Reviewerly/exHarmony huggingface dataset:
People’s Behaviour Analysis in Chat Message using Natural Language Processing

Nowadays, the mode of communication is mainly through messages. A lot of information has been conveyed through WhatsApp. WhatsApp is the most popular chat application with active users of more than 650 million. It has been widely used by all, especially among the business people and youngsters. Using several analyzing tools, users can analyse the WhatsApp group chat or personal chat. Authentically users wish to analyse their chat for several purposes. This research work is intended to perform a flirt analysis and time analysis. This project has many use cases like the parent, who wants to analyze their child chat; the police, who want to get valuable information from culprit chat; the business people, who wants to know the status of the business in the group chat. Using the Deep Learning model (NLP), sentimental analysis has been performed for each text. This helps to find the state of mind of the chatters. Further, this research work calculates the number of positive and negative statements that are used by each person in the text by using the text mining concept. As now due to this pandemic situation, every conversation and also the important discussion has been done through the WhatsApp and it was highly needed for the person who wants to check their child's conversation and also for the higher authority for enquiry and for the business chair person who are needed to analyse their business well being group can also be used for their personal usage of analyse using the algorithm in this method.
Pair Programming and the Level of Knowledge in the Formation of Pairs

The pandemic situation COVID-19 requires teachers to rethink some of the activities proposed to students since even though they are physically in the same room at the university, they cannot join and must maintain a social distance. The curricular unit of introduction to programming in the first year of computer courses requires several activities: students only learn by doing. One of the activities that has been used a lot in the academic community is pair programming: a single computer for two students working together. In the case that this article reports the pair works sharing a ZOOM room and alternates the position of driver (writes the code) with navigator (one that comments on the code). This article uses the technique of pair programming for writing top-down, algorithm and traces to verify the behavior of variables along the algorithm. If the pair programming technique has had very interesting results in terms of task speed and code quality far superior to programmers alone, there are some doubts about the problem of the constitution pf the groups. In this article we use two opposite strategies: group students with opposite knowledge or group students with similar knowledge. We found that students who prefer this activity are the worst ones (in both strategies) and the best students when grouped with colleagues with similar knowledge.
Android-Based Intangible Cultural Heritage Expert System In Banten Province Using Backward Chaining Method

Abstract Expert system is a system used to assist decision making that works by using the knowledge and analysis methods that have been defined by the expert. Intangible cultural heritage is a living cultural heritage that is recognized by some communities, groups or individuals. Banten Province has an intangible cultural heritage, but many people do not know there is many kinds of intangible cultural heritage that exist in Banten Province. The purpose of this paper is to make an Android-Based Intangible Cultural Heritage Expert System in Banten Province Using the Backward Chaining which is useful as a medium for the introduction of intangible cultural heritage that can be accessed easily. The software used is the Android Studio IDE which is installed with the Android SDK. The process of making an intangible cultural heritage expert system uses several stages, namely analysis, design, coding, implementation, and testing. The output of this research is that the application can be installed on an Android smartphone and data can be accessed offline.
Interpretation of SVM to Build an Explainable AI via Granular Computing

Machine learning (ML) Machine learning is known to be one of the chief tools used for extracting information from data and predict output with exceptional accuracy. However, this accuracy comes with a lack of explainabilityExplainability. This especially becomes a serious problem when it comes to analyzing and making diagnosis with medical data. These ML models are usually built by expert coders without any incorporation or feedback of contextual information from subject matter experts (physicians in our case). This in turn leads the physicians to be skeptical about these ML medical diagnosis models due to their lack of transparencyTransparency. Additionally, this may also cause the diagnosis given by the AI to have a gap in explainabilityExplainability. Thus, we aimed to overcome these challenges by incorporating physicians at an early development stage for our XAIXAI from the initial stage of validating the granular information extracted from the SVM algorithms to the final validation of explanations given by the XAIXAI mobile app. Through this, we were able to eliminate the gap in explainabilityExplainability which could have caused the AI to make incorrect diagnosis. Moreover, we also achieved higher levels of trust and improved the overall performance of the XAI whichXAI aided the physicians in various ways.
Machine Learning and Deep Learning applications in E-learning Systems: A Literature Survey using Topic Modeling Approach

E-learning has been one of the major trends in education and its becoming an attracting topic in the field of artificial intelligence and its subfields like machine learning and deep learning, that are considered the most promising technologies in our era where its application score is almost unlimited. Many researchers are showing interest in the topic with significant research results. The aim of this paper is to extract the applications of machine learning and deep learning in E-learning systems. In this work we collected research papers from five research databases: Springer Link, Science Direct, Scopus, IEEE Digital Library, and Web of Science for a topic modeling application using a machine learning technique known as Latent Dirichlet Allocation (LDA).
Facilitating student involvement in physics learning through worksheets assisted by augmented reality during the covid-19 pandemic: Analysis of teacher perceptions

Abstract One of the obstacles faced in physics learning during the COYID-19 pandemic is the low students’ involvement in the learning activities. This research was aimed to describe the views of Islamic school physics teachers on the importance of. Augmented Reality-assisted worksheet teaching materials in facilitating students’ involvement in the learning process This research involved 31 Islamic high school physics teachers in Lampung Province. Indonesia. The explanatory design mixed methods were used in this research. The results showed that the Augmented Reality-assisted worksheet could increase potentially student involvement in physics learning diuing the pandemic by implementing a learning management system smted to students’ learning needs and facilities. The results of this research also illustrated the importance of designing worksheets that support the effectiveness of online learning during the COYID-19 pandemic.
The impact of corruption and university education on African innovation: evidence from emerging African economies

Extant research has argued that university education plays an important role in engendering national level innovation. University education helps to increase innovation that in turn generates capital resources for the economy. This in turn supports R&D institutes and universities to generate and implement new creative ideas. This study, therefore, employed a panel data analysis for 19 African countries from 2011 to 2017 to investigate the role of university education in promoting African innovation. Sample data provided by UNESCO and The Global Economy were collected. The results indicated that university enrollment, graduates and expenditure are not major predictors of innovation and hi-tech export. But University graduate and expenditure have a negative impact on information technology export, whereas corruption extend the positive effect of university education (university enrolment, graduates, and expenditure) on innovation, hi-tech export, and information technology export. We recommend that standard operating procedures should be designed and implemented to ensure procedural foundations and awareness against elements of professional misconducts in education. Policymakers shall provide transparent criteria to ensure transparency in allocation of the university expenditures and promote anti-corruption education.
Mechanisms for improving the quality of education

In modern conditions, there is a competitive struggle among educational institutions for applicants in the educational space. Success is achieved only by an educational institution that has its own brand, based on high-quality education and the demand for its graduates in the real sector of the economy. Can increase your competitiveness by improving the quality of the educational process. This is possible through regular improvement of the educational process, introduction of new pedagogical technologies, and improvement of teachers pedagogical skills. The main directionsfor improving the quality of the educational process are work in the audience and work in the outside audience with students, as well as the activation of their scientific activities. It is possible to improve the quality of education by increasing the interest of students in the profession they are learning, so they will have internal motivation for better mastering the material and activating their educational and scientific activities. But to do this, the teacher must use modern pedagogical techniques and constantly develop himself. The article is written on the basis of the author’s own pedagogical experience. The article systematizes the directions of quality assessment, which can be used both for the organization of mechanisms for internal and external assessment of the quality of the educational process. Examples of experience in organizing the educational process are given.
The influence of cooperative model two stay-two stray assisted by digital literacy to improve student’s metacognitive at MTs Muhammadiyah Sukarame Bandarlampung

Abstract The purpose of this study was to determine the influence of cooperative model two stay-two stray assisted by digital literacy to improve student’s metacognitive at MTs Muhammadiyah Sukarame Bandarlampung. The research conducted was quasi-experiment research with a non-equivalent control-group design. The method used was quantitative and the sampling technique used was the saturated sampling technique. The sample in this study was class VIII U1 as the experimental class that received the treatment of Two-Stay-Two-Stray assisted by digital literacy and class VIII U2 as the control class that was treated using the Group Investigation model. The results of this study revealed that the average metacognitive ability of the experimental class was 73,04 and the control class was 67,56. Based on the result of the t-test, Two Stay-Two Strays assisted by digital literacy affects the students’ metacognitive ability with the results of t observed = 3,17 and t critical = 1,66 which indicated that H 1 is accepted. Thus, the Two Stay-Two Strays cooperative model assisted by digital literacy affected the metacognitive ability of MTs Muhammadiyah Sukarame Bandarlampung students. Increased metacognitive abilities affected the improvement of students’ learning outcomes.
A Conjecture on Optimal Ternary Linear Codes

We give a conjecture on the achievement of the Griesmer bound for ternary linear codes. We prove that our conjecture is valid for at most 7 dimensions.
Five Hundred Most-Cited Papers in the Computer Sciences: Trends, Relationships and Common Factors

This study reveals common factors among highly cited papers in the computer sciences. The 500 most cited papers in the computer sciences published between January 2013 and December 2017 were downloaded from the Web of Science (WoS). Data on the number of citations, number of authors, article length and subject sub-discipline were extracted and analyzed in order to identify trends, relationships and common features. Correlations between common factors were analyzed. The 500 papers were cited a total of 10,926 times: the average number of citations per paper was 21.82 citations. A correlation was found between author credibility (defined in terms of the QS University Ranking of the first named author’s affiliation) and the number of citations. Authors from universities ranked 350 or higher were more cited than those from lower ranked universities. Relationships were also found between journal ranking and both the number of authors and the article length. Higher ranked journals tend to have a greater number of authors, but were of shorter length. The article length was also found to be correlated with the number of authors and the QS Subject Ranking of the first author’s affiliation. The proportion of articles in higher ranked journals (journal quartile), the length of articles and the number of citations per page were all found to correlate to the sub-discipline area (Information Systems; Software Engineering; Artificial Intelligence; Interdisciplinary Applications; and Theory and Methods).
Industrial and OSS developers’ profiles: a family of experiments to evaluate a pioneering neuro-linguistic method for preferred representational systems automatic detection

Abstract Software projects use mailing lists as the primary tool for collaboration and coordination. Mailing lists can be an important source for extracting behavioral patterns in the software development. A new approach for that is the use of Neurolinguistic theory to determine what is the Preferred Representational cognitive System (PRS) of software engineers in that specific context. Different resources and cognitive channels are used by developers in order to achieve software understanding. An important question on this matter is: What types of representational systems are preferred by software engineers? This paper presents a psychometrically based neurolinguistic method to identify the PRS of software developers. Experimental evaluation of the approach was carried out in three experiments to assess the Preferred Representational System of developers at Industrial and OSS (Apache server and Postgresql) mailing lists. For the OSS projects, the results showed that the PRS scores of the top-committers clearly differ from the general population of the projects. For industry, the experiment showed that the developers indeed have a PRS. Finally, for both scenarios, the qualitative analysis also indicated that the PRS scores obtained are aligned with the developers’ profiles, considering that alignment is essential to effective communication within the team and enhances the development process due to a better software comprehension.
Reversible Color Image Watermarking Scheme Based on Contourlet Transform and Principal Component Analysis

It became an important issue to protect the ownership and prevent unauthorized access. A blind and reversible watermarking technique is introduced in this work. The watermark will be generated from the host image and owner identity. Gray Level Co-occurrence Matrix can be used for key generation. Contourlet Transform is used to partition the image into multiple sub-bands. Principal Component Analysis is used to identify the appropriate bands that can be used for embedding the watermark. For the extraction of watermark, secret key and ownership id are required. This technique ensures superior performance in contrast with other techniques while calculating Bit Error Rate and Peak Signal to Noise Ratio.
Blockchain for Secure and Transparent Track-and-Trace in Manufacturing

This chapter will first introduce the reader to blockchain technology and the different types of blockchain systems. The underlying principle on how it achieves consensus and guarantees immutability of records in a completely distributed manner is explained. Some use cases where blockchain can address challenging issues in manufacturing environment are given. A particular use case of end-to-end track-and-trace including the manufacturing process on the shop floor is then explained, and showcased in [28] by implementing the blockchain-based track-and-trace system with Hyperledger Fabric 2.0. Additional challenges that need to be tackled pertaining to transaction speed and volume are described. To catalyze the adoption, seamless integration of different blockchain systems is needed. This will involve integrating a number of different blockchain protocols. A discussions and recommended considerations for blockchain interoperability, which allow exchange of information/tokens across different systems running different protocols are then given. Finally, this chapter ends with a summary where some of the best practices for a successful blockchain deployment are discussed.
Enhancing the Security of Confidential Data Using Video Steganography

Privacy has always become more increasing impact on modern applications and the growth in term of technology. Security and confidentiality of information have become very crucial and challenging aspects for transmitting the secret data to the recipient. Therefore, many techniques are created for providing high security of data. Steganography is used for hiding the secret data behind any medium like text, audio, video, image, etc. It is not sufficient just to hide data. For this purposes, we used encryption of data before hiding it. Cryptography is used for encryption of secret data. Video steganography gives better performance for data hiding because of large amount of video frames are used for data hiding as compare to image steganography. The frame selection approach helps for selection of frames from video. This hybrid combination gives high security to data. It helps for providing better security in military, law enforcement agencies, securely storing data, etc. The main purpose of this research is to provide multilevel security with high embedding capacity of data. The PSNR ratio is getting increase.
The Legendre pseudorandom function as a multivariate quadratic cryptosystem: security and applications

Abstract Sequences of consecutive Legendre and Jacobi symbols as pseudorandom bit generators were proposed for cryptographic use in 1988. Major interest has been shown towards pseudorandom functions (PRF) recently, based on the Legendre and power residue symbols, due to their efficiency in the multi-party setting. The security of these PRFs is not known to be reducible to standard cryptographic assumptions. In this work, we show that key-recovery attacks against the Legendre PRF are equivalent to solving a specific family of multivariate quadratic (MQ) equation system over a finite prime field. This new perspective sheds some light on the complexity of key-recovery attacks against the Legendre PRF. We conduct algebraic cryptanalysis on the resulting MQ instance. We show that the currently known techniques and attacks fall short in solving these sparse quadratic equation systems. Furthermore, we build novel cryptographic applications of the Legendre PRF, e.g., verifiable random function and (verifiable) oblivious (programmable) PRFs.
Deep Learning based Framework for Text Detection

At present, object detection is estimated as a core assignment in the province of computer vision. Strategies for text detection dependent on deep learning have been applied in variety of fields, like smart transportation systems and self-sufficient driving system. In recent times deep learning based strategies used for localization of text have acquired cheerful results with respect to diverse datasets, but, they typically fall behind when presented to precarious circumstance. Still there is a challenge among speed and precision for text discovery. Customarily for text detection generally the two phase detectors like Faster-RCNN, Fast-RCNN and R-CNN were investigated. In this work, a deep learning based framework for detecting text in scene pictures is proposed. Proposed framework is based on one stage object detection system identified as YOLOv5 which is nearly quicker and precise, difference to existing object detectors. It gives quick and exact identification of text from basic scene pictures. Analyses performed on condition of craftsmanship datasets including ICDAR 2013, ICDAR 2015, ICDAR 2003, SVT, MSRA-TD-500 show that the proposed framework essentially performs better aside from ICDAR2015, contrast with existing strategies regarding rightness. Proposed framework obtains optimum results for ICDAR2013 dataset. The size of model produced through our framework is small.
The Research on Improving the Precision of The Polymer IFHI By BP Neural Network of The Method Data Normalization

Abstract BP neural network has a good effect on data prediction. Using BP neural network to predict polymer fire risk index (IFHI) is a cross research method based on computer intelligent learning model and traditional fire risk assessment. In order to solve the defect of low precision of BP neural network in IFHI prediction, a solution to standardize the initial data processing is proposed to optimize the BP neural network simulation model. In the construction of BP neural network model, the range, threshold, and weight of the initial data need to be processed globally, and then the BP neural network is simulated and trained. Finally, the data is simulated and predicted by using MATLAB. The output results of the neural network with different data standardization processing methods are compared to verify the simulation accuracy. The results show that the input node parameters of BP neural network are processed by Z-score normalization method, which improves the learning ability of the model, improves the accuracy of the prediction effect, and reduces the IFHI error of prediction.
Sentiment Analysis Related of International Festival of Living Arts Loja-Ecuador Employing Knowledge Discovery in Text

Nowadays, the use of social networks is part of the daily life of most people, especially young people, to share content and opinions. Twitter is one of the most popular social networks, in which users are the first actors to generate a large amount of information. The analysis of Twitter data requires a systematic process of collection, processing and classification. The main objective of this work is to classify the data tweets in three different classes: Positive, Negative and Neutral Opinions, corresponding to the “International Festival of the Living Arts in Loja (FIAVL)” in the years between 2016 and 2019. The official account of the “FIAVL” produced a total of 18k tweets in Spanish language, which followed the different phases of the Knowledge Discovery in Text (KDT) methodology for its analysis and study. Vector Support Machines (SVM) and Naive Bayes (NB) were used to classify the classes, where an accuracy rate of 98.7% was obtained, with the neutral opinion prevailing over the rest of the classes with 57%, thus it could be concluded that there are no positive or negative opinions about the FIAVL.