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24th KES International Conference on Knowledge-Based and Intelligent information and Engineering systems, KES 2020

作者： Ammar, Randa Ben Ayed, Yassine Ben Multimedia Information System and Advanced Computing Laboratory Sfax University Tunisia

Alzheimer's disease (AD) is the most leading symptom of neurodegenerative dementia;AD is defined now as one of the most costly chronic diseases. For that automatic diagnosis and control of Alzheimer's disease may have a significant effect on society along with patient well-being. Language disorder is regarded to be among the most common symptoms of AD, as a direct and natural result of cognitive impairment. Hence, the diagnosis of Alzheimer's disease using speech-based features is gaining growing attention. The aim of this study is to extract linguistic features following a proposed taxonomy of language impairment of AD patients. Obtained results indicate that the proposed taxonomy of the linguistic features extracted from the speech samples can be used to differentiate between Alzheimer's disease patients and the healthy control group. Support Vector Machine (SVM) classifier obtained classification accuracy over 90 percent. © 2020 The Authors. Published by Elsevier B.V.

关键词： Taxonomies

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Efficient Hierarchical Exploration with An Active Subgoal Generation Strategy

Efficient Hierarchical Exploration with An Active Subgoal Ge...

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2022 IEEE International Conference on Robotics and Biomimetics, ROBIO 2022

作者： Xu, Xin Zuo, Guoyu Li, Jiangeng Huang, Gao The Faculty of Information Technology Beijing University of Technology Beijing100124 China The Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Faculty of Information Beijing University of Technology Beijing100124 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (纸本)9781665481090

Goal-conditioned hierarchical reinforcement learning (HRL) is a promising approach in long-horizon and complex tasks. It has been successfully used by decomposing tasks via subgoals. However, the existing goal-conditioned HRL methods often suffer from training inefficiency in the long-horizon tasks with sparse external rewards. For this problem, this paper proposes a novel framework that can train high-level policy efficiently with an effective sub goal generation method. The key component of the framework is an active subgoal generation strategy which consists of (a) an efficient high-level policy learning mechanism and (b) two measures of novelty and coverage for filtering effective subgoals. We also introduce the HER mechanism to the framework to solve the problem of sparse reward in the environment. Building upon the proposed framework, this paper develops an active hierarchical exploration strategy with an efficient subgoal generation method. We test our method on a variety of continuous control tasks. Experimental results demonstrate that the strategy outperforms the state-of-the-art HRL approaches. © 2022 IEEE.

关键词： Reinforcement learning

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An Efficient Color and Geometric Feature Fusion Module for 6D Object Pose Estiamtion 12

An Efficient Color and Geometric Feature Fusion Module for 6...

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12th International Conference on CYBER Technology in Automation, Control, and Intelligent systems, CYBER 2022

作者： Li, Jiangeng Liu, Hong Huang, Gao Zuo, Guoyu Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Computing Intelligence and Intelligent Systems Beijing100124 China Faculty of Information Beijing University of Technology Beijing100124 China Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing100081 China

ISBN: (数字)9781665472678

ISBN: (纸本)9781665472678

6D pose estimation is widely used in robot tasks such as sorting and grasping. RGB-D-based methods have recently attained brilliant success, but they are still susceptible to heavy occlusion. Our critical insight is that color and geometry information in RGBD images are two complementary data, and the crux of the pose estimation problem under occlusion is fully leveraging them. Towards this end, we propose a new color and geometry feature fusion module that can efficiently leverage two complementary data sources from RGB-D images. Unlike prior fusion methods, we conduct a two-stage fusion strategy to do color-depth fusion and local-global fusion successively. Specifically, we fuse the color features extracted from RGB images into the point cloud in the first stage. In the second stage, we extract local and global features from the fused point cloud using an ASSANet-like network and splice them together to obtain the final fusion features. We conducted experiments on the widely used LineMod and YCB-Video datasets, which shows that our method improves the prediction accuracy while reducing the training time. © 2022 IEEE.

关键词： Color

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Pronounced advance on typhoon track forecast with global convection-permitting model

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Science Bulletin 2025年第9期70卷 1424-1428页

作者： Gu, Jun Zhao, Chun Li, Gudongze Feng, Jiawang Xu, Mingyue Du, Qiuyan Xia, Zihan Li, Yubin Chen, Guanghua Hao, Xiaoyu Chen, Junshi An, Hong Deep Space Exploration Laboratory/School of Earth and Space Sciences/CMA-USTC Laboratory of Fengyun Remote Sensing/State Key Laboratory of Fire Science/Institute of Advanced Interdisciplinary Research on High-Performance Computing Systems and Software University of Science and Technology of China Hefei230026 China Laoshan Laboratory Qingdao266237 China CAS Center for Excellence in Comparative Planetology University of Science and Technology of China Hefei230026 China School of Atmospheric Physics Nanjing University of Information Science and Technology Nanjing210044 China Key Laboratory of Cloud-Precipitation Physics and Severe Storms Institute of Atmospheric Physics Chinese Academy of Sciences Beijing100029 China School of Computer Science and Technology University of Science and Technology of China Hefei230026 China

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Understanding Learner Engagement in a Virtual Learning Environment 18th

Understanding Learner Engagement in a Virtual Learning Envir...

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Joint Conferences on 18th International Conference on Intelligent systems Design and Applications, ISDA 2018 and 10th World Congress on Nature and Biologically Inspired computing , NaBIC 2018

作者： Hlioui, Fedia Aloui, Nadia Gargouri, Faiez Multimedia Information System and Advanced Computing Laboratory University of Sfax Sfax Tunisia

ISBN: (纸本)9783030166595

The past few years has seen the rapid growth of educational data mining approaches for the analysis of data obtained from the virtual learning environments (VLE). However, due to the open and online characteristics of VLEs, vast majority of learners may enroll and drop a course freely, resulting in high dropout rates problem. One of the key elements in reducing dropout rates is the accurate and prompt identification of learners’ engagement level and providing individualized assistance. In this respect, this paper proposes a survival modeling technique to study various factors’ impact on attrition over the Open University in UK. We aim to perceive the learning from a psychological engagement perspective, which is necessary to gain a better understanding of learner motivation and subsequent knowledge and skill acquisition. In this way, we provide an innovative process that may help the tutor to interfere with weak learner at the appropriate time, such as dialog prompts, or learning resources to enhance the learning efficiency. It can help developers to evaluate the VLE effectively and expand system function for future development trend. © 2020, Springer Nature Switzerland AG.

关键词： Data mining

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Ray-Tracing based CDL Modeling for C-V2X Scenarios with Multiple Roadside Vehicles 7

Ray-Tracing based CDL Modeling for C-V2X Scenarios with Mult...

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7th IEEE International Conference on Electronic information and Communication Technology, ICEICT 2024

作者： Wang, Yiran He, Danping Duo, Hao An, Hao Guan, Ke Tian, Dao Hrovat, Andrej Beijing Jiaotong University School of Electronic and Information Engineering Beijing100044 China CTTL-Terminal China Academy of Information and Communication Technology Beijing100083 China Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation Beijing100044 China ZTE Corporation State Key Laboratory of Mobile Network and Mobile Multimedia Technology Shenzhen518000 China Jožef Stefan Institute Department of Communication Systems Ljubljana1000 Slovenia Jožef Stefan International Postgraduate School Ljubljana1000 Slovenia

ISBN: (纸本)9798350384437

Cellular vehicle-to-everything (C-V2X) communication has provided safer and more convenient interactions between vehicles and surroundings. To support the improvement of intelligent C-V2X communication, it is essential to understand the characteristics of the wireless channel. In this paper, channel measurement is performed in the urban C-V2X scenario at 5.915 GHz. The material electromagnetic (EM) parameters are calibrated based on the measured results, which are used for multipath data obtaining in ray-tracing (RT) simulation. After that, the clustered delay line (CDL) models for the C-V2X scenario are proposed to study the spatial characteristics of the significant multipath components. Related cluster and in-cluster parameters are extracted and analyzed based on CDL modeling. This paper provides useful calibrated EM parameters for extensive simulations. The complex channel data in the C-V2X scenarios with and without multiple roadside vehicles are effectively simulated and studied. The proposed CDL models can offer guidance for channel modeling research in the C-V2X scenario. © 2024 IEEE.

关键词： C-V2X CDL model clustering ray-tracing

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Evaluating the Knowledge in Test Engineering Education using a Solar-powered Remote-controlled Flying Probe-Inspired In-Circuit Tester

Evaluating the Knowledge in Test Engineering Education using...

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International Symposium for Design and Technology of Electronics Packages (SIITME)

作者： Sorin Liviu Jurj Raul Rotar Ioana Brîncovan Flavius Oprițoiu Mircea Vlăduțiu Computers and Information Technology Department Advanced Computing Systems and Architectures (ACSA) Laboratory “Politehnica” University of Timisoara Timisoara Romania Department of Automation and Applied Informatics Aurel Vlaicu University Arad Romania

ISBN: (数字)9798331539511

ISBN: (纸本)9798331539528

This paper presents a solar-powered remote-controlled Flying Probe-Inspired In-Circuit Tester (FPICT) designed to improve individuals’ knowledge and practical skills in the Test Engineering Education (TEE) domain. The proposed system consists of a user-friendly modern website that offers three main functions: (a) access to TEE-related documentation and tests, (b) a real-time webcam feed for remote monitoring, access, control, and execution of real-time tests on multiple Printed Circuit Boards (PCBs) using the FPICT platform, and (c) performance assessment of users’ TEE knowledge improvement. The effectiveness of the FPICT in enhancing TEE knowledge is evaluated by comparing the score distribution of users before and after the initial tests. Additionally, the power consumption of the FPICT platform is measured, and the feasibility of powering the system with solar energy is investigated. Experimental results demonstrate that the remote-controlled FPICT successfully increases the TEE-related knowledge of web users by an average of $26 \%$ over an average duration of 50 minutes, highlighting its efficacy in teaching theoretical concepts of In-Circuit Testing (ICT) and developing practical skills in the Flying Probe Testing (FPT) domain.

关键词： Knowledge engineering Power measurement Webcams Printed circuits Solar energy Real-time systems Probes Engineering education Remote monitoring Testing

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A Cross-Platform Instant Messaging User Association Method Based on Spatio-temporal Trajectory 8th

A Cross-Platform Instant Messaging User Association Method B...

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8th International Conference on Artificial Intelligence and Security , ICAIS 2022

作者： Zhou, Pei Luo, Xiangyang Du, Shaoyong Li, Lingling Yang, Yang Liu, Fenlin School of Cyber Science and Engineering Zhengzhou University Zhengzhou450000 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450000 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Intelligent Engineering Zhengzhou University of Aeronautics Zhengzhou450000 China School of Computing and Information Systems Singapore Management University Singapore188065 Singapore

ISBN: (纸本)9783031067600

The current research on cross-platform instant messaging user association is mainly divided into two categories: based on user attributes and based on user behavior. Methods based on user attributes mainly identify users through multiple attributes such as user name and multi-platform user information association based on cell phone number, but user association is not possible when multi-platform user information is inconsistent and users do not grant their address book permissions. Methods based on user behavior mainly calculate the similarity between user trajectories features such as geographic location frequency and co-occurrence, but this method lacks the user’s information, which leads to the inability to fully excavate the sequential features of the trajectory and affects the accuracy of trajectory matching. In order to solve this problem, this paper proposes a cross-platform instant messaging user association algorithm based on temporal trajectories (CPTrajst). We firstly place probes in the area where the target may appear so as to obtain user information, gets user trajectory, then processes the trajectory and performs two trajectory matches, finally associate users of different platforms whose trajectories match, thus increasing the accuracy and reliability of cross-platform instant messaging user association. We conducts specific experiments for users of WeChat and Momo, the most commonly used instant messengers in China. The results show that we can achieve reliable association for these two types of instant messaging users and the user association accuracy can reach 99.5%, which is better than the existing user association algorithms based on trajectory matching. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Trajectories

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Joint Super-Resolution and Classification Based on Bidirectional Mapping and Multiple Constraints

Joint Super-Resolution and Classification Based on Bidirecti...

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IEEE International Conference on multimedia and Expo (ICME)

作者： Zijian Yuan Kan Chang Zhiquan Liu Xinjie Wei Boning Chen School of Computer and Electronic Information Guangxi University Nanning China Guangxi Key Laboratory of Multimedia Communications and Network Technology Guangxi University Nanning China School of Computing and Information Systems The University of Melbourne Melbourne Australia

Since discriminant features are insufficient in the low-resolution (LR) images, it is challenging to accurately classify them. To address this issue, this paper proposes a joint super-resolution (SR) and classification network (JSRCN) based on bidirectional mapping and multiple constraints. In JSRCN, there is a SR sub-network containing a forward mapping path and several backward mapping paths. In the forward mapping path, high resolution (HR) features are progressively reconstructed. On the other hand, the backward mapping paths are used to alleviate the hardship of directly learning a nonlinear mapping from the LR space to the HR space. To effectively restore discriminant features for the image classification sub-network, multiple perceptual loss and multi-scale feature loss are presented to enhance the representation ability for the multi-scale features in images with different resolutions. Experiments show that compared with other competing methods, the proposed method achieves the highest accuracy.

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Triangle Matters! TopDyG: Topology-aware Transformer for Link Prediction on Dynamic Graphs 25

Triangle Matters! TopDyG: Topology-aware Transformer for Lin...

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34th ACM Web Conference, WWW 2025

作者： Zhang, Xin Cai, Fei Zheng, Jianming Pan, Zhiqiang Chen, Wanyu Chen, Honghui Chen, Chonghao National Key Laboratory of Information Systems Engineering National University of Defense Technology Changsha China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China College of Electronic Countermeasures National University of Defense Technology Hefei China

ISBN: (纸本)9798400712746

Dynamic graph link prediction is widely utilized in the complex web of the real world, such as social networks, citation networks, recommendation systems, etc. Recent Transformer-based link prediction methods on dynamic graphs not only fail to model the fine-grained structures such as triangles with the vanilla Transformers in the graph serialization process, but also amplify the imbalanced distribution of graphs because of their over-estimation of high-degree nodes. To tackle these issues, we propose a Topology-aware Transformer on Dynamic Graph (TopDyG) for link prediction, consisting of a topology injected Transformer (Ti-Transformer) and a mutual information learning (Mi-Learning). The Ti-Transformer explores the explicit structure of serialized graphs, capturing the topological features. The Mi-Learning mines the relationship between nodes by modeling the mutual information with a prior knowledge, alleviating the over-estimation of high-degree nodes when applying the Transformer-based models for the dynamic graph link prediction task. Extensive experiments on four public datasets containing both transductive and inductive settings present the superiority of our proposal. In particular, TopDyG presents an improvement of 43.27% and 28.75% against the state-of-the-art baselines in terms of NDCG and Jaccard, respectively. The advantages are especially obvious on the high-density graphs. © 2025 Copyright held by the owner/author(s).

关键词： Recommender systems

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