"Boring formal methods" or "Sherlock Holmes deduction methods"?

This paper provides an overview of common challenges in teaching of logic and formal methods to Computer Science and IT students. We discuss our experiences from the course IN3050: Applied Logic in Engineering, introduced as a "logic for everybody" elective course at at TU Munich, Germany, to engage pupils studying Computer Science, IT and engineering subjects on Bachelor and Master levels. Our goal was to overcome the bias that logic and formal methods are not only very complicated but also very boring to study and to apply. In this paper, we present the core structure of the course, provide examples of exercises and evaluate the course based on the students' surveys.Comment: Preprint. Accepted to the Software Technologies: Applications and Foundations (STAF 2016). Final version published by Springer International Publishing AG. arXiv admin note: substantial text overlap with arXiv:1602.0517

Reduced order modeling of fluid flows: Machine learning, Kolmogorov barrier, closure modeling, and partitioning

In this paper, we put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements. We build on the fact that in a realistic application, there are uncertainties in initial conditions, boundary conditions, model parameters, and/or field measurements. Moreover, conventional nonlinear ROMs based on Galerkin projection (GROMs) suffer from imperfection and solution instabilities due to the modal truncation, especially for advection-dominated flows with slow decay in the Kolmogorov width. In the presented LSTM-Nudge approach, we fuse forecasts from a combination of imperfect GROM and uncertain state estimates, with sparse Eulerian sensor measurements to provide more reliable predictions in a dynamical data assimilation framework. We illustrate the idea with the viscous Burgers problem, as a benchmark test bed with quadratic nonlinearity and Laplacian dissipation. We investigate the effects of measurements noise and state estimate uncertainty on the performance of the LSTM-Nudge behavior. We also demonstrate that it can sufficiently handle different levels of temporal and spatial measurement sparsity. This first step in our assessment of the proposed model shows that the LSTM nudging could represent a viable realtime predictive tool in emerging digital twin systems

The Issue of Tourist Accommodation in the Scandinavian Journal of Hospitality and Tourism

The aim of the study is to analyze the contents of the articles published in the Scandinavian Journal of Hospitality and Tourism with special attention paid to texts describing tourist accommodation in its broadest sense. The list of references was collected in a survey of Taylor & Francis Online1 which includes online editions of the journal

The similarity metric

A new class of distances appropriate for measuring similarity relations between sequences, say one type of similarity per distance, is studied. We propose a new ``normalized information distance'', based on the noncomputable notion of Kolmogorov complexity, and show that it is in this class and it minorizes every computable distance in the class (that is, it is universal in that it discovers all computable similarities). We demonstrate that it is a metric and call it the {\em similarity metric}. This theory forms the foundation for a new practical tool. To evidence generality and robustness we give two distinctive applications in widely divergent areas using standard compression programs like gzip and GenCompress. First, we compare whole mitochondrial genomes and infer their evolutionary history. This results in a first completely automatic computed whole mitochondrial phylogeny tree. Secondly, we fully automatically compute the language tree of 52 different languages.Comment: 13 pages, LaTex, 5 figures, Part of this work appeared in Proc. 14th ACM-SIAM Symp. Discrete Algorithms, 2003. This is the final, corrected, version to appear in IEEE Trans Inform. T

Demand Forecasting Tool For Inventory Control Smart Systems

With the availability of data and the increasing capabilities of data processing tools, many businesses are leveraging historical sales and demand data to implement smart inventory management systems. Demand forecasting is the process of estimating the consumption of products or services for future time periods. It plays an important role in the field of inventory control and Supply Chain, since it enables production and supply planning and therefore can reduce delivery times and optimize Supply Chain decisions. This paper presents an extensive literature review about demand forecasting methods for time-series data. Based on analysis results and findings, a new demand forecasting tool for inventory control is proposed. First, a forecasting pipeline is designed to allow selecting the most accurate demand forecasting method. The validation of the proposed solution is executed on Stock&Buy case study, a growing online retail platform. For this reason, two new methods are proposed: (1) a hybrid method, Comb-TSB, is proposed for intermittent and lumpy demand patterns. Comb- TSB automatically selects the most accurate model among a set of methods. (2) a clustering-based approach (ClustAvg) is proposed to forecast demand for new products which have very few or no sales history data. The evaluation process showed that the proposed tool achieves good forecasting accuracy by making the most appropriate choice while defining the forecasting method to apply for each product selection

Using electronic resources to support dialogue in undergraduate small‐group teaching: The ASTER project

Learning through dialogue is an important element of UK higher education, supported by tutorial, seminar and workshop classes. Since 1998, the ASTER project has been exploring how Information and Communication Technologies support learning in small groups (http://cti‐psy.york.acuk/aster/). Electronic resources are developed and used in courses to support a wide range of learning needs, from delivery of content to interactive teaching tools and assessment. The manner in which they are integrated into a course dictates the extent to which they support and extend learning. The ASTER survey has identified the use of a range of new technologies to support learning through dialogue in a variety of contexts. Many of the uses are common across disciplines, though we have observed some differences in the range of tools used, and how they are implemented in and beyond the classroom. These differences are partly determined by the subject content of resources, and by the activities that ICT tools support. Another factor influencing this variation seems to be traditions of academic discourse. The findings suggest that educational technology needs to support both generic education practice, and the special needs of particular disciplines

Citation Analysis: A Comparison of Google Scholar, Scopus, and Web of Science

When faculty members are evaluated, they are judged in part by the impact and quality of their scholarly publications. While all academic institutions look to publication counts and venues as well as the subjective opinions of peers, many hiring, tenure, and promotion committees also rely on citation analysis to obtain a more objective assessment of an author’s work. Consequently, faculty members try to identify as many citations to their published works as possible to provide a comprehensive assessment of their publication impact on the scholarly and professional communities. The Institute for Scientific Information’s (ISI) citation databases, which are widely used as a starting point if not the only source for locating citations, have several limitations that may leave gaps in the coverage of citations to an author’s work. This paper presents a case study comparing citations found in Scopus and Google Scholar with those found in Web of Science (the portal used to search the three ISI citation databases) for items published by two Library and Information Science full-time faculty members. In addition, the paper presents a brief overview of a prototype system called CiteSearch, which analyzes combined data from multiple citation databases to produce citation-based quality evaluation measures

Get yourself connected: conceptualising the role of digital technologies in Norwegian career guidance

This report outlines the role of digital technologies in the provision of career guidance. It was commissioned by the c ommittee on career guidance which is advising the Norwegian Government following a review of the countries skills system by the OECD. In this report we argue that career guidance and online career guidance in particular can support the development of Norwa y’s skills system to help meet the economic challenges that it faces.The expert committee advising Norway’s Career Guidance Initiativ