Reviewerly/exHarmony huggingface dataset:
Geological structure and conditions of formation of Strizhkovskoe barite-polymetallic deposit (Ore Altai)

Abstract Strizhkovskoe barite-polymetallic deposit is a typical medium-low-temperature hydrothermal-metasomatic deposit. It’s located within north-west part of Zmeinogorsk-Bystrushinskiy downfold; host rocks are volcano-terrigenous Devonian rocks. Ore bodies are related to sublatitudinal and submeridional faults intercrossing. Wallrock quartz-chlorite-sericite metasomatites formed prior to ores formation. Ore mineralization process began in Middle Devonian. Main ore minerals are sphalerite, galena, and chalcopyrite. Also there are gold and silver occurring as electrum, acanthite; silver is present in fahlores as an isomorphous admixture. Formation of the deposit subdivides into two main stages and five phases; according to obtained results of isotope study of the sulfides, it’s possible to conclude that there was a mixed source of ore matter (mantle source replacing with crust source). The temperature of main ore formation stage varied form 230 to 180 degrees. Solutions salinity varied from 9 to 15 % wt of NaCl equiv. According to fluid inclusions study results, the solutions consisted of water and following compounds CO 2 , NH 3 , SO 2 , HCO 3 .
Construction of quasi-professional tasks for the methodical training of foreign language teachers

Recognizing the relevance of the competence-based approach and the implementation of context-based learning in contemporary education, researchers in this scientific field often distinguish the quasi-professional level of teacher training, which acts as an intermediate, transitional level from theoretical to practical, professional level.There are a lot of research devoted to quasi-professional activities in teacher training (G.Kh.Valeev, L.F. Nugumanova, N.V. Bekuzarova, E.V. Ermolovich; T.V. Pushkareva, E.V. Kuzevanova).At the same time, there are only a few studies devoted to quasi-professional activities in the methodological training of a teacher of a foreign language (J.S. Fritzko, E.S. Nadtocheva, M.G.Makarchenko).This article is devoted to the parameters for construction of quasi-professional tasks (QPT) for the methodical preparation of a foreign language teacher (FL teacher) on planning, analyzing and implementing teaching activities.The definition and characteristics of these tasks are presented.Examples of quasiprofessional tasks for the methodical training of a foreign language teacher, including the authors' tasks, are given.
Investigating the Information Perception Value (IPV) Model in Maintaining the Information Security

This pioneering study is conducted among 150 employees from various ministries of Brunei Darussalam regarding their perception in maintaining the information security and to validate the IPV model using linear regression data analysis techniques. The IPV model identifies the factors that affect the user's perception of information values and to further assess as how these perceptions of information value affect their behavior in information security environment. The results show that IPV model have significant predicting power the employees' behavior with more than half of the variance (59%) in information value is shared by these six contextual variables. However, four out of six antecedent variables monetary value, ministerial jurisdiction, spiritual, and social values are significantly predicting the information value. The study has significant impact both for the researchers and practitioners and will add value to the current repository of broad knowledge in information security behavior.
Enhancing Document-Camera Images

Document Camera digitalization devices are low-cost, easy to use, produce good quality images, are able to digitalize pages of bound books without damaging their spine, etc. On the other hand, they may bring two serious problems. The first one appears if the document to be digitalized is printed on glossy paper. The paper reflects the different illumination sources from the environment producing a specular noise in the document image. The second problem occurs when the document to be digitalized does not lay flat on the digitalization surface. This paper presents solutions to both problems. The results obtained in almost 400 test images may be considered satisfactory.
Designing a Support Tool for API Debugging

Coding often relies on Application Programming Interfaces (APIs), yet APIs are hard to learn, use, and debug. Debugging is a notoriously difficult skill, and is made even more challenging by the complexities of APIs. Yet, little work has focused on support for debugging code that relies on an API. In this poster, we present an early design of debugging worked examples to support programmers in debugging an unfamiliar API. In our formative study, novices could access the information through a prototype tool. This works-in-progress presents preliminary data about programmers' use of this tool and the implications for future work.
Optimality Criteria for the Path Planning of Autonomous Industrial Vehicles

The trajectories planning problem for autonomous industrial vehicles requires facing significant issues concerning the efficiency and the safety of the plant. The solution of such issues is complicated by the necessity of generating paths and time-laws in different moments: plant layouts must be designed a priori, while velocity profiles must be generated in real time in order to manage variable operating conditions and safety concerns. One possible solution to the real-time velocity planning problem has been recently proposed in the framework of the European project ECHORD++. The study in this work will conversely focus on the path planning problem. More precisely, the proposed investigation aims to establish the most appropriate offline path planning criterion to be used for the design of autonomous warehouses, in order to improve the plant performances. The goal is to obtain curves which allow swift direction changes which minimally impact on the system efficiency.
User Perception Towards ICT Services and Facilities in Maulana Azad Library, Aligarh Muslim University

This chapter examines users' opinion regarding various aspects of ICT-based services and facilities provided by the Maulana Azad Library, Aligarh Muslim University, India. The authors used a quantitative study based on the survey method along with the questionnaire as a tool for data collection. Statistical Package for Social Sciences (SPSS) was used for analysis and interpretation of 278 selected questionnaires. Findings of the study reveal a majority of respondents are satisfied with ICT-based services and facilities offered by the library. However, most of the respondents have reported that it is essential to improve the quality of ICT facilities like the speed of Internet connection, scanner facility, and photocopy facility. They also cited a need to increase the number of printing machines. The findings also show that users have a positive attitude towards ICT tools which are used by the library such as various Social Networking Sites (SNSs), e-mail, blogs, etc. for spreading quick information.
Intelligent System for Collecting, Analyzing and Managing Data in the Field of Medicine

The subject of the research is a constriction and inner architecture of the hybrid intelligent system for the healthcare called "Lazar". The goal of the study is to present all the inner layers of the construction of the hybrid intelligent system for the healthcare. The methodology of the study is a representation of the work of the modules and their structure within one intelligent system: it's triggers, productive rules work and data analysis modules. The results of the study are presented on a Table 1. It is important representation of the system's core. This field of computer science, which is engaged in the development of intelligent computer systems, i.e. systems that have the capabilities that we traditionally associate with the human mind - understanding the language, learning, the ability to reason, solve problems, etc. A. N. Kolmogorov: "Any material system with which one can discuss the problems of science for a long time ..." is intellectual. A. Turing: If in the process of dialogue people fail to establish that one of the participants is a machine, then such a machine has intelligence.
A Novel Document Summarization System for Albanian Language

Summarization is a Natural Language Processing application that may seem trivial to a person, but in a time where the quantity of information provided is continuously growing, the possibility of implementing a "helper" in order to summarize it, has become a necessity. Most of the existing scientific studies in automatic text summarization has been paying attention primarily to English with only some recent attempts in other major languages. To the best of our knowledge, no prior approaches handle automatic summarization for Albanian documents. This paper is proposed to fill this gap by implementing a novel extractive summarization system, designed specifically for Albanian Language. We showed experimentally that the enrichment of the summarization system with language-dependent elements improves the systems' performance and the compression rate.
Vehicle Recognition Systems Using Speed-Up Robust Features and Non-Maxima Suppression

The proposed work aims to implement non learning computer vision method using features detection in real live traffic condition to recognize vehicle by its physical features. The algorithm uses speed up robust features that is invariant to both scaling and rotation of an image. The use of speed up robust features (SURF) technique is based on its capability to correctly recognize images with different sizes and various angles of rotation. The proposed work uses a modified SURF that uses non maxima suppression in finding local maxima in order to point exactly the highest value of gradient magnitude. Object cloning was as well implemented in order to avoid memory violation of the observed and model images and increases the speed of algorithm. The system was tested against several conditions including tested towards finding moving and stationed object from the observed video. Additionally, it was as well tested towards real live traffic with minimum light (at night) and during light rain that increase the noise and distortion level of a live monitored environment. The accuracy result is promising with an average of 90% correctness and average performance of 688 millisecond per frame. The work as well aimed to provide conclusion whether the use of a modified scale and rotation invariant technique such as SURF and non-maxima suppression could contribute positively in the area of live vehicle recognition systems.
Babel VR: Multimodal Virtual Reality Environment for Shelf Browsing and Book Discovery

This paper presents the results of the development of a system called Babel VR, part of a work-in-progress research project aimed at obtaining insights into how a multimodal virtual reality environment can enhance shelf browsing and the discovery of books that exist in a physical library. Babel VR provides a virtual browsing experience of library items which display bibliographic attributes such as title, author, topic and location in a library, aided by the use of voice commands. The representation of physical library shelves enables the possibility of having some affordances of physical browsing within a virtual environment, while at the same time providing readers with a browsing experience enhanced by multimodal features such as the use of voice commands for searching and browsing.
Multi-channel data storage format definition for visualization tasks on the example of SPOT-4 images

One of the features of a remote sensing data storage is the widespread utilization of large-capacity disk arrays. Emergency situations arising from the use of arrays can lead to the fact that the remote sensing data, usually stored in uncompressed form, may become partially damaged. But even with incomplete recovery, this kind of data can be used in the future to solve production problems. However, this recovery is sometimes hampered by incomplete knowledge of the format of the corrupted data. The article describes an approach to automatic recognition of multichannel data interleaving type (BIP, BIL or BSQ) and its application to a recovery of SPOT-4 remote sensing data stored in the segment format "SEG", which were damaged after a disk array failure.
An Optimized Selective Scale Space Based Fuzzy C-Means Model for Image Segmentation

Clustering is a widely used technique for segmentation of images. In this paper, a method is proposed for image segmentation, which used an Optimized Selective Scale Based Fuzzy c-Means approach. This approach is used to improve the quality of image segmentation. An algorithm named Fuzzy c-means (FCM) is used for data clustering in which an element can belong to multiple clusters. This algorithm results in the transformation of data elements in such a way that closer elements will come more closer and remaining elements will scatter farther. Genetic Algorithm is used as an optimization technique in this model. Genetic Algorithm is one of the commonly used methods to decide the optimal value of a criterion. The optimal value is determined by simulating the evolution of population until the best fitted individuals among the population is not encountered. It is obtained by mutation selection and crossover of individuals from the existing population.
Web Documents Structures as Source for Machine-Understandable Document

The web documents are not understandable by computers. And, web documents cross the web hold nourmance amount of information. To gain the maximum precision of reaching precise results, understanding these web documents is vital task. Moreover, it has always been the case that the Semantic Web is an extension of the current web. The Semantic Web or Web 3.0 is a collection of components that work together so that a machine is able to process and understand information. In order for this vision to be realized, formal standards for representing and interpreting data must be formulated and imposed. These formal standards include machine understandable ontologies Ontology Web Language (OWL). The Semantic Web vision provides the current Web with necessary infrastructure that allows computer to process knowledge and one of the cornerstones of this infrastructure is the Ontology. As this survey focuses on "web documents" as source for machine-understable document, we will discuss it in details how the researchers dealt with these web documents in order to build ontology.
Three-Dimensional Point Cloud Feature Change Detection Algorithm of Open-pit Mine Based on Discrete Curvature Analysis

Open-pit mine is an industrial production site produced by human intervention, easy to make human and nonhuman changes. Landslide and stairs are the main model structure of open-pit characteristics, changes of landslide and stairs can be clearly described by using the changes of stairs feature lines, but changes of stairs feature lines correspond to the changes of landslide and stairs. The slope sliding and change monitoring have affected safety production and management of open-pit mine. Based on the algorithm research, two laser scanning data of open-pit mine are used for experiments in the paper. The correctness of the algorithm in this dissertation were conducted.
Privacy Policies Model in Access Control

With the increasing advancement of services on the Internet, due to the strengthening of cloud computing, the exchange of data between providers and users is intense. Management of access control and applications need data to identify users and/or perform services in an automated and more practical way. Applications have to protect access to data collected. However, users often provide data in cloud environments and do not know what was collected, how or by whom data will be used. Privacy of personal data has been a challenge for information security. This paper presents the development and use of a privacy policy strategy, i. e., it was proposed a privacy policy model and format to be integrated with the authorization task. An access control language and the preferences defined by the owner of information were used to implement the proposals. The results showed that the strategy is feasible, guaranteeing to the users the right over their data.
Impact of In-domain Vector Representations on the Classification of Disease-related Tweets

A number of methods have been proposed for the construction of vector representations for natural language processing (NLP) tasks. These methods have been applied to various domains and each has its own pros and cons. Despite their effectiveness, the proposed approaches usually ignore the sentiment information concerning specific tasks. In this paper, we examined various types of word vectors and their impact on the performance of a sentiment classification problem in the area of infectious diseases. Vectors were used in the embedding layer of a word-based convolutional neural network (CNN) to identify tweets pertaining to avian influenza. We proposed a new approach to build effective word embeddings for the sentiment analysis task. Furthermore, the performance of the language model was compared in terms of using various corpus sizes and vector dimensions. Our experiments indicated that initializing the sentiment learning network with domain-specific word embeddings outperforms general domain embeddings. We found that the proposed method leads to a considerable improvement in the classification performance.
Wearable Computing and Human-Centricity

Wearable computing has gained significant traction with the advancement of computing technology and the resulting increase in efficiency and power within smaller form factors. The interfaces and applications of these devices have evolved over centuries from very primitive implementations to those which adapt to and anticipate user needs. However, the principles of human-centric design have only recently been defined and understood in the context of wearable computers. These devices introduce an additional set of requirements to the traditional concepts of human-centric computers which have yet to be defined in an adaptable framework. The failures of many recently launched wearable devices highlight the importance of these considerations throughout the design and development process. Human-centricity currently serves as one of the major challenges to the ubiquity and future success of wearable devices.
Geometric Interpretation of CNNs’ Last Layer

Training Convolutional Neural Networks (CNNs) remains a non-trivial task that in many cases relies on the skills and experience of the person conducting the training. Choosing hyper-parameters, knowing when the training should be interrupted, or even when to stop trying training strategies are some difficult decisions that have to be made. These decisions are difficult partly because we still know very little about the internal behaviour of CNNs, especially during training. In this work we conduct a methodical experimentation on MNIST public database of handwritten digits to better understand the evolution of the last layer from a geometric perspective: namely the classification vectors and the image embedding vectors. The visual inspection of these vectors during training have revealed misalignment issues, which otherwise would have not being obvious to detect. We show that by constraining the norms of the classifiers during training these issues are mitigated as well as the time to converge is reduced by 40%. Within this context we present the problem of the variability across equal set-up trainings due to the random component of the initialisation method. We propose a novel approach that guides the initialisation of the parameters in the classification layer. This method reduces 12% the variability across repetitions and leads to accuracies 18% higher on average.
A Cognitive Modeling Approach on Ironical Phraseology in Twitter

The development and growth of social networks evidence human creativity via the use of figurative language including irony. Recent studies on modeling irony and irony detection in social media have looked at it from a traditional perspective and have focused primarily on developing natural language processing systems, thus ignoring the mental processes the participants experience during ironic speech acts. As a result, irony has been misinterpreted and mixed by the experimental literature with other disparate phenomena, such as jokes, understatements or banter. On the other hand, scholars from the field of Cognitive Linguistics have studied the cognitive processes operating in the creation of ironic remarks. With regard to this, Ruiz de Mendoza’s [9] development of the echoic account focuses on ironic discourse and categorizes verbal irony. Yet, no study to date has explored ironical phraseology in terms of cognitive modeling based on bigdata. This study, therefore, aims to examine how Spanish-speakers conceptualize and express irony in Twitter. Results revealed that irony was frequently misconceived and, as a consequence, additional cues such as explicit ironic hashtags prevented readers from interpreting the message literally, especially in explicit-echoic ironic cases. A more frequent interaction between text-hashtag as compared to text-emoji was also evinced for all potentially ironic linguistic signs. It is expected that our findings contribute to research on Spanish as a foreign language (ELE in the native-language acronym) teaching by enhancing the intercultural sensitivity in the learner, as well as to the field of computational linguistics in adding feature types.
VALUE OF HIGHER EDUCATION AS A DEMAND FACTOR

The article highlights the problem of quality of higher education in domestic universities that cannot be solved exclusively by educational institutions, because it requires to change the environment. Consumers of higher education are willing to pay for its value, depending on the career prospects and earnings of graduates. Success in career and earnings for consumers of higher education is determined by their productivity as employees or as entrepreneurs in private business. The effectiveness of University graduates depends on competition in the labor market among employers, competition for consumers and universities competition for the best teachers. Countries with a relatively high share of the public sector also presented low figures of labor productivity due to sluggish competition for skilled labor among firms and low motivation of consumers of higher education to acquire high qualifications. The ability of universities to realize their potential in creating value for consumers depends on the degree of their autonomy from government regulation. Russian universities, which are subject to a heavy burden of state regulation, for this reason may lose in global competition to foreign universities. The increase in the degree of economic freedom has a positive impact on wages in various countries of the world. Freedom from government regulation and control allows universities to make effective decisions that contribute to creating value for consumers at the lowest cost, which positively affects the efficiency of their graduates. The most important resource of universities in the process of production of utility for consumers are highly qualified scientists who are able to create innovations that bring the highest return among other investments. The growth of teachers' salaries is accompanied by the growth of the rating among universities on the quality of higher education. Attractive salary for scientists combined with high requirements for their qualifications in the best universities in the world, allows you to select the best staff for training students and produce the most effective specialists.