Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards efficient distribution of high-volume content

We consider the problem of a source distributing a large file, such as a software package or multimedia content, to a large number of clients in the least possible time. We first tackle it under the assumption that the order of data delivery is immaterial. We obtain a cooperative algorithm analytically, and prove that it is optimal for a simple homogeneous client model. This algorithm is at least twice as fast as the well-known multicast tree method. We also explore, by simulation, a simple randomized version, and find that this algorithm performs surprisingly close to optimal. We then consider a scenario with heterogeneous client bandwidths and design an algorithm to handle this case. Next, we analyze and simulate a non-cooperative scenario in which the clients need incentives to upload data to each other. Specifically we consider different algorithms based on "barter" and find that certain relaxations of barter perform almost as well as the optimal cooperative algorithm. Our work is inspired by the popular BitTorrent [8] protocol, which loosely incorporates mechanisms for efficiency and incentives. We explore BitTorrent's performance via simulation, studying the impact of different parameters. We find that BitTorrent's completion time is around twice the time of an optimal algorithm. BitTorrent's scaling behavior can be improved closer to the optimal algorithm's, but only if carefully tuned. Finally, we propose an architecture that allows applications to customize the content delivery algorithms we develop. We propose that the delivery mechanism be divided into two layers, one application-specific, and one application-independent. The former interfaces to the latter via a narrow block prioritization channel. We argue that this is simple and powerful enough to accommodate a wide variety of applications. We design two different application-specific schemes as proof of concept, and evaluate them via simulations. These results indicate that our priority-based algorithms allow high rates of delivery of streaming or ordered data.
Polarity Control in Migrating Vascular Smooth Muscle Cells: N-cadherin Localization and Function

Vascular endothelial cell loss initiates directional migration of medial smooth muscle cells into the arterial intima contributing to in-stent restenosis, atherosclerosis and coronary arterial by-pass graft failure. N-cadherin is a cell-cell adhesion molecule that mediates the interaction between vascular endothelial cells and the innermost smooth muscle cells to stabilize the arterial wall. Upon injury, I reasoned that relocalization of N-cadherin on the inner most smooth muscle cells to the posterior-lateral borders stimulates cell polarization to enable directional migration. Using an in vitro scratch-wound model to stimulate cell polarity and locally remove cell-cell contacts at one pole of smooth muscle cells, I found that Ncadherin localization provides signaling cues via a Cdc42/GSK pathway that promote polarized reorganization of the cytoskeleton and directional cell migration. I also found that N-cadherin was important to functions of lamellipodia at the anterior of migrating cells. In lamellipodia, actin polymerization drives protrusion of the leading edge and coincident, but more posterior, actin depolymerization results in retrograde flow of actin and associated plasma membrane structures. Using live cell imaging, I found that clusters of N-cadherinGFP repeatedly accumulated at the leading edge specifically at the neck of large pinocytotic vesicles called macropinosomes that were internalized and transported away from the leading edge. This localization is consistent with a role for N-cadherin in closure and scission of vesicles during macropinocytosis.
Characterization and classification of EEG attention level

This paper proposed improved fuzzy entropy(FuzzyEn) to calculate attention levels from single channel EEG,on the basis of approximate entropy.EEG attention level signals collected from twelve healthy subjects were characterized by FuzzyEn and other methods,and Support Vector Machine(SVM) was used to classify all EEG attention levels.The experimental results demonstrate that average identification rate of FuzzyEn feature extraction method reaches 76.3%,and the fuzzy entropy method can effectively characterize the complexity of EEG attention level.
Research and Design of System Management Software on the Basis of Three-tier Architecture

The paper proposes an architecture model for design system management software.The model consists of a management platform and one or more management applications that run upon the platform.The management platform is actually a policy-based,three-tiers,center-oriented,multi-tiered framework.Management application is a software module that performs concrete management functions.The most important advantage of the model is that it has good extensibility.It lays emphasis on the design of the management platform.
Malicious Code Detection Model Based on Traffic Statistical Fingerprinting

In order to detect malicious codes which utilize encryption technology and tunnels encapsulation,a new malicious code detection model based on traffic statistical fingerprinting is presented.The packet-level features and flow-level features are extracted from each flow in a training set.The flow-level features are filtered by the Principal Component Analysis.The detection model is constructed after malicious code's traffic statistical fingerprinting is got from these features' probability density functions.Experimental results indicate that this model can effectively detect encrypted or tunneled malicious codes.
ASSISTÊNCIA ÀS MULHERES VÍTIMAS DE VIOLÊNCIA DOMÉSTICA EM UMA UNIDADE DE URGÊNCIA E EMERGÊNCIA

Introducao: A violencia domestica se refere a todas as formas de violencia praticada no ambiente familiar sendo um problema de saude publica uma violacao dos direitos humanos (MOURA et al., 2012). A atuacao dos profissionais da saude e fundamental para a solucao do problema sendo importante que o enfermeiro se pauta por acoes planejadas, capacitacao pessoal e interacao com a equipe multidisciplinar. Objetivo: Analisar a assistencia prestada as mulheres vitimas de violencia domestica em uma unidade de urgencia e emergencia. Metodologia: Realizou-se um estudo de abordagem qualitativa, descritiva e exploratoria. A pesquisa foi realizada na UPA IPASE do municipio de Varzea Grande, MT. Atraves de entrevistas semiestruturada. Participaram da pesquisa quatro profissionais: uma medica, duas enfermeiras e uma assistente social. Utilizou-se analise de conteudo tematica. Resultados: Os resultados foram organizados em duas categorias 1: Assistencia profissional as mulheres vitimas de violencia domestica na UPA, compreende-se que o cuidado a vitima de violencia exige mais do que as habilidades tecnicas, requerendo uma atencao individualizada que ultrapasse o sentido de curar e tratar; e, 2: Desenvolvimento Profissional para Atendimento da Mulher Vitima de Violencia Domestica, nos servicos de saude os processos educativos visam ao desenvolvimento dos profissionais por uma serie de atividades genericamente denominadas de capacitacoes, treinamentos e cursos emergenciais ou pontuais, estruturados e continuo. Conclusao: Diante as informacoes obtidas verificou-se as fragilidades e potencialidades da assistencia na unidade, sendo o acolhimento crucial para a qualidade e a humanizacao do atendimento. Os profissionais envolvidos devem ter capacitacoes periodicamente visando uma melhor resolutividade. Palavras-chave: Violencia contra a Mulher; Violencia Domestica; Atencao a Saude; Acao Intersetorial.
Dynamic configuration of RED parameters

RED's performance is sensitive to the operating parameters,this paper propose an algorithm based on the adaptive RED.The algorithm tune the parameter based on both the tendency of the traffic load and the change rate of the average queue length.The simulation results show that this algorithm has improved the adaptability of the RED through dynamically tuning the parameters,and show a better performance on the drop rate,average queue length and link utilization.
Recognition of Chinese Chunk

A novel methodology is presented to enhance Chinese text chunking with the aid of transductive Hidden Markov Models (transductive HMMs),where the chunking is considered as a special tagging problem. An attempt is thus made to utilize it via a number of transformation functions to introduce as much relevant contextual information as possible in model training. These functions enable the models to make use of contextual information to a greater extent and keep away from costly changes of the original training and tagging process. Each of them results in an individual model with certain pros and cons. Through a number of experiments, the best two models are integrated into a significantly better one. The chunking experiments were carried out on the HIT Chinese Treebank corpus, of which the results show that it is an effective approach to the recognition of Chinese chunk, achieving an F score of 8238%.
Scenario analysis and PRA: Overview and lessons learned

The recent trend to use a best estimate plus uncertainty (BEPU) approach to nuclear reactor safety analysis instead of the traditional conservative approach can lead to the production of very large amounts of data. Therefore the need for methodologies that can facilitate the analysis of high volumes of data in terms of both the cardinality (due to the high number of uncertainties included in the analysis) and the dimensionality (due to the complexity of systems) arises. Clustering methodologies offer powerful tools that can help the user to identify scenario groups that are representative of the data and thus can reduce the effort involved in data analysis. In this paper, we consider data that are generated using the dynamic event tree approach for nuclear power reactor transients which contain a large set of state variables (e.g., temperature, pressure of specific nodes in the simulator) and information regarding the status of specific components/systems. Techniques for clustering of the raw data, using in particular Mean-Shift methodology, are discussed and evaluated. We highlight the lessons learned from the research activities at The Ohio State University and possible future research directions. In addition, pre-processing of the raw data and dimensionality reduction techniques are described and compared using several examples.
Tratamento da informação: um desafio para professores dos anos iniciais do ensino fundamental

A pesquisa teve como objetivo investigar relacoes com os saberes referentes ao tratamento da informacao estabelecidas por professores dos anos iniciais em formacao continuada e suas implicacoes na pratica de ensino desse tema. O estudo realizado foi de cunho quantitativo e qualitativo, cujo instrumento para obtencao dos dados foi um questionario com questoes abertas e fechadas. Os participantes deste trabalho foram vinte (20) docentes que participam do Centro de Formacao de Professores do Municipio de Belem do Para. Os dados analisados evidenciaram que existem dificuldades por parte desses professores em trabalhar com o bloco Tratamento da Informacao, apontando para a necessidade do estabelecimento de relacoes mais proficuas com este tema.
A FLEXIBLE AND TESTABLE SOFTWARE ARCHITECTURE: APPLYING PRESENTER FIRST TO A DEVICE SERVER FOR THE DOOCS ACCELERATOR CONTROL SYSTEM OF THE EUROPEAN XFEL

Presenter First (PF) uses a variant of Model View Presenter design pattern to add implementation flexibility and to improve testability of complex event-driven applications. It has been introduced in the context of GUI applications, but can easily be adapted to server applications. This paper describes how Presenter First methodology is used to develop a device server for the Programmable Logic Controls (PLC) of the European XFEL undulator systems, which are Windows PCs running PLC software from Beckhoff. The server implements a ZeroMQ message interface to the PLC allowing the DOOCS accelerator control system of the European XFEL to exchange data with the PLC by sending messages over the network. Our challenge is to develop a well-tested device server with a flexible architecture that allows integrating the server into other accelerator control systems like EPICS.
Pure fuzzy Hall effect sensors for permanent magnet synchronous motor

An investigation about Hall effect sensors’ efficiency is confirmed in permanent magnet synchronous motor (PMSM) drive systems. A fuzzy control algorithm is used as an artificial intelligence controller. Large scale and low slopes are used for creating membership functions and a sensitive controller is obtained. Speed is wanted to be taken under control and a minimum error value is aimed. PMSM drive systems are established using MATLAB-Simulink/SimPower. Simulations are realized with real-time parameters in discrete mode. A fuzzy logic controller is designed by using the MATLAB/Fuzzy Logic Toolbox. A normalization technique and high resolution output of the fuzzy logic controller (FLC) are unique aspects of the work. Input variable bandwidths are narrowed and the number of membership functions is reduced. The taken steps provide positive effects against variation of input parameters. Finally, sharp impacts are not produced by the fuzzy controller. Results verified that the Hall effect supported controller system gives better results than sensorless one. This is especially observed at change points of reference speed. Low complitude oscillations and short settling time are achieved on speed and torque signals.
Exercise, postprandial lipaemia and lipoprotein lipase activity

Impaired clearance of triacylglycerol-rich lipoproteins contributes to atherogenesis. It can be argued that exercise may decrease the risk of atherosclerotic diseases through its potential to improve the metabolic capacity for triacylgycerol and hence, clearance of triacylglycerol-rich lipoproteins. The investigations described in this thesis focused on the influence of exercise on postprandial lipid and lipoprotein metabolism. [Continues. ]
Modelo de estimación del esfuerzo, tiempo y coste de desarrollo de un software de gestión

El objetivo el presente estudio fue elaborar un modelo que permita estimar el esfuerzo, tiempo de desarrollo y coste de desarrollo de un software de gestion de manera confiable. Pues, la estimacion de un proyecto software es una actividad critica a la hora de tomar decisiones de planificacion. En la investigacion se realizo el seguimiento a cinco proyectos software de gestion de tamano mediano desarrollados por empresas trujillanas y en base a la muestra obtenida y haciendo uso de analisis de regresion se logro elaborar el modelo de estimacion de un proyecto software, que sigue una tendencia exponencial y que permite estimar el esfuerzo, tiempo de desarrollo y coste de desarrollo de un software de gestion.Palabras clave: Estimacion, software de gestion
Projections aléatoires pour l'apprentissage compressif

L'apprentissage compressif a pour objectif de reduire drastiquement le volume de grandes collections d'entrainement via des sortes de projections aleatoires, tout en preservant l'information necessaire a l'apprentissage. En s'appuyant sur quelques exemples en apprentissage non-supervise, nous proposons un tour d'horizon des outils utilises, des garanties theoriques a la fois en termes de preservation d'information et de respect de la vie privee, pour finir avec quelques problemes ouverts.
Estudio psicosocial del emprendimiento: antecedentes de la intención emprendedora en el marco de la teoría de la acción planificada

Esta Tesis analiza dentro del marco teorico de la teoria de la accion planificada (TAP; Ajzen, 1991) la influencia de una serie de antecedentes psicosociales interrelacionados sobre la intencion emprendedora en Espana: el contexto universitario y familiar, el riesgo percibido de emprender, la creatividad percibida, la influencia de los medios de comunicacion y los estereotipos de genero. El analisis en profundidad de estos aspectos que influyen en la intencion emprendedora se orienta a identificar que factores son mas relevantes para poner en marcha futuras iniciativas especificas que incidan de manera mas focalizada en aquellos aspectos que favorecen el desarrollo de la intencion emprendedora. Los antecedentes psicosociales que se contemplan en esta Tesis han sido estudiados de forma exploratoria en estudios recientes, aunque no de forma integrada ni en general en el contexto de Espana. Por tanto, esta Tesis persigue contribuir a la investigacion internacional mas actual en esta area de Psicologia del Emprendimiento, ampliando el modelo propuesto por la teoria de la accion planificada y sometiendo a prueba empiricamente un modelo extendido, asi como abordar especificamente el estudio de diversas variables relevantes en el emprendimiento con el fin de mejorar la educacion emprendedora. Para ello, se plantea una aproximacion cuantitativa, correlacional y transversal a traves de la realizacion de cinco estudios con muestras independientes y empleando cuestionarios para la recogida de datos: Estudio 1 – El entorno universitario y la intencion emprendedora: el papel mediador de los componentes de la teoria de la accion planificada; Estudio 2 – Estudio psicosocial de la creatividad percibida y la intencion emprendedora en una muestra de estudiantes de nivel universitario; Estudio 3 – Percepcion de la representacion del emprendimiento en los medios de comunicacion e intencion emprendedora; Estudio 4 – Influencia de la activacion de un modelo de rol de mujer emprendedora de exito en la intencion emprendedora; y Estudio 5 – Orientacion a la tarea y a la relacion autopercibidas e intencion emprendedora. En cuanto a la muestra, se distingue entre estudiantes de nivel universitario (Estudios 1 y 2) y poblacion general (Estudios 3, 4 y 5). De la presente Tesis se derivan una serie de implicaciones practicas ademas de teoricas, ya que un mejor conocimiento de los aspectos que conducen a diferentes colectivos a emprender puede contribuir a un diseno mas adecuado de los programas de formacion emprendedora que se estan impulsando en muchas universidades y desde otro tipo de instituciones.
Energy-Saving QoS Resource Management of Virtualized Networked Data Centers for Big Data Stream Computing

In this research contribution, we develop the scheduler which optimizes the energy-vs.-performance trade-off in Software-as-a-Service (SaaS) Virtualized Networked Data Centers (VNetDCs) that support real-time Big Data Stream Computing (BDSC) services. Our objective is to minimize the communication-plus-computing energy which is wasted by processing streams of Big Data under hard real-time constrains on the per-job computing-plus-communication delays. For this purpose, the proposed scheduler operates at the Middleware layer of the underlying protocol stack and performs the adaptive joint allocation of the task sizes, computing rates, communication rates and powers of the available VNetDC. In order to deal with the inherently nonconvex nature of the resulting resource management optimization problem, we develop a solving approach that leads to the lossless decomposition of the afforded problem into the cascade of two simpler sub-problems. The resulting optimal scheduler is amenable of scalable and distributed adaptive implementation. The performance of a Xen-based prototype of the scheduler is tested under several Big Data workload traces and compared with the corresponding ones of some state-of-the-art static and sequential schedulers.
A Method for Geo Semantic Spatial Index on SPARQL Query

In order to query geo semantic data efficiently,based on the analysis of traditional RDF(resource description framework)data organization methods and spatial index methods,Firstly,ageospatial quad data model—GeoQuad was proposed,and then,on the basis of this model a geo semantic spatial index method was constructed.Finally,using tools such as Jena,ARQ and JTS topology suite,a Geo SPARQL spatial query was achieved,which supported semantic query specification--SPARQL.Experiments show that this method is feasible,which is not only able to locate the spatial RDF nodes quickly,but also can quickly spatial query on RDF data by returning the results in the form of RDF node.
Emerging Statistical Models for the Analysis of Genomic Data

In this paper we review important emerging statistical models that have been recently developed and used for genomic data analysis. First, we summarize general background and some critical issues in genomic data mining. We then describe a novel concept of statistical significance, so-called false discovery rate, the rate of false positives among all positive findings, which has been suggested to control the error rate of numerous false positives in large screening biological data analysis. In the next section two recent statistical testing methods---significance analysis of microarray (SAM) and local pooled error (LPE) tests are introduced. We next introduce statistical modeling in genomic data analysis such as ANOVA and heterogeneous error modeling (HEM) approaches that have been suggested for analyzing microarray data obtained from multiple experimental and/or biological conditions. ™ gene expression, Misclassification penalized posterior (MiPP), Significance analysis of microarray (SAM), Supervised learning, Unsupervised learning
Face Image Analysis using Adaboost Learning and Non-Square Differential LBP

In this study, we presented a method for non-square Differential LBP operation that can well describe the micro pattern in the horizontal and vertical component. We proposed a way to represent a LBP operation with various direction components as well as the diagonal component. In order to verify the validity of the proposed operation, Differential LBP was investigated with respect to accuracy, sensitivity, and specificity for the classification of facial expression. In accuracy comparison proposed LBP operation obtains better results than Square LBP and LBP-CS operations. Also, Proposed Differential LBP gets better results than previous two methods in the sensitivity and specificity indicators 'Neutral’, 'Happiness', 'Surprise'
SVM based Chinese Web page automatic classification

This paper deals with Chinese web page classification based on support vector machine (SVM). First, Some methods are proposed for feature extraction and selection based on textual keywords. Then Special problems are discussed on statistic learning theory, support vector machine and their application in classification. Quadratic program algorithm is also described for constructing the SVM classifier. In the experiment part, the sample set, including 5096 samples, is chosen from the web version of Chinese People's Daily. It is separated into two sets, the training set with 3398 samples and the test set with 1698 samples. Two kinds of kernel function, polynomial and radial basis function, are considered in constructing the SVM classifier. The final classification correct rates are 89.81%, 86.51% for the two classifiers, respectively.