Reviewerly/exHarmony huggingface dataset:
A Fraud Detection System Using Decision Trees Classification in An Online Transactions

Individuals and businesses are frequently seen engaging in a fraud scheme, which results in the loss of funds, rights, and assets. This paper aims to provide an empirical analysis and study of a supervised learning technique, decision trees (DT), on a credit card transaction dataset as a benchmark. The proposed approach can be employed to reduce FPs and FNs through supervised machine learning (ML) technique. The fraud detection system also uses historical data to construct a training set and then analyses it to identify fraudulent activity. In this study, suspicious activity is detected using the decision tree classifier. The obtained results showed that FPs and FNs in an online transaction or activity can be reduced to a large extent using DT classification. This method achieved a promising result in terms of performance measures such as precision (99.7%), accuracy (92.25%), recall (81.49%), and F1_score (86.47%) respectively. As a result, the use of ML classifiers can reduce FPs and FNs, increasing customer satisfaction in an online transaction.
kNN Imputation Versus Mean Imputation for Handling Missing Data on Vulnerability Index in Dealing with Covid-19 in Indonesia

The COVID-19 virus has rapidly spread throughout the world, and the WHO declared it a pandemic on March 11, 2020. Previous research considered five domains associated with the social vulnerability index in the context of pandemic infection management and mitigation in the community, such as socioeconomic conditions, demographic composition, housing and hygiene, availability of health care facilities, and epidemiological factors related to COVID-19. The Katadata Insight Center (KIC) investigates the vulnerability index of Indonesian provinces to the coronavirus based on the risks of regional characteristics, population health, and mobility. There is a chance that the supporting data is either incomplete or missing, which is a common flaw that influences the prediction system's results and renders it ineffective. This paper will compare the kNN-based imputation method with the mean imputation to handle missing data, which causes the provincial vulnerability index in Indonesia to be measured incorrectly. The vulnerability index associated with COVID-19 should be one of the factors considered by the Indonesian government when making decisions or establishing a lockdown strategy and large-scale restriction rules in each province. When missing data is discovered, kNN imputation and mean imputation can be used as a solution. Based on the results of the experiments, the mean imputation has a much lower average RMSE performance than the kNN imputation method in the dataset of vulnerability index in dealing with COVID-19 in Indonesia.
IoT system to monitor health conditions of elderly

Elderly people face different old-age-related diseases where some of such diseases transform into chronic diseases. However, without assistance, aged persons are helpless. Even informing someone about their health issues is a challenge for them. In developing countries new technologies, including the internet of things, are being used to monitor old adults’ health and activities, thus enabling them to live safely and independently at home as they age. This study aimed at designing and prototyping a system intended to help improve the health of elderly people in Sub-Saharan Africa. This can be achieved by providing a relatively easy way to monitor the health of elderly people remotely and provide vital signs information of elderly people periodically with automatic alerts in case of emergency issues and tracking the location of elderly people. Therefore, architecture for context awareness and IoT- based systems is proposed to improve the health monitoring of elderly people in Africa and thus improve the quality of life of elderly people. The system uses a heartbeat sensor, Temperature sensor, and GPS Module to collect data with Arduino Uno as Microcontroller. The collected data is sent to the cloud using GSM Module. The cloud data is stored, processed, analyzed, and then sends to the caregiver and Doctor. This system will enhance the quality of life for elderly people by enabling them to get real-time health information and medication from any location. Additionally, it will enable healthcare practitioners to remotely monitor them.
Question Difficulty Prediction with External Knowledge

The difficulty of test questions is an important indicator for educational examination and recommendation of personalized learning resources. Its evaluation mainly depends on the experience of experts, which is subjective. In recent years, question difficulty prediction (QDP) using neural networks has attracted more and more attention. Although these methods improve the QDP efficiency, it works ill for questions involving abstract concepts, such as numerical calculation, date, and questions whose answers require background knowledge. Therefore, we propose a difficulty prediction model based on rich knowledge fusion (RKF+), which solves the problem that the difficulty prediction models cannot obtain conceptual knowledge and background knowledge. The key is to introduce the attentional mechanism with a sentry vector, which can dynamically obtain the text representation and external knowledge representation of test questions. To further fusion the acquired external knowledge, our model added a bi-interaction layer. Finally, the validity of this model is verified on three different datasets. Besides, the importance of attentional mechanism and external knowledge representation is further analyzed by ablation experiment. In addition, based on a real English reading comprehension test dataset, we explore the influence of two kinds of external knowledge on the question difficulty prediction model.
A Comparison of Four Metaheuristic Algorithms for the Problem of Test Redundancy Reduction

Abstract. Finding the optimal solution out of all reasonable solutions is the goal of an optimization problem. Numerous metaheuristic algorithms have been created in the literature during the past 30 years. It is essential to assess each algorithm's performance using broad case studies in order to assist engineers in selecting the optimal metaheuristic algorithm for the given problem. In this research, we give a comparative analysis of four metaheuristic algorithms used to solve the test redundancy reduction problem: the teaching-learning-based optimization (TLBO), the jaya algorithm (JA), the sine-cosine algorithm (SCA), and the sparrow-search algorithm (SSA). To achieve statistical significance, the evaluation of these algorithms' performance is carried out by running every algorithm thirty times. Finding a minimal subset of the test suite that satisfies the specified test criteria is the aim of test redundancy reduction. It was discovered that, in terms of average reduction rate and runtime effectiveness, the SSA is the more efficient metaheuristic algorithm for the test redundancy reduction problem among all the competing metaheuristic algorithms.
String Figure Simulation with Multiresolution Wire Model

String figure is a traditional game with a loop of a string played by hooking and/or unhooking strands of the loop from fingers to produce patterns representing certain objects. The patterns of the string figure change dynamically by the string manipulations by fingers. A computer-based method based on knot theory has been proposed to make string figure patterns. This method represents a string in the string figure as an extended knot diagram and generates string figure patterns by monotonically decreasing the number of crossing points. However, there are string figures where this method does not make the patterns correctly. In contrast, this paper proposes a method using physical simulation from the viewpoint that the string figure patterns are determined according to the tension and frictional force applied to the string. To evaluate the proposed method, we conducted two types of experiments. In the first experiments, we showed that physical simulations using an adaptive multi-resolution wire model have sufficient capability to compute the string figure patterns. In the second, we conducted experiments to make patterns for instances of the string figure. The experimental results indicate that the proposed method can correctly make the string figure patterns in most cases. We also found that the proposed method can make some of the string figure patterns that cannot be generated by conventional methods.
Transplanting of Environments between Closures in the lambda calculus

In programming languages, a function closure is a function whose body includes possibly free variables and that is accompanied by association with their values. The association of variables with their values is called an environment. If a programming language uses lexical scope and treats functions as first-class objects, then function closures are mandatory. Function closures are provided in Common Lisp, Emacs Lisp, Scheme and other dialects of LISP, as well as in modern scripting languages such as Python, JavaScript and Ruby, and procedural languages such as C++.
Multi-Attribute BERT for Preferences Completion in Multi-Criteria Recommender System

For a multi-criteria recommender system (MCRS), a complete set of criteria ratings is necessary to produce an accurate recommendation. Incomplete preferences, known as the "partial preferences problem," is one of the problems in MCRS. This issue affects the performance of MCRS due to an increase in data sparsity. Criteria rating prediction is one method for completing the preferences. Therefore, this study proposes a new method for preferences completion, that is a multi-attribute Bidirectional Encoder Representations from Transformers (BERT). The proposed method incorporates reviews and overall ratings to predict incomplete criteria ratings. Rule-based adjustment is also performed to enhance the performance of the proposed method in predicting the worst rating. This study shows that the proposed method outperforms the baseline method. The proposed method is also evaluated on MCRS using a user-based multi-criteria collaborative filtering approach. The result is that it has a positive impact on the recommendation system.
A Framework for Actor-Oriented Automated Hate Speech Detection

The development of automated hate speech detection research has not yet detailed the actor who is the source of hatred, even though the forms of hate speech intervention for perpetrators and supporters are different. Previous research on this topic has paid much attention to the action component in the form of sentimental words. However, it has yet to pay attention to the actor component that can differentiate legal hate speech from illegal hate speech and transforms hateful or offensive speech into free speech, thus dealing with misclassification, as reported in a previous study. This research proposes a framework for the automated detection of hate speech that provides an actor and action component to solve the problem of such errors and meets the need for comprehensive interventions. This study shows how to apply the framework in a rule-based approach by considering the actor component in predicting hate speech and differentiating it from others. The prediction process is called actor-oriented automated hate speech detection.
Unstructured Data to Visualized Information: Improving process of master list preparation for university ranking assessment and decision making

Information is crucial in any organization especially with regard to university ranking assessment. The information retrieval by using the words of “universiti kuala lumpur” will retrieve a publication from others university entity that contain those words. The most common data included was from Infrastructure University Kuala Lumpur. Besides, the data received from Scopus are in the form of authors initial and it will take some time for the Publication Unit officer to identify the author of the papers and to indicate the campus. This makes the process of preparing the master list taking longer time. This initiative is to shorten the process of preparing the university ranking document. The processed data will be visualized accordingly for head of campus and top management university and the publication performance can be assessed. It is found that the system developed enable the Publication Unit officers to process the data received from the library within 3 days for 200 data. Previously, the process took almost two weeks or more due to process of findings the owner of the publication. This finding confirmed the argument made by Patel (2021) that the information system is an enabler to achieve the final goal and in this case is the goal are to the prepare MyRA and SETARA publication master list and to provide the publication analysis for university and campus for decision making and strategic planning.
Aesthetic Image Synthesis Using Multiple-Aesthetic-Aware GAN

The use of synthesized images in fields that prioritize visual appearance, particularly the field of art, places a high value on aesthetics, with the system's output images required to adhere to several aesthetic rules. There are two approaches to aesthetic image synthesis with architecture based on GAN. The first strategy is to modify the loss function on the GAN so that, in addition to the loss from the GAN architecture, aesthetic loss and content/semantic loss are also calculated and the total loss is the sum of the three loss calculations. However, the outcomes still fall short of the expected natural image appearance. The second strategy involves modifying the GAN architecture by adding a new layer to the GAN generator and discriminator network, while the loss function calculation remains unchanged. The results of this second approach to image synthesis have not been optimized in terms of producing meaningful images across multiple semantic classes. This study proposes a method for increasing the aesthetic value of the synthesized image by modifying the two approaches and employing the multiple-aesthetic-aware GAN method. The proposed method takes conditional semantic information and conditional aesthetic information into account not only in the GAN architecture, but also in the loss function value calculation. The proposed method is the result of ongoing research and will be evaluated using the Inception Score (IS), the Frechet Inception Distance (FID), and an aesthetic value metric.
Assessment on Multiple Aspects of Online Book Recommendation Systems

The recommendation system plays a major role in the online platform that instructs consumers to buy the items they like. Most e-commerce websites, such as online library, online games, Netflix and amazon, recommend products that are preferred by consumers using the recommended system. Consumers use the recommendation techniques to select the interested items and similar types of items they are looking for. Recommended system that mostly uses techniques such as collaborative filtering, content-based filtering and hybrid approach. Book recommendation system (BRS) aids consumers in recommending the best books of their interest and the same type books they preferred earlier. Most research articles uses data set comprises book data, user data and rating data. This article compares well-known recommendation system techniques and the data set used to recommend books to consumers. We observed that hybrid technique named commenders is the best among the other research articles, but data sparsity is the complex problem among existing recommendation systems.
Anomaly Detection from Video Surveillances Using Adaptive Convolutional Neural Network

Anomaly detection is finding various anomalous activities taking place in the video. Using an unsupervised learning technique, surveillance videos identify various real-time video anomalies in the dataset. This paper proposed an adaptive convolutional neural network (ACNN) deep learning model and classified the anomalies into two classes: anomaly and non-anomaly. The two main key points that improve the learning algorithm of ACNN are as follows: first, extract the hidden key features from the frames obtained by converting the videos to images using an adapted convolutional neural network. Second, along with traditional CNN layers, proposed model contains customized dropout and dense layers, which improves the performance during training. Performance evaluation is done through the UCF standard dataset. It consists of full-framed real-world videos obtained through CCTVs which are real anomalies like accidents on the roads, fire explosions, and fighting. Here, we predicted various anomalous activities in video anomaly detection by utilizing normal videos and videos containing abnormal activities. The proposed ACNN model produced better accuracy of 91% as compared to other existing algorithms used for the same dataset.
A Relevant Image Recommendation Based on Social Images

Because of the colossal increment in picture database sizes, just as its immense organization in different applications, many image retrieval frameworks have been created. The test, in any case, is in structuring a framework with the capacity to recover best matches if there should arise an occurrence of having all sort of pictures as inquiry. The intensity of such framework profoundly relies upon the highlights it utilizes to play out the coordinating procedure. In our project, images are collected from Flickr Dataset. In this task, an image retrieval framework has been structured and created. Right off the bat, this report diagrams a portrayal of some crude highlights of a picture, which have been used in the introduced framework. These highlights are extricated and utilized as the reason for a similitude check between pictures. The calculations, used to figure the similitude between separated highlights, are then clarified. The last outcome was a MATLAB-manufactured programming application, with a picture database, that used diverse component of the pictures in the database as the premise of correlation and recovery.
Partial CMMI V2.0 Assessment Using Multi-grade Fuzzy for Healthcare and Insurance Segment in Software Services

Capability and Maturity Model Integration (CMMI) is a process-level improvement training and appraisal program. It was developed at Carnegie Mellon University and administered by the CMMI Institute. It is required by many organizations, especially for software development. Healthcare and Insurance segment is one of the key segments in software services. Regulations play a key role in the Healthcare and Insurance segment of Financial Services. The current study aims to evaluate a sub-section of the software development process for this segment using the multi-grade fuzzy approach. The study takes a global software consulting and services organization as a case for the analysis. Importance Performance Analysis (IPA) is used to identify the critical capabilities to be addressed by the delivery heads of the organization. The Partial CMMI V2.0 Index of case organization is computed at 7.88, which is at Level 4 Maturity Level indicating the process as “Quantitatively managed and capable.” Improvement is required in the service management area to develop and keep updated the organization standards, and description of services and in the service agreement area to analyze existing service agreements and obtain service data for preparing updated agreements. They are in the Developing and Managing Services Capability of the organization. This model can be expanded to all the capability areas and used as a full-fledged assessment and continuous improvement tool for software development projects.
Object Detection with Voice Feedback for Blind People Using Computer Vision

Computer vision is a field in machine learning that makes computer to learn by itself and allows the computer to see what human sees and recognizes. Today, computer can recognize objects faster than humans, and research shows that computer vision accuracy has been increased from 40 to 85% in the past 10 years. Out of 285 million peoples, 39 million are blind and visually challenged. People 50 years and older are 82% of all blinds, and they cannot live their own life without others’ help but with the help of computer vision to train the computer to identify the object and give them as a voice feedback so that they can experience the real-world object with the help of computer vision. Object detection and recognition are done on a single-board computer represented by Raspberry Pi-3 as an embedded solution. The goal of this work is to make a smart framework, which recognizes the object for the visually impaired client and measures its precision and presents the output as sound signs to caution the visually impaired customer of the prevention ahead.
A Comparative Analysis of Customer Behavior Using Sophisticated Machine Learning Techniques

Data is growing in leaps and bounds day by day with the rapid growth in cloud and internet technologies. Social media has become an integrated part of life as it is used to share content, opinions, reviews, suggestions, etc. Anybody can voice out their opinions through social media. This data can be reused or analyzed to understand the customer and cater to his needs. In particular, e-Commerce platform enables the users to write product reviews to help other buyers purchase better products and be benefited from their opinions. The e-Commerce platform can also mine the data for sentiment analysis to understand the customer opinion and build recommendation systems to understand the customer needs and suggest products. Recommendation systems give a personalized shopping experience to users and thereby increases sales and profits. In this project, we use n-gram analysis to understand the data in the dataset and then apply multiple machine learning algorithms to the selected dataset to identify the appropriate classification algorithm that gives the best accuracy. We also try to build a recommender system that uses collaborative filtering to recommend appropriate products as per the customer needs.
All About Links

So far, this book has discussed many of the components of an email message and some indicators that can help us determine the validity of a message. The last chapter focused on content and various ways that content can be used as an attack method. Email messages can contain far more than just content. Often, messages are sent with links or attachments to help convey additional information to support the content in the main body. This chapter focuses on links and how attackers use them to exploit and attack victims.
Remediation and Lessons Learned

Thus far, this book has largely discussed identifying and detecting phishing messages and suspicious activity deriving from phishing messages on a computer or network. Once you identify this behavior, what should you do next? You learned the PICERL process and are nearing the final stages of our incident response process. These stages include eradication, remediation, and lessons learned. When complete, this closes out our process and brings our systems back to a normal state while analyzing what you’ve learned.
Phishing Tactics and Techniques

Chapter 1 covered the basic components of email messages and headers. Now it is time to get into attack techniques. Attackers use techniques and tricks to entice end users to click a link, open an attachment, or send sensitive information unwittingly. Understanding these techniques helps you identify and scrutinize email messages that employ these or similar tactics. Before jumping into the nitty-gritty details, let’s go over some of the basics.