Reviewerly/exHarmony huggingface dataset:
Naïve Bayes and K-Nearest Neighbor Algorithm Approach in Data Mining Classification of Drugs Addictive Diseases

Indonesia, with its very large population, is a potential market for drugs trafficking. Hence, seriousness is needed in cracking down or preventing drug trafficking. Narcotics are substances or drugs that can cause dependence or addicted and other negative impacts on users. The problem is that drug users do not realize and even ignore diseases caused by drug addiction. The diseases can be life-threatening for users, such as inflammation of the liver, heart disease, hypertension, stroke, and others. The prevalence rate of drug abuse in West Nusa Tenggara (NTB) is included in the high category, reaching 292 cases or around 37.24% cases. This study aimed to create an application that can classify various diseases of drug users using the naïve bayes and KNN methods. The results of this study indicated that there was a very close relationship between drug users and various deadly diseases. The prediction results showed that the naive bayes method provided a prediction accuracy of 94.5% while the KNN showed a prediction accuracy of 92.5%. This shows that the naive bayes method provides better predictive performance than the KNN in the data set of drug addicts in NTB.
Application of General Regression Neural Network Algorithm in Data Mining for Predicting Glass Sales and Inventory Quantity

FF Jaya Glass is a shop that supplies and installs 3 mm to 12 mm glass. The store obtained glass from suppliers to be processed in shape and size according to customers’ order. After completing the customer's order, the shop worker will install the glass at the requested location. Unfortunately, currently stores do not utilize sales data to predict sales either manually or by utilizing technology. As a result, the store cannot predict when the number of glass orders will increase or decrease. In addition, errors often occur when ordering glass for the next period. As a result, stores often run out of glass supplies due to the large number of glass orders so that the achievement of profits is not optimal. This study aims to identify sales variables in glass sales data and build a general regression neural network model as a data mining method. In addition, this study aims to iterate to find the best value in the sales data training process, design and create applications according to user needs, and conduct system validation tests. The general regression neural network method is used to predict sales. The results of this study indicate that the application of general regression neural networks can be used to predict sales. This will make it easier for the store to provide glass supplies in the coming months with an accuracy of 98.1%.
Evaluating The Application of Library Information System Technology using the PIECES Method in Remote Areas

Over five years, the implementation of the library information system at IKIP Muhammadiyah Maumere faced a challenge, frequent errors during data input that hindered users from fully utilizing the system. These issues not only affected users’ interest but also highlighted the significance of the human factor in shaping the quality of an information system. To make full use of the system, it was crucial to identify and address the problems associated with it. This research delved into the experiences of 242 library information system users, including lecturers, students, and librarians, by using the PIECES method. The goal was to analyze users’ satisfaction and uncover any underlying issues within the system. The results of the PIECES analysis revealed average satisfaction scores, showcasing users' contentment with the system's performance (3.77), information (3.79), economy (3.80), control (3.77), efficiency (3.77), and service (3.89). These findings suggest that the library information system has been meeting users' expectations. However, a significant problem emerged in the performance variable, particularly in the system stability. Additionally, issues related to data compatibility, duplication in storage, and users’ authority management, access control, and system errors were observed in the information and control variables. Based on these identified challenges, recommendations for system improvement were made by targeting low satisfaction levels. Proposed solutions involve enhancing data management, storage practices, user access control, and reducing the risk of system errors, ensuring more efficient and reliable library information system
Machine Learning-Based Hybrid Optimization Model of Personnel Digital Development Planning

The object of our research is the organization personnel management department. The subject of our study is planning of the organization's employees digital competencies” development. The purpose of our work is to create a hybrid optimization model that allows for effective planning of the digital competencies development of the organization's personnel based on digital competencies assessments. The relevance of the research lies in the fact that with the active transition of world into the information environment all spheres. It is imperative that employees of organizations have the appropriate knowledge and skills. In our work, we considered and analyzed different approaches to effective planning for the personnel digital competencies” development, we developed and tested a hybrid optimization model that allows for planning the organization's employees digital competencies development based on assessments of digital competencies using machine learning and discrete optimization methods. The developed model has shown its effectiveness on test data. The use of the model will result in a more structured process of developing digital competencies of the organization's workers via a generated development schedule, as well as saving time an employee of the HR department would spend drawing up such a schedule.
Understanding Data Types and File Formats for Ansible

This chapter covers data and data types used in Ansible. It begins by explaining what data is and its relationship with Ansible, followed by discussing Ansible's dependency on Python data types and providing examples of basic Python data types. The chapter then delves into Ansible configuration files, including Ansible inventory in INI, JSON, and YAML formats, inventory priority, and the ansible.cfg file. It also provides YAML examples to familiarize readers with different data types. We will also talk about the preferred playbook module names. Additionally, the chapter covers Ansible data interchange and explores JSON and Jinja2 templates with simple examples. By the end of the chapter, readers will have a broader understanding of data and data types used in Ansible, how to use Ansible configuration files, and how to effectively read and understand JSON and Jinja2 templates for data interchange and rendering files.
Word Embedding Enrichment for Dictionary Construction: An Example of Incivility in Cantonese

Dictionary-based methods remain valuable to measure concepts based on texts, though supervised machine learning has been widely used in much recent communication research. The present study proposes a semi-automatic and easily implemented method to build and enrich dictionaries based on word embeddings. As an example, we create a dictionary of political incivility that contains vulgarity and name-calling words in Cantonese. The study shows that dictionary-based classification outperforms supervised machine learning methods, including deep neural network models. Furthermore, a small number of random seed words can generate a highly accurate dictionary. However, the uncivil content detected is only weakly correlated with uncivil perceptions, as we demonstrate in a population-based survey experiment. The strengths and limitations of dictionary-based methods are discussed.
Integrating Elder and Youth into Co-Design Approach—A Case Study of Medication Aids Design

Designs for the elderly should consider their characteristics and needs. However, in previous research, the role of the elderly has been chiefly as consultants or testers, and they rarely actively participate in the design process. This study integrates steps and methods based on experience based co-design and design thinking, developing an elder and youth co-creation model. This model is applied to the medication-assistance design closely related to the needs of the elderly, exploring the impact and benefits brought by the elder and youth co-creation model for both the elderly and young designers. In this research, nine elder individuals, ranging in age from 61 to 74, and eight graduate students, primarily from design fields, participated in the co-creation workshops. This study implements the elder and youth co-creation model in the form of workshops, organizing five co-creation workshops to address the existing medication problems of the elderly and redesign medication assistance. Focus group interviews are conducted after the end of the workshop. The results show that through this model, young designers can enhance empathy with users, discover the potential needs of target customers, and design products that meet the needs of the target customer group. Furthermore, the model can enhance the confidence level of the elderly in creativity and serve as a means to present the abilities and experiences of the elderly.
Overlaps in field generated circular planar nearrings

We investigate circular planar nearrings con-structed from finite fields as well the complex number field using amultiplicative subgroup of order k, and characterize the overlaps of the basic graphs which arise in the associated 2-designs.
Reading Strategies Used by Elementary Students When Comprehending Digital Texts

Filipino youths are shifting away from reading printed texts in the 21st century as more information becomes accessible via the Internet. This study aims to determine the digital reading comprehension levels of Grade six pupils, their use of online reading strategies, and whether proficient and less proficient digital readers use different reading strategies. One hundred seventy-four sixth-grade students at a public elementary school in Naga City, Philippines, took digital reading comprehension and strategy-use tests during the 2019-2020 academic year to achieve this purpose. Of the three types of online reading strategies (global, problem-solving, and support), the elementary students reported employing more support strategies than the other types. Moreover, proficient digital readers highly use reading strategies across the three types, while less-proficient digital readers employ the strategies moderately. However, most participants showed a lack of proficiency in digital reading skills. It is recommended that literacy teachers teach the judicious use of online reading strategies to assist elementary students in comprehending digital texts.
A DESCRIPTIVE STUDY ON ENGLISH LEARNING MOTIVATION OF TENTH YEAR STUDENTS IN THE MIDST OF COVID-19

This research is a descriptive study of students motivation using descriptive quantative approach. This research aimed to know the student motivation in learning English on the tenth year student In the Midst of Covid-19 at MAU (Featured Islamic School) Ihya’ Ulumiddin Singojuruh academic year 2020-2021. The researcher used questionnaire to collect the data. The indicators of the questionnaire were divided into two part; indicators of integrative and instrumental motivations. The researcher used the Likert scale with four alternative answers, those are strongly agree, agree, disagree and strongly disagree. The researcher analyzed the data of questionnaires by formula : P = n/N x 100%. The research results that the students’ integrative motivation in learning English is categorized as very high motivation and the students’ instrumental motivation is categorized as very high motivation.
An Analysis of Homestay Hosts Interest in Learning English at Tamansari Tourism Village

English is important language to communicate with the foreign guests who stay at the homestay. This research aimed to describe how the Homestay hosts interest in learning English at Tamansari tourism village. The research design was descriptive qualitative. The respondents were 22 homestay hosts in Tamansari tourism village which determined by using purposive sampling. The data were taken by using questionnaire and interview. The data from questionnaire was analyzed by using Likert scale. The data analysis was determined based on theory of interest with four indicators. They are attention, participation, willingness, and feeling need. Analysis questionnaire and interview data showed that homestay hosts in Tamansari tourism village have high interest in learning English and the index percentage was 74%. It consists of attention level 78%, participation level 64%, willingness level 71%, and feeling need level 74%. Therefore the homestay hosts in Tamansari tourism village need English as communication medium with the foreign tourists. On the other case, there were some problems faced by the homestay hosts that are pronunciation, lack of time, lack of partner to learn, fluctuation of foreign tourists visit, and low awareness to learn made them practice English less maximal.
Enhancing Mobile Multimedia Trustworthiness through Federated AI-based Content Authentication: Enhancing Mobile Multimedia

The rapid proliferation of mobile devices and multimedia content has led to an increased need for ensuring trustworthiness and authentication of the shared data. Traditional centralized methods have proven to be insufficient in maintaining privacy and addressing scalability issues. This paper presents a novel approach to enhancing mobile multimedia trustworthiness through the application of Federated AI-based content authentication techniques. By leveraging the benefits of distributed machine learning and edge computing, our proposed framework efficiently authenticates multimedia data while preserving user privacy and reducing latency. Our system employs a federated learning model that trains AI algorithms on local devices, allowing them to collaboratively build a robust and accurate authentication model. Additionally, this research introduces a blockchain-based decentralized trust management system to further enhance the integrity and traceability of the authentication process. Through extensive evaluations, this research demonstrate that our proposed framework significantly improves the trustworthiness of mobile multimedia content while minimizing the overhead and resource consumption associated with traditional centralized approaches.
Learning full context feature for human motion prediction

Human motion prediction aims to predict the target poses given the previous poses. Most existing methods are devoted to extracting richer motion features from only the given previous poses to predict the target poses. However, we consider that the post poses after the target poses are helpful in acquiring the context feature and constraint between neighbor motions, which is also important for motion prediction. In this paper, we explore to make use of the post motion information for a powerful human motion prediction method. Specifically, we propose a human motion prediction model which learns the motion constraint from both the previous and post poses, in order to fully utilize the context features of the target poses. During training, the proposed memory dictionary module is used to learn the mapping from previous features to post features. In testing, the proposed memory dictionary module fully exploits the learned mapping to calculate the future motion feature according to the input previous feature. Thus, the context feature of human motion is enriched in our method. We evaluate the proposed method on two large-scale datasets, Human3.6M and CMU-Mocap. The experimental results demonstrate that our method improves the motion prediction performance, especially for long-term human motion.
Geographic Information System for Customer Distribution Using the Haversine Algorithm

Distribution customers of PT. Dinamika Lubsindo Utama, which does not yet have information, especially location information, has resulted in couriers who will deliver products often traveling long distances to get to the location of the customer who placed the order due to repeating the route they have previously taken. In this research, a geographic information system will be built that can provide information on the distribution customer locations of PT. Dinamika Lubsindo Utama has placed an order by carrying out calculations using the Haversine algorithm to get the distance from the system user's location to the customer's location. The system will display customers who have placed orders to the admin, complete with name, address, location point, and distance from the courier to the customer's location in order from closest to farthest, so that the courier can deliver customer orders more effectively.
Recursive neural programs: A differentiable framework for learning compositional part-whole hierarchies and image grammars

Abstract Human vision, thought, and planning involve parsing and representing objects and scenes using structured representations based on part-whole hierarchies. Computer vision and machine learning researchers have recently sought to emulate this capability using neural networks, but a generative model formulation has been lacking. Generative models that leverage compositionality, recursion, and part-whole hierarchies are thought to underlie human concept learning and the ability to construct and represent flexible mental concepts. We introduce Recursive Neural Programs (RNPs), a neural generative model that addresses the part-whole hierarchy learning problem by modeling images as hierarchical trees of probabilistic sensory-motor programs. These programs recursively reuse learned sensory-motor primitives to model an image within different spatial reference frames, enabling hierarchical composition of objects from parts and implementing a grammar for images. We show that RNPs can learn part-whole hierarchies for a variety of image datasets, allowing rich compositionality and intuitive parts-based explanations of objects. Our model also suggests a cognitive framework for understanding how human brains can potentially learn and represent concepts in terms of recursively defined primitives and their relations with each other.
Determinants of factors impacting buying intention towards e-retailing: a special reference to Jordan context

The immediate aim of the present paper is to examine the effect of trust, risk and attitude on purchase intention in Jordan. Moreover, the study also aims to compare the male-female mean value in terms of purchase intention. The sampling technique of the study follows non-probability approach. Convenient sampling technique was employed in the present article. A self-administrated survey questionnaire was used for the purpose of research. The questionnaires were distributed mainly in Amman, Maan and Al Karak region. The total numbers of respondents were 143. The data has been analysed by SPSS program and AMOS. T test and structural equation modelling were undertaken. The study concludes that trust and attitude significantly impact purchasing intention, whereas perceive risk has no significant effect on purchasing intention. The findings also show positive relationships between variables. The article at hand reflects a step ahead in studies into how private predispositions and safety and privacy signals of customers impact their intention to buy.
GEOGRAPHICAL INFORMATION SYSTEM: APPLICATION TO THE GEOLOCATION OF THE NEAREST HEALTH FACILITIES IN THE TOWN OF KANANGA

New technology now makes it possible to determine the location of an object or person with a certain degree of accuracy. This technology is generally based on the GPS system or the communication interfaces of a mobile phone. Geolocation has a wide range of applications and purposes, from navigation assistance and personal contact to real-time management of human and vehicle resources for individuals or companies. This work, linked to mobile applications, shows how a well-defined entity can be located if, for example, it goes missing. Today, new technology remains an essential tool in geographic information systems. Every user is expected to have precise knowledge of how to use geolocation when necessary. The tool chosen for this work can be a smartphone, a tablet, an Android phone or any other model the user chooses. The user enters a reference and, by pressing the search button, a new window opens showing the structure closest to his position and the steps he should take to reach his destination as quickly as possible. Once the destination has been found, the user simply selects the shortest route to get there. Then there is the issue of geolocation, something that should never be forgotten. First and foremost, geolocation raises the issue of personal data protection and privacy. The importance of the application that has been put in place is to help anyone who doesn't know a place that is sometimes remote, without having to make a lot of effort or go through a person, but he ends up getting there, or discovering someone and something else.
A Stable, Efficient, and High-Precision Non-Coplanar Calibration Method: Applied for Multi-Camera-Based Stereo Vision Measurements

Traditional non-coplanar calibration methods, represented by Tsai’s method, are difficult to apply in multi-camera-based stereo vision measurements because of insufficient calibration accuracy, inconvenient operation, etc. Based on projective theory and matrix transformation theory, a novel mathematical model is established to characterize the transformation from targets’ 3D affine coordinates to cameras’ image coordinates. Then, novel non-coplanar calibration methods for both monocular and binocular camera systems are proposed in this paper. To further improve the stability and accuracy of calibration methods, a novel circular feature points extraction method based on region Otsu algorithm and radial section scanning method is proposed to precisely extract the circular feature points. Experiments verify that our novel calibration methods are easy to operate, and have better accuracy than several classical methods, including Tsai’s and Zhang’s methods. Intrinsic and extrinsic parameters of multi-camera-systems can be calibrated simultaneously by our methods. Our novel circular feature points extraction algorithm is stable, and with high precision can effectively improve calibration accuracy for coplanar and non-coplanar methods. Real stereo measurement experiments demonstrate that the proposed calibration method and feature extraction method have high accuracy and stability, and can further serve for complicated shape and deformation measurements, for instance, stereo-DIC measurements, etc.
Foundations

In this chapter, we will not be concerned with programming languages but with the limits of the programs that we can write, asking whether there exist problems that no program can solve. A motivation for this research is the question that we asked at the end of Sect. 2.3 : that is, is it possible to construct a static semantic analyser which can verify constraints imposed by the programming language’s definition? We will soon discover, however, that the answer to the question is rather more general. It is a kind of absolute limit to what can (and cannot) be done with a computer. Although the material in this chapter can appear abstract, our treatment is wholly elementary.