A Linear Time Algorithm for Computing Longest Paths in Cactus Graphs

ACM Computing Classification System (1998): G.2.2.

A New Method for Computing the Eccentric Connectivity Index of Fullerenes

ACM Computing Classification System (1998): G.2.2, G.2.3.

A Variable Neighborhood Search Approach for Solving the Maximum Set Splitting Problem

This paper presents a Variable neighbourhood search (VNS) approach for solving the Maximum Set Splitting Problem (MSSP). The algorithm forms a system of neighborhoods based on changing the component for an increasing number of elements. An efficient local search procedure swaps the components of pairs of elements and yields a relatively short running time. Numerical experiments are performed on the instances known in the literature: minimum hitting set and Steiner triple systems. Computational results show that the proposed VNS achieves all optimal or best known solutions in short times. The experiments indicate that the VNS compares favorably with other methods previously used for solving the MSSP. ACM Computing Classification System (1998): I.2.8.

An Open Virtual World for Professional Development

The paper presents a study that focuses on the issue of sup-porting educational experts to choose the right combination of educational methodology and technology tools when designing training and learning programs. It is based on research in the field of adaptive intelligent e-learning systems. The object of study is the professional growth of teachers in technology and in particular that part of their qualification which is achieved by organizing targeted training of teachers. The article presents the process of creating and testing a system to support the decision on the design of training for teachers, leading to more effective implementation of technology in education and integration in diverse educational contexts. ACM Computing Classification System (1998): H.4.2, I.2.1, I.2, I.2.4, F.4.1.

Classifier PGN: Classification with High Confidence Rules

ACM Computing Classification System (1998): H.2.8, H.3.3.

Mapping and Merging of Anatomical Ontologies

This article presents the principal results of the Ph.D. thesis Intelligent systems in bioinformatics: mapping and merging anatomical ontologies by Peter Petrov, successfully defended at the St. Kliment Ohridski University of Sofia, Faculty of Mathematics and Informatics, Department of Information Technologies, on 26 April 2013.

Classification of Paintings by Artist, Movement, and Indoor Setting Using MPEG-7 Descriptor Features

ACM Computing Classification System (1998): I.4.9, I.4.10.

On the Busy Period in One Finite Queue of M/G/1 Type with Inactive Orbit

The paper deals with a single server finite queuing system where the customers, who failed to get service, are temporarily blocked in the orbit of inactive customers. This model and its variants have many applications, especially for optimization of the corresponding models with retrials. We analyze the system in non-stationary regime and, using the discrete transformations method study, the busy period length and the number of successful calls made during it. ACM Computing Classification System (1998): G.3, J.7.

VisibleZ: A Mainframe Architecture Emulator for Computing Education

This paper describes a PC-based mainframe computer emulator called VisibleZ and its use in teaching mainframe Computer Organization and Assembly Programming classes. VisibleZ models IBM’s z/Architecture and allows direct interpretation of mainframe assembly language object code in a graphical user interface environment that was developed in Java. The VisibleZ emulator acts as an interactive visualization tool to simulate enterprise computer architecture. The provided architectural components include main storage, CPU, registers, Program Status Word (PSW), and I/O Channels. Particular attention is given to providing visual clues to the user by color-coding screen components, machine instruction execution, and animation of the machine architecture components. Students interact with VisibleZ by executing machine instructions in a step-by-step mode, simultaneously observing the contents of memory, registers, and changes in the PSW during the fetch-decode-execute machine instruction cycle. The object-oriented design and implementation of VisibleZ allows students to develop their own instruction semantics by coding Java for existing specific z/Architecture machine instructions or design and implement new machine instructions. The use of VisibleZ in lectures, labs, and assignments is described in the paper and supported by a website that hosts an extensive collection of related materials. VisibleZ has been proven a useful tool in mainframe Assembly Language Programming and Computer Organization classes. Using VisibleZ, students develop a better understanding of mainframe concepts, components, and how the mainframe computer works. ACM Computing Classification System (1998): C.0, K.3.2.

Signal processing of physiological signals for automated assessment of stress in computer users

The need to provide computers with the ability to distinguish the affective state of their users is a major requirement for the practical implementation of affective computing concepts. This dissertation proposes the application of signal processing methods on physiological signals to extract from them features that can be processed by learning pattern recognition systems to provide cues about a person's affective state. In particular, combining physiological information sensed from a user's left hand in a non-invasive way with the pupil diameter information from an eye-tracking system may provide a computer with an awareness of its user's affective responses in the course of human-computer interactions. In this study an integrated hardware-software setup was developed to achieve automatic assessment of the affective status of a computer user. A computer-based "Paced Stroop Test" was designed as a stimulus to elicit emotional stress in the subject during the experiment. Four signals: the Galvanic Skin Response (GSR), the Blood Volume Pulse (BVP), the Skin Temperature (ST) and the Pupil Diameter (PD), were monitored and analyzed to differentiate affective states in the user. Several signal processing techniques were applied on the collected signals to extract their most relevant features. These features were analyzed with learning classification systems, to accomplish the affective state identification. Three learning algorithms: Naïve Bayes, Decision Tree and Support Vector Machine were applied to this identification process and their levels of classification accuracy were compared. The results achieved indicate that the physiological signals monitored do, in fact, have a strong correlation with the changes in the emotional states of the experimental subjects. These results also revealed that the inclusion of pupil diameter information significantly improved the performance of the emotion recognition system. ^

Developing a Data-Driven Classification of South Florida Plant Communities

A comprehensive, broadly accepted vegetation classification is important for ecosystem management, particularly for planning and monitoring. South Florida vegetation classification systems that are currently in use were largely arrived at subjectively and intuitively with the involvement of experienced botanical observers and ecologists, but with little support in terms of quantitative field data. The need to develop a field data-driven classification of South Florida vegetation that builds on the ecological organization has been recognized by the National Park Service and vegetation practitioners in the region. The present work, funded by the National Park Service Inventory and Monitoring Program - South Florida/Caribbean Network (SFCN), covers the first stage of a larger project whose goal is to apply extant vegetation data to test, and revise as necessary, an existing, widely used classification (Rutchey et al. 2006). The objectives of the first phase of the project were (1) to identify useful existing datasets, (2) to collect these data and compile them into a geodatabase, (3) to conduct an initial classification analysis of marsh sites, and (4) to design a strategy for augmenting existing information from poorly represented landscapes in order to develop a more comprehensive south Florida classification.

Evaluating High-Resolution Aerial Photography Acquired by Unmanned Aerial Systems for Use in Mapping Everglades Wetland Plant Associations

Mapping of vegetation patterns over large extents using remote sensing methods requires field sample collections for two different purposes: (1) the establishment of plant association classification systems from samples of relative abundance estimates; and (2) training for supervised image classification and accuracy assessment of satellite data derived maps. One challenge for both procedures is the establishment of confidence in results and the analysis across multiple spatial scales. Continuous data sets that enable cross-scale studies are very time consuming and expensive to acquire and such extensive field sampling can be invasive. The use of high resolution aerial photography (hrAP) offers an alternative to extensive, invasive, field sampling and can provide large volume, spatially continuous, reference information that can meet the challenges of confidence building and multi-scale analysis.

Developing a classification system of social communication functioning of preschool children with autism spectrum disorder

Background: Impairments in social communication are the hallmark feature of autism spectrum disorder (ASD). Operationalizing ‘severity’ in ASD has been challenging; thus stratifying by functioning has not been possible. Purpose: To describe the development of the Autism Classification System of Functioning: Social Communication (ACSF:SC) and evaluate its consistency within and between parent and professional ratings. Methodology: (1)ACSF:SC development based on focus groups and surveys involving parents, educators and clinicians familiar with preschoolers with ASD; and (2)Evaluation of the intra- and inter-rater agreement of the ACSF:SC using weighted kappa(кw). Results: Seventy-six participants were involved in the development process. Core characteristics of social communication were ascertained: communicative intent; communicative skills and reciprocity; and impact of environment. Five ACSF:SC levels were created and content-validated across participants. Best capacity and typical performance agreement ratings varied as follows: intra-rater on 41 children was кw=0.61-0.69 for parents and кw=0.71-0.95 for professionals; inter-rater between professionals were кw=0.47-0.61 and between parents and professionals кw=0.33-0.53. Conclusions: Perspectives from parents, and professionals informed ACSF:SC development, providing common descriptions of the levels of everyday communicative abilities of children with ASD to complement DSM-5. Rater agreement demonstrates the ACSF:SC can be utilized with acceptable consistency in comparison to other functional classification systems.

Energy consumption of parallel algorithms for solving linear systems on HPC architecture

Modern High-Performance Computing HPC systems are gradually increasing in size and complexity due to the correspondent demand of larger simulations requiring more complicated tasks and higher accuracy. However, as side effects of the Dennard’s scaling approaching its ultimate power limit, the efficiency of software plays also an important role in increasing the overall performance of a computation. Tools to measure application performance in these increasingly complex environments provide insights into the intricate ways in which software and hardware interact. The monitoring of the power consumption in order to save energy is possible through processors interfaces like Intel Running Average Power Limit RAPL. Given the low level of these interfaces, they are often paired with an application-level tool like Performance Application Programming Interface PAPI. Since several problems in many heterogeneous fields can be represented as a complex linear system, an optimized and scalable linear system solver algorithm can decrease significantly the time spent to compute its resolution. One of the most widely used algorithms deployed for the resolution of large simulation is the Gaussian Elimination, which has its most popular implementation for HPC systems in the Scalable Linear Algebra PACKage ScaLAPACK library. However, another relevant algorithm, which is increasing in popularity in the academic field, is the Inhibition Method. This thesis compares the energy consumption of the Inhibition Method and Gaussian Elimination from ScaLAPACK to profile their execution during the resolution of linear systems above the HPC architecture offered by CINECA. Moreover, it also collates the energy and power values for different ranks, nodes, and sockets configurations. The monitoring tools employed to track the energy consumption of these algorithms are PAPI and RAPL, that will be integrated with the parallel execution of the algorithms managed with the Message Passing Interface MPI.

Transtornos psiquiátricos no pós-parto

OBJETIVO: O pós-parto é um período de alterações biológicas, psicológicas e sociais. Essa é considerada a época mais vulnerável para a ocorrência de transtornos psiquiátricos. A disforia puerperal, a depressão pós-parto e a psicose pós-parto têm sido classicamente relacionadas ao pós-parto. Atualmente, tem sido observado que os transtornos ansiosos também estão associados a esse período. MÉTODO: Neste artigo é feita uma revisão da bibliografia acerca de transtornos psiquiátricos no pós-parto a partir de artigos encontrados no PubMed e no SciELO entre os anos de 2000 e 2009. Livros, teses e outros artigos considerados relevantes citados no material consultado também foram incluídos. RESULTADOS: A disforia puerperal ocorre em 50% a 85% das mulheres, o quadro é leve e transitório e não requer tratamento. A depressão pós-parto tem prevalência em torno de 13%, pode causar repercussões negativas na interação mãe-bebê e em outros aspectos da vida da mulher e deve ser tratada. A psicose pós-parto é rara, aparecendo em cerca de 0,2% das puérperas. Tem quadro grave que envolve sintomas psicóticos e afetivos, havendo risco de suicídio e infanticídio e geralmente requerendo internação hospitalar. Os transtornos ansiosos podem ser exacerbados ou precipitados no pós-parto, especialmente o transtorno de ansiedade generalizada, o transtorno de estresse pós-traumático e o transtorno obsessivo-compulsivo. CONCLUSÃO: Apesar de não serem reconhecidos como entidades diagnósticas pelos sistemas classificatórios atuais, os transtornos mentais no puerpério apresentam peculiaridades clínicas que merecem atenção por parte de clínicos e pesquisadores.