@article{huang_pugh_kim_nam_2024, title={Brain dynamics of mental workload in a multitasking context: Evidence from dynamic causal modeling}, volume={152}, ISSN={["1873-7692"]}, DOI={10.1016/j.chb.2023.108043}, abstractNote={Multitasking is a common element in complex human-computer interactions and is known to impose deleterious mental workload demands. High mental workload is known to involve bilateral hemisphere activation, but the patterns of effective connectivity (directed causal influence or communication) among brain regions in such a context remain unclear. This study investigated the effect of mental workload on the causal influence brain regions exert over each other under a multitasking scenario. The Dynamic Causal Modeling (DCM) method was implemented to infer the flow of information and allocation of attentional resources. Thirty participants performed four subtasks with varying levels of workload on a computer-based multitasking program, simulating a pilot cockpit. Using eight brain regions commonly identified to be activated in multitasking conditions, nine candidate models were developed. Bayesian model averaging was then used to quantify the connectivity strengths among the brain regions. Linear regression was conducted to study the relationships between connection strengths and subtask performances. The results showed that the causal connections shifted from the left to both sides of the brain with increased workload. Linear regression analysis showed that the subtask performance could be predicted by connectivity strengths. Thus, by studying the brain dynamics of mental workload, we may be able to develop a predictor that supplements subjective self-report measures.}, journal={COMPUTERS IN HUMAN BEHAVIOR}, author={Huang, Jiali and Pugh, Zachary H. and Kim, Sangyeon and Nam, Chang S.}, year={2024}, month={Mar} }
 @article{park_kim_lee_2023, title={A User Preference and Intent Extraction Framework for Explainable Conversational Recommender Systems}, DOI={10.1145/3596454.3597178}, abstractNote={Conversational recommender systems (CRS) communicate with a user through natural language understanding to support the user finding necessary information. While the importance of user information extraction from a dialog is growing, previous systems rely on named-entity recognition to find out user preference based on deep learning methods. However, there is still scope for such recognition modules to perform better in terms of accuracy and richness of the elicited user preference information. Also, extracting user information solely depending on entities mentioned in user utterances might ignore contextual semantics. Besides, black-box recommender systems are widely used in previous CRSs whereas such methods undermine transparency and interpretability of recommended results. To alleviate these problems, we propose a novel framework to extract user preference and user intent and apply it to a recommender system. User preference is extracted from sets of an item feature entity detected by our item feature entity detection module and an estimated rating about each entity. Utilizing graph representation of user utterances, user intent is also elicited to consider the contextual semantic of each element word. Based on both outcomes, we implement recommendation by candidate selection and ranking, then provide explanation of the recommendation result to enhance interpretability and manipulability of the system. We illustrate how our framework works in practice by a sample conversation. Experiments present improvement and effectiveness of user information elicitation in recommendation.}, journal={COMPANION OF THE 2023 ACM SIGCHI SYMPOSIUM ON ENGINEERING INTERACTIVE COMPUTING SYSTEMS, EICS 2023}, author={Park, Jieun and Kim, Sangyeon and Lee, Sangwon}, year={2023}, pages={16–23} }
 @article{kim_choo_park_park_nam_jung_lee_2023, title={Designing an XAI interface for BCI experts: A contextual design for pragmatic explanation interface based on domain knowledge in a specific context}, volume={174}, ISSN={["1095-9300"]}, DOI={10.1016/j.ijhcs.2023.103009}, abstractNote={Domain experts utilize a decision-support system depending on an artificial intelligence (AI) algorithm. Likewise, researchers in brain-computer interface (BCI) have recently employed deep learning (DL) algorithms for decoding and analyzing neural signals. Despite its outstanding performance, the BCI technology with the DLs has pointed out that it has a potential problem of low transparency due to algorithmic complexity of the models. On this problem, explainable artificial intelligence (XAI) can be a solution to make an AI algorithm and its decisions more interpretable. However, the explanation from the XAI has been emphasized that it should be designed corresponding with the user's different expectations which are contextually variable. Thus, our study aims to propose an explanation interface for the BCI expert under Pragmatism structuralizing an explanation with scientific knowledge in a contrastive manner. For this work, we conduct a contextual design process with five BCI experts, specifically conducting a contextual inquiry and work modeling to extract design requirements from their expertise in their work environment; next, designing and evaluating an interactive prototype of the explanation interface. The results indicated that our prototype has the advantages of increasing contextual understanding and intuitive interface design. Yet, there were also challenges on the explanation for novice users without prior knowledge on the XAI and objective understanding of the AI model with enough interpretability. This study contributes to providing a theoretical framework based on Pragmatism and designing a user-centered XAI system for domain experts in a specific context.}, journal={INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES}, author={Kim, Sangyeon and Choo, Sanghyun and Park, Donghyun and Park, Hoonseok and Nam, Chang S. and Jung, Jae-Yoon and Lee, Sangwon}, year={2023}, month={Jun} }
 @article{choo_park_kim_park_jung_lee_nam_2023, title={Effectiveness of multi-task deep learning framework for EEG-based emotion and context recognition}, volume={227}, ISSN={["1873-6793"]}, DOI={10.1016/j.eswa.2023.120348}, abstractNote={Studies have investigated electroencephalogram (EEG)-based emotion recognition using hand-crafted EEG features (e.g., differential entropy) or the annotated emotion categories without any additional emotion factors (e.g., context). The effectiveness of raw EEG-based emotion recognition remains for further investigation. In this study, we investigated the effectiveness of multi-task learning (MTL) for raw EEG-based convolutional neural networks (CNNs) in emotion recognition with auxiliary context information. Thirty subjects participated in this study, where their brain signals were collected when watching six types of emotion images (social/nonsocial-fear, social/nonsocial-sad, and social/nonsocial-neutral). For the MTL architecture, we utilized temporal and spatial filtering layers from raw EEG-based CNNs as shared and task-specific layers for emotion and context classification tasks. Subject-dependent classifications and five repeated five-fold cross-validation were performed to test the classification accuracy for all comparison models. Our results showed that (1) the MTL classifier had a significantly higher classification accuracy and improved the performance of the single-task learnings (STLs) for both emotion and context, and (2) the ShallowConvNet was the best network architecture among the considered CNNs for the MTL with statistically significant improvement to the raw EEG-based STLs. This shows that the MTL can be a promising method for emotion recognition in utilizing the raw EEG-based CNN classifiers and emphasizes the importance of considering context information.}, journal={EXPERT SYSTEMS WITH APPLICATIONS}, author={Choo, Sanghyun and Park, Hoonseok and Kim, Sangyeon and Park, Donghyun and Jung, Jae-Yoon and Lee, Sangwon and Nam, Chang S.}, year={2023}, month={Oct} }
 @article{jeon_kim_lee_2023, title={Interactive Feedback Loop with Counterfactual Data Modification for Serendipity in a Recommendation System}, ISSN={["1532-7590"]}, DOI={10.1080/10447318.2023.2238369}, abstractNote={AbstractUsers often encounter tedious recommendations as they are continuously exposed to the recommendation system. In response to this issue, serendipity in a recommendation system has been introduced to generate novel and unexpected recommendations while keeping them relevant to users’ previous preferences. This study proposes an interactive feedback loop for a serendipity in a recommendation system that allows users to directly explore content via counterfactual manipulation of features. Specifically, users indicate their preferences through the “what-if” based customization of content meta-information, and these modifications influence their usage history, thereby enabling the elicitation of serendipitous items. To validate the proposed feedback loop, we conducted a scenario-based experiment and compared system-initiated and user-intervened recommendations. The results reveal that counterfactual exploration can help to generate serendipitous recommendations. This study contributes to providing a user-friendly recommendation system that can retrieve preference-reflected recommendations through user interaction.Keywords: Recommendation systemserendipityinteractive machine learningcounterfactual data modificationhuman intervention Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by Institute for Information & Communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No.2022-0-00078, Explainable Logical Reasoning for Medical Knowledge Generation).Notes on contributorsGyewon JeonGyewon Jeon is a graduate student in Department of Industrial and Management Engineering at Korea University. His academic interests lie in Serendipitous Recommender System, Interactive Machine Learning, and Human Artificial Intelligence Interaction.Sangyeon KimSangyeon Kim is a visiting scholar in North Carolina State University. He has obtained his PhD degree from Sungkyunkwan University in 2022. His academic interests lie in HCI, Intelligent user interface, and accessible computing.Sangwon LeeSangwon Lee is a Professor in School of Industrial and Management Engineering at Korea University. He has obtained his PhD and Master degrees from the Pennsylvania State University in 2010 and 2006, respectively. Also, he has graduated as B.S. from Korea University in 2004. His academic interests lie in HCI, UX, XAI, and affective computing.}, journal={INTERNATIONAL JOURNAL OF HUMAN-COMPUTER INTERACTION}, author={Jeon, Gyewon and Kim, Sangyeon and Lee, Sangwon}, year={2023}, month={Aug} }