Reviewerly/exHarmony huggingface dataset:
Bipolar Neutrosophic Finite Switchboard State Machines

The notion of bipolar neutrosophic finite switchboard state machines (BNFSSTMs), homomorphisms and strong homomorphisms of bipolar neutrosophic finite state machines (BNFSSTMs) are introduced and some related results are studied.
Quality in education in the policy of Heydar Aliyev

The purpose of the study is to provide quality education in the education system of our country, especially in the higher education system, and to determine ways to integrate into the world education system, taking into account the global processes taking place in the world and digital technologies resulting from the IV industrial revolution, as well as new requirements of the business world. and international communities to professional skills designed for the distant future in this direction.
Financial Literacy, Numeracy and Schooling: Evidence from Developed Countries

Financial literacy is low among young people and their uninformed choices may have costly and long-lasting consequences. This paper uses information on approximately 52,000 fifteen-year-old students participating in the 2018 OECD Programme for International Student Assessment (PISA) to provide fresh evidence on two drivers for youth financial skills: math skills and students’ exposure to financial education at school. Our results are threefold. First, mathematical skills have a positive impact on financial skills, and to a greater extent than reading skills. Second, an extension based on the 2012 wave of PISA suggests that the transfer of competences from mathematics to financial literacy can be enhanced when teaching strategies focus more on stimulating “cognitive activation”. Third, we show how the percentage of students having the chance to receive financial education at school varies widely across countries, and how having such an opportunity positively influences financial achievements.
The Prediction of Hydrometeorology Variables Using the Method of Recurrent Neuronal Networks

This scientific article evaluates the prediction of hydrometeorological variables, which refer to temperature, precipitation, and flow. The applied methodology is long-term bidirectional recurrent neural networks (BRNN) in a series of 40 years of study for a better perspective on the climatological conditions in Metropolitan Lima until the year 2050. The BRNN model is formed by a single series of past observations, which means that the model analyzes one variable simultaneously to project the next value in the sequence, and unlike other LSTM models, the bidirectional model can model complex and long-time series of sequences efficiently. The purpose of the model is to divide the neurons of a regular RNN into 2 directions, one of them is for the positive time direction (forward states), and one is for the negative time direction (reverse states). In addition, mean square error (MSE), root mean square error (RMSE), and Nash Sutcliffe efficiency ratio (NSE) were used as error metrics to assess prediction performance. Regarding the results, the prediction of average temperatures tends to increase between the ranges of 0.30–0.90 °C with estimated maximum temperatures up to 27 °C.
Machine Learning Model for the E-mail Spam Detection with Data Mining Techniques

E-mail customers get several hundred spam messages on regular basis with a fresh content, from fresh addresses which are robotically produced by robot programming tool. The traditional methods namely dark-white lists are ineffective to filter the spam. The text mining (TM) methods are implemented to an e-mail for maximizing the yield the filtering of e-mail spam. The e-mail spam detection techniques have various phases such as to pre-process the data, extract the attributes, and classify the data. The pre-processing phase will clean the dataset, and features are extracted to identify the features having great impact on the target set. The combination of multiple classifiers is used in this phase for the classification focuses on integrating the SVM, NB, and Random Forest. Python is employed to execute the introduced system and diverse metrics such as accuracy, precision, and recall are considered for analyzing the outcomes. The results of proposed model show high improvement for the e-mail spam prediction.
IIITA AlumniConnect: A Community Platform to Bridge the Gap Between Alumni, Students and Faculty

Every year a large number of students graduate from our institution, and the institution needs to communicate with those students as they represent the institution and become real-time or indirect advertisers for the institution. The purpose of IIITA AlumniConnect is to support this goal of providing opportunities for alumni students to enjoy the benefits of socializing and gaining the potential to form a network of alumni. Through community-based student organizations and programs, IIITA alumni can renew and strengthen its relationship with the Institute, developing its mission and values among alumni, students and faculty. This social media application provides an easy-to-use interface that enables the user to easily understand features offered by the application. The user will be able to register and log in to their account and interact with other users through community posts and personal chat options. The user can share ideas, digital photos and videos, jobs posts, posts, personal chat and inform others about online or real world activities and events. The Alumni Affairs Office, IIITA is conducting mentorship programs(guidance by alumni to current students) in several areas such as Placements, Jobs, Entrepreneurship, Higher Education, Management, Administrative and Civil Services for which this platform will come in handy to the institute.
Birth of a New Asset Class

While the first part of the book covered why cryptoassets are worth understanding, this second part dives into what they are in more detail. This chapter introduces how the first cryptoasset came to life, why it was meaningful, and what followed.
Websockets

HTTP by its nature is a one-directional, stateless protocol. This means one party, a client like a web browser, sends a single request to the server and waits for the returned data, eventually closing the connection once all the data from that single request is retrieved. From the protocol perspective, any subsequent request does not know about previous requests, which is why HTTP is called a stateless protocol.
Form-Based Authentication

Secured web applications typically use the HTTPS (SSL/TLS) communication protocol rather than HTTP and require users to authenticate themselves via a login dialog box. With the basic authentication procedure, the browser presents a standardized input form for the user credentials. Although it's easy enough to implement and configure, the basic authentication does not allow you to control the presentation and behavior of the login dialog box. Professional web applications use form-based authentication instead, which allows them to implement their own login dialog box that's compliant with company policies and design guidelines.
Handcrafted and Deep Features for Micro-expressions: A Study

Micro-expression emerges on the face when someone is striving to mask a true emotion. They are difficult to see with naked human eyes due to their short duration, necessitating the use of computer vision tools to recognize them. This paper presents a micro-expression analysis based on handcrafted and deep feature extractors. The former extractor is further classified in appearance, geometry, and optical flow-based methods; the latter discusses convolutional neural networks. This survey also includes the recent state of the art in each category, along with their contribution and performances.
COVID Teaching Experiences as Disrupting Events to Promote a Paradigm Shift in K-12 Mobile Technology Integration

Although many K–12 teachers were reluctant to integrate mobile devices into their instruction (also known as mobile learning) before the COVID-19 pandemic, the shift to emergency remote or hybrid learning disrupted business-as-usual educational practice. The present mixed methods study investigated 16 K–12 teachers’ perspectives about mobile learning and how COVID-19 influenced those perspectives in an online educational technology course held in the early months of the shift to remote or hybrid instruction in 2020. Data included participant responses to background questionnaires, mobile-learning readiness and technology adoption surveys as well as course artifacts such as discussion board posts. Findings included that the teachers considered mobile devices to bring new opportunities for learning, able to improve twenty-first century skills, enhance learning in more flexible ways, with an aim to improve student-to-student communication. Participants’ personal COVID teaching required many to use mobile devices for teaching and learning for the first time or in new ways. Their COVID-19 teaching experiences disrupted teacher participants’ mobile learning perspectives. Teachers observed students’ social and academic needs being met flexibly, newly recognizing mobile learning as critical and potentially transformational; the pandemic “changed everything.” Teachers, such as the participants of this study who have some training in mobile learning, are increasingly ready to be the change.
Real-Time Indian Sign Language Detection

Sign Language Recognition is one in all the foremost growing fields of research. The sign language is mainly used for the communication of deaf–dumb people. There has always been a communication gap between normal and deaf or dumb people. Large amount of research has been done on this topic, but no outcome has come that can be commercialized. There is currently no such system available that can help for perfect and complete communication among them. The aim of this paper is to review the Indian Sign Language System and explore different approaches for implementing the system. The input to the system will be real-time hand gestures or sign images. A camera attached to the computer will capture images of the hand in the two detection areas, and the contour feature extraction is used to recognize the hand gesture combinations of the person. Based on these recognized gestures, the relevant text will be displayed on the computer screen. If this project is developed further, Text to Speech Conversion can also be made possible by including the Google Text To Speech (GTTS) module of Python in the SLDS.
COVID-19 Social Distancing Detection and Email Violation Mechanisms

The technique of flattening the curve for coronavirus-infected cases is challenging in addressing the worldwide ongoing rampant novel COVID-19 pandemic crisis unless citizens take steps to halt the virus’s spread. Maintaining a safe space between individuals around us in public is one of the most important behaviors. Deep learning algorithms have been used in the proposed work to mitigate the spreading of the coronavirus utilizing social distance detection. This proposed work analyzes a pre-recorded video feed of walking pedestrians to alert people to maintain a safe distance. The goal is achieved using YOLOv3 and YOLOv4 for object detection in the video frame used as input. Furthermore, an email-based alert mechanism is also implemented if the number of violations exceeds the defined limit.
WDP-GAN: Weighted Graph Generation With GAN Under Differential Privacy

Many real-world networks can be represented as weighted graphs, where weights represent the closeness or importance of relationships between node pairs. Sharing these graphs is beneficial for many applications while potentially leading to privacy breaches. Variants of deep learning approaches have been developed for synthetic graph publishing, but privacy-preserving graph (especially weighted graph) publishing has not been fully addressed. To bridge this gap, we propose WDP-GAN, a generative adversarial network (GAN) based privacy-preserving weighted graph generation approach, which can generate unlimited synthetic graphs of a given weighted graph while ensuring individual privacy. To do this, we devise a new node sequence sampling method to generate the training set while preserving both the edge weight and topological structure of the original graph. Moreover, we apply the bi-directional long-short term memory (Bi-LSTM) network to capture the interdependence of node pairs. WDP-GAN then approximates the edge weight information using the frequencies of edges produced by the generator. Furthermore, we propose an adaptive gradient perturbation algorithm to improve the speed and stability of the training process while ensuring individual privacy. Theoretical analysis and experiments on real-world network datasets show that WDP-GAN can generate graphs that effectively preserve structural utility while satisfying differential privacy.
Assessment Framework

Before measuring the value of a cryptoasset, the diligent investor should ask critical questions to identify whether the asset has value in the first place. While the next chapter dives into valuation methods, this chapter covers preliminary questions to filter out candidates for these valuations.
LFTransNet: Light Field Salient Object Detection via a Learnable Weight Descriptor

Light Field Salient Object Detection (LF SOD) aims to segment the visually distinctive objects out of surroundings. Since light field images provide a multi-focus stack (many focal slices in different depth levels) and an all-focus image for the same scene, they record comprehensive but redundant information. Existing methods exploit the useful cue by long short-term memory with attention mechanism, 3D convolution, and graph learning. However, the importance of intra-slice and inter-slice in the focal stack is not well investigated. In the paper, we propose a learnable weight descriptor to simultaneously exploit different weights in slice, spatial region, and channel dimensions, and therefore propose an LF SOD method based on the learnable descriptor. The method extracts slice features and all-focus features from a weight-shared backbone and another backbone, respectively. A transformer decoder is used to learn the weight descriptor which both emphasizes the importance of each slice (inter-slice) and discriminates the spatial and channel importance of each slice (intra-slice). The learnt descriptor serves as the weight to make slice features attend to important slices, regions, and channels. Furthermore, we propose the hierarchical multi-modal fusion which aggregates high-layer features by modelling the long-range dependency to fully excavate common salient semantics and combines low-layer features by spatial constraint to eliminate the blurring effect of slice features. The experimental result exceeds the state-of-the-art methods at least 25% in terms of mean absolute error evaluation metric. It demonstrates a significant improvement in LF SOD performance via the designed learnable weight descriptor. <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/liuzywen/LFTransNet</uri>
Jakarta Mail

Being able to send emails from server processes can be a vital part of a company’s IT landscape. Consider, for example, asynchronously transmitting results from long-running calculations, sending automatically generated reports, or sending warning messages when a server process fails.
Texture Classification Using ResNet and EfficientNet

The texture is the fundamental unit of an image that aids in its identification. Analysis of texture forms the foundational basis of Computer Vision tasks like image recognition and image segmentation. Images of various domains like satellite and medical have been identifiable because of their texture. This paper aims to propose models for the task of texture classification, evaluate them on a standard data set and then compare the results of the models to the results attained by models proposed previously for the same task. The paper uses the transfer learning approach to achieve this objective. The pre-trained models are used for feature extraction, removing their top layer and freezing the rest. The two pre-trained models used in this paper are the ResNetV2 and the EfficientNet-B4 paper. The proposed models are trained and tested on the Kylberg data set, a widely used texture data set. The two models attained accuracies of 99.78% and 92.97%, respectively.
Evaluation of Semantic Parsing Frameworks for Automated Knowledge Base Construction

Semantic parsing is a subfield of natural language understanding that translates natural language utterances into detailed representations of their meaning. Though numerous meaning representation frameworks exist, none has been universally accepted. The current paper provides a comparative study of the most promising semantic representations of text, taking into account parsers, performance, extensibility, available corpora, and other aspects - used for the automated construction of a knowledge base for commonsense reasoning. In parallel, a corpus was constructed, and experiments were conducted to capture the linguistic attributes essential for automated reasoning. Based on the findings the author suggests using said parsers in an ensemble.
Stack Tag - Predicting the Stack Overflow Questions’ Tags Using Gated Recurrent Unit Networks

Stack Overflow like reputed forum has well-curated set of Questions and Answers pertaining to computer programming which is being increased exponentially. Thus, it has become more relevant than ever to provide an alternative to the currently in place tagging system. In this research paper, we proposed a conventional deep learning model that employs the concepts of Natural Language Processing with Gated Recurrent Neural Networks to predict the programming language of the question posted on Stack Overflow. The 10 most popular languages have been considered for the experiments. We built three classifier models separately with three different inputs; the first model requires only the title of the question, the second model requires only the body of the question, and the last model requires both title and the body as the input. Results shows that the classifier using the title only and body only as the input resulted in an accuracy score of 79.21% and 84.17% respectively, however, the classifier trained on both title and body from the question estimated at an astonishing accuracy of 97.38%. The underlying results demonstrate that our deep learning model tends to generalize more accurately when provided with both the title and body of the context.
Automatic Recognition of Check-Points for NCPR Video Review

The goal of the Neonatal Resuscitation Promotion Project is to promote neonatal cardiopulmonary resuscitation (NCPR), which is an indispensable skill to help doctors and nurses save the life of a newborn whose respiratory circulation is unstable immediately after birth. Workshops are held throughout Japan consisting of lectures, scenario training, and review in support of this goal. In the NCPR workshop, it is recommended to review student activities in the scenario practice by using video. However, the time available for review is limited, which makes it somewhat difficult to play and review the entire video. Our concept is to save review time by automatically detecting essential scenes in scenario practice. In this study, we tackled the detection of essential scenes the instructor wanted to emphasize. First, we developed and experimented with an annotation tool and analyzed the results to identify the essential scenes. Second, we utilized two techniques to detect student behavior, namely, sensor recognition and image recognition, and investigated which was more suitable. In future work, we plan to create a system that automatically generates video teaching materials from automatically extracted scenes.
Edge-Enabled Blockchain-Based V2X Scheme for Secure Communication Within the Smart City Development

As the high-mobility nature of the vehicles results in frequent leaving and joining the transportation network, real-time data must be collected and shared in a timely manner. In such a transportation network, malicious vehicles can disrupt services and create serious issues, such as deadlocks and accidents. The blockchain is a technology that ensures traceability, consistency, and security in transportation networks. In this study, we integrated edge computing and blockchain technology to improve the optimal utilization of resources, especially in terms of computing, communication, security, and storage. We propose a novel, edge-integrated, blockchain-based vehicle platoon security scheme. For the vehicle platoon, we developed the security architecture, implemented smart contracts for practical network scenarios in network simulator version 3, and integrated them with the simulation urban mobility traffic control interface API. We exhaustively simulated all the scenarios and analyzed the communication performance metrics, such as throughput, delay, and jitter, and the security performance metrics, such as mean squared error, communication, and computational cost. The performance results demonstrate that the developed scheme can solve security-related issues more effectively and efficiently in smart cities.
Sentiment Analysis Using Social Media and Product Reviews

In today’s time of the Internet, all the services are available over the Internet, and social networking and microblogging sites are becoming extremely popular among all age groups. Companies require an approach to gather information about their customers so that they can provide valuable and precise suggestions about their products based on the input provided by the user. Sentimental analysis comes here in the picture that analyzes the comments and reviews of the people over their product. In this paper, different classifiers have been used and compared based on their accuracy, precision, and recall. A string-to-word vector filter is used to convert the string dataset to a nominal dataset. The classifiers used are SVM, Naïve Bayes, and logistic regression and have procured an accuracy of 83.37%, 82.20%, and 82.78% with logistic regression, Naïve Bayes, and SVM, respectively.
Adding Scripting Languages

The Java language is not always the best choice for implementing business functionalities and algorithms. The language only slowly implements new features, which can be an advantage, because it is easier to maintain old code when the coding structures don’t change too often. And Java is statically typed, which means you have to take care of object types at a language level. This leads to more typing work, since type specifiers need to be entered often in order to make sure that type conformance works correctly. Contrary to that, scripting languages like Groovy and Python often include features to write code more concisely, and they are often typed dynamically. That means that type checks don’t take place during compiling and the code contains fewer boilerplate constructs to ensure variable type conformance in assignments.
Improved Deep Learning Approach for Underwater Image Enhancement

Recently, the domain of computer vision has experienced tremendous study and experimentation. Multiple machine learning (ML) and/or deep learning (DL) approaches are inculcated or proposed to address issues in computer vision tasks like image or video classification, object detection, image enhancement, etc. We present one such technique for enhancing underwater images. This experiment tweaks the earlier proposed FUnIE-GAN architecture to conducive images with a better visual perception. The UIEB data set is utilized for paired training. There is an attempt to apply U-Net architecture as a discriminator in place of the previously adopted Markovian patch GAN. The evaluation of the model uses PSNR and SSIM (full-reference evaluation metrics), along with entropy and UCIQE (non-reference evaluation metrics). The mean values of PSNR, SSIM, UCIQE and entropy metrics for 90 test images are 17.248, 0.6, 0.513 and 6.093, respectively, indicating that the improved FUnIE-GAN version generated underwater images with better visual perception.