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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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Sociotechnical AI Governance: Challenges and Opportunities for HCI

Sociotechnical AI Governance: Challenges and Opportunities f...

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

作者： Kevin Feng, K.J. Pang, Rock Yuren Kuo, Tzu-Sheng Winecoff, Amy Tseng, Emily Widder, David Gray Suresh, Harini Reinecke, Katharina Zhang, Amy X. University of Washington SeattleWA United States Paul G. Allen School of Computer Science & Engineering University of Washington SeattleWA United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States Center for Democracy & Technology WashingtonDC United States Microsoft Research New YorkNY United States Cornell Tech New YorkNY United States Brown University ProvidenceRI United States

ISBN: (纸本)9798400713958

Rapid advancements in and adoption of frontier AI systems have amplified the need for AI governance measures across the public sector, academia, and industry. Prior work in technical AI governance has proposed agendas for governing technical components in AI development, such as data, models, and compute. However, recent calls for more sociotechnical approaches recognize the critical role of social infrastructures surrounding technical ones in shaping governance decisions and efforts. While scholars and practitioners have advocated for sociotechnical AI governance, concrete research directions in this area are only beginning to emerge. This workshop aims to gather the expertise of researchers in HCI and adjacent disciplines to chart promising paths forward for sociotechnical AI governance. To make problems in this area more tangible, we outline four core governance challenges for contributions: anticipating high-priority risks to address with governance, identifying where to focus governance efforts and who should lead those efforts, designing appropriate interventions and tools to implement governance actions in practice, and evaluating the effectiveness of these interventions and tools in context. Through papers, panel discussions, keynotes, and collaborative drafting of a research agenda, this workshop will build community and empower actionable efforts to tackle AI governance through a sociotechnical lens. © 2025 Copyright held by the owner/author(s).

关键词： AI governance human-AI interaction responsible AI sociotechnical methods

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What does ChatGPT know about natural science and engineering?

arXiv

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arXiv 2023年

作者： Balhorn, Lukas Schulze Weber, Jana M. Buijsman, Stefan Hildebrandt, Julian R. Ziefle, Martina Schweidtmann, Artur M. Delft University of Technology Delft Netherlands Human-Computer Interaction Center Chair of Communication Science RWTH Aachen University Aachen Germany

ChatGPT is a powerful language model from OpenAI that is arguably able to comprehend and generate text. ChatGPT is expected to have a large impact on society, research, and education. An essential step to understand ChatGPT’s expected impact is to study its domain-specific answering capabilities. Here, we perform a systematic empirical assessment of its abilities to answer questions across the natural science and engineering domains. We collected 594 questions from 198 faculty members across 5 faculties at Delft University of Technology. After collecting the answers from ChatGPT, the participants assessed the quality of the answers using a systematic scheme. Our results show that the answers from ChatGPT are on average perceived as "mostly correct". Two major trends are that the rating of the ChatGPT answers significantly decreases (i) as the complexity level of the question increases and (ii) as we evaluate skills beyond scientific knowledge, e.g., critical attitude. Copyright © 2023, The Authors. All rights reserved.

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Discrepancies in online postoperative care protocols for Mohs surgery

JAAD Reviews

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JAAD Reviews 2025年 3卷 128-130页

作者： Afua Ofori-Darko Ashley M. Perez Devin Barzallo Jill Lehman Bryan T. Carroll Case Western Reserve University School of Medicine Cleveland Ohio Carnegie Mellon University Human-Computer Interaction Institute Pittsburgh Pennsylvania Department of Dermatology University Hospitals Cleveland Medical Center Cleveland Ohio

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Leveraging Part-of-Speech Tagging for Enhanced Stylometry of Latin Literature 1

Leveraging Part-of-Speech Tagging for Enhanced Stylometry of...

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1st Workshop on Machine Learning for Ancient Languages, ML4AL 2024

作者： Chen, Sarah Li Burns, Patrick J. Bolt, Thomas J. Chaudhuri, Pramit Dexter, Joseph P. Phillips Academy Andover United States Research Science Institute Center for Excellence in Education United States Department of Computer Science Stanford University United States Institute for the Study of the Ancient World New York University United States Department of Languages & Literary Studies Lafayette College United States Department of Classics University of Texas Austin United States Institute of Collaborative Innovation Department of Computer and Information Science University of Macau China Department of Human Evolutionary Biology Harvard University United States

ISBN: (纸本)9798891761445

In literary critical applications, stylometry can benefit from hand-curated feature sets capturing various syntactic and rhetorical functions. For premodern languages, calculation of such features is hampered by a lack of computational resources for accurate part-of-speech tagging and semantic disambiguation. This paper reports an evaluation of POS taggers for Latin and their use in augmenting a hand-curated stylometric feature set. Our analyses show that POS-augmented features not only provide more accurate counts but also perform well on tasks such as genre classification. In the course of this work, we introduce POS n-grams as a feature for Latin stylometry. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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The IoT Codex: A Book of Programmable Stickers for Authoring and Composing Embedded Computing Applications

The IoT Codex: A Book of Programmable Stickers for Authoring...

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IEEE Symposium on Visual Languages and human Centric Computing (VL/HCC)

作者： Kristin Williams Jessica Hammer Scott Hudson Computer Science Emory University Atlanta USA Human Computer Interaction Institute Carnegie Mellon University Pittsburgh USA Entertainment Technology Center Carnegie Mellon University Pittsburgh USA

The Internet-of-Things (IoT) promises to enhance everyday objects with computing, but rarely enables directly authoring or composing that behavior. Lightweight IoT approaches attach identifiers (e.g., RFID tags) to objects to enable networked services. Typically these tags are passive, and so, depend on activity recognition and predefined context. This limits interaction to invoking predetermined behavior. Instead, this work presents The IoT Codex: a lightweight approach to customizing everyday objects with IoT by enabling interactive attachable IDs (aIDs) to compose software-supported behavior in situ. This work contributes 1) paper engineering techniques to construct aIDs that embody state, and 2) a tangible, end user programming (EUP) language for customizing IoT within symbolic and idiosyncratic contexts. Here, we provide preliminary validation of our approach with an empirically informed design space, sample applications, and a small co-design workshop. In doing so, we offer preliminary evidence for tangible, end user programming to enable meaningful control over IoT services.

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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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We Are Sending You Back... to the Optimum! Fuzzy Time Travel Particle Swarm Optimization 28th

We Are Sending You Back... to the Optimum! Fuzzy Time Trav...

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28th European Conference on Applications of Evolutionary Computation, EvoApplications 2025, held as part of EvoStar 2025

作者： Papetti, Daniele M. Tangherloni, Andrea Coelho, Vasco Besozzi, Daniela Cazzaniga, Paolo Nobile, Marco S. Department of Informatics Systems and Communication University of Milano-Bicocca Milan Italy Department of Computer Science Bocconi University Milan Italy Department of Human and Social Sciences University of Bergamo Bergamo Italy Department of Environmental Sciences Informatics and Statistics Ca’ Foscari University of Venice Venice Italy Research Center Milan Italy

ISBN: (纸本)9783031900648

Particle Swarm Optimization (PSO) is a swarm intelligence meta-heuristics whose performance highly depends on the selection of its hyper-parameters, which control the particles’ exploration and exploitation capabilities during the search process. Since the tuning of the hyper-parameters is problem-dependent, settings-free methods are preferable. Fuzzy Self-Tuning PSO (FST-PSO) is a settings-free variant of PSO that exploits a Fuzzy Rule-Based System to determine the best hyper-parameter values for each particle dynamically. Despite this advantage, the optimization process might get stuck in local optima. Here, we propose a what-if strategy to generate and explore alternate swarm histories: what happens if we could go back in time and change something in the past so that one particle "was" different at the beginning of the optimization? Specifically, whenever the global best particle does not improve for a number of iterations, it is terminated and re-initialized, meanwhile sending the rest of the swarm back to the initial configuration. This approach, called "time travel" FST-PSO (FTT-PSO), works under the assumption that the convergence of any particle towards an optimum is strongly related to the influence of the global best particle. We compare the performance of FTT-PSO against FST-PSO on the benchmark suites used in IEEE CEC and GECCO competitions. Our results show that time traveling allows for outperforming both the standard and the multistart versions of FST-PSO. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Particle swarm optimization (PSO)

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How can we can create an equitable CHI

How can we can create an equitable CHI

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Extended Abstracts of the 2023 CHI Conference on human Factors in Computing Systems, CHI EA 2023

作者： Grady, Siobahn Day Bauer, Christine Wehbe, Rina Spiel, Katta Muller, Michael Harrington, Christina School of Library and Information Sciences North Carolina Central University DurhamNC United States Department of Information and Computing Sciences Utrecht University Utrecht Netherlands Computer Science Dalhousie University HalifaxNS Canada Faculty of Informatics Tu Wien Vienna Austria Human Centered Ai Ibm Research CambridgeMA United States Human-Computer Interaction Institute Carnegie Mellon University PittsburghPA United States

ISBN: (纸本)9781450394222

This panel aims to generate conversation toward creating a more equitable CHI. In recognizing our community's hard work thus far, this panel seeks to engage panelists and participants with thought-provoking questions to garner and promote actionable items for the community. We intend to have an open dialogue on allyship, diversity, equity, and inclusion to achieve a CHI for all. © 2023 Owner/Author.

关键词： allyship diversity equality equity inclusion

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Investigating fairness in machine learning-based audio sentiment analysis

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AI and Ethics 2024年第2期5卷 1099-1108页

作者： Luitel, Sophina Liu, Yang Anwar, Mohd Human-Centered AI (HCAI) Lab Department of Computer Science North Carolina Agricultural and Technical State University Greensboro USA Quantum Computing Research Center <QC|RC> Department of Computer Science North Carolina Agricultural and Technical State University Greensboro USA

Audio sentiment analysis is a growing area of research, however little attention has been paid to the fairness of machine learning models in this field. Whilst the current literature covers research on machine learning models’ reliability and fairness in various demographic groups, fairness in audio sentiment analysis with respect to gender is still an uninvestigated field. To fill this knowledge gap, we conducted experiments aimed at assessing the fairness of machine learning algorithms concerning gender within the context of audio sentiment analysis. In this research, we used 442 audio files of happiness and sadness—representing equal samples of male and female subjects—and generated spectrograms for each file. Then we performed feature extraction using bag-of-visual-words method followed by building classifiers using Random Forest, Support Vector Machines, and K-nearest Neighbors algorithms. We investigated whether the machine learning models for audio sentiment analysis are fair across female and male genders. We found the need for gender-specific models for audio sentiment analysis instead of a gender-agnostic-model. Our results provided three pieces of evidence to back up our claim that gender-specific models demonstrate bias in terms of overall accuracy equality when tested using audio samples representing the other gender, as well as combination of both genders. Furthermore, gender-agnostic-model performs poorly in comparison to gender-specific models in classifying sentiments of both male and female audio samples. These findings emphasize the importance of employing an appropriate gender-specific model for an audio sentiment analysis task to ensure fairness and accuracy. The best performance is achieved when using a female-model (78% accuracy) and a male-model (74% accuracy), significantly outperforming the 66% accuracy of the gender-agnostic model.

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