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Josef Küng

Institute for Application-oriented Knowledge Processing, Johannes Kepler University Linz, Austria

Title: Applied knowledge processing


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Dirk Draheim

Head of Large-Scale Systems Group of TUT Tallinn University of Technology, Estonian

Title: On Generalizing Association Rule Mining: Grand Pivot Reports and Genuine Impact

Abstract: In this talk, we elaborate on the potential of association rule mining in today’s data science tool landscape. We start by reviewing association rule mining in terms of probability theory. On the basis of this, we can generalize association rule mining from discrete-valued to real-valued target columns, i.e., by stepping from mining conditional probabilities to mining conditional expected values. Together with arbitrary dicing, we arrive at the notion of grand pivot reports. You can think of a grand pivot report as the complete unfolding of the pivot table over all involved factors; the idea and at the same time challenge is to incorporate each and every line of the complete report in decision making. Now: what are the tools needed to support the analyst with this obviously unmanageable task? Due to the Yule-Simpson effect, grand pivot reports are heavily vulnerable to misinterpretation. Therefore, grand pivot reports need refined measurements of interestingness that are robust against the Yule-Simpson effect and reveal the genuine impact of the multiple influencing factors. We discuss, how F.P. conditionalization can be exploited to define a notion of genuine impact.

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Sadok Ben Yahia

School of Information Technologies, Tallinn University of Technology, Estonia

Title: Test Suite Reduction (TSR) for Softwares

Abstract: I will present recent work on Test Suite Reduction (TSR) within the scope of Software testing, which is a widely accepted practice that ensures the quality of a System under Test. In this respect, TSR is considered as a potential approach to deal with the test suite size problem. Moreover, a complete automation support is highly recommended for software testing to adequately meet the thriving challenges raised by the big data era. The originality of the work that I will present stands in the unveil of a connection between the concept of minimal transversals of an hypergraph with the TSR issue. I will also highlight the connection between minimal transversals and the Formal Concept analysis. The latter has typically been steadily applied in the field of software engineering to support software maintenance and object-oriented class identification tasks.

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Vitalian Danciu

Ludwig-Maximilians-University Munich, Germany

Title: Group security and individual privacy

Abstract: The security of an organisation is affected by the privacy enjoyed by its member individuals. The large-scale change emergent from the gloabal proliferation of cloud computing, smart homes, the internet of things and machine learning requires a novel view on the flow of confidential information. The growing reciprocal influence between the knowledge about individuals and the knowledge about the groups or organisations they pertain to originates in the personal data about group members being processed outside of the domain where it originates. Thus, the degree of privacy of individuals affects the degree of confidentiality of the information pertaining to the groups the individual is a member of. This lecture discusses threats to individuals and organisations based on models of information flow and knowledge extraction.

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Phan Thanh An

Department of Numerical Analysis and Scientific Computing, Institute of Mathematics, Vietnam

Title: Optimization methods for computational geometry

Abstract: We present optimization methods, namely method of orienting curves (MOC) and method of multiple shooting (MMS), for solving efficiently fundamentation problems: convex hull, Delaunay triangulation and geometric shortest path problems. The methods can be used for these problems in digital geometry and therefore have applications in computer graphics and computer vision. Some advantages of MOC (MMS, respectively) are low running times (high accuracy of approximate solutions and less memory of the system, respectively). This is a joint work with N.N. Hai, T.V. Hoai, and L.H. Trang.

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Tae Myoung Chung

Sungkyunkwan University, Korea

Title: Current Severe Internet Attacks And Protecting Technology

Abstract: Main points: The keynote will discuss about popular attacks like ransomware, ATP, and supplier attacks, as well as the security technologies to prevent and protect those attacks.

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Fukuda Kensuke

Information Systems Architecture Research Division, National Institute of Informatics (NII), Tokyo, Japan

Title: Detecting large-scale network scanners in IPv4/IPv6 networks

Abstract: Detecting large-scale network scans is an important issue for network security because such scans may be a signal of large-scale attacks. In this talk, we will introduce our DNS-based network scan detection technique called DNS backscatter. DNS backscatter is a set of DNS reverse queries generated during network scans, and it reliably detects them at authoritative servers with help from machine learning techniques. We will demonstrate how DNS backscatter detects large-scale scans in IPv4 and IPv6 networks.