Reviewerly/exHarmony huggingface dataset:
Cloud Platforms and Technologies for Big Satellite Data Processing

This paper addresses the problem of processing large volumes of satellite data and compares different cloud platforms for potential solutions. Existing cloud platforms like Google Earth Engine, Amazon Web Services (AWS), and CREODIAS have been used to tackle this challenge. However, this study proposes an optimal pipeline for satellite data processing, taking into account the advantages and limitations of each platform. The specific focus is on solving machine learning problems using satellite data. In the experiment conducted, the effectiveness of each cloud platform was analyzed. It was found that cloud platforms offer benefits such as flexibility, access to computing resources, and parallel processing architectures, leading to increased productivity and cost reduction. CREODIAS, in particular, stands out due to its specialization in satellite data and easy access to various data types, along with tools for data searching and visualization. The experiment demonstrated that tasks, from data loading to classification, were executed fastest on CREODIAS resources. However, AWS performed data classification faster. The availability of its own internal data bucket was a significant advantage of CREODIAS, especially when considering ARD data. These findings contribute to the advancement of AI methodologies and have practical implications for solving satellite monitoring applications.
Explaining Sarcasm of Tweets using Attention Mechanism

Emotion identification from text can help boost the effectiveness of sentiment analysis models. Sarcasm is one of the more difficult emotions to detect, particularly in textual data. Even though several models for detecting sarcasm have been presented, their performance falls way short of that of other emotion detection models. As a result, few strategies have been introduced in the paper that helped to enhance the performance of sarcasm detection models. To compare performance, the model was tested using the TweetEval benchmark dataset. On the TweetEval benchmark, the technique proposed in this paper has established a new state-of-the-art. Besides the low performance, interpretability of existing sarcasm detection models are lacking compared to other emotion detection models like hate speech and anger. Therefore, an attention-based interpretability technique has been proposed in this paper that interprets the token importance for a certain decision of sarcasm detection model. The results of the interpretability technique aid in our comprehension of the contextual embeddings of the input tokens that the model has paid the greatest attention to while making a particular decision which outperforms existing transformer-based interpretability techniques, particularly in terms of visualisations.
Emotionally Wrapped Social Media Text: Approaches, Opportunities, and Challenges

With the evolution of several online platforms for information sharing such as social media, blogs, product review sites, and discussion forums, people have become more proactive in sharing their expectations, views, feelings, and experiences. This large amount of emotionally wrapped data motivates many researchers to perform data mining and present the crux of hidden emotions or mental states in a more presentable and comprehensible manner. It has several applications in different domains such as business, education, psychology, politics, and many more. This paper presents a detailed literature review projected to rigorously analyze the existing approaches to identify the mental or emotional state of a person from unstructured textual data. We include the most relevant papers which were published during 2001-2022. The selected papers are classified into three categories: granularity level, contextual level, and cognition level. Each category is carefully analyzed followed by a detailed and critical discussion. Finally, open challenges, opportunities, applications, and future directives are presented in-depth to facilitate the researchers working in the domain of emotion mining.
Confidential Training and Inference using Secure Multi-Party Computation on Vertically Partitioned Dataset

Digitalization across all spheres of life has given rise to issues like data ownership and privacy. Privacy-Preserving Machine Learning (PPML), an active area of research, aims to preserve privacy for machine learning (ML) stakeholders like data owners, ML model owners, and inference users. The Paper, CoTraIn-VPD, proposes private ML inference and training of models for vertically partitioned datasets with Secure Multi-Party Computation (SPMC) and Differential Privacy (DP) techniques. The proposed approach addresses complications linked with the privacy of various ML stakeholders dealing with vertically portioned datasets. This technique is implemented in Python using open-source libraries such as SyMPC (SMPC functions), PyDP (DP aggregations), and CrypTen (secure and private training). The paper uses information privacy measures, including mutual information and KL-Divergence, across different privacy budgets to empirically demonstrate privacy preservation with high ML accuracy and minimal performance cost.
Sentiment Analysis and Speaker Diarization in Hindi and Marathi Using using Finetuned Whisper

Automatic Speech Recognition (ASR) is a crucial technology that enables machines to automatically recognize human voices based on audio signals. In recent years, there has been a rigorous growth in the development of ASR models with the emergence of new techniques and algorithms. One such model is the Whisper ASR model developed by OpenAI, which is based on a Transformer encoder-decoder architecture and can handle multiple tasks such as language identification, transcription, and translation. However, there are still limitations to the Whisper ASR model, such as speaker diarization, summarization, emotion detection, and performance with Indian regional languages like Hindi, Marathi and others. This research paper aims to enhance the performance of the Whisper ASR model by adding additional components or features such as speaker diarization, text summarization, emotion detection, text generation and question answering. Additionally, we aim to improve its performance in Indian regional languages by training the model on common voice 11 dataset from huggingface. The research findings have the potential to contribute to the development of more accurate and reliable ASR models, which could improve human-machine communication in various applications.
Sentiment Analysis of Online Product Review using Deep Learning in Distributed Sensor Networks

Recently, sentiment analysis has been a major business practice for an organization that helps analyze online reviews of different products. An organization can understand the customer's perception of their organizational products by analyzing reviews. On the other hand, this analysis process also helps to understand the customer's emotions about the organizational products. In this process, deep learning and distributed sensor networks play crucial roles in analyzing emotions. Five different steps of this analysis will provide accurate information about the customers' emotions on products. This analysis will help increase the product's value by understanding the customer's perception of where negative feedback improves the products. Through the help of this analysis, an organization will get several benefits that enrich its organizational image. On the other hand, this analysis process will face polarity issues, tone issues, comparative sentence analysis issues, and understanding idioms and emojis issues. The implementation of live API and proper sentiment analysis tools help to increase the effectiveness of this analysis.
Speech Emotion Analysis of Short English Readings based on the CAM-SPAT Model

With the development of technology, voice sentiment analysis has also undergone rapid development, and its application fields are constantly expanding. Multimodal models have become a key focus of researchers due to their ability to better predict emotions. In order to help English learners improve their oral English proficiency, a deep learning based emotional analysis model for English short text reading is proposed, and this model is used to analyze emotions in English reading. Additionally, a cross-modal attention mechanism based on a prediction-assisted task was developed to identify emotions in English reading aloud in state and a two-layer attention-based bi-directional long- and short-term memory network was created to classify emotions in English reading aloud. The results of the research revealed that the classification model’s mean F1 value was 98.54%, the detection model’s mean F1 value was 85.13%, and the speech emotion analysis model’s mean F1 value was 73.55, which was not significantly different from the mean of the professionals’ ratings. The significance of the study lies in providing English learners with a method and pathway to improve their oral English proficiency.
Ensemble Hybrid Model for COVID-19 Sentiment Analysis with Cuckoo Search Optimization Algorithm

The COVID-19 pandemic has caused anxiety and fear worldwide, affecting people's physical and mental health. This research work proposes a sentiment analysis approach to better understand the public's perception of COVID-19 in India. Two datasets are created by collecting tweets regarding COVID-19 in India. Pre-processing and analysis of datasets are performed by using natural language processing (NLP) techniques. Various features are extracted from collected tweets using three-word embeddings GloVe, fastText, Elmo. The optimal features are selected by cuckoo search optimization algorithm. Finally, the proposed hybrid model of Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (BiLSTM) is used to categorize the tweets into three sentiment categories. Proposed model achieved 94.44% accuracy, 90.34% precision, 88.53% sensitivity, and 89.53% F1 score. It significantly improved over previous approaches, which achieved 80% accuracy.
Personalized Exercise Program Design with Machine Learning in Sensor Networks

The use of wearable solutions and sensors has reached the point of modern machine learning (ML) techniques in recent years. It contains the different programs that help to develop personalized opportunities for the betterment of the individuals. The research shed light upon the functions of ML and distributed sensor networks (DSN) in promoting healthcare among users. It can be said that the functions of the ML help to develop good fitness among the users, which results in good health. This dissertation consists of different kinds of ML algorithms and the usage of DSNs for the development of physical exercise programs. It has been found that most individuals use the sensors for their benefit in developing good health. The use of machine learning techniques not only helps to record an individual's health data but also sends emergency information to the nearest medical center for the user's benefit. The use of ML helps to the identification of the location of an individual with the help of GPS measures of real-time distance and users' health activities.
Design and Application of Corpus in Computational Linguistics based on Multimedia Virtual Technology

In this paper a design and application of corpus in computational linguistics is proposed based on multimedia technology in order to solve the problem of the combination of corpus technology and multimedia teaching methods. The hardware interface circuit is created based on the USB interface board, and the S3C6410 CPU is chosen to build a multimedia embedded processor. The proposed study is primarily concerned with creating and using a corpus for computational linguistics that is based on multimedia virtual technology. Multimedia virtual technologies will be employed in this study to set up an environment that is appropriate for gathering and analyzing linguistic data. The goal is to offer a language analysis method that is more effective and efficient, as standard approaches have trouble dealing with complex and varied data sources. The accuracy, scope, and variety of the language data are anticipated to be improved by the suggested methodology. The research also suggests a possible way to improve the Corpus’s design and use in computational linguistics. In this study, encouraging results and an in-depth examination of important issues, such as reliability, validity, and efficiency, are examined. These issues should be measured when considering whether adopting a new technology is the right decision. According to the testing findings, the authors' shared system had the least reaction time-about 1.9 seconds. In conclusion, the author's corpus is more adaptable, usable, and practical for use in the classroom.
A Feature Extraction based Improved Sentiment Analysis on Apache Spark for Real-time Twitter Data

This paper aims to improve the accuracy of sentiment analysis on Apache Spark for a real-time general twitter data. A lot of works exist on sentiment analysis on offline or stored twitter data that uses several classification algorithms on relevant features extracted using well-known feature extraction methodologies on pre-processed text data. However, not much works exist for sentiment analysis of real-time twitter data and especially for the generic data on big data processing platforms such as Apache Spark. This paper proposes a real-time sentiment analysis for generic twitter data through Apache Spark using six classification algorithms on N-gram and Term Frequency – Inverse Document Frequency (TF-IDF) feature extraction methodologies on the pre-processed data. An exhaustive comparison is done using Logistic Regression (LR), Multinomial Naive Bayes (MNB), Random Forest Classfier (RFC), Support Vector Machine (SVM), K-Nearest Neighbour (K-NN), and Decision Tree (DT) classification algorithms. It is observed that the trigram feature extraction method performs the best on LR and SVM and the RFC results are also comparable on the considered general tweets data.
Noise analysis of Grover and phase estimation algorithms implemented as quantum singular value transformations for a small number of noisy qubits

The quantum singular value transformation (QSVT) algorithm is a general framework to implement most of the known algorithms and provides a way forward for designing new algorithms. In the present work, the impact of noise on the QSVT algorithm is examined for bit flip, phase flip, bit-phase flip, and depolarizing noise models for a small number of qubits. The small number of noisy qubits approximates the currently available noisy quantum computers. For simulation results, the QSVT implementation of the Grover search and quantum phase estimation (QPE) algorithms is considered. These algorithms are among the basic quantum algorithms and form the building blocks of various applications of quantum algorithms. The results showed that the QSVT implementation of the Grover search and QPE algorithms has a consistently worse dependence upon noise than the original implementation for all four noise models. The probability of success of the Grover algorithm and phase measured by the QPE algorithm were found to exponentially depend upon the error probability in the noisy channels but only linearly dependent on the number of qubits.
Adaptive Learning and Correlative Assessment of Differential Usage Patterns for Students with-or-without Learning Disabilities via Learning Analytics

Learning Disabilities (LD) can be categorized into logical, analytical, grammatical, vocabulary, sequential, and inference disabilities. Analysis of such disabilities assists students to identify and strengthen their weak areas. A wide variety of analysis models is proposed by researchers to perform such tasks, but most of these models are highly complex and cannot be scaled for multimodal parameter sets. To overcome these issues, this text proposes a model for correlative assessment of differential usage patterns in students with-or-without learning disabilities via multimodal analysis. The proposed model initially collects real-time inference sets for students with Learning Disabilities (LD) and without LDs. These sets consist of question-specific recorded responses for “Addition,” “Carry Propagation,” “Basic to Advanced Grammar,” “Direct, Inference and Vocabulary Comprehension,” “Finding odd-man-out,” “Sequencing,” and “Pseudo and Sight Spelling” for different question sets. Answers to these questions and their metadata were processed via a correlative engine that assisted in evaluation of correctness, time needed per question per category, number of skips, number of revisits, and unanswered ratio for different students. This evaluation was combined with temporal analysis to identify per-category progress of students. Based on this progress, students were either upgraded to next level or given lower-level questions, which assisted them to incrementally improve their grades. The model proved that the performance of LD students is 55% less than the non-LD students and an average of 18 LD students have achieved an average of 33% of improvement after having multiple attempts of the adaptive lessons. The model uses a correlation function, which enables to identify answering patterns of LD and non-LD students with 98.4% accuracy, thus can be used for clinical scenarios.
Machine Learning and Natural Language Processing Algorithms in the Remote Mobile Medical Diagnosis System of Internet Hospitals

In order to alleviate the contradiction between supply and demand of professional pharmacists, integrate medical resources, and ensure the safety of patients' medication, the telemedicine diagnosis system has played a great role. Today, in this "Internet +" era, all walks of life have begun to integrate with Internet technology. The purpose of this paper was to discuss the practical utility of machine learning and natural language processing algorithms in the remote mobile medical diagnosis system of Internet hospitals, for which this paper conducted in-depth discussion. This paper first introduced the basic concepts, development and characteristics of machine learning and natural language processing algorithms in detail, and carefully studied and analyzed the development and culture of traditional offline medical diagnosis models. Based on machine learning and natural language processing algorithms, a remote mobile medical diagnosis is designed. By combining with the medical diagnosis system of traditional hospitals, a new type of remote mobile medical diagnosis system for Internet hospitals was designed and developed, and the combination of traditional medical industry and Internet technology was deeply studied. According to each functional requirement, the image module, heart rate measurement module and user setting module are designed respectively. Compared to traditional medical diagnosis systems, the accuracy of the remote mobile medical diagnosis system based on machine learning applied in internet hospital diagnosis in this article reached 80% or even higher. At the same time, it was found through experiments that when the evolution number was 3, the maximum fit value and average fit value were the same, both of which were 0.6. This indicates that the system can accommodate more than 10000 people at the same time, and patients can receive good treatment plans, with a very broad application prospect.
Contextualizing and Expanding Conversational Queries without Supervision

Most conversational passage retrieval systems try to resolve conversational dependencies by using an intermediate query resolution step. To do so, they synthesize conversational data or assume the availability of large-scale question rewriting datasets. To relax those conditions, we propose a zero-shot unified resolution–retrieval approach, that (i) contextualizes and (ii) expands query embeddings using the conversation history and without fine-tuning on conversational data. Contextualization biases the last user question embeddings towards the conversation. Query expansion is used in two ways: (i) abstractive expansion generates embeddings based on the current question and previous history, whereas (ii) extractive expansion tries to identify history term embeddings based on attention weights from the retriever. Our experiments demonstrate the effectiveness of both contextualization and unified expansion in improving conversational retrieval. Contextualization does so mostly by resolving anaphoras to the conversation and bringing their embeddings closer to the important resolution terms that were omitted. By adding embeddings to the query, expansion targets phenomena of ellipsis more explicitly, with our analysis verifying its effectiveness on identifying and adding important resolutions to the query. By combining contextualization and expansion, we find that our zero-shot unified resolution–retrieval methods are competitive and can even outperform supervised methods.
AlgBERT: Automatic Construction of Annotated Corpus for Sentiment Analysis in Algerian Dialect

Nowadays, sentiment analysis is one of the most crucial research fields of Natural Language Processing (NLP), and it is widely applied in a variety of applications such as marketing and politics. However, the Arabic language still lacks sufficient language resources to enable the tasks of opinion and emotion analysis comparing to other language such as English. Additionally, manual annotation requires a lot of effort and time. In this article, we address this problem and propose a novel automated annotation platform for sentiment analysis called AlgBERT by providing annotated corpus and using deep learning technology that includes many automatic natural language processing algorithms, which is the basis for text classification and opinion analysis. We suggest using BERT model as a method; it is the abbreviation of Bidirectional Encoder Representations from Transformers, as it is one of the most effective technologies in terms of results in different world languages. We used around of 54K comments collected from social networking (Twitter, YouTube) written in Arabic and Algerian dialects. Our AlgBERT system obtained excellent results with an accuracy of 91.04%, and this is considered as one of the best results for opinion analysis in Algerian dialect.
Challenges and Growing Opportunities for Healthcare Cooperatives

Post COVID-19 crisis, healthcare has become a priority for every government. Furthermore, the pandemic has also made us realise why do we need an affordable healthcare delivery service at the grassroots level. As a result, the Government of India has come out with the Ayushman Sahakar scheme. This scheme has been launched by the Union Government with an aim to assist the cooperatives in the creation of healthcare infrastructure in this country. It is pertinent to note that the cooperatives in the last few years have transformed rural areas and have pushed them out of poverty. As a result, it will be interesting to see the impact cooperatives will have in the field of healthcare.The authors in this work have discussed the history of healthcare cooperatives in India. After this, the authors have analysed the government schemes and legal provisions which regulate the functioning of healthcare cooperatives in this country. In the next part, the authors studied the Ayushman Sahakar scheme. The authors have discussed the features of the scheme and the impact it has generated in the field of healthcare. Lastly, the author has discussed the challenges which healthcare cooperatives face in this country and how we can overcome those challenges.