Reviewerly/exHarmony huggingface dataset:
Realities and Challenges of Cybercrimes in India: An Analytical Study

This investigative scrutiny delves into the veracity and complexities of digital wrongdoings in India.The nation has witnessed a momentous surge in the quantity of digital wrongdoings in the recent period, prompting financial predicaments and disrepute in personal eminence.The analysis scrutinizes the various categories of digital wrongdoings that prevail in India, encompassing the malevolent activities of hacking, pilfering of personal identities, deceiving ploys of phishing and the vicious assaults of ransomware.Additionally, it evaluates the elements that foster the escalation of digital wrongdoings, such as the rapid and widespread proliferation of technology, inadequate discernment among the populace and the insufficiency of robust measures in cybersecurity.The study further discusses the legal framework and institutional mechanisms in place to combat cybercrimes in India, including the Information Technology Act, 2000, and the National Cyber Security Policy, 2013.The current discourse highlights the obstacles faced by authorities in their pursuit of cybercriminals.This is primarily due to the intricate nature of cybercrimes.The study puts forth the necessity for a comprehensive approach in addressing cybercrimes in India.This would entail a combination of legal, technological, and educational measures aimed at raising public awareness and fortifying cybersecurity infrastructure.
(P.) GREGORIĆ and (G.) KARAMANOLIS (eds) Pseudo-Aristotle: <i>De Mundo (On the Cosmos)</i>: A Commentary. Cambridge and New York: Cambridge University Press, 2021. Pp. xi + 245: illus., maps; 24 cm. £75. 9781108834780.

(P.) GREGORIĆ and (G.) KARAMANOLIS (eds) Pseudo-Aristotle: De Mundo (On the Cosmos): A Commentary. Cambridge and New York: Cambridge University Press, 2021. Pp. xi + 245: illus., maps; 24 cm. £75. 9781108834780. - Volume 142
EASIER corpus: A lexical simplification resource for people with cognitive impairments

Thanks to technologies such as the Internet and devices now available to people, we have increasingly greater access to larger quantities of information. However, people with ageing disabilities or intellectual disabilities, non-native speakers, and others have difficulties reading and understanding information. For this reason, it is essential to provide text simplification mechanisms when accessing information. Natural Language Processing methods can be applied to simplify textual content and improve understanding. These methods often use machine learning algorithms and models which require resources, such as corpora, to be trained and tested. This article presents the EASIER corpus, a resource that can be used to build lexical simplification methods to process Spanish domain-independent texts. The EASIER corpus is composed of 260 annotated documents with 8,155 words labelled as complex and 5,130 words with at least one proposed context-aware synonym associated. Expert linguists in easy-to-read and plain language guidelines have annotated the corpus based on their experience adapting texts for people with intellectual disabilities. Sixteen annotation guidelines that discriminate between complex and simple words have been defined to help other groups of experts to generate new annotations. Additionally, an inter-annotator agreement test was performed to validate the corpus, obtaining a Fleiss Kappa coefficient of 0.641. Furthermore, a qualitative evaluation was conducted with 45 users (including people with intellectual disabilities, elderly people, and a control audience). Complex word identification tasks achieved moderate results, but the synonyms proposed to replace complex words achieved almost perfect ratings. This resource has been integrated into the EASIER platform, a tool that helps people with cognitive impairments and intellectual disabilities to read and understand texts more easily.
THE COLLEGE INQUIRY BOT (ARTIFICIAL INTELLIGENCE)

The goal of a chat-bot is to establish communication between computers and people.The device is equipped with the information necessary to distinguish between sentences and draw a conclusion in answer to a query.Chat-bots' whole user interface will be text-based, allowing the user to submit commands and get text and text to speech answers.Typically, chatbots are services that offer functionality by recalling previous instructions.By integrating chat-bot technology with well-known online services, it may be used safely by an even wider audience.The chat-bots for college inquiries will be created utilizing artificial algorithms that can comprehend and evaluate user messages and enquiries.The matching of an input statement from a user is the response principle.The User can ask about any college-related events via the chatbot without physically visiting the school.After analyzing the query, the system responds to the user.The technology provides the students' question with an artificial intelligence-based response.An efficient Graphical User Interface allows the system to respond to the user as if they were a real person.
An Interpretable Measure of Dataset Complexity for Imbalanced Classification Problems

The class imbalance problem is associated with harmful classification bias and presents itself in a wide variety of important applications of supervised machine learning. Measures have been developed to determine the imbalance complexity of datasets with imbalanced classes. The most common such measure is the Imbalance Ratio (IR). It is, however, widely accepted that the complexity of a classification task is the combined result of class imbalance and other factors, such as class overlap. Thus, in order to accurately assess the complexity of a problem, the data complexity measures ought to account for more than the simple IR. In this paper, we demonstrate that IR has a weak correlation with classifier performance in terms of macro averaged recall, gmean score, and precision. Other more complete measures such as the adapted N1 and N3 measures use neighborhood information to assess overlap. These measures show a strong negative correlation with classifier performance, but their reported values were hard to interpret. This motivates a new measure that estimates overlap complexity and returns a value with a clear interpretation. Here we propose such a measure based on the number of minority instances entangled in a Tomek Link. The proposed measure is evaluated on a large selection of synthetic and real datasets and is found to be as good as or better than the best competitors in terms of its negative correlation with respect to mean classifier performance.
A Topic-Specific Web Crawler using Deep Convolutional Networks

This paper presented a new focused crawler that efficiently supports the Turkish language. The developed architecture was divided into multiple units: a control unit, crawler unit, link extractor unit, link sorter unit, and natural language processing unit. The crawler's units can work in parallel to process the massive amount of published websites. Also, the proposed Convolutional Neural Network (CNN) based natural language processing unit can professionally classifying Turkish text and web pages. Extensive experiments using three datasets have been performed to illustrate the performance of the developed approach. The first dataset contains 50,000 Turkish web pages downloaded by the developed crawler, while the other two are publicly available and consist of “28,567” and “22,431” Turkish web pages, respectively. In addition, the Vector Space Model (VSM) in general and word embedding state-of-the-art techniques, in particular, were investigated to find the most suitable one for the Turkish language. Overall, results indicated that the developed approach had achieved good performance, robustness, and stability when processing the Turkish language. Also, Bidirectional Encoder Representations from Transformer (BERT) was found to be the most appropriate embedding for building an efficient Turkish language classification system. Finally, our experiments showed superior performance of the developed natural language processing unit against seven state-of-the-art CNN classification systems. Where accuracy improvement compared to the second-best is 10% and 47% compared to the lowest performance.
Permutation Elementary Cellular Automata: Analysis and Application of Simple Examples

This paper studies simple three-layer digital dynamical systems related to recurrent-type neural networks. The input to hidden layers construct an elementary cellular automaton and the hidden to output layers are one-to-one connection described by a permutation. Depending on the permutation, the systems generate various periodic orbits. Applications include walking robots, switching power converters, and reservoir computing. In order to analyze the dynamics, we introduce two feature quantities that evaluate complexity and stability of the periodic orbits. Calculating the feature quantities in simple example systems, we have clarified that the systems can generate various stable periodic orbits. Presenting an FPGA based hardware prototype, typical periodic orbits are confirmed experimentally.
A COSINE SIMILARITY-BASED RESUME SCREENING SYSTEM FOR JOB RECRUITMENT

In today's fast-paced job market, recruitment processes have become increasingly competitive, making it difficult for companies to select the most qualified candidates for a job position.In order to streamline the recruitment process, we have developed a resume screening website that allows HR managers to upload job descriptions and rank resumes based on their similarity to the job requirements.Our system uses cosine similarity as the primary metric to determine the relevance of a candidate's resume to the job description.The system has been designed to be user-friendly and efficient, making it easier for HR managers to identify the best candidates for a job position.The results of our experiments indicate that our system outperforms traditional resume screening methods in terms of accuracy and speed.
BANKING SECTOR BLOCKCHAIN DEPLOYMENT TO INCREASE SCALABILITY

The Blockchain is an encrypted database that keeps data statistics, or, to put it another way, it is a virtual ledger of any transactions or contracts that must be independently documented.One of Blockchain's main advantages is that this virtual record is dispersed across a number of vast computer networks and isn't necessarily guaranteed to be kept in a single location.The financial services industry has already begun to be disrupted by the Blockchain network, and it is this technology that powers bit coin transactions.Research on the impact of Blockchain technology on the financial sector is the focus of this essay.There is no doubt that everyone in the world is interested in learning how this potential technology will affect or shape the future.
TIC TAC TOE USING AI

This project seeks to develop a Tic Tac Toe game with AI and multiplayer features.The game's AI is built on the mini-max algorithm, which allows the computer to make the best plays based on the current state of the game.The multiplayer mode allows two players to compete against one other through a network, with the option of playing locally or remotely.The game was created with Python and Pygame, and it features an easy and userfriendly interface, numerous difficulty levels, and support for multiple concurrent connections.
Designing An E-Course In Light of Quality Matters Standards, And Measuring Its Effectiveness on Developing The Skills of Educational Infographic Design And Attitudes Towards The E-Course Among Postgraduate Female Students At Hail University.

This study aimed at designing an e-course in light of Quality Matters (QM) standards, and measuring its effectiveness on developing the skills of educational infographic design and attitudes towards the e-course among postgraduate female students at Hail university. The study adopted the quasi-experimental methodology based on one-group design. To achieve the objectives of the study, the course of "new trends in education technology" was designed as an e-course in light of QM standards, an achievement test for the cognitive aspect of the skills of educational infographic design was designed, an evaluation card for the educational infographic design, and an attitude scale towards the e-course were prepared. The study sample consisted of (22) female students of master's degree at Hail university. The results showed a statistically significant difference at level (α ≤ 0.05) between the mean scores of the study sample in the pre and post application of the achievement test, the evaluation card and the attitudes scale, the differences were in favor of the post application. The results also showed the effectiveness of the e-course on developing the performance side of infographic design skills, and in developing attitudes towards the e-course among the sample of the study.
Analysis of the Content of the Sixth-Grade Science Book in Elementary School in Lighting the Twenty-First-Century Skills

This study aimed to analyze the sixth-grade science book in the elementary school examining the twenty-first-century skills, the researcher used the descriptive analytical approach by preparing an analysis tool that was applied to the study sample, which is the sixth-grade science book in the first and second semester. and there are several skills under each of these main areas as follows: learning and innovation skills (creativity and innovation, communication and cooperation, critical thinking and problem solving), information culture and technology skills (communication and media culture, information culture, in-formation technology culture), career and life skills (adaptation and flexibility, initiative and self-direction, personal and societal responsibility). The results of the study were as follows: The education and innovation skills got the highest percentage, which is %64 in the first semester and %63.4 in the second semester. The career and life skills were in the second with a percentage of %25.1 in the first semester and %25.3 in the second semester. The information culture and technology skills were in the third that is %10.8 in the first semester and %11.3 in the second semester, and in light of the results it reached. The research presented a set of recommendations and suggestions.
PT module -A Traffic Signal Classification Model Based on Convolutional Neural Networks and Random Forests

Autonomous driving and image recognition are the hot directions of Internet development nowadays. In self-driving cars it is necessary to capture traffic signs in front of the vehicle by cameras. To ensure that the information of image recognition is correct, a set of image classification models with high accuracy should be used to classify the recognized objects in order to determine the next instruction of vehicle operation. There were many achievements in the research work of traffic sign recognition, but there are still some shortcomings. By combining CNN and random forests with PT module (a module that can improve the accuracy of feature extraction), we finally came up with a classification model that can efficiently place the received traffic sign images into a specified category, which we obtained 97% accuracy on the GTSRB dataset, which is much more accurate than traditional neural network methods or regression methods. We have also evaluated it on other datasets and the results obtained are more promising.
Ground Based-cloud Classification Based on Comparing Different Classification Models

Clouds are one of the most common things in our daily life, and different types of clouds will foreshadow different weather conditions. Therefore, accurately identifying and classifying them are important for human forecasting the local weather. However, artificially classifying clouds could possibly cause a minor error. With the helping of machine learning classification model could avoid this situation as much as possible. In this study, our goal is to get sufficiently high accuracy of based-cloud classification. We used LBP as a preprocessing approach, and used KSVM, MLP, Custom Vision, and Resnet to compare each result. The dataset contains around 15 thousand cloud images in 5 types in the same format and pixels. With the implementation of four classification models, experiments showed that ResNet34 came out a great result with a test accuracy 94%. Therefore, this study demonstrates ResNet34 is a good image classification model in based-cloud classification field.
Development of an Augmented Reality based Serious Game for the Training of Children with Dyslexia and Dysgraphia

Therapists have used paper-based exercises to treat learning disorders for many years. Dyslexia and Dysgraphia are one of the reading and writing disorders that are common nowadays. Different serious games and interventions are developed for the improvement of dyslexic and dysgraphic children. But these games cannot engage these children for a long time. Moreover, there is a need for an assessment criterion for the performance of dyslexic and dysgraphic children. Augmented Reality is an emerging technology that is used in the educational and medical fields. Augmented Reality in learning apps increases children's performance and makes them attentive. Research shows that it has a positive impact on dyslexic children. In this case study, an augmented reality-based serious game called ARLexic Game is developed to train children with Dyslexia and Dysgraphia. Design Science Research Methodology is used to design and develop the application. An experiment is conducted with specialists in special education and children with dyslexia and Dysgraphia aged 7 to 14 years old. The outcome shows that the ARLexic Game engagement level increases with time. Children learn quickly without being bored and enjoying it.
Neural Machine Translation in Translation and Program Repair

Translation is a challenge for humans since it needs a good command of two or more languages. When it comes to computer programs, it is even more complex as it is difficult for computers to imitate human translators. With the emergence of deep learning algorithms, especially neural network architectures, neural machine translation (NMT) models gradually outperformed previous machine translation models and became the new mainstream in practical machine translation (MT) systems. Nowadays, NMT has been developing for several years and has been applied in many fields. This paper is focused on studies on four different application categories of NMT models: 1) Text NMT; 2) Automatic program repair (based on NMT); 3) Simultaneous translation. Our work provides a summary of the latest research on different applications of NMT and makes comments on their development in the future. This paper also mentioned the shortcomings of existing studies in this essay and pointed out some possible research directions.
IOT-Based Document Tracking Software Development Process

In this paper, we introduce a development process for web-based software to keep track of the scientific achievements of academic staff at universities who have earned a degree and moved up in their jobs. This system was put in place because the government wanted to move toward electronic governance and digitization. It was also made to make it easier to track and speed up the process of getting a scientific update. The system was supposed to be a free Web app that could be updated and made bigger as new instructions came in. IoT (Internet of Things) technology is used in this system to send an SMS message to the student's phone number about the final grade. PHP, HTML, CSS, and My SQL are all programming languages that were used to build the system
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS ANDROID PADA MATERI SISTEM PERSAMAAN LINEAR DUA VARIABEL

This study is a research and development (R&D method), which aims to develop an Android-based learning media on the Linear Equation in Two Variable.The advantages of this android-based media can be used at any time and are easily accessed using a smartphone.The research development design used is a 4D model (define-design-develop-disseminate).However, in this article, the study has been carried out has reached the development stage.The dissemination stage becomes a future work which is a separate study from this article.Overall, this article briefly describes developing learning media by utilizing android-based technology, especially during the covid-19 pandemic.
Linguistic exegesis sources and their methods to use

Arabic language is the most important basis of interpretation because the Holy Qur'an was revealed in the Arabic language and the polytheists were challenged to imitate it, but they could not do it and will not do it until the Day of Resurrection‫۔‬ This is the Arabic language, and His style is the main component of Ijaz al-Qur'an, Therefore, interpretation through the Arabic Linguistic sources is of great importance and status, because only those who understand the Arabic language can understand the Holy Qur'an‫۔‬ Hence, the goal of this article is to highlight such sources of Arabic vocabulary that make it easier to understand the Qur'an‫۔‬ At the beginning of this article, these sources are divided into six parts‫۔‬ This part of the research is related to the first three parts, which includes Ma'ani ul-Qur'an, Ghareeb Al-Qur'an, and the basic books of Tafsir in which vocabulary is given importance‫۔‬ In this paper, an attempt has been made to highlight the authors scholarly content, and other scholarly aspects of these books, including the method of compilation‫۔‬ Keywards: Holy Qur'an, Interpretation, exegesis sources, linguistic interpretation, sources of interpretation‫۔‬
Proof of Mirror Theory for a Wide Range of $$\xi _{\max }$$

In CRYPTO’03, Patarin conjectured a lower bound on the number of distinct solutions $$(P_1, \ldots , P_{q}) \in (\{0,1\}^{n})^{q}$$ satisfying a system of equations of the form $$X_i \oplus X_j = \lambda _{i,j}$$ such that $$P_1, P_2, \ldots $$ , $$P_{q}$$ are pairwise distinct. This result is known as “ $$P_i \oplus P_j$$ Theorem for any $$\xi _{\max }$$ ” or alternatively as Mirror Theory for general $$\xi _{\max }$$ , which was later proved by Patarin in ICISC’05. Mirror theory for general $$\xi _{\max }$$ stands as a powerful tool to provide a high-security guarantee for many blockcipher-(or even ideal permutation-) based designs. Unfortunately, the proof of the result contains gaps that are non-trivial to fix. In this work, we present the first complete proof of the $$P_i \oplus P_j$$ theorem for a wide range of $$\xi _{\max }$$ , typically up to order $$O(2^{n/4}/\sqrt{n})$$ . Furthermore, our proof approach is made simpler by using a new type of equation, dubbed link-deletion equation, that roughly corresponds to half of the so-called orange equations from earlier works. As an illustration of our result, we also revisit the security proofs of two optimally secure blockcipher-based pseudorandom functions, and n-bit security proof for six round Feistel cipher, and provide updated security bounds.