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In the context of tree species recognition, botanists knowledge was used in different works specially when recognising tree species through leaves. In this paper, two sub-classification strategies for tree species recognition are proposed. For each sub-classification strategy, Basic belief assignment (Bba) was determined and obtained data were fused thanks to a totally adaptive fusion system implemented...
This paper proposes an entity recognition system in image documents recognized by OCR. The system is based on a graph matching technique and is guided by a database describing the entities in its records. The input of the system is a document which is labeled by the entity attributes. A first grouping of those labels based on a function score leads to a selected set of candidate entities. The entity...
Two of the major problems in social media message classification are the data sparseness issue and the high degree of lexical variation. Paraphrases, or synonyms, are alternative ways of expressing the same meaning using different lexical variations. In this study, we try to use paraphrases to improve tweet topic classification performance. We explored two approaches to generating paraphrases, WordNet,...
Top-k join is an essential tool for data analysis, since it enables selective retrieval of the k best combined results that come from multiple different input datasets. In the context of Big Data, processing top-k joins over huge datasets requires a scalable platform, such as the widely popular MapReduce framework. However, such a solution does not necessarily imply efficient processing, due to inherent...
Frequent sequence mining methods often make use of constraints to control which subsequences should be mined, e.g., length, gap, span, regular-expression, and hierarchy constraints. We show that many subsequence constraints—including and beyond those considered in the literature—can be unified in a single framework. In more detail, we propose a set of simple and intuitive "pattern expressions"...
Automated Border Control (ABC) systems are being increasingly used to perform a fast, accurate, and reliable verification of the travelers' identity. These systems use biometric technologies to verify the identity of the person crossing the border. In this context, fingerprint verification systems are widely adopted due to their high accuracy and user acceptance. Matching score normalization methods...
This paper presents a new methodology for detecting deterioration in performance of deep neural networks when applied to on line visual analysis problems and enabling fine-tuning, or retraining, of the network to the current data characteristics. Pre-trained deep neural networks which have a satisfactory performance on the problem under study constitute the basis of the approach, with efficient transfer...
In the last decades, data fusion techniques proved their performances especially in the case of complex recognition system. The idea is to use those techniques in the context of tree species recognition. In this paper, we propose to fuse information extracted from barks with those extracted from leaves. The goal is to increase the power of discrimination of the proposed fusion system and to achieve...
Today, health research and health care generate a steadily increasing amount of data. Making these available for secondary use cases is essential for efficiency gains in health research, e.g. by reducing time-and costs-intensive acquisition of data. In this contribution, we introduce our SAHRA software platform enabling reproducible research, e.g. by combining multiple data sources, performing data...
Privacy and security of big data is emerging as one among the most relevant research challenges of recent years, also stirred-up by a wide family of critical applications ranging from scientific computing to social network analysis and mining, from data stream management to smart cities, and so forth. Traditionally, the issue of making (even very-large) databases private and secure has a long history...
Prioritizing a database of items in response to a given query object is a fundamental task in information retrieval and machine learning. We examine a specific realization of this problem in the context of a collection of biomedical articles. Given a query PubMed article, we investigate the problem of identifying and ranking recommended papers that are topically related to the query article. The two...
This paper explores scalable implementation strategies for carrying out lazy schema evolution in NoSQL data stores. For decades, schema evolution has been an evergreen in database research. Yet new challenges arise in the context of cloud-hosted data backends: With all database reads and writes charged by the provider, migrating the entire data instance eagerly into a new schema can be prohibitively...
Using speech or text to predict articulatory movements can have potential benefits for speech related applications. Many approaches have been proposed to solve the acoustic-to-articulatory inversion problem, which is much more than the exploration for predicting articulatory movements from text. In this paper, we investigate the feasibility of using deep neural network (DNN) for articulartory movement...
This paper puts forward a knowledge push method which is based on multidimensional hierarchical context model for general business process, and constructs a multi-dimensional hierarchical model of business process and a context driven knowledge resource database model, which emphasizes the mapping relation between knowledge and the knowledge context. On this basis, a framework of the knowledge push...
Radio frequency fingerprinting, based on Wi-Fi signals is a popular approach for indoor localization. Recently a few works have explored applicability of machine learning techniques to this problem. However, the challenging task of accurately finding the position depends on prior efforts of fingerprinting. Another challenge is that, distance sensitivity of signal strength depends on proximity to the...
When learning a new word in language learning, there are two problems. One is how difficult the word itself is. The second is, in what kind of situation, it will be used. There is a research that defined quantitative ambiguity of words based on the structure of WordNet, then investigated the relationship between the ambiguity and the difficulty level of words. In this paper, we re-define ambiguity...
Online marketplaces are e-commerce websites where thousands of products are provided by multiple third parties. There are dozens of these differently structured marketplaces that need to be visited by the end users to reach their targets. This searching process consumes a lot of time and effort; moreover it negatively affects the user experience. In this paper, extensive analysis and evaluation of...
Queries posed by a user over a database do not always return the desired responses. It may sometimes result an empty set of answers especially when data are pervaded with uncertainty and imprecision. Thus, to address this problem, we propose an approach for relaxing a failing query in the context of evidential databases. The uncertainty in such databases is expressed within the belief function theory...
Several studies have been developed over the years in the areas of Text Mining, Social Network Analysis and Detection of Communities. In this paper, we present a new technique for community detection in social networking using the conversation of users in a social network. We showed that the proposal performed very well for a specific theme for defining a community and performed well for joint themes...
Analysis of lace texture images is a challenging problem because the lace is a soft and extensible material and can be easily deformed. This paper investigates a whole system for lace classification. A first step, based on Otsu's segmentation method, allows to remove the background. Then the lace texture is characterized using local binary patterns (LBP). In order to be robust against rotation the...
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