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Digital Mahjong System: Towards Precise Cognitive Assessment with IoT Technologies 1

24th International Conference on Human-computer Interaction, HCII 2022

作者： An, Ning Hu, Enze Guo, Yanrui Yang, Jiaoyun Au, Rhoda Ding, Huitong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Shenzhen Corecloud Innovation Technology Co. Ltd. Shenzhen China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China Department of Anatomy and Neurobiology Neurology and Framingham Heart Study Boston University School of Medicine Boston United States Department of Epidemiology Boston University School of Public Health Boston United States

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

To the best of our knowledge, this paper is the first to apply IoT technologies to transform the popular Mahjong game into a Digital Mahjong System (DMS) for digitally performing cognitive assessments. People have started exploring digital cognitive assessment tools for better objectivity and simplification. However, most of these tools are not friendly for older adults. This paper aims to address this issue by integrating IoT technologies with Mahjong to make cognitive assessment simpler and more engaging for older adults. DMS has the following features: 1) integrating motion tracking devices into Mahjong tiles and transferring them into Digital Mahjong Tiles (DMTs), which can precisely capture their moving trajectory during the assessment, 2) using a Near field Communication reader to configure DMT’s primary data, 3) supporting Over-The-Air Device Firmware Update that can be done remotely via Bluetooth, and 4) developing a charging base that can charge 16 DMTs simultaneously using Qi wireless charging standard. With further testing, the DMS has the potential to become a digital platform to implement and test various cognitive assessment tools integrating Mahjong elements. © 2022, Springer Nature Switzerland AG.

关键词： Internet of things

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A Reparameterization-Based Textual Adversarial Attack Approach with Multi-Objective Geometric Loss

SSRN

引用

SSRN 2022年

作者： Dong, Huoyuan Wan, Shaohua Dong, Jialiang Yuan, Shuai Guan, Zhitao School of Control and Computer Engineering North China Electric Power University Beijing102206 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China Brock University ON Canada

Maliciously designed adversarial samples entail severe security threats to the Pre-trained Language Models (PLMs), which are widely deployed in the natural language processing systems. Such systems would benefit from the research line on adversarial sample attacks by improving the adversarial robustness. Most existing literature focuses on achieving a high success rate of attacks in the vanilla model. They do not, however, consider the samples’ semantic consistency and fluency. Furthermore, computational complexity is another issue due to the frequent search on perturbations and queries on target models. In this study, we propose a simple yet effective adversarial attack framework by using the reparameterization methods on discreate token sequence. Particularly, a parameterized adversarial distribution based on the Gumbel distribution is optimized through perplexity regularization and embedding-based constraint, such that adversarial perturbations is obtained. In addition, we introduce the multi-objective geometric loss to address the learning imbalance issue on multiple tasks during optimization. To accommodate the pure black-box scenario, we further extend our framework by incorporating GPT-2 into our framework. Detailed experimental studies of the proposed work have been carried out using six benchmark datasets and four SOTA fine-tuned models. The results demonstrate significant reduction on the accuracy of the target model, while preserving fluency, semantic consistency and similarity under human evaluations. Finally in the hard label black-box settings, high level of performance can also be achieved by our approach with fewer queries in comparison with baseline methods. © 2022, The Authors. All rights reserved.

关键词： Natural language processing systems

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Variational Graph Generator for Multi-View Graph Clustering

arXiv

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arXiv 2022年

作者： Chen, Jianpeng Ling, Yawen Xu, Jie Ren, Yazhou Huang, Shudong Pu, Xiaorong Hao, Zhifeng Yu, Philip S. He, Lifang School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Department of Computer Science Virginia Tech BlacksburgVA24061 United States Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518000 China College of Computer Science Sichuan University Chengdu610065 China Department of Mathematics the College of Science Shantou University Shantou515063 China Department of Computer Science University of Illinois Chicago ChicagoIL60607 United States Department of Computer Science and Engineering Lehigh University PA18015 United States

Multi-view graph clustering (MGC) methods are increasingly being studied due to the explosion of multi-view data with graph structural information. The critical point of MGC is to better utilize view-specific and view-common information in features and graphs of multiple views. However, existing works have an inherent limitation that they are unable to concurrently utilize the consensus graph information across multiple graphs and the view-specific feature information. To address this issue, we propose Variational Graph Generator for Multi-View Graph Clustering (VGMGC). Specifically, a novel variational graph generator is proposed to extract common information among multiple graphs. This generator infers a reliable variational consensus graph based on a priori assumption over multiple graphs. Then a simple yet effective graph encoder in conjunction with the multi-view clustering objective is presented to learn the desired graph embeddings for clustering, which embeds the inferred view-common graph and view-specific graphs together with features. Finally, theoretical results illustrate the rationality of the VGMGC by analyzing the uncertainty of the inferred consensus graph with the information bottleneck principle. Extensive experiments demonstrate the superior performance of our VGMGC over SOTAs. The source code is publicly available at https://***/cjpcool/VGMGC. © 2022, CC BY.

关键词： Graph embeddings

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Pareto Self-Supervised Training for Few-Shot Learning

arXiv

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arXiv 2021年

作者： Chen, Zhengyu Ge, Jixie Zhan, Heshen Huang, Siteng Wang, Donglin AI Division School of Engineering Westlake University Institute of Advanced Technology Westlake Institute for Advanced Study College of Computer Science & Technology Zhejiang University

While few-shot learning (FSL) aims for rapid generalization to new concepts with little supervision, self-supervised learning (SSL) constructs supervisory signals directly computed from unlabeled data. Exploiting the complementarity of these two manners, few-shot auxiliary learning has recently drawn much attention to deal with few labeled data. Previous works benefit from sharing inductive bias between the main task (FSL) and auxiliary tasks (SSL), where the shared parameters of tasks are optimized by minimizing a linear combination of task losses. However, it is challenging to select a proper weight to balance tasks and reduce task conflict. To handle the problem as a whole, we propose a novel approach named as Pareto self-supervised training (PSST) for FSL. PSST explicitly decomposes the few-shot auxiliary problem into multiple constrained multi-objective subproblems with different trade-off preferences, and here a preference region in which the main task achieves the best performance is identified. Then, an effective preferred Pareto exploration is proposed to find a set of optimal solutions in such a preference region. Extensive experiments on several public benchmark datasets validate the effectiveness of our approach by achieving state-of-the-art performance. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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Task augmentation via channel mixture for few-task meta-learning

引用

Neural Networks 2025年 190卷 107609页

作者： Fan, Jiangdong Li, Yuekeng Bi, Jiayi Xu, Hui Shao, Jie School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China

Meta-learning is a promising approach for rapidly adapting to new tasks with minimal data by leveraging knowledge from previous tasks. However, meta-learning typically requires a large number of meta-training tasks. Existing methods often generate new tasks by interpolating fine-grained feature points, and such interpolation can compromise the continuity and integrity of the feature representations in the generated tasks. To address this problem, we propose task-level data augmentation to generate additional new tasks. Specifically, we introduce a novel task augmentation method called Task Augmentation via Channel Mixture (TACM). TACM generates new tasks by mixing channels from different tasks. This channel-level mixture preserves the continuity and integrity of feature information in channels during the mixture process, thereby enhancing the generalization ability of the model. Experimental results demonstrate that TACM outperforms other state-of-the-art methods across multiple datasets. Code is available at https://***/F-GOD6/TACM. © 2025 Elsevier Ltd

关键词： Meta-learning Overfitting Task augmentation

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A Tutorial on Movable Antennas for Wireless Networks

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IEEE Communications Surveys and Tutorials 2025年

作者： Zhu, Lipeng Ma, Wenyan Mei, Weidong Zeng, Yong Wu, Qingqing Ning, Boyu Xiao, Zhenyu Shao, Xiaodan Zhang, Jun Zhang, Rui National University of Singapore Department of Electrical and Computer Engineering 117583 Singapore National Key Laboratory of Wireless Communications Chengdu611731 China Southeast University National Mobile Communications Research Laboratory Frontiers Science Center for Mobile Information Communication and Security Nanjing210096 China Purple Mountain Laboratories Nanjing211111 China Shanghai Jiao Tong University Department of Electronic Engineering Shanghai200240 China Beihang University School of Electronic and Information Engineering Beijing100191 China University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Beijing Institute of Technology State Key Laboratory of CNS/ATM Miit Key Laboratory of Complex-field Intelligent Sensing Beijing100081 China The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China

Movable antenna (MA) has been recognized as a promising technology to enhance the performance of wireless communication and sensing by enabling antenna movement. Such a significant paradigm shift from conventional fixed antennas (FAs) to MAs offers tremendous new opportunities towards realizing more versatile, adaptive and efficient next-generation wireless networks such as 6G. In this paper, we provide a comprehensive tutorial on the fundamentals and advancements in the area of MA-empowered wireless networks. First, we overview the historical development and contemporary applications of MA technologies. Next, to characterize the continuous variation in wireless channels with respect to antenna position and/or orientation, we present new field-response channel models tailored for MAs, which are applicable to narrowband and wideband systems as well as far-field and near-field propagation conditions. Subsequently, we review the state-of-the-art architectures for implementing MAs and discuss their practical constraints. A general optimization framework is then formulated to fully exploit the spatial degrees of freedom (DoFs) in antenna movement for performance enhancement in wireless systems. In particular, we delve into two major design issues for MA systems. First, we address the intricate antenna movement optimization problem for various communication and/or sensing systems to maximize the performance gains achievable by MAs. Second, we deal with the challenging channel acquisition issue in MA systems for reconstructing the channel mapping between arbitrary antenna positions inside the transmitter and receiver regions. Moreover, we show existing prototypes developed for MA-aided communication/sensing and the experimental results based on them. Finally, the extension of MA design to other wireless systems and its synergy with other emerging wireless technologies are discussed. We also highlight promising research directions in this area to inspire future investigatio

关键词： Antenna arrays

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Temporal Correlation Meets Embedding: Towards a 2nd Generation of JDE-based Real-Time Multi-Object Tracking

arXiv

引用

arXiv 2024年

作者： Zhang, Yunfei Liang, Chao Gao, Jin Zhang, Zhipeng Hu, Weiming Maybank, Stephen Zhou, Xue Li, Liang School of Information Science and Technology Shanghaitech University Shanghai201210 China State Key Laboratory of Multimodal Artificial Intelligence System Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing101408 China Shenzhen Institute for Advanced Study University of Electronic Science and Technology of China Shenzhen518110 China KargoBot China Department of Computer Science and Information Systems Birkbeck College LondonWC1E 7HX United Kingdom School of Automation Engineering University of Electronic Science and Technology of China Chengdu611731 China Beijing Institute of Basic Medical Sciences Beijing100850 China

Joint Detection and Embedding (JDE) trackers have demonstrated excellent performance in Multi-Object Tracking (MOT) tasks by incorporating the extraction of appearance features as auxiliary tasks through embedding Re-Identification task (ReID) into the detector, achieving a balance between inference speed and tracking performance. However, solving the competition between the detector and the feature extractor has always been a challenge. Meanwhile, the issue of directly embedding the ReID task into MOT has remained unresolved. The lack of high discriminability in appearance features results in their limited utility. In this paper, a new learning approach using cross-correlation to capture temporal information of objects is proposed. The feature extraction network is no longer trained solely on appearance features from each frame but learns richer motion features by utilizing feature heatmaps from consecutive frames, which addresses the challenge of inter-class feature similarity. Furthermore, our learning approach is applied to a more lightweight feature extraction network, and treat the feature matching scores as strong cues rather than auxiliary cues, with an appropriate weight calculation to reflect the compatibility between our obtained features and the MOT task. Our tracker, named TCBTrack, achieves state-of-the-art performance on multiple public benchmarks, i.e., MOT17, MOT20, and DanceTrack datasets. Specifically, on the DanceTrack test set, we achieve 56.8 HOTA, 58.1 IDF1 and 92.5 MOTA, making it the best online tracker capable of achieving real-time performance. Comparative evaluations with other trackers prove that our tracker achieves the best balance between speed, robustness and accuracy. Code is available at https://***/yfzhang1214/TCBTrack. Copyright © 2024, The Authors. All rights reserved.

关键词： HTTP

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Security and Privacy Challenges in Distributed Database Management Systems

Security and Privacy Challenges in Distributed Database Mana...

引用

Information Technologies and Communications (GCITC), Global Conference on

作者： Santosh Kumar Kishor Kumar Dash Bhargav Reddy Piduru K. Rajkumar Bandi Bhaskar Mohit Tiwari Department of Computer Science ERA University Lucknow Uttar Pradesh Department of Botany and Journalism & Mass Communication Faculty & Academic Counsellor Balimela College of Science and Technology Berhampur University and Govt. Model Degree College Study Centre Odisha State Open University Hyundai Customer Experience Architect Irvine California (SG)/CSE Department National Engineering College Kovilpatti Tamilnadu India School of CS & AI SR University Warangal Telangana India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of Engineering Delhi

This research aims to investigate the challenge of recognizing and evaluating distributed database management system (DDBMS) security and privacy challenges and also to describe important danger indicators that may lead to possible data security difficulties in distributing resources, both tangible and functioning. An investigation of the primary DDBMS security and privacy challenges that can be found while developing and managing a standard DDBMS meant for supporting data procedures and offering data services was conducted as a component of this research. The data assessment was produced depending on the findings of surveys and questionnaires administered to DDBS security and privacy professionals with varying degrees of training and emphases in their operations within this expertise field. The findings of the research include a list of primary risk variables based on their significance and frequency in practice, and also a spotlight on the most important security and privacy measures. This article summarizes data on traditional methods to illustrate data security challenges employing statistical, qualitative, and mixed evaluation, and also the most recent techniques depending on smart categorization and examination of DDBS security and privacy challenge factors and functioning with huge amounts of data.

关键词：

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A Full-Scale Experimental Investigation of Natural Gas Explosion in a 710-M Long Utility Tunnel with Multiple Pipelines

SSRN

引用

SSRN 2023年

作者： Chen, Di Zhang, Huaixin Li, Jun Liu, Kuirong Wang, Yuwei Huang, Yuanjie Mao, Zhenglin Wu, Chengqing Computer Science School Southwest Petroleum University Chengdu610500 China School of Civil and Environmental Engineering University of Technology Sydney SydneyNSW2007 Australia PipeChina Southwest Pipeline Co. Ltd Sichuan Chengdu610000 China Sinopec Southwest Oil & Gas Company China Research Institute of Safety Environmental Protection and Technology Supervision PetroChina Southwest Oil and Gas Field Company China Research Institute of Exploration and Development PetroChina Southwest Oil and Gas Field Company China

Natural gas pipelines can pose significant threats to the safe operation of utility tunnels that house multiple critical infrastructure systems. Currently, there is controversy regarding whether pipelines will suffer damage from gas explosions, and no full-scale gas explosion test in utility tunnels has been found to be published to date. In this study, two 1:1-scale experiments were conducted for the first time in a 710-m long tunnel with crosssectional dimensions of 3.2 m × 2.6 m. Three pipelines, constructed according to industrial standards and with lengths of around 25 m, were used to investigate their damage behavior. The development of flow fields, such as flame images, flame signals, temperature, and overpressure, as well as pipeline responses were comprehensively captured. The results revealed a positive and a negative feedback loop drove the variations in overpressure in the combustion region. Furthermore, a significant pressure drop was observed when the airflow passed a local expansion. The experiments also demonstrated that while pipelines inside utility tunnels are unlikely to be directly damaged by gas explosions, accessories such as racks are at high risk of being severely destroyed. The findings provide essential guidance for the safety design of utility tunnels that handle flammable gases. © 2023, The Authors. All rights reserved.

关键词： Natural gas

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Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System

arXiv

引用

arXiv 2025年

作者： Wang, Yueqing Mei, Yikun Gao, Zhen Wan, Ziwei Ning, Boyu Mi, De Muhaidat, Sami School of Information and Electronics Beijing Institute of Technology Beijing100081 China MIIT Key Laboratory of Complex-Field Intelligent Sensing Beijing Institute of Technology Beijing100081 China Jiaxing314019 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250307 China National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China School of Computing and Digital Technology Birmingham City University BirminghamB55JU United Kingdom KU 6G Research Center Department of Computer and Information Engineering Khalifa University Abu Dhabi United Arab Emirates

The spatial diversity and multiplexing advantages of massive multi-input-multi-output (mMIMO) can significantly improve the capacity of massive non-orthogonal multiple access (NOMA) in machine type communications. However, state-of-the-art grant-free massive NOMA schemes for mMIMO systems require accurate estimation of random access channels to perform activity detection and the following coherent data demodulation, which suffers from excessive pilot overhead and access latency. To address this, we propose a pre-equalization aided grant-free massive access scheme for mMIMO systems, where an iterative detection scheme is conceived. Specifically, the base station (BS) firstly activates one of its antennas (i.e., beacon antenna) to broadcast a beacon signal, which facilitates the user equipment (UEs) to perform downlink channel estimation and pre-equalize the uplink random access signal with respect to the channels associated with the beacon antenna. During the uplink transmission stage, the BS detects UEs’ activity and data by using the proposed iterative detection algorithm, which consists of three modules: coarse data detection (DD), data-aided channel estimation (CE), and fine DD. In the proposed algorithm, the joint activity and DD is firstly performed based on the signals received by the beacon antenna. Subsequently, the DD is further refined by iteratively performing data-aided CE module and fine DD module using signals received by all BS antennas. Our simulation results demonstrate that the proposed scheme outperforms state-of-the-art mMIMO-based grant-free massive NOMA schemes with the same access latency. Simulation codes are provided to reproduce the results in this article: https://***/owenwang517/tvt-2025. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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