Reviewerly/exHarmony huggingface dataset:
Prediction of Bontang City COVID-19 Data Time Series Using the Facebook Prophet Method

The increasing trend of COVID-19 cases in Bontang makes it the first order of the highest incident rate in East Kalimantan, with a value of 1161.78 cases per 100 thousand inhabitants. The purpose of this study was to predict the increase in COVID-19 cases in Bontang City with a data set of positive confirmed cases, recovered and died of COVID-19 in Bontang city. The data set used starts from March 24, 2020 - March 1, 2021, using the Facebook Prophet method, the Jupyter Notebook application, and the Python programming language. The research process consists of the data collection stage, prediction implementation stage (data preprocessing, processing, performance evaluation, dashboard creation), and analysis of the result. The prediction was performed for up to 92 days until May 5, 2021. The result shows a trend of increasing cases of covid reaching the highest positive value, the highest recovery, and highest death, respectively, of 8695, 6099, and 156 people. According to the model, the average positive predictive error (MAE) and the average positive predictive accuracy value (MAPE) are 0.17 and 17.4%, indicating the positive prediction of contracting covid has good accuracy criteria. The next evaluation for the death prediction is accounted as reasonable accuracy criteria in which MAE and MAPE are 0.27 and 27%, respectively. Lastly, the recovery prediction has MAE of 0.17 and MAPE of 17.4%, implying good accuracy criteria. The study also provides recommendations to the COVID-19 Task Force to prepare the minimum number of PCR Tests by 870 tests and increase the hospitalization occupancy by 294 to control the spreading of the Coronavirus.
Does smarter mean better?

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings Saudi Arabian SMEs are not seeing the performance benefits to digital transformation and smart technology adoption. This will have to change if its economy is to truly diversify away from oil. Originality The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.
Developing EFL Learner's Reading Comprehension through a Smartphone-Assisted Reading Program

This study integrated a smartphone-assisted reading program into a college English class to develop English as a foreign language (EFL) learners’ reading comprehension. A class of 32 students in a Chinese college participated in the program. Reading exercises via smartphone applications were used as a complement to the intensive reading course. The study lasted approximately 14 weeks. Upon completion, all students completed an anonymous questionnaire about their perception of their learning experience, and five students were chosen at random for a semi-structured interview for detailed analysis. Results showed students generally accepted a mobile-assisted learning approach, but they did not form the habit of regular reading via smartphones. In addition, most students favored language learning applications over other platforms, and video was their preferred learning resource. Furthermore, the advantages and disadvantages of this learning approach were identified. With regard to the learning effect, most students agreed that this approach is was effective in improving increasing their learning interest, promoting their autonomous learning ability, and enhancing their reading proficiency.
Research of key technologies for multi‐rotor UAV automatic aerial recovery system

This study proposes an aerial recovery technology that can complete aerial recovery for target unmanned aerial vehicles (UAVs) by a platform UAV to overcome the limitation of ground recovery for multi-rotor UAVs. To ensure the safety of automatic aerial recovery, more requirements are made for the stability of multi-rotor UAVs. This study proposes a back-stepping trajectory tracking controller for feedforward compensation of uncertain disturbances. The results of the simulation show that the controller can effectively prevent external disturbance and meet requirements for the desired trajectory tracking. Furthermore, based on a you only look once (YOLO) network, this study designs a real-time image recognition and positioning system, which can achieve real-time positioning for platform UAV by the target UAV with an image-processing rate of 8 frames per second (FPS). With the main objective of safety, this study designs an automatic recovery guiding system for target UAVs using a fuzzy controller. The target UAVs can automatically realize a soft landing on the platform UAV effect of the guiding system. Finally, this study conducts dynamic recovery tests in-flight and verifies the feasibility of automatic aerial recovery technology for multi-rotor UAVs.
Character Education Competence of a Teacher in the Professional Life Cycle

The modern educational environment is characterized by a number of global challenges and the emergence of a new paradigm of value orientations at the time of unlimited freedom of self-determination, multiculturalism, and multiple identity of the individual. All this escalate the problem of understanding the true, false and pseudo values. In the world of absolute uncertainty, instability, the importance of the character education competence of the teacher is increasing as the teacher is responsible for designing the students’ educational environment. In today's reality the effective development of the character education competence of a teacher, which is not limited only to the period of studies at a teacher university, is of utmost importance. The authors consider the development track of the teacher's competence in character education throughout the entire professional life cycle in the regional socio-pedagogical cluster. Experimental research has confirmed the need to update the formation of character education competence both for the teaching staff of a teacher university, who are responsible for organizing the professional character education, and for the future teachers who will come to school tomorrow. The authors suggest that the character education competence of a teacher begins its formation long before entering the teaching profession and is determined by the axiological content of the quasi-professional communities in which an individual is involved.
Research on the Reconstruction Strategy of Public English Ecological Classroom under the Background of “Internet +”

In the era of “Internet plus,” the educational ecosystem is changing under the influence of modern educational technology, with teachers and students as the main players. The traditional teaching method of “books + classroom + large-class teaching” with teachers at the center has evolved into “two masters: teachers leading and students as the main body.” There are also some discussions in foreign language teaching about educational ecology, with a focus on ecological imbalance. Emotion analysis is divided into two categories based on emotional information: positive and negative. It can be considered a unique text classification, despite the fact that it differs from traditional text classification. Using the emotion recognition algorithm, this paper can provide simple access to WEB pages and carry out emotional experiences such as face emotion recognition. Third-party developers, on the other hand, have a lower entry barrier, allowing them to quickly create colorful applications based on their own ideas.
Improvements to the data search and validation functionality in the Crystallography Open Database

Crystallography Open Database (COD) [1] is the largest open-access FAIR [2] collection of small-molecule crystal structures that currently contains over 475 000 entries.In recent years, several notable improvements have been made to enhance the data curation process as well as expand the data search capabilities.
Thyroid Prediction on Imbalanced Datasets Using Machine Learning Classifiers

Classification is technique to classify our data into a desired (distinct) classis where we can allot an exclusive label to each class. Applications of classification include biometric identification, speech recognition, handwrit ing recognition, nondiabetic detection, document classification etc. Classifiers can be a Binary classifier where we have only 2 distinct classes or 2 possible outcomes (example: Fraudulent Transaction and Non-Fraudulent Transaction) or Multi-Class classifiers: where there are more than two distinct classes (For Example: Predicting Thyroid Disease, possible class labels are Hyperthyroidism, Hypothyroidism and Normal). Evaluating Machine learning classifier models can be a tough task. In this study we will attempt to explain Performance Metrics for Classification problems in Machine Learning. We will also try to explain how inferences drawn from confusion matrix can be misleading at times and also, we will see what other metrics we can turn to for better evaluation of the Machine learning classifier models. We will mostly focus on Binary classifiers and later would mention a few changes with regards to the Multi-class classifiers.
Chapter 17. The distribution of prosodic features in the Rhapsodie corpus

This chapter analyzes the interface between prosodic features and situational variables in the Rhapsodie treebank. First, we provide general quantitative information. Second, we present a preliminary set of statistical analyses performed on six specific prosodic variables, that provides two types of information. While Principal Component Analysis is useful to identify major trends in the data, inferential analysis is the first step in modeling the effect of the different modalities of each situational variable on the prosodic patterns, before considering predictive modeling. Finally, by focusing on the IPA unit, we present a third statistical method based on the computation of specificity indexes.
Directional Analytic Discrete Cosine Frames

Block frames called <i>directional analytic discrete cosine frames</i> (DADCFs) are proposed for sparse image representation. In contrast to conventional overlapped frames, the proposed DADCFs require a reduced amount of 1) computational complexity, 2) memory usage, and 3) global memory access. These characteristics are strongly required for current high-resolution image processing. Specifically, we propose two DADCFs based on discrete cosine transform (DCT) and discrete sine transform (DST). The first DADCF is constructed from parallel separable transforms of DCT and DST, where the DST is permuted by row. The second DADCF is also designed based on DCT and DST, while the DST is customized to have no DC leakage property which is a desirable property for image processing. Both DADCFs have rich directional selectivity with slightly different characteristics each other and they can be implemented as non-overlapping block-based transforms, especially they form Parseval block frames with low transform redundancy. We perform experiments to evaluate our DADCFs and compare them with conventional directional block transforms in image recovery.
Polynomial Tope (PT) Key Group Generation Based Received Signal Strength (RSS)

In the process of sending data wirelessly, a high data security system is needed to avoid data leakage. The key generation between wireless devices using Physical Layer Security has become an attractive alternative for wireless device communication security. In this paper, we propose a Polynomial Tope (PT) key group generation which is a group key generation system from several wireless devices using a star topology. The test is carried out in indoor and semi-outdoor environments by utilizing the randomness characteristics of the wireless channel. From the measurement results, RSS is generated which is converted into Difference of Signal Strength (DS) which will be used to generate the key. Based on the test, the average Key Generation Rate (KGR) is 4.2065 bps and the Key Disagreement Rate (KDR) is 0.172 for all scenarios. In addition, the key obtained has been able to meet all the randomness requirements of the National Institute of Standards and Technology (NIST) test with a value above 0.01.
ENGLISH TEACHERS’ STRATEGIES IN TEACHING VOCABULARY DURING COVID - 19

Abstract&#x0D; &#x0D; This study describes the strategies used by English teachers in teaching vocabulary during the Covid-19 pandemic. The type of research used is qualitative research, where the data collection process uses observation, interview, recording and triangulation methods. The results of research conducted on two English teachers at SMA Dharma Pancasila showed that the teachers at the school taught using various strategies and varied learning methods such as telling stories in the form of narrative, question and answer, and stimulating students to learn using attractive pictures. Besides that, teachers must also continue to motivate and arouse students' enthusiasm to study more actively during the Covid-19 pandemic, and it is also hoped that teachers and parents can work together to create more effective learning conditions.&#x0D; Keywords: Teacher Strategies, Covid-19, Vocabulary, Online learning
Classification of Invoice Images By Using Convolutional Neural Networks

Today, as the companies grow, the number of personnel working within the company and the number of supplier companies that the company works with are also increasing. In parallel with this increase, the amount of expenditure made on behalf of the company increases, and more invoices are created. Since the invoices must be kept for legal reasons, physical invoices are transferred to the digital environment. Since large companies have large numbers of invoices, labor demand is higher in digitalizing invoices. In addition, as the number of invoices to be transferred to digital media increases, the number of possible errors during entry becomes more. This paper aims to automate the transfer of invoices to the digital environment. In this study, invoices belonging to four different templates were used. Invoice images taken from a bank system were used for the first time in this study, and the original invoice dataset was prepared. Furthermore, two more datasets were obtained by applying preprocessing methods (Zero-Padding, Brightness Augmentation) on the original dataset. The Invoice classification system developed using Convolutional Neural Networks (CNN) architectures named LeNet-5, VGG-19, and MobileNetV2 was trained on three different data sets. Data preprocessing techniques such as correcting the curvature and aspect ratio of the invoices and image augmentation with variable brightness ratio were applied to create the data sets. The datasets created with preprocessing techniques have increased the classification success of the proposed models. With this proposed model, invoice images were automatically classified according to their templates using CNN architectures. In experimental studies, a classification success rate of 99.83% was achieved in training performed on the data set produced by the data augmentation method.
Research on Toy Design for Special Children Based on Sensory Integration Training, D-S Theory, and Extenics: Taking Physical Toys for ADHD Children as an Example

Based on sensory integration training, D-S theory and extenics, this paper discusses the innovation and development of ADHD children’s toys, so as to make the design scheme more real, scientific, and comprehensive and meet the internal needs of users. Considering the advantages of extenics and the D-S theory in dealing with uncertain and incompatible decision-making problems, an evaluation method of toy design scheme for special children based on extension analysis and the D-S theory is proposed. Through the combination of the D-S theory and extenics evaluation method and sensory integration training theory, the designed toy scheme has a certain auxiliary therapeutic effect on the rehabilitation of ADHD children. Through the design case of physical fitness for ADHD children, it is proved that the combination of the D-S theory and extenics based on the sensory integration training theory has a certain reference value for the generation of product scheme and makes the product design process more comprehensive and objective.
Q-polynomial coherent configurations

The concept of coherent configurations is a generalization of the concept of association schemes. In this paper, we introduce the concept of Q-polynomial coherent configurations and study the relationship among intersection numbers, Krein numbers, and eigenmatrices. The examples of Q-polynomial coherent configurations are provided from Delsarte designs in Q-polynomial schemes and spherical designs, Euclidean designs.
A survey of DCT based watermarking schemes

This International event is another small endeavor in the direction to bring together the experienced intellectual on one hand, and the young inquisitive minds on the other hand, to develop new thoughts, new ideas and new strategies to combat the challenges of changing pattern of world demand.I extend a warm welcome to all the participants from across the world to this mega event and hope that two days scientific deliberations of this august congregation will go a long way in shaping the philosophy and action, and make a valuable contribution to the field of Research and Development.
Deterministic Finite State Automatic Smartphone Sales at Handphone Store Using Vending Machine

Mobile phones are getting more sophisticated from year to year, and there are many features that have the latest in these cellphones. Mobile phones have the fastest network today with fourth generation technology so it's not slow to watch videos, play games, study online, and so on. Mobile phones sold have variations of Android and Iphone Operating System. The sale of the cellphones is through mall counters, roadside counters, and street vendor counters. Cellphone sales counters still use tables, cell phone holders, brochures to promote cellphones, and others seem boring, there must be something unique to attract customers' attention. This study aims to develop innovation in selling cellphones that are unique and different from other cellphone stores. The research method uses Deterministic Finite State Automata on the Vending Machine. State Diagram design to define features, main view design, and purchase flow. The results of the study using Deterministic Finite State Automata in the Vending Machine design make it easier for buyers to make purchases of cellphones independently and have their own uniqueness to attract customers' attention
Aceh's Historic Tourist Attractions: An Augmented Reality-Based Prototype of a Virtual Tour Application

Indonesia has attractive tourist destinations for tourism such as beautiful interior areas and historical places. The purpose of this research is to design and build a virtual tour application for Aceh tourism objects using augmented reality. One of the problems that occur in tourist objects is that foreign tourists do not have an idea about the tourist objects they want to visit. The technology used in this study and previous research is Augmented Reality, but previous research only displays 3D tourist objects, while in this study, augmented reality technology is incorporated into the design of the 4 Aceh tourist attractions by showing a 3-dimensional illustration of the object as a whole. for the outside of the building and displays a virtual tour image in the form of a video to illustrate the inside of the tourist attraction building on the Android mobile platform. Based on the results of distance and angle testing, the best (ideal) distance that produces clear and bright marker detection is found at a distance between 25 to 45 cm, while the best angle is between an angle with a slope of 0Â° to 60Â°. Measurements of distances and angles are carried out using threads, bows and measuring tapes. The 3D object is successfully displayed by pointing the camera at the marker to be detected.Â
Investigation and Application of Differential Privacy in Bitcoin

Bitcoin is one of the best-known cryptocurrencies, which captivated researchers with its innovative blockchain structure. Examinations of this public blockchain resulted in many proposals for improvement in terms of anonymity and privacy. Generally used methods for improvement include mixing protocols, ring signatures, zero-knowledge proofs, homomorphic commitments, and off-chain storage systems. To the best of our knowledge, in the literature, there is no study examining Bitcoin in terms of differential privacy, which is a privacy notion coming up with some mechanisms that enable running useful statistical queries without identifying any personal information. In this paper, we provide a theoretical examination of differential privacy in Bitcoin. Our motivation arises from the idea that the Bitcoin public blockchain structure can benefit from differential privacy mechanisms for improved privacy, both making anonymization and privacy breaches by direct queries impossible, and preserving the checkability of the integrity of the blockchain. We first examine the current Bitcoin implementation for four query functions using the differential privacy formulation. Then, we present the feasibility of the utilization of two differential privacy mechanisms in Bitcoin; the noise addition to the transaction amounts and the user graph perturbation. We show that these mechanisms decrease the fraction of the cases violating differential privacy, therefore they can be used for improving anonymity and privacy in Bitcoin. Moreover, we showcase the noise addition to transaction amounts by using IBM Differential Privacy Library. We compare four differential privacy mechanisms for varying privacy parameter values and determine the feasible mechanisms and the parameters.
Graph-based data analysis in Big Data Computing Environment: An investigation of Flight Network Datasets

The airline industry has always been a cornerstone in the growing economies of the world and also stands to be the most efficient mode of transport for many decades. Being such a prime industry, it requires vital operational analytics for productive decision-making, which is directly related to revenue generation, and being a data-driven industry, Big Data analytics and especially graph-based analytics turn out to be the appropriate match for generating actionable insights from airline network data. The research work here applies algorithms such as the PageRank algorithm and Label Propagation algorithm to the flight network data. The results generated are helpful in achieving business objectives such as discovering the most influential airports and finding airport communities among the airline network that ultimately leads to effective flight route planning.