The purpose of this paper 1 is to build a fundamental framework of discovering and analyzing a workflow-based social network formed through workflow-based organizational business operations. A little more precisely speaking, the framework formalizes a series of theoretical steps from discovering a workflow-based social network to analyzing the discovered social network. For the sake of the discovery phase, we conceive an algorithm that is able to automatically discover the workflow-based social network from a workflow procedure; while on the other hand, in the analysis phase we apply the degree centrality algorithm to the discovered social network, which is one of the well-known social network analysis algorithms in the literature. Consequently, the crucial implication of the framework is in quantifying the degree of work-intimacy among performers who are involved in enacting the corresponding workflow procedure. Also, as a conceptual extension of the framework, it can be applied to discovering and analyzing degree centrality or collaborative closeness and betweenness among architectural components and nodes of collaborative cloud workflow computing environments.Keywords-workflow-based social network, ICN-based workflow model, human network discovery, degree of work-intimacy, centrality analysis

This study investigated how humans interact socially with robots. Participants engaged in a hallway navigation task with a robot. Throughout twelve trials, the display on the robot and its proxemics behavior was varied while participants were tasked with first, reacting to the robot’s actions and second, interpreting its behavior. Results indicated that proxemic behavior and robotic display characteristics influence the degree to which individuals perceive the robot as socially present, with more human-like displays and assertive robotic behaviors resulting in greater assessments of social presence. When examined in isolation, repeated interactions over time do not appear to affect the perception of a socially present robot under these particular circumstances. Results are discussed in the context of how social signals theory inform research in human-robot interaction.

Sexual exploration is a natural part of adolescent development; yet, unmediated internet access has enabled teens to engage in a wider variety of potentially riskier sexual interactions than previous generations, from normatively appropriate sexual interactions to sexually abusive situations. Teens have turned to online peer support platforms to disclose and seek support about these experiences. Therefore, we analyzed posts (N=45,955) made by adolescents (ages 13--17) on an online peer support platform to deeply examine their online sexual risk experiences. By applying a mixed methods approach, we 1) accurately (average of AUC = 0.90) identified posts that contained teen disclosures about online sexual risk experiences and classified the posts based on level of consent (i.e., consensual, non-consensual, sexual abuse) and relationship type (i.e., stranger, dating/friend, family) between the teen and the person in which they shared the sexual experience, 2) detected statistically significant differences in the proportions of posts based on these dimensions, and 3) further unpacked the nuance in how these online sexual risk experiences were typically characterized in the posts. Teens were significantly more likely to engage in consensual sexting with friends/dating partners; unwanted solicitations were more likely from strangers and sexual abuse was more likely when a family member was involved. We contribute to the HCI and CSCW literature around youth online sexual risk experiences by moving beyond the false dichotomy of "safe" versus "risky". Our work provides a deeper understanding of technology-mediated adolescent sexual behaviors from the perspectives of sexual well-being, risk detection, and the prevention of online sexual violence toward youth.

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Empirical evaluations of uncertainty visualizations often employ complex experimental tasks to ensure ecological validity. However, if training for such tasks is not sufficient for naïve participants, differences in performance could be due to the visualizations or to differences in task comprehension, making interpretation of findings problematic. Research has begun to assess how training is related to performance on decision-making tasks using uncertainty visualizations. This study continues this line of research by investigating how training, in general, and feedback, in particular, affect performance on a simulated resource allocation task. Additionally, we examined how this alters metacognition and workload to produce differences in cognitive efficiency. Our results suggest that, on a complex decision-making task, training plays a critical role in performance with respect to accuracy, subjective workload, and cognitive efficiency. This study has implications for improving research on complex decision making, and for designing more efficacious training interventions to assess uncertainty visualizations.