Reviewerly/exHarmony huggingface dataset:
The study of human behaviour in a laboratory set-up with the use of innovative technology

Virtual reality technologies known since the 1990s have virtually disappeared. They have been replaced by their new editions characterised by a very faithful reproduction of details. The laboratory described in the article was created to study the intensity of various sensations experienced by a person in virtual worlds. A number of different scenarios have been developed that are designed to trigger specific reactions, to produce symptoms similar to those observed in arachnophobia, acrophobia or claustrophobia. The person examined can be equipped with a series of non-invasive sensors placed on the body in such a way that they do not interfere with immersion in VR. The laboratory instruments enable the acquisition and synchronisation of many signals. Body movement data is recorded by means of Kinect. Involuntary hand movements are measured with 5DT gloves. In addition, body temperature, ambient temperature and skin moisture are continuously monitored. Apart from recording the image from VR goggles, it is also possible to record the entire session on camera in 4K resolution in order to interpret facial expressions. The results are then analysed in detail and checked for patterns. The article describes both the test set-up itself as well as several test scenarios and presents the results of pilot studies.
Thomas Egan and Hildegunn Dirdal, <i>Cross-linguistic Correspondences. From Lexis to Genre</i>

Abstract This article reviews Cross-linguistic Correspondences. From Lexis to Genre 978 90 272 5956 1
Coronary Heart Disease Prognosis Using Machine-Learning Techniques on Patients With Type 2 Diabetes Mellitus

Heart diseases and stroke are the number one cause of death and disability among people with type 2 diabetes (T2D). Clinicians and health authorities for many years have expressed interest in identifying individuals at increased risk of coronary heart disease (CHD). Our main objective is to develop a prognostic workflow of CHD in T2D patients using a Holter dataset. This workflow development will be based on machine learning techniques by testing a variety of classifiers and subsequent selection of the best performing system. It will also assess the impact of feature selection and bootstrapping techniques over these systems. Among a variety of classifiers such as Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Alternating Decision Tree (ADT), Random Tree (RT) and K-Nearest Neighbour (KNN), the best performing classifier is NB. We achieved an area under receiver operating characteristics curve (AUC) of 68,06% and 74,33% for a prognosis of 3 and 4 years, respectively.
Education for the Innovative Development of a Region

The chapter describes the possible mechanism of supporting the development of education in a region through an organization of interaction between education and businesses into a regional network of schools and their partners (representatives of science, higher education, high-tech businesses) keen on innovation as the resource of the region's development. Mechanisms for an organization of such a network are based on the model of the innovative complex in education as a mechanism to ensure innovative pedagogical community at the interregional and regional levels through an organization of interaction of scientists, teachers, administrators, and people in a business. The chapter represents five years of experience of implementation of this model in the frameworks of the ROSNANO School League, the Russian national network program, and its influence on the establishment of the regional networks.
Shock filter‐based morphological scheme for texture enhancement

Coherence enhancing shock filters combine shock filtering with the orientation estimation of the structure tensors thus enhancing the coherent flow-like structures. The basic operations defined here are dilation and erosion that take place in the zones of influence. However, in order to achieve the goal of texture enhancement, in the proposed method, the authors have extended this notion to define an opening and closing based shock filter. Subsequently, the open–close filtered image is employed to locate and highlight the bright and dark texture features over the entirety of the image. Combining these feature images with the original image in a specific way will produce an image with texture features enhanced. Furthermore, we have performed these operations at different scales to achieve better enhancement of the texture features. The method has been formulated, implemented and tested over a number of synthetic and natural texture images and the experimental results establish the efficacy of the proposed method in enhancing the prominent texture parts in the image proportionately more than the non-prominent texture parts.
Blending Association Rules for Knowledge Discovery in Big Data

Data mining techniques have been widely applied in several domains to support a variety of business-related applications such as market basket analysis. For instance, basket market transaction accumulate large amounts of customer purchase data from their day-to-day operations. This paper delivers a strategy for the implementation of a systematic analysis framework built on the established principles used in data mining and machine learning areas.We employ Apriori and FP-growth algorithms coupled with support vector machine to implement our recommendation systems. Experiments are done using a real market dataset and the 0.632+ bootstrap method is used here in order to evaluate our framework. The obtained results suggest that the proposed framework will be able to generate benefits for grocery chain using a real-world grocery store data. FP-growth algorithm shows better performance over Apriori in terms of time complexity.
Homomorphic Encryption Scheme for Data Security in Cloud Using Compression Technique

Data generationChandrashekar, D. K. and consumption are becoming a main part of daily life. Huge volume of dataSrikantaiah, K. C. is available over the InternetVenugopal, K. R.; preprocessing of these data is a challenging task. The data preprocessing is used to process the raw data into precise data and store in the cloud. Providing security to this data is another challenging issue. Earlier, data owners were not ready to send their data to centralized sites because of the privacy issue. Nowadays, two types of encryption techniques are applied to data stored in the cloud and data is more secured. In this paper, we proposed compressed homomorphic encryption technique (CHET) which combines the compression technique to encrypt the data so it reduces the file transfer time and encrypt time, and it provides security. Data owner can decrypt the data when required. The homomorphic function is allowed to compute encryption time. Our experiment results show that CHET outperforms 10% in compression and encryption time and provides high security than RSA and BGV.
Can Computers Understand Picture Books and Comics?

The objective of this chapter is to develop a method for generating and analyzing creative work by using computers. In this chapter, picture books and comics are considered representative creative work because they contain multi-modal information represented by natural language and pictures. These works focus on interesting issues, which can be explained by computational approaches to narratology. In this chapter, the authors discuss the following two topics. First, a method of semi-automatic picture book generation by agent-based simulation is presented. Second, methods of generating and analyzing comics on the basis of the features of pictures and stories used are described. From that work, the authors introduce the construction of an original dataset.
A Practical Algorithm for Structure Embedding

This paper presents an algorithm for the structure embedding problem: given two finite first-order structures over a common relational vocabulary, does there exist an injective homomorphism from one to the other? The structure embedding problem is NP-complete in the general case, but for monadic structures (each predicate has arity $$\le 1$$ ) we observe that it can be solved in polytime by reduction to bipartite graph matching. Our algorithm, MatchEmbeds, extends the bipartite matching approach to the general case by using it as the foundation of a backtracking search procedure. We show that MatchEmbeds outperforms state-of-the-art SAT, CSP, and subgraph isomorphism solvers on difficult random instances and significantly improves the performance of a client model checker for multi-threaded programs.
Research and Application of Dual Active Disaster Preparedness System Based on Redo Read-Write Separation Cluster

At present, the security and reliability of information systems have directly related to the development of various businesses of the State Grid Corporation. In order to solve problems such as data security, business continuity, and performance pressure, and avoid the impact of system disruption or data loss caused by a disaster on the business, it is necessary to research the solution of active-active disaster recovery. In this paper, based on a thorough study of the principle of read-write separation and its technical advantages in solving the performance bottleneck of high-concurrency business systems, a read-write separation cluster based on redo is designed in the data layer of the active and standby systems, and at the same time, the information system The active layer, the application layer, the data layer, and the storage layer of the architecture are designed for active-active layer by layer and a set of remote active-active disaster recovery systems is constructed. Considering that Zhejiang Power’s OMS system is a highly concurrent business system and the standby system is a cold standby with full service capabilities, this design scheme is used to implement transformation and upgrade of Zhejiang Power’s OMS business system. Through comparative testing, the new system is capable of providing services after a disaster. Interrupted, data zero loss recovery capability, and system response speed increased by more than 50%.
Research on Digital Evaluation System for Experimental Score

To increase student’s learning interest and improve teaching efficiency in vocational education, the digital evaluation system of experimental Score is studied. It offers a new approach to record multiple experimental Score quickly using digital method rather than old paper way. The research focus on the system design including server and client, and the key technologies in system implementation.
An Exact Algorithm of Searching for the Largest Size Cluster in an Integer Sequence 2-Clustering Problem

A problem of partitioning a finite sequence of points in Euclidean space into two subsequences (clusters) maximizing the size of the first cluster subject to two constraints is considered. The first constraint deals with every two consecutive indices of elements of the first cluster: the difference between them is bounded from above and below by some constants. The second one restricts the value of a quadratic clustering function that is the sum of the intracluster sums over both clusters. The intracluster sum is the sum of squared distances between cluster elements and the cluster center. The center of the first cluster is unknown and determined as the centroid (i.e. as the mean value of its elements), while the center of the second one is zero. The strong NP-hardness of the problem is shown and an exact algorithm is suggested for the case of integer coordinates of input points. If the space dimension is bounded by some constant this algorithm runs in a pseudopolynomial time.
Targeted Ciphers for Format-Preserving Encryption

We introduce Targeted Ciphers, which typically encipher points on domain $$\mathcal {X}$$ , but can be easily modified to instead encipher points on some subset $$\mathcal{S}\subseteq \mathcal {X}$$ . Ciphers that can directly support this domain targeting are useful in Format-Preserving Encryption, where one wishes to encipher points on a potentially complex domain $$\mathcal{S}$$ . We propose two targeted ciphers and analyze their security. The first, Targeted Swap-or-Not, is a modification of the Swap-or-Not cipher proposed by Hoang, Morris, and Rogaway (CRYPTO 2012). The second, a new cipher we call Mix-Swap-Unmix, achieves the stronger notion of full security. Our targeted ciphers perform domain targeting more efficiently than the recently proposed Cycle Slicer algorithm of Miracle and Yilek (ASIACRYPT 2017).
JPSC KP

Advancement in technology has facilitated humans in various ways. However, the phenomenon has negative strain, too, in the form of weaponry and transport and communication technologies that have accelerated the momentum and broadened the spectrum of terrorist activities. Kidnappings, bomb blasts, vandalisms, target killings, physical harassments, etc. are a few of the various bad practices a racket or terrorist organization resorts to, to satisfy its thirst for blood and money. Some even resort to making extortion demands and threat calls to raise their finances and spread terror, respectively. These activities have become difficult to trace with the use of burner phones, outstation phone calls, and calls from unregistered service identity module (SIMs). Given this scenario, the proposed Joint Public Safety Cell app is intended to assist the general public in efficient registration of a complaint, and keeping updates of security forcers' action on their complaint. Details of JPSC app have been discussed in the chapter.
Wilhelm Widmark, Library Director, Stockholm University Library

Interview with Wilhelm Widmark, Library Director at Stockholm University. Mr. Widmark gives his thoughts on organizational development, trends within academic libraries as well as needs for new skills amongst staff in order to fill the needs for new services for the university.
Large Scale Text Categorization Based on Density Statistics Merging

In view of the problems faced by the effective classification and management of massive text, a new classification method for mass Web text information is proposed. The core idea is based on the characteristics of the low quantity, high value rate of the long text and the high quantity and low price rate of the short text in the current network environment. The feature selection method based on complex network is proposed. The number of features obtained by this method is more stable, and the accuracy of the selection of features in large text centralization is improved. Secondly, a text classification method based on density statistical merging is proposed, and the classification method is studied from the point of view of data sampling. The method is classified. In the process, we not only use the density information of the text feature set, but also use the difference information of each feature of the text obtained by the statistical merging criteria. Therefore, the algorithm has better robustness to noise and has a better classification effect to the large text set.
Experimental clarinetist Gleb Kanasevich rushes disturbing dark energy through his new record “Asleep”

Gleb Kanasevich is an experimental clarinetist, working in a variety of aesthetics, and his new doom/ambient album were premiering today marks a real ...
A Cloud Storage Data Access Control Scheme Based on Attribute-Sets Encryption

In order to solve the data security problem in cloud storage system, an access control scheme which supports for a finer attribute expression for cloud storage data based on CP-ASBE (Ciphertext-Policy Attribute-Sets Based Encryption) is proposed in this paper, which can solve the problem of attribute confusion based on attribute encryption algorithm. A multi-authorization center is used to address single-point security issues. The digest of plaintext is used to encrypt the plaintext, and then the CP-ASBE encryption key is used to improve the efficiency and save the storage space of the cloud storage. In terms of attribute revocation, access control lists are used to handle coarse-grained privilege revocation. For fine-grained attribute revocation, proxy re-encryption is used and the complex calculations are delegated to the computationally powerful DataNode node. The confidentiality, integrity, non-repudiation, availability and security of the scheme are analyzed and proved. The results show that the cloud storage data access control scheme based on CP-ASBE can effectively improve the security of user data in HDFS (Hadoop Distributed File System) cloud storage system.
Exploring Semantic Archival Collections: The Case of Piłsudski Institute of America

Over the last decades, a huge amount of available digital collections have been published on the Web, opening up new possibilities for solving old questions and posing new ones. However, finding pertinent information in archives often is not an easy task. Semantic Web technologies are rapidly changing the archival research by providing a way to formally describe archival documents. In this paper, we present the activities employed in building the semantic layer of the Piłsudski Institute of America digital archive. In order to accommodate the description of archival documents as well as historical references contained in these, we used the arkivo ontology, which aims at providing a reference schema for publishing Linked Data. Finally, we present some query examples that meet the domain experts’ information needs.
Institutions and the Economic Performance of Central African Countries

The purpose of this paper is to examine the combined effect of political and economic institutions on the economic performance of the countries of Central Africa.To do this, we use dynamic panel data over the period 1996-2013.Using Principal Component Analysis (PCA) to understand the multidimensionality of institutions through a composite indicator, and applying the estimation technique based on the Generalized Moment Method (GAM) to capture the impact of institutions on economic performance, we arrive at the following result: analyzed individually or in combination, institutions constitute a real obstacle to economic performance in Central Africa.
Introducing Augmented Reality and Internet of Things at Austrian Secondary Colleges of Engineering

In Austria technical education is taught at federal secondary colleges of engineering (HTL) at a quite high level of ISCED 5. Despite mechanical engineering, design with industrial standard 3D programs being state of the art in industry and education, technologies using Internet of Things (IoT) and Augmented Reality (AR) are still at an early stage. This publication describes the introduction of the IoT platform Thingworx at Austrian HTL for mechanical engineering, with focus on AR and IoT from an educational perspective as well as the training aspects from the platform developers’ perspective. In addition, an assessment using a system usability scale (SUS) test among students and teachers has been undertaken to obtain knowledge of how students and teachers see the usability of the IoT and AR platform Thingworx. The assessment results are presented in this paper.
Pedagogical Software Agents for Personalized E-Learning Using Soft Computing Techniques

Due to the emerging e-learning scenario, there is a need for software agents to teach individual users according to their skill. This chapter introduces software agents for intelligent tutors for personalized learning of English. Software agents teach a user English on the aspects of reading, translation, and writing. Software agents help user to learn English through recognition and synthesis of human voice and helps users to improve on handwriting. Its main objective is to understand what aspect of the language users wants to learn. It deals with the intuitive nature of users' learning styles. To enable this feature, intelligent soft computing techniques have been used.
The model of the Bodaibo graben formation: geological and geophysical aspects

The problem of the Bodaibo graben formation has been considered within the framework of the proposed tectonic scheme of the Baikal-Patom fold belt crystalline basement — the Paleoproterozoic Baikal-Viluisk granulite-gneiss belt, which divides the foundation of the Siberian platform into Anabar-Mirninsky, Nepsko-Botuobinsky and Aldan-Stanovoi geoblocks. The Baikal-Viluisk granulite-gneiss belt is expressed by a system of blocks having a common northeastern direction and forming uplifts and grabens. As a result of the transpession interaction of the Nepsko-Botuobinsky and Anabar-Myrninsky geoblocks with Aldan-Stanovoi one in the Paleoproterozoic, the frontal part of the former was elevated with the formation of metamorphic rocks of Sarma-Tonod zone of outcrops and with the simultaneous descending of the Aldan-Stanovoi geoblock marginal part and the formation of the Olokit and Bodaibo grabens. The Olokyt graben was filled with the detrital material from the Nepsko-Botuobinsky and Anabar-Mirninsky geoblocks, and the Bodaibo graben — mainly by the products of the metamorphic rocks destruction of the Aldano-Stanovoy shield, including the gold-bearing metabasite complexes.
Leveraging Ontological Knowledge for Neural Language Models

Neural Language Models such as Word2Vec and GloVe have been shown to encode semantic relatedness between words. Improvements in unearthing these embeddings can ameliorate performance in numerous downstream applications such as sentiment analysis, question answering, and dialogue generation. Lexical ontologies such as WordNet are known to supply information about semantic similarity rather than relatedness. Further, extracting word em-beddings from small corpora is daunting for data-hungry neural networks. This work shows how methods that conflate Word2Vec and Ontologies can achieve better performance, reduce training time and help adapt to domains with a minimum amount of data.
FASELOD: A Faceted Search Engine in Linked Open Datasets Using Voice Recognition

Commonly, web applications incorporate interfaces that are limited to data capture through traditional methods such as keyboard, mouse or touch screens, which makes interaction with these interfaces complicated and unnatural for less experienced users. For its part, the LOD (Linked Open Data) cloud covers a large number of domains including the medical domain, the information stored in the datasets of this domain have great high quality information related to drugs, dis-eases, studies, clinics, orphan drugs, to mention a few. On the other hand, the re-search of the NLP (Natural Language Processing) has presented great advances in the generation of artificially intelligent behaviors. This document presents the development of a faceted search engine on datasets that are part of the LOD cloud that provides a more natural and intuitive navigation through NLP. Through the use of facets, the user is provided with a list of results on which he performs an incremental refinement by selecting values of the facets of the data that become constraints on the dataset. FASELOD provides a mechanism based on Silk that allows obtaining other related results within other datasets that are part of the LOD cloud.
Leveraging Models of Human Reasoning to Identify EEG Electrodes in Images With Neural Networks

Humans have very little trouble recognizing discrete objects within a scene, but performing the same tasks using classical computer vision techniques can be counterintuitive. Humans, equipped with a visual cortex, perform much of this work below the level of consciousness, and by the time a human is conscious of a visual stimulus, the signal has already been processed by lower order brain regions and segmented into semantic regions. Convolutional neural networks are modeled loosely on the structure of the human visual cortex and when trained with data produced by human actors are capable of emulating its performance. By black-boxing the low-level image analysis tasks in this way, the authors model solutions to problems in terms of the workflows of expert human operators, leveraging both the work performed pre-consciously and the higher-order algorithmic solutions employed to solve problems.