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Data analytics have become increasingly complicated as the amount of data has increased. One technique that is used to enable data analytics in large datasets is data sampling, in which a portion of the data is selected to preserve the data characteristics for use in data analytics. In this paper, we introduce a novel data sampling technique that is rooted in formal concept analysis theory. This technique is used to create samples reliant on the data distribution across a set of binary patterns. The proposed sampling technique is applied in classifying the regions of breast cancer histology images as malignant or benign. The performance of our method is compared to other classical sampling methods. The results indicate that our method is efficient and generates an illustrative sample of small size. It is also competing with other sampling methods in terms of sample size and sample quality represented in classification accuracy and F1 measure. 1 2017 Elsevier Ltd This contribution was made possible by NPRP grant #07- 794-1-145 from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Scopus

In this paper, we present a validation of public transport origin-destination (OD) matrices obtained from smartcard and GPS data. These matrices are very valuable for management and planning but have not been validated until now. In this work, we verify the assumptions and results of the method using three sources of information: the same database used to make the estimations, a Metro OD survey in which the card numbers are registered for a group of users, and a sample of volunteers. The results are very positive, as the percentages of correct estimation are approximately 90% in all cases. (C) 2014 Elsevier Ltd. All rights reserved.

At the beginning of the 1980s, the Artificial Intelligence (AI) community showed little interest in research on methodologies for the construction of knowledge-based systems (KBS) and for knowledge acquisition (KA). The main idea was the rapid construction of prototypes with LISP machines, expert system shells, and so on. Over time, the community saw the need for a structured development of KBS projects, and KA was recognized as the critical stage and the bottleneck for the construction of KBS. Concerning KA, many publications have appeared since then. However, very few have focused on formal plans to manage knowledge acquisition and from multiple knowledge sources. This paper addresses this important problem. KAMET is a formal plan based on models designed to manage knowledge acquisition from multiple knowledge sources. The objective of KAMET is to improve, in some sense, the phase of knowledge acquisition and knowledge modeling process, making them more efficient.

Recently, IT technologies are becoming focused on different traditional industries. The large scaled intelligent video surveillance system is one of them and it integrates a large number of digitalized CCTV [1] devices through the Web. However, existing video devices have been deployed for many years by different vendors as different models with different specifications. To integrate these heterogeneous devices, the centralized management server (CMS) and its clients need a specialized architecture to deal with different types of media encodings and connection protocols etc. In particular, well-defined access control mechanism is required for a large scaled surveillance video network. In this paper, we designed and implemented a server that incorporates the architecture integrating and delivering multiple video streams from different types of video devices to multiple clients and securing the access to the video streams.

The application of various clustering techniques for large-scale gene-expression measurement experiments is a well-established method in bioinformatics. Clustering is also usually accompanied by functional characterization of gene sets by assessing statistical enrichments of structured vocabularies, such as the gene ontology (GO) [Gene Ontology Consortium. The gene ontology (GO) project in 2006. Nucleic Acids Research (Database issue), vol. 34; 2006. p. D322-6]. If different clusters are generated for correlated experiments, a machine learning step termed cluster meta-analysis may be performed, in order to discover relations among the components of such sets. Several approaches have been proposed: in particular, kernel methods may be used to exploit the graphical structure of typical ontologies such as GO. Following up the formulation of such approach [Merico D, Zoppis I, Antoniotti M, Mauri G. Evaluating graph kernel methods for relation discovery in GO-annotated clusters. In: KES-2007/WIRN-2007, Part IV, Lecture notes in artificial intelligence, vol. 4694. Berlin: Springer; 2007. p. 892-900; Zoppis I, Merico D, Antoniotti M, Mishra B, Mauri G. Discovering relations among GO-annotated clusters by graph kernel methods. In: Proceedings of the 2007 international symposium on bioinformatics research and applications. Lecture notes in computer science, vol. 4463. Berlin: Springer; 2007], in this paper we discuss, from an information-theoretic point of view, further results about its applicability and its performance.

The exponential growth of information available on the World Wide Web, and retrievable by search engines, has implied the necessity to develop efficient and effective methods for organizing relevant contents. In this field document clustering plays an important role and remains an interesting and challenging problem in the field of web computing. In this paper we present a document clustering method, which takes into account both contents information and hyperlink structure of web page collection, where a document is viewed as a set of semantic units. We exploit this representation to determine the strength of a relation between two linked pages and to define a relational clustering algorithm based on a probabilistic graph representation. The experimental results show that the proposed approach, called RED-clustering, outperforms two of the most well known clustering algorithm as k-Means and Expectation Maximization.

In this paper, an intelligent agent-based communication support platform for multimodal transport is developed. The rationale for doing so is found in the potential of such a system to increase cost efficiency, service and safety for different transport-related actors. Although, at present several comparable systems exist, their current implementation is far from successful because of technological and economic obstacles. The new expert communication platform put forward here (called MamMoeT) addresses these two issues by using a software agent-based approach. Software agents are pieces of software representing a single user. They are autonomous, communicative and intelligent. The MamMoeT system developed can be described as a real-time decision support system in which intelligent software agents handle communicative tasks, exchange desired amounts of information among different users using common exchange protocols which act as translators between different systems. ispartof: Expert Systems with Applications vol:36 issue:7 pages:10280-10287 status: published

This paper presents an approach to predict the operating conditions of machine based on\ud classification and regression trees (CART) and adaptive neuro-fuzzy inference system (ANFIS)\ud in association with direct prediction strategy for multi-step ahead prediction of time series\ud techniques. In this study, the number of available observations and the number of predicted\ud steps are initially determined by using false nearest neighbor method and auto mutual\ud information technique, respectively. These values are subsequently utilized as inputs for\ud prediction models to forecast the future values of the machines’ operating conditions. The performance of the proposed approach is then evaluated by using real trending data of low methane compressor. A comparative study of the predicted results obtained from CART and ANFIS models is also carried out to appraise the prediction capability of these models. The results show that the ANFIS prediction model can track the change in machine conditions and has the potential for using as a tool to machine fault prognosis.

The large amount of information available and the difficulty on processing it has made knowledge management a promising area of research. Several topics are related to it, for example distributed and intelligent information retrieval, information filtering and information evaluation, which became crucial. In this paper, we focus our attention on the knowledge evaluation problem. With the aim of evaluating information coded in the standard non-proprietary format SGML (as also in XML), we propose some evaluation methods based on L-grammars which are fuzzy grammars. In particular we apply these methods to the evaluation of documents in SGML-format and to the evaluation of HTML-pages in the World Wide Web. L-grammars generate recursively enumerable L-languages, as it has been proved in Gerla ((1991), Information Sciences 53), and so they can be used to generate fuzzy languages based on extensions of the document type definitions (DTD) involved by SGML. Given a DTD, we extend its associated language by adding a judgement label. By selecting a particular label and by taking the start symbol of the grammar associated to the DTD, we can generate any DTD-compliant document with a fuzzy degree of membership derived from the judgement label. In this way we fit the computational model underlying the recursively enumerable L-languages to the process of collecting different evaluations of the same document. Finally, we outline how the generalization of these methods of evaluation can be applied in different contexts and for different roles, as for example for information filtering.

In this paper we introduce DEGAS (Dynamic Entities Get Autonomous Status), an active temporal data model based on autonomous objects. The natural combination of active and temporal databases is discussed. The active dimension of DEGAS means that we define the behaviour of objects in terms of production rules. The temporal dimension means that the history of an object is included in the DEGAS data model. Further novel features of DEGAS are the encapsulation of the complete behaviour of an object, both potential and actual. Thus, DEGAS combines dynamic and structural specifications in one model. In addition, DEGAS allows easy evolution of object capabilities through a clear distinction between inherent types and capabilities that can be acquired and lost. This addon mechanism makes DEGAS very suitable as a formalism for role modelling. Finally, the rule model in DEGAS is both simple, through the use of finite automata, and general, because it allows different strategies for dealing with constraints and reacting to events in other objects.