The Booth: Bringing Out the Super Hero in You

Demo paper about the booth

Detecting stealthy attacks: Efficient monitoring of suspicious activities on computer networks

<p>Stealthy attackers move patiently through computer networks - taking days, weeks or months to accomplish their objectives in order to avoid detection. As networks scale up in size and speed, monitoring for such attack attempts is increasingly a challenge. This paper presents an efficient monitoring technique for stealthy attacks. It investigates the feasibility of proposed method under number of different test cases and examines how design of the network affects the detection. A methodological way for tracing anonymous stealthy activities to their approximate sources is also presented. The Bayesian fusion along with traffic sampling is employed as a data reduction method. The proposed method has the ability to monitor stealthy activities using 10-20% size sampling rates without degrading the quality of detection.</p>

Proposing the deep dynamic Bayesian network as a future computer based medical system

<p>The development of new learning models has been of great importance throughout recent years, with a focus on creating advances in the area of deep learning. Deep learning was first noted in 2006, and has since become a major area of research in a number of disciplines. This paper will delve into the area of deep learning to present its current limitations and provide a new idea for a fully integrated deep and dynamic probabilistic system. The new model will be applicable to a vast number of areas initially focusing on applications into medical image analysis with an overall goal of utilising this approach for prediction purposes in computer based medical systems.</p>

On Multi- Server Queues With Consultation With Main Server

Queueing Theory is the mathematical study of queues or waiting lines. Queues abound in every day life - in computer networks, in tra c islands, in communication of electro-magnetic signals, in telephone exchange, in bank counters, in super market checkouts, in doctor's clinics, in petrol pumps, in o ces where paper works to be processed and many other places. Originated with the published work of A. K. Erlang in 1909 [16] on congestion in telephone tra c, Queueing Theory has grown tremendously in a century. Its wide range applications includes Operations Research, Computer Science, Telecommunications, Tra c Engineering, Reliability Theory, etc.

Data Science Seminar: Generic Big Data Processing for Advancing Situation Awareness and Decision-Support

The generation of heterogeneous big data sources with ever increasing volumes, velocities and veracities over the he last few years has inspired the data science and research community to address the challenge of extracting knowledge form big data. Such a wealth of generated data across the board can be intelligently exploited to advance our knowledge about our environment, public health, critical infrastructure and security. In recent years we have developed generic approaches to process such big data at multiple levels for advancing decision-support. It specifically concerns data processing with semantic harmonisation, low level fusion, analytics, knowledge modelling with high level fusion and reasoning. Such approaches will be introduced and presented in context of the TRIDEC project results on critical oil and gas industry drilling operations and also the ongoing large eVacuate project on critical crowd behaviour detection in confined spaces.

Data Science Seminar: The data science revolution in Physics and Astronomy

Abstract Heading into the 2020s, Physics and Astronomy are undergoing experimental revolutions that will reshape our picture of the fabric of the Universe. The Large Hadron Collider (LHC), the largest particle physics project in the world, produces 30 petabytes of data annually that need to be sifted through, analysed, and modelled. In astrophysics, the Large Synoptic Survey Telescope (LSST) will be taking a high-resolution image of the full sky every 3 days, leading to data rates of 30 terabytes per night over ten years. These experiments endeavour to answer the question why 96% of the content of the universe currently elude our physical understanding. Both the LHC and LSST share the 5-dimensional nature of their data, with position, energy and time being the fundamental axes. This talk will present an overview of the experiments and data that is gathered, and outlines the challenges in extracting information. Common strategies employed are very similar to industrial data! Science problems (e.g., data filtering, machine learning, statistical interpretation) and provide a seed for exchange of knowledge between academia and industry. Speaker Biography Professor Mark Sullivan Mark Sullivan is a Professor of Astrophysics in the Department of Physics and Astronomy. Mark completed his PhD at Cambridge, and following postdoctoral study in Durham, Toronto and Oxford, now leads a research group at Southampton studying dark energy using exploding stars called "type Ia supernovae". Mark has many years' experience of research that involves repeatedly imaging the night sky to track the arrival of transient objects, involving significant challenges in data handling, processing, classification and analysis.

Software and Space: Investigating How a Cosmology Research Group Enacts Infrastructure by Producing Software

Thesis (Ph.D.)--University of Washington, 2016-08

Common Environment for Undergraduate Computer Programming

We present an Integrated Environment suitable for learning and teaching computer programming which is designed for both students of specialised Computer Science courses, and also non-specialist students such as those following Liberal Arts. The environment is rich enough to allow exploration of concepts from robotics, artificial intelligence, social science, and philosophy as well as the specialist areas of operating systems and the various computer programming paradigms.

The design and Evaluation of 'CAP Tools' - A computer aided parallelisation tool-kit

Abstract not available

Anomaly Detection in Noisy Images

Finding rare events in multidimensional data is an important detection problem that has applications in many fields, such as risk estimation in insurance industry, finance, flood prediction, medical diagnosis, quality assurance, security, or safety in transportation. The occurrence of such anomalies is so infrequent that there is usually not enough training data to learn an accurate statistical model of the anomaly class. In some cases, such events may have never been observed, so the only information that is available is a set of normal samples and an assumed pairwise similarity function. Such metric may only be known up to a certain number of unspecified parameters, which would either need to be learned from training data, or fixed by a domain expert. Sometimes, the anomalous condition may be formulated algebraically, such as a measure exceeding a predefined threshold, but nuisance variables may complicate the estimation of such a measure. Change detection methods used in time series analysis are not easily extendable to the multidimensional case, where discontinuities are not localized to a single point. On the other hand, in higher dimensions, data exhibits more complex interdependencies, and there is redundancy that could be exploited to adaptively model the normal data. In the first part of this dissertation, we review the theoretical framework for anomaly detection in images and previous anomaly detection work done in the context of crack detection and detection of anomalous components in railway tracks. In the second part, we propose new anomaly detection algorithms. The fact that curvilinear discontinuities in images are sparse with respect to the frame of shearlets, allows us to pose this anomaly detection problem as basis pursuit optimization. Therefore, we pose the problem of detecting curvilinear anomalies in noisy textured images as a blind source separation problem under sparsity constraints, and propose an iterative shrinkage algorithm to solve it. Taking advantage of the parallel nature of this algorithm, we describe how this method can be accelerated using graphical processing units (GPU). Then, we propose a new method for finding defective components on railway tracks using cameras mounted on a train. We describe how to extract features and use a combination of classifiers to solve this problem. Then, we scale anomaly detection to bigger datasets with complex interdependencies. We show that the anomaly detection problem naturally fits in the multitask learning framework. The first task consists of learning a compact representation of the good samples, while the second task consists of learning the anomaly detector. Using deep convolutional neural networks, we show that it is possible to train a deep model with a limited number of anomalous examples. In sequential detection problems, the presence of time-variant nuisance parameters affect the detection performance. In the last part of this dissertation, we present a method for adaptively estimating the threshold of sequential detectors using Extreme Value Theory on a Bayesian framework. Finally, conclusions on the results obtained are provided, followed by a discussion of possible future work.

Jeux de policiers et voleurs, Modèles et applications

Les jeux de policiers et voleurs sont étudiés depuis une trentaine d’années en informatique et en mathématiques. Comme dans les jeux de poursuite en général, des poursuivants (les policiers) cherchent à capturer des évadés (les voleurs), cependant ici les joueurs agissent tour à tour et sont contraints de se déplacer sur une structure discrète. On suppose toujours que les joueurs connaissent les positions exactes de leurs opposants, autrement dit le jeu se déroule à information parfaite. La première définition d’un jeu de policiers-voleurs remonte à celle de Nowakowski et Winkler [39] et, indépendamment, Quilliot [46]. Cette première définition présente un jeu opposant un seul policier et un seul voleur avec des contraintes sur leurs vitesses de déplacement. Des extensions furent graduellement proposées telles que l’ajout de policiers et l’augmentation des vitesses de mouvement. En 2014, Bonato et MacGillivray [6] proposèrent une généralisation des jeux de policiers-voleurs pour permettre l’étude de ceux-ci dans leur globalité. Cependant, leur modèle ne couvre aucunement les jeux possédant des composantes stochastiques tels que ceux dans lesquels les voleurs peuvent bouger de manière aléatoire. Dans ce mémoire est donc présenté un nouveau modèle incluant des aspects stochastiques. En second lieu, on présente dans ce mémoire une application concrète de l’utilisation de ces jeux sous la forme d’une méthode de résolution d’un problème provenant de la théorie de la recherche. Alors que les jeux de policiers et voleurs utilisent l’hypothèse de l’information parfaite, les problèmes de recherches ne peuvent faire cette supposition. Il appert cependant que le jeu de policiers et voleurs peut être analysé comme une relaxation de contraintes d’un problème de recherche. Ce nouvel angle de vue est exploité pour la conception d’une borne supérieure sur la fonction objectif d’un problème de recherche pouvant être mise à contribution dans une méthode dite de branch and bound.

The ecology of the ePundit: surveying the new opinion-making landscape.

This paper explores hybrid forms of contemporary political opinion-making online, which we name ePunditry. The ePundit utilizes Web 2.0 technologies and networks to distribute their work: changing and challenging the boundaries and hierarchies of the existing opinion space, across multiple platforms. Drawing on the language of media ecology we define and give examples of ePunditry. We also consider the impact of the ePundit upon the wider media landscape, alongside the empowered role of the readership.

Fallbasiertes E-Learning durch dynamische Verknüpfung von Fallstudien und Fachinhalten. Neue Diskussionsansätze zu einem vernachlässigten Konzept

Der Einsatz von Fallstudien kann als wichtiges Bindeglied zur Verknüpfung von Theorie und Praxis betrachtet werden. Fallstudien ermöglichen die Anwendung theoretischen Grundlagenwissens und die Entwicklung überfachlicher Kompetenzen. Damit können sie einen wichtigen Beitrag zur beruflichen Handlungskompetenz genau dort leisten, wo praktische Erfahrungen im Rahmen der Aus-und Weiterbildung nicht möglich sind. Der Einsatz von Fallstudien sollte aus diesem Grund nicht nur den „klassischen“ Anwendungsdisziplinen wie den Rechtswissenschaften, der Betriebswirtschaftslehre oder der Psychologie vorbehalten sein. Auch im Bereich der Informatik können sie eine wichtige Ergänzung zu den bisher eingesetzten Methoden darstellen. Das im Kontext des Projekts New Economy1 entwickelte und hier vorgestellte Konzept zur didaktischen und technischen Aufbereitung von Fallstudien am Beispiel der IT-Aus- und Weiterbildung soll diese Diskussion anregen. Mit Hilfe des vorgestellten Ansatzes ist es möglich, unterschiedliche methodische Zugänge zu einer Fallstudie für eine computerbasierte Präsentation automatisch zu generieren und mit fachlichen Inhalten zu verknüpfen. Damit ist ein entscheidender Mehrwert gegenüber den bisherigen statischen und in sich geschlossenen Darstellungen gegeben. Der damit zu erreichende Qualitätssprung im Einsatz von Fallstudien in der universitären und betrieblichen Aus- und Weiterbildung stellt einen wichtigen Beitrag zur praxisorientierten Gestaltung von Blended Learning-Ansätzen dar.(DIPF/Orig.)

Interaktives Präsentieren und kooperatives Modellieren. Szenarien akademischen Lehrens und Lernens in Informatik und Naturwissenschaften

Ausgehend von einem handlungsorientierten Medienbegriff werden in diesem Artikel neue Einsatzformen digitaler Medien in der Lehre thematisiert. Dabei spielen Hardware-Voraussetzungen wie berührungsempfindliche Bildschirme oder Funknetzwerke ebenso eine Rolle wie eine Reihe innovativer Softwarewerkzeuge, die insbesondere interaktiv-kooperative Szenarien unterstützen. Praktische Erfahrungen mit diesen Werkzeugen wurden in der akademischen Lehre an der Universität Duisburg-Essen sowie im schulischen Einsatz im Umfeld eines EU-Projektes gesammelt.(DIPF/Orig.)

Technical and Didactical Scenarios of Student-centered Teaching and Learning

In this paper we envision didactical concepts for university education based on self-responsible and project-based learning and outline principles of adequate technical support. We use the scenario technique describing how a fictive student named Anna organizes her studies of informatics at a fictive university from the first days of her studies to make a career for herself.(DIPF/Orig.)