Reviewerly/exHarmony huggingface dataset:
A Systematic Review on Machine Learning Insider Threat Detection Models, Datasets and Evaluation Metrics

Computers are crucial instruments providing a competitive edge to organizations that have adopted them. Their pervasive presence has presented a novel challenge to information security, specifically threats emanating from privileged employees. Various solutions have been tried to address the vice, but no exhaustive solution has been found. Due to their elusive nature, proactive strategies have been proposed of which detection using Machine Learning models has been favoured. The choice of algorithm, datasets and metrics are cornerstones of model performance and hence, need to be addressed. Although multiple studies on ML for insider threat detection have been done, none has provided a comprehensive analysis of algorithms, datasets and metrics for development of Insider Threat Detection models. This study conducts a comprehensive systematic literature review using reputable databases to answer the research questions posed. Search strings, inclusion and exclusion criteria were set for eligibility of articles published in the last decade.
Load Balancing Large Data Sets in a Hadoop Cluster

With the interconnection of one to many computers online, the data shared by users is multiplying daily. As a result, the amount of data to be processed by dedicated servers rises very quickly. However, the instantaneous increase in the volume of data to be processed by the server comes up against latency during processing. This requires a model to manage the distribution of tasks across several machines. This article presents a study of load balancing for large data sets on a cluster of Hadoop nodes. In this paper, we use Mapreduce to implement parallel programming and Yarn to monitor task execution and submission in a node cluster.
Code Swarm: A Code Generation Tool based on the Automatic Derivation of Transformation Rule Set

Automatic generation of software code from system design models remains an actively explored research area for the past several years. A number of tools are currently available to facilitate and automate the task of generating code from software models. To the best of our knowledge, existing software tools rely on an explicitly defined transformation rule set to perform the model-to-code transformation process. In this paper, we introduce a novel tool named Code Swarm, abbreviated as CodS, that automatically generates implementation code from system design models by utilizing a swarm-based approach. Specifically, CodS is capable of generating Java code from the class and state models of the software system by making use of the previously solved model-to-code transformation examples. Our tool enables the designers to specify behavioural actions in the input models using the Action Specification Language (ASL). We use an industrial case study of the Elevator Control System (ECS) to perform the experimental validation of our tool. Our results indicate that the code generated by CodS is correct and consistent with the input design models. CodS performs the process of automatic code generation without taking the explicit transformation rule set or languages metamodels’ information as input, which distinguishes it from all the existing automatic code generation tools.
Block Chain Technology and Internet of Things to Protect Financial Transactions in Crypto Currency Market

The integration of block chain and Internet of Things aims to address security concerns through a multifaceted approach. Block chain's inherent characteristics, such as decentralization and cryptographic hashing, contribute to the prevention of fraud and unauthorized access. Internet of Things devices, ranging from sensors to smart devices, play a pivotal role by providing real-time data that can be securely recorded on the block chain. This enables stakeholders to access timely and accurate information, enhancing decision-making processes. This article explores the synergies between block chain technology and the Internet of Things to bolster the security of financial transactions within the crypto currency market. With the growing prominence of digital assets, ensuring the integrity and confidentiality of transactions has become paramount. Block chain’s decentralized and immutable ledger capabilities provide a robust foundation, while IoT's real-time data generation adds a layer of transparency and efficiency. However, this integration is not without its challenges. The article delves into scalability issues, interoperability challenges, and the need for standardized protocols. Privacy concerns surrounding sensitive financial data and the intricacies of identity management in a decentralized environment are also addressed.
An Investigation of the Chemical Composition of Bottom Sediments from Dried Lake Gavur of Türkiye by Using XRF and Multivariate Data Analysis

Lake sediments offer valuable information about the geographical, climatic and environmental changes. This work examined the chemical elements of eleven sediment samples from the old bottom (0-5m; 0.5m increments) of the dried Lake Gavur in Türkiye by applying XRF along with PCA and PLSR data analyses. The highest but negative correlations were observed for Sr (-0.89), S+SO3 (-0.74) and Zr+ZrO2 (-0.61) with sampling depths meaning that their concentrations were lower in deeper locations (4.0-5.0 m). In contrast, Ti+TiO2 (0.60), Al+Al2O3 (0.53) and Cu+CuO (0.51) had high and positive correlations and their quantities increased as the depth increased. The age of two samples was determined as 4752±33 BP years (2.5 m) and 5470±35 BP (5.0 m). Ca+CaO content was found as lower and Ti+TiO was higher in the deeper sediment (5.0 m) indicating rainy periods. PCA clustered the samples into two groups as deeper samples (4.0-5.0 m) and other samples (0.0-3.5m) while PLSR grouped them into three clusters as deeper (4.0-5.0 m), medium-depth (1.5-3.5 m) and shallower (0.0-1.0m) samples. PLSR model had a good performance to estimate the sampling depth from the chemical elements (R2cal=0.95; R2val=0.67) which means that the variations of the chemicals were highly dependent on the sampling depth.
Mexpert: An Algorithm for Finding Cross-disciplinary Experts Using Data Mining Techniques

Multidisciplinary research is the practice of bringing together experts from different disciplines or fields of study to work collaboratively on a common problem, project, or research question. This study proposes an expert finding algorithm, namely Mexpert, to bridge the gap between experts using data mining techniques. Our method has three steps. The first process is data preparation, which involves extracting information from research papers. The first process includes collecting data, extracting keywords, and discovering topics. The second process involves creating a model to group experts with similar knowledge and expertise. The last process involves analyzing the expert profiles using their experience in terms of h-index, average number of publications and total citations, followed by profile ranking. The corpus contains 17,679 papers obtained from SCOPUS. The experimental results reveal that there are four clusters out of 7 fields. We analyzed each cluster obtained from Mexpert and found that most cluster members published multidisciplinary research papers together. These results suggested that our approach can be applied to find a group of experts with different expertise.
Perbandingan Algoritma Naïve Bayes dan Decision Tree Pada Sentimen Analisis

The development of information technology in recent years is fast, pushing internet usage. People use social media to share their opinions or interact simply with each other. Sentiment analysis is a branch of Natural Language Processing (NLP) that can filter and categorize people's views on social media. This study uses Twitter data to compare two algorithms: Naïve Bayes Classifier and Decision Tree. This study divided the data into two scenarios where the first one with 800 data and the second one with 200 data. Data from each scenario was divided again into 70% training data and 30% test data from 1000 data. The result shows that Naïve Bayes Classifier has much higher accuracy with 85% compared to the Decision Tree with 78% in the second scenario.
A IMPLEMENTATION ON SIGN LANGUAGUAGE TRANSLATOR

we propose an innovative Human-Computer Interaction (HCI) system: a hand gesture-controlled virtual mouse powered by AI algorithms. Utilizing computer vision through webcam or built-in camera recordings, the system captures and processes hand gestures, translating them into precise mouse movements. This offers an alternative interface, particularly beneficial for individuals facing challenges with conventional input devices. The vision-based approach enhances the system's reliability, enabling diverse mouse behaviors like left and right clicking. By eliminating the need for a physical mouse, this HCI solution marks a significant stride in advancing technology for seamless human-computer interaction. Keywords: HCI, Hand Gesture, Gesture Recognition, OpenCV, Media-pipe, pyAutoGUI.
Text Classification from positive and unlabeled examples using Support Vector Machine (SVM)

Support Vector Machines (SVMs) are a powerful machine learning algorithm that can be used for text classification. Traditional SVMs require both positive and negative examples to train the model. However, in many real-world scenarios, it can be difficult or expensive to obtain negative examples. This study explores the application of SVMs in textclassification when only positive and unlabeled examples are available. Theresults showed that the proposed approach achieved competitive performance compared to traditional supervised methods, even when trained on limited labeled examples. The utilization of SVC in the proposed approach is twofold. First, the SVC model is used to classify theunlabeled examples as positive or negative. Second, the SVC model is used to select the positive examples that are added to the training set. Thisiterative process of training and selecting examples helps to improve the classification accuracy of the SVM model. The proposed approach is a promising method for text classification when only positive and unlabeled examples are available. Theapproach is effective in achieving competitive performance compared to traditional supervised methods, even when trained on limited labeled examples. This work contributes to enhancing text classification techniques, particularly in situationswith resource constraints and challenging label acquisition. Keywords: Support Vector Machine(S VM), Text Classifications ,Text Mining, SVC, Supervised Methods
APLIKASI PREDIKSI STATUS GIZI BALITA BERBASIS WEB MENGGUNAKAN METODE K-NEAREST NEIGHBOR

The nutritional status of children under five is an important parameter in monitoring and caring for children's health, and this research aims to provide a reliable and efficient tool for stakeholders, including health workers and parents. The K-NN method was chosen as the basis for prediction because of its ability to classify nutritional status by comparing the similarity of new toddler data with existing data in the dataset. This application allows users to enter data such as the age, weight, height and gender of the toddler. Using the K-NN algorithm, this application automatically calculates the distance between the toddler to be predicted and the toddlers in the dataset that are most similar to it. Application testing results show a very satisfactory accuracy level of 91.94%. This shows that this application can provide predictions of the nutritional status of toddlers with a high level of confidence, potentially enabling early detection of nutritional problems and timely intervention.
Introduction to Python

Python is an object-oriented programming language that can support a wide range of applications like web development, desktop applications, etc. Its general-purpose programming features make coding easy, comprehensive and readable even for beginners.
Bridging Communication Gaps: A Comprehensive Survey on Sign Language Hand Gesture Smart Recognition Technology

Abstract: Efficient communication is crucial in human interaction, yet individuals with language and hearing challenges encounter obstacles. This article centers on Nepali Sign Language (NSL) and introduces the Hand Gesture Smart Recognition initiative, with the goal of translating NSL into English. The research explores hand gesture recognition technology, emphasizing the cultural subtleties inherent in both vision-based and glove-based approaches. Recent advancements, such as the integration of compact models and CNNs, signify advancements in gesture recognition. The article offers valuable insights into the development and application of this technology, fostering optimism regarding the future potential of culturally suitable human-computer interaction technologies.
File Handling

From the previous chapters, we anticipate that the readers have garnered enough working knowledge of Python and its elementary concepts. In this chapter, we introduce to the user the concept of files. These are one of the major built-in object types in Python. We can create, call, work, and close the files using several functions as enunciated below. One of the primary tasks of files is method exporting and common file-processing tasks such as input and output display to external files, flush buffers, etc.
Data Types, Operators, and Expressions

Now that you have been familiarised with the installation and basics of Python programming, it's time to dig in a little more and understand the different data types that are available along with the operators and expressions that make the programming more user-friendly. In this chapter, we will learn about data type categorization and the operators present. Here you will learn the following nuances: 1. Data types supported by the Python. 2. Use of variables to store and access the data. 3. Operators do mathematical works and logical functions. 4. Variety of expressions to serve the range of applications.
Tuple and Dictionaries

Another important data type of Python is dictionary. In the previous chapter, we acquainted the readers with the lists and strings. In this chapter, we shall discuss dictionaries and lists. lists are the ordered collection of objects but dictionaries are an unordered collection of objects. dictionary values are referred to using key-value pair instead of positional offset. Due to this, they have found great usage in search tables, records and aggregation. Another concept introduced in this chapter is tuples. These are immutable like strings and represent a stable collection of arbitrary items.
Pemanfaatan Teknologi Multimedia Sebagai Peningkatan Motivasi Pembelajaran Siswa Serta Keprofesionalisasian Guru

The purpose of this study was to determine the results of the use of multimedia technology regarding its effect on student learning motivation and teacher professionalism. The research method used in this study is library searching by searching for and collecting various other research sources to be studied in depth. The relationship between technological developments and the professionalism of a teacher provides a framework for understanding how important effective learning is in attracting students' interest in learning and understanding in the classroom. Technological developments in the form of using multimedia as a learning medium for students can be an effort to increase student learning motivation which in fact has a big influence on the professionalism of teaching staff. After using interactive multimedia, students' understanding increases. Students' motivation or enthusiasm for learning also increases so that the concepts of the learning material can be absorbed well.
LANGUAGE DETECTION USING MACHINE LEARNING

This paper presents an innovative Machine Learning (ML) model for language detection that combines the power of logistic regression with a multimodal approach. The proposed model is designed to handle three types of inputs: sequential text data, files, and image representations. The proposed model offers a versatile and accurate solution for identifying languages across diverse data modalities. The model architecture employs logistic regression to enhance interpretability and feature extraction from each input modality. Trained on a comprehensive multilingual dataset, the model exhibits robust performance, showcasing its applicability to real-world scenarios. The model’s ability to process text, files, and images makes it well-suited for applications in content filtering, cross-modal information retrieval, and multilingual sentiment analysis. This research contributes to the advancement of language detection models by offering a unified solution for handling diverse input types.
AUTOMATIC SPEECH RECOGNITION USING DEEP NEURAL NETWORKS

This research concentrates on the progression and present status of automatic speech recognition systems powered by deep neural networks. It discusses model architectures, training approaches, evaluating model efficacy, and recent advancements specific to deep neural networks applied in automatic speech recognition models. It considers the challenges faced in crafting these speech recognition models, such as data scarcity and the necessity for adaptability. Our exploration traces the evolution of automatic speech recognition through deep neural networks, presenting valuable insights aimed at propelling the domain of speech recognition for diverse applications, spanning from smart devices to healthcare. KEYWORDS - Automatic Speech Recognition, Deep Neural Networks, Language Modeling, Robustness to Noise, Speech Modelling
ANALYSIS OF THЕ ADVANTAGЕS AND DISADVANTAGЕS OF USING THЕ PROGRAM ARTICULATЕ STORYLINЕ 360 IN THЕ ЕDUCATIONAL PROCЕSS

In thе modеrn еducational procеss, incrеasing attеntion is paid to thе usе of information and communication tеchnologiеs (ICT) for lеarning. Thе program Articulatе Storylinе 360 offеrs a widе rangе of fеaturеs and tools for crеating intеractivе onlinе coursеs. Howеvеr, thеrе arе cеrtain difficultiеs facеd by tеachеrs and studеnts whеn using it in tеaching. Thе purposе of this articlе is to rеviеw thе advantagеs and main difficultiеs of using thе Articulatе Storylinе 360 program in tеaching and proposе solutions to ovеrcomе thеm.
Innovative Methods of Teaching Foreign Languages to Children Aged 10-16: Enhancing Learning Outcomes and Student Engagement

This research article examines the implementation and effectiveness of innovative teaching methods in the context of foreign language acquisition among children aged 10-16. By exploring the application of novel approaches such as gamification, technology integration, project-based learning, and communicative language teaching, this study aims to enhance learning outcomes, foster student engagement, and improve overall language proficiency.
APPLICATION OF NON-TRADITIONAL TECHNIQUES AND METHODS OF TEACHING FOR LANGUAGE LEARNERS

This article highlights how innovative pedagogical strategies, known as creative teaching methods, are designed to stimulate creativity and actively involve students in the learning process. These strategies encompass a broad spectrum of activities, from straightforward team-building exercises to intricate, open-ended problems that could necessitate an entire semester to resolve. They incorporate interactive lessons, the application of virtual reality technology, blended learning, project-based learning, among others. The objective of these strategies extends beyond mere knowledge transfer, they aim to cultivate creative thinking and problem-solving abilities in students.
PEER-EVALUATION AS A TRIGGER TO SELF-REFLECTION IN TEACHING PRACTICUM OF PRE-SERVICE ENGLISH TEACHERS

The author of the article discusses potential of peer evaluation implemented in teaching practicums within pre-service English teacher curriculum as a mechanism that encourages student teachers to practice self-reflection. The author also proposes that there is need to develop a model of peer evaluation that stipulates certain standards of carrying out peer evaluation in teaching practicums.
Professionalism and ELT (English language teaching)

In this article, the concept of professionalism and its significance in language teaching are explained thoroughly. New trends and the ways to achieve professionalism are explained relying on both scholars` notions and contemporary pedagogical sphere.
Clinicohaematological Study of Leukaemias at a Tertiary Care Centre, Vindhya Region of India: A Cross-sectional Study

Introduction: Leukaemia is characterised by widespread proliferation of leucocytes and their precursors in the body tissues and is usually associated with qualitative and quantitative changes in circulating white blood cells. The global burden of leukaemia is increasing. Aim: To study the morphology of leukaemia from Peripheral Blood Smears (PBS) and/or bone marrow smears and evaluate the clinical and haematological profile of leukaemia patients. Materials and Methods: This prospective observational study was conducted in a Tertiary Care Centre of the Vindhya region, in the Department of Pathology at Shyam Shah Medical College, Rewa, Madhya Pradesh, India. The study was carried out over a period of one year, from June 2019 to June 2020. A total of 90 newly diagnosed cases of all types of leukaemia during the study period. Detailed history, physical examination, complete blood counts, and bone marrow examinations were performed to confirm the diagnosis. Cytochemistry was used for the diagnosis and classification of acute leukaemia according to the French-American-British (FAB) classification. The data was compiled and analysed using appropriate statistical methods, and the mean was described with Standard Deviation (SD). Statistical significance was assessed using Analysis of Variance (ANOVA) test conducted with Statistical Package for Social Sciences (SPSS) version 22.0. Results: The study included 90 cases of all types of leukaemia, with a mean age of 36.5±22.5 years. There was a male preponderance in the study, with 65 (72.2%) males and 25 (27.8%) females, resulting in a male-to-female ratio of 2.6:1. Chronic Myeloid Leukaemia (CML) was the most common malignancy, accounting for total 40 (44.4%) cases. Non specific symptoms such as fever in 50 (55.6%), weight loss 49 (54.4%), and loss of appetite 42 (46.7%) cases were the most common symptoms across all types of leukaemia. Acute leukaemia also presented with leukopenia and normal leucocyte counts, while chronic leukaemia mostly showed markedly elevated Total Leucocyte Count (TLC) with a mean count of 1,30,912±63,792 per mm3. Conclusion: Light microscopic features of peripheral smear and bone marrow examination still play an important role in the diagnosis of leukaemia, especially in resource-poor health centres lacking immunotyping and cytogenetics.
Optimasi Zoom Meeting Sebagai Media Pembelajaran Virtual Synchronous

The Covid-19 pandemic indirectly requires educators and participants to master information and communication technology related to the learning process. The use of zoom meetings as a virtual synchronous online learning medium is an option to communicate or face-to-face virtually. Since the Covid-19 pandemic, Zoom has developed various breakthroughs to provide convenience to its users or customers by adding various interesting features. These features can be utilized as much as possible by educators in carrying out online learning so that the learning process can take place well.