Big Data Cogn. Comput., Volume 9, Issue 12 (December 2025) – 28 articles

Sentiment classification is a key technique for analyzing the emotional tendency of user reviews and is of great significance to movie recommendation systems. However, existing methods often face challenges in practical applications due to complex model structures, low computational efficiency, or difficulties in balancing local details with global contextual features. To address these issues, this paper proposes a Multi-Scale Feature Fusion Linear Attention model (MSFFLA). The model consists of three core modules: the BERT Encoder module for extracting basic semantic features; the Parallel Multi-scale Feature Extraction module (PMFE), which employs multi-branch dilated convolutions to accurately capture local fine-grained features; and the Global Multi-scale Linear Feature Extraction module (MGLFE), which introduces a Multi-Scale Linear Attention mechanism (MSLA) to efficiently model global contextual dependencies with approximately linear computational complexity. Extensive experiments were conducted on three public datasets: SST-2, Amazon Reviews, and MR. The results show that compared to the state-of-the-art BERT-CondConv model, our model achieves improvements in accuracy and F1-Score by 1.8% and 0.4%, respectively, on the SST-2 dataset, and by 1.5% and 0.3% on the Amazon Reviews dataset. This study not only validates the effectiveness of the proposed model but also provides an efficient and lightweight solution for sentiment classification tasks in movie recommendation systems, demonstrating promising practical application prospects. Full article

Tornado occurrence and detection are well established in mesoscale meteorology, yet the application of deep learning (DL) to radar-based tornado detection remains nascent and under-validated. This study benchmarks DL approaches on TorNet, a curated dataset of full-resolution, polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) radar volumes. We evaluate three canonical architectures (e.g., CNN, VGG19, and Xception) under five optimizers and assess the effect of replacing conventional MLP heads with Kolmogorov–Arnold Network (KAN) layers. To address severe class imbalance and label noise, we implement radar-aware preprocessing and augmentation, temporal splits, and recall-sensitive training. Models are compared using accuracy, precision, recall, and ROC-AUC. Results show that KAN-augmented variants generally converge faster and deliver higher rare-event sensitivity and discriminative power than their baselines, with Adam and RMSprop providing the most stable training and Lion showing architecture-dependent gains. We contribute (i) a reproducible baseline suite for TorNet, (ii) evidence on the conditions under which KAN integration improves tornado detection, and (iii) practical guidance on optimizer–architecture choices for rare-event forecasting with weather radar. Full article

In plateau and high-altitude areas, freeze-thaw cycles often alter the uniaxial compressive strength (UCS) of rock, thereby impacting the stability of geotechnical engineering. Acquiring rock samples in these areas for UCS testing is often time-consuming and labor-intensive. This study developed a hybrid model based on the XGBoost algorithm to predict the UCS of rock under freeze-thaw conditions. First, a database was created containing longitudinal wave velocity (Vp), rock porosity (P), rock density (D), freezing temperature (T), number of freeze-thaw cycles (FTCs), and UCS. Four swarm intelligence optimization algorithms—artificial bee colony, Newton–Raphson, particle swarm optimization, and dung beetle optimization—were used to optimize the maximum iterations, depth, and learning rate of the XGBoost model, thereby enhancing model accuracy and developing four hybrid models. The four hybrid models were compared to a single XGBoost model and a random forest (RF) model to evaluate overall performance, and the optimal model was selected. The results demonstrate that all hybrid models outperform the single models. The XGBoost model optimized by the sparrow algorithm (R² = 0.94, RMSE = 10.10, MAPE = 0.095, MAE = 7.22) performed best in predicting UCS. SHapley Additive exPlanations (SHAP) were used to assess the marginal contribution of each input variable to the UCS prediction of freeze-thawed rock. This study is expected to provide a reference for predicting the UCS of freeze-thawed rock using machine learning. Full article

Point-of-Interest (POI) recommendation predicts users’ future check-ins based on their historical trajectories and plays a key role in location-based services (LBS). Traditional approaches such as collaborative filtering and matrix factorization model user–POI interaction matrices fail to fully leverage spatio-temporal information and semantic attributes, leading to weak performance on sparse and long-tail POIs. Recently, Graph Neural Networks (GNNs) have been applied by constructing heterogeneous user–POI graphs to capture high-order relations. However, they still struggle to effectively integrate spatio-temporal and semantic information and enhance the discriminative power of learned representations. To overcome these issues, we propose Spatio-Temporal and Semantic Dual-Channel Contrastive Alignment for POI Recommendation (S²DCRec), a novel framework integrating spatio-temporal and semantic information. It employs hierarchical relational encoding to capture fine-grained behavioral patterns and high-level semantic dependencies. The model jointly captures user–POI interactions, temporal dynamics, and semantic correlations in a unified framework. Furthermore, our alignment strategy ensures micro-level collaborative and spatio-temporal consistency and macro-level semantic coherence, enabling fine-grained embedding fusion and interpretable contrastive learning. Experiments on real-world datasets, Foursquare NYC, and Yelp, show that S²DCRec outperforms all baselines, improving F1 scores by 4.04% and 3.01%, respectively. These results demonstrate the effectiveness of the dual-channel design in capturing both sequential and semantic dependencies for accurate POI recommendation. Full article

Upstream processes strongly influence downstream analysis in sequential data-processing workflows, particularly in machine learning, where data quality directly affects model performance. Conventional statistical imputations often fail to capture nonlinear dependencies, while deep learning approaches typically lack uncertainty quantification. We introduce a hybrid imputation model that integrates a deep learning autoencoder with Convolutional Neural Network (CNN) layers and a Transformer-based contextual modeling architecture to address systematic variation across heterogeneous data sources. Performing multiple imputations in the autoencoder–transformer latent space and averaging representations provides implicit batch correction that suppresses context-specific remains without explicit batch identifiers. We performed experiments on datasets in which 10% of missing data was artificially introduced by completely random missing data (MCAR) and non-random missing data (MNAR) mechanisms. They demonstrated practical performance, jointly ranking first among the imputation methods evaluated. This imputation technique reduced the root mean square error (RMSE) by 50% compared to denoising autoencoders (DAE) and by 46% compared to iterative imputation (MICE). Performance was comparable for adversarial models (GAIN) and attention-based models (MIDA), and both provided interpretable uncertainty estimates (CV = 0.08–0.15). Validation on datasets from multiple sources confirmed the robustness of the technique: notably, on a forensic dataset from multiple laboratories, our imputation technique achieved a practical improvement over GAIN (0.146 vs. 0.189 RMSE), highlighting its effectiveness in mitigating batch effects. Full article

Background: Retrieval-augmented generation (RAG) aims to reduce hallucinations and outdated knowledge by grounding LLM outputs in retrieved evidence, but empirical results are scattered across tasks, systems, and metrics, limiting cumulative insight. Objective: We aimed to synthesise empirical evidence on RAG effectiveness versus parametric-only baselines, map datasets/architectures/evaluation practices, and surface limitations and research gaps. Methods: This systematic review was conducted and reported in accordance with PRISMA 2020. We searched the ACM Digital Library, IEEE Xplore, Scopus, ScienceDirect, and DBLP; all sources were last searched on 13 May 2025. This included studies from January 2020–May 2025 that addressed RAG or similar retrieval-supported systems producing text output, met citation thresholds (≥15 for 2025; ≥30 for 2024 or earlier), and offered original contributions; excluded non-English items, irrelevant works, duplicates, and records without accessible full text. Bias was appraised with a brief checklist; screening used one reviewer with an independent check and discussion. LLM suggestions were advisory only; 2025 citation thresholds were adjusted to limit citation-lag. We used a descriptive approach to synthesise the results, organising studies by themes aligned to RQ1–RQ4 and reporting summary counts/frequencies; no meta-analysis was undertaken due to heterogeneity of designs and metrics. Results: We included 128 studies spanning knowledge-intensive tasks (35/128; 27.3%), open-domain QA (20/128; 15.6%), software engineering (13/128; 10.2%), and medical domains (11/128; 8.6%). Methods have shifted from DPR + seq2seq baselines to modular, policy-driven RAG with hybrid/structure-aware retrieval, uncertainty-triggered loops, memory, and emerging multimodality. Evaluation remains overlap-heavy (EM/$F_1$), with increasing use of retrieval diagnostics (e.g., Recall@k, MRR@k), human judgements, and LLM-as-judge protocols. Efficiency and security (poisoning, leakage, jailbreaks) are growing concerns. Discussion: Evidence supports a shift to modular, policy-driven RAG, combining hybrid/structure-aware retrieval, uncertainty-aware control, memory, and multimodality, to improve grounding and efficiency. To advance from prototypes to dependable systems, we recommend: (i) holistic benchmarks pairing quality with cost/latency and safety, (ii) budget-aware retrieval/tool-use policies, and (iii) provenance-aware pipelines that expose uncertainty and deliver traceable evidence. We note the evidence base may be affected by citation-lag from the inclusion thresholds and by English-only, five-library coverage. Funding: Advanced Research and Engineering Centre. Registration: Not registered. Full article

The reliable and early detection of promising research directions is of great practical importance, especially in cases of limited resources. It enables researchers, funding experts, and science authorities to focus their efforts effectively. Although citation analysis has been commonly considered the primary tool to detect directions for a long time, it lacks responsiveness, as it requires time for citations to emerge. In this paper, we propose a conceptual framework that detects new research directions with a contextual Top2Vec model, collects and analyzes reviews for those directions via Transformer-based classifiers, ranks them, and generates short summaries for the highest-scoring ones with a BART model. Averaging review scores for a whole topic helps mitigate the review bias problem. Experiments on past ICLR open reviews show that the highly ranked directions detected are significantly better cited; additionally, in most cases, they exhibit better publication dynamics. Full article

In wireless communication, information security, and anti-interference technology, modulation recognition of frequency-hopping signals has always been a key technique. Its widespread application in satellite communications, military communications, and drone communications holds broad prospects. Traditional modulation recognition techniques often rely on expert experience to construct likelihood functions or manually extract relevant features, involving cumbersome steps and low efficiency. In contrast, deep learning-based modulation recognition replaces manual feature extraction with an end-to-end feature extraction and recognition integrated architecture, where neural networks automatically extract signal features, significantly enhancing recognition efficiency. Current deep learning-based modulation recognition research primarily focuses on conventional fixed-frequency signals, leaving gaps in intelligent modulation recognition for frequency-hopping signals. This paper aims to summarise the current research progress in intelligent modulation recognition for frequency-hopping signals. It categorises intelligent modulation recognition for frequency-hopping signals into two mainstream approaches, analyses them in conjunction with the development of intelligent modulation recognition, and explores the close relationship between intelligent modulation recognition and parameter estimation for frequency-hopping signals. Finally, the paper summarises and outlines future research directions and challenges in the field of intelligent modulation recognition for frequency-hopping signals. Full article

Stock trading faces significant challenges due to market volatility and the complexity of integrating diverse data sources, such as financial texts and numerical market data. This paper proposes an innovative automated trading system that integrates advanced natural language processing (NLP) and deep reinforcement learning (DRL) to address these challenges. The system combines two novel components: PrimoGPT, a Transformer-based NLP model fine-tuned on financial texts using instruction-based datasets to generate actionable features like sentiment and trend direction, and PrimoRL, a DRL model that expands its state space with these NLP-derived features for enhanced decision-making precision compared to traditional DRL models like FinRL. An experimental evaluation over seven months of leading technology stocks reveals cumulative returns of up to 58.47% for individual stocks and 27.14% for a diversified portfolio, with a Sharpe ratio of 1.70, outperforming traditional and advanced benchmarks. This work advances AI-driven quantitative finance by offering a scalable framework that bridges qualitative analysis and strategic action, thereby fostering smarter and more equitable participation in financial markets. Full article

Shipping containers are vital to the transportation industry due to their cost-effectiveness and compatibility with intermodal systems. With the significant increase in container usage since the mid-20th century, manual tracking at port terminals has become inefficient and prone to errors. Recent advancements in Deep Learning for object detection have introduced Computer Vision as a solution for automating this process. However, challenges such as low-quality images, varying font sizes & illumination, and environmental conditions hinder recognition accuracy. This study explores various architectures and proposes a Container Code Localization Network (CCLN), utilizing ResNet and UNet for code identification, and a Container Code Recognition Network (CCRN), which combines Convolutional Neural Networks with Long Short-Term Memory to convert the image text into a machine-readable format. By enhancing existing shipping container localization and recognition datasets with additional images, our models exhibited improved generalization capabilities on other datasets, such as Syntext, for text recognition. Experimental results demonstrate that our system achieves $97.93\%$ accuracy at $64.11$ frames per second under challenging conditions such as varying font sizes, illumination, tilt, and depth, effectively simulating real port terminal environments. The proposed solution promises to enhance workflow efficiency and productivity in container handling processes, making it highly applicable in modern port operations. Full article

This paper presents an in-production Rhetorical Role Labeling (RRL) classifier developed for Hungarian judicial decisions. RRL is a sequential classification problem in Natural Language Processing, aiming to assign functional roles (such as facts, arguments, decision, etc.) to every segment or sentence in a legal document. The study was conducted on a human-annotated sentence-level RRL corpus and compares multiple neural architectures, including BiLSTM, attention-based networks, and a support vector machine as baseline. It further investigates the impact of late chunking during vectorization, in contrast to classical approaches. Results from tests on the labeled dataset and annotator agreement statistics are reported, and performance is analyzed across architecture types and embedding strategies. Contrary to recent findings in retrieval tasks, late chunking does not show consistent improvements for sentence-level RRL, suggesting that contextualization through chunk embeddings may introduce noise rather than useful context in Hungarian legal judgments. The work also discusses the unique structure and labeling challenges of Hungarian cases compared to international datasets and provides empirical insights for future legal NLP research in non-English court decisions. Full article

This work represents the natural continuation of the development of the cognitive architecture developed and named Sophimatics, organically integrating the spatio-temporal processing mechanisms of the Super Time Cognitive Neural Network (STCNN) with the advanced principles of Sophimatics. Sophimatics’ goal is as challenging as it is fraught with obstacles, but its ultimate aim is to achieve a more humanized post-generative artificial intelligence, capable of understanding and analyzing context and evaluating the user’s purpose and intent, viewing time not only as a chronological sequence but also as an experiential continuum. The path to achieving this extremely ambitious goal has been made possible thanks to some previous work in which the philosophical thinking of interest in AI was first inherited as the inspiration for the aforementioned capabilities of the Sophimatic framework, then the issue of mapping concepts and philosophical thinking in Sophimatics’ AI infrastructure was addressed, and finally a cognitive-inspired network such as STCNN was created. This work, on the other hand, addresses the challenge of how to endow the infrastructure with both chronological and experiential time and its powerful implications, such as the innate ability to resolve paradoxes, which generative AI does not have among its prerogatives precisely because of structural limitations. To reach these results, the model operates in the two-dimensional complex time domain ℂ², extending cognitive processing capabilities through the implementation of dual temporal operators that simultaneously manage the real temporal dimension, where past, present, and future are managed and the imaginary one, that considers memory, creativity, and imagination. The resulting architecture demonstrates superior capabilities in resolving informational paradoxes and integrating apparently contradictory cognitive states, maintaining computational coherence through adaptive Sophimatic mechanisms. In conclusion, this work introduces Phase 4 of the Sophimatic framework, enabling management of two-dimensional time within a novel cognitively inspired neural architecture grounded in philosophical concepts. It connects with existing research on temporal cognition, hybrid symbolic–connectionist models, and ethical AI. The methodology translates philosophical insights into formal computational systems, culminating in a mathematical formalization that supports two-dimensional temporal reasoning and paradox resolution. Experimental results demonstrate efficiency, predictive accuracy, and computational feasibility, highlighting potential real-world applications, future research directions, and present limitations. Full article

Federated learning has gained popularity in recent years to enhance IoT security because the model allows decentralized devices to collaboratively learn a shared model without exchanging raw data. Despite its privacy advantages, federated learning is vulnerable to poisoning attacks, where malicious devices introduce manipulated data or model updates to corrupt the global model. These attacks can degrade the model’s performance or bias its outcomes, making it difficult to ensure the integrity of the learning process across decentralized devices. In this research, our goal is to develop a defense mechanism against poisoning attacks in federated learning models. Specifically, we develop a spacetime model, that combines the three dimensions of space and the one dimension of time into a four-dimensional manifold. Poisoning attacks have complex spatial and time relationships that present identifiable patterns in that manifold. We propose SpaceTime-Deep Similarity Defense (ST-DSD), a deep learning recurrent neural network that includes space and time perceptions to provide a defense against poisoning attacks for federated learning models. The proposed mechanism is built upon a time series regression many-to-one architecture using spacetime relationships to provide an adversarial trained deep learning poisoning defense. Simulation results show that SpaceTime defense outperforms existing solutions for poisoning defenses in IoT environments. Full article

In the context of remanufacturing, particularly mobile device refurbishing, effective operator training is crucial for accurate defect identification and process inspection efficiency. This study examines the application of Natural Language Processing (NLP) techniques to evaluate operator expertise based on subjective textual responses gathered during a defect analysis task. Operators were asked to describe screen defects using open-ended questions, and their responses were compared with expert responses to evaluate their accuracy and consistency. We employed four NLP models, including finetuned Sentence-BERT (SBERT), pre-trained SBERT, Word2Vec, and Dice similarity, to determine their effectiveness in interpreting short, domain-specific text. A novel disagreement tagging framework was introduced to supplement traditional similarity metrics with explainable insights. This framework identifies the root causes of model–human misalignment across four categories: defect type, severity, terminology, and location. Results show that a finetuned SBERT model significantly outperforms other models by achieving Pearsons’s correlation of 0.93 with MAE and RMSE scores of 0.07 and 0.12, respectively, providing more accurate and context-aware evaluations. In contrast, other models exhibit limitations in semantic understanding and consistency. The results highlight the importance of finetuning NLP models for domain-specific applications and demonstrate how qualitative tagging methods can enhance interpretability and model debugging. This combined approach indicates a scalable and transparent methodology for the evaluation of operator responses, supporting the development of more effective training programmes in industrial settings where remanufacturing and sustainability generally are a key performance metric. Full article

Massive Open Online Courses (MOOCs) have gained increasing popularity in recent years, highlighting the growing importance of effective course recommendation systems (CRS). However, the performance of existing CRS methods is often limited by data sparsity and suffers under cold-start scenarios. One promising solution is to leverage course-level conceptual information as side information to enhance recommendation performance. We propose a general framework for integrating LLM-generated concepts as side information into various classic recommendation algorithms. Our framework supports multiple integration strategies and is evaluated on two real-world MOOC datasets, with particular focus on the cold-start setting. The results show that incorporating LLM-generated concepts consistently improves recommendation quality across diverse models and datasets, demonstrating that automatically generated semantic information can serve as an effective, reusable, and scalable source of side knowledge for educational recommendations. This finding suggests that LLMs can function not merely as content generators but as practical data augmenters, offering a new direction for enhancing robustness and generalizability in course recommendation. Full article

Depression represents a critical global mental health challenge, with social media offering unprecedented opportunities for early detection through computational analysis. We propose a novel BERT–CNN–BiLSTM architecture with attention mechanisms that systematically integrate emoji usage patterns—fundamental components of digital emotional expression overlooked by existing approaches. Evaluated on the SuicidEmoji dataset, our model achieves 97.12% accuracy, 94.56% precision, 93.44% F1-score, 85.67% MCC, and 91.23% AUC-ROC. Analysis reveals distinct emoji patterns: depressed users favour negative emojis (😔 13.9%, 😢 12.8%, 💔 6.7%) while controls prefer positive expressions (😂 16.5%, 😊 11.0%, 😎 10.2%). The attention mechanism identifies key linguistic markers, including emotional indicators, personal pronouns, and emoji features, providing interpretable insights into depression-related language. Our findings suggest that the integration of emojis substantially improves optimal social media-based mental health detection systems. Full article

As Large Language Models (LLMs) are core components in Retrieval-Augmented Generation (RAG) systems for knowledge-intensive tasks, concerns regarding hallucinations, redundancy, and unverifiable outputs have intensified, particularly in high-stakes domains, such as e-government. This study proposes a modular, multi-pipeline framework for statement-level faithfulness evaluation for characterizing hallucination and redundancy across both simple and agentic RAG pipelines. Using GPT-4.1, Claude Sonnet-4.0, and Gemini 2.5 Pro as LLM-based judges, this study examines how tool-specific attribution within agentic multi-tool architectures influences the interpretability and traceability of the generated content. By using a modular agentic RAG framework combining symbolic (GraphRAG), semantic (embedding), and real-time (web) retrieval, we benchmark hallucination and redundancy patterns, using state-of-the-art LLM judges. The study examines RAG and agent-based pipelines that attribute outputs to distinct tools, in contrast to traditional single-source RAG systems that rely on aggregated retrieval. Using e-government data sourced from the European Commission’s Press Corner, our evaluation framework assesses not only the frequency, but also the source-aware detectability of hallucinated content. The findings provide actionable insights into how source granularity and retrieval orchestration impact faithfulness evaluation across different pipeline architectures, while also suggesting new directions for explainability-aware RAG design. The study contributes a reproducible, modular framework for automated faithfulness assessment, with implications for transparency, governance compliance, and trustworthy AI deployment. Full article

The increasing scale of modern datasets has created a significant computational bottleneck for traditional scientific and statistical algorithms. To address this problem, the current paper describes and validates a high-performance method based on adaptive spline interpolation that can dramatically accelerate the calculation of foundational scientific and statistical functions. This is accomplished by constructing parsimonious spline models that approximate their target functions within a predefined, highly precise maximum error tolerance. The efficacy of the adaptive spline-based solutions was evaluated through benchmarking experiments that compared spline models against the widely used algorithms in the Python SciPy library for the normal, Student’s t, and chi-squared cumulative distribution functions. Across 30 trials of 10 million computations each, the adaptive spline models consistently achieved a maximum absolute error of no more than 1 × 10⁻⁸ while simultaneously ranging between 7.5 and 87.4 times faster than their corresponding SciPy algorithms. All of these improvements in speed were observed to be statistically significant at p < 0.001. The findings establish that adaptive spline interpolation can be both highly accurate and much faster than traditional scientific and statistical algorithms, thereby offering a practical pathway to accelerate both the analysis of large datasets and the progress of scientific inquiry. Full article

As a vital carrier of human intangible culture, dance plays an important role in cultural transmission through digital generation. However, existing dance generation methods rely heavily on high-precision motion capture and manually annotated datasets, and they fail to effectively model the culturally distinctive movements of Chinese ethnic folk dance, resulting in semantic distortion and cross-modal mismatch. Building on the Chinese traditional ethnic Helou Dance, this paper proposes a culture-aware Chinese ethnic folk dance generation framework, CAFE-Dance, which dispenses with manual annotation and automatically generates dance sequences that achieve high cultural fidelity, precise music synchronization, and natural, fluent motion. To address the high cost and poor scalability of cultural annotation, we introduce a Zero-Manual-Label Cultural Data Construction Module (ZDCM) that performs self-supervised cultural learning from raw dance videos, using cross-modal semantic alignment and a knowledge-base-guided automatic annotation mechanism to construct a high-quality dataset of Chinese ethnic folk dance covering 108 classes of curated cultural attributes without any frame-level manual labels. To address the difficulty of modeling cultural semantics and the weak interpretability, we propose a Culture-Aware Attention Mechanism (CAAM) that incorporates cultural gating and co-attention to adaptively enhance culturally key movements. To address the challenge of aligning the music–motion–culture tri-modalities, we propose a Tri-Modal Alignment Network (TMA-Net) that achieves dynamic coupling and temporal synchronization of tri-modal semantics under weak supervision. Experimental results show that our framework improves Beat Alignment and Cultural Accuracy by 4.0–5.0 percentage points and over 30 percentage points, respectively, compared with the strongest baseline (Music2Dance), and it reveals an intrinsic coupling between cultural embedding density and motion stability. The code and the curated Helouwu dataset are publicly available. Full article

Pooling strategies are fundamental to convolutional neural networks, shaping the trade-off between accuracy, robustness to spatial variations, and computational efficiency in modern visual recognition systems. In this paper, we present and validate ECA110-Pooling, a novel rule-based pooling operator inspired by elementary cellular automata. We conduct a systematic comparative study, benchmarking ECA110-Pooling against conventional pooling methods (MaxPooling, AveragePooling, MedianPooling, MinPooling, KernelPooling) as well as state-of-the-art (SOTA) architectures. Experiments on three benchmark datasets—ImageNet (subset), CIFAR-10, and Fashion-MNIST—across training horizons ranging from 20 to 50,000 epochs show that ECA110-Pooling consistently achieves higher Top-1 accuracy, lower error rates, and stronger F1-scores than traditional pooling operators, while maintaining computational efficiency comparable to MaxPooling. Moreover, when compared with SOTA models, ECA110-Pooling delivers competitive accuracy with substantially fewer parameters and reduced training time. These results establish ECA110-Pooling as a principled and validated approach to image classification, bridging the gap between fixed pooling schemes and complex deep architectures. Its interpretable, rule-based design highlights both theoretical significance and practical applicability in contexts that demand a balance of accuracy, efficiency, and scalability. Full article

Following PRISMA-ScR guidelines, this scoping review systematically maps the landscape of Large Language Models (LLMs) in mechanical engineering. A search of four major databases (Scopus, IEEE Xplore, ACM Digital Library, Web of Science) and a rigorous screening process yielded 66 studies for final analysis. The findings reveal a nascent, rapidly accelerating field, with over 68% of publications from 2024 (representing a year-on-year growth of 150% from 2023 to 2024), and applications concentrated on front-end design processes like conceptual design and Computer-Aided Design (CAD) generation. The technological landscape is dominated by OpenAI’s GPT-4 variants. A persistent challenge identified is weak spatial and geometric reasoning, shifting the primary research bottleneck from traditional data scarcity to inherent model limitations. This, alongside reliability concerns, forms the main barrier to deeper integration into engineering workflows. A consensus on future directions points to the need for specialized datasets, multimodal inputs to ground models in engineering realities, and robust, engineering-specific benchmarks. This review concludes that LLMs are currently best positioned as powerful ‘co-pilots’ for engineers rather than autonomous designers, providing an evidence-based roadmap for researchers, practitioners, and educators. Full article

Driving safety education remains a critical societal priority, and understanding traffic rules is essential for reducing road accidents and improving driver awareness. This study presents the development and evaluation of a virtual simulator for learning traffic rules, incorporating spherical video technology and interactive training scenarios. The primary objective was to enhance the accessibility and effectiveness of traffic rule education by utilizing modern virtual reality approaches without the need for specialized equipment. A key research component is using Petri net-based models to study the simulator’s dynamic states, enabling the analysis and optimization of system behavior. The developed simulator employs large language models for the automated generation of educational content and test questions, supporting personalized learning experiences. Additionally, a model for determining the camera rotation angle was proposed, ensuring a realistic and immersive presentation of training scenarios within the simulator. The system’s cloud-based, modular software architecture and cross-platform algorithms ensure flexibility, scalability, and compatibility across devices. The simulator allows users to practice traffic rules in realistic road environments with the aid of spherical videos and receive immediate feedback through contextual prompts. The developed system stands out from existing traffic rule learning platforms by combining spherical video technology, large language model-based content generation, and cloud architecture to create a more interactive, adaptive, and realistic learning experience. The experimental results confirm the simulator’s high efficiency in improving users’ knowledge of traffic rules and practical decision-making skills. Full article

This study addresses the problem of automatic attack detection targeting Linux-based machines and web applications through the analysis of system logs, with a particular focus on reducing the computational requirements of existing solutions. The aim of the research is to develop and evaluate the effectiveness of machine learning models capable of classifying system events as benign or malicious, while also identifying the type of attack under resource-constrained conditions. The Linux-APT-Dataset-2024 was employed as the primary source of data. To mitigate the challenge of high computational complexity, model optimization techniques such as parameter quantization, knowledge distillation, and architectural simplifications were applied. Experimental results demonstrate that the proposed approaches significantly reduce computational overhead and hardware requirements while maintaining high classification accuracy. The findings highlight the potential of optimized machine learning algorithms for the development of practical early threat detection systems in Linux environments with limited resources, which is particularly relevant for deployment in IoT devices and edge computing systems. Full article

This paper explores the replication of the DotA Plus prediction system using decision tree algorithms. The study implements and evaluates Extra Trees Classifier, Random Forest Classifier, and Hist Gradient Boosting Classifier, along with their combined average, for predicting the outcome of Defense of the Ancients (DotA) 2 matches. Data was collected using the OpenDotA API and the Steam API, and various features such as game duration, tower and barracks states, net-worth, assists, last hits, gold, level, gold per minute, and experience per minute were extracted for model training. Additionally, hero and item win rate features, derived from Dotabuff data, were incorporated to enhance the models’ predictive accuracy. The models were trained on datasets with varying match durations, including segments for matches under 10 min, between 10 and 20 min, and over 20 min. The experimental results show that the Extra Trees Classifier consistently outperformed other individual models and performed comparably to the averaged models, achieving a peak performance of 98.6% test accuracy on matches longer than 20 min when using match duration segmentation and hero/item embeddings. The study highlights the effectiveness of decision tree-based methods for real-time match outcome prediction in DotA 2 and offers insights into feature importance. The combined average of Extra Trees Classifier, Random Forest Classifier, and Hist Gradient Boosting Classifier models provides a robust and reliable prediction of DotA 2 match outcomes, thus showing potential as a real-time prediction system. Full article

Current crime spatiotemporal prediction models are limited by the insufficient ability of POI data to represent the continuity and mixed-use nature of urban spatial functions. To address this, our study applies an urban region representation method based on building footprints and validates its effectiveness in improving the accuracy of crime spatiotemporal prediction. Specially, we first use the Region Dual Contrastive Learning algorithm to generate region representations as a region graph by integrating building footprints and POI data. Then, the region graph combined with crime data is input into crime prediction models to predict four crime types, including Burglary, Robbery, Felony Assault, and Grand Larceny. Finally, ablation experiments are conducted to quantify the contribution of building footprints to prediction improvement. The experimental results on New York City crime data indicate that (1) the region representations significantly improve deep learning model performance, with the most improved LSTM achieving average increases of 5.66% in Macro-F1 and 18.57% in Micro-F1, particularly benefiting baseline models with lower accuracy, and (2) the region representations yield more significant improvements for low-frequency crime categories and mitigates temporal memory decay in long-term predictions. These findings confirm that incorporating urban region representation based on building footprints effectively enhances crime spatiotemporal prediction performance, providing a more precise and efficient tool for urban security management to optimize police resource allocation and crime prevention strategies. Full article

To enable fully automated medicine warehousing in intelligent pharmacy systems, accurately detecting disordered, stacked pillboxes is essential. This paper proposes a high-precision detection algorithm for such scenarios based on an improved YOLOv8 framework. The proposed method integrates a novel convolutional module that replaces traditional stride convolutions and pooling layers, enhancing the detection of small, low-resolution targets in computer vision tasks. To further enhance detection accuracy, the Bi-Level Routing Attention (BiFormer) Vision Transformer is incorporated as a Cognitive Computing module. Additionally, the circular Smooth Label (CSL) technique is employed to mitigate boundary discontinuities and periodic anomalies in angle prediction, which often arise in the detection of rotated objects. The experimental results demonstrate that the proposed method achieves a precision of 94.24%, a recall of 90.39%, and a mean average precision (mAP) of 94.16%—improvements of 3.34%, 2.53%, and 3.35%, respectively, over the baseline YOLOv8 model. Moreover, the enhanced detection model outperforms existing rotated-object detection methods while maintaining real-time inference speed. To facilitate reproducibility and future benchmarking, the full dataset and source code used in this study have been released publicly. Although no standardized benchmark currently exists for pillbox detection, our self-constructed dataset reflects key industrial variations in pillbox size, orientation, and stacking, thereby providing a foundation for future cross-domain validation. Full article

High-performing medical Large Language Models (LLMs) typically require extensive fine-tuning with substantial computational resources, limiting accessibility for resource-constrained healthcare institutions. This study introduces a confidence-driven multi-model framework that leverages model diversity to enhance medical question answering without fine-tuning. Our framework employs a two-stage architecture: a confidence detection module assesses the primary model’s certainty, and an adaptive routing mechanism directs low-confidence queries to Helper models with complementary knowledge for collaborative reasoning. We evaluate our approach using Qwen3-30B-A3B-Instruct, Phi-4 14B, and Gemma 2 12B across three medical benchmarks; MedQA, MedMCQA, and PubMedQA. Results demonstrate that our framework achieves competitive performance, with particularly strong results in PubMedQA (0.95) and MedMCQA (0.78). Ablation studies confirm that confidence-aware routing combined with multi-model collaboration substantially outperforms single-model approaches and uniform reasoning strategies. This work establishes that strategic model collaboration offers a practical, computationally efficient pathway to improve medical AI systems, with significant implications for democratizing access to advanced medical AI in resource-limited settings. Full article

Social media platforms are currently confronted with a substantial problem concerning the presence of fake accounts, which pose a threat by spreading harmful content, spam, and misinformation. This study aims to address the problem by differentiating between fake and real X accounts (formerly Twitter). The need to mitigate the negative impact of fake accounts on online communities serves as the driving force for this work, with the goal of developing an effective method for identifying fake accounts and their fraudulent activities, such as posting harmful links, engaging in spamming behaviors, and disrupting online communities. The scope of this work focuses specifically on fake Twitter account detection. A comprehensive approach is taken, leveraging user information and tweets to discern between genuine and fake accounts. Various deep learning architectures are proposed and implemented, utilizing different optimizers and evaluating performance metrics. The models are trained and tested using a collected dataset, augmented to cover diverse real-life scenarios. The results show promising progress in distinguishing between fake and real accounts, revealing that the inclusion of tweet content along with user metadata does not significantly improve the classification of fake accounts. It also highlights the importance of selecting appropriate optimizers. The implications of this study are relevant to social media platforms, users, and researchers. The findings provide insights into combating fake accounts and their fraudulent activities, contributing to the enhancement of online community safety. While the research is specific to Twitter, the methodology and insights gained may be potentially generalizable to other social media platforms. Full article