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Proceedings of the 2023 ACM Designing Interactive Systems Conference

作者： Jane Hsieh Miranda Karger Lucas Zagal Haiyi Zhu Software and Societal Systems Department Carnegie Mellon University United States Barnard College United States University of Utah United States Human Computer Interaction Institute Carnegie Mellon University United States

ISBN: (纸本)9781450398930

Gig workers, and the products and services they provide, play an increasingly ubiquitous role in our daily lives. But despite growing evidence suggesting that worker well-being in gig economy platforms have become significant societal problems, few studies have investigated possible solutions. We take a stride in this direction by engaging workers, platform employees, and local regulators in a series of speed dating workshops using storyboards based on real-life situations to rapidly elicit stakeholder preferences for addressing financial, physical, and social issues related to worker well-being. Our results reveal that existing public and platformic infrastructures fall short in providing workers with resources needed to perform gigs, surfacing a need for multi-platform collaborations, technological innovations, as well as changes in regulations, labor laws, and the public’s perception of gig workers, among others. Drawing from multi-stakeholder findings, we discuss these implications for technology, policy, and service as well as avenues for collaboration.

关键词： Design Methods Workplaces

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Towards expert gaze modeling and recognition of a user's attention in realtime 24

Towards expert gaze modeling and recognition of a user's att...

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24th KES International Conference on Knowledge-Based and Intelligent Information and Engineering Systems, KES 2020

作者： Castner, Nora Geßler, Lea Geisler, David Hüttig, Fabian Kasneci, Enkelejda Human-Computer Interaction University of Tübingen Germany Institute of Computer Science University of Tübingen Germany Department of Prosthodontics University Hospital Tübingen Germany

One of the appealing areas of expertise research is devoted to measuring the effectiveness of training programs for novices. With recent progress in eye tracking, gaze-based interaction systems recognize a user's attention and can direct it accordingly. Moreover, dynamic visualization of an expert gaze model facilitates novice training by guiding the gaze to relevant areas. In addition, the system should be aware of realtime attention to remove an overlay that could occlude relevant information. We use an implementation of subtle gaze direction (SGD) and the simplified scanpath of a dentist to train naive participants in finding anomalies in dental radiographs. We were able to effectively direct user gaze to relevant image features without occluding the area when attention was recognized. Additionally, participants reported that the intervention was helpful for image inspection. The results of the model intervention show minimal improvements in anomaly detection, which is expected of naive subjects. We advocate that the system has the potential to be highly effective for advanced students and trainees with a certain foundation of conceptual knowledge. © 2020 The Authors. Published by Elsevier B.V.

关键词： Eye tracking

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Passive Monitoring of Physiological Precursors of Stress Leveraging Smartwatch Data

Passive Monitoring of Physiological Precursors of Stress Lev...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Fazeli, Shayan Levine, Lionel Beikzadeh, Mehrab Mirzasoleiman, Baharan Zadeh, Bita Peris, Tara Sarrafzadeh, Majid UCLA Computer Science Department Los Angeles United States Chapman University Department of Psychology Los Angeles United States The Jane and Terry Semel Institute for Neuroscience and Human Behavior UCLA Los Angeles United States

ISBN: (纸本)9781665468190

Developing the capability to continuously and noninvasively monitor the mental health status of individuals is a critical focus in the mHealth domain. The use of passively-generated data gathered via smart and portable electronic devices to monitor specific indicators of mental health has shown potential to serve as an effective alternative to traditional intrusive survey-based approaches to monitoring mental health remotely. In this study, we propose a remote health monitoring framework for dynamic, flexible, and scalable assessment and detection of physiological precursors of a stress response. Our method comprises a smartwatch-based system for continuous monitoring of primary physiological signals, followed by a deep neural network architecture that performs the fusion and processing of the multi-modal sensor readings. We empirically validate our system on a cohort of university-affiliated members of the military. Our findings demonstrate the effectiveness of our passive-sensing system for tracking perceived stress, the results of which can be used to obtain a better understanding of patient behavior and improve personalized treatments1. © 2022 IEEE.

关键词： Deep neural networks

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A Nonsynaptic Memory Based Neural Network for Hand-Written Digit Classification Using an Explainable Feature Extraction Method 22

A Nonsynaptic Memory Based Neural Network for Hand-Written D...

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6th International Conference on Information System and Data Mining, ICISDM 2022

作者： Faghihi, Faramarz Cai, Siqi Moustafa, Ahmed Alashwal, Hany University of Bremen Germany School of Psychology Bond University Australia Department of Human Anatomy and Physiology University of Johannesburg South Africa Department of Computer Science and Software Engineering United Arab Emirates University United Arab Emirates

ISBN: (纸本)9781450396257

Deep learning methods have been developed for handwritten digit classification. However, these methods work as black-boxes' and need large training data. In this study, an explainable feature extraction method is developed for handwritten digit classification. The features of the digit image include horizontal, vertical, and orthogonal lines as well as full or semi-circles. In our proposed method, such features are extracted using 10 neurons as computational units. Specifically, the neurons store the features through network training and store them inside the neurons in a non-synaptic memory manner. Following that, the trained neurons are used for the retrieval of information from test images to assign them to digit categories. Our method shows an accuracy of 75 % accuracy using 0.016 % of the training data and achieves a high accuracy of 86 % using one epoch of whole training data of the MNIST dataset. To the best of our knowledge, this is the first model that stores information inside a few single neurons (i.e., non-synaptic memory) instead of storing the information in synapses of connected feed-forward layers. Due to enabling single neurons to compute individually, it is expected that such a class of neural networks can be combined with synaptic memory architectures that we expect to show higher performance compared to traditional neural networks. © 2022 ACM.

关键词： Extraction

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Utilizing New Temporal Complex Neutrosophic Aczel-Alsina Aggregation Operators for Multi-Criteria Decision Making: An Application to Tourist Destination Selection

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Neutrosophic Sets and Systems 2024年 70卷 45-63页

作者： Trang, Le Mai Thong, Nguyen Tho Nhi, Nguyen Thi Lan Economics Department Thuongmai Universiy Ho Tung Mau Hanoi Cau Giay Viet Nam Faculty of Computer Science and Engineering ThuyLoi University 175 Tay Son Hanoi Dong Da Viet Nam Vietnam - Japan Institute for Human Resources Development Foreign Trade University 91 Chua Lang Hanoi Dong Da Viet Nam Center for Economics and Community Development Ngoc Ha Doi Can Hanoi Ba Dinh Viet Nam

The Temporal Complex Neutrosophic Set (TCNS) is a powerful extension of the Neutrosophic theory, and TCNS has added advantage to represents uncertain, indeterminate, inconsistent and time related factors. In the literature TCNS has been used for multi-criteria decision-making problems, especially in the time-aware systems. In order to tackle effectively decision-making problems in the TCNS environment effectively, it is crucial to make suite aggregation operators that enable decision makers to process preferences in a nuanced manner. In this paper, we introduce novel TCNS aggregation operators based on Aczel and Alsina t-norm and t-conorm using Aczel-Alsina operations, and we propose a multi-criteria decision-making model that utilizes those operators for TCNS-based multi criteria decision making problems (MCDMs). Theorytically, the soundness and completeness of new aggregation operators are proved, and to demonstrate the practicality and effectiveness of our model a real world case study for tourist destination selection in Vietnam is completed along with analysis. The result proved the application potential of the proposal to solve the time-related MCDMs in practical, such as creating a MCDM model for tourist destination selection. © (2024), (Neutrosophic Sets and Systems). All rights reserved.

关键词： Aczel-Alsina operators Complex Neutrosophic Set MCDM Temporal Complex Neutrosophic Set

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A Modified Wavelet Threshold Approach for Reducing Various Noise with Statistical Results

A Modified Wavelet Threshold Approach for Reducing Various N...

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2022 International Conference on Artificial Intelligence of Things, ICAIoT 2022

作者： Hamed, Saad Hussein Abed Alhafidh, Omar Khalid Salih Younis, Younis Samir Yahia, Warif B. Meften, Saleem Ali, Ali Hasan Al-Qadisiyah University College of Computer Science and Information Technology Al-Diwaniyah Iraq University of Mosul College of Education for Human Sciences Mosul Iraq Ministry of Education Nineveh Education Directorate Nineveh Iraq Hodeidah University Department of Computer Science and Engineering Al-Hudaydah Yemen University of Basrah College of Education for Pure Sciences Basrah Iraq

ISBN: (纸本)9798350396768

Different types of noise play a role in reducing the details of the images and blur the features which are important in many purposes. Physical and medical images are fractal in nature and especially the ones that are taken from experiments related to a medical field. This work investigates several noise reduction methods and reducing the fractional Brownian motion noise in physical and medical images using a modified wavelet threshold approach. The performance of this approach is analyzed and compared with some other approaches by using PSNR (Peak Signal to Noise Ratio), MSE (Mean Square Error), and other criteria for the validity of the method. © 2022 IEEE.

关键词： Mean square error

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DECKER: Double Check with Heterogeneous Knowledge for Commonsense Fact Verification

arXiv

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arXiv 2023年

作者： Zou, Anni Zhang, Zhuosheng Zhao, Hai 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

Commonsense fact verification, as a challenging branch of commonsense question-answering (QA), aims to verify through facts whether a given commonsense claim is correct or not. Answering commonsense questions necessitates a combination of knowledge from various levels. However, existing studies primarily rest on grasping either unstructured evidence or potential reasoning paths from structured knowledge bases, yet failing to exploit the benefits of heterogeneous knowledge simultaneously. In light of this, we propose DECKER, a commonsense fact verification model that is capable of bridging heterogeneous knowledge by uncovering latent relationships between structured and unstructured knowledge. Experimental results on two commonsense fact verification benchmark datasets, CSQA2.0 and CREAK demonstrate the effectiveness of our DECKER and further analysis verifies its capability to seize more precious information through reasoning. The official implementation of DECKER is available at https://***/Anni-Zou/Decker. © 2023, CC BY.

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Global and Transdisciplinary Perspectives on Dark Patterns and Deceptive Design Practice

Global and Transdisciplinary Perspectives on Dark Patterns a...

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2025 CHI Conference on human Factors in Computing Systems, CHI EA 2025

作者： Seaborn, Katie Gray, Colin M. Gunawan, Johanna Mildner, Thomas Schäfer, René Chamorro, Lorena Sanchez Nakamura, Satoshi Department of Industrial Engineering and Economics Institute of Science Tokyo Tokyo Japan UXP2 Lab. Indiana University Bloomington IN United States Law and Tech Lab. Maastricht University Maastricht Netherlands University of Bremen Bremen Germany RWTH Aachen University Aachen Germany Human-Computer Interaction Research Group University of Luxembourg Esch-Sur-Alzette Luxembourg Meiji University School of Interdisciplinary Mathematical Sciences Nakano Tokyo Japan

ISBN: (纸本)9798400713958

Dark patterns and deceptive designs (DPs) refer to user interfaces (UIs) that trick people into interactions that benefit the service providers. Today, academic research, legal action, and media coverage has raised awareness among a diversity of stakeholders worldwide. Yet, the lens has focused on Western and English contexts. We propose a Special Interest Group (SIG) that centres on cross-cultural and interdisciplinary engagement. The organizing team, who hail from a plurality of nations and disciplines, will spark discussion by sharing their knowledge—findings, frameworks, methods, and tools—and culturally-sensitive perspectives on deception in modern digital products and services. Attendees will participate in a small group drawing activity, whereby culturally-specific DPs and disciplinary perspectives can be surfaced and communicated without reliance on a specific language or cultural frame. This SIG is expected to draw in a diversity of designers, researchers, security experts, and legal scholars concerned about ethical design practice. © 2025 Copyright held by the owner/author(s).

关键词： Dark Patterns Deceptive Design Ethics Manipulative User Interfaces Regulation

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Somatic Mutations and Alzheimer's Disease

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JOURNAL OF ALZHEIMERS DISEASE 2022年第2期90卷 475-493页

作者： [Anonymous] [a] Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong China [b] Department of Computer Science and Technology University of Cambridge UK [c] The Elton Laboratory for Molecular Neuroendocrinology Department of Human Molecular Genetics and Biochemistry Sackler Faculty of Medicine Adams Super Center for Brain Studies and Sagol School of Neuroscience Tel Aviv University Tel Aviv Israel

Alzheimer's disease (AD) represents a global health challenge, with an estimated 55 million people suffering from the non-curable disease across the world. While amyloid-beta plaques and tau neurofibrillary tangles in the brain define AD proteinopathy, it has become evident that diverse coding and non-coding regions of the genome may significantly contribute to AD neurodegeneration. The diversity of factors associated with AD pathogenesis, coupled with age-associated damage, suggests that a series of triggering events may be required to initiate AD. Since somatic mutations accumulate with aging, and aging is a major risk factor for AD, there is a great potential for somatic mutational events to drive disease. Indeed, recent data from the Gozes team/laboratories as well as other leading laboratories correlated the accumulation of somatic brain mutations with the progression of tauopathy. In this review, we lay the current perspectives on the principal genetic factors associated with AD and the potential causes, highlighting the contribution of somatic mutations to the pathogenesis of late onset Alzheimer's disease. The roles that artificial intelligence and big data can play in accelerating the progress of causal somatic mutation markers/biomarkers identification, and the associated drug discovery/repurposing, have been highlighted for future AD and other neurodegenerations, with the aim to bring hope for the vulnerable aging population.

关键词： Alzheimer's disease amyloid-beta peptide artificial intelligence big data late onset Alzheimer's disease somatic mutations tau

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EM Pre-training for Multi-party Dialogue Response Generation

arXiv

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arXiv 2023年

作者： Li, Yiyang Zhao, Hai 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 response generation requires an agent to generate a response according to the current dialogue history, in terms of which two-party dialogues have been well studied, but leaving a great gap for multi-party dialogues at the same time. Different from two-party dialogues where each response is a direct reply to its previous utterance, the addressee of a response utterance should be specified before it is generated in the multi-party scenario. Thanks to the huge amount of two-party conversational data, various pre-trained language models for two-party dialogue response generation have been proposed. However, due to the lack of annotated addressee labels in multiparty dialogue datasets, it is hard to use them to pre-train a response generation model for multi-party dialogues. To tackle this obstacle, we propose an Expectation-Maximization (EM) approach that iteratively performs the expectation steps to generate addressee labels, and the maximization steps to optimize a response generation model. Theoretical analyses and extensive experiments have justified the feasibility and effectiveness of our proposed method. The official implementation of this paper is available at https://***/EricLee8/MPDRG. © 2023, CC BY.

关键词： Iterative methods

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