Christian Servin

An Optimization Approach using Soft Constraints for the Cascade Vulnerability Problem

In the discipline of computer security, the field of trust management design is dedicated to the design of trusted systems, in particular trusted networks. One common trusted mechanism used these days is the multi-level security (MLS) mechanism, that allows simultaneous access to systems by users with different levels of security clearance in an interconnected network. Vulnerability arises when an intruder takes advantage of the network connectivity and creates an inappropriate flow of information across the network, leading to the so-called cascade vulnerability problem (CVP). In this article, we extend an existent approach to this problem proposed by Bistarelli et al. [1] that models, detects and properly eliminates the CVP in a network. This particular approach expresses a solution of the problem using constraint programming. We incorporate real-world criteria to consider into this approach, such as the bandwidth, electricity, cost of connections. Considering such features in CVP results in generating a constraint optimization problem.

How to Estimate Relative Spatial Resolution of Different Maps or Images of the Same Area

In this paper, we describe how to estimate relative spatial resolution of different maps or images of the same area under uncertainty. We consider probabilistic and fuzzy approaches and we show that both approaches lead to the same estimate - which makes us somewhat more confident that this joint result is reasonable.

How to Gauge Accuracy of Measurements and of Expert Estimates: Beyond Normal Distributions

To properly process data, we need to know the accuracy of different data points, i.e., accuracy of different measurement results and expert estimates. Often, this accuracy is not given. For such situations, we describe how this accuracy can be estimated based on the available data.

Propagation of Interval and Probabilistic Uncertainty in Cyberinfrastructure-related Data Processing and Data Fusion

On various examples ranging from geosciences to environmental sciences, thisbook explains how to generate an adequate description of uncertainty, how to justifysemiheuristic algorithms for processing uncertainty, and how to make these algorithmsmore computationally efficient. It explains in what sense the existing approach touncertainty as a combination of random and systematic components is only anapproximation, presents a more adequate three-component model with an additionalperiodic error component, and explains how uncertainty propagation techniques canbe extended to this model. The book provides a justification for a practically efficientheuristic technique (based on fuzzy decision-making). It explains how the computationalcomplexity of uncertainty processing can be reduced. The book also shows how totake into account that in real life, the information about uncertainty is often onlypartially known, and, on several practical examples, explains how to extract the missinginformation about uncertainty from the available data.

Computer Science Curricular Guidelines for Associate-Degree Transfer Programs (Abstract Only)

The ACM Committee for Computing Education in Community Colleges (CCECC) is updating the ACM curricular guidance for two-year transfer programs in computer science based on CS2013 with cybersecurity learning outcomes infused throughout. This BOF will provide a platform for two-year and four-year computer science faculty and academic administrators to discuss the newly revised associate-degree transfer guidance. The core task group writing the guidance consists of twelve community college faculty across the United States, led by the ACM CCECC and three task group leaders. The guidance has been informed by input from both two- and four-year educators in two rounds of public review and comment; a BOF, special session, and affiliated workshop at the prior two SIGCSE conferences; and international input at ITiCSE 2016. By SIGCSE 2017 the guidance will be in near-final form. The session will include an overview of the guidance, its relationship to CS2013, and infused cybersecurity. Discussion will center on implementing the guidance in two-year programs, gathering program exemplars, and facilitating transfer with four-year university partners.

Curricular Guidance for Associate-Degree Transfer Programs in Computer Science with Contemporary Cybersecurity Concepts (Abstract Only)

In 2015, under the auspices of the ACM Education Board the Committee for Computing Education in Community Colleges (CCECC) began an effort to update the ACM Computing Curricula 2009: Guidelines for Associate-Degree Transfer Curriculum in Computer Science with inclusion of contemporary cybersecurity concepts. To this end, the CCECC established a task force of community college educators to review the ACM/IEEE Computer Science Curricula 2013 (CS2013) and identify foundational material in CS2013 that is appropriate for the first two years of a computer science education. To further inform the guidance, the CCECC administered surveys to a global audience of computer science educators to solicit input related to CS2013 knowledge areas (KAs) and knowledge units (KUs) and on cybersecurity topics, which are appropriate for associate-degree computer science transfer programs. The guidance has been through two rounds of public review and comment

How to estimate expected shortfall when probabilities are known with interval or fuzzy uncertainty

To gauge the risk corresponding to a possible disaster, it is important to know both the probability of this disaster and the expected damage caused by such potential disaster (“expected shortfall”). Both these measures of risk are easy to estimate in the ideal case, when we know the exact probabilities of different disaster strengths. In practice, however, we usually only have a partial information about these probabilities: we may have an interval (or, more generally, fuzzy) uncertainty about these probabilities. In this paper, we show how to efficiently estimate the expected shortfall under such interval and/or fuzzy uncertainty.

Towards efficient algorithms for approximating a fuzzy relation by fuzzy rules: Case when “and”-and “or”-operation are distributive

A generic fuzzy relation often requires too many parameters to represent - especially when we have a relation between many different quantities X1,..., Xn, There is, however, a class of relations which require much fewer parameters to describe - namely, relations which come from fuzzy rules. It is therefore reasonable to approximate a given relation by fuzzy rules. In this paper, we explain how this can be done in an important case when “and”- and “or”-operation are distributive - and we also explain why this case is important.

Sparse fuzzy techniques improve machine learning

On the example of diagnosing cancer based on the microarray gene expression data, we show that fuzzy-technique description of imprecise knowledge can improve the efficiency of the existing machine learning algorithms. Specifically, we show that the fuzzy-technique description leads to a formulation of the learning problem as a problem of sparse optimization, and we use l1-techniques to solve the resulting optimization problem.

How to Describe and Propagate Uncertainty When Processing Time Series: Metrological and Computational Challenges, with Potential Applications to Environmental Studies

Time series comes from measurements, and often, measurement inaccuracy needs to be taken into account, especially in such volatile application areas as meteorology and economics. Traditionally, when we deal with an individual measurement or with a sample of measurement results, we subdivide a measurement error into random and systematic components: systematic error does not change from measurement to measurement while random errors corresponding to different measurements are independent. In time series, when we measure the same quantity at different times, we can also have correlation between measurement errors corresponding to nearby moments of time. To capture this correlation, environmental science researchers proposed to consider the third type of measurement errors: periodic. This extended classification of measurement error may seem ad hoc at first glance, but it leads to a good description of the actual errors. In this paper, we provide a theoretical explanation for this semi-empirical classification, and we show how to efficiently propagate all types of uncertainty via computations.