Reviewerly/exHarmony huggingface dataset:
The degree of using E-Learning websites by Jordanian universities’ student and Its relationship with quality assurance standards from the perspective of Quality Assurance Reprehensive: درجة استخدام طلبة الجامعات الأردنية لمواقع التعليم الإلكتروني وعلاقتها بمعايير الجودة الشاملة من وجهة نظر ممثلي الجودة فيها

This study aimed to identify the degree of using E-Learning websites by Jordanian universities’ student and its relationship with the comprehensive quality standards from the perspective of quality representatives in these universities. To achieve the objective of the study, the researcher used the descriptive correlation method and developed two tools. The first questionnaire examined the degree of using e-learning websites by Jordanian universities’ students and it consisted of (15) items. Where the second questionnaire, which examined the degree of applying the comprehensive quality standards, consisted of seven fields that cover (50) items. The validity and reliability of the two instruments were ascertained. The study sample consisted of (177) quality representatives from sixteen private and public universities located in the central district of the kingdom. The results of the study showed the following: The degree of using E-learning websites by Jordanian universities’ students was moderate (M=3.53), The degree of implementing E-learning quality assurance standards was high (M=3.71), and there is a positive correlation relationship with statistical significance at the level of significance (α=0.50) between the degree of using E-learning websites by Jordanian universities’ students and the implementation of E-learning quality assurance standards. The study recommended emphasizing the importance of applying quality standards as they have a significant role in improving the degree of students' use of e-learning sites.
Learning Cascaded Detection Tasks with Weakly-Supervised Domain Adaptation.

In order to handle the challenges of autonomous driving, deep learning has proven to be crucial in tackling increasingly complex tasks, such as 3D detection or instance segmentation. State-of-the-art approaches for image-based detection tasks tackle this complexity by operating in a cascaded fashion: they first extract a 2D bounding box based on which additional attributes, e.g. instance masks, are inferred. While these methods perform well, a key challenge remains the lack of accurate and cheap annotations for the growing variety of tasks. Synthetic data presents a promising solution but, despite the effort in domain adaptation research, the gap between synthetic and real data remains an open problem. In this work, we propose a weakly supervised domain adaptation setting which exploits the structure of cascaded detection tasks. In particular, we learn to infer the attributes solely from the source domain while leveraging 2D bounding boxes as weak labels in both domains to explain the domain shift. We further encourage domain-invariant features through class-wise feature alignment using ground-truth class information, which is not available in the unsupervised setting. As our experiments demonstrate, the approach is competitive with fully supervised settings while outperforming unsupervised adaptation approaches by a large margin.
Credit Card Fraud Detection using Machine Learning

The rapid growth in E-Commerce industry has led to an exponential increase in the use of credit cards for online purchases and consequently they have been surging in the fraud related to it. Nowadays, the development of technology is rapidly increasing, including the credit card fraud. The credit card fraud (CCF) is one of the problems our banking system is facing today. Fraudsters used many methods to attack the customer. The growth in electronic transactions has resulted in a greater demand for fast and accurate user identification and authentication. Conventional method of identification based on possession of pin and password are not all together reliable. Higher acceptability and convenience of credit card for purchases have not only given personal comfort to customers but also attracted a large number of attackers. In recent years, for banks has become very difficult for detecting the fraud in credit card system. Machine learning plays a vital role for detecting the credit card fraud in the transactions. For predicting these transactions banks make use of various machine learning methodologies, past data has been collected and new features are been used for enhancing the predictive power. The performance of fraud detecting in credit card transactions is greatly affected by the sampling approach on data-set, selection of variables and detection techniques used. The three techniques are applied for the dataset and work is implemented in python language. The performance of the techniques is evaluated for different variables based on sensitivity, specificity, accuracy and error rate. The comparative results will show which Methodologies are efficient.
Facebook will 'get better' on hate speech: COO

Major companies began boycotting Facebook ads in June as part of the Stop Hate For Profit campaign.
Layanan Referensi Virtual Perpustakaan Universitas Leiden Melalui Perpustakaan KITLV-Jakarta

Introduction. This study discusses Leiden University Library reference services through the KITLV-Jakarta. Since KITLV was acquired by the Leiden University Library, the KITLV-Jakarta Library which was a KITLV-Dutch representative, is now a representative of the Leiden University Library in Jakarta. Data Collection Method. This study used a qualitative approach with descriptive research involving observations and interviews as well as primary document data sources.Data Analysis. The data was analyzed by involving the process of data reduction, interpretation, and conclusion. Results and Discussions. The results of the study indicate that reference services, particularly for reference collections found in the Leiden University Library cannot be accessed directly through the KITLV-Jakarta Library. Only KITLV librarians can request the reference service from Leiden University librarians.Conclusion. Librarians at the KITLV-Jakarta Library do not have the same access as librarians at the Leiden University Library, even though the KITLV-Jakarta Library is a representative of the Leiden University Library in Jakarta.
An Exploration for Generative Adversarial Networks Via Adding A Screening Model

As an emerging deep learning model, generative adversarial networks has enough creativity and potential in the application and advanced studies. However, many problems should be tackled in its training process. Based on the exist studies of generative adversarial networks and relevent ideas of games theories, this article adds a new screening model for the framework of generative adversarial networks to solve vanishing gradient in the training process, which contains the function of filter and measurer. This model does not interfere training process, only find and delete discriminators which might cause vanishing gradient, and output a value to represent the progress of training process.
Dispositivos móviles como estrategias metodológicas para la enseñanza de Educación Cultural y Artística

The objective of this study was focused on determining the use of mobile devices by teachers in the ECA teaching-learning process, in order to propose them as a pedagogical alternative. The research assumed a quantitative, descriptive, non-experimental methodological approach. The results show that teachers do not use mobile devices effectively for teaching; although they affirm that their use could help improve the educational process if they are well selected and applied. It is concluded that at the present time most of the students have access to the internet and therefore, at least to a mobile device, in such a way the motivation for the use of these resources within the ECA classroom is a task of the teacher , which must be trained on the selection and application of resources that promote teaching - learning.
SKILLS REQUIRED FOR THE INDUSTRY 4.0 AFTER COVID-19

K. Ferreira Santos1A.P.B. Garcez1R. Moreira da Silva2J. Aires Moreira2C.E. Barbosa de Lima2P.V. Leitão Moreira2J.M. da Silva Neto2R.D. Paulino2A.P. Moraes Ventura2F.B. Charrua-Santos1
Modeling and evaluation of dispatching policies in IaaS cloud data centers using SANs

Infrastructure-as-a-Service (IaaS) clouds not only have to meet business requirements but also need to consider other important metrics that influence the quality of service, such as performance, availability, and power consumption. In this paper, we present a Stochastic Activity Network (SAN) based analytical approach that simultaneously computes these metrics under a given load for IaaS cloud data centers. The model's high-level abstraction of IaaS clouds allows us to evaluate different resource management strategies and features, including cloud federation and dispatching policies. In a first step, we model a single rack including its autonomous local manager responsible for scheduling IaaS requests onto available PMs. In a second step, we present a unified model that represents an entire data center, including an energy-aware central manager, to take advantage of structure-awareness for managing cloud resources in an optimized way. We introduce and evaluate several dispatching policies that can be used by the cloud central manager to demonstrate the applicability of the unified SAN model. The model is validated against the well-known CloudSim framework. Extended simulations are also conducted to apply the proposed model to a real-world IaaS cloud.
Digital Gold: The Case for Cryptocurrencies

Cryptocurrencies have the potential to improve upon both commodity and fiat monies. I consider the rise of bitcoin and its underlying blockchain technology. I discuss obstacles to greater adoption and explain how some alt-coins have taken steps to resolve some problems with bitcoin. Finally, I discuss the prospects for cryptocurrency to promote sound money.
SAND: an AND-RX Feistel lightweight block cipher supporting S-box-based security evaluations

We revisit designing AND-RX block ciphers, that is, the designs assembled with the most fundamental binary operations—AND, Rotation and XOR operations and do not rely on existing units. Likely, the most popular representative is the NSA cipher SIMON, which remains one of the most efficient designs, but suffers from difficulty in security evaluation. As our main contribution, we propose SAND, a new family of lightweight AND-RX block ciphers. To overcome the difficulty regarding security evaluation, SAND follows a novel design approach, the core idea of which is to restrain the AND-RX operations to be within nibbles. By this, SAND admits an equivalent representation based on a $$4\times 8$$ synthetic S-box (SSb). This enables the use of classical S-box-based security evaluation approaches. Consequently, for all versions of SAND, (a) we evaluated security bounds with respect to differential and linear attacks, and in both single-key and related-key scenarios; (b) we also evaluated security against impossible differential and zero-correlation linear attacks. This better understanding of the security enables the use of a relatively simple key schedule, which makes the ASIC round-based hardware implementation of SAND to be one of the state-of-art Feistel lightweight ciphers. As to software performance, due to the natural bitslice structure, SAND reaches the same level of performance as SIMON and is among the most software-efficient block ciphers.
Generating Explainable Rule Sets from Tree-Ensemble Learning Methods by Answer Set Programming.

We propose a method for generating explainable rule sets from tree-ensemble learners using Answer Set Programming (ASP). To this end, we adopt a decompositional approach where the split structures of the base decision trees are exploited in the construction of rules, which in turn are assessed using pattern mining methods encoded in ASP to extract interesting rules. We show how user-defined constraints and preferences can be represented declaratively in ASP to allow for transparent and flexible rule set generation, and how rules can be used as explanations to help the user better understand the models. Experimental evaluation with real-world datasets and popular tree-ensemble algorithms demonstrates that our approach is applicable to a wide range of classification tasks.
SOCIO-CULTURAL CONDITIONALITY OF THE DEVELOPMENT OF CONVERGENT PROCESSES IN MODERN EDUCATIONК

The article analyzes the understanding of the term “convergence” from the position of general scientific application and meaning. Attention is focused on the socio-cultural conditionality of the convergence of the natural science and humanitarian spheres of culture and the corresponding systems of scientific knowledge. It is stated that convergent processes, which are both a system-forming value and a mechanism for harmonizing modern culture, are reflected in the development of science, which in turn determines the development of convergent processes in education.
Innovative “Triple Helix” Projection: Favorable Environment For Digital Medicine For Making Medical Decisions

The article examines the global trends in the innovative development of medicine, the technological basis of which is nuclear medicine and pharmaceuticals, diagnostic and therapeutic systems, digital technologies used to solve the problems of the medical sphere and the nuclear industry.Areas are considered related to the development and production of new materials, in particular radio pharmaceuticals for medical purposes according to the GMP standard, modification of materials, ionizing processing of agricultural and food products.A perspective projection of the development of the triple helix system is presented, which is implemented through the creation in the city of Obninsk of the Kaluga region of the Innovative Scientific and Technological Center (INTC) Park of Atomic and Medical Technologies, which acts as a logical continuation of the process to improve the activities of the existing cluster of pharmaceuticals, biomedicine and biotechnologies.Thanks to the comprehensive support of the state, a promising environment for innovative development is being formed, providing unlimited opportunities for conducting research work, implementing international cooperation, and training highly qualified personnel.Opportunities for constructive cooperation between business associations and scientific communities have been maximally expanded, providing, on the one hand, the creation of a reliable financial and material and technical base of science, on the other, increasing productivity, efficiency, competitiveness, and other business indicators.In the end, the main task is being solved - improving the quality of life and preserving human health and life itself.The authors, as in previously published articles [1, 2], but at a higher quality level achieved thanks to new scientific results, consistently defend the position of the need to improve the “triple helix” system.On the example of the development of a specific project - the Park of Atomic and Medical Technologies and the cluster of pharmaceuticals, biomedicine and biotechnologies in Obninsk, the researchers demonstrate that constructive, mutually beneficial cooperation between the state, business organizations, scientific and educational communities and societies ensures the building of a new innovativeprojection of civilizational development.
INNOVATIVE TECHNOLOGIES IN ORGANISING EXHIBITION OF NATURE SECTOR IN KUZBASS STATE MUSEUM OF LOCAL HISTORY (KEMEROVO, RUSSIA) AND PECULIARITIES OF THEIR USE IN EXCURSION WORK

The paper analyses digital transformation of traditional technology of exhibiting museum items, methods of excursion service to museum audience. An experience of integration of information and telecommunication technologies (such as mapping, holographic projection, typhlokommentation, augmented reality, etc.) into activities run by Kuzbass State Museum of Local History (Kemerovo, Russia) is outlined. The authors reveal a few problems of carrying on excursion work in modernized exhibition areas of a museum.
Application of AHP to support the Marketing Management Promotion Strategy of Universities in Aceh

Marketing management promotion strategy is important by private universities, this hall because the high competition with public universities is very high and the number of study programs at public universities in Aceh so that competition to increase prospective students is very tight, especially in the selection of study programs in accordance with student interests.  The importance of conducting marketing management promotion strategies in convincing prospective new students to choose a privately schooled program according to the student's interests. Furthermore, the need for a system in providing major recommendations in accordance with interest is one of the strategies to attract students to enroll vocationally. With this recommendation system provides recommendations for students in choosing a major must be on target, effective and efficient. so that students can choose major recommendations with a more effective duration of time, not wasted time and students can choose a study program that suits their grades and abilities.  Therefore, it is important that the application model of application of decision support systems using the analitycal hierarchy process for students and this program strongly supports the university in looking at prospective students based on interests and talents that are in accordance with each student's abilities. The keriteria used in the ahp method of student subjects is processed in the ahp model and incorporated into the program for the suitability of majors and study programs. This research flow includes weighting menu to see the level of interest of criteria, menu set weight criteria in accordance with the initial input of criteria, menu set weights majors, menu list of majors to see the majors covered, design values and list of student names that include informatics, accounting and management. Kriteri value 98, 90, 75, 80 and 84, then for K-1 and alternative 1 namely, 0.2940, Alt 0.0702, Alt-3 0.003, alt 4 with a value of 0.4894 and the last stage is 0.01124 with the highest score Alt-1 0.1810, Alt2, 01586, 0, 1047, Alt4 0.4002 and Alt 5 0.1518. In addition, you see majors who are the same age as the campus.
Diagnosing the Current Information Systems Security Department in the Information Technology Department According to the International Standard (ISO / IEC 27001: 2013)

IT systems and networks of information are vulnerable to threats resulting from the breach or attempt to obtain data or reports, records and files that contain the information, which is available to persons, designated in the Ministry of the Interior.  This is because of its inherent specificity associated with work and the concern of the attempted destruction by the authorities that aim to misuse the information. The importance of research stems from the importance of the application of the standard because the information protection means the protection of the human resource in the Ministry of the Interior and protects all those who are working on this information.  It also protects them from loss and damage and access to it to get benefits from it by any person whose aim is to disrupt the order or sabotage.  Thus, it is required to manage it with the best methods of protection adopted globally. The research aims to diagnose the gap between the information security system in the department according to the international standard (ISO / IEC 27001    , 2013). It also aims to identify the gap using Likert Sibai scale. The size of the gap has reached the largest gap in the size of the requirement of leadership represented by about 49% and matching 51% due to lack of establishment of information security policy in the section.
Current problems of developing rare book collection at sci-tech library (case study of Central Scientific Agricultural Library)

The issues of methodological approaches toward and criteria of publication selection for rare and valuable book collections of sci-tech libraries are discussed. Based on the experience of CSAL, the need for comprehensive evaluation of books is substantiated: by materials and facilities, level of performance, physical condition and age, circulation, periodicity, and by value in scientific and sociocultural aspects.
L-Verse: Bidirectional Generation Between Image and Text

Far beyond learning long-range interactions of natural language, transformers are becoming the de-facto standard for many vision tasks with their power and scalability. Especially with cross-modal tasks between image and text, vector quantized variational autoencoders (VQ-VAEs) are widely used to make a raw RGB image into a sequence of feature vectors. To better leverage the correlation between image and text, we propose L-Verse, a novel architecture consisting of feature-augmented variational autoencoder (AugVAE) and bidirectional auto-regressive transformer (BiART) for image-to-text and text-to-image generation. Our AugVAE shows the state-of-the-art reconstruction performance on ImageNet1K validation set, along with the robustness to unseen images in the wild. Unlike other models, BiART can distinguish between image (or text) as a conditional reference and a generation target. L-Verse can be directly used for image-to-text or text-to-image generation without any finetuning or extra object detection framework. In quantitative and qualitative experiments, L-Verse shows impressive results against previous methods in both image-to-text and text-to-image generation on MS-COCO Captions. We furthermore assess the scalability of L-Verse architecture on Conceptual Captions and present the initial result of bidirectional vision-language representation learning on general domain.
Flatness-Based Model Predictive Trajectory Planning for Cooperative Landing on Ground Vehicles.

The autonomous landing of a fixed-wing unmanned aerial vehicle (UAV) on a moving unmanned ground vehicle (UGV) demands precise spatial synchronization of both vehicle systems. A promising strategy to achieve a successful landing in this heterogeneous rendezvous maneuver is the subdivision of the synchronization control task into a trajectory planner and underlying trajectory tracking controllers. For this purpose, a central trajectory planner, which is the subject of this paper, computes feasible trajectories for the UAV and UGV that converge towards each other. Current approaches mainly rely either on computationally expensive optimization problems or on potentially inaccurate linearization. In this paper, a new model predictive trajectory planning scheme based on the flatness property of kinematic models of the fixed-wing UAV and UGV is presented. By using vehicle models with this property, quadratic programs (QP) are formulated that can be solved efficiently in each time instance without linearization of the nonlinear vehicle models, which is a novel approach in this application. Crucial requirements for the trajectory planner are that it reduces the horizontal distance between the vehicles to below 0.5 m while maintaining safety constraints to allow safe landing, and a sampling rate of 40 Hz to allow rapid replanning in case of disturbances. To overcome the issue of input constraint transformation between the original and the flat models, different approximation methods are investigated. Simulation results with simple kinematic vehicle models and high-fidelity dynamic models using underlying trajectory tracking controllers and navigation filters are presented that demonstrate that the proposed trajectory planning method allows safe landing maneuvers with the considered vehicle setup.