Reviewerly/exHarmony huggingface dataset:
SMS CLASSIFICATION: CONJOINT ANALYSIS OF MULTINOMIAL NAIVE BAYES APPLICATION

Nowadays there are ham and spam messages that are sent to the users via SMS. The aim of this article is to show how machine learning and text processing technologies can be used in order to predict the trustworthiness of SMS messages. The data we are going to use is collected from Kaggle. This study is very important because it helps us to understand how machine learning and text processing can be used in order to predict message trustworthiness. At the time of writing this article, there was not an article explaining how this can be done using the Multinomial Naive Bayes algorithm. The methodology we used in this article consists of dataset collection, data cleaning, data analysis, text preparation, and training model. This will be seen in the methodology section in great detail. At the end of this article, we will show to u the accuracy that we have got when implementing a Multinomial Naive Bayes algorithm for the classification of SMS messages. This study was quite beneficial because anyone can see how Multinomial Naive Bayes algorithm usage can be beneficial in order to predict the trustworthiness of SMS messages.
UNDERSTANDING THE CLOUD COMPUTING ECOSYSTEM: A QUANTITATIVE ANALYSIS

A large number of companies are using cloud computing facilities to decrease budgets and rise the elasticity of their IT structure. This has sparked controversy among staff and investigators over the welfares and hazards of cloud computing. This study uses content analysis to test cloud computing systems. Analytical data includes high-quality investigation courses and staff-directed articles from journals and websites. We use n-gram and cluster algorithm k-methods for literature study. The input of this paper is twofold: first, it recognizes the key words and themes that are part of the cloud computing program that we have integrated with the entire model. Second, the paper reveals the sentiments of important issues expressed in both academic and educational contexts.
Динамика развития российского сегмента научных публикаций(по данным Web of Science Core Collection и Scopus)

The findings of the study of Russian publications array of 2010-2019 are presented. The dynamics for the ten-year period (2010-2019) is demonstrated. For completeness, two of the most authoritative international polythematic resources, i. e. Web of Science Core Collection (WoS CC) and Scopus, were analyzed in parallel. The dynamics of Russian publications is presented in two versions: all types of publications indexed by the systems, and selected types of publications (research articles, reviews, book chapters, and letters). The positive dynamics of the share of Russian publications within the global array was determined, as well as the increase in Russia’s rating positions in terms of the number of publications in the period 2010-2019, were determined. The publications with Russian participation are represented in all 153 science areas (the WOS CC classifier Research Areas), however, the barrier of 1% of the total segment of Russian publications reflected in the WoS CC was cleared just in 42 areas. It is demonstrated that in the recent years, along with the decreasing tendency for the share of publications in physics and chemistry within the Russian publication array, there has been an increase in the rate of publications in the technical, social sciences and the humanities. The Emerging Sources Citation Index (ESCI), a WoS CC member since 2015, promotes intensively the works in the humanities and social sciences to the world publishing space. Despite the decrease in the rate of physics and chemistry in the Russian publication segment, these areas remain the undisputed leaders in the share of publications in WoS CC.
探討達成不同貼文類型適用的圖像替代文字描述方式研究/A study of the description of alt text suitable for different types of posts.

近年來社群軟體的使用率提高，圖像、影像逐漸取代文字為主要的社交物件。視障者也是社群軟體的高度使用者，當視障者在使用社群軟體時，會為沒有替代文字的圖片感到困擾。臉書近年推出自動替代文字系統，該系統仍然有許多可改善的空間。本研究探討另一項長期做為視障者提供視覺資訊的口述影像服務後，提出臉書貼文中的圖像替代文字應該因應貼文類型調整敘述方式。研究發現，由於相繼型的貼文在文字內容中，解釋圖像資訊的比例較少，因此貼文適合使用特定的圖像替代文字。而說明型貼文的文字內容中，已解釋許多圖像資訊，因此可以使用特定或抽象的圖像替代文字。以研究結果彙整成建議圖像替代文字產出流程圖，以提供未來相關研究或者相關服務參考。 Recently, images and videos have replaced text as the main social objects on social software. When the visually impaired use social software, they will be troubled by pictures without alt text. Facebook has launched an automatic alt text system, which still has a lot of room for improvement. After exploring the Audio Description service that provides visual information for the visually impaired, this research proposes that the alt text of posts should be adjusted according to the types of posts. This research found that since successive posts spend a smaller proportion of the text explaining the image information, the posts are suitable for specific alt text. The text of the illustrated post contains a lot of image information, so these posts are appropriate for specific or abstract alt text. The research results are compiled into a flowchart of the different ways of producing alt text provides a reference for future related research or the development of related services.
Programming Crash Course for Composers

This chapter serves as a concise, understandable introduction to programming concepts, with a focus on principles useful specifically to someone planning to integrate music or sound into a game. When learning game audio as a field, it is far too easy to skip over basic programming principles that should be part of any game music composer’s foundation. Intricate details of programing basics are not the focus of this chapter, but rather general concepts that are essential for understanding how a programmer thinks and being able to read code in a scripting language such as C#. These general concepts are then applied through audio-specific demonstrations using C#.
Ensemble Methods for Learning: An approach towards handling Class Imbalance and Class Overlapping Problems

In this paper we performed different ensembling learning techniques to handle imbalance of classes on a given dataset and do further analysis of the result obtained. At first various techniques were identified to handle class imbalance such as data pre-processing, cost sensitive and ensemble learning methods. After separating testing and training dataset, we implemented the data pre-processing techniques such as oversampling and undersampling and then implemented neural networks which train on imbalance dataset by respecting the introduced class weights. We introduced the SMOTE algorithm and further refined it by adding Bagging and Boosting methods. Any errors that emerged in the streamlining of the process was taken into consideration as well. The results obtained by training the models on Training data were collected and further verified it by testing it on Testing data. Then results were compared based 175on their ROC value and various parameters obtained from the confusion matrix. During the comparative analysis, we found that data pre-processing by oversampling outperformed undersampling+random forest method based on ROC values. Among all the methods used, Neural Networks combined with the cost sensitive learning’s performance in terms on ROC value was the best. In general, ensembling learning mechanism based on boosting yielded better results than bagging and SMOTE+Bagging along with Neural Networks technique resulted in the best precision value.
Creation of Two Charginos and Neutralino Via Different Propagators

We investigated the creation of two Charginos ( ± ) and neutralino ( ) owing to electron-positron annihilation via the processand estimated the cross section for this interaction in the Minimal Supersymmetric Standard Model (MSSM).There are three gatherings of Feynman graphs which taken by the sorts of the propagators.Group (I) when and Z boson are propagators, Group (II) when and h (lighter Higgs boson) are propagators and Group (III) when and H (heavier Higgs boson) are propagators, where , = 1,2 !" ℓ = 1,2,3,4.here are (192) various potential circumstances from Feynman graph.Over a range of center of mass energy S (Gev), the cross-sections (pb) are determined based on (MSSM), the mechanisms of the process can be recognized as:+ % in group I. % + % → ℎ ' % + % → ( % + % in group II.% + % → * ' % + % → ( % + % in group III At S interval (1000-2100) Gev, the best value of σ is 0.072 Pb in-group (I).When masses of Charginos are m / 0 1 2 = 700 GeV, m / 0 6 7 = 700GeV and mass of neutralino is m 8 ℓ 9 = 800 GeV
Spiral CAPTCHA with Adversarial Perturbation and Its Security Analysis with Convolutional Neural Network

Human or bot? The first question comes in mind before deploying web services. A Completely Automated Public Turing test to tell Computer and Human Apart (CAPTCHA) is a tool which generally used to boost the security of web services by conducting a challenge-response test. This test helps to determine whether incoming requests from legitimate user or intelligent bots. A bot finds it difficult to predict distorted words which human can easily recognize. There are two major aspects of a CAPTCHA: easily identified CAPTCHAs by humans and the ability to prevent bot attacks. This paper presents a new text-based CAPTCHA design called Spiral CAPTCHA with an immutable adversarial noise (IAN) and validate it using convolutional neural network (CNN) attacks. A new text-based CAPTCHA “Spiral CAPTCHA” is designed using PHP. A dataset of 16,384 images has been created. The proposed CAPTCHA has also been tested for its security against convolutional neural network “Alexnet.” For evaluation purposes, the CNN model designed with a dataset containing 8,027 images and validated using 3441 images. Testing of model performed by randomly choosing bunches of 64, 128, 256, and 512 images from testing a dataset of 4916 images. For robustness checking against recognition, we initially tested CAPTCHA without any noise and later checked with noise. Finally, proposed CAPTCHA is evaluated using recognition rate, recognition speed, attack speed, and success rate. We achieved a maximum recognition rate for CAPTCHA without perturbation is 38.48% and with perturbation is 13.50%. However, success rate is very low (almost 0%) for both cases, which confirms that the proposed CAPTCHA is robust against recognition attacks.
PIANO STUDENTS' PERSPECTIVES AND EXPERIENCES WITH REMOTE PIANO INSTRUCTION

This qualitative phenomenological study explored student perspectives and experiences with remote piano instruction during COVID-19.Ten participating students responded to 13 original, vetted questions via phone/video conferencing or Google form, an online application for uploading responses to questions.The findings indicated that the students believed they benefitted from continuing their piano instruction via remote piano instruction.Research findings also revealed the students had to manage and cope with audio/video technological interruptions and socio-emotional issues associated with remote piano instruction.The research findings further indicated the students' use of their preferred virtual communication application to interact, see, hear, and maintain their established teacher/student relationship helped offset the problems associated with remote piano instruction.All ten students felt that they have continued to progress with their piano performance and music reading skills via remote piano instruction.The findings would benefit and encourage piano instructors, researchers, and technology developers to conduct more research, both qualitative and quantitative, to determine the best measures for improving students' and teachers' experiences with remote piano instruction.
Effect of Pipe Diameter Variation on Transmission of Porous Breakwater

Abstract The porous breakwater is a breakwater designed in a transitional sea with an upright position in the direction of the waves that protect the coastal area from erosion and abrasion as it is caused by wave energy. This study aims to determine the effect of pipe diameter and depth variation on wave transmission in porous breakwaters and to analyzing the parameters that affect the transmission coefficient on wave dampers. It was conducted at the Hydraulic Laboratory of the Faculty of Engineering, Hasanuddin University, Gowa. It uses an experimental-based method with a model. The resulting waveform consists of three variation periods (T; 1,0 s, T; 1,1 s, T; 1,2 s) and two water depth variations (d; 36 cm, d; 28 cm). The results of the study indicated that the parameters that influence the transmission coefficient (Kt) are wave height (Hi), wavelength (L), wave period (T), water depth (d), and pipe diameter (Ø).
Gate register tool with koha library automation software

The article presents an attempt to integrate gate registerwith open-source software Koha Integrated Library Management System. Using this option, the patron's library card will be scanned by the system during entry and exit at the library gate, and this information will be storedin the new table which we have added to the Koha database. Using this tool various reports can be generated for various library management purposes. In the final phase, we have developed a patch for Koha which has enabled the users of the library to update their profile in Koha without library staff help. This tiny addition to Koha has enabled it toward crowdsourcing mode. The above two tools are tested in the real-time environment and positive feedback has been reeived from library staff and users.
Design and Manufacturing of a Water Bike to Pick Up Garbage on Matano Lake

Garbage is one of the main factors that greatly affects and causes a decrease in environmental quality, especially waste in the water area. Matano Lake in Soroako experienced this condition. The garbage in Matano consists of plastic, cans, cloth and some types of daily activities waste of peoples surround the lake that can pollute the cleanliness and health of lake water. Matano Lake is the water source used by residents of Soroako for processing drinking water, cooking, bath, wash, and other household needs. The shape of the banks of the matano lake varies, in the form of slopes, swamps, and steep cliffs, so that some sides can't be cleaned from the land. Based on these conditions, research is needed to facilitate the cleaning of lakes from waste. Research in the form of design of bicycles that can float and move above the water surface, and has a component to pick up and accommodate garbage from the lake. Stages of research consist of data collection, analysis, design, manufacture, and testing. The design phase includes calculation of construction safety, selection of standard components, drawing of construction and components details to be made. Tool manufacturing phase through metal machining and fabrication processes. Testing phase includes testing of buoyancy ability, motion speed, and load ability. The main components consist of transmission elements, frames, buoys, drive systems, movement direction control, hooks, and trash cans.
Introduction to the Special Issue on GaLA Conf 2020

This special issue of the International Journal of Serious Games offers very valuable extensions to the best papers of the 2020 edition of the GaLA conference.&#x0D; The local organization committee was composed of computer scientists of Laval (France), affiliated to Le Mans Université.&#x0D; From the 9th to the 10th of December 2020, 500 participants attended to a well-organized conference, through their virtual avatars, and listened to 37 presentations on Serious Games and Gamification. A special session was related to Virtual Reality, in a pedagogical and gaming context. &#x0D; The four extended papers, published in this journal, significantly extend their original work, and were accepted through a regular peer-review process for this special issue.
Growing Neural Networks Achieve Flatter Minima

Deep neural networks of sizes commonly encountered in practice are proven to converge towards a global minimum. The flatness of the surface of the loss function in a neighborhood of such minima is often linked with better generalization performances. In this paper, we present a new model of growing neural network in which we incrementally add neurons throughout the learning phase. We study the characteristics of the minima found by such a network compared to those obtained with standard feedforward neural networks. The results of this analysis show that a neural network grown with our procedure converges towards a flatter minimum than a standard neural network with the same number of parameters learned from scratch. Furthermore, our results confirm the link between flatter minima and better generalization performances as the grown models tend to outperform the standard ones. We validate this approach both with small neural networks and with large deep learning models that are state-of-the-art in Natural Language Processing tasks.
Scope of Aesthetics in the Game Model of Artworks, which Contained Function of Augmented Reality

As I have repeatedly emphasized, processes in the field of technology and their application for the purposes of the visual arts have always played a major role in the development of my creative vision. An essential role in this direction has my acquaintance with Atanas Markov, with whom we complement each other in many respects and this has a beneficial effect on our teamwork. We have already shared in a previous edition of the conference "Cultural and Historical Heritage" about the work on the projects "Interactive Generative Formations", "Virtual Arena" and "Interaction with a technological organism". We are currently developing an extension of the last of the listed installations. This upgrade consists of the creation of a series of two-dimensional works in which an element of augmented reality must be included. This is essentially not something that has not already been done, but it is a very good element that would contribute the communication with the "Technological organism". The relation is also implemented at the level of interaction, and we rely on the game moment again. Through this act, we involve the observer, as an active participant in the artwork. Keywords: Augmented Reality, Interactive Art, Game, Game Model, Artwork
Incentives and Cryptographic Protocols for Bitcoin-like Blockchains

Bitcoin is a widely acknowledged digital currency that is designed in a decentralized manner. The recognition of Bitcoin has introduced the notion of cryptocurrencies and, in general, blockchain technology. Blockchain, within less than a decade, has become one of the most exciting technological developments. Among several exciting use cases and projects, there has been an inevitable hype in the industry as well. While in the research community, it has opened an interdisciplinary research field among cryptography, distributed systems, and economics. Notwithstanding the interest and great effort, blockchain is still a new and evolving technology, and numerous challenges need to be addressed. To name a few, security, privacy, scalability, smart contracts, and economic aspects with their manifold sub-challenges can be mentioned.Among the research challenges, in this thesis, we investigate three crucial ones for the long-term functionality of the Bitcoin-like blockchains, which are security, scalability, and economic aspects.Our works can be divided into two subjects: transaction propagation and payment channel networks. Transaction propagation or advertisement refers to the dissemination of newly created transactions of clients in the mining network.In this thesis, we investigate the lack of incentives for transaction propagation and provide an incentive mechanism for peer-to-peer mining networks. Moreover, we focus on the inefficient routing of the transactions and propose a smart routing mechanism. Payment channel networks (PCN) are promising layer-2 protocols aiming to improve the scalability of blockchains.In this thesis, we present three works on the PCNs.Firstly, we investigate the incentives to participate in multi-hop payments and propose a profit strategy that would encourage the use of PCNs. Secondly, we propose the first Bitcoin-compatible virtual channel constructions on payment channels that improve the efficiency and availability of multi-hop payments. Finally, we introduce the first post-quantum PCN utilizing our post-quantum adaptor signature scheme. Our works mainly focus on Bitcoin and its PCN, Lightning Network, yet they can be applied to the blockchains and cryptocurrencies having similar characteristics.
Classification of Kinematic Data Using Explainable Artificial Intelligence (XAI) for Smart Motion

Kinematics is the analysis of certain parameters related to human locomotion to detect potential behaviour and gait activities of a particular subject. Gait analysis is a systematic study of motion in humans which is characterized by measurement, description, and assessment of quantities. Most research has focused solely on signal or image of movement by involving several body segments for modelling which increases research complexity and computation time. Thus, the problem raised is how to capture modelled movements using flexible methods especially for smart cities. This chapter presents an investigation on the kinematics of jogging, walking, and hand-drawn motion path. It was a search for the best possible methods for motion tracking, analysis, and recognition using a wearable sensor that can potentially be applicable in smart cities. This was done by developing a low-cost recognition system utilizing one sensor to collect kinematic data, and a skeletonization model involving one to two body segments. Signal data was analyzed using a wearable sensor that used 3-space sensing as statistical classifiers as a classification learning system. The proposed recognition system achieved an accuracy of more than 98% to recognize human activities. The results indicate that motion based on the proposed system can be identified for smart motion detection. The study has shown that human locomotion can be successfully classified by the proposed kinematic data using Explainable Artificial Intelligence (XAI).