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i-com 2024年第1期23卷 1-5页

作者： Richter, Alexander Koch, Michael Prilla, Michael Computer Science Department Universität der Bundeswehr München München Germany Wellington School of Business and Government Victoria University of Wellington Wellington New Zealand Interactive Systems Department for Human Centered Computing and Cognitive Science Faculty for Computer Science University of Duisburg-Essen Duisburg Germany

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Data Bias Recognition in Museum Settings: Framework Development and Contributing Factors 25

Data Bias Recognition in Museum Settings: Framework Developm...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Quinto Lima, Stella Buraglia, Gabriela Kam-Kwai, Wong Roberts, Jessica School of Interactive Computing Georgia Institute of Technology Atlanta GA United States Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Hong Kong Interactive Computing Georgia Institute of Technology Atlanta GA United States

ISBN: (纸本)9798400713941

Critical thinking skills are increasingly important for comprehending our data-rich society. While museums provide data for discussion, visitors may not naturally question data in such displays due to the inherent authority of a museum. To investigate what factors can help visitors recognize bias in data, we interviewed visitors after they interacted with an augmented reality data map in an interactive data exhibition. Here, we present a qualitative analysis of fifteen semi-structured interviews with visitors who engaged with mapped data from the citizen science platform iNaturalist. The study revealed that 47% of participants were able to recognize bias, and familiarity was found to be a significant factor in this ability. We propose a three-layer framework to understand the cognitive processes of bias recognition in informal learning settings and apply this framework to our data to inform future work for designing displays to promote critical engagement with data in free-choice learning contexts. © 2025 Copyright held by the owner/author(s).

关键词： Augmented Reality Critical Thinking Human-Centered computing Human-Data Interaction Informal Learning Prior Knowledge

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Confirmation Bias: The Double-Edged Sword of Data Facts in Visual Data Communication 25

Confirmation Bias: The Double-Edged Sword of Data Facts in V...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Li, Shiyao Davidson, Thomas James Xiong Bearfield, Cindy Wall, Emily Computer Science Emory University Atlanta GA United States Interactive Computing Georgia Institute of Technology Atlanta GA United States

ISBN: (纸本)9798400713941

Incorporating data facts, which are natural language descriptions of data patterns, alongside visualizations can guide readers and enhance the visibility of data patterns. However, data facts might also induce confirmation bias in visual analysis. We conducted a series of crowdsourced experiments to explore the biasing effects of data facts. Our findings show that the presentation style, strength, and alignment of data facts with pre-existing beliefs significantly impact confirmation bias. Data facts that support prior beliefs can exacerbate confirmation bias, whereas those that refute an individual's beliefs can mitigate it. This effect is amplified when data facts are used in combination with visual annotations. Data facts describing variable correlations are perceived to be more compelling than ones describing average values and are associated with higher levels of confirmation bias. We underscore the persuasive influence of data facts in visualizations and caution against their indiscriminate use in efforts to mitigate bias. © 2025 Copyright held by the owner/author(s).

关键词： annotation confirmation bias data facts data visualization

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VizGroup: An AI-assisted Event-driven System for Collaborative Programming Learning Analytics 24

VizGroup: An AI-assisted Event-driven System for Collaborati...

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37th Annual ACM Symposium on User Interface Software and Technology, UIST 2024

作者： Tang, Xiaohang Wong, Sam Pu, Kevin Chen, Xi Yang, Yalong Chen, Yan Department of Computer Science Virginia Tech BlacksburgVA United States University of Washington SeattleWA United States Department of Computer Science University of Toronto TorontoON Canada School of Interactive Computing Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9798400706288

Programming instructors often conduct collaborative learning activities, like Peer Instruction, to foster a deeper understanding in students and enhance their engagement with learning. These activities, however, may not always yield productive outcomes due to the diversity of student mental models and their ineffective collaboration. In this work, we introduce VizGroup, an AI-assisted system that enables programming instructors to easily oversee students' real-time collaborative learning behaviors during large programming courses. VizGroup leverages Large Language Models (LLMs) to recommend event specifications for instructors so that they can simultaneously track and receive alerts about key correlation patterns between various collaboration metrics and ongoing coding tasks. We evaluated VizGroup with 12 instructors in a comparison study using a dataset collected from a Peer Instruction activity that was conducted in a large programming lecture. The results showed that VizGroup helped instructors effectively overview, narrow down, and track nuances throughout students' behaviors. © 2024 ACM.

关键词： Collaborative learning

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On Training Strategies for LSTMs in Sensor-Based Human Activity Recognition

On Training Strategies for LSTMs in Sensor-Based Human Activ...

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2023 IEEE International Conference on Pervasive computing and Communications Workshops and other Affiliated Events, PerCom Workshops 2023

作者： Shan, Shuai Guan, Yu Guan, Xin Missier, Paolo Plotz, Thomas University of Warwick Department of Computer Science Coventry United Kingdom School of Computing Newcastle University Newcastle upon Tyne United Kingdom School of Interactive Computing Georgia Institute of Technology Atlanta United States

ISBN: (纸本)9781665453813

Human Activity Recognition (HAR) is one of the core research areas in mobile and wearable computing. With the application of deep learning (DL) techniques such as CNN, recognizing periodic/static activities (e.g, walking, lying, cycling, etc.) has become a well-studied problem. What remains a major challenge though is the sporadic activity recognition (SAR) problem, where activities of interest tend to be non-periodic, and occur less frequently when compared with the often large amount of irrelevant 'background' activities. Recent works suggested that sequential DL models (such as LSTMs) have great potential for modeling non-periodic behaviours, and in this paper we studied some LSTM training strategies for SAR. Specifically, we proposed two simple yet effective LSTM variants, i.e., a delay model and an inverse model. The delay model can effectively exploit pre-defined delay intervals as additional contextual information, while the inverse model can learn patterns from the time-series in an inverse manner, which can be complementary to the forward model (i.e., LSTM). These two LSTM variants are very practical, and they can be deemed as training strategies without alteration of the LSTM fundamentals. We also studied some additional LSTM training strategies, which can further improve the accuracy. We evaluated our models on two public datasets, and the promising results suggested the effectiveness of our approaches in recognizing sporadic activities. © 2023 IEEE.

关键词： Long short-term memory

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Effect of PAIP1 on the metastatic potential and prognostic significance in oral squamous cell carcinoma

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International Journal of Oral science 2022年第1期14卷 113-122页

作者： Neeti Swarup Kyoung-Ok Hong Kunal Chawla Su-Jung Choi Ji-Ae Shin Kyu-Young Oh Hye-Jung Yoon Jae-Il Lee Sung-Dae Cho Seong-Doo Hong Department of Oral Pathology School of Dentistry and Dental Research InstituteSeoul National UniversitySeoulRepublic of Korea Department of Computer Science School of Interactive ComputingGeorgia Institute of TechnologyAtlantaGAUSA

Poly Adenylate Binding Protein Interacting protein 1(PAIP1) plays a critical role in translation initiation and is associated with the several cancer types. However, its function and clinical significance have not yet been described in oral squamous cell carcinoma(OSCC) and its associated features like lymph node metastasis(LNM). Here, we used the data available from Gene Expression Omnibus(GEO), The Cancer Genome Atlas(TCGA), and Clinical Proteomic Tumor Analysis Consortium(CPTAC) to analyze PAIP1 expression in oral cancer. The publicly available data suggests that PAIP1 mRNA and protein levels were increased in OSCC. The high PAIP1 expression was more evident in samples with advanced stage, LNM, and worse pattern of invasion. Moreover, the in vitro experiments revealed that PAIP1 knockdown attenuated colony forming, the aggressiveness of OSCC cell lines, decreasing MMP9 activity and SRC phosphorylation. Importantly, we found a correlation between PAIP1 and pSRC through the analysis of the IHC scores and CPTAC data in patient samples. Our findings suggest that PAIP1 could be an independent prognostic factor in OSCC with LNM and a suitable therapeutic target to improve OSCC patient outcomes.

关键词： squamous invasion metastasis

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'User AR' - Leveraging AI Based Augmented Reality for User Manuals 15

'User AR' - Leveraging AI Based Augmented Reality for User M...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Shanmugam, Thirumurugan Chandrashekar, Chirag Bhati, Vipin Subburaj, Maheswari Sivaraman, Arun Kumar Sultana, Ajmery University of Technology and Applied Sciences College of Computing and Information Sciences Suhar Oman Vellore Institute of Technology School of Computer Science and Engineering Chennai India Photon Interactive Inc Digital Engineering Solution Center TorontoON Canada Algoma University School of Computer Science and Technology BramptonON Canada

ISBN: (纸本)9798350370249

The objective of this research is to propose an AI based novel augmented reality (AR) software to assist users in operating a wide range of technical and non-technical devices. The project's aim is to make this AR application, which provides detailed explanations of all the device's functions, more widely used than traditional user manuals. Augmented reality, which overlays digital information onto the real world, can be employed to develop an AR-based user manual for products or devices. Mobile applications that use a camera to recognize a product or device and display dynamic graphics, such as 3 D models, animations, or text, are one example of AR user manuals. These augmented reality user manuals enable consumers to quickly and easily learn how to operate, assemble, repair, or troubleshoot their purchased products. Experiments show that the proposed algorithm was able to recognize the object accurately with a percentage of 95.2%. © 2024 IEEE.

关键词： Artificial Intelligence Augmented reality (AR) Object Recognition

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Enhancing Cyclone Center Identification in Radar Images through Deep Learning and Match Recognition 15

Enhancing Cyclone Center Identification in Radar Images thro...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Shanmugam, Thirumurugan Chandrashekar, Chirag Subburaj, Maheswari Sivaraman, Arun Kumar Sultana, Ajmery Thangavel, Senthil Kumar University of Technology and Applied Sciences College of Computing and Information Sciences Suhar Oman Vellore Institute of Technology School of Computer Science and Engineering Chennai India Photon Interactive Inc Digital Engineering Solution Center ON Canada Algoma University Brampton School of Computer Science and Technology ON Canada Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350370249

This research focuses on improving the identification of cyclone centers using deep learning and match recognition applied to radar images. Accurately pinpointing the cyclone's center is vital for predicting its intensity and trajectory. However, challenges persist in automatically locating the center due to the diverse nature of cyclone morphology and structure. To address this, the deep convolutional network's capability is leveraged to capture various structural features in images by proposing two-step approach for cyclone center localization. Initially, a pre-trained EfficientDet model is employed using transfer learning to obtain weights. Subsequently, these weights, along with the data, are utilized in a deep learning model to provide precise coordinates of the cyclone center in the respective image. The effectiveness of existing deep learning and machine learning models show that the cyclone prediction systems have an accuracy ranging from 86% to 92% or better, and cyclone eye detection accuracy surpassing 87%. Experiment outcomes indicate that the proposed methodology outperforms conventional methods and existing works, showcasing its potential for enhancing cyclone monitoring and forecasting. © 2024 IEEE.

关键词： Cyclone detection deep learning efficient det and YOLO model transfer learning

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The Goofus & Gallant Story Corpus for Practical Value Alignment 23

The Goofus & Gallant Story Corpus for Practical Value Alignm...

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23rd IEEE International Conference on Machine Learning and Applications, ICMLA 2024

作者： Al Nahian, Md Sultan Tasrin, Tasmia Frazier, Spencer Riedl, Mark Harrison, Brent Institute for Biomedical Informatics University of Kentucky LexingtonKY United States School of Interactive Computing Georgia Institute of Technology AtlantaGA United States University of Kentucky Department of Computer Science LexingtonKY United States

ISBN: (纸本)9798350374889

Values or principles are key elements of human society that influence people to behave and function according to an accepted standard set of social rules to maintain social order. As AI systems are becoming ubiquitous in human society, it is a major concern that they could violate these norms or values and potentially cause harm. Thus, to prevent intentional or unintentional harm, AI systems are expected to take actions that align with these principles. Training systems to exhibit this type of behavior is difficult and often requires a specialized dataset. This work presents a multi-modal dataset illustrating normative and non-normative behavior in real-life situations described through natural language and artistic images. This training set contains curated sets of images that are designed to teach young children about social principles. We argue that this is an ideal dataset to use for training socially normative agents given this fact. © 2024 IEEE.

关键词： Adversarial machine learning

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DISCRETE REPRESENTATIONS STRENGTHEN VISION TRANSFORMER ROBUSTNESS 10

DISCRETE REPRESENTATIONS STRENGTHEN VISION TRANSFORMER ROBUS...

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10th International Conference on Learning Representations, ICLR 2022

作者： Mao, Chengzhi Jiang, Lu Dehghani, Mostafa Vondrick, Carl Sukthankar, Rahul Essa, Irfan Google Research Computer Science Columbia University United States School of Interactive Computing Georgia Insitute of Technology United States

Vision Transformer (ViT) is emerging as the state-of-the-art architecture for image recognition. While recent studies suggest that ViTs are more robust than their convolutional counterparts, our experiments find that ViTs trained on ImageNet are overly reliant on local textures and fail to make adequate use of shape information. ViTs thus have difficulties generalizing to out-of-distribution, real-world data. To address this deficiency, we present a simple and effective architecture modification to ViT's input layer by adding discrete tokens produced by a vector-quantized encoder. Different from the standard continuous pixel tokens, discrete tokens are invariant under small perturbations and contain less information individually, which promote ViTs to learn global information that is invariant. Experimental results demonstrate that adding discrete representation on four architecture variants strengthens ViT robustness by up to 12% across seven ImageNet robustness benchmarks while maintaining the performance on ImageNet. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Image recognition

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