Distributed environment for efficient virtual machine image management in federated Cloud architectures
The use of virtual machines (VMs) in Cloud computing provides various benefits in the overall software engineering lifecycle. These include efficient elasticity mechanisms resulting in higher resource utilization and lower operational costs. The VMs as software artifacts are created using provider-specific templates, called virtual machine images (VMI), and are stored in proprietary or public repositories for further use. However, some technology-specific choices can limit the interoperability among various Cloud providers and bundle the VMIs with nonessential or redundant software packages, leading to increased storage size, prolonged VMI delivery, stagnant VMI instantiation, and ultimately vendor lock-in. To address these challenges, we present a set of novel functionalities and design approaches for efficient operation of distributed VMI repositories, specifically tailored for enabling (1) simplified creation of lightweight and size optimized VMIs tuned for specific application requirements; (2) multi-objective VMI repository optimization; and (3) efficient reasoning mechanism to help optimizing complex VMI operations. The evaluation results confirm that the presented approaches can enable VMI size reduction by up to 55%, while trimming the image creation time by 66%. Furthermore, the repository optimization algorithms can reduce the VMI delivery time by up to 51% and cut down the storage expenses by 3%. Moreover, by implementing replication strategies, the optimization algorithms can increase the system reliability by 74%.

Eating disorder in university students: an international multi-institutional study
Background: The purpose of this study was to determine the prevalence of eating disorders (ED) among university students and to identify associated demographic and behavioral profile in university students in the three countries. Methods: In 2018, a cross-sectional study was conducted in three universities: Biała Podlaska (Poland), Miskolc (Hungary) and Ternopil (Ukraine). Students completed an anonymous self-reported questionnaire compiled at the University of Rouen (EurECAS) and gave information on sex, age, marital status, financial difficulties, physical fitness (international Fitness Scale), height and weight for calculating BMI. Eating disorder was measured using SCOFF questionnaire with 2 levels (0=no problem, 1=eating disorders), psychological profile such as stress, anxiety and depression was measured using DASS-21 questionnaire. Results: A total of 1965 students was included in the survey (534 in Hungary, 708 in Poland and 723 in Ukraine). The prevalence of eating disorders was 21.0% in Hungary, 19.7% in Poland, and 36.9% in Ukraine (p < 10-4). Results of logistic regression indicated that ED was significantly associated with to be a female (OR = 2.57, 95% CI = 1.94-3.42), having lower age (OR=.94, 95% CI=.91-.97), higher BMI (OR = 1.13, 95% CI = 1.09-1.17), living alone (OR=.63, 95% CI=.48-.82), being susceptible to stress (OR = 1.10, 95% CI = 1.06-1.15) and anxiety (OR = 1.11, 95% CI = 1.06-1.16). Conclusions: Prevalence of ED, especially in Ukraine, was high in different socio-cultural context. Such stadies are encouraging health care professionals and academic individuals to strongly inquire about the increasing prevalence of eating disorders amongst university students as it is associated with other risk factors. Public health interventions in screening students in the university environment are highly recommended. Key messages: In this large, international study, 26.4% of students reported eating disorders. Female and single students are more affected by the problem. Public health interventions should be targeted to screen these disorders.

IntraClusTSP – An Incremental Intra-cluster Refinement Heuristic Algorithm for Symmetric Traveling Salesman Problem
The Symmetric Traveling Salesman Problem (sTSP) is an intensively studied NP-hard problem. It has many important real-life applications such as logistics, planning, manufacturing of microchips and DNA sequencing. In this paper we propose a cluster level incremental tour construction method called Intra-cluster Refinement Heuristic (IntraClusTSP). The proposed method can be used both to extend the tour with a new node and to improve the existing tour. The refinement step generates a local optimal tour for a cluster of neighbouring nodes and this local optimal tour is then merged into the global optimal tour. Based on the performed evaluation tests the proposed IntraClusTSP method provides an efficient incremental tour generation and it can improve the tour efficiency for every tested state-of-the-art methods including the most efficient Chained Lin-Kernighan refinement algorithm. As an application example, we apply IntraClusTSP to automatically determine the optimal number of clusters in a cluster analysis problem. The standard methods like Silhouette index, Elbow method or Gap statistic method, to estimate the number of clusters support only partitional (single level) clustering, while in many application areas, the hierarchical (multi-level) clustering provides a better clustering model. Our proposed method can discover hierarchical clustering structure and provides an outstanding performance both in accuracy and execution time.

Efficient Approximation for Counting of Formal Concepts Generated from FCA Context
The number of formal concepts generated from the input context is an important parameter in the cost functions of concept formation algorithms. The calculation of concept count for any arbitrary context is a hard, NP-complete problem and only rough approximation methods can be found in the literature to solve this problem. This paper introduces an efficient numerical approximation algorithm for contexts where attribute probabilities are independent from the objects instances. The preconditions required by the approximation method are usually met in the FCA applications, thus the proposed method provides an efficient tool for practical complexity analysis, too.

Tackling binge drinking in university students: a European public health challenge.
Background: Binge drinking (BD) and behavioural risk factors among students in higher education remain to be investigated. The aim was to identify the prevalence and the factors associated with the frequent BD in university students in the University of Miskolc (M) in Hungary and Rouen (R) in France. Methods: French and Hungarian students in higher education completed an anonymous self-questionnaire, online in Rouen and paper in Miskolc Hungary. The questionnaire collected age, gender, housing, curricula, smoking, cannabis consumption, alcohol abuse problems (ADOSPA test), risk of eating disorders (SCOFF test), and perceived stress (Cohen score). Frequent BD was determined as a consumption of five or most alcoholic drinks on one occasion at least twice a month. A risk profile of consumers was conducted according to the consumption of alcohol and frequency of BD. Results: A total of 2116 students were included, 659 in M and 1457 in R; with respectively in M and R, a mean age of 20.8 (SD = 3.2) and 20.5 (SD = 2.9) and a sex ratio M: F of 0.29 and 0.58. Smoking prevalence was in M 22.6% and 22.3% in R, 12.8% of students in M and 39.8% in R were occasionally consumers of cannabis (p < 10-4). The prevalence of frequent BD was 15.9% in M and 13.8% in R, with significant difference by gender in the two universities; 29.8% and 25.1% in R presented an alcohol abuse problems (p = 0.03). After logistic regression, the frequent BD was significantly associated with the male sex (AOR=4.65, 95%CI=3.40-6.37), living in rental (AOR=1.69, 95%CI=1.21-2.42), smoking (AOR=5.59, 95%CI=3.83-8.08) and consuming cannabis (AOR=12.76, 95%CI=9.09-17.93). Students granting and living in couple presented a significant lower risk of frequent BD. Alcohol consumption was at risk for near 70% of students. Conclusions: BD patterns were quite similar in Hungary and France, it concerns one on six students, especially male students. BD appears as a frequent, whose consequences remain to be investigated. Key messages: • Our study highlights the prevalence of binge drinking, with similar behaviours in university students in Hungary and France. • Specific target populations for public health interventions were identified.

Health-related quality of life of adolescents with type 1 diabetes in the context of resilience
Abstract Background Adolescents with type 1 diabetes (T1D) can be faced with deterioration in glycemic control (GC), reduced health-related quality of life (HRQoL), and other psychosocial problems. It is important to understand how the disease and its clinical conditions influence HRQoL and how adolescents are able to overcome the life adjustment difficulties. Objective To assess HRQoL of adolescents with T1D from demographic, clinical, personal, and behavioral point of view. Subjects A total of 229 adolescents with T1D (51.2% males) with a mean age of 15.35 (2.29) years old were recruited from three diabetes centers. The mean diabetes duration was 7.48 (3.87), the mean hemoglobin A1C (HbA1c) level was 10.3 (1.76) mmol/L. Methods A multicenter quantitative correlational design study was applied to investigate the influence of sex, age, diabetes duration, GC expressed by HbA1c, intensive insulin regimen, physical activity (PA), resilience (RS), and socioeconomic background on HRQoL. Results Presence of the diabetes symptoms and worry about the disease has negative impact on the patients' HRQoL. Stepwise multiple regression analyses indicated that insulin pump therapy, male sex, and higher level of RS were significantly related to an increase in HRQoL, whereas the higher level of PA, male sex, and better HRQoL was significantly related to positive change in RS. Patients treated with insulin pump therapy had significantly better HRQoL. Conclusions Significant association can be observed between HRQoL and RS. Supposedly, higher level of PA promotes higher level of RS that in turn helps increase HRQoL in adolescents with T1D. Treatment with insulin pump therapy also promotes better HRQoL.

Fuzzy Rule Interpolation methods and FRI Toolbox
FRI methods are less popular in the practical application domain. One possible reason is the missing common framework. There are many FRI methods developed independently, having different interpolation concepts and features. One trial for setting up a common FRI framework was the MATLAB FRI Toolbox, developed by Johanyák et. al. in 2006. The goals of this paper are divided as follows: firstly, to present a brief introduction of the FRI methods. Secondly, to introduce a brief description of the refreshed and extended version of the original FRI Toolbox. And thirdly, to use different unified numerical benchmark examples to evaluate and analyze the Fuzzy Rule Interpolation Techniques (FRI) (KH, KH Stabilized, MACI, IMUL, CRF, VKK, GM, FRIPOC, LESFRI, and SCALEMOVE), that will be classified and compared based on different features by following the abnormality and linearity conditions

Cloud computing based bushfire prediction for cyber–physical emergency applications
In the past few years, several studies proposed to reduce the impact of bushfires by mapping their occurrences and spread. Most of these prediction/mapping tools and models were designed to run either on a single local machine or a High performance cluster, neither of which can scale with users’ needs. The process of installing these tools and models their configuration can itself be a tedious and time consuming process. Thus making them, not suitable for time constraint cyber–physical emergency systems. In this research, to improve the efficiency of the fire prediction process and make this service available to several users in a scalable and cost-effective manner, we propose a scalable Cloud based bushfire prediction framework, which allows forecasting of the probability of fire occurrences in different regions of interest. The framework automates the process of selecting particular bushfire models for specific regions and scheduling users’ requests within their specified deadlines. The evaluation results show that our Cloud based bushfire prediction system can scale resources and meet user requirements.

Benchmarking morphological analyzers for the Hungarian language
In this paper we evaluate, compare and benchmark the four most widely used and most advanced morphological analyzers for the Hungarian language, namely Hunmorph-Ocamorph, Hunmorph-Foma, Humor and Hunspell. The main goal of the current research is to define objective metrics while comparing these tools. The novelty of this paper is the fact that the analyzers are compared based on their annotation token systems instead of their lemmatization features. The proposed metrics for the comparison are the following: how different their annotation token systems are, how many words are recognized by the different analyzers and how many words are there whose morphological structure is equivalent using a well-defined mapping among the annotation token systems. For each of these metrics, we define the concept of similarity and distance. For the evaluation we use a unique Hungarian corpus that we generated in an automated way from Hungarian free texts, as well as a novel automated token mapping generation algorithm. According to our experimental results, Hunmorph-Ocamorph gives the best results. Hunmorph-Foma is very close to it, but sometimes returns an invalid lemma. Humor is the third best analyzer, while Hunspell is far worse than the other three tools.

Optimization of the Morpher Morphology Engine Using Knowledge Base Reduction Techniques
Morpher is a novel morphological rule induction engine designed and developed for agglutinative languages. The Morpher engine models inflection using general string-based transformation rules and it can learn multiple arbitrary affix types, too. In order to scale the engine to training sets containing millions of examples, we need an efficient management of the generated rule base. In this paper we investigate and present several optimization techniques using rule elimination based on context length, support and cardinality parameters. The performed evaluation tests show that using the proposed optimization techniques, we can reduce the average inflection time to 0.52 %, the average lemmatization time to 2.59 % and the number of rules to 2.25 % of the original values, while retaining a high correctness ratio of 98 %. The optimized model can execute inflection and lemmatization in acceptable time after training millions of items, unlike other existing methods like Morfessor, MORSEL or MorphoChain.

New models and algorithms to solve integrated problems of production planning and control taking into account worker skills in flexible manufacturing systems
The paradigm of the cyber-physical manufacturing system is playing an increasingly important role in the development of production systems and management of manufacturing processes. This paper presents an optimization model for solving an integrated problem of production planning and manufacturing control. The goal is to create detailed production plans for a complex manufacturing system and to control the skilled manual workers. The detailed optimization model of the problem and the developed approach and algorithms are described in detail. To consider the impact of human workers performing the manufacturing primary operations, we elaborated an extended simulation-based procedure and new multi-criteria control algorithms that can manage varying availability constraints of parallel workstations, worker-dependent processing times, different product types and process plans. The effectiveness of the proposed algorithms is demonstrated by numerical results based on a case study

Clustering algorithms with prediction the optimal number of clusters
Clustering is a widely used technique for grouping of objects. The objects, which are similar to each other, should be in the same cluster. One disadvantage of general clustering algorithms is that the user must specify the number of clusters in advance, as input parameter. This is a major drawback since it is possible that the user cannot specify the number of clusters correctly, and the algorithm thus creates a clustering that puts very different elements into the same cluster. The aim of this paper is to present our representation and evaluation technique to determine the optimal cluster count automatically. With this technique, the algorithms themselves determine the number of clusters. In this paper, first, the classical clustering algorithms are introduced; then, the construction and improvement algorithms and then our representation and evaluation method are presented. Then the performance of the algorithms with the test results are compared.