The measure of sentence similarity is useful in various research fields, such as artificial intelligence, knowledge management, and information retrieval. Several methods have been proposed to measure the sentence similarity based on syntactic and/or semantic knowledge. Most proposals are evaluated on English sentences where the accuracy can decrease when these proposals are applied to other languages. Moreover, the results of these methods are unsatisfactory, as much relevant semantic knowledge, such as semantic class, thematic role and syntactico-semantic knowledge like the semantic predicates, are not taken into account. We must acknowledge that this kind of knowledge is rare in most of the lexical resources. Recently, the International Organization for Standardization (ISO) has published the Lexical Markup Framework (LMF) ISO-24613 norm for the development of lexical resources. This norm provides, for each meaning of a lexical entry, all the semantic and syntactico-semantic knowledge in a fine structure. Profiting from the availability of LMF-standardized dictionaries, we propose, in this paper, a generic method that enhances the measure of sentence similarity by applying semantic and syntactico-semantic knowledge. An experiment was carried out on Arabic, as this language is processed within our research team and an LMF-standardized Arabic dictionary is at hand where the semantic and the syntactico-semantic knowledge are accessible and well structured. Moreover, the experiments yielded better results, showing a high correlation with human ratings.

Multidimensional schemas are used to model data warehouse systems, a special type of large databases dedicated for decision support. Several approaches have been proposed in this domain to ensure the satisfaction of decision makers needs and the accuracy of the generated schemas. This paper presents an approach for the validation of multidimensional star schema assisted by repair solutions. Our approach aims to assist designers by detecting constraint violations and proposing repair solutions based on a number of error-based rules formalized in Prolog. Its efficiency stems from using both linguistic similarity computation and a set of heuristics developed by bottom-up design methods to produce warnings about semantic constraint violations and their impact on the quality of the analysis results.

The recent advances in the feature extraction techniques in recognition of handwritten digits attract researchers to work in this area. The present study includes recognition of handwritten digits using hybrid feature extraction technique including static and dynamic properties of handwritten digit images. In this paper, static properties include number of non-zero (white) pixels in square, horizontal, vertical and diagonal styles as sub regions of a binary image. The dynamic properties include features from recovery of drawing order of original image. The extraction of dynamic features include two stages: first stage recover the drawing order of an image and second stage compute the chain code directions from recovered drawing order. The algorithm for recovery of drawing order uses properties of writing behavior. The support vector machine has been used as recognition method for the proposed feature extraction scheme. We have achieved an overall error rate of 0.73 % for mnist data set including 60,000 training images and 10,000 test images. Our feature extraction technique results in feature vector length of an image equals to 356. The achieved results strengthen our proposed technique usability as error rate achieved is at par with literature (\(<\)1 %) and the length of feature vector per image is small in comparison to input feature vector length of 784 which has been commonly used in previous work. The developed system is stable and useful in real life applications.

Within case-based reasoning (CBR), terms concerning quality of a case base are mentioned in publications, but partially without clarifications of criteria. When developing a CBR system from scratch, an index for case base quality supports an assessment of the actual cases. In this approach, both theory and an application are demonstrated. An index was defined and subsequently applied within a proof of concept. In addition, various approaches concerning case base quality are demonstrated. Big data occur within a combination of high velocity, great volume and variety of incoming data. New cases are suitable if they are referring to an economic value. Defining an index to measure the case base quality copes with that. In this paper, an overview of the CBR-related index towards the big picture regarding data quality can be seen. To demonstrate weighting, concrete invocations of the defined subindices are itemized with respect to applied data sets. This paper depicts a generic easy-to-use index with respect to case bases.

In this work we summarize some of our studies on paraconsistent artificial neural networks (PANN) applied to electroencephalography. We give attention to the following applications: probable diagnosis of Alzheimer disease and attention-deficit /hyperactivity disorder (ADHD). PANNs are well suited to tackle problems that human beings are good at solving, like prediction and pattern recognition. PANNs have been applied within several branches and among them, the medical domain for clinical diagnosis, image analysis, and interpretation signal analysis, and interpretation, and drug development. For study of ADHD, we have a result of recognition electroencephalogram standards (delta, theta, alpha, and beta waves) with a median kappa index of 80 %. For study of the Alzheimer disease, we have a result of clinical diagnosis possible with 80 % of sensitivity, 73 % of specificity, and a kappa index of 76 %.

In this paper, we propose an adaptive algorithm for reversible data hiding by employing the characteristics and pyramidal relationships of original images. The major goal of reversible data hiding is to keep the reversibility of algorithm. By use of the pyramidal structure to explore the inherent characteristics of original images, regions with different smoothness levels can be determined, and then data hiding can be performed adaptively with the pre-determined threshold for balancing the output image quality and embedding capacity. On the one hand, larger capacity can be hidden into smoother regions with limited degradation of output image quality. On the other hand, the size of location map, which serves as the side information for keeping reversibility, can be reduced for embedding into smoother or less smooth regions of original image. By carefully manipulating difference values between layers in pyramidal structure, secret information can effectively be embedded. With our method, we observe better performances over relating methods with enhanced image quality, the more embedding capacity, and comparable amount of side information for decoding. More importantly, the reversibility of our method is guaranteed, meaning that original image and secret information can both be perfectly recovered at the decoder. Simulation results demonstrate that proposed method in this paper outperforms those in conventional algorithms.

We have already developed some kinds of paraconsistent annotated logic programs. In this paper we propose the paraconsistent process order control method based on a paraconsistent annotated logic program called before–after extended vector annotated logic program with strong negation (bf-EVALPSN) with a small example of pipeline process order verification. Bf-EVALPSN can deal with before–after relations between two processes (time intervals) in its annotations, and its reasoning system consists of two kinds of inference rules called the basic bf-inference rule and the transitive bf-inference rule. We introduce how the bf-EVALPSN-based reasoning system can be applied to the safety verification for process order.