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International Joint Conference on Neural Networks (IJCNN)

作者： Yan-Di Huang Guang-Yu Zhang Dong Huang Chang-Dong Wang Yang Liu Enbo Huang College of Mathematics and Informatics South China Agricultural University Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangdong Provincial Key Laboratory of Intellectual Property and Big Data Guangzhou China School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangxi Key Lab of Human-Machine Interaction and Intelligent Decision Nanning Normal University Nanning China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Contrastive clustering has recently been an emerging topic in deep unsupervised learning. Nevertheless, the previous works mostly adopt the stochastic data augmentations, which easily leads to the semantic drift problem by limited transformations. Moreover, these approaches ignore the data distribution information when generating the positive and negative pair-wise samples. In light of this, this paper proposes a simple yet effective unsupervised clustering network termed Confidence-oriented Contrastive Graph Clustering (CoCGC). Particularly, we design an end-to-end network paradigm with un-shared weights, among which a hybrid graph filter is utilized to generate two views of reliable augmentations. Guided by the non-dominated sorting theory, we further construct a confidence-oriented sample set from the latent data distribution perspective. By considering the local density and cluster distribution of the embedding representations, the discriminative sample pairs can be derived from the confidence-oriented sets in a two-view contrastive manner. Finally, a cross-view neighbor contrastive loss is devised for better exploiting the self-supervised network signals. Extensive experimental results on five benchmark datasets demonstrate the effectiveness of our method against the existing state-of-the-art deep graph clustering methods.

关键词： Network topology Semantics Neural networks Reliability theory Reliability engineering Information filters Filtering theory

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Lovely Spam! Wonderful Spam!

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Science 2013年第6137期340卷 1171页

作者： Jennifer Golbeck The reviewer is at the Human-Computer Interaction Lab University of Maryland College Park MD 20742 USA.

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Queer in HCI: Strengthening the Community of LGBTQIA+ Researchers and Research

Queer in HCI: Strengthening the Community of LGBTQIA+ Resear...

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2021 CHI Conference on human Factors in Computing Systems: Making Waves, Combining Strengths, CHI EA 2021

作者： Devito, Michael Ann Lustig, Caitlin Simpson, Ellen Allison, Kimberley Chuanromanee, Tya Spiel, Katta Ko, Amy Rode, Jennifer Dym, Brianna Muller, Michael Klaus Scheuerman, Morgan Marie Walker, Ashley Brubaker, Jed Ahmed, Alex Department of Information Science University of Colorado United States Human Centered Design and Engineering University of Washington United States School of Information Studies Syracuse University United States Department of Psychology Macquarie University Australia University of Notre Dame United States Human-Computer Interaction Group Tu Wien Austria The Information School University of Washington United States Knowledge Lab University College London United Kingdom Department of Information Science University of Colorado Boulder United States Ai Interactions Ibm Research United States Information Science University of Colorado Boulder United States Pitch Lab Northwestern University United States University of Colorado Boulder United States Carnegie Mellon University United States

ISBN: (纸本)9781450380959

As Queer human-computer interaction (HCI) becomes an established part of the larger field, both in terms of research on and with queer populations and in terms of employing queering theories and methods, the role of queer researchers has become a timely topic of discussion. However, these discussions have largely centered around member-researcher status and positionality when working with queer populations. Based on insights gathered at multiple ACM events over the past two years, we identified two pressing issues: (1) we need to better support queer people doing HCI research not specific to queer populations, and (2) we need to identify how to best support member-researchers in leading Queer HCI while including collaborators beyond the queer community. This Special Interest Group (SIG) aims to directly address these challenges by convening a broad community of queer researchers and allies, working not only on explicitly-queer topics but across a broad range of HCI topics. © 2021 Owner/Author.

关键词： Computation theory

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In-Silico Ear Model Based on Episcopic Images for Percutaneous Auricular Vagus Nerve Stimulation

In-Silico Ear Model Based on Episcopic Images for Percutaneo...

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EMF-Med World Conference on Biomedical Applications of Electromagnetic Fields

作者： Babak Dabiri Razlighi Stefan Kampusch Stefan H. Geyer Van Hoang Le Florian Thurk Simon Brenner Jozsef Constantin Szeles Wolfgang J. Weninger Eugenijus Kaniusas Institute of Electrodynamics Microwave and Circuit Engineering Vienna University of Technology Division of Anatomy MIC Medical University of Vienna Institute of Visual Computing & Human-Centered Technology Computer Vision Lab Vienna University of Technology Department for Vascular Surgery University Clinic for Surgery Medical University of Vienna

ISBN: (纸本)9781538659229

Auricular branch of vagus nerve stimulation is continuously evolving as a minimal-invasive treatment option in patients suffering from chronic pain. For optimization of stimulation mechanisms, we conducted insilico modelling of the electrode-tissue-nerve interface by a thorough literature review of the anatomical structures combined with nerve and vessel structures from episcopic images of a human ear. Four nerves and two vessels in the ear were implemented in a 3D CAD model for future optimization of stimulation regions and patterns. Mean diameters for nerves were 30 μm, and for vessels 100 μm. Mean distance between nerves and vessels was about 100 μm.

关键词： Auricular Vagus Nerve Stimulation In-Silico Modelling Episcopic Imaging

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Optimization Method for an Information interaction Interface from Error-Cognition to Information Feature Mapping

Research Square

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Research Square 2020年

作者： Wu, Xiaoli Caldwell, Sabrina School of Design Art & Medial Nanjing University of Science & Technology Nanjing210094 China Research School of Computer Science The Australian National University ACT Australia College of Mechanical and Electrical Engineering Hohai University Changzhou China Lab of Human Factors and Information System Interaction & Design Hohai University Changzhou China

Background- With the rapid development of digital and intelligent information systems, display of visual information in interfaces has become an important challenge in the field of human-computer interaction. Objective- We propose a method for the optimization of information interaction interfaces from error-cognition through the mapping of information characteristics. Method- A mapping method of matrix description is adopted to analyze the association properties between error-cognition sets and design information sets. Results- Based on the mapping relationship between the domains of error-cognition and design information, a cross-correlational analysis is carried out between error-cognition and design information. Conclusion- We obtain the relationship matrix between the error-cognition of correlation between design information and the degree of importance among design information. Application- Taking the task interface of a warfare navigation display as an example, error factors and the features of design information are extracted. Based on the results, we also propose an optimization design scheme for the monitoring task interface. © 2020, CC BY.

关键词： Errors

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IoT based Smart Water Quality Prediction for Biofloc Aquaculture

arXiv

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arXiv 2022年

作者： Mamunur Rashid, Md. Nayan, Al-Akhir Obaidur Rahman, Md. Simi, Sabrina Afrin Saha, Joyeta Kibria, Muhammad Golam Department of CSE University of Liberal Arts Bangladesh Dhaka Bangladesh Department of CSE European University of Bangladesh Dhaka Bangladesh Dept. of Human Computer Interaction University of Siegen Siegen Germany Department of ECE North South University Dhaka Bangladesh Department of CSE IoT Lab University of Liberal Arts Bangladesh Dhaka Bangladesh

Traditional fish farming faces several challenges, including water pollution, temperature imbalance, feed, space, cost, etc. Biofloc technology in aquaculture transforms the manual into an advanced system that allows the reuse of unused feed by converting them into microbial protein. The objective of the research is to propose an IoT-based solution to aquaculture that increases efficiency and productivity. The article presented a system that collects data using sensors, analyzes them using a machine learning model, generates decisions with the help of Artificial Intelligence (AI), and sends notifications to the user. The proposed system has been implemented and tested to validate and achieve a satisfactory result. © 2022, CC BY.

关键词： Aquaculture

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Incremental Feature Selection for Dynamic Incomplete Data Using Sub-Tolerance Relations

SSRN

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SSRN 2023年

作者： Zhao, Jie Ling, Yun See-To, Wing-Kuen Eric Huang, Faliang Wang, Jiahai School of Management Guangdong University of Technology Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

Feature selection is an important problem in Rough Set and is widely used in data analysis. The extended theory of tolerance relations can effectively deal with the realistic problem of incomplete data. However, the space and time complexity of computing the positive region using TRS increases dramatically. Simultaneously, redundant features are allowed to enter the feature subset, resulting in poor classification accuracy, because the positive region-based significance measure neglects uncertain information. To overcome these drawbacks, this paper introduces a subrelation-based tolerance class (STC) approach. STC transforms the TRS positive region calculation into a two-stage certainty measurement of objects, and thus divides the entire universe into two regions of certainty and uncertainty at each stage, by decomposing the tolerance relation into two subrelations. The computational space is reduced by progressively filtering out of the regions of certainty. Using STC, a new incremental mechanism is proposed. Furthermore, we propose a new significance measure using both certain and uncertain information, which effectively reduces the number of redundant features and subsequently improves computational efficiency and classification accuracy. Experiments on real data sets and comparisons with existing algorithms have been conducted to verify the superiority of the method. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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Data recording in primary care field studies: Patient records enhancement project

Data recording in primary care field studies: Patient record...

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2011 5th International Conference on Pervasive Computing Technologies for Healthcare and Workshops, PervasiveHealth 2011

作者： Axelrod, Lesley Henwood, Flis Fitzpatrick, Geraldine Cassell, Jackie Smith, Helen Nicholson, Amanda Rait, Greta Human Centred Technology Lab School of Informatics University of Sussex Falmer Sussex BN1 9QH United Kingdom Social Informatics Research Unit School of Applied Social Science University of Brighton Falmer Brighton Sussex BN1 9PH United Kingdom Human-Computer Interaction Group Technical University of Vienna Karlsplatz 13 1040 Vienna Austria Brighton and Sussex Medical School Mayfield House Falmer Brighton Sussex BN1 9PH United Kingdom Department of Primary Care and Population Health University College London Medical School London NW3 2NF United Kingdom

ISBN: (纸本)9781936968152

This position paper describes the human-computer interaction (HCI) field studies component of the multidisciplinary Patient Records Enhancement Project (PREP). PREP seeks to understand variability of data found in primary care electronic records, in particular the balance between coded data and doctor's 'free text' notes. HCI fieldwork will establish variables that affect recording practices. In field studies we observe and record data recording practices in general practice (GP) surgeries, interview staff, video consultations with real patients and video consultations with standardized patients (played by medical actors). By standardizing patients we can compare the impact of other variables: different doctors, in different surgeries, using different e-health systems. Our early findings suggest that variability is due to a complex web of reasons, driven by personal, contextual and organizational processes. Findings from thematic analysis will result in design implications for studies by epidemiologists and public health researchers, design of NHS training and work processes, and design of electronic health record interfaces. © 2011 ICST.

关键词： human computer interaction

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Exploring Uni-manual Around Ear Off-Device Gestures for Earables

arXiv

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arXiv 2024年

作者： Shimon, Shaikh Shawon Arefin Neshati, Ali Sun, Junwei Xu, Qiang Zhao, Jian School of Computer Science University of Waterloo 200 University Ave W WaterlooON Canada Ontario Tech University 2000 Simcoe St N OshawaONL1G 0C5 Canada Huawei Human-Machine Interaction Lab 19 Allstate Pkwy MarkhamON Canada School of Computer Science University of Waterloo 200 University Ave W WaterlooONN2L 3G1 Canada

Small form factor limits physical input space in earable (i.e., ear-mounted wearable) devices. Off-device earable inputs in alternate mid-air and on-skin around-ear interaction spaces using uni-manual gestures can address this input space limitation. Segmenting these alternate interaction spaces to create multiple gesture regions for reusing off-device gestures can expand earable input vocabulary by a large margin. Although prior earable interaction research has explored off-device gesture preferences and recognition techniques in such interaction spaces, supporting gesture reuse over multiple gesture regions needs further exploration. We collected and analyzed 7560 uni-manual gesture motion data from 18 participants to explore earable gesture reuse by segmentation of on-skin and mid-air spaces around the ear. Our results show that gesture performance degrades significantly beyond 3 mid-air and 5 on-skin around-ear gesture regions for different uni-manual gesture classes (e.g., swipe, pinch, tap). We also present qualitative findings on most and least preferred regions (and associated boundaries) by end-users for different uni-manual gesture shapes across both interaction spaces for earable devices. Our results complement earlier elicitation studies and interaction technologies for earables to help expand the gestural input vocabulary and potentially drive future commercialization of such devices. © 2024, CC BY.

关键词： Digital storage

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Consistency Approximation: Incremental Feature Selection Based on Fuzzy Rough Set Theory

SSRN

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SSRN 2023年

作者： Zhao, Jie Wu, Daiyang Wu, JiaXin Ye, Wenhao Huang, Faliang Wang, Jiahai See-To, Wing-Kuen Eric Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

Fuzzy Rough Set Theory (FRST)-based feature selection has been widely used as a preprocessing step to handle dynamic and large datasets. However, large-scale or high-dimensional datasets remain intractable for FRST-based feature selection approaches due to high space complexity and unsatisfactory classification performance. To overcome these challenges, we propose a Consistency Approximation (CA)-based framework for incremental feature selection. By exploring CA, we introduce a novel significance measure and a tri-accelerator. The CA-based significance measure provides a mechanism for each sample in the universe to keep members with different class labels within its fuzzy neighbourhood as far as possible, while keeping members with the same label as close as possible. Furthermore, our tri-accelerator reduces the search space and decreases the computational space with a theoretical lower bound. The experimental results demonstrate the superiority of our proposed algorithm compared to state-of-the-art methods on efficiency and classification accuracy, especially for large-scale and high-dimensional datasets. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

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