D3.1 User expectations and cross-modal interaction

This document is deliverable D3.1 “User expectations and cross-modal in-teraction” and presents user studies to understand expectations and reac-tions to content presentation methods for mobile AR applications and rec-ommendations to realize an interface and interaction design in accordance with user needs or disabilities

Visualizing the Motion Flow of Crowds

In modern cities, massive population causes problems, like congestion, accident, violence and crime everywhere. Video surveillance system such as closed-circuit television cameras is widely used by security guards to monitor human behaviors and activities to manage, direct, or protect people. With the quantity and prolonged duration of the recorded videos, it requires a huge amount of human resources to examine these video recordings and keep track of activities and events. In recent years, new techniques in computer vision field reduce the barrier of entry, allowing developers to experiment more with intelligent surveillance video system. Different from previous research, this dissertation does not address any algorithm design concerns related to object detection or object tracking. This study will put efforts on the technological side and executing methodologies in data visualization to find the model of detecting anomalies. It would like to provide an understanding of how to detect the behavior of the pedestrians in the video and find out anomalies or abnormal cases by using techniques of data visualization

Information scraps: how and why information eludes our personal information management tools

In this paper we describe information scraps -- a class of personal information whose content is scribbled on Post-it notes, scrawled on corners of random sheets of paper, buried inside the bodies of e-mail messages sent to ourselves, or typed haphazardly into text files. Information scraps hold our great ideas, sketches, notes, reminders, driving directions, and even our poetry. We define information scraps to be the body of personal information that is held outside of its natural or We have much still to learn about these loose forms of information capture. Why are they so often held outside of our traditional PIM locations and instead on Post-its or in text files? Why must we sometimes go around our traditional PIM applications to hold on to our scraps, such as by e-mailing ourselves? What are information scraps' role in the larger space of personal information management, and what do they uniquely offer that we find so appealing? If these unorganized bits truly indicate the failure of our PIM tools, how might we begin to build better tools? We have pursued these questions by undertaking a study of 27 knowledge workers. In our findings we describe information scraps from several angles: their content, their location, and the factors that lead to their use, which we identify as ease of capture, flexibility of content and organization, and avilability at the time of need. We also consider the personal emotive responses around scrap management. We present a set of design considerations that we have derived from the analysis of our study results. We present our work on an application platform, jourknow, to test some of these design and usability findings

Spatial and Temporal Learning in Robotic Pick-and-Place Domains via Demonstrations and Observations

Traditional methods for Learning from Demonstration require users to train the robot through the entire process, or to provide feedback throughout a given task. These previous methods have proved to be successful in a selection of robotic domains; however, many are limited by the ability of the user to effectively demonstrate the task. In many cases, noisy demonstrations or a failure to understand the underlying model prevent these methods from working with a wider range of non-expert users. My insight is that in many mobile pick-and-place domains, teaching is done at a too fine grained level. In many such tasks, users are solely concerned with the end goal. This implies that the complexity and time associated with training and teaching robots through the entirety of the task is unnecessary. The robotic agent needs to know (1) a probable search location to retrieve the task\u27s objects and (2) how to arrange the items to complete the task. This thesis work develops new techniques for obtaining such data from high-level spatial and temporal observations and demonstrations which can later be applied in new, unseen environments. This thesis makes the following contributions: (1) This work is built on a crowd robotics platform and, as such, we contribute the development of efficient data streaming techniques to further these capabilities. By doing so, users can more easily interact with robots on a number of platforms. (2) The presentation of new algorithms that can learn pick-and-place tasks from a large corpus of goal templates. My work contributes algorithms that produce a metric which ranks the appropriate frame of reference for each item based solely on spatial demonstrations. (3) An algorithm which can enhance the above templates with ordering constraints using coarse and noisy temporal information. Such a method eliminates the need for a user to explicitly specify such constraints and searches for an optimal ordering and placement of items. (4) A novel algorithm which is able to learn probable search locations of objects based solely on sparsely made temporal observations. For this, we introduce persistence models of objects customized to a user\u27s environment

Designing for Cross-Device Interactions

Driven by technological advancements, we now own and operate an ever-growing number of digital devices, leading to an increased amount of digital data we produce, use, and maintain. However, while there is a substantial increase in computing power and availability of devices and data, many tasks we conduct with our devices are not well connected across multiple devices. We conduct our tasks sequentially instead of in parallel, while collaborative work across multiple devices is cumbersome to set up or simply not possible. To address these limitations, this thesis is concerned with cross-device computing. In particular it aims to conceptualise, prototype, and study interactions in cross-device computing. This thesis contributes to the field of Human-Computer Interaction (HCI)—and more specifically to the area of cross-device computing—in three ways: first, this work conceptualises previous work through a taxonomy of cross-device computing resulting in an in-depth understanding of the field, that identifies underexplored research areas, enabling the transfer of key insights into the design of interaction techniques. Second, three case studies were conducted that show how cross-device interactions can support curation work as well as augment users’ existing devices for individual and collaborative work. These case studies incorporate novel interaction techniques for supporting cross-device work. Third, through studying cross-device interactions and group collaboration, this thesis provides insights into how researchers can understand and evaluate multi- and cross-device interactions for individual and collaborative work. We provide a visualization and querying tool that facilitates interaction analysis of spatial measures and video recordings to facilitate such evaluations of cross-device work. Overall, the work in this thesis advances the field of cross-device computing with its taxonomy guiding research directions, novel interaction techniques and case studies demonstrating cross-device interactions for curation, and insights into and tools for effective evaluation of cross-device systems