Reviewerly/exHarmony huggingface dataset:
ADAPTIVE MODULE AS AN EFFECTIVE MEANS OF TEACHING MATHEMATICS TO FIRST-YEAR STUDENTS WITH DIFFERENT LEVEL OF SECONDARY SCHOOL MATHEMATICS SKILLS

Statement of the problem. Over the past decade, there has been a decrease in the level of mathematics skills of secondary school students all over the world, resulting in the disruption of the continuity of mathematical education in the transition from secondary school to university. The purpose of the study is to analyse the mathematics placement test results of first-year students of the Siberian Federal University for the period from 2015 to 2018, as well as to assess the effectiveness of using an adaptive electronic learning resource (AELR) in the process of mastering the module of a mathematical discipline by students with different level of secondary school training. Methodology. The analysis of the placement test results and the learning outcomes of the students are carried out using correlation analysis. When developing the AELR, technologies of adaptive learning and microlearning are used. We also follow the didactic principles of modern higher education. Results and conclusions. The data analysis has revealed a number of topics of a secondary school mathematics course, the level of retained knowledge of which is below average from year to year. However, there is no significant correlation between the level of school mathematics skills of first-year students and their results of mastering the module of a mathematical discipline. It has been shown that the use of the AELR in the learning process allows all the students to achieve positive outcomes regardless of their level of school training in mathematics.
The Path Analysis of Rural Community Education Service on Rural Revitalization Strategy in China

In the new era, China has made an important strategic deployment of rural revitalization, reflecting the comprehensive realization of well-off new tasks and new requirements, which is the common aspirations and pursuit of the majority of farmers.The rural community education is the key strategic support to realize the rural revitalization, the development of rural community education is the basic premise of building a modern countryside and an important way to train new quality rural residents.However, the rural community education in China is confronted with such constraints and challenges as low status, imperfect system, weak faculty and weak sustainable development.Based on the current situation of rural community education, this paper proposes specific development strategies, so as to promote the better development of rural communities in China and help rural areas realize rural revitalization as soon as possible.
Emerging Computer Vision Based Machine Learning Issues for Smart Cities

Machine learning algorithmsKhan, Muhammad Murtaza boast remarkable predictive capabilities and deep learning, a branch of machineIlyas, Muhammad Usman learning, has already provided the much required breakthroughs forSaleem, Sajid recognition and authentication. This has enabled the deploymentAlowibdi, Jalal S. of a face recognition based biometric identification systemAlkatheiri, Mohammed Saeed by the Department of Homeland Security at U.S. airports. Deep learning algorithms require huge amounts of data for training. However, this shall not be an issue in smart cities. Instead, the ability to use the same deep learning technology for ulterior motives raise some issues. Machine learning algorithms have already been developed which can generate fake images and videos and rendering humans incapable of differentiating between real and generated content. Such content can be used for spreading disinformation regarding individuals and may lead to legal issues. Any system, relying on face recognition may be mislead using such technology. Similarly, researchers were able to create master finger prints, i.e., a set of finger prints that may be used instead of an original finger print to defeat authentication biometric systems. Alongside data pollution, these image and video processing issues may present significant challenges to governance of smart cities that shall rely on automated processing of such data. This paper presents an introduction to interpretability, attempts made to improve understanding of ML algorithms and their results. Area where we believe the definition of interpretability is lacking are also highlighted and a few example scenarios are presented. Finally, some possible directions for addressing the raised issues are introduced.
Onto2DB: towards an eclipse plugin for automated database design from an ontology

Ontologies are spreading more and more in the field of information technologies as a privileged solution allowing the formalization of knowledge. The theoretical model of ontologies is most promising. They are increasingly ubiquitous given the benefits they present. Despite the proliferation of research proposing approaches dedicated to the design of a database from an ontology, the tools to design a database from an ontology are rare or inaccessible. Thus, in this contribution, we present our approach for the development of an Eclipse Plug-in, in order to automatically generate a conceptual model of a relational database from an ontology. To evaluate the usefulness of our approach, we used our resulting Eclipse Plug-in to automatically generate a conceptual model of a relational database from an ontology, customize it, and automatically generate the corresponding SQL script for Data Definition. The results of this experiment showed that our Plug-in constitutes a concretization of our approach and a means of automatic translation from the ontological model to the relational model.
Textometric Methods and the TXM Platform for Corpus Analysis and Visual Presentation

Textometric approach has long been applied as a useful method for corpus analysis in various fields of humanities and social sciences.Textometry allows the non-linear quantitative and qualitative study of digital corpora, combining lexicometric and statistical research with developed corpus technologies.In this paper, the current version of the srpELTeC corpus was analyzed within the TXM program environment to illustrate the possibilities of the textometric approach and visual presentation of the obtained results.
The formation of conceptual-categorical apparatus of research methodology to the problems of education management

Т. В
A data modeling conceptual framework for ubiquitous computing based on context awareness

This paper introduces a framework for data modeling to support ubiquitous computing based on context-awareness. Data always grow in term of volume, variety, velocity, and value. The problem arises when it grows exponentially. Consequently, data is anywhere and requirements change in early data definitions then data design become not as the plan. Therefore, suitable approach with new paradigm and methods of data modeling needs to be enhanced to solve the problems in the real world. Data model must consider the active object that related to each other. Any objects may interact with each other in a ubiquitous way and recorded in digital technology. Sensors, actuator devices, and radio frequency identification technology may support communication between objects through ubiquitous computing. The data model in Ubiquitous Computing needs to restructure to become active and dynamic. Ubiquitous computing is a model that enable all objects around the people to communicate and invisible. In order to support this paradigm, a new perspective of how data are designed and stored on each object is needed. Furthermore, using ubiquitous computing, the pervasive network can request and response information, which means the devices may communicate and has the initiative to solve a problem without human intervention. Human wants more intelligence objects. Therefore, more sensors and memory are required. Data structures need to enhance or embedded into any devices that interact with the human.
PENGEMBANGAN PAKAR IPS (PAPAN KARAMBOL IPS) SEBAGAI MEDIA PEMBELAJARAN IPS KELAS IV SEKOLAH DASAR

&lt;p&gt;The background of this research is the implementation of a very minimal educational game tool and students are less attentive to the teacher's explanation during the learning process. So it affects the value of low students. This research aims to develop the instrument of Educational games Carrom Board IPS (EXPERTS IPS) on the subject of IPS materials economic activity and natural Resources class IV Elementary School and know the feasibility of the use of educational game tools of the EXPERT IPS. The research was conducted at the state Elementary School in Bekasi Regency with research subjects amounting to 8 students of IV A class. The type of research used is Research and Development (R&amp;amp;D). The development procedures used to refer to ADDIE's research and development model. The instruments used are observation and poll. The IPS EXPERT has been through the revision stage based on suggestions and input from 3 validators. The results showed that the resulting IPS EXPERTS deserves to be used as an educational tool game material of economic and natural resources in grade IV Elementary School. This is based on the results of expert validation of material with an average percentage of 97% Belong to a very decent quality category. The validation result of media experts with an average percentage of 93% which belongs to the category of quality is decent. Trials by students with an average percentage of 96% which belongs to the category of quality are decent. Conclusion: has been developed the educational game tools of the EXPERT IPS IPS and is worthy to be used as an educational tool in elementary school with an increase in the value of learning results of the average student IPS of 81.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords: papan karambol IPS; educative game; economic activity and natural resources&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;
An analysis of underlying constructs affecting the job satisfaction amongst accountants

The study aims to identify and measure factors affecting the job satisfaction amongst accountants in accounting service firms in Hanoi.Based on literature review and the results of some interviews, a total of 200 questionnaires were sent to research participants for recollection within 1 month; 145 of them met standard and were subject to analysis.The results of Exploratory Factor Analysis (EFA) and Multiple Regression Analysis (MRA) identified 5 main factors affecting job satisfaction amongst accountants: (i) salaries, (ii) welfare regimes; (iii) Recognized achievements and rewards, (iv) working conditions and (v) training and promotion.On this basis, some recommendations have been improved the job satisfaction of the accountant in the accounting service firms in Hanoi.
Comparing load balancing algorithms for web application in cloud environment

&lt;p&gt;Cloud computing has emerged as a new paradigm for providing on-demand computing resources and outsourcing software and hardware infrastructures. Load balancing is one of the major concerns in cloud computing environment means how to distribute load efficiently among all the nodes. For solving such a problem, we need some load balancing algorithms, so in this paper we will compare the existing algorithms for web application.and based on results obtained we choose the best among them.&lt;/p&gt;
How Many Users Are Enough? Exploring Semi-Supervision and Stylometric Features to Uncover a Russian Troll Farm

Social media has reportedly been (ab)used by Russian troll farms to promote political agendas.Specifically, state-affiliated actors disguise themselves as native citizens of the United States to promote discord and promote their political motives.Therefore, developing methods to automatically detect Russian trolls can ensure fair elections and possibly reduce political extremism by stopping trolls that produce discord.While data exists for some troll organizations (e.g., Internet Research Agency), it is challenging to collect ground-truth accounts for new troll farms in a timely fashion.In this paper, we study the impact the number of labeled troll accounts has on detection performance.We analyze the use of self-supervision with less than 100 troll accounts as training data.We improve classification performance by nearly 4% F1.Furthermore, in combination with self-supervision, we also explore novel features for troll detection grounded in stylometry.Intuitively, we assume that the writing style is consistent across troll accounts because a single troll organization employee may control multiple user accounts.Overall, we improve on models based on words features by ∼9% F1.
Viewing Experience of Augmented Reality Objects as Ambient Media - A Comparison of Multimedia Devices

Augmented reality objects as ambient media (i.e., media embedded in everyday contexts) are being deployed in diverse contexts, such as home, mobile, and work. Because media experience is mediated by the medium, the viewing experience of augmented reality objects should depend on the type of viewing device. In this paper, we report on a user study with 18 participants and three state-of-the-art devices (i.e., HoloLens, iPad Pro tablet, and iPhone X smartphone), comparing the influence of these mobile viewing devices on feelings of presence and workload during ambient media consumption. Study results include a significant main effect of device on participants ratings of Possibility to examine an AR object, which demonstrates that ambient media consumption experiences are determined by the choice of viewing device.
PEMODELAN APLIKASI MOBILE PELAYANAN PUBLIK DESA (SMART VILLAGE) BERBASIS CLOUD COMPUTING

Public service is a necessity that can not be negotiated in improving the quality of service every time, every agency or organization that provides public services certainly wants to provide the best service to everyone it serves. At present public services in the village Beringin Taluk Kec. Kuantan Tengah District Kuantan Singingi has done very well but there are some obstacles in terms of public services such as the absence of a safe data storage place so that data is easily lost and damaged. There is no integration between BUMDes in the village so that information cannot be reviewed periodically. There is no integrated media of information or complaints, so people need to come directly to the village office to find information and provide complaints. Modeling an application for cloud computing-based public services has contributed to increasing the achievement of the goals of a village to get to a smart village with an integrated system and facilitate the community to access all needs in terms of information and services.
Using the principles of quantum linguistics in information warfare

The article reviews the existing approaches of the quantum-linguistic study of text and related methods that implement the principles of quantum linguistics and can be used in obtaining the theoretical basis for further research on the technical aspect of the quantum linguistics’ use in providing the state information security in case of possible hostile propaganda of one person, as well as groups of people, provided both the known and the unknown psychological portrait of the attacker. As a result of studying the existing methods and approaches in quantum linguistics, it was found that the formal aspect of the textual quantum properties, which involves the simultaneous change in the states of quantum-semantic particles and the wave function of the general semantic line of the text, is not fully highlighted. The solution of the problem is proposed by using the methods of cybernetic hermeneutics, linguistic-combinatorial simulation of difficult-formalized systems and quantum semantics. The expediency of intensional logic approaches use was proved. It has been discovered that semiotic approaches can act only as additional means. It is proved that the main task of the text, created by quantum-linguistic methods, is to draw the attention of the intruder, and therefore there is a need of constructing his psychological portrait using the methods of the linguistic experiment. In formalizing the process of an objective semantic line search, it is proposed to use methods of computer hermeneutics based on interactive analytics. An overview of the methods and approaches that can help to implement the synthesis of text by the individual function has found the feasibility of using the formal principles of discursive analysis for the in its inverse task (generation of text). The necessity of carrying out further researches to automate the process of allocating a function that describes the objective semantic line of the text and to construct an individual function describing the individual subjective semantic line of text is noted.
Exploiting Category-Level Semantic Relationships for Fine-Grained Image Recognition

We present a label-based, semantic distance induced regularization learning method for Fine-grained image recognition (FGIR). In contrast to previous label-based methods that involve a nontrivial optimization in multi-task metric learning, our approach can be integrated into an end-to-end network without introducing any extra parameters, thus easy to be optimized. To this end, a category-level hierarchical distance matrix (HDM) that encodes semantic distance between subcategories through a tree-like label hierarchy is constructed. HDM is then incorporated into a DCNN to aggregate misclassified prediction probabilities for model learning, thus providing additional discriminative information for fine-grained feature learning. Experiments on three fine-grained benchmark datasets (Stanford Cars, FGVC-Aircraft, CUB-Birds) validate the effectiveness of our approach and demonstrate its improvements over previous methods.
Cooperative Localization by Set-theoretic Estimation

Among the vast variety of sensor localization techniques, set-theoretic estimation methods are especially appealing for large-scale networks with low-cost devices. Firstly, these methods usually exhibit comparatively low complexity. Secondly, the design of both centralized and distributed fixed-point algorithms for set-theoretic estimation problems is usually straightforward. Existing set-theoretic algorithms for range-based localization typically relax the spheric constraint sets to convex balls, which may lead to poor localization performance even if the range measurements are exact. In this paper, we formulate the spheric cooperative localization problem as a convex feasibility problem in a product Hilbert space. This formulation makes cooperative localization accessible by a multiplicity of simple iterative algorithms. In particular, we propose a cooperative localization technique that exploits the bounded perturbation resilience of a fixed-point algorithm for the spheric convex feasibility problem. We define perturbations that steer the iterates of the proposed algorithm towards a solution to a semidefinite relaxation of the problem. Moreover, we show that the localization accuracy can be improved with low effort by heterogeneous sensor measurements.
Decision Making Using Machine Learning Based Opinion Prediction Model for Smart Governance

Background: Decision making requires a rigorous process of evaluation, which is an analytical and organized process to figure out the present positive influences, favourable future prospects, existing shortcomings and ulterior complexities of any plan, program, practice or a polity. Evaluation of policy is an essential and vital process required to measure the performance or progression of the scheme. The main purpose of policy evaluation is to empower various stakeholders and enhance their socio-economic environment. A large number of policies or schemes in different areas are launched/constituted by government in view of the citizen welfare. Although, the governmental policies intend to better shape up the quality of life of people but may also impact their everyday's life. Objective: The contemplation of public opinion plays a very significant role in the process of policy evaluation. Therefore, the aim of this paper is to incorporate the concept of opinion mining in policy evaluation. An attempt has been made to elevate the process of policy evaluation by analyzing public opinion. Methods: A latest governmental scheme Saubhagya launched by the Indian government in 2017 has been selected for evaluation by applying supervised learning based opinion mining techniques. The data set of public opinion associated with this scheme has been captured by Twitter. Results: The result validates that the proposed methodology supports the optimizing process of policy evaluation and provides a more accurate and actual status of policy's effect among Indian citizen. As a result, this would aid in identifying and implementing the preventive and corrective measures required to be taken for a successful policy. Conclusion: The proposed methodology will stabilize and strengthen the process of policy evaluation which target towards favourable and flourishing future prospects concerning the socio-economic status of a nation. The results are quite exciting and further extension of work will be performed in order to develop and design a patent framework in the area of social big data analytics.
Using a Community of Practice Approach to Transform: How an Academic Library Collections Unit Reorganized to Meet Growing Demands for E-Resources and Services During a Time of Institutional Change

In recent years, technical services operations and collection management at the University of South Florida (USF) Library was operating with flat budgets, a staff organization in a state of transition, ineffective workplace communication, and a progressive loss of institutional knowledge and skill sets.During the same time, the university was characterized by a fast-developing dynamic research and learning environment.New research endeavors and academic programs were incorporating a variety of electronic resources in their activities.In response, library administrators implemented a strategy of organizing technical services operations into a community of practice with three key elements.The three elements included redefining technical staff organization, enhancing collaboration and communication, and building new work areas.This paper illustrates how the technical services operations at USF was remodeled by modifying staff organization and workspaces into collaborative teams.The team approach has focused skill sets on multiformat collection problem solving and enhanced service delivery to library patrons.The reorganization has transformed a department previously centered on processing print publications to a department focused on managing diverse activities including evidence-based acquisitions programs in both e-book and streaming formats as well as diverse e-journal and database subscriptions.The new Collections & Discovery Unit has a shared mission and vision.The unit staff members have built a knowledge base of diverse information and solutions to meet the challenges of a fast-changing academic landscape.
Quality of Service-Aware Complex Event Service Composition in Real-time Linked Dataspaces

The proliferation of sensor devices and services along with the advances in event processing brings many new opportunities as well as challenges for intelligent systems. It is now possible to provide, analyse, and react upon real-time, complex events in smart environments. When existing event services do not provide such complex events directly, an event service composition may be required. However, it is difficult to determine which event service candidates (or service compositions) best suit users’ and applications’ quality-of-service requirements. A sub-optimal service composition may lead to inaccurate event detection and lack of system robustness. In this chapter, we address these issues by first providing a Quality-of-Service (QoS) aggregation schema for complex event service compositions, and then developing a genetic algorithm to create near-optimal event service compositions efficiently. The approach is evaluated with both real sensor data collected via Internet of Things services and synthesised datasets.