Miracle-dz/UniFAR-FTUs huggingface dataset:
A Qualitative Analysis of Situated Web-Based Instruction

This paper presents results of research on an intensive online graduate level course in Web-based instruction (WBI) delivered via the same medium. The classes, offered concurrently at the University of Memphis and at Georgia State University, provide students with experiences centered around three areas: being a student in an online environment; being a designer and developer of online learning; and being a critic of WBI materials and resources. The results of analysis of the data collected over three semesters of this course are addressed, and findings are discussed in the areas of: synchronous versus asynchronous interaction; the importance of technical prowess; aids and barriers to the establishment of a learning community; and the affective dimensions of WBI. The paper concludes that careful consideration must be made of learners, the environment, and other issues associated with an educational system. Contains 18 references. (AEF) Reproductions supplied by EDRS are the best that can be made from the original document.
Analyzing Structural Relations in Multivariate Dyadic Binary Data

In social network studies, most often only a single relation (or link) between the actors is investigated. When more than one link has been recorded, the two-way sociomatrix becomes a three-way array with the set of links being the third way. In this paper, we present a model which simultaneously accounts for the three ways in the data. Random effects are used to model the between-actor variability, both on senders and receivers side. In addition, structural relations between the linking variables are investigated. The model is applied to a study of popularity and strength in a class of students. It is shown that popularity can be seen as a linear function of strength on the receivers’ side, but not on the senders’ side.
Evolving communicating agents based on genetic programming

The paper presents the emergence of the cooperative behavior for communicating agents by means of genetic programming (GP). Our experimental domain is the pursuit game, a multi agent test bed. The world consists of simulated robot agents and a simulated environment which is both dynamic and unpredictable. For the purpose of evolving the cooperative behavior, we use the co-evolutionary breeding strategy. We confirm the emergence of cooperation via communication. The effectiveness of GP based multi agent learning is discussed with comparative experiments.
Deep Extreme Learning Machine and Its Application in EEG Classification

Recently, deep learning has aroused wide interest in machine learning fields. Deep learning is a multilayer perceptron artificial neural network algorithm. Deep learning has the advantage of approximating the complicated function and alleviating the optimization difficulty associated with deep models. Multilayer extreme learning machine (MLELM) is a learning algorithm of an artificial neural network which takes advantages of deep learning and extreme learning machine. Not only does MLELM approximate the complicated function but it also does not need to iterate during the training process. We combining with MLELM and extreme learning machine with kernel (KELM) put forward deep extreme learning machine (DELM) and apply it to EEG classification in this paper. This paper focuses on the application of DELM in the classification of the visual feedback experiment, using MATLAB and the second brain-computer interface (BCI) competition datasets. By simulating and analyzing the results of the experiments, effectiveness of the application of DELM in EEG classification is confirmed.
Discontinuous feedback control of a 3 link planar PPR underactuated manipulator

In this paper we consider the synthesis of a point-to-point control law for a three degrees of freedom planar PPR underactuated manipulator moving in a horizontal plane. We apply a discontinuous feedback controller construction derived for a special class of underactuated systems. By using a coordinate transformation, we transform the system into chained form which makes it suitable for the application of the discontinuous control law. Simulation results are presented that demonstrate the effectiveness of the controller.
Concept maps as versatile tools to integrate complex ideas: From kindergarten to higher and professional education

Knowledge is getting increasingly more complex. Learners, from Kindergarten to higher education, require powerful tools to connect complex ideas. This paper explores the range of studies that investigated concept maps as learning, metacognitive, collaborative, and assessment tools to support integrating complex ideas. Research suggests that concept maps can be successfully implemented in a wide variety of settings, from K12 to higher and professional education. However, the effectiveness of concept maps depends on different factors, such as concept map training and choosing a suitable form of concept map to match the task and learner. Developing proficiency in concept mapping takes time and practice and should not be first introduced in higher education. Concept map training could start as early as Kindergarten and include concept map generation, interpretation, and revision. This paper concludes that, if implemented thoughtfully, concept maps can be versatile tools to support knowledge integration processes towards a deeper understanding of the relations and structures of complex ideas and facilitate life-long learning.
Graphical Model for Three-Way Living Donor Kidney Exchange

Kidney transplantation is the treatment of choice for patients with end-stage renal disease (ESRD). There are three possible organ sources for these transplants; cadaver, living, and good samaritan donors. The living donors are usually friends or relatives of the patient. The benefits of living donors in kidney exchanges are to increase the patients’ chance of receiving an organ sooner than patients waiting for cadaver donors, as well as providing them with a higher graft survival rate. In cases where a living donor is incompatible with their loved-one in need of a transplant, kidney paired exchanges are possible. Kidney paired exchanges involve two donor-recipient pairs where each donor cannot give a kidney to the intended recipient because of immunological incompatibility, but each recipient can receive a kidney from the other donor. This type of exchange offers a lifesaving alternative to waiting for a kidney from a deceased-donor waiting list. We explore how three-way exchanges can expand the opportunity for incompatible pairs to find compatible donors for their recipients and also how it can ease the burden for reciprocal compatibility. In this project, we generate a simulated population of incompatible donor-recipient pairs using data from the U.S. general population and the Organ Procurement and Transplantation Network. We assign each individual in a pair a blood type. From these assignments, we create a directed graph, where nodes represent incompatible pairs and directed edges represent possible exchanges determined by blood type. In addition to blood type, the model includes other kidney allocation considerations, such as the age of the recipient, immunologic sensitization and the hospital or treatment location of incompatible pairs. We assign these factors as priorities or weights to the nodes and to the directed edges of the graph. We find all possible three-way exchanges in the graph and present an algorithm to identify maximum weighted kidney three-way exchanges from the simulated population of incompatible pairs.
Representational Inquiry in Science Learning Games

Using learning games in education gives rise to a learning situation where game culture meets school culture and the result can be successful or corrupting for both. In this paper I present a case study of school classes and their teachers playing the game ‘Homicide’, a game where children play the roles as forensic experts who solve a series of murder cases. When teachers use this type of games, they have to adapt to new teaching situations and roles. This includes the fictional role in a game, but also the role as a supervisor for a group of students that play the role as professional experts. I present examples of teachers who adopt different roles in the game, and discuss how understanding the background for these roles can help us define the game-based learning situation. Finally I discuss what consequences the problems presented here may have for the design of future learning games. Author
Iterative uncertainty reduction via Monte Carlo simulation : a streamlined life cycle assessment case study

Life cycle assessment (LCA) is one methodology for assessing a product’s impact on the environment. LCA has grown in popularity recently as consumers and governments request more information concerning the environmental consequences of goods and services. In many cases, however, carrying out a complete LCA is prohibitively expensive, demanding large investments of time and money to collect and analyze data. This thesis aims to address the complexity of LCA by highlighting important product parameters, thereby guiding data collection. LCA streamlining is the process of reducing the necessary e↵ort to produce acceptable analyses. Many methods of LCA streamlining are unfortunately vague and rely on engineering intuition. While they can be e↵ective, the reduction in e↵ort is often accompanied by a commensurate increase in the uncertainty of the results. One nascent streamlining method aims to reduce uncertainty by generating random simulations of the target product’s environmental impact. In these random Monte Carlo simulations the product’s attributes are varied, producing a range of impacts. Parameters that contribute significantly to the uncertainty of the overall impact are targeted for resolution. To resolve a parameter, data must be collected to more precisely define its value. This research project performs a streamlined LCA case study in collaboration with a diesel engine manufacturer. A specific engine is selected and a complex model of its production and manufacturing energy use is created. The model, consisting of 184 parameters, is then sampled randomly to determine key parameters for resolution. Parameters are resolved progressively and the resulting decrease in uncertainty is examined. The primary metric for evaluating model uncertainty is False Error Rate (FSR), defined here as the confusion between two engines that di↵er in energy use by 10%. Initially the FSR is 21%, dropping to 6.1% after 20 parameters are resolved, and stabilizing at 5.8% after 39 parameters are resolved. The case study illustrates that, if properly planned, a streamlined LCA can be performed that achieves desired resolution while vastly reducing the data collection burden. Thesis Supervisor: Timothy G. Gutowski Title: Professor of Mechanical Engineering 3
Application of controllability measure for actuator placement in a civil engineering structure

An eight storey flexible building is considered for active control with an emphasis on the problem of efficient actuator placement. An active bracing system (ABS) is used as the control mechanism. Several practically feasible ABS configurations are evaluated using a recently proposed controllability measure and, based on this evaluation, one of these is selected as the design configuration. As a practical constraint, at most two ABS and three sensors are assumed to be available. An output feedback linear quadratic regulator is used as a controller. To verify the conclusions based on the controllability measure, performance of this control system is evaluated with these actuator configurations using time-history analyses. Important response quantities, such as peak and root-mean-square (RMS) relative displacement, absolute acceleration, control force and energy requirement are used for the analysis.
Acquisition, processing and display for 3D live-action cinema and television

Three-dimensional cinema and television involves the presentation of a separate image to a viewer’s left and right eyes, in order to invoke a depth perception. Three-dimensional cinema and television provides filmmakers with an additional cue to aid in their storytelling. Current acquisition and manipulation approaches make it difficult to effectively exploit the additional depth dimension. In this thesis we examine the pipeline of acquisition, processing and display, and propose methods and approaches which make it easier to exploit the depth dimension, while also aiming to improve the quality of the three-dimensional viewing experience. Computing a depth value for each pixel in the video images of a captured scene is a difficult task. We propose an acquisition system where a central, high quality film camera is supported with additional satellite sensors. Rather than using sensors of a single modality, e.g. visible light cameras, we propose to use additional modalities. Besides lower quality visible light cameras, we also incorporate a Time-of-Flight depth camera and a thermal camera. By combining sensors of different modalities we aim to provide more information for computing per-pixel depth. The satellite cameras allow for better occlusion reasoning of the scene. A depth camera provides a direct measure of scene depth, albeit at a low resolution. Finally, a thermal imaging camera provides information to correctly discern between different scene elements, when those scene elements are imaged as regions with similar colors. We propose a method to combine the information from multiple modalities and demonstrate that we can compute high quality depth maps. Since we are dealing with motion pictures, it’s not sufficient to compute depth only for a single instant in time. The computed depth should be temporally consistent for the video. We argue that the temporally consistent depth is of most importance for foreground objects in a scene. We propose an interactive approach which propagates segmented foreground objects from a begin and end frame of a shot, to the frames in between. By grouping pixels with similar photometric and thermal properties into so-called superpixels, we reduce the complexity from per-pixel to per-superpixel. We then pose the problem as a labeling problem for superpixels over time, where the label that is assigned to each superpixel indicates to which segment that superpixel belongs. We show that this information can be directly exploited in the depth computation, where the segments are used as prior knowledge in that computation.
Lernen der visiomotorischen Kontrolle eines Robotergreifers mit Gaborfiltern

Wir prasentieren ein biologisch motiviertes, lernendes System zur Ansteuerung von optimalen Greifpositionen eines Robotergreifers. Die Bilder von einer am Endeffektor befestigten Handkamera werden mit Hilfe von Gaborfiltern, die das Antwortverhalten von speziellen Neuronen im visuellen Kortex simulieren, analysiert und die fur die MLP-basierte Vorhersage der Korrekturbewegungen relevanten Bildmerkmale extrahiert. Das System zeigt nach kurzer Trainingsdauer eine hohe Genauigkeit und Geschwindigkeit sowie eine grose Robustheit gegenuber Beleuchtungsveranderungen. Um das Greifen neuer Objekttypen zu realisieren, genugt die einmalige Vorgabe der zu erlernenden Greifposition. Wir vergleichen das System mit zwei Verfahren, die das gleiche Ziel verfolgen und z.T. die identische Hardware benutzen: einem PCA Fuzzy-Control System und einer auf Hough-Transformation basierenden PSOM. Alle drei Verfahren realisieren die Feinpositionierung auf der Grundlage eines allgemeinen Ansatzes zur Feinpositionierung, verwenden jedoch unterschiedliche Objektreprasentationen, die hier vorgestellt und verglichen werden1.
Vers une forme d'humanisation des interfaces digitales : transposition du concept d'hospitalité au commerce électronique et impact de ses dimensions sur les réactions affectives, l'absorption cognitive et les comportements des consommateurs

Cette these s’interesse a une nouvelle forme de marketing orientee vers des valeurs plus humaines et sociales. Le concept d’hospitalite permet de proposer une humanisation du commerce electronique pour repondre aux objectifs du marketing 3.0. Il s’agit alors de s’interesser a comment ancrer les strategies de web-commercants pure players dans les valeurs du marketing 3.0. Le croissement des champs du marketing et des systemes d’information permet une approche globale et precise du concept d’hospitalite afin de le transposer aux environnements digitalises. Pour comprendre ces mecanismes, nous adoptons une approche quantitative qui mobilise un plan factoriel complet permettant de verifier l’impact de l’hospitalite virtuelle sur les comportements des internautes. Ainsi, les resultats de cette recherche soulignent l’interet de creer des sites Internet marchand base sur le concept d’hospitalite. Cela permet en effet d’accroitre les reactions affectives a valence positive, l’absorption cognitive, les comportements d’approche et l’intention d’achat. A l’inverse, les reactions affectives a valence negative et les comportements d’evitement sont reduits. La these permet de conclure qu’il est possible d’humaniser une interface digitale mais que cette forme d’humanisation ne remplace pas une presence humaine reelle. Cependant, elle apporte des benefices pour les consommateurs qui en tirent une certaine experience de navigation pour le consommateur au profit des organisations envers lesquelles les intentions d’achat sont plus importantes
Nonlinear neural network controller for dynamic system

A learning type of controller using a neural network is proposed and compared with a conventional controller. The learning neural network controller can use not only quadratic error but also a more general cost function. A practical design method is proposed, and the advantages of the learning type of neural network controller in comparison with the adaptive type are discussed. Simulated and experimental results confirm the realization of nonlinear optimal control using the proposed controller. <<ETX>>
Comparative Study of Face Recognition Techniques

The aim of this paper is to compare various methods for face recognition. In this paper we are going to discuss various techniques for face recognition. This paper includes a brief description of PCA (Principle Component Analysis), LDA (Linear Discriminant Analysis), LBP (Local Binary Pattern), Gabor filter, LGBP (Local Gabor Binary Pattern). Comparative study of various methods is discussed in this paper and from this study a suitable and more efficient technique is found out which will give more effective result. Keywords— Face recognition, LDA (Linear Discriminant Analysis), PCA (Principle Component Analysis), LBP (Local binary pattern), Gabor Filter, LGBP (Local Gabor Binary Pattern).
Validación de la escala de evaluación para la prevención del dopaje en escolares (CUPIAD) (Validation of the scale of assessment for the prevention of doping in school (CUPIAD))

El dopaje es un termino relacionado con el deporte y hoy en dia afecta a todos sus niveles, siendo utilizado cada vez, en mayor medida, por personas mas jovenes. Los estudios muestran que existe un gran desconocimiento del mismo y sus efectos sobre la salud, lo que provoca que no haya un rechazo total y rotundo al uso de sustancias dopantes. En la actualidad, el consumo de sustancias dopantes se esta usando fuera del deporte con otros objetivos siendo principalmente la mejora de la imagen corporal. Los instrumentos creados para saber los conocimientos, creencias y actitudes sobre el mismo han sido diversos, pero ninguno que se adapte a la poblacion escolar espanola. El objetivo de este estudio ha sido validar un instrumento que permita conocer los conocimientos, creencias y actitudes que tienen los escolares espanoles en materia de dopaje. La poblacion de estudio esta formada por 540 escolares de 1o de la ESO de diferentes escuelas, todas ellas de la Comunidad Autonoma de Aragon y mas concretamente de la ciudad de Zaragoza en el Noreste de Espana. Se realizo un analisis estadistico de los items, un analisis factorial exploratorio, un analisis factorial confirmatorio, pruebas de invarianza factorial, asi como pruebas de fiabilidad y validez. Los resultados garantizan la validez y fiabilidad del instrumento denominado Cuestionario Programa de Intervencion Antidopaje (CUPIAD) para medir los conocimientos, creencias y actitudes que tienen los jovenes espanoles sobre el dopaje, compuesto por 17 items agrupados en 5 dimensiones. Abstract. Doping is a term related to sport and today affects all levels increasingly being used to a greater extent by younger people. Studies show that there is great ignorance of it and its effects on health, causing no full and resounding rejection of the use of doping substances. Currently, the use of doping substances is being used outside the sport with other objectives being mainly improving body image. The instruments created to know the knowledge, beliefs and attitudes about it have been different, but none that fits the Spanish school population. The aim of this study was to validate a tool to determine the knowledge, beliefs and attitudes that have Spanish schools on doping. The study population consists of 540 students of 1st ESO different schools. a statistical analysis of the items, an exploratory factor analysis, confirmatory factor analysis, factorial invariance tests and tests of reliability and validity was performed. The results guarantee the validity and reliability of the instrument called CUPIAD to measure knowledge, beliefs and attitudes that young Spaniards have on doping, consisting of 17 items grouped into 5 dimensions.
Programs for Machine Learning

Algorithms for constructing decision trees are among the most well known and widely used of all machine learning methods. Among decision tree algorithms, J. Ross Quinlan's ID3 and its successor, C4.5, are probably the most popular in the machine learning community. These algorithms and variations on them have been the subject of numerous research papers since Quinlan introduced ID3. Until recently, most researchers looking for an introduction to decision trees turned to Quinlan's seminal 1986 Machine Learning journal article [Quinlan, 1986]. In his new book, C4.5: Programs for Machine Learning, Quinlan has put together a definitive, much needed description of his complete system, including the latest developments. As such, this book will be a welcome addition to the library of many researchers and students.
Optimierung der Gesichtsklassifikation bei der Erkennung von Akromegalie

Optimising Strategies for Face Classification in the Detection of Acromegaly. It has been shown that face classification software might help distinguish between subjects with and without acromegaly on regular photographs. In this project, we investigated several aspects that will be necessary and helpful to bring this recognition method closer to clinical application. Face classification was based on “nodes” placed on frontal and lateral photographs of individuals and analysis of the underlying texture and geometric relations. We analysed whether omission of nodes considered less relevant would change classification rates, and how a novel set of nodes referring to the most common morphological changes of the face will affect the classification rate. Internal verification was done on a data set with 57 acromegalic patients and 59 controls. External verification was done on a data set consisting of 13 acromegalic patients (7 female, 6 male) and 45 controls (25 female, 20 male). Preliminary data in the validation set showed improvement using graphs with omission of irrelevant nodes and inclusion of new nodes. In conclusion, adaptation, such as reduction of nodes associated with unwanted noise and graph design referring to the most common morphological changes in the face, can improve correct classification rates in the detection of acromegaly by means of a face classification software. These results are encouraging. Further analyses in an increasingly large database are ongoing. J Klin Endokrinol Stoffw 2013; 6 (3): 20–4.