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Automatic Life Event Tree Generation for Older Adults 1

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

作者： Gui, Fang Wu, Xi Hu, Min Yang, Jiaoyun School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

Studies have shown that learning personal stories could help provide individualized eldercare services. However, personal stories are often disordered because of the scattered collection, including informal interviews or daily interactions, which brings difficulties in acquiring valuable information quickly. One solution to this problem is to extract events from personal stories and automatically organize them in chronological order. Events extracted by current methods from social media or news corpus are mainly organized in a linear structure. These works usually focus on the event time and ignore the consistency of event contents when organizing events. This paper aims to organize events into a tree structure based on an event network, with stem nodes representing key event topics and branch nodes representing detailed events. Social workers or caregivers can clarify the life experience of the older adults quickly through the event tree and have a preliminary understanding of them. The experiments show that the event tree generated by our method has a better performance in consistency than current event organization methods. A survey study shows that our method achieves the highest logical coherence for the event tree branches compared with other algorithms. © 2022, Springer Nature Switzerland AG.

关键词： Trees (mathematics)

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A Subspace-Based Non-Dominated Subset Selection Method

A Subspace-Based Non-Dominated Subset Selection Method

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2023 IEEE Congress on Evolutionary Computation, CEC 2023

作者： Tan, Qingshan Li, Changhe Zeng, Sanyou Yang, Shengxiang Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

ISBN: (纸本)9798350314588

Environmental selection is an important process in multi-objective evolutionary algorithms (MOEAs). As the evolution progresses, the number of non-dominated solutions increases. This paper is focused on selecting a subset from excess non-dominated solutions for evolutionary or the final output. However, traditional selection methods in classical MOEAs encounter difficulties when dealing with candidate solutions that possess irregular topologies. Although the distance-based subset selection methods are not sensitive to the topologies of the candidate points, they have significant room for reducing computational complexity. In order to address the above issues, a subspace selection method is proposed in this paper. It partitions the objective space into multiple subspaces that have comparable volumes and shapes. The maximal minimum distance of each solution is considered to ensure that the sparsest solution is always chosen first. To save computational costs, only the solutions in the neighboring subspaces are taken into account. The experimental results demonstrate that the proposed subspace selection method outperforms classical selection methods in solving problems with various shapes of the Pareto front. © 2023 IEEE.

关键词： Multiobjective optimization

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Prediction Method of Truck Travel Time in Open Pit Mines Based on LSTM Model 42

Prediction Method of Truck Travel Time in Open Pit Mines Bas...

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42nd Chinese Control Conference, CCC 2023

作者： Ao, Mengting Li, Changhe Yang, Shengxiang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

ISBN: (纸本)9789887581543

Aiming at the prediction of truck travel time in open pit mines, we established a prediction model based on long short-term memory(LSTM). This model fully accounts for 11 factors, including the nature of trucks, weather, road conditions, and driver's behaviors, as well as the influence of neighbor road segments in the route on the current predicted road segment. The experiment shows that the error of the LSTM prediction model is significantly reduced compared with SVR and BP models. In addition, the maximum absolute mean error under different conditions is less than 12 seconds. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Long short-term memory

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Hierarchy-Aware and Channel-Adaptive Semantic Communication for Bandwidth-Limited Data Fusion

arXiv

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arXiv 2025年

作者： Guo, Lei Chen, Wei Sun, Yuxuan Ai, Bo Pappas, Nikolaos Quek, Tony State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University China School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Department of Computer and Information Science Linköping University Linköping Sweden Information Systems Technology and Design Singapore University of Technology and Design Singapore

Obtaining high-resolution hyperspectral images (HR-HSI) is costly and data-intensive, making it necessary to fuse low-resolution hyperspectral images (LR-HSI) with high-resolution RGB images (HR-RGB) for practical applications. However, traditional fusion techniques, which integrate detailed information into the reconstruction, significantly increase bandwidth consumption compared to directly transmitting raw data. To overcome these challenges, we propose a hierarchy-aware and channel-adaptive semantic communication approach for bandwidth-limited data fusion. A hierarchical correlation module is proposed to preserve both the overall structural information and the details of the image required for super-resolution. This module efficiently combines deep semantic and shallow features from LR-HSI and HR-RGB. To further reduce bandwidth usage while preserving reconstruction quality, a channel-adaptive attention mechanism based on Transformer is proposed to dynamically integrate and transmit the deep and shallow features, enabling efficient data transmission and high-quality HR-HSI reconstruction. Experimental results on the CAVE and Washington DC Mall datasets demonstrate that our method outperforms single-source transmission, achieving up to a 2 dB improvement in peak signal-to-noise ratio (PSNR). Additionally, it reduces bandwidth consumption by two-thirds, confirming its effectiveness in bandwidth-constrained environments for HR-HSI reconstruction tasks. Copyright © 2025, The Authors. All rights reserved.

关键词： Bandwidth

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Radar Probing Optimization for Joint Beamforming and UAV Trajectory Design in UAV-Enabled Integrated Sensing and Communication

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IEEE Transactions on Communications 2024年

作者： Liu, Yaxi Mao, Wencan He, Boxin Huangfu, Wei Huang, Tianyao Zhang, Haijun Long, Keping University Of Science And Technology Beijing Beijing Advanced Innovation Center For Materials Genome Engineering Beijing Engineering And Technology Research Center For Convergence Networks And Ubiquitous Services School Of Computer And Communication Engineering Beijing100083 China University Of Science And Technology Shunde Innovation School Beijing China National Institute Of Informatics Information Systems Architecture Science Research Division Tokyo101-8430 Japan

Unmanned aerial vehicle (UAV)-enabled massive multiple-input-multiple-output (MIMO) integrated sensing and communication (ISAC) is an emerging platform to perform communication and sensing efficiently and flexibly. However, the existing works barely consider the radar probing tasks and neglect the benefits of the dedicated sensing signal. In this paper, we focus on joint optimizations in radar probing tasks, and a novel indicator is introduced, namely radar probing error. Two optimizations in radar probing tasks are established: i) joint transmit beamforming design for large-scale regional radar probing and communication task;ii) joint transmit beamforming and UAV trajectory design for communication enhancement and radar probing task. For the former task, we adopt both communication and novel sensing precoders to further support the MIMO radar. A semidefinite relaxation is utilized to relax the original non-convex problem, which is proven to be tight. For the latter task, we adopt block coordinate descent to alternately optimize the precoders and UAV trajectory where the fractional programming approach and successive convex approximation are further adopted. Experiment results testify the validation of the proposed methods for radar probing tasks in UAV-enabled MIMO ISAC. Moreover, results show the fundamental trade-off between the dual functions and reveal the effectiveness of the introduced sensing precoder. © 1972-2012 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Tunable collective electromagnetic induced transparency-like effect due to coupling of dual-band bound states in the continuum

arXiv

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arXiv 2024年

作者： Chen, Jian Huang, Rixing Zhao, Xueqian Fan, Qingxi Chang, Kan Zhang, Zhenrong Li, Guangyuan Guangxi Key Laboratory of Multimedia Communications and Network Technology School of Computer Electronics and Information Guangxi University Nanning530004 China School of Electrical Engineering Guangxi University Nanning530004 China CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen518055 China

The coupling between dual-band or multi-band quasi-bound states in the continuum (q-BICs) is of great interest for their rich physics and promising applications. Here, we report tunable collective electromagnetic induced transparency-like (EIT-like) phenomenon due to coupling between dual-band collective electric dipolar and magnetic quadrupolar q-BICs, which are supported by an all-dielectric metasurface composed of periodic tilted silicon quadrumers. We show that this collective EIT-like phenomenon with strong slow light effect can be realized by varying the nanodisk diameter or the tilt angle, and that the transparency window wavelength, the quality factor, and the group index can all be tuned by changing the nanodisk size. We further find that as the nanodisk size decreases, the slow light effect becomes stronger, and higher sensitivity can be obtained for the refractive index sensing. Interestingly, the sensitivity first increases exponentially and then reaches a plateau as the nanodisk size decreases, or equivalently as the group index increases. We therefore expect this work will advance the understanding of the collective EIT-like effect due to coupling between q-BICs, and the findings will have potential applications in slow-light enhanced biochemical sensing. © 2024, CC0.

关键词： Refractive index

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A Little Chit-Chat Goes a Long Way: Design and Evaluation of Task-and Person-Oriented Styles for Social Robots

A Little Chit-Chat Goes a Long Way: Design and Evaluation of...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Elie Saad Joost Broekens Mark A. Neerincx Department of Intelligent Systems Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands School of Engineering Holy Spirit University of Kaslik Jounieh Lebanon The Leiden Institute of Advanced Computer Science Leiden University Leiden The Netherlands The Netherlands Organization for Applied Scientific Research (TNO) Soesterberg The Netherlands

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

Whereas the reception task is a promising application domain for social robots, knowledge is lacking about how to design the appropriate re-usable communication styles for a reception robot. This paper presents the use and evaluation of an iterative interaction-design (ID) method with which task- and person-oriented multi-modal communication styles have been designed for such a robot. First, we report on an evaluation study of the ID-method with Industrial Design students (N =13) who designed these two communication styles for a Pepper robot. This provided a set of distinct designs of the two styles, for which the differences in design parameters were in line with social science theory. The task-oriented style showed a more formal, shorter and less chatty communication. Second, we present findings from a Mechanical Turk study conducted to evaluate the perception of these style designs. Participants (N =301) were presented with videos showing the robot acting as a receptionist and were asked to rate their perception of the robot, the service experience and the orientation of the designs. Overall, the interaction with the robot was appreciated well. The robot with a person-oriented style was perceived to be more animate and likeable. Analysis showed that chit-chat was the main contributor to the perceived difference between the person-oriented and task-oriented styles. This is an important finding as it gives interaction designers a validated best-practice approach to make interaction style more or less personal.

关键词： Service robots Social robots Design tools Behavioral sciences Iterative methods Robots Videos

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Machine learning algorithms for predicting the risk of chronic kidney disease in type 1 diabetes patients: a retrospective longitudinal study

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Neural Computing and Applications 2024年第26期36卷 16545-16565页

作者： Chowdhury, Md Nakib Hayat Reaz, Mamun Bin Ibne Ali, Sawal Hamid Md Crespo, María Liz Cicuttin, Andrés Ahmad, Shamim Haque, Fahmida Bakar, Ahmad Ashrif A. Razak, Mohd Ibrahim Bin Shapiai Abd Bhuiyan, Mohammad Arif Sobhan Department of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia UKM Selangor Bangi43600 Malaysia Saidpur Cantonment Saidpur Nilphamari5310 Bangladesh Department of Electrical and Electronic Engineering Independent University Bangladesh Dhaka Bashundhara Bangladesh Department of Electrical Electronic and Systems Engineering Centre of Advanced Electronic and Communication Engineering Universiti Kebangsaan Malaysia UKM Selangor Bangi43600 Malaysia Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia Trieste34151 Italy Department of Computer Science and Engineering University of Rajshahi Rajshahi6205 Bangladesh Artificial Intelligence Resource Molecular Imaging Branch National Cancer Institute BethesdaMD United States Department of Electrical and Electronics Engineering Xiamen University Malaysia Selangor Bandar Sunsuria Sepang43900 Malaysia

Chronic kidney disease (CKD) is a significant concern for individuals with type 1 diabetes (T1D), impacting their quality of life and healthcare costs. Identifying T1D patients at greater risk of developing CKD is crucial for preventive measures. However, it is challenging due to the asymptomatic progression of CKD and limited nephrologist availability in many countries. This study explores machine learning algorithms to predict CKD risk in T1D patients using ten years of retrospective data from the Epidemiology of Diabetes Interventions and Complications clinical trial. Eleven machine learning algorithms were applied to twenty-two readily available features from T1D patients’ routine check-ups and self-assessments to develop 10-year CKD risk prediction models. In addition, we also proposed a heterogeneous ensemble model (STK) using a stacking generalization approach. The models’ performance was evaluated using different evaluation metrics and repeated stratified k-fold cross-validation. Several predictive models showed reliable performance in CKD risk prediction, with the proposed ensemble model being the best performing with an average accuracy of 0.97, specificity of 0.98, sensitivity/recall of 0.96, precision of 0.98, F1 score of 0.97, Kappa and MCC score of 0.94, AUROC of 0.99, and Precision-Recall curve of 0.99. The proposed machine learning approach could be applicable for CKD risk prediction in T1D patients to ensure the necessary precautions to overcome the risk. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Machine learning

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An Augmented Reality Game's User Experience and Its Effect on Immersion Leading to Intention

An Augmented Reality Game's User Experience and Its Effect o...

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International Conference on ICT For Smart Society (ICISS)

作者： Tanty Oktavia Kawang Utomo Ford Lumban Gaol Takaaki Hosoda Information Systems Management Department BINUS Graduate Program - Master of Information System Management Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Doctor of Computer Science Bina Nusantara University Jakarta Indonesia Decision Science Department Advanced Institute of Industrial Technology Tokyo Japan

ISBN: (数字)9798350389654

ISBN: (纸本)9798350389661

Since the emergence of Pokémon Go in 2016, the world has been introduced to Augmented Reality (AR) games. Many IT companies have begun developing augmented reality (AR) games due to the great commercial potential in this industry. Certain products may have managed to remain viable in the market, while others were forced to close due to one or two factors. Since the emergence of Pokémon Go, no other game has managed to surpass its unprecedented success. The company responsible for developing Pokémon Go has decided to shut down one of their games due to a lack of consumer interest. The technology of augmented reality (AR) is consistently associated with the concept of immersion. Immersion is a key aspect of the game that allows users to deeply engage and feel fully involved in the experience. This study will employ a quantitative methodology, utilising a questionnaire that will be distributed to and completed by those who have engaged in augmented reality (AR) games. The data will be analysed using Smart PLS to examine the impact of user experience on immersion, which in turn influences intention. After gathering over 200 participants by spreading google form questionnaires and doing data analysis, the findings indicate that only a few aspects of user experience, namely Brand Experience and User Need Experience, have an impact on reasons. Niantic, the developer of Pokémon Go, was unable to replicate their success with a similar augmented reality game called Harry Potter: Wizards Unite. The initial release occurred on June 21, 2019, and the shutdown took place on December 21, 2021. In the beginning, they shown a profound enthusiasm for the game but were unable to sustain it, much as Pokémon Go. According to Niantic CEO John Hanke, the primary reason for the game's shutdown was its immersion. Immersion has a notable impact on User Confirmation, which in turn has a notable impact on Satisfaction, and Satisfaction has a notable impact on Intention. Moreover, there have be

关键词： Surveys Industries Data analysis Games Companies User experience Software Hardware Internet Augmented reality

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A collision-free fast planning algorithm for snake-like redundant manipulators via reinforcement learning 4

A collision-free fast planning algorithm for snake-like redu...

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4th International Conference on Pattern Recognition and Artificial Intelligence, PRAI 2021

作者： Qu, Ji Zhang, Zeliang Lin, Yue Zhang, Yunong Wang, Jianming Xiao, Xuan School of Computer Science and Technology Tiangong University Tianjin300387 China School of Information Science Japan Advanced Institute of Science and Technology Ishikawa Nomi Japan Tiangong University Tianjin Key Laboratory Autonomous Intelligence Technology and Systems Tianjin300387 China

ISBN: (纸本)9781665413220

This paper presents a planning algorithm via reinforcement learning to fast generate a collision-free path in the narrow and complex space for snake-like redundant manipulators. First, the snake-like redundant manipulator is introduced and its kinematic is analyzed. Second, the algorithm strategy is designed by combining the snake-inspired crawling motion and reinforcement learning. Finally, the simulations are conducted to test and verify the proposed algorithm. As a result, the snake-like redundant manipulator can explore the target position in the pipeline with a 93.33% success rate at an average planning time cost of 49.2s. © 2021 IEEE.

关键词： Redundant manipulators

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