Computer security and the data encryption standard : proceedings of the Conference on Computer Security and the Data Encryption Standard held at the National Bureau of Standards in Gaithersburg, Maryland, on February 15, 1977 /

Mode of access: Internet.

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.

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.

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.

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.

Optimization, high availability and redundancy in information communications and technology using networks and systems virtualization architectures

Mestrado em Engenharia Informática

Multinationals, technology networks and international takeovers

We formulate a knowlegde--based model of direct investment through mergers and acquisitions. M&As are realized to create comparative advantages by exploiting international synergies and appropriating local technology spillovers requiring geographical proximity, but can also represent a strategic response to the presence of a multinational rival. The takeover fee paid tends to increase with the strength of local spillovers which can thus work against multinationalization. Seller's bargaining power increases the takeover fee, but does not influence the investment decision. We characterize losers and winners from multinationalization, and show that foreign investment stimulates research but could result in a synergy trap reducing multinationals' profits.

Promoting Metacognitive Skills in a CSCL environment: implications for instructional and technology design

This paper aims to better understand the development of students’ learning processes when participating actively in a specific Computer Supported Collaborative Learning system called KnowCat. To this end, a longitudinal case study was designed, in which eighteen university students took part in a 12-month (two semesters) learning project. During this time period, the students followed an instructional process, using some elements of KnowCat (KnowCat key features) design to support and improve their interaction processes, especially peer learning processes. Our research involved both supervising the students’ collaborative learning processes throughout the learning project and focusing our analysis on the qualitative evolution of the students’ interaction processes and on the development of metacognitive learning processes. The results of the current research reveal that the instructional application of the CSCL-KnowCat system may favour and improve the development of the students’ metacognitive learning processes. Additionally, the implications of the design of computer supported collaborative learning networks and pedagogical issues are discussed in this paper.

Document Image Segmentation and Compression using Artificial Neural Networks and Evolutionary Methods

The work is intended to study the following important aspects of document image processing and develop new methods. (1) Segmentation ofdocument images using adaptive interval valued neuro-fuzzy method. (2) Improving the segmentation procedure using Simulated Annealing technique. (3) Development of optimized compression algorithms using Genetic Algorithm and parallel Genetic Algorithm (4) Feature extraction of document images (5) Development of IV fuzzy rules. This work also helps for feature extraction and foreground and background identification. The proposed work incorporates Evolutionary and hybrid methods for segmentation and compression of document images. A study of different neural networks used in image processing, the study of developments in the area of fuzzy logic etc is carried out in this work

Boosting optimum-path forest clustering through harmony Search and its applications for intrusion detection in computer networks

In this paper we propose a nature-inspired approach that can boost the Optimum-Path Forest (OPF) clustering algorithm by optimizing its parameters in a discrete lattice. The experiments in two public datasets have shown that the proposed algorithm can achieve similar parameters' values compared to the exhaustive search. Although, the proposed technique is faster than the traditional one, being interesting for intrusion detection in large scale traffic networks. © 2012 IEEE.

A study evaluating the effectiveness of science and technology networks operating in the subregion

Includes bibliography

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.