Fuller, Stephanie Singh, Manmeet Arora, Priyansh Parlikad, Ajith Kumar Stankovski, Vlado Abraham, Ajith Ghosh, Soumya K. Lutfiyya, Hanan Kanhere, Salil S. Bahsoon, Rami Rana, Omer Dustdar, Schahram Sakellariou, Rizos Uhlig, Steve Buyya, Rajkumar School of Electronic Engineering and Computer Science Queen Mary University of London London United Kingdom Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Raygun Performance Monitoring Wellington New Zealand Center for Applied Mathematics Tianjin University Tianjin China Department of Science Kings Education London United Kingdom Cymax Group Technologies BC Canada QM Academy Queen Mary University of London London United Kingdom Jackson School of Geosciences University of Texas at Austin AustinTX United States Centre for Climate Change Research Indian Institute of Tropical Meteorology Pune India Microsoft Hyderabad India Institute for Manufacturing Department of Engineering University of Cambridge Cambridge United Kingdom Faculty of Computer and Information Science University of Ljubljana Ljubljana Slovenia Machine Intelligence Research Labs AuburnWA United States Faculty of Computing and Data Science FLAME University Maharashtra Pune India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Department of Computer Science University of Western Ontario London Canada Sydney Australia School of Computer Science University of Birmingham United Kingdom School of Computer Science and Informatics Cardiff University Cardiff United Kingdom Distributed Systems Group Vienna University of Technology Vienna Austria Department of Computer Science University of Manchester Oxford Road Manchester United Kingdom Laboratory School of Computing and Information Systems The University of Melbourne Australia

ChatGPT, an AI-based chatbot, offers coherent and useful replies based on analysis of large volumes of data. In this article, leading academics, scientists, distinguish researchers and engineers discuss the transformative effects of ChatGPT on modern education. This research discusses ChatGPT capabilities and its use in the education sector, identifies potential concerns and challenges. Our preliminary evaluation shows that ChatGPT perform differently in different subject areas including finance, coding, maths, and general public queries. While ChatGPT has the ability to help educators by creating instructional content, offering suggestions and acting as an online educator to learners by answering questions, transforming education through smartphones and IoT gadgets, and promoting group work, there are clear drawbacks in its use, such as the possibility of producing inaccurate or false data and circumventing duplicate content (plagiarism) detectors where originality is essential. The often reported "hallucinations" within GenerativeAI in general, and also relevant for ChatGPT, can render its use of limited benefit where accuracy is essential. What ChatGPT lacks is a stochastic measure to help provide sincere and sensitive communication with its users. Academic regulations and evaluation practices used in educational institutions need to be updated, should ChatGPT be used as a tool in education. To address the transformative effects of ChatGPT on the learning environment, educating teachers and students alike about its capabilities and limitations will be crucial. © 2023 The Authors

关键词： Machine learning

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