Alex Dekhtyar

Direct observation is a ground-truth measure for physical behavior, but the high cost limits widespread use. The purpose of this study was to develop and test machine learning methods to recognize aspects of physical behavior and location from videos of human movement: Adults (N = 26, aged 18–59 y) were recorded in their natural environment for two, 2- to 3-h sessions. Trained research assistants annotated videos using commercially available software including the following taxonomies: (1) sedentary versus non-sedentary (two classes); (2) activity type (four classes: sedentary, walking, running, and mixed movement); and (3) activity intensity (four classes: sedentary, light, moderate, and vigorous). Four machine learning approaches were trained and evaluated for each taxonomy. Models were trained on 80% of the videos, validated on 10%, and final accuracy is reported on the remaining 10% of the videos not used in training. Overall accuracy was as follows: 87.4% for Taxonomy 1, 63.1% for Taxonomy 2, and 68.6% for Taxonomy 3. This study shows it is possible to use computer vision to annotate aspects of physical behavior, speeding up the time and reducing labor required for direct observation. Future research should test these machine learning models on larger, independent datasets and take advantage of analysis of video fragments, rather than individual still images.

As software engineering (SE) practitioners, we can help society by using our communities of experts to address a software need of a socially conscious organization. Doing so can benefit society in the locale of a SE conference and provide access to international experts for local organizations. Furthermore, established SE researchers as well as practitioners and students have the opportunity for a unique learning experience. While the SE community has already realized the importance of addressing human values and promoting social good objectives in software development, we are unaware of previous attempts to leverage SE conferences for this activity. Conferences present an opportunity to enjoy the assembly of SE practitioners, researchers, and students for the purpose of a philanthropic endeavor. Over the past four years of running a “Requirements Engineering for Social Good” event called RE Cares, co-located with the International Conference on Requirements Engineering, we worked with the stakeholders local to the conference venue. We selected stakeholders who would not necessarily have ready access to requirements engineering, software design, and development expertise otherwise, to build software targeting “good causes.” In the last two years, this event was altered to adapt to the constraints induced by COVID-19, moving to a hybrid mode and changing many of its practices accordingly. This paper summarizes and generalizes our experiences, discussing our lessons learned in the context of the pandemic and beyond and providing a framework for conducting similar social contribution in any SE conferences in general.

As one part of an NSF-sponsored Data Science Fellowship at Cal Poly, San Luis Obispo, a group of faculty offered a unique one-unit quarter-long seminar on the history of ideas behind the core principles of Data Science. We present an overview of this seminar, its learning objectives, and outcomes and lessons learned.

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[Context and motivation] Trace matrices are lynch pins for the development of mission- and safety-critical software systems and are useful for all software systems, yet automated methods for recovering trace links are far from perfect. This limitation makes the job of human analysts who must vet recovered trace links more difficult. [Question/Problem] Earlier studies suggested that certain analyst behaviors when performing trace recovery tasks lead to decreased accuracy of recovered trace relationships. We propose a three-step experimental study to: (a) determine if there really are behaviors that lead to errors of judgment for analysts, (b) enhance the requirements tracing software to curtail such behaviors, and (c) determine if curtailing such behaviors results in increased accuracy. [Principal ideas/results] We report on a preliminary study we undertook in which we modified the user interface of RETRO.NET to curtail two behaviors indicated by the earlier work. We report on observed results. [Contributions] We describe and discuss a major study of potentially unwanted analyst behaviors and present results of a preliminary study toward determining if curbing these behaviors with enhancements to tracing software leads to fewer human errors.

Can biology students without programming skills solve problems that require computational solutions? They can if they learn to cooperate effectively with computer science students. The goal of the in-concert teaching approach is to introduce biology students to computational thinking by engaging them in collaborative projects structured around the software development process. Our approach emphasizes development of interdisciplinary communication and collaboration skills for both life science and computer science students.