Search

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]

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]

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]

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]

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]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

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]

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]

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]

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]

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]

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]

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.

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 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]

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]

This paper contributes to the dialogue around how to increase inclusion in higher education, taking the lecturer perspective as the point of departure. Theoretically, disability is understood as an interaction, which means that lecturers partake in the constitution of dis/ability in learning situations. Two qualitative interviews were conducted with an interdisciplinary lecturer employed in an institution of higher education in Norway. These data are used for this single case study to illustrate and reflect on the challenges of diversity in learning situations. Findings suggest that the lecturer struggles to encounter an increasingly diverse student population inclusively. Overall, her experiences unpack the outcome of a structural lack of prioritization to ensure accessibility for disabled students at an institutional level at the university where she is employed. This article emphasizes that the responsibility to ensure an inclusive teaching practice in higher education must be recognized and treated as an institutional obligation. To signal its priority, all institutions of higher education should make inclusive training obligatory for lecturers, as well as assigning hours to work on enhancing the development of inclusive skills in this group. [ABSTRACT FROM AUTHOR]

The combination of UAV camera and intelligent algorithm is a promising method for non-contact bridge crack detection. In this paper, an inspection tool based on UAV Image Acquisition Technology (UAVIAT) and Improved Intelligent Target Detection Technology (IITDT) called Improved Intelligent Real-Time Crack Detection Method for Bridges (IIRTCDMB) is proposed for efficient crack detection. The contributions of this paper are (1) The Squeeze-Excitement (SE) attention module is integrated into the target detection algorithm - You Only Look Once version 7 (YOLOv7) model to improve the learning ability of the feature channel. A Focal-efficient intersection over union (Focal-EIoU) loss function is also introduced to improve the regression accuracy of the model. As a result, a new crack image detection algorithm, YOLOv7-CD, is proposed. (2) A training process based on two-stage transfer learning (TSTL) is established, and hyper-parameter optimization of YOLOv7-CD is carried out. The feasibility and excellent performance of the proposed method are verified by applying it on the Cuntan Yangtze River Bridge. The results show that the average precision (AP) of the YOLOv7-CD model is improved by 3.19% compared with the original YOLOv7 model. After TSTL and hyperparameter optimization, the AP of the YOLOv7-CD model for bridge crack detection reaches 98.01%, which is higher than that of the popular target detection models. The IIRTCDMB proposed in this paper can acquire bridge surface images more safely and efficiently, and provide inspectors with more accurate structural crack information with lower computational and hardware requirements, which can provide technical support for the assessment of structural safety conditions and the formulation of maintenance programs. [ABSTRACT FROM AUTHOR]

Physical activity breaks in the classroom are an efficient way of promoting healthy behaviors in students, enhancing the development of their cognitive and academic abilities. The effects of active breaks, which are exclusive to the academic environment, are almost entirely positive, only a few papers did not show significant improvements, but they did not show any negative impact on academic performance either. This research was carried out to identify the properties and consequences of active breaks and physical activity during classes in school age. We have examined multiple databases, and we have selected 41 papers accounting for a time span of 15 years. Psychology, medicine, and education databases were the most browsed Most papers revealed positive outcomes for improving skills related to mathematics, language, reading, comprehension, as well as cognitive and executive functions. Overall, the results of this review corroborate the theory that physical education in schools is positively associated with academic performance in children. Education researchers keep investigating in order to fully assess the positive impact on academic performance, behavior during classes, and cognitive functions. [ABSTRACT FROM AUTHOR]

Personalized education aims to provide cooperative and exploratory courses for students by using computer and network technology to construct a more effective cooperative learning mode, thus improving students' cooperation ability and lifelong learning ability. Based on students' interests, this paper proposes an effective student grouping strategy and group-oriented course recommendation method, comprehensively considering characteristics of students and courses both from a statistical dimension and a semantic dimension. First, this paper combines term frequency–inverse document frequency and Word2Vec to preferably extract student characteristics. Then, an improved K-means algorithm is used to divide students into different interest-based study groups. Finally, the group-oriented course recommendation method recommends appropriate and quality courses according to the similarity and expert score. Based on real data provided by junior high school students, a series of experiments are conducted to recommend proper social practical courses, which verified the feasibility and effectiveness of the proposed strategy. [ABSTRACT FROM AUTHOR]

Bio-controllers inspired by the characteristics of the human lower limb play an important role in the study of lower limb rehabilitation robots (LLRRs). However, the inverse dynamics modeling of robots for human lower limb rehabilitation remains a challenging issue due to the non-linear and strong coupling characteristics of the bio-controller. To further improve the inverse dynamics model's accuracy, this paper proposes the use of a non-parametric modeling approach in order to learn it. In detail, the main idea is to use the motion data of the main joints of the lower limbs of healthy people as an input and the corresponding joint moments as an output, which are learned through the training of a neural network. To ensure that the learned model can be used on LLRRs, all data collected in this paper are real data from human lower limbs. In addition, since the type of data collected is time series, this paper proposes the use of the long short-term memory (LSTM) and gated recurrent unit (GRU) networks to learn the inverse dynamics model of the robot-like human lower limb and to compare the learning effects of the two networks. The evaluation metric for both network models is the root mean square error (RMSE). The experimental results show that both networks have sound learning effects, and that the GRU network has a more significant learning ability than the LSTM network. [ABSTRACT FROM AUTHOR]

In unsupervised domain adaptation (UDA), knowledge is transferred from label-rich source domains to relevant but unlabeled target domains. Current most popular state-of-the-art works suggest that performing domain alignment from the class perspective can alleviate domain shift. However, most of them based on domain adversarial which is hard to train and converge. In this paper, we propose a novel contrastive learning to improve diversity and discriminability for domain adaptation, dubbed as IDD_ICL, which improve the discriminativeness of the model while increasing the sample diversity. To be precise, we first design a novel implicits contrastive learning loss at sample-level by implicit augment sample of the source domain. While augmenting the diversity of the source domain, we can cluster the samples of the same category in the source domain together, and disperse the samples of different categories, thereby improving the discriminative ability of the model. Furthermore, we show that our algorithm is effective by implicitly learning an infinite number of similar samples. Our results demonstrate that our method doesn't require complex technologies or specialized equipment, making it readily adoptable and applicable in practical scenarios. [ABSTRACT FROM AUTHOR]

Deep learning network has the advantages of strong learning ability, strong adaptability, and good portability. Therefore, synthetic aperture radar (SAR) automatic target recognition (ATR) based on deep network is widely used in both military and civilian fields. However, due to the imaging conditions, radar angle, imaging distance, and other reasons, it is difficult to obtain efficient and usable SAR image datasets. SAR images’ recognition under small sample conditions is still a challenging problem. In this paper, a SAR target recognition method based on multi-view differential feature fusion network is proposed to address this problem. Considering the correspondence between RCS and target features, the network extracts dissimilarities between features from SAR images of different angles of the same target and fuses them with the original features of one angle to form new features, which enriches the available training data. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) public dataset show that the proposed method has a higher target recognition rate than other deep network methods, as well as single angle input recognition methods. [ABSTRACT FROM AUTHOR]

Generally, bearing early failure-induced impulses is weak and easily submerged by strong background noise. How to extract weak impulses is a big challenge. In this paper, based on the K Singular Value Decomposition (KSVD) algorithm, an improved dictionary learning model (WID-MSKW-KSVD) is proposed, which integrates a special denoising method and a multi-scale transformation into a typical dictionary learning model and it successfully overcomes two major shortcomings of traditional KSVD model including the lack of multi-scale learning process and the ability of dictionary atoms to resist strong noise interference. Furthermore, the kurtosis of the envelope spectrum is weighted into the proposed model as a fault matrix to enhance the fault feature. Finally, the feasibility of the proposed model is demonstrated by numerical simulation and practical experiments, and the superiority and reliability of this model in suppressing noise interference and extracting fault features are verified by comparison with other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Multi-label Short Text Classification (MSTC) is a challenging subtask of Multi-Label Text Classification (MLTC) for tagging a short text with the most relevant subset of labels from a given set of labels. Recent studies have attempted to address MSTC task using MLTC methods and Pre-trained Language Models (PLM) based fine-tuning approaches, but suffering the low performance from the following three reasons, 1) failure to address the issue of data sparsity of short texts, 2) lack of adaptation to the long-tail distribution of labels in multi-label scenarios and 3) an implicit weakness in the encoding length for PLM, which limits the ability of the prompt learning paradigm. Therefore, in this paper, we propose KSSVPT, a Knowledge and Separating Soft Verbalizer based Prompt Tuning method for MSTC to address the above challenges. Firstly, to mitigate the sparsity issue in short texts, we propose a novel approach that enhances the semantic information of short texts by integrating external knowledge into the soft prompt template. Secondly, we construct a new soft prompt verbalizer for MSTC, called separating soft prompt verbalizer, to adapt to the long-tail distribution issue aggravated by multiple labels. Thirdly, we propose a mechanism of label cluster grouping in building a prompt template to directly alleviate limited encoding length and capture the label correlation. Extensive experiments conducted on six benchmark datasets demonstrate the superiority of our model compared to all competing models for MLTC and MSTC in the tackling of MSTC task. [ABSTRACT FROM AUTHOR]

As one of the advanced automotive chassis technologies, the steer-by-wire system offers a high level of precision, responsiveness, and controllability in the driving experience. It can also adjust and optimize parameters to adapt to the preferences of different drivers. However, when faced with the steer-by-wire system, both experienced drivers and novice drivers are in the novice stage, exhibiting learning or adaptation behaviors when using this steering system. In this paper, a small-scale pilot evaluation was conducted by means of a questionnaire survey and driving-simulator experiment, and the learning behavior and adaptability of four experienced and four novice drivers to the steer-by-wire system were analyzed when using the traditional steering system. The study found that experienced drivers show significant changes in their adaptation to the steering system, mainly due to their habitual driving with traditional steering systems. In contrast, novice drivers show no significant changes in their adaptation to the steering system, which is attributed to their lack of driving experience and skills, resulting in less sensitivity to changes in the steering system. Additionally, the study found that novice drivers under the steer-by-wire system grasp control over speed and steering-wheel angle more quickly. This research provides a reference for improving drivers' learning and adaptation abilities to the steer-by-wire system and optimizing the design of the steer-by-wire system. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Elementary Education / Revija za Elementarno Izobraževanje is the property of University of Maribor, Faculty of Education and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Neuromorphic computation draws inspiration from the remarkable features of the human brain including low energy consumption, parallelism, adaptivity, cognitive functions, and learning ability. These qualities hold the promise of unlocking groundbreaking computational techniques that surpass the limitations of traditional computing systems. This paper reports a remarkable photo‐synaptic behavior in the field of rare earth ion‐doped luminescent oxides by using long‐persistent luminescence (LPL). This system utilizes electron trap states to regulate the synaptic behavior, operating through a fundamentally different mechanism from that of electronic‐based synaptic devices. To realize this strategy, Tb3+ doped CaSnO3, which shows a significant LPL property under UV‐light excitation, is prepared. The luminescent system shows key neuromorphic characteristics such as paired‐pulse facilitation, pulse‐number/timing dependent potentiation, and pulse‐number/timing dependent short‐ to long‐term plasticity transition, which are required for realizing synaptic devices. This feature expands the way for advanced neuromorphic technologies employing light stimuli. [ABSTRACT FROM AUTHOR]

A long-standing research goal is to develop computing technologies that mimic the brain's capabilities by implementing computation in electronic systems directly inspired by its structure, function, and operational mechanisms, using low-power, spike-based neural networks. The Loihi neuromorphic processor provides a low-power, large-scale network of programmable silicon neurons for brain-inspired artificial intelligence applications. This paper exploits the Loihi processors and a theory-guided methodology to enable unsupervised learning of spike patterns. Our method ensures efficient and rapid selection of the network's hyperparameters, enabling the neuromorphic processor to generate attractor states through real-time unsupervised learning. Precisely, we follow a fast design process in which we fine-tune network parameters using mean-field theory. Moreover, we measure the network's learning ability regarding its error correction and pattern completion aptitude. Finally, we observe the dynamic energy consumption of the neuron cores for each millisecond of simulation equal to 23 μ J/time step during the learning and recall phase for four attractors composed of 512 excitatory neurons and 256 shared inhibitory neurons. This study showcases how large-scale, low-power digital neuromorphic processors can be quickly programmed to enable the autonomous generation of attractor states. These attractors are fundamental computational primitives that theoretical analysis and experimental evidence indicate as versatile and reusable components suitable for a wide range of cognitive tasks. [ABSTRACT FROM AUTHOR]

Accurately capturing children's word learning abilities is critical for advancing our understanding of language development. Researchers have demonstrated that utilizing more complex statistical methods, such as mixed-effects regression and hierarchical linear modeling, can lead to a more complete understanding of the variability observed within children's word learning abilities. In the current paper, we demonstrate how a person-centered approach to data analysis can provide additional insights into the heterogeneity of word learning ability among children while also aiding researchers' efforts to draw individual-level conclusions. Using previously published data with 32 typically developing and 32 late-talking infants who completed a novel noun generalization (NNG) task to assess word learning biases (i.e., shape and material biases), we compare this person-centered method to three traditional statistical approaches: (1) a t-test against chance, (2) an analysis of variance (ANOVA), and (3) a mixed-effects regression. With each comparison, we present a novel question raised by the person-centered approach and show how results from the corresponding analyses can lead to greater nuance in our understanding of children's word learning capabilities. Person-centered methods, then, are shown to be valuable tools that should be added to the growing body of sophisticated statistical procedures used by modern researchers. [ABSTRACT FROM AUTHOR]

Despite its dominance in the field of teaching English as a second/foreign language, the implementation of the Communicative Language Teaching (CLT) approach continues to be challenging and problematic. A similar set of constraints – including but not limited to challenges related to educational cultures, contextual and conceptual factors, and lack of authentic materials and facilities – have been reported as factors hindering CLT implementations in many contexts. Language teaching and learning materials and facilities are crucial elements that have been found to affect communicative language teaching implementation. However, the issue of how those material elements can affect CLT implementation has rarely been the focus of research in CLT implementation studies. In this paper, the researcher examines the effect of language teaching and learning materials on teachers’ ability to teach communicatively. Thus, informed by sociomateriality this paper attends to a gap in the literature about how material elements of the curriculum hinder the implementation of the communicative language teaching approach in the Saudi context. The data examined in this study were collected through classroom observations and semi-structured interviews. The analysis of the findings indicated that material elements in the curriculum exerted agency and power, hindering teachers’ ability to teach communicatively and learners’ ability to improve their learning experiences. The report concludes with practical implications related to the complexity of curriculum development and implementation and the emergent nature of such processes as webs of entangled human/nonhuman relations that give rise to education. [ABSTRACT FROM AUTHOR]

Aiming at the limited learning ability of a single model, the objective of this paper is to investigate situational awareness of the network security which is established on the fusion model. In this paper, a convolutional neural network (CNN) and long short-term memory (LSTM)-based model for situational assessment of the network security condition are provided. According to different fusion methods, the parallel and serial CNN-LSTM fusion models were constructed to evaluate the UNSW-NB15 data set, and both the situation values and levels were obtained. The investigational outcomes illustrate that the evaluation accuracy of the two models can reach up to 85.19% and 92.59%, respectively. A situation prediction model called IPSO-ABiLSTM is suggested and is based on improved particle swarm optimization (IPSO) and attention fusion bidirectional long short-term memory (ABiLSTM). The IPSO has the characteristics of faster convergence speed to optimize the ABiLSTM network parameters and obtain the optimal parameters for situation prediction. The investigational outcomes illustrate that the suggested IPSO-ABiLSTM model has a fitting degree of up to 0.9922, which can effectively achieve the situation prediction in the network security. [ABSTRACT FROM AUTHOR]

In order to solve the problem of the slow transmission rate of high-frequency information in smart grid and improve the efficiency of information transmission, a research method of high-frequency information transmission in smart grids based on the CNN-LSTM model is proposed. It effectively combines the superiority of the CNN algorithm for high-frequency information feature extraction and the learning ability of the LSTM algorithm for global features of high-frequency information. Meanwhile, the client buffer is divided by the VLAN area division method, which avoids the buffer being too large due to line congestion. The intelligent control module is adopted to change the traditional control concept. In addition, the neural network optimization control module is used for intelligent control, which ensures the feedback speed of the control terminal and avoids the problem of increasing the buffer area caused by the feedback time difference. The experimental results show that via the method in this paper, the total efficiency of single-channel transmission reaches 96% and the transmission rate reaches 46 bit/s; the total efficiency of multiplex transmission is 89% and the transmission rate reaches 75 bit/s. It is verified that the method proposed in this paper has a fast transmission rate and high efficiency. [ABSTRACT FROM AUTHOR]

This paper outlines the process and results of an authentic assessment of student work using a revised version of the AAC&U's Information Literacy VALUE rubric. This rigorous assessment, which included the scoring of nearly 900 student papers from four different stages across the undergraduate curriculum, revealed much about the process of authentic assessment of student learning, the struggles and competencies of our students, and a clear path forward for improving practice. It also gave us a broad view of student learning, allowing us to immerse ourselves in student work and providing a stronger narrative to share with stakeholders. [ABSTRACT FROM AUTHOR]

At present, substation operation ticket system is developed based on an expert system, which has some problems such as knowledge base redundancy, intelligence deficiency and automatic learning ability. To solve this problem, this paper proposes an operation sequence reasoning model based on the knowledge base of the Neo4j knowledge graph and DuelingDQN (Dueling Deep Q Network) algorithm. Firstly, the diagram structure model of substation main wiring was established using the Neo4j knowledge graph. Based on the diagram structure model, the operable equipment set of the operation task was searched to form the task space, action space and action selection model of DuelingDQN. The reward and punishment function was designed based on the "five defense" rules and the state change of equipment. Make DuelingDQN model and Neo4j model interact in real time, and automatically learn the operation sequence. The test results show that the method proposed in this paper can automatically deduce the correct operation steps under different wiring modes and realize the transfer within the station, which is of great significance to the intellectualization of the operation ticket system. [ABSTRACT FROM AUTHOR]

Speech emotion recognition (SER) technology is significant for human–computer interaction, and this paper studies the features and modeling of SER. Mel-spectrogram is introduced and utilized as the feature of speech, and the theory and extraction process of mel-spectrogram are presented in detail. A deep residual shrinkage network with bi-directional gated recurrent unit (DRSN-BiGRU) is proposed in this paper, which is composed of convolution network, residual shrinkage network, bi-directional recurrent unit, and fully-connected network. Through the self-attention mechanism, DRSN-BiGRU can automatically ignore noisy information and improve the ability to learn effective features. Network optimization, verification experiment is carried out in three emotional datasets (CASIA, IEMOCAP, and MELD), and the accuracy of DRSN-BiGRU are 86.03%, 86.07%, and 70.57%, respectively. The results are also analyzed and compared with DCNN-LSTM, CNN-BiLSTM, and DRN-BiGRU, which verified the superior performance of DRSN-BiGRU. [ABSTRACT FROM AUTHOR]

Classroom-based assessment validation has received considerable critical attention and many conceptualisations have emerged. While these conceptualisations are helpful in advancing our assessment knowledge, there is a need for a more learning-oriented teacher assessment practice validation. This paper builds on previous validation theories and approaches to redefine the validity of classroom-based assessment in terms of practical, useful, and trustworthy interpretation and uses of classroom assessment in enhancing learning and teaching. Further, the paper sets relevant inferences and prioritises teachers as sources of evidence in assessment evaluation based on pragmatic principles and Vygotsky's sociocultural theory. This explication is valuable in exploring a learning-centred validation approach for evaluating classroom assessment. The paper suggests practical principles for evaluating learning-oriented, teacher-based assessment. Lastly, the paper concludes by articulating implication of the approach in any contemporary assessment system. [ABSTRACT FROM AUTHOR]

The primary sight control system of a tank gunner has image stabilization as one of its primary functions. The image stabilization deviation in the aiming line is a key indicator for evaluating the operational status of Gunner's Primary Sight control system. Employing image detection technology to measure image stabilization deviation enhances the effectiveness and accuracy of the detection process and allows for the evaluation of image stabilization functionality. Hence, this paper proposes an image detection method aimed at the Gunner's Primary Sight control system of a specific tank which utilizes an enhanced You Only Look Once version 5 (YOLOv5) sight-stabilizing deviation algorithm. At first, a dynamic weight factor is integrated into SCYLLA-IoU (SIOU), creating δ-SIOU, which replaces Complete IoU (CIoU) as the loss function of YOLOv5. After that, the Spatial Pyramid Pool module of YOLOv5 was enhanced to improve the multi-scale feature fusion ability of the model, thereby elevating the performance of the detection model. Finally, the C3CA module was created by embedding the Coordinate Attention (CA) attention mechanism into the CSK-MOD-C3 (C3) module. The Bi-directional Feature Pyramid (BiFPN) network structure was also incorporated into the Neck network of YOLOv5 to improve the model's ability to learn target location information and image detection accuracy. Based on data collected by a mirror control test platform, experimental results indicate an improvement in the detection accuracy of the model by 2.1%. These findings offer valuable insights into measuring the image stabilization deviation in the aiming line and facilitating the development of the parameter measurement system for Gunner's Primary Sight control system. [ABSTRACT FROM AUTHOR]