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This paper presents Personalized Affect Detection with Minimal Annotation (PADMA), a user-dependent approach for identifying affective states from spontaneous facial expressions without the need for expert annotation. The conventional approach relies on the use of key frames in recorded affect sequences and requires an expert observer to identify and annotate the frames. It is susceptible to user variability and accommodating individual differences is difficult. The alternative is a user-dependent approach, but it would be prohibitively expensive to collect and annotate data for each user. PADMA uses a novel Association-based Multiple Instance Learning (AMIL) method, which learns a personal facial affect model through expression frequency analysis, and does not need expert input or frame-based annotation. PADMA involves a training/calibration phase in which the user watches short video segments and reports the affect that best describes his/her overall feeling throughout the segment. The most indicative facial gestures are identified and extracted from the facial response video, and the association between gesture and affect labels is determined by the distribution of the gesture over all reported affects. Hence both the geometric deformation and distribution of key facial gestures are specially adapted for each user. We show results that demonstrate the feasibility, effectiveness and extensibility of our approach.

Many studies have attested to the efficacy of integrating innovative methods of teaching computing and engineering, especially for introductory students and at the K-12 level. As an example, robots have been successfully used to teach a wide range of subjects, from introductory programming to artificial intelligence. As a discipline, wearable computing is seen to be practical, yet futuristic and exciting, and it captures the attention and interest of people who might not otherwise be drawn to technology. Recent developments in this field have also raised the possibility of moving wearable computing construction within the reach of hobbyists and novices. However, there still exist substantial obstacles toward the adoption of wearable computing into education. This paper presents a framework with the objective of facilitating the integration of wearable computing into outreach and introductory computer science and engineering education. We also present a comprehensive evaluation of our platform, including a comparison with the current practice of sewing-based wearable computing.

Given a set of clusters, we consider an optimization problem which seeks a subset of clusters that maximizes the microaverage F-measure. This optimal value can be used as an evaluation measure of the goodness of clustering. For arbitrarily overlapping clusters, finding the optimal value is NP-hard. We claim that a greedy approximation algorithm yields the global optimal solution for clusters that overlap only by nesting. We present a mathematical proof of this claim by induction. For a family of n clusters containing a total of N objects, this algorithm has an {\rm O}(n;{2}) time complexity and O(N) space complexity.