Reviewerly/exHarmony huggingface dataset:
Predictive Food Safety Risk Monitoring

Due to the dynamic nature of the food supply ecosystem and rising reports on food-borne disease outbreaks, effective food safety risk monitoring has become crucial for public health and socioeconomic development. The Singapore Food Agency (SFA) is a statutory board that oversees the food safety and security of Singapore. Part of its functions is to monitor and detect potential food-borne incidents. Currently, SFA scientists rely on manual efforts to gather relevant information from various internal datasets for risk assessments and decision making. This is not only time-consuming but also susceptible to human errors. To tackle the problem, this paper proposes a system called SFA-FSM, which applies advanced AI technologies for predictive food safety risk monitoring. In particular, we fine-tune Transformer-based pre-trained language models such as BERT and XLNet into SFA-FSM for online food safety news monitoring, customer feedback monitoring, and named entity extraction. Moreover, we have conducted experiments to evaluate the proposed food safety monitoring approaches. The experimental results have shown that the proposed approaches have achieved promising performance.
XLM-RoBERTa Model for Key Information Extraction on Military Document

Key Information Extraction (KIE) is the process of extracting important data in a text or an image, such as the content of a military document. One popular method for performing KIE is by utilizing pattern matching with Regular Expression on texts from documents. However, this method is not efficient due to the fixed nature of RegEx, where we need an exclusive RegEx rule to extract certain data entities. To solve this flexibility issue, a deep learning method with Named Entity Recognition (NER) has been proposed. NER is applied to the scanned document using a transformer-based model called XLM-RoBERTa. This model has been chosen for the development of the KIE module as it supports Indonesian and has demonstrated good accuracy in previous studies. The model has been trained on a military document dataset in the Indonesian language, achieving an f1-score of 86.55%. This method has also been proven to be flexible, as the model can extract the same entities across different document types.
A Missed Opportunity? Making Sense of the Low Adoption Rate of <i>COVID Alert</i>, Canada’s Contact-Tracing Application

Background: This study reflects on the low adoption rate of COVID Alert, the Canadian contact-tracing application, during the COVID-19 pandemic. Analysis: Using data from two representative surveys conducted in Canada in 2020, this article examines how the application’s attributes and citizens’ attitudes on the sanitary crisis and on technology may have influenced the low adoption rate of COVID Alert. Conclusions and implications: The results suggest that the application design was socially acceptable, that it was seen as useful, and that it was not perceived as risky or hard to use. Contrasting this evidence with the unpopularity of COVID Alert leads to the suggestion that the barrier to greater uptake may have partly come from the inability of its developers to effectively promote the application.
A Class Balancing Based Machine Learning Approach for Fraudulent Credit Card Transaction Detection

The increased usage of credit cards for online and offline transactions in the past several years has also attracted criminals to commit fraudulent activities too. Since the volume and number of such transactions are increasing machine learning methods find a lot of applications in the detection of such anomalies. The data sets available for such cases are also highly imbalanced which add to the difficulty in the detection of such fraudulent activities. In this paper six machine learning algorithms namely Logistic Regression (LR), Multi-Layer Perceptron (MLP), Gradient Boosting Classifier(GBC), Random Forest Classifier(RFC), Decision Tree Classifier(DTC), Extreme Gradient Boosting Classifier(XGB) have been used along with a class balancing technique called Borderline SMOTE. The combination of class balancing with machine learning enhances the performance of all classifiers with the Extreme Gradient Boosting Classifier being the best.
A High-Performance Algorithm and Tool for Detecting Critical Vulnerabilities

As web applications continue to advance and permeate various aspects of our lives, the prevalence of dangerous website security vulnerabilities, including SQL injection and cross-site scripting, poses a significant threat. These vulnerabilities create an enticing opportunity for hackers to exploit websites for personal gain, political motives, or to gain notoriety. While some research and commercial software have been developed to scan and identify these vulnerabilities, this paper introduces an efficient algorithmic approach and accompanying tool specifically designed for web security vulnerability detection. The proposed method addresses the need for accurate vulnerability detection by presenting a comprehensive study and tool. Through experimental evaluations, it is demonstrated that the algorithm achieves a high level of accuracy in detecting vulnerabilities. Moreover, the tool outperforms popular commercial software available in the market, boasting faster performance and the ability to identify less commonly known vulnerabilities such as shell injection or file inclusion.
Bi-Sigmoidal Function Based Adaptive Gamma Correction for Dark Image Enhancement

The proposed Bi-sigmoidal function-based gamma correction method addresses the challenge of enhancing dark images containing light sources. Traditional methods often suffer from over-enhancement in bright regions, leading to information loss and degraded image quality. In contrast, the novel approach employs two modified sigmoidal functions, specifically tailored for dark and bright regions, achieving simultaneous enhancement while maintaining balance. The Bisigmoidal piecewise mapping function estimates local gamma factors based on local mean intensity, ensuring effective enhancement across different intensity regions. Experimental results demonstrate the method's superiority over existing techniques in both objective and subjective measures. By considering local characteristics and employing tailored sigmoidal functions, the proposed method effectively enhances dark images, delivering visually pleasing and high-quality results without sacrificing image details in bright regions.
Modification of Vigenère Cipher to Overcome Kasiski and Friedman Attacks

Since the advent of wireless communication, the safe transmission of data from one point to another has been of prime significance and concern. As a result, many algorithms have been developed to encrypt the data being transmitted from the sender's side to avoid unauthorized access. Yet, cybercriminals tend to always find loopholes in algorithms and develop a way to bypass or crack encryption to obtain confidential data. Vigenère Cipher is among the oldest and best-known polyalphabetic ciphers, which uses polyalphabetic substitution with the help of a series of Caesar ciphers to encrypt data. Yet, attackers have found ways to crack the encryption by analyzing the frequency of letters and predicting the key. This paper introduces a new methodology to overcome frequency analysis using the transposition of plain text characters based on the value of the characters from the key being used to encrypt them. As a result, Kasiski and Friedman attacks are nullified on the encrypted text.
Adaptive Cubature Quadrature Kalman Filter in Square Root Framework for Nonlinear State Estimation with Unknown Measurement Noise Covariance Matrix

An adaptive quadrature filter which is designed for nonlinear state estimation with unknown measurement noise covariance matrix has been formulated in this paper. The proposed filter is designed with the essential framework of square root version of Gaussian filters with Cubature Quadrature rule. During state estimation of nonlinear systems when the measurement noise covariance matrix remains unidentified the proposed filter ensures acceptable estimation performance by adapting the standard deviation of measurement noise at each time instant which also retains the numerical stability of the estimator. Efficacy of the proposed filter has been validated during state estimation of significantly nonlinear Lorenz attractors. Monte Carlo simulation indicates that the accuracy of the proposed filter is demonstrably superior to its competing algorithms and comparable with adaptive square root Gauss Hermite filter at considerably lower computation effort.
Artificial Intelligence and Machine Learning on Human Gesture Interpretation

Human Gesture control is a new age technology which allows humans to interact with computers and other devices using natural body movements. Earlier, human-computer interaction was dependent on physical peripheral input controls such as keyboards, mouse, joysticks and physical touch screens. However, Human Gesture control takes this interaction to a new level. In the present paper various uses and benefits of Gesture recognition technique using Artificial Intelligence and Machine Learning discussed. The field where this technology can be applied is vast like Gaming and Entertainment, Home Automation. Human and robot working together is now not a concept but it is a working model. Effective communication medium between humans and robots are important for successful human-robot collaboration, as they allow robots to support humans in heavy tasks. Gesture recognition has been a reliable interface between humans and computers for a long time. Hand gesture is very used thing in the daily life while communicating having good hand gesture represents a good communicator. Having gesture while communication also represents the importance, emphasis on various aspects. For the continuous interaction with the computer Gestures are used. The present paper aims to explore and review the new technology with respect to human gesture interpretation using Machine Learning and Artificial Intelligence. The paper emphasizes on the necessity of awareness of this upcoming technology which can be understood and utilized for a smart society.
Generative Chatbot Adaptation for Odia Language: A Critical Evaluation

Large Language Models (LLMs) have gained significant attention in the field of Natural Language Processing (NLP) and Artificial Intelligence (AI) due to their ability to generate human-like text and facilitate conversational interactions. However, the majority of LLMs are majorly developed for English, limiting their accessibility and effectiveness for non-English speaking populations. In India, where only 10% of the population is proficient in English, the need for LLM models adapted to regional languages becomes crucial. This research paper focuses on the adaptability of LLMs to the Odia language, spoken by approximately 50 million people in India. With a primary objective to cater to the Odia-speaking community, we aim to evaluate existing LLM models such as ChatGPT, and Olive, an instruction following Odia LLM, specifically in the context of generating conversational outputs in Odia. We employ a critical evaluation approach to assess the performance, language understanding, and response generation capabilities of the LLM models for the Odia language. By conducting experiments and comparative analysis, we seek to determine the strengths, weaknesses, and potential areas of improvement for the existing LLM models. Our findings will contribute to the development of more effective and contextually accurate generative chatbots for the Odia language, enabling better communication and accessibility for the Odia-speaking population.
Organizational psychology and its role in improving the health of the work environment at King Khalid University - Saudi Arabia

The study aimed to identify organizational psychology and its role in improving the health of the work environment among workers at King Khalid University - Saudi Arabia. The analytical descriptive approach, where the study population is (7759) employees and faculty members, where (2400) questionnaires were distributed and (2250) questionnaires were retrieved, with a rate of 93.75%. The study concluded that there is a statistical significance at the level of significance (α ≤ 0.05) between organizational psychology and improving the health of the internal and external work environment among King Khalid University employees. At King Khalid University, due to the difference in personal and functional characteristics (age, gender, educational qualification, job position), but there are statistically significant differences between the level of interest of the study sample members due to the experience variable in favor of those with more than 15 years of experience. The study recommended paying attention to improving the health of the work environment so that the employee's work becomes more productive, less dangerous, and less tiring and stressful. It also recommended that King Khalid University work on the comfort and dignity of employees to increase and improve their productivity.
Animated Video-based E-Flashcard Learning Media for Early Childhood Pre-Reading Abilities

The implementation of flashcards is still focused on image recognition and has yet to go too deep into letter recognition. This research aims to analyze the effect of animated video-based e-flashcards on the pre-reading abilities of young children. This type of research is quantitative with a quasi-experimental method and a nonequivalent control group design. The population of this study was early childhood children from group B class. The research sample consisted of two groups, namely group B1 as the control class and group B3 as the experimental class, totaling 38 children. Data collection methods use observation and documentation. Data analysis techniques use inferential statistics. The research results show the influence of the use of animated video-based e-flashcard learning media on the pre-reading abilities of early childhood. E-flashcard learning media can improve the pre-reading abilities of young children. Animated video-based e-flashcard learning media is equipped with additional features such as animation and sounds that can attract children's attention so that children can focus on the information conveyed.
THE STUDENTS’ PERCEPTION ON ONLINE LEARNING DURING THE PANDEMIC COVID-19: THE CASE OF ENGLISH STUDENTS OF UIN SULTHAN THAHA SAIFUDDIN JAMBI

The objectives of the research to find out the students’ perception on online learning during the covid-19 at the Fifth Semester of English Education Department in UIN Sulthan Thaha Saifuddin Jambi in the year 2021/2022. The researcher used qualitative research as design method because it was comparable to collect the data from students’ perception, and gave interview to collecting the data. The data was collected from the fifth semester which have been learned using online learning. The sample were 3 students selected by purposive sampling. The research found that the majority of students had a positive perception of online learning based on the results of their interviews. They improved their learning through online learning and found a new understanding of the new media used by the lecturer in media online learning. On the other hand, some students gave perception that online learning has obstacles affected by a poor network, the quality of the student's mobile phone, and the capacity of the online learning application.
Audio-Visual as Media in Reading: Students’ Responses and Challenges

Some experts believe that audio-visual media is beneficial in learning a second language, including English. It can increase not only students’ motivation to learn, but also their focus on the learned material. In reading, audio-visual media is considered effective in enhancing students’ comprehension. Most of the studies focus on the positive impact of using audio-visual media, but they have not yet investigated the obstacle that might be experienced by the students on its usage. Therefore, this study intends to examine the students’ obstacles of using audio-visual media in reading as well as their responses to its implementation. The qualitative method was applied. The data were gained through questionnaires and observation. The respondents were 37 ninth-grade students. The findings revealed that although the students responded positively to the use of audio-visual media in reading, they had issues with its implementation. The issues dealt with language and comprehension. In the term of language, the students found it hard to understand the meaning of some words. As a result, it impacted their comprehension. Thus, it is suggested for the teachers to first consider the students’ vocabulary mastery before using audio-visual media in teaching reading. If the students have a limited vocabulary, they can use a video that has familiar words known by the students.
APPLICATION OF THE K-MEANS METHOD FOR CLUSTERING BEST SELLING PRODUCTS IN ICE CREAM SALES

From youngsters to adults, ice cream is a culinary item that is highly sought after by a variety of demographics. The ice cream combination, which includes a variety of culinary items including dairy products, sugars, stabilizers, flavor enhancers, and eggs, is frozen to create this dish. Aice Ice Cream has a wide variety of goods, which makes it popular with customers. According to various data that the author has observed from Agent Aice Harun, including the fact that there are still challenges in the stock of goods available to meet consumer demand, it is necessary to forecast sales of Ice Cream Aice products that are most in demand by consumers in order to facilitate stock provision. Due to these problems, the researchers conducted. The K-Means Clustering Algorithm approach will be applied manually during the clustering process, and Python data mining tools will be used for the implementation. The final calculation results are the same, showing that 48 things are the most desirable, 6 items are pretty attractive, and 6 items are less desirable, according to the results of determining centroid values arbitrarily using the Euclidian Distance formula manually using Python Tools. Agent Aice Harun is able to supply goods that are in line with consumer preferences as a result. Python computations and manual calculations get the same conclusion. Therefore, it is evident from these data that the accuracy value attained is 100%.
DESIGN AND BUILD SCHEDULING INFORMATION SYSTEM FOR RSUD DR. RM. DJOELHAM

Hospitals are institutions that provide complete individual health services, which provide facilities in the form of inpatient, outpatient and emergency services. Currently scheduling at RSUD Dr. RM. Djoelham has constraints related to service management that is not optimal, causing delays, this is related to an erratic schedule that makes shift changes that occur not optimal. Genetic algorithms can be used to help schedule employees RSUD Dr. RM. Djoelham automatically. Scheduling automation can be accomplished by properly coding into chromosomes. To realize the management information system RSUD Dr. RM. Djoelham can use the PHP CodeIgniter framework. The purpose of designing a management information system at the hospital is expected to facilitate data collection on employee schedules and speed up schedule data collection. And can minimize the possibility of errors that occur during the scheduling process. The results of this study the system can schedule automatically, making schedules can also be done for each division. Functional testing is carried out using a black box where the test results show that all features can run as they should.
AN ANALYSIS OF CODE MIXING USED BY EFL STUDENTS IN THE CLASSROOM PRESENTATION

This descriptive qualitative research aims to have a descriptive account of the types of code mixing used by the EFL students in the classroom presentation for the English course provided. The researchers conducted this research at the English Education Faculty of Institut Parahikma Indonesia, Gowa (IPI Gowa) purposely taking 1 lecturer teaching the English course and 1 class of the fourth semester students consisting of 10 students having a classroom presentation as one of the learning activities designed. It employed a classroom observation and open interview to collect the data in which their results were classified based on Musyken’s theory (2000) and analyzed using the theory of Miles, Hubberman, and Saldana (2020) coming with three stages of the interactive model data analysis, namely data condensation, data display, and conclusion drawing or verification. The result of data analysis showed that the code-mixing types utilized by the students when doing the presentation were three, that is (1) insertion (word) taking the most dominant type used with 27 extracts, (2) alternation (clause) taking more dominant type used with 15 extracts, and (3) congruent lexicalization (dialect) taking the least with 12 extracts in the classroom presentation. These findings simply means that the use of code mixing becomes the students’ communicative strategy channeling the presenters and the audiences’ ways of understanding the points of their discussion during the classroom presentation.
IMPLEMENTATION OF LINEAR REGRESSION METHOD FOR PREDICTING CIMORY MILK SALES

PT. Rasa Prima Sejati Wall's is a company engaged in the field of ice cream food and Cimory drinks. This company has various types of ice products and Cimory drinks to offer. Every year the company can create new products, especially in various flavors, not only that, the company guarantees the quality of the products it produces. The problem faced by the company is that the sale of available goods does not match consumer demand. The company also has not used predictors or plans for the sale of goods so that there is a buildup of goods which results in losses in the company. In this case the research will make predictions by looking at past sales data. The data taken is only sales data for Cimory products for the last three years from 2019-2021. Research using the Liner Regression method is one of the methods in the function of predicting sales. Linear regression is a statistical method used to construct a model or relationship between one or more independent variables X and response variable Y. The software used to support data processing is Rapidminer. The purpose of this study is to use data mining to identify the most popular, least popular, and desirable products or items at Pt. Rasa Prima so that companies can use this information as a guideline for managing their inventory and attending to customer disappointment properly available using a simple linear regression method, the prediction results for 2022 and RapidMinier are 770000. This prediction can help companies make decisions and reduce inventory shortages.