Lunchtime Seminar

Archive Winter Semester 2016/2017

Preoperative planning for rigid and non-rigid conditions for image-guided minimally invasive surgery

Lecturer:
Noura Hamze
Postdoctoral researcher at IGS group, University of Innsbruck

Date: Thursday, 26th of January 2017, 12:00 – 1:00

Venue: SR 1/2, ICT Building, Technikerstraße 21a, 6020 Innsbruck

Abstract:
Image-guided minimally invasive surgery is becoming very common in hospitals today. Despite its advantages compared to conventional open surgery, its major difficulties are the reduced visibility inside the body, and the limited maneuvering of surgical tools. Therefore, a precise preoperative planning of the surgical tools trajectories is a key factor to a successful intervention. In this talk, I will present our previous work on preoperative path planning for surgical tools, and show how we could increase intervention safety levels by considering intra-operative deformation during the preoperative planning phase. Our methods combine geometry-based optimization techniques with physics-based simulations. The developed techniques are widely applicable; examples of two different surgical procedures will be shown: percutaneous procedures for hepatic tumor thermal ablation, and neurosurgical deep brain stimulation. Finally, I will also briefly outline our ongoing and future research on forearm orthopedic surgery planning in the Interactive Graphics and Simulation Group.


Strong Modular Proof Assistance: Reasoning across Theories

Lecturer:
Cezary Kaliszyk
Research assistant at CL group, University of Innsbruck

Date: Thursday, 19th of January 2017, 12:00 – 1:00

Venue: SR 1/2, ICT Building, Technikerstraße 21a, 6020 Innsbruck

Abstract:
As proofs of correctness of programs become more important in modern complex designs, automatically providing proof advice becomes a principal challenge. The strongest general purpose advice and automation for formal proofs is today provided by learning-reasoning systems called hammers. In this talk we will discuss several limitations of the current early generation of hammer systems and discuss new AI methods that will combine the knowledge and the reasoning techniques present in the current systems into a smart learning and reasoning system working over a large part of today’s body of formalized knowledge. We will also show how the uniform learning methods and encoding components generalize advice for different proof assistants into a general advice system for semi-formal and informal proofs.


Discriminative models for multi-instance problems with tree-structure

Lecturer:
Tomas Pevny
researcher at Agent Technology Center (ATC), Czech Technical University, Prague

Date: Thursday, 12th of January 2017, 12:00 – 1:00

Venue: SR 1/2, ICT Building, Technikerstraße 21a, 6020 Innsbruck

Abstract:
Modeling network traffic is gaining importance in order to counter modern threats of ever increasing sophistication. It is though surprisingly difficult and costly to construct reliable classifiers on top of telemetry data due to the variety and complexity of signals that no human can manage to interpret in full. Obtaining training data with sufficiently large and variable body of labels can thus be seen as prohibitive problem. The goal of this work is to detect infected computers by observing their HTTP(S) traffic collected from network sensors, which are typically proxy servers or network firewalls, while relying on only minimal human input in model training phase.
We propose a discriminative model that makes decisions based on all computer’s traffic observed during predefined time window (5 minutes in our case). The model is trained on collected traffic samples over equally sized time window per large number of computers, where the only labels needed are human verdicts about the computer as a whole (presumed infected vs. presumed clean). As part of training the model itself recognizes discriminative patterns in traffic targeted to individual servers and constructs the final high-level classifier on top of them. We show the classifier to perform with very high precision, while the learned traffic patterns can be interpreted as Indicators of Compromise. In the following we implement the discriminative model as a neural network with special structure reflecting two stacked multi-instance problems. The main advantages of the proposed configuration include not only improved accuracy and ability to learn from gross labels, but also automatic learning of server types (together with their detectors) which are typically visited by infected computers.


Development of a Risk-Based Test Strategy and its Industrial Evaluation

Lecturer:
Michael Felderer
Senior Researcher at QE group, University of Innsbruck

Date: Thursday, 15th of December 2016, 12:00 – 1:00

Venue: SR 1/2, ICT Building, Technikerstraße 21a, 6020 Innsbruck

Abstract:
Risk-based testing has a high potential to improve the software test process as it helps to optimize the allocation of resources and provides decision support for the management. But for many organizations the integration of risk-based testing into an existing test process is a challenging task. An essential first step when introducing risk-based testing in an organization is to establish a risk-based test strategy which considers risks as the guiding factor to support all testing activities in the entire software lifecycle. In this presentation we provide an overview of risk-based testing and present a process for risk-based test strategy development and refinement. The process has been created as part of a research transfer project on risk-based testing that provided the opportunity to get direct feedback from industry and to evaluate the ease of use, usefulness and representativeness of each process step together with five software development companies. Furthermore, we present an outlook on ongoing research on the integration of defect prediction and risk-based testing.


A One-for-All Exams Generator: Written Exams, Online Tests, and Live Quizzes with R

Lecturer:
Achim Zeileis
Professor at Department of Statistics, University of Innsbruck

Date: Thursday, 1st of December 2016, 12:00 – 1:00

Venue: SR 1/2, ICT Building, Technikerstraße 21a, 6020 Innsbruck

Abstract:
A common challenge in large-scale courses is that many variations of similar exercises are needed for written exams, online tests conducted in learning management systems (such as Moodle, OLAT, Blackboard, etc.), or live quizzes with voting via smartphones or tablets. Here, we introduce a set of open-source tools – tied together by the R package “exams” (https://CRAN.R-project.org/package=exams) – which facilitate these tasks. The package is based on individual exercises that are either in R/LaTeX or R/Markdown format and can contain questions/solutions with some random numbers, text snippets, or even individualized datasets. The exercises can be combined to exams and easily rendered into a number of output formats including PDF, HTML, XML for Moodle or OLAT, etc. It will be illustrated how the Department of Statistics at Universität Innsbruck manages its large statistics and mathematics courses using PDF exams that can be automatically scanned and evaluated, online tests in the OpenOLAT learning management system, and live quizzes in the ARSnova audience response system.


Reliable Analysis of Functional Logic Programs

Lecturer:
Thomas Sternagel
Research assistant at CL group, University of Innsbruck

Date: Thursday, 17th of November 2016, 12:00 – 1:00

Venue: 3W03, ICT Building, 2nd floor, Technikerstraße 21a, 6020 Innsbruck

Abstract:
More and more often computer programs run in parallel on multiple cores, CPUs, or even distributed systems. The likelihood of errors in a program increases with its complexity. If we are lucky these errors do not show up in practice, but more likely they will surface at some point and cause negative financial impact, the loss of life, or both. Testing parallel programs is becoming more difficult and clearly not enough.
What we want are methods to prove properties of programs formally and automatically. In this regard already the choice of programming language is crucial. We will go for functional logic programs to preclude certain kinds of concurrency problems from the start. A suitable model of computation for functional logic programs is conditional term rewriting.
This talk will be about how to formalize, implement, and certify methods for conditional term rewriting in order to check certain properties of functional logic programs.


From Plagiarism Detection to Bible Analysis: The Potential of Grammar-Based Text Analysis

Lecturer:
Michael Tschuggnall
Postdoctoral Researcher at DBIS, University of Innsbruck

Date: Thursday, 10th of November 2016, 12:00 – 1:00

Venue: SR 1/2, ICT Building, Technikerstraße 21a, 6020 Innsbruck

Abstract:
The amount of textual data available from digitalized sources such as free online libraries or social media posts has increased drastically in the last decade. As one consequence, it becomes easier for a plagiarist to find suitable sources, where on the other side it gets harder for automated tools to detect fraud.
This talk gives an overview of how textual analysis can help to reveal potential plagiarism, i.e., by inspecting the grammatical writing style of authors. Moreover, related tasks like authorship attribution or author profiling can be tackled using similar algorithms, which aim to identify the writer of a document or to extract meta information like gender and age by investigating the writing style. Finally, also analyses on the original Bible writings in Old Hebrew were conducted, revealing promising results.


Skill learning by robotic playing

Lecturer:
Simon Hangl
Research assistant at IIS group, University of Innsbruck

Date: Thursday, 3rd of November 2016, 12:00 – 1:00

Venue: SR 1/2, ICT Building, Technikerstraße 21a, 6020 Innsbruck

Abstract:
Robots are widely used in industry. However, they only work well in highly restricted and controlled environments, in which it is relatively easy to program the robots. The next step is to enable robots to work in unstructured environments, in which the applications are countless (e.g. household robots). One important and still unsolved problem is (semi-) autonomous skill acquisition. Current approaches mostly require a big set of training samples and/or high task priors in the learning method itself. We investigated a method for robotic playing for autonomous skill acquisition that can be applied in unstructured environments. We further introduce concepts like boredom, creativity or curiosity to robots in order to guide them during the learning process.


Uncertainity in Workflow Scheduling and Execution in the Cloud

Lecturer:
Sashko Ristov
Postdoctoral Researcher at DPS group, University of Innsbruck

Date: Thursday, 27th of October 2016, 12:00 – 1:00

Venue: SR 1/2, ICT Building, Technikerstraße 21a, 6020 Innsbruck

Abstract:
The performance of cloud resources is uncertain because of elastic resource provisioning and unstable performance of multitenant VMs over time. This reflects the performance of workflow applications even more due to the data and control dependencies and opens two challenges that we will present:
Modeling the uncertainty in workflow scheduling and workflow execution. Our scheduling model improves the estimation of the Pareto optimal set of scheduling solutions that resist against fluctuations in processing times.
Additionally, the workflow execution model shows closer simulation than state of the art with simpler configuration of a simulator.


Multimedia forensics: a deterministic approach

Lecturer:
Cecilia Pasquini
Postdoctoral Researcher at SEC group, University of Innsbruck

Date: Thursday, 20th of October 2016, 12:00 – 1:00

Venue: SR 1/2, ICT Building, Technikerstraße 21a, 6020 Innsbruck

Abstract:
The increasing availability and pervasiveness of multimedia data, coupled with the easy access to user-friendly editing software, motivates research on multimedia forensics, which develops forensic tools for verifying the authenticity of multimedia data. This is mostly done by studying traces left in the signal by any operation that could have been employed as post-processing, either for malicious purposes or simply to improve their content or presentation.
The majority of forensic approaches are based on statistical properties of the signal and operation considered. However, we explore the possibility of defining and exploiting in the forensic analysis properties that are deterministically related to a certain processing operation. With this respect, we present an approach targeted to the detection in 1D data of a common data smoothing operation, the median filter. The main peculiarity of this method is the ability of providing a deterministic response on the presence of median filtering traces in the data under investigation.


Tuning Task Parallelism: Granularity Control and Context-aware Optimization

Lecturer:
Peter Thoman
Postdoctoral Researcher at DPS group, University of Innsbruck

Date: Thursday, 13th of October 2016, 12:00 – 1:00

Venue: SR 1/2, ICT Building, Technikerstraße 21a, 6020 Innsbruck

Abstract:
For many algorithms – including divide-and-conquer methods and branch-and-bound computations – nested task parallelism is a more natural fit than flat data parallelism. However, the latter is far more widely used in high-performance parallel programs at this point in time. We identify reasons for the current divide between parallelism theory and application development reality, and present some approaches to mitigate the underlying issues. The focus is on providing solutions which allow application programmers to focus on simply expressing the parallelism available in their algorithms, rather than concerning themselves with hardware-, system- and application-specific performance tuning. The methods presented include compiler optimizations, runtime system tuning, and context-aware parallel API design.


Implementing Threat Intelligence Sharing Platforms: Challenges and obstacles

Lecturer:
Christian Sillaber
Research assistant at QE group, University of Innsbruck

Date: Thursday, 6th of October 2016, 12:00 – 1:00

Venue: SR 1/2, ICT Building, Technikerstraße 21a, 6020 Innsbruck

Abstract:
In the last couple of years, organizations have demonstrated an increased willingness to participate in threat intelligence sharing platforms. The exchange of information about threats, vulnerabilities, incidents and mitigation strategies results from the organizations’ growing need to collectively protect against today’s sophisticated cyber-attacks. However, the increasing amount of data that is shared via these platforms, multiple data sources to be integrated, a lack of proper quality controls as well as a frequent mismatch between the value proposition of such platforms and the organizations’ requirements lead to high friction in early stages of platform implementation. In a series of workshops and interviews we identified challenges early adopters of threat intelligence sharing platforms face and how they can be mitigated. The findings of the workshops and interviews show that the successful implementation of threat intelligence sharing platforms requires a good alignment between information system security risk management and the business environment, direct integration into the existing security management tool landscape and capable analysis mechanisms. We present results from an ongoing research project spotlighting data quality challenges in threat intelligence sharing platforms and related frictions organizations face when implementing such platforms.

Nach oben scrollen