004 Datenverarbeitung; Informatik
Refine
Has Fulltext
- yes (207) (remove)
Year of publication
Document Type
- Article (110)
- Doctoral Thesis (64)
- Conference Proceeding (13)
- Master's Thesis (8)
- Postprint (6)
- Monograph/Edited Volume (2)
- Bachelor Thesis (1)
- Habilitation Thesis (1)
- Moving Images (1)
- Preprint (1)
Keywords
- Informatik (17)
- Didaktik (15)
- Hochschuldidaktik (14)
- Ausbildung (13)
- Maschinelles Lernen (7)
- Antwortmengenprogrammierung (5)
- Computer Science Education (5)
- Machine Learning (5)
- answer set programming (5)
- E-Learning (4)
Institute
- Institut für Informatik und Computational Science (207) (remove)
Die 7. Fachtagung für Hochschuldidaktik, die 2016 erneut mit der DeLFI E-Learning Fachtagung Informatik stattfand, setzte das erfolgreiche Modell einer Tagung fort, die sich mit hochschuldidaktischen Fragen und der Gestaltung von Studiengängen der Informatik beschäftigt.
Thema der Tagung waren alle Fragen, die sich der Vermittlung von Informatikgegenständen im Hochschulbereich widmen. Dazu gehörten u.a.:
• fachdidaktische Konzepte der Vermittlung einzelner Informatikgegenstände
• methodische Lösungen, wie spezielle Lehr- und Lernformen, Durchführungskonzepte
• empirische Ergebnisse und Vergleichsstudien
• E-Learning-Ansätze, wenn sie ein erkennbares didaktisches Konzept verfolgen
• Studienkonzepte und Curricula, organisatorische Fragen, wie Gewinnung von Studierenden, Studieneingangsphase, Abbrecher.
Die Fachtagung widmete sich ausgewählten Fragestellungen dieses Themenkomplexes, die durch Vorträge ausgewiesener Experten, durch eingereichte Beiträge und durch Präsentationen und Poster intensiv behandelt wurden.
Unser besonderer Dank gilt dem Programmkomitee und den hier nicht genannten Helfern für ihren Einsatz bei der Vorbereitung und Durchführung der Tagung.
Die Vermittlung von Modellierungsfähigkeiten in der Softwaretechnik-Ausbildung konzentriert sich meist auf Modellierungskonzepte, Notationen und Entwicklungswerkzeuge. Die Betrachtung der Modellierungsaktivitäten, etwa die Entwicklung und Gegenüberstellung alternativer Modellvorschläge, steht weniger im Vordergrund. Die vorliegende Studie untersucht zwei Formen des kollaborativen Modellierens am Tabletop in Bezug auf ihren Einfluss auf die Modellierungsaktivitäten in kleinen Gruppen. Die Ergebnisse zeigen, dass sowohl selbstorganisierte als auch moderierte Modellierungssitzungen das Entwickeln eines gemeinsamen Modellverständnisses fördern. In moderierten Sitzungen wurden zudem mehr alternative Lösungsideen entwickelt und in stärkerem Maße diskutiert.
The main objective of this dissertation is to analyse prerequisites, expectations, apprehensions, and attitudes of students studying computer science, who are willing to gain a bachelor degree. The research will also investigate in the students’ learning style according to the Felder-Silverman model. These investigations fall in the attempt to make an impact on reducing the “dropout”/shrinkage rate among students, and to suggest a better learning environment.
The first investigation starts with a survey that has been made at the computer science department at the University of Baghdad to investigate the attitudes of computer science students in an environment dominated by women, showing the differences in attitudes between male and female students in different study years. Students are accepted to university studies via a centrally controlled admission procedure depending mainly on their final score at school. This leads to a high percentage of students studying subjects they do not want. Our analysis shows that 75% of the female students do not regret studying computer science although it was not their first choice. And according to statistics over previous years, women manage to succeed in their study and often graduate on top of their class. We finish with a comparison of attitudes between the freshman students of two different cultures and two different university enrolment procedures (University of Baghdad, in Iraq, and the University of Potsdam, in Germany) both with opposite gender majority.
The second step of investigation took place at the department of computer science at the University of Potsdam in Germany and analyzes the learning styles of students studying the three major fields of study offered by the department (computer science, business informatics, and computer science teaching). Investigating the differences in learning styles between the students of those study fields who usually take some joint courses is important to be aware of which changes are necessary to be adopted in the teaching methods to address those different students. It was a two stage study using two questionnaires; the main one is based on the Index of Learning Styles Questionnaire of B. A. Solomon and R. M. Felder, and the second questionnaire was an investigation on the students’ attitudes towards the findings of their personal first questionnaire. Our analysis shows differences in the preferences of learning style between male and female students of the different study fields, as well as differences between students with the different specialties (computer science, business informatics, and computer science teaching).
The third investigation looks closely into the difficulties, issues, apprehensions and expectations of freshman students studying computer science. The study took place at the computer science department at the University of Potsdam with a volunteer sample of students. The goal is to determine and discuss the difficulties and issues that they are facing in their study that may lead them to think in dropping-out, changing the study field, or changing the university. The research continued with the same sample of students (with business informatics students being the majority) through more than three semesters. Difficulties and issues during the study were documented, as well as students’ attitudes, apprehensions, and expectations. Some of the professors and lecturers opinions and solutions to some students’ problems were also documented. Many participants had apprehensions and difficulties, especially towards informatics subjects. Some business informatics participants began to think of changing the university, in particular when they reached their third semester, others thought about changing their field of study. Till the end of this research, most of the participants continued in their studies (the study they have started with or the new study they have changed to) without leaving the higher education system.
Computer Security deals with the detection and mitigation of threats to computer networks, data, and computing hardware. This
thesis addresses the following two computer security problems: email spam campaign and malware detection.
Email spam campaigns can easily be generated using popular dissemination tools by specifying simple grammars that serve as message templates. A grammar is disseminated to nodes of a bot net, the nodes create messages by instantiating the grammar at random. Email spam campaigns can encompass huge data volumes and therefore pose a threat to the stability of the infrastructure of email service providers that have to store them. Malware -software that serves a malicious purpose- is affecting web servers, client computers via active content, and client computers through executable files. Without the help of malware detection systems it would be easy for malware creators to collect sensitive information or to infiltrate computers.
The detection of threats -such as email-spam messages, phishing messages, or malware- is an adversarial and therefore intrinsically
difficult problem. Threats vary greatly and evolve over time. The detection of threats based on manually-designed rules is therefore
difficult and requires a constant engineering effort. Machine-learning is a research area that revolves around the analysis of data and the discovery of patterns that describe aspects of the data. Discriminative learning methods extract prediction models from data that are optimized to predict a target attribute as accurately as possible. Machine-learning methods hold the promise of automatically identifying patterns that robustly and accurately detect threats. This thesis focuses on the design and analysis of discriminative learning methods for the two computer-security problems under investigation: email-campaign and malware detection.
The first part of this thesis addresses email-campaign detection. We focus on regular expressions as a syntactic framework, because regular expressions are intuitively comprehensible by security engineers and administrators, and they can be applied as a detection mechanism in an extremely efficient manner. In this setting, a prediction model is provided with exemplary messages from an email-spam campaign. The prediction model has to generate a regular expression that reveals the syntactic pattern that underlies the entire campaign, and that a security engineers finds comprehensible and feels confident enough to use the expression to blacklist further messages at the email server. We model this problem as two-stage learning problem with structured input and output spaces which can be solved using standard cutting plane methods. Therefore we develop an appropriate loss function, and derive a decoder for the resulting optimization problem.
The second part of this thesis deals with the problem of predicting whether a given JavaScript or PHP file is malicious or benign. Recent malware analysis techniques use static or dynamic features, or both. In fully dynamic analysis, the software or script is executed and observed for malicious behavior in a sandbox environment. By contrast, static analysis is based on features that can be extracted directly from the program file. In order to bypass static detection mechanisms, code obfuscation techniques are used to spread a malicious program file in many different syntactic variants. Deobfuscating the code before applying a static classifier can be subjected to mostly static code analysis and can overcome the problem of obfuscated malicious code, but on the other hand increases the computational costs of malware detection by an order of magnitude. In this thesis we present a cascaded architecture in which a classifier first performs a static analysis of the original code and -based on the outcome of this first classification step- the code may be deobfuscated and classified again. We explore several types of features including token $n$-grams, orthogonal sparse bigrams, subroutine-hashings, and syntax-tree features and study the robustness of detection methods and feature types against the evolution of malware over time. The developed tool scans very large file collections quickly and accurately.
Each model is evaluated on real-world data and compared to reference methods. Our approach of inferring regular expressions to filter emails belonging to an email spam campaigns leads to models with a high true-positive rate at a very low false-positive rate that is an order of magnitude lower than that of a commercial content-based filter. Our presented system -REx-SVMshort- is being used by a commercial email service provider and complements content-based and IP-address based filtering.
Our cascaded malware detection system is evaluated on a high-quality data set of almost 400,000 conspicuous PHP files and a collection of more than 1,00,000 JavaScript files. From our case study we can conclude that our system can quickly and accurately process large data collections at a low false-positive rate.
Services that operate over the Internet are under constant threat of being exposed to fraudulent use. Maintaining good user experience for legitimate users often requires the classification of entities as malicious or legitimate in order to initiate countermeasures. As an example, inbound email spam filters decide for spam or non-spam. They can base their decision on both the content of each email as well as on features that summarize prior emails received from the sending server. In general, discriminative classification methods learn to distinguish positive from negative entities. Each decision for a label may be based on features of the entity and related entities. When labels of related entities have strong interdependencies---as can be assumed e.g. for emails being delivered by the same user---classification decisions should not be made independently and dependencies should be modeled in the decision function. This thesis addresses the formulation of discriminative classification problems that are tailored for the specific demands of the following three Internet security applications. Theoretical and algorithmic solutions are devised to protect an email service against flooding of user inboxes, to mitigate abusive usage of outbound email servers, and to protect web servers against distributed denial of service attacks.
In the application of filtering an inbound email stream for unsolicited emails, utilizing features that go beyond each individual email's content can be valuable. Information about each sending mail server can be aggregated over time and may help in identifying unwanted emails. However, while this information will be available to the deployed email filter, some parts of the training data that are compiled by third party providers may not contain this information. The missing features have to be estimated at training time in order to learn a classification model. In this thesis an algorithm is derived that learns a decision function that integrates over a distribution of values for each missing entry. The distribution of missing values is a free parameter that is optimized to learn an optimal decision function.
The outbound stream of emails of an email service provider can be separated by the customer IDs that ask for delivery. All emails that are sent by the same ID in the same period of time are related, both in content and in label. Hijacked customer accounts may send batches of unsolicited emails to other email providers, which in turn might blacklist the sender's email servers after detection of incoming spam emails. The risk of being blocked from further delivery depends on the rate of outgoing unwanted emails and the duration of high spam sending rates. An optimization problem is developed that minimizes the expected cost for the email provider by learning a decision function that assigns a limit on the sending rate to customers based on the each customer's email stream.
Identifying attacking IPs during HTTP-level DDoS attacks allows to block those IPs from further accessing the web servers. DDoS attacks are usually carried out by infected clients that are members of the same botnet and show similar traffic patterns. HTTP-level attacks aim at exhausting one or more resources of the web server infrastructure, such as CPU time. If the joint set of attackers cannot increase resource usage close to the maximum capacity, no effect will be experienced by legitimate users of hosted web sites. However, if the additional load raises the computational burden towards the critical range, user experience will degrade until service may be unavailable altogether. As the loss of missing one attacker depends on block decisions for other attackers---if most other attackers are detected, not blocking one client will likely not be harmful---a structured output model has to be learned. In this thesis an algorithm is developed that learns a structured prediction decoder that searches the space of label assignments, guided by a policy.
Each model is evaluated on real-world data and is compared to reference methods. The results show that modeling each classification problem according to the specific demands of the task improves performance over solutions that do not consider the constraints inherent to an application.
KEYCIT 2014
(2015)
In our rapidly changing world it is increasingly important not only to be an expert in a chosen field of study but also to be able to respond to developments, master new approaches to solving problems, and fulfil changing requirements in the modern world and in the job market. In response to these needs key competencies in understanding, developing and using new digital technologies are being brought into focus in school and university programmes. The IFIP TC3 conference "KEYCIT – Key Competences in Informatics and ICT (KEYCIT 2014)" was held at the University of Potsdam in Germany from July 1st to 4th, 2014 and addressed the combination of key competencies, Informatics and ICT in detail. The conference was organized into strands focusing on secondary education, university education and teacher education (organized by IFIP WGs 3.1 and 3.3) and provided a forum to present and to discuss research, case studies, positions, and national perspectives in this field.
Die Studieneingangsphase stellt für Studierende eine Schlüsselphase des tertiären Ausbildungsabschnitts dar. Fachwissenschaftliches Wissen wird praxisfern vermittelt und die Studierenden können die Zusammenhänge zwischen den Themenfeldern der verschiedenen Vorlesungen nicht erkennen. Zur Verbesserung der Situation wurde ein Workshop entwickelt, der die Verbindung der Programmierung und der Datenstrukturen vertieft. Dabei wird das Spiel Go-Moku1 als Android-App von den Studierenden selbständig entwickelt. Die Kombination aus Software (Java, Android-SDK) und Hardware (Tablet-Computer) für ein kleines realistisches Softwareprojekt stellt für die Studierenden eine neue Erfahrung dar.
Erstsemester-Studierende sind mit den Anforderungen des Lehr-/ Lernprozess einer Universität oder Fachhochschule noch nicht vertraut. Ihre Erwartungen orientieren sich vielmehr an ihrer bisherigen Lerngeschichte (Abitur, Fachabitur, o. ä.). Neben den fachlichen Anforderungen des ersten Semesters müssen die Studierenden also auch Veränderungen im Lehr-/Lernprozess erkennen und bewältigen. Es wird anhand einer Output-orientierten
informatischen Lehrveranstaltung aufgezeigt, dass sich aus deren strengen Anforderungen der Messbarkeit klare Kompetenzbeschreibungen ergeben, die besonders dem Orientierungsbedürfnis Erstsemester-Studierender entgegenkommen.
Auf der Grundlage der Planung, Durchführung, Evaluation und Revision eines gemeinsamen Seminars von Medienpädagogik und Didaktik der Informatik stellen wir in diesem Aufsatz dar, wo die Defizite klassischer Medienbildung in Bezug auf digitale bzw. interaktive Medien liegen und welche Inhalte der Informatik für Studierende aller Lehrämter – im allgemeinbildenden Sinne – aus dieser Perspektive relevant erscheinen.
Die Tagung HDI 2014 in Freiburg zur Hochschuldidaktik der Informatik HDI wurde erneut vom Fachbereich Informatik und Ausbildung / Didaktik der Informatik (IAD) in der Gesellschaft für Informatik e. V. (GI) organisiert. Sie dient den Lehrenden der Informatik in Studiengängen an Hochschulen als Forum der Information und des Austauschs über neue didaktische Ansätze und bildungspolitische Themen im Bereich der Hochschulausbildung aus der fachlichen Perspektive der Informatik.
Die HDI 2014 ist nun bereits die sechste Ausgabe der HDI. Für sie wurde das spezielle Motto „Gestalten und Meistern von Übergängen“ gewählt. Damit soll ein besonderes Augenmerk auf die Übergänge von Schule zum Studium, vom Bachelor zum Master, vom Studium zur Promotion oder vom Studium zur Arbeitswelt gelegt werden.