Changing the Face of Traditional Education

At large, research universities, a common approach for teaching hundreds of undergraduate students at one time is the traditional, large, lecture-based course. Trends indicate that over the next decade there will be an increase in the number of large, campus courses being offered as well as larger enrollments in courses currently offered. As universities investigate alternative means to accommodate more students and their learning needs, Web-based instruction provides an attractive delivery mode for teaching large, on-campus courses. This article explores a theoretical approach regarding how Web-based instruction can be designed and developed to provide quality education for traditional, on-campus, undergraduate students. The academic debate over the merit of Web-based instruction for traditional, on-campus students has not been resolved. This study identifies and discusses instructional design theory for adapting a large, lecture-based course to the Web.

Mathe-Üben in Studium und Abitur

Fehlende Grundkenntnisse in der Mathematik zählen zu den größten Hindernissen für einen erfolgreichen Start in ein Hochschulstudium. Studienanfänger in einem MINT-Studium bringen inzwischen deutlich unterschiedliche Vorrausetzungen mit: „Mathe-Angst“ gilt als typisches Phänomen und der Übergang in ein selbstbestimmtes Lernverhalten stellt eine große Herausforderung dar. Diese Fall-Studie beschreibt, wie mit Hilfe einer Mathe-App bereits zu Beginn des Studiums aktives Lernen unterstützt und selbstbestimmtes Lernen eingeübt werden kann. Das neue Kurskonzept mit App-Unterstützung stößt an der Hochschule Offenburg auf breite Akzeptanz. Der mobile BYOD-Ansatz ermöglicht Lern-Szenarien, die über PC- bzw.- Laptop-gebundene eLearning-Lösungen nicht realisierbar sind. Der Inhalt des MassMatics-Vorbereitungskurs orientiert sich am Mindestanforderungskatalog des cosh-Arbeitskreises für den Übergang Schule-Hochschule. In der Zwischenzeit wurde der App-gestützte Kurs mit seinen über 500 Aufgaben von mehr als 1000 Studierenden besucht.

Integriertes Informationsmanagement für Bildungseinrichtungen

Der CampusSource Workshop fand vom 10. bis 12. Oktober 2006 an der Westfälischen Wilhelms Universität (WWU) in Münster statt. Kernpunkte der Veranstaltung waren die Entwicklung einer Engine zur Verknüpfung von e-Learning Anwendungen mit Systemen der HIS GmbH und die Erstellung von Lehr- und Lerninhalten mit dem Ziel der Wiederverwendung. Im zweiten Kapitel sind Vorträge der Veranstaltung im Adobe Flash Format zusammengetragen. Zur Betrachtung der Vorträge ist der Adobe Flash Player, mindestens in der Version 6 erforderlich

Generisches Framework Studi-App

Mit der Idee eines generischen, an vielfältige Hochschulanforderungen anpassbaren Studierenden-App-Frameworks haben sich innerhalb des Arbeitskreises Web der ZKI ca. 30 Hochschulen zu einem Entwicklungsverbund zusammengefunden. Ziel ist es, an den beteiligten Einrichtungen eine umfassende Zusammenstellung aller elektronischen Studienservices zu evaluieren, übergreifende Daten- und Metadatenmodelle für die Beschreibung dieser Dienste zu erstellen und Schnittstellen zu den gängigen Campusmanagementsystemen sowie zu Infrastrukturen der elektronischen Lehre (LMS, Druckdienste, elektronischen Katalogen usw.) zu entwickeln. In einem abschließenden Schritt werden auf dieser Middleware aufsetzende Studienmanagement-Apps für Studierende erstellt, die die verschiedenen Daten- und Kommunikationsströme der standardisierten Dienste und Kommunikationskanäle bündeln und in eine für den Studierenden leicht zu durchschauende, navigationsfreundliche Aufbereitung kanalisiert. Mit der Konzeption eines dezentralen, über eine Vielzahl von Hochschulen verteilten Entwicklungsprojektes unter einer zentralen Projektleitung wird sichergestellt, dass redundante Entwicklungen vermieden, bundesweit standardisierte Serviceangebote angeboten und Wissenstransferprozesse zwischen einer Vielzahl von Hochschulen zur Nutzung mobiler Devices (Smartphones, Tablets und entsprechende Apps) angeregt werden können. Die Unterstützung der Realisierung klarer Schnittstellenspezifikationen zu Campusmanagementsystemen durch deren Anbieter kann durch diese breite Interessensgemeinschaft ebenfalls gestärkt werden. Weiterhin zentraler Planungsinhalt ist ein Angebot für den App-Nutzer zum Aufbau eines datenschutzrechtlich integeren, persönlichen E-Portfolios. Details finden sich im Kapitel Projektziele weiter unten.

Building an Academic Cloud for 500,000 Users

Under the brand name “sciebo – the Campuscloud” (derived from “science box”) a consortium of more than 20 research and applied science universities started a large scale cloud service for about 500,000 students and researchers in North Rhine-Westphalia, Germany’s most populous state. Starting with the much anticipated data privacy compliant sync & share functionality, sciebo offers the potential to become a more general cloud platform for collaboration and research data management which will be actively pursued in upcoming scientific and infrastructural projects. This project report describes the formation of the venture, its targets and the technical and the legal solution as well as the current status and the next steps.

A nature-inspired approach to speed up optimum-path forest clustering and its application to intrusion detection in computer networks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Deep learning for computer vision: a comparison between convolutional neural networks and hierarchical temporal memories on object recognition tasks

In recent years, Deep Learning techniques have shown to perform well on a large variety of problems both in Computer Vision and Natural Language Processing, reaching and often surpassing the state of the art on many tasks. The rise of deep learning is also revolutionizing the entire field of Machine Learning and Pattern Recognition pushing forward the concepts of automatic feature extraction and unsupervised learning in general. However, despite the strong success both in science and business, deep learning has its own limitations. It is often questioned if such techniques are only some kind of brute-force statistical approaches and if they can only work in the context of High Performance Computing with tons of data. Another important question is whether they are really biologically inspired, as claimed in certain cases, and if they can scale well in terms of "intelligence". The dissertation is focused on trying to answer these key questions in the context of Computer Vision and, in particular, Object Recognition, a task that has been heavily revolutionized by recent advances in the field. Practically speaking, these answers are based on an exhaustive comparison between two, very different, deep learning techniques on the aforementioned task: Convolutional Neural Network (CNN) and Hierarchical Temporal memory (HTM). They stand for two different approaches and points of view within the big hat of deep learning and are the best choices to understand and point out strengths and weaknesses of each of them. CNN is considered one of the most classic and powerful supervised methods used today in machine learning and pattern recognition, especially in object recognition. CNNs are well received and accepted by the scientific community and are already deployed in large corporation like Google and Facebook for solving face recognition and image auto-tagging problems. HTM, on the other hand, is known as a new emerging paradigm and a new meanly-unsupervised method, that is more biologically inspired. It tries to gain more insights from the computational neuroscience community in order to incorporate concepts like time, context and attention during the learning process which are typical of the human brain. In the end, the thesis is supposed to prove that in certain cases, with a lower quantity of data, HTM can outperform CNN.

Towards continuous surveillance of fruit flies using sensor networks and machine vision

In Australia, the Queensland fruit fly (B. tryoni), is the most destructive insect pest of horticulture, attacking nearly all fruit and vegetable crops. This project has researched and prototyped a system for monitoring fruit flies so that authorities can be alerted when a fly enters a crop in a more efficient manner than is currently used. This paper presents the idea of our sensor platform design as well as the fruit fly detection and recognition algorithm by using machine vision techniques. Our experiments showed that the designed trap and sensor platform is capable to capture quality fly images, the invasive flies can be successfully detected and the average precision of the Queensland fruit fly recognition is 80% from our experiment.

Multi-objective models and techniques for analysing the absolute capacity of railway networks

Railway capacity determination and expansion are very important topics. In prior research, the competition between different entities such as train services and train types, on different network corridors however have been ignored, poorly modelled, or else assumed to be static. In response, a comprehensive set of multi-objective models have been formulated in this article to perform a trade-off analysis. These models determine the total absolute capacity of railway networks as the most equitable solution according to a clearly defined set of competing objectives. The models also perform a sensitivity analysis of capacity with respect to those competing objectives. The models have been extensively tested on a case study and their significant worth is shown. The models were solved using a variety of techniques however an adaptive E constraint method was shown to be most superior. In order to identify only the best solution, a Simulated Annealing meta-heuristic was implemented and tested. However a linearization technique based upon separable programming was also developed and shown to be superior in terms of solution quality but far less in terms of computational time.

Optical processing devices and techniques for next generation optical networks

Este trabalho surge do interesse em substituir os nós de rede óptica baseados maioritariamente em electrónica por nós de rede baseados em tecnologia óptica. Espera-se que a tecnologia óptica permita maiores débitos binários na rede, maior transparência e maior eficiência através de novos paradigmas de comutação. Segundo esta visão, utilizou-se o MZI-SOA, um dispositivo semicondutor integrado hibridamente, para realizar funcionalidades de processamento óptico de sinal necessárias em nós de redes ópticas de nova geração. Nas novas redes ópticas são utilizados formatos de modulação avançados, com gestão da fase, pelo que foi estudado experimentalmente e por simulação o impacto da utilização destes formatos no desempenho do MZI-SOA na conversão de comprimento de onda e formato, em várias condições de operação. Foram derivadas regras de utilização para funcionamento óptimo. Foi também estudado o impacto da forma dos pulsos do sinal no desempenho do dispositivo. De seguida, o MZI-SOA foi utilizado para realizar funcionalidades temporais ao nível do bit e do pacote. Foi investigada a operação de um conversor de multiplexagem por divisão no comprimento de onda para multiplexagem por divisão temporal óptica, experimentalmente e por simulação, e de um compressor e descompressor de pacotes, por simulação. Para este último, foi investigada a operação com o MZI-SOA baseado em amplificadores ópticos de semicondutor com geometria de poço quântico e ponto quântico. Foi também realizado experimentalmente um ermutador de intervalos temporais que explora o MZI-SOA como conversor de comprimento de onda e usa um banco de linhas de atraso ópticas para introduzir no sinal um atraso seleccionável. Por fim, foi estudado analiticamente, experimentalmente e por simulação o impacto de diafonia em redes ópticas em diversas situações. Extendeu-se um modelo analítico de cálculo de desempenho para contemplar sinais distorcidos e afectados por diafonia. Estudou-se o caso de sinais muito filtrados e afectados por diafonia e mostrou-se que, para determinar correctamente as penalidades que ocorrem, ambos os efeitos devem ser considerados simultaneamente e não em separado. Foi estudada a escalabilidade limitada por diafonia de um comutador de intervalos temporais baseado em MZI-SOA a operar como comutador espacial. Mostrou-se também que sinais afectados fortemente por não-linearidades podem causar penalidades de diafonia mais elevadas do que sinais não afectados por não-linearidades. Neste trabalho foi demonstrado que o MZI-SOA permite construir vários e pertinentes circuitos ópticos, funcionando como bloco fundamental de construção, tendo sido o seu desempenho analisado, desde o nível de componente até ao nível de sistema. Tendo em conta as vantagens e desvantagens do MZI-SOA e os desenvolvimentos recentes de outras tecnologias, foram sugeridos tópicos de investigação com o intuito de evoluir para as redes ópticas de nova geração.

Computer vision and color measurement techniques for inline monitoring of cheese curd syneresis

Optical characteristics of stirred curd were simultaneously monitored during syneresis in a 10-L cheese vat using computer vision and colorimetric measurements. Curd syneresis kinetic conditions were varied using 2 levels of milk pH (6.0 and 6.5) and 2 agitation speeds (12.1 and 27.2 rpm). Measured optical parameters were compared with gravimetric measurements of syneresis, taken simultaneously. The results showed that computer vision and colorimeter measurements have potential for monitoring syneresis. The 2 different phases, curd and whey, were distinguished by means of color differences. As syneresis progressed, the backscattered light became increasingly yellow in hue for circa 20 min for the higher stirring speed and circa 30 min for the lower stirring speed. Syneresis-related gravimetric measurements of importance to cheese making (e.g., curd moisture content, total solids in whey, and yield of whey) correlated significantly with computer vision and colorimetric measurements..

An Optimum-Path Forest framework for intrusion detection in computer networks

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems

One objective of the feeder reconfiguration problem in distribution systems is to minimize the power losses for a specific load. For this problem, mathematical modeling is a nonlinear mixed integer problem that is generally hard to solve. This paper proposes an algorithm based on artificial neural network theory. In this context, clustering techniques to determine the best training set for a single neural network with generalization ability are also presented. The proposed methodology was employed for solving two electrical systems and presented good results. Moreover, the methodology can be employed for large-scale systems in real-time environment.