Reviewerly/exHarmony huggingface dataset:
Online Fraud Detection using Machine Learning

Fraudsters find it easy to commit credit card fraud because it is an easy target. There has been an increase in online payment modes in due to e-commerce and other online platforms, there is now a higher danger of online fraud. Due to an increase in fraudulent online transactions, researchers have begun to evaluate and detect fraud using machine learning. In order to examine past transaction information and extract consumer behavioral patterns, our main goal in this study, a novel fraud detection algorithm for streaming transaction data is built and created. A system that clusters cardholders according to the amount of their transactions. In order to extract the behavioral pattern of the groups, we should aggregate the sliding window method transactions done by cards from various groupings. It is then decided which classifier with the best rating score can be chosen as one of the best methods to predict frauds after training different classifiers over the groups separately. The paper shows how the model is related to convolutional neural networks and afterward adding classifiers algorithms, for example, Isolation Forest, Local Outlier, and SVM can be utilized to recognize misrepresentation. As a result, concept drift can be solved via a feedback mechanism. We used the Kaggle credit card fraud dataset for this article.
Analyzing the Impact of Feature Correlation on Classification Acuracy of Machine Learning Model

Machine Learning has been widely used in building classification models for early detection of diseases using electronic health records of patients or variety of biosensor data on the characteristics of tissues or bio-fluids. For example, machine learning algorithms have been extensively applied for cancer detection and heat disease classification using open-source clinical data repository. However, there has been increasing number of challenges and complexities in building accurate classification models from diverse healthcare datasets available today that are basically measured under a variety of clinical settings and measurement techniques. Models that were shown to attain highest accuracy often suffer from overfitting problem. A key element in enhancing the accuracy of machine learning models is selecting correlated features or eliminating redundant features associated with a given dataset. Therefore, this study examines how the degree of correlation among features affects the predictive accuracy of three popular classifier models of breast cancer detection using clinical, demographic, anthropometric and blood metabolic data (with 10 features), and cytological characteristics of breast tissues (with 11 features) of patients. It was demonstrated that eliminating highly correlated features from the input dataset does not have any negative impact on the accuracy of classification models. SVM was found to be the best classifier with highest accuracy for both the datasets. The present results suggest that increasing the number of features increases the performance of breast cancer classifiers, and there is a modest change in prediction accuracy with the removal of highly correlated features.
Studying the Effect of an Online Realm Based on Wearable Technology on Learners' Understanding

An interactive system called imperative (AR) creates a realistic and genuine educational and learning environment. The latest crop clearly prefers innovation training over conventional teaching methods. Studies on scientific test techniques are still lacking, despite the fact that earlier research on virtual reality has shown some information on how it might enhance learning results in several subjects. Students need experimental information to better understand the phenomenon. The page discusses the steps involved in constructing & implementing a mobile application for a lab experiment that makes use of augmented reality technology. An extra educational resource for high school seniors is this. The prime purpose of this mobile-based augmented reality program for a scientific experiment is to provide thorough information in addition to conventional processes and educational materials. The experiments that will be shown in ElectroAR include a number of them. An professional review is conducted to evaluate input. In the future, user testing of ElectroAR in actual classroom environments will be conducted.
An Ensemble-based approach for assigning text to correct Harmonized system code

The world customs organization (WCO) administers the Harmonized System (HS) that provides guidance on common nomenclature to describe traded goods by names and numbers. Inaccurate or incomplete usage of HS codes lead to regulatory violations by the company for which the goods are traded. Shipment often gets delayed because of the inaccuracies encountered. The customs often classify these products into ‘other’ category which turns out to be expensive as high tariff rates are charged. Hence a system is required for accurate assignment of text-description to HS code as per WCO manual. In order to develop an accurate commodity classification engine this paper has provided solutions under two scenarios. Under the first scenario where sufficient and reliable training data is available, we build a BERT-transformer based hierarchical conditional multi-class classification model that outperforms the flat classification model by approximately 16 percentage on a publicly available data. As the HS codes are hierarchical in nature, building a model at each digit gives a clear edge. The reason is that the algorithm arrives at the final class by traversing all the possibilities at each digit level conditional upon the digit chosen at previous level. Under the second scenario, where training data is unreliable as is often the case due to unverified user input stored in the backend, we demonstrate how correct assignment can be arrived at by modelling the WCO manual as a NER based knowledge-graph and then computing the similarity of example text description with the nodes and edges of the graph. We assign the text-description to a HS code based on the highest similarity measure. The knowledge-graph also introduces AI based auditing functionality in the system which is a novelty and addresses questions of responsible AI.
Development of Critical Information Framework by Big Data Analytics and Artificial Intelligence to Prevent Cyber Attacks in WSN

Sadly, we now have to deal with the phenomenon of cyber-attacks as a result of our growing dependence on internet information. As technology has evolved and gotten more complex, so have the sorts of malware used in these assaults. At current moment, CIIs including financial institutions and telecommunications are the targets of an increasing number of targeted cyberattacks. APTs are a specific kind of malware that is difficult to identify and remove due to its covert nature, intricate design, and use in targeted attacks. These computer viruses may injure and assault people (in the targeted systems). Considering how long conventional cyber security techniques take, this is rather concerning. Defense strategies fail to thwart these assaults. To make knowledge decisions and warn of imminent dangers, researchers must have a realistic understanding of this vast amount of data. This expertise can only be achieved through automation. Cyber defense is primarily based on big data analytics and artificial intelligence. Methods of big data analytics are typically used on large data sets containing a limited number of data types. It is the goal of this research to identify patterns, connections, themes, and other pertinent information.
Web-based Pretrained Transformer Model for Scientific Paper Summarization (WPT-SPS)

The rapid growth of scientific publications becomes challenging for researchers to swiftly learn about breakthroughs in their domains. This challenge is addressed by scientific summarization, which provides summaries of the important contributions of scientific papers. This paper proposes a transfer learning technique for scientific paper summarization to generated abstractive summaries of scientific papers in a particular domain. The proposed model attained improvement of around 12% and 20% than the state-of-the art models such as BART and Longformers.
A Thorough Study of Implications of Something Like the IoT Devices (Technology) Building More Design Data feed and on Local Variables Limits Imposed via Correlations

Design Computation (MDP) or Design technology (MDE) is an intriguing method in lately to construct mock -ups of traditional operations. Other effects stated include excellent productivity, satisfying users' demands the list goes on. The research was thus carried out to examine the procedures and equipment used in MDP. To determine which tools and strategies are most frequently employed in this new activity, a major random sampling methodology has been applied. The quantity of Minlp usage across various platforms has been tallied using a randomly selected sample technique. Since the sample method is random, no specific field was looked at while determining the conclusion. The sample consequently sustained the instrument and model choices randomly Srs components.Findings revealed that Low Computational cost Architecture (RSA) is utilised largely for creating the designs. Other applications featured include Simulation and Sputnik that are also use; nevertheless, owing to having extensive capability in RSA, that RSA is utilised largely. Aside from this, both Computer Science (ML) and CNN's (Cn) are employed in MDP relating to distinct domains. The general favourable and negative qualities have been established in this investigation.
Extractive Text Summarization of Indian Election Commission Manuals

A summary which is a shorter version of the original text highlights the major points from the much longer subject to help the reader get the gist in a short period of time. The objective of this work is to generate a concise document description of Indian Election Domain manuals that is more revealing than a title but short enough to be absorbed in a short time and can be used for rapid relevance assessment. Extractive approach was used in this work which involved preprocessing of the manuals available in pdf format, scoring of sentences by comparing them with a vector representing the most significant words in the text and selection of top scoring sentences to be included in the summary after eliminating redundant sentences. Due to the absence of reference summaries the results were evaluated for linguistic and semantic quality and it was concluded that the approach produces satisfactory summaries.
Crime Analysis and Forecasting using Twitter Data in the Indian Context

Since the late 1990s, social media has added more features and users. Due to the rise of social media, blogs and posts by common people are now a part of mainstream journalism. Twitter is a place where people can share their ideas about culture, society, the economy, and politics. India's large population and rising crime rate make it hard for law enforcement to find and stop illegal activities. This article shows the use of Twitter data to analyse, forecast, and visualise criminal activity using statistical and machine learning models and geospatial visualisation techniques. This helps law enforcement agencies make the best use of their limited resources and put them in the right places. The research aims to present a spatial and temporal picture of crime in India and is split into three parts: Classification, Visualisation, and Forecasting. Crime tweets are identified using a hashtag query argument in the tweepy python package's search_tweets function, followed by substring-keyword classification. The visualisation uses gmaps and bokeh python packages for geospatial and matplotlib for analytical applications. The forecasting portion compares AR, ARIMA, and LSTM to determine the best model for time series forecasting of crime tweet count.
LightPEN: Optimizing the Vulnerability Exposures for Lightweight Penetration Test

Penetration Testing (PenTest) is crucial to an organization’s system security. It helps ensure the confidentiality, integrity, and availability of the system and reduces exposures to future risks. Specifically, the PenTest process is usually initiated after the vulnerability assessment (VA) scanning where its results are used to undertake the PenTest. Significantly, PenTest requires expert testers to test each vulnerability found in the VA stage thoroughly. Hence, the process is expert-dependent and time-consuming. To optimize the set of vulnerabilities to be tested in the PenTest process, we introduce the scheme called LightPEN to support the extraction of known vulnerabilities obtained from existing sources such as local code scanning, notice from vendors and developers, and previous VA reports. In addition, our system provides exploitable scripts for the PenTest process. Finally, we conducted the experiment to demonstrate the efficiency of our proposed system.
A Dynamic and Efficient Crypto-Steganography System for Securing Multiple Files in Cloud

Traditional steganography is generally based on the way of hiding payloads into a cover image. In this way, the quality of the visibility is subject to the size of cover image and the payload size. Existing works provided the attempts to manage multiple payloads to multiple cover images simultaneously. However, the secure and fine-grained access control for stego images has not been addressed. In this paper, we propose a system scheme called CryptSteg to support a secure and fine-grained data hidden in the image files shared on cloud. Essentially, we integrate the ciphertext policy-attribute based encryption (CP-ABE) and the symmetric encryption to secure the payload and the secret key in the cryptographic steganography setting. In addition, we proposed an algorithm to embed the ciphertext payload into multiple cover images. For the evaluation, we conducted the experiment to demonstrate the efficiency of our proposed scheme.
Block-Chain Implementation in Industry 4.0: Critical Review

ICT has exploded over the past few decades, and as a result, the use of numerous smart applications—such as those for smart farming, smart healthcare, supply-chain & logistics, business, tourism and hospitality, and energy management—has increased exponentially. Due to the use of the open channel, or the Internet, for data transfer in all of the aforementioned apps, security and privacy are key issues. Although numerous security standards and solutions have been put forth during the recent years in order to increase the level of security of the smart applications mention previously, the current solutions either rely on centralized architecture or have high computational and communication cost. Furthermore, the majority of the security solutions currently available have a narrow focus and do not address important parameter called stability. Block-chain technology may provide a remedy for the aforementioned problems. These facts served as the impetus for the rigorous review of several Block-chain-dependent technologies with suitability for numerousindustrial applications that we offer in this article. We made 4 different contributions to this paper. First, we investigated the most cutting-edge Block-chain applications for smart apps currently available. The reference engineering for the Block-chain’s material in various Industry 4.0 applications was demonstrated further. The advantages and disadvantages of conventional security systems are then contrasted with their responses. In order to give readers a detailed understanding of the applicability of existing Block-chain-based security solutions in diverse applications, we finally offered a comparison of those solutions using a variety of characteristics.
Multiple Words to Single Word Associations Using Masked Language Models

In this paper, we examine a word association task that predicts a correct associative word from five stimulus words using Masked Language Models (hereafter referred to as MLMs). For MLMs, we used BERT and gMLP. Since our word association task uses only nouns for both stimulus and associative words, we trained new models by restricting masked tokens to nouns. In our experiment, we input sentences such as “The prefecture associated with Mt. Fuji, Lake Hamana, … and eels is MASK. (富士山、浜名湖、⋯、うなぎから連想する都道府県は MASK です。),” so that MASK outputs an associative word. In the experiments, we also examined adding Japanese quotation marks 「」 before and after the MASK, i.e., 「MASK」. The experiment results showed that the highest percentage of correct answers, 49%, was obtained by adding 「」 before and after the MASK (74% of the correct answers were within the top five words).
An Approach for Enhancing Data Storage Capacity in Quick Response Code using Zip Compression Technique

A 2-D matrix barcode known as Quick Response Code stores data in four distinct encoding modes. Nowadays, it can be found on almost every product, billboard, digital transaction, and so on because of its growing popularity. It is now so user- and mobile-friendly that users can seamlessly transit from offline to online environments. It is used to store the information, allowing people to interact in real world using Smartphone and also as an information sharing medium. The primary objective of this work is to use ZIP compression to improve QR code data storage. This paper will provide a brief explanation of how QR code data storage capacity is enhanced by ZIP compression. As ZIP compression is a lossless compression technique, the result demonstrates how more data can be stored in QR codes of different sizes without losing any information in the form of compressed data. Additionally, after scanning the QR code, the compressed data is decompressed to retrieve the original data.
Integration of IOT with Block Chain Technology for the Technology Advancement

The technique that enables that data storage of any data collection is known as bitcoin. With some of its applied different, it offers a secure system, as well as a visible with the set of data being readily available to all users. Blockchain-based applications for cryptocurrency are called attention for their dependability, robustness, and efficiency. The cryptocurrency therefore is made accessible to reach enormous populations. Despite being the thing of the future, the bitcoin has a number of drawbacks that vary depending on the use case. This research assessed how well different blockchain networks functioned in various applications. The pros and cons of such a system, which itself is referred to as the technological advancement of the future, was thoroughly studied and contrasted.
Analysis of Decentralized Pharmaceutical Supply Chain: A Systematic Review

Supply chain optimization has recently emerged as a key research area in process operations and management. There has been a great deal of research done on facility sites and layouts, inventory and distribution planning, capacity and production planning, and complex scheduling. This information only briefly and infrequently addresses issues unique to the pharmaceutical sector. Moreover, effective supply chain management is a problem in any industry, but there is added complexity and risk in healthcare due to the direct impact that a supply chain disruption might have on patient security and health outcomes. One solution for improving the security, authenticity, data provenance, and utility of the health supply chain is blockchain technology. Our goal is to present an overview of the potential problems connected with the adoption and use of blockchain in the medical supply chain, with a focus on the public health, medical device, and pharmaceutical supply sectors.
Recommender System for Mobile Applications

Recommender Systems have turn out to be a critical part of any e-commerce internet site. It can used for any product primarily based on web sites or movie streaming websites. This paper is centered towards the recommender systems that can be used for recommending movies. The recommendation systems are not uncommon for movies but they lack a certain opinion poll of the people about the recommended movie. The system makes use of the popularity opinion of the people which can be drawn from the sentiment analysis of popular micro blogging website such as twitter. The content based filtering method of machine learning is used to find recommendation. Cosine similarities score is used for the movie recommendation system. Further the sentiment analysis of the recommended movies gives validity whether they are actually recommendable or not. The proposed system will develop an android application for recommendation system. This android application is named Ticket.
An Algorithm for Peer Reviewer Recommendation Based on Scholarly Activity Assessment

Journal editors are putting a lot of effort into selecting appropriate reviewers for fair and reliable peer review of submitted manuscripts. Editors consider whether the reviewers have no affinity with any of the authors of manuscripts and have sufficient expertise in reviewing the manuscripts. The affinity can be evaluated by whether any of the reviewers has been a coauthor and/or a coworker in a common institution with any of the authors of the manuscript. The expertise depends on the similarity of the research topic between the reviewer’s published papers and the submitted manuscripts. In this paper we propose an algorithm to recommend appropriate reviewers to editors, based on the assessment of these scholarly activities and achievements. To implement this algorithm, TextRank and GenSim library are used to extract feature sets from abstract and introduction sections of both submitted manuscripts and the reviewer candidates’ papers. Based on the extracted feature sets, affinity and expertise check are implemented. An experiment has been conducted with a data set of over 1,000 papers in the field of DB research to evaluate the performance of this algorithm. The experiment consists of affinity check by using 2-mode network matrix operations and expertise check based on Max Similarity and/or topic clustering. Experimental results show that the recommendation algorithm is reasonable on the basis of scholarly activity assessment and achievements. Our method is designed to optimize the reviewer pool for each journal, client, and this algorithm is designed with an open license and has the advantage of being free to use and operate. In addition, our algorithm has the advantage of journal-specific optimization by designing to adjust the expertise of the reviewer selection and the weights for exclusion of interests to reflect journal-specific policies.
Difficulties and Potential Ulnerabilities in the IOT Architecture

The Internet of Things has significant restrictions on privacy and protection. IoT problems might include user inexperience, inadequate device updates, a lack of strong and efficient security precautions, and unreliable fluid gadget tracking. The study's objectives include identifying (a) varied confidentiality worries, (b) techniques for trying to secure IoT-enabled configurations and constructions, (c) available today safety agreements, and (d) a most suitable data protection depictions for the differing stages of IoT-powered applications. Scientists proposed a new, generally expanded layer model that included security and confidentiality tools as well as layer information. the recommended cloud/edge-based Internet of Things system's implementation and assessment. Internet of Thing (IoT) devices built as virtual servers using Amazon Web make the lowest layer (AWS). The topcoat was developed by researchers using the cloud-enabled IoT platform of AWS (the cloud). To allow data transfer between the envisioned cloud/edge authorized IoT representation's levels, researcher developed ssl certificates.