VRMoViAn - An Immersive Data Annotation Tool for Visual Analysis of Human Interactions in VR

Understanding human behavior in virtual reality (VR) is a key component for developing intelligent systems to enhance human focused VR experiences. The ability to annotate human motion data proves to be a very useful way to analyze and understand human behavior. However, due to the complexity and multi-dimensionality of human activity data, it is necessary to develop software that can display the data in a comprehensible way and can support intuitive data annotation for developing machine learning models able recognize and assist human motions in VR (e.g., remote physical therapy). Although past research has been done to improve VR data visualization, no emphasis has been put into VR data annotation specifically for future machine learning applications. To fill this gap, we have developed a data annotation tool capable of displaying complex VR data in an expressive 3D animated format as well as providing an easily-understandable user interface that allows users to annotate and label human activity efficiently. Specifically, it can convert multiple motion data files into a watchable 3D video, and effectively demonstrate body motion: including eye tracking of the player in VR using animations as well as showcasing hand-object interactions with level-of-detail visualization features. The graphical user interface allows the user to interact and annotate VR data just like they do with other video playback tools. Our next step is to develop and integrate machine learning based clusters to automate data annotation. A user study is being planned to evaluate the tool in terms of user-friendliness and effectiveness in assisting with visualizing and analyzing human behavior along with the ability to easily and accurately annotate real-world datasets

A stigmergy-based analysis of city hotspots to discover trends and anomalies in urban transportation usage

A key aspect of a sustainable urban transportation system is the effectiveness of transportation policies. To be effective, a policy has to consider a broad range of elements, such as pollution emission, traffic flow, and human mobility. Due to the complexity and variability of these elements in the urban area, to produce effective policies remains a very challenging task. With the introduction of the smart city paradigm, a widely available amount of data can be generated in the urban spaces. Such data can be a fundamental source of knowledge to improve policies because they can reflect the sustainability issues underlying the city. In this context, we propose an approach to exploit urban positioning data based on stigmergy, a bio-inspired mechanism providing scalar and temporal aggregation of samples. By employing stigmergy, samples in proximity with each other are aggregated into a functional structure called trail. The trail summarizes relevant dynamics in data and allows matching them, providing a measure of their similarity. Moreover, this mechanism can be specialized to unfold specific dynamics. Specifically, we identify high-density urban areas (i.e hotspots), analyze their activity over time, and unfold anomalies. Moreover, by matching activity patterns, a continuous measure of the dissimilarity with respect to the typical activity pattern is provided. This measure can be used by policy makers to evaluate the effect of policies and change them dynamically. As a case study, we analyze taxi trip data gathered in Manhattan from 2013 to 2015.Comment: Preprin

The Reality of Measuring Human Service Programs: Results of a Survey

In the summer of 2013, Idealware created and distributed a survey to learn how human service organizations from their own mailing list are actually using technology to measure and evaluate the outcomes of their programs. The suvey looked at a general overview of outcomes measurement and program evaluation topics, from how frequently they look at data and how much time they spend doing so to what types of metrics the organizations were tracking. To further understand the realities of measuring program effectiveness, Idealware conducted a site visit and interview of three human service organizations in Portland, Maine. The results clearly show that the respondents are struggling to measure their programs

Is participatory design associated with the effectiveness of serious digital games for healthy lifestyle promotion? : a meta-analysis

Background: Serious digital games can be effective at changing healthy lifestyles, but large differences in their effectiveness exist. The extent of user involvement in game design may contribute to game effectiveness by creating a better fit with user preferences. Participatory design (PD), which represents active user involvement as informant (ie, users are asked for input and feedback) or codesigner (ie, users as equal partners in the design) early on and throughout the game development, may be associated with higher game effectiveness, as opposed to no user involvement or limited user involvement. Objective: This paper reports the results of a meta-analysis examining the moderating role of PD in the effectiveness of serious digital games for healthy lifestyle promotion. Methods: Four databases were searched for peer-reviewed papers in English that were published or in press before October 2014, using a (group-) randomized controlled trial design. Effectiveness data were derived from another meta-analysis assessing the role of behavior change techniques and game features in serious game effectiveness. Results: A total of 58 games evaluated in 61 studies were included. As previously reported, serious digital games had positive effects on healthy lifestyles and their determinants. Unexpectedly, PD (g=0.075, 95% CI 0.017 to 0.133) throughout game development was related to lower game effectiveness on behavior (Q=6.74, P<.05) than when users were only involved as testers (g=0.520, 95% CI 0.150 to 0.890, P<.01). Games developed with PD (g=0.171, 95% CI 0.061 to 0.281, P<.01) were also related to lower game effectiveness on self-efficacy (Q=7.83, P<.05) than when users were not involved in game design (g=0.384, 95% CI 0.283 to 0.485, P<.001). Some differences were noted depending on age group, publication year of the study, and on the specific role in PD (ie, informant or codesigner), and depending on the game design element. Games developed with PD were more effective in changing behavioral determinants when they included users in design elements on game dynamics (beta=.215, 95% CI .075 to .356, P<.01) and, more specifically, as an informant (beta=.235, 95% CI .079 to .329, P<.01). Involving users as informants in PD to create game levels was also related to higher game effectiveness (Q=7.02, P<.01). Codesign was related to higher effectiveness when used to create the game challenge (Q=11.23, P<.01), but to lower game effectiveness when used to create characters (Q=4.36, P<.05) and the game world (Q=3.99, P<.05). Conclusions: The findings do not support higher effectiveness of games developed with PD. However, significant differences existed among PD games. More support was found for informant roles than for codesign roles. When PD was applied to game dynamics, levels, and game challenge, this was associated with higher effectiveness than when it was applied to game aesthetics. Since user involvement may have an important influence on reach, adoption, and implementation of the intervention, further research and design efforts are needed to enhance effectiveness of serious games developed with PD

Strategic HRM Measurement in the 21st Century: From Justifying HR to Strategic Talent Leadership

Measurement will be vital to the evolution of human resource management in the coming century, but in this chapter we propose that it will not be measurement as usual. The future of HRM will require a decision science for talent resources that is as logical, reliable, consistent and flexible as Finance, the decision science for financial resources, and Marketing, the decision science for customer resources. In this chapter we describe the elements of this new decision science, which we call “Talentship,” and its implications for the future of strategic HR measurement. Using this framework, we review leading measurement approaches, describe their contributions, and identify the significant opportunities for improvement in future HR measurement systems

Resources and Tools:A Step-by-Step Methodological Guide for Costing HIV/AIDS Activities

Many developing countries have recognized the need for comprehensive national reforms and comprehensive prevention, treatment, and care and support initiatives to reduce future transmission of and to meet the growing demand for HIV/AIDS services. As a part of these national health reform initiatives, governments are exploring ways to allocate resources in the most efficient and effective way to mitigate the HIV/AIDS epidemic. However, many countries lack information on the level and nature of the costs of HIV/AIDS programs. This document provides an introduction to the procedure for calculating and analyzing the costs of HIV/AIDS programs and describes how to measure directly the actual costs of a program that is up and running. The step-by-step guide is intended to provide project managers in the field with a framework for how to do measure costs for a single, recent year in the life of an HIV/AIDS program. An illustrative activities list in the report annex will assist the user to develop an activities-based framework. The information gleaned from the costing framework will enable policymakers and program managers to make informed resource allocation decisions

Keeping Context In Mind: Automating Mobile App Access Control with User Interface Inspection

Recent studies observe that app foreground is the most striking component that influences the access control decisions in mobile platform, as users tend to deny permission requests lacking visible evidence. However, none of the existing permission models provides a systematic approach that can automatically answer the question: Is the resource access indicated by app foreground? In this work, we present the design, implementation, and evaluation of COSMOS, a context-aware mediation system that bridges the semantic gap between foreground interaction and background access, in order to protect system integrity and user privacy. Specifically, COSMOS learns from a large set of apps with similar functionalities and user interfaces to construct generic models that detect the outliers at runtime. It can be further customized to satisfy specific user privacy preference by continuously evolving with user decisions. Experiments show that COSMOS achieves both high precision and high recall in detecting malicious requests. We also demonstrate the effectiveness of COSMOS in capturing specific user preferences using the decisions collected from 24 users and illustrate that COSMOS can be easily deployed on smartphones as a real-time guard with a very low performance overhead.Comment: Accepted for publication in IEEE INFOCOM'201