Reviewerly/exHarmony huggingface dataset:
Emotion Deduction from Social Media Text Data Using Machine Learning Algorithm

Emotion represents the feeling of an individual in a given situation. There are various ways to express the emotions of an individual. It can be categorized into verbal expressions, written expressions, facial expressions and gestures. Among these various ways of expressing the emotion, the written method is a challenging task to extract the emotions, as the data is in the form of textual dat. Finding the different kinds of emotions is also a tedious task as it requires a lot of pre preparations of the textual data taken for the research. This research work is carried out to analyse and extract the emotions hidden in text data. The text data taken for the analysis is from the social media dataset. Using the raw text data directly from the social media will not serve the purpose. Therefore, the text data has to be pre-processed and then utilised for further processing. Pre-processing makes the text data more efficient and would infer valuable insights of the emotions hidden in it. The preprocessing steps also help to manage the text data for identifying the emotions conveyed in the text. This work proposes to deduct the emotions taken from the social media text data by applying the machine learning algorithm. Finally, the usefulness of the emotions is suggested for various stake holders, to find the attitude of individuals at that moment, the data is produced.
Exploiting Deep Learning Techniques for the Verification of Handwritten Signatures

Biometric featuresare common measures of identity verification where signaturesarethe most used type. The digital technology has given birth to new ways of biometric identification, such as fingerprints, iris and face recognition,while dealing with handwritten signatures is still a challenging task, because handwritten signatures are more prone to forgery than other means of verification due to issues like computer error, insufficient datasets, and loss of information. This work aims to develop a system that takes a signature image as its input and determines whether the signature is genuine written by its author or forged by another individual. The system is based on a neural network algorithm called Convolutional Siamese Neural Networks, which is used for deep learning and computer vision as well as other machine learning tasks such as natural language processing and digital signal processing.A Contrastive Loss function which compares the Euclidean distance of the output feature vectors is used, and a writer-independent model is used for training and image classification. This work’s objective is toenhance the precision of signature verification and take it as a base for future work on signature verification and use it in user identification, fraud detection and prevention, and forensic investigation applications. The system can be applied in banking, government and private organizations, and forensic investigation for identity and document verification, impersonation and fraud detection and prevention, crime and judicial investigation, and passport verification
The Usage of MALL for Vocabulary Acquisition: A Systematic Review (2019–2023)

Vocabulary plays a crucial role in developing strong language skills for English language learners, contributing to their overall educational sustainability. However, engaging students in vocabulary activities can be a persistent challenge. Previous research has suggested that Mobile Assisted Language Learning (MALL) could be an effective approach in English classrooms. However, there is still a lack of recent research that specifically examines the use of MALL for improving vocabulary acquisition. Therefore, this article presents a systematic review of literature focused on the usage of Mobile Assisted Language Learning with a specific emphasis on vocabulary acquisition. The aim of this review is to explore the types of MALL used in teaching English and how it is implemented in English language teaching. The review follows the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines and includes 15 relevant articles obtained from the Scopus, ERIC and Web of Science (WoS) databases. Through careful screening and application of exclusion and inclusion criteria, the selected articles are analysed to shed light on the usage of MALL in ESL classroom for vocabulary acquisition. The findings highlight the types of MALL used in English classrooms and how these approaches were implemented to foster transformative learning.
A Systematic Literature Review on the Perceptions of Year four ESL Primary School Pupils on the Usage of Quizlet in Improving English Vocabulary Skills

Technology has revolutionized online education systems, offering exciting prospects worldwide. Teaching has changed greatly since the technology was introduced. Higher education institutions have actively adopted technology into their instructional procedures, while basic schools remain wary. Despite modern technology and fast internet in many Malaysian schools, many still lack equipment and use it inefficiently. This study explores fourth graders' views on using Quizlet to improve their English as a second language vocabulary. This research investigated twelve primary school pupils. The teacher taught vocabulary using Quizlet. Interviews with small cohorts of students, one-on-one interviews, classroom observation, and document review provided data. The study used Burnard's analytical methods to assess participants' views on Quizlet's impact on English vocabulary learning. The participants liked Quizlet for language learning. Quizlet was liked by users for its many benefits. This study emphasizes the need for technology integration training for instructors, especially ESL teachers, to improve students' vocabulary learning. Quizlet is a technology application that improves vocabulary, especially for English learners.
Image Enhancement of Colon Cancer Images using a Two-Stage Hybrid Approach of TV and Shift-Invariant Filtering

Medical imaging holds a critical position in both disease diagnosis and treatment strategies, including colon cancer. However, the quality of medical images can often be compromised by noise and artifacts, making accurate interpretation challenging. Here, we suggest a innovative two-stage hybrid method aimed at enhancing colon cancer images, leveraging the strengths of Total Variation (TV) denoising and shift-invariant filtering techniques. The primary objective of this study is to increase visual superiority as well as diagnostic accurateness of colon cancer image while preserving crucial anatomical information.The first stage of our approach employs Total Variation (TV) denoising to reduce noise and enhance image contrast. TV regularization is known for its ability to preserve edges and fine details, making it well-suited for medical image enhancement. In the second stage, we apply shift-invariant filtering to further enhance the image quality. This technique is designed to address the limitations of traditional filtering methods and adapt to the specific characteristics of colon cancer images.&#x0D; To evaluate the effectiveness of our hybrid approach, we conducted a comprehensive set of experiments using a relevant dataset. We employed a range of quantitative metrics, including the Global Relative Error (EGRAS), Root Mean Squared Error (RMSE), Universal Image Quality Index (UQI), and Pixel-Based Visual Information Fidelity (VIFP), to assess the quality and fidelity of enhanced images. Our results demonstrate that the hybrid combination consistently outperforms existing methods, yielding superior image quality and diagnostic potential. This study makes a valuable contribution to the realm of medical imaging by introducing a robust and effective method to improve the quality of colon cancer images. Findings suggest that the proposed two-stage hybrid method holds promise for improving the accuracy of diagnosis and treatment planning. Further research in this direction may lead to advancements in medical image enhancement techniques, ultimately benefiting patient care and medical research.
RAU: Novel Activation Function for Deep Learning Neural Network

In Deep learning neural networks (DNNs) activation functions perform a vital role. In each neuron activation function is responsible for generating output signals from given input signals. Hence, activation function is one of the factors that influence the performance of DNN. A novel activation unit RAU (Reciprocal activation unit) is proposed in this paper. Most of the popular algorithms given more importance to positive signals, but proposed method handles the negative and positive inputs equally. The proposed RAU tested with both multiclassification and binary classification datasets. Iris flower and Wisconsin Breast Cancer datasets are used for the analysis. In Breast cancer dataset RAU provides 99.25% and 97.08% accuracy for classification of train and test sets respectively. In Iris dataset RAU provides 99.05% and 97.78% accuracy for the classification of train and test sets. Analysis of the same datasets are performed with the existing activation functions- Sigmoid, RMAF, Swish, Tanh and ReLU. Results showed that RAU performed better than other activation functions.
Sentence Semantic Similarity based Complex Network approach for Word Sense Disambiguation

Word Sense Disambiguation is a branch of Natural Language Processing(NLP) that deals with multi-sense words. The multi-sense words are referred to as the polysemous words. The term lexical ambiguity is introduced by the multi-sense words. The existing sense disambiguation module works effectively for single sentences with available context information. The word embedding plays a vital role in the process of disambiguation. The context-dependent word embedding model is used for disambiguation. The main goal of this research paper is to disambiguate the polysemous words by considering available context information. The main identified challenge of disambiguation is the ambiguous word without context information. The discussed complex network approach is disambiguating ambiguous sentences by considering the semantic similarities. The sentence semantic similarity-based network is constructed for disambiguating ambiguous sentences. The proposed methodology is trained with SemCor, Adaptive-Lex, and OMSTI standard lexical resources. The findings state that the discussed methodology is working fine for disambiguating large documents where the sense of ambiguous sentences is on the adjacent sentences.
BIDRN: A Method of Bidirectional Recurrent Neural Network for Sentiment Analysis

Text mining research has grown in importance in recent years due to the tremendous increase in the volume of unstructured textual data. This has resulted in immense potential as well as obstacles in the sector, which may be efficiently addressed with adequate analytical and study methods. Deep Bidirectional Recurrent Neural Networks are used in this study to analyze sentiment. The method is categorized as sentiment polarity analysis because it may generate a dataset with sentiment labels. This dataset can be used to train and evaluate sentiment analysis models capable of extracting impartial opinions. This paper describes the Sentiment Analysis-Deep Bidirectional Recurrent Neural Networks (SA-BDRNN) Scheme, which seeks to overcome the challenges and maximize the potential of text mining in the context of Big Data. The current study proposes a SA-DBRNN Scheme that attempts to give a systematic framework for sentiment analysis in the context of student input on institution choice. The purpose of this study is to compare the effectiveness of the proposed SA- DBRNN Scheme to existing frameworks to establish a robust deep neural network that might serve as an adequate classification model in the field of sentiment analysis.
Manfaat Irigasi Springkler pada Lahan Fasum untuk Peningkatan Produksi Pertanian Kampung Tangguh Poharin

This community service is the result of research in 2022 which will be poured on community service on the fasum land in Pondok Harapan Indah housing, Jalan Sigura-Gura Malang. Installation of pipes and sprinkler nozzles based on distance and height according to the type of plant that exists. With the potential for water sources and limited land area, farmers' businesses are expected to be able to utilize water efficiently. The sprinkler irrigation system is thought to be an alternative in the provision of irrigation and for this reason it is necessary to test the ability of the irrigation system to vary the distance between the sprinklers and the high sticks to the irrigation radius and its uniformity within a few minutes duration. For a stick height of 1 m with the same sprinkler spacing, a uniformity coefficient of less than 80% is obtained and the optimal irrigation radius is with an average spacing of 6 meters
Development of efficient techniques for ASR System for Speech Detection and Recognization system using Gaussian Mixture Model- Universal Background Model

Some practical uses of ASR have been implemented, including the transcription of meetings and the usage of smart speakers. It is the process by which speech waves are transformed into text that allows computers to interpret and act upon human speech. Scalable strategies for developing ASR systems in languages where no voice transcriptions or pronunciation dictionaries exist are the primary focus of this work. We first show that the necessity for voice transcription into the target language can be greatly reduced through cross-lingual acoustic model transfer when phonemic pronunciation lexicons exist in the new language. Afterwards, we investigate three approaches to dealing with languages that lack a pronunciation lexicon. Secondly, we have a look at the efficiency of graphemic acoustic model transfer, which makes it easy to build pronunciation dictionaries. Thesis problems can be solved, in part, by investigating optimization strategies for training on huge corpora (such as GA+HMM and DE+HMM). In the training phase of acoustic modelling, the suggested method is applied to traditional methods. Read speech and HMI voice experiments indicated that while each data augmentation strategy alone did not always increase recognition performance, using all three techniques together did. Power normalised cepstral coefficient (PNCC) features are tweaked somewhat in this work to enhance verification accuracy. To increase speaker verification accuracy, we suggest employing multiple “Gaussian Mixture Model-Universal Background Model (GMM-UBM) and SVM classifiers”. Importantly, pitch shift data augmentation and multi-task training reduced bias by more than 18% absolute compared to the baseline system for read speech, and applying all three data augmentation techniques during fine tuning reduced bias by more than 7% for HMI speech, while increasing recognition accuracy of both native and non-native Dutch speech.
A Knowledge-Enhanced Hierarchical Reinforcement Learning-Based Dialogue System for Automatic Disease Diagnosis

Deep Reinforcement Learning is a key technology for the diagnosis-oriented medical dialogue system, determining the type of disease according to the patient’s utterances. The existing dialogue models for disease diagnosis cannot achieve good performance due to the large number of symptoms and diseases. In this paper, we propose a knowledge-enhanced hierarchical reinforcement learning model for strategy learning in the medical dialogue system for disease diagnosis. Our hierarchical strategy alleviates the problem of a large action space in reinforcement learning. In addition, the knowledge enhancement module integrates a learnable disease–symptom relationship matrix and medical knowledge graph into the hierarchical strategy for higher diagnosis success rate. Our proposed model has been proved to be effective on a medical dialogue dataset for automatic disease diagnosis.
Behavioral features of the speech signal as part of improving the effectiveness of the automatic speaker recognition system

The current reality is saturated with intelligent telecommunications solutions, and automatic speaker recognition systems are an integral part of many of them. They are widely used in sectors such as banking, telecommunications and forensics. The ease of performing automatic analysis and efficient extraction of the distinctive characteristics of the human voice makes it possible to identify, verify, as well as authorize the speaker under investigation. Currently, the vast majority of solutions in the field of speaker recognition systems are based on the distinctive features resulting from the structure of the speaker's vocal tract (laryngeal sound analysis), called physical features of the voice. Despite the high efficiency of such systems - oscillating at more than 95% - their further development is already very difficult, due to the fact that the possibilities of distinctive physical features have been exhausted. Further opportunities to increase the effectiveness of ASR systems based on physical features appear after additional consideration of the behavioral features of the speech signal in the system, which is the subject of this article.
METHODS OF FORMING COMPETENCIES IN APPLICANTS FOR THE SPECIALTY «CYBERSECURITY» WHEN PERFORMING A COURSE ASSIGNMENT IN THE DISCIPLINE «LOCAL NETWORKS»

The methodology of «AttackDetectionLAN» for the formation of professional and subject competencies of applicants for the degree «Bachelor» in the specialty «Cybersecurity» in the course assignment in the discipline «Local Networks» is proposed: 1) obtaini
Binary Arithmetic Optimization Algorithm with Machine Learning based Intrusion Detection System

Intrusion Detection Systems (IDS) are significant for preventing and identifying malicious actions in computer networks. Machine Learning (ML) approaches are extremely executed for recognizing intrusion since it is investigating huge volumes of network traffic data and recognize designs indicative of intrusions. But, the performance of these ML approaches is greatly dependent upon the choice of relevant features which efficiently represent the network traffic data. Feature Selection (FS) is the procedure of recognizing the most informative and discriminative aspects in a given database. As part of Intrusion Detection (ID) utilizing ML, FS purposes for identifying the subset of features that are efficiently differentiated between normal network behaviour and malicious activities. This article proposes a Binary Arithmetic Optimization Algorithm with Machine Learning based Intrusion Detection System (BAOA-MLIDS) technique. The BAOA-MLIDS technique employs FS with an optimal ML classifier for the ID process. To accomplish this, the BAOA-MLIDS technique performs data preprocessing to scale the input data. Besides, the BAOA-MLIDS technique comprises BAOA based FS approach to choose optimal features. Moreover, Extreme Learning Machine (ELM) approach is utilized for the identification of the intrusions. Furthermore, Hunger Games Search Optimization (HGSO) approach was employed for the hyperparameter optimization of the ELM approach. The performance assessment of the BAOA-MLIDS model was examined on a standard dataset and the outputs outperformed the advancement of the BAOA-MLIDS model in the ID process.
A Federated Consensus for Proof of Authority in IoT-Blockchain Applications

The growing adoption of Internet of Things (IoT) devices and the need for secure and scalable blockchain applications pose significant challenges in the realm of consensus protocols. This paper proposes a novel consensus mechanism called Federated Consensus for Proof of Authority (Fed-PoA), which combines the advantages of Proof of Authority (PoA) and federated learning to achieve secure and scalable IoT-Blockchain applications. The Fed-PoA ensures efficient data sharing, privacy preservation, and decentralized operation. Performance evaluation of this model in a simulated environment demonstrates superior convergence and memory usage compared to a representative work in this context.
Profil literasi energi terbarukan pembangkit listrik tenaga air (PLTA) Jatiluhur pada siswa SMA di Kabupaten Purwakarta

The purpose of this study was to determine the effectiveness of writing to learn strategies in improving cognitive abilities and to identify students' self-regulated learning profiles. The research method used was a quasi experimental design with Nonequivalent Control Group Design. The population of this study were all students of class XI in a high school in Bandung. The research sample consisted of 60 students of class XI in a high school in Bandung who were selected by using purposive sampling technique. The instruments used were cognitive ability tests, self regulated learning questionnaires, and learning implementation observation sheets. The results showed that the implementation of the writing to learn strategy further improved the cognitive abilities of students with the n-gain value of the experimental class of 0.825 which was in the high category and the n-gain value of the control class of 0.623 which was in the moderate category. The implementation of the writing to learn strategy is effective in improving the cognitive abilities of students based on a significant difference between the experimental class and the control class and the effect size value of 0.823 which is in the high category. The self-regulated learning profiles of students in the experimental class and control class were mostly in the moderate category.
"A STUDY ON THE PATHWAY OF IMPROVING THE SUBJECT CORE LITERACY OF CHINESE RURAL SECONDARY SCHOOL TEACHERS BASED ON MOBILE LEARNING"

In this era of globalization, mobile learning is increasingly becoming a new paradigm to promote teachers' core literacy. Inthis study, teachers in rural secondary schools in South China were selected as respondents, and the correlation
MULTIDIMENSIONAL DIH4CPS ONTOLOGY

Digital Innovation Hub are of utmost importance when they sustain cooperation in innovative technological domains likethe Cyber-Physical System. In this paper, we introduce, in a first part, a DIH ontology that we extend with CPS entities.For
"PROMOTING STUDENTS’ CRITICAL THINKING &amp; PROBLEM SOLVING SKILLS VIA MOBILE-SUPPORTED LABS"

Critical Thinking & Problem Solving belong to 21st century skills that enhance ways of thinking, learning, working andliving in the world. When combined with well-designed educational activities, mobile technology has the capacity to fosterthese
SISTEM INFORMASI LES PRIVAT KOTA KEDIRI BERBASIS ANDROID

Demanding knowledge Made of things that must be and must be implemented for every human being. One example of the demand for science at official official institutions such as in school. Currently there is no science that exist in schools that can improve the quality of students. Some of the learners at school may not be full-fledged. Lessons are taught in schools. For the exams undertaken the learners need to learn by alternative methods outside the school such as learning to learn or private lessons. Not a few individuals who also provide ease in the form of private lessons, in addition to alternative side jobs to help and improve the potential participants better locked in understanding the lessons in school or to face the school exam. The methods used are methods that still use manual methods, and are not effective in finding target participants and also participants who will choose a location close to home. Therefore, this research has resulted in an information application that allows users to access services and services that exist among the participants of the personal service and the learners. For the development of personal information system applications in cities using android systems still need to improve the security system because the system used is still needed and still needed, but also need to develop some features such as giving the rating on tentor, learners comment feature on tentor and validity period for account tentor or want a date or application.