Collaborative video searching on a tabletop

Almost all system and application design for multimedia systems is based around a single user working in isolation to perform some task yet much of the work for which we use computers to help us, is based on working collaboratively with colleagues. Groupware systems do support user collaboration but typically this is supported through software and users still physically work independently. Tabletop systems, such as the DiamondTouch from MERL, are interface devices which support direct user collaboration on a tabletop. When a tabletop is used as the interface for a multimedia system, such as a video search system, then this kind of direct collaboration raises many questions for system design. In this paper we present a tabletop system for supporting a pair of users in a video search task and we evaluate the system not only in terms of search performance but also in terms of user–user interaction and how different user personalities within each pair of searchers impacts search performance and user interaction. Incorporating the user into the system evaluation as we have done here reveals several interesting results and has important ramifications for the design of a multimedia search system

Global-Scale Resource Survey and Performance Monitoring of Public OGC Web Map Services

One of the most widely-implemented service standards provided by the Open Geospatial Consortium (OGC) to the user community is the Web Map Service (WMS). WMS is widely employed globally, but there is limited knowledge of the global distribution, adoption status or the service quality of these online WMS resources. To fill this void, we investigated global WMSs resources and performed distributed performance monitoring of these services. This paper explicates a distributed monitoring framework that was used to monitor 46,296 WMSs continuously for over one year and a crawling method to discover these WMSs. We analyzed server locations, provider types, themes, the spatiotemporal coverage of map layers and the service versions for 41,703 valid WMSs. Furthermore, we appraised the stability and performance of basic operations for 1210 selected WMSs (i.e., GetCapabilities and GetMap). We discuss the major reasons for request errors and performance issues, as well as the relationship between service response times and the spatiotemporal distribution of client monitoring sites. This paper will help service providers, end users and developers of standards to grasp the status of global WMS resources, as well as to understand the adoption status of OGC standards. The conclusions drawn in this paper can benefit geospatial resource discovery, service performance evaluation and guide service performance improvements.Comment: 24 pages; 15 figure

Evaluating Visual Explanations for Similarity-Based Recommendations: User Perception and Performance

Recommender system helps users to reduce information overload. In recent years, enhancing explainability in recommender systems has drawn more and more attention in the field of Human-Computer Interaction (HCI). However, it is not clear whether a user-preferred explanation interface can maintain the same level of performance while the users are exploring or comparing the recommendations. In this paper, we introduced a participatory process of designing explanation interfaces with multiple explanatory goals for three similarity-based recommendation models. We investigate the relations of user perception and performance with two user studies. In the first study (N=15), we conducted card-sorting and semi-interview to identify the user preferred interfaces. In the second study (N=18), we carry out a performance-focused evaluation of six explanation interfaces. The result suggests that the user-preferred interface may not guarantee the same level of performance

Making Sense of Document Collections with Map-Based Visualizations

As map-based visualizations of documents become more ubiquitous, there is a greater need for them to support intellectual and creative high-level cognitive activities with collections of non-cartographic materials -- documents. This dissertation concerns the conceptualization of map-based visualizations as tools for sensemaking and collection understanding. As such, map-based visualizations would help people use georeferenced documents to develop understanding, gain insight, discover knowledge, and construct meaning. This dissertation explores the role of graphical representations (such as maps, Kohonen maps, pie charts, and other) and interactions with them for developing map-based visualizations capable of facilitating sensemaking activities such as collection understanding. While graphical representations make document collections more perceptually and cognitively accessible, interactions allow users to adapt representations to users’ contextual needs. By interacting with representations of documents or collections and being able to construct representations of their own, people are better able to make sense of information, comprehend complex structures, and integrate new information into their existing mental models. In sum, representations and interactions may reduce cognitive load and consequently expedite the overall time necessary for completion of sensemaking activities, which typically take much time to accomplish. The dissertation proceeds in three phases. The first phase develops a conceptual framework for translating ontological properties of collections to representations and for supporting visual tasks by means of graphical representations. The second phase concerns the cognitive benefits of interaction. It conceptualizes how interactions can help people during complex sensemaking activities. Although the interactions are explained on the example of a prototype built with Google Maps, they are independent iv of Google Maps and can be applicable to various other technologies. The third phase evaluates the utility, analytical capabilities and usability of the additional representations when users interact with a visualization prototype – VIsual COLlection EXplorer. The findings suggest that additional representations can enhance understanding of map-based visualizations of library collections: specifically, they can allow users to see trends, gaps, and patterns in ontological properties of collections

A DOMAIN-CENTRIC APPROACH TO DESIGNING USER INTERFACES OF VIDEO RETRIEVAL SYSTEMS

Thesis (PhD) - Indiana University, Information Science, 2007User- and task-centric efforts in video information retrieval (IR) research are needed because current experiments are showing few significant results. It is our belief that unsatisfactory results in video IR can be partially attributed to the overemphasis on technologically-driven approaches to interface development and system evaluation. This study explored variables that have been consistently overlooked in video retrieval efforts, including those related to domain and search tasks. The underlying goal of this study is to promote alternative means for evaluating video retrieval systems, and to make progress toward developing new design principles and a video seeking model. A series of interactive search runs were conducted using a video retrieval system called ViewFinder. ViewFinder was implemented to search and browse the NASA K - 16 Science Education Programs. The system includes new design features that take into account the unique characteristics of the domain and associated tasks. Users with a background in Science Education, including teachers and academic majors, were recruited to perform a number of search tasks. Results from the search experiments were collected and analyzed using both objective and subjective measures. From these results, researchers gained further knowledge about domain-centric video search tasks, including how textual, visual, and hybrid tasks were all deemed important by science educators. Further analysis of experimental results also revealed associations between search tasks, user interaction, interface features and functions, and system effectiveness. The evaluation of individual interface features and functions exhibited that keyword searching was significant for retrieving Science Education video. However, these experiments also produced positive results for various visual search features. Unlike keyword searching, which was consistent and effective across many task types, the use and effectiveness of visual search and browse features were shown to be task dependent. Overall, the results from this study highlight the importance of user- and task-centric methods in video retrieval, as they provided researchers with additional understanding of the influences of domain-specific search tasks on user interaction with video systems. In addition, the experimental methodology employed for this study encourages future foundations for developing and evaluating video search interfaces designed for specific domains and search tasks

Controllability and explainability in a hybrid social recommender system

The growth in artificial intelligence (AI) technology has advanced many human-facing applications. The recommender system is one of the promising sub-domain of AI-driven application, which aims to predict items or ratings based on user preferences. These systems were empowered by large-scale data and automated inference methods that bring useful but puzzling suggestions to the users. That is, the output is usually unpredictable and opaque, which may demonstrate user perceptions of the system that can be confusing, frustrating or even dangerous in many life-changing scenarios. Adding controllability and explainability are two promising approaches to improve human interaction with AI. However, the varying capability of AI-driven applications makes the conventional design principles are less useful. It brings tremendous opportunities as well as challenges for the user interface and interaction design, which has been discussed in the human-computer interaction (HCI) community for over two decades. The goal of this dissertation is to build a framework for AI-driven applications that enables people to interact effectively with the system as well as be able to interpret the output from the system. Specifically, this dissertation presents the exploration of how to bring controllability and explainability to a hybrid social recommender system, included several attempts in designing user-controllable and explainable interfaces that allow the users to fuse multi-dimensional relevance and request explanations of the received recommendations. The works contribute to the HCI fields by providing design implications of enhancing human-AI interaction and gaining transparency of AI-driven applications

Quantifying, Modeling and Managing How People Interact with Visualizations on the Web

The growing number of interactive visualizations on the web has made it possible for the general public to access data and insights that were once only available to domain experts. At the same time, this rise has yielded new challenges for visualization creators, who must now understand and engage a growing and diverse audience. To bridge this gap between creators and audiences, we explore and evaluate components of a design-feedback loop that would enable visualization creators to better accommodate their audiences as they explore the visualizations. In this dissertation, we approach this goal by quantifying, modeling and creating tools that manage people’s open-ended explorations of visualizations on the web. In particular, we: 1. Quantify the effects of design alternatives on people’s interaction patterns in visualizations. We define and evaluate two techniques: HindSight (encoding a user’s interaction history) and text-based search, where controlled experiments suggest that design details can significantly modulate the interaction patterns we observe from participants using a given visualization. 2. Develop new metrics that characterize facets of people’s exploration processes. Specifically, we derive expressive metrics describing interaction patterns such as exploration uniqueness, and use Bayesian inference to model distributional effects on interaction behavior. Our results show that these metrics capture novel patterns in people’s interactions with visualizations. 3. Create tools that manage and analyze an audience’s interaction data for a given visualization. We develop a prototype tool, ReVisIt, that visualizes an audience’s interactions with a given visualization. Through an interview study with visualization creators, we found that ReVisIt make creators aware of individual and overall trends in their audiences’ interaction patterns. By establishing some of the core elements of a design-feedback loop for visualization creators, the results in this research may have a tangible impact on the future of publishing interactive visualizations on the web. Equipped with techniques, metrics, and tools that realize an initial feedback loop, creators are better able to understand the behavior and user needs, and thus create visualizations that make data and insights more accessible to the diverse audiences on the web