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International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Ibrahim A. Tahmid Lee Lisle Kylie Davidson Kirsten Whitley Chris North Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech US Department of Defense

Eye gaze patterns vary based on reading purpose and complexity, and can provide insights into a reader’s perception of the content. We hypothesize that during a complex sensemaking task with many text-based documents, we will be able to use eye-tracking data to predict the importance of documents and words, which could be the basis for intelligent suggestions made by the system to an analyst. We introduce a novel eye-gaze metric called ‘GazeScore’ that predicts an analyst’s perception of the relevance of each document and word when they perform a sensemaking task. We conducted a user study to assess the effectiveness of this metric and found strong evidence that documents and words with high GazeScores are perceived as more relevant, while those with low GazeScores were considered less relevant. We explore potential real-time applications of this metric to facilitate immersive sensemaking tasks by offering relevant suggestions.

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Evaluation Criteria and Assessments for Effective User Experiences, Web Accessibility, and interaction for Visually Impaired Users 8th

Evaluation Criteria and Assessments for Effective User Exper...

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8th International Conference on Computational science and Technology, ICCST 2021

作者： Jantan, Azrul Hazri Yazid, Mariam Azwa Husain, Wan Nur Liyana Wan Human-Computer Interaction Research Lab Faculty of Computer Science and Information Technology Universiti Putra Malaysia Selangor43400 Malaysia

ISBN: (纸本)9789811685149

To date, the advancement in web and internet technologies have changed the way on how people interact with computer software/application. Information is much easier to accessed, processes can be performed faster, and interaction can be controlled fully by users. However, the complexity of the web accessibility, application interaction, and components of user experience were also increasing. Thus, to fulfil the web usability requirements and its comprehensiveness, web experts’ verification and validation are required to support the overall design modelling including its internal strength of the domain. This paper will elaborate and discuss the main criteria included in the verification and validation processes towards visually impaired users. We built our evaluation (usability) criteria based on previous research and represent them as questionnaire for qualitative assessment of a web application. The main objective is to propose the best evaluation criteria for a web evaluation and assessment usability to support the design modelling and interaction effectiveness among visually impaired users. It will include better technology understanding on both design and evaluation criteria of the whole domain structure including their interactivity that reflect the web accessibility, application interaction, and user experience components. The proposed evaluation criteria will be focusing on three section, called interaction characteristics, user experience components, and the outcome of the final judgement. We envisage effective and comprehensive evaluation criteria that could be integrated and provide positive feedback from the experts’ review and their findings. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Usability engineering

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MIGRATE: Cross-Lingual Adaptation of Domain-Specific LLMs through Code-Switching and Embedding Transfer 31

MIGRATE: Cross-Lingual Adaptation of Domain-Specific LLMs th...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Hong, Seongtae Lee, Seungyoon Moon, Hyeonseok Lim, Heuiseok Department of Computer Science and Engineering Korea University Korea Republic of Human-inspired AI Research Korea Republic of

ISBN: (纸本)9798891761964

Large Language Models (LLMs) have rapidly advanced, with domain-specific expert models emerging to handle specialized tasks across various fields. However, the predominant focus on English-centric models demands extensive data, making it challenging to develop comparable models for middle and low-resource languages. To address this limitation, we introduce MIGRATE, a novel method that leverages open-source static embedding models and up to 3 million tokens of code-switching data to facilitate the seamless transfer of embeddings to target languages. MIGRATE enables effective cross-lingual adaptation without requiring large-scale domain-specific corpora in the target language, promoting the accessibility of expert LLMs to a diverse range of linguistic communities. Our experimental results demonstrate that MIGRATE significantly enhances model performance in target languages, outperforming baseline and existing cross-lingual transfer methods. This approach provides a practical and efficient solution for extending the capabilities of domain-specific expert models. © 2025 Association for Computational Linguistics.

关键词： Embeddings

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Opportunities for Serious Game Technologies to Engage Children with Autism in a Pakistani Sociocultural and Institutional Context: An investigation of the design space for serious game technologies to enhance engagement of children with autism and to facilitate external support provided 34

Opportunities for Serious Game Technologies to Engage Childr...

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34th Australian computer-human interaction Conference: Connected Creativity, OzCHI 2022

作者： Muneeb, Sara Sitbon, Laurianne Ahmad, Faizan Research Center for Human Computer Interaction Comsats University Islamabad Lahore Campus Pakistan Department of Computer Science Queensland University of Technology Australia Department of Computer Science School of Technologies Cardiff Metropolitan University United Kingdom

ISBN: (纸本)9798400700248

Usability and design of serious game technologies have been widely investigated as academic interventions tools for children with autism. However, besides the technology, the sociocultural and institutional context also impacts the gameplay activities of children with autism as well as the external support provided by instructors. Therefore, this paper investigates i) the impact of sociocultural and institutional context over the engagement of children with autism with serious game technologies, ii) the types of external support children with autism are offered or require during gameplay session, and iii) the type of rewards and encouragers that can act as unbiased support to increase the engagement of the children with autism during gameplay. This paper investigates these factors with an inductive analysis of educational gameplay activities of 23 children with autism in Lahore, Pakistan. Our study found that sociocultural and institutional context has substantial impact over the gameplay activities of children with autism. Children required both physical and verbal support during the gameplay activities, and institutional context influences the behavior of instructors as well. Finally, dynamic animations, rewards and encouragers can keep the children engaged for longer in the gameplay. © 2022 ACM.

关键词： Serious games

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Federated Learning and Convolutional Neural Networks for Intrusion Detection Systems 6

Federated Learning and Convolutional Neural Networks for Int...

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6th International Conference on Pattern Analysis and Intelligent Systems, PAIS 2024

作者： Abid, Malika Benkaddour, Mohammed Kamel Benouis, Mohamed Computer Science Department Ouargla Algeria University of Augsburg Human-Centered Artificial Intelligence Augsburg Germany

ISBN: (纸本)9798350350265

Recently, there has been a growing focus on network traffic threats due to the proliferation of mobile devices and the expanding Internet of Things (IoT). The centralised anomaly detection approach implies sharing the user's data with a cloud server, which mostly exposes the user to the data breach issue. Given the above issues, this paper proposes a federated learning intrusion detection system (FedIDS) to improve the performance of intrusion detection while protecting user privacy. Motivated by recent exploits into deep learning for intrusion detection, we propose a convolutional neural network (CNN) model to learn and train the record data collected from independent and non-independent identically distributed (Non-IID, IID)data. This work conducts experiments on the NSL-KDD Data set for network intrusion detection. Experimental results show that FedIDS has achieved a competitive advantage over state-of-the-art centralised machine learning methods. © 2024 IEEE.

关键词： Intrusion detection

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Spaces to Think: A Comparison of Small, Large, and Immersive Displays for the Sensemaking Process

Spaces to Think: A Comparison of Small, Large, and Immersive...

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International Symposium on Mixed and Augmented Reality (ISMAR)

作者： Lee Lisle Kylie Davidson Leonardo Pavanatto Ibrahim A. Tahmid Chris North Doug A. Bowman Department of Computer Science Center for Human-Computer Interaction Virginia Tech Department of Computer Science Sanghani Center Virginia Tech

Analysts need to process large amounts of data in order to extract concepts, themes, and plans of action based upon their findings. Different display technologies offer varying levels of space and interaction methods that change the way users can process data using them. In a comparative study, we investigated how the use of single traditional monitor, a large, high-resolution two-dimensional monitor, and immersive three-dimensional space using the Immersive Space to Think approach impact the sensemaking process. We found that user satisfaction grows and frustration decreases as available space increases. We observed specific strategies users employ in the various conditions to assist with the processing of datasets. We also found an increased usage of spatial memory as space increased, which increases performance in artifact position recall tasks. In future systems supporting sensemaking, we recommend using display technologies that provide users with large amounts of space to organize information and analysis artifacts.

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How do we counter dangerous speech in Italy? 10

How do we counter dangerous speech in Italy?

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10th Italian Conference on Computational Linguistics, CLiC-it 2024

作者： Tonini, Vittoria Frenda, Simona Stranisci, Marco Antonio Patti, Viviana Computer Science Department University of Turin Torino Italy Aequa-Tech Torino Italy Interaction Lab Heriot-Watt University Edinburgh United Kingdom

The phenomenon of online dangerous speech is a growing challenge and various organisations try to prevent its spread answering promptly to hateful messages online. In this context, we propose a new dataset of activists' and users' comments on Facebook reacting to specific news headlines: AmnestyCounterHS. Taking into account the literature on counterspeech, we defined a new schema of annotation and applied it to our dataset, in order to examine the most used counter-narrative strategies in Italy. This research aims to support the future development of automatic counterspeech generation. This paper presents also a comparative analysis of our dataset with other two datasets in Italian (Counter-TWIT and multilingual CONAN) containing dangerous speech and counter narratives. Through this analysis, we will understand how the environment (artificial vs. ecological) and the topics of discussions online influence the nature of counter narratives. Our findings highlight the predominance of negative sentiment and emotions, the varying presence of stereotypes, and the strategic differences in counter narratives across datasets. © 2024 CEUR-WS. All rights reserved.

关键词： Abusive language Counter narrative Italian language Linguistic analysis

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Generating a General Culturing Microorganism Pattern Using a Delta Parallel Robot and Cam-in-Hand Calibration Method 11

Generating a General Culturing Microorganism Pattern Using a...

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11th RSI International Conference on Robotics and Mechatronics, ICRoM 2023

作者： Pasiar, Navid Mohammadi, Arvin Ahangar, Sajjad Khomami, Navid Asadi Masouleh, Mehdi Tale University of Tehran Human and Robot Interaction Lab. Department of Mechanical Eng. Tehran Iran University of Tehran Human and Robot Interaction Lab. Tehran Iran University of Tehran Human and Robot Interaction Lab. School of Electrical and Computer Eng. Tehran Iran

ISBN: (纸本)9798350308105

Robots can play a vital role in laboratory tasks, especially in culturing microorganisms. Currently, many of these operations are performed manually, which leads to biased and irreproducible results. This paper explores a calibration method for minimizing errors of a performing two certain laboratory tasks of swabbing and pipetting. This leads to enhanced accuracy and productivity. This research intends to culture microorganisms in a petri-dish in a specific pattern, measuring the coordinates of each object in the output image so the object location in the real world can be calculated. This research investigates the Cam-in-Hand method for a Delta Parallel Robot, meaning that the camera is mounted on the End-Effector. In this approach, a method of translating image coordinates into real-world coordinates is introduced. It is essential to provide an additional coordinate (in this case $z -$value) before the conversion, given that the camera outputs a 2D image. A vector-based image, in SVG format, as input to the algorithm generates a set of coordinates which determines the main points for swabbing or pipetting operation. Using this data, an array of coordinates is linearly interpolated in 3D space for the swabbing operation. Conversely, the trajectory generated for pipetting uses the 4-5-6-7 interpolating polynomial. The robot then adheres to following the interpolated array of coordinates as a function of time, using a PID controller. The pipetting device is built with a 3D printer using PLA materials. The calibration is done in different heights. The Camin-Hand method leads to ±1 cm precision as a result. Minimum, maximum, and mean errors are 0.092,1.61 and 0.76 respectively for the central point. © 2023 IEEE.

关键词： Calibration

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A fast high throughput plant phenotyping system using YOLO and Chan-Vese segmentation

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Soft Computing 2024年第20期28卷 12323-12336页

作者： Jain, S. Ramesh, Dharavath Damodar Reddy, E. Rathod, Santosha Ondrasek, Gabrijel Jharkhand Dhanbad826004 India Department of Computer Science and Engineering National Institute of Technology Goa Farmagudi403401 India ICAR—Indian Institute of Rice Research Telangana Hyderabad500030 India Faculty of Agriculture University of Zagreb Zagreb10000 Croatia Department of Computer Science University of Economics and Human Sciences Warsaw Poland

Understanding plant traits is essential for decoding the behavior of various genomes and their reactions to environmental factors, paving the way for efficient and sustainable agricultural practices. Image-based plant phenotyping has become increasingly popular in modern agricultural research, effectively analyzing large-scale plant data. This study introduces a new high-throughput plant phenotyping system designed to examine plant growth patterns using segmentation analysis. This system consists of two main components: (i) A plant detector module that identifies individual plants within a high-throughput imaging setup, utilizing the Tiny-YOLOv4 (You Only Look Once) architecture. (ii) A segmentation module that accurately outlines the identified plants using the Chan-Vese segmentation algorithm. We tested our approach using top-view RGB tray images of the ‘Arabidopsis Thaliana’ plant species. The plant detector module achieved an impressive localization accuracy of 96.4% and an average Intersection over Union (IoU) of 77.42%. Additionally, the segmentation module demonstrated strong performance with dice and Jaccard scores of 0.95 and 0.91, respectively. These results highlight the system’s capability to define plant boundaries accurately. Our findings affirm the effectiveness of our high-throughput plant phenotyping system and underscore the importance of employing advanced computer vision techniques for precise plant trait analysis. These technological advancements promise to boost agricultural productivity, advance genetic research, and promote environmental sustainability in plant biology and agriculture. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Agriculture

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Demand Forecasting in Transportation: A Graph Attention Networks for Predicting Bus Ridership 27

Demand Forecasting in Transportation: A Graph Attention Netw...

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27th International Conference on Advanced Communications Technology, ICACT 2025

作者： Munkhbayar, Tsetsentsengel Dashdorj, Zolzaya Jargalsaikhan, Zoljargal Tae-Koo, Kang Altangerel, Erdenebaatar Department of Computer Science Mongolian University of Science and Technology Ulaanbaatar Mongolia Department of Human Intelligence and Robot Engineering Sangmyung University Seoul Korea Republic of

ISBN: (纸本)9791188428137

Demand prediction in transportation systems plays a critical role in optimizing resources and improving service efficiency. This study explores demand prediction for Ulaanbaatar's public transportation network using Graph Attention Networks (GATs), Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs). GATs effectively capture spatial relationships, achieving the best performance while GANs struggle with stability and convergence issues. The findings emphasize the potential of using graph-based methods that incorporate key stations in the analysis of public transportation networks for predicting transit demand. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.

关键词： Generative adversarial networks

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