Reviewerly/exHarmony huggingface dataset:
Teaching with a companion: the case of gravity

Virtual Reality (VR) has repeatedly proven its effectiveness in student learning. However, despite its benefits, the student equipped with a personal headset remains isolated from the real world while immersed in a virtual space and the classic student-teacher model of learning is difficult to transpose in such a situation. This study aims to bring the teacher back into the learning process when students use a VR headset. We describe the benefits of using a companion for educational purposes, taking as a test case the concept of gravity. We present an experimental setup designed to compare three different teaching contexts: with a physically present real teacher, using a live video of the teacher, and with a VR avatar of the teacher. We designed and evaluated three scenarios to teach the concept of gravity: an introduction to the concept of free fall, a parabolic trajectory workshop and a final exercise combining both approaches. Due to sanitary conditions, only pre-tests are reported. The results showed that the effectiveness of using the VR simulations for learning and the self-confidence level of the students increased as well. The interviews show that the students ranked the teaching modes in this order: VR companion mode, video communication and real teacher.
Introductory Review of Swarm Intelligence Techniques

With the rapid upliftment of technology, there has emerged a dire need to ‘fine-tune’ or ‘optimize’ certain processes, software, models or structures, with utmost accuracy and efficiency. Optimization algorithms are preferred over other methods of optimization through experimentation or simulation, for their generic problem-solving abilities and promising efficacy with the least human intervention. In recent times, the inducement of natural phenomena into algorithm design has immensely triggered the efficiency of optimization process for even complex multi-dimensional, non-continuous, non-differentiable and noisy problem search spaces. This chapter deals with the Swarm intelligence (SI) based algorithms or Swarm Optimization Algorithms, which are a subset of the greater Nature Inspired Optimization Algorithms (NIOAs). Swarm intelligence involves the collective study of individuals and their mutual interactions leading to intelligent behavior of the swarm. The chapter presents various population-based SI algorithms, their fundamental structures along with their mathematical models.
IoT Terminal Recognition Method Based on Linear Discriminant Spectral Analysis

Access to a large number of wireless devices brings serious security risks to the Internet of Things (IoT). The RF fingerprint (RFF) identification technology realizes the device’s unique authentication by extracting the subtle characteristics of the wireless device hardware, which can effectively solve the wireless network security problem. Aiming that the accuracy of traditional RFF identification methods declines rapidly with the decrease of SNR, this paper proposed an RFF identification method based on linear discriminate spectral analysis (LDSA). First, we calculated the optimal projection matrix, then extracted the average power spectral density characteristics of multiple consecutive preambles of the candidate device, and finally used the minimum centroid criterion in the subspace for device identification. Based on theoretical analysis, we conduct experiments on different software-defined radios and achieves an accuracy of more than 75% above 15 dB. Compared with the baseline algorithms, the identification accuracy is improved by more than 10% on average.
Software quality metrics in the automatic evaluation of Python introductory programming

Numerous virtual environments with automatic program evaluation have emerged to assist the teaching-learning process, allowing timely feedback. In a review of these environments, we find few studies that focus on an approach centered on refactoring: where students are strongly encouraged to refactor, improving the submitted code to also meet quality criteria. In a traditional environment, the student submits the answer and if it is dynamically correct, he goes to the next question. In this work, we propose a complementary approach based on software engineering metrics, which allow a finer evaluation of the code where the programmer, after having his dynamically correct answer, is invited and encouraged to refactor his solution towards an optimal code that also meets the software quality metrics. The work is based on source code in the Python language and shows which software quality metrics can be used with the purpose of encouraging students to refactor their code in programming fundamentals disciplines.
Fuzzy Keyword Search Over Encrypted Data using Cloud Computing

“Cloud” is a collective term for a large number of developments and possibilities. It is not an invention, but more of a “practical innovation”, combining several earlier inventions into something new and compelling. A cloud computing platform dynamically provisions, configures, reconfigures, and de provisions servers as needed [8]. Servers in the cloud can be physical machines or virtual machines. Security is a critical issue in cloud computing due to the variety of IT services that can be provided through a cloud environment. This paper focuses on the aspect of searching keywords over encrypted data while maintaining integrity of the data. Using a traditional algorithm like AES. Unlike traditional searching algorithms, here we try to implement a fuzzy logic which is based on a NLP technique called N gram. This fuzzy keyword searching significantly increases the efficiency and safety over cloud. This will keep the searching time efficient and acquire great results. The n-gram logic will be used to make sets of keywords which will used in the search implementation. To achieve more accurate results, Jaccard Coefficient will be used to find the similarity between the sets of keywords and rank them based on that. The purpose of this paper is to improve the traditional keyword search over encrypted data using cloud computing using advanced algorithms without compromising over security. Through rigorous security analysis, we show that our proposed solution is secure and maintains the privacy of the file server while efficiently using the fuzzy logic
Synchronizing a quantum local area network with White Rabbit

We demonstrate a scalable quantum local area network architecture using White Rabbit timing components. Synchronizing three distant nodes with ultralow timing jitter, we obtain significantly improved entanglement distribution fidelity over previous results with GPS clocks.
GraphZoo: A Development Toolkit for Graph Neural Networks with Hyperbolic Geometries

Hyperbolic spaces have recently gained prominence for representation learning in graph processing tasks such as link prediction and node classification. Several Euclidean graph models have been adapted to work in the hyperbolic space and the variants have shown a significant increase in performance. However, research and development in graph modeling currently involve several tedious tasks with a scope of standardization including data processing, parameter configuration, optimization tricks, and unavailability of public codebases. With the proliferation of new tasks such as knowledge graph reasoning and generation, there is a need in the community for a unified framework that eases the development and analysis of both Euclidean and hyperbolic graph networks, especially for new researchers in the field. To this end, we present a novel framework, GraphZoo, that makes learning, designing and applying graph processing pipelines/models systematic through abstraction over the redundant components. The framework contains a versatile library that supports several hyperbolic manifolds and an easy-to-use modular framework to perform graph processing tasks which aids researchers in different components, namely, (i) reproduce evaluation pipelines of state-of-the-art approaches, (ii) design new hyperbolic or Euclidean graph networks and compare them against the state-of-the-art approaches on standard benchmarks, (iii) add custom datasets for evaluation, (iv) add new tasks and evaluation criteria.
The structure of information and the sources of the "Trade and Industrial Newspaper" (the 90s of the XIX - early XX century)

The subject of research in this article is the "Commercial and Industrial Newspaper" published by the Ministry of Finance of Imperial Russia, the circumstances and process of its appearance, the goals and objectives set for its editorial office, the structure, content and quality of the information published in it, the place and role of the newspaper among other periodicals of the Ministry of Finance and similar Russian periodicals of the period under review (The 90s of the XIX century – 1906). As an object of research, the structure of information in the "Trade and Industrial Newspaper", its content and changes, as well as sources of obtaining this information are considered. The purpose of the study is to show how the "Trade and Industrial Newspaper", thanks to the published materials, the extensive and multilateral information provided by it, contributed to the development of the domestic trade and industrial sphere, the formation of an economically educated Russian entrepreneurial class, its self–awareness and consolidation. The role of the newspaper in ensuring the professional and class interests of the Russian bourgeoisie, in carrying out the state information policy is considered. The "Trade and Industrial Newspaper", which emerged as a publication of a specific professional direction, over time turned out to be one of the most innovative, energetic, knowledgeable, in-demand mass media bodies, which had a great influence not only on the similar publications that were being formed, but also on the information policy of the Russian state.
A novel convolution kernel-based robust watermarking scheme applied in medical image

When the existing digital watermarking algorithm is applied to the medical image, the watermark embedded in the Region of Interest (ROI) will change the properties of the original medical image, causing misdiagnosis. To solve these problems, a robust watermarking method for medical images based on feature extraction of convolution kernels is proposed in this paper. First, the embedded watermark is encrypted by logistic mapping to improve the security of the transmission of patients' personal information on the internet. Then, the convolution kernels are used to perform feature extraction on the original medical image to increase the algorithm's ability to resist geometric attacks. Finally, the zero-watermarking technology is used to organically combine the watermark and the medical image to prevent the region of interest of the medical image from being damaged by the watermark. Experimental results show that the algorithm has good performance on security and robustness.
Penerapan Model Discovery Learning untuk Meningkatkan Hasil Belajar Siswa Pada Tema Peduli Makhluk Hidup Siswa Kelas IV SD Inpres Mamajang II Kota Makassar

The low learning outcomes of fourth graders at SD Inpres Mamajang II Kota Makassar. For this reason, it is necessary to apply the Discovery learning model to improve student learning outcomes. The research used is the Kemmis and Taggart class action research (CAR), with 9 research subjects in grade IV at SDI Mamajang II. This study consisted of 2 cycles, each cycle consisting of stages of planning, implementation, observation, and reflection. Data collection techniques used in this study were observation, interviews and multiple choice tests. The results showed that there was an increase in the average score of students in the first cycle which was 66.67 and in the first cycle it increased to 81.11. Mastery of student learning in the first cycle is 44,44% and in the second cycle has increased to 88.89%. The application of the Discovery learning model can improve the learning outcomes of fourth grade students at SD Inpres Mamajang II Kota Makassar.
ECCE: Entity-centric Corpus Exploration Using Contextual Implicit Networks

In the Digital Age, the analysis and exploration of unstructured document collections is of central importance to members of investigative professions, whether they might be scholars, journalists, paralegals, or analysts. In many of their domains, entities play a key role in the discovery of implicit relations between the contents of documents and thus serve as natural entry points to a detailed manual analysis, such as the prototypical 5Ws in journalism or stock symbols in finance. To assist in these analyses, entity-centric networks have been proposed as a language model that represents document collections as a cooccurrence graph of entities and terms, and thereby enables the visual exploration of corpora. Here, we present ECCE, a web-based application that implements entity-centric networks, augments them with contextual language models, and provides users with the ability to upload, manage, and explore document collections. Our application is available as a web-based service at http://dimtools.uni.kn/ecce.
Error analysis on abstracts of the students' final writing projects

Background: Writing final project is one of the requirements for graduation from the college and abstract is one of the elements that students need to write too. Students' errors in writing abstracts need to be analyzed. Purpose: This research was conducted to find out errors made by the students. Design and Methods: In this qualitative research, the data were collected from students' abstracts of their final writings. There were six abstracts from six students collected and investigated. The data were analyzed by using theory from Dulay, Burt and Krashen (1982). The errors were classified as omission, addition, misformation and misordering. Results: Based on the research, it was found that the students had 34 errors in their abstracts. The students made 10 errors of omission, 2 errors of addition, 20 errors of misformation and 2 errors for misordering. The result of this research is expected to help the lecturers find out the solutions to overcome the errors in students’ writing. For the students themselves, it is expected that the research shows them the common errors and difficulties in academic writing, especially abstract.
Study on Various Self-supervised Video Representation Learning Methods

In the field of video representation, the self-supervised learning method was efficiently applied to the pre-training domain to downstream tasks using large and many unlabeled datasets. The basic approaches are typically based on a pre-text task method and a contrastive learning method. First, in a pre-text task method, a user defines a new problem and uses it as a proxy for self-supervised learning. Second, contrastive learning is a method of predicting the relationship between instances by assuming that feature values extracted through a certain model will have similar information between instances. According to the recent popularity of unsupervised learning, various self-supervised methods as well as the above methods are used in the field of video representation learning. Effective video representation learning is performed by fusing the multimodality advantages of video and the features of audio-visual information with various deep learning techniques. In this paper, recent representative methods of self-supervised video representation learning are summarized and described. Additionally, we provide a brief overview of how to utilize multimodality (audio-visual) information, which is the strength of the video.
Impelementation of Podcast Technology in Increasing Motivation to Learn Akidah Akhlak in Madrasah Aliyah Bilingual Students Krian Sidoarjo

This article aims to describe the implementation of Podcasts to increase the learning motivation of Akidah Akhlak in Bilingual Madrasah Aliyah students from Sidoarjo. Starting from the teaching and learning process on Akidah Akhlak subjects with Podcast learning media has somewhat significant meaning because so far, the results of learning Akidah Akhlak are still ineffective, and often teachers do not consider other essential components that can help the learning process so that students are motivated in the study. The teaching method used is still monotonous and does not understand the needs of students. This study uses qualitative descriptive research and data collection by interview, observation, and documentation. Analytical techniques include data reduction, data presentation, drawing conclusions, and triangulation data validity techniques. The findings of this study reinforce the existence of a learning process in Gagne's cognitive theory because Gagne's theory emphasizes the learning process rather than learning outcomes. The learning process in this theory is interconnected or influences between intrinsic and extrinsic factors to achieve capability or competence in learning.
Artistic choral education in Bukovina in the 19th century

The article analyzes certain aspects of cultural and educational processes that took place in Bukovina in the second half of the nineteenth century. Characterizes the content of the dynamics of the development of trends that contributed to the formation of artistic education and the creative heritage of those artists who determined their activities. The facts of the pedagogical, composer, public, and conductor activities of the outstanding artists of Bukovina of the XIX century, which contributed to the integrative development of educational, professional-performing and cultural-educational traditions of the western region, are presented.
Deep Learning Techniques for Banner Image Classification

For many centuries, people use many tactics to display their disagreement or rejection of an action or behavior that is not in favor of them. Usually, they assemble in large numbers and use banners, flags, slogans, etc., to exhibit their anger and frustration. Actually, the banners & flags work as an instigator for violent behavior in a crowd, on the other side; it also helps law enforcement agencies to understand the intent of the crowd. The goal of security forces is to identify and isolate unlawful material that may instigate violent action in the crowd. Thus, identification of banners at the genesis level of the crowd is very important to control and manage the probability of violent behavior in the crowd. To support security forces in their decision-making process, here we developed a system that employs pre-trained convolutional neural network (CNN) models to automatically recognize banners in an image or video. To achieve this, the present work also releases the DIAT Banner Dataset our novel dataset, mostly based on Indian protest events that include 1017 images. Using this dataset, we train pre-trained models and illustrate its result. A novel data augmentation technique referred to as the sliding window with histogram matching is introduced for dealing with the data insufficiency problem and also demonstrates its effectiveness as it improves the accuracies of all pre-trained models by an average of 7.37%. In this study, ResNet50 exceeds other pre-trained CNN models by obtaining 99.4% accuracy, after comparing the performance of several pre-trained models.
MEASURING THE RELATIONSHIP BETWEEN KNOWLEDGE MANAGEMENT, ORGANIZATIONAL LEARNING, AND INNOVATION: A STUDY OF JAMMU &amp; KASHMIR TOURISM DEVELOPMENT CORPORATION, INDIA

The present research focuses on the impact of Knowledge Management and Organizational Learning on Innovation in tourism organization. This study works on a model with Knowledge Management and Organizational Learning as inputs to promote Innovation. The study has used the descriptive research method, for which the data were collected through a questionnaire from 101 employees of Jammu and Kashmir Tourism Development Corporation (JKTDC) by adopting a census approach. Further regression analysis was applied to find the cause and effect relationship between Knowledge Management, Organizational Learning, and Innovation. Overall the study found that Knowledge Management has no significant effect on Innovation in the organization. While Organizational Learning significantly affects Innovation. It was also found that there is a significant difference in practicing Knowledge Management, Organizational Learning, and Innovation between levels of management. No significant difference was found regarding practicing Knowledge Management, Organizational Learning, and Innovation based on gender in the organization.
Frustrated arrays of nanomagnets for efficient reservoir computing (Conference Presentation)

We present initial experimental results and simulate a nanomagnet reservoir computer (NMRC) solving tasks requiring high memory content, with an area-energy-delay product ten-million times lower than CMOS systems. We manufactured a small nanomagnet reservoir demonstrating a frustrated state. We evaluated the performance on two novel tasks. Our results indicate the reservoir’s short-term memory capabilities and ability to integrate information from multiple concurrent inputs. In the end, our system saw a reduction in area by a factor of 50,000, in energy by a factor of 60, and in period by a factor of four as compared with an equivalent CMOS reservoir.
Imaging through unknown, random diffusers using diffractive all-optical computing (Conference Presentation)

We report a computer-free method to image through random, new diffusers at the speed of light using passive diffractive optical networks composed of spatially-engineered transmissive layers. These diffractive layers were designed using deep learning in a computer with image pairs containing diffuser distorted optical fields and the corresponding distortion-free images (ground truth). After this one-time training effort, the resulting diffractive layers were fabricated to form a physical network to all-optically reconstruct unknown objects through random, unknown diffusers, without requiring any power except for illumination. This diffractive computational imager might find applications in various fields, e.g., atmospheric sciences, biomedical imaging, defense/security.
Sync-Sequence based Synchronization Scheme in QKD-Secured Access Networks

Nowadays, more and more attention has been paid to the information security of access network, so the application of quantum key distribution (QKD) technology in access network has gradually become a feasible scheme. However, with the application of QKD technology in access network, the support of synchronization technology becomes more important. This paper provides a synchronization scheme in QKD-Secured access network scenario and realizes the synchronization function. Based on this, a sync-sequence based synchronization scheme is proposed to reduce the synchronization error of the system, and then reduce the probability of encrypted service transmission failure caused by non-synchronization.
Multiple-Layer Candidate Sieves Against Serial Cryptographic Implementations

The existing multiple-layer candidate sieve exploits collisions to filter the candidates to achieve a much smaller space for easier key recovery, and tries to recover the key ranking at very deep candidate space. However, it leads to enormous computation yet achieves very low success probability. In this paper, we build a novel Simple Multiple-Layer Sieve (SMLS) from Correlation Power Analysis (CPA) and achieve better performance than the existing one. Furthermore, we build two combined sieves named Two-Layer Stacking Sieve (TLSS) and Full-Layer Stacking Sieve (FLSS) since same operations in serial cryptographic implementation generate similar leakage. The experimental results verify their superiority.