Next Generation Sequencing is a technology for genome sequencing used in genetics for diseased diagnosis. NGS provides the list of all mutations in a genome, so identifying the one which causes a disease is not trivial. A number of applications for variant prioritization was developed, but the data they provide is rather a suggestion than a diagnosis, moreover they suffer from issues as identifying nonpathogenic variant as a causal one or inability to identify the causal gene. These issues inspired us to create a strategy for variant prioritization which includes the use of Exomiser and OmimExplorer result sets improved by semantic analysis of abstracts and articles freely available from PubMed and PubMed Central databases. For the wider scope of scientific articles Google Scholar repository will be used. Described approach enables to present latest and most accurate information about potential pathogenic variants.

We present our research at Onet, the largest Polish news portal, aimed at constructing meaningful user profiles that are most descriptive of their interests in the context of the media content they browse. We used two distinct state-of-the-art numerical text-representation techniques: LDA topic modeling and Word2Vec word embeddings. We trained our models on the corpora of articles in Polish and compare them with a baseline model built on a general language corpora. We compared the performance of algorithms on two distinct tasks - similar articles retrieval and users gender classification. Our results show that the choice of text representation depends on the task - Word2Vec is more suitable for text comparison, especially for short texts such as titles. In the user profiling task, the best performance was obtained with a combination of features: topics from the article text and word embeddings from the title.

The aim of the ISMOP project is to study processes in earthen flood embankments: water filtration, pore pressure changes, and temperature changes due to varying water levels in the riverbed. Developing a system for continuous monitoring of flood embankment stability is the main goal of this project. A full-size earthen flood embankment with built-in sensors was built in Czernichow and used to conduct experiments involving the simulation of different flood waves, with parameters mostly measured at time intervals of 15 minutes. Numerical modelling—in addition to providing information about phenomena occurring in the embankment due to external factors and changes in water level—could be used to assess the state of the embankment. Modelling was performed using Itasca Flac 2D 7.0 with an assumed grid cell size of 10x10 cm. The water level in the embankment simulated the water flow in the Wisła River and the temperature of the air and water. Data about the state of the flood embankment was exported every hour.Using numerical models and real experiment data, a model-driven module was used to perform comparisons. Analyses of each half-section of the flood embankment were carried out separately using similarity measures and an aggregate window.For the tests, the North-West (NW) half cross-section of the embankment was chosen, which contains pore pressure and temperature sensors UT6 to UT10. The water level in the embankment was raised to a height of 3m; the best numerical model was considered the one that best matched the actual data recorded by the sensors during the experiment. The experiment period was from 9pm on 29/08/2016 to 9am on 03/09/2016.Seventeen numerical models of the water level rising to 2, 3, and 4 meters were compared against real experimental data from the NW half cross-section. The first step was to verify the similarity between the incoming data from the sensors. If the correlation value exceeded 0.8, the data from the sensors was averaged. The experimental data was then compared against the numerical models using least absolute deviations L1-Norm. The L1-Norm varied from 26 to 32, depending on window length and the numerical model used.

Organizations across the globe gather more and more data, encouraged by easy-to-use and cheap cloud storage services. Large datasets require new approaches to analysis and processing, which include methods based on machine learning. In particular, symbolic regression can provide many useful insights. Unfortunately, due to high resource requirements, use of this method for large-scale dataset analysis might be unfeasible. In this paper, we analyze a bottleneck in the open-source implementation of this method we call hubert. We identify that the evaluation of individuals is the most costly operation. As a solution to this problem, we propose a new evaluation service based on the Apache Spark framework, which attempts to speed up computations by executing them in a distributed manner on a cluster of machines. We analyze the performance of the service by comparing the evaluation execution time of a number of samples with the use of both implementations. Finally, we draw conclusions and outline plans for further research.

为了研究简化轮数的SNAKE（2）算法抵抗碰撞攻击的能力,根据算法的一个等价结构,给出了SNAKE（2）算法的一个6轮区分器。通过在此区分器前后加适当的轮数,对7/8/9轮的SNAKE（2）算法实施了攻击。其攻击的数据复杂度依次为O（2^6）、O（2^6.52）、O（2^15）,时间复杂度依次为O（2^9.05）、O（2^18.32）、O（2^26.42）,攻击结果优于对SNAKE（2）算法的Square攻击。

This paper describes the application of hypergraph grammars to drive a linear computational cost solver for grids with point singularities. Such graph gram- mar productions are the first mathematical formalisms used to describe solver algorithms, and each indicates the smallest atomic task that can be executed in parallel, which is very useful in the case of parallel execution. In particular, the partial order of execution of graph grammar productions can be found, and the sets of independent graph grammar productions can be localized. They can be scheduled set by set into a shared memory parallel machine. The graph- grammar-based solver has been implemented with NVIDIA CUDA for GPU. Graph grammar productions are accompanied by numerical results for a 2D case. We show that our graph-grammar-based solver with a GPU accelerator is, by order of magnitude, faster than the state-of-the-art MUMPS solver.

This paper presents the rationale behind performing an analysis of Internet resilience in the sense of maintaining a connection of autonomous systems in the presence of failures or attacks — on a level of a single country. Next, the graph of a network is constructed that represents interconnections between autonomous systems. The connectivity of the graph is examined for cases of link or node failure. Resilience metrics are proposed, focusing on a single autonomous system or on overall network reliability. The process of geographic location of networking infrastructure is presented, leading to an analysis of network resilience in the case of a joint failure of neighboring autonomous systems.

This work proposes context aware handover algorithms for mobile positioning systems. The algorithms perform handover among positioning systems based on important contextual factors related to position determination with efficient use of battery. The proposed solution which consists of the algorithms is implemented in the form of an Android application named Locate@nav6. The performance of the proposed solution was tested in selected experimental areas. The handover performance was compared with other existing location applications. The proposed solution performed correct handover among positioning systems in 95% of cases studied while two other applications performed correct handover in only 50% of cases studied. Battery usage of the proposed solution is less than one third of the battery usage of two other applications. The analysis of the positioning error of the applications demonstrated that, the proposed solution is able to reduce positioning error indirectly by handing over the task of positioning to an appropriate positioning system. This kept the average error of positioning below 42.1 meters for Locate@nav6 while the average error for two other applications namely Google Latitude and Malaysia maps was between 92.7 and 171.13 meters.

The need for cost optimization in a broad sense constitutes the basis of operation of every enterprise. In the case of IT structure, which is present in almost every field of activity these days, one of the most commonly applied technologies leading to good cost-to-profit adjustment is virtualization. It consists in locating several operational systems with IT systems on a single server. In order for such optimization to be carried out correctly it has to be strictly controlled by means of allocating access to resources, which is known as performance isolation. Modern virtualizers allow to set up this allocation in quantitative terms (the number of processors, size of RAM, or disc space). It appears, however, that in qualitative terms (processor's time, RAM or hard disc bandwidth) the actual allocation of resources does not always correspond with this configuration. This paper provides an experimental presentation of the achievable level of performance isolation of the Hyper-V virtualizer.

This paper regards the Jelly View technology, which provides a new, practical methodology for knowledge decomposition, storage, and retrieval within Relational Database Management Systems (RDBMS). Intensional Knowledge clauses (rules) are decomposed and stored in the RDBMS founding reusable components. The results of the rule-based processing are visible as regular views, accessible through SQL. From the end-user point of view the processing capability becomes unlimited (arbitrarily complex queries can be constructed using Intensional Knowledge), while the most external queries are expressed with standard SQL. The RDBMS functionality becomes extended toward that of the Deductive Databases