Reviewerly/exHarmony huggingface dataset:
Operational experience of a Solar Power Plant with a Dual-Axis Solar Tracking System in the conditions of the Southern Urals

The paper substantiates the need for the introduction of solar tracking systems for photovoltaic plants and presents a brief review of the scientific and technical literature on the development of solar trackers. Principal scheme of a heliostation with a dual-axis solar tracker of Orenburg State University has been designed. A comparative analysis of the generated electricity by a statically located solar module and a solar module with the dual-axis solar tracker is represented. Data for analysis have been obtained in Orenburg (Russia) using the developed automated system for remote diagnostics and monitoring of photovoltaic system parameters and a wireless weather station HP2000. The use of a dual-axis heliotracker for the solar plant in the conditions of the Southern Urals has allowed to increase the generation of electricity by 34.7% compared to a statically located PV panel.
Discrete signals: sampling, quantization and coding

Abstract Digital signal processing and machine learning require digital data which can be processed by algorithms on computer. However, most of the real-world signals that we observe are real numbers, occurring at real time values. This means that it is impossible in practice to store these signals on a computer and we must find some approximate signal representation which is amenable to finite, digital storage. This chapter describes the main methods which are used in practice to solve this representation problem.
Context-Aware Applications for Disaster Management: A Systematic Literature Review

Activities in all phases of disaster management, i.e. disaster risk analysis, disaster preparedness, early warning systems, emergency relief, and reconstruction, have used information technology (IT) applications intensively. Nowadays context-aware applications are widely used as they can adapt their behavior to their environment or contexts. The objective of this study is to investigate the extent to which context-aware applications are used in disaster management. The study is carried out as a systematic literature review on 14 selected papers from leading academic databases in the fields of computer science and information technology. The result shows that the applications are mainly for emergency relief and targeted to institutions that are responsible for disaster management. The applications use context in decision making, decision support, prediction, and instruction. While many applications already use a number of contexts, not many applications use contexts with different types, i.e. computing, user, physical, and time contexts. Also, we identify four generic components of the applications, namely device, sensor, communication protocol, and processing.
Assessing the Impact of Enterprise Software Design Patterns on Maintainability: A Case Study

Software design patterns are used to improve the software quality. There are distinct kinds of software, as well as design patterns. One of them is patterns for enterprise application architecture. Although enterprise design patterns are said to be able to improve the software quality, there is a lack of literature that discusses the evidence of the impact. This preliminary study proposes a method to assess the impact of enterprise software design pattern on maintainability. We use the Academic Information System (AIS) of Institut Teknologi Sepuluh Nopember (ITS) as a case study. It is enterprise software which is frequently maintained and need to be evolved. The software maintainability is measured by using C&K metrics. Then we calculate the relative change and evaluate it based on viewpoints of the metrics. Transaction Script pattern is selected to verify the method. As a result, it decreases the maintainability instead of improving it. The result of this preliminary study shows that design patterns did not improve the software maintainability. Based on the previous researches, it depends on several factors which may be studied in the future work. Thus, we need to propose a method to select the appropriate patterns or whether we use design patterns or not.
Foreground Object Image Masking via EPI and Edge Detection for Photogrammetry with Static Background

In automated photogrammetry of a small object, rotating the object provides an easier setting and more stable camera positions than moving the camera around the object. However, the static features in the background can confuse the structure from motion, which leads to the failure of reconstruction. We are addressing the problem by proposing a masking algorithm based on light field epipolar-plane images (EPIs). Using a simple EPI analysis and edge detection technique, a single light field image is enough to create an initial mask, which acts as a region of interest for an edge image. Lastly, binary morphological techniques are applied to obtain the final mask image. The result shows promising performances of 93.84% recall and outperforms comparable algorithms in accuracy, precision, JI, and F1 scores with 98.39%, 97.75%, 91.86%, and 95.75%, respectively.
Development of web platform for dissemination of design with social impact methodology

Since the distant history of mankind, the phenomenon of the social impact of design is noticed. The term design with social impact has appeared in frequent use for the last decade, but the interest of designers for the impact of their works in social change has long been there. The designer's approach has undergone major changes over the years, focusing on the outward appearance of products, their features, usability, versatile customer testing, to the involvement of users in the design process. The main goal of this master's thesis is to study the essence of the approach design with social impact and its importance in today's society, to study the methods that emerge from it and to contribute to the promotion, dissemination and application of the knowledge and experiences from the realized activities of this field in our society. The research emphasized the study of the most modern scientific methods in the field of design, as ethnographic research and grounded theory. In order to clarify and define the essence of this methodology, realized projects from the held workshops within the international project "Design with Social Impact" were presented and analyzed. The drawn conclusions are presented in the form of guidelines on how it should be applied, what are its advantages, and why its application is useful. As a final result of this research a web platform was created aimed at spreading the idea about the possibilities of the methodology design with social impact in our society as a factor that can affect the social changes, and at the same time as an opportunity to exchange knowledge and experiences of the designers. This paper is expected to promote the methodology of design with social impact in our surroundings in order to contribute to raising awareness about the importance of designers and the creative way of thinking as an important factor for solving numerous problems in contemporary communities and contemporary living.
Design of Low-Power USB Audio System Based on LPM Protocol

Universal Serial Bus (USB) audio Devices was applied extensively in current mobile communication equipment, at the same time the low power consumption and high USB bandwidth must be satisfied, we design audio equipment based on USB2.0 High Speed (HS), adopt the latest USB Link Power Management (LPM) protocol issued by USB organization, use Field Programmable Gate Array (FPGA) as the main controller, USB2.0 HS physical layer interface (PHY) was designed externally, and then the USB audio class 3.0 equipment was realized to play music. System analysis and test results show that the system can transmit based on USB2.0 HS, and the average power consumption is lower than the USB1.1 audio equipment, has solved the demand of the high bandwidth and low power consumption of USB audio device effectively.
Target Tracking Based on Visual Saliency Combining Super-Pixel Segmentation and Conditional Number Blocking

Accurate characterization of targets, target occlusion and other complex changes are common problems in video tracking. Studies have shown that human visual saliency mechanisms can screen targets of interest and suppress background. Inspired by the visual salience, this paper proposes a target tracking method combining super-pixel segmentation and conditional number blocking based on visual saliency tracking. The super-pixel region of the target image is generated by using the super-pixel segmentation algorithm, and the segmentation of the tracking target is realized by combining the condition numbers of the Hessian matrix. Next, we judge whether the target block is blurred or not and modify the corresponding target block. Then, we construct the association of each block according to the distance between the block and the target center point, and then extract the Hog features of the relevant samples. Finally, the final target position is determined according to the Bayesian classifier. The method was evaluated on 50 public test sequences of OTB-2013 dataset. Experimental data and analysis show that our method surpasses some other advanced tracking methods, which can effectively solve the fuzzy problem in tracking and improve real-time performance.
Semantic tracking and recommendation using fourfold similarity measure from large scale data using hadoop distributed framework in cloud

Purpose The purpose of this paper is to propose a fourfold semantic similarity that results in more accuracy compared to the existing literature. The change detection in the URL and the recommendation of the source documents is facilitated by means of a framework in which the fourfold semantic similarity is implied. The latest trends in technology emerge with the continuous growth of resources on the collaborative web. This interactive and collaborative web pretense big challenges in recent technologies like cloud and big data. Design/methodology/approach The enormous growth of resources should be accessed in a more efficient manner, and this requires clustering and classification techniques. The resources on the web are described in a more meaningful manner. Findings It can be descripted in the form of metadata that is constituted by resource description framework (RDF). Fourfold similarity is proposed compared to three-fold similarity proposed in the existing literature. The fourfold similarity includes the semantic annotation based on the named entity recognition in the user interface, domain-based concept matching and improvised score-based classification of domain-based concept matching based on ontology, sequence-based word sensing algorithm and RDF-based updating of triples. The aggregation of all these similarity measures including the components such as semantic user interface, semantic clustering, and sequence-based classification and semantic recommendation system with RDF updating in change detection. Research limitations/implications The existing work suggests that linking resources semantically increases the retrieving and searching ability. Previous literature shows that keywords can be used to retrieve linked information from the article to determine the similarity between the documents using semantic analysis. Practical implications These traditional systems also lack in scalability and efficiency issues. The proposed study is to design a model that pulls and prioritizes knowledge-based content from the Hadoop distributed framework. This study also proposes the Hadoop-based pruning system and recommendation system. Social implications The pruning system gives an alert about the dynamic changes in the article (virtual document). The changes in the document are automatically updated in the RDF document. This helps in semantic matching and retrieval of the most relevant source with the virtual document. Originality/value The recommendation and detection of changes in the blogs are performed semantically using n-triples and automated data structures. User-focussed and choice-based crawling that is proposed in this system also assists the collaborative filtering. Consecutively collaborative filtering recommends the user focussed source documents. The entire clustering and retrieval system is deployed in multi-node Hadoop in the Amazon AWS environment and graphs are plotted and analyzed.
A Scientometric Review of Research Evolution in Digital Forensics

In this paper, we reviewed the scientific literature of digital forensics between 1990 to 2019. The visualized charts of keyword cloud, surging keywords and co-citation networks were generated by CiteSpace V. Based on the analysis, it was found that: (1) the research contents of digital forensics are focused on eight aspects. They are basic theory and methods, physical equipment and forensic methods, image forgery identification, file recovery and data extraction, smart phones and social network forensics, case-based forensics and crime forensics, automatic identification technology and tools, cloud computing and cloud forensics. (2) The research evolutional paths were followed by storage media, image forensics technologies, data recovery technologies, encryption and decryption technologies, human and social characteristics. (3) The breakthrough of image forgery identification technology accelerated the development of digital forensics; smart phone and social network forensics and automated forensics technologies bring important paradigm shift; in the future, automatic forensics and cloud forensics will be hot research directions.
Literature Search—Reactance in Literature

The first step in the literature search was finding relevant papers that dealt with reactance in the context of human–computer interactionHuman-computer interaction. In order to find such papers, a literature search was performed in the ACM digital library ( https://scholar.google.com/ ) publication database, Google Scholar ( https://dl.acm.org/ ), as well as in ScienceDirect ( https://www.sciencedirect.com/ ) and also in the references of identified papers. The results of the research that is described in Chaps. 2 and 3 have been published in [7]. The texts in this chapter roughly follow the published paper but data has been reanalyzed. That publication also includes the publications [5, 6] which were published by the author of this thesis. These publications cover experiments that are presented in Chaps. 4 and 8 and are therefore not included in the current literature analysis.
Intelligent Workload Scheduling in Distributed Computing Environment for Balance between Energy Efficiency and Performance

Global digital transformation requires more productive large-scale distributed systems. Such systems should meet lots of requirements, such as high availability, low latency and reliability. However, new challenges become more and more important nowadays. One of them is energy efficiency of large-scale computing systems. Many service providers prefer to use cheap commodity servers in their distributed infrastructure, which makes the problem of energy efficiency even harder because of hardware inhomogeneity. In this chapter an approach to finding balance between performance and energy efficiency requirements within inhomogeneous distributed computing environment is proposed. The main idea of the proposed approach is to use each node’s individual energy consumption models in order to generate distributed system scaling patterns based on the statistical daily workload and then adjust these patterns to match the current workload while using energy-aware Power Consumption and Performance Balance (PCPB) scheduling algorithm. An approach is tested using Matlab modeling. As a result of applying the proposed approach, large-scale distributed computing systems save energy while maintaining a fairly high level of performance and meeting the requirements of the service-level agreement (SLA).
FORMATION OF FUTURE FOREIGN LANGUAGE TEACHERS’ PROJECT COMPETENCE (BY THE EXAMPLE OF MORDOVIAN STATE PEDAGOGICAL INSTITUTE NAMED AFTER M. E. EVSEVIEV)

The article reveals the importance of a future teacher’s project competence under the conditions of new educational standards, examines the content and structure of this competence. The author clarifies the definition of a foreign language teacher’s project competence, describes its structure, introduces a model of its formation, and identifies the criteria to assess the level of this competence. Special attention is paid to practical tasks aimed to form future teachers’ readiness to organize project-based activity at foreign language lessons.
Deformation of Properties of Identity System in the Process of Professional Burnout in Specialists of the Deontological Profile

According to the system approach, a person is considered as a multidimensional self-developing system, while identity is a reflection in the personality consciousness of its individual "dimensions". Being an element of self-consciousness, individual identities, in turn, are organized into a system reflecting the holistic view of the personality about itself, i.e. Me-concept. Professional activity is one of the most important areas of life. At the level of self-awareness, the attitude towards one’s profession and oneself as a professional is reflected in professional identity. Professional identity begins to form at the stage of vocational education and is often maintained even after retirement. Any significant contradictions that arise during the professionalization are reflected in one’s identity. The research features the changes that arise in the system of identity of the individual in the process of its professional burnout. The results of the empirical study of professional burnout of doctors substantiate the changes in the properties of the identity system, expressed in the increase of diffuse and predetermined identity. It also affects its structure and reduces the importance of professional, communicative and reflexive identity, as well as the structural deformation of the professional identity. The work with self-consciousness and identity is an important component of prevention and correction of professional burnout.
Design Processes and Decisions

This chapter discusses the design of serious games and presents the CENTRIC Serious Games Developmental Framework to guide design processes and decisions. Considering aspects such as pedagogy, immersion, fidelity and presence, this framework discusses a holistic design method, which utilises the potential of co-development to create a successful serious game.
Optimizing Natural Language Processing Pipelines: Opinion Mining Case Study

This research presents NLP-Opt, an Auto-ML technique for optimizing pipelines of machine learning algorithms that can be applied to different Natural Language Processing tasks. The process of selecting the algorithms and their parameters is modelled as an optimization problem and a technique was proposed to find an optimal combination based on the metaheuristic Population-Based Incremental Learning (PBIL). For validation purposes, this approach is applied to a standard opinion mining problem. NLP-Opt effectively optimizes the algorithms and parameters of pipelines. Additionally, NLP-Opt outputs probabilistic information about the optimization process, revealing the most relevant components of pipelines. The proposed technique can be applied to different Natural Language Processing problems, and the information provided by NLP-Opt can be used by researchers to gain insights on the characteristics of the best-performing pipelines. The source code is made available for other researchers. In contrast with other Auto-ML approaches, NLP-Opt provides a flexible mechanism for designing generic pipelines that can be applied to NLP problems. Furthermore, the use of the probabilistic model provides a more comprehensive approach to the Auto-ML problem that enriches researcher understanding of the possible solutions.
Cross-disciplinary Collaborations to Enrich Access to Non-Western Language Material in the Cultural Heritage Sector

The British Library is home to millions of items representing every age of written civilisation, including books, manuscripts and newspapers in all written languages. Large digitisation programmes currently underway are opening up access to this rich and unique historical content on an ever increasing scale. However, particularly for historical material written in non-Latin scripts, enabling enriched full-text discovery and analysis across the digitised output, something which would truly transform access and scholarship, is still out of reach. This is due in part to commercial text recognition solutions currently on the market today having largely been optimised for modern documents and Latin scripts. This paper will report on a series of initiatives undertaken by the British Library to investigate, evaluate and support new research into enhancing text recognition capabilities for two major digitised collections of non-Western language collections: printed Bangla and handwritten Arabic. It seeks to present lessons learned and opportunities gained from cross-disciplinary collaboration between the cultural heritage sector and researchers working at the cutting edge of text recognition, with a view towards informing and encouraging future such partnerships.
A Design and an Implementation of Forecast Sentence Extractor

Strategic planning is a practical approach for researchers to conduct the STEEP analysis. One of the most promising approaches for strategic planning is the Foresight Framework. In the very first steps of Foresight Framework, however, the environmental scanning is involved. This process is time-consumed since a very large amount of data must be explored. To alleviate the time in such a process, this study proposes a design and an implementation of the forecast sentence extractor by using natural language processing and machine learning algorithm. The proposed algorithm digests a long article and then provides a short list of forecast sentences. Three feature selection approaches are tested. From the experimental studies, the accuracy of the proposed algorithm is up to 85.10%.
Bayesian assessment of predictors’ contributions to variation in the predictive performance of a logistic regression model

The logistic regression model is the algorithm most commonly applied in business analytics applications for classifying objects into binary categories among industrial users. This paper presents a Bayesian approach to assessing the contributions of predictors to the predictive performance of the classification model. Our proposed approach has two novel features that distinguish it from the usual approaches for such purpose. First, our approach ranks different predictors based on their contributions to variation in a model’s predictive performance, thus addressing the challenges of prediction risk and suggesting modelling strategy. Second, our approach can evaluate the contributions of every individual predictor each pair of two predictors. Hence, it can provide valuable information for managers on highly defined and detail-oriented business inquiries, complementary to the routine information conveyed by the usual methods for variable and feature selection purpose. We demonstrate the proposed approach using an example in credit risk management.
Development of webquest using Google Site in teaching Circulatory System

Abstract This study aimed to develop a webquest in teaching circulatory system using Google site for Grade VI pupils. The developed webquest was an instructional material designed to enhance pupil’s performance in engaging learning activities especially in science lessons based from the K-12 Science Curriculum. The developed webquest was evaluated and rated Excellent by the ICT Experts, In-Service Teachers and Pre-Service Teachers. The comments and suggestions were used to improve the developed webquest. The researchers also made use of the Flesh-Kencaid Readablity Test to determine the readability of the developed webquest and the results revealed that it is appropriate for Grade VI pupils.