Reviewerly/exHarmony huggingface dataset:
The conceptual foundations of educational cooperation of universities and companies of Industry 4.0

Purpose The purpose of this paper is to specify the conceptual foundations of educational cooperation of universities and companies of Industry 4.0 for maximizing its efficiency. Design/methodology/approach For determining the efficiency of educational cooperation of universities and companies of Industry 4.0 in modern Russia, the authors use the information and analytical materials on the results of monitoring of effectiveness of the activities of educational organizations of higher education in 2018. They use the indicators of the number of cooperation agreements and efficiency of cooperation – employment of graduates and the share of non-budget assets in revenues from the educational activities. These indicators are studied with the help of the method of regression analysis. The objects of the research are the flagship universities of the Central and the Southern Federal District of the Russian Federation. Findings Low efficiency of the educational cooperation of universities and companies of Industry 4.0 in modern Russia is observed. The authors show by the example of the flagship universities of the Central and Southern Federal Districts of the Russian Federation in 2018 that multiple agreements on their cooperation with the companies of regions in which they are located do not ensure high values of the indicators of employment of their graduates and the share of non-budget assets in revenues from the educational activities. Originality/value An algorithm of developing the educational cooperation of universities and companies of Industry 4.0 and a model of educational cooperation of universities and companies of Industry 4.0 are offered. The authors’ recommendations specify the conceptual foundations of the educational cooperation of universities and companies of Industry 4.0, as they orient the participants of this cooperation at achievement of specific formalized results and show the mechanism of achieving them.
Learning a Resource Scale for Collectible Card Games

In Collectible Card Games like Magic: the Gathering, one of the developers’ main challenges is creating new and interesting cards that are not too strong or game-braking, pertaining the game’s overall balance. One way to address this issue is through the analysis of the cards resource costs. Powerful cards need more resource to be played while weaker ones need less resource. This work proposes a recommender system to a card’s resource scale. In summary, we model the problem as a classification task and present and in-depth analysis of our results. We propose using LSTMs to learn a vector representation for text followed by XGBoost models to incorporate remaining features. Our approach is capable of reaching a Mean Reciprocal Rank of 0.8064 despite superficially identical cards having different mana costs. The analysis provided indicate that the model was able to learn useful rules for predicting a card’s resource cost and highlight key insights for future research.
A Comparison of Denoising Algorithms for Effective Edge Detection in X-Ray Fluoroscopy

X-ray fluoroscopy provides various diagnosis and is widely used in interventional radiology. However, the low-dose involved in fluoroscopy generates an intense Poisson-distributed quantum noise. Object recognition and tracking help in many fluoroscopic applications. Edge-detection is essential, but common derivative operators require noise suppression to provide reliable results. Moreover, homoscedasticity of noise is generally assumed, but is not the case of fluoroscopic images. However, the Anscombe transform can stabilize the quantum noise variance. This study presents a comparison of two denoising algorithms to evaluate their performance in edge-detection for real fluoroscopic sequences. VBM4D is one of best video-processing method for Additive White Gaussian Noise (AWGN), while Noise Variance Conditioned Average (NVCA) is a recent, real-time, algorithm specifically tailored for fluoroscopy. Some real fluoroscopic sequences screening the motion of lumbar spine were processed. Noise parameters were estimated using image sequences of a static scene: the relationship between the luminance and the noise variance was obtained. Generalised Anscombe transform and its inverse were applied to use the VBM4D algorithm. Edge-detection was performed by means of the Sobel operator. The Anscombe transform resulted able to stabilise the noise variance and consequently allow the use of algorithms designed for AWGN. The results show that both approaches provide effective identification of object contours (i.e. vertebral bodies). Despite of its simplicity the NVCA algorithm shows better performances than VBM4D on delineation of boundaries of examined spine fluoroscopic scenes. Furthermore, the NVCA algorithm can be realized in hardware and can offer real-time fluoroscopic processing.
Reflective approach to improve self-adaptation of Web service compositions with autonomic middleware

Purpose This paper aims to propose an approach for the self-adaptation of the Web composition called SAMIWA. The SAMIWA framework helps users during the search, invocation and composition of the appropriate Web service. Design/methodology/approach The authors’ approach allows expressing requirements by taking into account potential users’ context in addition to the functional one. Findings In this paper, the authors introduce a new context-aware approach that provides a dynamic adaptation of service compositions. Originality/value The author has implemented a Web application that enables selection and composition of the most appropriate composite service.
Situation-Aware Decision-Support During Man-Made Emergencies

Situation identification and its analysis lead to some situation-aware state of knowledge. Uncertainties occur during this process and vastly influence the advice or alerts generated or recommendations attained. A common methodological framework based on non-monotonic logic will greatly help in the formalization and mitigation of situation awareness. The default logic allows reasoning under uncertainty along with reasoning with incomplete information, hence found the best candidate to deal with non-monotonic logic. This paper provides an approach for the study of the non-monotonicity of human commonsense reasoning, specifically for concepts like psychological prototypes and the philosophical Natural Kinds by exploiting semantic web technologies. Providing a quick at the same time right decision-support greatly depends upon the apprehension of the situation. We provide ways to perceive the reality timely, analyze it using past experiences, anticipate and predict next steps and possible actions for subsequent decision making and performance. Ordinarily, decision-makers pay attention to knowledge abstractions while taking a little notice of lower-level details. Making the end users not only identify the situation but getting aware of it will fetch all the benefits. We have utilized a hybrid of top-down and bottom-up reasoning along with the Case-based and rule-based reasoning to bestow decision support in the domain of man-made emergencies.
TCEC Cup 3

This is a report of the 'TCEC Cup 3' computational experiment, the 3rd TCEC knockout event and the second major part of TCEC’s 15th season. It featured the best 32 engines of the ‘TCEC 15’ league event and the games were held at Blitz tempo. The different format called for different optimal settings in the engines, thereby enabling a comparison with performance at slower tempi. LEELA CHESS ZERO retained the TCEC Cup, defeating STOCKFISH this time. All decisive games in the supplementary data have been played out by FRITZ17 at search depth 24 to enable and benchmark endgame practice.
Towards an Anatomy of Software Requirements

Requirements engineering is crucial to software development but lacks a precise definition of its fundamental concepts. Even the basic definitions in the literature and in industry standards are often vague and verbose. To remedy this situation and provide a solid basis for discussions of requirements, this work provides precise definitions of the fundamental requirements concepts and two systematic classifications: a taxonomy of requirement elements (such as components, goals, constraints...); and a taxonomy of possible relations between these elements (such as “extends”, “excepts”, “belongs” ...). The discussion evaluates the taxonomies on published requirements documents; readers can test the concepts in two online quizzes. The intended result of this work is to spur new advances in the study and practice of software requirements by clarifying the fundamental concepts.
INFORMATION TECHNOLOGIES IN THE CONTEXT OF FORMING THE DIGITAL COMPETENCE OF FUTURE TEACHERS

The article analyzes information technologies as components of the formation of the digital competence of future primary school teachers, focuses on practically oriented online resources (Canva, Paint Kards, VivaVideo-Video Editor &amp; Photo Movie, mozaBook, etc.). Modernization of all spheres of modern life results in changes in the educational environment. The integration of information technologies into the formation of the digital competence of future teachers at the lessons of the linguistic and literary direction is due to the problem of losing the interest of schoolchildren in the book and reading in general. Therefore, it is quite logical to use close to modern children new technologies, since the student of the XXI century needs not only the updating of technical support, but also teaching methods. Thus, a modern teacher of the primary school in the context of the formation of digital competence must have the full range of available innovative instruments. It has been highlighted that modern information technologies, combining various possibilities, being universal, can imitate educational toys and games, at the same time they act as an equal partner, able to respond subtly to relevant actions and inquiries. In this area, educational institutions are being increasingly provided with computers, projectors, interactive whiteboards, tablets, e-books, whiteboards of the new generation of ЕdPrо Tоuch and mozaBook software. All this requires a teacher to be a professional who can work in a team, creatively approach the organization of the educational process.
An improved Fully Homomorphic Encryption model based on N-Primes

Cloud computing is the provision of computing services over the internet, which provides unlimited computing capabilities to its users. Cloud Service Providers (CSP) in the distanced places helps the users such as businesses and individuals to use its software and hardware means. The physical distance between the users and providers allows third parties to be capable of accessing the data which threats the privacy of the users. Thus, its security is the main concern when it comes to transform data from a locally owned storage to cloud storage. Cloud providers are required to save an encrypted version of user’s data on their storage. The traditional encryption schemes have been used for data encryption prior to sending them to the provider. Thought, the secret key has to be provided by the users to the server so as to decrypt the information prior to the requirement of calculations. Therefore, the traditional cryptographic schemes cannot be used to process cloud’s data. After the encryption of the information data are revealed to calculation in clouds, so confidentiality is not guaranteed and this result in difficulty in using cloud. In Homomorphic Encryptions calculation on ciphertext can be performed with no need for decryption. This paper, develops and designs a new mathematical model to achieve the characteristics of the Fully Homomorphic Encryption. The proposed model’s security depends on the problem of Factorization the integers to their primary numbers. In this paper, instead of dealing with two prime numbers it is expanded to deal with n prime numbers. The security of the presumptive algorithm to be more efficient in front of the security challenges facing cloud computing. What distinguishes this proposed system is that it deals with the explicit text after converting it to the ASCII code instead of converting it to the binary system as it is in the existing systems, thus providing speed in the encryption process and returns the encryption.
A certifying extraction with time bounds from Coq to call-by-value $\lambda$-calculus

We provide a plugin extracting Coq functions of simple polymorphic types to the (untyped) call-by-value $\lambda$-calculus L. The plugin is implemented in the MetaCoq framework and entirely written in Coq. We provide Ltac tactics to automatically verify the extracted terms w.r.t a logical relation connecting Coq functions with correct extractions and time bounds, essentially performing a certifying translation and running time validation. We provide three case studies: A universal L-term obtained as extraction from the Coq definition of a step-indexed self-interpreter for Ł, a many-reduction from solvability of Diophantine equations to the halting problem of L, and a polynomial-time simulation of Turing machines in L.
Modelling and Identification of the Musicians Blowing Part and the Flute Musical Instrument

The implementation of digital musical instruments has been growing in the last few years. The musical domain is relying on the use of electrical musical systems that are still manually controlled by humans. Although the full control of such systems remains more theoretical especially when the melody is not well known and/or saved within this electrical musical system, this work tries to pass those boundaries by creating a fully autonomous control system for the flute instrument. As this paper is part of a bigger project, the modelling and identification part of the system formed by the musician lungs, mouth, and the exciter of the flute will be presented. The static and the dynamic model identification will be verified and will lead to a well-defined transfer function for the system. This TF will be used in future works for the control purpose.
Anaphoric Connectives and Long-Distance Discourse Relations in Czech

This paper is a linguistic as well as technical survey for the development of a shallow discourse parser for Czech. It focuses on long-distance discourse relations signalled by (mostly) anaphoric discourse connectives. Proceeding from the division of connectives on “structural” and “anaphoric” according to their (in)ability to accept distant (non-adjacent) text segments as their left-sided arguments, and taking into account results of related analyses on English data in the framework of the Penn Discourse Treebank, we analyze a large amount of language data in Czech. We benefit from the multilayer manual annotation of various language aspects from morphology to discourse, coreference and bridging relations in the Prague Dependency Treebank 3.0. We describe the linguistic parameters of long-distance discourse relations in Czechin connection with their anchoring connective, and suggest possible ways of their detection. Our empirical research also outlines some theoretical consequences for the underlying assumptions in discourse analysis and parsing, e.g. the risk of relying too much on different (language-specific?) part-of-speech categorizations of connectives or the different perspectives in shallow and global discourse analyses (the minimality principle vs. higher text structure).
Augmented Reality to Enhance the Clinical Eye: The Improvement of ADL Evaluation by Mean of a Sensors Based Observation

The paper proposes the use of Augmented Reality (AR) in a rehabilitative clinical setting with the purpose of supporting the therapist during the clinical observation by providing real-time augmentations using an Optical See-Through Head-Mounted Display. Developed in the context of project AUSILIA (Assisted Unit for Simulating Independent Living Activities), the solution has the objective to track a monitored person inside a sensorized domestic environment while performing activities of daily living. A distributed sensing infrastructure enables the collection and display of several information regarding his physiological status and his interaction capabilities with the furniture. The obtained system allows the therapist to appreciate new information, enhancing the clinical assessment with new objective elements, at the same time without denying any of the classic evaluation methods.
A New Approach for Twitter Event Summarization Based on Sentence Identification and Partial Textual Entailment

Recent trend of information propagation on any real-time event in Twitter makes this platform more and more popular than any other online communication media. This trend creates a necessity to understand real-time events quickly and precisely by summarizing all the relevant tweets. In this paper, we propose a two-phase summarization approach to produce abstract summary of any Twitter event. The approach first extracts key sentences from the whole set of event relevant tweets and eliminates maximum redundant information by exploring Partial Textual Entailment (PTE) relation between sentences. Next, generates an abstract summary over the least redundant key sentences. We conduct experiments to evaluate the performance of our propose approach and report that the approach outperforms over the baseline approach as well as state-of-the-art event summarization approach.
Automatic Question Generation with Classification Based on Mind Map

In Indonesia's national education system, a question within a national standardized school examination (Ujian Sekolah Berstandar Nasional, USBN) is generated based on a predetermined grid. A predetermined grid is a matrix that contains a set of criteria that all question should abide. Students who would like to take the final school exam usually prepare themselves by learning through the existing predetermined grid then extend their knowledge around the predetermined grid. But extending knowledge around the predetermined grid is not easy because it depends on the student's creativity to associate the material that exists. The contribution of this research is to generate various types of questions that have relevance to the predetermined grid. The dataset that used is learning the material of all grade in 4, 5, and 6 of elementary school science subjects. The question generation process is done by the rule which generated from the mind map. The mind map will help to determine the interrelationship between the learning material. This study uses 50 competencies from the predetermined grid as input to generate questions. The results of question generation are then validated by an expert. Expert state that all inputs used can generate questions according to competence. The experiment result shows that the proposed method can be used to generate questions that are related to the predetermined grid.
NONYM!ZER: Mitigation Framework for Browser Fingerprinting

Not only recent compelled cookies regulations have radically restrained their threats but also increased people awareness has played a fundamental part. This has placed huge pressure on enterprises to find alternatives to bridge this gap and satisfy business demands. Since then fingerprinting has gained enormous popularity. In this paper, we introduce "nonym!zer" as a mitigation framework for browser fingerprinting. It helps to hinder or impede browser fingerprinting on desktop browsers that web servers use such as WebGL or Canvas technologies.
GNSS Threat Monitoring and Reporting with the Android Raw GNSS Measurements and STRIKE3

Reliable radio navigation signals are of extreme importance. Nowadays, we rely on Global Navigation Satellite System (GNSS) related technologies for an array of uses ranging from agricultural, financial, transportation, and military applications. As such, providing existing systems with the tools to combat the threat presented by malicious spoofing or jamming attacks is critical. The paper, by adequately assessing fundamental measurements offered in the raw GNSS engine, and using software tools native to the Android Operating System (OS), will show the effectiveness of Android devices in reporting interference events as per the Standardization of GNSS Threat Reporting and Receiver Testing through International Knowledge Exchange, Experimentation and Exploitation (STRIKE3) Threat Monitoring and Reporting standard. This work also proposes noninvasive improvements to the aforementioned standard that aim to improve the reporting procedures, extend the support for non-GPS signals, and increase support for smartphones. Finally, this work also presents a detailed study of the infrastructure required to reproduce the findings of this work in terms of bandwidth, server costs, and false alarm consideration due to user dynamics.
Three-dimensional extension of blood vessel network by combining multiple ultrasound volumes from different directions

We have previously proposed the use of acoustic radiation force in blood vessels for therapeutic application of ultrasound. For this purpose, we have developed a blood vessel network reconstruction algorithm to fuse between B-mode and Doppler-mode volumes. However, a size of ultrasound volume was insufficient to recognize the network for treatment. Therefore, using multiple ultrasound volumes, we propose a method to extend a network with neighbor networks. First, an ultrasound volume was analyzed to extract tree-structure including the nodes and the edges. Then we configured an extension method between two tree-structures, which performs the insertion of nodes in a source tree to a target tree. Next, similarity between two networks were evaluated by introducing edge lengths in the network and the edit distance. By analyzing in vivo blood vessel of porcine liver, we confirmed that the construction of the network was reliable according to the extension with the similarity of 60% compared with CT data. We confirmed that the proposed method is effective for reconstructing blood vessel network.
A joint-feature learning-based voice conversion system for dysarthric user based on deep learning technology

Dysarthria speakers suffer from poor communication, and voice conversion (VC) technology is a potential approach for improving their speech quality. This study presents a joint feature learning approach to improve a sub-band deep neural network-based VC system, termed J_SBDNN. In this study, a listening test of speech intelligibility is used to confirm the benefits of the proposed J_SBDNN VC system, with several well-known VC approaches being used for comparison. The results showed that the J_SBDNN VC system provided a higher speech intelligibility performance than other VC approaches in most test conditions. It implies that the J_SBDNN VC system could potentially be used as one of the electronic assistive technologies to improve the speech quality for a dysarthric speaker.