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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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QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation

arXiv

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arXiv 2024年

作者： Fu, Weiping Wei, Bifan Hu, Jianxiang Cai, Zhongmin Liu, Jun School of Computer Science and Technology Xi’an Jiaotong University Xi’an China School of Continuing Education Xi’an Jiaotong University Xi’an China MOE KLINNS Lab School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an China

Automatically generated questions often suffer from problems such as unclear expression or factual inaccuracies, requiring a reliable and comprehensive evaluation of their quality. Human evaluation is widely used in the field of question generation (QG) and serves as the gold standard for automatic metrics. However, there is a lack of unified human evaluation criteria, which hampers consistent and reliable evaluations of both QG models and automatic metrics. To address this, we propose QGEval, a multi-dimensional Evaluation benchmark for Question Generation, which evaluates both generated questions and existing automatic metrics across 7 dimensions: fluency, clarity, conciseness, relevance, consistency, answerability, and answer consistency. We demonstrate the appropriateness of these dimensions by examining their correlations and distinctions. Through consistent evaluations of QG models and automatic metrics with QGEval, we find that 1) most QG models perform unsatisfactorily in terms of answerability and answer consistency, and 2) existing metrics fail to align well with human judgments when evaluating generated questions across the 7 dimensions. We expect this work to foster the development of both QG technologies and their evaluation. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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The Survey on Multi-Source data Fusion in Cyber-Physical-Social Systems: Foundational Infrastructure for Industrial Metaverses and Industries 5.0

arXiv

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arXiv 2024年

作者： Wang, Xiao Wang, Yutong Yang, Jing Jia, Xiaofeng Li, Lijun Ding, Weiping Wang, Fei-Yue School of Artificial Intelligence Anhui University Hefei266114 China Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China National Engineering Laboratory for Big Data Collaborative Security Technology Beijing100015 China School of Automation Southeast University Nanjing210018 China School of Information Science and Technology Nantong University Nantong226019 China Faculty of Innovation Engineering Macau University of Science and Technology 999078 China

As the concept of Industries 5.0 develops, industrial metaverses are expected to operate in parallel with the actual industrial processes to offer "Human-Centric" Safe, Secure, Sustainable, Sensitive, Service, and Smartness "6S" manufacturing solutions. Industrial metaverses not only visualize the process of productivity in a dynamic and evolutional way, but also provide an immersive laboratory experimental environment for optimizing and remodeling the process. Besides, the customized user needs that are hidden in social media data can be discovered by social computing technologies, which introduces an input channel for building the whole social manufacturing process including industrial metaverses. This makes the fusion of multi-source data cross Cyber-Physical-Social Systems (CPSS) the foundational and key challenge. This work firstly proposes a multi-source-data-fusion-driven operational architecture for industrial metaverses on the basis of conducting a comprehensive literature review on the state-of-the-art multi-source data fusion methods. The advantages and disadvantages of each type of method are analyzed by considering the fusion mechanisms and application scenarios. Especially, we combine the strengths of deep learning and knowledge graphs in scalability and parallel computation to enable our proposed framework the ability of prescriptive optimization and evolution. This integration can address the shortcomings of deep learning in terms of explainability and fact fabrication, as well as overcoming the incompleteness and the challenges of construction and maintenance inherent in knowledge graphs. The effectiveness of the proposed architecture is validated through a parallel weaving case study. In the end, we discuss the challenges and future directions of multi-source data fusion cross CPSS for industrial metaverses and social manufacturing in Industries 5.0. Copyright © 2024, The Authors. All rights reserved.

关键词： data fusion

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Rtrl: Relation-Aware Transformer with Reinforcement Learning for Deep Question Generation

SSRN

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SSRN 2024年

作者： Zeng, Hongwei Wei, Bifan Liu, Jun BYD Auto Engineering Research Institute Guandong Shenzhen518118 China School of Computer Sciences and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Shaanxi Xi’an710049 China School of Continuing Education Xi’an Jiaotong University Shaanxi Xi’an710049 China

Deep question generation is an important yet challenging NLP task where multi-step reasoning chain connecting multiple documents is required to answer the questions. In this paper, we propose a Relation-aware Transformer with Reinforcement Learning (RTRL) to improve the performance of deep question generation. Specifically, RTRL first utilize the relation-aware Transformer to capture both the explicit linguistic and implicit semantic relations for better text representation. Besides, RTRL utilizes the sentence-level reward in a reinforcement learning framework to deal with the metric inconsistency between training and evaluation, and thus improve evaluation metrics with reference questions. Experimental results on HotpotQA dataset shows that RTRL outperforms compared baselines, highlighting its efficiency in generating deep questions. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Multimodal Dialogue Systems via Capturing Context-aware Dependencies of Semantic Elements 20

Multimodal Dialogue Systems via Capturing Context-aware Depe...

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28th ACM International Conference on Multimedia, MM 2020

作者： He, Weidong Li, Zhi Lu, Dongcai Chen, Enhong Huai, Baoxing Yuan, Jing Anhui Province Key Lab of Big Data Analysis and Application School of Computer Science and Technology University of Science and Technology of China China School of Data Science University of Science and Technology of China China HUAWEI Technologies Hangzhou China

ISBN: (纸本)9781450379885

Recently, multimodal dialogue systems have engaged increasing attention in several domains such as retail, travel, etc. In spite of the promising performance of pioneer works, existing studies usually focus on utterance-level semantic representations with hierarchical structures, which ignore the context-aware dependencies of multimodal semantic elements, i.e., words and images. Moreover, when integrating the visual content, they only consider images of the current turn, leaving out ones of previous turns as well as their ordinal information. To address these issues, we propose a Multimodal diAlogue systems with semanTic Elements, MATE for short. Specifically, we unfold the multimodal inputs and devise a Multimodal Element-level Encoder to obtain the semantic representation at element-level. Besides, we take into consideration all images that might be relevant to the current turn and inject the sequential characteristics of images through position encoding. Finally, we make comprehensive experiments on a public multimodal dialogue dataset in the retail domain, and improve the BLUE-4 score by 9.49, and NIST score by 1.8469 compared with state-of-the-art methods. © 2020 ACM.

关键词： Semantics

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GPTR: Gestalt-Perception Transformer for Diagram Object Detection

arXiv

引用

arXiv 2022年

作者： Hu, Xin Zhang, Lingling Liu, Jun Fan, Jinfu You, Yang Wu, Yaqiang Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering School of Computer Science and Technology Xi’an Jiaotong University China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University China Department of Control Science and Engineering Tongji University Shanghai China Department of Computer Science National University of Singapore Singapore Lenovo Research Beijing China

Diagram object detection is the key basis of practical applications such as textbook question answering. Because the diagram mainly consists of simple lines and color blocks, its visual features are sparser than those of natural images. In addition, diagrams usually express diverse knowledge, in which there are many low-frequency object categories in diagrams. These lead to the fact that traditional data-driven detection model is not suitable for diagrams. In this work, we propose a gestalt-perception transformer model for diagram object detection, which is based on an encoder-decoder architecture. Gestalt perception contains a series of laws to explain human perception, that the human visual system tends to perceive patches in an image that are similar, close or connected without abrupt directional changes as a perceptual whole object. Inspired by these thoughts, we build a gestalt-perception graph in transformer encoder, which is composed of diagram patches as nodes and the relationships between patches as edges. This graph aims to group these patches into objects via laws of similarity, proximity, and smoothness implied in these edges, so that the meaningful objects can be effectively detected. The experimental results demonstrate that the proposed GPTR achieves the best results in the diagram object detection task. Our model also obtains comparable results over the competitors in natural image object detection. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean label

arXiv

引用

arXiv 2022年

作者： Hu, Shengshan Zhou, Ziqi Zhang, Yechao Zhang, Leo Yu Zheng, Yifeng He, Yuanyuan Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China School of Information Technology Deakin University VIC3216 Australia School of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security HUST Wuhan430074 China Cluster and Grid Computing Lab HUST Wuhan430074 China

Due to its powerful feature learning capability and high efficiency, deep hashing has achieved great success in large-scale image retrieval. Meanwhile, extensive works have demonstrated that deep neural networks (DNNs) are susceptible to adversarial examples, and exploring adversarial attack against deep hashing has attracted many research efforts. Nevertheless, backdoor attack, another famous threat to DNNs, has not been studied for deep hashing yet. Although various backdoor attacks have been proposed in the field of image classification, existing approaches failed to realize a truly imperceptive backdoor attack that enjoys invisible triggers and clean label setting simultaneously, and they also cannot meet the intrinsic demand of image retrieval backdoor. In this paper, we propose BadHash, the first generative-based imperceptible backdoor attack against deep hashing, which can effectively generate invisible and input-specific poisoned images with clean label. Specifically, we first propose a new conditional generative adversarial network (cGAN) pipeline to effectively generate poisoned samples. For any given benign image, it seeks to generate a natural-looking poisoned counterpart with a unique invisible trigger. In order to improve the attack effectiveness, we introduce a label-based contrastive learning network labCLN to exploit the semantic characteristics of different labels, which are subsequently used for confusing and misleading the target model to learn the embedded trigger. We finally explore the mechanism of backdoor attacks on image retrieval in the hash space. Extensive experiments on multiple benchmark datasets verify that BadHash can generate imperceptible poisoned samples with strong attack ability and transferability over state-of-the-art deep hashing schemes. © 2022, CC BY.

关键词： Deep neural networks

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Hierarchical GAN-Tree and Bi-Directional Capsules for multi-label image classification

引用

Knowledge-Based Systems 2022年 238卷

作者： Wang, Boyan Hu, Xuegang Zhang, Chenwei Li, Peipei Yu, Philip S. Hefei University of Technology Anhui Province Hefei China Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Anhui Province Hefei China Anhui Province Key Laboratory of Industry Safety and Emergency Technology Anhui Province Hefei China *** Inc. Seattle WA United States University of Illinois at Chicago Chicago IL United States

Compared with the flat multi-label image classification, the hierarchical structure reserves a richer source of structural information to represent complicated relationships between labels in the real world. However, existing multi-label image classification methods focus on the accuracy of label prediction, ignoring the structural information embedded in the hierarchical label space. Furthermore, they hardly form the relevant visual feature space corresponding to the hierarchical label structure. In this paper, we propose a novel hierarchical framework based on the feature and label structural information named Hierarchical GAN-Tree and Bi-Directional Capsules (HGT&BC) to address these problems. We conduct Hierarchical GAN-Tree for feature space representation and Hierarchical Bi-Directional Capsules for label space classification, respectively. Hierarchical GAN-Tree generates hierarchical feature space using the unsupervised divisive clustering pattern according to the hierarchical structure, alleviating the mode-collapse of generators and the overfitting manifestation of conventional GANs. Hierarchical Bi-Directional Capsules utilize the hierarchical label structure in iterations of top-down and bottom-up processes: the top-down process integrates hierarchical relationships into the probability computation to enhance partial hierarchical relationships;the bottom-up process modifies the dynamic routing mechanism between capsules to represent semantic objects for the comprehensive global hierarchical classifiers. Owing to the two components, HGT&BC successfully expresses the hierarchical relationships in both feature and label space and improves the performance of multi-label image classification. Extensive experimental results on four benchmark datasets demonstrate the effectiveness and efficiency of our hierarchical framework in practice. © 2021 Elsevier B.V.

关键词： Capsule Neural Network Generative Adversarial Network Hierarchical learning Multi-label image classification

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A Rational Delegating Computation Protocol Based on Smart Contract 14th

A Rational Delegating Computation Protocol Based on Smart Co...

引用

14th EAI International Conference on Mobile Multimedia Communications, Mobimedia 2021

作者： Ma, Juan Chen, Yuling Liu, Guoxu Zhu, Hongliang Peng, Changgen State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China Blockchain Laboratory of Agricultural Vegetables Weifang University of Science and Technology Shouguang China Engineering Lab for Cloud Security and Information Security Center of BUPT Beijing China

ISBN: (纸本)9783030898137

The delegating computation has become an irreversible trend, together comes the pressing need for fairness and efficiency issues. To solve this problem, we leverage game theory to propose a smart contract-based solution. Firstly, according to the behavioral preferences of the participants, we design an incentive contract to describe the motivation of the participants. Secondly, to satisfy the fairness of the rational delegating computation, we propose a delegating computation protocol based on the smart contract. Specifically, rational participants are to gain the maximum utility and reach the Nash equilibrium in the protocol. Besides, we design a reputation mechanism with a reputation certificate, which measures the reputation from multiple dimensions. And the reputation is used to assure the client’s trust in the computing party to improve the efficiency of the protocol. Finally, the analysis results show that the proposed protocol solves the complex traditional verification problem. Meanwhile, we prove the fairness and correctness of the protocol. © 2021, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Smart contract

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HOPE-L: A Lossless database Watermarking Method in Homomorphic Encryption Domain

HOPE-L: A Lossless Database Watermarking Method in Homomorph...

引用

Smart City and Informatization (iSCI), IEEE International Conference on

作者： Xueqi Zhang Haiyong Xie Hui Lin University of Science and Technology of China Anhui China National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data Beijing China

ISBN: (纸本)9781665400411

Cloud computing has been widely adopted in the Internet economy; however, this poses numerous risks including illegal data copying and digital copyright infringement, leading to the challenge on protecting the security and copyright of key data stored in databases. In this paper, We propose a novel method, Lossless database Watermarking in Homomorphic Encryption domain (HOPE-L), to address this challenge. Our method combines the database encryption (i.e., homomorphic encryption, order-preserving encryption) and information hiding (i.e., lossless databased watermarking) technologies. More specifically, the information hiding algorithm leverages the homomorphic properties of the homomorphic encryption algorithm to embed secret data. No distortion is introduced throughout the data embedding process. Therefore, we can embed the watermark into the encrypted data losslessly. The watermark can be used to authenticate the copyright, and the recipients can directly recover the original database without loss. Analysis and theorem prove that HOPE-L can achieve more embedding space and no distortion. Extensive experiments show that the whole operation process is time-efficient, the embedded watermark is robust, and HOPE-L performs more robust than the existing algorithms and can resist common database attacks.

关键词： data privacy databases Smart cities Conferences Watermarking Resists Copyright protection

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