Reviewerly/exHarmony huggingface dataset:
Dimensions of nonbinary antiprimitive BCH codes and some conjectures

Bose-Chaudhuri-Hocquenghem (BCH) codes have been intensively investigated. Even so, there is only a little known about primitive BCH codes, let alone non-primitive ones. In this paper, let q>2 be a prime power, the dimension of a family of non-primitive BCH codes of length n=qm+1 (also called antiprimitive) is studied. These codes are also linear codes with complementary duals (called LCD codes). Through some approaches such as iterative algorithm, partition and scaling, all coset leaders of Cx modulo n with q⌈m2⌉<x≤2q⌈m2⌉+2 are given for m≥4. And for odd m the first several largest coset leaders modulo n are determined. Furthermore, a new kind of sequences is introduced to determine the second largest coset leader modulo n with m even and q odd. Also, for even m some conjectures about the first several coset leaders modulo n are proposed, whose complete verification would wipe out the difficult problem to determine the first several coset leaders of antiprimitive BCH codes. After deriving the cardinalities of the coset leaders, we shall calculate exact dimensions of many antiprimitive LCD BCH codes.
Factors Influencing the Willingness to Download Contact Tracing Apps among the American Population

The COVID-19 pandemic, which began in 2020, necessitated the roll-out of contact tracing apps (CTAs) worldwide. However, there is limited work to understand the various factors that influence the adoption of CTAs, and the moderating effect of persuasive design and smartphone usage experience. Consequently, we carried out an empirical study among the American population (n = 160) using screenshots of key interfaces of an existing CTA available on the market. The interfaces comprise two types of designs: persuasive version (equipped with persuasive features) and control version (unequipped with persuasive features). Our Partial Least Square Path Model showed that perceived usefulness (β = 0.23, p < 0.05) and perceived compatibility (β = −0.33, p < 0.01) are important to downloading the control version of the app. However, perceived trust (β = 0.45, p < 0.05) is important to downloading the persuasive version of the app. Moreover, perceived usefulness (β = 0.26, p < 0.05) and perceived risk (β = −0.26, p < 0.05) is important for the high-experience group (people with more than 10 years of experience in smartphone usage) and perceived trust (β = 0.46, p < 0.05) for the low-experience group. We discuss the implications of the findings.
The ascent of network traffic classification in the dark net: A survey

The Darknet is a section of the internet that is encrypted and untraceable, making it a popular location for illicit and illegal activities. However, the anonymity and encryption provided by the network also make identifying and classifying network traffic significantly more difficult. The objective of this study was to provide a comprehensive review of the latest advancements in methods used for classifying darknet network traffic. The authors explored various techniques and methods used to classify traffic, along with the challenges and limitations faced by researchers and practitioners in this field. The study found that current methods for traffic classification in the Darknet have an average classification error rate of around 20%, due to the high level of anonymity and encryption present in the Darknet, which makes it difficult to extract features for classification. The authors analysed several quantitative values, including accuracy rates ranging from 60% to 97%, simplicity of execution ranging from 1 to 9 steps, real-time implementation ranging from less than 1 second to over 60 seconds, unknown traffic identification ranging from 30% to 95%, encrypted traffic classification ranging from 30% to 95%, and time and space complexity ranging from O(1) to O(2n). The study examined various approaches used to classify traffic in the Darknet, including machine learning, deep learning, and hybrid methods. The authors found that deep learning algorithms were effective in accurately classifying traffic on the Darknet, but the lack of labelled data and the dynamic nature of the Darknet limited their use. Despite these challenges, the study concluded that proper traffic classification is crucial for identifying malicious activity and improving the security of the Darknet. Overall, the study suggests that, although significant challenges remain, there is potential for further development and improvement of network traffic classification in the Darknet.
MXFaaS: Resource Sharing in Serverless Environments for Parallelism and Efficiency

Although serverless computing is a popular paradigm, current serverless environments have high overheads. Recently, it has been shown that serverless workloads frequently exhibit bursts of invocations of the same function. Such pattern is not handled well in current platforms. Supporting it efficiently can speed-up serverless execution substantially.
Children on ChatGPT Readability in an Educational Context: Myth or Opportunity?

In this work, we present the results of a preliminary exploration aiming to understand whether the use of ChatGPT in an educational context can be an asset to meet the specific needs of the students. In particular, we focus on the possibility of adapting the responses to online inquiries related to the primary school curriculum to meet the expectations of readers with different literacy levels. The analysis of feedback elicited from children (9- to 10-year-olds) in three 4th grade classrooms indicates that ChatGPT can adapt its responses to the 4th grade level. However, it still needs improvement to reach the right level of readability. Outcomes from this work can inspire future research directions involving technologies like ChatGPT to adapt learning paths to suit a broad range of students with varied cognitive skills. The potential of such tools to support teachers in their effort to adapt to individual learning needs is still to be fully exploited.
Exploring fusion strategies for accurate RGBT visual object tracking

We address the problem of multi-modal object tracking in video and explore various options available for fusing the complementary information conveyed by the visible (RGB) and thermal infrared (TIR) modalities, including pixel-level, feature-level and decision-level fusion. Specifically, in contrast to the existing approaches, we propose and develop the paradigm for combining multi-modal information for image fusion at pixel level. At the feature level, two different kinds of fusion strategies are investigated for completeness, i.e., the attention-based online fusion strategy and the offline-trained fusion block. At the decision level, a novel fusion strategy is put forward, inspired by the success of the simple averaging configuration which has shown so much promise. The effectiveness of the proposed decision-level fusion strategy owes to a number of innovative contributions, including a dynamic weighting of the RGB and TIR contributions and a linear template update operation. A variant of the proposed decision fusion method produced the winning tracker at the Visual Object Tracking Challenge 2020 (VOT-RGBT2020). A comprehensive comparison of the innovative pixel and feature-level fusion strategies with the proposed decision-level fusion method highlights the advantages fusing multimodal information at the decision score level. Extensive experimental results on five challenging datasets, i.e., GTOT, VOT-RGBT2019, RGBT234, LasHeR and VOT-RGBT2020, demonstrate the effectiveness and robustness of the proposed method, compared to the state-of-the-art approaches. The Code is available at https://github.com/Zhangyong-Tang/DFAT.
Energy-aware images: Quality of Experience vs Energy Reduction

The production, transmission and display of video content requires significant amounts of energy. Whether broadcasting or streaming a video, its display on modern televisions is responsible for a significant proportion of the energy consumption. This paper proposes a framework for analysing and processing video frames that allows a modern screen to use less energy when displaying these frames. The content is analyzed prior to encoding and transmission, generating metadata that is attached to the content. After reception by a display, the metadata is then used along with display parameters and user settings to adapt the content prior to display. Two use cases are discerned: one whereby the energy is reduced as much as possible under the constraint that visual quality is maintained, and another where the highest visual quality is sought under the constraint of a fixed reduction of energy use. These use cases would well serve broadcasting and streaming services, respectively.
The digital edge: examining the relationship between digital competency and language learning outcomes

Technology in education, including language learning, is on the rise. Digital competency is essential for teachers to effectively integrate technology and enhance language teaching. It enables access to authentic materials, interactive exercises, and collaboration opportunities. However, integrating technology poses challenges for teachers.This empirical research aimed to investigate the impact of digital competency on language learning outcomes in the context of "smart education," which incorporates sustainable practices and digital technologies in the language classroom.The study adopted a quantitative approach to collect and analyze data. The sample population for the study comprised of 344 language teachers at various language schools in a metropolitan city. The data collection carried out with a digital competency questionnaire. The data were analyzed using descriptive statistics and multivariate technique, i.e., structural equation modeling.The study found that digital competency positively correlated with language proficiency outcomes. Participants with higher levels of digital competency achieved better language learning outcomes compared to those with lower levels of digital competency. Additionally, the study found that incorporating sustainable practices, such as digitalized learning materials and virtual classrooms, positively contributed to language learning outcomes. The findings of this study suggest that digital competency plays a vital role in language learning outcomes in the context of "smart education."Teachers should consider incorporating digital tools and sustainable practices into their language teaching to enhance language learning outcomes. The study recommends that language educators should focus on developing digital competency and integrating sustainable practices into their language classroom to promote effective language learning.
Simultaneously Teaching Mathematics and Prolog in School Curricula: A Mutual Benefit

Created in the 1970s, Prolog has its roots in mathematical logic. Its use to model logic problems is natural, but beyond logic, we suggest that using and learning Prolog for most of the topics in the high school math curriculum (probability, algebra, analysis or geometry) allows for a better assimilation of the course concepts. We argue that using Prolog is helpful in that it asks to properly model a problem, which is essential to develop problem-solving skills since it is often the key for finding a solution. At the same time, high school students discover a programming language that is easier to learn than imperative languages since the syntax is close to natural language and the language specification is more synthetic than traditional imperative languages.
Fractional order adaptive hunter-prey optimizer for feature selection

Proposing a reliable feature selection approach is the primary stone for endorsing the prediction performance; therefore, this paper proposes an enhanced optimization technique to identify the features of diverse datasets. The proposed approach employs the memory dependency of the fractional calculus operators to boost the agents' motion during the diversification and the intensification of the hunter-prey optimization algorithm (HPO). Moreover, a chaotic sine map is applied to balance the transition between the exploitation and exploration cores, aiming to improve further convergence. As a result, a novel variant named fractional adaptive HPO (F-AHPO) is proposed as a modified feature selection (FS) model. The proposed algorithm's applicability and performance are validated by using various UCI datasets. For the purpose of evaluating the proposed approach performance, its results are assessed and demonstrated versus a set of state-of-art techniques representing the literature. The results show the high ability of F-AHPO to enhance classification accuracy by removing irrelevant features. F-AHPO provides better results than other methods based on several performance measures.
Continual learning for predictive maintenance: Overview and challenges

Deep learning techniques have become one of the main propellers for solving engineering problems effectively and efficiently. For instance, Predictive Maintenance methods have been used to improve predictions of when maintenance is needed on different machines and operative contexts. However, deep learning methods are not without limitations, as these models are normally trained on a fixed distribution that only reflects the current state of the problem. Due to internal or external factors, the state of the problem can change, and the performance decreases due to the lack of generalization and adaptation. Contrary to this stationary training set, real-world applications change their environments constantly, creating the need to constantly adapt the model to evolving scenarios. To aid in this endeavor, Continual Learning methods propose ways to constantly adapt prediction models and incorporate new knowledge after deployment. Despite the advantages of these techniques, there are still challenges to applying them to real-world problems. In this work, we present a brief introduction to predictive maintenance, non-stationary environments, and continual learning, together with an extensive review of the current state of applying continual learning in real-world applications and specifically in predictive maintenance. We then discuss the current challenges of both predictive maintenance and continual learning, proposing future directions at the intersection of both areas. Finally, we propose a novel way to create benchmarks that favor the application of continuous learning methods in more realistic environments, giving specific examples of predictive maintenance.
Privacy-Enhancing Technologies in Federated Learning for the Internet of Healthcare Things: A Survey

Advancements in wearable medical devices using the IoT technology are shaping the modern healthcare system. With the emergence of the Internet of Healthcare Things (IoHT), efficient healthcare services can be provided to patients. Healthcare professionals have effectively used AI-based models to analyze the data collected from IoHT devices to treat various diseases. Data must be processed and analyzed while avoiding privacy breaches, in compliance with legal rules and regulations, such as the HIPAA and GDPR. Federated learning (FL) is a machine learning-based approach allowing multiple entities to train an ML model collaboratively without sharing their data. It is particularly beneficial in healthcare, where data privacy and security are substantial concerns. Even though FL addresses some privacy concerns, there is still no formal proof of privacy guarantees for IoHT data. Privacy-enhancing technologies (PETs) are tools and techniques designed to enhance the privacy and security of online communications and data sharing. PETs provide a range of features that help protect users’ personal information and sensitive data from unauthorized access and tracking. This paper comprehensively reviews PETs concerning FL in the IoHT scenario and identifies several key challenges for future research.
A novel agile ontology engineering methodology for supporting organizations in collaborative ontology development

Ontologies can represent technological enablers for knowledge elicitation and management in different kinds of organizations, especially with the exponential growth of sources and types of data fostered by digital transformation. However, their adoption in business applications is still limited, with existing Ontology Engineering Methodologies (OEMs) lacking adequate support during knowledge elicitation, authoring and reuse phases. This paper introduces a novel agile ontology engineering methodology (AgiSCOnt) to support ontologists (especially novice ones) in ontology development workflow, fostering collaboration with domain experts in an iterative, flexible and customizable approach. AgiSCOnt combines macro-level instructions with micro-level guidance, leveraging existing techniques and a management framework to help novice ontologists throughout the whole ontology engineering process. The methodology is compared to existing OEMs and assessed with three other agile methodologies (UPONLite, SAMOD, and RapidOWL). The evaluation is conducted with a sample of novice ontologists in a learning environment on Industry 4.0 technologies. Both the development process with a methodology from a user perspective and the quality of the developed ontologies were considered in the evaluation. Preliminary results show that AgiSCOnt effectively supports authoring and reuse, with developed ontologies of good quality. It is perceived as clear and simple, while being flexible and adaptable enough, thus supporting knowledge management and sharing in industrial organizations through the documentation of the ontologies.
Risk and UCON-based access control model for healthcare big data

Abstract The rapid development of healthcare big data has brought certain convenience to medical research and health management, but privacy protection of healthcare big data is an issue that must be considered in the process of data application. Access control is one of the methods for privacy protection, but traditional access control models cannot adapt to the dynamic, continuous, and real-time characteristics of healthcare big data scenarios. In this paper, we propose an access control model based on risk quantification and usage control (RQ-UCON). The model adds a risk quantification module to the traditional UCON model to achieve privacy protection of medical data. This module classifies risks into direct and indirect risks and quantifies them based on the physician's visit history. The model stores the quantified risk values as subject attributes. The RQ-UCON model uses an improved Exponentially Weighted Moving Average (EWMA) and penalty factors to predict risk value and to update the risk values of the subject attributes in real-time. The RQ-UCON model uses agglomerative hierarchical clustering to cluster the risk values of physicians within the department, resulting in risk intervals for each physician's operational behavior. Each risk interval is stored as a condition in the RQ-UCON model. Finally, according to the model whether the subject attributes meet the model conditions to determine whether the subject has the corresponding access rights, and according to the risk interval to grant the subject the corresponding access rights. Through the final experiment, it can be seen that the access control model proposed in this paper has a certain control on the excessive access behavior of doctors and has a certain limitation on the privacy leakage of healthcare big data.
What makes evolutionary multi-task optimization better: A comprehensive survey

Evolutionary multi-task optimization (EMTO) is a new branch of evolutionary algorithm (EA) that aims to optimize multiple tasks simultaneously within a same problem and output the best solution for each task. EMTO utilizes the strengths of EA to perform global optimization without relying on the mathematical properties of the problem. Therefore, EMTO is particularly suitable for complex, non-convex and nonlinear problems. Unlike traditional single-task EA, EMTO can deal with multiple optimization problems at once and can automatically transfer knowledge among these different problems. EMTO provides a novel approach for solving multi-task optimization problems and has attracted the attention of many researchers in the field of evolution. Due to the strong parallel search capability of EMTO, many excellent theoretical and applied research has been proposed on EMTO. To better organize these respectable research works and inspire future researchers, this paper reviews the related works on EMTO in the following three aspects. Firstly, many works focus on improving the performance of EMTO through various optimization strategies. Through an in-depth analysis and review of the current literature on this topic, we provide a comprehensive summary of these strategies. Secondly, we provide examples of real-world applications of EMTO, as well as its combination with other optimization paradigms. These examples demonstrate the wide applicability of EMTO. Finally, we propose some potential directions for future research in EMTO to inspire researchers in this field.
The Effect of Scaffolding Using the Peer Tutoring Method on Biology Learning Outcomes in the Material of the Human Excretory System

Purpose of the study: To find out the effect of scaffolding using the peer tutoring learning method on the cognitive, affective and psychomotor aspects of class IX students of SMP Negeri 1 Muaro Jambi on the subject of the excretory system in humans.&#x0D; Methodology: This type of research is a real experiment or true experiment. While the research design used in this study is the Posttest-Only Control Design. The sample in this study was class IX A as the experimental class and class IX D as the control class. The types of data and data sources in this study are quantitative data obtained from the results of tests carried out or data that describe cognitive aspects and qualitative data, namely data obtained from affective and psychomotor aspects. The instrument used for assessing learning outcomes in the cognitive aspect is a written test. Multiple choice questions are used for the posttest in the experimental class and the control class. For the assessment of learning outcomes on the affective aspect a questionnaire was used, and for the assessment of learning outcomes on the psychomotor aspect a worksheet was used for students.&#x0D; Main Findings: Scaffolding (providing assistance) using the Peer Tuthoring learning method does not affect the learning outcomes of biology in class IX students of SMP Negeri 1 Muara Jambi in the cognitive aspect, but affects the affective and psychomotor aspects.&#x0D; Novelty/Originality of this study: It can be used as material for consideration and information for teachers in selecting appropriate, effective, and efficient learning models in the biology learning process. So that it can improve student learning outcomes in Biology subjects.