A GPU-based Evolution Strategy for Optic Disk Detection in Retinal Images

La ejecución paralela de aplicaciones usando unidades de procesamiento gráfico (gpu) ha ganado gran interés en la comunidad académica en los años recientes. La computación paralela puede ser aplicada a las estrategias evolutivas para procesar individuos dentro de una población, sin embargo, las estrategias evolutivas se caracterizan por un significativo consumo de recursos computacionales al resolver problemas de gran tamaño o aquellos que se modelan mediante funciones de aptitud complejas. Este artículo describe la implementación de una estrategia evolutiva para la detección del disco óptico en imágenes de retina usando Compute Unified Device Architecture (cuda). Los resultados experimentales muestran que el tiempo de ejecución para la detección del disco óptico logra una aceleración de 5 a 7 veces, comparado con la ejecución secuencial en una cpu convencional.Parallel processing using graphic processing units (GPUs) has attracted much research interest in recent years. Parallel computation can be applied to evolution strategy (ES) for processing individuals in a population, but evolutionary strategies are time consuming to solve large computational problems or complex fitness functions. In this paper we describe the implementation of an improved ES for optic disk detection in retinal images using the Compute Unified Device Architecture (CUDA) environment. In the experimental results we show that the computational time for optic disk detection task has a speedup factor of 5x and 7x compared to an implementation on a mainstream CPU

Slaying the SA-Demons – Humans vs. Technology – A Content analysis

This paper examines Situation Awareness (SA) and the application of Endsley’s SA-Demons in different contexts and research areas. We perform content analysis to examine how they are used, and to what degree they are perceived as stemming from human-error or weaknesses in technology and if any suggestions for mitigation are primarily focused on the human or the technology side. Based on our findings, we propose Universal Design as a tool that can counter the effects of the SA-Demons by improving the usability and accessibility of SA-supporting technology and thereby removing barriers to SA, rather than challenging the users to overcome not only barriers that are a result of the complexity of the situation itself, but also additional barriers that are caused by inferior and suboptimal design of the technology in use.publishedVersio

Enhancing Performance of Parallel Self-Organizing Map on Large Dataset with Dynamic Parallel and Hyper-Q

Self-Organizing Map (SOM) is an unsupervised artificial neural network algorithm. Even though this algorithm is known to be an appealing clustering method,many efforts to improve its performance are still pursued in various research works. In order to gain faster computation time, for instance, running SOM in parallel had been focused in many previous research works. Utilization of the Graphics Processing Unit (GPU) as a parallel calculation engine is also continuously improved. However, total computation time in parallel SOM is still not optimal on processing large dataset. In this research, we propose a combination of Dynamic Parallel and Hyper-Q to further improve the performance of parallel SOM in terms of faster computing time. Dynamic Parallel and Hyper-Q are utilized on the process of calculating distance and searching best-matching unit (BMU), while updating weight and its neighbors are performed using Hyper-Q only. Result of this study indicates an increase in SOM parallel performance up to two times faster compared to those without using Dynamic Parallel and Hyper-Q

Booleovská faktorová analýza atraktorovou neuronovou sítí

Import 23/08/2017Methods for the discovery of hidden structures of high-dimensional binary data rank among the most important challenges facing the community of machine learning researchers at present. There are many approaches in the literature that try to solve this hitherto rather ill-defined task. The Boolean factor analysis (BFA) studied in this work represents a hidden structure of binary data as Boolean superposition of binary factors complied with the BFA generative model of signals, and the criterion of optimality of BFA solution is given. In these terms, the BFA is a well-defined task completely analogous to linear factor analysis. The main contributions of the dissertation thesis are as follows: Firstly, an efficient BFA method, based on the original attractor neural network with increasing activity (ANNIA), which is subsequently improved through a combination with the expectation-maximization method(EM),so LANNIA method has been developed. Secondly, the characteristics of the ANNIA that are important for LANNIA and ANNIA methods functioning were analyzed. Then the functioning of both methods was validated on artificially generated data sets. Next, the method was applied to real-world data from different areas of science to demonstrate their contribution to this type of analysis. Finally, the BFA method was compared with related methods, including applicability analysis.Jednou z nejdůležitějších výzev současnosti, která stojí před komunitou badatelů z oblasti strojového učení je výzkum metod pro analýzu vysoce-dimenzionálních binárních dat s cílem odhalení jejich skryté struktury. V literatuře můžeme nalézt mnoho přístupů, které se snaží tuto doposud poněkud vágně definovanou úlohu řešit. Booleovská Faktorová Analýza (BFA), jež je předmětem této práce, předpokládá, že skrytou strukturu binárních dat lze reprezentovat jako booleovskou superpozici binárních faktorů tak, aby co nejlépe odpovídala generativnímu modelu signálů BFA a danému kritériu optimálnosti. Za těchto podmínek je BFA dob��e definovaná úloha zcela analogická lineární faktorové analýze. Hlavní přínosy disertační práce, jsou následující: Za prvé byl vyvinut efektivní způsob BFA založený na původní atraktorové neuronové síti s rostoucí aktivitou (ANNIA), která byla následně zlepšena kombinací s metodou expectation–maximization (EM)a tak vytvo5ena metoda LANNIA. Dále byly provedeny analýzy charakteristik ANNIA, které jsou důležité pro fungování obou metod. Funkčnost obou metod byla také ověřena na uměle vytvořených souborech dat pokrývajících celou škálu parametrů generativního modelu. Dále je v práci ukázáno použití metod na reálných datech z různých oblastí vědy s cílem prokázat jejich přínos pro tento typ analýzy. A konečně bylo provedeno i srovnání metod BFA se podobnými metodami včetně analýzy jejich použitelnosti.460 - Katedra informatikyvyhově

A COMPARATIVE STUDY OF TWO METHODOLOGIES FOR BINARY DATASETS ANALYSIS

Abstract: Studied are differences of two approaches targeted to reveal latent variables in binary data. These approaches assume that the observed high dimensional data are driven by a small number of hidden binary sources combined due to Boolean superposition. The first approach is the Boolean matrix factorization (BMF) and the second one is the Boolean factor analysis (BFA). The two BMF methods are used for comparison. First is the M8 method from the BMDP statistical software package and the second one is the method suggested by Belohlavek & Vychodil. These two are compared to BFA, especially with the Expectationmaximization Boolean Factor Analysis we had developed earlier has, however, been extended with a binarization step developed here. The well-known bars problem and the mushroom dataset are used for revealing the methods' peculiarities. In particular, the reconstruction ability of the computed factors and the information gain as the measure of dimension reduction was under scrutiny. It was shown that BFA slightly loses to BMF in performance when noise-free signals are analyzed. Conversely, BMF loses considerably to BFA when input signals are noisy

Käyttäjätyytyväisyyden ennustaminen tekstimuotoisista arvosteluista

Tässä työssä hyödynnetään piirteenirrotus- ja luokittelumenetelmiä ja testataan niiden toimivuutta käyttäjätyytyväisyyden arviointiin tekstimuotoisesta aineistosta. Piirteiden irrottamiseen tekstistä käytetään TF-IDF-algoritmia, jonka antamat piirteet syötetään vertailtaville koneoppimismenetelmille. Käytettävät koneoppimismenetelmät ovat satunnainen metsä ja tukivektorikone, josta käytetään lineaarista ja radiaalista kerneliä käyttäviä toteutuksia. Koneoppimismenetelmistä vertaillaan sekä luokitteluun että regressioon perustuvia versioita menetelmistä. Valitut menetelmät ovat alan julkaisujen perusteella yleisesti käytössä tekstin merkityksen ja sävyn analysointiin liittyvissä ongelmissa. Algoritmien esittelyn lisäksi käydään läpi aineiston käsittelystä alkaen koko aiheeseen liittyvä koneoppimisprosessi. Työssä esitellään algoritmien testauksen tulokset ja arvioidaan niiden pohjalta käytettyjen menetelmien soveltuvuutta käyttäjätyytyväisyyden ennustamiseen tekstimuotoisten arvostelujen pohjalta

Panel: The Architectural Touch: Gestural Approaches to Library Search

This panel centers on the LibViz project—a touch and gesture-based interface that allows users to navigate through library collections using visual queries—and the issues surrounding such efforts. The LibViz project, for which we have done initial research and constructed a prototype, aims to increase the discoverability of library materials, particularly those of non-textual objects, which are difficult to access via traditional search and which do not circulate. Many collections are currently preparing large scale digitizing of threedimensional objects and it is imperative to develop appropriate methods to work with this new kind of data. The established methods only do a poor job at providing access to 3D-object data. Based in theories of “grounded cognition,” the LibViz interface will be optimized for use on personal mobile devices, but it can also be used on large format touch screens equipped with depth cameras that track user gestures. In other words, the interactive flow of LibViz allows both gestural interaction and touch commands, effectively extending the sensory modalities involved in the cognitive processing of the search results. By engaging a fuller range of human cognitive capabilities, the LibViz interface also hopes to help transform search. The amount of data generated in the digital era is growing exponentially, and so we must find novel ways of analyzing and interpreting these vast data archives. Moreover, the ways in which information is categorized and databases are created are value laden. As such, the processes by which these structures are established should be more transparent than conventional systems currently allow. The project turns library search into a powerful and pleasurable experience, stimulating engagement with the collections and the library itself

Reasoning with user's preferences in ambient assisted living environments

Understanding the importance of preference management in ambient intelligent environments is key to providing systems that are better prepared to meet users' expectations. Preferences are fundamental in decision making, so it is an essential element in developing systems that guides the choices of the users. These choices can be decided through argument(s) which are known to have various strengths, as one argument can rely on more certain or vital information than the other. The analysis of survey conducted on preferences handling techniques in Artificial Intelligence (AmI), indicates that most of existing techniques lack the ability to handle ambiguity and/or the evolution of preferences over time. Further investigation identified argumentation technique as a feasible solution to complement existing work. Argumentation provides a means to deal with inconsistent knowledge and we explored its potentials to handle conflicting users preferences by applying to it several real world scenarios. The exploration demonstrates the usefulness of argumentation in handling conflicting preferences and inconsistencies, and provides effective ways to manage, reason and represents user's preferences. Using argumentation technique, this research provide a practical implementation of a system to manage conflicting situations, along with a simple interface that aids the flow of preferences from users to the system, so as to provide services that are better aligned with the users' behaviour. This thesis also describes the functionalities of the implemented system, and illustrates the functions by solving some of the complexities in users' preferences in a real smart home. The system detects potential conflict(s), and solves them using a redefined precedence order among some preference criteria. The research further show how the implemented Hybrid System is capable of interacting with external source's data. The system was used to access and filter live data (groceries products) of a UK supermarket chain store, through their application programming interface (API), and advise users on their eating habits, based on their set preference(s)

Zpráva o výsledcích vědecko-výzkumné a vývojové činnosti na VŠB-TU Ostrava za rok 2011 : určeno pro zasedání Vědecké rady VŠB-TUO dne 9. 3. 2012

Ostrav

Proceedings of the Third International Conference on Intelligent Human Computer Interaction (IHCI 2011), Prague, Czech Republic, August, 2011

XI, 306 p. 109 illus., 48 illus. in color.online