Reviewerly/exHarmony huggingface dataset:
An MCTS-Based Recommender System for Education Complex

Although recommender systems in the education industry filter overload information and bring convenience to both learners and education facilities, challenges such as the coldstart problem and issue of timeliness, however, are more significant than recommending traditional goods while providing educational advice for students. To address this issue, this paper proposes an innovative recommender system based on Monte Carlo tree search (MCTS), which performs root cause analysis of the increased number of students during a certain period in each education facility, followed by computation of similarities between root cause groups and the rest of the students using Mahalanobis Distance. An experiment with a six-month dataset obtained from an education complex named Luoyang City Hall was conducted, and the results of A/B testing indicate that the treatment group experiences a higher student attendance rate than samples using traditional recommendation methods.
Unsupervised single image dehazing network

In image dehazing algorithms, most algorithms use supervised learning method for image dehazing. Because the ideal data set is difficult to collect, many supervised learning methods use composite data set, but the effect of the model trained in composite data set is often not ideal when applied to the real-world images. To solve this problem, a single image dehazing network has been proposed for the purpose of unsupervised single image dehazing network. The contributing factors include: (1) solving the problem of hard acquisition of ideal data sets, (2) proposing unsupervised loss functions, (3) introducing a method for embedding a model unsupervised neural network. We create unsupervised loss functions according to existing knowledge: the bright channel loss, the dark channel loss and the dark channel energy loss, we propose unsupervised loss functions that guarantee image dehazing algorithms. Since there are many experiments on synthetic and real-world images, the proposed method can improve the details, structure and texture of the image by better modifying the three advanced dehazing methods.
BiLSTM-ATT Chinese Sentiment Classification Model Based on Pre-training Word Vectors

Chinese text sentiment classification is an important branch of natural language processing, and deep learning-based sentiment classification has been a hot research topic for sentiment classification tasks in recent years. In this paper, the model introduces pre-training word vectors in the word embedding layer, extracts textual contextual features in the feature extraction layer using a bidirectional long and short-term memory neural network, and adds an attention mechanism to the network structure. The classification layer uses a support vector machine to classify the incoming textual features to achieve the analysis of Chinese text sentiment. The model achieves an accuracy of 87% in a publicly available takeaway review binary classification task.
A Cryptosystem of Skewed Affine Cipher of Multiple Keys

In this era, where communication over technology has become vital, the reliability of the same is of utmost need. Cryptography ensures confidentiality, user authentication, and integrity of data. One of the techniques is the Elliptic Curve Cryptography (ECC). Several classical ciphers are designed based on mathematical backgrounds. In this paper, we focus on combining Affine Cipher and ECC to magnify the security provided by an Affine cipher. Hence a skewed Affine cipher that uses multiple keys over Elliptic curves is proposed. The keys chosen are derived from the points on the specified Elliptic curve, which forms a cyclic group or a cyclic subgroup.
Can the Market of Cryptocurrency Be Followed with the Technical Analysis?

Abstract: This paper focuses on the role of technical analysis in cryptocurrency and whether cryptocurrency can be followed with technical analysis or not. During this study, we have used the most established technical tools and indicators like the Candlestick pattern, volumes, trends, the Moving Average, Moving Average Convergence Divergence, the Relative Strength Index, Bollinger Bands, Fibonacci Retracement, etc. By analyzing Bitcoin charts, the results indicate that the indicators can be used to generate a significantly positive return. It is found that cryptocurrency can be followed with technical analysis like equity market and forex market. We have used charts that explain how Bitcoin can be traded through technical analysis which is the most hyped-up topic for today’s generation. Keywords: Technical Analysis, Cryptocurrency, bearish, bullish, traders, candlestick pattens, technical indicators, securities, Moving Average Convergence Divergence (MACD), Relative Strength Index (RSI), Bollinger Bands and, trends.
An Overview of Semantic Based Document Summarization in Different Languages

The evolution of Artificial Intelligence (AI) gave insight into how humans should interact with machines. Natural Language Processing (NLP) is an area under AI, which aims to minimize the communication gap between human and machine. Text summarization is a sub-domain of NLP, which primarily focuses on summarizing text documents into a meaningful summary. Text summarization is useful in the areas, such as media monitoring, search engine optimization (SEO), question answering systems, etc. Lots of research works are carried out in the area of text summarization. A majority of the research works are adopted syntactic approaches. However, the semantic based approaches provide more meaningful summary compared to syntactic approaches. Due to the complexity of the semantic based methods, only few works are reported especially in Indian languages. In this paper, an overview of various semantic based text summarization methods are explored. An extensive review is done in languages, such as English, Arabic, Chinese, Hindi, Kannada, Tamil, and Malayalam. At the end of this survey, a critical analysis was done on various semantic based text summarization techniques. This analysis will not only give insights to potential prospective researchers, but also come up with an improved framework or method for semantic based abstractive text summarization.
Eyeball Movement Cursor Control Using OpenCV

The upcoming generation is going to be evolved with an interaction that will be nonverbal, which is called the eye gaze and it also develops a new mode of communication for disabled people. The basic need for this kind of a system is that it can provide the assistance that a third person gives for the physically disabled people and hence by the means of tracking the eyeball movement this is made possible. The right, left, top, and bottom movements are incorporated concerning the movement of the eyeball. The advanced version of this system comes with a chai that has wheels on the ends, hence this can also be mounted with the chair and hence it makes the user take the system wherever and everywhere he moves around. The tracking is generally done by taking the trace of the center of the pupil and hence with the traces it generates the center, top, right, left, and bottom.
The Role of Behavioural Intentions in Implementation of Bitcoin Digital Currency Factors in Terms of Usage and Acceptance in New Zealand: Cyber Security and Social Influence

Bitcoin, Dogecoin, Litecoin, Dash coin, Ripple, and Stellar are just a few of the numerous cryptocurrencies available today. Bitcoin is the most widely used cryptocurrency in various parts of the world. The objective, of this research to ascertain analyses the beneficial effects of social affect and cybersecurity on the use and acceptance of bitcoin.to ascertain and analyses the beneficial influence of examines the behavioural intention as a moderating factor, the relationship between social influence and cyber security and bitcoin use behaviour and acceptance in New Zealand. 497 questionnaires were distributed, and 328 completed questionnaires were returned and analysed, yielding a response rate of 68%. respondents provided their information voluntary a unique link emailed to every one of our samples. the survey. Used hierarchical moderated regression analysis to test the hypotheses (H1, H2, H3, H4). Spss were used to test two sets of linear regression models. the data collection instrument for this study was an online questionnaire. according to the findings, behavioural intention has a significant beneficial effect on social influence and cybersecurity on bitcoin use behaviour and acceptance. additionally, social influence, as well as cybersecurity, have a support positive impact of bitcoin use behaviour and acceptance in New Zealand. the implications for the research on the use of bitcoin are expected to provide education about bitcoin. enhance the security of financial transactions, provide informative pertaining for the regulation that permit use and provide benefits for entice and retain customers in terms bitcoin users. Technological advancements have resulted in a significant
Block Chain and Edge Computing Base Design Secured Framework for Tender Allocation

Online technology has improved a lot and it plays a major part in this generation. The government and private sectors are actively moving through this technology to make their work faster, more efficient, and grow cost optimization. One such technology that has grown in this past year is blockchain. It has been established in all the parts of the domain because it is more secured and available at a low cost provides more security and improved traceability. Normally government will post the tender details to some of the other companies as outsourcing. We are going to create a secured website for government tender using blockchain technology. Blockchain is a system of recording information. It is difficult to cheat or hack the system. With the help of blockchain, the government data will be stored securely. Cryptography technique is used for security and it is difficult for hackers to hack. The goal of our work is to provide industry innovation and infrastructure.
Factors Impacting Educators’ Intention Towards E-Learning Adoption

Educators play a vital role in learning system and in the present scenario E-learning highly in use in the pandemic. There are gaps in studies. Most of the research are student centric whereas educator’s role is more important as a content developer as well an effective presenter. The challenges for educators are getting familiar with the resources and application platforms changing concurrently like time constraints, negative attitude from parties involved, a lack of technical skills, infrastructure, organizational strategies support, and cyber threats. This study with taking the support of UTAUT2 in academic context tries to discover the factors impacting educators' intention with E-Learning technology. Most of the research carried out has not worked on the risk factors like technological, evaluation, cyber, psychological, interaction, and time involved in E-learning, so the current study proposes a behavioral model TECPIT (Technological, Evaluation, Cyber, Psychological, Interaction, and Time) to overcome the risk factors.
Designing a Framework to Improve the Learning Experience for Middle School Students in Geographical Education

With the growing research in the use of Augmented Reality (AR) for education, various applications have been developed for catering to day-to-day needs of living, learning, social, and physical. Augmented Reality applications improve various skills, such as motivation, collaboration, self-learning, critical thinking, and content understanding in the fields of education. Many AR-based applications are available for increasing the student learning experience in the distinct subjects of science, engineering, and mathematics when compared to the conventional approach. This study aims to accelerate the knowledge of eighth-grade students in geographical studies by assimilating the real-world environment with context-based information. This paper aims at the development and implementation of an AR-based system for analyzing and providing the information on how effective the usage of augmented reality applications can be to improve the learning outcomes of the eighth-grade students in their learning of the topics of geography relative to traditional teaching.
Machine Learning Based Face Recognition System

This task intends to provide progressed protection by using way of allowing a patron to understand who is in reality attending to the framework using facial acknowledgment. The framework allows sincerely permitted customers to get entrance. Python is a programming language implemented alongside machine getting to know methods and an open supply library that is applied to configuration, manufacture, and educate machine gaining knowledge of fashions. An interface tool is moreover accommodated unapproved clients to enlist to acquire the front with the sooner consent from the Admin.
Design of a Model in Machine Learning For Credit Card Fraud Detection

In e-commerce, fraud has become a major problem, and much effort is being invested into identifying and preventing it.Currently, fraud detection and prevention systems are only able to detect and prevent a small percentage of fraudulent transactions, resulting in billions of dollars in losses.Because online transactions are likely to grow dramatically in the coming year, better fraud detection and prevention is critical.We provide a datadriven strategy for estimating the likelihood of a fraudulent or legal transaction based on machine learning techniques applied to large data sets.To predict the likelihood of a customer's next transaction being fraudulent, the algorithm was trained using past e-commerce credit card transaction data.Random Forest, Support Vector Machine, Gradient Boost, and combinations of these are used to compare the performance of supervised machine learning approaches.The problem of class imbalance is considered before the model is trained on a classifier, and methods such as oversampling and data pre-processing are employed.
A Secure and Cost-Effective Platform for Employee Management System Using Lightweight Standalone Framework Over Diffie Hellman’s Key Exchange Algorithm

Aim: Cloud computing has become one of the most popular storage and computing technologies. The aim of this paper is to provide a secure and cost effective platform for the employee management system. Materials and Methods: This study contains a total of 2 groups, the Diffie Hellman key exchange algorithm is analyzed in the group 1 (434 samples), and the advanced encryption standard is analyzed in the group 2 (434 samples). The accuracy and efficiency of each of the models are compared. Result: This paper is to improve the efficiency of the algorithm used in the employee management system. The proposed model appears to be efficient and faster than the existing algorithm and the mean accuracy of detection is ±1SD. Conclusion: The final outcome of the existing system, Diffie Hellman’s algorithm is compared with the outcome of the advanced encryption standard algorithm, and the proposed model proved to be having higher efficiency (1.2 times faster) than the existing model.
Challenges for Designing of Children Speech Corpora: A State-of-the-Art Review

Automatic Speech Recognition (ASR) is the use of computer hardware and software-based techniques to identify and process human voices. These systems used data from both male and female speakers. The majority of commercial ASR systems available on adult speech are working efficiently. Speech data collected from both male and female speakers were used in these systems. In recent decades, ASR systems for children have emerged, such as reading tutors, aids for foreign language learning, and computer games. Child ASR systems are essential but poorly understood in the field of computer speech recognition. The child data collection is a very complex task. Child corpus is not available publicly, and variability of children speakers and ASR developed for a particular age group is not suitable for other age groups. These are some of the reasons for less and ineffective child ASR systems. However, the non-availability of child corpus publicly is a primary reason for ineffective child ASR. Designing and developing child corpus is a very tedious task. Therefore, the primary focus of this state-of-the-art review is to discuss various challenges encountered while designing and developing child corpus.
Use of Machine Learning and Detect Fake Profiles in a Social Media Network

The social network has become an important part of our lives today which is prone to online impersonation as well as fake accounts. These online profiles are used by intruders to cause harm in online social networks (OSNs) in order to perform malicious activities in form of privacy intrusion like theft identity. So it becomes necessary in OSN, to identify the account whether it is real or fake. In this article we have proposed several algorithms for classification like deep neural network and SVM (Support Vector Machine) algorithm. It also studies comparisons of these methods for classification based on the spam account dataset that is used to select the best.
Awareness and Perception of Chatbots/Chatbox Amongst Customers

As businesses are all moving to the online platform, interacting with the customers is becoming a challenge. Traditionally, businesses could connect with their customers through the middlemen involved in selling their products or through their sales force. Today, one of the platforms where business people interact with customers are chatbots. Since the use of chatbots is fast becoming a popular feature on the website of mainly customer-oriented businesses, it is important to know the ease and comfort of the people in interacting with the chatbots. In the current paper, we have tried to analyze the awareness and perception of chatbots amongst customers. The data was collected using a survey method. The questionnaire included data related to customer demographics and constructs to understand awareness and perception about chatbots amongst people. The data was collected from people residing in and around Pune city. Data would be analyzed using one-way ANOVA and Pearson correlation regressions tests. The results would have both academic as well as practical implications.
The Mechanism of Spam Comment Detection Using Count Vectorizer and Naive Bayes Machine Learning Algorithms in Python

AbstractAim : The aim of this study is to predict Novel spam comments using Naive Bayes and SVM algorithms to improve the accuracy. Spamming is the process of posting unwanted and awkward comments on specific posts in any type of social sharing medium or video sharing medium. Materials and Methods : The Dataset used for training and testing of proposed text precision models is created using the comments of some popular videos from Youtube with 5 attributes and Sample size = 150. The framework that retrieves suitable data adapts SVM algorithm and compares it with Naive Bayes algorithm. Results and Discussion : The retrieval accuracy of SVM classifier is (96.72%) and Naive Bayes algorithm is (90.89%). There exists a statistical significant difference between SVM and Naive Bayes with (p&lt;0.05) Conclusion : The work has confirmed that the efficiency of the support vector machine algorithm has given more accuracy when compared to the Naive Bayes machine learning algorithm.
Virtual Voice Assistant for Smart Devices

Digital assistants have been a budding technology lately. In modern society, chatbots have found their way in many sectors of e-commerce. With widespread advancements in the field of artificial intelligence, developing machines to ease real life tasks is the norm of the day. Voice Assistants have gained a lot of popularity in this era of home automation and smart devices. These personal assistants, voice or text, can be easily programmed to perform many tasks by simply commanding them to. High tech industries like, Google have publicized voice-based search that is, especially, a bonanza for many like senior citizens who are not comfortable using the keypad/keyboard or English language as these assistants offer support in other languages like Hindi too. Any voice-based assistant works on two pivotal functions namely - listening to(detecting) commands and responding to the given commands given by user. Along with these two base functions, we need customized directive that will define the functionality of the voice assistant.
A literature review on classification of phishing attacks

Phishing is a fraudulent activity through which the phisher/attacker tries to lure internet users into stealing their personal information/credentials to gain financially [1].Phishing is comparable to fishing, which gives a different meaning; in phishing, the attacker uses bait (sending an email with an embedded hyperlink that redirects to a fraudulent site) to capture the internet users' credentials.Earlier, the hackers were known as Phreaks (a Phreak is someone who breaks into the telephone networks illegally to make free long-distance phone calls or to tap phone lines) and are closely related to each other.The reason for replacing "f" with "ph" is to link the phishing scams with phreaks [2, 3].For the past two decades, phishing has become the most dangerous attack, and plenty of attack is taking place every day [4][5][6].The first phishing scam was recorded on 2nd Jan 1996 on American online (AOL) that provides internet services [2].
Z-Score Normalized Features with Maximum Distance Measure Based k-NN Automated Blood Cancer Diagnosis System

Leukemia is a blood-forming cancer disease characterized by the abnormal growth of White Blood Cells. Early detection and treatment of cancer reduce mortality and increase the survival rate. Most of the k-NN approaches used a different combination of statistical and geometrical features of the nucleus and cytoplasm with and without normalization, resulting in an uncertain range of features. Minkowski, Euclidean, City-block, Correlation, and Cosine distance metrics discover the average similarity between features predicts Leukemia with less accuracy. This paper proposes a Z-Score normalized feature set combined with a k-NN maximum distance measure-based automated blood cancer diagnosis system to address this problem. Initially, the features are normalized using the Z-score normalization method, which eliminates the mis-classification outcomes. The Chebyshev distance measure, which finds the maximum similarities between the features when k=1, has the highest accuracy of 97.92%. Furthermore, tuning the parameters by grid-search improves the performance rate by 98.65% .
Geometrical error correction based on sensitivity equations for accurate specular surface measurement with deflectometry

Deflectometry systems are very sensitive to geometry since the geometric uncertainty could easily generate low-order shape errors. The geometric calibration of the display screen is one of the important geometric calibration procedures. We have analyzed the geometry sensitivity of the display screen for flat mirror measurement with deflectometry. Sensitivity equations are derived to explicitly describe the relationship between the geometric uncertainty and the shape errors. The correctness and effectiveness of the proposed sensitivity equations have been verified with numerical simulations and preliminary experiments. The proposed theory can help to understand the effect of geometry sensitivity on deflectometry tests and evaluate the system performance. Moreover, it provides a guide for the improvement of the testing accuracy for low-order shapes.