Reviewerly/exHarmony huggingface dataset:
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Support, Barriers, and Expectations of the Reader: On Textual and Visual Choices in a Picturebook Based on the Animated Feature Film Cars&#x0D; Whenever an animated feature film aimed at children is released from a bigger production company, tie-ins will follow, including picturebooks. These books can be found in everyday grocery stores and have the potential to reach many children. Although discussed, few systematic descriptions of the literature in question exist. In this article, I examine textual and visual choices in the picturebook Bilar (Cars, 2015) based on the Disney &amp; Pixar film with the same name (Lesseter). I investigate what barriers and what support it offers the reader to grip the story, but also whom the book addresses as a reader. The analysis focuses on resources important for comprehension, namely temporality and cohesion. Close-up analysis of text and pictures reveals and tries to explain temporal and logical gaps. The analysis shows that Bilar is quite complex, with features that contribute to distancing the story from the reader. The temporal unfolding advances quickly, but quite evenly, with few pauses. Leaps are drastic and linguistically unmarked, and intentions and events are presented indirectly. The text has few explicit causal ties and deviates from what is expected. Overall, the pictures repeat information in the text, but they also contradict the text in a way that suggests it was unintentional. Although the text manages to mediate the plot and intention – to the adult reader – it is hard to grasp in total without the movie in mind. The addressed reader, then, is plural: an adult and a child who have watched the movie and can fill the gaps, and who do not care too much about logical details. However, the study and the conclusion is based on a traditional view of the book and of reading. An investigation of the book in use might change or widen the understanding of this type of product.
Case Study on the Moving of Patients Using Blockchains’ Federation

He patients moving between medical institutions is a process that takes place on a daily basis, either to continue or improve their treatment. At each moment of the moving (origin hospital, means of transport and destination hospital) information is generated in isolation, which is recorded in each of the health information systems. This limits the interoperability needed to provide quality services to the patient. Also, it is not possible to have the trajectory of the information when a patient is moved, generating problems in medical decision making, since only a small vision of the actors in each system is available. In this paper, a case study of patients moving between medical institutions is presented, applying the FedBlocks tool, which uses the concept of Blockchain federation, where a higher order Blockchain is in charge of federating a set of lower level Blockchains, generating a view of the patient’s entire medical history. Likewise, all interconnected systems communicate and can share authorized data of their patients, providing a benefit for these systems and for the actors of the entire ecosystem.
Can Public Blockchains Mitigate Semiconductors Supply Chain Disruption?

Abstract Today's supply chain is more complex in composition and structure. Information loss and late transmission aggravated the bullwhip effect. Lack of reliable information storage, traceability, and accountability have also slowed supply chain management. Blockchain supports distributed networking, information synchronization among nodes, digital encryption, traceable information, and unchangeable block content, making it suitable for the supply chain. This paper presents the public blockchain as a solution to the "bullwhip effect problem in supply chain (SC). The framework is implemented to address the disruption of semiconductors' SC. The framework consists of a checklist to determine the SC's readiness for blockchain adoption. The checklist determines the motivation, timing, and the appropriate platform for adopting blockchain. The framework was validated in the Semiconductors SC by polling four executives: each represents a large company that has many branches in Egypt and the Middle East; each represents a different stage, and they are retail, distribution, manufacturing, and supply. The results showed that the executives agreed that there are perceived benefits to adopting blockchain. At the same time, they are neutral about whether it is the right time for blockchain adoption and disagree that the BC technology is ready for their SC. Finally, some of the public blockchain platforms that can be used and implemented in supply chains were introduced to help enterprises choose the most suitable platform to adopt.
DEVELOPING A MODEL SEMANTIC-BASED IMAGE RETRIEVAL BY COMBINING KD-TREE STRUCTURE WITH ONTOLOGY

The paper proposes an alternative approach to improve the performance of image retrieval. In this work, a framework for image retrieval based on machine learning and semantic retrieval is proposed. In the preprocessing phase, the image is segmented objects by using Graph-cut, and the feature vectors of objects presented in the image and their visual relationships are extracted using R-CNN. The feature vectors, visual relationships, and their symbolic labels are stored in KD-Tree data structures which can be used to predict the label of objects and visual relationships later. To facilitate semantic query, the images use the RDF data model and create an ontology for the symbolic labels annotated. For each query image, after extracting their feature vectors, the KD-Tree is used to classify the objects and predict their relationship. After that, a SPARQL query is built to extract a set of similar images. The SPARQL query consists of triple statements describing the objects and their relationship which were previously predicted. The evaluation of the framework with the MS-COCO dataset and Flickr showed that the precision achieved scores of 0.9218 and 0.9370 respectively.
Anomaly Identification Model for Telecom Users Based on Machine Learning Model Fusion

With the development of economic globalization and modern information and communication technology, the situation of communication fraud is becoming more and more serious. How to identify fraudulent calls accurately and effectively has become an urgent task in current telecommunications operations. Affected by the sample set and the current state of the art, the current machine learning methods used to identify the imbalanced distribution dataset of positive and negative samples have low recognition accuracy. Therefore, in this paper, we propose a new hybrid model solution that uses feature construction, feature selection and imbalanced classes handling. A stacking model fusion algorithm composed of a two-layer stacking framework with several state-of-the-art machine learning classifiers is adopted. The results show that the risk user identification model based on mobile network communication behavior established by our stacking model fusion algorithm can accurately predict the category labels of telecom users and improve the risk of telecom users. The generalization performance of the identification is high, which provides a certain reference for the telecommunications industry to identify risk users based on mobile network communication behaviors.
Trajectory Control Using Fuzzy Logic Applied to a Parallel Robot

Abstract This work presents the development of Mamdani-type fuzzy controllers for tracking linear trajectories in the workspace of a delta-type linear parallel robot. The system comprises three positioner actuators driven by direct current motors with incremental encoders for displacement reading. A multifunctional data module made the interface between the computer and the machine, capturing the signals coming from the encoders and supplying the control signals to the power circuits that drive the motors. Labview® software was used to implement the controller, using the fuzzy logic toolkit, and Matlab® to analyze and present the experimental results. There are three phases of control: velocity control, position control with trajectory tracking, and trajectory generation. In the analysis of the system's response curves and the obtained data, it was verified a very satisfactory performance of the controllers acting with 100 mm/s. The maximum steady-state error was -1.50mm for X-axis motion, -1.76mm for Y-axis motion, and 1.02mm for Z-axis motion, resulting in a trajectory tracking error of 1.26%.
Blockchain Technology Application in Security: A Systematic Review

Blockchain technology is a promising technology that attracts popularity among researchers. However, it was first introduced with digital currencies in particular Bitcoin, but nowadays it is also known as one of the most frequently used techniques for securing networks. This systematic review research identifies studies that use blockchain for their security challenges. In addition, different fields in blockchain usage, blockchain categorization type, consensus mechanism, smart contract usage, and integration with other software-based algorithm is also investigated. Our results maintain that the internet of things (IoT) is the main field that blockchain is used to provide security.
Diagnosis, a Predominant Aspect in Furcation Management: A Review

The diagnosis of a disease is of utmost importance and the entire outcome of the treatment depends upon it.For a proper diagnosis, different traditional as well as new modalities are there and many more are still under progression.The traditional and the gold standard diagnosis for furcation depends on the clinical examination using a Naber's probe along with the periapical radiography.Various new modalities such as Computed tomography, Cone beam computed tomography, substraction radiography, digital radiography have been introduced with various latest fronteirs like fiberscopes, ultrasonography etc.This article reviews the traditional as well as new frontiers that have made the diagnosis more precise and feasible.
Synchronous and Asynchronous Modalities for Mathematics Instruction during the Covid-19 Pandemic

&lt;p style="text-align: justify;"&gt;The purpose of this study was to evaluate synchronous and asynchronous mathematics teaching modalities at Isabela State University. The qualitative research method was used to collect information, opinions, and experiences of Isabela State University mathematics faculty in employing synchronous and asynchronous modes in teaching mathematical courses in terms of strengths, weaknesses, possibilities, and problems. The study's subjects were 15 Mathematics Instructors chosen at random from Isabela State University's nine campuses. A structured interview was created and distributed to participants using Google Form. The limitations on face-to-face encounters prompted the use of such data-gathering technique. The researcher followed up with another video call interview to validate the participants' responses. The data was transcribed and processed using thematic analysis. The findings demonstrated that the synchronous and asynchronous learning modalities both have strengths and disadvantages that influence the quality of the teaching-learning process throughout the epidemic. Given this, distant learning is thought to be more effective when both modalities are used rather to just one of the aforementioned. This is because the strengths of one of the two modalities can solve the flaws highlighted in the other. As a result, mathematics instructors may receive more in-depth training in both asynchronous and synchronous teaching approaches, as well as strategies for becoming more successful teachers during the present school closures.&lt;/p&gt;
Limitations of Load Balancing and Performance Analysis Processes and Algorithms in Cloud Computing

In the modern IT industry, cloud computing is a cutting-edge technology. Since it faces various challenges, the most significant problem of cloud computing is load balancing, which degrades the performance of the computing resources. In earlier research studies, the management of the workload to address all resource allocation challenges that caused by the participation of a large number of users has received important attention. When several people are attempting to access a given web application at once, managing all of those users becomes exceedingly difficult. One of the elements affecting the performance stability of cloud computing is load balancing. This article evaluates and discusses load balancing, the drawbacks of the numerous methods that have been suggested to distribute load among nodes, and the variables that are taken into account when determining the best load balancing algorithm.
"LEARNING IN THE DIGITAL ERA. DIGITAL CITIZENS "

The paper focuses on learning in the digital era and digital citizens. The evolution of modern information technologies and the Internet have revolutionized education and traditional forms of training. The development of modern forms of learning has directed the evolution of multimedia and communication technologies to the development of digital infrastructures demanded by today's computerized societies that provide everyone with easy access to the content of massive virtual libraries. Using new technologies, everyone concerned with their professional development can communicate with their field specialists and other colleagues to share information, knowledge and experience. Digital citizens belong to the digital society. They use technology to get involved in the good course of society. Digital citizenship empowers people to harvest the benefits of digital technology in a safe and efficient way. In the world of the 21st century, the skills of the digital age cannot be neglected. Once these skills are mastered, trust and autonomy arise. Young people learn not only to master a wide range of digital tools but also to understand how they work and how they are created.
TEACHING SAMPLING TECHNIQUES USING R AND PYTHON

The free software R (http://www.r-project.org)allows us to perform and obtain any type of statistical analysis needed, since it has available multiple function packages prepared to perform the task you want, in any field or area.This is thanks to its large community of users who are responsible for creating, developing, publishing and updating these packages.The field of sampling, which is key in any type of survey or research, has a wide range of functions and packages to be used, and in particular for complex sampling designs there are many packages that can help us to perform any technique we are interested in using.That is why in this paper we will address the use of the statistical environment of R for learning sampling for students of the degree in Statistics and any degree in which these contents are taught.But it may be the case that the teachers are reluctant to use this programming software, despite being simple to use, simply because they are unfamiliar with it.Another programming software that is currently on the rise, and which has wide applications in fields as important for large companies as data mining, is the free software Python (https://www.python.org).More and more companies of all kinds are looking for employees with extensive knowledge in this software due to the infinite applications it has, being already one of the most used programming software in the world.In the case of statistical analysis, it has a large number of utilities, although in the specific case of sampling, especially for complex sample designs, there is not so much variety.In spite of this, the "samplics" package for Python, developed for selecting, weighting and analysing data from complex sampling designs, was released this November.Knowing about this package, in this paper we will also address the use of programming software Python for learning survey sampling techniques through this package, looking for similarities and differences between this method and the one we would use with programming software R.
Multi-Label Emotion Classification Using Physically Explicated Dataset

Multi-label emotions detection and classification in Facebook, Twitter, and other social media may be a demanding work because of general nature of the linguistics employed in these styles of platforms. Most of the previous studies mainly targeting single-label emotion detection, which detected only one emotion in a very given piece of text. But human expressions are multiplex, it should have multiple emotions with different semantics. So, during this study, we mainly target multi-label emotion classification which may classify possible different emotions in a very given text or data. Multi-label categorization has become the first solution among classification problems. Multi-label emotion categorizing may be a supervised learning that focuses on classifying multi-label emotions from a given data which contains a wide selection of implementations in Marketing, Electronic learning, education, and medical management, etc. to form the classification more efficient we use manually curated datasets labelled for basic eight emotion categories, these 8 basic emotions are based on Plutchik's model, which has the physiological purpose of each.
Government Fund Allocation and Tracking System over Blockchain

The state government working involves a large number of trans- actions activities towards various operations throughout the state. This includes new actions, initiatives, projects, granting contracts, farmer schemes, and so on. One of the most challenging factors that top governments face is low- level corruption which at times is hard to follow and denies the state progress. As a result of the current system, tracking is very problematic and this provides needy people with a service that is sometimes difficult to track, which deprives them. In this case, we use blockchain which enables cryptography and transaction security at every stage while maintaining transparency so that every transaction is backed up with proof of its authenticity. Hence, we present a framework that uses blockchain innovation and a fullproof fund transfer system. Blockchain contains growing list of records called blocks. Crypto- graphically hashed data, a timestamp, and recent transactions are included in each block.
Digital Capabilities of Teachers: A Comparative Case Study

Online and digital education, which was once perceived as demanding financially and academically, became the most viable option to continue teaching and learning during the COVID-19 pandemic. Despite their lack of prior experience, capabilities, or willingness, many teachers shifted their courses online in a short period to continue teaching during the pandemic. To understand the efficacy in making such a shift, the nature of digital capabilities the teachers recognize within themselves and if these capabilities correlate with their socio-cultural and geographical backgrounds are to be realized. In this study, two cases from the global North and South are compared to investigate the similarities and differences in making choices of digital tools, and, to capture their digital affordances and ability to manage technology in the teaching and learning process. A sample of 50 teachers from two higher education institutions is used to capture the perceptions. The discovered trends in the choice of tools may shed light on region-specific disparities in digital affordances in technology-enhanced learning and global digital education development.
Build a Blockchain-based confidentiality and privacy solution using cryptographic techniques

This study aims to apply security problems such as homomorphic encryption algorithm, elliptic curve digital signature algorithm, and secure hash algorithms in building your own bitcoin virtual currency based on the Blockchain system; while still ensuring the confidentiality and privacy of the generated bitcoin virtual currency. The result achieved by the author team, who created their virtual currency bitcoin, has integrated security techniques (including secure hash algorithms with a length size of 256 bits; elliptic curve digital signature algorithm with a length size of 384 bits; full homomorphic encryption) and ensure privacy. The bitcoin virtual currency created by the team of creators achieves these security and privacy properties in Blockchain-based systems and applications.
PENGARUH PEMAHAMAN DAN PEMANFAATAN SISTEM INFOMASI AKUNTANSI TERHADAP PENINGKATAN PRODUKTIVITAS PADA PERUSAHAAN PT. PRODUK SAWITINDO JAMBI

Purpose: This study aims to determine and analyze the effect of understanding accounting and the use of accounting information systems on increasing productivity at PT. Sawitindo Jambi products. Design/Methodology/Approach: The research method used is descriptive quantitative analysis with primary data through surveys, multiple linear regression analysis tools. Findings: The results showed that simultaneously showed that the variables of accounting understanding and accounting system utilization together had a positive and significant effect on increasing productivity at PT. Sawitindo Jambi products. while partially showing that the variable of accounting understanding has a positive and significant effect on increasing the productivity of PT. Sawitindo Jambi products. and the variable of accounting system utilization has a positive and significant effect on increasing productivity at PT. Sawitindo Jambi products. Research Implications: The implication of the research results is that the company should conduct a review of the understanding of accounting and the use of AIS in making financial reports so that the productivity of the company's financial statements can continue to be improved.
MIXED-REALITY TOPICS FOR COURSE CURRICULUM: CURRENT STATE AND FUTURE PERSPECTIVES

Appears in: INTED2023 Proceedings Publication year: 2023Pages: 5927-5931ISBN: 978-84-09-49026-4ISSN: 2340-1079doi: 10.21125/inted.2023.1557Conference name: 17th International Technology, Education and Development ConferenceDates: 6-8 March, 2023Location: Valencia, Spain
Structure and Sequence Aligned Code Summarization with Prefix and Suffix Balanced Strategy

Source code summarization focuses on generating qualified natural language descriptions of a code snippet (e.g., functionality, usage and version). In an actual development environment, descriptions of the code are missing or not consistent with the code due to human factors, which makes it difficult for developers to comprehend and conduct subsequent maintenance. Some existing methods generate summaries from the sequence information of code without considering the structural information. Recently, researchers have adopted the Graph Neural Networks (GNNs) to capture the structural information with modified Abstract Syntax Trees (ASTs) to comprehensively represent a source code, but the alignment method of the two information encoder is hard to decide. In this paper, we propose a source code summarization model named SSCS, a unified transformer-based encoder-decoder architecture, for capturing structural and sequence information. SSCS is designed upon a structure-induced transformer with three main novel improvements. SSCS captures the structural information in a multi-scale aspect with an adapted fusion strategy and adopts a hierarchical encoding strategy to capture the textual information from the perspective of the document. Moreover, SSCS utilizes a bidirectional decoder which generates a summary from opposite direction to balance the generation performance between prefix and suffix. We conduct experiments on two public Java and Python datasets to evaluate our method and the result show that SSCS outperforms the state-of-art code summarization methods.