Reviewerly/exHarmony huggingface dataset:
Vehicle Feature Recognition via a Convolutional Neural Network with an Improved Bird Swarm Algorithm

&lt;p&gt;Accurate vehicle feature recognition is an important element in traffic intelligence systems. To address the problems of slow convergence and weak generalization ability in using convolutional neural networks to improve vehicle feature recognition, we propose an improved bird swarm algorithm to optimize convolutional neural networks (IBSA-CNNs) for vehicle recognition strategies. First, we use the center of gravity backward learning strategy and similarity- and aggregation-based optimization strategy in population initialization and foraging behavior, respectively, to improve the algorithm performance and avoid falling into a local optimum. Second, the improved bird swarm algorithm is used to optimize the weights of the convolutional and pooling layers of the convolutional neural network to improve the neural network performance. Finally, we tested the performance of the improved bird swarm algorithm in simulation experiments using benchmark functions. The recognition performance of IBSA-CNN was tested by the UCI dataset, and in the traffic vehicle dataset BIT-Vehicle, it improved 4.9% and 6.8% compared with R-CNN and CNN, respectively, indicating that IBSA-CNN has better vehicle feature recognition.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Building Learning Analysis System with GQM Methodology and ELK Stack

&lt;p&gt;With the development of Internet technology, Massive Open Online Courses (MOOCs) are becoming popular around the world, and more and more people are learning new knowledge through online learning platforms. Learners generate a lot of information on learning platforms every day, and most of the information is recorded in logs. However, it is difficult to start to extract valuable information from the huge and messy log data of learning platforms, which requires a methodology to guide and a lot of time and cost to process the data. Therefore, this research proposes a data analysis process, using the GQM (Goal Question Metric) method as a guide for the analysis process combined with the Banerjee analysis model to build a series of question lists and metrics to evaluate students&amp;rsquo; learning behavior performance, and using the ELK Stack (Elasticsearch Logstash Kibana) as an analysis environment to solve the problem of data processing. Finally, we conduct a case study of a programming course in the OpenEdu e-learning platform to help educators transform the log data into analyzable information to understand students&amp;rsquo; learning behaviors in the online course and to propose effective decisions for improving learning outcomes.&lt;/p&gt; &lt;p&gt;&amp;nbsp;&lt;/p&gt;
Analysis of Factors Affecting Motivation and Interest in Learning English for Non-English Students

Students at tertiary institutions are required to take general English courses which act as a support for the main courses in the study program. However, observations show that students are still not skilled in using English. Some of the factors that cause this include low motivation, lack of vocabulary, lack of interest in learning, limited time available, and other factors. The purpose of this study is to identify the factors that influence the motivation and interest in learning non-English students. It also aims to evaluate which motivational subfactors have more influence on student learning. This study used descriptive qualitative method. Data was collected through descriptive techniques based on events in the field, with data sources coming from scientific journals and researchers as analyzers and interpreters of research data. The results show that to increase interest and motivation to learn English, comprehensive support from universities or colleges is needed, such as providing adequate resources, using innovative learning methods, introducing English culture, holding extracurricular activities related to English, and providing support from teachers and administrative staff.
The Dynamic Model of Water Balance in A Sub-Basin

Management of water resources in a river basin requires a new approach. The availability of water is a basic thing that must be considered given the increasing need for water. This study aimed to develop a water balance model in the Upper Bekasi River Sub Basin. In this study water balance model of the Bekasi River Sub Basin was built with several variables that affect water demand and availability, namely population growth rate, public facilities, and efforts to utilize resources. In this research, water supply and demand modelling was carried out using a dynamic system supported by Vensim software. Simulations were carried out from 2012 to 2045. The increase in population growth was directly proportional to the increased in population water needs. This dynamic model simulation used the application of rainwater harvesting and water-saving behaviour. Water demand for the Bekasi River Sub Basin was equal to 156.649.000 m3/year. The availability of surface water was 138.127.680 m3/year and had not been able to meet the water needs. The dynamic model showed that the water balance will be deficit in 2031, namely 51.883.200 m3/year. However, with the application of the water saving scenario, the water balance has a surplus of 258.564.000 m3 / year until the end of the simulation. Water reducing behavior can be used as an efficient method of water utilization. Keywords: Population, Rainwater harvesting, Simulation, Water management, Water resource
Designing Criteria for the Evaluation Research Findings in the Field of Social Science and Humanity: some Theoretical Issues

As society evolves, so do people's needs for increasing awareness and knowledge. This is the driving force behind scientific advancement, including social sciences and humanities. The findings of humanities and social science research have made significant contributions to the ideological and scientific foundations of policymaking and development programs. Due to the distinctiveness of originality and creativity with varied values from scientific values, social practical values, informational values, etc., scientific and technical activities need to be identified, managed, and evaluated in accordance with the proper concepts, criteria, and standards. In a scientific research institution, evaluating scientific research outcomes is a critical task. One of the most crucial roles of evaluation is to comprehend the true scientific value of research findings. This article's objective is to provide a theoretical foundation for ongoing efforts to develop a comprehensive framework for evaluating research outputs in the humanities and social sciences.&#x0D; Keywords: Evaluation; scientific research findings; evaluation criteria; social science and humanities research; criteria; scientific research outputs.&#x0D; &#x0D; &#x0D;
The System Architecture of Intelligent Student Relationship Management Based on Cognitive Technology with Conversational Agent for Enhancing Student’s Loyalty in Higher Education

This paper presents the conceptual framework, value chain model and the system architecture of intelligent student relationship management based on cognitive technology with conversational agent for enhancing student&amp;rsquo;s loyalty in higher education. The purposes were to synthesize the conceptual framework and apply it to develop the value chain model and the system architecture of intelligent student relationship management based on cognitive technology with conversational agent for enhancing student&amp;rsquo;s loyalty in higher education and assess the developed value chain model and system architecture. The questionnaire was employed as the instrument to assess and certify the value chain model and the system architecture by the experts. The 5 point-Likert scale was used to identify the level of agreement of the value chain model and system architecture certification assessment. The instrument was verified by five experts using content validity index (CVI). After that, the value chain model and the system architecture were verified based on the consensus assessments of seventeen experts using mean, standard deviation (S.D.), median, interquartile range and quartile deviation. The results revealed that the experts had a consensus on the value chain model developed based on the conceptual framework (Mean = 4.89, S.D. = 0.27, Median = 5, Interquartile Range: I.R. = 0.00, Quartile Deviation: Q.D. = 0.00). They also had a consensus to approve the system architecture developed based on the value chain model (Mean = 4.70, S.D. = 0.55, Median = 5, Interquartile Range: I.R. = 1.00, Quartile Deviation: Q.D. = 0.50).
A Bibliometric Overview of Orbis Litterarum from 1983 to 2020

Orbis Litterarum is an international journal devoted to the study of European, American and related literature. This study analyzes the journal publications from 1983 to 2020 in a bibliometric approach after extracting bibliography data from the Web of Science database. The study covered all the published articles of Orbis Litterarum from 1983 until 2020, aiming to inform readers and researchers about the journal with both quantitative and qualitative data. The bibliometric study includes the most cited sources, top contributing countries, universities and authors, research fields and publication dynamics. Hopefully, it may help researchers to optimize their research decisions for in-depth studies on literary scholarship, criticism and theory.
THE CORRELATION BETWEEN STUDENTS’ VOCABULARY MASTERY AND READING COMPREHENSION AT THE SECOND GRADE OF SMA NEGERI 10 KOTA TERNATE

This research is purposed to know the correlation between vocabulary mastery and reading comprehension at the second grade of SMA N 10 Kota Ternate. There are two variables in this research. Students vocabulary as independent variable (variable X) and Reading comprehension as dependent variable (Variable Y). The population of this research was all second grade of SMA N 10 Kota Ternate while the sample of the research were 30 respondents that taken from class 2 IPA1. This research used convenience sampling and applied quantitative descriptive with Pearson’s r correlation coefficient technique to analyze the data. The data were collected by using a test. The test consist of 30 items of vocabulary mastery test and 30 items of reading comprehension. The result of the research found that there is a significant correlation between vocabulary mastery and reading comprehension is 31.77. While in Pearson’s r table with the degree of significance 5% is 0.367. It means that correlation coefficient of 31.77 is significant and the null hypothesis is rejected because rxy&gt;rt = 31.77&gt;0.367. In other words, the alternative hypothesis that stated there is significant correlation between vocabulary mastery and reading comprehension is accepted. the suggestion of this research is that in developing the vocabulary mastery and reading comprehension teacher should encourage the students to have and use dictionary as a tool to help them with difficult words and the teacher should support the students expectation about reading and arouse their interest to increase their reading comprehension.
A review on the criteria for adoption of cloud computing in South African Institutions and Business enterprises

Cloud computing has taken over the IT industry. Big enterprises such as Oracle, Microsoft and Amazon have developed world class state of the art products and services for Cloud Computing. These products serve and the needs and power many corporations, business institutions and enterprises across the world. Cloud computing is continuing to growing and attracting more businesses and birthing and transforming industries such as music streaming and online gaming. As we progress and move towards a 5G and become a 4th industrial society more cloud computing products and services will be developed and used. However, there are many issues that arise such as stability, reliability, security and fraud. Addressing these problems introduces complexity towards adopting cloud comput ing for small business enterprises leaving only big enterprises to be able to afford reliable cloud computing products. There are many outstanding works that have been previously published by scholars on cloud computing. This research paper provides a systematic review and a summary of some the outstanding works of scholars recently published and the different criterias used for clouding computing adoption by South African Institutions and Business enterprises.
The Analysis of a GPT-based Sepedi Text Generation Model

Text generation is defined as a component of natural language processing that makes use of computational linguistics techniques to produce text that cannot be distinguished from human written text. This study aims to develop and analyse a Generative Pre-Trained Transformer 2 (GPT-2) language model to generate Sepedi phrases. The under-resourced Sepedi language is regarded as a disjunctive language. The Sepedi language orthographic representation presents challenges and has limited resources. The GPT-2 transformer requires large datasets, as well as state-of-the-art computational resources. The unstructured National Centre for Human Language Technology (NCHLT) Sepedi text dataset was used. The text generation model developed with the small dataset managed to get the lowest loss value of 2.36. The output text generated using this model produces a text that is syntactically correct with instances of grammatical errors. The model performed better than previously developed Sepedi text generation models by using transformer-based technique.
Improving Accuracy of Credit Card Fraud Detection Using Supervised Machine Learning Models and Dimension Reduction

Credit card fraud is a serious crime, and it is a common type of identity theft. Financial institutions and consumers are experiencing economical losses due to financial fraud caused by credit card transactions. Machine Learning Models can aid and alleviate credit card fraud by providing real time detection of credit card fraud before it takes place. The problem that arises with machine learning models is poor performance in terms of accuracy if the data objects in dataset have high dimensionality. In this paper we have tested and compared six machine learning models in detecting credit card fraud. Furthermore, dimension reduction techniques was used to improve the performance of these machine learning models. The results show improved accuracy on the machine learning models after applying dimension reduction and removing anomalies and imbalance.
A Bridge to Machine Learning for Zero Computer Science Community: A Machine Learning Path with Linear Regression

Based on LinkedIn poll results, professionals from non-computer science backgrounds have developed an interest in data science and most intend to enter the field through Machine Learning, but do not know where to start (Webb et al., 2021) The aim of this study is to introduce an ideal systematic approach and workflow to get started with Machine Learning. Since the Linear Regression algorithm is the main algorithm used to introduce the concept of Machine Learning, it was chosen for this study to lay a fundamental platform. Using the proposed algorithm and appropriate python libraries, efficient methods of explanatory data analysis and procedural steps towards model training are also explained. Briefly, this study gives an insight on how to approach other Machine Learning algorithms as well. The first steps in Machine Learning involve data pre-processing. The dataset was chosen to meet data manipulation techniques suitable for people without a computer science background.
EdgeAI Use Cases for Algorithmic Government

In this chapter we review some use cases of EdgeAI for algorithmic government. Some of the use cases include Facial Recognition for Suspects at Public Places, Social Network Analysis (SNA) for analyzing citizen behavior, Voice enabled AI-based personal assistants, and Industrial safety through cameras and sensors. We propose different architectures and enabling technologies for each use case, aimed at optimizing the various key performance indicators of model training and inference in those particular scenarios.
THE DEVELOPMENT OF A JĀTAKA STORY-BASED COMIC BOOK FOR ELEMENTARY BUDDHIST SUNDAY SCHOOL STUDENTS

The purpose of this study is to explain the manufacturing process and determine the feasibility level of jātaka comic books. The method used in this research is Research and Development using the ADDIE development model (Analysis, Design, Development, Implementation, and Evaluation). The data collection techniques used include observation, interviews, questionnaires, and documentation. The data credibility is assessed by transferability, dependability, and confirmability. The data analysis techniques used for qualitative data are reduction, presentation, conclusion and verification. Meanwhile, quantitative data are analyzed using descriptive percentage theory. The result of the research is the process of making jātaka comic books based on the ADDIE development model. Based on the results of the product feasibility assessment from experts, jātaka comic book get a feasibility percentage of 89.95% from material experts and 75% from media experts. The test subjects involved were 2 BSS teachers and 7 BSS students at the Elementary School level at BSS Dhamma Mettā. The responses of BSS students during product trials seemed interested, enthusiastic, and able to understand the contents of the jātaka comic book. The BSS teacher said that jātaka comic books were easy to use and suitable as teaching materials. Based on the results of the percentage assessment and responses from the test subjects, it can be said that the jātaka comic book is suitable to be used as teaching material for BSS at the elementary school level at BSS Dhamma Mettā.
IMPLEMENTATION OF MODIFIED SCRIPT FOR LINUX BASED OPERATING SYSTEMS USING A LINEAR ALGORITHM FOR NETWORK PORT SCANNING

The Python programming language has various module libraries for network scanning of hosts. In this paper the performance and implementation of a modified script for Linux based operating systems using a linear algorithm for network port scanning is presented.
Algorithmic Government

In this chapter, we introduce the concept of Algorithmic Government and highlight the motivations and benefits of AI in governance by reviewing several use cases. We then discuss the importance of Large-Scale Decision-Making (LSDM) events in the context of Algorithmic Government and talk about relevant definitions, characteristics of LSDM events, and the different consensus reaching processes. We also elaborate on the various ways AI is integrated into LSDM events to enhance its efficacy. Next, we discuss the shortcomings of the cloud and fog architectures currently used to implement AI in federal agencies. The issues identified are latency, communication overhead, and bandwidth consumption, focusing on security and privacy concerns. Finally, we discuss these challenges and drive demand for new computing technologies.
DEVELOPMENT OF INTERACTIVE LEARNING MEDIA BASED ON GOOGLE SITES WEBSITES FOR SCHOOL SUBJECTS OF BUDDHISM AND MORALS FOR JUNIOR HIGH SCHOOL CLASS VIII DURING IN THE PANDEMIC

This study aims to explain the process of creating a google sites website to produce an online learning media that will help BME learning activities at the junior high school level. This type of research is research and development with the ADDIE approach. Data collection techniques used are interviews and questionnaires. Data credibility was assessed by transferability, dependability, confirmability, and logical validity. Data analysis techniques used for qualitative data are reduction, presentation, and conclusion. Quantitative data were analyzed by descriptive percentage theory. The product validation from the material experts obtained a feasibility percentage of 91.1% and from the website, the media expert obtained a feasibility percentage of 71.8%. The results of the user trial, namely the BME teachers and five Buddhist class VIII students also gave good responses to the product developed. The BME teachers conveyed that the learning media products based on the google sites were very effective. Some students also gave positive responses, that this learning media is quite interesting and more interactive.
DEVELOPMENT OF BUDDHIST SYMBOL FOR THE ADHICITTA BUDDHIST SUNDAY SCHOOL

The limited learning media containing Buddhist symbols materials in the Buddhist Sunday Schools causes the lack of visualization in children's learning material. One alternative is to use an application creation program that utilizes Adobe Flash. Based on this problem, a learning media for Buddhist symbols was developed for the Adhicitta Buddhist Sunday School, Kenteng Village, Susukan District, Semarang Regency. This research is research and development. Data collection instruments used questionnaires and documentation and were analyzed using descriptive statistics. The data collection technique was carried out by the ADDIE process to see the level of the product validity and its development stage and to use a questionnaire for the success of the learning media. The results of the learning media validation of Buddhist symbols showed 81.176% of the Material Experts and the results of the validation of the media expert show that the learning media developed was feasible with an average acquisition of 80%. Based on the results of research conducted, it can be concluded that the learning media of Buddhist symbols can be used in the Buddhist Sunday School learning program.
Comparative Analysis of Forensic Software on Android-based MiChat using ACPO and DFRWS Framework

Instant Messaging (IM) is a popular and widely used communication application. MiChat is a multi-platform instant chat service with several features that can attract various segments of the population to use it as a tool for committing cybercrimes. A forensic framework and several forensic tools are needed to carry out physical evidence investigation procedures. This study focuses on analyzing and comparing the forensic tools used during the research, based on defined digital evidence parameters and applying a specific mobile forensic framework. The results show that Final Mobile Forensic has the highest ability to obtain digital evidence and can recover deleted data, while Oxygen Forensic Detective has advantages in terms of audio, images, and video but cannot recover data. The best framework is DFRWS, which has the most complete stages so that it can support the investigation process. The best digital evidence is text chat and contacts, which can be used to support valid legal claims.
Anonymous Communication and Shuffle Model in Federated Learning

In the previous two chapters, we have discussed how to keep privacy through encrypting content transported in federated learning, namely, encrypt the information exchanged between different parties. Another way of privacy protection in federated learning is to disconnect the contents from their sources, in other words, hiding the ID of a piece of information, which is called anonymous communication.
Differential Privacy in Federated Learning

In this chapter, we briefly introduce the concept and basic terms of differential privacy, which is a very popular new tool designed for privacy protection. Then, we present the applications of differential privacy in federated learning for security and privacy protection.
The Future Work

At the end of this book, we would like to present the future work in line of the theme that we have discussed so far under the big picture of the information age.