Reviewerly/exHarmony huggingface dataset:
Using Crypto-Asset Pricing Methods to Build Technical Oscillators for Short-Term Bitcoin Trading

This paper examines the trading performances of several technical oscillators created using crypto-asset pricing methods for short-term bitcoin trading. Seven pricing models proposed in the professional and academic literature were transformed into oscillators, and two thresholds were introduced to create buy and sell signals. The empirical back-testing analysis showed that some of these methods proved to be profitable with good Sharpe ratios and limited max drawdowns. However, the trading performances of almost all methods significantly worsened after 2017, thus indirectly confirming an increasing financial literature that showed that the introduction of bitcoin futures in 2017 improved the efficiency of bitcoin markets.
Meme Detection of Journalists from Social Media by Using Data Mining Techniques

With regard to today's social media networks, memes have become central. Share a million memes per second on different social media. The detection of memes is a very concentrated and demanding subject in the current field of research. Share about a million memes on various social media. Today's social media (What's App, Twitter, and Facebook) is widespread around the world. Since people in all countries are heavily used, use a set of social media application data (Twitter). As social media has an enormous amount of data overall the world. That will use that data set of Twitter for meme detection of journalists in Pakistan. So, this is done by getting data set from social media (Twitter, What’s App, and Facebook) by using their authenticated APIs. For this analysis use some opinion mining techniques and sentiment analysis like the statistical descriptive and content analysis. In our society, it is the better way to analysis about any journalists because social media can provide very huge amounts of data about any journalist which is true or false but I can make approximate correct perceptions by using sentiment analysis and text mining techniques. It will provide highly wanted and hidden characteristics and perceptions for searchers and demanding people about journalists. Finally use for sentiment analysis by using Python.
Challenges and possibilities in the academic libraries of assam in promoting positive climate and culture in the educational institutes in the current scenario

Educational institutes provide education to the learners with a vision to create a generation that can design and build a better society in near future. Libraries can be treated as the knowledge hub for educational institutes. The institutes spend lots of money in the libraries so that users can get their required information to carry the intellectual practices. Though the parent institutes spent so much money in the libraries, in this study, it is seen that about 30% of the users are not satisfied with the current service of their academic libraries and many users expect some more from the libraries; which affects the positive climate and culture of the parent institute. This study is based on a descriptive, mix-method research approach. In this paper, the challenges and possibilities carried by the academic libraries of Assam in promoting a positive climate and culture in the institutions are traced and discussed. Many major issues like absence in the library orientation program, Communication gap between the library staff and the users, lack of professional and permanent staff, etc. are identified during the study which affects the academic libraries of Assam. Some recommendations are also made in the paper to enhance the library services in the academic institution.
The Employment of Language Politeness Strategies in Student-Lecturer Virtual Interaction

Language politeness is a critical element to be incorporated in communication for its worth across social status, power, or position. In this study, WhatsApp text conversations between students and lecturers were utilised in analysing its politeness strategies used by the students. Purposive sampling which involved WhatsApp texts from two classes of English for Oral Presentation (ELC590) subject was conducted and text messages from thirty (30) students were selected. The text messages were then analysed using descriptive qualitative and quantitative analysis studies according to the four (4) politeness sub-strategies by Brown and Levinson (1987). All four (4) types of language politeness strategies were observed in the respondents’ texts. Negative politeness strategy is the highest strategy used among respondents, followed by bald-on, positive politeness, and lastly off-record strategies. Based on the results, a number of respondents were not communicatively competent as some of the text messages contained Face-threatening Acts (FTA) that would damage the lecturers’ face and be considered as impolite thus breaking down effective communication. Hence, it is recommended that other elements could be explored in future research such as time, use of emoji, punctuation or capitalisation. The findings would result in better understanding of language politeness strategies used in students’ communication with lecturers.
Quantum Key Distribution for Secure Communications

Data protection and information security have been the essence of communication in today's digital era. Authentication and secrecy of secure communication are achieved using key-based cryptographic primitives; the security of which significantly relies upon the underlying computationally complex mathematics. Moreover, these existing cryptographic primitives are considered to be non-deterministic on the basis of the existing computational capabilities. However, the considerable advancements in the development of quantum computers have significantly enhanced parallel computations; thereby, posing a great threat to these existing encryption primitives. Thus, in the future, the physical manifestation of a large successful quantum computer is likely to break all the existing public-key encryption algorithms in no time. This has led to a remarkable surge of interest in propelling quantum mechanics into existence; subsequently, leading cryptographers to research various viable domains to offer quantum-resistant secure communications. Resultantly, quantum cryptography/quantum key distribution has emerged as a futuristic replacement for classical cryptography as it offers unconditionally secure communication along with the inherent detection of any unintended user. Thus, keeping in view the significance of this relatively newer domain of cryptography, this research focuses on presenting a consolidated review of the various Quantum Key Distribution (QKD) protocols. A comparative analysis of the working mechanism of the prominent QKD protocols is presented along with an overview of the various emerging trends that have been proposed to optimize the implementational efficiency of the BB84 protocol.
Development of Story Books Containing MELESAT (Mathematics, Existence, Literacy, Engineering, Science, Art, Technology) to Improve Literacy Skills among Group B of Early Childhood Education

The purpose of this study was to develop a storybook containing Mathematics, Existence, Literacy, Engineering, Science, Art, Technology (MELESAT) to improve literacy skills among group B of early childhood education and to test the feasibility of designing a storybook containing MELESAT. This type of research is a Research and Development with Pretest - Posttest One Group Design, using the ADDIE model which includes five stages, namely Analysis, Design, Develop, Implementation, and Evaluate. Subjects are children aged 5-6 years, including group B early childhood education totally 20. The instrument used a validation questionnaire and literacy tests. Results of this research indicate that the validation of the material obtained 3.04 of 4, validation of book design obtained 2.55 of 4 and validation of the media obtained 2.89 of 4. The effectiveness based on giving pretest and posttest to students totaled 20 subjects. The results showed that the pre-test score of 57% was developing as expected, and 43% was starting to develop. Post-test scores showed 7.5% progressed beyond expectations, 87.5% progressed as expected, and 5% started to develop. Through the content, design, and use of MELESAT books, there is an increase of literacy skills of children aged 5-6 years including group B early childhood education.
Vertically Federated Learning with Correlated Differential Privacy

Federated learning (FL) aims to address the challenges of data silos and privacy protection in artificial intelligence. Vertically federated learning (VFL) with independent feature spaces and overlapping ID spaces can capture more knowledge and facilitate model learning. However, VFL has both privacy and utility problems in framework construction. On the one hand, sharing gradients may cause privacy leakage. On the other hand, the increase in participants brings a surge in the feature dimension of the global model, which results in higher computation costs and lower model accuracy. To address these issues, we propose a vertically federated learning algorithm with correlated differential privacy (CRDP-FL) to meet FL systems’ privacy and utility requirements. A privacy-preserved VFL framework is designed based on differential privacy (DP) between organizations with many network edge devices. Meanwhile, feature selection is performed to improve the algorithm’s efficiency and model performance to solve the problem of dimensionality explosion. We also propose a quantitative correlation analysis technique for VFL to reduce the correlated sensitivity and noise injection, balancing the utility decline due to DP protection. We theoretically analyze the privacy level and utility of CRDP-FL. A real vertically federated learning scenario is simulated with personalized settings based on the ISOLET and Breast Cancer datasets to verify the method’s effectiveness in model accuracy, privacy budget, and data correlation.
Reading Anxiety Among Junior High School Students in Mataram: A Survey Study

This study aims to identify reading anxiety among junior high school students. The research design employed a quantitative approach in the form of a survey study. There were 80 respondents who were willing to participate in this study. The respondents were students from one of junior high schools in Mataram, Indonesia. All respondents were students from grades 8 and 9. To collect the data, this study adapted a questionnaire from Ahmad et.al. (2013) which consists of five domains of reading anxiety, i.e., (1) lack of vocabulary, (2) unfamiliar topic, (3) unfamiliar culture, (4) afraid of making errors, and (5) worry about reading effects. Based on the results, the statement with the highest score is “I enjoy reading a text in English text in English when I know its translation” (M=4.15; SD=.730). Meanwhile, the statement with the lowest score is “In reading aloud in the class I do not understand the text even though it is easy” (M=3.00; SD=1.055). The data imply that the most significant factor in students’ reading anxiety is lack of vocabulary and the least factor is worrying about reading effects.
Feature Extraction for Patch Matching in Patch-based Denoising Methods

Patch-based image denoising is a popular topic in recent years. In existing works, the distance between two patches was calculated as their Euclidian distance. When the noise level is high, this approach may not be desirable in image denoising. In this paper, we propose to extract noise-robust feature vectors from image patches and match the image patches by their Euclidian distance of the feature vectors for grey scale image denoising. Our modification takes advantage of the fact that the mean of the Gaussian white noise is zero. For every patch in the noisy image, we use lines to divide the patch into two regions with equal area and we take the mean of the right region for each line. Hence, a number of features can be extracted. We use these extracted features to match the patches in the noisy image. By introducing feature-based patch matching, our method performs favourably for highly noisy images.
Code-Mixing in WhatsApp Group Discussions of Online English Classes

The COVID-19 pandemic has forced the educational world to switch from offline classes to online classes. Limited access to the internet has encouraged many people to use WhatsApp to teach instead of academic platforms. Although WhatsApp is not designed for educational purposes, many teachers prefer using it to conduct online discussions, including in English classes. This new model of communication using WhatsApp for English classes has resulted in the interesting phenomenon of code-mixing. This paper aims to investigate the types and functions of code-mixing used in WhatsApp groups of online English classes. This study employed a qualitative method. The sample of this research was conversations between the lecturers and students in WhatsApp group chats of online English classes during the pandemic. The data were collected through observation. The collected data were then analyzed by using textual analysis. This study revealed three types of code-mixing used in online English classes on WhatsApp group chats: insertion, alternation, and congruent lexicalization. Next, this study found five functions of code-mixing in online English classes on WhatsApp group chats: quotation, address specification, interjection, message qualification, personalization, and objectification. Address specification, personalization, and objection were dominantly found because specific sentences frequently refer to people, things, or arguments during discussions on WhatsApp group chats.
Sistem Informasi Pembayaran SPP pada Sekolah di Kecamatan Gedung Tataan dengan Metode Extreme Programming

School financial administration is a step in managing school finances from acceptance to how the finances used are accounted for in an objective and systematic manner. Student financial administration systems in schools such as payment of tuition fees. SPP for schools in the Kelurahan Gedung Tataan still uses a manual process and has not been computerized. The system that runs the process of SPP payment transactions. The development of this system is carried out through several stages of data collection methods. The next stage is software development using the Extreme Programming method and using object-oriented PHP and MySQL programming applications including Unified Modeling Language (UML), as well as application testing using ISO 25010. Testing the application using ISO 25010 with a questionnaire filled out by 7 respondents on the web-based tuition payment system that has been built resulting in a total of 90% with the predicate Very Good, it is concluded that the system built has been considered feasible to be applied in managing tuition transactions in the building district arrangement.
Pengembangan E-Book Berjenis E-Flip PDF Dengan Belajar Mandiri Terbimbing Berbasis Problem Posing Menggunakan Ide Kreatif Scamper

The purpose of this study was to develop a valid, practical and effective E-Book learning media of E-Flip PDF with Guided Self-Learning on student learning outcomes on matrix material in class XI of SMA Negeri 1 Nanga Mahap. The method used is a 4D research method which is limited to Define, Design and Develo. The subjects in this study were 29 students of class XI SMA Negeri 1 Nanga Mahap, 3 material and media experts. The instruments used in this research are in the form of material expert validation sheets and media expert validation sheets, teacher response questionnaires, student response questionnaires and posttest questions based on the results of research that have been carried out, the results of the validation of the E-Book learning media are E-Flip PDF with Independent Learning. Supervised has a level of validity of 93.66% with very valid criteria, the level of practicality of learning media is 89.93% with very practical criteria and the effectiveness level of learning media is 82.75% with very effective criteria.&#x0D; &#x0D;
Outlier Detection in Test Samples using Standard Deviation and Unsupervised Training Set Selection

Outlier detection is a technique to identify and remove significantly different data from the more correct and consistent data in a data set. Outlier data can have negative impact on classification and clustering performance; that should be identified and removed to improve the classification efficiency. Regardless of whether a classifying technique classifies an outlier correctly, the very notion of identifying a data as outlier is of great significance. In this paper, a new approach is proposed for outlier data detection within a test data set along with unsupervised training set selection. The selected training set is used for two-step classification. After unsupervised clustering the training set, the closest cluster to a test sample is selected using the Euclidean distance measure. Then, the outlier in the test sample is identified with the concepts of standard deviation and mean value. The results showed by evaluating the distance of each sample of the test set with the new selected data set. the accuracy of the classifiers is enhanced after detection and elimination of outlier data.
Software components selection: An optimized selection criterion for component-based software engineering (CBSE)

Component-based software engineering (CBSE) is becoming the frequently used approach for software development concerning time and cost constraints. While most people’s lifestyle depends on the use of software applications, the development cost and time of software applications are becoming more difficult to achieve. Also, high-quality and competent applications that fit users’ needs became essential. Therefore, most software development organizations are reusing commercial-off-the-shelf (COTS) to reduce development costs and time. However, most organizations and developers face problems selecting components that fit customer needs to integrate with the target system. So, decisions regarding software component selection are hard to consider regarding the entire quality of the software system. This study aims to investigate the most important matters to software industry practitioners and experts involved in component selection. First, the author asked the practitioners to select the most important quality criteria for an online bookstore from a list by providing their subjective judgment and evaluation grades; after that, utilizing the evidential reasoning (ER) approach for software component selection problems due to its ability to analyze decisions with multilevel evaluations and information uncertainty. Moreover, the main features of the ER approach, such as weight normalization, probability assessment, dealing with uncertainty, and utility intervals, offer several advantages for COTS selection problems, including cost and time minimizing, improved software reliability, effectiveness, and efficiency. This study concentrated on assessing the quality criteria for an online bookstore using the ER approach. Analysis results are provided depending on the computational steps of this approach. Finally, the findings show the rank of the four components according to their weights, evaluation grades, and belief degrees for the selection.
A Comparative Study on the Effects of Pooling on FER CNN Models

Emotion recognition has attracted much attention in ArtificialIntelligence in order to make machines understand emotional sentiments, with many industries trying hard to incorporate emotion recognition technologies into their products.The easiest way to detect a person's emotion is recognizing their facial expressions.In this work, the researchers tend to use FER as a problem and use the Deep Convolutional Neural Network (DCNN), which extracts the features automatically and therefore surpasses and outperforms the limitations of traditional machine learning.This work provides a comparative study of various existing pre-trained model architectures.MobileNetV2, MobileNetV3_Small, NASNetMobile, ResNet50, ResNet50V2, ResNet152V2, DenseNet169 and DenseNet201 with modification in their pooling layer to achieve high accuracy and have the potential for implementation in embedded systems.In this project, various deep learning pre-trained models were trained, tested, and compared on a modified subset of the FER 2013 Dataset for Face Emotion Recognition under all the conditions of pooling, i.e., None, Min, Avg, and Max.FER 2013 being one of the most challenging dataset, and due to limited run-time cost available, the MobileNetV2 model gave the highest testing accuracy of 83.64% with a training accuracy of 97.87% on average pooling.The models were compared on the following evaluation metrics: Accuracy, Loss, Precision, Recall and F1-score.For a practical approach, they integrate the model into a mobile application so that models can be run on devices in real time.
Generating 2-D and 3-D Master Faces for Dictionary Attacks With a Network-Assisted Latent Space Evolution

A master face is a face image that passes face-based identity authentication for a high percentage of the population.These faces can be used to impersonate, with a high probability of success, any user, without having access to any user information.We optimize these faces for 2D and 3D face verification models, by using an evolutionary algorithm in the latent embedding space of the StyleGAN face generator.For 2D face verification, multiple evolutionary strategies are compared, and we propose a novel approach that employs a neural network to direct the search toward promising samples, without adding fitness evaluations.The results we present demonstrate that it is possible to obtain a considerable coverage of the identities in the LFW or RFW datasets with less than 10 master faces, for six leading deep face recognition systems.In 3D, we generate faces using the 2D StyleGAN2 generator and predict a 3D structure using a deep 3D face reconstruction network.When employing two different 3D face recognition systems, we are able to obtain a coverage of 40%-50%.Additionally, we present the generation of paired 2D RGB and 3D master faces, which simultaneously match 2D and 3D models with high impersonation rates.
Grover's Quantum Search Algorithm of Causal Multiloop Feynman Integrals

A proof-of-concept application of a quantum algorithm to multiloop Feynman integrals in the Loop-Tree Duality (LTD) framework is applied to a representative four-loop topology. Bootstrapping causality in the LTD formalism, is a suitable problem to address with quantum computers given the straightforward possibility to encode the two on-shell states of a propagator on the two states of a qubit. A modification of Grover's quantum search algorithm is developed and the quantum algorithm is successfully implemented on IBM Quantum and QUTE simulators.