169,076 research outputs found
Overview of VideoCLEF 2009: New perspectives on speech-based multimedia content enrichment
VideoCLEF 2009 offered three tasks related to enriching video content for improved multimedia access in a multilingual environment. For each task, video data (Dutch-language television, predominantly documentaries) accompanied by speech recognition transcripts were provided.
The Subject Classification Task involved automatic tagging of videos with subject theme labels. The best performance was achieved by approaching subject tagging as an information retrieval task and using both speech recognition transcripts and archival metadata. Alternatively, classifiers were trained using either the training data provided or data collected from Wikipedia or via general Web search. The Affect Task involved detecting narrative peaks, defined as points where viewers perceive heightened dramatic tension. The task was carried out on the âBeeldenstormâ collection containing 45 short-form documentaries on the visual arts. The best runs exploited affective vocabulary and audience directed speech. Other approaches included using topic changes, elevated speaking pitch, increased speaking intensity and radical visual changes. The Linking Task, also called âFinding Related Resources Across Languages,â involved linking video to material on the same subject in a different language.
Participants were provided with a list of multimedia anchors (short video segments) in the Dutch-language âBeeldenstormâ collection and were expected to return target pages drawn from English-language Wikipedia. The best performing methods used the transcript of the
speech spoken during the multimedia anchor to build a query to search an index of the Dutch language Wikipedia. The Dutch Wikipedia pages returned were used to identify related English pages. Participants also experimented with pseudo-relevance feedback, query translation and methods that targeted proper names
Inattentive Consumers in Markets for Services
In an experiment on markets for services, we find that consumers are likely to stick to default tariffs and achieve suboptimal outcomes. We find that inattention to the task of choosing a better tariff is likely to be a substantial problem in addition to any task and tariff complexity effect. The institutional setup on which we primarily model our experiment is the UK electricity and gas markets, and our conclusion is that the new measures by the UK regulator Ofgem to improve consumer outcomes are likely to be of limited impact
Human-Machine Collaborative Optimization via Apprenticeship Scheduling
Coordinating agents to complete a set of tasks with intercoupled temporal and
resource constraints is computationally challenging, yet human domain experts
can solve these difficult scheduling problems using paradigms learned through
years of apprenticeship. A process for manually codifying this domain knowledge
within a computational framework is necessary to scale beyond the
``single-expert, single-trainee" apprenticeship model. However, human domain
experts often have difficulty describing their decision-making processes,
causing the codification of this knowledge to become laborious. We propose a
new approach for capturing domain-expert heuristics through a pairwise ranking
formulation. Our approach is model-free and does not require enumerating or
iterating through a large state space. We empirically demonstrate that this
approach accurately learns multifaceted heuristics on a synthetic data set
incorporating job-shop scheduling and vehicle routing problems, as well as on
two real-world data sets consisting of demonstrations of experts solving a
weapon-to-target assignment problem and a hospital resource allocation problem.
We also demonstrate that policies learned from human scheduling demonstration
via apprenticeship learning can substantially improve the efficiency of a
branch-and-bound search for an optimal schedule. We employ this human-machine
collaborative optimization technique on a variant of the weapon-to-target
assignment problem. We demonstrate that this technique generates solutions
substantially superior to those produced by human domain experts at a rate up
to 9.5 times faster than an optimization approach and can be applied to
optimally solve problems twice as complex as those solved by a human
demonstrator.Comment: Portions of this paper were published in the Proceedings of the
International Joint Conference on Artificial Intelligence (IJCAI) in 2016 and
in the Proceedings of Robotics: Science and Systems (RSS) in 2016. The paper
consists of 50 pages with 11 figures and 4 table
User evaluation of a pilot terminologies server for a distributed multi-scheme environment
The present paper reports on a user-centred evaluation of a pilot terminology service developed as part of the High Level Thesaurus (HILT) project at the Centre for Digital Library Research (CDLR) in the University of Strathclyde in Glasgow. The pilot terminology service was developed as an experimental platform to investigate issues relating to mapping between various subject schemes, namely Dewey Decimal Classification (DDC), Library of Congress Subject Headings (LCSH), the Unesco thesaurus, and the MeSH thesaurus, in order to cater for cross-browsing and cross-searching across distributed digital collections and services. The aim of the evaluation reported here was to investigate users' thought processes, perceptions, and attitudes towards the pilot terminology service and to identify user requirements for developing a full-blown pilot terminology service
User centred evaluation of an automatically constructed hyper-textbook
As hypertext systems become widely available and their popularity increases, attention has turned to converting existing textual documents into hypertextual form. An important issue in this area is the fully automatic production of hypertext for learning, teaching, training, or self-referencing. Although many studies have addressed the problem of producing hyper-books, either manually or semi-automatically, the actual usability of hyper-books tools is still an area of ongoing research. This article presents an effort to investigate the effectiveness of a hyper-textbook for self-referencing produced in a fully automatic way. The hyper-textbook is produced using the Hyper-TextBook methodology. We developed a taskbased evaluation scheme and performed a comparative usercentred evaluation between a hyper-textbook and a conventional, printed form of the same textbook. The results indicate that the hyper-textbook, in most cases, improves speed, accuracy, and user satisfaction in comparison to the printed form of the textbook
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