McNair Scholars: Identifying and Overcoming the Obstacles in Academia Faced by Underrepresented Populations

Using qualitative methodology, this study identifies obstacles faced by students underrepresented in academia (i.e., first-generation, low income, race/ethnic and gender minorities) participating in a high impact program, and how self-efficacy is maintained while combating obstacles. This study adds a new dimension to understanding the relationship between underserved populations in academia, self-efficacy and student success. Findings, based on interviews with nine McNair Scholars, identified multiple themes as both obstacles and benefits to their academic education e.g., minority status, support, knowledge, time/ organization, stability and community. A better understanding of the complex relationship between success and hardship for non-dominant identities in academia is needed before adequate changes can be implemented, promoting success for all students

Olfoto: designing a smell-based interaction

We present a study into the use of smell for searching digi-tal photo collections. Many people now have large photo libraries on their computers and effective search tools are needed. Smell has a strong link to memory and emotion so may be a good way to cue recall when searching. Our study compared text and smell based tagging. For the first stage we generated a set of smell and tag names from user de-scriptions of photos, participants then used these to tag pho-tos, returning two weeks later to answer questions on their photos. Results showed that participants could tag effec-tively with text labels, as this is a common and familiar task. Performance with smells was lower but participants performed significantly above chance, with some partici-pants using smells well. This suggests that smell has poten-tial. Results also showed that some smells were consistently identified and useful, but some were not and highlighted issues with smell delivery devices. We also discuss some practical issues of using smell for interaction

Smelly Maps: The Digital Life of Urban Smellscapes

Smell has a huge influence over how we perceive places. Despite its importance, smell has been crucially overlooked by urban planners and scientists alike, not least because it is difficult to record and analyze at scale. One of the authors of this paper has ventured out in the urban world and conducted smellwalks in a variety of cities: participants were exposed to a range of different smellscapes and asked to record their experiences. As a result, smell-related words have been collected and classified, creating the first dictionary for urban smell. Here we explore the possibility of using social media data to reliably map the smells of entire cities. To this end, for both Barcelona and London, we collect geo-referenced picture tags from Flickr and Instagram, and geo-referenced tweets from Twitter. We match those tags and tweets with the words in the smell dictionary. We find that smell-related words are best classified in ten categories. We also find that specific categories (e.g., industry, transport, cleaning) correlate with governmental air quality indicators, adding validity to our study.Comment: 11 pages, 7 figures, Proceedings of 9th International AAAI Conference on Web and Social Media (ICWSM2015

Are Smell-Based Metrics Actually Useful in Effort-Aware Structural Change-Proneness Prediction? An Empirical Study

Bad code smells (also named as code smells) are symptoms of poor design choices in implementation. Existing studies empirically confirmed that the presence of code smells increases the likelihood of subsequent changes (i.e., change-proness). However, to the best of our knowledge, no prior studies have leveraged smell-based metrics to predict particular change type (i.e., structural changes). Moreover, when evaluating the effectiveness of smell-based metrics in structural change-proneness prediction, none of existing studies take into account of the effort inspecting those change-prone source code. In this paper, we consider five smell-based metrics for effort-aware structural change-proneness prediction and compare these metrics with a baseline of well-known CK metrics in predicting particular categories of change types. Specifically, we first employ univariate logistic regression to analyze the correlation between each smellbased metric and structural change-proneness. Then, we build multivariate prediction models to examine the effectiveness of smell-based metrics in effort-aware structural change-proneness prediction when used alone and used together with the baseline metrics, respectively. Our experiments are conducted on six Java open-source projects with up to 60 versions and results indicate that: (1) all smell-based metrics are significantly related to structural change-proneness, except metric ANS in hive and SCM in camel after removing confounding effect of file size; (2) in most cases, smell-based metrics outperform the baseline metrics in predicting structural change-proneness; and (3) when used together with the baseline metrics, the smell-based metrics are more effective to predict change-prone files with being aware of inspection effort

Shear Strength of Franciscan Complex Melange as Calculated from Back-Analysis of a Landslide

A methodology was developed to evaluate causation of a complex landslide that occurred in Franciscan Complex melange during the winter of 1996-1997 in San Mateo County, California. Conventional back-analysis methods to evaluate shear strength parameters were insufficient because the basal failure plane traversed three materials (fill, block-poor melange, and block-rich melange). Instead, using field observations of landslide kinematics, review of available subsurface data, and previous experience with Franciscan melange, a methodology was developed that incorporates the location of critical failure surfaces generated by random search routines in PCSTABL5M as an additional constraint. The methodology was employed to evaluate the relative effects of various destabilizing modifications to the slope

The sweet smell of success: Enhancing multimedia applications with olfaction

This is the Post-Print version of the Article. The official published version can be accessed from the link below - Copyright @ 2012 ACMOlfaction, or smell, is one of the last challenges which multimedia applications have to conquer. As far as computerized smell is concerned, there are several difficulties to overcome, particularly those associated with the ambient nature of smell. In this article, we present results from an empirical study exploring users' perception of olfaction-enhanced multimedia displays. Findings show that olfaction significantly adds to the user multimedia experience. Moreover, use of olfaction leads to an increased sense of reality and relevance. Our results also show that users are tolerant of the interference and distortion effects caused by olfactory effect in multimedia