Reviewerly/exHarmony huggingface dataset:
AI Curriculum Design for Korea K-12 AI Education Through Analyzing AI Education Curriculum

This paper deals with the curriculum design of Korean K-12 AI education. AI is the core technology of 4th wave and it is impact is wide and strong. Therefore, every country has s strong nurture system for AI manpower. However, there are few materials about curriculum design and operation experience documentation because AI was an interest since 2016, and its education is very new. In Korea, they started a program for teacher manpower education in 2020. Therefore, there is no manpower that had an experience in the design and operation of the K-12 AI curriculum. The AI curriculum is a core parameter for AI manpower. So, AI-advanced countries try to develop AI education methods. This paper analyzes the K-12 AI curriculum of the advanced countries, and then designs and suggests Korea’s K-12 AI curriculum.
Exploring effective methods for automated essay scoring of non-native speakers

Automated essay scoring (AES) has become a valuable tool in educational settings, providing efficient and objective evaluations of student essays. However, the majority of AES systems have primarily focused on native English speakers, leaving a critical gap in the evaluation of non-native speakers’ writing skills. This research addresses this gap by exploring the effectiveness of automated essay-scoring methods specifically designed for non-native speakers. The study acknowledges the unique challenges posed by variations in language proficiency, cultural differences, and linguistic complexities when assessing non-native speakers’ writing abilities. This work focuses on the automated student assessment prize and Khon Kaen University academic English language test dataset and presents an approach that leverages variants of the long short-term memory network model to learn features and compare results with the Kappa coefficient. The findings demonstrate that the proposed framework and approach, which involve joint learning of different essay representations, yield significant benefits and achieve results comparable to state-of-the-art deep learning models. These results suggest that the novel text representation proposed in this paper holds promise as a new and effective choice for assessing the writing tasks of non-native speakers. The result of this study can apply to advance educational assessment practices and promote equitable opportunities for language learners worldwide by enhancing the evaluation process for non-native speakers
Unpacking Privacy Calculus and Interplay of Data Privacy and Healthcare: Paths Towards Safeguarding Patient Empowerment

The modern healthcare landscape is mainly data-driven, with clinical and consumer uses for patient data. Moreover, patient data is used for research and analytics to enhance healthcare results. Yet, this exponential expansion of data is accompanied by grave privacy risks, including data breaches and unlawful access. This chapter examines the data privacy implications of the contemporary healthcare environment and identifies seven significant challenges. In addition, we investigate the idea of privacy calculus and how it impedes patient empowerment and healthcare management effectiveness in the healthcare ecosystem. We highlight the influence of privacy calculus on patients, healthcare professionals, and researchers through five case studies. Lastly, we explore prevalent healthcare rules, their aims, and their primary purposes, and suggest how they might be used to solve privacy calculus. This chapter illuminates the crucial need for privacy protection in the healthcare business and the steps that may be taken to preserve patient data while advancing healthcare research and administration.
What size should my music studio be?

In Everest, F. Alton, and Ken C Pohlmann, &lt;em&gt;Master Handbook of Acoustics&lt;/em&gt; (New York: McGraw-Hill, 2009), p. 247, a range of room ratios are listed to achieve optimal modal distributions using the Bolt range and a set of external sources. In other words, this addresses the question: what size should I make my room to achieve the best even distribution of sound throughout?
Research on obstacle avoidance path planning of UAV in complex environments based on improved Bézier curve

Obstacle avoidance path planning is considered an essential requirement for unmanned aerial vehicle (UAV) to reach its designated mission area and perform its tasks. This study established a motion model and obstacle threat model for UAVs, and defined the cost coefficients for evading and crossing threat areas. To solve the problem of obstacle avoidance path planning with full coverage of threats, the cost coefficients were incorporated into the objective optimization function and solved by a combination of Sequential Quadratic Programming and Nonlinear Programming Solver. The problem of path planning under threat full coverage with no solution was resolved by improving the Bézier curve algorithm. By introducing the dynamic threat velocity obstacle model and calculating the relative and absolute collision cones, a path planning algorithm under multiple dynamic threats was proposed to solve the difficulties of dynamic obstacle prediction and avoidance. Simulation results revealed that the proposed Through-out method was more effective in handling full threat coverage and dynamic threats than traditional path planning methods namely, Detour or Cross Gaps. Our study offers valuable insights into autonomous path planning for UAVs that operate under complex threat conditions. This work is anticipated to contribute to the future development of more advanced and intelligent UAV systems.
Artificial Bee Colony Algorithm Based on Improved Search Strategy

The standard Artificial Bee Colony (ABC) algorithm exhibits slow convergence speed and a tendency to get trapped in local optima under certain circumstances. To overcome these limitations, researchers have proposed a new ABC algorithm (GABC) by using a modified search strategy. During the process of searching for solutions, the GABC algorithm incorporates some randomly selected individuals and the global best individual. However, the GABC algorithm still has drawbacks such as low search accuracy and slow convergence speed. In response to these issues, an improved artificial bee colony algorithm (IABC) is proposed in this paper. The IABC algorithm introduces a dynamic inertia weight factor based on the GABC algorithm. A set of standard test functions are used to test the optimization of the improved artificial bee colony algorithm. Experimental results demonstrate that the proposed algorithm outperforms both the standard ABC algorithm and the GABC algorithm in terms of search accuracy and convergence speed.
Runtime Verification for Trustworthy Computing

Autonomous and robotic systems are increasingly being trusted with sensitive activities with potentially serious consequences if that trust is broken. Runtime verification techniques present a natural source of inspiration for monitoring and enforcing the desirable properties of the communication protocols in place, providing a formal basis and ways to limit intrusiveness. A recently proposed approach, RV-TEE, shows how runtime verification can enhance the level of trust to the Rich Execution Environment (REE), consequently adding a further layer of protection around the Trusted Execution Environment (TEE). By reflecting on the implication of deploying RV in the context of trustworthy computing, we propose practical solutions to two threat models for the RV-TEE monitoring process: one where the adversary has gained access to the system without elevated privileges, and another where the adversary gains all privileges to the host system but fails to steal secrets from the TEE.
Safe and Robust Robot Behavior Planning via Constraint Programming

The safe operation of an autonomous system is a complex endeavor, one pivotal element being its decision-making. Decision-making logic can formally be analyzed using model checking or other formal verification approaches. Yet, the non-deterministic nature of realistic environments makes these approaches rather troublesome and often impractical. Constraint-based planning approaches such as Tumato have been shown to be capable of generating policies for a system to reach a stated goal and abiding safety constraints, with guarantees of soundness and completeness by construction. However, uncertain outcomes of actions in the environment are not explicitly modeled or accounted for, severely limiting the expressiveness of Tumato. In this work, we extend Tumato with support for non-deterministic outcomes of actions. Actions have a specific intended result yet can be modeled to have alternative outcomes that may realistically occur. The adapted solver generates a policy that enables reaching the goals in a safe manner, even when alternative outcomes of actions occur. Furthermore, we introduce a purely declarative way of defining safety in Tumato, increasing its expressiveness. Finally, the addition of cost or duration values to actions enables the solver to restore safety when necessary, in the most preferred way.
An adaptive gravitational search optimization (<scp>AGSO</scp>)‐based convolutional neural network—long short‐term memory (<scp>CNN‐LSTM</scp>) approach for face recognition and classification

Summary Face recognition and classification have gained increasing attraction in the recent decades due to their widespread adoption in real time application systems. Most of the conventional research efforts focused on developing face recognition frameworks using enhanced optimization‐based classification methods, they are hampered by issues such as computational complexity, increased overhead, limited capacity to handle large datasets, and lengthy processing time. The novel contribution of this paper is to develop a highly competent and precise face recognition methodology through an innovative mechanism. In this framework, the initial step involves face detection from input images using an analytical face parts detection methodology. Subsequently, the tutor face filtering (TFF) technique is applied to preprocess the face image, enhancing its quality and filtering out noise content. Following this preprocessing step, features are extracted from the processed image using the direction‐based pattern extraction (DBPE) model. To improve classifier accuracy, a novel adaptive gravitational search optimization (AGSO) technique is employed to select the optimal features during model training. Finally, an integrated deep learning model, referred to as convolutional neural network — long short‐term memory (LSTM), is utilized for accurate face image recognition based on the selected optimal features. To assess and compare the system's performance, various metrics are employed in the results analysis to demonstrate the superiority of the proposed approach.
Reasoning about Intuitionistic Computation Tree Logic

In this paper, we define an intuitionistic version of Computation Tree Logic. After explaining the semantic features of intuitionistic logic, we examine how these characteristics can be interesting for formal verification purposes. Subsequently, we define the syntax and semantics of our intuitionistic version of CTL and study some simple properties of the so obtained logic. We conclude by demonstrating that some fixed-point axioms of CTL are not valid in the intuitionistic version of CTL we have defined.
ENGINEERING STUDENT-CENTRIC PROTOTYPE DESIGN FOR EFFICIENT MANAGEMENT OF DIGITAL LIBRARY COLLECTIONS AND RESOURCES UNDER JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY KAKINADA

In the ever-evolving landscape of academic institutions, efficient library management systems are essential to support research, learning, and knowledge dissemination. This abstract provides an overview of JNTUKLIBCON, a comprehensive library management system developed for Jawaharlal Nehru Technological University, Kakinada (JNTUK). JNTUKLIBCON is designed to streamline and enhance the library's operations by offering three distinct interfaces: one for administrators, one for librarians, and one for students. Each interface caters to specific user roles, ensuring efficient management of library resources, user accounts, and material requisitions. The system leverages web technologies, including HTML, Java Server Pages (JSP), Tomcat Web server, and a MySQL database, to create a user-friendly and responsive platform. It encompasses a wide range of features, from user authentication to the management of various library materials, such as books, e-books, e-journals, and research reports. Administrators have the privilege of overseeing user accounts, student records, and library resources, while librarians can efficiently manage materials and track material requisitions. Students benefit from a simplified interface for checking material availability and accessing electronic resources. The system's deployment promises to enhance resource management, increase accessibility to electronic materials, and improve overall user experiences within the library. It offers a centralized platform that simplifies library administration and empowers users to make the most of the available resources.
Data Centers

A data center is a facility that centralizes the shared IT operations and equipment of an organization for the storage and processing of data, information, and applications. Traditionally, data centers were dedicated physical spaces within a building or group of buildings, meticulously controlled and managed on premises.
EGFA-NAS: a neural architecture search method based on explosion gravitation field algorithm

Abstract Neural architecture search (NAS) is an extremely complex optimization task. Recently, population-based optimization algorithms, such as evolutionary algorithm, have been adopted as search strategies for designing neural networks automatically. Various population-based NAS methods are promising in searching for high-performance neural architectures. The explosion gravitation field algorithm (EGFA) inspired by the formation process of planets is a novel population-based optimization algorithm with excellent global optimization capability and remarkable efficiency, compared with the classical population-based algorithms, such as GA and PSO. Thus, this paper attempts to develop a more efficient NAS method, called EGFA-NAS, by utilizing the work mechanisms of EGFA, which relaxes the search discrete space to a continuous one and then utilizes EGFA and gradient descent to optimize the weights of the candidate architectures in conjunction. To reduce the computational cost, a training strategy by utilizing the population mechanism of EGFA-NAS is proposed. In addition, a weight inheritance strategy for the new generated dust individuals is proposed during the explosion operation to improve performance and efficiency. The performance of EGFA-NAS is investigated in two typical micro search spaces: NAS-Bench-201 and DARTS, and compared with various kinds of state-of-the-art NAS competitors. The experimental results demonstrate that EGFA-NAS is able to match or outperform the state-of-the-art NAS methods on image classification tasks with remarkable efficiency improvement.
Fuzzy Inference System Based-AI for Diagnosis of Esophageal Cancer

Due to global changes, the incidence and mortality rate of esophagus cancer has skyrocketed in the last decades, with about 500,000 new cases. Esophageal cancer is a real life problem with uncertain data and human error, giving room for possible misdiagnosis. This study developed a fuzzy intelligent system (FIS) to screen and provide predictive diagnosis of esophageal cancer. Fuzzy IF THEN rules were generated from a combination of esophageal symptoms, general risk factors, and diagnostic tests, under expert considerations. MATLAB software was used to design and run the FIS. The data was retrieved from a hospital in Erbil for 7 patients. The system provides recommendations with each predictive diagnosis, whether a patient is positive or negative for esophageal cancer or something suspicious is wrong with the esophagus. After implementing the data on FIS, the system shows an overall system accuracy of 95.24%, with an even higher accuracy of 98% for each patient’s prediction. For future studies, it is highly recommended that the fuzzy rules be expanded to include more variables and dataset.
A brief note on the RHS Plan S report

The most recent Royal Historical Society document on Plan S says the following about the Open Library of Humanities (OLH): “It fails, however, to reference or explore the funding base that has made these important initiatives both possible and (to date) sustainable.
Skin Detection System Based Fuzzy Neural Networks for Skin Identification

Machine learning is an effective technology for big data augmentation and analysis. This research proposes an integrated machine learning algorithm (Skin Detection System) based on clustering technique and fuzzy neural networks on extremely big datasets for identification of skin. We explored the Takagi–Sugeno fuzzy network utilizing Fuzzy C-Mean (FCM) clustering and cross-validation approach with the gradient descent learning algorithm. Using clustering technique, targeted features were extracted from the large datasets and utilized for the implementation of the Skin Detection System (SDS). We also incorporated cross-validation technique utilizing fuzzy c-means and gradient descent algorithms to update the SDS parameters. Finally, the SDS was trained and tested on the domain. The comparative results are provided in Table 3 to depict the efficiency of using the proposed approach to classifying big datasets.
A Semantic Portal to Improve Search on Rivers State’s Independent National Electoral Commission

Semantic search portals in electoral processes have emerged as a promising approach to enhance the accessibility, efficiency, and transparency of electoral information. This research focuses on applying a semantic search portal within Nigeria’s context of the Independent National Electoral Commission (INEC). The objective is to provide an overview of the benefits and implications of implementing a semantic search portal in the electoral domain. The semantic search portal leverages the power of semantic web technologies and ontologies to enable efficient and intelligent information retrieval. The portal facilitates accurate and context-aware search results by representing electoral data and knowledge in a structured and interconnected manner. It allows users, including citizens, researchers, and electoral officials, to access and retrieve relevant information regarding the electoral process, candidates, and policies. Developing and implementing the semantic search portal in collaboration with INEC Nigeria entails several crucial steps. These include the creation of a comprehensive ontology that captures the complexity of the electoral domain, integrating diverse data sources, utilizing natural language processing techniques for query understanding, and incorporating machine learning algorithms for search relevance and personalization. By embracing a semantic search portal, INEC Nigeria can realize numerous benefits, such as improved access to electoral information, enhanced decision-making processes, increased citizen engagement, and greater transparency in the electoral process. The portal can also contribute to data-driven insights, predictive analytics, and policy evaluation, thus empowering stakeholders to make informed decisions and strengthen democratic practices.
Face Mask Recognition System-Based Convolutional Neural Network

The use of face masks has been widely acknowledged as an effective measure in preventing the spread of COVID-19. Scientists argue that face masks act as a barrier, preventing virus-carrying droplets from reaching other individuals when coughing or sneezing. This plays a crucial role in breaking the chain of transmission. However, many people are reluctant to wear masks properly, and some may not even be aware of the correct way to wear them. Manual inspection of a large number of individuals, particularly in crowded places such as train stations, theaters, classrooms, or airports, can be time-consuming, expensive, and prone to bias or human error. To address this challenge, an automated, accurate, and reliable system is required. Such a system needs extensive data, particularly images, for training purposes. The system should be capable of recognizing whether a person is not wearing a face mask at all, wearing it improperly, or wearing it correctly. In this study, we employ a convolutional neural network (CNN)-based architecture, to develop a face mask detection/recognition model. The model achieved an impressive accuracy of 97.25% in classifying individuals into the categories of wearing masks, wearing them improperly, or not wearing masks at all. This automated system offers a promising solution to efficiently monitor and enforce face mask usage in various settings, contributing to public health and safety.
Blockchain Technology, Artificial Intelligence, and Big Data in Education

The way we approach learning and teaching may be completely changed if blockchain technology, artificial intelligence, and big data are integrated into the educational system. While artificial intelligence can tailor and improve the learning experience for each student, blockchain technology can offer safe and open record-keeping for educational institutions. Big data can be utilized to evaluate and enhance many facets of the educational process, including resource allocation and curriculum creation. This study intends to investigate how big data, artificial intelligence, and blockchain are currently being used in education and to evaluate any prospective effects on the educational system. In order to do this, we reviewed prior studies on the subject and examined case studies of effective uses of these technologies in education. In order to gain more information about the benefits and challenges of integrating blockchain, artificial intelligence, and big data into education, we also spoke with educators and industry leaders in-person. According to our research, the use of big data, AI, and blockchain in education has the potential to increase the effectiveness, equity, and customization of the educational system. There are obstacles to be overcome, too, such as the requirement for the right infrastructure and the potential for data privacy issues. Overall, this study advances knowledge of the possibilities and restrictions of utilizing blockchain, artificial intelligence, and big data in education and offers suggestions for future study and application.
The second OA liberated article from my Leverhulme Prize: Natural language generation and authorial labour

Some time around 2016 I was invited by Kasia Boddy and David Winters to contribute to a special issue of &lt;i&gt;Critical Quarterly&lt;/i&gt; that they were putting together. The issue was centred on notions of authorship in the digital age. This came at an opportune time as I had been playing with training a recurrent neural network on the back corpus of the literary studies journal, &lt;i&gt;Textual Practice&lt;/i&gt;, and had some promising results.