Biblio

The presence of robots is becoming more apparent as technology progresses and the market focus transitions from smart phones to robotic personal assistants such as those provided by Amazon and Google. The integration of robots in our societies is an inevitable tendency in which robots in many forms and with many functionalities will provide services to humans. This calls for an understanding of how humans are affected by both the presence of and the reliance on robots to perform services for them. In this paper we explore the effects that robots have on humans when a service is performed on request. We expose three groups of human participants to three levels of service completion performed by robots. We record and analyse human perceptions such as propensity to trust, competency, responsiveness, sociability, and team work ability. Our results demonstrate that humans tend to trust robots and are more willing to interact with them when they autonomously recover from failure by requesting help from other robots to fulfil their service. This supports the view that autonomy and team working capabilities must be brought into robots in an effort to strengthen trust in robots performing a service.

This Innovate Practice Full Paper describes our experience with teaching cybersecurity topics using guided inquiry collaborative learning. The goal is to not only develop the students' in-depth technical knowledge, but also “soft skills” such as communication, attitude, team work, networking, problem-solving and critical thinking. This paper reports our experience with developing and using the Guided Inquiry Collaborative Learning materials on the topics of firewall and IPsec. Pre- and post-surveys were conducted to access the effectiveness of the developed materials and teaching methods in terms of learning outcome, attitudes, learning experience and motivation. Analysis of the survey data shows that students had increased learning outcome, participation in class, and interest with Guided Inquiry Collaborative Learning.

Trust in human-automation interaction is increasingly imperative as AI and robots become ubiquitous at home, school, and work. Interdependence theory allows for the identification of one-on-one interactions that require trust by analyzing the structure of the potential outcomes. This paper synthesizes multiple, formerly disparate research approaches by extending Interdependence theory to create a unified framework for outcome-based trust in human-automation interaction. This framework quantitatively contextualizes validated empirical results from social psychology on relationship formation, stability, and betrayal. It also contributes insights into trust-related concepts, such as power and commitment, which help further our understanding of trustworthy system design. This new integrated interactional approach reveals how trust and trustworthiness machines from merely reliable tools to trusted teammates working hand-in-actuator toward an automated future.

Once the requirement is gathered in agile, it is broken down into smaller pre-defined format called user stories. These user stories are then scoped in various sprint releases and delivered accordingly. Release planning in Agile becomes challenging when the number of user stories goes up in hundreds. In such scenarios it is very difficult to manually identify similar user stories and package them together into a release. Hence, this paper suggests application of natural language processing algorithms for identifying similar user stories and then scoping them into a release This paper takes the approach to build a word corpus for every project release identified in the project and then to convert the provided user stories into a vector of string using Java utility for calculating top 3 most occurring words from the given project corpus in a user story. Once all the user stories are represented as vector array then by using RV coefficient NLP algorithm the user stories are clustered into various releases of the software project. Using the proposed approach, the release planning for large and complex software engineering projects can be simplified resulting into efficient planning in less time. The automated commercial tools like JIRA and Rally can be enhanced to include suggested algorithms for managing release planning in Agile.

Agile methods frequently have difficulties with qualities, often specifying quality requirements as stories, e.g., "As a user, I need a safe and secure system." Such projects will generally schedule some capability releases followed by safety and security releases, only to discover user-developer misunderstandings and unsecurable agile code, leading to project failure. Very large agile projects also have further difficulties with project velocity and scalability. Examples are trying to use daily standup meetings, 2-week sprints, shared tacit knowledge vs. documents, and dealing with user-developer misunderstandings. At USC, our Parallel Agile, Executable Architecture research project shows some success at mid-scale (50 developers). We also examined several large (hundreds of developers) TRW projects that had succeeded with rapid, high-quality development. The paper elaborates on their common Critical Quality Factors: a concurrent 3-team approach, an empowered Keeper of the Project Vision, and a management approach emphasizing qualities.

As robotic capabilities improve and robots become more capable as team members, a better understanding of effective human-robot teaming is needed. In this paper, we investigate failures by robots in various team configurations in space EVA operations. This paper describes the methodology of extending and the application of Work Models that Compute (WMC), a computational simulation framework, to model robot failures, interruptions, and the resolutions they require. Using these models, we investigate how different team configurations respond to a robot's failure to correctly complete the task and overall mission. We also identify key factors that impact the teamwork metrics for team designers to keep in mind while assembling teams and assigning taskwork to the agents. We highlight different metrics that these failures impact on team performance through varying components of teaming and interaction that occur. Finally, we discuss the future implications of this work and the future work to be done to investigate function allocation in human-robot teams.

In this paper, we address the problem of peer grouping employees in an organization for identifying security risks. Our motivation for studying peer grouping is its importance for a clear understanding of user and entity behavior analytics (UEBA) that is the primary tool for identifying insider threat through detecting anomalies in network traffic. We show that using Louvain method of community detection it is possible to automate peer group creation with feature-based weight assignments. Depending on the number of employees and their features we show that it is also possible to give each group a meaningful description. We present three new algorithms: one that allows an addition of new employees to already generated peer groups, another that allows for incorporating user feedback, and lastly one that provides the user with recommended nodes to be reassigned. We use Niara's data to validate our claims. The novelty of our method is its robustness, simplicity, scalability, and ease of deployment in a production environment.