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Nowadays, computer games occupy a large share of the entertainment industry market: according to data for 2019, the market volume was $ 152.1 billion (for comparison, the market volume in 2018 was $ 137.9 billion). The growing popularity of the industry led to the inclusion of computer games in 2005 on the UNESCO World Heritage List, and in 2003 – to the establishment of a video game award by the British Academy of Film and Television Arts. Computer games are increasingly penetrating our lives, and thus follows the language of “gamers”, exerting an increas-ing influence on the normative language. Computer games are a vast topic for research and study, especially in terms of language and teaching. Teaching translation in the modern world should in-clude a comprehensive preparation of future specialists for work. In the current education system of the Russian Federation, there is no professional training of translators for the localization of computer games, therefore, translators who wish to work in this area need to undergo additional training outside the higher education system. This brings to the fore the issue of needing to identify the features of the translation process and localization of computer games in order to improve the quality of teaching translation. We consider the features of translation and localization from English into Russian based on the texts of the computer game “The Witcher 3: Wild Hunt”.

Introduction: High-quality training of tractor drivers is possible only with the correct development of professional skills. The skills necessary for a tractor driver to reliably operate tractor equipment can be trained with simulators at the initial stage of training. The use of simulators makes it possible to automate the actions the tractor driver takes when driving the vehicle, and avoid the trainee’s exposure to risks. Material and Methods: The articlepresents the classification of simulators used for training tractor drivers, by their models and information the tractor driver acquires in the process of driving tractor equipment. The existing information models of simulators in a generalized form can be divided into two groups: the first, which involves only reproduction of visual information, without imitation of vestibular information, and the second, which combines both visual and vestibular information. The analysis of information models of existing simulators is carried out, their advantages and disadvantages are reflected. Results: For the presented classification of the information model of simulators, two systematizing features are proposed, namely, the viewing angle of the presented visual information and the presence of imitation of vestibular information. Results and Discussions: The research carried out can be useful for the further development of science in this area, as well as for the choice of simulators for the educationalprocess.

The article discusses the relevance of training simulators for controlling tractor equipment and MTA. Using agricultural machinery as an example, the relevance of AR and VR development prospects is shown. The drawbacks of modern training simulators and the possibility of their elimination using VR and AR technologies have been identified. The main directions in augmented reality are considered.

Nowadays, at the time of rapid growth of various branches of science and technology, the evolution of national languages is also observed. Each separate language expands its vocabulary through the inclusion of new terms, words and expressions that appeared in the language with the beginning of the technical revolution in the military and civilian spheres. As a result of the process impact of production modernization and updating the technical base around the world, completely new concepts and terms have appeared that have a narrow specialization, complexity, and are professionally oriented. As a result of this, natural difficulties and peculiarities arise in the translation of technical vocabulary lead to the question of the need for streamlining and in-depth analysis, and comparison of polysyllabic lexical units of the terminological fund of the Chinese language for further reproduction of an adequate translation into English or Russian. We determine lexical features of the terminology of the military Chinese sublanguage, study the composition of its terminological fund and ways of replenishing vocabulary, as well as study the main features of the military Chinese sublanguage translation and analyze the lexical, stylistic and syntactic features of the translation of the military Chinese sublanguage.

Several features of n-dimensional binary space topology are considered in the paper. A number of special rules for encoding the signatures of state-transition diagram of FSM according to the binary space features have been formulated. The asynchronous FSM encoding algorithm, according the described rules is implemented as a software module. The algorithm that generating additional transitional states sequences is in accordance with “differs exactly one bit state” switching condition is described in details. Evaluation subsequent with the comparative analysis of obtained encoding results is also presented.

Artificial intelligence (AI) can extract subtle visual information from digitized histopathology slides and yield scientific insight on genotype-phenotype interactions as well as clinically actionable recommendations. Classical weakly supervised pipelines use an end-to-end approach with residual neural networks (ResNets), modern convolutional neural networks such as EfficientNet, or non-convolutional architectures such as vision transformers (ViT). In addition, multiple-instance learning (MIL) and clustering-constrained attention MIL (CLAM) are being used for pathology image analysis. However, it is unclear how these different approaches perform relative to each other. Here, we implement and systematically compare all five methods in six clinically relevant end-to-end prediction tasks using data from N=4848 patients with rigorous external validation. We show that histological tumor subtyping of renal cell carcinoma is an easy task which approaches successfully solved with an area under the receiver operating curve (AUROC) of above 0.9 without any significant differences between approaches. In contrast, we report significant performance differences for mutation prediction in colorectal, gastric and bladder cancer. Weakly supervised ResNet-and ViT-based workflows significantly outperformed other methods, in particular MIL and CLAM for mutation prediction. As a reason for this higher performance we identify the ability of ResNet and ViT to assign high prediction scores to highly informative image regions with plausible histopathological image features. We make all source codes publicly available athttps://github.com/KatherLab/HIA, allowing easy application of all methods on any end-to-end problem in computational pathology.

Video graphics array (VGA) is a still commonly used display interface standard in embedded systems. Due to traditional microprocessor cannot provide timings for high-resolution VGA interface it is often implemented as a part of FPGA-based image processing flow. The main problem is that FPGA-based project need to be covered with an adequate set of tests which allow verifying the functionality the whole image processing flow even without availability of real FPGA IC and/or monitor. This, in turn, remains difficult without the visualization of resulted image. A hardware verification technique of digital VGA generator synthesized on the FPGA basis that uses of an unsynthesized subset of the System Verilog language has been proposed in this paper. A full-featured object-oriented package for BMP-files processing is developed and its integration inside the FPGA-project is shown. Moreover, the synthesizable part of design has been hardware tested on the DE10-Lite board.

Deep learning can detect microsatellite instability (MSI) from routine histology images in colorectal cancer (CRC). However, ethical and legal barriers impede sharing of images and genetic data, hampering development of new algorithms for detection of MSI and other biomarkers. We hypothesized that histology images synthesized by conditional generative adversarial networks (CGANs) retain information about genetic alterations. To test this, we developed a 'histology CGAN' which was trained on 256 patients (training cohort 1) and 1457 patients (training cohort 2). The CGAN synthesized 10 000 synthetic MSI and non-MSI images which contained a range of tissue types and were deemed realistic by trained observers in a blinded study. Subsequently, we trained a deep learning detector of MSI on real or synthetic images and evaluated the performance of MSI detection in a held-out set of 142 patients. When trained on real images from training cohort 1, this system achieved an area under the receiver operating curve (AUROC) of 0.742 [0.681, 0.854]. Training on the larger cohort 2 only marginally improved the AUROC to 0.757 [0.707, 0.869]. Training on purely synthetic data resulted in an AUROC of 0.743 [0.658, 0.801]. Training on both real and synthetic data further increased AUROC to 0.777 [0.715, 0.821]. We conclude that synthetic histology images retain information reflecting underlying genetic alterations in colorectal cancer. Using synthetic instead of real images to train deep learning systems yields non-inferior classifiers. This approach can be used to create large shareable data sets or to augment small data sets with rare molecular features. (c) 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd. on behalf of The Pathological Society of Great Britain and Ireland.

The paper presents a description and testing of an improved states encoding algorithm for asynchronous finite state machine (FSM). It is based on the concept of anti-race encoding, according to which the transition between two adjacent FSM states should be accompanied by switching of only a single trigger. During the research process, in order to find the best solution, a number of original algorithms were proposed, among which was a heuristic encoding algorithm that matches the FSM state transition diagram with n-dimensional binary space. In addition, an exhaustive (or "brutal-force") search algorithms for all possible states was developed. As a separate accompanying task, an algorithm for generation of transitional states sequences between the given set of generated state signatures was created. All the developed algorithms were implemented in the form of software modules (SM) and were used to search for optimal FSM state encoding configurations. A comparative analysis of the encoding results obtained by these SM is also presented.

The application of deep learning for automated segmentation (delineation of boundaries) of histologic primitives (structures) from whole slide images can facilitate the establishment of novel protocols for kidney biopsy assessment. Here, we developed and validated deep learning networks for the segmentation of histologic structures on kidney biopsies and nephrectomies. For development, we examined 125 biopsies for Minimal Change Disease collected across 29 NEPTUNE enrolling centers along with 459 whole slide images stained with Hematoxylin & Eosin (125), Periodic Acid Schiff (125), Silver (102), and Trichrome (107) divided into training, validation and testing sets (ratio 6:1:3). Histologic structures were manually segmented (30048 total annotations) by five nephropathologists. Twenty deep learning models were trained with optimal digital magnification across the structures and stains. Periodic Acid Schiff-stained whole slide images yielded the best concordance between pathologists and deep learning segmentation across all structures (F-scores: 0.93 for glomerular tufts, 0.94 for glomerular tuft plus Bowman’s capsule, 0.91 for proximal tubules, 0.93 for distal tubular segments, 0.81 for peritubular capillaries, and 0.85 for arteries and afferent arterioles). Optimal digital magnifications were 5X for glomerular tuft/tuft plus Bowman’s capsule, 10X for proximal/distal tubule, arteries and afferent arterioles, and 40X for peritubular capillaries. Silver stained whole slide images yielded the worst deep learning performance. Thus, this largest study to date adapted deep learning for the segmentation of kidney histologic structures across multiple stains and pathology laboratories. All data used for training and testing and a detailed online tutorial will be publicly available.

This paper proposes a new photovoltaic panel maximum-power-point optimizer based on a buck converter. It can be connected to the DC-link distributed energy harvesting system that should perform the true maximum-power-point tracking algorithm based on maintaining a constant DC link voltage. The algorithm is based on the sensorless hysteresis control and ensures high efficiency. Three different realizations of proposed hysteresis optimizers have been analyzed in the paper, including operation principle and adjustment of hysteresis intervals. An experimental study has been performed for a portable low-power photovoltaic system in case of different loads and irradiance levels. The efficiency of maximum power point tracking has been calculated analytically for different hysteresis intervals and validated by experiment, which proved a 97-98 % efficiency of tracking for different PV panel temperatures. The proposed solution is recommended to be used in small- and medium-sized power systems where the price of the conventional maximum power point tracking converter is very high and is comparable to the cost of the individual panel

Autonomous systems based on the "Internet of Things" paradigm have become widespread. The Internet of Things devices are used for collecting and analyzing data, control electrical systems. The Internet of Things the most common fields of use are smart houses, smart cities, smart traffic, environment monitoring, healthcare etc. With the automation to the degree of autonomy of such processes as cargo delivery and human transportation, the Internet of Things paradigm begins to extend not only to stationary devices, but also to mobile, primarily small unmanned aerial vehicles. UAV can be used not only for civil use but for police or military operations too. This poses a potential threat to skilled criminals such as terrorists, smugglers and drug couriers. There is an urgent problem of secure transmission of data and control signals at distances up to tens of kilometers without loss of communication and the possibility of interception of control. Wireless communication technologies are widely used in all areas of the economy: control systems, environmental safety monitoring, industrial automation, logistics, etc. Wireless networks have many characteristics in common with wireline networks, and therefore, many security issues of wireline networks apply to the wireless environment. Wireless data is easy to intercept by potential eavesdroppers. Issue of security and privacy become more notable with wireless networks. The paper substantiates the transition to cryptographically protected wireless communication channels in autonomous control systems for both fixed and mobile performance. Possible attack vectors in such systems are considered. An analytical review and classification of modern cryptographic protection (encryption) algorithms used at the representative, session and channel levels of communication interfaces together and functional diagrams for some of them are performed. Selected criteria for comparing cryptographic algorithms, which allows you to choose the best depending on the functions performed and the conditions of use of a particular autonomous system.

A novel engineering software-based solution for contact debounce in embedded systems is proposed by dint of a specialized finite-state machine. A cross-platform C-language implementation of algorithm in conjunction with maintain data structures description is shown. Independence of hardware is guaranteed by using of pointer to port read function. An integration paradigm into existing embedded project is given. An auto-repeat feature allows emulating of unique repetition behavior in case of long pushing state. Proposed algorithm guaranties the robust input data and excludes the influence of unsuccessful mechanical implementation of any type of input devices with contacts.

A novel engineering solution for all-digital frequency and phase comparators is proposed, implemented and tested. It is not only allows to measure the inequality sign between two periodic processes but also the numeric degree of difference between them. They in turn can be used for regulator input value, or directly for modulator input. Both comparators were implemented scalable using generic features of hardware description language, and were equipped with synchronous pulse shorteners and edge detectors, which increases the noise immunity and solves problem of detection of almost equal frequencies and phases, that in turn, resolved of two actual engineering tasks that is grew based on analysis of existing similar blocks. Proposed blocks can be used for motor control purposes in pulse-frequency domain (or spike-based domain) or as a part of control system for quasi-resonant converters for directly processing of feedback signals from Hall-sensors and reference moving generator that results in robustness of control system and reducing of its complexity.

Patient flow modeling in healthcare plays a large role in understanding the operation of the system and its characteristics. Besides, modeling techniques can significantly improve the effectiveness of the medical facilities. The existing level of automation in these facilities enables the accumulation of large amounts of various data. Therefore, the collected data might be considered as the resource of new valuable knowledge. A novel approach to automatically identify the groups of similar clinical pathways based on event hospital data is presented in the paper. More specifically, the approach summarizes the most frequent pathways by implementing hard and soft clustering algorithms in order to describe the behavior patterns. The obtained clusters of clinical pathways serve as a starting point for the development of a personalized approach in modelling the heterogeneous patient flow in urban medical facilities. The results indicate the suitability of multidimensional time series clustering and Additive Regularization of Topic Models (ARTM) for the clinical event data.

a combined algorithm for automated finding the optimal configuration of cluster distribution is presented. Clusters are considered as idealized subnets of a multi-level computer network, which can have different methods of data transferring. The use of genetic algorithm is considered as a method for finding the best solution in a multidimensional problem with a large number of local minimums. In assessing the optimality, the distance from the center of the cluster to the place of its location brought into line to the expenses of its operation. The combined algorithm was software-implemented and was analyzed by results of solving a particular problem of transferring data.

This paper presents an algorithm for automated method of finding the optimal position of cluster centers in logistics tasks. This algorithm has been considered as part of a complex algorithm of finding the optimal solution of logistics tasks with georeferenced. In the work the cluster centers has been considered as a temporary storages, which is connected with other terminals by shortest path. Several algorithms of cluster centers finding was evaluated in terms obtaining the optimal plan for transportation of cargo. The costs amount for transportation and servicing of a cargo is considered as an estimation of optimality. The dependence of total transport expenses on the number of a cluster centers was taken into account also. The algorithm was realized in software prototype and was analyzed solving a particular transportation problem.

The problem of scalable and optimal behavior-style description of the priority encoder into FPGA was examined. The structural synthesis technique of a scalable priority resolving circuits in sequential and parallel forms, which allow improving the FPGA resource utilization in comparison with typical description, was proposed. A novel approach of scalable synthesis of the triangular threshold-logic gate array was also proposed. The results of research and development, which have presented by structure generation circuit and algorithm, were confirmed by correct register-transfer level representation and during timing simulation. The numeric comparative assessments of FPGA recourses utilizing depending both the encoder inputs number, priority resolving circuit topology and threshold-logic gate unit was implemented.

Business Process Management is a powerful approach forthe automation of collaborative business processes. Recently conceptshave been introduced to allow batch processing in business processesaddressing the needs of different industries. The existing batch activityconcepts are limited in their flexibility. In this paper we contribute differentstrategies for modeling and executing processes including batch workto improve the flexibility 1) of business processes in general and 2) of thebatch activity concept. The strategies support different flexibility aspects(i.e., variability, looseness, adaptation, and evolution) of batch activities.The strategies provide a systematic approach to categorize existing andfuture batch-enabled BPM systems. Furthermore, the paper provides asystem architecture independent from existing BPM systems, which allowsfor the support of all the strategies. The architecture can be usedwith different process languages and existing execution environments ina non-intrusive manner.

International audience; This double volumes LNCS 11229-11230 constitutes the refereed proceedings of the Confederated International Conferences: Cooperative Information Systems, CoopIS 2018, Ontologies, Databases, and Applications of Semantics, ODBASE 2018, and Cloud and Trusted Computing, C&TC, held as part of OTM 2018 in October 2018 in Valletta, Malta. The 64 full papers presented together with 22 short papers were carefully reviewed and selected from 173 submissions. The OTM program every year covers data and Web semantics, distributed objects, Web services, databases, informationsystems, enterprise workflow and collaboration, ubiquity, interoperability, mobility, grid and high-performance computing.

An efficient particle clump generation algorithm for use in discrete element methods (DEMs) was developed to simulate actual particles in a granular specimen. The algorithm requires many fewer circles than existing methods, particularly for angular particles. The procedure is a logical extension of a computational method for determining the classic Wadell particle roundness in which circles are fitted to the corners of particles. Hence, the new method perfectly preserves the location, size and shape of particle corners and is appropriately termed the corner preserving algorithm. The remaining perimeter of the particle, which includes concave and flat sections, is fitted with non-corner circles. Unlike earlier methods, the corner preserving algorithm requires only a single control parameter to generate the circles. This parameter is the ratio of the clump area to the original particle area, AR. An algorithm seeks a corresponding optimum clump roughness that achieves the user-prescribed AR. The method can easily be incorporated in existing soil particle characterization systems in which binary images or even images of particle assemblies are produced. Examples illustrate the simplicity and advantages of the corner preserving algorithm for DEM clump generation.

To introduce a new method of measuring sound localization ability based on eye-tracking and to test this method by analysing the influence of mild induced conductive hearing loss on sound localization.Sound signals were presented from different angles, and the participant's responses were measured using an eye-tracking device. For validation, a comparison of responses to visual stimuli was performed. To test the clinical application of this method, a mild conductive hearing loss was simulated, and the impact of this change on sound localization was measured.Fifteen participants.The system provided repeatable measurements, and there was a good correlation of sound and visual signals. A large number of trials could be completed fairly rapidly. Following the induced conductive hearing loss, a decline of 5.5° in the accuracy of sound localization in the horizontal plane was found towards the side of the non-impaired ear for frontal presentations.Quantifying sound localization by eye-tracking was found to be feasible, fast and accurate. A mild conductive hearing loss caused a slight degradation of sound localization accuracy within the 30° frontal sector, which is in good agreement with results found using methods requiring more extensive instrumentation.

Modern problems of energy efficiency analysis and electromagnetic compatibility currently presented in Ukraine are analyzed. The use of combined algorithm based both on PQ-theory and the Fast Fourier Transform (FFT) for analysis of electric energy parameters and power components is given. The architecture of embedded system for the analysis of energy efficiency in “smart grids” based on previously simulated algorithm is proposed.

The discrete element method (DEM) simulates large discontinuous deformations as a natural outcome of discrete particle interactions. The method is well suited for problems such as plowing, penetration and hopper flows. However, verification of DEM simulations has been largely limited to comparisons with laboratory stress-strain diagrams of two dimensional simulations of at most a few thousand ideally-shaped particles. This paper presents an automated video tracking and digital image analysis system that has been developed to obtain soil particle displacement fields and velocities from small-scale laboratory experiments. A three-dimensional simulation of a laboratory plowing experiment is performed in which a one-to-one correspondence is achieved between the number of particles and their size distribution in simulation and physical experiment. The data obtained from the video tracking system are used to evaluate the qualitative and quantitative ability of the simulation to model the experiment kinematics.

Laboratory experiments are typically performed on particulate media to study stress-deformation behavior and to verify or calibrate computer models from controlled or measured boundary stresses and displacements. However, such data do not permit the formation of shear bands, displacement fields within flowing granular media, and other small-scale localized deformation phenomena to be identified. Described are two semiautomated computer vision techniques for accurately determining the two-dimensional displacement field in granular soils from video images obtained through a transparent planar viewing window. The techniques described are applicable for studying the behavior of particulate media under plane strain and certain axisymmetric test conditions. Digital image processing and analysis routines are used in two different computer programs, Tracker and Tracer, Tracker uses a graphical user interface that allows individual particles to be selected and tracked through a sequence of digital video images. A contrast edge detection algorithm delineates the two-dimensional projected boundaries of particles. The location of the centroid of each particle selected for tracking is determined from the boundary to quantify the trajectory of each particle. Tracer maps the trace or trajectory of specially dyed fluorescent particles in a sequence of video frames. A thresholding technique segments individual particle trajectories. Together, Tracker and Tracer provide a set of tools for identifying small-scale displacement fields in particulate assemblies deforming under either quasi-static or rapid loading (such as gravity flow).

Fuzzy DL-Lite has been studied as a means to answer queries w.r.t. vague or imprecise knowledge and facts. Existing approaches consider only the Zadeh semantics or are limited to precise terminological knowledge, where only facts are graded. We study the problem of answering conjunctive queries over fuzzy DL-Lite\(_R\) ontologies which allow also graded axioms, and whose semantics is based on mathematical fuzzy logic. We show that for the Godel t-norm, the degree of an answer is computable through repeated calls to a classical query answering engine. For non-idempotent t-norms, we show the difficulty in dealing with degrees, and provide some partial solutions.

This paper studies Knowledge Bases (KBs) in PSOA RuleML and IDP, aligning, interoperating, and co-executing them for a use case of Air Traffic Control (ATC) regulations. We focus on the common core of facts and rules in both languages, explaining basic language features. The used knowledge sources are regulations specified in (legal) English, and an aircraft data schema. In the modeling process, inconsistencies in both sources were discovered. We present the discovery process utilizing both specification languages, and highlight their unique features. We introduce three extensions to this ATC KB core: (1) While the current PSOA RuleML does not distinguish the ontology separately from the instance level, IDP does. Hence, we specify a vocabulary-enriched version of ATC KB in IDP for knowledge validation. (2) While the current IDP uses relational modeling, PSOA additionally supports graph modeling. Hence, we specify a relationally interoperable graph version of ATC KB in PSOA. (3) The KB is extended to include optimization criteria to allow the determination of an optimal sequence of more than two aircraft.

The DMN standard allows users to build declarative models of their decision knowledge. The standard aims at being simple enough to allow business users to construct these models themselves, without help from IT staff. To this end, it combines simple decision tables with a clear visual notation. However, for real-life applications, DMN sometimes proves too restrictive. In this paper, we develop an extension to DMN’s decision table notation, which allows more knowledge to be expressed, while retaining the simplicity of DMN. We demonstrate our new notation on a real-life case study on product design.

Digital marketing is a domain where data analytics are a key factor to gaining competitive advantage and return of investment for companies running and monetizing digital marketing campaigns on, e.g., search engines and social media. In this paper, we propose an end-to-end approach to enrich marketing campaigns performance data with third-party event data (e.g., weather events data) and to analyze the enriched data in order to predict the effect of such events on campaigns’ performance, with the final goal of enabling advanced optimization of the impact of digital marketing campaigns. The use of semantic technologies is central to the proposed approach: event data are made available in a format more amenable to enrichment and analytics, and the actual data enrichment technique is based on semantic data reconciliation. The enriched data are represented as Linked Data and managed in a NoSQL database to enable processing of large amounts of data. We report on the development of a pilot to build a weather-aware digital marketing campaign scheduler for JOT Internet Media—a world leading company in the digital marketing domain that has amassed a huge amount of data on campaigns performance over the years—which predicts the best date and region to launch a marketing campaign within a seven-day timespan. Additionally, we discuss benefits and limitations of applying semantic technologies to deliver better optimization strategies and competitive advantage.

Neuro-symbolic integration is a current field of investigation in which symbolic approaches are combined with deep learning ones. In this work we start from simple non-relational knowledge that can be extracted from text by considering the co-occurrence of entities inside textual corpora; we show that we can easily integrate this knowledge with Logic Tensor Networks (LTNs), a neuro-symbolic model. Using LTNs it is possible to integrate axioms and facts with commonsense knowledge represented in a sub-symbolic form in one single model performing well in reasoning tasks. In spite of some current limitations, we show that results are promising.

We present STYPES, a system that rewrites ontology-mediated queries with linear tuple-generating dependencies and conjunctive queries to equivalent nonrecursive datalog (NDL) queries. The main feature of STYPES is that it produces polynomial-size rewritings whenever the treewidth of the input conjunctive queries and the size of the chases for the ontology atoms as well as their arity are bounded; moreover, the rewritings can be constructed and executed in LOGCFL, indicating high parallelisability in theory. We show experimentally that Apache Flink on a cluster of machines with 20 virtual CPUs is indeed able to parallelise execution of a series of NDL-rewritings constructed by STYPES, with the time decreasing proportionally to the number of CPUs available.

© Springer Nature Switzerland AG 2018. Modelling decisions in organisations is a challenging task. Deciding which modelling language to use for the problem at hand is a fundamental question. We investigate the Decision Model and Notation (DMN) standard and the IDP knowledge base system (KBS) in their effectiveness to model and solve specific real-life case problems. This paper presents two cases that are solved with DMN and IDP: (1) Income taxation for foreign artists temporarily working in Belgium; and (2) Registration duties when purchasing real-estate in Belgium. DMN is used as a front-end method, assisting the business analyst in the analysis and modelling of the business domain and communication with the domain expert. It is complemented with the representation of the logic in IDP as back-end system, which allows more forms of inference. ispartof: pages:248-263 ispartof: Proceedings of RuleML+RR 2018 vol:11092 pages:248-263 ispartof: ruleML + RR location:Luxembourg date:17 Sep - 21 Sep 2018 status: published

Research on inconsistency-tolerant query answering usually assumes that the terminological knowledge is correct, and only the facts (ABox) need to be repaired. In this paper we study the problem of answering instance queries over inconsistent ontologies, by repairing the whole knowledge base (KB). Contrary to ABox repairs, when KB repairs are considered, instance checking in \(\textit{DL}\text {-}{} \textit{Lite}_\textit{Horn}\) w.r.t. the brave semantics remains tractable, and the intersection semantics allow for an any-time algorithm. We also show that inconsistency-tolerant instance checking w.r.t. ABox repairs is intractable even if only polynomially many ABox repairs exist.

Traditionally, nurse call systems in hospitals are rather simple: patients have a button next to their bed to call a nurse. Which specific nurse is called cannot be controlled, as there is no extra information available. This is different for solutions based on semantic knowledge: if the state of care givers (busy or free), their current position, and for example their skills are known, a system can always choose the best suitable nurse for a call. In this paper we describe such a semantic nurse call system implemented using the EYE reasoner and Notation3 rules. The system is able to perform OWL-RL reasoning. Additionally, we use rules to implement complex decision trees. We compare our solution to an implementation using OWL-DL, the Pellet reasoner, and SPARQL queries. We show that our purely rule-based approach gives promising results. Further improvements will lead to a mature product which will significantly change the organization of modern hospitals.

In the business environment, knowledge of company data is essential for a variety of tasks. The European funded project euBusinessGraph enables the establishment of a company data platform where data providers and consumers can publish and access company data. The core of the platform is the semantic data model that is the conceptual representation of company data in a common way so that it is easier to share and interlink company data. In this paper we show how the unified model and Grafterizer, a tool for manipulating and transforming raw data into Linked Data, support the linking challenge proposed in FEIII 2019. Results show that geographical enrichment of RDF data supports the interlinking process between company entities in different datasets.

Detecting small sets of relevant patterns from a given dataset is a central challenge in data mining. The relevance of a pattern is based on user-provided criteria; typically, all patterns that satisfy certain criteria are considered relevant. Rule-based languages like Answer Set Programming (ASP) seem well-suited for specifying such criteria in a form of constraints. Although progress has been made, on the one hand, on solving individual mining problems and, on the other hand, developing generic mining systems, the existing methods either focus on scalability or on generality. In this paper we make steps towards combining local (frequency, size, cost) and global (various condensed representations like maximal, closed, skyline) constraints in a generic and efficient way. We present a hybrid approach for itemset, sequence and graph mining which exploits dedicated highly optimized mining systems to detect frequent patterns and then filters the results using declarative ASP. To further demonstrate the generic nature of our hybrid framework we apply it to a problem of approximately tiling a database. Experiments on real-world datasets show the effectiveness of the proposed method and computational gains for itemset, sequence and graph mining, as well as approximate tiling. Under consideration in Theory and Practice of Logic Programming (TPLP)., 29 pages, 7 figures, 5 tables