Search Results (848)

This paper introduces DK-PRACTICE, an intelligent educational platform that combines Knowledge Tracing (KT) and recommendation systems to support personalized learning in higher education. The platform utilizes a novel Paired Bipolar Bag-of-Words (PB-BoW) model to assess students’ knowledge states, forecast performance, and offer targeted recommendations. To test its effectiveness in real-world settings, DK-PRACTICE was implemented in the “Computer Organization and Architecture” undergraduate course, involving 138 students in Pre-Test and 106 in Post-Test. Empirical analysis of benchmark datasets and a newly created course dataset showed that the PB-BoW model outperformed an RNN-based KT model in predictive accuracy. Student surveys indicated high levels of satisfaction with usability, relevance of recommendations, and overall learning support, with most participants expressing willingness to reuse the platform in other courses. These results demonstrate the potential of DK-PRACTICE as a scalable and adaptable tool for improving personalized learning and bridging the gap between AI-driven KT research and classroom implementation. Full article

Contactless text entry is increasingly important in immersive and constrained computing environments, yet most vision-based approaches rely on character-level recognition or key localization, which are fragile under monocular sensing. This study investigates the feasibility of recognizing natural QWERTY typing motions directly at the word level using only a single RGB camera, under a fixed single-user and single-camera configuration. We propose a word-level contactless typing framework that models each word as a distinctive spatiotemporal finger motion pattern derived from hand joint trajectories. Typing motions are temporally segmented, and direction-aware finger displacements are accumulated to construct compact motion representations that are relatively insensitive to absolute hand position and typing duration within the evaluated setup. Each word is represented by multiple motion prototypes that are incrementally updated through online learning with a trial-delayed adaptation protocol. Experiments with vocabularies of up to 200 words show that the proposed approach progressively learns and recalls word-level motion patterns through repeated interaction, achieving stable recognition performance within the tested configuration at realistic typing speeds. Additional evaluations demonstrate that learned motion representations can transfer from physical keyboards to flat-surface typing within the same experimental setting, even when tactile feedback and visual layout cues are reduced. These results support the feasibility of reframing contactless typing as a word-level motion recall problem, and suggest its potential role as a complementary component to character-centric camera-based input methods under constrained monocular sensing. Full article

Limited study time constrains university EFL vocabulary learning, so efficiency should be evaluated alongside accuracy. A web-based multimodal environment was developed that uses a large language model for contextualized drills and tutoring, text-to-speech for pronunciation and listening rehearsal, and an interactive 3D mastery view for self-regulated tracking. Vocabulary knowledge is modeled as a discrete mastery state (m = 0–5), updated after each attempt, and an adaptive scheduler allocates practice across mastery strata. Learning ROI is defined as newly mastered words per hour and computed from logged study time and mastery transitions. In a three-month deployment (N = 171), learners achieved a mean ROI of 9.8 words/hour, about 60% higher than conventional estimates (5–6 words/hour); high-adherence users reached 17–21 words/hour. End-of-trial surprise review results indicated retention above 85%. For CET-4, the platform cohort obtained the highest mean score (457.66) and pass rate (74.24%) compared with Baicizhan (442.22; 64.81%) and traditional instruction (428.60; 53.70%). The results provide quantitative support for the hypothesis that multimodal personalization improves time-based vocabulary gains and their durability. Full article

This study investigates the evolution of public sentiment and discourse surrounding artificial intelligence through a comprehensive multi-method analysis of 28,819 Reddit comments spanning March 2015 to May 2024. Addressing three research questions—(1) what dominant topics characterize AI discourse, (2) how has sentiment changed over time, particularly following ChatGPT 5.2’s release, and (3) what linguistic patterns distinguish positive from negative discourse—we employ 28 distinct analytical techniques to provide validated insights into public AI perception. Methodologically, the study integrates VADER sentiment analysis, Linguistic Inquiry and Word Count (LIWC) analysis with regression validation, dual topic modeling using Latent Dirichlet Allocation and Non-negative Matrix Factorization for cross-validation, four-dimensional tone analysis, named entity recognition, emotion detection, and advanced NLP techniques including sarcasm detection, stance classification, and toxicity analysis. A key methodological contribution is the validation of LIWC categories through linear regression (R² = 0.049, p < 0.001) and logistic regression (61% accuracy), moving beyond the descriptive statistics typical of prior linguistic analyses. Results reveal a pronounced decline in positive sentiment from +0.320 in 2015 to +0.053 in 2024. Contrary to expectations, sentiment decreased following ChatGPT’s November 2022 release, with negative comments increasing from 31.9% to 35.1%—suggesting that direct exposure to powerful AI capabilities intensifies rather than alleviates public concerns. LIWC regression analysis identified negative emotion words (β = −0.083) and positive emotion words (β = +0.063) as the strongest sentiment predictors, confirming that affective rather than technical engagement drives public AI attitudes. Topic modeling revealed nine coherent themes, with facial recognition, algorithmic bias, AI ethics, and social media misinformation emerging as dominant concerns across both LDA and NMF analyses. Network analysis identified regulation as a central hub (degree centrality = 0.929) connecting all major AI concerns, indicating strong public appetite for governance frameworks. These findings contribute to theoretical understandings of technology risk perception, provide practical guidance for AI developers and policymakers, and demonstrate validated computational methods for tracking public opinion toward emerging technologies. Full article

With the rapid development of intelligent computing and artificial intelligence, there is an increasing demand for efficient, interpretable, and interactive frameworks for fine-grained text analysis. In the field of argument mining, existing approaches are often constrained by sentence-level processing, limited exploitation of key linguistic markers, and a lack of human–AI collaborative mechanisms, which restrict both recognition accuracy and computational efficiency. To address these challenges, this paper proposes KFF-Transformer, a computing-oriented human–AI collaborative framework for fine-grained argument element identification based on Toulmin’s model. The framework first employs an automatic key marker mining algorithm to expand a seed set of expert-labeled linguistic cues, significantly enhancing coverage and diversity. It then employs a lightweight deep learning architecture that combines BERT for contextual token encoding with a BiLSTM network enhanced by an attention mechanism to perform word-level classification of the six Toulmin elements. This approach leverages enriched key markers as critical features, enhancing both accuracy and interpretability. It should be noted that while our framework leverages BERT—a Transformer-based encoder—for contextual representation, the core sequence labeling module is based on BiLSTM and does not implement a standard Transformer block. Furthermore, a human-in-the-loop interaction mechanism is embedded to support real-time user correction and adaptive system refinement, improving robustness and practical usability. Experiments conducted on a dataset of 180 English argumentative essays demonstrate that KFF-Transformer identifies key markers in 1145 sentences and achieves an accuracy of 72.2% and an F1-score of 66.7%, outperforming a strong baseline by 3.7% and 2.8%, respectively. Moreover, the framework reduces processing time by 18.9% on CPU and achieves near-real-time performance of approximately 3.3 s on GPU. These results validate that KFF-Transformer effectively integrates linguistically grounded reasoning, efficient deep learning, and interactive design, providing a scalable and trustworthy solution for intelligent argument analysis in real-world educational applications. Full article

High degree conductive disorders (CD) requiring permanent pacemaker implantation (PPI) have modestly decreased over time and remain the main complications of TAVR. Furthermore, management strategies for CD occurring after TAVR remain controversial. We proposed a review evaluating mechanisms and risk of CD after TAVR, focusing on the role of ECG evaluation but also on the importance of anatomic parameters analyzed in multi-slice computed tomography (MSCT), as well as regarding procedural aspects. Considering the lack of clear recommendations for the evaluation of risk of CD and indications of PPI, this review tries to summarize strategies to anticipate and detect the risk of high degree CD, to decrease incidence of CD and to optimize PPI indications. Perspectives regarding ambulatory monitoring, use of machine learning and new pacing techniques are proposed. This review was narrative and included selection of literature using key words including: conductive disorders, TAVR and pacemaker implantation. Full article

Prader–Willi (PWS) and Angelman (AS) syndromes were the first examples in humans with errors in genomic imprinting, usually from de novo 15q11-q13 deletions of different parent origin (paternal in PWS and maternal in AS). Dozens of genes and transcripts are found in the 15q11-q13 region, and may play a role in PWS, specifically paternally expressed SNURF-SNRPN and MAGEL2 genes, while AS is due to the maternally expressed UBE3A gene. These three causative genes, including their encoding proteins, were targeted. This review article summarizes and illustrates the current understanding and cause of both PWS and AS using strategies to include the literature sources of key words and searchable web-based programs with databases for integrated gene and protein interactions, biological processes, and molecular mechanisms available for the two imprinting disorders. The SNURF-SNRPN gene is key in developing complex spliceosomal snRNP assemblies required for mRNA processing, cellular events, splicing, and binding required for detailed protein production and variation, neurodevelopment, immunodeficiency, and cell migration. The MAGEL2 gene is involved with the regulation of retrograde transport and promotion of endosomal assembly, oxytocin and reproduction, as well as circadian rhythm, transcriptional activity control, and appetite. The UBE3A gene encodes a key enzyme for the ubiquitin protein degradation system, apoptosis, tumor suppression, cell adhesion, and targeting proteins for degradation, autophagy, signaling pathways, and circadian rhythm. PWS is characterized early with infantile hypotonia, a poor suck, and failure to thrive with hypogenitalism/hypogonadism. Later, growth and other hormone deficiencies, developmental delays, and behavioral problems are noted with hyperphagia and morbid obesity, if not externally controlled. AS is characterized by seizures, lack of speech, severe learning disabilities, inappropriate laughter, and ataxia. This review captures the clinical presentation, natural history, causes with genetics, mechanisms, and description of established laboratory testing for genetic confirmation of each disorder. Three separate searchable web-based programs and databases that included information from the updated literature and other sources were used to identify and examine integrated genetic findings with predicted gene and protein interactions, molecular mechanisms and functions, biological processes, pathways, and gene-disease associations for candidate or causative genes per disorder. The natural history, review of pathophysiology, clinical presentation, genetics, and genetic-phenotypic findings were described along with computational biology, molecular mechanisms, genetic testing approaches, and status for each disorder, management and treatment options, clinical trial experiences, and future strategies. Conclusions and limitations were discussed to improve understanding, clinical care, genetics, diagnostic protocols, therapeutic agents, and genetic counseling for those with these genomic imprinting disorders. Full article

The emergence of low-cost edge devices has enabled the integration of automatic speech recognition (ASR) into IoT environments, creating new opportunities for real-time language assessment. However, achieving reliable performance on resource-constrained hardware remains a significant challenge, especially on the Artificial Internet of Things (AIoT). This study presents an AIoT-based framework for automated English-speaking assessment that integrates architecture and system design, ASR benchmarking, and reliability analysis on edge devices. The proposed AIoT-oriented architecture incorporates a lightweight scoring framework capable of analyzing pronunciation, fluency, prosody, and CEFR-aligned speaking proficiency within an automated assessment system. Seven open-source ASR models—four Whisper variants (tiny, base, small, and medium) and three Vosk models—were systematically benchmarked in terms of recognition accuracy, inference latency, and computational efficiency. Experimental results indicate that Whisper-medium deployed on the Raspberry Pi 5 achieved the strongest overall performance, reducing inference latency by 42–48% compared with the Raspberry Pi 4 and attaining the lowest Word Error Rate (WER) of 6.8%. In contrast, smaller models such as Whisper-tiny, with a WER of 26.7%, exhibited two- to threefold higher scoring variability, demonstrating how recognition errors propagate into automated assessment reliability. System-level testing revealed that the Raspberry Pi 5 can sustain near real-time processing with approximately 58% CPU utilization and around 1.2 GB of memory, whereas the Raspberry Pi 4 frequently approaches practical operational limits under comparable workloads. Validation using real learner speech data (approximately 100 sessions) confirmed that the proposed system delivers accurate, portable, and privacy-preserving speaking assessment using low-power edge hardware. Overall, this work introduces a practical AIoT-based assessment framework, provides a comprehensive benchmark of open-source ASR models on edge platforms, and offers empirical insights into the trade-offs among recognition accuracy, inference latency, and scoring stability in edge-based ASR deployments. Full article

This study examines how different programming paradigms are associated with learning experiences and cognitive challenges as encountered by non-computer science novice learners. Using a case-study approach situated within specific instructional contexts, we integrate survey data from undergraduate students with large-scale public question-and-answer data from Stack Overflow to explore paradigm-related difficulty patterns. Four instructional contexts—C, Java, Python, and Prolog—were examined as pedagogical instantiations of imperative, object-oriented, functional-style, and logic-based paradigms using text clustering, word embedding models, and interaction-informed complexity metrics. The analysis identifies distinct patterns of learning challenges across paradigmatic contexts, including difficulties related to low-level memory management in C-based instruction, abstraction and design reasoning in object-oriented contexts, inference-driven reasoning in Prolog-based instruction, and recursion-related challenges in functional-style programming tasks. Survey responses exhibit tendencies that are broadly consistent with patterns observed in public Q&A data, supporting the use of large-scale community-generated content as a complementary source for learner-centered educational analysis. Based on these findings, the study discusses paradigm-aware instructional implications for programming education tailored to non-major learners within comparable educational settings. The results provide empirical support for differentiated instructional approaches and offer evidence-informed insights relevant to curriculum-oriented teaching and future research on adaptive learning systems. Full article

In addition to standardized lateral cephalometric radiographs, comprehensive assessment using dental cone-beam computed tomography (CBCT) and CT has become commonplace in the diagnosis and treatment of jaw deformities. Simulation based on cephalometric and CT data is particularly useful in the management of jaw deformities, both for evaluation and prognostic prediction. As such imaging examinations cover a wide anatomical region, it is not uncommon for various incidental pathologies to be discovered. This review emphasizes the necessity of evaluating the entire imaged area in addition to the chief complaint. Furthermore, it outlines the essential anatomical structures that should be assessed during diagnostic imaging performed prior to representative surgical procedures for jaw deformities (e.g., sagittal split ramus osteotomy and Le Fort I osteotomy). This review paper is descriptive in nature, incorporating our facility’s empirical aspects, and presents representative cases in a narrative format; it is not a systematic review. In other word, as the evidence-based literature does not cover all aspects of pretreatment evaluation, these criteria are based on the past experience of the authors. Full article

Accurate measurement of semantic similarity between geographic terms is a fundamental challenge in geographic information science, directly influencing tasks such as knowledge retrieval, ontology-based reasoning, and semantic search in geographic information systems (GIS). Traditional ontology-based approaches primarily rely on a narrow set of features (e.g., semantic distance or depth), which inadequately capture the multidimensional and context-dependent nature of geographic semantics. To address this limitation, this study proposes an ontology-driven semantic similarity model that integrates a backpropagation (BP) neural network with multiple ontological features—hierarchical depth, node distance, concept density, and relational overlap. The BP network serves as a nonlinear optimization mechanism that adaptively learns the contributions of each feature through cross-validation, balancing interpretability and precision. Experimental evaluations on the Geo-Terminology Relatedness Dataset (GTRD) demonstrate that the proposed model outperforms traditional baselines, including the Thesaurus–Lexical Relatedness Measure (TLRM), Word2Vec, and SBERT (Sentence-BERT), with Spearman correlation improvements of 4.2%, 74.8% and 80.1%, respectively. Additionally, comparisons with Linear Regression and Random Forest models, as well as bootstrap analysis and error analysis, confirm the robustness and generalization of the BP-based approach. These results confirm that coupling structured ontological knowledge with data-driven learning enhances robustness and generalization in semantic similarity computation, providing a unified framework for geographic knowledge reasoning, terminology harmonization, and ontology-based information retrieval. Full article

Rainstorm floods constitute one of the major natural hazards threatening the safe and stable operation of power grid facilities. Constructing a rapid and accurate prediction model is of great significance in order to enhance the disaster prevention capacity of the power grid. This study proposes a rapid prediction model for urban rainstorm flood targeting power grid facilities based on deep learning. The model utilizes computational results of high-precision mechanism models as data-driven input and adopts a dual-branch prediction architecture of space and time: the spatial prediction module employs a multi-layer perceptron (MLP), and the temporal prediction module integrates convolutional neural network (CNN), long short-term memory network (LSTM), and attention mechanism (ATT). The constructed water dynamics model of the right bank of Liangshui River in Fengtai District of Beijing has been verified to be reliable in the simulation of the July 2023 (“23·7”) extreme rainstorm event in Beijing (the July 2023 event), which provides high-quality training and validation data for the deep learning-based surrogate model (SM model). Compared with traditional high-precision mechanism models, the SM model shows distinctive advantages: the R² value of the overall inundation water depth prediction of the spatial prediction module reaches 0.9939, and the average absolute error of water depth is 0.013 m; the R² values of temporal water depth processes prediction at all substations made by the temporal prediction module are all higher than 0.92. Only by inputting rainfall data can the water depth at power grid facilities be output within seconds, providing an effective tool for rapid assessment of flood risks to power grid facilities. In a word, the main contribution of this study lies in the proposal of the SM model driven by the high-precision mechanism model. This model, through a dual-branch module in both space and time, has achieved second-level high-precision prediction from rainfall input to water depth output in scenarios where the power grid is at risk of flooding for the first time, providing an expandable method for real-time simulation of complex physical processes. Full article

Marine protected areas (MPAs) are crucial for marine ecosystems, but they often pose significant challenges for the local fishing communities that rely on these ecosystems for their livelihoods. Identifying approaches that maintain ecological integrity while improving community livelihoods and well-being has become a central issue in marine sustainability. This study investigates the adaptive livelihood strategies of a community on Qi’ao Island, located in China’s Pearl River Estuary, which has gradually transitioned from traditional fisheries to tourism-induced livelihoods. Based on Actor–network theory (ANT), we developed a multi-level approach to examine interactions between human and non-human actors, institutions, and policies during livelihood adaptation. A mixed-methods approach was adopted, combining semi-structured interviews (n = 47), extended field observation, and policy analysis. Computational text analysis techniques included word frequency analysis, sentiment analysis, and co-occurrence network analysis using Python 3.8. These were integrated with thematic analysis and coding conducted in NVivo 15. This study demonstrates that the sustainability of tourism-based livelihood adaptation depends on equitable benefit sharing, flexible governance, and sustained community participation. Theoretically, this research extends livelihood studies by demonstrating how ANT captures the relational and processual dynamics of adaptation. Practically, it offers policy-relevant insights for designing adaptive and participatory governance strategies that reconcile conservation objectives with community well-being in MPAs. Full article

With the advancement of artificial intelligence and educational informatization, there is a growing demand for intelligent teaching assistance systems in universities. Focusing on the university “Algorithms” course in the computer science department, this study develops a multi-terminal collaborative knowledge service system, Course-Oriented Knowledge Service–Based AI Teaching Assistant System (CKS-AITAS), which consists of a PC terminal and a mobile terminal, where the PC terminal integrates functions including knowledge graph, semantic retrieval, intelligent question-answering, and knowledge recommendation. While the mobile terminal enables classroom check-in and teaching interaction, thus forming a closed-loop platform for teaching organization, resource acquisition, and knowledge inquiry. For the document retrieval module, paragraph-level semantic modeling of textbook content is conducted using Word2Vec, combined with approximate nearest neighbor indexing, and this module achieves an MRR@10 of 0.641 and an average query time of 0.128 s, balancing accuracy and efficiency; the intelligent question-answering module, based on a self-built course FAQ dataset, is trained via the BERT model to enable question matching and answer retrieval, achieving an accuracy rate of 86.3% and an average response time of 0.31 s. Overall, CKS-AITAS meets the core teaching needs of the course, provides an AI-empowered solution for university teaching, and boasts promising application prospects in facilitating education sustainability. Full article

Natural language processing is the technology used to interact with computers using human languages. An overlapping technology is Information Retrieval (IR), in which a user searches for the demanded or required documents from among a number of documents that are already stored. The required document is retrieved according to the relevance of the query of the user, and the results are presented in descending order. Many of the languages have their own IR systems, whereas a dedicated IR system for Sindhi still needs attention. Various approaches to effective information retrieval have been proposed. As Sindhi is an old language with a rich history and literature, it needs IR. For the development of Sindhi IR, a document database is required so that the documents can be retrieved accordingly. Many Sindhi documents were identified and collected from various sources, such as books, journal, magazines, and newspapers. These documents were identified as having potential for use in indexing and other forms of processing. Probabilistic modeling and pattern discovery were used to find patterns and for effective retrieval and relevancy. The results for Sindhi Information Retrieval systems are promising and presented more than 90% relevancy. The time elapsed was recorded as ranging from 0.2 to 4.8 s for a single word and 4.6 s with a Sindhi sentence, with the same starting time of 0.2 s. The IR system for Sindhi can be fine-tuned and utilized for other languages with the same characteristics, which adopt Arabic script. Full article