Miracle-dz/UniFAR-FTUs huggingface dataset:
A generic metadata description for hydrodynamic model data

Sharing of data sets between numerical models is considered an important and pressing issue in the modeling community, because of (i) the time consumed to convert data sets and (ii) the need to connect different types of numerical codes to better map inter-connectedness of aquatic domains. One of the reasons for the data sharing problem arises from the lack of sufficient description of the data, or lack of metadata, which is due to the absence of a standardized framework for these metadata sets. This paper describes the development of a metadata framework for hydrodynamic data descriptions using the Geographic Information Metadata, 19115:2003 standard published by the International Standards Organization (ISO). This standard has been chosen not only because of its extent and adequacy to describe geospatial data, but also because of its widespread use and flexibility to extend the coverage. The latter is particularly important, as further extensions of the metadata standard are needed to provide a comprehensive metadata representation of hydrodynamics and their I/O data. in order to enable the community to share and reuse numerical code data sets, however, they need to be published in both human and machine understandable format. One such format is the Web Ontology language (OWL), whose syntax is compliant with the Extensible Markup Language (XML). In this paper, we present an extensive metadata profile using the available elements of ISO 19115:2003 as well as its extension rules. Based on the metadata profile, an explicit specification or ontology for the model data domain has been created using OWL. The use of OWL not only permits flexibility when extending the coverage but also to share data sets as resources across the internet as part of the semantic web. we demonstrate the use of the framework using a two-dimensional finite element code and its associated data sets.
Bayesian network for post-earthquake infrastructure risk assessment and decision

A framework for post-earthquake risk assessment and decision making for infrastructure systems is developed. Use is made of the Bayesian network methodology for modeling earthquake hazards and system performance, as well as for probabilistic updating in light of an evolving state of information gained from ground motion sensors and observations of the system component states. The Bayesian network is extended by addition of decision and utility nodes to construct an influence diagram, which is used for post-earthquake decision making regarding the type of inspection to perform or for setting the performance level of components. A value-of-information heuristic is used to determine the best sequence of component inspections. The methodology is demonstrated by its application to a hypothetical model of a segment of the California high-speed rail system.
SIMULTANEOUS MULTIVIEW FACE TRACKING AND RECOGNITION IN VIDEO USING PARTICLE FILTERING by Naotoshi Seo

Title of thesis: SIMULTANEOUS MULTI-VIEW FACE TRACKING AND RECOGNITION IN VIDEO USING PARTICLE FILTERING Naotoshi Seo, Master of Science, 2009 Thesis directed by: Professor Rama Chellappa Department of Electrical and Computer Engineering Recently, face recognition based on video has gained wide interest especially due to its role in surveillance systems. Video-based recognition has superior advantages over image-based recognition because a video contains image sequences as well as temporal information. However, surveillance videos are generally of lowresolution and contain faces mostly in non-frontal poses. We propose a multi-view, video-based face recognition algorithm using the Bayesian inference framework. This method represents an appearance of each subject by a complex nonlinear appearance manifold expressed as a collection of simpler pose manifolds and the connections, represented by transition probabilities, among them. A Bayesian inference formulation is introduced to utilize the temporal information in the video via the transition probabilities among pose manifolds. The Bayesian inference formulation realizes video-based face recognition by progressively accumulating the recognition confidences in frames. The accumulation step possibly enables to solve face recognition problems in low-resolution videos, and the progressive characteristic is especially useful for a real-time processing. Furthermore, this face recognition framework has another characteristic that does not require processing all frames in a video if enough recognition confidence is accumulated in an intermediate frame. This characteristic gives an advantage over batch methods in terms of a computational efficiency. Furthermore, we propose a simultaneous multi-view face tracking and recognition algorithm. Conventionally, face recognition in a video is performed in trackingthen-recognition scenario that extracts the best facial image patch in the tracking and then recognizes the identity of the facial image. Simultaneous face tracking and recognition works in a different fashion, by handling both tracking and recognition simultaneously. Particle filter is a technique for implementing a Bayesian inference filter by Monte Carlo simulation, which has gained prevalence in the visual tracking literature since the Condensation algorithm was introduced. Since we have proposed a video-based face recognition algorithm based on the Bayesian inference framework, it is easy to integrate the particle filter tracker and our proposed recognition method into one, using the particle filter for both tracking and recognition simultaneously. This simultaneous framework utilizes the temporal information in a video for not only tracking but also recognition by modeling the dynamics of facial poses. Although the time series formulation remains more general, only the facial pose dynamics is utilized for recognition in this thesis. SIMULTANEOUS MULTI-VIEW FACE TRACKING AND RECOGNITION IN VIDEO USING PARTICLE FILTERING
Self-report differentiation of anxiety and depression in an anxiety disorders sample.

There is a prevalent belief that self-reports of anxiety and depression are often indistinguishable, based largely on findings with college students or mixed psychiatric samples. This study focused on a homogeneous sample of clinically anxious subjects who presented to an anxiety disorders clinic. A factor analysis was conducted using the responses of 298 anxiety disorder patients on the Beck Depression Inventory and the State Anxiety subscale of the State-Trait Anxiety Inventory. Separate anxiety and depression factors emerged with almost no overlap. State Anxiety was comprised of separate positive and negative descriptort but this may be an artifact of the subscale. These results suggest that anxiety and depression can be reliably differentiated in a homogeneous clinical sample and await replication in a unipolar depressed sample
Die familiäre Lernumwelt und ihre Bedeutung für die kindliche Kompetenzentwicklung

Dieser Artikel widmet sich der familiaren Lernumwelt und ihrer Bedeutung fur die Kompetenzentwicklung bei Vorschul- und Grundschulkindern. Wahrend im englischsprachigen Raum bereits viele Befunde zum Einfluss der Home Learning Environment (HLE) auf schriftsprachliche und mathematische Kompetenzen vorliegen, sind Untersuchungen in Deutschland bislang vergleichsweise selten. Der vorliegende Artikel soll deshalb einen Uberblick uber die bisherigen Erkenntnisse liefern und noch offene Fragen diskutieren. Zunachst werden ein theoretischer Bezugsrahmen sowie Begrifflichkeiten und Operationalisierungen der HLE vorgestellt. Danach wird anhand bisheriger Studienergebnisse der Einfluss der HLE auf die kindliche Kompetenzentwicklung belegt. Erste Interventionsansatze zur Verbesserung der HLE werden vorgestellt und schlieslich offene Fragen im Kontext der familiaren Lernumwelt diskutiert.
A theory of need in international political theory : autonomy, freedom, and a global obligation

The thesis is concerned with the nature of human action presupposed by normative theory; it is about recognising and articulating the fragility of the human within the context of human needs and assumptions made by international ethical theory. The primary aim of the thesis is to establish the existence of two basic needs necessary for moral action, to determine a global obligation to enable the meeting of those needs, and to articulate a necessary reconceptualisation of the state system in line with the demands of that obligation. The thesis makes this argument in three parts. By exploring and revealing the vulnerability and finitude of the individual actor, looking at the notion and language of ‘need’, and demonstrating what is involved in being held morally responsible, Part One seeks to provide an objective and universalist account of the prerequisites of moral action, establishing two basic needs: autonomy and freedom. The second Part of the thesis is dedicated to showing why there is a corresponding obligation – a ‘Global Principle’ – to meet these needs, an obligation which is cosmopolitan in scope and source. In its attempt to articulate a rationally-derived core and primary principle of justice, the thesis hopes to contribute to the cosmopolitan discourse of IPT. Part Three shows in what way the international system, with an emphasis on the state, needs to be reconceived; it argues that the state needs to be reconceptualised as a transparent enactor of the derivative duties of the Global Principle (through political and socio-economic reform) ensuring identification of the individual as the primary actor of responsibility within the international. Overall, the thesis aims to identify and acknowledge the limitations of the human and the necessity of some external provisions in order to enable her to become a normatively accountable actor. It aims to highlight what normative theory both assumes and reinforces about human action, arguing that only once the discourse of IPT has recognised the uniquely needing nature of the individual can she become a meaningful and free actor within the international arena.
Evaluation of machine-learning-based approaches to load estimation based on sEMG and inertial signals during lifting movements of the upper body

In a previous project at Fraunhofer IPA, a power orthosis has been established, which is aimed to support a user when lifting heavy objects as well as reducing strains on a healthy user’s upperand underarm. [1] [2] The orthosis as been developed with two control modes in mind one for support and one for transparent mode. In order to evalute the level of required support by the orthosis, surface Electromyography (sEMG) should be used in conjunction with various machine learning algorithms. [3] In this work the student has to research the state-of-the-art of surface EMG and its application to load and user intent detection. Furthermore current applications of machine learning algorithms in the field of neural signal processing, especially surface EMG have to be evalued. After that the student has to establish a connection from a professional wireless surface EMG to a PC. As a final step, the student has to estimate the level of support required with various machine learning algorithms.
Brief overview of survey sampling techniques with R

For the last decade, many R packages have been suggested for performing sample selection according to various sampling designs as well as design-based estimation of finite population interest parameters and of their variance. The goal of this article is to give a brief overview of some of these packages with application on two datasets. A special attention is given to calibration issues.
Biological image analysis using deep learning-based methods: Literature review

Automatic processing large amount of microscopic images is important for medical and biological studies. Deep learning has demonstrated better performance than traditional machine learning methods for processing massive quantities of images; therefore, it has attracted increasing attention from the research and industry fields. This paper summarizes the latest progress of deep learning methods in biological microscopic image processing, including image classification, object detection, and image segmentation. Compared to the traditional machine learning methods, deep neural networks achieved better accuracy without tedious feature selection procedure. Obstacles of the biological image analysis with deep learning methods include limited training set and imperfect image quality. Viable solutions to these obstacles are discussed at the end of the paper. With this survey, we hope to provide a reference for the researchers conducting biological microscopic image processing.
Application of finite element analysis on balloon expandable coronary stents: A review

Numerical analysis of complex physical environment continues to be preferred over “build and test” approach in product development process. Finite Element Analysis (FEA) of coronary artery stenting is studied and researched worldwide for many years. Potential of using FEA for mimicking in-vivo is high as experimental test is ruled out for variety of reasons. This review aims at discussing issues and challenges of numerical simulation based on part of available literature on usage of FEA techniques for investigating behavior of balloon expandable (BE) coronary stents inside artery. Literatures of past 16 years of study on the structural analysis is summarized and potential issues for research is discussed. Study tries to investigate deployment characteristics and biomechanical response of artery post stenting and significance of non-physiological conditions induced. Effects of geometrical parameters, simulation strategies are summarized. Study mainly underscores the potential challenges of reliable numerical investigation. Scope of FEA in predicting contributor for in-stent restenosis (ISR), a major drawback of stenting procedure, by correlating the engineering aspect of stent design and its clinical significance supported by clinical trials are highlighted. Study is expected to serve as qualitative assessment for cardiologists to minimize procedural failure and quantitative tool for the designers for stent optimization.
Condition Monitoring for Wireless Sensor Network-Based Automatic Weather Stations

Wireless Sensor Network (WSN)-based Automatic Weather Stations (AWSs) perform automatic collection and transmission of weather data. These AWSs face challenges, which lower their performance. Hence, a need for regular monitoring to reduce down time. We propose condition monitoring, comprised of a data receiver, analyser, problem classifier and reporter and visualizer, to mine data relationships, identify possible causes of problems and perform reporting of AWS status. The data receiver uses an M/M/1/k queuing model. We use Successive Pairwise REcord Differences (SPREDs) algorithm to compare arrival rates and packet content so as to establish sensor, node and AWS level performance. We also perform a hybrid of Grubb outlier detection and correlations amongst related variables for data validation. Problems take on one of four states. One connection can receive data at a rate as low as 1ms, without loss while problem identification especially in high density network is improved
Correlation, conflict, and choice.

We examined the degree to which individuals adapt their decision processes to the degree of interattribute correlation and conflict characterizing a decision problem. On the basis of an effort-accuracy framework for adaptive decision making, we predicted that the more negatively correlated the attribute structure, the more people will use strategies that process much of the relevant information and make trade-offs. A computer simulation study supported these predictions, and two experiments using process-tracing techniques to monitor information acquisition indicated that individuals did indeed respond to interattribute correlation by shifting their processing strategies in ways that are adaptive according to the effort-accuracy framework
Religion and the rise of capitalism : an historical study

In one of the true classics of twentieth-century political economy, R. H. Tawney addresses the question of how religion has affected social and economic practices. He tracks the influence of religious thought on capitalist economy and ideology since the Middle Ages, shedding light on the question of why Christianity continues to exert a unique role in the marketplace. In so doing, the book offers an incisive analysis of the morals and mores of contemporary Western culture. Religion and the Rise of Capitalism is more pertinent now than ever, as today the dividing line between the spheres of religion and secular business is shifting, blending ethical considerations with the motivations of the marketplace.
The way to design web-based (EMP) course in EFL context: A bridge from evaluation to course design

As the worldwide use of English increases it has become indispensable in educational, vocational and professional environments. As a result, English for specific purposes (ESP) has a central role in teaching English at the tertiary level. With rapid advances in technological development, technology has emerged as one of the key aspects of teaching and learning English in classrooms. Based on the students' needs to use technology in learning English for medical purposes (EMP), the internet has turned into a valuable EMP resource. However, the selection of appropriate websites that match the learners’ and teachers’ needs can turn into a time-consuming process.
Analysis of parallel computational models for clustering

Clustering is one of the main task of data mining, where groups of similar objects are discovered and grouping of similar data as well as outliers detection are performed. Processing of huge datasets requires scalable models of computations and distributed computing environments, therefore efficient parallel clustering methods are required for this purpose. Usually for parallel data analytics the MapReduce processing model is used. But growing computer power of heterogeneous platforms based on graphic processors and FPGA accelerators causes that CUDA and OpenCL models may be interesting alternative to MapReduce. This paper presents comparative analysis of effectiveness of applying MapReduce and CUDA/OpenCL processing models for clustering. We compare different methods of clustering in terms of their possibilities of parallelization using both models of computation. The conclusions indicate directions for further work in this area.
Design and analysis of a mobile robot for storage and retrieval system

The growth of the automation industry has been staggering and its continued growth has imbibed life to an aging industry. The application of robots has reduced the workload of humans and frees up the workforce off of monotonous, non-creative jobs and can be efficiently utilized for more fulfilling jobs. The aim of this research is to design and analyze a mobile robot for a storage and retrieval system. The robot has the objective of translating long racks in storage houses to different locations, instead of transporting the individual components in the racks. The main task of lifting the racks will be accomplished using a lead screw mechanism and a differential drive is used as the main drive. The challenges faced were mainly, distributing the load concentration equally along the chassis to ensure maximum stability and limiting the weight ratio of the robot to the racks to an optimal number. Design and analysis of the robot were carried out using CATIA and ANSYS, respectively.
Mapping the onset of psychosis: the Comprehensive Assessment of At‐Risk Mental States

Objective: Recognizing the prodrome of a first psychotic episode prospectively creates the opportunity of intervention, which could delay, ameliorate or even prevent onset. Valid criteria and a reliable methodology for identifying possible prodromes are needed. This paper describes an instrument, the Comprehensive Assessment of At-Risk Mental States (CAARMS), which has been designed for such a purpose. It has two functions: (i) to assess psychopathology thought to indicate imminent development of a first-episode psychotic disorder; and (ii) to determine if an individual meets criteria for being at ultra high risk (UHR) for onset of first psychotic disorder. This paper describes the pilot evaluation of the CAARMS.Method: Several methodologies were used to test the CAARMS. First, CAARMS scores in a group of UHR young people and the association between CAARMS scores and the risk of transition to psychotic disorder, were analysed. Second, CAARMS scores in a UHR group were compared to a control group. To asses.
GRAMI-2: DESARROLLO DE UN TEST PARA EVALUAR LA COORDINACIÓN MOTRIZ GLOBAL EN LA EDUCACIÓN PRIMARIA

This study presents the development and validation of a motor test for school children 8 to 12 years. A panel of experts was responsible for the selection of the tasks that formed the initial composition of the test, which was applied to a sample of 1360 school children aged between 7 and 12 years, of whom 709 were boys and 651 girls. The second version of the test was composed of 8 tests. It was applied to another sample of 1664 school children 8 to 12 years, of whom 864 were boys and 800 girls. The data obtained were analyzed using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA), results showed a model with a single component and 6 indicators, so the final test was consisted of 6 tasks. Reliability by calculating the internal consistency and test-retest reliability was very satisfactory, both above . 80. The criteria validity through its relationship with a scale of osbervacion of motor competence was very satisfying relationships.
The role of stable accommodation in reducing recidivism: what does the evidence tell us?

Purpose – This paper aims to review the available evidence on the role of stable accommodation in reducing the risk of recidivism. It seeks to answer questions about the nature and extent of such a role, whether stable accommodation on its own plays a role or as part of an overall programme, and what the implications arising from the current evidence are for policy makers and practitioners.Design/methodology/approach – The research strategy consisted of a number of steps. First, existing systematic reviews in the field were reviewed to identity relevant evidence. Following this, a standard search of the literature was undertaken to identity potential research for further consideration. Articles and books identified were subject to a three part test to determine relevance and robustness of method.Findings – The paper suggests that the evidence base is less than clear about the role of stable accommodation in reducing risk of recidivism. The extant literature can be classified as two types; the first utilis.
Performance comparison of perceived image color difference measures

This paper deals with techniques for analysis of perceived color differences in images. Impact of color artifacts in image processing chain is a critical factor in the assessment of overall Quality of Experience (QoE). At first, an overview of color difference measures is presented. The performance of the methods is compared based on the results from subjective studies. Possible utilization of publicly available datasets with associated subjective scores is discussed. Majority of the datasets contain images distorted by various types of distortions not necessarily with controlled color impairments. Dedicated database of images with common color distortions and associated subjective scores is introduced. Performance evaluation and comparison of objective image color difference measures is presented using conventional correlation performance measures and robust receiver operating characteristic (ROC) based analyses.