410 research outputs found
Slicing and dicing the information space using local contexts
In recent years there has been growing interest in faceted grouping of documents for Interactive Information Retrieval (IIR). It is suggested that faceted grouping can offer a flexible way of browsing a collection compared to clustering. However, the success of faceted grouping seems to rely on sufficient knowledge of collection structure. In this paper we propose an approach based on the local contexts of query terms, which is inspired by the interaction of faceted search and browsing. The use of local contexts is appealing since it requires less knowledge of the collection than existing approaches. A task-based user study was carried out to investigate the effectiveness of our interface in varied complexity. The results suggest that the local contexts can be exploited as the source of search result browsing in IIR, and that our interface appears to facilitate different aspects of search process over the task complexity. The implication of the evaluation methodology using high complexity tasks is also discussed
Graph search and beyond:SIGIR 2015 workshop summary
Modern Web data is highly structured in terms of entities and relations from large knowledge resources, geo-temporal references and social network structure, resulting in a massive multidimensional graph. This graph essentially unifies both the searcher and the information resources that played a fundamentally different role in traditional IR, and "Graph Search" offers major new ways to access relevant information. Graph search affects both query formulation (complex queries about entities and relations building on the searcher's context) as well as result exploration and discovery (slicing and dicing the information using the graph structure) in a completely personalized way. This new graph based approach introduces great opportunities, but also great challenges, in terms of data quality and data integration, user interface design, and privacy. We view the notion of "graph search" as searching information from your personal point of view (you are the query) over a highly structured and curated information space. This goes beyond the traditional two-term queries and ten blue links results that users are familiar with, requiring a highly interactive session covering both query formulation and result exploration. The workshop attracted a range of researchers working on this and related topics, and made concrete progress working together on one of the greatest challenges in the years to come
Human activity recognition for pervasive interaction
PhD ThesisThis thesis addresses the challenge of computing food preparation context in the kitchen. The automatic
recognition of fine-grained human activities and food ingredients is realized through pervasive sensing
which we achieve by instrumenting kitchen objects such as knives, spoons, and chopping boards with
sensors. Context recognition in the kitchen lies at the heart of a broad range of real-world applications. In
particular, activity and food ingredient recognition in the kitchen is an essential component for situated
services such as automatic prompting services for cognitively impaired kitchen users and digital situated
support for healthier eating interventions. Previous works, however, have addressed the activity
recognition problem by exploring high-level-human activities using wearable sensing (i.e. worn sensors
on human body) or using technologies that raise privacy concerns (i.e. computer vision). Although such
approaches have yielded significant results for a number of activity recognition problems, they are not
applicable to our domain of investigation, for which we argue that the technology itself must be genuinely
âinvisibleâ, thereby allowing users to perform their activities in a completely natural manner.
In this thesis we describe the development of pervasive sensing technologies and algorithms for finegrained
human activity and food ingredient recognition in the kitchen. After reviewing previous work on
food and activity recognition we present three systems that constitute increasingly sophisticated
approaches to the challenge of kitchen context recognition. Two of these systems, Slice&Dice and Classbased
Threshold Dynamic Time Warping (CBT-DTW), recognize fine-grained food preparation
activities. Slice&Dice is a proof-of-concept application, whereas CBT-DTW is a real-time application
that also addresses the problem of recognising unknown activities. The final system, KitchenSense is a
real-time context recognition framework that deals with the recognition of a more complex set of
activities, and includes the recognition of food ingredients and events in the kitchen. For each system, we
describe the prototyping of pervasive sensing technologies, algorithms, as well as real-world experiments
and empirical evaluations that validate the proposed solutions.Vietnamese governmentâs 322 project, executed by the Vietnamese Ministry of
Education and Training
Fabrication, structure and properties of epoxy/metal nanocomposites
Gd2O3 nanoparticles surface-modiïŹed with IPDI were compounded with epoxy. IPDI provided an anchor into the porous Gd2O3 surface and a bridge into the matrix, thus creating strong bonds between matrix and Gd2O3. 1.7 vol.-% Gd2O3 increased the Youngâs modulus of epoxy by 16â19%; the surface-modiïŹed Gd2O3 nanoparticles improved the critical strain energy release rate by 64.3% as compared to 26.4% produced by the unmodiïŹed nanoparticles. The X-ray shielding efïŹciency of neat epoxy was enhanced by 300â360%, independent of the interface modiïŹcation. Interface debonding consumes energy and leads to crack pinning and matrix shear banding; most fracture energy is consumed by matrix shear banding as shown by the large number of ridges on the fracture surface
An Analysis of the Current Program Slicing and Algorithmic Debugging Based Techniques
This thesis presents a classification of program slicing based techniques. The classification allows us to identify the differences between existing techniques, but it also allows us to predict new slicing techniques. The study identifies and compares the dimensions that influence current techniques.Silva Galiana, JF. (2008). An Analysis of the Current Program Slicing and Algorithmic Debugging Based Techniques. http://hdl.handle.net/10251/14300Archivo delegad
Interactive visual exploration of a large spatio-temporal dataset: Reflections on a geovisualization mashup
Exploratory visual analysis is useful for the preliminary investigation of large structured, multifaceted spatio-temporal datasets. This process requires the selection and aggregation of records by time, space and attribute, the ability to transform data and the flexibility to apply appropriate visual encodings and interactions. We propose an approach inspired by geographical 'mashups' in which freely-available functionality and data are loosely but flexibly combined using de facto exchange standards. Our case study combines MySQL, PHP and the LandSerf GIS to allow Google Earth to be used for visual synthesis and interaction with encodings described in KML. This approach is applied to the exploration of a log of 1.42 million requests made of a mobile directory service. Novel combinations of interaction and visual encoding are developed including spatial 'tag clouds', 'tag maps', 'data dials' and multi-scale density surfaces. Four aspects of the approach are informally evaluated: the visual encodings employed, their success in the visual exploration of the clataset, the specific tools used and the 'rnashup' approach. Preliminary findings will be beneficial to others considering using mashups for visualization. The specific techniques developed may be more widely applied to offer insights into the structure of multifarious spatio-temporal data of the type explored here
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