Reviewerly/exHarmony huggingface dataset:
A Neuro-NLP Induced Deep Learning Model Developed Towards Comment Based Toxicity Prediction

The comments sections of online forums and social media platforms have become the new playing field for cyber harassment. Correspondingly, various organizations and companies have decided to abolish toxic and nasty comments altogether to avoid this kind of issue. To protect authorized and genuine users from being exposed to comments which contain offensive language on online mediums or social media platforms, organizations have started flagging such comments and they are blocking those users who are using unpleasant forms of language. Most of the organizations use computerized algorithms for instinctive discovery of comment toxicity using machine learning and artificial intelligence based systems. In the present research study, we have tried to build multi headed comment toxicity detection models. We have built three toxicity detection models using deep learning techniques and compared the accuracy and results. We have also developed a menu driven interface which will help to link machine learning models which is uncomplicated for non programmers and this connection of model to interface will be convenient for making interactive programming interfaces with great accuracy and operationality.
Finger Motion Controlled Biomedical Wheelchair

The Wheelchairs have provided good assistance for the people who are suffering from disabilities. As a lot of disabled people require access to a wheelchair controlled by themselves, the proposed prototype is of great importance. Recent advancements in wheelchair systems includes innovative ways of controlling the wheelchair and at the same time making it affordable to the user. The motive of this project is to provide the user complete control of the wheelchair with the help of finger motion. Flex sensors take input from the user in the form of finger motion and they drive the motor, which helps the wheels to move in the required direction. Also this system consists of different health monitoring parameters and wireless charging with emergency buzzer.
Finding Experts in Community Question Answering System Using Trie String Matching Algorithm with Domain Knowledge

In recent days, different community question-answering systems have been evolving as online forums for knowledge sharing among users across the world. However, finding experts on the questions is a key issue in the community question-answering system. To address this issue, a novel approach is proposed in this paper for finding experts from the community question-answering website using an exact string-matching algorithm along with domain knowledge. The proposed system is designed with three phases: i) User Profile Modeling, ii) Question Preprocessing, and iii) Expert Recommendation. Initially, the matrix factorization model is adopted for user profile modeling where the tags and the answerer’s information are represented in the form of a matrix. Then, the domain-wise grouping of tags is done to minimize the search time of the tags. The question preprocessing is done using a keyword extraction algorithm to extract the keywords. Finally, the expert recommendation is accomplished using the trie string matching algorithm and key-value mapping process. For doing experiments, a stack overflow community question-answering website is utilized in this work. The performance of the system is measured and the results section proved that the system achieved 91.2% accuracy.
Spam email classification and sentiment analysis based on semantic similarity methods

Electronic mail has widely been used for communication purposes, and the spam filter is required in the e-mail to save storage and protect from security issues. Various techniques based on NLP methods are used to increase spam detection efficiency. Spam detection cannot handle the unbalanced classes and lower efficiency due to irrelevant feature extraction in existing approaches. In this research, sentiment analysis-based semantic FE and hybrid FS techniques were used to increase the spam and non-spam detection efficiency in e-mail. The sentiment analysis is carried out in this proposed method with semantic feature extraction and hybrid FS. The sentiment analysis measures the polarity of the input text and used for e-mail spam classification. Different semantic similarity feature extraction methods are used in this proposed method. The TF-IDF, Information Gain, and Gini Index were used. The proposed semantic similarity and hybrid FS were evaluated with various classifiers. The experimental analysis shows that the Gini index FS technique, word2vec FE, and SVM classifier show the higher performance of 95.17% and RF with Gini index and word2vec methods has 93.3% accuracy in e-mail spam detection.
Teaching and Training System Based on WEB3.0 Technology

The research on Application-oriented Undergraduate Teaching Reform Based on data mining technology is a research to investigate the research problems and solutions in the field of data mining. The purpose of this paper is to summarize the existing research in this field and discuss some new directions for future research. This paper will be divided into five parts: the first part briefly introduces data mining, the second part discusses the current research activities related to teaching reform, the third part describes the main findings of previous research and puts forward new directions for future research, and the fourth part discusses the limitations and conclusions. The purpose of this study is to study the effect of data mining technology on applied undergraduate teaching. The application of data mining technology can provide a lot of information about students, teachers and other people involved in education. This information can be used in various ways to improve the quality of teaching.
E-LEARNING SYSTEM FRAMEWORK USING ELLIPTIC CURVE CRYPTOGRAPHY AND SEARCHABLE ENCRYPTION

E-learning represents one of the foremost wide technique in modern education. Our framework is starting from the training effectiveness idea, in order for the learners not to acquire knowledge and skills independently, but instead to allow the learner to have access to their material in a customize fashion which will meet their needs without any barriers, such as time or space. Our framework is designed with respect for: Business Intelligence and in the same time is capable to offer a framework fully-loaded with Business Intelligence to give a full oversight over the organizational training; competencies management based on mapped organization plan which will identify smartly the gaps; with monitor and management courses; from e-course to conventional or VR and AR; and with a fully-loaded business intelligence features which will give the possibility to have multiple dashboard analytics. Current paper describes an e-learning framework that will assure the security of the data using elliptic curve cryptography, the data are being stored in a big data environment. The aim of our framework is to optimize the quantity of data and to offer security protection for the data, which is available on front end computers. The security will be guaranteed by a scheme based on elliptic curve cryptography and searchable encryption mechanisms. The role of searchable encryption will allow accessing the data on encrypted cloud and searches will be performed over encrypted content. Our framework will be dependent on some learner's behavior characteristics. The learner behavior will be understood as 'behavior in context' and characterized as an important security parameter for each learner.
CONSTRUCTION AND PROGRAMMING OF ROBOTS IN REAL AND SIMULATION ENVIRONMENT

The article is focused on educational robotics - on the construction and programming of robots in school environment. It characterises the forms in which the teaching of work with robotic kit can take place. It also brings the results of research into students? attitudes to work with robotic kits, whether their use has a positive effect on improving students? relationship to programming and technology as such. The work with LEGO MINDSTORMS EV3 robotic kit is described from the point of view of the construction and programming of robots in the extracurricular activity of computer science for pupils of the second primary education stage of elementary school. The article describes how to proceed if a sufficient number of real robotic kits is not available or if full-time form of education is changed to distance form. We also point out how to teach the construction and programming of robots in a simulation environment. We had had previous experience with using Virtual Robotics Toolkits during the present-form learning and therefore we used it in the distance form of teaching as well. This virtual kit allows to construct and program robots LEGO Mindstorms in 3D environment, which simulated various problem situations. Pupils can construct robot models at home in LEGO Digital Designer software using virtual blocks and then use Virtual Robotics Toolkits to continue working with the robot and control it by program. It replaces the use of physical models. It is a full-featured replacement of programming real robot kits. In the article we report on our experience in the construction and programming of robots in both full-time and distance forms of teaching.
EVALUATING THE PERFORMANCE OF AUTHENTICATION ALGORITHMS BASED ON KEYSTROKE DYNAMICS USED IN ONLINE EDUCATIONAL PLATFORMS

The field of education had noticeable challenges regarding in 2020. With the COVID-19 pandemic the authorities have introduced new regulations with reference to education. A decisive number of universities have had to adapt to the unfamiliar circumstances, moving all their activities to the online environment. These limitations have led to an unprecedented leap in online education. Suddenly, both teachers and students, were forced by the newly implemented conditions to move their entire activity to online educational platforms and thus continue their courses in this manner. This process has led to a development of the e-learning section. This unforeseen change brought a series of challenges, one of which we have focused on in this paper: how to supervise students during an exam session, considering that each one is physically in a different place. To emphasize one method that partially solves the problem of identifying the students that are attending the exams, the paper focused on the method of continuous authentication based on keystroke dynamics, the unique way of pressing the keys while a student writing the subject of the exam. This paper presents the state of scientific research in the field of keystroke dynamics and focuses on the methods of evaluating the performance of authentication algorithms based on keystroke dynamics in online educational platforms. Also, an analysis of the evolution of the MOOC phenomenon is made and the evolution of the main platforms is presented. The MOOC phenomenon (Massive Open Online Courses), courses attended by a large number of students from any corner of the world online, was born in 2008. This phenomenon reached a first maximum in 2012, and in 2020 there was an exponential increase in the number of students enrolled. These increases justify research research which is concerned with the success with which users are identified in an online education system, such as the present.
USING BLOCKCHAIN TECHNOLOGY IN SMART UNIVERSITY

The evolution of information and communications technology (ICT) has a huge impact in all spheres of activity, leading to major changes in every process, gaining important benefits but also facing challenges, especially those related to data security and protection. In the educational sector, there was a switch from traditional university to ICT based university. The use of SOA (Service Oriented Architecture), Cloud Computing, Internet of Things (IoT) and other technologies paves the way to switch to a new stage: intelligent university. Although IoT offers numerous benefits to the educational system, scalability and security of increasing number of objects connected to the network are major challenges. Switching to a decentralized IoT may be a solution for this challenge. Blockchain technology allows decentralization of process management thus being able to solve many of current problems of IoT, especially regarding the security. Also, the grows of e-learning and m-learning sectors requires using technologies that improve the trust and value of distance education. Additionally, increasing amount of electronic content, new law requirements regarding personal data security and protection and storing data in clouds require the adoption of new technologies that can solve current problems (see the Fog Computing paradigm). Blockchain technology may improve the educational system in various aspects, like: trust, collaboration, transparency, data security, issuing and security of certificates, management and protection of intellectual property, fighting intellectual fraud, lifelong high quality education, decentralized education, etc. Use of blockchain technology in educational system is in early stage. Very few universities have started using this technology, most of them only for validation of certificates/diplomas. There are few researches addressing the use of blockchain in education and the benefits and challenges of adopting this technology. This paper approaches three major issues: benefits of adopting blockchain in universities, challenges of adopting blockchain technology in universities, state of using blockchain technology in Romania.
Mineral Association Analysis: Predicting unknown mineral occurrences and improving our understanding of mineralogy

Minerals are the oldest surviving materials from the formation of our solar system. They are time capsules that store and provide information about the evolution of Earth and other planetary bodies. In addition to being a cornerstone of geoscience research, minerals also have economic, industrial and commercial importance in many sectors of society. One of the fundamental questions in mineralogy and geosciences in general is &amp;#8220;Where to find minerals?&amp;#8221;. Due to the complex and intertwined nature of natural systems, it has been hard to predict the occurrences of minerals. However, with increase in the volume and accuracy of mineral data and rise of mineral informatics, data science and analytics methods can be developed to answer this fundamental question in mineralogy.&amp;#160;&amp;#160;In this contribution, we present &amp;#8220;mineral association analysis&amp;#8221;, a method to: 1) Predict the mineral inventory for any existing locality. 2) Predict previous unknown localities for any given mineral. Mineral association analysis is a machine learning method that uses association rule learning to find interesting patterns based on mineral occurrence data. Using mineral association analysis, we have been able to predict locations of critical minerals, such as minerals with Li- and Th-bearing phases, predict the mineral inventory of mars analogue sites, and even understand how mineralization and mineral associations changed through deep time.
Symmetry statistics of mineral – paragenetic mode pairs

Characterizing the types of crystalline structures that form in different environments helps us to better interpret the geologic record and deepens our understanding of mineral stability. To this end, the Dolivo &amp;#8211; Dobrovol&amp;#8217;sky symmetry index provides a convenient way to quantify the statistical trends in the symmetry of minerals over time (Bermanec et al. 2022). Behavior of the Dolivo - Dobrovol&amp;#8217;sky symmetry index was investigated for different paragenetic modes of minerals (Hazen and Morrison 2022). Two datasets were used and compared (code and datasets are available on https://github.com/NoaVidovic/pgm-mineral-pairs-pg). The first one used only minerals, and each mineral was considered just once. In contrast, in the mineral&amp;#8211;paragenetic mode pair dataset, minerals were counted once for each of the paragenetic modes in which they occurred.The paragenetic mode dataset incorporates a number of properties associated with each of more than 60 modes of formation, including relative age and order of that mode&amp;#8217;s first appearance, estimated minimum and maximum temperature and pressure of formation, and duration. Paragenetic mode order does not substantially affect the symmetry index of minerals. However, some trends are evident when inspecting the properties of given paragenetic modes. The symmetry indices show a strong correlation with the maximum temperature, maximum pressure, and minimum pressure of paragenetic modes they belong to (Hazen et al. 2022) with correlation coefficients of 69%, 84% and 95%, respectively when using the mineral dataset. These trends show that minerals formed at higher temperature display higher overall symmetry. Trends for pressure are enigmatic: correlations show that minerals formed at higher minimum pressure tend to favor lower symmetry, whereas minerals formed at higher maximum pressure tend to favor higher symmetry.When using the mineral&amp;#8211;paragenetic mode dataset, the correlation coefficients are significantly lower at 42%, 30% and 89% for maximum temperature, maximum pressure, and minimum pressure, respectively. The lower correlation coefficients obtained using the mineral&amp;#8211;paragenetic mode pairs might indicate that the paragenetic mode is not as important in terms of trends in symmetry as initially thought. On the other hand, considering a much higher correlation coefficient for the mineral dataset, perhaps there is a more dominant effect where certain P-T conditions tend to favor certain types of symmetry at equilibrium.
Complexity of Russian Academic Texts as the Function of Syntactic Parameters

Providing students with academic materials of steadily increasing complexity is equally critical for textbook publishers, educators, and test developers, therefore automation of text complexity assessment is relevant in a number of areas. The authors propose an innovative approach addressing limitations of the existing Russian text complexity tools based on descriptive and lexical features only. We examined the impact of 75 syntactic features on texts measured with ETAP-3, contrasted them in texts of different grade levels in Russian Readability Corpus of 1.2 million tokens and suggest an original model of text complexity. We confirm statistically significant correlation of text complexity and 28 syntactic features in Russian texts. More research is needed to examine the model’s accuracy in texts of different types and genres as well as its applicability in automated text analyzers.
Addressing the Issue of Unavailability of Parallel Corpus Incorporating Monolingual Corpus on PBSMT System for English-Manipuri Translation

This research paper work establishes an important concept of improving Phrase based Statistical Machine Translation System incorporating monolingual corpus on the target side of the English to Manipuri translation language pair. However, there has been no work that focuses on translating one of the Indian Minority Tibeton-Burman Manipuri language pair. This Phrase based Statistical Machine Translation system has been developed using the Moses open-source toolkit and evaluated carefully using various automatic and human evaluation techniques. PBSMT achieves a BLEU Score of 10.15 as compared to the baseline PBSMT of BLEU Score 9.89 using the same training, tuning, and testing datasets. This research paper work addresses the issue of limited availability of parallel text corpora (English-Manipuri pair).
Hypernymy Detection for Vietnamese Using Dynamic Weighting Neural Network

The hypernymy detection problem aims to identify the “is-a" relation between words. This problem has recently received attention from researchers in the field of natural language processing because of its application to varied downstream tasks. So far, fairly effective methods for hypernymy detection in English have been reported. In Vietnamese, this problem has not been effectively solved. In this study, we applied a number of hypernymy detection methods based on word embeddings and supervised learning for Vietnamese. We propose an improvement on the dynamic weighting neural network model introduced by Luu Anh Tuan et al. [18] by weighting context words proportionally to the semantic similarity between them and the hypernym. Based on Vietnamese WordNet, three datasets for hypernymy detection were built. Experimental results showed that our proposal can increase the efficiency from 8% to 10% in terms of accuracy compared to the original method.
Text-Image Sentiment Analysis

Expressiveness varies from one person to another. Most images posted on Twitter lack good labels and the accompanying tweets have a lot of noise. Hence, in this paper we identify the contents and sentiments in images through the fusion of both image and text features. We leverage on the fact that AlexNet is a pre-trained model with great performance in image classification and the corresponding set of images are extracted from the web. In particular, we present a novel method to extract features from Twitter images and the corresponding labels or tweets using deep convolutional neural networks trained on Twitter data. We consider fine tuning AlexNet pre-trained CNNs to initialize the model and AffectiveSpace of English concepts as text features. Lastly, to combine the image and text predictions we propose a novel sentiment score. Our model is evaluated on Twitter dataset of images and corresponding labels and tweets. We show that accuracy by merging scores from text and image models is higher than using any one system alone.
Gaze Tracking: A Survey of Devices, Libraries and Applications

Gaze tracking is a technological discipline that offers an alternative interaction between human and computer. A common solution based on gaze tracking has two elements: a hardware device to obtain data from user’s gaze and a library to process the gathered data. This work makes a formal analysis of previous works in gaze tracking. It presents the most used devices and libraries in gaze tracking solutions in the last 5 years as well as the most important characteristics of each of them. To fulfill this purpose, a search for articles related with eye tracking solutions was carried out in different scientific databases. After that, a classification of results was made, as well as a review of them. In addition, the article offers the areas of IoT in which gaze tracking has had the most influence. Finally, various selection criteria for devices and libraries are offered. These criteria are based on features such as price, technical performance and compatibility.
CTDP Depacking with Guided Depth Upsampling Networks for Realization of Multiview 3D Video

With the advancement of 3D display technology, the original 3D stereo-view systems requiring 3D glasses have evolved to multiple-view naked-eyes 3D displays. For 2D broadcasting systems, we can pack one view and its corresponding depth map into a frame in the transmission side and generate multiple views by the depth image-based rendering (DIBR) engine at the receivers. Up to now, the centralized texture depth packing (CTDP) frame-compatible format is the best solution for view and depth packing. In this paper, we propose an improved CTDP depacking method and realize it with DIBR process to achieve real-time 3D multiview exhibition. To maintain the quality of the depacked depth map, in the CTDP depacking process, we suggest a deep learning-based guided depth upsampling network. The experimental results demonstrate that the proposed 3D multiview system can successfully depack CTDP video and generate 9-view 3D movies in real-time. The proposed guided depth upsampling network achieves a better quality of the generated views than the traditional algorithms.