Reviewerly/exHarmony huggingface dataset:
Language Identification for Multilingual Sentiment Examination

Social media is most popular platform on which users can share their views, reviews and knowledge about various topics, news, products etc. Identifying sentiments or opinions of users is valuable for many e-commerce companies, Hotels, e-learning etc. This opinion analysis is useful for companies to improve their service and products. Due to increase in web users across globe, users happen to post their views freely over the internet. Many different languages are spoken across globe, supporting multilingual nature of social media makes analysis of such text difficult. Sentiment analysis can be conducted using videos, image, text, where text sentiment analysis is most popular form because of freely available contents in the form of blogs, reviews, comments etc. Because of development of social media platform, people can post comment in any language, creates the need for Multilingual sentiment analysis. Sentiment analysis task needs phases such as data collection, pre-processing, sentiment classification and polarity identification. The Multilingual nature needs Script Identification on the input text by labelling the different words used in text along with scripts used to denote them. Various languages used in the text are identified and the Hindi language text written in Romanized script is transliterated to Devanagari script. Text is then completely translated into English language and POS(Parts of Speech) tagging is performed on the obtained text. The aim and purpose of this study is to survey different techniques of multilingual sentiment analysis, and language identification of source text, where n-grams model outperforms all.
Challenges Potential and Future of Internet of Things Integrated with Blockchain

In the Internet of Things (IoT) idea, regular gadgets end up savvy and self-governing. As we are seeing idea behind this is transforming into a realism on account of achievement in innovation, but we still face challenges, especially in some field like security e.g., information dependability. Taking account, the upcoming advancement in the field of IoT, it seems very important to give trust in the field of enormous approaching data foundation. Blockchain has given us the new way to share our data with others. Building such a trust in discrete condition with is term as the key factor of blockchain in which we don’t need any specialists is a hi-tech development that can possibly change numerous enterprises, the IoT among them. Troublesome advances, for example, IoT used enormous information and distributed to beat its restrictions, and we blockchain can be one of the accompanying ones. This paper centers around this correlation, explores difficulties in blockchain with IoT implementation, and reviews recent significant work so as to investigate about how the existing blockchain technology can change the way we work in IoT.
Gift-exchange Game Theory for Gamification on Digital Data Collection Systems

Gamification is widely used to increase user motivation by applying game elements to things that are not game-based. For example, in a data collection system. Gamification is used to increase the motivation of users to want to get involved. The elements of the game that are often used are rewards, badges, and leaderboards. But some implementations have not been able to ensure increased user motivation. Gamification applied needs to consider the use of game theory. Game theory is used to determine the right gamification model for digital data collection. This paper discusses the application of gift-exchange-games (GEG) in the gamification of digital data collection systems. GEG is used to model player interactions in the gamification system. GEG implementation of Gamification can improve user involvement in the system. This theoretical model can later be used for a more efficient data collection system development platform.&#x0D; Keywords: gamification, game theory, data collection, gift-exchange-game, geg
Correction to: Photocatalytic kinetics and cyclic stability of photocatalysts Fe-complex/TiO2 in the synergistic degradation of phenolic pollutants and reduction of Cr(VI)

The article Photocatalytic kinetics and cyclic stability of photocatalysts Fe-complex/TiO2 in the synergistic degradation of phenolic pollutants and reduction of Cr(VI), written by Tongtong Guo, Shuang Yang, Yunning Chen, Lu Yang, Yingnan Sun and Qingkun Shang, was originally published electronically on the publisher’s internet portal (currently SpringerLink) on 20 October 2020 with open access.
Remaining Useful Life Prediction Using Enhanced Convolutional Neural Network on Multivariate Time Series Sensor Data

All machines in power plants need high reliability and to be continuous run at all times in the production process. The Remaining Useful Life (RUL) prediction of machines is an estimation for planning maintenance activities in advance to save the cost of corrective and preventive maintenance. Most existing models analyze sensor data separately. This univariate analysis never considers the relationship between sensors and time simultaneously. In this paper, we applied a Convolutional Neural Network (CNN), which considered both dimensions of and sensors; a multivariate time series analysis. Furthermore, we applied many techniques to enhance the framework of deep learning, including dropout, L2 Regularization, and the Adaptive Gradient Descent (AdaGrad). For the experiment, we conducted our method and showed the performance in term of Root Mean Square Error (RMSE) on a standard benchmark and for real-case datasets.
Few Matches or Almost Periodicity: Faster Pattern Matching with Mismatches in Compressed Texts

A fundamental problem on strings in the realm of approximate string matching is pattern matching with mismatches: Given a text t, a pattern p, and a number k, determine whether some substring of t has Hamming distance at most k to p; such a substring is called a k-match.As real-world texts often come in compressed form, we study the case of searching for a small pattern p in a text t that is compressed by a straight-line program.This grammar compression is popular in the string community, since it is mathematically elegant and unifies many practically relevant compression schemes such as the Lempel-Ziv family, dictionary methods, and others.We denote by m the length of p and by n the compressed size of t.While exact pattern matching, that is, the case k = 0, is known to be solvable in near-linear time Õ(n + m) [Jeż TALG'15], despite considerable interest in the string community, the fastest known algorithm for pattern matching with mismatches runs in time Õ(n √ m poly(k)) [Gawrychowski, Straszak ISAAC'13], which is far from linear even for very small k.In this paper, we obtain an algorithm for pattern matching with mismatches running in time Õ((n + m) poly(k)).This is near-linear in the input size for any constant (or slightly superconstant) k.We obtain analogous running time for counting and enumerating all k-matches.Our algorithm is based on a new structural insight for approximate pattern matching, essentially showing that either the number of k-matches is very small or both text and pattern must be almost periodic.While intuitive and simple for exact matches, such a characterization is surprising when allowing k mismatches.
Handling Optimization Problem, and the Scope of Varied Artificial Bee Colony (ABC) Algorithms: A Contemporary

One of the most successful search algorithms of the last decade is Artificial Bee Colony (ABC) algorithm. It was first coined by Dervis Karaboga, 2005. Since then a group of variants of the algorithm have been anticipated to find solutions for the problems of optimization. The motivation for the algorithm is the search process of honey bees for food sources. The present paper aimed to bring out the evolutionary developments of the algorithm that cover numerous versions of the algorithm with the strategic changes to meet the optimization needs of the adopted problem contexts. This survey clearly reviewed the basic types, advancements, application areas, and the relevance of the ABC algorithm addressing various problem contexts. The efforts made by the research community since the last two decades along with the success stories are discussed in detail. The attachment of the optimization process of ABC with data mining is dealt in particular. Finally the opportunities and the scope of the application of the algorithm in large areas of problem domains are highlighted.
Automated Essay Scoring using Ontology Generator and Natural Language Processing with Question Generator based on Blooms Taxonomy’s Cognitive Level

Essay writing examination is commonly used learning activity in all levels of education and disciplines. It is advantageous in evaluating the student’s learning outcomes because it gives them the chance to exhibit their knowledge and skills freely. For these reasons, a lot of researchers turned their interest in Automated essay scoring (AES) is one of the most remarkable innovations in text mining using Natural Language Processing and Machine learning algorithms. The purpose of this study is to develop an automated essay scoring that uses ontology and Natural Language Processing. Different learning algorithms showed agreeing prediction outcomes but still regression algorithm with the proper features incorporated with it may produce more accurate essay score. This study aims to increase the accuracy, reliability and validity of the AES by implementing the Gradient ridge regression with the domain ontology and other features. Linear regression, linear lasso regression and ridge regression were also used in conjunction with the different features that was extracted. The different features extracted are the domain concepts, average word length, orthography (spelling mistakes), grammar and sentiment score. The first dataset used is the ASAP dataset from Kaggle website is used to train and test different machine learning algorithms that is consist of linear regression, linear lasso regression, ridge regression and gradient boosting regression together with the different features identified. The second dataset used is the one extracted from the student’s essay exam in Human Computer Interaction course. The results show that the Gradient Boosting Regression has the highest variance and kappa scores. However, we can tell that there are similarities when it comes to performances for Linear, Ridge and Lasso regressions due to the dataset used which is ASAP. Furthermore, the results were evaluated using Cohen Weighted Kappa (CWA) score and compared the agreement between the human raters. The CWA result is 0.659 that can be interpreted as Strong level of agreement between the Human Grader and the automated essay score. Therefore, the proposed AES has 64-81% reliability level.
Machine learning

The field of machine learning is introduced at a conceptual level. Ideas such as supervised and unsupervised as well as regression and classification are explained. The tradeoff between bias, variance, and model complexity is discussed as a central guiding idea of learning. Various types of model that machine learning can produce are introduced such as the neural network (feed-forward and recurrent), support vector machine, random forest, self-organizing map, and Bayesian network. Training a model is discussed next with its main ideas of splitting a dataset into training, testing, and validation sets as well as performing cross-validation. Assessing the goodness of the model is treated next alongside the essential role of the domain expert in keeping the project real. The chapter concludes with some practical advice on how to perform a machine learning project.
Contribution to the Geological, Geochemical and Mineralogical Studies of Gabal Serbal Granitic Rocks, Southwestern Sinai, Egypt

The area is located between latitudes 28˚ 32′-28˚ 40′ 43˝ N and longitudes 33˚ 34′-33˚ 41′ 30˝ E (Figs. 1a andb).El-Shazly et al. (1974) and Shimron (1980) concluded that gneisses and migmatites are the oldest rock unit in Sinai reaching more than 1100 Ma.Feiran gneisses and migmatites were studied by many authors, some of them are Akaad et al., (1967), El-Gaby and
Multi-class instance-incremental framework for classification in fully dynamic graphs

Existing work in the area of graph classification is mostly restricted to static graphs. These static classification models prove ineffective in several real life scenarios that require an approach capable of handling data of a dynamic nature. Further, the limited work in the domain of dynamic graphs mainly focuses on solely incremental graphs which fail to accommodate fully dynamic graphs (FDG). Hence, in this paper, we propose a comprehensive framework targeting multi-class classification in fully dynamic graphs by utilising the efficient Weisfeiler-Lehman graph kernel (W-L) with a multi-class support vector machine (SVM). The framework iterates through each update using the instance-incremental method while retaining all historical data in order to ensure higher accuracy. Reliable validation metrics are utilised for the model parameter selection and output verification. Experimental results over four case studies on real-world data demonstrate the efficacy of our approach.
Translating Time: Modelling the (Re)Processing of Emerging Meaning

The choice between substance ontology and process ontology has been haunting our thinking since, at least, Ancient Greek philosophy. The assumption seems that things are the way they are and that one has to put work into changing them. Constancy or substance, in this view, is primary and change (or process) secondary. In translation studies, this plays out in the source text as the stable starting point that has to be transformed into a target text. Based on Peirce’s process semiotics and other process thinkers, I inverse the above argument, arguing that change or process is primary and constancy or substance secondary. Because the universe is subject to the Second Law of Thermodynamics, it is a process taking form rather than a form changing. Any text is a process that has been constrained materially to be relatively stable, but the stability is not original; it is the effect of semiotic work, translation. My interest is in the semiotic work done to constrain the semiotic process into some form of stability and how one can get to know or understand these constraints. Part of this paper explores some of the implications of process thinking for translation studies. However, this reversal of ground and figure also challenges the modeling of translation. If translation is a process, how do we model it in a static medium such as print? Therefore, I explore the affordances that new computational technology offers for translating static models into dynamic ones.
UNIQUE METHODOLOGY TO MITIGATE ANTI-FORENSICS IN CLOUD USING ATTACK- GRAPHS

In this challenging era of technology where cloud computing has expanded its application wings in almost all areas. From individual end-users, small scale business to large organizations. All of them are now free from the maintenance, updating the software and taking the advantage of low-cost investments just by hiring the services and required applications from the cloud rather than building and maintaining the same. Though these advantages are creating much demand for cloud services with more required features at the same time, a challenge comes on the other side from attacks on client's data in the cloud. It becomes more difficult for the investigator to carry out the investigation during cyber-attacks on the cloud. During the attacks, there is a need for a quick response when certain anti-forensics is carried out to mislead the investigators. They may lead to waste of time, efforts, and spent cost. This paper presents a unique methodology to mitigate anti-forensics in a cloud with the effective usage of the relevant graphs. With the effective usage of graphs by the generation of attack, evidence and anti-forensic graphs helpful for the investigator to understand the cause and to carry out relatively countermeasures right way without being deviated by the anti-forensics.
The Acquiring of the Knowledge about Standards in the Digital Era

The knowledge from external sources has been recognized as critical for improving the capabilities of small and medium enterprises (SMEs) to be competitive on the global market.The aim of this paper is to explore main factors which affect SMEs' choice of a dominant source for acquiring information and the knowledge about standards.The questionnaire survey research method was used to seek the response from representatives of 130 SMEs operating in Serbia.Our findings indicate four sourcesrelevant websites, services of consultants, inputs of customers as well as information received from business partners.Our results showed that larger SMEs, with a longer tradition in business, will have more chances to use Websites as a primary source for gaining knowledge about standards.Domestic SMEs shape their decisions to hire consultants predominantly because of a negative perception of standards.The usage of customer inputs as a dominant source for gaining information and transfer of knowledge about standards are influenced by SMEs' experience in business and negative perception of the benefits of standards.In the digital era, SMEs still suffer from a lack of resources or capabilities to use the Web and the Internet for acquiring information and knowledge about standards in the digital era.
An Efficient Fault Tolerance Mechanism Using Tcsa To Ensure Reliabilty In Cloud

Distributed computing gives on-request benefit in computing innovation. This innovation offers a combination of programming and assets which displays dynamic adaptability in nature. These frameworks are advantageous; however, there is likewise a burden for the framework. In the event that the framework has side effects of disappointment, it doesn't give its usefulness. So, the arrangement is to blame expectation and relief. It gives the ability to the framework to respond easily when the framework has turned out badly or any surprising equipment or programming disappointment in the association. The paper portrays a higher objective behind faulty speculations and mitigation techniques, schemes, and how the cloud analyst uses the new novel method to solve these problems. In this document, fault prediction will be overcome with the schemes speculated blame for faults and mitigation will be clarified and achieved.
Isogeny-Based Certificateless Identification Scheme

In this paper, we propose a new certificateless identification scheme based on isogenies between elliptic curves that is a candidate for quantum-resistant problems. The proposed scheme has the batch verification property which allows verifying more than one identity by executing only a single challenge-response protocol.
Sign Language to Text-Speech Translator Using Machine Learning

Communication with deaf and dumb people is quite difficult task for others. So, through sign language can communicate with deaf and mute persons but it is difficult for normal people to understand the sign language hence it creates a huge gap between them and it's uneasy to exchange their ideas, thoughts with others. This gap has existed for years in order to minimize this, new technologies should be emerged. Therefore, an interpreter is necessary which acts as a bridge between deaf-mute and others. This paper proposed system which is a sign language translator. The system used American Sign Language (ASL) dataset which is pre-processed based on threshold and intensity. This system recognizes sign language alphabet and by joining the letters it creates a sentence then it converts the text to speech. As the system is based on hand, hand gesture is used in sign language recognition system, for that the efficient hand tracking technique which is given by media pipe cross platform is used and it exactly detects the hand after that by using the ANN architecture the model has trained and which classifies the images. The system has achieved 74% accuracy and recognize almost all the letters. The system which also converts sign text to speech so that it will also helpful for blind people.
Post-Quantum Security: CoreVUE Breaks Through PKI A Look at an Emerging Technology in Cybersecurity

Existing methods of encryption and key exchange are seeing their effectiveness reduced as quantum algorithms are released. This research and resulting proof of concept has created technology simplifying the protection of data in transit using a revolutionary post-quantum encryption key management system that eliminates the need for PKI.
Key Phrase Extraction by Term Clustering using Proposed Graph Based Ranking Model Method

This paper proposes a novel keyphrase extraction procedure which is referred as term clustering based keyphrase extraction (TCKE).The proposed graph-based keyphrase extraction method using TCKE is employed and the objective of this work is to extract the most important terms from the journal document.The proposed model consists of three sets namely document pre-processing, display of terms in graphical formats, and efficient retrieval of keywords.In pre-processing stage, the document is tokenized and removed the stop words from the text document.Further, the important terms are extracted and clustered into keyphrases.The term clustering proposed in this paper is employed to find the candidate keyphrases.Using this candidate keyphrases, keyphrase graph is constructed.The experimental analysis is carried out in journal article and the results depict that the proposed graph-based keyphrase extraction technique can effectively extracts the most important terms from journal document.
Automated identification and counting of proliferating mesenchymal stem cells in bone callus

The assessment of cell count is essential for the evaluation of biological cell proliferation development. Manual counting can be time consuming and subject to human error as it depends on visual inspection. On the other hand, automated counting using software based morphological analysis can eliminate or reduce these disadvantages and provide statistical reliability. In this study, we employ a software-based method for the automated counting of mesenchymal stem cells (MSCs) proliferation in the bone callus of Wistar rats to evaluate fracture healing. The proposed method started with extracting the green component of the digital image acquired using a light microscope. The subsequent stages involved: contrast enhancement, adaptive thresholding and false detection reduction. This method was tested using 48 MSCs images and the results were evaluated by a specialist. The average of precision, recall and F-measure were found to be 87.14%, 88.04% and 87.50% respectively.
Research on Collision Avoidance Technology of Manipulator based on AABB Hierarchical Bounding Box Algorithm

With the acceleration of the intelligent process of domestic industry, the manipulator in the production workshop is increasingly widely used, and the collision problem of the manipulator is becoming more and more prominent. Aiming at this problem, the AABB hierarchical bounding box algorithm is designed based on the AABB bounding box algorithm. The algorithm and the commonly used bounding box algorithm are used to envelope the human 3D point cloud data model. The results show that the operation time of this algorithm is 18.11% and 25.04% shorter than that of OBB and k-DOPs algorithms, but the proportion of human body in the volume of bounding box is only reduced by 6.65% and 11.20%, and the comprehensive effects are the best. Based on the algorithm, a simulation collision avoidance detection model is built on MATLAB. The simulation results show that the algorithm can realize the collision avoidance function of the manipulator effectively. The research provides a certain reference value for optimizing the collision avoidance ability of the manipulator.