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Semantic attributes represent an adequate knowledge that can be easily transferred to other domains where lack of information and training samples exist. However, in the classical object recognition case, where training data is abundant, attribute-based recognition usually results in poor performance compared to methods that used image features directly. We introduce a generic framework that boosts...
The ambiguity of named entity refers to one named entity with multiple entity concepts. We use the text contextual information and other external repository to cope with the ambiguity of named entity. Then we can make sure the truly allegations of a named entity. Our system can improve the performance of the online recommendation system, the ability to extract information and other practical applications...
The aim of automatic multi-document abstractive summarization is to create a compressed version of the source text and preserves the salient information. Existing graph based summarization methods treat sentence as bag of words, rely on content similarity measure and did not consider semantic relationships between sentences. These methods may fail in determining redundant sentences that are semantically...
This paper briefly describes and evaluates XSDF, a new XML Semantic Disambiguation Framework, taking as input: an XML document and a general purpose semantic network, and then producing as output a semantically augmented XML tree made of unambiguous semantic concepts. Experiments demonstrate the effectiveness of XSDF in comparison with alternative methods.
In this paper, we introduce a new method that explores spatio-temporal-theme correlations between physical and social streaming data for event detection and pattern interpretation from heterogeneous sensors. Particularly, we employ a basic two-phase framework in pattern recognition (i.e. feature extraction and detection) with the novel improvement that concerns the use of semantic information acquired...
In most model-free tracking algorithms, context of the target is usually taken as source of negative examples for training the appearance model and thus not fully utilized. In fact, the target is embedded in its context, and there are spatial constraints between them with the potential motion correlations and relative locations. This paper presents a part-based model to describe the spatial constraints...
Image semantics recognition is a long-standing research topic and has been used to many application areas, including medical diagnose, public security, etc. However, how to teach a social robot to have the intelligence to recognize images through user interactions still remains open and ambitious. In this paper we propose a novel framework of semi-supervised human-robot interactive image recognition...
This paper proposes a logical and linguistic model for automatic identification of collocation similarity. The method of component analysis is proposed to determine the semantic equivalence between collocates. The set of semantic and grammatical characteristics of collocates is identified by means of algebra of predicates to formalize collocation similarity.
In view of the lack of semantic information description in commonly-used knowledge representation modes such as framework and object-oriented mode, the domain ontology of electronic protective equipment fault diagnosis is designed by analyzing and summarizing the characteristics of the equipment fault cases. Considering that the traditional VSM (vector space model) ignores the role of the position...
Policy conflicts problem is the bottleneck restricting of multi-domain access control. In this paper, we study the factors and detection methods of multi-domain access control policy conflict. Focused on the typical multi-domain access control model of RBAC, we proposed a policy conflicts detection method based on synthesis strategy and correction set, which is improved rely on updating global policy...
During search and discovery of Web Services (WS) the user specifies a set of parameters that is compared against the WS operations, this means that one of the main steps in WS discovery is the comparison process. Most of related work aim at improving the WS search process based in the following approaches: semantic description of operations, semantic relation on name functions, workflows, and functions...
In this paper, we represent the context based weighting scheme for vector space model to evaluate the relation between concept and context in Information storage and retrieval system. A meaning of a word is relatively decided by a context dynamically. A vector space model, generally use a static weighting scheme for term document matrix like latent semantic indexing (LSI), co-occurrence or correlations...
In recent years the technological world has grown by incorporating billions of small sensing devices, collecting and sharing real-world information. As the number of such devices grows, it becomes increasingly difficult to manage all these new information sources. There is no uniform way to share, process and understand context information. In previous publications we discussed efficient ways to organize...
Nowadays, in such a high-tech living lifestyle, profusion of multimedia data are produced and propagated around the world. To identify meaningful semantic concepts from the large amount of data, one of the major challenges is called the data imbalance problem. Data imbalance occurs when the number of positive instances (i.e., instances which contain the target concept) is greatly less than the number...
Person re-identification is an important computer vision task with many applications in areas such as surveillance or multimedia. Approaches relying on handcrafted image features struggle with many factors (e.g. lighting, camera angle) which lead to a large variety in visual appearance for the same individual. Features based on semantic attributes of a person's appearance can help with some of these...
Mining based on opinions can extract useful information from users' comments. After doing cluster and analysis on the information, users can get a detailed understanding of the commodity, then determine to buy the commodity or not. In this paper, firstly, we extract evaluation objects and evaluation words, then cluster the evaluation objects. Next based on SO-PMI algorithm, judge the polarity of evaluation...
Soft computing and computing with words (CWW) operate with uncertain natural language (NL) statements to make NL statements more exact. Currently these NL statements in CWW are limited by quantitative concepts. This paper expands CWW beyond quantitative words to texts that contain incongruities. Incongruities are common in jokes, ironies and contradictory texts. A dynamic model, an algorithm, and...
In this paper we investigate the use of fuzzy rule-based classifiers for multi-label classification. This classification task deals with problems where more than one label could be assigned simultaneously to a given instance. We concentrate on problem transformation methods, which use different strategies to transform a multi-label problem into a different single-label classification problems. This...
Fuzzy sentence semantic similarity measures are designed to be applied to real world problems where a computer system is required to assess the similarity between human natural language and words or prototype sentences stored within a knowledge base. Such measures are often developed for a specific corpus/domain where a limited set of words and sentences are evaluated. As new “fuzzy” measures are...
Now, cross-modal retrieval similarity on multimedia with texts and images have attracted scholars' more and more attention. The difficulty of cross-modal retrieval is how to effectively construct correlation between multi-modal heterogeneous data. According to canonical correlation analysis, most existing cross-modal methods embed the heterogeneous data into a joint abstraction space by linear projections...
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