Reviewerly/exHarmony huggingface dataset:
CONDITIONS FOR FORMATION AND INCREASE OF DIGITAL LITERACY IN CHILDREN AND STUDENTS OF PRESCHOOL AND PRIMARY SCHOOL AGE

The formation of digital skills in children and young students is especially relevant for pedagogical practice. The subsequent development and formation of digital literacy depends on the successful creation of a digital base. The ability to work with modern information technologies is an integral part of the successful realization of the modern personality.In this paper, an attempt is made to derive the conditions that are suitable for the formation and increase of digital literacy in children and students of preschool and primary school age. The results of a survey within teachers working in kindergartens and schools (grades 1-4), parents of children in the study group, students studying to be teachers are proposed. The positive conditions are presented, but also factors that prevent the creation of a successful environment for the acquisition of digital knowledge and skills are taken into account.As a result of the study, the main digital skills are derived, which according to the respondents should be formed in the considered age group. Skills and competencies that can be developed in the implementation of specialized activities for the formation of digital literacy are described. Depending on the derived digital skills, technological solutions are offered, with the help of which they can be formed.In order to form digital literacy, the presence of a digital environment is a must. Depending on the nature of the digital environment - spontaneous or educational, the degree and direction in which adolescents' interest in technology and their application develops is determined.The role of educational institutions is to organize, assist and guide the activities for the formation of digital skills of children and young students. The existence of a system for the formation of digital skills in children provides a spiral development and upgrade of knowledge and skills. This provides a solid basis for building digital literacy and competence.
Towards World Identification in Description Logics

The logical analysis of the usability of nominals, that are introduced by Hybrid Logic, informal descriptions of the world (within modern knowledge representation systems and semantic systems) is very important because nominals, as second sorts of propositional symbols, can support logical identification of the described world at specific states. This paper will focus on answering the philosophical-logical question of ‘how a fundamental world description in the description logic (DL) and a nominal can be related to each other?’. Based on my assumption that nominals can support more adequate identification of the world in DL, this paper will deal with the concept of ‘world identification’. Accordingly, based on a logical-terminological analysis of nominals, it will analyse hybridised fundamental world descriptions. Relying on the outcomes, this research will reach the idea that we can have a hybrid description logic with the analysed concepts.
Research on the Application of Blended Learning in the Platform Course of “Management”

In today’s Internet era, “blended learning” is a relatively advanced and effective learning method, which is of great significance for promoting curriculum teaching reforms and improving teaching quality. This paper explores the blended teaching design method of the platform course of “Management”, clarifies the teaching objectives, overall design and unit design, and implements in teaching. A comprehensive evaluation of the teaching effect was made in three areas of the usage of the platform, analysis of students’ assessment grades and feedback of students’ questionnaire, which shows that blended teaching has achieved good results. There are still some problems in the implementation of blended teaching, including the quantity and quality of teaching resources, as well as the students’ assessment results need to be improved, and need to be improved in future research.
Secure SMS Using Pseudo-Random Bit Generator Based on Chaotic Map, and AES on Arduino UNO Board and SIM 900 Module

Amid the fame of the WhatsApp application, Short Message Service (SMS) is still a popular used to communication via cell phone for many people in rural area or in broadband-underdeveloped area. A mechanism that can secure the SMS is needed so that the information sent is not easily stolen by unauthorized parties. This study aims to secure messages sent using SMS by implementing the AES algorithm to secure messages with keys generated by chaotic logistic map-based PRBG algorithms with input in the form of random numbers agreed by the sender and recipient of the message to be a guarantee that messages sent can only be decrypted by the legal recipient. A secure SMS device can produce 128-bit long encrypted messages with an average message delivery time of 2.118 seconds. Sending messages without encryption takes an average of 1.539 seconds.
Leakage-Resilient Inner-Product Functional Encryption in the Bounded-Retrieval Model

We propose a leakage-resilient inner-product functional encryption scheme (IPFE) in the bounded-retrieval model (BRM). This is the first leakage-resilient functional encryption scheme in the BRM. In our leakage model, an adversary is allowed to obtain at most l-bit knowledge from each secret key. And our scheme can flexibly tolerate arbitrarily leakage bound l, by only increasing the size of secret keys, while keeping all other parts small and independent of l. Technically, we develop a new notion: Inner-product hash proof system (IP-HPS). IP-HPS is a variant of traditional hash proof systems. Its output of decapsulation is an inner-product value, instead of the encapsulated key. We propose an IP-HPS scheme under DDH-assumption. Then we show how to make an IP-HPS scheme to tolerate $$l'$$ -bit leakage, and we can achieve arbitrary large $$l'$$ by only increasing the size of secret keys. Finally, we show how to build a leakage-resilient IPFE in the BRM with leakage bound $$l=\frac{l'}{n}$$ from our IP-HPS scheme.
The New Normal Is Cell Phones in the Classroom

This chapter seeks to compare and contrast a classroom without student cell phone use in 2000 to the ubiquitous use of cell phones in today's classroom and society. Cell phone use and how to include them in today's classroom is a hot topic in school systems and teacher education programs in America and therefore relevant to establishing instructional guidelines for cell phone inclusion in the classroom. The author will share lived classroom experience in the 20 years from cell phone infancy to usage by more than 90% of students in today's classroom. The author will share effective use of cell phones in the classroom and pitfalls to allowing cell phones in the class without a well-planned instruction basis for cell phone use.
Characteristics of a Random Vector Obtained Using Human-Computer Interaction

The paper is devoted to the analysis of a software random number generator based on human-computer interaction (HCI) for software and cryptographic applications. The article describes method of generating random numbers based on human-computer interaction using time and positions on a computer screen or smartphone. Based on this method, a random number generator was developed and implemented in software. In the course of the work the authors carry out correlation analysis of random numbers obtained by the algorithm, thus, 150001 random vectors consisting of 24 values were generated. Test software for this method has been designed, statistical values and the interdependence of generated random variables are considered. It is shown that random variables are probably independent of each other. The advantage of the considered algorithm for generating random numbers is simplicity in preparation and operation.
Investigation of Quasi-Optimal Motion of a Mobile Robot: the Maximum Principle Based Approach

This paper concerns the quasi time-optimal motion in a simplified model of a mobile robot with constraints imposed on the state variables. As it is known, in this kind of problems involving the so-called unicycle type of dynamics, the classical assumptions of regularity with respect to the state constraints are not fulfilled. This fact greatly complicates the analysis of such control problems through the use of Pontryagin’s maximum principle. Moreover, the difficulties are also due to the presence of a singular control mode which is related to the angular velocity. The paper proposes a certain regularization approach in order to overcome the obstacles above and to develop a tool for the subsequent numerical implementation.
Data Association Rules Mining Method Based on RBF Neural Network Optimization Algorithm

In order to achieve real-time and accurate data mining, this paper proposes a data association rules mining method based on RBF neural network optimization algorithm. On the basis of considering the constraint association rules, the data frequent itemsets are reduced to get the corresponding candidate data sets that meet the rules, and the update of the data frequent itemsets for user needs is completed. The candidate data set is input to RBF neural network for training, the network output is optimized by combining with rough set theory, the user demand data table and quadruple are constructed, the user demand attributes are described, and the user demand data is obtained by using upper bound pruning method. Through the upper bound pruning method, the user demand data is obtained and real-time mining is realized. Compared with other data mining algorithms, the simulation results show that the performance of the data mining algorithm proposed in this paper is less constrained by the proportion of redundant data and the size of data, the accuracy of data mining is higher, and it has better stability.
Research on Wireless Ad Hoc Network Technology for Building Monitoring

Abstract Aiming at the difficulties of equipment expansion, high data collection cost, and difficulty in data sharing faced by traditional building monitoring systems, this paper proposes a wireless ad hoc network technology for building monitoring. This method establishes a wireless ad hoc network through wireless network structure selection, wireless network self-organization and dynamic maintenance. Experimental results show that the wireless ad hoc network technology greatly reduces the cost of networking and debugging of intelligent systems, and can well meet the needs of intelligent monitoring of existing traditional buildings.
Case Study of Cloud Computing Security and Emerging Security Research Challenges

In this technological era, cloud computing is bombarded with immense benefits that includes availability, flexibility, ubiquitary access, and cost effectiveness. Cloud Computing offers its services to different kinds of users with the help of the World Wide Web on the virtual platform regardless of devices. Hence, all the resources kept on the same-shared storage device, which will lead to a considerable rise in various cloud security concerns for both; user and their private information. Data privacy can be compromised with a broad usage of cloud as smaller companies to bigger ones are adapting this versatile system. This paper examines several recent security attacks and proposed solutions of secure cloud computing from the perspective of organizations. Threat action varieties, other attacks with date, information exposed and number of record breach is presented with IT attack at risk. Finally, we have presented research challenges that can be worth noticing.
Chaining Polysemous Senses for Evocation Recognition

In this paper we present a new individual measure for the task of evocation strength prediction. The proposed solution is based on Dijkstra’s distances calculated on the WordNet graph expanded with polysemy relations. The polysemy network was constructed using chaining procedure executed on individual word senses of polysemous lemmas. We show that the shape of polysemy associations between WordNet senses has a positive impact on evocation strength prediction and the measure itself could be successfully reused in more complex ML frameworks.
Duration-selectable Story Generation Using Route Planning and Graph-to-text Conversion

Every story is more than the collection of texts. In the past, people created stories depending on a storyline and designed various events in episodes of the story. Recently, researchers investigate story generation using the computer instead of the human being. In this paper, we propose a scheme to produce stories with various descriptions. The produced graph represents a storyline, and route planning is applied to it to find the proper route. According to the selected route, the graph-to-text conversion transforms characters' action as sentences. The experiment results demonstrate that the proposed scheme is capable of generating duration-selectable stories.
Conditions for Feedback Shift Registers to Generate Permutations

Kammadanam, Vamshi Krishna Sule, Virendra Hong, YiThe paper explores conditions to be satisfied by feedback shift registers (FSRs) to generate a permutation (alternatively a nonsingular map) of the state space over various fields. Such a condition is well known over the binary field $$\mathbb {F}_{2}$$ . This is extended to small finite fields and a simple sufficient condition is stated for larger fields. Permutations with cycles or periodic sequences generated by their action of lengths which are of exponential order of the register length are attractive for construction of pseudorandom sequences which have many applications.
Unmanned vehicle's optimal control based on the selection function and network operators

The paper presents a method for optimal control of an unmanned vehicle taking into account real-time constraints; environmental parameters are not known in advance. At the first step, an unmanned vehicle is identified at the training ground, then the optimal trajectory is selected. To construct the control function and the selection function, evolutionary methods of symbolic regression are used. The synthesis is carried out by a network operator, which was modeled in gazebo. Also, the way of using the proposed method on the example of the mobile robot's movement along a track with dynamic and static restrictions is presented.
A Survey of Graph Curvature and Embedding in Non-Euclidean Spaces

Interest has been growing lately towards learning representations for non-Euclidean geometric data structures. Such kinds of data are found everywhere ranging from social network graphs, brain images, sensor networks to 3-dimensional objects. To understand the underlying geometry and functions of these high dimensional discrete data with non-Euclidean structure, it requires their representations in non-Euclidean spaces. Recently, graph embedding in Riemannian spaces has been explored to successfully capture the geometric properties of networks and achieve the state-of-the-art quality in graph representation learning tasks. In this survey, we provide an overview on graph embeddings based on Riemannian geometry with different curvature spaces. We further present recent developments in various application areas using graph embedding models in non-Euclidean domains.
Predicting Structure &amp; Clarity of software projects with Machine Learning

The software development domain is very dynamic and only around 28% of the projects that follow project management methodologies stay on schedule. This paper shows methods that try to solve this by aiming identification of the problems encountered by team members faster. The goal of the project is to allow project managers to find meaningful insights on the clarity and structure felt by team members on the tasks they are working on. The system we developed collects data from third party tools used for the development of software projects and correlates it with the marks given by team members for Structure & Clarity daily. We built models for prediction of Structure & Clarity using Random Forest Regression, Artificial Neural Networks (ANNs) and Long Short-Term Memory (LSTM).
Finding Facial Emotions From the Clutter Scenes Using Zernike Moments-Based Convolutional Neural Networks

Finding faces in the clutter scenes is a challenging task in automatic face recognition systems as facial images are subjected to changes in the illumination, facial expression, orientation, and occlusions. Also, in the cluttered scenes, faces are not completely visible and detecting them is essential as it is significant in surveillance applications to study the mood of the crowd. This chapter utilizes the deep learning methods to understand the cluttered scenes to find the faces and discriminate them into partial and full faces. The work proves that MTCNN used for detecting the faces and Zernike moments-based kernels employed in CNN for classifying the faces into partial and full takes advantage in delivering a notable performance as compared to the other techniques. Considering the limitation of recognition on partial face emotions, only the full faces are preserved, and further, the KDEF dataset is modified by MTCNN to detect only faces and classify them into four emotions. PatternNet is utilized to train and test the modified dataset to improve the accuracy of the results.
Smart Car Detection and Tracking Control Based on Panoramic Image

Real-time tracking control is one of the fundamental challenges in intelligent control systems. Current control strategy for smart car which relies on in-vehicle cameras, cannot be got the environment information of the road completely. For this problem, smart car based on panoramic image is presented for detecting and tracking. Using external camera installed in the road side to get the road and smart car images. The car is detected according to the color blocks on the smart car using image process and get the real-time position of the car. By contrast with the predetermined track to get the bias and then sent to the smart car using wireless local area networks(WLAN). The steering control based on bias is established depending on PD control algorithm, and the PD algorithm based on bias is used to control the direction and angle of the steering gear respectively. Through test of the field, the smart car could move smoothly along the predetermined track with satisfied accuracy and real time.
An algorithm of the recognition of inferior barcode

Abstract Objective: Bar code recognition is widely used. This paper introduces a method of using digital image algorithm to complete the identification of stain bar code. Methods: firstly, the barcode image was binarized and positioned; secondly, the pixels in the barcode image were stacked on the abscissa axis by projection technology to remove the barcode stains; finally, the barcodes were effectively identified by the center distance algorithm. Results: the experiments show that the barcodes with different degrees of pollution are well recognized by our method, which is better than the traditional recognition algorithm. Conclusion: This paper proposes a new barcode recognition algorithm, which can quickly and accurately identify the bad barcode
Role of Police in the Criminal Justice System of Bangladesh: Need for Reformation

Police and their functions are very important in criminology. Because it is the principal duty of the police to arrest criminals and conduct them until the conclusion of trial for preventing crime. Police are legally authorized to use force and other means of coercion to execute public and social order. The basic knowledge of crime and criminology is must for the police and that’s why almost in every country of the world has a criminology division for police. And police are manually trained for the knowledge of criminology. It is true that an honest, sincere and effective police force can ensure a happy and peaceful society. Although it is not possible for the police to reduce crime from society completely but it can be controlled and retained in a satisfactory stage. Otherwise, trick, corrupted, unlettered and disingenuous police force can give facilities to the criminals and make the life of the general citizens miserable. This study has an assertion and provided some recommendations to the knowledge of criminology for police and to reform the police system in Bangladesh. This article provides strategic policy guidance for the police personnel.
The Role of the Game of Balanced Nutrition Ladder Snake in Influencing Changes in Knowledge and Attitudes of Children in Muhammadiyah Tanjung Morawa Elementary School

This study aims to determine the effect of the educational game of balanced nutrition ladder snakes on changes in knowledge and attitudes and actions in elementary school children in Muhammadyah school, Tanjung Morawa district. This type of research is a Quasi-Experimental study (quasi-experimental design) with the design of One Group Pretest- Posttest. The sample in this study was 75 elementary school children Muhammadiyah Kec. Tanjung Morawa. Data was collected using a questionnaire that was filled in by the respondent himself. If the data is normally distributed then the type of test used is the dependent T-test (in pairs). If the data is not normally distributed then the test used is the ranking test. The statistical test results obtained p-value = 0,000 < 0.05, which shows that the influence of snake ladder education game balanced nutrition to changes in knowledge and attitudes in school children. The conclusion in this study is the influence of the educational game of balanced nutrition ladder snakes on changes in knowledge and attitudes in school children.
Penguatan Perpustakaan Untuk Pembangunan: Sebuah Konsep Transformasi Berbasis Inklusi Sosial

Goals of national development as the purpose of the “Pembukaan Undang-Undang Dasar 1945” are building a smart nation’s life and advancing public welfare. Library has a strategic role in national development by become a place who can used by people without limitation of social group. This concept is called library service based on social inclusion. In this article, the author tries to find definition of library service based on social inclusion and describe what changes are needed.
The number of affine equivalent classes and extended affine equivalent classes of vectorial Boolean functions

Affine equivalent classes and extended affine equivalent (EA-equivalent for short) classes of vectorial Boolean functions have important applications in cryptography, logic circuit, sequences for communications, etc. Recently, Y. Zhang et al., computed the number of affine equivalent classes of n-variable Boolean functions when 1≤n≤10 by group isomorphism (Zhang et al., 2016). However, the case for affine equivalent vectorial Boolean function remains a challenging open problem. Furthermore, little result for EA-equivalent vectorial Boolean function is known except a trivial lower bound mentioned in Mullen and Panario (2013, P246). In this paper, we focus on the challenging problem of calculating the number of affine equivalent classes and EA-equivalent classes of vectorial Boolean functions. First, for EA-equivalence, we prove that the trivial lower bound proposed in Mullen and Panario (2013) has at least 3 effective figures if n is not too small. We then show that the lower bound also holds for affine equivalent classes. Furthermore, we give an explicit formula and calculate the exact number of affine equivalent classes of (n,m)-functions with 1≤m,n≤11 by GAP. The results in this paper are helpful for the theory and applications of the classifications of vectorial Boolean functions.