Reviewerly/exHarmony huggingface dataset:
Analysis of ICT Implementation in Teaching and Learning using Analytic Hierarchy Process (AHP): A Comparison of Rural and Urban Areas in Kedah

Nowadays, digital technology has been increasingly ubiquitous, particularly in teaching pedagogy. Digital technology-based teaching has the potential to significantly increase the students’ learning outcomes as well as their critical thinking and problem-solving skills. As a result, the Ministry of Education has initiated, supported, and promoted the incorporation of information and communications technology (ICT) into educational policy, as well as infusing classrooms with digital learning tools and resources. The Ministry of Education believes that implementing ICT into teaching and learning processes could assist and improve educational standards. However, due to some barriers, ICT integration in teaching has not yet been effectively adopted in Malaysia’s secondary schools. The aim of this study was to analyze and rank the factors that impede the implementation of ICT in urban and rural secondary schools using the analytic hierarchy process (AHP) approach. A t-test analysis is also carried out to determine the significant difference between factors from both urban and rural areas. The finding shows that there is no significant difference between factors from areas. The new mean values have been computed for each factor in order to determine the new ranking for each factor. The workload, lack of accessibility and network connection, and lack of support assistance are the top three factors that impede the ICT adoption in secondary schools in Kedah according to the new mean values obtained. In contrast, a lack of confidence has the least impact factor in this issue, accounting for only 3.5% of the total.
Model of project activity organization in bachelor’s of technology education preparing

The article considers the place of project activity in training bachelors of technology in the context of technology education modernization. The author describes the model of bachelors’ project activity organization that can be used by teacher in future independent pedagogical practice. An innovative element of the model is a development of digital electronic environment, whose existence provides the conditions for a wide multi-aspect inclusion of students in different types of project activities in technological education and stimulates the showing of various forms of activity.
Profiling of CNNs using the MATLAB FPGA-based Deep Learning Processor

In this paper we assess the performance of the new MATLAB Deep Learning Processor. It is a hardware architecture meant for FPGA devices which is able to infer Convolutional Neural Networks. The system is deployed on a Xilinx ZCU102 SoC and we customize it with the aim to maximize its processing performance. We evaluate the hardware resources utilization, the maximum achievable clock frequency, and the power dissipation of the system. Our goal is to find the best performing networks on FPGA and, eventually, to compare the results with a GPU-based counterpart. We conduct an experimental campaign where the FPGA execution time of several CNNs is profiled and compared to the execution time on the NVIDIA Titan RTX GPU platform. This allows a comparative performance analysis when the same network is inferred on different systems. We consider all the available CNNs of the MATLAB suite which have been pretrained with the ImageNet dataset. Finally, to pinpoint the most cost-effective network, the FPGA prediction time is put in relation with the accuracy on the aforementioned dataset.
Design And Build The AMANAH Vocational School Main Book Application Using The Waterfall Model

The benefits of information technology in the field of education include improving educational services, facilitating the collection and dissemination of educational information, educational data storage media, improving teaching skills, motivating students, thus helping communication activities in education. The concept of this information system has the effect of transforming the filling of the conventional ledger into digital form, by carrying out this transformation it can affect the easier filling of the master book and secure data storage and a longer time. After the process of accepting new students at SMK AMANAH is completed, the administrative officer or administrative administration enters student data, the value of student report cards per semester into the student master book, the difficulties encountered by administrative officers during the process of entering the data, officers have difficulty in terms of The main book used has a large size and the storage of the master book is still less effective and does not last long because it can be damaged, even though the master book should be able to be used for a long time as a document that is needed at any time. The research method used to design a student master book system is to use the waterfall or waterfall method to produce a design for the school's main book information system (SIBIS). Starting from the login page, the new student registration page, the grade submission page, and reports to help the school.
Digital Image Copyright Protection with Spatial Domain Public Image Watermarking Scheme

The resulting digital image certainly has the copyright attached to the image. There needs to be protection of digital image works because the form of storage of digital imagery works that are vulnerable to piracy, claims of unauthorized authorities, illegal duplication, or unauthorized modification. The Spatial Domain Public Image Watermarking method is used to protect digital imagery using applications designed to facilitate the watermarking process on several different digital images as well as with different image formats. The reason for using this method is because it has low complexity. In this study, it consisted of three processes carried out, namely the process of making watermarking, the process of checking watermarking and comparison of input imagery and image results. In the process of making watermarking do image input as a watermarking media, input inserted imagery in the form of binary images / text, key inputs and watermark results. In process of checking watermarking performs image input that becomes watermark media, watermark results input, key input, and checking results. The process of creating watermarking takes a relatively short time while the process of extracting watermarking processing time depends on the size of the image file. From some image testing conducted using imagery with three formats, namely: JPG with an average time accuracy of 9:5,310(42.15%), .GIFs with an average time of 8:27.207(21.63%), and. BMP has an average time accuracy of 5:2.989(36.22%). Thus, the determination of the image format used is adjusted to the original image, so that it can perform time efficiency.
Generating Consistent Multimodal Dialogue Responses with Emoji Context Model

Emoji plays a critical role in human-to-human conversations. It is straightforward to understand emotional feelings of others via the emojis in a dialogue response. However, incorporating emoji into a chatbot is a challenging task given the heterogeneity of the data sources. Motivated by previous work using emojis for sentiment analysis, we pave a novel way of creating a chatbot that can generate consistent multimodal responses. In this paper, we propose to develop an Emoji Context Model to generate emoji in dialogues. Also, considering dialogue historical information, responses generated by our proposed model achieve better performance in terms of coherency comparing with the state-of-the-art. The experimental results demonstrate our proposed model improves conversation consistency and provides effective multimodal responses with emojis.
Pedagogical Heritage of Professor S.G. Vershlovsky and his Contribution to Andragogy Development

The article substantiates the methods of studying the scientist’s pedagogical heritage, identifies sources and directions for the study of the biography and scientific works of S. G. Vershlovsky; the main stages of the biography and work of the scientist and teacher are given.
Context-Aware Memory Attention Network for Video-Based Action Recognition

Human action recognition is very popularly researched in the computer vision community. The current challenge is to render it efficient enough for wide deployment. In this paper, we propose a human action recognition model which does not require optical flow extraction and 3D convolution, called Context-Aware Memory Attention Network (CAMA-Net). It consists of an attention module called Context-Aware Memory Attention Module which is used to calculate the relevance score between the key and value pairs from the backbone output. The proposed method is evaluated and tested on popular public action recognition datasets, UCF101 and HMDB51. The results demonstrate the strength of our proposed model as it outperforms existing baseline models.
Standards column: VQEG

The Video Quality Experts Group (VQEG) was born from a need to bring together experts in subjective video quality assessment and objective quality measurement. The first VQEG meeting, held in Turin in 1997, was attended by a small group of experts drawn from ITU-T and ITU-R Study Groups. VQEG was first grounded in basic subjective methodology and objective tool development/verification for video quality assessment such that the industry could be moved forward with standardization and implementation. At the beginning it was focused around measuring the perceived video quality since the distribution path for video and audio were limited and known. Over the last 20 years from the formation of VQEG the ecosystem has changed dramatically and thus so must the work. Multimedia is now pervasive on all devices and methods of distribution from broadcast to cellular data networks. This shift has the expertise within VQEG to move from the visual (no-audio) quality of video to Quality of Experience (QoE). The march forward of technologies means that VQEG needs to react and be on the leading edge of developing, defining and deploying methods and tools that help address these new technologies and move the industry forward. This also means that we need to embrace both qualitative and quantitative ways of defining these new spaces and terms. Taking a holistic approach to QoE will enable VQEG to drive forward and faster with unprecedented collaboration and execution. VQEG is open to all interested from industry, academia, government organizations and Standard-Developing Organizations (SDOs). There are no fees involved, no membership applications and no invitations are needed to participate in VQEG activities. Subscription to the main VQEG email list (ituvidq@its.bldrdoc.gov) constitutes membership in VQEG. VQEG conducts work via discussions over email reflectors, regularly scheduled conference calls and, in general, two face-to-face meetings per year. There are currently more than 500 people registered across 11 email reflectors, including a main reflector for general announcements relevant to the entire group, and different project reflectors dedicated to technical discussions of specific projects. A LinkedIn group exists as well.
Teacher in the Space of Distance Learning: New Opportunities

The article makes an attempt to comprehend the new requirements for the teacher’s organizational and managerial activities in the context of distance learning. The possibilities, problems and risks of educational subjects in the interactive educational environment, a new model of organizing the interaction of educational subjects are considered.
Pedagogical Ethics in Social Media

The article examines the ethics of teacher’s behavior in social networks and formulates recommendations for teacher’s behavior on the Internet.
Statistical Evaluation of Orientation Correction Algorithms in a Real-Time Hand Tracking Application for Computer Interaction

A new approach to correct the orientation estimate for a miniature Magnetic-Angular Rate-Gravity (MARG) module is statistically evaluated in a hand motion tracking system. Thirty human subjects performed an experiment to validate the performance of the proposed orientation correction algorithm in both non-magnetically distorted (MN) and magnetically distorted (MD) areas. The Kruskal-Wallis tests show that the orientation correction algorithm using Gravity and Magnetic Vectors with Double SLERP (GMV-D), the correction using Gravity and Magnetic Vectors with Single SLERP (GMV-S) and the on-board Kalman-Filter (KF) performed similarly in non-magnetically distorted areas. However, the statistical tests show that, when operating in the magnetically distorted region, the level of error in the orientation estimates produced by the three methods is significantly different, with the proposed GMV-D method yielding lower levels of error in the three Euler Angles Phi, Theta and Psi. This indicates that the GMV-D method was better able to provide orientation estimates that are more robust against local disturbances of the magnetic field that might exist in the operating space of the MARG module.
High-Resolution Image Classification with Rich Text Information Based on Graph Convolution Neural Network

High-resolution image classification with rich text information is becoming increasingly common in real life. For example, in a live stream, viewers may receive a high-resolution image with rich text that contains product information sent by a commercial broadcaster. Under this circumstance, image classification is more challenging because it must take both high resolution and text information into consideration. In this study, we propose a novel graph neural network-based method for solving the problem of high-resolution image classification with rich text information. Specifically, the proposed method consists of the following three steps. First, we adopt an optical character recognition model to extractrich text information from a target image. Then, using location information, we construct a graph to represent the relationship between the text in the image. Third, a graph neural network is used to capture the semantic features of the original image. To validate the proposed methodology, we design two models: a node-based model and graph-based model. Experimental evaluations show that both models take better advantage of high resolution and text content and outperform other conventional image classification methods based on convolutional neural networks. Finally, to promote more efficient academic communication, the code and dataset are publicly available at https://github.com/kann-tsukasa/High-Resolution-Image-Classification-with-Rich-Text-Information.
What is the Future of Cyber Deterrence?

Scholars and practitioners alike have debated the feasibility of applying deterrence models to cyberspace. Advocates of "cyber persistence theory," for instance, posit that deterrence strategies are unlikely to succeed in the cyber domain. In contrast, the Cyberspace Solarium Commission's March 2020 report advocates for updating traditional deterrence concepts to account for the implications of emerging technologies, calling for the United States to implement a strategy of "layered cyber deterrence" In this article, we unpack the concept of cyber deterrence from three perspectives: definitional differences; distinguishing between general and specific deterrence; and the role of thresholds. Based on our analysis, we demonstrate why cyber strategies anchored in persistent engagement and near-constant offensive maneuver are insufficient to address the range of threat actor behavior in cyberspace. Instead, we offer a theoretical framework that articulates the conditions under which deterrence is possible in cyberspace. Finally, we conclude by providing policy recommendations for the United States.
What is the Expected Return on Bitcoin? Extracting the Term Structure of Returns from Options Prices

We infer the forward-looking Bitcoin risk premium from options contracts. Using data from 2018 to 2020, we show that the expected excess returns for Bitcoin are time-varying and significantly higher than in equities or gold, averaging almost 80% per annum. A temporal analysis of the term structure of Bitcoin risk premia reveals an upward sloping term structure during Bitcoin bull markets and downward sloping during Bitcoin bear markets.
SGMiner: A Fast and Scalable GPU-Based Frequent Pattern Miner on SSDs

Frequent itemset mining is extensively employed as an essential data mining technique. Nevertheless, as the data size grows, the applicability of this method decreases owing to the relatively poor performance of the existing methods. Though numerous efficient sequential frequent itemset mining methods have been developed, the performance that can be achieved is clearly limited by the fact that they exploit only one thread. To overcome these limitations, a number of parallel methods using multi-core central processing units (CPUs), multiple machines or many-core graphic processing units (GPU) have been proposed. However, these methods are relatively slow in performance and have low scalability, mainly owing to large memory requirements for intermediate data, significant disk I/Os, and heavy computation. In this study, to resolve the aforementioned problems, we propose SGMiner, which is a new, fast, and scalable GPU- and disk-based method on a single machine equipped with multiple graphic processing units (GPUs) and multiple solid-state drives (SSDs) for extracting frequent patterns. It is based on an algorithm similar to the Apriori algorithm and neither has intermediate data nor large disk I/O overheads owing to its exploitation of SSDs. Moreover, we propose storing transaction databases, namely bitmap transaction chunks, in SSDs, streaming the chunks to GPU device memory via the main memory with reduced I/O overhead, and performing fast support counting with GPUs based on the chunks. In addition, when exploiting multiple GPUs and SSDs, it proposes a concept of replicating bitmap transaction chunks stored in SSDs to GPUs in a streaming fashion. This could allow an almost equal workload to be distributed evenly across multiple GPUs with reduced I/O overheads. The experiments we conducted demonstrate that SGMiner outperforms the existing methods in terms of scalability and performance with enhanced robustness.
Sequential Normalization: Embracing Smaller Sample Sizes for Normalization

Normalization as a layer within neural networks has over the years demonstrated its effectiveness in neural network optimization across a wide range of different tasks, with one of the most successful approaches being that of batch normalization. The consensus is that better estimates of the BatchNorm normalization statistics (μ and σ2) in each mini-batch result in better optimization. In this work, we challenge this belief and experiment with a new variant of BatchNorm known as GhostNorm that, despite independently normalizing batches within the mini-batches, i.e., μ and σ2 are independently computed and applied to groups of samples in each mini-batch, outperforms BatchNorm consistently. Next, we introduce sequential normalization (SeqNorm), the sequential application of the above type of normalization across two dimensions of the input, and find that models trained with SeqNorm consistently outperform models trained with BatchNorm or GhostNorm on multiple image classification data sets. Our contributions are as follows: (i) we uncover a source of regularization that is unique to GhostNorm, and not simply an extension from BatchNorm, and illustrate its effects on the loss landscape, (ii) we introduce sequential normalization (SeqNorm) a new normalization layer that improves the regularization effects of GhostNorm, (iii) we compare both GhostNorm and SeqNorm against BatchNorm alone as well as with other regularization techniques, (iv) for both GhostNorm and SeqNorm models, we train models whose performance is consistently better than our baselines, including ones with BatchNorm, on the standard image classification data sets of CIFAR–10, CIFAR-100, and ImageNet ((+0.2%, +0.7%, +0.4%), and (+0.3%, +1.7%, +1.1%) for GhostNorm and SeqNorm, respectively).
Multivariate Symmetric Interpolating Dual Multiwavelet Frames

The construction of symmetric multiwavelets in the multivariate case with useful in applications properties is a challenging task, mainly due to the complexity of the matrix extension problem. Nevertheless, for the interpolating case, a general technique can be developed. For an appropriate pair of symmetry group H and matrix dilation M and for a given H-symmetric interpolating refinable matrix mask, a method for the construction of H-symmetric dual refinable matrix masks with a preassigned order of sum rule is suggested. Wavelet matrix masks are constructed using a certain explicit matrix extension algorithm, and their symmetry properties are studied via its polyphase components. The resulting multiwavelet systems form dual multiwavelet frames, where wavelet functions have symmetry properties, preassigned order of vanishing moments and preassigned order of the balancing property. Several examples are presented.
Digital Competencies of Higher Education Institutions in Mexico: A Systematic Literature Review

The objective of this publication is to identify the digital competencies that teachers in training have, within Higher Education Institutions in Mexico, through the analysis and systematization of previous research in 20 years from 2001 to date. For this purpose, a systematic review of the literature has been carried out through a systematic, explicit, and reproducible method. For this review, two filters were carried out: a practical criterion and a methodological quality criterion. As part of the first filter, five sources of information were used: EBSCO, Web of Science, Scopus, Google Scholar, and Redalyc. As a result, we found several articles with different approaches and research methodologies. It was found that teachers in higher education institutions have a medium–low mastery of digital competencies; the digital competencies with the highest mastery being those linked to the commitment and social responsibility of teachers in the use of Information and Communication Technology. Different institutions in Mexico have implemented different training programs in digital competencies for university teachers. As part of the training objectives of these projects, it is sought that teachers understand virtual environments, to make the change in their functions. It highlights the need for adequate training, not only in technology but also in pedagogy and curriculum, adequate infrastructure, and changes in institutional policies.
Art Talents in the Context of a Specific Research Focus

The scientific study presents a survey of the outcomes and research results obtained through a research tool by a questionnaire from the teachers of the basic art schools in Slovakia. Interpretation of research data in the form of categories - characters and determinants of artistic talent and talent of children and pupils has a representative function. Results of the mentioned group of respondents will be further compared with groups of teachers to different levels of schools.
Parameter Adaptation in Harmony Search with Shadowed Type-2 Fuzzy Approach for Designing Optimized Interval Type-2 Fuzzy Controllers

Currently, fuzzy logic has ventured into different areas and has shown that its use improves greatly compared to traditional methods. This is due to the handling of uncertainty that is needed in real-world situations. For this reason, the use of shadowed set theory is presented for parameter adaptation of harmony search, which is inspired by music specifically in jazz improvisation. In this article, the goal is the optimization of an interval type-2 controller utilizing the variant of harmony search with fuzzy adaptation. The study case with which the experimentation was carried out is a direct current motor, its main characteristic is to maintain the stability of the motor position. A fuzzy system is utilized to carry out the experiment, which was compared with existing methods in the literature, to demonstrate the improvement of the method.