Reviewerly/exHarmony huggingface dataset:
Improving Autoencoder-Based Recommendation Systems

Recommendation systems help in providing suggestions regarding the products/services that the users might be interested in by analyzing ratings, reviews, and other feedback obtained from the users on their previous purchases. These types of systems can be seen in domains like e-commerce, entertainment, news, etc. The ratings and reviews in e-commerce are obtained from user feedback which are far less in number when compared with the users and items count. This leads to data sparsity issues where the recommendation systems are prone to provide in-accurate recommendations. To counter this issue, in our study, we used both autoencoder and sentiment analysis on the reviews using the BERT language model to generate a more accurate version of the rating matrix. The empirical studies done on the Amazon baby product reviews dataset have shown that the approach has significantly increased the accuracy of predicted ratings.
Investigating the Application of Multi-lingual Transformer in Graph-Based Extractive Text Summarization for Hindi Text

Generating meaningful summary for the given natural language text is one of the challenging and popular task in present era. Researchers have come up with various techniques for the abstractive and extractive summarization. This experimental study is focused on the extractive summarization. In graph-based extractive text summarization techniques, the sentences of the input document are used as the nodes of the graph and various similarity measurements are used to weight the edges of the graph. Each node’s rating is determined using the graph ranking algorithms, and the top-ranked nodes (sentences) are then added to the output extractive summary. In this work, we first translate the publicly available dataset into Hindi text using the Google Translate service. Next, we apply a pre-trained multi-lingual transformer to generate embedding vectors of each sentence of the document. We use these embedding vectors as the nodes of the graph. Rest of the approach remains unchanged. At last, we evaluate the generated extractive summaries on the basis on ROUGE score. Evaluation results indicate that the use of pre-trained multi-lingual transformer can be effective to generate more meaningful extractive summaries.
Graphology-Based Behavior Prediction: Case Study Analysis

The handwriting of a human being carries the richest information which helps gain insights into one’s physical, emotional, and mental state. Graphology helps in interpreting a person’s characteristics by analyzing their handwriting. It is the investigation of a sensible state of mind at the time of writing. This gives an insight into the morality, enthusiasm, and hidden talent of the writer. The state of the cerebrum is reflected in handwriting as the signals move from the cerebrum to the fingers. This has been studied for almost 400 years. Graphology has wide applications in the fields of medicine, education, criminology, etc. Integrative graphology primarily focuses on different strokes of the written word and the relation to an individual’s personality. Holistic graphology considers the form, use of space, and movement during writing. Features of handwriting such as concavity of letters, margin, spacing, pen pressure, baseline, size of letters, loops of alphabets are considered for the above-mentioned applications. These factors lead to the analysis and study of cases based on criminology and depression which focuses on examining the handwriting of the individual and keep a track of their behavior.
ARTSAM: Augmented Reality App for Tool Selection in Aircraft Maintenance

Aircraft Maintenance is an advanced task requiring highly skilled engineers. Facilitating the Aircraft maintenance by providing proper tools and equipment is essential in ensuring good maintenance work. Aircraft Maintenance Technicians (AMTs) require precise knowledge and customized tools to perform their duties. They are responsible for an airplane’s safety and efficiency, and rely on a few basic pieces of equipment for a wide range of jobs pertaining to airplane maintenance. Specific maintenance tasks require unique tools. And while the AMTs could probably improvise and get the job done anyway, specialized tools exist for a reason–they help get the job done correctly and improvising will lead to unnecessary labor and a compromised aircraft. For example, an incorrectly sized screwdriver or screw causes wear and tear and makes the job harder. Besides, traditional tool management requires employees to manually check in and out each tool, which is time consuming. A Tool Selector app which recognises and tags tools in real time will help AMTs in determining how it is used in a particular task. Through this app, the AMTs can be guided through animations to perform specific tasks, such as replacement of Oil Filter from an aircraft engine.
Comparative Study of Depth Estimation for 2D Scene Using Deep Learning Model

Estimating depth is critical for 3D scene reconstruction and visualization. Usually, depth is measured using depth cameras, LIDAR, and stereo cameras. However, these devices have limitations in terms of range and calibration. Depth estimation using a monocular RGB image is the subject of extensive research as it eliminates the need for physical depth measuring instruments. For this objective, many deep learning models are employed. Deep learning models have achieved significant advances in accuracy in recent years, addition to the fact that this increases the computing cost. We examine and compare two deep learning models such as DenseDepth and U-Net and their properties such as training parameters, loss function, accuracy, and so on in this study. Furthermore, we explore challenges in estimating monocular depth.
Quantum Web-Based Health Analytics System

In this work, a quantum web-based health analytics system has been proposed. The system uses a neuro-fuzzy algorithm which uses a selective rule-based strategy to process datasets. When a query is being raised, the datasets required are identified and processed to formulate inference for a knowledge repository. The knowledge repository is decentralized and ensures authentic access for a particular query. The quantum block chain algorithm reduces processing time and memory consumed by 24 and 28%, compared to the conventional block chain approach.
Automatic Text Summarization Using Word Embeddings

Text summarization generates a summary that highlights key sentences while condensing all information from the original document into a few sentences. Abstractive and extractive are the two types of summaries seen in general text summarization process. A hybrid summarization technique which generates an abstractive summary over an extractive summary is proposed in this paper. The initial phase incorporates the use of a semantic model to generate word embeddings for each sentence, and these embeddings are used to improve the lack of semantic disintegrity in extractive summaries. The second phase takes in the concept of WordNet, Lesk algorithm, and POS tagging for generating an abstractive summary from the extractive summary. The paper uses two different data sets: DUC 2004 and Daily Mail/CNN for evaluating the performance over ROUGE and BLEU metrics. The results highlight the relevance of developing hybrid approaches to summarization compared to complex abstractive techniques.
The NFT Wave and Better Use Cases—Using NFTs for Documents, Identification, and College Degrees

With the innovation of smart contracts, the tokenization of digital assets reached great heights in the form of NFTs. While NFTs have opened up various use cases and opportunities in the form of digital art and collectibles, the potential of NFTs remains unclear. Office documents, as well as educational and identification documents, play a significant role in our daily lives. However, in this environment, forgery, duplication, and manipulation are all too common. This paper aims to address the gap between present and potential use cases. It proposes utilizing NFTs for better trust, transparency, and security by demonstrating the efficacy of NFTs in the domain of document verification and presenting the usefulness in tokenizing documents such as driving licenses, college certificates and other identity documents to prevent fraud and improve transparency over the authentication domain. This paper discusses NFTs in detail, the working, challenges, current applications, and an innovative approach to moving documents from off-chain to on-chain for validation of data with the help of decentralized oracles following prisoner’s dilemma and ERC-721 standards.
Application of Machine Learning in Customer Services and E-commerce

Nowadays, machine learning (ML) plays the important role in the E-commerce industry and its customer relations to perform different kinds of tasks such as prediction of purchases, segmentation of customers according to their reviews/sentiments, and recommendation of products to the active users. Various ML algorithms are implemented to get trained with data patterns to perform the above-mentioned tasks. In this paper, the customer segmentation and recommendation of women's clothing based on the reviews are presented. The comparative study is done using five different ML algorithms, namely regression analysis, Naïve Bayes, decision trees, support vector machines, and clustering analysis. The results show that the Naïve Bayes algorithm has better performance when compared to other algorithms by showing better accuracy.
Aspect-Based Product Recommendation System by Sentiment Analysis of User Reviews

Over the past decade, online shopping has become the norm. Everything that a person needs is available on online platforms such as Amazon and Flipkart. There is no dearth in the quantity and categories of products available on these online platforms. Given this influx of products in these online marketplaces and the rapid shift shown by people toward online shopping, it becomes imperative for an efficient product recommendation system to be in place in order to make product recommendations for a user as effective and seamless as possible. In this paper, a method for a recommendation system that is based on the aspects present in the user inputs and reviews is proposed, implemented, and evaluated. As opposed to just depending on user ratings and the sentiment scores and orientation (positive versus negative) of user reviews to recommend products, the proposed method performs aspect extraction of the given user reviews, calculates the sentiment associated with the aspects along with its positive and negative connotation and orientation and based on this, recommends relevant products to the user as per the requirements entered by the user in our system. The classifiers used for this purpose are k-NN, SVM, SentiWordNet, and NLTK VADER. Thus, based on the product requirements entered by the end user in our recommendation system, relevant products are recommended to the user.
STMP-Net: A Spatiotemporal Prediction Network Integrating Motion Perception

This article proposes a video prediction network called STMP-Net that addresses the problem of the inability of Recurrent Neural Networks (RNNs) to fully extract spatiotemporal information and motion change features during video prediction. STMP-Net combines spatiotemporal memory and motion perception to make more accurate predictions. Firstly, a spatiotemporal attention fusion unit (STAFU) is proposed as the basic module of the prediction network, which learns and transfers spatiotemporal features in both horizontal and vertical directions based on spatiotemporal feature information and contextual attention mechanism. Additionally, a contextual attention mechanism is introduced in the hidden state to focus attention on more important details and improve the capture of detailed features, thus greatly reducing the computational load of the network. Secondly, a motion gradient highway unit (MGHU) is proposed by combining motion perception modules and adding them between adjacent layers, which can adaptively learn the important information of input features and fuse motion change features to significantly improve the predictive performance of the model. Finally, a high-speed channel is provided between layers to quickly transmit important features and alleviate the gradient vanishing problem caused by back-propagation. The experimental results show that compared with mainstream video prediction networks, the proposed method can achieve better prediction results in long-term video prediction, especially in motion scenes.
Transformation of an Esvecees (SVCS) Value to Spherical Coordinates as the Result of the Earthquake Forecasting Using SLHGN

The main issue, which has existed since the development of Single Layer Hierarchical Graph Neuron (SLHGN) started, is the representation for location data that will be fed to SLHGN structure. Similar issue, the double-value characteristics of the ordinary and current coordinate system have slowed down the enhancement of a sophisticated earthquake forecasting technology that uses SLHGN. To deal with the problem, a new way of representing locations on the earth, called Single Value Coordinate System (SVCS), has been researched and developed. Since the location of a potential earthquake—after being elaborated by the earthquake forecaster—is represented through esvecees (SVCS) values, people would have difficulties to understand and to locate it. To make the earthquake forecasting results be understandable and locatable for targeted people, those esvecees values should therefore be transformed into ordinary coordinates, which comprise longitude and latitude values. For that purpose, a technology for the transformation from esvecees values to ordinary coordinates have been successfully developed. The experiment results show that the location of a potential earthquake can now be gained as longitude and latitude values. This means that the earthquake forecasting using Single Layer Hierarchical Graph Neuron (SLHGN) is getting closer to its complete functionalities.
Privacy by Design in CBRN Technologies Targeted to Vulnerable Groups: The Case of PROACTIVE

This paper provides the results of the privacy impact assessment conducted for the PROACTIVE solutions and its subsequent privacy by design integration process. PROACTIVE is a solution for fostering coordination and communication between stakeholders before and during CBRNe events focusing on providing guidance to vulnerable populations. Based on extensive fieldwork activities with practitioners and vulnerable groups, the results of the analysis reveal the main risk focus, including the need to filter data by competent authorities (data controllers) and potential solutions for each of them, which allow establishing standard procedures for systems used in similar contexts.
Cyber Security Policies in Crisis Response: Exploring the Predicament of Creating Safe But Workable Systems

As societies become more connected, the nature and response to crises are becoming increasingly complex as well. Crises can affect our societies in unpredictable ways and thus require different actors to bring information together to take appropriate actions. However, as the number and diversity of actors increases, so does the quality and variety of information systems. Especially considering the increased availability and accessibility of technologies available together, process and exchange information. While these developments provide a high potential to improved information sharing, these options also present certain risks. Individual organizations may have measures, training, and policies in place for daily routines to mitigate these risks. During a crisis however, these measures can become a constraint, especially when sharing information in an inter-organizational and cross-boundary context. People in the crisis response team may not appreciate the risks, need to improvise, or even circumvent measures. In this paper, we examine the increased cybersecurity risks associated with the increased use of information technologies used in and facilitating information management during a crisis response. Using a serious gaming research method we examine how, in the context of crisis response, these factors are exacerbated under the pressure of time, uncertainty and coordination challenges. From this we identify the need for increased awareness about the risks of information technologies and sharing in heterogenous stakeholder environments. Specifically, a cultural change and need for additional capacities to understand, assess, and mitigate risks involved as the number of actors, systems, and technological options keep increasing.
COVID-19 Sāvdhān: Harnessing the Telecom Infrastructure for COVID-19 Management

The tremendous challenges brought on by the COVID-19 pandemic has brought to light the significance of inclusive and effective risk communication as a top priority for preparedness and response in health emergencies. A comprehensive emergency management strategy must include effective communication systems assisted with state-of-the-art Information and Communication Technologies (ICTs). For enabling communication, SMS messages utilizing the vast telecommunication sector have proven to be a very efficient tool. Messages should be precise, scientifically accurate, context-sensitive, entrusted, understandable, as well as relevant for the segment of society they are intended for. Geo-targeting helps in reducing over-alerting by minimizing the annoyance and the subsequent opt-out behavior caused by unnecessary alerts. This study provides an in-depth description of the established platform known as ‘COVID-19 Sāvdhān’, which allows for the SMS dissemination of pandemic-related messages to the geo-targeted population, including but not limited to information about vaccination, quarantine facilities, testing centers, hotspots, lockdown, essential supplies, and law and order situations. The platform has been widely used to disseminate more than 3.4 billion targeted SMS in 10 different languages across the length and breadth of India during COVID situation.
Digital Certificate System for Verification &amp; Validation of Educational Certificates using Block chain Technology

Abstract: With the proliferation of mobile devices in recent years, there is a tendency to move almost all real-world activities to the cyber world. Although this makes our daily life easier, it violates many security rules due to the anonymous nature of the Internet. Phishing attacks are the easiest way to get sensitive information from innocent users. Phishers aim to obtain sensitive information such as usernames, passwords, and bank account information. Cybersecurity professionals are looking for reliable and consistent detection methods to detect phishing websites. This project deals with machine learning technology to detect phishing URLs by extracting and analyzing various features of legitimate and phishing URLs. Decision trees, random forests, and support vector machine algorithms are used to identify phishing websites in a two-step process of first visualizing and extracting features of URLs using python libraries and then training them into a model using Gradient Classifier Algorithm to predict real-time phishing websites.
Patterns Of Cultural Communication In Selling Activities In The Limbung Traditional Market, Gowa Regency

This research started from the finding of communication phenomena in traditional markets. In the traditional market the process of buying and selling transactions with interpersonal communication (face to face) between the seller and the buyer. Bargaining in buying and selling in traditional markets is common in the process of transactions (payments) or even without transactions because there is no agreement between the seller and the buyer. The process of buying and selling transactions begins with greetings, the process of bargaining until the transaction is colored by conversations between the seller and the buyer that even leaves the main topic. The process in these stages forms a communication pattern. The formulation of the problem to be answered in this study is how the pattern of communication between sellers and buyers in buying and selling transactions in traditional markets is. This study examines the communication patterns of sellers and buyers in the transaction process in traditional markets. The focus in this research is the communication component in buying and selling transactions in traditional markets. Using the ethnographic method of communication, with participant observation and in-depth interviews with five seller informants and five buyer informants who were selected using a purposive sampling technique with predetermined criteria. The results of this study indicate that sellers and buyers in transactions use dyadic communication patterns, there is a symbolic interactionism model communication process between the seller and the buyer, the unique communication component is found in verbal (language) and non-verbal messages (clothing, touch, body language) and effects and context of communication. The effect of communication in the process of buying and selling transactions that can give birth to intimacy between the seller and the buyer. The context of communication is not only about the topic of buying and selling transactions, but also about other topics such as personal gossip, economics, education, religion, culture, social and even politics. This research requires further research with a different paradigm so that it can be further developed in an ethnographic communication research.&#x0D;
Enhancing ICT for female economic participation in sub-Saharan Africa

Purpose This study investigates how enhancing information and communication technology (ICT) affects female economic participation in sub-Saharan African nations. Design/methodology/approach Three female economic participation indicators are used, namely female labour force participation, female unemployment and female employment rates. The engaged ICT variables are fixed broadband subscriptions, mobile phone penetration and Internet penetration. The Generalized Method of Moments is used for the empirical analysis. Findings The following main findings are established: First, there is a (1) negative net effect in the relevance of fixed broadband subscriptions in female labour force participation and female unemployment and (2) positive net effects from the importance of fixed broadband subscriptions on the female employment rate. Secondly, an extended analysis is used to establish thresholds at which the undesirable net negative effect on female labour force participation can be avoided. From the corresponding findings, a fixed broadband subscription rate of 9.187 per 100 people is necessary to completely dampen the established net negative effect. Hence, the established threshold is the critical mass necessary for the enhancement of fixed broadband subscriptions to induce an overall positive net effect on the female labour force participation rate. Originality/value This study complements the extant literature by assessing how increasing penetration levels of ICT affect female economic inclusion and by extension, thresholds necessary for the promotion of ICT to increase female economic inclusion.