Human Motion Trajectory Prediction: A Survey

With growing numbers of intelligent autonomous systems in human environments, the ability of such systems to perceive, understand and anticipate human behavior becomes increasingly important. Specifically, predicting future positions of dynamic agents and planning considering such predictions are key tasks for self-driving vehicles, service robots and advanced surveillance systems. This paper provides a survey of human motion trajectory prediction. We review, analyze and structure a large selection of work from different communities and propose a taxonomy that categorizes existing methods based on the motion modeling approach and level of contextual information used. We provide an overview of the existing datasets and performance metrics. We discuss limitations of the state of the art and outline directions for further research.Comment: Submitted to the International Journal of Robotics Research (IJRR), 37 page

A phenomenological study of the impact of knowledge intensity and environmental velocity on in source or hosted contact centres.

Contact centres exist in order to focus the final step of the intra organisational value chain which then delivers optimalcustomer satisfaction. In this paper we analyse a centre with a view to investigating the impact of outsourcing or the inhouselocus of provision. Such centres exhibit agency/principal characteristics, bringing knowledge management into sharp focus, aspects of information intensity which impact on the organisational dynamics, and the learning of the employees. A phenomenological approach to determine the essence of the activities was deployed rather than a methodological initiative based post positivistic strategic analysis. The characteristics of contact centres investigated coalesce into two distinct categories; a framework to depict this is presented

Psychological counseling in the Italian academic context: Expected needs, activities, and target population in a large sample of students

University psychological counseling (UPC) is receiving growing attention as a means to promote mental health and academic success among young adults and prevent irregular attendance and dropout. However, thus far, little effort has been directed towards the implementation of services attuned to students' expectations and needs. This work intends to contribute to the existing literature on this topic, by exploring the perceptions of UPC among a population of 39,277 students attending one of the largest universities in the South of Italy. Almost half of the total population correctly identified the UPC target population as university students, and about one third correctly expected personal distress to be the main need that UPC should target. However, a large percentage did not have a clear idea about UPC target needs, activities, and population. When two specific student subsamples were analyzed using a person-centered analysis, namely (i) those who expressed their intention to use the counseling service but had not yet done so and (ii) those who had already used it, the first subsample clustered into two groups, characterized by an "emotional" and a "psychopathological" focus, respectively, while the second subsample clustered into three groups with a "clinical", "socioemotional", and "learning" focus, respectively. This result shows a somewhat more "superficial" and "common" representation of UPC in the first subsample and a more "articulated" and "flexible" vision in the second subsample. Taken together, these findings suggest that UPC services could adopt "student-centered" strategies to both identify and reach wider audiences and specific student subgroups. Recommended strategies include robust communication campaigns to help students develop a differentiated perception of the available and diverse academic services, and the involvement of active students to remove the barriers of embarrassment and shame often linked to the stigma of using mental health services

Online Popularity and Topical Interests through the Lens of Instagram

Online socio-technical systems can be studied as proxy of the real world to investigate human behavior and social interactions at scale. Here we focus on Instagram, a media-sharing online platform whose popularity has been rising up to gathering hundred millions users. Instagram exhibits a mixture of features including social structure, social tagging and media sharing. The network of social interactions among users models various dynamics including follower/followee relations and users' communication by means of posts/comments. Users can upload and tag media such as photos and pictures, and they can "like" and comment each piece of information on the platform. In this work we investigate three major aspects on our Instagram dataset: (i) the structural characteristics of its network of heterogeneous interactions, to unveil the emergence of self organization and topically-induced community structure; (ii) the dynamics of content production and consumption, to understand how global trends and popular users emerge; (iii) the behavior of users labeling media with tags, to determine how they devote their attention and to explore the variety of their topical interests. Our analysis provides clues to understand human behavior dynamics on socio-technical systems, specifically users and content popularity, the mechanisms of users' interactions in online environments and how collective trends emerge from individuals' topical interests.Comment: 11 pages, 11 figures, Proceedings of ACM Hypertext 201

Quality in MOOCs: Surveying the Terrain

The purpose of this review is to identify quality measures and to highlight some of the tensions surrounding notions of quality, as well as the need for new ways of thinking about and approaching quality in MOOCs. It draws on the literature on both MOOCs and quality in education more generally in order to provide a framework for thinking about quality and the different variables and questions that must be considered when conceptualising quality in MOOCs. The review adopts a relativist approach, positioning quality as a measure for a specific purpose. The review draws upon Biggs’s (1993) 3P model to explore notions and dimensions of quality in relation to MOOCs — presage, process and product variables — which correspond to an input–environment–output model. The review brings together literature examining how quality should be interpreted and assessed in MOOCs at a more general and theoretical level, as well as empirical research studies that explore how these ideas about quality can be operationalised, including the measures and instruments that can be employed. What emerges from the literature are the complexities involved in interpreting and measuring quality in MOOCs and the importance of both context and perspective to discussions of quality

Predictive Coding for Dynamic Visual Processing: Development of Functional Hierarchy in a Multiple Spatio-Temporal Scales RNN Model

The current paper proposes a novel predictive coding type neural network model, the predictive multiple spatio-temporal scales recurrent neural network (P-MSTRNN). The P-MSTRNN learns to predict visually perceived human whole-body cyclic movement patterns by exploiting multiscale spatio-temporal constraints imposed on network dynamics by using differently sized receptive fields as well as different time constant values for each layer. After learning, the network becomes able to proactively imitate target movement patterns by inferring or recognizing corresponding intentions by means of the regression of prediction error. Results show that the network can develop a functional hierarchy by developing a different type of dynamic structure at each layer. The paper examines how model performance during pattern generation as well as predictive imitation varies depending on the stage of learning. The number of limit cycle attractors corresponding to target movement patterns increases as learning proceeds. And, transient dynamics developing early in the learning process successfully perform pattern generation and predictive imitation tasks. The paper concludes that exploitation of transient dynamics facilitates successful task performance during early learning periods.Comment: Accepted in Neural Computation (MIT press