Reviewerly/exHarmony huggingface dataset:
On the variants of RSA cryptosystem and its related algebraic cryptanalysis

The RSA cryptosystem is the earliest public key cryptosystem which came into existence since 1978 and has become the most broadly used public key cryptosystem in the world. So far, RSA is being implemented as a default cryptosystem in most of web browsers and also most commonly used feature to secure internet banking systems. For decades, studies on improving the efficiency of RSA in terms of its encryption and decryp-tion time, and also its security were conducted. Hence, many variants of RSA were proposed to overcome such said issues. Essentially this review article attempts to analyze the variants of RSA cryptosystem which shared a similarity of possessing its public key e and private key d satisfying this particular key equation of the form ed − k(p 2 − 1)(q 2 − 1) = 1 where the product of (p 2 − 1)(q 2 − 1) is referred as modified Euler totient function. This review article also emphasizes on the algebraic cryptanaly-sis methods proposed on those variants cryptosystems specifically via the continued fractions method and the lattice reduction method
Smart Shopping Tablet / Atiq Nadzirah Nasaruddin

Nowadays, online shopping is growing 3 times faster than traditional retail. However, previous research found that most of the people still prefer in store shopping especially for those who wants to shop for groceries, food and clothing. It is because people would like to see and touch the product in person before they buy it. However, there are several problems encountered by the shoppers while doing in store shopping. Crowding and queuing in long line at billing counters is the main stressors when shopping products for regular used in supermarket. This is because customers have to wait in long queues to scan their products using barcode scanner and get it billed. This situation can leads to time consuming and it is inconvenient situation these day as people are living in a busy environment. Therefore, the company invents the new innovation product with RFID technology where able to total up the price of the items in the shopping trolley that can solve the problem faced by the shoppers during at the billing counter. The new innovation product also was invented with navigation or directory. It is specifically designed to assists the shoppers to find the product location within the store and avoid of wasting their time. The product location like aisle and shelf number will be displayed on the screen of the smart tablet. Moreover, the screen of the smart tablet able to display important information related to the store and items within the store. It is able to display the item with variety of brand and price. This information is important to avoid their purchasing items in not their budget limit. Additionally, the screen also can display about the specials offers and advertising the information of the store. It is to ensure that the shoppers will never miss out the great deals offered in the store. Since the tablet’s screen is the important part of the product, thus the company decided to cover it with high quality glass protector which is Gorilla Glass 5. It is to make sure that the screen of the smart tablet is not easily cracked and safe for long-term used.
ImplementasiAlgoritma NaÃ¯ve Bayes Classifier (NBC) Untuk Analisis Sentimen Komentar Kebijakan Full Day School

Sentiment analysis is an important research topic and is currently being developed. Sentiment analysis is carried out to see the opinion or tendency of a person's opinion on a problem or object, whether it tends to have a negative or positive view. The main purpose of this research is to find out public sentiment towards the Full Day school policy comments from the Facebook Page of the Ministry of Education and Culture of the Republic of Indonesia and to determine the performance of the Na-Ã¯ve Bayes Classifier Algorithm. The results of this study indicate that the public's negative sentiment towards the Full Day School policy is higher than positive or neutral sentiment. The highest accuracy value is the NaÃ¯ve Bayes Classifier algorithm with the trigram feature selection of the 300 data training model with a value of 80%. This simulation has proven that the larger the training data and the selection of features used in the NBC Algorithm affect the accuracy of the results. Meanwhile, the simulation results from 10 test data with 5 different NBC and Lexicon algorithms also show that the Full Day School Policy proposed by the Indonesian Minister of Education and Culture has a higher negative sentiment than positive or neutral by most Facebook users who express opinions through comments. The highest accuracy value is the NaÃ¯ve Bayes Classifier algorithm with the trigram feature selection of the 300 data training model with a value of 80%. This simulation has proven that the larger the training data and the selection of features used in the NBC Algorithm affect the accuracy of the results. Meanwhile, the simulation results from 10 test data with 5 different NBC and Lexicon algorithms also show that the Full Day School Policy proposed by the Indonesian Minister of Education and Culture has a higher negative sentiment than positive or neutral by most users. Facebook that expresses opinions through comments. The highest accuracy value is the NaÃ¯ve Bayes Classifier algorithm with the tri-gram feature selection of the 300 data training model with a value of 80%. This simulation has proven that the larger the training data and the selection of features used in the NBC Algorithm affect the accuracy results.
Disparate Impacts and GINA: Congress's Unfinished Business

The Genetic Information Nondiscrimination Act of 2008 (GINA) deviated from preceding employment discrimination laws by excluding disparate impact liability, an important enforcement component to promote substantive equality. Nevertheless, Congress did not intend for this to be a permanent exclusion and, instead, assigned itself future work: after six years, a commission was to educate Congress on genetic discrimination incidents, update Congress on relevant scientific advances in genetics/omics, and provide recommendations to Congress on the need to enable disparate impact liability. Ten years after GINA became law, it seems appropriate to take a look back at the broader employment law context within which Congress made this decision to exclude disparate impact liability for genetic discrimination, explore how and why Section 208 became inserted into GINA, and provide a status update on the additional policy work mandated. After reasonable investigation, there is no information to indicate that Congress fulfilled its statutory obligation to appoint members to a Genetic Nondiscrimination Study Commission or that any policy work envisioned by Section 208 has commenced. To fulfill a promise of fairness and equality, Congress must revisit the issue and enable disparate impact liability to value genetic diversity and prevent any ‘genetic underclasses’ from forming.
Research on Enterprise Risk Management and Risk-Oriented Internal Audit Based on Cloud Computing Model

Risk management is an important guarantee to strengthen the basic management of internal control, improve the operational efficiency and economic benefits of enterprises, and realize the sustainable and healthy development of enterprises. Modern auditing technology and method system are gradually developed on the basis of original auditing technology. Risk-centered internal audit means that the internal auditor of a company effectively evaluates risks and management capabilities through tests related to risk supervision, and puts forward audit evaluation and suggestions to achieve the management objectives of the company. Risk-oriented audit is superior to traditional audit mode mainly in that it changes passive risk control into active risk management, and effectively controls the disadvantages of traditional audit that makes organizational structure constantly refined and rules and regulations complicated. This paper expounds the meaning of risk-oriented audit and risk-oriented internal audit, introduces the procedures of risk-oriented internal audit, and discusses the role and construction assumption of risk-oriented internal audit in enterprise risk management based on cloud computing model.
The search of alternative ways for professional training of future teachers to the innovation-related risks

The modern educational challenges define the relevance of the future teacher's capacity formation problem towards the revealing of the pedagogical risks in the innovative activities and assessment of their own abilities in the minimisation of the negative consequences. It is caused by the fact that the modern school requires the teacher's high performance aimed at the formation of human capital assets. It is stated that the innovative pedagogical activities as a nonlinear process of the educational reforms are accompanied by the consolidation of such values as initiative, creative work, and risk. The paper contains the results of the experimental work with the bachelor's students with the specialty in 'pedagogical education' in the approbation of the variable course 'pedagogical risks in the teacher's innovation activities' in the structure of the general professional training model 'pedagogics' as part of the future teacher's riskological competence formation.
Migration of Oracle DB (on-premise) to AWS redshift DB (Cloud)

“Migration of Oracle Db (On-permise) to AWS redshift DB (Cloud)” is one of the solution for Database Freedom Service initiated by AWS focused on accelerating enterprise migrations from commercial database engines (Oracle, SQL Server, Teradata etc.) to AWS native database services like Amazon Redshift, Amazon Aurora, Amazon DynamoDB and Amazon ElastiCache, their by saving overall cost spent by business in terms of database licenses. The main objective of this project is to Assess and identify the challenges in migrating Oracle database to AWS redshift DB. Provide, implement and test the solution to overcome the challenges.
ULMFiT for Question Classification in Question Answering Systems

Question answering systems are used for retrieving relevant information that when a query is posted a direct response is provided rather than furnishing references containing answers. The main goal of these systems is to provide exact responses to users’ queries posed in natural language. The main aim of this paper is to perform question classification based on machine learning algorithms and transfer learning approach which helps in identifying the category to which a query belongs to. If the question asked by the user is “Will COVID end soon?”, then the Question answering system needs to retrieve information related to pandemic duration only. So, proper question categorization must be done by the system to help find similar questions that fall under the category of ” pandemic duration”. Then a comparison has been performed between two approaches. The experimental results on COVID-Q dataset demonstrate that the ULMFiT approach is better compared to TF-IDF and Word2Vec models combined with machine learning algorithms-Logistic Regression, MLP classifier, and SVM.
Visualisierung von Software-Historie in Virtual Reality - Erweiterung einer Insel-Metapher zur Darstellung der Evolution OSGi-basierter Software-Projekt

Software visualisations using metaphors can provide an intuitive approach to the system’s architecture. An island metaphor is especially suitable for modular software systems. Apart from the current status of a project, its history is a crucial factor. It provides, among others, information about logical dependencies to help plan tasks for further development and maintenance of the software. Therefore, a visualisation of software history would be useful. At the moment, there are no visualisations using an island metaphor existing which simultaneously represent the history of the software project.This thesis presents a concept to extend the island metaphor in order to visualise software history. Its focus is to maintain the user’s mental map of the representation while the components of the archipelago are continuously adapted due to changes of the software system. The concept is implemented as a prototype integrated in the application IslandViz . This application already shows static software architecture in a virtual environmentusing an island metaphor.A subsequent user study compares the developed prototype to an existing, static software visualisation, which depicts software architectures independently at different points in time. The study shows that in order to identify changes, it is helpful that the user’s overview of the visualisation is kept over time. Users, on average, solve tasks up to 50% faster and answer questions regarding the depicted changes 33% more accurately in the newly developed prototype. At the same time, the user’s perceived mental effort is about 21% lower. In addition, the study reveals aspects for further improvement of this concept.The presented concept to visualise the history of modular software projects using an island metaphor will help plan development and maintenance tasks in the future.
Requirements of Formulating a National Strategy for Developing the Cybersecurity System in Iraq According to GCI.v4(2019) Index

The present study aims to present a proposed realistic and comprehensive cyber strategy for the Communications Directorate for the next five years (2022-2026) based on the extent of application and documentation of cybersecurity measures in the Directorate and the scientific bases formulating the strategy. The present study is significant in that it provides an accurate diagnosis of the capabilities of the cyber directorate in terms of strengths and weaknesses in its internal environment and the opportunities and threats that surround it in the external environment, based on the results of the assessment of the reality of cybersecurity according to the global Cybersecurity index, which provides a strong basis for building its strategic direction Which is expressed by its vision, mission, and realistic strategic objectives. The researcher adopted the case study method for its objectivity and being the closest and best method to prove the research problem and provide objective and realistic treatments for it. As for the field of application of the present study, it is represented by the Communications Directorate due to its great importance and its close relationship to the communications system in Iraq and national Cybersecurity, in addition to the fact that it has not been subjected to testing the extent to which Cybersecurity measures are applied and documented before. The checklist was used according to the Global Cybersecurity Index (GCI.v42019). In order to measure the extent of the application and documentation of cybersecurity measures in the Directorate, personal observation and interviews were used as tools for data collection. The SWOT matrix was adopted to analyze the internal and external environment factors.
Efficient Scene Text Detection in Images with Network Pruning and Knowledge Distillation

Modern deep learning models draw a progressive graph of success in many different areas. However, enrichment of these models and increase in their success rates depend on the systems which need high computing resources. In addition, the need for more computational power limits the real life usage of these models trained with deep learning. Scene Text Detection is one of the most popular topics, which must work under these limitations. To this end, network pruning and knowledge distillation, which are popular methods for network compression, were implemented with a text detection system, whereby faster, lighter and more accurate models are obtained. Moreover, training strategies and their details of these models are shared in this paper. The results of comprehensive experiments and model outputs demonstrate the success of the methods.
Visual representation of negation: Real world data analysis on comic  image design

There has been a widely held view that visual representations (e.g., photographs and illustrations) do not depict negation, for example, one that can be expressed by a sentence "the train is not coming". This view is empirically challenged by analyzing the real-world visual representations of comic (manga) illustrations. In the experiment using image captioning tasks, we gave people comic illustrations and asked them to explain what they could read from them. The collected data showed that some comic illustrations could depict negation without any aid of sequences (multiple panels) or conventional devices (special symbols). This type of comic illustrations was subjected to further experiments, classifying images into those containing negation and those not containing negation. While this image classification was easy for humans, it was difficult for data-driven machines, i.e., deep learning models (CNN), to achieve the same high performance. Given the findings, we argue that some comic illustrations evoke background knowledge and thus can depict negation with purely visual elements.
User Perception of Wearables in Everyday Learning Contexts: The Impact of Prior Device Experience

Wearable devices are ubiquitous technology, which is attached to the user itself, allowing it to be available in various everyday life settings. With the growing popularity and increasing affordability of smart wearables devices, their uses are also growing. Traditionally wearables have been used for health and fitness tracking, but now wearable are used for various educational purposes as well. Wearable devices can take the form of daily use accessories like a watch, glasses, clip, necklace, etc. The abundance of form factors brings the question of what preferences people have for these form factors and how prior experience shapes these preferences. In this paper, we explore peoples’ attitudes towards different wearable form factors and their preferences of wearable form factors in an everyday learning context. We conducted a survey-based study to find differences between users with and without prior experience with wearable devices. This study will help designers understand why certain wearable devices are preferred and the role of prior experience. In the survey, nine different fictional scenarios of daily life were presented, and participants were asked to imagine themselves using a wearable for learning in those scenarios. Results show a significant relationship between users’ prior device experience and which form factor of wearable device they prefer to use for learning. Also, participants with prior experience with fitness trackers rated the social influence of wearable devices significantly lower compared to participants without wearable experience.
Multiplicative coding and factorization in vector symbolic models of cognition

Author(s): Kent, Spencer | Advisor(s): Olshausen, Bruno A; Efros, Alexei | Abstract: This dissertation covers my attempts to confront the challenge and promise of multiplicative representations, and their attendant factorization problems, in the brain. This is grounded in a paradigm for modeling cognition that defines an algebra over high-dimensional vectors and presents a compelling factorization problem. The proposed solution to this problem, a recurrent neural network architecture called Resonator Networks, has several interesting properties that make it uniquely effective on this problem and may provide some principles for designing a new class of neural network models. I show some applications of multiplicative distributed codes for representing visual scenes and suggest how such representations may be a useful tool for unifying symbolic and connectionist theories of intelligence.
LibGuides: Learn at the Libraries: Searching

Learn more about university level writing, how to search the library, and the correct way to use references.
Motivated reasoning in the public domain: Three variations on a theme

Author(s): Rodriguez, Cristian Guillermo | Advisor(s): Ditto, Peter H. | Abstract: The concept of motivated reasoning describes when people’s processing of information is driven by prior motivations, such as self-preservation, social-identity threats and worldview maintenance. In the public domain, strong political and social identities shape political and moral debates. In this dissertation I present three papers that address motivated reasoning in the context of three public debates: abortion, hate speech and COVID-19 policies. The first paper examines how abortion attitudes are related to other attitudes not directly related to termination of pregnancy, testing competing ideological explanations abortion attitudes. Across three studies, data show support for the liberal and conservative explanations of abortion attitudes, but the evidence is stronger for the latter. The second paper studies how people judge an ambiguous expression as hate speech, based on the group being targeted and the person’s ideology. I found that liberals are particularly attuned to denounce an ambiguous statement as hate speech, and that the target’s perceived victimhood partially drives these effects. The third paper studies the relationship between COVID-19 related policies, partisanship and exposure to the virus. No relationship was found between the exposure to the virus and changes in policies: people keep partisan policy views, despite of pathogen threats. All studies confirm the main tenet of themotivated reasoning approach: people’s views are often not driven by rational considerations, but rather by motivations to defend their worldview and their social groups.
Data Security in Cloud Computing

Cloud computing is a model which enables widespread access to a shared pool of resources including the characteristics of scalability, virtualization and many others. The most important service offered by cloud is storage wherein the users store the required data. Security is a concern here as the data is stored on remote server with multi user capabilities. The data is at the risk of unauthorized access thereby reducing reliability and privacy. The key issue in cloud regarding security is the openness of the host or service providers. A gateway to the hacker is being provided when a test environment is set on a cloud. Cloud allows exchange of information among its services which requires standards. This development of standards is tough due to the interoperability issues. An intruder can provide malicious threat to the cloud data. The developments of standards are still a concern in security of the cloud. Though there is increasing research done to enhance the security, new issue arises, or the security method becomes inappropriate for the scalable services. Program clustering and slicing for user efficiencies is difficult for the same reason.
PENGARUH MODEL PEMBELAJARAN SINTETIK DENGAN BAHAN AJAR GAMIFIKASI: KEMAMPUAN KOMUNIKASI MATEMATIS (SELF-EFFICACY)

This research aims to know the influences of synthetic learning model assisted by gamification teaching materials on mathematical communication skills in terms of Self-Efficacy. This research is kind of Quasy Experiment Design by adopting Posttest Only Control Design. The technique sampling is taken by Non Probability Sampling using Porposive Sampling technique. The researcher used ANOVA (one way in different cells) in calculating the data. The result of this research showed that there is an influence of synthetic learning model assisted by gamification teaching materials on mathematical communication skills in terms of Self-Efficacy, students communication ability using gamification were better than conventional technique. In addition, there is an influence between Self-Efficacy(high, medium, and low) on students’ mathematical communication. Futhermore, there is no interaction between synthetic learning model by using gamification in terms of Self-Efficacyon students’ mathematical communication skills.&#x0D; &#x0D; Keywords: Synthetic Learning Model, Gamification Self-Efficacy