Reviewerly/exHarmony huggingface dataset:
Crimes’ forensic characteristics features in the computer information field

The purpose of the research is to identify the main crimes’ forensic characteristics features in the computer information field, inasmuch it makes a great importance in the modern world, as computer crimes are becoming more widespread and threaten the information security, critical infrastructure, personal life and citizens’ finances, organizations and the entire state. The terminological apparatus is analyzed, the classifications of modern cybercriminals are studied, the main approaches to the commission of crimes in the field of computer information are determined. The following methods are used in the research: analysis and synthesis, induction and deduction, system method, dialectical and materialistic methods, formal legal method. The study and analysis of the forensic characteristics allows us to determine the most common ways of committing such crimes, to develop effective counteraction measures. It is established that the main difficulty in the investigation of these crimes is the complex technological nature and the order of their commission. Many criminals are constantly improving their skills, the most qualified of them try to be careful, develop their own countermeasures against law enforcement agencies, using ciphers, special remote access programs to make it as difficult as possible to collect evidence and search activities. Among other things, the difficulty of cybercriminals holding liable is complicated if intruders are located in another state, and unfortunately, one cannot count on international cooperation in the field of law and order and public security with many Western countries today.
Detection of Phishing Attack Using Machine Learning

It is thought that a spate of phishing scandals first occurred in the mid-1990s, when a group of criminals pretended to be AOL employees and used instant messaging and email to purloin users’ passwords and hijack their accounts. Today with the increasing use of the internet, cybercrime is an easy way to steal sensitive information from users. Attackers are keenly aware of important user information such as name of the user, their password and bank details. This paper identifies criminal URLs for stealing sensitive information by finding various features in URLs. With the use of databases in training our model, it will provide a distinction between a website for identity theft and sensitive information. The aim of our paper is to find the best algorithm for training and training in the largest database that provides the most accurate result. Ways to find criminal websites for stealing sensitive information are being developed, divided into three categories: list-based, heuristic methods and machine learning methods. The result also shows that the accuracy of the detection of criminal websites for stealing sensitive information increases as additional databases are used as training databases. All dividers do well when 90. Our work analyzes various machine learning strategies when applied on a database of related attribute websites and their associated information that might be helpful in locating a website could be a crime for stealing sensitive information.
Problems in determining substantive law norms to protect museum objects and collections as intellectual property

The importance of the research is conditioned by the ongoing commercialization and expansion of masterpiece and museum objects use limits, as well as museum collections in the context of ensuring the rights and legitimate interests of museums as corresponding intellectual property objects’ right holders. This predetermined the study of museums’ participation peculiarities in civil legal relations taking into account the legal regime of museum objects and museum collections as the purpose of this research. The tasks include the establishment of the museum’s civil legal status as a relevant legal relations subject, civil turnover characterization of museum objects and museum collections and their legal regulation sources, current legislation norms’ application practice analysis on the use of museum objects. The theoretical basis of the research is provided by civilization science representatives’ works of recent years, the subject of which affects public relations connected with the museum objects and museum collections protection as intellectual property objects. The normative basis is formed by the legislation provisions on culture, museum legislation, legislation on intellectual property. The empirical basis is the materials of court cases on disputes on the museum objects use and collections for commercial purposes.
Optimization of Classification Algorithm for Improving Semantic-Based Text Classification

Recently, one of the most popular research areas is text classification. The most critical issue is semantic problems that arise in the classification. As a result, identifying and removing duplicate semantic words should be done to improve a classifier’s efficiency. This chapter focuses on optimizing the feature space with the use of the dictionary method. We implemented our proposed methodology in three standard benchmark datasets, and the experimental results show that it outperforms existing algorithms. The method of SVM achieved a maximum accuracy of nearly 96%.
Optical Flow-Based Movement Alert System for Assisting Visually Impaired People

The home security system is accepted as an important system, and it benefits many people, especially visually impaired people. This is due to an increase in the number of home burglaries, incidents of theft which have made people more aware and vigilant regarding the same. In this work, we have used the optical flow method to track and detect the movement of objects precisely. The optical flow method is used to recognize the movement by observing video streams. Objects observed under surveillance in the surveillance area are studied and analyzed with reference to the surroundings. This provides a crucial element for detecting and predicting anomalies based on the behaviors of observed objects. An alarm system is proposed which can track the moving object and raise an alert.
A Short Review on XAI Techniques in Text Data

This review focuses on the state-of-the-art algorithms for explainability of artificial intelligence (XAI) systems using different techniques. A plethora of explainability techniques have evolved which are useful for language processing, text classification and other computational text analytics tasks. Various research ideas are to be elevated to improve the potential of the feature extraction techniques in text analytics to endeavor the explanation of the prediction toward more standard, optimum, robust and trustworthy. The survey presented in this article consolidates the related research papers on explainability on text analytics using deep learning techniques and other frameworks.
Photovoltaic Energy Prediction Using Machine Learning Techniques

Solar energy is becoming one of the most promising power sources in residential, commercial, and industrial applications. Solar photovoltaic (PV) facilities use PV cells that convert solar irradiation into electric power. PV cells can be used in either standalone or grid-connected systems to supply power for home appliances, lighting, and commercial and industrial equipment. Managing uncertainty and fluctuations in energy production is a key challenge in integrating PV systems into power grids and using them as steady, standalone power sources. For this reason, it is very important to forecast solar energy power output. In this paper, we analyze and compare various methods to predict the production of photovoltaic energy for individual installations and network areas around the world, using statistical methods for time series and different machine learning techniques.
Intelligent System for Detecting Spam Emails Using Machine Learning Classifiers

The major issue with the email spam is a major issue nowadays, as Internet users grow rapidly. The attacker, or the spammer, mainly used it for phishing or to commit fraud by sending malicious links in spam emails that could harm our system. It is fair to say that, as email communication has increased in recent years in this Internet age, owing to its low cost and simplicity in use for messaging and sharing important information with others, spamming has become a significant problem, with email spam being the most widely recognized form of the problem. Spam emails have a financial impact on organizations, but they also irritate individual email users and frequently cause inconvenient behavior. In this paper, we have designed an intelligent system for the identification of spam or ham email via the use of several machine-learning algorithms. Various approaches were utilized in this research; however, according to the results of the experiment, the random forest algorithm exceeds all other algorithms with an accuracy of 98.5% when compared to all other algorithms.
SIA-SMOTE: A SMOTE-Based Oversampling Method with Better Interpolation on High-Dimensional Data by Using a Siamese Network

SMOTE is an effective method for balancing imbalanced datasets by interpolating between existing samples in the minority class. However, if the synthetic samples generated through interpolation are based on noisy data points, then they may also be noisy and can lead to overfitting and reduced performance on unseen data. This paper proposes a new method SIA-SMOTE, which uses SMOTE for oversampling the minority class and a siamese network for synthetic image selection. SIA-SMOTE also explores the decision boundary to better capture data distribution of the minority class. The proposed method has been compared to random oversampling, SMOTE, and ASN-SMOTE on MNIST, FMNIST, and three medical image datasets. The results show that SIA-SMOTE achieved the best overall performance in terms of three evaluation metrics.
Named Entity Recognition Using Deep Learning and BERT for Tamil and Hindi Languages

Named Entity Recognition (NER) is a subclass of Information Extraction (IE) activity that captures text information and meaning of words from unstructured input. NER’s goal is to identify and categorize every word or symbol in a document into predefined categories. Information Extraction (IE) from Indian language text is gaining popularity among the Indian researchers. The extraction of meaningful and relevant information from structured data is crucial, and NER plays a key part in this process. The NER extraction is performed on the data in multilingual languages Hindi and Tamil. The work performed Word2vec, fastText, and BERT to create embeddings which are further used with deep learning and machine learning (ML) models. The model is proposed with three distinct neural architectures of the Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory-Convolutional Neural Network (Bi-LSTM-CNN), Long Short-Term Memory-Convolutional Neural Network (LSTM-CNN) on Word2vec, and fastText embeddings with an overall F-measure of 85–88%. LSTM-CNN with fastText embeddings produced an F-measure of 88.3% for Hindi data. LSTM-CNN with Word2vec embeddings produced an F-measure of 88.66% for Tamil data. The predictions of the entities are evaluated using machine learning (ML) models Decision Tree, SVM, Logistic Regression, and Random Forest on the Bidirectional Encoder Representation from Transformers (BERT) embeddings. The F-measure achieved on these embeddings with SVM is 99.6%, 99.44% for Hindi and Tamil data, respectively.
Intelligent Online Voting System for Twenty-First Century and Smart Cities 5.0: An Empirical Approach through Blockchain with ML Techniques

A digital voting system that allows people to vote sitting at their homes based on their face recognition. The traditional voting system does not allow people to vote while sitting in their homes. Considering the situation of COVID-19, everything is going digital. Questions on EVM from losing parties regarding some malfunctioning. Digital, secured, reliable, user-friendly, and a low-cost system. DDoS assaults, polling booth capturing, vote tampering and manipulation, malware attacks, and other security issues are all present in today’s voting systems. The voting process may be made more safe, transparent, immutable, and dependable by utilizing blockchain technology. Our current voting system is questioned by a variety of stakeholders, including political parties and ordinary citizens because votes are manipulated, citizens find voting a time-consuming task, and, most importantly, the current pandemic situation has digitized almost all areas of interest in our county. As a result, to resolve the issue produced by the physical voting system. The system we are creating is a blockchain-based decentralized app which uses machine learning for the identification of the user, it uses principles like face matching, checking confidence level, and then allows the user to vote based on the verification of her/his documents and eligibility to vote without compromising the right to vote anonymously.
Development of a Natural Calamity Alert System for Tourists: An Application of PEAS Technology

Tourists travel from one place to another under a tourist package plan. The starting dates of journey from home to different selected places are usually pre planned. Natural calamity can occur during this period of the tour. A tourist may have started the journey for the tour or maybe not, when the news of the probable calamity is announced. The tourism authority takes the responsibility of informing all those tourists who are set to visit the particular tourist spot, where the calamity may occur. The tourists may reach the spot very shortly or nearly in a day or two. Thus, it becomes very urgent and quick flow of information is required to be transferred to the tourists by detecting their current location. Hence to find all the tourists who are near to the upcoming calamity zone requires a smart application. This can be developed using artificial intelligence.
Seamless Passenger Experience for the Airport Environment: Research at DARTeC

Surveillance systems have been used in daily life for security purposes. There is a wide range of applications such as visual tracking, crowd area tracking, queue detection, and face recognition that are used to integrate within the airport. In recent years, tracking passenger in airports have become more and more automated. Face detection and recognition in visual tracking framework is a significant pipeline for digital aviation. Despite the importance of that, there remains a lack of the presence of occlusion regarding previous research. The challenges have been increased due to Covid-19 restrictions and the necessity of mask-wearing. In this paper, we have developed a facial recognition system for the DARTeC environment, which is one of the subclasses of our seamless passenger experience at the airport research.
Detection of Fallen Trees and Vehicles Plying on the Road for Safety of Visually Impaired People

The problem of obstacle detection for blind people is addressed in this paper. In day-to-day life, visually impaired people face a lot of problems like navigating around places, finding reading material, and arranging clothes. The white stick works in many ways to help people with poor eyesight. The white cane is moved to the side to side to alert the person of possible obstacles in their path. The proposed model detects obstacle on road and raise audio alert, thus making the user aware prior to hitting the obstacle. The dataset contains two classes, namely fallen trees and vehicles. SIFT feature descriptor is used for feature extraction. Dimensionality is reduced by using principal component analysis. The classification performance is observed on five different models. Among SVM ovo, SVM ovr, decision tree, random forest, KNN classifiers, SVM gave highest accuracy of 94.26%.
Acid Sulfate Soils Classification and Prediction from Environmental Covariates Using Extreme Learning Machines

This paper explores the performance of the Extreme Learning Machine (ELM) in an acid sulfate soil classification task. ELM is an Artificial Neuron Network with a new learning method. The dataset comes from Finland’s west coast region, containing point observations and environmental covariates datasets. The experimental results show similar overall accuracy of ELM and Random Forest models. However, ELM implementation is easy, fast, and requires minimal human intervention compared to conventional ML methods like Random Forest.
Audio Based Text Summarization Using Natural Language Processing

The domain of summarization of multimedia is rapidly evolving and creating an impact in the present world in several verticals. An enormous amount of hardware resources are involved in the communication ecosystem. The scalable nature along with diversity and specificity is a major challenge in establishing effective summarization algorithms. The present algorithms are incapable of capturing semantic relationships among words as they focus majorly upon the extractive version of summarization. The analysis focuses entirely upon abstractive summarization of textual corpus. The system incorporates heuristic approaches to train the system and predict test data. The entire model is developed using state-of-the-art architectures by incorporating the Transformer model. The encoder and decoder schemes help in effective summaries as compared to the extractive approach.
A Study on Hiding Internet of Things (IoT) Communication

Internet of Things (IoT) has been a part of our day-to-day lives for a few decades, and it has been utilised to make our lives easier and more pleasant in many ways. However, as much as it provides more comfort, it also presents a myriad of challenges. The majority of these challenges are caused by security risks and bad intentions on the part of the users. The threats were mostly focussed on data confidentiality and privacy, among other things. The Internet of Things (IoT) has sparked widespread anxiety about the potential for information leakage. As a result, a secure environment is essential to ensure that data is transmitted from its devices over the network without being compromised. Energy and computational power, on the other hand, are two key limitations for any IoT device, since it is often run on a remote system with limited energy supply and computing capacity; it also transfers data into the world of the Internet in a relatively unprotected manner. In this work, the authors attempted to identify several current steganographic processes that may be used with Internet of Things devices while requiring little computing and energy resources.
BERT-Based Dependency Parser for Hindi

Dependency parsing is concerned with obtaining its grammatical structure and establishing the relations between the words in the sentence, namely the headwords and the words that alter those heads. This paper presents a transformer-based dependency parser for Hindi. The transformer model, BERT and sentence transformer LaBSE were used to generate the embeddings for the Hindi tokens, further applied to the machine learning algorithms to make predictions. The accuracy and the Unlabelled attachment score (UAS) are calculated for the models using Multilingual BERT cased and uncased (MBERT), Multilingual Representations for Indian Languages (MuRIL) and LaBSE. The performance in the case of MBERT ranges between 55 and 65%, while the results observed on using the LaBSE are 3–4% higher for UAS score and 2–3% greater for the accuracy of most models. The Decision Tree algorithm provides the UAS score of 61% with MBERT and 65% with LaBSE, and Random Forest provides a score of 63% and 66% with MBERT and LaBSE, respectively. The UAS score offered by SVM for three kernels–Linear, RBF, and Polynomial–was obtained to be 63%, 60% each, respectively, using the MBERT, while the scores 64% 66% and 66.50% were obtained using the LaBSE embedding. The Logistic Regression is observed to provide a score of 62% for MBERT and 63.7% for LaBSE.
Game Theory Strategies in Cloud Security with Implementation

In the recent past, the IT sector has escalated to greater heights with the introduction of cloud computing. Along with the advancement in technology, the concerns in security have also increased simultaneously. Though cloud computing has numerous advantages and security provisions, still there exists some demerits leading to security concerns. In contempt of this matter, cloud providers hesitate to disclose any information regarding their security measures. This causes disinterest among the provider’s clients as they are debarred from knowing the security measures, and hence, they are uncertain about any malicious attack on their applications. Adding to that, clients cannot implement their own security measures as they are not granted any kind of access to the network layer. In this paper, we put forward the concepts of game theory and apply it to the security transparency models in cloud computing. Precisely, we model the security transparency dispute as a dynamic non-cooperative game theoretic problem, whereby the provider and client are modeled as players in the game. A theoretical analysis by which the provider or the client can compute his/her best strategy to reach the Nash equilibrium is presented.
Augmented Reality in Chemistry Education: An Exploratory Analysis

Augmented Reality (AR) is a part of immersive technology. It is growing very rapidly in this digitization era. Almost in every domain, we can find applications of augmented reality. In education, it is playing a very vital role. AR educational application has become a popular learning platform since it no longer requires expensive machinery and hardware like head-mounted displays for learning. Chemistry provides us fundamental knowledge of the matter’s structure around us. Studying the subject basic is very important for students. Hence, chemistry is chosen as parameter for this survey. The objective of this paper is to survey last five years (2017–2021), research work on Augmented Reality in Chemistry Education.
Time Series Forecasting with Quantum Neural Networks

In this work we explore the use of Quantum Computing for Time Series forecasting. More specifically, we design Variational Quantum Circuits as the quantum analogy of feedforward Artificial Neural Networks, and use a quantum neural network pipeline to perform time series forecasting tasks. According to our experiments, our study suggests that Quantum Neural Networks are able to improve results in error prediction while maintaining a lower number of parameters than its classical machine learning counterpart.