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Factors Affecting Interior Acceleration Sound Preference for Electric Vehicles: A Path Analysis

Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2022年第1期66卷 1902 - 1907页

作者： May Jorella Lazaro Sungho Kim Adriance Wilfred Anggang Minsik Choi Myung Hwan Yun 1Interdisciplinary Program in Cognitive Science Seoul National University Seoul Korea Department of Industrial Engineering Seoul National University Seoul Korea

Vehicle sound design is gaining attention in the automotive industry, especially for Electric Vehicles (EV). For EVs, acceleration sound is critical for both user experience (UX) and safety. Despite the abundance of UX-related studies investigating the external presentation of acceleration sound for EVs, internal presentation of acceleration sound seems to be overlooked. Thus, further understanding on what influences the user preferences for internal EV sound is essential for better EV sound design. This study aims to explore and develop a simple theoretical path model to help understand the relationship between pragmatic quality, hedonic quality, novelty, and user preferences for EV internal acceleration sounds. Thirty-two participants evaluated twenty-seven EV acceleration sound samples using a 12-item semantic differential scale with bipolar adjective pairs that describe the measured variables in a controlled experimental setting. The relationship between the modeled variables was analyzed using bias-corrected factor score path analysis (BCFSPA). Results showed that the modified model yielded good model fit indices and partially confirmed the initial hypotheses. It was found that pragmatic and hedonic quality had a positive relationship with user preference, whereas, novelty had a negative relationship with hedonic quality and user preference for EV sounds. This study contributes to the understanding of factors that affects user preference for EV sound and provides initial accounts to different approaches and methods for model testing.

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Unbiased Parameter Estimation for Partially Observed Diffusions

arXiv

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arXiv 2023年

作者： Awadelkarim, Elsiddig Jasra, Ajay Ruzayqat, Hamza Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal23955-6900 Saudi Arabia

In this article we consider the estimation of static parameters for partially observed diffusion process with discrete-time observations over a fixed time interval. In particular, we assume that one must time-discretize the partially observed diffusion process and work with the model with bias and consider maximizing the resulting log-likelihood. Using a novel double randomization scheme, based upon Markovian stochastic approximation we develop a new method to unbiasedly estimate the static parameters, that is, to obtain the maximum likelihood estimator with no time discretization bias. Under assumptions we prove that our estimator is unbiased and investigate the method in several numerical examples, showing that it can empirically out-perform existing unbiased *** Codes 60J22, 62M05, 65C40, 62M20 © 2023, CC BY.

关键词： Parameter estimation

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Multi-Turn Dialogue Comprehension from a Topic-Aware Perspective

SSRN

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SSRN 2023年

作者： Ma, Xinbei Xu, Yi Zhao, Hai Zhang, Zhuosheng Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China

Dialogue Machine Reading Comprehension requires language models to effectively decouple and model multi-turn dialogue passages. As a dialogue development goes after the intentions of participants, its topic may not remain constant throughout the whole passage. Hence, it is non-trivial to detect and leverage the topic shift in dialogue modeling. Topic modeling, although has been widely studied in plain text, deserves far more utilization in dialogue reading comprehension. This paper proposes to model multi-turn dialogues from a topic-aware perspective. We start with a dialogue segmentation algorithm to split a dialogue passage into topic-concentrated fragments in an unsupervised way. Then we use these fragments as topic-aware language processing units in further dialogue comprehension. On one hand, the split segments indict specific topics rather than mixed intentions, thus showing convenience on in-domain topic detection and location. For this task, we design a clustering system with a self-training auto-encoder, and we build two constructed datasets for evaluation. On the other hand, the split segments are an appropriate element of multi-turn dialogue response selection. For this purpose, we further present a novel model, Topic-Aware Dual-Attention Matching (TADAM) Network, which takes topic segments as processing elements and matches response candidates with a dual cross-attention. Empirical studies on three public benchmarks show great improvements over baselines. Our work continues the previous studies on document topic, and brings the dialogue modeling to a novel topic-aware perspective with exhaustive experiments and analyses. © 2023, The Authors. All rights reserved.

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The Implementation of TOGAF for Enterprise System Planning on XYZ Campus

The Implementation of TOGAF for Enterprise System Planning o...

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Networking, Electrical engineering, computer science, and Technology (IConNECT), International Conference on

作者： Tamsir Hasudungan Sirait Ford Lumban Gaol Suhono Harso Supangkat Benny Ranti Binus Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department Bina Nusantara University Jakarta Indonesia School of Electrical Engineering and Informatics Bandung Institute of Technology Bandung Indonesia Computer Science Department Universitas Indonesia Jakarta Indonesia

Universities can employ information technology as one means of achieving their goals and objectives. Universities can get advantages from information technology, such as effective resource management and information management for decision-making. However, don't forget that the campus must consider what technology is appropriate to assist them achieve their goals, particularly in the current industrial era 4.0 where technology is available with many different choices. The campus requires an enterprise architecture in order to design, manage, and coordinate information technology infrastructure, applications, and processes strategically and thoroughly. The adoption of enterprise information system architecture (EA) is also intended to improve the quality of services provided to internal and external stakeholders. In this case, Enterprise Architecture can help an organization to match its information technology resources with business processes and strategies to achieve their goals. This research was conducted using TOGAF ADM, also known as the Open Group Architecture Framework Architecture Development Method. This method offers best practices for creating enterprise architecture and emphasizes several steps that include creating an architectural vision, information systems, business architecture modeling to help XYZ campus manage all their information technology.

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Age Effects on Decision-Making, Drift Diffusion Model

arXiv

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arXiv 2023年

作者： Kavian, Zahra Hajisadeghi, Kimia Shahir, Yashar Rezazadeh Faraji, Mehrbod Ebrahimpour, Reza Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Computer Engineering Shahid Rajaee Teacher Training University Tehran Iran Center for Cognitive Science Institute for Convergence Science and Technology Sharif University of Technology Tehran Iran

Training can improve human decision-making performance. After several training sessions, a person can quickly and accurately complete a task. However, decision-making is always a trade-off between accuracy and response time. Factors such as age and drug abuse can affect the decision-making process. This study examines how training can improve the performance of different age groups in completing a random dot motion (RDM) task. The participants are divided into two groups: old and young. They undergo a three-phase training and then repeat the same RDM task. The hierarchical drift-diffusion model analyzes the subjects’ responses and determines how the model’s parameters change after training for both age groups. The results show that after training, the participants were able to accumulate sensory information faster, and the model drift rate increased. However, their decision boundary decreased as they became more confident and had a lower decision-making threshold. Additionally, the old group had a higher boundary and lower drift rate in both pre and post-training, and there was less difference between the two group parameters after training. © 2023, CC BY-NC-SA.

关键词： Decision making

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Multi-branch Attention Consistency Network for Facial Expression Recognition

Multi-branch Attention Consistency Network for Facial Expres...

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Annual Conference of Industrial Electronics Society

作者： Jing Li Tianyu Hu Gaoxiang Ouyang School of Computer Science and Engineering Tianjin University of Technology Tianjin China State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China

Due to the high inter-class similarity and subjective annotation of facial expressions, annotation uncertainty has become the key challenge in recent years. In this paper, we propose a Multi-branch Attention Consistency Network for facial expression recognition by combining latent label distribution learning and attention consistency to alleviate the annotation uncertainty. To be specific, we design three modules, namely multi-branch feature classification (MFC), multi-branch latent distribution learning (MLD) and multi-class attention consistency (MAC). The MFC classifies uncertain expressions through multiple auxiliary branches, which obtains attention maps and the degree of confidence for different facial categories. The MLD guides the target branch to learn latent label distributions from auxiliary branches. The MAC learns attention regions by multi-class attention consistency between auxiliary and target branches. Finally, we demonstrate the effectiveness of our proposed method by conducting experiments on three popular facial expression datasets. Experimental results show that our method achieves the state-of-the-art results of 90.16%, 89.98%, 63.12% accuracy on RAF-DB, FERPlus and AffectNet datasets, respectively.

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3D Image Generation from X-Ray Projections Using Generative Adversarial Networks

3D Image Generation from X-Ray Projections Using Generative ...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Mohammad AlShurbaji Salam Dhou Biomedical Engineering Graduate Program American University of Sharjah Sharjah UAE Department of Computer Science and Engineering American University of Sharjah Sharjah UAE

The human brain can effortlessly imagine a 3D image from only 2D images with a little expertise and imagination, but for machines, this is not a trivial task. Because of this, reconstructing 3D images from 2D ones is a hot topic and has many applications. In this paper, we propose a Generative Adversarial Network (GAN)-based approach that generates CT-like images using pairs of orthogonal X-ray projections taken from different angles. In this work, a variety of orthogonal pairs from different angles, ranging from 0°&90° to 60°&150°, were considered as input to the 3D image generation model. The effectiveness of the proposed method was assessed by measuring the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR), which resulted in values of 0.641 and 29.21, respectively. Furthermore, the model's ability to capture the respiratory motion in the input projections and reflect it in the generated images was also assessed. This work demonstrated the feasibility of generating CT-like images from X-ray projections captured from different orthogonal angles taking into consideration the respiratory motion exhibited in these projections.

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Innovations in Social Informatic: A Patent Landscape Review 7

Innovations in Social Informatic: A Patent Landscape Review

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7th Information Systems International Conference, ISICO 2023

作者： Afia, Nur Purnomo, Agung Maulana, Fairuz Iqbal Prasetyo, Yogi Tri Gumasing, Ma Janice J. Rosyidah, Elsa Persada, Satria Fadil Institute for Research and Community Services Universitas Nahdlatul Ulama Sidoarjo Sidoarjo61234 Indonesia English Language Education Universitas Nahdlatul Ulama Sidoarjo Sidoarjo61234 Indonesia Entrepreneurship Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Bina Nusantara University Jakarta11480 Indonesia School of Industrial Engineering and Engineering Management Mapúa University Manila1002 Philippines International Bachelor Program in Engineering Yuan Ze University Taoyuan32003 Taiwan

Patent intellectual property in social informatics with entrepreneurship support creates greater opportunities to improve human life. It has the potential to open enormous innovation opportunities in various fields, such as technology, health, services, and society. This research was conducted to review the patent landscape and the main trends of patents on social informatics in all countries. Researchers applied a patent landscape analysis using data on 487 patent documents over 16 years from the *** database. The study results show yearly patented development patterns related to social informatics. There is a royalty-free opportunity by using 15 patent discontinued. Patent related to social informatics was dominated by company ownership and inventors from the United States. The patent physics (G) section overwhelmed patents related to social informatics. There were 334 simple family patents on social informatics in nine jurisdictions;the United States was the highest. © 2023 The Authors. Published by Elsevier B.V.

关键词： Patents and inventions

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SEQUEL: Semi-Supervised Preference-based RL with Query Synthesis via Latent Interpolation

SEQUEL: Semi-Supervised Preference-based RL with Query Synth...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Daniel Marta Simon Holk Christian Pek Iolanda Leite Division of Robotics Perception and Learning School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Digital Futures Dept. of Cognitive Robotics TU Delft Delft The Netherlands

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Preference-based reinforcement learning (RL) poses as a recent research direction in robot learning, by allowing humans to teach robots through preferences on pairs of desired behaviours. Nonetheless, to obtain realistic robot policies, an arbitrarily large number of queries is required to be answered by humans. In this work, we approach the sample-efficiency challenge by presenting a technique which synthesizes queries, in a semi-supervised learning perspective. To achieve this, we leverage latent variational autoencoder (VAE) representations of trajectory segments (sequences of state-action pairs). Our approach manages to produce queries which are closely aligned with those labeled by humans, while avoiding excessive uncertainty according to the human preference predictions as determined by reward estimations. Additionally, by introducing variation without deviating from the original human’s intents, more robust reward function representations are achieved. We compare our approach to recent state-of-the-art preference-based RL semi-supervised learning techniques. Our experimental findings reveal that we can enhance the generalization of the estimated reward function without requiring additional human intervention. Lastly, to confirm the practical applicability of our approach, we conduct experiments involving actual human users in a simulated social navigation setting. Videos of the experiments can be found at https://***/view/rl-sequel

关键词： Interpolation Uncertainty Navigation Estimation Reinforcement learning Semisupervised learning Robot learning

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AKIP Process Automation Platform: A Framework for the Development of Process-Aware Web Applications 18

AKIP Process Automation Platform: A Framework for the Develo...

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18th International Conference on Web Information Systems and Technologies, WEBIST 2022

作者： Neto, Ulisses Telemaco Oliveira, Toacy Pillat, Raquel Alencar, Paulo Cowan, Don Melo, Glaucia David R. Cheriton School of Computer Science University of Waterloo Waterloo Canada System Engineering and Computing Program Federal University of Rio de Janeiro Rio de Janeiro Brazil

ISBN: (纸本)9789897586132

An increasing number of platforms for Business Process Automation (BPA) have been developed in recent years, including open-source and proprietary solutions. However, there are still some unsolved problems and limitations related to the adoption of these solutions, which include: vendor lock-in, limited UI/UX, limited integration, outdated technology stack, and lack of support for non-process features. The framework presented in this paper addresses these problems and limitations by providing an open-source platform to facilitate the development of process-aware information systems (PAISs) based on code generation techniques. The platform is capable of generating a functional process-aware Web application from a business process model defined in BPMN. To the best of our knowledge, there is no other software tool that generates fully functional process-aware Web applications. The presented framework has been evaluated in the academy and industry and used to develop dozens of non-profit and commercial process-aware applications. Copyright © 2022 by SCITEPRESS – science and Technology Publications, Lda. All rights reserved.

关键词： Open systems

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