98,248 research outputs found
Switcher-random-walks: a cognitive-inspired mechanism for network exploration
Semantic memory is the subsystem of human memory that stores knowledge of
concepts or meanings, as opposed to life specific experiences. The organization
of concepts within semantic memory can be understood as a semantic network,
where the concepts (nodes) are associated (linked) to others depending on
perceptions, similarities, etc. Lexical access is the complementary part of
this system and allows the retrieval of such organized knowledge. While
conceptual information is stored under certain underlying organization (and
thus gives rise to a specific topology), it is crucial to have an accurate
access to any of the information units, e.g. the concepts, for efficiently
retrieving semantic information for real-time needings. An example of an
information retrieval process occurs in verbal fluency tasks, and it is known
to involve two different mechanisms: -clustering-, or generating words within a
subcategory, and, when a subcategory is exhausted, -switching- to a new
subcategory. We extended this approach to random-walking on a network
(clustering) in combination to jumping (switching) to any node with certain
probability and derived its analytical expression based on Markov chains.
Results show that this dual mechanism contributes to optimize the exploration
of different network models in terms of the mean first passage time.
Additionally, this cognitive inspired dual mechanism opens a new framework to
better understand and evaluate exploration, propagation and transport phenomena
in other complex systems where switching-like phenomena are feasible.Comment: 9 pages, 3 figures. Accepted in "International Journal of
Bifurcations and Chaos": Special issue on "Modelling and Computation on
Complex Networks
Personalized human computation
Significant effort in machine learning and information retrieval has been devoted to identifying personalized content such as recommendations and search results.
Personalized human computation has the potential to go beyond existing techniques like collaborative filtering to provide personalized results on demand, over personal data, and for complex tasks. This work-in-progress compares two approaches to personalized human computation. In both, users annotate a small set of training examples which are then used by the crowd to annotate unseen items. In the first approach, which we call taste-matching, crowd members are asked to annotate the same set of training examples, and the ratings of similar users on other items are then used to infer personalized ratings. In the second approach, taste-grokking, the crowd is presented with the training examples and asked to use them predict the ratings of the target user on other items
A Literature Review on the Development of Multimedia Information Retrieval (MIR) and the Futere Challenges
 Abstrak Multimedia information retrieval (MIR) adalah proses pencarian dan pengambilan informasi (information retrieval/IR) dalam content berbentuk multimedia, seperti suara, gambar, video, dan animasi. Penelitian ini menggunakan metode kajian literatur (literature review) terhadap perkembangan MIR saat ini dan tantangan yang akan dihadapi di masa depan bagi para periset di bidang IR. Berbagai penelitian MIR saat ini meliputi komputasi yang berpusat pada manusia (aktor) terhadap pencarian informasi, memungkinkan mesin melakukan pembelajaran (semantik), memungkinkan mesin meminta koreksi (umpan balik), penambahan fitur atau faktor baru, penelitian pada media baru, perangkuman informasi dari content multimedia, pengindeksan dengan performa tinggi, dan mekanisme terhadap teknik evaluasi. Di masa yang akan datang, tantangan yang menjadi potensi penelitian MIR meliputi peran manusia yang tetap menjadi pusat (aktor) terhadap pencarian informasi, kolaborasi konten multimedia yang lebih beragam, dan penggunaan kata kunci sederhana (folksonomi). Kata kunci: multimedia information retrieval, multimedia, komputasi, semantik, pencarian informasi  Abstract Multimedia information retrieval (MIR) is the process of searching and retrieving information (information retrieval/IR) in multimedia content, such as audio, image, video, and animation. This study uses literature review method against current MIR conditions and what challenges to be faced in the future for researchers in the field of IR. Various studies of MIR currently include human centered computation for IR, allowing machine to do the learning (semantics); allowing machine to request feedback, add new features or factors, research on new media, summarize information from multimedia content, high-performance indexing, and evaluation techniques. In the future, the potential of MIR research includes the human-centered role for information retrieval, more diverse collaborative multimedia content, and the use of simple keyword (folksonomy). Keywords: multimedia information retrieval, multimedia, computation, semantics, information searchÂ
Summarizing Text Using Lexical Chains
The current technology of automatic text summarization imparts an important role in the information retrieval and text classification, and it provides the best solution to the information overload problem. And the text summarization is a process of reducing the size of a text while protecting its information content. When taking into consideration the size and number of documents which are available on the Internet and from the other sources, the requirement for a highly efficient tool on which produces usable summaries is clear. We present a better algorithm using lexical chain computation. The algorithm one which makes lexical chains a computationally feasible for the user. And using these lexical chains the user will generate a summary, which is much more effective compared to the solutions available and also closer to the human generated summary
Integration and coordination in a cognitive vision system
In this paper, we present a case study that exemplifies
general ideas of system integration and coordination.
The application field of assistant technology provides an
ideal test bed for complex computer vision systems including
real-time components, human-computer interaction, dynamic
3-d environments, and information retrieval aspects.
In our scenario the user is wearing an augmented reality device
that supports her/him in everyday tasks by presenting
information that is triggered by perceptual and contextual
cues. The system integrates a wide variety of visual functions
like localization, object tracking and recognition, action
recognition, interactive object learning, etc. We show
how different kinds of system behavior are realized using
the Active Memory Infrastructure that provides the technical
basis for distributed computation and a data- and eventdriven
integration approach
Modeling social information skills
In a modern economy, the most important resource consists in\ud
human talent: competent, knowledgeable people. Locating the right person for\ud
the task is often a prerequisite to complex problem-solving, and experienced\ud
professionals possess the social skills required to find appropriate human\ud
expertise. These skills can be reproduced more and more with specific\ud
computer software, an approach defining the new field of social information\ud
retrieval. We will analyze the social skills involved and show how to model\ud
them on computer. Current methods will be described, notably information\ud
retrieval techniques and social network theory. A generic architecture and its\ud
functions will be outlined and compared with recent work. We will try in this\ud
way to estimate the perspectives of this recent domain
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