Television has always been a popular entertainment medium with considerable impact on consumer purchase behaviors. However, unlike mobiles and PCs, it has yet to support an explicit search capability that extends beyond simple content navigation. A core reason for this is lack of usable input interfaces for TV coupled with the difficulty of creating and executing queries based on rich media context. In this paper, we present TV Answers a system that combines a novel context capture capability (Freeze-Frame) with an intelligent Web services mediator (Edge Proxy) to "crowd-source" user-generated queries. We describe the design and implementation of an early prototype of TV Answers and highlight the challenges and opportunities presented by such systems. Our goal is to demonstrate the viability of using "social search" as a complement to existing algorithm-driven search solutions for rich media consumption.

Television and video consumption are growing rapidly worldwide, driving usage of second and third screens for related interactions and information. Since television has a proven impact on consumer purchase behaviors, there is commercial interest in technologies that model viewers' intent, interests

We present the design and implementation of a new indexing technique, Mondrian tree. The Mondrian tree indexing method partitions the entire universe of discourse into spatial alarm monitoring regions and alarm-free regions. This enables us to reduce the number of on-demand alarm-free region computations, significant savings of both server load and client-to-server communication cost. We evaluate the efficiency of the Mondrian tree indexing approach and show that the Mondrian tree offers significant performance enhancements on spatial alarm processing at both the server side and the client side.

Searching for small objects (e.g., logos) in images is a critical yet challenging problem. It becomes more difficult when target objects differ significantly from the query object due to changes in scale, viewpoint or style, not to mention partial occlusion or cluttered backgrounds. With the goal to retrieve and accurately locate the small object in the images, we formulate the object search as the problem of finding subimages with the largest mutual information toward the query object. Each image is characterized by a collection of local features. Instead of only using the query object for matching, we propose a discriminative matching using both positive and negative queries to obtain the mutual information score. The user can verify the retrieved subimages and improve the search results incrementally. Our experiments on a challenging logo database of 10,000 images highlight the effectiveness of this approach.

Richer communication, computational and content- handling capabilities on mobile devices are driving the adoption of more interactive and collaborative applications. These motivate a need for data-sharing paradigms that are flexible, efficient and lightweight - but also accessible across the different applications that may be involved in an ongoing user task. Our AppBus framework provides a unique "short term memory" facility for mobile devices, facilitating such data-sharing across co-resident applications. With the Remote AppBus extension, we make this facility accessible in a consistent manner across address spaces on a device, and potentially across devices. In this paper, we describe the design and implementation of remote AppBus, and discuss its utility in enabling task collaboration on device, and in facilitating task continuity across devices. We conclude with a look at related efforts, challenges and future directions for the research.

The explosion in content availability, coupled with the diversity in consumption devices, has fragmented the market. Users need new mechanisms for content discovery and navigation, while providers and advertisers need new ways to analyze and monetize usage. In this paper, we advocate 'social bookmarking' as a solution, given its proven utility in enabling affinity-based discovery online. But, adapting such web-centric ideas to rich media consumption raises technical and business challenges. With TV Clips, we introduce a system that tackles these concerns for broadcast television with extensibility to 3-screen usage. We describe the initial architecture and prototype, and discuss its evolution to support product-centric requirements like scalability and rights management. A secondary goal is to share our experiences in developing this prototype for a commercial environment (i.e., TV) and motivate further research in adapting 'social search' strategies to such media-rich domains.

As online Collaborative Question Answering (CQA) servicessuch as Yahoo! Answers and Baidu Knows are attracting users, questions, and answers at an explosive rate, the truly urgent and important questions are increasingly getting lost in the crowd. That is, questions that require immediate responses are pushed out of the way by the trivial but more recently arriving questions. Unlike other questions in collaborative question answering (CQA) for which users might be willing to wait until good answers appear, urgent questions are likely to be of interest to the asker only if answered in the next few minutes or hours. For such questions, late responses are either not useful or are simply not applicable. Unfortunately, current collaborative question-answering systems do not distinguish urgent questions from the rest, and could thus be ineffective for urgent information needs. We explore text- and data- mining methods for automatically identifying urgent questions in the CQA setting. Our results indicate that modeling the question context (i.e., the particular forum/category where the question was posted) can increase classification accuracy compared to the text of the question alone.

Abstract An interceptor is a software mechanism that provides the hooks that are needed to introduce additional code dynamically into the execution path of an application. By exploiting interceptors, developers can enhance and potentially modify the behavior of an application at runtime without having to revise or recompile the application code. We have identified three distinct interception points for the Java Remote Method Invocation (JavaRMI) model, at the proxy level, the transport level and the shared library level of the JavaRMI model. The interceptors implemented at these interception points employ the DynamicProxy API, the RMISocketFactory API, and a library mediation approach, respectively. Our interceptor implementations are novel in that they are transparent to the application, add nominal latency overheads and are easy to deploy, requiring only minimal modifications to the application. We describe how the interceptors can be exploited to introduce additional services (such as logging and profiling mechanisms) to the JavaRMI runtime. In particular, we describe the use of interceptors in the Aroma System to enhance the existing JavaRMI model with support for fault‐tolerance through the consistent replication of JavaRMI objects. Copyright © 2001 John Wiley & Sons, Ltd.

Electronic commerce technology offers the opportunity to integrate and optimize the global production and distribution supply chain. The computers of the various corporations, located throughout the world, will communicate with each other to determine the availability of components, to place and confirm orders, and to negotiate delivery timescales. We describe MAgNet, a system for networked electronic trading that is based on the Java mobile agent technology, called aglets. Aglets are dispatched by the buyer to the various suppliers, where they negotiate orders and deliveries, returning to the buyer with their best deals for approval. MAgNET handles the deep supply chain, where a supplier may need to contact further suppliers of subcomponents in order to respond to an enquiry. Experimental results demonstrate the feasibility of using the Java aglet technology for electronic commerce.