Reviewerly/exHarmony huggingface dataset:
Bit-serial systolic accelerator design for convolution operations in convolutional neural networks

The accuracy of Convolutional Neural Networks (CNNs) has exceeded the human level in many fields, but the high computation complexity is one of the main challenges for CNNs applied in the mobile or embedded devices. In this paper, we provide a hardware accelerator scheme for the convolution operations in CNNs, which adopts the bit-serial systolic architecture. Implementation results show that the proposed scheme can reduce the area by about 64%, increase the maximum frequency by about 4.4 times and increase the hardware efficiency by about 1.2 times compared with the state-of-the-art Eyeriss architecture.
Supra-professional competences of pedagogues of the XXI century

Digitalization and advancement of knowledge economy, formation and proliferation of the new technological didactics based on the didactic tetrahedron, lead to requirements changes in the development of pedagogical competencies. These trends influence the selection of the topic of research aimed at studying the relevance of formation of supra-professional competencies of future pedagogues. The goal of this work consists in carrying out an empirical study, based on the content analysis of previous research, of the opinions of students of pedagogical universities on the importance of self-evaluation and evaluation of opportunities for the development of supra-professional competencies essential for the pedagogues of the XXI century. The online survey involved 150 students from three pedagogical universities &amp;ndash; Ural State Pedagogical University, South Ural State Humanitarian Pedagogical University, and Russian State Vocational Pedagogical University. The article presents an original definition of the concept of &amp;ldquo;supra-professional competence&amp;rdquo;, as well as makes an overview of national and foreign literature on the matter. The authors revealed the results of pilot study conducted among students of the aforementioned pedagogical universities. As a result of the research, based on the collected opinions of the students of 7 fields of study in two perspectives &amp;ndash; self-evaluation of the level of development of students&amp;rsquo; competencies and assessment of the importance of competences on the job market. The acquired results of assessment of opportunities for the development of supra-professional competences testify to the fact that there is room for improvement of educational process.
Creating explorable extended reality environments with semantic annotations

Abstract The main element of extended reality (XR) environments is behavior-rich 3D content consisting of objects that act and interact with one another as well as with users. Such actions and interactions constitute the evolution of the content over time. Multiple application domains of XR, e.g., education, training, marketing, merchandising, and design, could benefit from the analysis of 3D content changes based on general or domain knowledge comprehensible to average users or domain experts. Such analysis can be intended, in particular, to monitor, comprehend, examine, and control XR environments as well as users’ skills, experience, interests and preferences, and XR objects’ features. However, it is difficult to achieve as long as XR environments are developed with methods and tools that focus on programming and 3D modeling rather than expressing domain knowledge accompanying content users and objects, and their behavior. The main contribution of this paper is an approach to creating explorable knowledge-based XR environments with semantic annotations. The approach combines description logics with aspect-oriented programming, which enables knowledge representation in an arbitrary domain as well as transformation of available environments with minimal users’ effort. We have implemented the approach using well-established development tools and exemplify it with an explorable immersive car showroom. The approach enables efficient creation of explorable XR environments and knowledge acquisition from XR.
Hubungan Self Efficacy dan Dukungan Sosial Teman Sebaya Dengan Self Regulated Learning Pada Mahasiswa Universitas Potensi Utama Medan

This study aims to examine the relationship between Self Efficacy and peer social support with Self Regulated Learning at Potensi Utama University students. Where the subject of research is a student of Potensi Utama University with a sample of 131 students. Sampling through simple random sampling technique. Data collection is done by using Likert scale which consists of three scale that is Self Efficacy scale, Social Peer Support scale and Self Regulated Learning scale. Data analysis method used is Multiple Regression Analysis. The results obtained that there is a positive relationship between Self Efficacy and social support peers with Self Regulated Learning students of Potensi Utama University. With the value F = 9.511 and the value p = 0.000. Self Efficacy contributes to Self Regulated Learning of 9.3%. Friends' social support contributes to Self Regulated Learning of 7.3%. Self Efficacy and peer social support to Self Regulated Learning as much as 12.9% this means there are still 87.1% other factors that can affect Self Regulated Learning. From these results it is expected for the Potensi Utama University Medan to improve students' ability in managing Self Efficacy in a better direction, build a conducive atmosphere that leads to peer social support, and create learning conditions that lead students to learn independently.
Towards a Natural Human-Robot Interaction in an Industrial Environment

Nowadays, modern industry has adopted robots as part of their processes. In many scenarios, such machines collaborate with humans to perform specific tasks in their same environment or simply guide them in a natural, safe and efficient way. Our approach improves a previously conducted work on a multi-modal human-robot interaction system with different audio acquisition and speech recognition modules for a more natural communication. The semantic interpreter, with the aid of a knowledge manager, parses the resulting transcription and, using contextual information, selects the order that the operator has uttered and sends it to the robot to be executed. This setup is evaluated in a real manufacture scenario in a laboratory environment with a large set of end users both quantitatively and qualitatively. The gathered results reveal that the system behaves robustly and that the assignment was also considered by the end users as manageable, whilst the system in overall was received with a high level of trust and usability.
Cloud computing concept in education systems

Cloud technology is a system that enables the use of IT resources more efficiently, simply and cheaply. The cost-effectiveness of applying this technology is reflected in the optimization of operating costs, the optimization of capital investments, as well as the reduction of costs. Electronic education as a system began to "live" in the late 20th century in the form of IT applications. Nowadays, these applications are in a new environment - Cloud Computing. The amount of data that needs to be processed grows and has a tendency to accelerate. This huge amount of data is necessary to store, transfer and process in a unit of time this requires the development of certain systems. The given systems that enable the management of the given resources are big data systems, internet of things, cloud platforms, virtualization, artificial intelligence, 5g, etc. The platform structure that carries these systems is cloud computing. The use of cloud computing as a new technology for the realization of e-learning enhances educational and research systems, in terms of storage and flow capacity, data processing speed and availability of information with the aim of a better educational environment with greater controllability, is more economical, efficient and safer in relation to the traditional concept. The paper presents the impact of ICT and cloud computing on educational systems, which should be part of new trends due to the advantages that given technology brings.
A quantum procedure for map generation

Quantum computation is an emerging technology that promises a wide range of possible use cases. This promise is primarily based on algorithms that are unlikely to be viable over the coming decade. For near-term applications, quantum software needs to be carefully tailored to the hardware available. In this paper, we begin to explore whether near-term quantum computers could provide tools that are useful in the creation and implementation of computer games. The procedural generation of geopolitical maps and their associated history is considered as a motivating example. This is performed by encoding a rudimentary decision making process for the nations within a quantum procedure that is well-suited to near-term devices. Given the novelty of quantum computing within the field of procedural generation, we also provide an introduction to the basic concepts involved.
Guidelines for Selecting Appropriate Knowledge Management System Implementation Frameworks

The preservation of knowledge is a universal requirement for every organisation that strives to gain a competitive advantage. Knowledge management systems are a modern business enabler and driver of success. Furthermore, the implementation of knowledge management systems is a comprehensive process which integrates people, business processes, the business universe, technology, and customers. However, some evidence suggests that organisations are investing a multiplicity of resources in implementing knowledge management systems with little success. Therefore, the aim of this study is to identify and understand the types of frameworks available for implementing knowledge management systems and present a comprehensive implementation framework including the relevant critical success factors and measurement tools that organisations may use for implementing knowledge management system projects. The successful implementation of knowledge management systems lie in the adoption of a suitable strategy and well-defined framework.
Learning Graph Laplacian with MCP

We consider the problem of learning a graph under the Laplacian constraint with a non-convex penalty: minimax concave penalty (MCP). For solving the MCP penalized graphical model, we design an inexact proximal difference-of-convex algorithm (DCA) and prove its convergence to critical points. We note that each subproblem of the proximal DCA enjoys the nice property that the objective function in its dual problem is continuously differentiable with a semismooth gradient. Therefore, we apply an efficient semismooth Newton method to subproblems of the proximal DCA. Numerical experiments on various synthetic and real data sets demonstrate the effectiveness of the non-convex penalty MCP in promoting sparsity. Compared with the existing state-of-the-art method, our method is demonstrated to be more efficient and reliable for learning graph Laplacian with MCP.
Rethinking pooling in graph neural networks

Graph pooling is a central component of a myriad of graph neural network (GNN) architectures. As an inheritance from traditional CNNs, most approaches formulate graph pooling as a cluster assignment problem, extending the idea of local patches in regular grids to graphs. Despite the wide adherence to this design choice, no work has rigorously evaluated its influence on the success of GNNs. In this paper, we build upon representative GNNs and introduce variants that challenge the need for locality-preserving representations, either using randomization or clustering on the complement graph. Strikingly, our experiments demonstrate that using these variants does not result in any decrease in performance. To understand this phenomenon, we study the interplay between convolutional layers and the subsequent pooling ones. We show that the convolutions play a leading role in the learned representations. In contrast to the common belief, local pooling is not responsible for the success of GNNs on relevant and widely-used benchmarks.
A Digital Forensics Analysis for Detection of The Modified COVID-19 Mobile Application

Thanks to the flexible and developable structure of the Android operating system, application packages (APK files) can be easily installed on mobile devices. This situation paved the way for many different applications to be developed and used without supervision. In addition to well-developed applications, this structure of the Android operating system has enabled the use of pirated applications targeting user data and anti-forensic applications developed for different purposes. Therefore, the detection and analysis processes of pirated applications targeting user data and applications displaying anti forensic computing behaviors are quite difficult and complex. In this study, the detection and analysis methods of such applications are mentioned, and then the proposed methods are tested with sample application analysis. In the sample case analysis, an analysis of the suspicious application, which does not match the package name and application name, was carried out. As a result of the analysis, it has been determined that the harmful application uses the icon and name of a known application, and the package name was randomly generated irrelevantly to reduce detectability. The study is thought to be useful for the detection of such suspicious applications. In the case study, an analysis of the application in which one of the popular mobile applications was imitated due to the covid-19 pandemic was performed.
The Safety Area of Digital Competence: A Mixed Method Study in Galician Primary Education Students

This article aims to evaluate the safety area of digital competence in pre-adolescents schooled in primary education as well as to understand the processes that interfere with the development of that area. A mixed research methodology was used through an exploratory sequential design with a qualitative phase (multiple case study) followed by quantitative phase (assessment test on safety). Particularly noteworthy among the results obtained is the influence of the family on the subcompetence of health protection as it is the one that worries families the most and where students score the highest on the test conducted.
Anchored Bayesian Gaussian mixture models

Finite mixtures are a flexible modeling tool for irregularly shaped densities and samples from heterogeneous populations.When modeling with mixtures using an exchangeable prior on the component features, the component labels are arbitrary and are indistinguishable in posterior analysis.This makes it impossible to attribute any meaningful interpretation to the marginal posterior distributions of the component features.We propose a model in which a small number of observations are assumed to arise from some of the labeled component densities.The resulting model is not exchangeable, allowing inference on the component features without post-processing.Our method assigns meaning to the component labels at the modeling stage and can be justified as a data-dependent informative prior on the labelings.We show that our method produces interpretable results, often (but not always) similar to those resulting from relabeling algorithms, with the added benefit that the marginal inferences originate directly from a well specified probability model rather than a post hoc manipulation.We provide asymptotic results leading to practical guidelines for model selection that are motivated by maximizing prior information about the class labels and demonstrate our method on real and simulated data.
A Compression System for Unicode Files Using an Enhanced Lzw Method

Data compression plays a vital and pivotal role in the process of computing as it helps in space reduction occupied by a file as well as to reduce the time taken to access the file. This work relates to a method for compressing and decompressing a UTF-8 encoded stream of data pertaining to Lempel-Ziv-welch (LZW) method. It is worth to use an exclusive-purpose LZW compression scheme as many applications are utilizing Unicode text. The system of the present work comprises a compression module, configured to compress the Unicode data by creating the dictionary entries in Unicode format. This is accomplished with adaptive characteristic data compression tables built upon the data to be compressed reflecting the characteristics of the most recent input data. The decompression module is configured to decompress the compressed file with the help of unique Unicode character table obtained from the compression module and the encoded output. We can have remarkable gain in compression, wherein the knowledge that we gather from the source is used to explore the decompression process.
Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data

Directional data consist of observations distributed on a (hyper)sphere, and appear in many applied fields, such as astronomy, ecology, and environmental science. This paper studies both statistical and computational problems of kernel smoothing for directional data. We generalize the classical mean shift algorithm to directional data, which allows us to identify local modes of the directional kernel density estimator (KDE). The statistical convergence rates of the directional KDE and its derivatives are derived, and the problem of mode estimation is examined. We also prove the ascending property of the directional mean shift algorithm and investigate a general problem of gradient ascent on the unit hypersphere. To demonstrate the applicability of the algorithm, we evaluate it as a mode clustering method on both simulated and real-world data sets.
The Pecularities Of Automatic Headlights

Today, the types of vehicles and their functions are increasing, creating favorable conditions for humans. Sensors play an important role in performing functions such as driving, tracking, traffic regulation. The main function of sensors is to receive incoming information and transmit it to other forms of data. For example, we can take photoelectric sensors. Photoelectric sensors receive incoming light current and transmit information in the form of electrical voltage.
A Hybrid Computational Intelligence based Technique for Automatic Cryptanalysis of Playfair Ciphers

The Playfair cipher is a symmetric key cryptosystem-based on encryption of digrams of letters. The cipher shows higher cryptanalytic complexity compared to mono-alphabetic cipher due to the use of 625 different letter-digrams in encryption instead of 26 letters from Roman alphabets. Population-based techniques like Genetic algorithm (GA) and Swarm intelligence (SI) are more suitable compared to the Brute force approach for cryptanalysis of cipher because of specific and unique structure of its Key Table. This work is an attempt to automate the process of cryptanalysis using hybrid computational intelligence. Multiple particle swarm optimization (MPSO) and GA-based hybrid technique (MPSO-GA) have been proposed and applied in solving Playfair ciphers. The authors have attempted to find the solution key applied in generating Playfair crypts by using the proposed hybrid technique to reduce the exhaustive search space. As per the computed results of the MPSO-GA technique, correct solution was obtained for the Playfair ciphers of 100 to 200 letters length. The proposed technique provided better results compared to either GA or PSO-based technique. Furthermore, the technique was also able to recover partial English text message for short Playfair ciphers of 80 to 120 characters length.
Ontological Approach for Question Generation and Knowledge Control

With the development of intelligent information technology, automatic generation of questions and automatic verification of answers have become one of the main functions of the intelligent tutoring systems. Although some existing approaches to automatic generation of questions and automatic verification of answers are introduced in the literature, these approaches only allow to generate very simple objective questions and verify user answers with very simple semantic structure. So, this article proposes an approach for designing a general subsystem of automatic generation of questions and automatic verification of answers in intelligent tutoring systems built using OSTIS technology. The designed subsystem allows to automatically generate various types of questions based on information from the knowledge bases and multiple question generation strategies, and the subsystem can also automatically verify the correctness and completeness of user answers in the form of semantic graphs. Compared with existing approaches, the subsystem designed using the approach proposed in this article can not only generate various complex types of questions, such as multiple-choice questions, fill in the blank questions, questions of definition interpretation, etc., but also verify user answers with complex semantic structures.
Functional Modeling of Mechanical Scanning Radar Based on IDEF0 Method

Abstract At present, research of radar survivability is another important research field after the six characteristics of radar equipment. Research on the vulnerability of radar equipment is the key issue among them and function analysis of radar equipment is the basis of it. Aiming at the traditional mechanical scanning radar, by analyzing the traditional function tree modeling method and on the basis of it, a radar function modeling method based on IDEF0 is proposed. This method comprehensively analyzes the radar early warning function, comprehensively considers the relationship between its internal functions and the connection with external activities. Finally, through comparison with the traditional function tree modeling method, it shows its superiority and is vulnerable to radar equipment, which can provide some reference for the research of radar equipment vulnerability.
Meta-analysis: google classroom on mathematics learning in Indonesia as an alternative online media during the COVID-19 pandemic

Abstract The purpose of this study was to determine the research profile consisting of articles related to Google Classroom on mathematics learning in Indonesia. This research is meta-analysis research by conducting research journal studies. The research journals were chosen purposively based on the latest and suitability of the research theme, namely the use of Google Classroom in mathematics learning in Indonesia. This study examines 17 research journals in the last 5 years (2016 - 2020) originating from the Google Scholar database. The instrument in this study was the human instrument, which was the researcher himself who acted as the research instrument. The data collected is a research profile, research objectives, research designs, research instruments, and analysis techniques in research related to Google Classroom on mathematics learning in Indonesia. Analysis of the data used is descriptive qualitative. The results of this meta-analysis show that Google Classroom has a positive impact on mathematics learning in Indonesia.