Reviewerly/exHarmony huggingface dataset:
Prevention of SQL Injection Using a Comprehensive Input Sanitization Methodology

Databases are essential to modern-day web applications. Structured Query Language (SQL) used to retrieve information from relational database is prone to injection attack by a malicious user. Losing access to application database destroys all credibility for the organization. Mitigating against SQL injection attacks is a critical concern. This paper proposes a methodology for the safe flow of SQL query from the user layer to the database layer. The proposed methodology uses the process of client-side input sanitization to validate user input before a dynamic SQL query is constructed. On the server-side, another process provides server-side query sanitization to ensure a full-proof validation of the SQL query before it is run on the database. The client-side input sanitization process is presented in the paper. On comparison with related methodologies, the proposed methodology is robust, lightweight, and fast.
Query Recommendation for Search Engine Results Using Machine Learning

Now a days, web content over the World Wide Web is growing fast, it has become tougher to satisfy the necessities of the client’s queries results. This paper provides a technique for advising a sequence of queries that are similar with the client’s input search. The related searches are based on past given searches by the clients itself. This method tells about the clustering process where syntactical or semantically similar searches in groups are found. This also proposes some queries which are similar or related to the queries submitted by the client to get the required information which is relevant and efficient. This method does not only detect the similar and related searches and queries but can also rank them considering the similarity measure. And this technique is executed using real data sets from search engine query log. It gives queries in websites like Google, yahoo, Bing etc.
Contributions of Leadership Styles to Digital Transformation-An Exploratory Study of the Opinions of a Sample of Administrative Leaders at the Northern Technical University

The study was conducted to find out the extent of the success of the senior leaders in higher education organizations in the digital transformation process, as well as its practical side, it found the effect of leadership patterns, which are the transformational leader, the reciprocal leader, the democratic leader, and finally the autocratic leader, and its reflection on the digital roaming with its dimensions represented in electronic governance.Digital technology, physical aspects, digital strategies, one of the good things for the senior management is to make the administrative work linked to the Internet and archive it properly and correctly, allowing the user to find the required information in an easy and easy way.The study community at the Administrative Technical University was identified and a sample of 217 electronic questionnaires to be analyzed using the SPSS Amos program.The study reached a number of conclusions, the most important of which is the conformity of the study model with the hypothetical model using the chi-square for good matching of observational frequencies and dealing with the modeling with structural equations to analyze the data and the phenomenon that matched the field data.
Development of functional literacy among students in preparation for intellectual competitions in biology

The issue of schoolchildren's functional literacy development is the main topic of the study, which is based on pedagogical literature. Analysis and testing are done on the outcomes of students both before and after the application of test tasks influencing the development of functional literacy in schoolchildren. The following characteristics of how these tasks affected the intellectual kids in two classes are taken into consideration: 8 and 10 In order to be prepared for the intellectual competitions in biology, it is found that complicated tasks have an impact on the growth of functional literacy. The style of inquiry was used with pupils between the ages of 14 and 16. The level of FL students was found to be impacted by tasks for the development of functional literacy Автор
Automatic Modulation Classification Using a Support Vector Machine-Based Pattern Recognition Algorithm

Modulation format recognition is an essential part of intelligent receivers of wireless communication systems, especially for adaptive radio systems (ARS). This paper presents a detailed investigation of automatic modulation classification (AMC) using pattern recognition classifiers (PRC) under fading and AWGN conditions. A variety of classifiers with different kernel functions and Support Vector Machine (SVM) classifiers have been developed for the classification of higher-order digital modulation signals. In addition, an extensive investigation of the extraction of various higher-order statistical features from each of the modulated classes and the choice of appropriate features for training classifiers are presented. In addition, the performance of the SVM classifier is evaluated under a variety of training rates and suboptimal channel conditions. Further, the performance of SVM classifiers is compared to that of existing techniques to demonstrate the effectiveness of the SVM classifiers for modulation categorization.
Constructing Condensed Memories in Functorial Time

If episodic memory is constructive, experienced time is also a construct. We develop an event-based formalism that replaces the traditional objective, agent-independent notion of time with a constructive, agent-dependent notion of time. We show how to make this agent-dependent time entropic and hence well-defined. We use sheaf-theoretic techniques to render agent-dependent time functorial and to construct episodic memories as sequences of observed and constructed events with well-defined limits that maximize the consistency of categorizations assigned to objects appearing in memories. We then develop a condensed formalism that represents episodic memories as pure constructs from single events. We formulate an empirical hypothesis that human episodic memory implements a particular time-symmetric constructive functor, and discuss possible experimental tests.
ANALYSIS ON CAPSULE NETWORKS TO DETECT FORGED IMAGES AND VIDEOS

Modern day computer vision tasks requires efficient solution to problems such as image recognition, natural language processing, object detection, object segmentation and language translation. Symbolic Artificial Intelligence with its hard coding rules is incapable of solving these complex problems resulting in the introduction of Deep Learning (DL) models such as Recurrent Neural Networks and Convolutional Neural Networks (CNN). However, CNNs require lots of training data and are incapable of recognizing pose and deformation of objects leading to the introduction of Capsule Networks. And these are the new sensation in Deep Learning. They have lived to this expectation as their performance in relation to the above problems has been better than Convolutional Neural Networks. Even with this promise in performance, lack of architectural knowledge and inner workings of Capsules serves as a hindrance for researchers to take full advantage of this breakthrough. In this paper, we provide a comprehensive review of the state of the art architectures, tools and methodologies in existing implementations of capsule networks. We highlight the successes, failures and opportunities for further research to serve as a motivation to researchers and industry players to exploit the full potential of this new field. The main contribution of this survey article is that it explains and summarizes significant current state of the art Capsule Network architectures and implementations. The method introduced in this paper uses a capsule network to detect various kinds of spoofs, from replay attacks using printed images or recorded videos to computer generated videos using deep convolutional neural networks. It extends the application of capsule networks beyond their original intention to the solving of inverse graphics problems.
Reproducible research practices and transparency across linguistics

Scientific studies of language span across many disciplines and provide evidence for social, cultural, cognitive, technological, and biomedical studies of human nature and behavior. By becoming increasingly empirical and quantitative, linguistics has been facing challenges and limitations of the scientific practices that pose barriers to reproducibility and replicability. One of the proposed solutions to the widely acknowledged reproducibility and replicability crisis has been the implementation of transparency practices, e.g. open access publishing, preregistrations, sharing study materials, data, and analyses, performing study replications and declaring conflicts of interest. Here, we have assessed the prevalence of these practices in randomly sampled 600 journal articles from linguistics across two time points. In line with similar studies in other disciplines, we found a moderate amount of articles published open access, but overall low rates of sharing materials, data, and protocols, no preregistrations, very few replications and low rates of conflict of interest reports. These low rates have not increased noticeably between 2008/2009 and 2018/2019, pointing to remaining barriers and slow adoption of open and reproducible research practices in linguistics. As linguistics has not yet firmly established transparency and reproducibility as guiding principles in research, we provide recommendations and solutions for facilitating the adoption of these practices.
Mimeme Attribute Classification using LDV Ensemble Multimodel Learning

One of the most common types of social networking interaction is memes. Memes are innately multimodal, so studying and processing them is a hot issue currently. This study's analysis of the DV dataset comprises classifying memes according to their irony, humour, motive, and overarching mood. The effectiveness of three different creative transformer-based strategies has been carefully examined. The DV Dataset used here is created by own meme data for this implementation analysis of hateful memes. Out of all of our strategies, the proposed ensemble model LDV obtained a macro F1 score of 0.737 for humour classification, 0.775 for motivation classification, 0.69 for sarcasm classification, and 0.756 for overall sentiment of the meme.
Recognization Holic- Medicine Detection using Deep Learning Techniques

Everyone's life is significantly impacted by medicine. The rate at which a certain disease's treatment is discovered is rising daily. Therefore, it is important for people to be attentive when taking their medications and that they are knowledgeable about them. Providing various medication-related capabilities such as reminders to take medications on time, prescription information, and to aid chronic patients in taking numerous medications appropriately and avoiding taking the wrong medications, which may create drug interactions. The RECOGNIZATION HOLIC programme uses deep learning to create an intelligent system for identifying medications. By matching the text in the image with the dataset, which contains various drug names and their descriptions, the dataset can be displayed in this application. The scanned image is used to extract the text. By using an optical character recognition algorithm and tensor flow character training in the Android operating system, the text is recovered from the scanned image of the medication label. The reminder option with the medicine photo and the time to take their medicine is added into this application to help the people who might take the wrong medicine or forget to acquire the medicine. This software assists patients in taking their medications properly and at the ideal time.
Workflow Scheduling in Cloud Computing Environment by Combining Particle Swarm Optimization and Grey Wolf Optimization

Scheduling workflows is a vital challenge in cloud computing due to its NP-complete nature and if an efficient workflow task scheduling algorithm is not used then it affects the system’s overall performance. Therefore, there is a need for an efficient workflow task scheduling algorithm that can distribute dependent tasks to virtual machines efficiently. In this paper, a hybrid workflow task scheduling algorithm based on a combination of Particle Swarm Optimization and Grey Wolf Optimization (PSO GWO) algorithms, is proposed. PSO GWO overcomes the disadvantages of both PSO and GWO algorithms by improving the exploitation (local search) of PSO algorithm and exploration (global search) of GWO algorithm. This leads to better balance between exploration and exploitation, consequently it minimizes the makespan with 5.52% compared to GWO and 3.68% compared to PSO. The degree of imbalance reduced upto 33.22% compared to GWO and 17.61% compared to PSO, improves the convergence rate as well depending on number tasks and iterations. CloudSim tool is used to evaluate the proposed algorithm. The simulation results confirmed that the proposed method performs better than both of the standard PSO and GWO in terms of makespan, degree of imbalance and convergence rate
Detection of Face Mask and Glass using Deep Learning Algorithm

The rapid spread of the coronavirus has been a major health concern for the entire world. Direct human contact is one of the key factors contributing to the virus's rapid transmission. Wearing face masks in public areas is one of the numerous precautions that may be taken to stop the spread of this infection. To reduce the chance of the virus spreading, it is important to find ways to detect face masks in public locations. An automated system for face mask detection utilizing deep learning algorithm has been presented to address these issues and effectively stop the spread of this contagious disease. The proposed method combines a face mask identification model and a glass detection model using an algorithm to assess the results using deep learning models.
Cognitive Anxiety and Habits of Mind: A Study of the Interrelationships with Biology Learning Outcomes

Biology learning outcomes are related to several factors, namely cognitive anxiety and habits of mind. The purpose of this study is to analyze the relationship between cognitive anxiety, habits of mind, and biology learning outcomes. The method of research applied was correlational research with a stratified random sampling technique. The population of this study is all class XII students at four senior high schools in Bandung with a total sample of 145 students. The instruments used were questionnaires and Biology study results report documents. Habits of mind in this study refer to Costa and Kallick’s 16 categories of habits of mind as many as 50 questionnaire coverage. Students' cognitive anxiety was measured through a cognitive anxiety questionnaire which consists of 27 statement items based on Cassady and Johnson's Cognitive Anxiety Test. The learning outcomes were obtained from the scores of grade XII National Exam Practice. The instruments were tested for validity, reliability, and readability. It was found that there was a positive relationship between biology learning outcomes and cognitive anxiety (r = 0,045, α &lt;0.025 ) as well as between habits of mind and biology learning outcomes (r= 0,326, α &lt;0.025). No correlation was observed between habits of mind and cognitive anxiety. It can be summarized that habits of mind and cognitive anxiety have a positive relationship with biology learning outcomes. These findings indicate that biology learning outcomes are closely related to cognitive anxiety and habits of mind. Finally, we discuss the relationship of cognitive anxiety and the habits of mind to biology learning outcomes and provide suggestions for further work.
The Effect of Online Learning on Student Learning Interest and Motivation

: The purpose of this study was to determine the effect of online learning on the interest and learning motivation of Hamzanwadi University economics students. The type of research is quantitative research using simple random sampling technique, and the data collection technique is a Google form questionnaire using a Liker scale, namely strongly agree 3, agree 2, and disagree 1. The data analysis technique uses simple linear regression analysis. Then the results of the study show that online learning on learning interest is obtained tcount 7.697 &gt; t table 1.989, at a significant level of 5%. The magnitude of the influence of online learning on learning interest based on the results of the hypothesis test is 42.9% while the remaining 57.1% is influenced by other factors not examined. While the results of data analysis on online learning variables on learning motivation obtained tcount 5.156 &gt; ttable 1.989, at a significant level of 5%. The magnitude of the influence of online learning on learning interest based on the results of the hypothesis test is 25.2% while the remaining 74.8% is influenced by other factors not examined. Keywords: Online Learning, Learning Interest and Learning Motivation Abstrak : Tujuan dari penelitian ini adalah untuk mengetahui pengaruh Pembelajaran Daring terhadap Minat Dan Motivasi Belajar Mahasiswa Pendidikan Ekonomi Universitas Hamzanwadi. Jenis penelitian yaitu penelitian kuantitatif dengan teknik simple random sampling, dan teknik pengumpulan data yaitu angket google form menggunakan skala liker yakni sangat setuju 3, setuju 2, dan tidak setuju 1. Teknik analisis data menggunakan analisis regresi linier sederhana. Maka hasil penelitian menunjukkan bahwa pembelajaran daring terhadap minat belajar diperoleh thitung 7,697 &gt; t tabel 1,989, pada taraf signifikan 5%. Adapun besarnya pengaruh pembelajaran daring terhadap minat belajar berdasarkan hasil uji hipotesis 42,9% sedangkan sisanya yakni 57,1% dipengaruhi oleh faktor lain yang tidak diteliti. Sedangkan hasil analisis data variabel pembelajaran daring terhadap motivasi belajar diperoleh thitung 5,156 &gt; ttabel 1.989, pada taraf signifikan 5%. Adapun besarnya pengaruh pembelajaran daring terhadap minat belajar berdasarkan hasil uji hipotesis 25,2% sedangkan sisanya yakni 74,8% dipengaruhi oleh faktor lain yang tidak diteliti. Kata Kunci: Pembelajaran Daring, Minat Belajar dan Motivasi Belajar
PERFORMANCE STUDY OF THE TEXT ANALYSIS MODULE IN THE PROPOSED MODEL OF AUTOMATIC SPEAKER’S SPEECH ANNOTATION

The global spread and use of remote and online learning systems at various educational levels puts forward a number of requirements for existing systems and needs for expansion of functionality. The current problem in Ukraine is the unstable operation of the energy infrastructure due to frequent hostile shelling, so it is problematic for residents of Ukraine to join online classes on time, to listen to lectures by lecturers and teachers completely, to take part in conferences and master classes in full. This determines the need to provide the opportunity of familiarization with educational materials at a convenient time in a form convenient for understanding and mastering. The lecture recording provides access to audio files that are intended for listening, but are not intended for printed reproduction. Therefore, the expansion of existing digital educational platforms with the possibility of forming an annotation (summary, abstract) of a lecture and presenting it in the form of text-and-graphic materials for further use by course students on paper media is an urgent task and can improve the quality assessment of a remote educational resource from the point of view of the content and methodological aspect. The aim of the study is to create a generalized hybrid model of automatic annotation of the speaker’s speech, which provides for the possibility of recognizing the speech, transforming the available data into text and, at the last stage, summarizing the given text, keeping only the important meaningful part of a lecture. The desired aim was achieved due to the creation of a generalized hybrid model of automatic annotation of input audio data, taking into account the effectiveness and features of existing methods of automatic text annotation obtained after converting speech into text. The uniqueness of this study is the use of marker words at the stage of text summarization, as well as the comparison of the efficiency of data processing at different stages of operation of this model when using different hardware. The results of computational experiments on graphics processing units with the Turing architecture showed that when the scope of input data increases by almost 30 times, the time also increases proportionally, but the use of a more powerful graphics processing unit NVIDIA Tesla T4 gives an speedup of more than 2.5 times compared to the graphics processing unit NVIDIA GeForce GTX GPU 1650 Mobile for both English and Ukrainian languages. For texts in the Ukrainian language, the text compression obtained (the ratio of the word count of the input text array to the word count in the resulting annotation) is 89.7%, for English – 94.15%. The proposed use of marker words showed an increase in the logical connection of input information internally, but obliges speakers to use predefined marker words to preserve the structure of the annotation formed.
DEVISING ARCHITECTURE FOR REMOTE EDUCATION ORGANIZATION BASED ON A SINGLE-BOARD COMPUTER

The presented paper sets the goal of providing computer engineering courses based on a single-board computers (SBC) with the organization of the remote education process. The devised approach relies on the connection to the local area network (wired or wireless) of the university, making the board accessible to the internal users, and creation of a network tunnel between the remote user and connected parts. These actions are accompanied by assignments of necessary permissions to the users of the system. First, the requirements of each involved actor were considered and as a result of that further understanding of the network communication process, network management and administration, software requirements, etc. was acquired. According to these results, analysis of the capabilities of SBC in terms of its connectivity was performed and, finally, the architecture was realized on the available hardware with regard to the peculiarities of the specific subject. The article establishes all the necessary details on the implementation of the proposed architecture and discusses results of the testing performed as a part of the specific course. During the testing stage, the deployed system based on FriendlyARM Nano Pi allowed us to organize the course on Real-Time Embedded Computer Systems and successfully finish all related activities under circumstances of remote education. It was demonstrated that the proposed organization is reliable as demonstrates conceivable performance even under condition of multiple simultaneous connections to the target board. From the administrative point of view, the solution is highly configurable which is a huge advantage for the lecturer and for the administrator. The devised architecture has a potential in terms of improvement and scalability because it consumes relatively low network capacity and is easy to deploy under any circumstances.