Search Results (187)

Polygon triangulations and their generalizations to $\left(m+2\right)-$angulations are fundamental in combinatorics and computational geometry. This paper presents a unified linear-time framework that establishes explicit bijections between $m-$Dyck words, planted $\left(m+1\right)-$ary trees, and $\left(m+2\right)-$angulations of convex polygons. We introduce stack-based and tree-based algorithms that enable reversible conversion between symbolic and geometric representations, prove their correctness and optimal complexity, and demonstrate their scalability through extensive experiments. The approach reveals a hierarchical decomposition encoded by Fuss–Catalan numbers, providing a compact and uniform representation for triangulations, quadrangulations, pentangulations, and higher-arity angulations. Experimental comparisons show clear advantages over rotation-based methods in both runtime and memory usage. The framework offers a general combinatorial foundation that supports efficient enumeration, compressed representation, and extensions to higher-dimensional or non-convex settings. Full article

The extensive use of large language models (LLMs), particularly in the finance sector, raises concerns about the authenticity and reliability of generated text. Developing a robust method for distinguishing between human-written and AI-generated financial content is therefore essential. This study addressed this challenge by constructing a dataset based on financial tweets, where original financial tweet texts were regenerated using six LLMs, resulting in seven distinct classes: human-authored text, LLaMA3.2, Phi3.5, Gemma2, Qwen2.5, Mistral, and LLaVA. A context-aware representation-learning-based model, namely DeBERTa, was extensively fine-tuned for this task. Its performance was compared to that of other transformer variants (DistilBERT, BERT Base Uncased, ELECTRA, and ALBERT Base V1) as well as traditional machine learning models (logistic regression, naive Bayes, random forest, decision trees, XGBoost, AdaBoost, and voting (AdaBoost, GradientBoosting, XGBoost)) using Word2Vec embeddings. The proposed DeBERTa-based model achieved an impressive test accuracy, precision, recall, and F1-score, all reaching 94%. In contrast, competing transformer models achieved test accuracies ranging from 0.78 to 0.80, while traditional machine learning models yielded a significantly lower performance (0.39–0.80). These results highlight the effectiveness of context-aware representation learning in distinguishing between human-written and AI-generated financial text, with significant implications for text authentication, authorship verification, and financial information security. Full article

This paper presents accounts of preposition selection and agreement in English within Dynamic Syntax. To achieve this, I introduce two new, non-semantic, labels into the tree language: $Ph$ that takes as values phonological forms which are modelled as ordered sets of phonemes and $Md$ which takes as values sets of $Ph$ values, the phonological forms of certain words and forms with which a particular word can collocate. While these labels are not grounded in semantic concepts like type and formula, they are nevertheless grounded in phonological concepts and thus ultimately in phonetic phenomena. These labels are introduced through the parsing of words and are used to constrain the forms of other words they can felicitously appear with, such as nouns and certain determiners or verbs with selected prepositions or prepositional phrases, in a straightforward manner. It is shown how the remnant agreement and selection patterns in modern (standard) English can be captured without any recourse to traditional categories such as gender, person and number. Certain disagreement phenomena are discussed as are the broader implications of the approach. Full article

The growth of electronic word-of-mouth (eWOM) on digital platforms has heightened the need to distinguish authentic user-generated content from covert promotional material. This study proposes an integrated framework combining Natural Language Processing (NLP), machine learning, and Latent Dirichlet Allocation (LDA) to classify sentiment and detect advertising features in online game reviews. Reviews from the Steam platform were analyzed using Support Vector Machine (SVM), Decision Tree, and Naïve Bayes classifiers, with class imbalance addressed through SMOTE and SMOTE–Tomek techniques. The SMOTE-augmented SVM achieved the highest performance, with 98.18% overall accuracy and 97.52% negative sentiment detection. LDA and Quality Function Deployment (QFD) further uncovered latent promotional themes, providing insights into how advertising elements manifest in positive reviews and how negative feedback reflects genuine user concerns. The framework assists platform managers in enhancing eWOM credibility and supports marketers in designing data-driven advertising strategies. By bridging sentiment analysis with covert marketing detection, this research contributes a novel methodological approach for assessing review trustworthiness, improving transparency, and fostering consumer trust in digital information environments. Full article

A widely used repository of violent death records is the U.S. Centers for Disease Control National Violent Death Reporting System (NVDRS). The NVDRS includes narrative data, which researchers frequently utilize to go beyond its structured variables. Prior work has shown that NVDRS narratives vary in length depending on decedent and incident characteristics, including race/ethnicity. Whether these length differences reflect differences in narrative information potential is unclear. We use the 2003–2021 NVDRS to investigate narrative length and complexity measures among 300,323 suicides varying in decedent and incident characteristics. To do so, we operationalized narrative complexity using three manifest measures: word count, sentence count, and dependency tree depth. We then employed regression methods to predict word counts and narrative complexity scores from decedent and incident characteristics. Both were consistently lower for black non-Hispanic decedents compared to white, non-Hispanic decedents. Although narrative complexity is just one aspect of narrative information potential, these findings suggest that the information in NVDRS narratives is more limited for some racial/ethnic minorities. Future studies, possibly leveraging large language models, are needed to develop robust measures to aid in determining whether narratives in the NVDRS have achieved their stated goal of fully describing the circumstances of suicide. Full article

Target-oriented opinion words extraction (TOWE) is a novel subtask of aspect-based sentiment analysis (ABSA), which aims to extract opinion words corresponding to a given opinion target within a sentence. In recent years, neural networks have been widely used to solve this problem and have achieved competitive results. However, when faced with complex and long sentences, the existing methods struggle to accurately identify the semantic relationships between distant opinion targets and opinion words. This is primarily because they rely on literal distance, rather than semantic distance, to model the local context or opinion span of the opinion target. To address this issue, we propose a neural network model called DTOWE, which comprises (1) a global module using Inward-LSTM and Outward-LSTM to capture general sentence-level context, and (2) a local module that employs BiLSTM combined with DT-LCF to focus on target-specific opinion spans. DT-LCF is implemented in two ways: DT-LCF-Mask, which uses a binary mask to zero out non-local context beyond a dependency tree distance threshold, α, and DT-LCF-weight, which applies a dynamic weighted decay to downweigh distant context based on semantic distance. These mechanisms leverage dependency tree structures to measure semantic proximity, reducing the impact of irrelevant words and enhancing the accuracy of opinion span detection. Extensive experiments on four benchmark datasets demonstrate that DTOWE outperforms state-of-the-art models. Specifically, DT-LCF-Weight achieves F1-scores of 73.62% (14lap), 82.24% (14res), 75.35% (15res), and 83.83% (16res), with improvements of 2.63% to 3.44% over the previous state-of-the-art (SOTA) model, IOG. Ablation studies confirm that the dependency tree-based distance measurement and DT-LCF mechanism are critical to the model’s effectiveness, validating their ability to handle complex sentences and capture semantic dependencies between targets and opinion words. Full article

Linear models are not always able to sufficiently capture the structure of a dataset. Sometimes, combining predictors in a non-parametric method, such as deep neural networks (DNNs), would yield a more flexible modeling of the response variables in the predictions. Furthermore, the standard statistical classification or regression approaches are inefficient when dealing with more complexity, such as a high-dimensional problem, which usually suffers from multicollinearity. For confronting these cases, penalized non-parametric methods are very useful. This paper proposes two heuristic approaches and implements new shrinkage penalized cost functions in the DNN, based on the elastic-net penalty function concept. In other words, some new methods via the development of shirnkaged penalized DNN, such as ${\mathrm{DNN}}_{\mathrm{elastic-net}}$ and ${\mathrm{DNN}}_{\mathrm{ridge}\&\mathrm{bridge}}$, are established, which are strong rivals for ${\mathrm{DNN}}_{\mathrm{Lasso}}$ and ${\mathrm{DNN}}_{\mathrm{ridge}}$. If there is any dataset grouping information in each layer of the DNN, it may be transferred using the derived penalized function of elastic-net; other penalized DNNs cannot provide this functionality. Regarding the outcomes in the tables, in the developed DNN, not only are there slight increases in the classification results, but there are also nullifying processes of some nodes in addition to a shrinkage property simultaneously in the structure of each layer. A simulated dataset was generated with the binary response variables, and the classic and heuristic shrinkage penalized DNN models were generated and tested. For comparison purposes, the DNN models were also compared to the classification tree using GUIDE and applied to a real microbiome dataset. Full article

Path flow always plays a critical role in extracting vehicle travel patterns and reflecting network-scale traffic features. However, the comprehensive topological structure of urban road networks induces massive route choices, so frequent travel routes have been gradually regarded as an ideal countermeasure to represent traffic states. Widely used license plate recognition (LPR) devices can collect the abundant traffic features of all vehicles, but their sparse spatial distributions restrict the conventional models in frequent travel identification. Therefore, this study develops a network reconstruction method to construct a topological network from the LPR dataset, avoiding the adverse effects caused by the sparse distribution of detectors on the road network and further uses the Snake algorithm to fully utilize the road network structure and traffic attributes for clustering to obtain various travel patterns, with frequent routes under different travel patterns finally identified based on Steiner trees and frequent item recognition. To address the sparse spatial distribution of LPR devices, we utilize the word2vec model to extract spatial correlations among intersections. A threshold-based method is then applied to transform the correlation matrix into a reconstructed network, connecting intersections with strong vehicle transition relationships. This community structure can be interpreted as representing different travel patterns. Consequently, the Snake algorithm is employed to cluster intersections into distinct categories, reflecting these varied travel patterns. By leveraging the word2vec model, the detector installation rate requirement for Snake is significantly reduced, ensuring that the clustering results accurately represent the intrinsic relevance of traffic roads. Subsequently, frequent routes are identified from both macro- and micro-perspectives using the Steiner tree and Frequent Pattern Growth (FP Growth) algorithm, respectively. Validated on the LPR dataset in Changsha, China, the experiment results demonstrate that the proposed method can effectively identify travel patterns and extract frequent routes in the sparsely installed LPR devices. Full article

Measuring the small tree trunk variations during the day–night cycle, seasonal cycles, as well as those caused by the plant’s growth and health regime is a very important action in horticulture or forestry because by analyzing the collected data, assessments can be made on the health of the trees, but also on the climatic conditions and changes in a certain region. This can be performed with devices called dendrometers. This paper presents a new type of approach to these measurement types in which the trunk volume changes are highly sensitively converted into the axial stress on sensitive elements made of magnetic materials in wire form in which the giant stress impedance effect occurs. Finally, by electronic processing of the signals provided by the sensitive elements, digital words with a decimal value proportional to the diameter variations are obtained. This paper presents the operating principle, the constructive details and the experimental results obtained by testing the device in the laboratory and in-field. The proposed dendrometer, compared to those available commercially, has the advantage of good resolution and sensitivity, good immunity to temperature variations, the possibility of transmitting the result remotely, robustness and low price. Some metrological parameters obtained from the experimental testing are the following: resolution 1.6 µm, linearity 1.4%, measurement range 0 to 5 mm, temperature coefficient 0.012%/°C. Full article

Depression is now one of the most common mental health concerns in the digital era, calling for powerful computational tools for its detection and its level of severity estimation. A multi-level depression severity detection framework in the Reddit social media network is proposed in this study, and posts are classified into four levels: minimum, mild, moderate, and severe. We take a dual approach using classical machine learning (ML) algorithms and recent Transformer-based architectures. For the ML track, we build ten classifiers, including Logistic Regression, SVM, Naive Bayes, Random Forest, XGBoost, Gradient Boosting, K-NN, Decision Tree, AdaBoost, and Extra Trees, with two recently proposed embedding methods, Word2Vec and GloVe embeddings, and we fine-tune them for mental health text classification. Of these, XGBoost yields the highest F1-score of 94.01 using GloVe embeddings. For the deep learning track, we fine-tune ten Transformer models, covering BERT, RoBERTa, XLM-RoBERTa, MentalBERT, BioBERT, RoBERTa-large, DistilBERT, DeBERTa, Longformer, and ALBERT. The highest performance was achieved by the MentalBERT model, with an F1-score of 97.31, followed by RoBERTa (96.27) and RoBERTa-large (96.14). Our results demonstrate that, to the best of the authors’ knowledge, domain-transferred Transformers outperform non-Transformer-based ML methods in capturing subtle linguistic cues indicative of different levels of depression, thereby highlighting their potential for fine-grained mental health monitoring in online settings. Full article

The autonomous positioning of drone-based remote sensing plays an important role in navigation in urban environments. Due to GNSS (Global Navigation Satellite System) signal occlusion, obtaining precise drone locations is still a challenging issue. Inspired by vision-based positioning methods, we proposed an autonomous positioning method based on multi-view reference images rendered from the scene’s 3D geometric mesh and apply a bag-of-words (BoW) image retrieval pipeline to achieve efficient and scalable positioning, without utilizing deep learning-based retrieval or 3D point cloud registration. To minimize the number of reference images, scene coverage quantification and optimization are employed to generate the optimal viewpoints. The proposed method jointly exploits a visual-bag-of-words tree to accelerate reference image retrieval and improve retrieval accuracy, and the Perspective-n-Point (PnP) algorithm is utilized to obtain the drone’s pose. Experiments are conducted in urban real-word scenarios and the results show that positioning errors are decreased, with accuracy ranging from sub-meter to 5 m and an average latency of 0.7–1.3 s; this indicates that our method significantly improves accuracy and latency, offering robust, real-time performance over extensive areas without relying on GNSS or dense point clouds. Full article

Vehicular ad hoc networks (VANETs) aim to manage traffic, prevent accidents, and regulate various parts of traffic. However, owing to their nature, the security of VANETs remains a significant concern. This study provides insightful information regarding VANET vulnerabilities and attacks. It investigates a number of security models that have recently been introduced to counter VANET security attacks with a focus on machine learning detection methods. This confirms that several challenges remain unsolved. Accordingly, this study introduces a lightweight machine learning model with a gain information feature selection method to detect VANET attacks. A balanced version of the well-known and recent dataset CISDS2017 was developed by applying a random oversampling technique. The developed dataset was used to train, test, and evaluate the proposed model. In other words, two layers of enhancements were applied—using a suitable feature selection technique and fixing the dataset imbalance problem. The results show that the proposed model, which is based on the Random Forest (RF) classifier, achieved excellent performance in terms of classification accuracy, computational cost, and classification error. It achieved an accuracy rate of 99.8%, outperforming all benchmark classifiers, including AdaBoost, decision tree (DT), K-nearest neighbors (KNNs), and multi-layer perceptron (MLP). To the best of our knowledge, this model outperforms all the existing classification techniques. In terms of processing cost, it consumes the least processing time, requiring only 69%, 59%, 35%, and 1.4% of the AdaBoost, DT, KNN, and MLP processing times, respectively. It causes negligible classification errors. Full article

Background/Objectives: Cochlear implantation (CI) significantly enhances speech perception and quality of life in patients with severe-to-profound sensorineural hearing loss, yet outcomes vary substantially. Accurate preoperative prediction of CI outcomes remains challenging. This study aimed to develop and validate a machine learning model predicting postoperative speech recognition using a large, single-center dataset. Additionally, we compared model performance with expert clinical predictions to evaluate potential clinical utility. Methods: We retrospectively analyzed data from 2571 adult patients with postlingual hearing loss who received their cochlear implant between 2000 and 2022 at Hannover Medical School, Germany. A decision tree regression model was trained to predict monosyllabic (MS) word recognition score one to two years post-implantation using preoperative clinical variables (age, duration of deafness, preoperative MS score, pure tone average, onset type, and contralateral implantation status). Model evaluation was performed using a random data split (10%), a chronological future cohort (patients implanted after 2020), and a subset where experienced audiologists predicted outcomes for comparison. Results: The model achieved a mean absolute error (MAE) of 17.3% on the random test set and 17.8% on the chronological test set, demonstrating robust predictive performance over time. Compared to expert audiologist predictions, the model showed similar accuracy (MAE: 19.1% for the model vs. 18.9% for experts), suggesting comparable effectiveness. Conclusions: Our machine learning model reliably predicts postoperative speech outcomes and matches expert clinical predictions, highlighting its potential for supporting clinical decision-making. Future research should include external validation and prospective trials to further confirm clinical applicability. Full article

Background: This systematic review assessed the current literature regarding the analgesic treatment of central neuropathic pain (CNP) in central nervous system (CNS) conditions, such as spinal cord injuries, multiple sclerosis, post-stroke disorders, and Parkinson’s disease. The aim of this systematic review was to compare the current algorithmic treatment of CNP, which generally does not discriminate among underlying conditions, with RCTs investigating algorithm-recommended and non-algorithm-recommended drugs for differing underlying conditions. Methods: The PubMed and EMBASE databases were used to identify relevant randomized control trials (RCTs). MeSH terms and EmTree terms were searched as well as free text words in the title/abstract of the studies. A risk of bias tool was used to assess all included studies. Results: A total of 903 RCTs were identified from the initial search. Thirty-eight RCTs published between January 2002 and November 2024 fulfilled all the inclusion criteria and none of the exclusion criteria. The review investigated progressive and stable neurological diseases and conditions with associated CNP. Conclusions: From the majority of the included studies, the current recommended treatment algorithm seems to be effective and safe; however, the underlying condition seems to influence how the patient responds to tier-appropriate medication. Full article

Existing studies on ontology evolution lack automated mechanisms to balance semantic coherence and adaptability under real-time uncertainties, particularly in resolving spatiotemporal asymmetry and multidimensional coupling imbalances in dam-break scenarios. Traditional methods such as WordNet’s tree symmetry and FrameNet’s frame symmetry fail to formalize dynamic adjustments through quantitative metrics, leading to path dependency and delayed responses. This study addresses this gap by introducing a novel symmetry-entropy-constrained matrix fusion algorithm, which integrates algebraic direct sum operations and Hadamard product with entropy-driven adaptive weighting. The original contribution lies in the symmetry entropy metric, which quantifies structural deviations during fusion to systematically balance semantic stability and adaptability. This work formalizes ontology evolution as a symmetry-driven optimization process. Experimental results demonstrate that shared concepts between ontologies (s = 3) reduce structural asymmetry by 25% compared to ontologies (s = 1), while case studies validate the algorithm’s ability to reconcile discrepancies between theoretical models and practical challenges in evacuation efficiency and crowd dynamics. This advancement promotes the evolution of traditional emergency management systems towards an adaptive intelligent form. Full article