Search Results (389)

Arabic raw audio datasets were initially gathered to produce a corresponding signal spectrum, which was further used to extract the Mel-Frequency Cepstral Coefficients (MFCCs). The pronunciation dictionary, language model, and acoustic model were further derived from the MFCCs’ features. These output data were processed into Baidu’s Deep Speech model (ASR system) to attain the text corpus. Baidu’s Deep Speech model was implemented to precisely identify the global optimal value rapidly while preserving a low word and character discrepancy rate by attaining an excellent performance in isolated and end-to-end speech recognition. The desired outcome in this work is to forecast the next word and character in a sequential and systematic order that applies under natural language processing (NLP). This work combines the trained Arabic language model ARABERT with the potential of Long Short-Term Memory (LSTM) networks to predict the next word and character in an Arabic text. We used the pre-trained ARABERT embedding to improve the model’s capacity and, to capture semantic relationships within the language, we educated LSTM + CNN and Markov models on Arabic text data to assess the efficacy of this model. Python libraries such as TensorFlow, Pickle, Keras, and NumPy were used to effectively design our development model. We extensively assessed the model’s performance using new Arabic text, focusing on evaluation metrics like accuracy, word error rate, character error rate, BLEU score, and perplexity. The results show how well the combined LSTM + ARABERT and Markov models have outperformed the baseline models in envisaging the next word or character in the Arabic text. The accuracy rates of 64.9% for LSTM, 74.6% for ARABERT + LSTM, and 78% for Markov chain models were achieved in predicting the next word, and the accuracy rates of 72% for LSTM, 72.22% for LSTM + CNN, and 73% for ARABERET + LSTM models were achieved for the next-character prediction. This work unveils a novelty in Arabic natural language processing tasks, estimating a potential future expansion in deriving a precise next-word and next-character forecasting, which can be an efficient utility for text generation and machine translation applications. Full article

Background: In this narrative review, we have surveyed results obtained from a research program dealing with the role of semantic memory disorders as a predictor of progression from mild cognitive impairment (MCI) to Alzheimer’s disease (AD). Objectives: In this research program, we have taken into account many different putative markers, provided of a different complexity in the study of the semantic network. These markers ranged from the number of words produced on a semantic fluency task to the following: (a) the discrepancy between scores obtained on semantic vs. phonemic word fluency tests; (b) the presence, at the single-word level, of features (such as a loss of low typical words on a category verbal fluency task) typical of a degraded semantic system; or (c) the presence of more complex phenomena (such as the semantic distance between consecutively produced word pairs) concerning the organization of the semantic network. In the present review, all these studies have been presented, providing separate subsections for (a) methods, (b) results, and (c) a short discussion. Some tentative general conclusions have been drawn at the end of the review. We found that at baseline all these markers are impaired in MCI patients who will later convert to AD, but also that they do not necessarily show a linear worsening during the progression to AD and allow one to make different predictions about the time of development of AD. Our conclusions were that, rather than searching for the best marker of conversion, we should use a range of different markers allowing us to obtain the information most appropriate to the goal of our investigation. Full article

Background: Aortic valve stenosis (AVS) is a common valvular heart disease affecting millions of people worldwide. It leads to significant neurocognitive and neuropsychological impairments, impacting patients’ quality of life. Objective: The objective of this article is to identify and discuss the potential neurocognitive effects on patients with aortic stenosis before and after undergoing surgical aortic valve replacement (SAVR). Method: Our study involved the assessment of 64 patients undergoing aortic valve replacement (SAVR) using a neurocognitive evaluation comprising a battery of 11 different cognitive tests. These tests were designed to analyze the patients’ overall cognitive functioning, executive abilities, short- and long-term memory, and attentional performance. The tests were administered to patients before the aortic valve surgery (T0) and after the surgery (T1). From a statistical perspective, numerical variables are presented as means (±standard deviation) and medians (IQR), while categorical variables are presented as counts and percentages. Normality was assessed using the Shapiro–Wilk test. T0 and T1 scores were compared with the Wilcoxon signed rank test, with p < 0.05 considered significant. Analyses were performed using SAS version 9.4. Results: Conducted as part of a fully financed Italian Ministry of Health project (RF-2016-02361069), the study found that most patients showed normal cognitive functioning at baseline. Cognitive assessments showed that executive functions, attention, language, and semantic knowledge were within the normal range for the majority of participants. After SAVR, cognitive outcomes remained stable or improved, particularly in executive functions and language. Notably, verbal episodic memory demonstrated significant improvement, with the percentage of patients scoring within the normal range on the BSRT increasing from 73.4% at T0 to 92.2% at T1 (p < 0.0001). However, visuospatial and visuoconstructive abilities showed stability or slight decline, while attentional skills remained relatively stable. The Clock Drawing Test indicated the maintenance of cognitive functions. Conclusions: The findings of our study indicate a global stability in cognitive status among patients after undergoing SAVR, with significant improvement noted in verbal episodic memory. While other cognitive domains did not demonstrate statistically significant changes, these insights are valuable for understanding the cognitive effects of SAVR and can guide future research and clinical practice in selecting the most effective surgical and rehabilitative options for patients. Monitoring cognitive outcomes in patients undergoing aortic valve replacement surgery remains crucial. Full article

Although grid maps help mobile robots navigate in indoor environments, some lack semantic information that would allow the robot to perform advanced autonomous tasks. In this paper, a semantic map production system is proposed to facilitate indoor mobile robot navigation tasks. The developed system is based on the employment of LiDAR technology and a vision-based system to obtain a semantic map with rich information, and it has been validated using the robot operating system (ROS) and you only look once (YOLO) v3 object detection model in simulation experiments conducted in indoor environments, adopting low-cost, -size, and -memory computers for increased accessibility. The obtained results are efficient in terms of object recognition accuracy, object localization error, and semantic map production precision, with an average map construction accuracy of 78.86%. Full article

Introduction: Cognitive impairment, marked by a decline in memory and attention, is frequently underdiagnosed, complicating effective management. Cardiovascular risk factors (CVR) and anticholinergic burden (ACB) are significant contributors to dementia risk, with ACB often stemming from medications prescribed for neuropsychiatric disorders. This study evaluates cognitive profiles through three brief cognitive tests, analyzing the impact of CVR and ACB presence. Methods: This cross-sectional study was performed between 2019 and 2023 in community pharmacies and an outpatient clinic in Valencia, Spain. Eligible participants were patients with subjective memory complaints 50 years or older with clinical records of cardiovascular factors. Patients with conflicting information regarding diabetes diagnosis or not taking concomitant medications were excluded. Three brief cognitive tests (Memory Impairment Screening (MIS), Semantic Verbal Fluency Test, and SPMSQ) were assessed. CVR was calculated using the European SCORE2 table, and ACB was assessed using the CALS scale. Results: Among 172 patients with memory complaints and CVR factors, 60% failed at least one cognitive test. These patients were on significantly more medications and had higher blood pressure and HbA1c levels. An increase in CVR and ACB was associated with more failed tests. Additionally, elevated SCORE2 scores were associated with a greater failure rate on the MIS test, while patients with elevated ACB more frequently failed the SPMSQ test. Conclusions: Selecting an adequate brief cognitive test according to patients’ characteristics offers an opportunity to screen patients who are probably cognitively impaired. Whereas the MIS test may be helpful for patients with cardiovascular risk, SPMSQ stands out among patients with significant ACB. Full article

Named Entity Recognition (NER) is a crucial component of Natural Language Processing (NLP). When dealing with the high diversity and complexity of the Chinese language, existing Chinese NER models face challenges in addressing word sense ambiguity, capturing long-range dependencies, and maintaining robustness, which hinders the accuracy of entity recognition. To this end, a Chinese NER model based on multi-level representation learning is proposed. The model leverages a pre-trained word-based embedding to capture contextual information. A linear layer adjusts dimensions to fit an Extended Long Short-Term Memory (XLSTM) network, enabling the capture of long-range dependencies and contextual information, and providing deeper representations. An adaptive multi-head attention mechanism is proposed to enhance the ability to capture global dependencies and comprehend deep semantic context. Additionally, GlobalPointer with rotational position encoding integrates global information for entity category prediction. Projected Gradient Descent (PGD) is incorporated, introducing perturbations in the embedding layer of the pre-trained model to enhance stability in noisy environments. The proposed model achieves F1-scores of 96.89%, 74.89%, 72.19%, and 80.96% on the Resume, Weibo, CMeEE, and CLUENER2020 datasets, respectively, demonstrating improvements over baseline and comparison models. Full article

Event detection is a crucial task in information extraction. Existing research primarily focuses on machine automatic detection tasks, which often perform poorly in certain practical applications. To address this, an interactive event-detection mode of “machine recommendation-human review–machine incremental learning” was proposed. In this mode, we study a few-shot continual class-incremental learning scenario, where the challenge is to learn new-class events with limited samples while preserving memory of old class events. To tackle these challenges, we propose a class-incremental learning method for interactive event detection via Interaction, Contrast and Distillation (ICD). We design a replay strategy based on representative and confusable samples to retain the most valuable samples under limited conditions; we introduce semantic-boundary-smoothness contrastive learning for effective learning of new-class events with few samples; and we employ hierarchical distillation to mitigate catastrophic forgetting. These methods complement each other and show strong performance. Experimental results demonstrate that, in the 5-shot 5-round class incremental-learning settings on two Chinese event-detection datasets ACE and DuEE, our method achieves final recall rates of 71.48% and 90.39%, respectively, improving by 6.86% and 3.90% over the best baseline methods. Full article

Medical named entity recognition (NER) focuses on extracting and classifying key entities from medical texts. Through automated medical information extraction, NER can effectively improve the efficiency of electronic medical record analysis, medical literature retrieval, and intelligent medical question–answering systems, enabling doctors and researchers to obtain the required medical information more quickly and thereby helping to improve the accuracy of diagnosis and treatment decisions. The current methods have certain limitations in dealing with contextual dependencies and entity memory and fail to fully consider the contextual relevance and interactivity between entities. To address these issues, this paper proposes a Chinese medical named entity recognition model that combines contextual dependency perception and a new memory unit. The model combines the BERT pre-trained model with a new memory unit (GLMU) and a recall network (RMN). The GLMU can efficiently capture long-distance dependencies, while the RMN enhances multi-level semantic information processing. The model also incorporates fully connected layers (FC) and conditional random fields (CRF) to further optimize the performance of entity classification and sequence labeling. The experimental results show that the model achieved F1 values of 91.53% and 64.92% on the Chinese medical datasets MCSCSet and CMeEE, respectively, surpassing other related models and demonstrating significant advantages in the field of medical entity recognition. Full article

Semi-supervised medical image segmentation models often face challenges such as empirical mismatch and data imbalance. Traditional methods, like the two-stream perturbation model, tend to over-rely on strong perturbation, leaving weak perturbation and labeled images underutilized. To overcome these challenges, we propose an innovative hybrid copy-paste (HCP) method within the strong perturbation branch, encouraging unlabeled images to learn more comprehensive semantic information from labeled images and narrowing the empirical distribution gap. Additionally, we integrate contrastive learning into the weak perturbation branch, where contrastive learning samples are selected through semantic grouping contrastive sampling (SGCS) to address memory and variance issues. This sampling strategy ensures more effective use of weak perturbation data. This approach is particularly advantageous for pixel segmentation tasks with severely limited labels. Finally, our approach is validated on the public ACDC (Automated Cardiac Diagnosis Challenge) dataset, achieving a 90.6% DICE score, with just 7% labeled data. These results demonstrate the effectiveness of our method in improving segmentation performance with limited labeled data. Full article

Developments in drones and imaging hardware technology have opened up countless possibilities for enhancing structural condition assessments and visual inspections. However, processing the inspection images requires considerable work hours, leading to delays in the assessment process. This study presents a semantic segmentation architecture that integrates vision transformers with Laplacian pyramid scaling networks, enabling rapid and accurate pixel-level damage detection. Unlike conventional methods that often lose critical details through resampling or cropping high-resolution images, our approach preserves essential inspection-related information such as microcracks and edges using non-uniform image rescaling networks. This innovation allows for detailed damage identification of high-resolution images while significantly reducing the computational demands. Our main contributions in this study are: (1) proposing two rescaling networks that together allow for processing high-resolution images while significantly reducing the computational demands; and (2) proposing Dmg2Former, a low-resolution segmentation network with a Swin Transformer backbone that leverages the saved computational resources to produce detailed visual inspection masks. We validate our method through a series of experiments on publicly available visual inspection datasets, addressing various tasks such as crack detection and material identification. Finally, we examine the computational efficiency of the adaptive rescalers in terms of multiply–accumulate operations and GPU-memory requirements. Full article

Generating food images aims to convert textual food ingredients into corresponding images for the visualization of color and shape adjustments, dietary guidance, and the creation of new dishes. It has a wide range of applications, including food recommendation, recipe development, and health management. However, existing food image generation models, predominantly based on GANs (Generative Adversarial Networks), face challenges in maintaining semantic consistency between image and text, as well as achieving visual realism in the generated images. These limitations are attributed to the constrained representational capacity of sparse ingredient embedding and the lack of diversity in GAN-based food image generation models. To alleviate this problem, this paper proposes a food image generation network, named MLA-Diff, in which ingredient and image features are learned and integrated as ingredient-image pairs to generate initial images, and then image details are refined by using an attention fusion module. The main contributions are as follows: (1) The enhanced CLIP (Contrastive Language-Image Pre-Training) module is constructed by transforming sparse ingredient embedding into compact embedding and capturing multi-scale image features, providing an effective solution to alleviate semantic consistency issues. (2) The Memory module is proposed by embedding a pre-trained diffusion model to generate initial images with diversity and reality. (3) The attention fusion module is proposed by integrating features from diverse modalities to enhance the comprehension between ingredient and image features. Extensive experiments on the Mini-food dataset demonstrate the superiority of the MLA-Diff in terms of semantic consistency and visual realism, generating high-quality food images. Full article

Background: Repetitive transcranial magnetic stimulation (rTMS) has shown therapeutic effects in neurological patients by inducing neural plasticity. In this pilot study, we analyzed the modifying effects of high-frequency (HF-)rTMS applied to the dorsolateral prefrontal cortex (DLPFC) of patients with mild cognitive impairment (MCI) using an advanced approach of functional connectome analysis based on network control theory (NCT). Methods: Using local-to-global functional parcellation, average and modal controllability (AC/MC) were estimated for DLPFC nodes of prefrontal-lateral control networks (R/LH_Cont_PFCl_3/4) from a resting-state fMRI series acquired at three time points (T0 = baseline, T1 = T0 + 4 weeks, T2 = T1 + 20 weeks) in MCI patients receiving regular daily sessions of 10 Hz HF-rTMS (n = 10, 68.00 ± 8.16 y, 4 males) or sham (n = 10, 63.80 ± 9.95 y, 5 males) stimulation, between T0 and T1. Longitudinal (group) effects on AC/MC were assessed with non-parametric statistics. Spearman correlations (ρ) of AC/MC vs. neuropsychological (RBANS) score %change (at T1, T2 vs. T0) were calculated. Results: AC median was reduced in MCI-rTMS, compared to the control group, for RH_Cont_PFCl_3/4 at T1 and T2 (vs. T0). In MCI-rTMS patients, for RH_Cont_PFCl_3, AC % change at T1 (vs. T0) was negatively correlated with semantic fluency (ρ = −0.7939, p = 0.045) and MC % change at T2 (vs. T0) was positively correlated with story memory (ρ = 0.7416, p = 0.045). Conclusions: HF-rTMS stimulation of DLFC nodes significantly affects the controllability of the functional connectome in MCI patients. Emerging correlations between AC/MC controllability and cognitive performance changes, immediately (T1 vs. T0) and six months (T2 vs. T0) after treatment, suggest NCT could help explain the HF-rTMS impact on prefrontal-lateral control network, monitoring induced neural plasticity effects in MCI patients. Full article

In this narrative review, we explore the latest evidence on semantic interventions for older adults, including both prevention and rehabilitation/remediation efforts, discussing them particularly in the context of dementia. Cognitive interventions vary in their level of structure, encompassing standardized (task-focused tasks) and unstandardized tasks (person-centered tasks). These interventions also differ in their target: rehabilitation or prevention. Addressing semantic knowledge/semantic memory/semantics is important, primarily because its efficiency impacts other cognitive domains. Semantic tasks are commonly included in preventive and rehabilitation programs, typically as standardized tasks with pre-defined semantic referents. On the other hand, person-centered approaches introduce personally relevant semantics, allowing patients to share thoughts and experiences with expressive language. Although these approaches offer benefits beyond cognitive improvement, their lack of structure may pose challenges. Our question club (CQ) program blends structured activities with personally relevant semantics, aiming to harness the advantages of both methods. Additionally, in this narrative review, we discuss future challenges and directions in the field of semantic interventions. Full article

Named entity recognition (NER) is a fundamental task in Natural Language Processing (NLP). During the training process, NER models suffer from over-confidence, and especially for the Chinese NER task, it involves word segmentation and introduces erroneous entity boundary segmentation, exacerbating over-confidence and reducing the model’s overall performance. These issues limit further enhancement of NER models. To tackle these problems, we proposes a new model named KCB-FLAT, designed to enhance Chinese NER performance by integrating enriched semantic information with the word-Boundary Smoothing technique. Particularly, we first extract various types of syntactic data and utilize a network named Key-Value Memory Network, based on syntactic information to functionalize this, integrating it through an attention mechanism to generate syntactic feature embeddings for Chinese characters. Subsequently, we employed an encoder named Cross-Transformer to thoroughly combine syntactic and lexical information to address the entity boundary segmentation errors caused by lexical information. Finally, we introduce a Boundary Smoothing module, combined with a regularity-conscious function, to capture the internal regularity of per entity, reducing the model’s overconfidence in entity probabilities through smoothing. Experimental results demonstrate that the proposed model achieves exceptional performance on the MSRA, Resume, Weibo, and self-built ZJ datasets, as verified by the F1 score. Full article

In the era of information explosion, Chinese social media has become a repository for massive geographic information; however, its unique unstructured nature and diverse expressions are challenging to toponym entity recognition. To address this problem, we propose a Chinese social media named entity recognition (CSMNER) model to improve the accuracy and robustness of toponym recognition in Chinese social media texts. By combining the BERT (Bidirectional Encoder Representations from Transformers) pre-trained model with an improved IDCNN-BiLSTM-CRF (Iterated Dilated Convolutional Neural Network- Bidirectional Long Short-Term Memory- Conditional Random Field) architecture, this study innovatively incorporates a boundary extension module to effectively extract the local boundary features and contextual semantic features of the toponym, successfully addressing the recognition challenges posed by noise interference and language expression variability. To verify the effectiveness of the model, experiments were carried out on three datasets: WeiboNER, MSRA, and the Chinese social named entity recognition (CSNER) dataset, a self-built named entity recognition dataset. Compared with the existing models, CSMNER achieves significant performance improvement in toponym recognition tasks. Full article