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1. BrainEx: Interactive Visual Exploration and Discovery of Sequence Similarity in Brain Signals

   https://doi.org/10.1145/3534516  

   Howell-Munson, Alicia; Micek, Christopher; Li, Ziheng; Clements, Michael; Nolan, Andrew C.; Powell, Jackson; Solovey, Erin T.; Neamtu, Rodica (June 2022, Proceedings of the ACM on Human-Computer Interaction)

   Technology advances and lower equipment costs are enabling non-invasive, convenient recording of brain data outside of clinical settings in more real-world environments, and by non-experts. Despite the growing interest in and availability of brain signal datasets, most analytical tools are made for experts in the specific device technology, and have rigid constraints on the type of analysis available. We developed BrainEx to support interactive exploration and discovery within brain signals datasets. BrainEx takes advantage of algorithms that enable fast exploration of complex, large collections of time series data, while being easy to use and learn. This system enables researchers to perform similarity search, explore feature data and natural clustering, and select sequences of interest for future searches and exploration, while also maintaining the usability of a visual tool. In addition to describing the distributed architecture and visual design for BrainEx, this paper reports on a benchmark experiment showing that it outperforms other existing systems for similarity search. Additionally, we report on a preliminary user study in which domain experts used the visual exploration interface and affirmed that it meets the requirements. Finally, it presents a case study using BrainEx to explore real-world, domain-relevant data. 

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2. Review-Based Cross-Domain Collaborative Filtering: A Neural Framework

   Doan, T.N; Sahebi, S. (January 2019, Proceedings of the Third Workshop on Recommendation in Complex Scenarios)

   Cross-domain collaborative filtering recommenders exploit data from other domains (e.g., movie ratings) to predict users’ interests in a different target domain (e.g., suggest music). Most current cross-domain recommenders focus on modeling user ratings but pay limited attention to user reviews. Additionally, due to the complexity of these recommender systems, they cannot provide any information to users to support user decisions. To address these challenges, we propose Deep Hybrid Cross Domain (DHCD) model, a cross-domain neural framework, that can simultaneously predict user ratings, and provide useful information to strengthen the suggestions and support user decision across multiple domains. Specifically, DHCD enhances the predicted ratings by jointly modeling two crucial facets of users’ product assessment: ratings and reviews. To support decisions, it models and provides natural review-like sentences across domains according to user interests and item features. This model is robust in integrating user rating and review information from more than two domains. Our extensive experiments show that DHCD can significantly outperform advanced baselines in rating predictions and review generation tasks. For rating prediction tasks, it outperforms cross-domain and single-domain collaborative filtering as well as hybrid recommender systems. Furthermore, our review generation experiments suggest an improved perplexity score and transfer of review information in DHCD. 

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3. TransCrossCF: Transition-based Cross-Domain Collaborative Filtering

   https://doi.org/10.1109/ICMLA51294.2020.00059  

   Doan, Thanh-Nam; Sahebi, Shaghayegh (December 2020, 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA))

   null (Ed.)

   The success of cross-domain recommender systems in capturing user interests across multiple domains has recently brought much attention to them. These recommender systems aim to improve the quality of suggestions and defy the cold-start problem by transferring information from one (or more) source domain(s) to a target domain. However, most cross-domain recommenders ignore the sequential information in user history. They only rely on an aggregate or snapshot of user feedback in the past. Most importantly, they do not explicitly model how users transition from one domain to another domain as users continue to interact with different item domains. In this paper, we argue that between-domain transitions in user sequences are useful in improving recommendation quality, dealing with the cold-start problem, and revealing interesting aspects of how user interests transform from one domain to another. We propose TransCrossCF, transition-based cross-domain collaborative filtering, that can capture both within and between domain transitions of user feedback sequences while understanding the relationship between different item types in different domains. Specifically, we model each purchase of a user as a transition from his/her previous item to the next one, under the effect of item domains and user preferences. Our intensive experiments demonstrate that TransCrossCF outperforms the state-of-the-art methods in recommendation task on three real-world datasets, both in the cold-start and hot-start scenarios. Moreover, according to our context analysis evaluations, the between-domain relations captured by TransCrossCF are interpretable and intuitive. 

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4. What Ails End-User Composition: A Cross-Domain Qualitative Study

   https://doi.org/10.1007/978-3-319-58735-6 5  

   Dwivedi, V.; Herbsleb, J.; Garlan, D. (January 2017, International Symposium on End User Development)

   Across many domains, end-users need to compose computational elements into novel configurations to perform their day-to-day tasks. End-user composition is a common programming activity performed by such end-users to accomplish this composition task. While there have been many studies on end-user programming, we still need a better understanding of activities involved in end-user composition and environments to support them. In this paper we report a qualitative study of four popular composition environments belonging to diverse application domains, including: Taverna workflow environment for life sciences, Loni Pipeline for brain imaging, SimMan3G for medical simulations and Kepler for scientific simulations. We interview end-users of these environments to explore their experiences while performing common compositions tasks. We use “Content Analysis” technique to analyze these interviews to explore what are the barriers to end-user composition in these domains. Furthermore, our findings show that there are some unique differences in the requirements of naive end-users vs. expert programmers. We believe that not only are these findings useful to improve the quality of end-user composition environments, but they can also help towards development of better end-user composition frameworks. 

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5. Crossed Eyes: Domain Adaptation for Gaze-Based Mind Wandering Models

   https://doi.org/10.1145/3448017.3457386  

   Bixler, Robert E. (May 2021, ETRA '21 Full Papers: ACM Symposium on Eye Tracking Research and Applications)

   null (Ed.)

   The effectiveness of user interfaces are limited by the tendency for the human mind to wander. Intelligent user interfaces can combat this by detecting when mind wandering occurs and attempting to regain user attention through a variety of intervention strategies. However, collecting data to build mind wandering detection models can be expensive, especially considering the variety of media available and potential differences in mind wandering across them. We explored the possibility of using eye gaze to build cross-domain models of mind wandering where models trained on data from users in one domain are used for different users in another domain. We built supervised classification models using a dataset of 132 users whose mind wandering reports were collected in response to thought-probes while they completed tasks from seven different domains for six minutes each (five domains are investigated here: Illustrated Text, Narrative Film, Video Lecture, Naturalistic Scene, and Reading Text). We used global eye gaze features to build within- and cross- domain models using 5-fold user-independent cross validation. The best performing within-domain models yielded AUROCs ranging from .57 to .72, which were comparable for the cross-domain models (AUROCs of .56 to .68). Models built from coarse-grained locality features capturing the spatial distribution of gaze resulted in slightly better transfer on average (transfer ratios of .61 vs .54 for global models) due to improved performance in certain domains. Instance-based and feature-level domain adaptation did not result in any improvements in transfer. We found that seven gaze features likely contributed to transfer as they were among the top ten features for at least four domains. Our results indicate that gaze features are suitable for domain adaptation from similar domains, but more research is needed to improve domain adaptation between more dissimilar domains. 

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