540 research outputs found
A FOUNDATION FOR OPEN INFORMATION ENVIRONMENTS
Traditionally, information systems were developed within organizations for use by known audiences for known purposes. Advances in information technology have changed this landscape dramatically. The reach of information systems frequntly extends beyond organizational boundaries for use by unknown audiences and for purposes not originally anticipated. Individuals and informal communities can generate and use information in ways previously restricted to formal organizations. We term applications with these characteristics open information environments (OIEs). OIEs are marked by diversity of information available, flexibility in accommodating new sources, users and uses, and information management with minimal controls on structure, content, and access. This creates opportunities to generate new information and use it in unexpected ways. However, OIEs also come with challenges in managing the semantic diversity, flexibility of use, and information quality issus arising from the range of users and lack of controls. In this paper, we propose a set of principles for managing OIEs effectively. We outline a research program to examine the potential of OIEs, the challenges they present, and how to design OIEs to realize the benefits while mitigating the challenges. We highlight our ongoing research in this area, and conclude with a call for more research on this important phenomenon
Online annotations tools for micro-level human behavior labeling on videos
Abstract. Successful machine learning and computer vision approach generally require significant amounts of annotated data for learning. These methods including identification, retrieval, classification of events, and analysis of human behavior from a video. Micro-level human behavior analysis usually requires laborious efforts for obtaining the precise labels. As the quantity of online video grows, the crowdsourcing approach provides a method for workers without a professional background to complete the annotation task. These workers require training to understand implicit knowledge of human behavior. The motivation of this study was to enhance the interaction between annotation workers for training purposes. By observing experienced local researchers in Oulu, the key problem with annotation is the precision of the results. The goal of this study was to provide training tools for people to improve the label quality, it illustrates the importance of training. In this study, a new annotation tool was developed to test workers’ performance in reviewing other annotations. This tool filters very noisy input by comment and vote feature. The result indicated that users were more likely to annotate micro behavior and time that refer to other opinions, and it was a more effective and reliable way to train. Besides, this study reported the development process with React and Firebase, it emphasized the use of more Web resources and tools to develop annotation tools
Toward Super-Creativity
What is super creativity? From the simple creation of a meal to the most sophisticated artificial intelligence system, the human brain is capable of responding to the most diverse challenges and problems in increasingly creative and innovative ways. This book is an attempt to define super creativity by examining creativity in humans, machines, and human-machine interactions. Organized into three sections, the volume covers such topics as increasing personal creativity, the impact of artificial intelligence and digital devices, and the interaction of humans and machines in fields such as healthcare and economics
Judgment Sieve: Reducing Uncertainty in Group Judgments through Interventions Targeting Ambiguity versus Disagreement
When groups of people are tasked with making a judgment, the issue of
uncertainty often arises. Existing methods to reduce uncertainty typically
focus on iteratively improving specificity in the overall task instruction.
However, uncertainty can arise from multiple sources, such as ambiguity of the
item being judged due to limited context, or disagreements among the
participants due to different perspectives and an under-specified task. A
one-size-fits-all intervention may be ineffective if it is not targeted to the
right source of uncertainty. In this paper we introduce a new workflow,
Judgment Sieve, to reduce uncertainty in tasks involving group judgment in a
targeted manner. By utilizing measurements that separate different sources of
uncertainty during an initial round of judgment elicitation, we can then select
a targeted intervention adding context or deliberation to most effectively
reduce uncertainty on each item being judged. We test our approach on two
tasks: rating word pair similarity and toxicity of online comments, showing
that targeted interventions reduced uncertainty for the most uncertain cases.
In the top 10% of cases, we saw an ambiguity reduction of 21.4% and 25.7%, and
a disagreement reduction of 22.2% and 11.2% for the two tasks respectively. We
also found through a simulation that our targeted approach reduced the average
uncertainty scores for both sources of uncertainty as opposed to uniform
approaches where reductions in average uncertainty from one source came with an
increase for the other
Usage-driven Maintenance of Knowledge Organization Systems
Knowledge Organization Systems (KOS) are typically used as background knowledge
for document indexing in information retrieval. They have to be maintained
and adapted constantly to reflect changes in the domain and the terminology. In
this thesis, approaches are provided that support the maintenance of hierarchical
knowledge organization systems, like thesauri, classifications, or taxonomies, by
making information about the usage of KOS concepts available to the maintainer.
The central contribution is the ICE-Map Visualization, a treemap-based visualization
on top of a generalized statistical framework that is able to visualize almost
arbitrary usage information. The proper selection of an existing KOS for available
documents and the evaluation of a KOS for different indexing techniques by means
of the ICE-Map Visualization is demonstrated.
For the creation of a new KOS, an approach based on crowdsourcing is presented
that uses feedback from Amazon Mechanical Turk to relate terms hierarchically.
The extension of an existing KOS with new terms derived from the documents
to be indexed is performed with a machine-learning approach that relates
the terms to existing concepts in the hierarchy. The features are derived from text
snippets in the result list of a web search engine. For the splitting of overpopulated
concepts into new subconcepts, an interactive clustering approach is presented that
is able to propose names for the new subconcepts.
The implementation of a framework is described that integrates all approaches
of this thesis and contains the reference implementation of the ICE-Map Visualization.
It is extendable and supports the implementation of evaluation methods
that build on other evaluations. Additionally, it supports the visualization of the
results and the implementation of new visualizations. An important building block
for practical applications is the simple linguistic indexer that is presented as minor
contribution. It is knowledge-poor and works without any training.
This thesis applies computer science approaches in the domain of information
science. The introduction describes the foundations in information science; in the
conclusion, the focus is set on the relevance for practical applications, especially
regarding the handling of different qualities of KOSs due to automatic and semiautomatic
maintenance
Knowledge-based Biomedical Data Science 2019
Knowledge-based biomedical data science (KBDS) involves the design and
implementation of computer systems that act as if they knew about biomedicine.
Such systems depend on formally represented knowledge in computer systems,
often in the form of knowledge graphs. Here we survey the progress in the last
year in systems that use formally represented knowledge to address data science
problems in both clinical and biological domains, as well as on approaches for
creating knowledge graphs. Major themes include the relationships between
knowledge graphs and machine learning, the use of natural language processing,
and the expansion of knowledge-based approaches to novel domains, such as
Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages
with 3 table
Retrieval Enhancements for Task-Based Web Search
The task-based view of web search implies that retrieval should take the user perspective into account. Going beyond merely retrieving the most relevant result set for the current query, the retrieval system should aim to surface results that are actually useful to the task that motivated the query.
This dissertation explores how retrieval systems can better understand and support their users’ tasks from three main angles: First, we study and quantify search engine user behavior during complex writing tasks, and how task success and behavior are associated in such settings. Second, we investigate search engine queries formulated as questions, and explore patterns in a large query log that may help search engines to better support this increasingly prevalent interaction pattern. Third, we propose a novel approach to reranking the search result lists produced by web search engines, taking into account retrieval axioms that formally specify properties of a good ranking.Die Task-basierte Sicht auf Websuche impliziert, dass die Benutzerperspektive berücksichtigt werden sollte. Über das bloße Abrufen der relevantesten Ergebnismenge für die aktuelle Anfrage hinaus, sollten Suchmaschinen Ergebnisse liefern, die tatsächlich für die Aufgabe (Task) nützlich sind, die diese Anfrage motiviert hat.
Diese Dissertation untersucht, wie Retrieval-Systeme die Aufgaben ihrer Benutzer besser verstehen und unterstützen können, und leistet Forschungsbeiträge unter drei Hauptaspekten: Erstens untersuchen und quantifizieren wir das Verhalten von Suchmaschinenbenutzern während komplexer Schreibaufgaben, und wie Aufgabenerfolg und Verhalten in solchen Situationen zusammenhängen. Zweitens untersuchen wir Suchmaschinenanfragen, die als Fragen formuliert sind, und untersuchen ein Suchmaschinenlog mit fast einer Milliarde solcher Anfragen auf Muster, die Suchmaschinen dabei helfen können, diesen zunehmend verbreiteten Anfragentyp besser zu unterstützen. Drittens schlagen wir einen neuen Ansatz vor, um die von Web-Suchmaschinen erstellten Suchergebnislisten neu zu sortieren, wobei Retrieval-Axiome berücksichtigt werden, die die Eigenschaften eines guten Rankings formal beschreiben
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