Reviewerly/exHarmony huggingface dataset:
Combining Blockchain Multi Authority and Botnet to Create a Hybrid Adaptive Crypto Cloud Framework

Blockchain technology has now spread like wildfire across the internet. Blockchain has emerged as a game-changing technology for complex industrial processes as a result of its openness, availability, and security. In this study, we used a hybrid adaptive crypto cloud framework that combines blockchain technology with multiple authorities and a botnet framework to improve cloud security while reducing computation time. The proposed adaptive crypto cloud system divides the cloud security framework into stages to organize secure data communication and reduce communication latency while detecting internal and external threats. In order to compute authentication using hash mapping and deploy an authentication system to safeguard the various users&#x0027; authentication information, the whole system placed a major emphasis on block chain technology. The technology not only improves security but also makes the role-based access control system and anonymous authentication system more user-friendly.
A geometrical deep learning model for the lexicalisation of “unseen” RDF triples

A considerable amount of data, presented in a structured form, is available on the Web nowadays. For the informational content of such data to be made accessible and understandable to users, its translation into text is preferable. This task is named &#x201C;data-to-text generation&#x201D; in the state-of-the-art, and it is an instance of the Natural Language Generation. In order to generate some valuable text from data, also known as lexicalisation, some approaches have begun to consider the Resource Description Format (RDF) data present within the Knowledge Graphs. In this context, it is possible to identify two main categories of lexicalisation approaches that use neural networks: pipeline and end-to-end. The former has better performances but is more complex to adapt. The latter, the end-to-end systems, has much simpler architectures but is less precise. In this work, in order to get the best from the two categories, we propose a new hybrid approach, TripleEnc, which, thanks to the concept of vector similarity between RDF triples, identifies the best approach for lexicalisation. Empirical comparisons demonstrate that the novel approach improves the quality of the generated text.
PENERAPAN METODE EXTREME PROGRAMMING PADA PENGEMBANGAN SISTEM INFORMASI EVALUASI HAFALAN AL-QUR’AN

Monitoring refers to the process of gathering and analyzing information that is systematic and continuous related in improving the program. The Al-Qur’an memorization monitoring system that has been running is systematic, but not yet efficient. Recording memorization that uses conventional method requires a great deal of time to recap memorization. This research aimed to develop a system that can act as a medium for recording and monitoring the Al-Qur’an memorization. This research used the Extreme Programming method. This research result is the development of web based the Al-Qur’an memorization monitoring system with unit testing and acceptance testing that suit the needs of users. The Al-Qur’an memorization monitoring system can see memorization recap, record memorization, see memorized surah and see memorization chart. The Al-Qur’an memorization monitoring system can evaluation in the memorized Al-Qur’an.
Software Development Methodologies for Virtual Reality

Virtual Reality (VR) is the emerging field of digital environment simulation through specialized hardware such as a helmet, gloves or controllers which can be used to manipulate features of the environment. VR software covers a wide variety of applications, from educational media, games, to collaborative tools for professional teams. Over the past five to ten years, VR for software development has seen a great number of changes to the development process of creating, maintaining and improving cohesion between teams. Nowadays the society around us is changing and evolving at an accelerated rate. In this paper, we will discuss different software development methodologies for applications in VR software development, advancements within the field and answer challenges development teams currently face.
DILEMMAS OF DIGITAL SCHOOL

The new technologies, including WWW, computer hardware and software have become a necessary complement to human activity — also at all stages of education. The extraordinary appeal of these technologies has encouraged too hasty, in risky way, adopt the model for entry of the information technologies into school practice.
Estimation of Problematic Portions in Presentation Slides Based on Analysis of Audience’s Unconscious Reactions and Slide Data

Opportunities have increased to make presentations using slides to explain the results and progress of work. Therefore, there is an increasing need for students to develop the ability to improve presentation slides. To make students experienced in refining slides by themselves, it is important to suggest problematic portions in slides to them. In this research, we have developed a mechanism for automatically estimating problematic portions in presentations on the basis of an analysis of the compositional features of slides and gaze movements of audiences. We have also been working on the development of a system that presents the results of this estimation in an understandable form so that students can use them in their refinement of slides. This paper mainly describes the features of a system that estimates the problematic portions in slides.
Self-training Two-Stage 3D Object Detection With Noisy Student

As the core technology of autonomous driving, 3D object detection has attracted a great interest of lots of researchers. Many 3D object detection methods achieved promising performance. However, current 3D object detection method relies heavily on 3D annotation. The quantity and quality of annotation directly determine the performance of current 3D object detector, but 3D annotation is very expensive and require lots of human labor. Based upon the predicament described above, we proposed ST-PointRCNN: a semi-supervised 3D object detection method in autonomous driving, and achieves good results. Specifically, we apply student-teacher framework to train and improve the accuracy of the student model. We filter the predictions of teacher model and only keep high confidence predictions as pseudo labels. To expand the unlabeled data set and make the pseudo labels relatively more accurate than the predictions of student model, we design consistency losses for two-stage 3D object detection method to improve the robustness of the detector to data noise. Finally, we also conduct extensive experiments on KITTI dataset. The results show that comparing to the supervised method our method improve the average of car, pedestrian, and cyclist for easy difficulty by 8.8&#x0025;, 9.4&#x0025; and 7.1 &#x0025; respectively when using 5&#x0025; of label data.
Quantifying the Impact of Vulnerabilities of the Components of an Information System towards the Composite Rise Exposure

To compensate the lacking of any concrete scoring formula of the CVSS v3 for the category of "Environment", in this paper, we present a novel formula for objectively quantifying composite vulnerability exposures for non-terminal components of an information system. The paper examines limitations of the CVSS v3 calculator definition, notably the capacity to characterize vulnerabilities from a composite perspective, providing a means to output a composite CVSS-compliant vulnerability score for aggregated system components. We provide the definitions for related concepts, formulas for determining component vulnerability, and a formula for calculating composite vulnerability. The common implementation of a Linux, Apache, MySQL, PHP (LAMP) stack provides a practical demonstration of the foundational formulas.
Spatial-temporal-spectral transformer for 3D human pose estimation

Human motion exhibits a high spatial-temporal correlation, and further exploration of the intrinsic correlation of joint motion trajectories is beneficial to improve the performance of 3D pose estimation. Therefore, we propose a novel spatial-temporal-spectral transformer for high-quality 3D human pose estimation in videos, which mainly includes the spatial-temporal transformer at the joint level and the spectral transformer at the joint trajectory level. The former explores the dependencies of the joint level from the skeleton graphic structure and the sequence to obtain a richer feature representation. The latter explores the dependence of joint motion trajectories in the spectral domain. To obtain a more accurate 3D pose estimation of the center frame, a multi-layer stride convolution module is used to realize the estimation from the full frame to the center frame. In addition, since the 2D and 3D pose sequences have the same motion trajectory in the <tex>$xy$</tex> plane, we add the consistency constraint to obtain more accurate estimation results. Extensive experiments show that the proposed framework achieves state-of-the-art performance on Human3.6M.
Machine Learning for Multiple Stage Phishing URL Prediction

Phishing is a fraudulent process and a form of cybercrime where an attacker tries to obtain sensitive information for malicious use. A phisher uses social engineering and technical deception to fetch private information from the web user. Previous Machine Learning (ML) approaches have been used to detect whether URLs are valid, or invalid. The purpose of this work is to detect, or predict, the three stages of Phishing URLs starting with valid, not enough info and invalid URLs. We will investigate different potential models that are trained by Machine Learning algorithms and find out which of these models has better accuracy.
A Comparative Analysis of Various Models for Assessment of Trust in Digital Age Accelerated by Covid-19

The Coronavirus pandemic has made Irreversible changes in our society and the business world. Almost all aspects of the business and daily routine have shifted to the digital platforms and various forms of personal, indirect communications and suit the current environment in guarding us against coronavirus. The outbreak also brought a refreshing load of creativity in the people who found new ways to solve everyday problems. The key to solving problems is effective communication. With the help of mobile devices and computers, people were able to change their environment so that their expression of thoughts and their tasks of daily routine got aligned with social media platforms. People express themselves as if they are not going to get another chance to express themselves. They use doodles, poetic tweets, and many other forms of colloquial language. Using mixed language such as Hinglish became a norm for the commoner. In this research work, an attempt has been made to review techniques that can be used to work trust models from which meaning insights can be drawn in times such as covid-19 pandemic. From this study it can be inferred that no single approach of modeling complex scenarios such trust in times of covid-19 can be done. There is an urgent need to take inspiration from multiple techniques and approaches to assess the trust level in the digital society.
Continuous Variable Quantum Compilation

In recent years quantum computing has evolved from a research concept subject to reality. Companies and scientists around the world are creating physical quantum systems using different quantum technologies to advance the field. Although most of this focus is on superconducting, qubit-based implementations, recently there has been work in the development of boson-based quantum computers. The basic processing unit for these devices are continuous variable (CV), multi-photonic modes in Gaussian states. Due to the novel nature of this technology there is a lack of work on analyzing the compilation process of current photonic devices. One of the leading companies in this field developed a cross-platform Python library for simulating and executing programs on quantum photonic hardware, Strawberry Fields (SF). This paper will focus on analyzing the SF compilers, and proposing an additional compilation step that will enable future, more flexible hardware implementations.
Starvation Avoidance Task Scheduling Algorithm for Heterogeneous Computing Systems

High-performance and cost-effective Heterogeneous Computing Systems (HCS) are highly dependent on the efficiency of task scheduling, which has been proven to be NP-complete. This makes scheduling problem a vital issue which researchers have attempted optimization in different aspects under the expense of tradeoffs. The problem of scheduling a single Directed Acyclic Graph (DAG) has been extensively researched and studied, however most of the researches in this area did not consider the occurrence of multiple applications represented as multiple DAGs in real case scenarios. This paper aims to fill this gap by providing a fairness-aware, time-efficient, HCS static scheduling algorithm that meets deadlines, reduces makespan, and avoids starvation. The propose algorithm considers the possibility of having to schedule tasks of multiple applications concurrently. The algorithm is called Fair, Deadline-Aware, Time Efficient, Starvation Avoiding (FDATESA) scheduler, and relies on four pillars: (i) mapping the DAGs into information abundant maps (ii) Task priority heuristic calculation (iii) Assigning tasks to processors&#x2019; queues (iv) elastic processor time slot calculation for starvation avoidance.
Methods of Sentiment Analysis for Hindi and English Languages

Social media is widely regarded as one of the most important unstructured data. Analyzing and extracting meaning from such data is a time-consuming process. Because of the enormous data available on social media platforms, sentiment extraction has gotten a lot of attention. Microblogging is a relatively new phenomenon, with Twitter being the most widely utilized. It&#x0027;s one of the most comprehensive free and open data sources available. Today&#x0027;s society sees a lot of differing viewpoints on Twitter. Researchers can use opinion mining to obtain the present emotion and mood of the public. Sentiment Analysis is defined as the technique of extracting and finding the polarity of a given material to get insight into the hidden information, emotion, feeling contained within a text. The ultimate objective of sentiment analysis is to extract meaningful material from various sources of information. The first analysis of tweets was done using the Natural Language Processing (NLP) method. For further analysis of the opinionated data, two approaches are available: the Lexicon Based Approach (LBA) and the Machine Learning Approach (MLA) based on supervised learning. The LBA approach employs a resource dictionary, namely the Hindi SentiWordNet, and a Hybrid Based Approach (HBA) that joins the Lexicon based and Machine learning for categorizing tweets as positive or negative
A Data-Driven Password Strength Meter for Cybersecurity Assessment and Enhancement

Password-based authentication is the most popular authentication mechanism over insecure networks due to its simplicity and convenience. To ensure the security of this authentication mechanism, measuring the strength of users&#x0027; passwords is a crucial task not only to guide users to create strong passwords but to protect systems from unauthorized access. Password strength meters are usually used for measuring the strength of passwords, often in real-time. However, password strength meters are only helpful if they are accurate. Passwords meters that do not accurately reflect the actual passwords strengths, e.g., providing a high score for a weak password, may misinform users and hinder the overall security of password-based authentication mechanisms. Also, a user-friendly password strength meter is important in guiding users to create a strong password for further authentication. While many password strength meters were proposed in the literature, using the most appropriate password meter remains a difficult process. In this paper, we propose a data-driven password meter. Our system scrapes and collects large datasets to be used to measure the strength of user passwords based on proven password strength policies and we offer a user-friendly mechanism to help users create a strong password. We evaluate our system by measuring the influence of the proposed meter at guiding users to create stronger passwords by tracking their eye movements. We conducted our experiment with a pilot study on a testing web service and monitored the eye movements of 240 participants using an eye-tracking tool. Our results exhibited a significant improvement by influencing users to create stronger passwords, with an average of 110.97 years for password cracking time.
A Study on Development of Convertible Global Citizenship Education Instructional Strategy and Module of Group Polarization Thinking

In this study, as a part of global citizenship education, an educational strategy was sought for the possibility of shifting extreme thinking and an example of module was developed based on this. In order to establish the lesson strategy that reflects the characteristics of extreme thinking that occurs in society as well as schools and makes it flexible, 8 core contents are extracted from 9 topics in the field of global citizenship education. The core concepts of the lesson module from 8 core contents were polarization thinking, shifting thinking, and practice overcoming them. Through the education instructional module in this study, first, it will be possible to overcome the gap between existing global citizenship and actual lesson content and form, and to reduce the logical gap between society and student behavior. Second, global citizenship, which exists as knowledge, will become a reality, so that ‘real’ global citizenship can be acquired. Third, the global citizenship acquired through this module will provide practical norms, knowledge, and theory applicable in practical situations for students.
A Secure Block-chain Enabled Approach for E-Heath-care System

Security of data has been a necessity in this era and block-chain is emerging as the most secure and reliable platform in many sectors. One among them is the health care industry where we can optimize the daily life applications. Examination about block-chain and medical services is presently restricted, yet block-chain is near the very edge of changing the medical services framework; through its decentralized standards, block-chain can further develop openness and security of patient data, and can hence topple the medical care pecking order and construct another framework wherein patients deal with their own consideration. In this paper, we survey existing writing and applications accessible for the medical services framework utilizing block-chain innovation. Further we additionally propose a Secure Healthcare Management System utilizing Block-chain.
The Legacy of Andrzej Kossakowski

I review here a small part of Andrzej Kossakowski scientific activity focusing on his pioneering contribution to the evolution of open quantum systems (celebrated Gorini-Kossakowski-Lindblad-Sudarshan (GKLS) master equation) and positive maps in operator algebras. These particular topics turned out to be of primary importance for fundamentals of quantum physics and the rapid development of modern quantum information theory.
Evaluating Accuracy and Adversarial Robustness of Quanvolutional Neural Networks

Machine learning can thrust technological advances and benefit different application areas. Further, with the rise of quantum computing, machine learning algorithms have begun to be implemented in a quantum environment; this is now referred to as quantum machine learning. There are several attempts to implement deep learning in quantum computers. Nevertheless, they were not entirely successful. Then, a convolutional neural network (CNN) combined with an additional quanvolutional layer was discovered and called a quanvolutional neural network (QNN). A QNN has shown a higher performance over a classical CNN. As a result, QNNs could achieve better accuracy and loss values than the classical ones and show their robustness against adversarial examples generated from their classical versions. This work aims to evaluate the accuracy, loss values, and adversarial robustness of QNNs compared to CNNs.
Prototyping: Sample Selection for Imbalanced Data

In the context of supervised learning, we are concerned with the task of identifying a subset of the example instances which maximize the predictive performance. In contrast to sampling, we do not generate new instances because we do not know how to reliably label them. We propose a simple and effective method with complexity that is linear in the size of the source data, and logarithmic in the size of the number of examples selected. We demonstrate empirically that very significant improvements are achievable on skewed data across a wide range of model types and data sets. In particular, we observe that the fraction achieving peak performance is proportional to the square root of the reciprocal of the skewness.
Low-Resolution Face Recognition in Multi-person Indoor Environments Using Convolutional Neural Networks

Face recognition has been widely applied in many systems in our lives. These applications have reached good accuracies on face recognition tasks when face images can be captured with good quality, particularly when they have a high enough resolution. However, in an indoor environment, a surveillance camera often covers a wide area with multiple persons; this leads to only lower resolutions of face images are available in face recognition. This paper presents a face recognition approach for low resolution images using convolutional neural network (CNN) in a multi-person indoor environment. Our methods first detect face regions with the YOLOv3 approach and then recognize face images using the trained CNN model. Experiments are performed in an indoor classroom to capture face images with resolutions ranging from 20x20 to 70x70. Moreover, face images are extracted over 4 months to test the stability of our proposed face recognition model.
Cryptographic algorithms in IoT - a detailed analysis

IOT is quickly becoming a fundamental platform for interconnected devices. With such an advancement in technology, interconnected devices are also having security issues. As a matter of fact, Internet of Things (IoT) security has become a critical challenge. Because scalable systems and services are subject to a variety of vulnerabilities and data breaches, more confidentiality and anonymity are necessary. This paper presents cryptographic approach using symmetric and asymmetric encryption techniques for IoT security along with their comparison. This research aims to provide a detailed examination of RSA, Blowfish, Diffie-Hellman, ECC and other cryptographic schemes.
Generative Adversarial Network Applications in Creating a Meta-Universe

Generative Adversarial Networks (GANs) are machine learning methods that are used in many important and novel applications. For example, in imaging science, GANs are effectively utilized in generating image datasets, photographs of human faces, image and video captioning, image-to-image translation, text-to-image translation, video prediction, and 3D object generation to name a few. In this paper, we discuss how GANs can be used to create an artificial world. More specifically, we discuss how GANs help to describe an image utilizing image/video captioning methods and how to translate the image to a new image using image-to-image translation frameworks in a theme we desire. We articulate how GANs impact creating a customized world.