Reviewerly/exHarmony huggingface dataset:
Telugu Tweets Sentiment Analysis Based on Ordinal Regression

Nowadays, twitter is a platform where users can share their opinion on specific topic that occurring in the society. To analyze their opinions, sentiment analysis is a technique to exact the user’s sentiment on a particular topic. Much research has been done on machine learning and deep learning algorithms for analyzing the tweets. In world, human tweets are in different languages like Persian, Urdu, Hindi, Telugu, and Tamil. The current work is focused on analysis of Telugu language tweets. Here the comprehensive dataset that consists of Telugu tweets has been prepared for sentiment analysis by using deep learning approaches. For estimating the sentiment polarity and emotions, the deep long short-term memory modules were used and the findings show the best results in terms of accuracy.
Improved Multiscale Retinex for Image Enhancement Using Guided Filter and Customized Sigmoid Function

A digital image conveys much information as these are the two-dimensional representation of a three-dimensional real-world scene, usually captured by a camera. This characteristic of the image is advantageous and is used in several applications, viz. security, weather forecasting, surveillance, health care, etc. Due to low-lighting conditions, the camera’s performance gets affected severely, resulting in poor quality of the captured images. Many times the acquired image contains areas that have poor contrast. Color losses can be observed in the captured image in certain situations. To overcome these issues, an image enhancement technique is incorporated to increase the overall quality of the image. Various image enhancement techniques have been developed in the past. In this paper, an image enhancement technique has been proposed based on multiscale retinex using a guided filter and a customized sigmoid function. The proposed technique gives better qualitative and quantitative performance evaluation results than existing techniques.
Facial Recognition System with Secured Dynamic Implementation and Time Restriction

In today’s era, there is an increasing need for improved security measures as privacy and security are significant issues in information systems. Security plays an important role in everyday life examples in offices, institutions, laboratories, houses, banks, ATMs, and others. With the increase in prominence of automation and security, facial recognition systems are widely being used at entrances for human identification and access particularly for security functions. The already existing facial recognition system can be improved by making it dynamically implementable and time-bound by adding additional security and operative measures which has been focused in this paper. Facial recognition system captures human images to compare with stored database images to give authorization to the user to pass through the door. However, in the majority of systems, it is necessary to create the database for the photos of persons in advance before using them. The proposed system has secured dynamic face storing, where the images of a new user can be stored on-site without any security complications, which is secured using WhatsApp and OTP request to the authorized person(s). Additionally, a system enhancement that can be used in a variety of situations is time restriction.
Design of Blockchain Mining Algorithms for ASIC Implementation

In this paper, we have on the development and implementation of blockchain algorithm using Verilog. Currently, the blockchain process (mining) is performed using high-powered GPUs and CPUs which consumes a lot of energy and produces enormous amount of green house gasses which can harm the environment. We have implemented the blockchain algorithm using Verilog and have performed the simulations which have provided the accurate output. The simulations are performed using Modelsim and Xilinx ISE Design Suite Software. Since, we got the desired results of the verilog simulation, as a next step, we will focus on the design of the ASIC Chip which will be capable of performing the blockchain mining process. Thus, instead of bulky apparatus like GPUs, only a small ASIC Chip will be needed to perform blockchain mining. ...
Object Detection Using Deep Learning Approaches

Computer vision is the field of science that studies how computers and software can recognise and understand images and scenes. Computer vision has many different parts, such as recognising images, finding objects, making images, making images bigger, and more. Object detection in real time is a big, busy, and hard area of computer vision. Object detection is when more than one object needs to be found in an image. Image localization is when there is only one object to find in an image. This finds the objects of a class that make sense in digital pictures and videos. Real-time object detection can be used for many things, such as tracking objects, video surveillance, recognising pedestrians, counting people, self-driving cars, recognising faces, following a ball in sports, and many more. As deep learning grows quickly, more powerful tools that can learn semantic, high-level, and deeper features are made available to solve problems in traditional architectures. The network architecture, training strategy, and optimization function of these models are all different. Convolution neural network is an example of a deep learning tool that can be used to find objects with OpenCV, which is a library of programming functions that are mostly used for real-time computer vision. Most accidents happen because of things that get in the way, like other people, cars, people, fire hydrants, traffic signs, and so on. This is mostly about finding these kinds of problems that can lead to disaster.
A Survey on Indian Sign Language Translation Using Artificial Intelligence

This survey paper presents a summary of current work on the Indian Sign Language Translation in real time. Various approaches to this translation and the use of artificial intelligence in Indian Sign Language recognition and translation systems have been covered. The paper also discusses Indian Sign Language datasets and artificial intelligence models, including their results and comparisons to global approaches. The paper concludes by discussing techniques that have contributed to the advancement of real time, continuous translation of Indian Sign Language.
Domestic IoT Smart Home Its Future Roles and Human Behavior Through Interaction

Although objects can be abstract in their meanings, they will be referred to in their physical sense, i.e., physical objects or material objects. Also, the objects described here are inanimate. An object is a “thing consisting of matter.” Objects have physical representations that enable human beings to experience them through the senses, to see them, touch them, taste them, smell them, and hear them. Interpretations of objects are often limited to their views as equipments or instruments to fulfill tasks like work or survival. However, objects are more than that. This paper shall present some important roles of objects. Physical inactivity has increased significantly over the past years, and the advancement of technology has contributed to it. Paradoxically, domestic IoT shapes human behavior through human interaction. As everyday objects become a part of the Internet of Things (IoT), this paper aims to investigate how the IoT devices and everyday objects can collaborate with humans to address growing physical inactivity. Using a speculative and critical design approach, design proposals in the form of physical and video prototypes are constructed and discussed in a series of workshops. Participation in the workshops moves the participants from being passive consumers of technology to citizens that actively debate and design their own future. The outcomes of the paper are themes that critically address the implications of domesticating technology and its future roles and functions. Also, a set of characteristics is outlined to illustrate desirable, undesirable, and preferred characteristics of networked technologies that may encourage physical activity.
Attention-Based Approach for English to Hindi Translation

Artificial Intelligence (AI) is cleverly evolving with time and in large volumes of computational and processing strength and excessive call for the final quarter, greater than a billion dollars. Through language translation, people from different parts of the world can communicate, work together, and develop relationships. Machine Translation is undergoing a major transformation thanks to the use of neural networks in machine learning (MT). Looking at the identical aspect, a Machine Translation for English to Hindi has been proposed using the likes of Neural Machine Translation techniques along with attention mechanisms. Neural Machine Translation (NMT) is a modern approach which gives extremely good enhancements in evaluation of traditional system translation techniques. Neural Machine Translation has been capable of reaping massive development over ancient techniques: Rule primarily based model and Statistical Machine Translation. Aiming on the trouble of managing a lengthy distance dependency, attention mechanism is incorporated into the interpretation model, as a result the preprocessing module, encoder-decoder framework, and attention module of the system also are adopted.
Automated Cash Liquidity Flow Monitoring and Registry Using Deep Learning

The purpose of this initiative is to monitor the flow of bank notes inside the country and identify any illegal actions. This may be accomplished by putting in place the illicit money identification by counter automation system using deep learning in banking institutions, merchants, manufacturing areas, and so onto effectively tackle the issues, we employ computer vision as a project domain. The primary goal of this project is to automate the billing areas by creating a log out of bank notes flow with its unique serial number, determining if the bank note is legitimate or not, and tracking looted bank notes if used in any places where this project is applied.
Study and Analysis of Different Convolution Neural Network Techniques to Classify Indian Visual Arts

A convolution neural network is used to solve our day-to-day activities related to vision. In this paper, different soft computing models based on convolution neural networks were used for analyzing and classifying the Visual Arts data. The models used in the study for performing experimental activity were either used earlier or were new ones that are not used earlier. These models were AlexNet, SqueezNet, CNN cf6, and VGG 16. Out of four listed models, AlexNet and CNN cf6 were already used to solve the similar types of classification, and newly used were SqueezNet, and VGG 16. The results of applied models were analyzed based on performance matrix classification accuracy. The classification accuracy for the AlexNet model was best among all the used models. The best classification accuracy was seventy-one percent.
Federated Machine Learning Architecture for Heterogeneous Quantum Devices

Quantum computing shows immense potential for solving traditional, near to impossible, complex problems. It also shows how the performance and speed can be improved for the solution of existing classical problems by using quantum mechanics. However, the computation power of current computers, servers, and supercomputers is well enough to speed up the existing problem solutions. But as data generated from various resources increases rapidly, in current scenarios more than 2.5 quintillion bytes of data is generated daily and is expected to be 200+ zettabytes on the cloud by 2025. Handling and storing such a massive amount of data is a significant issue for current computing and storage devices. Quantum computing has the capability to resolve this issue with quantum rules. In this paper, the task of pattern finding and recognizing in data (i.e., machine learning) is explored with the help of quantum computing and introducing a collaborative learning architecture. It enables collaborative computation from heterogeneous quantum computing devices for machine learning tasks. Also, it can be adapted to any quantum machine learning approach and speed up the learning phase with better data privacy. The proposed scheme is implemented with the MNIST dataset to compare with current existing research work. Results show that the proposed architecture matches existing research work’s performance with the additional features of privacy and heterogeneity of quantum devices.
An Ensemble BERT Model for English-Hindi-Marathi Multilingual Question Answering

Multilingual Question Answering (MQA) is an application of NLP that deals with retrieving an accurate answer to the user’s question from the context despite the language of the question and context. Currently, multilingual-cased BERT models perform well for all fine-tuning settings, such as zero-shot, monolingual, cross-lingual, and multilingual settings of multilingual tasks of NLP. Of which the recent multilingual-cased BERT models are mBERT and IndicBERT, pre-trained on multiple languages of the world (104 languages) and India (17 languages), respectively. Hence, in this work, mBERT and IndicBERT are ensembled and then fine-tuned by training them on the in-house ILMQuAD dataset for all three languages. The obtained result evidenced that the ensemble BERT model with fine-tuning improves the result and becomes the new state-of-the-art performance for all three languages. The proposed ensemble BERT model is also evaluated on the MMQA, Translated SQuAD, MLQA, and XQuAD evaluation dataset for the English-Hindi MQA task.
Nucleus Segmentation Using K-Means Clustering for Analysis of Microscopy Images

Analysis of microscopy images involves scanning of information related to color and texture. The color levels of nucleus and its neighbors especially in blood smear images helps to identify malignancy of the cells. The motive of this work is to perform color-based segmentation on the blood smear images. Primarily, these images are subjected to contrast stretching using dark contrast algorithm (DCA). This enhancement of the image increases the contrast of the nucleus and its adjoining components. To perform color-based segmentation on this enhanced microscopic image, k-means clustering is used. It is a type of iterative hard clustering where the segmentation criteria is dependent upon the similarity error which depends on Euclidean distance and position of centroids. The segmented image obtained in the form of color-based clusters is evaluated using overlap ratio (OR) which is beneficial in analysis of microscopic images.
Online Advertising Dataset Using ANN (Artificial Neural Networks) and LR (Linear Regression Techniques)

Advanced displaying provides online sentiment digital advertisers with information into their sales. Sales are critical in increasing the business when sentimental digital marketing methods are used. Sentiment digital marketing is one approach for providing information about their company, gadgets, and products. Utilizing web-based advertising metrics like aggregation are designed to save you time when you need the most recent information for a show or report you're dealing with against a cutoff time. Business members can use our unique time span to expand their business. The current system has a high error rate, a poor business visualization strategy, and a high time complexity. AI technology has been employed in a variety of ways to improve the reach of target audiences in online targeted digital advertising. Recent study shows that improved computational force promotes capacity-focused granular crowds. This study explores and identifies several AI technologies used to improve specific web-based advertising. These three categories widely recognize and divide the word-of-mouth, client-driven, and content-driven promotion via radio, television, and newspapers methodologies that compose AI-based Internet designed promoting strategies. The proposed AI computation accurately predicts information at 94.50% using linear regression and neural network methods.
Multi-step Online Hate Speech Detection and Classification Using Sentiment and Sarcasm Features

With the increased popularity of different platforms like Instagram, Facebook, and Twitter, many people use these platforms to freely express their opinions. These platforms sometimes act as a bane for society as they have led to the rapid propagation of hatred. Therefore, Hate Speech Detection and Classification is a crucial task for moderators of these platforms, so that appropriate actions are taken against the offenders and make the Internet a safe place for all humans. This work proposes a two-step novel approach that detects online content as “Hate” or “Non-hate” speech in the first step and then subclassifies hate speech as “Racist”, “Sexist” or “Neither”. For rigorous testing of the trained models, a testing dataset was created by scrapping tweets about the controversial topic “#NupurSharma” from Twitter. Machine learning and Deep learning models with strong features like Sarcasm, Sentiment, TF-IDF and Pattern features were trained for this classification task and outperformed the existing works with an accuracy of 94.1% and 91.27% for primary and subclass classification tasks, respectively.
Comparison of Pollard’s rho Algorithm Based on Cycle Finding Methods

The cryptosystems which require small key size to implement a public key cryptosystem, and which are more efficient and secure are elliptic curve cryptosystems. Many public key schemes such as Diffie–Hellman and El Gamal schemes solve the elliptic curve discrete logarithm problem (ECDLP). The security of the Elliptic Curve Cryptographic System depends on the difficulty of the elliptic curve discrete logarithm problem (ECDLP). The major attention of any public key systems is the problem to solve ECDLP. Best way to solve ECDLP is to have an exponential time complexity of within the underlying field size. This study shows comparison of Pollard’s rho algorithm based on cycle finding methods and parallelization.
Blockchain Technology and Its Impact on the Accounting

Blockchain technology was initially developed for use as a public ledger for bitcoin and other cryptocurrency transactions. However, since then, many other uses have been found for this technology, especially in the fields of finance. Our objective in this paper is to present the blockchain technology and how it works, and we question the potential transformations in accounting brought about by this technology and identify the potential impacts on the professions of corporate accountants linked to the diffusion of this technology.
Machine Learning Classifiers Performance Comparison for Breast Cancer Detection

Breast cancer, worldwide is the top reason of mortality amongst women. Breast cancer early detection can minimize mortality and increase the likelihood of survival at a reduced cost. This study assesses the performance of machine learning (ML) classifiers using the Wisconsin (original) breast cancer (WBC) dataset and proposes the classifier with the best breast cancer diagnosis evaluation criteria. On the WBC dataset, the machine learning classifiers K-nearest neighbor, Naive Bayes, Random forest, Neural network (NN), Support vector machine, and Logistic regression were chosen for comparison. Logistic regression scored the most excellent AUC value of 0.9943, while the NN classifier achieved the highest accuracy score of 0.989. The research will aid in selecting a ML classifier for a computer-aided breast cancer diagnosis system.
Learning on Structured Documents for Conditional Question Answering

Conditional question answering (CQA) is an important task in natural language processing that involves answering questions that depend on specific conditions. CQA is crucial for domains that require the provision of personalized advice or making context-dependent analyses, such as legal consulting and medical diagnosis. However, existing CQA models struggle with generating multiple conditional answers due to two main challenges: (1) the lack of supervised training data with diverse conditions and corresponding answers, and (2) the difficulty to output in a complex format that involves multiple conditions and answers. To address the challenge of limited supervision, we propose LSD (Learning on Structured Documents), a self-supervised learning method on structured documents for CQA. LSD involves a conditional question generation method and a contrastive learning objective. The model is trained with LSD on massive unlabeled structured documents and is fine-tuned on labeled CQA dataset afterwards. To overcome the limitation of outputting answers with complex formats in CQA, we propose a pipeline that enables the generation of multiple answers and conditions. Experimental results on the ConditionalQA dataset demonstrate that LSD outperforms previous CQA models in terms of accuracy both in providing answers and conditions.
Rethinking Label Smoothing on Multi-Hop Question Answering

Multi-Hop Question Answering (MHQA) is a significant area in question answering, requiring multiple reasoning components, including document retrieval, supporting sentence prediction, and answer span extraction. In this work, we present the first application of label smoothing to the MHQA task, aiming to enhance generalization capabilities in MHQA systems while mitigating overfitting of answer spans and reasoning paths in the training set. We introduce a novel label smoothing technique, F1 Smoothing, which incorporates uncertainty into the learning process and is specifically tailored for Machine Reading Comprehension (MRC) tasks. Moreover, we employ a Linear Decay Label Smoothing Algorithm (LDLA) in conjunction with curriculum learning to progressively reduce uncertainty throughout the training process. Experiment on the HotpotQA dataset confirms the effectiveness of our approach in improving generalization and achieving significant improvements, leading to new state-of-the-art performance on the HotpotQA leaderboard.
The Contextualized Representation of Collocation

Collocate list and collocation network are two widely used representation methods of collocations, but they have significant weaknesses in representing contextual information. To solve this problem, we propose a new representation method, namely the contextualized representation of collocate (CRC), which highlights the importance of the position of the collocates and pins a collocate as the interaction of two dimensions: association strength and co-occurrence position. With a full image of all the collocates surrounding the node word, CRC carries the contextual information and makes the representation more informative and intuitive. Through three case studies, i.e., synonym distinction, image analysis, and efficiency in lexical use, we demonstrate the advantages of CRC in practical applications. CRC is also a new quantitative tool to measure lexical usage pattern similarities for corpus-based research. It can provide a new representation framework for language researchers and learners.