Reviewerly/exHarmony huggingface dataset:
The Role of Augmented Reality in The Online Shopping Experience: How Can Clothing Brands Use Mobile Augmented Reality Applications to Offer a More Immersive Shopping Experience to Their Customers?.

This study aims to explore the potential of augmented reality (AR) in enhancing the online shopping experience for customers of clothing brands. It investigates the effective utilization of mobile AR applications by clothing brands to offer a more immersive and interactive shopping experience. This research fills a void in the literature concerning the specific application of AR in the clothing industry, making it a crucial contribution.
Rancang Bangun Chatbot Pengaduan Kekerasan Perempuan Anak Dengan Metode Fuzzy String Matching Dan Enhanced Confix Stripping Stemmer

Violence against women is a serious issue affecting many individuals in society. In Indonesia, including Kota Probolinggo, the number of cases of violence against women remains high. This research aims to design a chatbot application as a means for reporting violence against women and children (VAWC) in Kota Probolinggo. The application provides an anonymous and safe space for victims to report their complaints without revealing their identities. Additionally, the chatbot delivers information and education about different types of violence against women and preventive measures. The development of the chatbot application utilizes Fuzzy String Matching, Enhanced Confix Stripping Stemmer, and preprocessing methods to enhance data search accuracy and provide appropriate responses to users. The testing results show that the chatbot offers responses with satisfactory accuracy. Leveraging AI technology in the form of a chatbot is expected to provide an effective solution in addressing and preventing violence against women, offering a secure and anonymous platform for victims to report incidents. The application is envisioned to offer better support and assistance to victims of violence.&#x0D; The chatbot application implements fuzzy string matching, the Levenshtein distance algorithm, and Enhanced Confix Stripping Stemmer to process user input before matching it with the database. During the Enhanced Confix Stripping Stemmer process, sentences are transformed into an array of basic words, enhancing the accuracy of data matching when using fuzzy string matching and the Levenshtein distance algorithm. Testing results indicate a 4.77% difference between manual calculations and the chatbot system. This difference may be attributed to variations in text processing and other factors.
Modelling of Hybrid Meta heuristic Based Parameter Optimizers with Deep Convolutional Neural Network for Mammogram Cancer Detection

Breast cancer (BC) is the common type of cancer among females. Mortality from BC could be decreased by identifying and diagnosing it atan earlierphase. Different imaging modalities are used to detect BC, like mammography. Even withproven records as a BC screening tool, mammography istime-consuming and hasconstraints, namely lower sensitivity in women with dense breast tissue. Computer-Aided Diagnosis or Detection (CAD) system assistsaproficient radiologist to identifyBC at an earlier stage. Recently, the advancementin deep learning (DL)methodsareemployed to mammography assist radiologists to increase accuracy and efficiency. Therefore, this study presents a metaheuristic-based hyperparameter optimization with deep learning-based breast cancer detection on mammogram images (MHODL-BCDMI) technique. The presented MHODL-BCDMI technique mainly focused on the recognition and classification of breast cancer on digital mammograms. To achieve this, the MHODL-BCDMI technique employs pre-processing in two stages: Wiener Filter (WF) based noise elimination and contrast enhancement. Besides, the MHODL-BCDMI technique exploits densely connected networks (DenseNet201) model for feature extraction purposes. For BC classification and detection, a hybrid convolutional neural network with a gated recurrent unit (HCNN-GRU) model is used. Furthermore, three hyperparameter optimizers are employed namely cat swarm optimization (CSO), harmony search algorithm (HSA), and hybrid grey wolf whale optimization algorithm (HGWWOA). Finally, the U2Net segmentation approach is used for the classification of benign and malignant types of cancer. The experimental analysis of the MHODL-BCDMI method is tested on a digital mammogram image dataset and the outcomes are assessed in terms of diverse metrics. The simulation results highlighted the enhanced cancer detection performance of the MHODL-BCDMI technique over other recent algorithms.
Lightweight Multi-Objective and Many-Objective Problem Formulations for Evolutionary Neural Architecture Search with the Training-Free Performance Metric Synaptic Flow

Neural architecture search (NAS) with naive problem formulations and applications of conventional search algorithms often incur prohibitive search costs due to the evaluations of many candidate architectures. For each architecture, its accuracy performance can be properly evaluated after hundreds (or thousands) of computationally expensive training epochs are performed to achieve proper network weights. A so-called zero-cost metric, Synaptic Flow, computed based on random network weight values at initialization, is found to exhibit certain correlations with the neural network test accuracy and can thus be used as an efficient proxy performance metric during the search. Besides, NAS in practice often involves not only optimizing for network accuracy performance but also optimizing for network complexity, such as model size, number of floating point operations, or latency, as well. In this article, we study various NAS problem formulations in which multiple aspects of deep neural networks are treated as multiple optimization objectives. We employ a widely-used multi-objective evolutionary algorithm, i.e., the non-dominated sorting genetic algorithm II (NSGA-II), to approximate the optimal Pareto-optimal fronts for these NAS problem formulations. Experimental results on the NAS benchmark NATS-Bench show the advantages and disadvantages of each formulation.
An Automatic Labeling Method for Subword-Phrase Recognition in Effective Text Classification

Text classification methods using deep learning, which is trained with a tremendous amount of text, have achieved superior performance than traditional methods. In addition to its success, multi-task learning (MTL for short) has become a promising approach for text classification; for instance, a multi-task learning approach employs the named entity recognition as an auxiliary task for text classification, and it showcases that the auxiliary task helps make the text classification model higher classification performance. The existing MTL-based text classification methods depend on auxiliary tasks using supervised labels. Obtaining such supervision signals requires additional human and financial costs in addition to those for the main text classification task. To reduce these costs, this paper proposes a multi-task learning-based text classification framework reducing the additional costs on supervised label creation by automatically labeling phrases in texts for the auxiliary recognition task. A basic idea to realize the proposed framework is to utilize phrasal expressions consisting of subwords (called subword-phrase) and to deal with the recent situation in which the pre-trained neural language models such as BERT are designed upon subword-based tokenization to avoid out-of-vocabulary words being missed. To the best of our knowledge, there has been no text classification approach on top of subword-phrases, because subwords only sometimes express a coherent set of meanings. The proposed framework is novel in adding subword-phrase recognition as an auxiliary task and utilizing subword-phrases for text classification. It extracts subword-phrases in an unsupervised manner, particularly the statistics approach. In order to construct labels for effective subword-phrase recognition tasks, extracted subword-phrases are classified for document classes so that subword-phrases dedicated to some classes can be distinguishable. The experimental evaluation of the five popular datasets for text classification showcases the effectiveness of the involvement of the subword-phrase recognition as an auxiliary task. It also shows comparative results with the state-of-the-art method, and the comparison of various labeling schemes indicates insights for labeling common subword-phrases among several document classes.
A Hybrid Deep Learning Approach to Keyword Spotting in Vietnamese Stele Images

In order to access the rich cultural heritage conveyed in Vietnamese steles, automatic reading of stone engravings would be a great support for historians, who are analyzing tens of thousands of stele images.Approaching the challenging problem with deep learning alone is difficult because the data-driven models require large representative datasets with expert human annotations, which are not available for the steles and costly to obtain.In this article, we present a hybrid approach to spot keywords in stele images that combines data-driven deep learning with knowledge-based structural modeling and matching of Chu Nom characters.The main advantage of the proposed method is that it is annotation-free, i.e. no human data annotation is required.In an experimental evaluation, we demonstrate that keywords can be successfully spotted with a mean average precision of more than 70% when a single engraving style is considered.
Optimized Deeplearning Algorithm for Software Defects Prediction

Accurate software defect prediction (SDP) helps to enhance the quality of the software by identifying potential flaws early in the development process. However, existing approaches face challenges in achieving reliable predictions. To address this, a novel approach is proposed that combines a two-tier-deep learning framework. The proposed work includes four major phases:(a) pre-processing, (b) Dimensionality reduction, (c) Feature Extraction and (d) Two-fold deep learning-based SDP. The collected raw data is initially pre-processed using a data cleaning approach (handling null values and missing data) and a Decimal scaling normalisation approach. The dimensions of the pre-processed data are reduced using the newly developed Incremental Covariance Principal Component Analysis (ICPCA), and this approach aids in solving the “curse of dimensionality” issue. Then, onto the dimensionally reduced data, the feature extraction is performed using statistical features (standard deviation, skewness, variance, and kurtosis), Mutual information (MI), and Conditional entropy (CE). From the extracted features, the relevant ones are selected using the new Euclidean Distance with Mean Absolute Deviation (ED-MAD). Finally, the SDP (decision making) is carried out using the optimized Two-Fold Deep Learning Framework (O-TFDLF), which encapsulates the RBFN and optimized MLP, respectively. The weight of MLP is fine-tuned using the new Levy Flight Cat Mouse Optimisation (LCMO) method to improve the model's prediction accuracy. The final detected outcome (forecasting the presence/ absence of defect) is acquired from optimized MLP. The implementation has been performed using the MATLAB software. By using certain performance metrics such as Sensitivity, Accuracy, Precision, Specificity and MSE the proposed model’s performance is compared to that of existing models. The accuracy achieved for the proposed model is 93.37%.
Cross-Layer Fragment Indexing based File Deduplication using Hyper Spectral Hash Duplicate Filter (HSHDF) for Optimized Cloud Storage

Cloud computing and storage processing is a big service for maintaining a large number of data in a centralized server to store and retrieve data depending on the use to pay as a service model. Due to increasing storage depending on duplicate copy presence during different sceneries, the increased size leads to increased cost. To resolve this problem, we propose a Cross-Layer Fragment Indexing (CLFI) based file deduplication using Hyper Spectral Hash Duplicate Filter (HSHDF) for optimized cloud storage. Initially, the file storage indexing easy carried out with Lexical Syntactic Parser (LSP) to split the files into blocks. Then comparativesector was created based on Chunk staking. Based on the file frequency weight, the relative Indexing was verified through Cross-Layer Fragment Indexing (CLFI). Then the fragmented index gets grouped by maximum relative threshold margin usingIntra Subset Near-Duplicate Clusters (ISNDC). The hashing is applied to get comparative index points based on hyper correlation comparer using Hyper Spectral Hash Duplicate Filter (HSHDF). This filter the near duplicate contentdepending on file content difference to identify the duplicates. This proposed system produces high performance compared to the other system. This optimizes cloudstorage and has a higher precision rate than other methods.
Utilization of Augmented Reality for Human Organ Analysis

This research paper investigates the utilization of augmented reality (AR) technology for human organ analysis in medical education. The study aims to develop and evaluate an AR application that provides an immersive and interactive learning experience for medical students. The research follows a quantitative methodology, to develop and test the effectiveness of the AR application in improving learning outcomes. The research examines the impact of the AR application on student engagement, retention of information, and performance on assessments. The results show that the AR application has a significant positive impact on learning outcomes. The use of AR technology improves student engagement, retention of information, and performance on assessments. The application's design and functionality were found to be intuitive and user-friendly, making it accessible for both students and educators. The research highlights the potential of AR technology in medical education and provides insights into its effectiveness in improving learning outcomes. The findings suggest that AR technology can be a valuable tool in medical education, enhancing the way students learn about human anatomy. This research can contribute to the existing literature on the use of AR technology in education, paving the way for future research and innovation in the field. Ultimately, the study shows that the integration of AR technology in medical education can significantly enhance the learning experience for students, providing them with an immersive and interactive approach to learning about human anatomy.
Breast Cancer Analytics Classification using MEFBUD DCNN Techniques

Breast cancer is the most dangerous and deadly form of cancer. Initial detection of breast cancer can significantly improve treatment effectiveness. The second most common cancer among Indian women in rural areas. Early detection of symptoms and signs is the most important technique to effectively treat breast cancer, as it enhances the odds of receiving an earlier, more specialist care. As a result, it has the possible to significantly improve survival odds by delaying or entirely eliminating cancer. Mammography is a high-resolution radiography technique that is an important factor in avoiding and diagnosing cancer at an early stage. There are numerous procedures and approaches for detecting cancer in the tissues of the breast. This work presents the image processing, segmentation, and deep learning methodologies and approaches for the diagnosis of breast cancer. This research will help people make better decisions and use trustworthy techniques to find breast cancer early enough to save a woman's life. Pre-processing, segmentation, and classification are some of this system's steps. We've included a thorough study of several techniques or processes, along with information on how they're used and how performance is measured. The stated results lead to the conclusion that, in order to increase the chances of surviving breast cancer, it is crucial to develop new procedures or techniques for early diagnosis. For researchers to effectively diagnose breast cancer, segmentation and classification phases are also difficult. Therefore, the precise diagnosis and categorization of breast cancer still require the use of more advanced equipment and techniques.
Micro Expression Spotting through Appearance Based Descriptor and Distance Analysis

Micro-Expressions (MEs) are a typical kind of expressions which are subtle and short lived in nature and reveal the hidden emotion of human beings. Due to processing an entire video, the MEs recognition constitutes huge computational burden and also consumes more time. Hence, MEs spotting is required which locates the exact frames at which the movement of ME persists. Spotting is regarded as a primary step for MEs recognition. This paper proposes a new method for ME spotting which comprises three stages; pre-processing, feature extraction and discrimination. Pre-processing aligns the facial region in every frame based on three landmark points derived from three landmark regions. To do alignment, an in-plane rotation matrix is used which rotates the non-aligned coordinates into aligned coordinates. For feature extraction, two texture based descriptors are deployed; they are Local Binary Pattern (LBP) and Local Mean Binary Pattern (LMBP). Finally at discrimination stage, Feature Difference Analysis is employed through Chi-Squared Distance (CSD) and the distance of each frame is compared with a threshold to spot there frames namely Onset, Apex and Offset. Simulation done over a Standard CASME dataset and performance is verified through Feature Difference and F1-Score. The obtained results prove that the proposed method is superior than the state-of-the-art methods.
Effective Workflow Scheduling in Cloud using Constriction Factor based Inertia Weight Particle Swarm Optimization

Cloud computing allows rapid provision of resources based on the need. This enables users to execute the independent tasks and dependent tasks called workflows on the cloud system. Workflow scheduling is a crucial problem that is NP Hard and is still a challenging problem. Particle Swarm Optimization (PSO) is one of the commonly used metaheuristic algorithms for solving task scheduling problems, but it has issues with premature convergence and lack of diversity. In recent years, chaotic maps have been employed in PSO to enhance its performance. This study proposes a Constriction factor-based inertia weight in PSO for workflow scheduling (CFPSO). The proposed algorithm utilizes a constriction factor for updating the inertia weight, which enhances the exploration ability of the algorithm thereby avoid local optima. The algorithm considers a fitness function with an aim to minimize makespan, service cost, and maximize load balance. The proposed algorithm is evaluated using a set of benchmark workflows, and the obtained results are compared with the standard PSO algorithm, Grey Wolf Optimizer (GWO) algorithm and Chaotic PSO algorithm. The extensive experimentation performed show that the proposed algorithm outperforms the other algorithms in terms of makespan, service cost, and load balance. The proposed CFPSO shows reduction of 20% of makespan, 2% of the service cost and 18% load balance rate compared to the conventional algorithms on Montage workflow with 1000 tasks. The use of constriction factor enhances the performance of the algorithm and makes it suitable for solving complex problems with multiple objectives. The proposed algorithm can be used in real-world applications to optimize workflow scheduling in cloud computing environments.
DECISION SUPPORT SYSTEM FOR TEACHER PERFORMANCE APPRAISAL WITH SIMPLE ADDITIVE WEIGHTING METHOD

Teachers are the most influential component in creating quality educational processes and outcomes. Therefore, improvement efforts are made to improve the quality of education. Schools will not make a significant contribution without the support of professional and qualified teachers. Teachers are really required to have high performance. To improve the quality of teacher performance, the 064022 State Elementary School conducts teacher performance appraisals every 3 months but the mechanism is less effective due to lack of transparency. Research on teacher performance appraisal using the system is applied for efficiency and transparency. The system was developed using the Simple Additive Weighting method. The Simple Additive Weighting method is part of the Decision Support System (DSS) used to help determine decisions based on alternative data and criteria data.&#x0D; &#x0D; Keywords: Decision Support System, Teacher Performance, Simple Addictive Weighting.
Hybrid Cryptography and Steganography-Based Security System for IoT Networks

Despite the fact that many IoT devices are publicly accessible to everyone on the network, understanding the security risks and threats posed by cyber attacks is critical; as a result, it should be safeguarded. Plain text is constructed into encrypted text, before being delivered by using cryptography, and is then reconstructed back to plain text after receiving a response from the recipient. The steganography technique can be used to hide sensitive information incorporated in a text, audio, or video file. One approach is to hide data in bits that correspond to successive rows of pixels with the same color in an image file. As a consequence, the image file retains the original's appearance while also containing "noise" patterns made out of common, unencrypted data. To do this, the encrypted data is subtly applied to the redundant data. In this work, it is suggested that IoT network data be encrypted using cryptography, and that an encrypted message be concealed inside an image file using steganography. Additionally, it is suggested to enhance the number of bits that may be stored within a single picture pixel. The payload that may be sent through an image is significantly increased by incorporating Convolutional Neural Networks into the classic steganography technique. In this work, we propose, design, and train Convolutional Neural Networks (CNN) to enhance the amount of data that can be securely encrypted and decrypted to show the original message.
Reading Material-Based Content Analysis in Senior High School English Textbooks in Indonesia

English textbooks play a vital role in providing students with language learning input in class activities. Unfortunately, the selection and evaluation of English textbooks has often received insufficient attention and thought. Content such as reading material receives less attention than layout analysis, design, and cultural understanding. The purpose of this study is to investigate the extent to which reading material cover the genres and skills presented in senior high school English textbooks in Indonesia. The qualitative-based content analysis method was used in this study. There were three English textbooks observed, namely those for grades 10, 11, and 12 published by Ministry of Education and Culture in Indonesia. The aspects observed in reading materials include reading genres and skills based on Brown's theory. The study discovered textbooks are dominated by personal reading with 17 reading material and 14 academic materials. Meanwhile, job-related reading is limited to class 10 textbooks. When it comes to reading skills, macroskills take precedence over microskills. Furthermore, based on these findings it can be concluded that the textbooks need to be developed to provide a balanced portion of reading genres and skills in each chapter. The limitations and suggestions are further discussed.