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10th International Conference on Kansei Engineering and Emotion Research, KEER 2024

作者： Mustafa, Nur Batrisyia Damia Hamidi, Saidatul Rahah Hussein, Surya Sumarni Shuhidan, Shuhaida Mohamed College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Center for Research in Data Science Computer and Information Sciences Department Universiti Teknologi PETRONAS Perak Malaysia

ISBN: (纸本)9789819798896

In today's rapidly growing digital world, effective and user-centered experiences rely on integrating emotions into the design of digital products. This study focuses on investigating the LEIQ™ paradigm and creating computer-aided assistive tools for enhancing the evaluation of emotion-driven user experience (UX) in web-based solutions. Given the increasing incorporation of technology into our everyday routines, it is critical to understand the impact of emotions on user interactions. Emotional experiences play a crucial role in the efficacy of online design and development, as they have a substantial impact on user engagement, contentment, and product loyalty. The study acknowledges the limitations of existing methods for evaluating emotions, which include challenges in managing and analyzing the large datasets generated by frameworks like LEIQ™. Thus, this paper aims to develop a prototype assistive tool specifically designed for LEIQ™ emotion measurement, with the intention of addressing these shortcomings. This advancement reduces the need for a human workforce by providing the opportunity to quickly and accurately acquire information using automated technology. Potential gains for LEIQ™ practitioners, IT artifact designers, developers, and product organizations are envisaged by the research. The ensuing solution, which goes beyond LEIQ™ data consolidation, has the potential to completely alter emotional UX research by going deeper and encompassing all areas of LEIQ™ data processing. Finally, the assistive tool's usefulness may improve by making data interpretation in the digital age easier across a range of prospects and disciplines. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Assistive Tool Emotional UX Lokman's Emotion and Importance Quadrant (LEIQ)™

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Quantum Algorithms and Lower Bounds for Finite-Sum Optimization

arXiv

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arXiv 2024年

作者： Zhang, Yexin Zhang, Chenyi Fang, Cong Wang, Liwei Li, Tongyang School of Electronics Engineering and Computer Science Peking University China Computer Science Department Stanford University United States National Key Lab of General Artificial Intelligence School of Intelligence Science and Technology Peking University China Institute for Artificial Intelligence Peking University China Center on Frontiers of Computing Studies Peking University China School of Computer Science Peking University China

Finite-sum optimization has wide applications in machine learning, covering important problems such as support vector machines, regression, etc. In this paper, we initiate the study of solving finite-sum optimization problems by quantum computing. Specifically, let f1,...,fn: Rd → R be -smooth convex functions and ψ: Rd → R be a µ-strongly convex proximal function. The goal is to find an ǫ-optimal point for F(x) = n1 Pni=1 fi(x) + ψ(x). We give a quantum algorithm with complexity O(n + √d + p/µ(n1/3d1/3 + n−2/3d5/6))1 improving the classical tight bound Θ(n + pn/µ). We also prove a quantum lower bound Ω( n + n3/4(/µ)1/4) when d is large enough. Both our quantum upper and lower bounds can extend to the cases where ψ is not necessarily stronglyconvex, or each fi is Lipschitz but not necessarily smooth. In addition, when F is nonconvex, our quantum algorithm can find _an ǫ-critial point using O(n + (d1/3n1/3 + √d)/ǫ2) queries. Copyright © 2024, The Authors. All rights reserved.

关键词： Support vector machines

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Emotion Recognition from Eye Movements Using Multi-way Autoregressive Model

Emotion Recognition from Eye Movements Using Multi-way Autor...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Tian-Fang Ma Xuan-Hao Liu Wei-Long Zheng Bao-Liang Lu Center for Brain-Like Computing and Machine Intelligence Department of Computer Science and Engineering Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The application of physiological signals in emotion recognition is a popular research topic in human-computer interactions. Eye movement, as an important physiological signal, plays an essential role in medicine, psychology, cognitive science, and other scientific research fields. Previous studies have successfully identified human emotions by combining various eye-related measurements, selecting features, and utilizing machine learning techniques. However, the exploration of eye movement signals in emotion recognition remains insufficient. In this study, we utilize eye tracking heatmap and eye movement trajectory data for emotion recognition for the first time. Based on the two types of eye movement data, we develop a multi-way autoregressive model capable of processing multi-view eye movement data. Compared to traditional deep learning baseline models, our model better adapts to the structure of eye movement data and significantly improves the classification performance. Furthermore, we integrate heatmap and trajectory data with commonly used eye-related measurement features, which further enhance the performance of emotion recognition beyond previous methods.

关键词： Heating systems Emotion recognition Adaptation models Accuracy Psychology Gaze tracking Biomedical measurement Data models Physiology Trajectory

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Speaker-Independent Depression Detection based on Adversarial Training Method

Speaker-Independent Depression Detection based on Adversaria...

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European Signal Processing Conference (EUSIPCO)

作者： Minggang WANG Shohei KATO Wen GU Jun YAN Fenghui REN Department of Computer Science and Engineering Nagoya Institute of Technology Nagoya Japan School of Computing and Information Technology University of Wollongong Wollongong Australia Center for Innovative Distance Education and Research Japan Advanced Institute of Science and Technology Nomi Japan

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Depression, a worldwide mental health problem, significantly affects people's lives and may even lead to suicide in severe cases. As speech contains a large number of features that can reflect the speaker's mental condition, research has been conducted on depression detection based on speech analysis recently. However, personalized features from various speakers in speech can be mingled with depression-related features, causing a personalization bias in the features and reducing feature generalization. In this paper, we propose an adversarial training method based on the gradient reversal technique to remove speaker characteristics from speech signals, eliminating the personalization bias caused by speaker characteristics on depression features. Experiments carried out on the Distress Analysis Interview Corpus - Wizard of Oz (DAIC-WoZ) dataset, demonstrate that our method improved the detection performance through more generalized depression feature extraction.

关键词： Training Analytical models Speech analysis Europe Mental health Signal processing Depression Feature extraction Interviews

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ADAMANIP: ADAPTIVE ARTICULATED OBJECT MANIPULATION ENVIRONMENTS AND POLICY LEARNING

arXiv

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arXiv 2025年

作者： Wang, Yuanfei Zhang, Xiaojie Wu, Ruihai Li, Yu Shen, Yan Wu, Mingdong He, Zhaofeng Wang, Yizhou Dong, Hao Center on Frontiers of Computing Studies School of Computer Science Peking University China Beijing University of Posts and Telecommunications China Inst. for Artificial Intelligence Peking University China Nat’l Eng. Research Center of Visual Technology Peking University China State Key Laboratory of General Artificial Intelligence Peking University China

Articulated object manipulation is a critical capability for robots to perform various tasks in real-world scenarios. Composed of multiple parts connected by joints, articulated objects are endowed with diverse functional mechanisms through complex relative motions. For example, a safe consists of a door, a handle, and a lock, where the door can only be opened when the latch is unlocked. The internal structure, such as the state of a lock or joint angle constraints, cannot be directly observed from visual observation. Consequently, successful manipulation of these objects requires adaptive adjustment based on trial and error rather than a one-time visual inference. However, previous datasets and simulation environments for articulated objects have primarily focused on simple manipulation mechanisms where the complete manipulation process can be inferred from the object’s appearance. To enhance the diversity and complexity of adaptive manipulation mechanisms, we build a novel articulated object manipulation environment and equip it with 9 categories of objects. Based on the environment and objects, we further propose an adaptive demonstration collection and 3D visual diffusion-based imitation learning pipeline that learns the adaptive manipulation policy. The effectiveness of our designs and proposed method is validated through both simulation and real-world experiments. Our project page is available at: https://*** Copyright © 2025, The Authors. All rights reserved.

关键词： Three dimensional computer graphics

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DNN Partitioning and Assignment for Distributed Inference in SGX Empowered Edge Cloud

DNN Partitioning and Assignment for Distributed Inference in...

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International Conference on Distributed computing Systems

作者： Yuepeng Li Deze Zeng Lin Gut Song Guo Albert Y. Zomaya School of Computer Science China University of Geosciences Wuhan China Engineering Research Center of Natural Resource Information Management and Digital Twin Engineering Software Ministry of Education Wuhan China School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing The Hong Kong Polytechnic University Hong Kong School of Computer Science The University of Sydney Australia

ISBN: (数字)9798350386059

ISBN: (纸本)9798350386066

Distributed Deep Neural Network (DNN) inference is a promising technology to explore the distributed resources in edge cloud to realize edge intelligence. Meanwhile the inherent resource sharing nature of edge cloud infrastructure also raises serious concerns on security and privacy. Software Guard Ex-tensions (SGX) emerges as a potential hardware-level solution but its limited secure memory (i.e., enclave page cache) imposes new challenges, especially in contrast to memory-hungry DNN models. A task's performance will be severely affected when its memory footprint is beyond the enclave page cache size, due to expensive secure page swapping. In this case, how to appropriately partition a DNN model and assign the partitions to distributed edge servers to efficiently utilize edge resources for fast secure inference becomes a challenging problem. In this paper, we first show that this problem is NP-hard. We further propose a MEmory -aware Distributed Inference Acceleration (MEDIA) algorithm, whose guaranteed approximation ratio is also formally analyzed. We have implemented a prototype system and applied some well-known representative DNN models to evaluate MEDIA's performance. Through extensive experiments, we verify the efficiency of MEDIA by the fact that it reduces the inference time by 19.5%-38.1 % in comparison with state-of-the-art approaches.

关键词： Computational modeling Prototypes Artificial neural networks Media Approximation algorithms Inference algorithms Software

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Self-Supervised Multi-LiDAR Object View Generation Using Single LiDAR 29

Self-Supervised Multi-LiDAR Object View Generation Using Sin...

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29th IEEE International Conference on Embedded and Real-Time computing Systems and Applications, RTCSA 2023

作者： Kuo, Yi-Hung Shih, Chi-Sheng Lin, Hsiang-Jui Hung, Shih-Hao Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan National Taiwan University Department of Computer Science and Information Engineering Taipei Taiwan High Performance and Scientific Computing Center National Taiwan University Taipei Taiwan Mohamed Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates

ISBN: (纸本)9798350337860

3D object detection using point clouds has been widely used for self-driving vehicles, roadside vehicle detection, and tracking. However, the detection accuracy using single-LiDAR-scanned point clouds suffers from occlusion to have accurate bounding boxes. Several previous works proposed compensating for the point cloud to improve 3D detection. This work designed a self-labeling method that exploits the characteristics of multiple LiDAR-scanned point clouds collected on roadside units. This labeling method allows the point completion model to be trained without human-labeled bounding boxes. The point completion model uses the single-LiDAR-scanned point cloud as input and uses the extracted object points from the fused point cloud as the training target. The voxel IoU and recall of the point-completed point clouds increased by 17% and 22% compared to that of the raw point clouds. Additionally, the detection performance of DBSCAN, i.e., 3D AP@IoU=0.25 increased by 20% after applying the point completion algorithm. © 2023 IEEE.

关键词： Roadsides

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Towards a Deep Learning Pain-Level Detection Deployment at UAE for Patient-Centric-Pain Management and Diagnosis Support: Framework and Performance Evaluation 14

Towards a Deep Learning Pain-Level Detection Deployment at U...

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14th International Conference on Ambient Systems, Networks and Technologies Networks, ANT 2023 and The 6th International Conference on Emerging Data and Industry 4.0, EDI40 2023

作者： Ismail, Leila Waseem, Muhammad Danish Lab School of Computing and Information Systems Faculty of Engineering and Information Technology The University of Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates

The outbreak of the COVID-19 pandemic revealed the criticality of timely intervention in a situation exacerbated by a shortage in medical staff and equipment. Pain-level screening is the initial step toward identifying the severity of patient conditions. Automatic recognition of state and feelings help in identifying patient symptoms to take immediate adequate action and providing a patient-centric medical plan tailored to a patient's state. In this paper, we propose a framework for pain-level detection for deployment in the United Arab Emirates and assess its performance using the most used approaches in the literature. Our results show that a deployment of a pain-level deep learning detection framework is promising in identifying the pain level accurately. © 2023 Elsevier B.V.. All rights reserved.

关键词： computer vision

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Prediction of Flight Arrival Delay Time Using U.S. Bureau of Transportation Statistics

Prediction of Flight Arrival Delay Time Using U.S. Bureau of...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Li, Jiarui Ji, Ran Li, Cheng'ao Yang, Xiaoying Li, Jiayi Li, Yiran Xiong, Xihan Fang, Yutong Ding, Shusheng Cui, Tianxiang School of Computer Science University of Nottingham Ningbo China Ningbo China School of Aerospace Engineering University of Nottingham Ningbo China Ningbo China Imperial College London Department of Computing London United Kingdom Ningbo Open University Teaching Research Center Ningbo China Business School Ningbo University Ningbo China

ISBN: (纸本)9781665430654

According to the data from the Bureau of Trans-portation Statistics (BTS), the number of passengers and flights has been increasing year by year. However, flight delay has become a pervasive problem in the United States in recent years due to various factors, including human factors such as security regulations, as well as natural factors such as bad weather. Flight delay not only affects the profits of airlines but also affects the satisfaction of passengers. Therefore, a model that can predict the arrival time of airplanes needs to be developed. Machine learning methods have been widely applied to prediction problems. In this paper, a variety of machine learning and computational intelligence methods, including linear regression, decision tree (DT), random forest (RF), gradient boosting (GB), gaussian regression models and genetic programming were trained on the U.S. department of Transportation's (DOT) BTS dataset. The results show that genetic programming performs best and can be used to predict the arrival time of the U.S. flights in advance, which is beneficial for airlines and passengers to make timely decisions. © 2023 IEEE.

关键词： Air flight Airport Big data Computational intelligence Delay Machine learning Prediction Regression

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The Odyssey Journey: Top-Tier Medical Resource Seeking for Specialized Disorder in China

arXiv

引用

arXiv 2024年

作者： Chan, Ka I. Hu, Siying Wang, Yuntao Xu, Xuhai Lu, Zhicong Shi, Yuanchun Institute Tsinghua University Beijing China Department of Computer Science City University of Hong Kong Hong Kong Key Laboratory of Pervasive Computing Ministry of Education Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science and Technology Qinghai University Xining China Google New York CityNY United States Department of Computer Science George Mason University FairfaxVA United States Department of Computer Science and Technology Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China Qinghai University Xining China

It is pivotal for patients to receive accurate health information, diagnoses, and timely treatments. However, in China, the significant imbalanced doctor-to-patient ratio intensifies the information and power asymmetries in doctor-patient relationships. Health information-seeking, which enables patients to collect information from sources beyond doctors, is a potential approach to mitigate these asymmetries. While HCI research predominantly focuses on common chronic conditions, our study focuses on specialized disorders, which are often familiar to specialists but not to general practitioners and the public. With Hemifacial Spasm (HFS) as an example, we aim to understand patients’ health information and top-tier1 medical resource seeking journeys in China. Through interviews with three neurosurgeons and 12 HFS patients from rural and urban areas, and applying Actor-Network Theory, we provide empirical insights into the roles, interactions, and workflows of various actors in the health information-seeking network. We also identified five strategies patients adopted to mitigate asymmetries and access top-tier medical resources, illustrating these strategies as subnetworks within the broader health information-seeking network and outlining their advantages and challenges. Copyright © 2024, The Authors. All rights reserved.

关键词： Electronic health record

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