Reviewerly/exHarmony huggingface dataset:
Upaya Peningkatan Minat Belajar di Era New Normal Pada Anak-Anak Warga Dusun II Desa Tumpatan

The problem of online learning is one of the problems felt by all children and adults. As in the learning era in the midst of a pandemic, learning has not yet obtained maximum results and is even lagging behind other countries. The purpose of this community service is so that school-age children do not feel bored because of online learning and have a high interest in learning in the future. Then knowledge and understanding are given to the target regarding the procedures for building interest in learning, so that it is hoped that they can learn according to government regulations. The target achieved in this service is to increase interest in learning and will increase knowledge in being a student in the midst of the Covid 19 pandemic. The method used is to provide socialization on procedures for building interest in learning to redefine knowledge that was left behind due to the pandemic. The resulting learning process to the government and provide knowledge, understanding and skills of learners. The lecture method and visits from participants as well as evaluating explanations about increasing interest in learning using learning media will provide a more real assessment of the target for the success of this activity. It is hoped that the target will participate in the socialization seriously and is targeted to produce knowledge and skills in general knowledge both online so that it can provide even better benefits in increasing knowledge.
A Data Mining Approach for Filtering out Social Spammers in Large-Scale Twitter Data Collections

Social networking services – such as Facebook.com and Twitter.com – are fast-growing enterprise platform that has become a prevalent and essential component of daily life. Due to its popularity, Twitter draws many spammers or other fake accounts to post malicious links and infiltrate legitimate users' accounts with many spam messages. Therefore, it is crucial to recognize and screen spam tweets and spam accounts. As a result, spam detection is highly needed but still a difficult challenge. This article applied several Bio-inspired optimization algorithms to reduce the features' dimensions in the first stage. Then we used several classification schemes in the second stage to enhance the spam detection rate in three real Twitter data collections. The performance of the chosen classifiers also revealed that Random Forest and C4.5 classifiers achieved the highest Accuracy, Precision, Recall, and F1-score even on class imbalance.
Blockchain Security : Enhanced Control Evaluation Approach to Protect Organizations’ Accounting Information

Blockchain technology can revolutionize transaction processing and reimage organizations’ traditional business practices. The literature argues that blockchain may also hurt organizations when key controls necessary to guide its implementation are not in place. The literature points to inadequacies in blockchain implementations, particularly related to the effective selection and implementation of blockchain controls in organizations. This research develops an approach that addresses the weaknesses in the literature. Such approach will allow organizations to be more precise in their implementation of controls to achieve successful blockchain implementation. The proposed approach uses fuzzy set theory to prioritize business controls more precisely in organizations. It is argued that fuzzy set theory allows for a more accurate assessment of imprecise criteria than traditional assessment methodologies, and therefore generates more accurate assessments critical for decision-making. Through a case, the developed approach proved successful in providing accurate implementation of controls to protect organizations’ accounting information.
A Systematic Literature Review on Security Indicators for Open-Source Enterprise Resource Planning Software

Open-source enterprise resource planning (ERP) software has become a preferred alternative for modern organizations due to its affordable cost, availability and ease of access. Open-source software allows access to customizable code which in most instances may have security loop holes due to the nature of its releases. The study is motivated by need for accountability and security assurance by stakeholders and the need for justification of investments towards information security. The objective was to analyse security indicators for open-source resource planning software. Papers and journals published between 2017 and 2021 from IEEE, ACM, Springer, arXiv, Wiley online library and EBSCO were reviewed. Out of the publications generated through the Google search, 62 publications were selected by reading the title, abstract, introduction and full text. Results indicate un-updated software, full access rights, inadequate training, failure to comply, single authentication and unauthorized software as some of the factors that indicate open-source enterprise resource planning software security. In conclusion effectiveness of mitigation measures to address these factors shows security or insecurity. Notably, there is need to institute security control measures and metrics for the identified factors to help assess security posture of enterprises during ERP software implementation. We recommend the design of security a measurement framework and definition of a metrics suite for assessing open-source ERP software security.
Building Reliable Cloud Systems through Chaos Engineering

Cloud computing systems need to be reliable so that they can be accessed and used for computing at any given point in time. The complex nature of cloud systems is the motivation to conduct research in novel ways of ensuring that cloud systems are built with reliability in mind. In building cloud systems, it is expected that the cloud system will be able to deal with high demands and unexpected events that affect the reliability and performance of the system. In this paper, chaos engineering is considered a heuristic method that can be used to build reliable cloud systems. Chaos engineering is aimed at exposing weaknesses in systems that are in production. Chaos engineering will help identify system weaknesses and strengths when a system is exposed to unexpected knocks and shocks while it is in production. Chaos engineering allows system developers and administrators to get insights into how the cloud system will behave when it is exposed to unexpected occurrences.
Complexity Metrics for Statechart Diagrams

Model-Driven Development and the Model-Driven Architecture paradigm have in the recent past been emphasizing on the importance of good models. In the Object-Oriented paradigm one of the key artefacts are the Statechart diagrams. Statechart diagrams have inherent complexity which keeps increasing every time the diagrams are modified, and this complexity poses problems when it comes to comprehending the diagrams. Statechart diagrams provide a foundation for analysing the dynamic behaviour of systems, and therefore, their quality should be maintained. The aim of this study is to develop and validate metrics for measuring the complexity of UML Statechart diagrams. This study used design science which involved the definition of metrics, development of a metrics tool, and theoretical and empirical validation of the metrics. For the measurement of the cognitive complexity of statechart diagrams, this study proposes three metrics. The defined metrics were further used to calculate the complexity of two sample statechart diagrams and found relevant. Also, theoretical validation of the defined metrics was done using the Weyuker’s nine properties and revealed they are mathematically sound. Empirical validations were performed on the metrics and results indicate that all the three metrics are good for the measurement of the cognitive complexity of statecharts.
Inherent Quality Metrics for Continuous Software Quality Enhancement

Traditional software quality metrics based on bug number and pass rate can only provide us afterthought post validation &amp; product release. In this paper, we propose some new inherent software quality metrics for proactive quality control during development phase, including Lines of Code (LOC#), Cyclomatic Complexity and Code Churn. In this paper, citing one ultra-large-scale software - Intel Media Driver as one example, we introduce the reason to choose those metrics, our experience on leveraging those metrics to improve the software quality, and our turn-key solution for automatic metrics data collection and analysis. We expect the identified metrics can help more researchers to form the corresponding research agendas and the experiences sharing can help following practitioners to apply similar enhancements.
Peningkatan Hasil Belajar Keterampilan Membaca Melalui Strategi Pembelajaran Card Sort Pada Siswa MTs. Darut Taqwa 02 Sengonagung Purwosari

Learning outcomes in learning reading skill there are 31 students who are still under the KKM. This is because the teacher still uses the lecture method and tadribat only, so that in focused learning with books that can cause students to feel bored and students are still not able to understand the meaning or information contained in the Arabic text. The objectives of this study include; to find out how to use the card sort learning strategy, and to find out how the effect of the card sort learning strategy on improving the learning outcomes of reading skill. This research is a quantitative research type of pre-experiment. The method used is one group pretest-posttest design. The results of this study show that card sort learning strategies have an effect on improving student reading skill learning outcomes.&nbsp; This can be seen from the fact that the calculation is greater than the ttabel, namely 11,614 &gt; 2,030 and Sig. (2 tailed) = 0.000 &lt; 0.05, then Ho was rejected and Ha was accepted. So it can be concluded that there is an influence on the use of card sort learning strategies on improving the learning outcomes of reading skill students of Junior High School Darut Taqwa 02 Sengonagung Purwosari.
Smartphone as additional learning tool with reference to EFL students in selected Universities in Chad

Today, mobile technology is a growing trend in learning languages. A mobile technology like smartphone is used by university students in their daily lives anywhere and anytime. There are number of language learning applications on smartphone that can help develop students’ language skills. The present study aims at investigating how the students perceive the usefulness of smartphone for learning English and how they use it to improve their linguistic skills. A 30-item questionnaire was administered to 513 students in the university of N’djamena and Doba. The data collected from respondents were analysed using basic descriptive statistics. The Findings reveal that there is a high positive agreement toward engagement in using smartphone as tools for learning among EFL students. In addition, it was found that students mostly embark into some linguistic activities (listening and reading) which help gain more practice on some skills than the others (writing and speaking) through their smartphone.
Performance Evaluation of Yolov4 and Yolov4- Tiny for Real-time Face-mask Detection on Mobile Devices

The viral outbreak of COVID-19 that started in the year 2019, radically changed our everyday life, with a detrimental impact on the simple, daily habits of citizens. In many countries around the world, the usage of mask is necessary as a protection measure against covid-19. Every service, organization, various stores, schools, universities, hospitals, companies and many other places, which are attended by hundreds of people every day, make the use of a mask necessary to enter them. This fact requires the control of the persons when they enter the respective spaces to determine if they are wearing a mask when entering the area. In this research we compared performance on YOLOv4 and the Tiny-YOLOv4 algorithm on images, recorded video, and real time video. In the next step we will implement the YOLOv4 TFlite and Tiny YOLOv4 TFlite model for mobile applications using the Android Studio platform. On the proposed dataset YOLOv4 achieved 92.91% mAP and training took around 2 hours for 1000 iterations. On the other hand, YOLOv4-tiny achieved 74.75% mAP and training took less than half an hour for 1000 iterations. For further improvement we convert YOLOv4 and YOLOv4-tiny to YOLOv4 TFlite and YOLOv4-tiny TFlite respectively. After this step we compare YOLOv4 TFlite and YOLOv4-tiny TFlite model performance on mobile device. YOLOv4 TFlite achieved 96.92% accuracy on real time video at 5017ms and YOLOv4-tiny 74.72% accuracy on real time video at 491ms.
Assessment of the Impact of a Firm’s Corporate Social Responsibility (CSR) on its Profitability in Nigeria: A Case Study of Akure Firms in Ondo State

This study critically examined the impact of a firm’s Corporate Social Responsibility (CSR) on its profitability in Nigeria. The specific objective of the study was to assess the effect of customer orientation on the performance of SMEs in South West, Nigeria; examine the effect of competitor orientation on the performance of SMEs in South West, Nigeria and investigate the effect of inter-functional coordination the on the performance of SMEs in Ondo state, Nigeria. A descriptive survey research design was adopted and the population of the study covered all the 23,289 SMEs across all the states in the South West region of Nigeria; out of which, 393 small and medium scale enterprises’ owners were selected using the Yamane model (1967). An adapted questionnaire was used and it was subjected to a validity and reliability test. Data gathered via the questionnaire was analyzed using frequency and percentage for background information of the respondents and items on the questions, Pearson correlation to show the relationship between the predictor and outcome variable and simple linear regression to reveal the effect of all the predictors on the outcome variable. The result of the analysis revealed that customer orientation has a positive and significant effect on the performance of Small and Medium Scale Enterprises (SMEs) in South West, Nigeria to the tune of 0.302(p=0.000&lt;0.05), Thus, it was concluded that all the predictors (customer orientation, competitor orientation and inter-functional orientation) have a positive and significant effect on the performance of SMEs in South West, Nigeria.
Robust counting of engine blades in aircraft engine video endoscopy

To ensure the safe operation of aircraft, regular endoscopy of the engines is mandatory. Since the blade stages are particularly susceptible to defects, they must be inspected especially frequently. In the process, a worker must inspect each blade individually. All findings must be carefully documented and assigned to the respective blade. Since there are no individual markings to identify the blades, the operator must count all blades as they pass through the endoscope image. Although electric rotary devices with automatic blade counting are available for some engines, manual counting is often necessary. Simultaneously inspecting and counting blades is tedious and error-prone. We present a novel algorithm for automatic blade counting during jet engine inspection in this paper. The algorithm’s central part is a Pearson correlation of individual video frames as the blades pass before the endoscopes during turning. Adaptive thresholding of the correlation function is used to count the blades. Rotation direction and speed are determined using the Farneback method of optical flow. By using correlation instead of classical image features, the algorithm is highly robust to metallic reflections and smooth blade surfaces without significant image features. In addition, the algorithm is robust to different rotation speeds and directions. Compared to existing approaches, the algorithm is robust and universally applicable for counting engine blades on almost any engine without the need for customization.
Enabling Supply Chains by Adopting Blockchain Technology

This paper is aimed at studying the enablement of supply chains by adopting blockchain technology. The implementation of blockchain in supply chain management can solve the current issues faced in the supply chain ecosystem. Supply chains are part and parcel of every business and have multiple inefficiencies in the system. Some of these inefficiencies can be managed by usage of blockchain Platform .Technology, Intra-organizational, Inter-Organizational, external factors, and innovation are critically evaluated for adoption of blockchain in supply chain. A pilot study is conducted in form survey for analysis of these factors. Hypotheses are derived for these factors for quantitative research. Subsequently these hypotheses are examined with the help of ADANCO2.3 for structural equation modelling. As an outcome, it is evident that Innovation and External factors are significantly impacting the adoption of blockchain in supply chain management.
Development of Chat Application

Abstract: Conversation is a way of using technology to connect people with ideas outside of local boundaries. The technology has been available for years but adoption has only recently taken place. Our project is an example of a chat server. It is made up of two applications - the client application, which runs on the user's web browser and the server application, running on any network servers. To start chatting the client must be connected to a server where they can conduct private and group chat. Safety measures were taken at the last moment. The latest developments in the internet have brought the world into our hands. Everything happens online from information transfer to purchasing. The internet makes the world a little round. This project is also online. This paper highlights the importance of the use of dialogue in everyday life and its impact on the world of technology. This project is for the development of a chat system based on Javascript programming language and network concept. The app allows people to transmit messages both privately and publicly .It also enables the feature to share resources such as files, photos, videos, etc. This online application is designed to communicate or chat with others online. It is more reliable and secure than any other traditional system available. Javascript, React.js and the client server concept were used to develop a web-based chat app. This app is built with the right structures for future development. It can be planted in all private organizations such as Colleges, IT parks, etc. Keywords: Javascript , React.js, Internet.
A Survey on Face Mask Detection and Social Distancing Measurement Using Machine Learning

Abstract: The purpose of the project “Implementation of Covid-19 Monitoring System using ML-based Face Mask Detectionand Social Distancing Measurement” is to create a tool that identifies the image of a human that can calculate the probability that he/she is wearing a mask or not and another motive of our project is to detect the people and check whether they aremaintaining the social distancingnorms. Using Machine Learning and Object Detection, this study seeks to detect face masksand social separation. The contribution is a strategy for merging more complex classifiers in a "cascade" that allows the image's background areas to be swiftly rejected while more computation is spent on promising object-like regions. While deep learning-based methods for general object detection have progressed dramatically in the previous two years, most approaches to face detection still use the CNN framework, which results in low accuracy and processing performance. We examine the use of MobileNet(pretrained model) and feature extraction in this project, which will yield outstanding results on a variety of object detection benchmarks. YOLO Object detection was used to detect persons in a frame and calculate the distance between them to check for social distancing. As a result, in a real-time situation, this technology tracks persons wearing or not wearing masks and provides social separation by triggering an alert if there is a violation in the scene or in public spaces. This can be combined with current embedded camera infrastructure to allow these analytics, which can be used in a variety of verticals as well as in offices and airport terminals/gates. Keywords: COVID–19, Face Mask, Social Distancing, Machine Learning,MobileNet, YOLOv3, Tensorflow, Keras, OpenCV, Object Detection. Abbreviations: DNN (Deep Neural Network), YOLOv3 (You Only Look Once Version3)
Speech Emotion Recognition Using Deep Learning

Abstract: Emotional state identification based on analysis of vocalisations is a challenging subject in the field of HumanComputer Interaction (HCI). In the research that has been done on speech emotion recognition (SER). A wide range of research approaches has been used in order to extract feelings from a variety of inputs, including a number of well- known ways to speech analysis and categorization that are already known. Recent research has suggested the use of deep learning algorithms. as potential alternatives to the approaches that are traditionally used in SER. This article offers a summary of more in- depth topics learning methodologies, as well as current research employing it, are discussed to identify the feelings conveyed by verbal expressions. The analysis will consider the feelings that were recorded in the databases that were utilised were: the contributions to both speech and emotion that were removed the restrictions that were found, as well as the discoveries that were made discovered. Keywords: Speech emotions, Real-time Speech Classification, Transfer Learning, HCI Bandwidth Reduction, SER, LSTM
A Mathematical Theory of Big Data

This article presents a cardinality approach to big data, a fuzzy logicbased approach to big data, a similarity-based approach to big data, and a logical approach to the marketing strategy of social networking services. All these together constitute a mathematical theory of big data. This article also examines databases with infinite attributes. The research results reveal that relativity and infinity are two characteristics of big data. The relativity of big data is based on the theory of fuzzy sets. The relativity of big data leads to the continuum from small data to big data, big data-driven small data analytics to become statistical significance. The infinity of big data is based on the calculus and cardinality theory. The infinity of big data leads to the infinite similarity of big data. The proposed theory in this article might facilitate the mathematical research and development of big data, big data analytics, big data computing, and data science with applications in intelligent business analytics and business intelligence.
A Note on the Irrationality of Angles of Kloosterman Sums over Finite Field

We prove that the angles of Kloosterman sums over arbitrary finite field are incommensurable with the constant π.