A Representation of Binary Matrices

Христина Костадинова, Красимир Йорджев - В статията се обсъжда представянето на произволна бинарна матрица с помощта на последователност от цели неотрицателни числа. Разгледани са някои предимства и недостатъци на това представяне като алтернатива на стандартното, общоприето представяне чрез двумерен масив. Показано е, че представянето на бинарните матрици с помощта на наредени n-торки от естествени числа води до по-бързи алгоритми и до съществена икономия на оперативна памет. Използуван е апарата на обектно-ориентираното програмиране със синтаксиса и семантиката на езика C++.

How different are the visual representations used for object recognition in middle childhood and adulthood?

Recent experimental studies have shown that development towards adult performance levels in configural processing in object recognition is delayed through middle childhood. Whilst partchanges to animal and artefact stimuli are processed with similar to adult levels of accuracy from 7 years of age, relative size changes to stimuli result in a significant decrease in relative performance for participants aged between 7 and 10. Two sets of computational experiments were run using the JIM3 artificial neural network with adult and 'immature' versions to simulate these results. One set progressively decreased the number of neurons involved in the representation of view-independent metric relations within multi-geon objects. A second set of computational experiments involved decreasing the number of neurons that represent view-dependent (nonrelational) object attributes in JIM3's Surface Map. The simulation results which show the best qualitative match to empirical data occurred when artificial neurons representing metric-precision relations were entirely eliminated. These results therefore provide further evidence for the late development of relational processing in object recognition and suggest that children in middle childhood may recognise objects without forming structural description representations.

An Algorithmic Approach to Inferring Cross-Ontology Links while Mapping Anatomical Ontologies

ACM Computing Classification System (1998): J.3.

Building OWL Ontology of Unique Bulgarian Bells Using Protégé Platform

We describe an ontological representation of data in an archive containing detailed description of church bells. As an object of cultural heritage the bell has general properties such as geometric dimensions, weight, sound of each of the bells, the pitch of the tone as well as acoustical diagrams obtained using contemporary equipment. We use Protégé platform in order to define basic ontological objects and relations between them.

Query Enrichment for Image Collections by Reuse of Classification Rules

User queries over image collections, based on semantic similarity, can be processed in several ways. In this paper, we propose to reuse the rules produced by rule-based classifiers in their recognition models as query pattern definitions for searching image collections.

Semantic representation for comparison of performance measurement indicators

Indicators are widely used by organizations as a way of evaluating, measuring and classifying organizational performance. As part of performance evaluation systems, indicators are often shared or compared across internal sectors or with other organizations. However, indicators can be vague and imprecise, and also can lack semantics, making comparisons with other indicators difficult. Thus, this paper presents a knowledge model based on an ontology that may be used to represent indicators semantically and generically, dealing with the imprecision and vagueness, and thus facilitating better comparison. Semantic technologies are shown to be suitable for this solution, so that it could be able to represent complex data involved in indicators comparison.

Riccati Representation for Elements in H^(-1) and Its Applications

2002 Mathematics Subject Classification: 35L05, 34L15, 35D05, 35Q53

Branching Particle Representation of a Class of Semilinear Equations

2000 Mathematics Subject Classification: 60J80, 60J85

Accent Recognition for Noisy Audio Signals

It is well established that accent recognition can be as accurate as up to 95% when the signals are noise-free, using feature extraction techniques such as mel-frequency cepstral coefficients and binary classifiers such as discriminant analysis, support vector machine and k-nearest neighbors. In this paper, we demonstrate that the predictive performance can be reduced by as much as 15% when the signals are noisy. Specifically, in this paper we perturb the signals with different levels of white noise, and as the noise become stronger, the out-of-sample predictive performance deteriorates from 95% to 80%, although the in-sample prediction gives overly-optimistic results. ACM Computing Classification System (1998): C.3, C.5.1, H.1.2, H.2.4., G.3.

Piecewise Convex Curves and their Integral Representation

2000 Mathematics Subject Classification: 52A10.

Harnessing linked knowledge sources for topic classification in social media

Topic classification (TC) of short text messages offers an effective and fast way to reveal events happening around the world ranging from those related to Disaster (e.g. Sandy hurricane) to those related to Violence (e.g. Egypt revolution). Previous approaches to TC have mostly focused on exploiting individual knowledge sources (KS) (e.g. DBpedia or Freebase) without considering the graph structures that surround concepts present in KSs when detecting the topics of Tweets. In this paper we introduce a novel approach for harnessing such graph structures from multiple linked KSs, by: (i) building a conceptual representation of the KSs, (ii) leveraging contextual information about concepts by exploiting semantic concept graphs, and (iii) providing a principled way for the combination of KSs. Experiments evaluating our TC classifier in the context of Violence detection (VD) and Emergency Responses (ER) show promising results that significantly outperform various baseline models including an approach using a single KS without linked data and an approach using only Tweets. Copyright 2013 ACM.

A gloss composition and context clustering based distributed word sense representation model

In recent years, there has been an increasing interest in learning a distributed representation of word sense. Traditional context clustering based models usually require careful tuning of model parameters, and typically perform worse on infrequent word senses. This paper presents a novel approach which addresses these limitations by first initializing the word sense embeddings through learning sentence-level embeddings from WordNet glosses using a convolutional neural networks. The initialized word sense embeddings are used by a context clustering based model to generate the distributed representations of word senses. Our learned representations outperform the publicly available embeddings on half of the metrics in the word similarity task, 6 out of 13 sub tasks in the analogical reasoning task, and gives the best overall accuracy in the word sense effect classification task, which shows the effectiveness of our proposed distributed distribution learning model.

Culture, ethnicity and migration after communism:the Pontic Greeks

This book addresses the issue of emerging transnationalism in the conditions of post-socialism through focussing on migrants’ identity as a social construction resulting from their experience of the ‘transnational circuit of culture��� as well as from post-Soviet shifts in political and economic conditions in their home regions. Popov draws upon ethnographic research conducted among Greek transnational migrants living on the Black Sea coast and in the North Caucasus regions of Russia who have become involved in extensive cross-border migration between the former Soviet Union (the Russian Federation, Kazakhstan and Georgia) and Greece (as well as Cyprus). It is estimated that more than 150,000 former Soviet citizens of Greek origin have resettled in Greece since the late 1980s. Yet, many of those who emigrate do not cut their connections with the home communities in Russia but instead establish their own transnational circuit of travel between Greece and Russia. This study demonstrates how migrants employ their ethnicity as symbolic capital available for investment in profitable transnational migration. Simultaneously they rework their practices of family networking, property relations and political participation in a way which strengthens their attachment to the local territory. The findings presented in the book imply that the social identities, economic strategies, political practices and cultural representation of the Russian Greeks are all deeply embedded in the shifting social and cultural landscape of post-Soviet Russia and extensively influenced by the global movement of ideas, goods and people.

Design software architecture models using ontology

Software architecture plays an essential role in the high level description of a system design, where the structure and communication are emphasized. Despite its importance in the software engineering process, the lack of formal description and automated verification hinders the development of good software architecture models. In this paper, we present an approach to support the rigorous design and verification of software architecture models using the semantic web technology. We view software architecture models as ontology representations, where their structures and communication constraints are captured by the Web Ontology Language (OWL) and the Semantic Web Rule Language (SWRL). Specific configurations on the design are represented as concrete instances of the ontology, to which their structures and dynamic behaviors must conform. Furthermore, ontology reasoning tools can be applied to perform various automated verification on the design to ensure correctness, such as consistency checking, style recognition, and behavioral inference.

An integrative approach to face and expression recognition from 3D scans

Biometrics is afield of study which pursues the association of a person's identity with his/her physiological or behavioral characteristics.^ As one aspect of biometrics, face recognition has attracted special attention because it is a natural and noninvasive means to identify individuals. Most of the previous studies in face recognition are based on two-dimensional (2D) intensity images. Face recognition based on 2D intensity images, however, is sensitive to environment illumination and subject orientation changes, affecting the recognition results. With the development of three-dimensional (3D) scanners, 3D face recognition is being explored as an alternative to the traditional 2D methods for face recognition.^ This dissertation proposes a method in which the expression and the identity of a face are determined in an integrated fashion from 3D scans. In this framework, there is a front end expression recognition module which sorts the incoming 3D face according to the expression detected in the 3D scans. Then, scans with neutral expressions are processed by a corresponding 3D neutral face recognition module. Alternatively, if a scan displays a non-neutral expression, e.g., a smiling expression, it will be routed to an appropriate specialized recognition module for smiling face recognition.^ The expression recognition method proposed in this dissertation is innovative in that it uses information from 3D scans to perform the classification task. A smiling face recognition module was developed, based on the statistical modeling of the variance between faces with neutral expression and faces with a smiling expression.^ The proposed expression and face recognition framework was tested with a database containing 120 3D scans from 30 subjects (Half are neutral faces and half are smiling faces). It is shown that the proposed framework achieves a recognition rate 10% higher than attempting the identification with only the neutral face recognition module.^