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Let's Influence Algorithms Together: How Millions of Fans Build Collective Understanding of Algorithms and Organize Coordinated Algorithmic Actions 25

Let's Influence Algorithms Together: How Millions of Fans Bu...

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

作者： Xiao, Qing Zheng, Yuhang Fan, Xianzhe Zhang, Bingbing Lu, Zhicong Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States University of Amsterdam Amsterdam Amsterdam Netherlands Tsinghua University Beijing China School of Journalism and Mass Communication University of Iowa iowa CityIA United States Department of Computer Science George Mason University FairfaxVA United States

ISBN: (纸本)9798400713941

Previous research pays attention to how users strategically understand and consciously interact with algorithms but mainly focuses on an individual level, making it difficult to explore how users within communities could develop a collective understanding of algorithms and organize collective algorithmic actions. Through a two-year ethnography of online fan activities, this study investigates 43 core fans who always organize large-scale fans collective actions and their corresponding general fan groups. This study aims to reveal how these core fans mobilize millions of general fans through collective algorithmic actions. These core fans reported the rhetorical strategies used to persuade general fans, the steps taken to build a collective understanding of algorithms, and the collaborative processes that adapt collective actions across platforms and cultures. Our findings highlight the key factors that enable computer-supported collective algorithmic actions and extend collective action research into the large-scale domain targeting algorithms. © 2025 Copyright held by the owner/author(s).

关键词： Algorithm Folk Theory of Algorithm Collective Understanding of Algorithm Collective Algorithmic Action Fandom Studies

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An EEG Method to Identify Image Preference With an Explicit/Implicit Task Brain-computer Interface

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IEEE Transactions on Affective Computing 2025年

作者： Li, Yulei Li, Shuyi Qi, Hongzhi Tianjin University Academy of Medical Engineering and Translational Medicine Tianjin300072 China Haihe Laboratory of Brain-Computer Interaction and Human-Machine Integration Tianjin300092 China Tianjin Key Laboratory of Brain Science and Neuroengineering Tianjin300072 China

Accurately determining an individual's preference for images remains a major challenge in the field of emotional research. This study proposes a novel paradigm for identifying individual image preferences using electroencephalography (EEG) signals and brain-computer interface (BCI). The paradigm involves both explicit and implicit tasks, where participants perform a typical event-related potential-based brain-computer interface(ERP-BCI) operation and their subjective image preferences are identified, respectively. Two experiments with a total of 27 participants demonstrate that event-related potential (ERP) signals during explicit BCI tasks are significantly influenced by target image preferences, enabling high-accuracy image preference recognition. Online experiments selecting positive and negative preference images from a candidate pool show top-1 accuracy approaching 100% and top-3 accuracy exceeding 90%. These results indicate the effectiveness of the proposed EEG-based image preference recognition paradigm, laying the groundwork for preference analysis applications. © 2010-2012 IEEE.

关键词： Brain mapping

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Simulating the Dilution of Sulfuric Acid on Mixed Reality Platform 8

Simulating the Dilution of Sulfuric Acid on Mixed Reality Pl...

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8th International Conference on Virtual Reality, ICVR 2022

作者： Pan, Zhipeng Miao, Jinda Cai, Ning Luo, Tianren Zhang, Mingmin Pan, Zhigeng Research Institute of Virtual Reality and Intelligent Systems Hangzhou Normal University Hangzhou China Zhejiang University College of Computer Science and Technology Hangzhou China Institute of Software Chinese Academy of Sciences State Key Laboratory of Computer Science and Beijing Key Lab of Human-Computer Interaction Beijing China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing China

ISBN: (数字)9781665479110

ISBN: (纸本)9781665479110

Augmented reality technology has been widely used in experimental education. Augmented reality provides virtual-real integration, real-Time interaction and three-dimensional immersion, which provides a new development direction for simulating the teaching environment and promoting learning interaction. We have developed an MR experiment of diluting concentrated sulfuric acid, which helps students learn experimental manipulations and scientific concepts before conducting real chemical experiments. Virtual experiments can avoid the risks of real chemical experiments and reduce the waste of chemical materials. At the same time, we correctly render the occlusion relationship between the hand, the beaker and the virtual liquid, providing a realistic experimental effect. The tracking of multiple cameras allows students to interact more naturally. We compared the availability and students' attitudes of three virtual experiments about diluting concentrated sulfuric acid. The results show that the usability of the MR experimental system reaches the level of user satisfaction. The realistic visual effects and natural interaction method of the MR experiment have been recognized by the students. © 2022 IEEE.

关键词： Students

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Technology, Movement, and Play Is Hampering and Boosting Interactive Play 23

Technology, Movement, and Play Is Hampering and Boosting Int...

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10th ACM SIGCHI Annual Symposium on computer-human interaction in Play, CHI PLAY 2023

作者： Van Delden, Robby Reidsma, Dennis Postma, Dees Weijdom, Joris Márquez Segura, Elena Turmo Vidal, Laia Vega-Cebrián, José Manuel Tajadura-Jiménez, Ana Waern, Annika Park, Solip Hämäläinen, Perttu Font, José Maria Johnsson, Mats Rasmussen, Lærke Schjødt Elbæk, Lars Human Media Interaction University of Twente Enschede Netherlands Hku Theatre Hku University of the Arts Netherlands Department of Computer Science and Engineering Universidad Carlos Iii de Madrid Madrid Spain Ucl Interaction Centre University College London London United Kingdom Department of Informatics and Media Uppsala University Uppsala Sweden Aalto University Finland Department of Computer Science and Media Technology Faculty of Technology and Society Malmö University Sweden Department of Sport Sciences Faculty of Education and Society Malmö University Sweden Department of Sports Science and Clinical Biomechanics University of Southern Denmark Odense Denmark

ISBN: (纸本)9798400700293

In this paper, we highlight how including technology, movement or play can boost a design process but with unbalanced amounts can also hamper the process. We provide a set of examples where we miscalculated the amount of technology, movement, or play that was needed in a design activity in such a way that it became counterproductive and for each example mention possible adaptations. Finally, we highlight three existing approaches that can balance the overabundance of technology, movement, and play in design processes: activity-centered design, somaesthetic design, and perspective-changing movement-based design. © 2023 Owner/Author.

关键词： and process balancing movement play product technology

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Enhance Sketch Recognition’s Explainability via Semantic Component-Level Parsing

arXiv

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arXiv 2023年

作者： Zhu, Guangming Wang, Siyuan Wu, Tianci Zhang, Liang School of Computer Science and Technology Xidian University China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province China Xi’an Key Laboratory of Intelligent Software Engineering China

Free-hand sketches are appealing for humans as a universal tool to depict the visual world. humans can recognize varied sketches of a category easily by identifying the concurrence and layout of the intrinsic semantic components of the category, since humans draw free-hand sketches based a common consensus that which types of semantic components constitute each sketch category. For example, an airplane should at least have a fuselage and wings. Based on this analysis, a semantic component-level memory module is constructed and embedded in the proposed structured sketch recognition network in this paper. The memory keys representing semantic components of each sketch category can be self-learned and enhance the recognition network’s explainability. Our proposed networks can deal with different situations of sketch recognition, i.e., with or without semantic components labels of strokes. Experiments on the SPG and SketchIME datasets demonstrate the memory module’s flexibility and the recognition network’s explainability. The code and data are available at https://***/GuangmingZhu/SketchESC. © 2023, CC BY.

关键词： Semantics

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Ruffle&Riley: Towards the Automated Induction of Conversational Tutoring Systems

arXiv

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arXiv 2023年

作者： Schmucker, Robin Xia, Meng Azaria, Amos Mitchell, Tom Machine Learning Department Carnegie Mellon University United States Human-Computer Interaction Institute Carnegie Mellon University United States Department of Computer Science Ariel University

Conversational tutoring systems (CTSs) offer learning experiences driven by natural language interaction. They are known to promote high levels of cognitive engagement and benefit learning outcomes, particularly in reasoning tasks. Nonetheless, the time and cost required to author CTS content is a major obstacle to widespread adoption. In this paper, we introduce a novel type of CTS that leverages the recent advances in large language models (LLMs) in two ways: First, the system induces a tutoring script automatically from a lesson text. Second, the system automates the script orchestration via two LLM-based agents (Ruffle&Riley) with the roles of a student and a professor in a learning-by-teaching format. The system allows a free-form conversation that follows the ITS-typical inner and outer loop structure. In an initial between-subject online user study (N = 100) comparing Ruffle&Riley to simpler QA chatbots and reading activity, we found no significant differences in post-test scores. Nonetheless, in the learning experience survey, Ruffle&Riley users expressed higher ratings of understanding and remembering and further perceived the offered support as more helpful and the conversation as coherent. Our study provides insights for a new generation of scalable CTS technologies. Copyright © 2023, The Authors. All rights reserved.

关键词： Natural language processing systems

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Semantic-Preserving Adversarial Code Comprehension 29

Semantic-Preserving Adversarial Code Comprehension

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29th International Conference on Computational Linguistics, COLING 2022

作者： Li, Yiyang Wu, Hongqiu 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

Based on the tremendous success of pre-trained language models (PrLMs) for source code comprehension tasks, current literature studies either ways to further improve the performance (generalization) of PrLMs, or their robustness against adversarial attacks. However, they have to compromise on the trade-off between the two aspects and none of them consider improving both sides in an effective and practical way. To fill this gap, we propose Semantic-Preserving Adversarial Code Embeddings (SPACE) to find the worst-case semantic-preserving attacks while forcing the model to predict the correct labels under these worst cases. Experiments and analysis demonstrate that SPACE can stay robust against state-of-the-art attacks while boosting the performance of PrLMs for code © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Semantics

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Learning Robust Representations with Information Bottleneck and Memory Network for RGB-D-based Gesture Recognition

Learning Robust Representations with Information Bottleneck ...

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International Conference on computer Vision (ICCV)

作者： Yunan Li Huizhou Chen Guanwen Feng Qiguang Miao School of Computer Science and Technology Xidian University China Xi’an Key Laboratory of Big Data and Intelligent Vision China Key Laboratory of Smart Human-Computer Interaction and Wearable Technology of Shaanxi Province China

Although previous RGB-D-based gesture recognition methods have shown promising performance, researchers often overlook the interference of task-irrelevant cues like illumination and background. These unnecessary factors are learned together with the predictive ones by the network and hinder accurate recognition. In this paper, we propose a convenient and analytical framework to learn a robust feature representation that is impervious to gesture-irrelevant factors. Based on the Information Bottleneck theory, two rules of Sufficiency and Compactness are derived to develop a new information-theoretic loss function, which cultivates a more sufficient and compact representation from the feature encoding and mitigates the impact of gesture-irrelevant information. To highlight the predictive information, we further integrate a memory network. Using our proposed content-based and contextual memory addressing scheme, we weaken the nuisances while preserving the task-relevant information, providing guidance for refining the feature representation. Experiments conducted on three public datasets demonstrate that our approach leads to a better feature representation and achieves better performance than state-of-the-art methods. The code of our method is available at: https://***/Carpumpkin/InBoMem.

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Critically Examining the Broader Implications of Methodological Design in Cross-Cultural, Multi-sited Case Studies of Engineering Ethics Education [Work in Progress]

Critically Examining the Broader Implications of Methodologi...

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2023 ASEE Annual Conference and Exposition - The Harbor of Engineering: Education for 130 Years, ASEE 2023

作者： Clancy, Rockwell Franklin Zhu, Qin Streiner, Scott Thorpe, Ryan The Department of Humanities Arts and Social Sciences The Colorado School of Mines The Department of Values Technology and Innovation Delft University of Technology Netherlands The Department of Engineering Education and Affiliate Faculty The Department of Science Technology & Society The Center for Human-Computer Interaction Virginia Tech United States School of Engineering the University of Pittsburgh United States

Ethics has long been recognized as crucial to responsible engineering, but the increasingly globalized environments present challenges to effective engineering ethics training. This paper is part of a larger research project that aims to examine the effects of culture and education on ethics training in undergraduate engineering students at universities in the United States, China, and the Netherlands. We are interested in how students’ curricular and extra-curricular (e.g., internships, service projects) experiences and training impact their ethical reasoning and moral dispositions, and how this differs cross-culturally. To understand this, we are conducting mixed methods research longitudinally over four years to engineering students at our participating universities to gauge their moral dispositions and ethical reasoning skills and to measure any change in these. This work-in-progress paper, however, is not about the direct outcomes of this research project. Rather, it critically examines our own practices and methods in doing this research. We begin the paper by briefly introducing the larger research project and motivating the use of comparative, multi-institutional case studies as necessary for contextualizing, complementing, and interpreting quantitative data on ethical reasoning and moral dispositions. Because the conditions related to engineering ethics education differ widely per participating institution for institutional (and also likely cultural) reasons, interpreting and analyzing quantitative survey data will require understanding contextual conditions of education at each institution. Comparative case studies can supply missing contextual information to provide a more complete picture of the engineering ethics educational contexts, strategies, and practices at each of the participating universities. However, in considering how to design and conduct these case studies, we realized we were operating under certain assumptions such as ethics in engineeri

关键词： Students

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ArT: All-round Thinker for Unsupervised Commonsense Question Answering 29

ArT: All-round Thinker for Unsupervised Commonsense Question...

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29th International Conference on Computational Linguistics, COLING 2022

作者： Wang, Jiawei 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

Without labeled question-answer pairs for necessary training, unsupervised commonsense question-answering (QA) appears to be extremely challenging due to its indispensable unique prerequisite on commonsense source like knowledge bases (KBs), which are usually highly resource consuming in construction. Recently pre-trained language models (PLMs) show effectiveness as an alternative for commonsense clues when they play a role of knowledge generator. However, existing work either relies on large-scale in-domain or out-of-domain labeled data, or fails to generate knowledge of high quality in a general way. Motivated by human thinking experience, we propose an approach of All-round Thinker (ArT) by fully taking association during knowledge generating. In detail, our model first focuses on key parts in the given context, and then generates highly related knowledge on such a basis in an association way like human thinking. Besides, for causal reasoning, a reverse thinking mechanism is especially added to further enhance bidirectional inferring between cause and effect. ArT is totally unsupervised and KBs-free. We evaluate it on three commonsense QA benchmarks: COPA, SocialIQA and SCT. On all scales of PLM backbones, ArT shows its brilliant performance and outperforms previous advanced unsupervised models. Our code is available at https://***/WangJW424/ commonsenseQA-ArT. © 2022 Proceedings - International Conference on Computational Linguistics, COLING. All rights reserved.

关键词： Computational linguistics

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