Reviewerly/exHarmony huggingface dataset:
A class-specific metric learning approach for graph embedding by information granulation

Graphs have gained a lot of attention in the pattern recognition community thanks to their ability to encode both topological and semantic information. Despite their invaluable descriptive power, their arbitrarily complex structured nature poses serious challenges when they are involved in learning systems. Typical approaches aim at building a vectorial representation of the graph in a suitable embedding space by leveraging on the selection of relevant prototypes that enable the use of common pattern recognition methods. An emerging paradigm able to synthesize prototypes in a data-driven fashion can be found in Granular Computing. Nonetheless, these methods often require a core dissimilarity measure defined directly in the graph domain that usually relies on a set of suitable parameters which are heavily problem-dependent. The automatic selection of these parameters is of utmost importance for building embedding spaces able to preserve the semantic contents between the structured and vector domains. In this paper, we propose an evolutionary-based approach for learning multiple dissimilarity measures tailored on each of the problem-related classes for the classification problem at hand. The learnt class-specific metrics contribute in synthesizing prototypes with high informative content related to each class by means of a Granular Computing approach. Such prototypes induce an embedding space where the graph classification can take place with common pattern recognition techniques for vector data. Tests conducted on publicly available datasets corroborate the effectiveness of the proposed approach both in terms of learning performances and interpretability of the model, as measured by the classification accuracy and number of meaningful prototypes considered in the synthesized model.
Assessment of global solar resource development

With the increasing severity of environmental problems, many countries have set energy transition targets to promote the realization of the Paris Agreement. There has been a global consensus on utilizing solar energy resources as alternatives to conventional sources to support this energy transition. In this regard, analyzing the “location,” “quantity,” and “quality” of global solar energy resources will not only assist an individual country to efficiently utilize these resources but also promote the realization of large-scale intercontinental resource utilization and complementation. This study established the basic database, model methods, and platform tools for global solar energy assessment, Then, a global solar energy resource assessment was conducted, which included the theoretical reserves (TRs), technical installed potential capacity (TPIC), and average development cost (ADC). A comparative analysis of the assessment results for all continents was also performed. After that, based on big data analysis and geographic information system (GIS) calculations, the distribution characteristics of the global solar power TPIC were calculated with the two core indicators, namely the capacity factor and ADC. Furthermore, a data-driven quantitative evaluation of the refined development potential of solar energy resources was performed. Finally, the reasonableness and coincidence analysis of the resource assessment results were verified using data from global and specifically Chinese photovoltaic (PV) bases.
Research and Application of Cloud Computing and Big Data Technology

Abstract The combined application of new network technologies with the traditional Internet and industry has enabled new businesses such as the Internet of Things and cloud computing to be gradually derived. To realize the construction of a modern society in China, great attention should be paid to the application of new network technologies such as cloud computing in actual work. Now through data integration and correlation analysis, the accuracy and innovation of information are further realized, and the overall work efficiency and work quality are improved. This paper systematically analyzes the network big data technology and applications of cloud computing and Internet of Things, and conducts research from data collection, data integration, data processing, and data governance. Its main purpose is to make the value and function of these new network technologies fully utilized
Digital literacy of indonesian prospective physics teacher: Challenges beyond the pandemic

Abstract Pandemic covid-19 reinforces the needs of digital technology in all aspects, including education. Digital literacy skills become essential for all teachers and also prospective teachers. This study aims to find an overview of the digital literacy capabilities of prospective physics teachers. This study was a quantitative descriptive research with data collection using an instrument in a questionnaire that included 29 items from 4 aspects of digital literacy: Accessing content, creating content, transliteracy, and evaluating content. The sample consisted of 171 physics education students from 3 State Universities in Indonesia. The result showed the level of digital literacy in the moderate category. From the four aspects, creating content related to creating or presenting information on various digital platforms showed lower results than accessing content, transliteracy, and evaluating content. In a prospective science teacher curriculum, creating learning content on digital platforms needs to be developed. This research finding suggests that digital literacy needs to be enhanced during the prospective science teachers’ program.
Machine Learning Algorithms based on Feature Selection Method used for the Prediction of Breast Cancer

Cancer is a dangerous disease, which is driven by abnormal growth of cells. Breast cancer is one among the most common type of cancer occurs in women. In 2012, around 12 percent of all new malignancy cases and 25 percent of all tumours were seen in women [6]. Breast cancer growth is regularly analysed in 140 of 184 nations around the world [17]. In breast cancer, when the abnormal cells spread in other part of body, then it is considered last stage of cancer. At this stage chances of survival rate are very low. The only solution is to detect the signs in its early stage. In past scientists created Computer aided diagnosis (CAD) frameworks that help the radiologist to recognize variations from the norm in a proficient way. Because of complex nature of micro calcification and masses, radiologist technology fails to analyse the sign of breast cancer. These days, advancement of technology is beneficial for increase in detection rate. Machine learning techniques are used for improved accuracy in early stage of cancer. In the machine learning technology, train the model and it predict the output on the basis of past experience. This research work presents an ensemble model by applying machine learning techniques which are Support Vector Machine (SVN), Artificial Neural Network (ANN), Random Forest (RF) using Feature Selection method on UCI repository breast cancer dataset. Feature Selection methods are commonly utilized to eliminate the unnecessary features before machine learning method could be applied.
Path Planning for Robotic Manipulators in Dynamic Environments Using Distance Information

In this paper, we present a novel algorithm – DRGBT (Dynamic Rapidly-exploring Generalized Bur Tree), intended for motion planning in dynamic environments. The main idea behind DRGBT lies in a so-called adaptive horizon, consisting of a set of prospective target nodes that belong to a predefined $\mathcal{C}$-space path, which originates from the current node. Each node is assigned a weight that depends on relative distances and captured changes in the environment. The algorithm continuously uses a suitable horizon assessment to decide when to trigger the replanning procedure. A comprehensive simulation study is performed, covering a variety of manipulators, where DRGBT is compared to a state-of-the-art algorithm. Results indicate some promising features of the proposed method.
Methodology for the implementation and use of text reuse detection systems in secondary education

The article analyses the situation with the lack of systematic control over the originality of texts of students’ written works in secondary schools, including individual projects, provided by the Russian Federal State Educational Standards. The authors point out that compliance with the academic program’s requirements to observe the norms and rules of citation and absence of plagiarism in the texts of individual projects is not ensured in practice in any way. In particular, schools have not developed evaluation criteria and procedures for monitoring. Also, there is no access to the necessary text reuse detection tools. The article substantiates the necessity for secondary education to check students’ for reused text by using specialized software on the example of the “Antiplagiat” system. As a result of the study, a methodology of introducing digital tools for text reuse detection in schools is presented. We developed such a methodology for secondary schools for the first time. It will allow avoiding the repetition of methodological errors made during the introduction of tools for text reuse detection in higher education, provide methodological support to teachers, and establish adequate criteria for assessing the originality of works, considering the specifics of the school.
Criteria and Levels of Formation of Intellectual Creativity of Future IT Specialists

The article presents the criteria and levels of formation of intellectual creativity of students in the “Applied Informatics” field of training. The significance of the study lies in the verification of the thesis that it is advisable to provide scientific and methodological support for the implementation of pedagogical conditions for the effective development of intellectual creativity of future IT specialists. The proposed conditions are focused on ensuring the purposeful mastery of systemic knowledge, skills, and competence for working with various IT technologies in the process of learning programming languages.
Collocation ranking: frequency vs semantics

Collocations play a very important role in language description, especially in identifying meanings of words. Modern lexicography’s inevitable part of meaning deduction are lists of collocates ranked by some statistical measurement. In the paper, we present a comparison between two approaches to the ranking of collocates: (a) the logDice method, which is dominantly used and frequency-based, and (b) the fastText word embeddings method, which is new and semantic-based. The comparison was made on two Slovene datasets, one representing general language headwords and their collocates, and the other representing headwords and their collocates extracted from a language for special purposes corpus. In the experiment, two methods were used: for the quantitative part of the evaluation, we used supervised machine learning with the area-under-the-curve (AUC) ROC score and support-vector machines (SVMs) algorithm, and in the qualitative part the ranking results of the two methods were evaluated by lexicographers. The results were somewhat inconsistent; while the quantitative evaluation confirmed that the machine-learning-based approach produced better collocate ranking results than the frequency-based one, lexicographers in most cases considered the listings of collocates of both methods very similar.
An Intelligent Mechanism to Automatically Discover Emerging Technology Trends: Exploring Regulatory Technology

Technology trend analysis uses data relevant to historical performance and extrapolates it to estimate and assess the future potential of technology. Such analysis is used to analyze emerging technologies or predict the growing markets that influence the resulting social or economic development to assist in effective decision-making. Traditional trend analysis methods are time-consuming and require considerable labor. Moreover, the implemented processes may largely rely on the specific knowledge of the domain experts. With the advancement in the areas of science and technology, emerging cross-domain trends have received growing attention for its considerable influence on society and the economy. Consequently, emerging cross-domain predictions that combine or complement various technologies or integrate with diverse disciplines may be more critical than other tools and applications in the same domain. This study uses a design science research methodology, a text mining technique, and social network analysis (SNA) to analyze the development trends concerning the presentation of the product or service information on a company's website. This study applies regulatory technology (RegTech) as a case to analyze and justify the emerging cross-disciplinary trend. Furthermore, an experimental study is conducted using the Google search engine to verify and validate the proposed research mechanism at the end of this study. The study results reveal that, compared with Google Trends and Google Correlate, the research mechanism proposed in this study is more illustrative, feasible, and promising because it reduces noise and avoids the additional time and effort required to perform a further in-depth exploration to obtain the information.
Implementation of Geographic Information System Base On Google Maps API to Determine Bidikmisi Scholarship Recipient Distribution in Central Sulawesi Indonesia

The presence of information technology in web-based digital mapping, especially using the Google API, has helped various parties make decisions regarding mapping to find out the distribution of various objects. Bidikmisi scholarship recipients are a group of university students who receive fundings Indonesian government every year. They come from various regions and economic backgrounds. However, the distribution of origin of students receiving the Bidikmisi scholarships has never been investigated accurately. As a result, information related to the geographical distribution of Bidikmisi scholarship recipients, both from an economic point of view and the quality of their education, has not yet been mapped. This study, therefore, developed a geographic information system (GIS) application based on google API to map the distribution of students receiving Bidikmisi scholarship based on region and economic geography. Data were gathered through surveys and interviews with experts and stakeholders from the State Islamic University of Datokarama Palu, Indonesia, to obtain information regarding the GIS design and also the criteria for Bidikmisi scholarship recipients. The results of the study show that 305 students received the scholarship from twelve regencies in Central Sulawesi, Indonesia. The origins of the students were successfully mapped throughout the province. Our study helped universities, especially in the field of student affairs and the Quality Assurance Department, to monitor the distribution of Bidikmisi scholarship recipients. Our study can be used as a reference in making quick and accurate decisions in future scholarship distribution.
Learning Component Size Distributions for Software Cost Estimation: Models Based on Arithmetic and Shifted Geometric Means Rules

Understanding software size distribution is critical to software cost estimation using COCOMO model and design of reliable production function model. This paper proposes and validates a theoretical framework based on the maximization of Shannon entropy to learn component size distribution of software systems when partial information about the moments is given. Specification of appropriate moment constraints either in the form of shifted geometric mean or arithmetic mean or both geometric and arithmetic means are considered. The models are validated using 30 real datasets. The analysis reveals that software systems where component sizes depict power-law behavior are governed by shifted geometric mean whereas those systems in which component size distribution shows exponential behavior are described by arithmetic mean. Another type of software system is also considered where the component size distribution is found to depict gamma distribution. Such systems are characterized by specification of both arithmetic and geometric means. The study underlines that the use of modern object-oriented programming languages adheres to power-law distribution indicating the existence of team synergies leading to substantial containment of software costs when compared to the use of traditional procedural programming languages.
A Strategic Approach to Russian Media System Analysis: Defining Mission, Values and Priorities

Modern media has transformed into a full-fledged industry, embedded in the national economy and inextricably linked with other industries. The strong dependence between the main economic indicators of the media industry and macroeconomic indicators determines the importance of a long-term media strategy. A successful strategy of media system development may improve the national economy and ensure social prosperity in the sense of Aristotelian eudaimonia. The research objective was to develop a new methodology for media strategizing with its mission, values, objectives, and strategic priorities. The study was based on strategy theory, media theory, mathematical economics, econometric methods of strategic media monitoring, and a new method of game-theoretic models of the three-sided market. The object of strategizing was a complex multi-level environment formed by media institutions in their interaction with each other and society, media market, infrastructure, manufacturers, developers, and distributors. The Russian media system was subjected to a primary OTSW analysis, which revealed five priorities. The author developed a quantitative assessment methodology for each priority. The research is a foundation for a separate interdisciplinary discipline “Theory of Media Strategy”.
Comparison of Different CNN Model used as Encoders for Image Captioning

Image captioning is the process of automatically describing the content of an image which connects computer vision and natural language processing. In this paper, we compare five popular convolutional neural networks architecture. They are Vgg16, InceptionV3, Resnet50, Densenet201 and Xception Model. By using these preprocessing model for same image captioning model. Encoder-Decoder model is a part of recurrent neural networks for sequence-to-sequence prediction problems. Encoders are usually used pre-trained convolutional neural networks for large datasets. There are many different types of Encoder-Decoder architecture used for generating caption. But it is very complicated to evaluate the performance of the architecture. In this paper, we used categorical-crossentropy for loss function, RMSprop for optimizer in Vgg16, Resnet50, InceptionV3, Densenet201 and Xception model.
Implementation of Blended Learning based Leseverstehen Teaching Materials in Germany Students of KKNI

The research background began with Indonesian students learning German who were less motivated in learning. This study aims to apply the effectiveness of Corel Video Studio learning media in Leseverstehen learning. The Leseverstehen course is one of the reading skills-oriented courses that must be mastered in German. In this course, students are required to make KKNI assignments. The purpose of further research is to develop the abilities of students in a learning process. The method used is quantitative descriptive, namely by using the Corel Studio video application in which there are various interesting themes from various activities. The results show that there is a significant increase in learning outcomes using Corel Video Studio in Leseverstehen learning. This study limits the study of skills to interest in learning and reading for foreign language learners. This research is urgent and important to carry out during the Covid 19 pandemic.
UCM4IoT: A Use Case Modelling Environment for IoT Systems

The engineering of IoT systems brings about various challenges due to the inherent complexities associated with such adaptive systems. Addressing the adaptive nature of IoT systems in the early stages of the development life cycle is essential for developing a complete and precise system specification. In this paper, we propose a use case modelling environment, UCM4IoT, to support requirements elicitation and specification of IoT systems. Our UCM4IoT language takes into account the heterogeneity of IoT systems and provides domain-specific language constructs to model the different facets of IoT. The language also incorporates the notion of exceptional situations and adaptive system behaviour. Our language is supported with a textual modelling environment to assist modellers in writing use cases. The environment supports syntax-directed editing, validation of use case models, and requirements analysis. The proposed language and tool is demonstrated with a smart store case study.
Construction of a Cryptographically Secure Pseudorandom Sequence Generator Based on the Blender Algorithm

The paper presents a scheme of a cryptographically secure pseudorandom sequence generator. The generator consists of three blocks that provide random data collection and processing, the internal state preparation and pseudorandom sequences generation. The main algorithm for all blocks is a blender from the 2-EXT extractor. For the first block, authors developed a special bit mixing algorithm that increases the min-entropy of raw data. The mixing procedure and the following processing with the help of a blender have shown high effectiveness. The generator software implementation results were analyzed using the NIST 800-90 and NIST 800-22 tests, as well as Pearson criterion. The study showed that the output sequences of the constructed generator have a high level of min-entropy, good statistical properties and a distribution close to uniform. The comparative analysis confirmed that the proposed generator is similar in quality of output sequences with generators based on “Kuznechik” and AES ciphers. However, application of strong blender and the special procedure of forming and using keys increase its cryptographic strength.
Risk-Based User Authentication for Mobile Passenger ID Devices for Land and Sea Border Control

Although the continuously increasing number of visitors entering the European Union through land-border crossing points or seaports brings tremendous economic benefits, novel border control solutions, such as mobile devices for passenger identification for land and sea border control, are essential to promote further the comfort of passengers. Nevertheless, the highly sensitive information handled by this type of devices makes them an attractive target for malicious actors. Therefore, novel secure and usable user authentication mechanisms are required to increase the level of security of this kind of devices without interrupting border control activities. Towards this direction, we, firstly, discuss risk-based authentication for mobile devices as a suitable approach to deal with the security vs. usability challenge. Besides that, an overview of existing risk estimation approaches – both qualitative and quantitative – is given to provide a foundation for organizing research efforts towards the design and development of proper risk estimation mechanisms for risk-based user authentication for mobile passenger identification devices used by border control officers at land and sea borders.