Reviewerly/exHarmony huggingface dataset:
Detecting Fake Job Posting Using ML Classifications and Ensemble Model

In this project, we create a fraudulent checker tool to detect fake job postings using NLP (Natural Language Processing) and ML approaches (Random Forest Classifiers, Logistic Regression, Support Vector Machines, and XGBoost Classifiers). These approaches will be compared and then combined into an ensemble model which is used for our job detector. The aim is to predict using machine learning for real or fake job prediction results with the highest accuracy. Dataset analysis is performed by supervised machine learning techniques (SMLT) and collects a variety of information such as variable identification, missing value handling, and data validation analysis. Data cleaning and preparation along with visualization are performed on the entire dataset. The ensemble model is created at the end using ML Algorithms like XGBoost, SVM, Logistic Regression, and Random Forest Classifier by choosing 4 of the best contributing features. The model produced at the end will be implemented in a Flask application for demonstration.
SIGN BOT Extending an Ability to Communicate by Creating an Indian Sign Language

There is a communication lag between deaf-mutes and normal people. To overcomethat, we are providing information access and services to deaf-mute people in Indian Sign Language (ISL) and developing a flexible project that can be enlarged to capture the entire lexicon of Indian Sign Language via physical gestures like hand expressions and non-manual signs like facial expressions by developing and building a training model using machine learning algorithms. Sign language recognition uses image-based manual and non-manual gestures. Here we used figure recognition to identify manual and non-manual gestures. Finding expression gestures and analyzing finger movements to determine what the deaf-dumb individual is saying. In Python, the MediaPipe recognizes the hand signs and facial gestures of a person. These modules were developed to assist people with non-identical motions. This paper presents figure identification of Indian Sign Language via hand and facial gestures, as well asits integration with a chatbot as transcript output.
Candidate Prioritization Using Automated Behavioural Interview with Deep Learning

The objective of a project is to make an integrated technological solution to assist and help companies in their hiring process. We have come up with the concept for prioritization of candidates using automated behavioural interview. This is a web-based portal that will be supporting the recruitment operations by prioritizing candidates from a large pool of candidates based on skills and experiences relevant to the job. Facilitating an informed interview process for a candidate with systematic transparent procedure. Our project aims to apply our technical skills to facilitate interviewers and address the challenges faced by the organization in its hiring process. This has been addressed using recording the answers of random behavioural questions with limited time to prepare, then video will be analysed to find the behavioural aspect of the candidate. Behavioural analysis of the interview is done by applying facial emotion recognition based on VGG Net Architecture.
Regularized CNN Model for Image Forgery Detection

Digital images play a very important role in different areas in the modern technological scenario. Changing and manipulating the content of the digital image is a very easy task by using powerful image editing tools. In today's technology environment, digital photographs serve a critical function in a variety of fields. Using advanced image editing tools, changing and rearranging the content of a digital image is a simple operation. It is now possible to add, edit, or remove essential aspects from an image despite leaving any perceptible alterations. In addition to determining if the picture is authentic or forged, the metadata of the image may be examined, however, metadata can be altered. In this example, the authors use Error Level Analysis on each picture and matching parameters for error rate analysis to detect images of modifications using Deep Learning on a dataset of a false image and real photos. This experiment shows that by running through 100 epochs, we obtain the best training accuracy of 99.17 % and 95.11 % of accuracy validating.
Social Media User Oppression Detection Technique Using Supervised and Unsupervised Machine Learning Algorithms

Social media is the highly gained popular medium of interaction.And because of using the social media in our day to day life more oftenly,the increase in cyberbullying has also increased,especially to the young people who are more into the social media platform.So by cyberbullying detection technique we can make a world or environment more safe.cyberbullying detection technique will categorize tweets or comments as bullying and non bullying words. The previous existing system uses algorithms such as Naïve Bayes which is slow and less accurate in identifying cyber bullying and also high false positives is observed.Instead here CNN and LSTM is proposed to identify cyberbullying comments and to provide good amount of accuracy
Phishing Website Detection Using Natural Language Processing and Deep Learning Algorithm

Due to rapid growth of the internet most of the people started using internet through mobile and web apps to satisfy their needs. Such as online shopping and banking. Under OWSAP top 10 vulnerabilities, sensitive data exposure is one of the common threats that is identified in recent years and phishing is found to be a key source. Sensitive data exposure is majorly occurring in the internet using various phishing techniques and phishing is found to be a key sources of data stealing. Attackers, not only targeted the financial sectors and e-commerce industries, also in the field of defense and security . To detect the phishing attacks in webpages, many software was used. Some of the method of detection the phishing is, by using the URL of the webpage and by using contents of the webpage. Still, there is no robust and accurate software solution to detect the phishing attacks. The purpose of the research is to use both URL and contents of the webpage to identify the phishing. The proposed work is to build an automated and hybrid model using Random Forest (RF) algorithm in Machine learning with the Convolutional Neural network algorithm (CNN) in Deep Learning is applied to detect and classify the phishing in URL and web page contents in an automated manner .
Converging Blockchain for Organ Donation

Allograft has emerged as a preeminent province in medical management. In today's contemporary world, many emerging technologies have evolved and are digitally automatized with the help of Artificial intelligence, Cloud, Blockchain, etc, and it brings innovative solutions for the critical problems to make new vogue [7]. practitioners are given the challenge of forcibly acquiring, and companionating health records [18]. The most highly demanded aspects of the interweb are privacy and security, and this is where blockchain plays a predominant role. It has features such as transparency, decentralization, and immutability that make it indispensable. This technology has captivated remarkable attention from researchers as well as government organizations [6]. By using blockchain, electronic medical records (EMR) and donor details can be stored securely. The electronic medical records are hashed through a distributed IFPS file server that is secure and it is immutable. It is done with the aid of user signs acquired digitally at the time of saving, which cannot be redo anymore. This paper aims to/ design a web portal to collect unused medicine and give those to needy people through hospitals or NGOs that will be beneficial to them. It can be done after checking the medicines and it is approved by the admin or hospitals.
Sentiment Analysis of National Eligibility-Cum Entrance Test on Twitter Data Using Machine Learning Techniques

People around the world use social media to communicate and share their perceptions about a variety of topics. Social media analysis is crucial to interacting, distributing, and stating people's opinions on various topics. Governments and organizations can take action on alarming issues more quickly with the help of such textual data investigation. The key purpose of this effort is to perform sentiment analysis of textual data regarding National Eligibility-cum Entrance Test (NEET), perform classification and determine how people feel about NEET. In this study, 11 different machine learning classifiers were used to analyze tweet sentiment, along with natural language processing (NLP). Tweepy is the python library which is used to get user opinion about NEET Exam. Annotating the data is accomplished using TextBlob and Vader. Text data is pre-processed with a natural language toolkit. The dataset downloaded from Twitter shows that unigram models perform well compared to bigram and trigram models. TF-IDF models are more accurate than count vectorizer which is based on word frequency. classifier achieves an average accuracy of 92%. Perceptron also receives the uppermost average accuracy of 91%. According to the data from the experiment, most people have a neutral opinion of NEET.
Fitting Bayesian Stochastic Differential Equation Models with Mixed Effects through a Filtering Approach

Recent advances in technology contribute to a fast-growing number of studies utilizing intensive longitudinal data, and call for more flexible methods to address the demands that come with them. One issue that arises from collecting longitudinal data from multiple units in time is nested data, where the variability observed in such data is a mixture of within-unit changes and between-unit differences. This article aims to provide a model-fitting approach that simultaneously models the within-unit changes with differential equation models and accounts for between-unit differences with mixed effects. This approach combines a variant of the Kalman filter, the continuous-discrete extended Kalman filter (CDEKF), and the Markov chain Monte Carlo method often employed in the Bayesian framework through the platform Stan. At the same time, it utilizes Stan’s functionality of numerical solvers for the implementation of CDEKF. For an empirical illustration, we applied this method in the context of differential equation models to an empirical dataset to explore the physiological dynamics and co-regulation between couples.
An Applied Computational Linguistics Approach to Clustering Scientific Research Papers

Scientific data available on the internet is rarely labelled. Most popular research paper repository sites contain papers without any annotation for grouping data. Classification of text via words, sentences and even paragraphs has become a key resource for a lot of industries looking to help their computers understand human language – the next stage in Artificial Intelligence. Using valuable Computational Linguistics ideas, some industrial applications have been able to streamline their processes to effectively and efficiently process and interpret language data. Continuing in this trend, in this paper, we aim to effectively clustering scientific research papers into topic-based differentiators, in the most efficient manner. Using multiple algorithms that have revolutionized the industry in the previous years, we compute over 800,000 entries of scientific research articles across 200+ domains that have been uploaded to accurately predict domains for each of these articles. We use clustering techniques like the K-Means algorithm to derive the topics for these papers with an accuracy of nearly 80%. We also use BERT to create topic clusters that generate topics based on frequently occurring contexts within the text. Beyond BERT, we use offspring algorithms that tackle specific, niche issues that BERT does not account for. We also fine-tune the parameters of the algorithms used to generate over 50 stronger topics that more accurately define scientific articles.
A Cumulative Tool for Docker Vulnerability Scanner

Docker container technology is a new virtualization technique that is extremely efficient throughout the development and deployment phases. Although Docker container technology is more convenient than traditional virtualization technology (virtual machines); it suffers from weak security due to inexperienced Docker image auditing techniques. To protect the host computer or local Docker containers from malicious Docker containers, it is required to detect potential hazards in Docker images and identify risks when Docker container instances are running on the host computer. This paper proposes a tool to give the cumulative report of the three major open-source vulnerability scanners like Trivy, Clair, and Grype.
Rock, Paper and Scissor Using AI- Random Forest Algorithm

Rock, paper, scissors (RPS) is a game of crucial decision-making that can be used to test various learning algorithms due to its simplicity. Random Forest is a Rock Paper Scissors Agent that makes predictions using the Random Forest Classification with a bit of random. The randomness on this agent has been limited only while losing (and partially due to window length), displaying the algorithm's true potential. A simulation of various AI agents is developed to compare and analyze against the Random forest algorithm.
Data poisoning attacks detection based on the convergence of the Peter and Clark algorithm and Bayesian adversarial federated learning

Effective Bayesian network structure learning algorithms, such as the Peter and Clark (PC) algorithm, must be designed and used with data integrity as a top priority. Deep learning models can be jointly built by thousands of participants; thanks to the innovative distributed learning technology known as federated learning. This research proposes a technique for the detection of data poisoning attacks along with enhancing secure data transmission and routing by utilizing the integration of the PC algorithm and federated learning. Data integrity of the network is likewise enhanced using the Convergence of the PC algorithm. Detection of attacks in established secure data transmission is carried out using Bayesian adversarial federated learning. We provided an optimization-based model poisoning approach and introduced adversarial neurons into the redundant area of a neural network by assessing model capacity. It should be highlighted that while those redundant neurons have little relevance to the primary goal of federated learning, they are crucial for poisoning attacks. Numerical tests show that the suggested approach can get beyond defense mechanisms and have a high attack success rate.
The Relationship between Audit Review of Private University and Audit Fee and Audit Hour

This study is to investigate audit fee and hour of private universities in order to measure the effectiveness of audit review.BRAfter audit review, there is a possibility that the auditors who audited the private universities and universities will be disadvantaged, which will put pressure on the auditing work. Therefore, audit review is highly likely to change the behavior of private universities and auditors. In this study, we examine ① audit fee and hour of audit reviewed universities(AR), ② audit fee and hour of AR after review, ③ the difference of audit fee and hour between AR and non-AR.BRAs a result, AR target year
Study on an Operating Rate Metrics Model Based on Process Loss Structures and a FOM-solution-based Data Analysis Method

The aim of this study is to present a new and improved metrics model for measuring the operating rate data based on the loss structure during the manufacturing process and a study on the data analysis methodology. First, the loss structure is classified, analyzed, and presented. Subsequently, the overall equipment effectiveness (OEE) metrics based on the total productive maintenance (TPM) losses are presented and the applicability problem is analyzed. The total production effectiveness (TPE) metrics, which includes both the equipment efficiency and 4M efficiency factors in the calculation of its operating rate new model, is then presented to minimize the problems of OEE. And the man-hour effectiveness (MHE) metrics is presented for measuring the man-hour during manual work with structured man-hour losses. Finally, factory operation management (FOM), which is a solution based on 4M multi-dimension analysis methodology, is applied for the analysis of the operating rate data.
Preparing for crisis: are you ready to be attacked?

How can you tell if your organisation is really prepared for a cyber attack? Focusing too heavily on technological solutions can result in key aspects being overlooked – including the necessary skills and awareness among your staff and whether you have the right processes in place, and the people ready and prepared to use them, in case of an attack.
PKI – friend or foe?

Network SecurityVol. 2023, No. 2 The FirewallPKI – friend or foe?Colin TankardColin TankardDigital PathwaysSearch for more papers by this authorColin TankardPublished Online:27 Feb 2023https://doi.org/10.12968/S1353-4858(23)70007-XAboutSectionsView articleView Full TextView EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article FiguresReferencesRelatedDetails 1 February 2023Volume 2023Issue 2ISSN (print): 1353-4858ISSN (online): 1872-9371 Metrics History Published online 27 February 2023 Published in print 1 February 2023 Information© MA Business Ltd.
Tracing template injection attacks to North Korea

Network SecurityVol. 2023, No. 2 FeaturesTracing template injection attacks to North KoreaTom McVeyTom McVeyMenlo SecuritySearch for more papers by this authorTom McVeyPublished Online:27 Feb 2023https://doi.org/10.12968/S1353-4858(23)70011-1AboutSectionsView articleView Full TextView EPUB ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareShare onFacebookTwitterLinked InEmail View article References 1. Petrosyan, Ani. ‘Estimated cost of cybercrime worldwide from 2016 to 2027’. Statista, 2 Dec 2022. Accessed Feb 2023. www.statista.com/statistics/1280009/cost-cybercrime-worldwide/. Google Scholar2. ‘What does 2023 have in store for cybercrime? Here are 16 predictions for a hacky new year’. Thales, 27 Dec 2022. Accessed Feb 2023. www.thalesgroup.com/en/worldwide-digital-identity-and-security/enterprise-cyber security/magazine/what-does-2023-have-store. Google Scholar3. McVey, Tom. ‘Talking template injection attacks’. Network Security, Dec 2022. Accessed Feb 2023. www.magonlinelibrary.com/doi/full/10.12968/S1353-4858%2822%2970067-0. Google Scholar4. Saengphaibul, Val; Gutierrez, Fred. ‘Attackers taking advantage of the Coronavirus/Covid-19 media frenzy’. Fortinet, 4 Mar 2020. Accessed Feb 2023. www.fortinet.com/blog/threat-research/attackers-taking-advantage-of-the-coronavirus-covid-19-media-frenzy. Google Scholar5. ‘A detailed analysis of Lazarus APT malware disguised as Notepad++ shell extension’. Cyber Geeks, 31 Jan 2022. Accessed Feb 2023. https://cybergeeks.tech/a-detailed-analysis-of-lazarus-malware-disguised-as-notepad-shell-extension/ Google Scholar6. Twitter thread, @PhantomXSec. Accessed Feb 2023. https://twitter.com/PhantomXSec/status/1541408773444513792 Google Scholar FiguresReferencesRelatedDetails 1 February 2023Volume 2023Issue 2ISSN (print): 1353-4858ISSN (online): 1872-9371 Metrics History Published online 27 February 2023 Published in print 1 February 2023 Information© MA Business Ltd.
Automated Enforcement and Traffic Safety

Traffic safety has been a persistent societal and public policy challenge. Conventional law enforcement by human police is often cost-ineffective due to the information asymmetry and negative externality that arise from unsafe driving behaviors. Recent decades have seen a rise of automated enforcement (in the form of traffic cameras), yet there has been limited understanding of its impact on road safety. Using a longitudinal dataset of traffic camera installations and police reports of road accidents in a metropolitan city in China, we study accident trends near the road intersections where traffic cameras are present. We analyze two types of cameras: advanced ones (that detect various traffic violations via constant video capture and analytics) and conventional ones (that detect limited violations via temporary image capture). Employing an event study design, we observe a significant and persistent decline in accidents near advanced cameras, compared to the locations without cameras or only with conventional cameras. We examine the underlying mechanisms and obtain supporting evidence: First, advanced cameras augment violation detection by capturing more violations (enforcement augmentation). Second, they constantly monitor and record traffic conditions, enhancing accident cause identification (evidence gathering). Finally, both capabilities gradually give rise to technology-enabled deterrence, such that the presence of cameras can deter risky driving and reduce accidents, regardless of whether such cameras are capable of detecting relevant traffic violations. This work contributes to the intersection of information systems, transportation economics, and criminology; and it offers crucial policy insights into the effective implementation of automated enforcement for traffic safety.
Low Cost Mobile Inverted Pendulum Using Kalman Filter with Fuzzy Based PID Controller

In the “Mobile Inverted Pendulum (MIP),” the robot with two-wheels can move front and back in a horizontal path and take the role of the cart in the basic inverted pendulum system. The usage of expensive sensors, like the SCC2130-D08-05 Murata, is a flaw in the current system. Our work includes modeling the MIP's State Space equation and utilizing MATLAB to examine the system's observability and controllability. To estimate the angular position of the MIP, a combination gyro sensor, accelerometer sensor along with Kalman filter is developed thereby reducing the cost of the model. PID controllers with FUZZY tuning are used in the proposed system. The MIP is stabilized using the PID. The PID gain settings in this case are tuned using a fuzzy method.
Framework Design of Intelligent Career Recommendation System for Undergraduates

The development of artificial intelligence technology has proudly enhanced the quality of life and education of students. The outbreak of COVID-19 in early 2020 dealt a huge blow to the world economy and workplace environment, therefore planning a career path before graduation is a primary and core task for undergraduate students to succeed in this era. This paper introduces the framework design of an intelligent career recommendation system, which is based on the analysis of the required career ability and students’ individual ability to achieve accurate career recommendations.