Reviewerly/exHarmony huggingface dataset:
Stability Analysis for Power Depth Vision of New Power System Based on Black-Box Adversarial Attack

The energy industry has reached a consensus on establishing a digital and intelligent new power system that prioritizes safe and stable operation. Artificial intelligence (AI) is a key technology for efficient scientific decision-making in this system, and the power industry is actively exploring and implementing AI research and practices. Power depth vision technology is critical for intelligent analysis, processing, and decision-making of power images in the new power system. However, well-trained power depth vision models are vulnerable to misjudging vision images that have been maliciously tampered with by indistinguishable noise (i.e., adversarial images), posing a threat to the safe and stable operation of the new power system. Therefore, we propose a stability analysis algorithm for the power depth vision of the new power system based on a black-box adversarial attack. Our algorithm involves a real-world noise simulator with a black-box attack switch (BBAS) that searches for on-manifold adversarial images and steers the generative network in the attack direction. To regulate the BBAS state, we present hard and soft filters with distinct constraints as alternatives. Additionally, we propose neighbor random sampling based on Taylor series approximation to define a bag of imitations of the generative noise image, which widens the search space of the adversarial images. Finally, we offer two stability analysis patterns for the training and testing procedure, and our experiments on insulator string detection demonstrate the effectiveness of our algorithm.
Text Validity Application Forming Functional and Non-Functional Requirements Based on Documentation Analysis on TESA Applications

This research relates to application development and specification documents (SRS) in software development. The SRS document contains essential functional and non-functional requirements in application development. Each element in the SRS must correspond to the results of the elicitation carried out. However, opinions sometimes differ when analyzing the document. This research aims to develop an application to validate the correspondence between elicitation results and artifacts in SRS semantically using the word2vec technique. The results showed that this application gave the highest score for the validation of functional requirements (0.533) and non-functional requirements (0.719). In addition, the value of questionnaire reached 0.71. The Cohen Kappa index will assess these results. If the score is below 0.21, the artifact will be repaired to achieve a score above that threshold. This research has created a text validity application that can read text files in Txt format, extract input through text pre-processing, and perform semantic suitability assessments using the Word2Vec method.
Research Article Matching Methods using Attention, Hybrid, RNN, and SIF

In the digital era, search engines such as Google Scholar make it easier to access scientific information, but there are challenges in determining the relevance and accuracy of articles. One technique used to select scientific articles relevant to a particular topic is entity matching. This study aims to analyze the performance of entity matching using four models owned by DeepMatcher. The models include Attention, Hybrid, RNN, and SIF, which are applied to a dataset of articles with keyword fairness in AI. The collected dataset consisted of 50 articles retrieved from Publish or Perish 8. The dataset was preprocessed up to pairing, resulting in a final dataset of 1225 rows and eight columns. The columns follow the column types that must be present in the dataset that will be processed by DeepMatcher, namely the "left," "right," "label," and "ID" attributes. Based on the results of the analysis, it was found that the most suitable DeepMatcher model, namely the RNN model, had the highest average F1 score of 54.08%.
Sampling - Variational Auto Encoder - Ensemble: In the Quest of Explainable Artificial Intelligence

Explainable Artificial Intelligence (XAI) models have recently attracted a great deal of interest from a variety of application sectors. Despite significant developments in this area, there are still no standardized methods or approaches for understanding AI model outputs. A systematic and cohesive framework is also increasingly necessary to incorporate new techniques like discriminative and generative models to close the gap. This paper contributes to the discourse on XAI by presenting an empirical evaluation based on a novel framework: Sampling - Variational Auto Encoder (VAE) - Ensemble Anomaly Detection (SVEAD). It is a hybrid architecture where VAE combined with ensemble stacking and SHapley Additive exPlanations is used for imbalanced classification. The finding reveals that combining ensemble stacking, VAE, and SHAP can not only lead to better model performance but also provide an easily explainable framework. This work has used SHAP combined with Permutation Importance and Individual Conditional Expectations to create a powerful interpretability of the model. The finding has an important implication in the real world, where the need for XAI is paramount to boost confidence in AI applications.
Robust Audio Forgery Detection Method Based on Capsule Network

Audio copy-move forgery is an audio forgery method that aims to generate forged audio by hiding some words or duplicating the words in the same audio. The significance of audio forgery detection lies in its pivotal role in maintaining the integrity of audio content, crucial for sectors such as law enforcement, journalism, and entertainment. Detecting manipulated audio, like copy-move forgery, ensures trustworthiness and credibility in various real-world applications, including authentication of evidence, preserving the authenticity of audio records, and safeguarding the integrity of media content. Post-processing operations such as compression, noise adding, and median filtering applied to fake audio to hide traces of forgery make audio copy-paste detection very difficult. In the proposed study, an audio copy-move forgery detection method based on Capsule networks resistant to post-processing operations is proposed. This is the first method to employ Capsule Network with Mel spectrogram of audio for the detection of audio copy-move forgery. For this purpose, the suspicious audio file taken as input is converted into mel spectrogram images. Features are extracted from Mel spectrogram images using EfficientNet. The capsule network is trained with these features, and the audio file given as a test is labeled as forged or original. The proposed method and other studies have been tested on the database created by us. Obtained results show that the proposed method is highly resistant to post-processing operations and outperforms other studies with more than 34% in accuracy.
Usability Analysis on Digital Library Information System using System Usability Scale (SUS)

The library plays an important role as a source of information for both academic and non-academic users. With the advent of technology, the library infrastructure has also evolved, embracing the implementation of digital library information systems. These systems aim to streamline library management through integration and automation. Universitas Pendidikan Ganesha (Undiksha) has undertaken the implementation of the INLISLite digital library information system. INLISLite helps the librarian with tasks ranging from cataloguing collections to facilitating book loans. The success of a system depends on the role of the user and the quality of the system produced. The consideration of user satisfaction is crucial for ensuring the efficiency of the system in the future. One approach to measuring user satisfaction is through usability testing, with the SUS method being one such tool to assess user satisfaction levels. The test with SUS uses a questionnaire consisting of 10 questions. The questionnaire was distributed to 15 respondents who have already used the INLISLite Undiksha system. Based on the calculations, the SUS score for the system was 65, which put it in grade C. It is included in the below average category on percentile ranks. The good category includes the adjective value, the marginal category includes the acceptable value, and the passive category includes the NPS value. The results of interviews with users indicate that they still have difficulties using some of the features on the system. Therefore, improvements to the system are needed to address the problems found and increase user satisfaction.
Enhancing Aerospace Engineering Students’ Speaking Ability through Instagram Vlog

This study aimed to determine whether or not the Instagram Vlog improved speaking skills. The study of the study is to determine how the Instagram vlog project aids students in improving their speaking skills and how students' perceptions of the project improve their speaking ability. This study had 49 students participating. The interview was demonstrated to determine the students' perceptions of Instagram Vlog usage in English class. While the pre-test and post-test were given to find out how Instagram Vlog affected the students’ performance. The interviews were used to find out how the students feel, and they believe that Instagram Vlog improves their ability to speak English. In contrast, the students' improvement following the Instagram Vlog implementation indicated that the Instagram Vlog's performance in the Bahasa Inggris I subject contributed to their improved speaking ability. The findings of this study should be considered by educational institutions that prepare students for future employment.
Comparative Study of Deep Learning Models for Document Classification

In the last few years, deep learning has shown promising improvements in natural language processing (NLP). LSTM, Bi-LSTM, and Convolutional Neural Networks (CNNs) are examples of neural networks (NNs) that are utilized for a variety of NLP applications, including sentiment analysis, information retrieval, and document classification. We compare these models to the BBC News dataset consisting of five categories: business, entertainment, politics, sport, and technology in this paper. Each model is vectorized using TF-IDF, Word2vec, and Glove. And then the performance of each model is evaluated by precision, recall, and F1-Score. In comparison with other models, Word2vec and CNN achieved a higher F1-Score.
Performance analysis of token-based text augmentation techniques on text classification tasks in Indic languages

Due to the increasing availability of large-scale datasets, deep learning models have become the first choice to tackle NLP tasks. However, training these models might require a huge amount of labeled data. It might be difficult to achieve great results if sufficient data is not available for training, which might be the case with low-resource languages. Fortunately, text augmentation has garnered considerable attention in recent years for improving the performance of natural language processing tasks, particularly in low-resource settings. The majority of these recently developed text data augmentation techniques have been thoroughly tested and analyzed in the resource-rich English language. However, there is a lack of similar performance analysis of text augmentation techniques in Indian languages. In this study, we focus on the performance of token-based text augmentation techniques for text classification tasks in Indian languages. We conduct performance analysis of token-based augmentation technique methods on headline and sentiment classification tasks in Indian languages using models like BiLSTM and the recently introduced ALBERT based multilingual model IndicBERT. We observed a significant performance boost across different languages and tasks with the BiLSTM model as compared to IndicBERT. IndicBERT, being pretrained on 12 Indian languages, has already learned semantic and contextual knowledge and thus does not benefit from added variations using augmentation.
A Multi-Dataset Comparative Analysis of Keras and GloVe-Based BiLSTM Models for Sentiment Analysis

The form of text on the web expresses feelings, thoughts, and opinions about entities such as brands, products, and people, generating a vast amount of data. These sentiments have a significant impact on businesses and market research. This paper presents an investigation into the performance of the a deep NN in the domain of Natural Language Processing (NLP). Specifically, we implement the BiLSTM model using two different kinds of word embeddings, namely Keras and Global Vectors for Word Representation (GloVe). We further assess the model’s performance on various datasets, which include the airline industry, a crisis scenario, a political party, and a technology company, among others, using various performance metrics in order to show the significance of different embeddings.
Vulnerability Scanning and Analysis Tool (VSAT)

Due to exponential technological advancement, the number of cyber attacks has been steadily increasing and leading to substantial loss of profits, productivity losses, reputational damage as well as judicial liability. To ensure that cyber attacks do not occur, it is important to identify vulnerabilities in web applications. This paper presents a vulnerability scanning tool that involves continuous monitoring and testing of Web applications which analyses the various vulnerabilities that are present in a system. Different types of scans that are performed to detect these vulnerabilities are also explained.
Development of Automatic Speech Recognition System for Kannada Language/Dialects

Many end-to-end automatic speech recognition (ASR) systems have been developed for Indian and International languages, but no efficient ASR system is developed for Kannada language. This paper attempts to demonstrate the development of end-to-end (E2E) ASR system for Kannada language/dialects under noisy conditions. We develop an ASR system by combining speech-to-text (STT) and text-to-speech (TTS) systems under noisy conditions. To develop an ASR system, a task specific speech data plays a vital role, and it is collected from the speakers. The dictionary and phoneme set are much indeed to transcribe the collected speech data. The language and acoustic models for the validated speech data are developed using Kaldi speech recognition toolkit. The text output of an STT system is given as input to TTS system. The texts are normalized, converted to phonemes, and compared with the original speech database. The phoneme that suites the best is concatenated to words and waveforms are generated to get back speech as the output.
Improved Methods of Imputation for Clustering

Clustering has been extensively researched in the areas of data mining and machine learning. It focuses on identifying the underlying specific structure of each cluster using the provided set of data. Although clustering algorithms show promising performance in a number of applications, the majority of them now in use are unable to handle clustering tasks with the incomplete functionality that are frequently encountered in real world applications. When we think about applications in the real world, measurement mistakes, human error, device failure, and other unreliable elements can all lead to incompleteness and ambiguity in the data. We suggest improved approaches for imputation prior to clustering to address this problem. To assess the effectiveness of the suggested strategies, we carried out an experimental investigation on five UCI benchmark datasets. The outcomes amply proved our effectiveness.
Spoken Indian Language Identification using MFCC and Vowel Onset Points

Conversation between individuals encompasses information on the speakers, languages, and contents. By taking the language-specific information out of the speech, the language of the utterance can be efficiently ascertained. Language Identification, or LID is the approach aimed at identifying spoken language. Speech recognition, speech translation, and voice-activated automatic systems all reap the benefits of having the knack to identify languages from speech. The LID system may be paramount for recognizing speakers since it can narrow down the search field by identifying languages. The Random Forest (RF) classification methodology has been implemented in this research to put forward an approach for language identification that relies on Vowel Onset Points (VOPs) and Mel Frequency Cepstral Coefficients (MFCCs) characteristics. Vocal tract features comprise VOP and MFCC. Language and speaker-related information are included in the VOP and MFCC characteristics extracted from spoken words that are spoken. Extraction of language-specific characteristics is a prerequisite for language identification. Therefore, the combination of these characteristics surpasses each one separately. The database of multilingual clean speech signals (Hindi, Assamese, and Malayalam) that was received from the IITKGP dataset has been the subject of the experiment. A random selection of 835 voice signals from the aforementioned database has been employed in the proposed model. Eighty percent of the 835 speech samples were utilized for training, while twenty percent from the same dataset were used to assess the suggested models. MFFCs characteristics and combined features are used to evaluate language models. When compared to taking MFFC features one at a time, experiments that employ both features concurrently get more accurate outcomes. Additionally, we compare the performance of the random forest and K nearest neighbor classifiers in the same convolved file. The proposed method incorporates an amalgam of the same features and a random forest model is used to increase language identification accuracy to 84.1% while the KNN gives only 71.9% accuracy.
Comparative Study on Sentiment Analysis in Image-Based Memes

In this electronic age of computers and mobile devices, users frequently share and express their opinions through videos and images. Memes are shared as a means of expressing the user’s thoughts, feelings, experiences, and sentiments. Memes are being used extensively, which has both pros and cons. Alarmingly more offensive and sensitive memes are being shared, which may be upsetting to other people’s social and cultural values. In order to classify these memes as positive or negative, this study will investigate their meaning. Thus, it will allow us to comprehend the user’s opinions on many topics and concepts. We have deployed different multimodal and unimodal on Facebook’s dataset of hateful memes and categorized the memes into positive and negative types. The best modal found was $BERT$, which outperformed all other algorithms with an accuracy of 93%.
Evolutionary Swarming Particles To Speedup Neural Network Parametric Weights Updates

Gradient-based neural networks weight optimizers such as stochastic gradient descent, adam, adagrad or adadelta provides robust and reliable results, and they are very slow to train as they slowly and steadily iterate over the solution search space. These days deep learning models are getting very large, having billions or even trillions of parameters. Updating these many parameters with gradient-based methods would require large computational resources and time. Therefore, an alternative, reliable and fast parametric update rule is required. Particle swarm optimizer is a popular algorithm used for solving various optimization problems like design control systems, feature selection, signal denoising, compression techniques, etc. In the state of the art, the closest application of particle swarm in a neural network is to tune hyperparameters to find the best combinations for a given set of problems. But no study has explicitly used evolutionary swarming particles to directly update weights and biases of a neural network. With some modifications on both the general batch weight update mechanism of a simple neural network and the particle swarm, we successfully combined the two together. The tests were performed on two datasets, one for kidney stone prediction and one for breast cancer classification showing that the classification accuracy with the proposed modification is better than the state of the art in multiple cases. But the most important observation was that the proposed model was found to be up to 147 times faster than AdaDelta and 110 times faster than stochastic gradient descent.
Learning Disability Predictions using Machine Learning: A detailed Evaluation on Predicting risk of Dyslexia

Dyslexia, a specific learning disability affecting 10% of the population, has significant societal implications. It hampers individuals’ acquisition and processing of information, impacting academic performance and self-esteem. Dyslexia’s effects extend to professional lives, as challenges in reading and writing can limit career choices. Socially, difficulties in communication and comprehension can lead to misunderstandings and isolation. Early identification using machine learning algorithms can facilitate tailored interventions, enabling individuals with dyslexia to thrive academically, professionally, and socially. Learning Disability (LD) predictions refer to the use of machine learning algorithms to predict the likelihood of an individual having the risk of a learning disability. Machine learning algorithms can be trained on data from individuals with and without Dyslexia to identify the patterns in the dataset that are indicative of the condition. Once trained, We aim to propose an approach to find the best-performing machine learning model that can then be used to predict the likelihood of an individual having Dyslexia based on their demographic information and performance measures from an online gamified test. The approach compares the performance of different classification algorithms such as Random Forest Classifier, Logistic Regression, Extra Trees Classifier, Bernoulli Naive Bayes Classifier, K-Nearest Neighbors Classifier, Linear Support Vector Classifier, Extreme Gradient Boosting Machine, Light Gradient Boosting Machine. Our study offers a novel dataset and the best fit classifier to predict the likelihood of a learning disability using less expensive computational methods. The results of the work will be used to create an easy-to-use tool for people in the education domain to predict the presence of learning disabilities in their children, and to help in identifying high-risk individuals early on.
Genesis of Various Optical Arrangements of Circular Polariscope in Digital Photoelasticity

In the early developments for automation of photoelasticity several researchers have proposed phase shifting techniques (PST) with different optical arrangements. Several ways of calculating the isoclinic and isochromatic parameters were also reported. In many cases though the optical arrangements were different, the intensity equations remained the same. Although theoretically, each of the techniques provide the evaluation of isoclinics and isochromatics, when applied to experimental images only some of the techniques remain robust. This indicates that specific optical arrangements do play a very important role on the success of the phase shifting technique experimentally. One of the main sources of error is due to the mismatch of quarter wave plate. In this paper, the role of quarter wave plates in influencing the selection of appropriate optical arrangement for PST is studied systematically. Different possibilities of obtaining the photoelastic parameters with least error are explored.