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To address the challenges encountered by Chinese primary school students, particularly left-behind and migrant children, who exhibit a preference for animations, video games, and short videos over reading books and struggle with Chinese-English bilingual skills, this study introduces an educational robot AIassisted method for simultaneous bilingual reading. To assess the effectiveness of this method, a 6-month Chinese-English bilingual extracurricular reading comparative experiment was conducted involving 85 grade 5 students from two classes in a primary school in Hangzhou, China. The AI-assisted class freely read 100 bilingual/English electronic picture books and 200 Chinese electronic classic serial picture books by employing the AI-assisted human-computer interactive electronic reading mode of the "Educational Robot+Audio Electronic Picture Book+Character-play Based Reading." In contrast, the paper book group read the same content presented in the traditional paper book format, following the "regular independent reading" mode. Post-experimental analyses were conducted employing t-tests and MANCOVA and the results revealed that: the primary factors influencing reading effectiveness are the choice of reading materials, reading tools, and reading mode, while reading time does not emerge as the principal influencing factor. Furthermore, students in the AI class demonstrated significant enhancements in bilingual reading motivation, reading amount, reading comprehension, independent learning ability, pronunciation proficiency, and test scores compared to their peers in the paper book class. The AI-assisted reading mode utilizing educational robots garnered positive feedback from teachers, parents, and students. It offers the potential to effectively substitute parental involvement in parent-child reading and English tutoring, while also enabling the simultaneous acquisition of bilingual proficiency in both Chinese and English. This approach proves to be highly effective, cost-efficient, and convenient, particularly for enhancing children's foreign language abilities. Moreover, it fosters positive reading habits and independent learning skills among primary school students, contributes to the establishment of lofty aspirations, and enhances bilingual performance. Overall, this innovative mode offers an effective means of facilitating children's acquisition of bilingualism and foreign language skills, as well as promoting reading education. [ABSTRACT FROM AUTHOR]

Some animals are thought to exhibit cognitive specialization, as they have specialized cognitive modules that solve specific social or ecological problems instead of one general-purpose mechanism that addresses diverse problems. Although there are many examples of specialized cognition, little is known about whether specialization develops through experience or is produced by innate, species-specific differences. Previous work has shown that Polistes fuscatus wasps use face recognition to individually identify other wasps and that P. fuscatus are specialized for learning conspecific faces. Here, we test how experimentally altering face experience in three Polistes species influences the development of face specialization. We show face learning is influenced by both experience and innate, species-specific differences. In P. fuscatus , experience with conspecific faces is not required for the development of face specialization. In two related Polistes species that naturally lack individual face recognition, Polistes metricus and Polistes dominula , experience has different effects on specialization. Polistes metricus , a close relative of P. fuscatus , develops face specialization with experience. However, P. dominula , a more distant relative, uses general pattern recognition to learn faces regardless of experience. Therefore, some species have innate mechanistic architecture that facilitates the development of face specialization, while other species do not. These results suggest that selection shapes animal minds in a modular manner. The capacity for specialized cognitive skills evolves in response to specific ecological or social demands, such as social benefits associated with accurate individual face recognition. Highlights • We test whether cognitive specialization is inherited or requires experience. • We experimentally altered face experience in three species of paper wasps. • Experience produces face specialization in some wasp species but not others. • Face specialization in wasps is influenced by both inheritance and experience. [ABSTRACT FROM AUTHOR]

The aim of this academic paper is to discuss and illustrate the significant role and duties of a lecturer, a tutor and a teacher in the process of learning and explain their utmost contribution to the promotion of the students educationally, emotionally and ethically. At the beginning of the academic paper, a brief description has been explained to distinguish between the teacher and the lecturer and between the tutor and the instructor. Then, the duties and responsibilities of a lecturer, a tutor and a teacher have been clarified. It is recommended that teachers and lecturers should be real leaders to their students and encourage them to improve their standard of education and promote their personalities by adopting better qualities and ethics in their life. Educational centers are expected to give more emphasis on the importance of training teachers, lecturers and tutors in order to help them teach and react to students positively and efficiently. [ABSTRACT FROM AUTHOR]

In this qualitative case study, the researcher explored an early-stage L2 doctoral student's reflective practice about her supervisor's feedback on the drafts of her research article and the role of reflection in her academic discourse socialization (ADS) as a researcher and an English academic writer. Data was collected over one and a half year from semi-structured interviews with the student and her supervisor, the student's drafts with supervisory feedback, informal conversations with the student, the student's written self-reflection, and observation of the student in academic activities. Qualitative and inductive analysis revealed that the supervisor-student dyad had a shared goal of paper writing, which was to provide the student cognitive skill training for research and academic writing. They also had different goals of paper writing, which characterized the social and negotiated nature of the student's ADS. Moreover, the student digested feedback through an interactive process of reflection-on-feedback , reflection-in-feedback , and reflection-for-feedback at different stages of writing, showing that reflective practice is indispensable for feedback to serve as useful and readily available resources for writing. Such reflective practice about feedback was found to contribute to the student's ADS cognitively, emotionally, and socially. The findings provide implications for L2 doctoral supervision, learning and socialization. [ABSTRACT FROM AUTHOR]

The growing implementation of digital education comes with an increased need to understand the impact of digital tools on learning. Previous behavioral studies have shown that handwriting on paper is more effective for learning than typing on a keyboard. However, the impact of writing with a digital pen on a tablet remains to be clarified. In the present study, we compared learning by handwriting with an ink pen on paper, handwriting with a digital pen on a tablet, and typing on a keyboard. Behavioral and electroencephalographic indices were measured immediately after learning with each writing tool. The moods of the subjects during the training were also assessed. The participants were divided according to their use of digital pen in their everyday lives, allowing us to take into account the effect of the familiarity with the digital pen on the learning process (familiar group vs. unfamiliar group). We performed an EEG experiment applying a repetition priming paradigm. In each trial, a learned foreign language word (prime word) and a mother tongue word (target word) were consecutively presented. The target word was either semantically identical to the prime word (repetitive condition) or different (non-repetitive condition). We assumed that a larger priming effect on N400 reflects larger learning progress. The familiar group showed a greater N400 priming effect for words learned with the digital or ink pen than those learned with the keyboard. The unfamiliar group showed the greater N400 priming effect for words learned with the ink pen compared with words learned by typing. In addition, positive mood during learning was significantly higher during handwriting than during typing, regardless of the groups. On the other hand, the behavioral indices were not influenced by the writing tool. These results suggest that the movements involved in handwriting allow a greater memorization of new words. The advantage of handwriting over typing might also be caused by a more positive mood during learning. Finally, our results show that handwriting with a digital pen and tablet can increase the ability to learn compared with keyboard typing once the individuals are accustomed to it. [ABSTRACT FROM AUTHOR]

Academic research is crucial to the development of science and technology and is an important factor that affects national strength. When writing an academic research paper, a rhetorical structure is typically used to present the paper's ideas, but this task is quite difficult for junior researchers. To solve this problem, some studies have adopted text mining to assist with the writing, but the existing methods still require human intervention to generate sentences. Recently, due to the increasing maturity of deep learning technology and the ability to address the problem of automatic text generation, progress has been made in this area. The highly complex deep learning operations can correctly generate sequences and find correlations between sequences. When a user provides a few keywords and key sentences, the proposed algorithm can generate an introduction section for the user. The results show that the generated introduction is more coherent, clearer, and more fluent than existing summarization methods. In addition, the method proposed in this study improves the accuracy compared with traditional text extraction methods. The manuscript produced by this study has been evaluated to show that the study can produce a comprehensive introduction compared with previous studies. • The proposed framework can generate a comprehensive introduction based on users' brief inputs. • This paper demonstrates a three-testing-threshold approach to check the content quality. • The introduction generated through this method has less than 3% similarity to the original text by comparison. [ABSTRACT FROM AUTHOR]

Glocalization is a trend that requires individuals to be literate in their native languages and a target language. The use of a foreign language or a non-native language leads to language anxiety. This review paper aims to look into the commonalities and the gaps in the read studies. The researcher found that language anxiety happens in foreign language learning and teaching and at the macro-level of learning. With this, the studies used scales to measure the levels of anxiety of the respondents; they also aimed to study the precedents and effects of language anxiety and the coping strategies of learners and teachers to lessen language anxiety. Finally, gaps in the studies include a need for a language teaching anxiety focusing on macro skills of the teachers; mixedmethod studies are rare for qualitative methods and a need to employ effective strategies to cope with language anxieties. [ABSTRACT FROM AUTHOR]

Due to their multifunctionality, tablets offer tremendous advantages for research on handwriting dynamics or for interactive use of learning apps in schools. Further, the widespread use of tablet computers has had a great impact on handwriting in the current generation. But, is it advisable to teach how to write and to assess handwriting in pre- and primary schoolchildren on tablets rather than on paper? Since handwriting is not automatized before the age of 10 years, children's handwriting movements require graphomotor and visual feedback as well as permanent control of movement execution during handwriting. Modifications in writing conditions, for instance the smoother writing surface of a tablet, might influence handwriting performance in general and in particular those of non-automatized beginning writers. In order to investigate how handwriting performance is affected by a difference in friction of the writing surface, we recruited three groups with varying levels of handwriting automaticity: 25 preschoolers, 27 second graders, and 25 adults. We administered three tasks measuring graphomotor abilities, visuomotor abilities, and handwriting performance (only second graders and adults). We evaluated two aspects of handwriting performance: the handwriting quality with a visual score and the handwriting dynamics using online handwriting measures [e.g., writing duration, writing velocity, strokes and number of inversions in velocity (NIV)]. In particular, NIVs which describe the number of velocity peaks during handwriting are directly related to the level of handwriting automaticity. In general, we found differences between writing on paper compared to the tablet. These differences were partly task-dependent. The comparison between tablet and paper revealed a faster writing velocity for all groups and all tasks on the tablet which indicates that all participants--even the experienced writers--were influenced by the lower friction of the tablet surface. Our results for the group-comparison show advancing levels in handwriting automaticity from preschoolers to second graders to adults, which confirms that our method depicts handwritin performance in groups with varying degrees of handwriting automaticity. We conclud that the smoother tablet surface requires additional control of handwriting movement and therefore might present an additional challenge for learners of handwriting. [ABSTRACT FROM AUTHOR]

Manuscript binarization is used to convert each pixel in the script image into text and background. Many manuscript binarization methods have been proposed, such as the Otsu, Bernsen, Sauvola, Niblack, Phansalkar and Singh methods. These methods only focus on one problem of a degraded manuscript. In this research, a deep learning approach based on the U-Net method is applied for binarization of degraded ancient manuscripts. Adding layers to the U-Net architecture can cause more parameters and excessive computational calculations. Residual U-Net (ResUNet) is a development of the U-Net method. ResUNet, with its residual blocks, enables efficient and effective feature extraction, capturing fine details of degraded documents. This is important for identifying and distinguishing text from various artifacts and noise in the document. ResUNet can handle various types of image degradation thanks to its residual blocks that prevent gradient loss and strengthen features over the network. Convolutional Long Short-Term Memory (ConvLSTM) is a variant of LSTM (Long Short-Term Memory) designed for spatial data such as images. ConvLSTM combines the ability of LSTM to learn long-term dependencies with the power of CNN in processing spatial data. The combination of ResUNet and ConvLSTM for binarization of degraded documents is a powerful strategy that leverages the power of both architectures to improve quality and accuracy in separating text from degraded background. The aim of this research is to determine the performance evaluation results of the combination of ResUNet and ConvLSTM architectures on the binarization of degraded ancient Javanese manuscripts. The trial was conducted using datasets taken from several museums. The dataset consists of 1200 images of Javanese ancient manuscripts that were damaged in the form of perforated paper, ink bleed through from the previous page, and red or brownish spots. The proposed method produces a loss value of 0.0559, F-Measure 92.89%, PSNR 18.52 dan IoU 0.85. [ABSTRACT FROM AUTHOR]

This paper presents an alternative approach of teaching, learning and solving systems of two linear equations for two unknowns that yield no solutions. We conducted a desk-based research on methods that have been used by in-service mathematics teachers for teaching systems of two linear equations in ordinary secondary schools. It was found that five common methods (substitution, elimination, graphical, inverse matrix, Cramer's rule) have been used for teaching a system of two linear equations for two unknowns that yield no solution and all methods yield the same answer regardless of having different ways of approaching the system. We realized that a crossing method (alternative approach) is not found in the literature and yet not used by teachers for teaching students a system of two linear equations that yield no solution. But this crossing method yields similar answers with that resulted when using the five common methods. We present this alternative method in this paper by comparing with the answers obtained using five methods while focusing on two systems of two linear equations that yield no solution. This new approach has implications in teaching, learning and solving systems of two linear equations that yield no solutions in ordinary secondary schools, including mathematics teachers and educators can use this method for teaching students in solving systems of two linear equations for two unknowns that yield no solutions. [ABSTRACT FROM AUTHOR]

Things done are better remembered than things that are just heard or seen. In Project based learning pedagogy, the course content is delivered by assigning problem statements pertaining to real life situations to a group of students. For the assessment and evaluation of the course, the students have to give a presentation and a demo of the solution for the given problem at the end of course. So the students get sufficient time to work on the solution for the given problem. However, the work of the students is regularly monitored which helps in refining the proposed solution, looking for alternative solutions etc. In the process of solving these problem statements, the students apply the knowledge they have gained in various courses they have undertaken. In this process, the problem solving skills and employability of the students is greatly enhanced. In this paper, the authors have implemented project based learning pedagogy for two third year courses in Instrumentation Engineering Curriculum. The paper describes the aptness of this teaching pedagogy for the selected courses, activity planning, formulation of problem statements, progress reviews, and feedback. Through this activity, the authors found out that technical and nontechnical skills of the students were greatly enhanced. Technical skills like, Problem formulation, System designing, etc are developed among the students. Effective communication, working in a team, developing a multidisciplinary approach, finance related planning are some of the non-technical skills found to be cultivated among the students. This ultimately develops life learning ability among the students. [ABSTRACT FROM AUTHOR]

In educational and psycho-diagnostic assessment procedures, computerised assessment is gradually replacing paper-based instruments. In computerised instruments, measuring reasoning skills, such as the ability to solve analogies, few differences are found with their paper-based counterparts; however young children may form an exception. Often in these comparisons of assessment mode, only accuracy is considered, whereas examining solution strategies and measuring learning over time are of particular interest to educational psychologists. The current study investigates whether the learning of analogical reasoning and progression of strategy change follow the same patterns in a computerised setting. The equivalence of paper-based and computerised versions of figural analogy puzzles were examined with regard to both accuracy and strategy change in 69 5-year old children (M=5.4 years). All children were asked to solve figural analogy matrices by constructing their answers from puzzle pieces. In the paper condition (N=33), the puzzle pieces could be grabbed and turned over by hand and then placed in the empty quadrant to solve the analogy. In the computer condition (N=36), the children used a computer mouse to carry out these manoeuvres. The assessment of children's solution strategy progression took place during weekly sessions over four consecutive weeks. Results indicate that the computerised figural analogy task is comparable to the original paper-based version with regard to both difficulty level and progression of strategy change. However, the computerised version took significantly less time to administer and analyse. Another advantage is that testing with computers allows for the registration of supplementary information that could prove useful for more individualised intervention. We conclude that computerised assessment is a sound method for educational psychologists to evaluate 5-year-olds' analogical reasoning ability and development. [ABSTRACT FROM AUTHOR]

The current study compared the effects of technological environments with that of the paper-and-pencil environment on reasoning about the concept of derivatives in the context of maximum and minimum problems. The data consisted of clinical interviews conducted with three pre-service secondary mathematics teachers and a newly registered graduate student all of whom had quite a lot of mathematics courses in their repertoire. The study revealed that participants mostly depended on and were limited to analytical reasoning within paper-and-pencil environments, whereas they were able to refer to practical and creative reasoning with the help of the facilities technology environments provided. On the other hand, although participants made progress and used different reasoning types within technology environments, there were cases where they could not move beyond analytical reasoning even within the presence of technological tools. The reason for such a limitation seemed to be because of the way they treated technology as an analytical tool and they depended on 'learning from technology' instead of 'learning with technology' [Hanna, G., 1989, Proofs that prove and proofs that explain. In: G. Vernaud, J. Rogalski and M. Artigue (Eds), Proceedings of the International Group for the Psychology of Mathematics Education (Paris: ERIC), pp. 45-51]. [ABSTRACT FROM AUTHOR]

The recognition of food images is of great significance for nutrition monitoring, food retrieval and food recommendation. However, the accuracy of recognition had not been high enough due to the complex background of food images and the characteristics of small inter-class differences and large intra-class differences. To solve these problems, this paper proposed a food image recognition method based on transfer learning and ensemble learning. Firstly, generic image features were extracted by using the convolutional neural network models (VGG19, ResNet50, MobileNet V2, AlexNet) pre-trained on the ImageNet dataset. Secondly, the 4 pre-trained models were transferred to the food image dataset for model fine-tuning. Finally, different basic learner combination strategies were adopted to establish the ensemble model and classify feature information. In this paper, several kinds of experiments were performed to compare the results of food image recognition between single models and ensemble models on food-11 dataset. The experimental results demonstrated that the accuracy of the ensemble model was the highest, reaching 96.88%, which was superior to any base learner. Therefore, the convolutional neural network model based on transfer learning and ensemble learning has strong learning ability and generalization ability, and it is feasible and practical to apply the method to food image recognition. [ABSTRACT FROM AUTHOR]

This paper responds to challenges around how to generate robust evidence in keeping with the principles of an asset-based approach based on mobilization of community strengths. The design of a collaborative evaluation of a multi-site Asset Based Community Development program is described and emergent learning discussed. A qualitative mixed method design was used to capture changes at community and program level drawing on diverse sources of evidence. Shared principles on the conduct of the evaluation were developed with program leads and community practitioners and opportunities for shared learning were built in. The paper distils learning on evaluation into six design features including the asset-based model as a framework, understandings of evidence and outcomes, ethical conduct, and the centrality of a collaborative and developmental approach. The paper concludes that these features form a coherent approach to asset-based evaluation which can link the theory and practice of Asset Based Community Development. [ABSTRACT FROM AUTHOR]

Multimodal Sentiment Analysis (MSA) has become an essential area of research to achieve more accurate sentiment analysis by integrating multiple perceptual modalities such as text, vision, and audio. However, most previous studies failed to align the various modalities well and ignored the differences in semantic information, leading to inefficient fusion between modalities and generating redundant information. In order to solve the above problems, this paper proposes a transformer-based network model, Tri-CLT. Specifically, this paper designs Integrating Fusion Block to fuse modal features to enhance their semantic information and mitigate the secondary complexity of paired sequences in the transformer. Meanwhile, the cross-modal attention mechanism is utilized for complementary learning between modalities to enhance the model performance. In addition, contrastive learning is introduced to improve the model's representation of learning ability. Finally, this paper conducts experiments on CMU-MOSEI aligned and unaligned data, and the experimental results show that the proposed method outperforms the existing methods. [ABSTRACT FROM AUTHOR]

Purpose The purpose of this paper is to explore the learning outcomes of students and the importance of research supervisors in writing research.Design/methodology/approach A questionnaire covering three dimensions: (A) skill set, (B) course arrangement, and (C) supervisor guidance was distributed to research students in Hong Kong and Chengdu, China, to examine their perceptions of a research module.Findings The regression results presented in this paper suggest that clear expectations of the course and the communication skills of supervisors or peers can explain around 20-30 percent of the change in the dependent variables of “awareness of using strategies to learn effectively,” “building self-confidence” and “quality of interaction.” With regard to the learning outcomes of students, the skills of using various resources, writing literature reviews and communication skills were perceived as being very important. For research supervisors, the quality of interaction, research experience and being approachable were all identified as being important.Practical implications The management of higher educational institutes could consider these findings when designing research curricula and selecting research supervisors so as to enhance learning outcomes.Originality/value The paper details learning outcomes from supervisors of writing research, which is important for the educational sector and the business world. [ABSTRACT FROM AUTHOR]

The first issue of Research in Learning Technology (RLT) was published in 1993. Over 30 years, the journal has comprised an informal research and development facility for new ideas and practices in technology enhanced learning. This paper takes nine published articles from RLT: the three most downloaded in the period January 2021 - March 2023 (but published at any time); the three most downloaded articles published from January 2021 to March 2023; and the three most cited articles published from January 2018 to March 2023. The aim is to identify different areas of current interest and influence, different areas of practice, and different scholarly approaches. The authors are the journal's current editorial team. This paper identifies diversity of technology enhanced learning-related subject matter and different approaches, too, but with ongoing interest in efficacy and in the 'how' of technology enhanced learning: how technology can be applied to truly enhance learning, comprising an approachable community, generating influence. [ABSTRACT FROM AUTHOR]

This special section focuses on the state-of-the-art technologies on parallel and distributed computing techniques for artificial intelligence (AI), machine learning (ML), and deep learning (DL). AI, ML, and DL can enable computers the ability to learn from a large amount of data and use the learned model to optimize a complex problem or discover rules in a complicated system. AI, ML and DL can be applied to push forward the boundaries for many domains and significantly influence our daily life. [ABSTRACT FROM AUTHOR]

The stratospheric airship, as a near-space vehicle, is increasingly utilized in scientific exploration and Earth observation due to its long endurance and regional observation capabilities. However, due to the complex characteristics of the stratospheric wind field environment, trajectory planning for stratospheric airships is a significant challenge. Unlike lower atmospheric levels, the stratosphere presents a wind field characterized by significant variability in wind speed and direction, which can drastically affect the stability of the airship's trajectory. Recent advances in deep reinforcement learning (DRL) have presented promising avenues for trajectory planning. DRL algorithms have demonstrated the ability to learn complex control strategies autonomously by interacting with the environment. In particular, the proximal policy optimization (PPO) algorithm has shown effectiveness in continuous control tasks and is well suited to the non-linear, high-dimensional problem of trajectory planning in dynamic environments. This paper proposes a trajectory planning method for stratospheric airships based on the PPO algorithm. The primary contributions of this paper include establishing a continuous action space model for stratospheric airship motion; enabling more precise control and adjustments across a broader range of actions; integrating time-varying wind field data into the reinforcement learning environment; enhancing the policy network's adaptability and generalization to various environmental conditions; and enabling the algorithm to automatically adjust and optimize flight paths in real time using wind speed information, reducing the need for human intervention. Experimental results show that, within its wind resistance capability, the airship can achieve long-duration regional station-keeping, with a maximum station-keeping time ratio (STR) of up to 0.997. [ABSTRACT FROM AUTHOR]

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The rapid advancement of digital technology has not only affected the world of work but also students' learning. Digital learning competence (DLC) is one of the essential skills students need for effective learning in a digital environment. Despite the significant presence of secondary vocational school students in China, constituting one-third of the high school demographic, research on their digital learning needs remains sparse. Addressing this gap, this paper attempted to propose the elements and structural model of digital learning competence for secondary vocational school students (V-DLC). A corresponding questionnaire was compiled, and an analysis was carried out with 872 valid survey data of secondary vocational school students achieved by convenient sampling. A five-factor model for the V-DLC was established through exploratory and confirmatory factor analyses, cross-validity, and criterion validity tests. This paper suggests that evaluating students' digital learning competence in secondary vocational schools can be achieved by considering the dimensions of cognitive processing and reading, technology use, thinking skills, activity management, and will management, combined with students' learning experiences in school and other fields. Given the global focus on digital learning competence, this framework will pave the way for empirical research on digital learning and guide the enhancement of student learning ability in vocational settings, adapting to the digital era. Furthermore, transitioning to a digitalized vocational education system is essential for preparing students for a digitally-driven workforce, aligning with modern job market demands and global trends. [ABSTRACT FROM AUTHOR]

This paper seeks to examine the questions of what criteria definitions of Artificial Intelligence (AI) use to define AI, what the disagreements that revolve around the term AI are based on, and what correlations can be drawn to other parameters. Framed as a problem of classification, a random sample of 45 definitions from various text sources was subjected to a qualitative content analysis. The criteria found are concluded in five dimensions, namely (1) learning ability, (2) human likeness, (3) state of "mind", (4) complexity of the problem, and (5) successfulness. Further, the results support the view that there is no consensus neither on which of these criteria are crucial to define AI nor on how these criteria must be fulfilled. By opposing the frequencies of the dimensions found with the metadata collected, it can be seen that most of these, e.g., country, scientific field, or gender of the author, are statistically independent of content variables, while the medium in which the definition was published shows a strong correlation. Since different mediums target different purposes and different readers, it must be taken into account that writing a definition of AI is to be seen in the context of its distribution area and its goal. [ABSTRACT FROM AUTHOR]

Hole detection is an important means of crack detection for aero-engine blades, and the current technology still mainly relies on manual operation, which may cause safety hazards for visual reasons. To address this problem, this paper proposes a deep learning-based, aero-engine blade crack detection model. First, the K-means++ algorithm is used to recalculate the anchor points, which reduces the influence of the anchor frame on the accuracy; second, the backbone network of YOLOv5s is replaced with Mobilenetv3 for a lightweight design; then, the slim-neck module is embedded into the neck part, and the activation function is replaced with Hard Sigmoid for redesign, which improves the accuracy and the convergence speed. Finally, in order to improve the learning ability for small targets, the SimAM attention mechanism is embedded in the head. A large number of ablation tests are conducted in real engine blade data, and the results show that the average precision of the improved model is 93.1%, which is 29.3% higher; the number of parameters of the model is 12.58 MB, which is 52.96% less, and the Frames Per Second (FPS) can be up to 95. The proposed algorithm meets the practical needs and is suitable for hole detection. [ABSTRACT FROM AUTHOR]

The Internet of Multimedia Things (IoMT) refers to a network of interconnected multimedia devices that communicate with each other over the Internet. Recently, smart healthcare has emerged as a significant application of the IoMT, particularly in the context of knowledge‐based learning systems. Smart healthcare systems leverage knowledge‐based learning to become more context‐aware, adaptable, and auditable while maintaining the ability to learn from historical data. In smart healthcare systems, devices capture images, such as X‐rays, Magnetic Resonance Imaging. The security and integrity of these images are crucial for the databases used in knowledge‐based learning systems to foster structured decision‐making and enhance the learning abilities of AI. Moreover, in knowledge‐driven systems, the storage and transmission of HD medical images exert a burden on the limited bandwidth of the communication channel, leading to data transmission delays. To address the security and latency concerns, this paper presents a lightweight medical image encryption scheme utilising bit‐plane decomposition and chaos theory. The results of the experiment yield entropy, energy, and correlation values of 7.999, 0.0156, and 0.0001, respectively. This validates the effectiveness of the encryption system proposed in this paper, which offers high‐quality encryption, a large key space, key sensitivity, and resistance to statistical attacks. [ABSTRACT FROM AUTHOR]

Salient object detection (SOD) aims to identify the most visually striking objects in a scene, simulating the function of the biological visual attention system. The attention mechanism in deep learning is commonly used as an enhancement strategy which enables the neural network to concentrate on the relevant parts when processing input data, effectively improving the model's learning and prediction abilities. Existing saliency object detection methods based on RGB deep learning typically treat all regions equally by using the extracted features, overlooking the fact that different regions have varying contributions to the final predictions. Based on the U2Net algorithm, this paper incorporates the split coordinate channel attention (SCCA) mechanism into the feature extraction stage. SCCA conducts spatial transformation in width and height dimensions to efficiently extract the location information of the target to be detected. While pixel-level semantic segmentation based on annotation has been successful, it assigns the same weight to each pixel which leads to poor performance in detecting the boundary of objects. In this paper, the Canny edge detection loss is incorporated into the loss calculation stage to improve the model's ability to detect object edges. Based on the DUTS and HKU-IS datasets, experiments confirm that the proposed strategies effectively enhance the model's detection performance, resulting in a 0.8% and 0.7% increase in the F1-score of U2Net. This paper also compares the traditional attention modules with the newly proposed attention, and the SCCA attention module achieves a top-three performance in prediction time, mean absolute error (MAE), F1-score, and model size on both experimental datasets. [ABSTRACT FROM AUTHOR]

This paper aims to develop a new model of Project-Based Instructional Taxonomy (PBIT) that provides a tool of course design that facilitates Computational Thinking (CT) development as creative action in solving real-life problems. Theoretically, PBIT is built on an integrative framework bringing together with studies on CT education, creativity, Bloom's Taxonomy, and Project-Based Instruction (PBI). This guides the course design to make alignment between diverse elements including education objectives, categories of CT, levels of learning ability, process of project facilitation, and methods of grading. A case will be discussed that focuses on a course Deep Learning and Technologies in AI bachelor program at Northeastern University (NEU) in China. It also shows how PBIT is applied in teaching practice that benefits students' CT development. As the conclusion indicates, this paper has contributions to future research on creativity, PBI, CT, and AI education. [ABSTRACT FROM AUTHOR]

Analyzing eye movement data to evaluate learning status has become crucial in intelligent education. The eye movement scanning path can directly or indirectly reflect changes in thinking patterns and psychological states. By analyzing the scanning path, we can explore the commonality and differences in learners' eye movement behaviors and provide essential references for improving visual content and giving guidance. This paper first studies the time series representation and clustering of the learner's scanning path under the same task. Then, the three learning states of concentration, mind-wandering, and information wandering are evaluated through the clustering results. Specifically, the improved DBA algorithm (iDBA) is proposed to extract group eye movement patterns, combined with the dynamic time warping (DTW) algorithm to calculate the similarity of scanning paths and determine the clustering seeds, while the distance density clustering (DDC) algorithm is used for clustering. Experiments show that time series-based eye movement pattern mining can identify group viewing behaviors. Meanwhile, clustering reveals different reading strategies and provides the ability to assess learning status. [ABSTRACT FROM AUTHOR]

As globalization accelerates, the linguistic diversity and semantic complexity of in-vehicle communication is increasing. In order to meet the needs of different language speakers, this paper proposes an interactive attention-based contrastive learning framework (IABCL) for the field of in-vehicle dialogue, aiming to effectively enhance cross-lingual natural language understanding (NLU). The proposed framework aims to address the challenges of cross-lingual interaction in in-vehicle dialogue systems and provide an effective solution. IABCL is based on a contrastive learning and attention mechanism. First, contrastive learning is applied in the encoder stage. Positive and negative samples are used to allow the model to learn different linguistic expressions of similar meanings. Its main role is to improve the cross-lingual learning ability of the model. Second, the attention mechanism is applied in the decoder stage. By articulating slots and intents with each other, it allows the model to learn the relationship between the two, thus improving the ability of natural language understanding in languages of the same language family. In addition, this paper constructed a multilingual in-vehicle dialogue (MIvD) dataset for experimental evaluation to demonstrate the effectiveness and accuracy of the IABCL framework in cross-lingual dialogue. With the framework studied in this paper, IABCL improves by 2.42% in intent, 1.43% in slot, and 2.67% in overall when compared with the latest model. [ABSTRACT FROM AUTHOR]

Automatic navigation with collision‐free navigation has become a critical challenge for unmanned surface vehicles (USVs) to expand their application scenarios. Conventional methods for achieving automatic navigation of USVs typically rely on finely modeling the environment, thus exhibiting poor generalization capabilities. Methods based on deep reinforcement learning possess powerful learning abilities and have achieved promising results in USV‐automatic navigation‐tasks. However, the increase in the complexity of network model structures has led to instability during the training process. Therefore, generating more robust navigation strategies, namely ensuring robust reward‐score trends during training and smoother action trajectories of the USV, is crucial for automatic navigation and constitutes the main research question of this study. In this paper, an improved deep deterministic policy gradient (DDPG) algorithm has been proposed for stable automatic navigation of USVs in complex environments. In this algorithm, first, we construct a stable training framework that incorporates the stable feature‐sharing module with constrained gradient backpropagation, which bolsters the USV's scene memorization capacity, reduces model training fluctuations during navigation policy learning, and improves the training stability of the navigation model. Second, we ensure the decision adaptability of the USV by constraining the extent of action change between adjacent time‐steps by using a reward‐function, which improves the USV‐action smoothly. Finally, we design typical USV‐automatic‐navigation‐scenarios to validate the performance of the Algorithm. Experimental results validate our algorithm's capability to achieve collision‐free navigation, outperforming the traditional DDPG algorithm in terms of convergence speed, effective sailing distance, and rudder angle maneuver consumption, among other performance metrics. [ABSTRACT FROM AUTHOR]

In steel production, defect detection is crucial for preventing safety risks, and improving the accuracy of steel defect detection in industrial environments remains challenging due to the variable types of defects, cluttered backgrounds, low contrast, and noise interference. Therefore, this paper introduces a steel surface defect detection model, DBCW-YOLO, based on YOLOv5. Firstly, a new feature fusion strategy is proposed to optimize the feature map fusion pair model using the BiFPN method to fuse information at multiple scales, and CARAFE up-sampling is introduced to expand the sensory field of the network and make more effective use of the surrounding information. Secondly, the WIoU uses a dynamic non-monotonic focusing mechanism introduced in the loss function part to optimize the loss function and solve the problem of accuracy degradation due to sample inhomogeneity. This approach improves the learning ability of small target steel defects and accelerates network convergence. Finally, we use the dynamic heads in the network prediction phase. This improves the scale-aware, spatial-aware, and task-aware performance of the algorithm. Experimental results on the NEU-DET dataset show that the average detection accuracy is 81.1, which is about (YOLOv5) 6% higher than the original model and satisfies real-time detection. Therefore, DBCW-YOLO has good overall performance in the steel surface defect detection task. [ABSTRACT FROM AUTHOR]

This paper demonstrates the regulation of women's pregnant bodies through and by discourses of care. It illustrates how present practices reflect those of slavery where the dressing of the body, styling of the hair, and dancing, communicated gender difference that complicated sexual difference. 1 The presentation explores the body as material, fleshy, changing, and regulated. It demonstrates that even when motherhood is seen as a social construct, there is a sexual attachment to certain practices of mothering (such as pregnancy and breastfeeding) that speaks to its materiality which intersect with the ideological aspect of motherhood. Michel Foucault argues that the social actor is a mere body, and power is exercised upon individuals as bodies are categorized, trained in procedures, and placed in hierarchies. The body therefore becomes docile through its ability to learn, and to internalize discourses that regulate thoughts and behaviors. Using the narratives of ten CARICOM mothers residing in Barbados, this paper unpacks the body politic of motherhood, noting specifically how motherhood operates as institution. [ABSTRACT FROM AUTHOR]

INTRODCTION: By analyzing the problem of self-monitoring in English online learning and constructing a strategyintegrated evaluation method, we can not only enrich the theoretical research results of self-monitoring in online learning, but also improve the independent learning ability and self-monitoring ability of students in English online learning. OBJECTIVES: To address the problem of poor optimization performance of current fusion optimization methods.METHODS:This paper proposes an online learning self-monitoring strategy fusion method based on improved nuclear reaction heuristic intelligent algorithm. First, the problems and enhancement strategies of online learning selfmonitoring are analyzed; then, the online learning self-monitoring strategy fusion model is constructed by improving the nuclear reaction heuristic intelligent algorithm; finally, the proposed method is verified to be effective and feasible through the analysis of simulation experiments. RESLUTS: The results show that the fusion method of learning self-monitoring strategies on the line at the 20th iteration number starts to converge to optimization with less than 0.1s optimization time, and the error of the statistical score value before and after weight optimization is controlled within 0.05. CONCLUSION:Addressing the Optimization of Convergence of Self-Monitoring Strategies for English Online Learning. [ABSTRACT FROM AUTHOR]

Entry of children to primary schools in India marks a sudden shift in the pedagogical approach as play-based pedagogy gives way to formal instruction. This paper argues that children's play motives do not subside when they enter school, rather play takes a more mature form and represents a strong linkage between their thought and reality. The recently introduced National Education Policy 2020 in India mandates a large-scale achievement survey at the age of 8, the fear is that this will lead to formalisation and academisation of early schooling. The paper presents a case study of a school in rural India that follows a play-based pedagogy in the primary years. The study uses dialectical-interactive approach for data collection and analysis. The analysis shows that play creates a pedagogic opportunity for the teacher and children to engage with each other's motives thus creating a shared 'object of activity' that ensures children's motivated engagement in their learning. [ABSTRACT FROM AUTHOR]

With strong self-learning and data analysis capabilities, deep learning is essential in cloud detection. However, many high-quality samples are the key to deep learning cloud detection methods. Different satellite image cloud detection techniques need to select adequate representative and high-quality samples and make corresponding cloud masks, which not only requires professional knowledge but also consumes a lot of workforce and time. To improve the generalization ability of the deep learning cloud detection model and quickly apply it to the cloud detection of different satellite images, this paper proposes a deep learning cloud detection method based on spectral assimilation for multiple types of satellite images (SAUNetCD). Under the condition of using fewer deep learning data samples, the deep learning model is used to achieve automatic cloud detection of multiple satellite data. Taking Landsat 8 OLI, Landsat 9 OLI, GF-1 WFV, and Sentinel 2A as examples, this paper selects Landsat 8 OLI data as the source data of cloud detection. The experimental results show that the spectral assimilation method improves the generalization ability of the deep learning cloud detection model and improves the cloud detection accuracy by nearly 20%. It realizes the fast cloud detection application of different satellite images by deep learning. It provides an effective way for cloud detection of multiple types of satellite images. [ABSTRACT FROM AUTHOR]

In this era of intelligence, the learning methods of learners have substantially changed. Many learners choose to learn through online education platforms. Although learners may enjoy more high-quality educational resources, when they are faced with an abundance of resource information, they are prone to become lost in knowledge, among other problems. To solve this problem, a multi-algorithm collaborative, personalized, learning path recommendation model is proposed to provide learning guidance for learners of online learning platforms. First, the learner model is constructed from four perspectives: cognitive level, learning ability, learning style, and learning intensity. Second, the association rule algorithm is employed to generate a sequence of knowledge points and to plan the learning sequence of knowledge points for learners. Last, the swarm intelligence algorithm is utilized to ensure that each knowledge point is matched with personalized learning resources with a higher degree of adaptability so that learners can learn using a more targeted approach. The experimental results show that the research results of this paper can, to a certain extent, recommend ideal learning paths to target users, effectively improve the accuracy of recommended resources, and thus improve the learning quality and learning effect of users. [ABSTRACT FROM AUTHOR]

The article presents a classroom activity called "Paper Towers" that is ideal for middle school students. They are instructed to build a paper tower as tall as possible that will resist being blown over by the teacher from one arm's length away. The activity aimed to provide students with several learning goals, both cognitive and affective, such as the ability to identify appropriate problems for technological design, design a solution or product, and evaluate the completed design. Moreover, it helps the students develop a comprehensive understanding of the applications of science and technology.

The goal of Text-to-SQL task is to map natural language queries into equivalent structured query languages(NL2SQL). On the WikiSQL dataset, the method used by the state-of-the-art models is to decouple the NL2SQL task into subtasks and then build a dedicated decoder for each subtask. There are some problems in this method, such as the model is too complicated, and the ability to learn the dependency between different subtasks is limited. To solve these problems, this paper innovatively introduces the sharing mechanism of multi-task learning into the NL2SQL task and realizes sharing by letting different subtasks share the same decoder. Firstly, sharing decoders for different subtasks can effectively reduce the complexity of the model, and at the same time, allows different subtasks to share knowledge during the training process so that the model can better learn the dependencies between different subtasks. This paper also designed a re-weighted loss to balance the complexity of the SELECT clause and the WHERE clause. We have evaluated the method in this article on the WikiSQL dataset. The experimental results show that the accuracy of the proposed model is better than state-of-the-art on the WikiSQL without execution guided decoding. [ABSTRACT FROM AUTHOR]

This paper aims to highlight the life of computer technologies to understand what kind of 'technological intentionality' is present in computers based upon the phenomenological elements constituting the objects in general. Such a study can better explain the effects of new digital technologies on our society and highlight the role of digital technologies by focusing on their activities. Even if Husserlian phenomenology rarely talks about technologies, some of its aspects can be used to address the actions performed by the digital technologies by focusing on the objects' inner 'life' thanks to the analysis of passive synthesis and phenomenological horizons in the objects. These elements can be used in computer technologies to show how digital objects are 'alive.' This paper focuses on programs developed through high-order languages like C++ and unsupervised learning techniques like 'Generative Adversarial Model.' The phenomenological analysis reveals the computer's autonomy within the programming stages. At the same time, the conceptual inquiry into the digital system's learning ability shows the alive and changeable nature of the technological object itself. [ABSTRACT FROM AUTHOR]

This collection of papers on ""future-readiness in education" aims to re-examine the purposes of education to include country-specific contexts and trajectories in the future. The purposes of education are developing learners for "Living, Learning, and Lifework." The contexts – economic, environmental, societal, and learning are deeply intertwined and interconnected with the education system. The papers examined the changing realities of these contexts and explore the implications of school leadership, teaching, learning, assessment, and educational outcomes. [ABSTRACT FROM AUTHOR]

Graph Neural Networks (GNNs) have achieved excellent performance of graph representation learning and attracted plenty of attentions in recent years. Most of GNNs aim to learn embedding vectors of the homogeneous graph which only contains single type of nodes and edges. However, the entities and their interactions in real world always have multiple types and naturally form the heterogeneous graph with rich structural and semantic information. As a result of this, it is beneficial to advance heterogeneous graph representation learning that can effectively promote the performance of complex network analysis. Existing survey papers of heterogeneous graph representation learning summarize all possible embedding techniques for graphs and make insufficient analysis for deep neural network models. To tackle this issue, in this paper, we systematically summarize and analyze existing heterogeneous graph neural networks (HGNNs) and categorize them based on their neural network architecture. Meanwhile, we collect commonly used heterogeneous graph datasets and summarize their statistical information. In addition, we compare the performances between HGNNs and shallow embedding models to show the powerful feature learning ability of HGNNs. Finally, we conclude the application scenarios of HGNNs and some possible future research directions. We hope that this paper can provide a useful framework for researchers who interested in HGNNs. [ABSTRACT FROM AUTHOR]

This paper explores the teachers' experiences of remote assessment in the Junior High School of Rizal Technological University. The qualitative study was used to determine the assessment methods used during the remote learning in terms of written and performance task and identify the most effective among other difficulties faced by the teachers in comparison with face-to-face. Data of the paper were obtained by conducting interviews that use open-ended questions from 10 participants who teach in the School Year 2021-2022. The analysis of the study was done according to themes and categories and participants' answers were quotes excerpted from the transcripts. The study's conclusion emphasizes the significance of carefully planning exams to guarantee academic integrity. With emphasis on the need to focus on cognitive, emotional, and psychomotor abilities when designing learning exams, especially for online learning, as well as the use of technology tools to monitor results and prevent dishonest behavior during online assessments. Finally, a complementary technique for evaluating students while they are engaged in online learning through teaching resources or learning management systems must be available. [ABSTRACT FROM AUTHOR]

This systematic literature review aimed to provide updated information on lifelong learning in educational research by examining theoretical documents and empirical papers from 2000 to 2022. This review sought to identify concepts, theories, and research trends and methods linked to lifelong learning in educational research in different countries. Our review findings showed that theoretical papers, such as reports, policies, and concepts of lifelong learning, are generally much more extensive than empirical studies. Word cloud analysis revealed that the most prominent concepts were lifelong learning skills, lifelong learning competencies, and the three types of lifelong learning (formal, nonformal, and informal). Following the inductive analysis, this study investigated three common research trends: conceptual framework or policies of lifelong learning, lifelong learning abilities, and influencing factors of lifelong learning and/or lifelong learning abilities. Regarding methodology, this study identified only three studies that used mixed methods, which is insufficient in the field. In addition, heterogeneity was also observed between research instruments in lifelong learning. Different data analysis techniques can be applied in this field, including content analysis, descriptive analysis, and inferential analysis. Finally, the participants involved in the examined studies were students, primary and secondary school teachers, undergraduates, postgraduates, student teachers, European Union Lifelong Learning experts, young adults, teacher educators, administrators, and academic staff. [ABSTRACT FROM AUTHOR]

Driver distraction detection not only helps to improve road safety and prevent traffic accidents, but also promotes the development of intelligent transportation systems, which is of great significance for creating a safer and more efficient transportation environment. Since deep learning algorithms have very strong feature learning abilities, more and more deep learning-based driver distraction detection methods have emerged in recent years. However, the majority of existing deep learning-based methods are optimized only through the constraint of classification loss, making it difficult to obtain features with high discrimination, so the performance of these methods is very limited. In this paper, to improve the discrimination between features of different classes of samples, we propose a high-discrimination feature learning strategy and design a driver distraction detection model based on Swin Transformer and the highly discriminative feature learning strategy (ST-HDFL). Firstly, the features of input samples are extracted through the powerful feature learning ability of Swin Transformer. Then, the intra-class distance of samples of the same class in the feature space is reduced through the constraint of sample center distance loss (SC loss), and the inter-class distance of samples of different classes is increased through the center vector shift strategy, which can greatly improve the discrimination of different class samples in the feature space. Finally, we have conducted extensive experiments on two publicly available datasets, AUC-DD and State-Farm, to demonstrate the effectiveness of the proposed method. The experimental results show that our method can achieve better performance than many state-of-the-art methods, such as Drive-Net, MobileVGG, Vanilla CNN, and so on. [ABSTRACT FROM AUTHOR]