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The transformative wave of generative AI is reshaping the creative thinking processes of learners, posing a significant challenge to education and industry in cultivating technological literacy and creativity. This study delves into the exploration of how learners can effectively tackle new challenges by deconstructing fragments from a macro perspective and generating innovative methods or concepts. In the Scratch visual programming environment, learners in the self-regulated learning mode observed entire functioning projects, facilitating easy disassembly and learning, namely by using Code Decomposed by Learner (CDBL). A total of 104 fifth-grade students were divided into two learning scaffoldings: (1) domain-general, learning from the top down (CDBL-TD), and (2) domain-specific, learning from the bottom up (CDBL-BU). Students in the CDBL-TD group exhibited a high degree of completion, strong exploration abilities, and the willingness to experiment with unlearned functions. Although there was no significant difference in originality between the two groups, students in the CDBL-TD group showcased greater uniqueness in designing characters or items within the game. This study introduces a novel programming learning scaffolding, offering instructors a tool to guide students’ creativity and enhance their programming capabilities.

After the Taiwanese government launched the Blueprint for Developing Taiwan into a Bilingual Nation by 2030, the Implementation Project of Bilingual Instruction in several domains of primary and junior high school education was promoted by the Taiwan Ministry of Education. Content and language integrated learning (CLIL) is a dual-objective strategy in which students simultaneously acquire language skills and subject knowledge. CLIL has been widely implemented and proven to be successful in European countries. This strategy will become the primary method of bilingual education for instructors in grades K-12. Other non-European countries, such as Latin America and Indonesia, however, found difficulties applying CLIL in class. The issues need to be identified to encourage researchers and practitioners to find solutions. The purpose of this paper is to identify the barriers to implementing CLIL from the perspective of K-12 teachers. We surveyed 102 K-12 teachers in Keelung using a questionnaire. The findings indicate that (1) 49.0% of teachers feel concerned if they are required to teach half of their content in English, (2) 64.8% of teachers are willing to improve their English proficiency in their spare time for implementing CLIL in class, and (3) only 36.3% of teachers are willing to adopt CLIL in class, while 50.0% of teachers are willing to implement CLIL if a teaching assistant system supports them with English course materials. Teaching assistant systems that provide English course materials might influence teachers’ willingness to implement CLIL in class.

When controversial products are introduced, effective promotion efforts and eventual public acceptance require consideration of multiple factors such as existing social network structures, numbers of pioneers and their locations, and appropriate methods for product information diffusion. These factors have been the focus of marketing, investment and other studies for many years, most recently in computer information science. Researchers are especially motivated to understand diffusion processes for new technologies and controversial products within and across social networks. While many product diffusion simulation models have been proposed, most suffer from assumptions of unchanging internal agent attitudes toward products, no opinion exchanges between agents, and non-significant relationships between agent internal opinion attitudes and diffusion thresholds. In this paper we propose an opinion dynamics model that assumes both agent interaction and changes in agent attitudes over time. Social psychology theory is used to explain interactions between opinion and diffusion dynamics, with changing agent attitudes and behaviors affected by interpersonal relationship factors. Simulations were used to study dynamic diffusion processes involving controversial products (e.g., vaccines and genetically modified foods) in different social networks. Results indicate that the proposed model accurately reflects several kinds of social phenomena, including pioneer influences, rural marketing strategies, and the influence of social network structure. This effort to identify instances of product diffusion under various social conditions is offered in support of research in communication dynamics and social media-centered marketing strategies.

We study the problem of controllable citation text generation by introducing a new concept to generate citation texts. Citation text generation, as an assistive writing approach, has drawn a number of researchers’ attention. However, current research related to citation text generation rarely addresses how to generate the citation texts that satisfy the specified citation intents by the paper’s authors, especially at the beginning of paper writing. We propose a controllable citation text generation model that extends a pre-trained sequence to sequence models, namely, BART and T5, by using the citation intent as the control code to generate the citation text, meeting the paper authors’ citation intent. Experimental results demonstrate that our model can generate citation texts semantically similar to the reference citation texts and satisfy the given citation intent. Additionally, the results from human evaluation also indicate that incorporating the citation intent may enable the models to generate relevant citation texts almost as scientific paper authors do, even when only a little information from the citing paper is available.

Lung sounds remain vital in clinical diagnosis as they reveal associations with pulmonary pathologies. With COVID-19 spreading across the world, it has become more pressing for medical professionals to better leverage artificial intelligence for faster and more accurate lung auscultation. This research aims to propose a feature engineering process that extracts the dedicated features for the depthwise separable convolution neural network (DS-CNN) to classify lung sounds accurately and efficiently. We extracted a total of three features for the shrunk DS-CNN model: the short-time Fourier-transformed (STFT) feature, the Mel-frequency cepstrum coefficient (MFCC) feature, and the fused features of these two. We observed that while DS-CNN models trained on either the STFT or the MFCC feature achieved an accuracy of 82.27% and 73.02%, respectively, fusing both features led to a higher accuracy of 85.74%. In addition, our method achieved 16 times higher inference speed on an edge device and only 0.45% less accuracy than RespireNet. This finding indicates that the fusion of the STFT and MFCC features and DS-CNN would be a model design for lightweight edge devices to achieve accurate AI-aided detection of lung diseases.

The authors use four criteria to examine a novel community detection algorithm: (a) effectiveness in terms of producing high values of normalized mutual information (NMI) and modularity, using well-known social networks for testing; (b) examination, meaning the ability to examine mitigating resolution limit problems using NMI values and synthetic networks; (c) correctness, meaning the ability to identify useful community structure results in terms of NMI values and Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks; and (d) scalability, or the ability to produce comparable modularity values with fast execution times when working with large-scale real-world networks. In addition to describing a simple hierarchical arc-merging (HAM) algorithm that uses network topology information, we introduce rule-based arc-merging strategies for identifying community structures. Five well-studied social network datasets and eight sets of LFR benchmark networks were employed to validate the correctness of a ground-truth community, eight large-scale real-world complex networks were used to measure its efficiency, and two synthetic networks were used to determine its susceptibility to two resolution limit problems. Our experimental results indicate that the proposed HAM algorithm exhibited satisfactory performance efficiency, and that HAM-identified and ground-truth communities were comparable in terms of social and LFR benchmark networks, while mitigating resolution limit problems.

One of the most important assessment indicators of computer virus infections is epidemic tipping point. Although many researchers have focused on the effects of scale-free network power-law connectivity distributions on computer virus epidemic dynamics and tipping points, few have comprehensively considered resource limitations and costs. Our goals for this paper are to show that (a) opposed to the current consensus, a significant epidemic tipping point does exist when resource limitations and costs are considered and (b) it is possible to control the spread of a computer virus in a scale-free network if resources are restricted and if costs associated with infection events are significantly increased.