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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Pragati Maheshwary Qiawen Liu Rui Mao Human-Computer Interaction Institute Carnegie Mellon University United States Department of Psychology University of Wisconsin-Madison United States College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (数字)9798331530631

ISBN: (纸本)9798331530648

Metaphorical expressions are widely used in daily communication between humans to improve the understanding of both complex and abstract concepts. However, it is unclear whether the usage of metaphorical language is helpful for enhancing the conversational engagement between humans and AI. In this work, we leverage a state-of-the-art computational metaphor processing tool to gain insights from human and ChatGPT conversations. Our quantitative analysis finds that although metaphors may enhance the quality of human-AI interactions, they do not directly lead to higher levels of conversational engagement, which is measured by the duration of the conversation. However, regression analysis shows a notable relationship between user and ChatGPT metaphor usage, suggesting that ChatGPT is adept at reflecting the linguistic style of users, especially in terms of metaphorical language. Additional topic modeling and concept mapping analyses further explored the patterns of metaphorical language across various engagement levels and topics in user and ChatGPT messages.

关键词： Analytical models Conferences Oral communication Linguistics Chatbots Regression analysis Data mining Artificial intelligence

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Bringing Instant Neural Graphics Primitives to Immersive Virtual Reality

Bringing Instant Neural Graphics Primitives to Immersive Vir...

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Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Ke Li Tim Rolff Susanne Schmidt Reinhard Bacher Simone Frintrop Wim Leemans Frank Steinicke Department of Informatics Human-Computer Interaction Group Universität Hamburg Deutsches Elektronen-Synchrotron DESY Germany Department of Informatics Computer Vision Group Universität Hamburg

Neural radiance field (NeRF), in particular, its extension by instant neural graphics primitives is a novel rendering method for view synthesis that uses real-world images to build photo-realistic immersive virtual scenes. Despite its enormous potential for virtual reality (VR) applications, there is currently little robust integration of NeRF into typical VR systems available for research and benchmarking in the VR community. In this poster paper, we present an extension to instant neural graphics primitives and bring stereoscopic, high-resolution, low-latency, 6-DoF NeRF rendering to the Unity game engine for immersive VR applications. 1 1 Link to the repository: https://***/uhhhci/immersive-ngp

关键词： Three-dimensional displays Stereo image processing Conferences Virtual reality Games User interfaces Rendering (computer graphics)

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Cruising Queer HCI on the DL: A Literature Review of LGBTQ+ People in HCI 24

Cruising Queer HCI on the DL: A Literature Review of LGBTQ+ ...

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Proceedings of the CHI Conference on human Factors in Computing Systems

作者： Jordan Taylor Ellen Simpson Anh-Ton Tran Jed R. Brubaker Sarah E Fox Haiyi Zhu Human-Computer Interaction Institute Carnegie Mellon University United States Department of Information Science University of Colorado United States Georgia Institute of Technology United States Department of Information Science University of Colorado Boulder United States

ISBN: (纸本)9798400703300

LGBTQ+ people have received increased attention in HCI research, paralleling a greater emphasis on social justice in recent years. However, there has not been a systematic review of how LGBTQ+ people are researched or discussed in HCI. In this work, we review all research mentioning LGBTQ+ people across the HCI venues of CHI, CSCW, DIS, and TOCHI. Since 2014, we find a linear growth in the number of papers substantially about LGBTQ+ people and an exponential increase in the number of mentions. Research about LGBTQ+ people tends to center experiences of being politicized, outside the norm, stigmatized, or highly vulnerable. LGBTQ+ people are typically mentioned as a marginalized group or an area of future research. We identify gaps and opportunities for (1) research about and (2) the discussion of LGBTQ+ in HCI and provide a dataset to facilitate future Queer HCI research.

关键词： LGBTQ+ people Literature Review Marginalized Communities Queer HCI Queer People

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Block Chain for Cloud Security: Enhancing Trust and Data Integrity in AI-Based Systems

Block Chain for Cloud Security: Enhancing Trust and Data Int...

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2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Swain, Prakash Chandra Mahajan, Kirti Rawat, Ruchira Bhavadharani, K. Patjoshi, Pramod Kumar Balaraman, N. School of Social Financial & Human Sciences Odisha Bhubaneswar India Bharati Vidyapeeth Deemed University Institute of Management & Entrepreneurship Development Pune411030 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India VSB Engineering College Department of Computer Science and Engineering Karur India Centurion University of Technology and Management School of Management India St. Martins Engineering College Department of Computer Science and Engineering Telangana Secunderabad India

ISBN: (纸本)9798350319200

The ontology process in research methodology denoted the multiple usage of methodologies in a single study based on various emphases. The concept of multimodal interactions from the perspective of block chain technology in artificial intelligence has successfully secured the security of the cloud infrastructures of various business organizations. With the help of block chain along with the integration of artificial intelligence, large-scale business organizations have integrated server less cloud security. The overall success has provided trustworthiness to business organizations from the core perspectives of the block chain cloud. © 2023 IEEE.

关键词： Cloud security hyper ledger fabric infrastructure Ontology Semantic technologies

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Data Augmentation for Classifying Multiple Sclerosis Severity through Inertial Measurement Unit-Based Gait Analysis

Data Augmentation for Classifying Multiple Sclerosis Severit...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Li, Jessica B. Farrell, John W. Valles, Damian Northwestern University Department of Statistics Department of Computer Science Evanston United States Texas State University Department of Health and Human Performance San Marcos United States Texas State University Ingram School of Engineering San Marcos United States

ISBN: (纸本)9798350386226

Gait impairments are highly prevalent in persons with multiple sclerosis (PwMS), contributing to difficulties in daily activities. Disability is commonly assessed using the Expanded Disability Status Scale (EDSS). However, the EDSS lacks detailed gait analysis for assessing the severity of multiple sclerosis. To address this, gait analysis tools such as inertial measurement units are commonly used to understand walking patterns in PwMS. Another concern is that collecting sufficient gait data becomes challenging due to limited participation in studies. This research acknowledges these limitations and proposes using a variational autoencoder to address this issue. Additionally, the study explores the feasibility of classification models aimed at assisting the quantification of disability of PwMS based on individuals' gait patterns. © 2024 IEEE.

关键词： Gait analysis

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Head-wise Shareable Attention for Large Language Models

Head-wise Shareable Attention for Large Language Models

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Cao, Zouying Yang, Yifei 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 Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China

ISBN: (纸本)9798891761681

Large Language Models (LLMs) suffer from huge number of parameters, which restricts their deployment on edge devices. Weight sharing is one promising solution that encourages weight reuse, effectively reducing memory usage with less performance drop. However, current weight sharing techniques primarily focus on small-scale models like BERT and employ coarse-grained sharing rules, e.g., layer-wise. This becomes limiting given the prevalence of LLMs and sharing an entire layer or block obviously diminishes the flexibility of weight sharing. In this paper, we present a perspective on head-wise shareable attention for large language models. We further propose two memory-efficient methods that share parameters across attention heads, with a specific focus on LLMs. Both of them use the same dynamic strategy to select the shared weight matrices. The first method directly reuses the pre-trained weights without retraining, denoted as DirectShare. The second method first post-trains with constraint on weight matrix similarity and then shares, denoted as PostShare. Experimental results reveal our head-wise shared models still maintain satisfactory capabilities, demonstrating the feasibility of fine-grained weight sharing applied to LLMs. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Marine Predators Algorithm with Deep Learning-Based Leukemia Cancer Classification on Medical Images

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computer Modeling in Engineering & sciences 2024年第10期141卷 893-916页

作者： Sonali Das Saroja Kumar Rout Sujit Kumar Panda Pradyumna Kumar Mohapatra Abdulaziz S.Almazyad Muhammed Basheer Jasser Guojiang Xiong Ali Wagdy Mohamed Department of Computer Science&Engineering Gandhi Institute for TechnologyBPUTBhubaneswar752054India Department of Information Technology Vardhaman College of Engineering(Autonomous)Hyderabad501218TelanganaIndia Department of Electronic&Communication Engineering Vedang Institute of TechnologyBhubaneswar752010India Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11543Saudi Arabia Department of Computing and Information Systems School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Research Centre for Human-Machine Collaboration(HUMAC) School of Engineering and TechnologySunway UniversityJalan UniversitiBandar SunwaySelangor Darul Ehsan47500Malaysia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical EngineeringGuizhou UniversityGuiyang550025China Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Applied Science Research Center Applied Science Private UniversityAmman11931Jordan

In blood or bone marrow,leukemia is a form of cancer.A person with leukemia has an expansion of white blood cells(WBCs).It primarily affects children and rarely affects *** depends on the type of leukemia and the extent to which cancer has established throughout the *** leukemia in the initial stage is vital to providing timely patient *** image-analysis-related approaches grant safer,quicker,and less costly solutions while ignoring the difficulties of these invasive *** can be simple to generalize computer vision(CV)-based and image-processing techniques and eradicate human *** researchers have implemented computer-aided diagnosticmethods andmachine learning(ML)for laboratory image analysis,hopefully overcoming the limitations of late leukemia detection and determining its *** study establishes a Marine Predators Algorithm with Deep Learning Leukemia Cancer Classification(MPADL-LCC)algorithm onMedical *** projectedMPADL-LCC system uses a bilateral filtering(BF)technique to pre-process medical *** MPADL-LCC system uses Faster SqueezeNet withMarine Predators Algorithm(MPA)as a hyperparameter optimizer for feature ***,the denoising autoencoder(DAE)methodology can be executed to accurately detect and classify leukemia *** hyperparameter tuning process using MPA helps enhance leukemia cancer classification *** results are compared with other recent approaches concerning various measurements and the MPADL-LCC algorithm exhibits the best results over other recent approaches.

关键词： Leukemia cancer medical imaging image classification deep learning marine predators algorithm

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Evaluation of Extended Reality Authoring Tool for General Aviation Weather Training 43

Evaluation of Extended Reality Authoring Tool for General Av...

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43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024

作者： Kim, Jiwon Wang, Kexin Dorneich, Michael Winer, Eliot Brown, Lori Whitehurst, Geoffrey Iowa State University Industrial and Manufacturing Systems Engineering AmesIA United States Iowa State University Human Computer Interaction AmesIA United States Iowa State University Mechanical Engineering AmesIA United States Western Michigan University Aviation Flight Science KalamazooMI United States

ISBN: (纸本)9798350349610

This paper presents the preliminary evaluation results of an extended reality (XR) authoring tool designed for aviation weather training. XR has the potential to improve educational outcomes in flight training programs. However, the complexity and time demands of existing XR authoring tools have slowed wider adoption. This work strives to address these gaps by developing and continuously refining an instructor-centered, user-friendly XR authoring tool. Seventeen flight instructors participated in developing two XR lessons and evaluated the tool's effectiveness. Results indicated that the introduction of the XR authoring tool significantly boosted the instructors' confidence in creating XR content and alleviated concerns related to time consumption. Additionally, the findings showed that the XR authoring tool is highly usable and demands a low workload, with the XR lessons produced being highly satisfactory. By employing an instructor-centered approach, the XR authoring tool enabled instructors who are not XR experts to improve students' learning experiences, focusing on aviation weather content without the burden of technical complexities. © 2024 IEEE.

关键词： Educational technology

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SIAMIXFORMER: A FULLY-TRANSFORMER SIAMESE NETWORK WITH TEMPORAL FUSION FOR ACCURATE BUILDING DETECTION AND CHANGE DETECTION IN BI-TEMPORAL REMOTE SENSING IMAGES

arXiv

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arXiv 2022年

作者： Mohammadian, Amir Ghaderi, Foad Human-Computer interaction lab Electrical and Computer Engineering Department Tarbiat Modares University Tehran Iran

Building detection and change detection using remote sensing images can help urban and rescue planning. Moreover, they can be used for building damage assessment after natural disasters. Currently, most of the existing models for building detection use only one image (pre-disaster image) to detect buildings. This is based on the idea that post-disaster images reduce the model’s performance because of presence of destroyed buildings. In this paper, we propose a siamese model, called SiamixFormer, which uses pre- and post-disaster images as input. Our model has two encoders and has a hierarchical transformer architecture. The output of each stage in both encoders is given to a temporal transformer for feature fusion in a way that query is generated from pre-disaster images and (key, value) is generated from post-disaster images. To this end, temporal features are also considered in feature fusion. Another advantage of using temporal transformers in feature fusion is that they can better maintain large receptive fields generated by transformer encoders compared with CNNs. Finally, the output of the temporal transformer is given to a simple MLP decoder at each stage. The SiamixFormer model is evaluated on xBD, and WHU datasets, for building detection and on LEVIR-CD and CDD datasets for change detection and could outperform the state-of-the-art. Copyright © 2022, The Authors. All rights reserved.

关键词： Disasters

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Audio-Driven Facial Landmark Generation in Violin Performance using 3DCNN Network with Self Attention Model 48

Audio-Driven Facial Landmark Generation in Violin Performanc...

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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Lin, Ting-Wei Liu, Chao-Lin Su, Li Taiwan International Graduate Program Social Networks and Human-Centered Computing Taiwan Academia Sinica Institute of Information Science Taipei Taiwan National Chengchi University Department of Computer Science Taipei Taiwan

ISBN: (纸本)9781728163277

In a music scenario, both auditory and visual elements are essential to achieve an outstanding performance. Recent research has focused on the generation of body movements or fingering from audio in music performance. The audio-driven face generation technique in music performance is still deficient. In this paper, we compile a violin soundtrack and facial expression dataset (VSFE) for modeling facial expressions in violin performance. To our knowledge, this is the first dataset mapping the relationship between violin performance audio and musicians' facial expressions. We then propose a 3DCNN network with self-attention and residual blocks for audio-driven facial expression generation. In the experiments, we compare our methods with three baselines on talking face generation. The codes and dataset are available on the Github (https://***/kevinlin91/icassp-music2face). © 2023 IEEE.

关键词： Music

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