Reviewerly/exHarmony huggingface dataset:
Feature engineering strategies based on a One-point Crossover for fraud detection on Big Data Analytics

Abstract A wide range of new opportunities for fraudulent online activities has arisen with the growing popularity of online shopping and big data issue. E-payment fraud schemes are collecting billions of dollars from customers, distributors and service providers every year. A lot of machine learning methods for fraud detection problems have been proposed which can be categorized into supervised, unsupervised and semi-supervised methods. In this paper, we proposed biologically inspired technic in the feature engineering phase for handling imbalanced data to increase the total data of a small number of classes by oversampling. One-point crossover used to generate the new data of minority classes. The best algorithm performance obtained to predict the fraud transaction from various machine learning models is Classification and Regression Tree with the corresponding accuracy, precision, recall, and F-1 Score are 96%.
Attacking RSA Using an Arbitrary Parameter

In this paper, we introduce a parameter u that is related to N via an arbitrary relation. By knowing the parameter along with RSA public key pairs, (N, e), we conduct two new attacks on the RSA cryptosystem. The first attack works on the equation $$eX - uY= Z-\phi _b$$ where $$\phi _b$$ is the best known lower bound of $$\phi (N)$$ . It combines the continued fraction method and Coppersmith’s method to factor N in polynomial time. The second attack shows that given $$(N_i,e_i)$$ for $$1\le i\le k$$ and a fixed X, we can simultaneously factor the k RSA moduli. It manipulates the result from diophantine approximation to enable the conditions of Coppersmith’s method. These attacks show that there are more possible weak RSA key pairs.
Making Things Better

In this chapter I shift the focus from questions of epistemology/methodology and turn, instead, to consider the social and political aspects of Dewey’s philosophy and their relation to design practice and research. The chapter opens with an exploration of how Dewey’s work has been drawn upon in participatory design discourse, looking in particular how individuals such as Pelle Ehn, Christopher Le Dantec and Carl DiSalvo as well as a number of related science and technology theorists have appropriated his ‘publics’ concept. From this presentation, I then turn my attention, again, to his original writings, with his stance in relation to democracy and ethics, as well as his melioristic perspective (i.e., the belief that human action can lead to positive change) all being highlighted in turn. The chapter draws to a close with a discussion of how these concepts can be related to contemporary understandings of the democratic/ethical within design practice and research. I conclude with the proposal that Dewey’s work might provide socially/politically motivated design practice and research with a value-based philosophic grounding that gives articulation to some pre-existing concerns.
Preparing Students for the Challenging Job Markets over the Next Decade

The introduction of high-speed communication systems over the past few years has created many opportunities for academic and business communities, while some challenges resulted in its by-product. The high-speed information systems have opened the door for e-commerce and resulted in the appearance of numerous online cyber entrepreneurial activities. While the cyber entrepreneurship and e-commerce heralded the lower costs for potential consumers, these systems limited the number of customers visiting the “brick and mortar” stores and shrunk the traditional commercial activities. Although business and commerce schools have been at the forefront of e-commerce, the engineering schools have tried to incorporate e-commerce and online education to their array of courses offered to their students. Many engineering schools have long realized the new shifts demanded in their traditional classes based on the advice of their constituencies and the industry advisory board members. Other areas that have highly been focused on by Texas A&M University at Qatar campus are leadership and communications. Texas A&M University at Qatar has heavily concentrated on strengthening students’ skills in communications and leadership. Almost 100% of students enrolled at Texas A&M University at Qatar engineering programs are bilingual where English is their second language. The multinational petrochemical industry operating in Qatar demand proficiency in English and the ability to lead and manage companies’ different divisions and initiatives. This article presents Texas A&M University at Qatar’s experience over the past few years in revisiting its curricula offered in four engineering degree programs in general and the petroleum Engineering Program in particular with regard to the introduction of computer-intensive courses aimed at acquainting students with e-commerce and strengthening students’ skills in communications and leadership.
Pose Invariant Face Recognition Using Principal Component Analysis

Srivastava , Akash Krishna Sneha , H. Diksha Singh, Koushlendra KumarFacial recognition is a propitious field that has been actively studied and researched upon in the recent years.The main challenge in face recognition is the various poses exhibited by subject. It makes it difficult to accurately identify the individual based on facial recognition. The present work encompasses of a new frontalization algorithm and facial recognition using principal component analysis. The three-dimensional face geometry model has been used to produce better facial features. The Zhu-Ramanan detector has been used for detection of facial features. Principal component analysis is a statistical approach used for face recognition which involves dimensionality reduction. The proposed approach was validated on two different databases. Outcomes of the proposed approach clearly show the reduction in computational complexity of process of face recognition. The approach successfully recognizes faces up to $$\pm 70^{\circ }$$ yaw. It outperforms other methods for face recognition in terms of its efficiency to recognize faces.
Opinion Mining to Aid User Acceptance Testing for Open Beta Versions

Social media enables the sharing of opinions, ideas, interests, thoughts, hobbies, etc., by creating social platforms such as Twitter, Facebook, TripAdvisor, Yelp, Rooter, Goodreads, etc. The above sharing generates a lot of information over various social media platforms, which is used in different research areas. Among the many research areas, one is opinion mining, which identifies and extracts subjective information from source materials by using text analysis, natural language processing, and computational linguistics. The application of opinion mining is abundantly seen in the areas of marketing, governance, tourism industry, etc. However, the application of opinion mining is not much explored in the area of user acceptance testing. Therefore, in this research article, we propose a model for opinion mining and use it as a supporting tool for user acceptance testing. Specifically, we use the opinion mining on “Twitter” tweets to analyze the response of an Open Beta, i.e., Public Beta versions of the software to quantify acceptance criteria qualities. Hence, we try to inculcate opinion mining in earlier stages of software development to aid user acceptance testing in order to get the user feedback as accurately as possible to make change in the concerned software before its final version is released. The model is implemented and evaluated for Open Beta versions of IOS 13 and AndroidQ using machine learning techniques such as decision tree, K-nearest neighbor, multilayer perceptron, and logistic regression. Our evaluation shows that we achieved the best result with K-nearest neighbor having an accuracy of 97.12%, which is 14.37% higher as compared to the highest accuracy ever obtained in this area.
An Approach Towards Enhancement of Review Summarization Using Sequence Model And Word Embedding

Traditionally, extraction-based text summarization techniques have simply relied upon statistical measures for extraction of major points in a document. With popularization of deep learning in solving problems in the field of Natural Language Processing, there is a need to incorporate deep learning to improve the effectiveness of summarization methods. In this paper, we explore the results and findings of our approach to improve review summarization as well as to delineate the capabilities of these language models in improving Natural Language Understanding. The aim of our summarization approach is to generate a gist of customers' feedback of a product which would provide vendors and other customers with the necessary understanding in order to make informed purchases. Our proposed summarization approach is split into four major steps: Keyword Extraction, Sentiment Analysis, Adjective Mining and Sentence Extraction. Afterwards, all the outputs of these steps are combined to provide the user with summary of all reviews about a product.
A Top-Down Three-Way Merge Algorithm for HTML/XML Documents

Collaborative work, with the need to keep HTML/XML code up-to-date, is now becoming vital particularly in the Web Development field. In order to fully support collaborative work and resolve related problems the need has arisen for an optimum solution to the automated editing of a number of parallel copies originating from a single original HTML/XML code document with the additional requirement to subsequently merge the copies into a single updated document. A number of algorithms have been used in the past for the purpose, such as: Diff3, XmlDiff, DeltaXML & 3DM, but HTML/XML code complexity related issues have now called for an algorithm that is more specifically designed for the purpose. In this paper a new algorithmic approach to merging HTML/XML code documents is presented that is based on the “Three-way Merge” approach and the “Node-per-Node” comparison between ordered trees. For the creation of the actual merging function operating at the heart of the algorithm, a particular methodology was followed in which only the two “Current Versions” are required for the generation of the updated document, and no involvement of the “Original Document” is necessary. This is an important improvement over the currently existing algorithms because eliminates the well-known “Idempotent” problem in merging two HTML/XML documents. In addition, the algorithmic approach presented here allows for the identification and treatment of all the conflicts that arise during a HTML/XML code merging in an ordered and clearly specified manner.
The Content Analysis of Electoral Act in Indonesia: The Comparison of Percent Agreement and Cohen Kappa

Abstract This study investigates the legality of practitioners in interpreting UU PP (Electoral Act) to provide guidance in technical and managerial aspects especially in relation with principle of privacy by applying content analysis with percent agreement and Cohen Kappa. Thus, this study design code paper to be distributed to 21 coders to gain more understanding the approach which is appropriate to synergize various interest in election to bring more appropriate management to align with voters expectation especially in regard to privacy policy. The highest disagreement among coders occurred between coder 8 and 20 with -0.285 of Cohen Kappa and 0 of percent agreement while the highest agreement between coder 2 and 7 with 0.727 of Cohen Kappa and 93.2 of percent agreement.
Search Engine Optimization Using Unsupervised Learning

Nowadays, web has emerged as the most demanding tool for retrieving information over a large repository. As the amount of information on the world wide web grows, it becomes increasingly difficult to accurately find what we want. The existing search engines mostly display the content based on many factors and not just the quality of the content. These include sponsored links, advertisements, paid appreciation, etc. Our project aims at developing a tool to rank the search results solely on the basis of the content, and not by keeping in consideration that which article would the user be most likely to click. Hence there will be no problem of Clickbait. Therefore, the aim is to create a tool that could scan the web on a specific topic and create a synthesis of the content found. We do this by gathering search results from various search engines using a web crawler and processing the results obtained from them by using our custom made ranking algorithm which clusters the results and ranks them on the basis of the content quality. After crawling through the web and retrieving the information, we will be using ‘term frequency – inverse document frequency’ as our weighting algorithm, followed by ‘singular value decomposition’, for decomposition of the weighted matrix. Lastly, we will be using ‘spherical K-means’ and custom ranking algorithm to display rich content. In order to give more efficient results, our project presents a new algorithm to rank web pages in accordance to the relevance of the user’s query.
Augmented Reality Based Scientific Gateway as Education Form

A Science Gateway is a community-developed set of tools, applications, and data that are integrated via a portal or a suite of applications, usually in a graphical user interface, that is further customized to meet the needs of a specific community. Gateways enable entire communities of users associated with a common discipline to use national resources through a common interface that is configured for optimal use. Researchers can focus on their scientific goals and less on assembling the cyber infrastructure they require. Gateways can also foster collaborations and the exchange of ideas among researchers. Our paper deal with position the augmented and mixed reality tool as a one of main component the scientific gateway and portals. The scientific portals and gateways are cumulative all classes of visualization, Augment and Mixed reality.
Developing the integrated SIMPAKDOS and its impact on employees’ intention to promote academic position

Abstract This study aims at developing an integrated Management Information System for Lecturers Credit Score Assessment (SIMPAKDOS). This research is an R&amp;D research conducted in March-September 2018. The system itself is developed by conducting FAST method (Framework for the Application of Systems Techniques). Data collection methods used are interviews, questionnaires, and documentation. The data analysis method used are descriptive analysis, FGD (focus group discussion), and linier regression analysis. The development of Integrated SIMPAKDOS has been finished with the FAST approach. The results of the needs analysis show that the lecturers and staffs need Integrated SIMPAKDOS with approved content and conditions. The logical design prepared has been through FGD, expert validation, and trials to obtain input and validation from various stakeholders. System validation is carried out in two stages (development expert validation and system content expert validation) and the results show that Integrated SIMPAKDOS is valid and feasible to use. The test results also show that the interest of lecturers applying for promotion is included in the very high category, which is as many as 65% after using SIMPAKDOS. The use of Integrated SIMPAKDOS proved to have a positive and significant effect on the intention of lecturers to propose rank promotions.
Research on Pressure Controls of High Pressure Tubing Based on Simulation Model

Abstract High pressure oil pipeline is the basic component of many fuel engines, and it has great application and development value in transportation, aerospace and other national defense military. This paper examines ways to control the pressure change in the high pressure tubing under the condition of intermittent fuel entry and ejection. On the basis of determining the balance equation of each part of the injection system, this paper presents different design schemes of the high pressure fuel system under different operating parameters of the high pressure tubing by establishing the simulation model.
Understanding research methodologies when combining virtual reality technology with machine learning techniques

Virtual Reality (VR) technology represents a new medium to provide immersive solutions in different fields. The analysis of a user while interacting in VR, through data science and machine learning (ML) techniques, might provide insights to deliver customized functionalities that enhance productivity and efficiency in learning tasks in education or rehabilitation processes in healthcare. However, empirical research involving VR often borrows methods from human-computer interaction intending to evaluate human behavior through technology, whereas ML intend to create mathematical models, usually with non-empirical approach. Their opposite nature might cause confusion for early-stage researchers wanting to understand and follow the methodological approaches and communicative practices in empirical studies that merge both VR and ML. This paper presents a scoping review of methodological strategies undertaken in 21 peer-reviewed research articles that involve both VR and ML. Results show and appraise different methodological approaches in research projects, and outline a set of recommendations to combine metrics from inferential statistics and evaluation of ML models to increase validity, reliability and trustworthiness in future research projects that intersect VR and ML.
Strategic Inference in Adversarial Encounters Using Graph Matching

There are many situations where we need to determine the most likely strategy that another team is following. Their strategy dictates their most likely next actions by selecting the optimal policy from their set of policies. In this scenario, there is a hierarchical, multi-agent, multi-team environment where the teams are built on layers of agents working together at each level to coordinate their behaviors, such as SiMAMT. We can think of a strategy as a hierarchically layered policy network that allows for teams to work together as a group while maintaining their own personalities. They can also shift from one policy to another as the situation dictates. SiMAMT creates an environment like this where sets of such teams can work together as allies or team up against others as adversaries. In this context, we wish to have a set of teams working as allies facing another set of teams as adversaries. One alliance should be able to analyze the actions of another alliance to determine the most likely strategy that they are following, thus predicting their next actions as well as the next best actions for the current alliance. To accomplish this, the algorithm builds graphs that represent the alignment (i.e., the constellation) of the various agent’s policies and their movement dependency diagrams (MDDs). These graphs are a clear way to represent the individual agent’s policies and their aggregation into a strategy. In this instance, the edges of the graphs represent choices that the policy can make while the vertices represent the decision junctures. This creates a map of the various agents as they move through a progression of decisions, where each decision is made at a decision juncture, and each edge shows the probabilistic progression from each of those decisions. These graphs can show the likelihood of actions taken at each level of the hierarchy, thus encoding the behaviors of the agents, their groups, the teams, and the alliances. We wish to demonstrate that these graphs can represent large sets of teams or alliances and that each alliance can use these representations to coordinate their own behavior while analyzing the behaviors of other alliances. Further, we wish to show that an alliance can make a decision in interactive time on which policy from within their strategy set should be in place based on their observations of other alliance’s strategies. To do so, the algorithm will build a probabilistic graph based on the observed actions of the other alliances by observing the actions taken by each agent within that alliance. It can then compare that probabilistic graph with known graph strategies or those that it has learned along the way. We present this methodology and verify it with experimentation confirmed in the conclusions in this paper.
An Economic Rationalization Framework for Higher Education

The marginal rate of returns (MRR) of higher education programs affect a countryâ€™s economy. It is in this light that the current study primarily intends to determine which higher education programs the state should fully subsidize and which programs the private education service providers should take charge of. The net effect of this identification is to rationalize the supply side of the economy. Quantitative non-experimental retrospective explanatory design and data mining techniques were used. Consequently, based on the programsâ€™ marginal rate of returns, indeed there are certain higher education services that bring about public good while some others do not. Agriculture, services, humanities and arts, sciences, health and welfare, engineering, manufacturing and construction, and education programs demonstrate greater public good while social sciences, business, and law generally promote greater private returns to the individuals rather than to the society.
Hosting and Sharing Content Policy

The purpose of this policy is to define hosting rules for content distribution on the University’s IT Resources by Users or authorized third parties.
The Roadmap to Keeping Oregon Open Part I: Establishing the COVID-19 Capacity for Testing and Contact Tracing Required to Keep the State Safe and Open

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Systematic Mapping on Embedded Semantic Markup Validated with Data Mining Techniques

The lack of structured information in the Web does not enable search engines to present accurate information in search results. Some technologies have arisen to produce structured data, including embedded semantic markup that aims to improve the quality and significance of the information exhibit in search results. An indicator of the success of a technology is the interest of researchers to address its particularities. To this end, in this work, we conducted a systematic mapping of research articles published into the embedded semantic markup field to expose the maturity of this technology. Moreover, the novelty of this systematic mapping is the use of data mining techniques aimed to confirm the topic-specific categorization conducted by the expert human regarding the research articles presented in this mapping.
Designing a <i>Radio-Tuner-Thing;</i> Transparency as Metaphor for our Experience of Objects and Things

The exhibited work explores how Thing Theory can be used in the design of interactive artefacts. In particular, it takes the metaphor of transparency used by Bill Brown to distinguish between objects and things as a creative stimulus. The resulting object allows the designers, and users, to explore nuances in how we might engage with objects as things and points to a rich conceptual framework that might enable new qualities of interaction.