Reviewerly/exHarmony huggingface dataset:
Localization and delocalization in networks with varied connectivity

We study the phenomenon of localization and delocalization in a circuit-QED network with connectivity varying from finite-range coupling to all-to-all coupling. We find a fascinating interplay between interactions and connectivity. In particular, we consider (i) harmonic, (ii) Jaynes-Cummings, and (iii) Bose-Hubbard networks. We start with the initial condition where one of the nodes in the network is populated and then let it evolve in time. The time dynamics and steady state characterize the features of localization (self-trapping) in these large-scale networks. For the case of harmonic networks, exact analytical results are obtained, and we demonstrate that all-to-all connection shows self-trapping whereas the finite-ranged connectivity shows delocalization. The interacting cases (Jaynes-Cummings and Bose-Hubbard networks) are investigated both via exact quantum dynamics and via a semiclassical approach. We obtain an interesting phase diagram when one varies the range of connectivity and the strength of the interaction. We investigate the consequence of imperfections in the cavity or qubit and the role of inevitable disorder. Our results are relevant especially given recent experimental progress in engineering systems with long-range connectivity.
A Distributed Task Scheduling Approach for Cloud Computing Based on Ant Colony Optimization and Queue Load Information

Cloud computing is an important computing paradigm based on large scale distributed infrastructures offering resources to consumers in a pay-as-you-go manner. An important aspect of cloud infrastructure management is the task scheduling problem. In this problem, tasks submitted by users and encapsulated in virtual machines are allocated to compute nodes in order to optimize some performance metric. In this paper a distributed task scheduling approach based on swarm intelligence is proposed, where schedulers distributed on different nodes make local task allocation decisions based on principles of ant colony optimization. Ant colony optimization is combined with queue load information for mitigating delayed reward problem that results from high load condition. Experimental evaluation in a simulated environment shows improved results compared to a distributed scheduling approach based on ant colony or queue load information only.
EvoDCMMO: Benchmarking and solving dynamic constrained multimodal optimization problems

In the field of evolutionary computation, many dynamic optimization problems whose fitness values and/or constraints are changed over time have been studied. As the research progresses, complex dynamic optimization problems draw the attention of researchers, such as the dynamic constrained optimization problems with constraints and the dynamic multimodal optimization problems with multiple optima in each environment. Dynamic, constrained, and multimodal properties frequently appear in real-world applications, but the dynamic constrained multimodal optimization problems (DCMMOPs) that simultaneously possess all these properties have not been studied yet. This paper is the extended version of our conference work, aiming to investigate the effect of different techniques for solving DCMMOP. Compared with the previous work, we proposed a more challenging benchmark suite and designed a framework called EvoDCMMO to make further research on DCMMOPs. The new challenge of the proposed benchmark problems arises from the additional constraint based on the fitness values. More techniques, including the optimizers, niching methods, constraint handling techniques and dynamic response strategies are implemented in EvoDCMMO to carry out the comparative studies for solving DCMMOPs and discussed in our work.
Age dynamics of the teaching staff of the higher school: current trends and assessment of prospects

The article is devoted to the analysis of the problems of age transformation of the teaching staff of universities during the modernization of the higher education system. The revealed trends in the transformation of the age structure of teachers indicate a gradual “aging” of the teaching staff, which cannot be assessed as an unambiguously negative process, since in a certain sense it determines the continuity between generations of teachers and provides the necessary level of quality of training of specialists. The analysis of the dynamics of the age structure of teachers differentiated by various professional qualification segments reflects a slight shift in the career trajectories of university teachers towards an increase in age. An assessment of the risks and prospects for the development of the personnel potential of universities is given.
Narratives in the educational process of critical thinking

In modern higher education, the number of educational programs aimed at improving the quality of thinking, reasoning abilities, and creativity is growing. The campus course «Logic and Critical Thinking» is aimed at improving the intellectual dispositions of students involved in the professional and personal sphere. Narrative in the educational process is considered as a didactic technique in teaching logic and critical thinking. The purpose of the article is to analyze the narrative as a supporting construct of the emerging discursive model, to study the possibility of the narrative as a role model in the process of forming their own mental practice of the project participants. The flexible narrative model used for interpretation is designed to awaken the deep curiosity of the student, the subjective need to solve logical problems and form the skill to think critically. The author studies the experience of narrative in the concept of an oral narrative of such a form of education as a seminar lesson. The trigger mechanism of students’ reflexive activity is a narrative text, the value of the narrative is shown in the recognition of qualitative questions, their clear formulation and the direction of their own research. It is concluded that an effective pedagogical narrative is content-variable and subject-oriented. Narrativization of the educational process is important not only for the development of critical thinking of students, but also for the teacher who reflects on the issues of building a more effective educational strategy.
Design of A Real-Time Object Detection Prototype System with YOLOv3 (You Only Look Once)

Object detection is an activity that aims to gain an understanding of the classification, concept estimation, and location of objects in an image. As one of the fundamental computer vision problems, object detection can provide valuable information for the semantic understanding of images and videos and is associated with many applications, including image classification. Object detection has recently become one of the most exciting fields in computer vision. Detection of objects on this system using YOLOv3. The You Only Look Once (YOLO) method is one of the fastest and most accurate methods for object detection and is even capable of exceeding two times the capabilities of other algorithms. You Only Look Once, an object detection method, is very fast because a single neural network predicts bounded box and class probabilities directly from the whole image in an evaluation. In this study, the object under study is an object that is around the researcher (a random thing). System design using Unified Modeling Language (UML) diagrams, including use case diagrams, activity diagrams, and class diagrams. This system will be built using the python language. Python is a high-level programming language that can execute some multi-use instructions directly (interpretively) with the Object Oriented Programming method and also uses dynamic semantics to provide a level of syntax readability. As a high-level programming language, python can be learned easily because it has been equipped with automatic memory management, where the user must run through the Anaconda prompt and then continue using Jupyter Notebook. The purpose of this study was to determine the accuracy and performance of detecting random objects on YOLOv3. The result of object detection will display the name and bounding box with the percentage of accuracy. In this study, the system is also able to recognize objects when they object is stationary or moving.
Information and Communication Technology Competencies Clustering For Students For Vocational High School Students Using K-Means Clustering Algorithm

The k-Means Clustering algorithm is intended to partition data into one or more groups, where data that has similarities in one group and data has differences in another. Information and Communication Technology (ICT) Competency data clustering in educational units is considered necessary to facilitate educational facilitation based on the differences in student abilities, determine advanced ICT guidance groups and become a reference in determining the place of Industrial Work Practices (Prakerin). This study aims to find out how the K-Means Clustering algorithm can be applied in clustering the ICT competencies of students at the State Vocational High School (SMK) 3 Lhokseumawe. The benefits generated in this study are in the form of visualization of data clustering that can help teachers and school management in formulating ICT policies at SMKN 3 Lhokseumawe. The data used in this study is the Information and Communication Technology (ICT) competency test score data for the 2021/2022 academic year. The data was obtained through a competency test process that refers to the Minister of Education and Culture Regulation Number 45 of 2015 concerning the Role of ICT/KKPI Teachers in the Implementation of the 2013 Curriculum where ICT competence includes the skills to search, store, process, present and disseminate data and information. Data processing in this study uses the K-means Clustering method and the RapidMiner application. Data processing using the RapidMiner application starts with data preparation, determining the number of clusters, and configuring the method. This study uses 3 (three) cluster configurations, namely the Very Competent, Competent, and Less Competent clusters. Testing data processing using the RapidMiner application resulted in 80 (eighty) students in cluster_0 with a Very Competent rating, 64 (sixty-four) students in cluster_1 with a Competent rating, and 10 (ten) students in cluster_2 with a Less Competent rating.
Data Structures and Algorithms with Python

Data Structures allow the efficient organization of data to enhance various data operations. They are critical and core to solving complex computer problems and providing complete solutions. Implementing Data Structures results in reusable code that can be applied on a variety of everyday computational and algorithmic tasks. This chapter introduces to some of the most basic data structures including static and dynamic stacks and queues, trees and binary trees and graphs. It also explains algorithmic concepts such as searching and sorting both on static and dynamic structures.
Heuristics for computer-mediated human interactions based on consumer rights

It highlights the importance of human relations mediated by computers, since these relations are placed before the existence of a democratic state of law. Thus, it points out the obligations that the design of this type of construction must have, observing principles established in consumer law, such as the principles of vulnerability and information. It establishes the discussion within the democratic context by considering the construction of systems and interfaces as scalable innovations that can interfere in society as it is known. It also establishes 7 heuristics to be observed within the field of computing derived from consumer rights based on a documental analysis of legal devices, as well as a bibliographic review of scientific literature in the legal field. The research is based on other areas of knowledge, such as sociology, philosophy, laws, and computer science. It concludes by proposing softwares needs to observe the democratic logic in order to respect the legal guarantees.
Students’ Subjectness, Self-Efficacy and Psychological Well-Being: Comparative Research of Russian and Kazakhstan Students

The relevance of the study is due to the need to resolve the contradictions between the requirements of higher education for the personality of a young person and his real capabilities, manifested in the ability or inability to be the subject of his activity and his development. The need to resolve this contradiction has determined the purpose of this study – to identify the level of expression and structure of the subjective properties of students’ personality, their relationship with self-efficacy and psychological well-being. The work used a set of theoretical and empirical research methods, in particular, questionnaires to identify the level of an individual subjectivity development (M.A. Shchukina), general and social self-efficacy (M. Sherer and J. Maddux, adaptation by A.V. Boyarintseva), psychological well-being (K. Riff, adaptation by T.D. Shevelenkova and P.P. Fesenko). Mathematical processing was carried out using the criterion ϕ* – Fisher’s angular transformation, as well as methods of correlation analysis using the linear Pearson correlation coefficient. The subjects were students of a number of universities of the Russian Federation and the Republic of Kazakhstan. A total of 277 students majoring pedagogy and psychology took part in the study, among them 266 (96%) women, 11 (4%) men, average age 19.5 years (SD=1.5). As a result, no fundamental differences were revealed in the structure and manifestations of subjectivity depending on the place of residence, national and cultural traditions, which indicates the existence of some general patterns in the formation of the subjectivity of a future teacher or psychologist. A high level of subjectivity was identified in 57.04% respondents, medium – in 38.98%, low – in 3.98%. It has been established that the level of subjectivity positively correlates with self-efficacy in objective activity, with social self-efficacy and with the psychological well-being of an individual. It was revealed that students with low subjectivity can also have a high level of psychological well-being, while performing a compensatory function. The results obtained can be used in the course of student training, as well as in the work of curators of student groups and in the activities of the psychological service of universities.
A Study of Integrating Multicultural Education into Primary English Based on Multicultural Children’s Literature: Focusing on Pre-service Teacher Education

Objectives This study is to examine the effects of multicultural pre-service teacher training for the integration of multicultural and English education. It aims to study how intensive teacher training affects teaching strategies utilizing multicultural children’s literature and teachers’ multicultural attitude. It also investigates positive and negative factors in applying what they have learned from teacher training to educational contexts. Methods Mixed methods of quantitative and qualitative research methods were adopted. Attitude toward ract/ethnic diversity, multicultural understanding and attitude, English reading and writing ability were analyzed by utilizing SPSS/PC+ 21.0, and teaching journals, work samples, and interview data were also examined through phenomenological research method. Results The findings of quantitative data analysis using t-test showed that multicultural education using children’s literature positively affected both teachers’ and students’ multicultural attitude. The differences were statistically significant. Through the teacher training, per-service teachers could understand how to utilize multicultural children’s literature using culturally responsive pedagogy and how to integrate English and multicultural education. In the teaching contexts, using multicultural picture books had positive impacts on understanding diverse cultures, and positively affected students’ English reading and writing ability. Engaging with multicultural picture books gave students the opportunities to actively participate in the lessons. On the other hand, there were cases where students won’t accept different perspectives about other cultures. Some pre-service teachers also pointed out that meeting with multicultural students in the real world would be integrated into the teacher education program. Conclusions This study addressed not only the positive impacts of teacher training on implementing English and multicultural education utilizing multicultural children’s literature, but also challenges in implementing multicultural education in educational contexts.
In pursuit of the hidden features of GNN’s internal representations

We consider the problem of explaining Graph Neural Networks (GNNs). While most attempts aim at explaining the final decision of the model, we focus on the hidden layers to examine what the GNN actually captures and shed light on the hidden features built by the GNN. To that end, we first extract activation rules that identify sets of exceptionally co-activated neurons when classifying graphs in the same category. These rules define internal representations having a strong impact in the classification process. Then – this is the goal of the current paper – we interpret these rules by identifying a graph that is fully embedded in the related subspace identified by the rule. The graph search is based on a Monte Carlo Tree Search directed by a proximity measure between the graph embedding and the internal representation of the rule, as well as a realism factor that constrains the distribution of the labels of the graph to be similar to that observed on the dataset. Experiments including 6 real-world datasets and 3 baselines demonstrate that our method DISCERN generates realistic graphs of high quality which allows providing new insights into the respective GNN models.
Does Blockchain Technology Promote the Quality of Enterprise Accounting Information?

Purpose - A crucial technology of the information society, blockchain technology has been introduced to more and more companies because blockchain technology can improve operation efficiency. This study aims to find how to apply blockchain technology to improve the quality of accounting information, so as to help investors better understand the company.&#x0D; Design/Methodology/Approach - This study conducts a fixed-effect regression analysis on the data of Chinese A-share listed companies from 2010 to 2019 to examine whether blockchain technology impacts the quality of accounting information.&#x0D; Findings - The empirical results show that investment in blockchain technology indeed positively affects the quality of enterprise accounting information. Furthermore, we found that the greater the attention of analysts, the higher the degree to which blockchain technology improves the quality of accounting information. Also, when the company has a stronger innovation ability, there is a higher degree to which blockchain technology improves the quality of accounting information.&#x0D; Research Implications - This study enriches the relevant research of the impact of blockchain technology on enterprise accounting information quality. It suggests that companies devoted to blockchain technology can promote trust between all parties and improve information transparency. Meanwhile, companies should increase R&amp;D investment so that blockchain can effectively improve the quality of accounting information.
A Modular and Extendable GNSS Python Library

This paper introduces gnss lib py as a modular Python tool for parsing, analyzing, and visualizing Global Navigation Satellite Systems (GNSS) data. gnss lib py provides an intuitive and modular framework allowing users to quickly prototype, implement, and visualize the results of GNSS algorithms. gnss lib py is modular in the sense that multiple types of algorithms can be easily exchanged for each other and applied on multiple data sources with minimal modification. gnss lib py is extendable as its functionality is documented and tested, which facilitates user-specific extensions of existing implementations. This paper describes the design considerations behind the creation of the gnss lib py Python library, the existing functionality of the library, and example usages of the library. gnss lib py is open-source on GitHub: github.com/stanford-navlab/gnss lib py.
Forming a Model for Innovative Strategies of Universities in Bulgaria

In a context of change and uncertainty for universities, it is important to identify, invest and support their vision for innovation in teaching, learning and assessment – in such a way as to adapt to the unknown. Innovation is part of the culture and is integrated into the strategy, operational plans and vision. The analysis of the genesis of innovative approaches to the development of universities shows that it is an example of increasing the sustainability of the educational system in the evolutionary period of development. The main purpose of this article is to clarify the types of innovations and innovation strategies of universities in Bulgaria. The main result of this article is the creation of a model for the formation of innovation strategies of the university. The main research methods used in the development are the method of analysis and synthesis, questionnaire research, intuitive and systematic approach.
A Patch-Based Algorithm for Diverse and High Fidelity Single Image Generation

Image generation is the task of producing new samples from one or several example images. Until recently, this has been done using large image databases, in particular using Generative Adversarial Networks (GANs). However, Shaham et al. [1] recently proposed the SinGAN method, which achieves this generation using a single image example. At the same time, researchers are realizing that classical patch- based methods can replace certain neural networks, with no costly training. In this paper, we present a purely patch-based method, named Patches for Single image generation (PSin), which requires no training and generates samples in seconds. Our algorithm is based on the minimization of a global, patch- based energy functional, which ensures the visual fidelity of the result to the original image. We also ensure diversity of the results by carefully choosing the initialization of the algorithm. We propose two initialization variants. We compare our results to both the original SinGAN and another recent patch-based image generation approach, both qualitatively and quantitatively using multiple metrics.
H-DAC: discriminative associative classification in data streams

Abstract In this paper, we propose an efficient and highly accurate method for data stream classification, called discriminative associative classification . We define class discriminative association rules (CDARs) as the class association rules (CARs) in one data stream that have higher support compared with the same rules in the rest of the data streams. Compared to associative classification mining in a single data stream, there are additional challenges in the discriminative associative classification mining in multiple data streams, as the Apriori property of the subset is not applicable. The proposed single-pass H-DAC algorithm is designed based on distinguishing features of the rules to improve classification accuracy and efficiency. Continuously arriving transactions are inserted at fast speed and large volume, and CDARs are discovered in the tilted-time window model. The data structures are dynamically adjusted in offline time intervals to reflect each rule supported in different periods. Empirical analysis shows the effectiveness of the proposed method in the large fast speed data streams. Good efficiency is achieved for batch processing of small and large datasets, plus 0–2% improvements in classification accuracy using the tilted-time window model (i.e., almost with zero overhead). These improvements are seen only for the first 32 incoming batches in the scale of our experiments and we expect better results as the data streams grow.