PhD Student, Universidad de las Américas Puebla
A pattern-based approach to activity design in creative work supported by surface computing.
Creativity can be supported by various media and tools, ranging from sheets of paper, colored pens, scissors and glue, to full-fledged technological tools that operate on platforms such as mobile phones, tablets and large-sized multi-touch interactive surfaces. Large-sized multi-touch interactive surfaces appear as an interesting alternative for supporting creativity processes and for supporting synchronous collocated collaboration. However, they have mostly been used for visualization and navigation purposes. Their use as authoring tools has remained largely unexplored. From a thorough review of the literature, a significant gap has been detected between the level of development tools and end-user applications that aim to support creativity processes using interactive surfaces. We conducted qualitative research in which we applied Activity Centered Design (ACD) to analyze creativity methods as used in various settings, including some that already incorporate interactive surfaces. Thus, based on this research we propose ISCALI (Innovation Solutions Centered on Activities for Large-sized Interfaces), a model for introducing multi-touch surfaces into the creative process. In accordance to ACD ISCALI comprises three major components: activities, actions and operations. The central activities within the processes of creativity comprise generation, organization and evaluation of ideas. Each of these activities encompass sets of actions. Finally, several operation sets achieve the goal of each of the actions. In order to demonstrate the potential of ISCALI in describing different creativity methods, we designed a general architecture and we propose the development of a set of building blocks for supporting creativity software applications.
Abstract: Point-of-Interest (POI) recommendation has received increasing attention in Location-based Social Networks (LBSNs). It involves user behavior analysis, movement pattern model and trajectory sequence prediction, in order to recommend personalized services to target user. Existing POI recommendation methods are confronted with three problems: (1) they only consider the location information of users' check-ins, which causes data sparsity; (2) they fail to consider the order of users' visited locations, which is valuable to reflect the interest or preference of users; (3) users cannot be recommended the suitable services when they move into the new place. To address the above issues, we propose a semantical pattern and preference-aware service mining method called SEM-PPA to make full use of the semantic information of locations for personalized POI recommendation. In SEM-PPA, we firstly propose a novel algorithm to classify the locations into different types for location identification; then we construct the user model for each user from four aspects, which are location trajectory, semantic trajectory, location popularity and user familiarity; in addition, a potential friends discovery algorithm based on movement pattern is proposed. Finally, we conduct extensive experiments to evaluate the recommendation accuracy and recommendation effectiveness on two real-life datasets from GeoLife and Beijing POI. Experimental results show that SEM-PPA can achieve better recommendation performance in particular for sparse data and recommendation accuracy in comparison with other methods.
Pub.: 11 Jan '17, Pinned: 06 Jul '17
Abstract: Clinical data repositories (CDR) have great potential to improve outcome prediction and risk modeling. However, most clinical studies require careful study design, dedicated data collection efforts, and sophisticated modeling techniques before a hypothesis can be tested. We aim to bridge this gap, so that clinical domain users can perform first-hand prediction on existing repository data without complicated handling, and obtain insightful patterns of imbalanced targets for a formal study before it is conducted. We specifically target for interpretability for domain users where the model can be conveniently explained and applied in clinical practice.We propose an interpretable pattern model which is noise (missing) tolerant for practice data. To address the challenge of imbalanced targets of interest in clinical research, e.g., deaths less than a few percent, the geometric mean of sensitivity and specificity (G-mean) optimization criterion is employed, with which a simple but effective heuristic algorithm is developed.We compared pattern discovery to clinically interpretable methods on two retrospective clinical datasets. They contain 14.9% deaths in 1 year in the thoracic dataset and 9.1% deaths in the cardiac dataset, respectively. In spite of the imbalance challenge shown on other methods, pattern discovery consistently shows competitive cross-validated prediction performance. Compared to logistic regression, Naïve Bayes, and decision tree, pattern discovery achieves statistically significant (p-values < 0.01, Wilcoxon signed rank test) favorable averaged testing G-means and F1-scores (harmonic mean of precision and sensitivity). Without requiring sophisticated technical processing of data and tweaking, the prediction performance of pattern discovery is consistently comparable to the best achievable performance.Pattern discovery has demonstrated to be robust and valuable for target prediction on existing clinical data repositories with imbalance and noise. The prediction results and interpretable patterns can provide insights in an agile and inexpensive way for the potential formal studies.
Pub.: 22 Apr '17, Pinned: 06 Jul '17
Abstract: The current study explored the persistence of event model organizations and how this influences the experience of interference during retrieval. People in this study memorized lists of sentences about objects in locations, such as "The potted palm is in the hotel." Previous work has shown that such information can either be stored in separate event models, thereby producing retrieval interference, or integrated into common event models, thereby eliminating retrieval interference. Unlike prior studies, the current work explored the impact of forgetting up to 2 weeks later on this pattern of performance. We explored three possible outcomes across the various retention intervals. First, consistent with research showing that longer delays reduce proactive and retroactive interference, any retrieval interference effects of competing event models could be reduced over time. Second, the binding of information into events models may weaken over time, causing interference effects to emerge when they had previously been absent. Third, and finally, the organization of information into event models could remain stable over long periods of time. The results reported here are most consistent with the last outcome. While there were some minor variations across the various retention intervals, the basic pattern of event model organization remained preserved over the two-week retention period.
Pub.: 19 May '17, Pinned: 06 Jul '17