Search Results (17,058)

Augmenting the author’s prior research on lyrical code-switching, as presented in Picone, “Artistic Codemixing”, published in 2002, various conceptual frameworks are made explicit, namely the enlistment of multimodal and intertextual approaches for their methodological usefulness in analyzing and interpreting message-making that incorporates lyrical code-switching as one of its components. Conceived as a bipolarity, the rooted (or local) and the transcendent (or global), each having advantages in the negotiation of identity, is also applied to the analysis. New departures include the introduction of the notion of “curated lyrical code-switching” for the purpose of analyzing songs in which multiple performers are assigned lyrics in different languages, as a function of their respective proficiencies, as curated by the person or persons having authorial agency and taking stock of the social semiotics relevant to the anticipated audience. Moving beyond the negotiation of the identity of the code-switching composer or performer, in another new departure, attention is paid to the musical identity of the listener. As a reflection of the breadth of lyrical code-switching, a rich assortment of examples draws from the musical art of Beyoncé, Jon Batiste, Stromae, Shakira, BTS, NewJeans, Indigenous songsmiths, Cajun songsmiths, Latin Pop and Hip-Hop artists, songs composed for international sports events, and other sources. Full article

In recent years, dogs that appear to communicate with people by pressing buttons on soundboards that replay pre-recorded English words have become very popular on social media online. We explore how these dogs belong to a historical tradition that dates back at least to the Middle Ages and peaked in the early twentieth century. Through analyses of short videos, books, and training manuals, we identify several paradoxes inherent in this phenomenon. These include how the dogs appear to provide unmediated access to their thoughts, and yet, their button presses are typically incoherent and require interpretation. They also require months of training to “spontaneously” express themselves. There is also an anthropomorphism and -centrism in claiming that a human skill—language—is required for a dog to express mental states that it already possesses. This approach to communicating with dogs quiets canine forms of expression such as barking, whining, bodily postures, and odors and replaces them with endearing but infantile human voices. We suggest that, while this endeavor may be well intentioned and often playful, it runs the risk of skewing people’s perception of dogs towards fur-clad infants rather than adult members of a different species and of making people less attentive to canine nonverbal communication. Full article

Patient satisfaction is a key measure of the quality of healthcare, directly impacting the success and competitiveness of healthcare providers in an increasingly demanding market. Traditional feedback collection methods often fall short of capturing the full spectrum of patient experiences, leading to skewed satisfaction reports due to patients’ reluctance to criticize services and the inherent limitations of survey designs. To address these issues, advanced Natural Language Processing (NLP) techniques such as aspect-based sentiment analysis are emerging as essential tools. Aspect-based sentiment analysis breaks down the feedback text into specific aspects and evaluates the sentiment for each aspect, offering a more nuanced and actionable understanding of patient opinions. Despite its potential, aspect-based sentiment analysis is under-explored in the healthcare sector, particularly in the Arabic literature. This study addresses this gap by performing an Arabic aspect-based sentiment analysis on patient experience data, introducing the newly constructed Hospital Experiences Arabic Reviews (HEAR) dataset, and conducting a comparative study using Bidirectional Embedding Representations from Transformers (BERT) combined with machine learning classifiers, as well as fine-tuning BERT models, including MARBERT, ArabicBERT, AraBERT, QARiB, and CAMeLBERT. Additionally, the performance of GPT-4 via OpenAI’s ChatGPT is evaluated in this context, making a significant contribution to the comparative study of BERT with traditional classifiers and the assessment of GPT-4 for aspect-based sentiment analysis in healthcare, ultimately offering valuable insights for enhancing patient experiences through the use of AI-driven approaches. The results show that the joint model leveraging MARBERT and SVM achieves the highest accuracy of 92.14%, surpassing other models, including GPT-4, in both aspect category detection and polarity tasks. Full article

Deep learning has made significant strides, driving advances in areas like computer vision, natural language processing, and autonomous systems. In this paper, we further investigate the implications of the role of additive shortcut connections, focusing on models such as ResNet, Vision Transformers (ViTs), and MLP-Mixers, given that they are essential in enabling efficient information flow and mitigating optimization challenges such as vanishing gradients. In particular, capitalizing on our recent information bottleneck approach, we analyze how additive shortcuts influence the fitting and compression phases of training, crucial for generalization. We leverage Z-X and Z-Y measures as practical alternatives to mutual information for observing these dynamics in high-dimensional spaces. Our empirical results demonstrate that models with identity shortcuts (ISs) often skip the initial fitting phase and move directly into the compression phase, while non-identity shortcut (NIS) models follow the conventional two-phase process. Furthermore, we explore how IS models are still able to compress effectively, maintaining their generalization capacity despite bypassing the early fitting stages. These findings offer new insights into the dynamics of shortcut connections in neural networks, contributing to the optimization of modern deep learning architectures. Full article

Image retargeting is a common computer graphics task which involves manipulating the size or aspect ratio of an image. This task often presents a challenge to the artist or user, because manipulating the size of an image necessitates some degree of data loss as pixels need to be removed to accommodate a different image size. We present an image retargeting framework which implements a confidence map generated by a segmentation model for content-aware resizing, allowing users to specify which subjects in an image to preserve using natural language prompts much like the role of an art director conversing with their artist. Using computer vision models to detect object positions also provides additional control over the composition of the retargeted image at various points in the image-processing pipeline. This object-based approach to energy map augmentation is incredibly flexible, because only minor adjustments to the processing of the energy maps can provide a significant degree of control over where seams—paths of pixels through the image—are removed, and how seam removal is prioritized in different sections of the image. It also provides additional control with techniques for object and background separation and recomposition. This research explores how several different types of deep-learning models can be integrated into this pipeline in order to easily make these decisions, and provide different retargeting results on the same image based on user input and compositional considerations. Because this is a framework based on existing machine-learning models, this approach will benefit from advancements in the rapidly developing fields of computer vision and large language models and can be extended for further natural language directorial controls over images. Full article

Through fieldwork conducted between 2014 and 2016 in Arkhangelsk, Naryan-Mar, Krasnoye, and Saint Petersburg, this paper investigates the endangered status of Tundra Nenets, an underrepresented and understudied Samoyedic minority language in northern Russia. Criteria for assessing language endangerment are applied to Tundra Nenets while also taking into consideration the interplay between language identity, reactive ethnicity, negative attitudes, and state politics. The personal story of NC, a Tundra Nenets woman, serves as a case study and exemplifies the impact of decades of marginalization, stigmatization, and discrimination on the cultural and linguistic identity. NC’s narrative illustrates how negative attitudes are exacerbating the decline of Tundra Nenets, further threatening its survival. Because of its absence from schools and institutions, Tundra Nenets seems to be turning into a heritage language in its own homeland. This paper studies the complex interplay between identity, language, and societal pressures, illustrating the broader challenges faced by the Tundra Nenets and other minority communities in maintaining their linguistic and cultural heritage. While the situation remains dire and political action is called for, efforts in boosting language awareness, documentation, and revitalization offer potential pathways for the preservation of Tundra Nenets, drawing on successful examples from other endangered language communities. Full article

The proliferation of deep learning has transformed artificial intelligence, demonstrating prowess in domains such as image recognition, natural language processing, and robotics. Nonetheless, deep learning models are susceptible to adversarial examples, well-crafted inputs that can induce erroneous predictions, particularly in safety-critical contexts. Researchers actively pursue countermeasures such as adversarial training and robust optimization to fortify model resilience. This vulnerability is notably accentuated by the ubiquitous utilization of ReLU functions in deep learning models. A previous study proposed an innovative solution to mitigate this vulnerability, presenting a capped ReLU function tailored to bolster neural network robustness against adversarial examples. However, the approach had a scalability problem. To address this limitation, a series of comprehensive experiments are undertaken across diverse datasets, and we introduce the dynamic-max-value ReLU function to address the scalability problem. Full article

As user requirements become increasingly complex, the demand for product personalization is growing, but traditional hardware-centric production relies on fixed procedures that lack the flexibility to support diverse requirements. Although bespoke manufacturing has been introduced, it provides users with only a few standardized options, limiting its ability to meet a wide range of needs. To address this issue, a new manufacturing concept called the software-defined factory has emerged. It is an autonomous manufacturing system that provides reconfigurable manufacturing services to produce tailored products. Reinforcement learning has been suggested for flexible scheduling to satisfy user requirements. However, fixed rule-based methods struggle to accommodate conflicting needs. This study proposes a novel federated digital twin scheduling that combines large language models and deep reinforcement learning algorithms to meet diverse user requirements in the software-defined factory. The large language model-based literacy module analyzes requirements in natural language and assigns weights to digital twin attributes to achieve highly relevant KPIs, which are used to guide scheduling decisions. The deep reinforcement learning-based scheduling module optimizes scheduling by selecting the job and machine with the maximum reward. Different types of user requirements, such as reducing manufacturing costs and improving productivity, are input and evaluated by comparing the flow-shop scheduling with job-shop scheduling based on reinforcement learning. Experimental results indicate that in requirement case 1 (the manufacturing cost), the proposed method outperforms flow-shop scheduling by up to 14.9% and job-shop scheduling by 5.6%. For requirement case 2 (productivity), it exceeds the flow-shop method by up to 13.4% and the job-shop baseline by 7.2%. The results confirm that the literacy DRL scheduling proposed in this paper can handle the individual characteristics of requirements. Full article

Migrant adolescents face unique emotional and relational challenges that can hinder their well-being and development. While prior research has identified many of these challenges, there is limited work exploring migrant adolescents’ perspectives on their needs. This study aims to bridge that gap by adopting a participatory approach to investigate the emotional and relational needs of migrant adolescents in Spain and the solutions they propose to address them. Using Bronfenbrenner’s Ecological Systems Theory as the theoretical framework, we conducted qualitative participatory research with migrant adolescents. They identified their emotional and relational needs, which were categorised into six thematic areas distributed across the ecological levels. The themes include supporting their families, receiving recognition and emotional support from relatives, improving school and societal experiences, learning the host language, gaining empathy from the local population, and regularising their legal status. The key actors identified to help meet their needs include parents, teachers, peers, society, and policymakers. The participants proposed self-directed solutions to these challenges, such as fostering peer relationships and advocating for policy reforms. The findings suggest that migrant adolescents have valuable insights into their emotional and relational needs, emphasising the importance of involving them in shaping interventions that support their inclusion and mental health. Full article

(1) Background: In recent years, large language models (LLMs) such as ChatGPT have gained significant attention in various fields, including dentistry. This scoping review aims to examine the current applications and explore potential uses of LLMs in the orthodontic domain, shedding light on how they might improve dental healthcare. (2) Methods: We carried out a comprehensive search in five electronic databases, namely PubMed, Scopus, Embase, ProQuest and Web of Science. Two authors independently screened articles and performed data extraction according to the eligibility criteria, following the PRISMA-ScR guideline. The main findings from the included articles were synthesized and analyzed in a narrative way. (3) Results: A total of 706 articles were searched, and 12 papers were eventually included. The applications of LLMs include improving diagnostic and treatment efficiency in orthodontics as well as enhancing communication with patients. (4) Conclusions: There is emerging research in countries worldwide on the use of LLMs in orthodontics, suggesting an upward trend in their acceptance within this field. However, the potential application of LLMs remains in its early stage, with a noticeable lack of extensive studies and tailored products to address specific clinical needs. Full article

With the advancement of the large language model (LLM), the demand for data labeling services has increased dramatically. Big models are inseparable from high-quality, specialized scene data, from training to deploying application iterations to landing generation. However, how to achieve intelligent labeling consistency and accuracy and improve labeling efficiency in distributed data middleware scenarios is the main difficulty in enhancing the quality of labeled data at present. In this paper, we proposed an asynchronous federated learning optimization method based on the combination of LLM and digital twin technology. By analysising and comparing and with other existing asynchronous federated learning algorithms, the experimental results show that our proposed method outperforms other algorithms in terms of performance, such as model accuracy and running time. The experimental validation results show that our proposed method has good performance compared with other algorithms in the process of intelligent labeling both in terms of accuracy and running solves the consistency and accuracy problems of intelligent labeling in a distributed data center. Full article

Background/Objectives: Slow oscillation (SO) brainwaves observed during sleep have been shown to reflect the process of memory consolidation, that underlies the critical role of sleep in learning, memory, and other cognitive functions. Closed-loop auditory stimulation (CLAS) uses tones presented in phase with SOs to increase their amplitude and number, along with other brainwave signatures related to memory consolidation. Prior studies have found that CLAS maximizes the ability to perform rote memorization tasks, although this remains controversial. The present study examined whether CLAS affects a broader range of learning tasks than has been tested previously, including a rote language learning task requiring basic memorization and also two discovery learning tasks requiring insight, hypothesis testing, and integration of experience, all processes that benefit from memory consolidation. Methods: Twenty-eight healthy participants performed language and discovery learning tasks before sleeping in our laboratory for three continuous nights per week over two weeks, with verum or control CLAS using a prototype NeuroGevity system (NeuroGeneces, Inc., Santa Fe, NM, USA) in a crossed, randomized, double-blind manner. Results: Language learning showed a 35% better word recall (p = 0.048), and discovery learning showed a 26% better performance (p < 0.001) after three continuous nights of CLAS vs. control. EEG measures showed increased SO amplitude and entrainment, SO-spindle coupling, and other features that may underlie the learning benefits of CLAS. Conclusions: Taken together, the present results show that CLAS can alter brain dynamics and enhance learning, especially in complex discovery learning tasks that may benefit more from memory consolidation compared with rote word pair or language learning. Full article

Background: This article introduces SC-Phi2, a fine-tuned StarCraft II small language model. Small language models, like Phi2, Gemma, and DistilBERT, are streamlined versions of large language models (LLMs) with fewer parameters that require less computational power and memory to run. Method: To teach Microsoft’s Phi2 model about StarCraft, we create a new SC2 text dataset with information about StarCraft races, roles, and actions and use it to fine-tune Phi-2 with self-supervised learning. We pair this language model with a Vision Transformer (ViT) from the pre-trained BLIP-2 (Bootstrapping Language Image Pre-training) model, fine-tuning it on the StarCraft replay dataset, MSC. This enables us to construct dynamic prompts that include visual game state information. Results: Unlike the large models used in StarCraft LLMs such as GPT-3.5, Phi2 is trained primarily on textbook data and contains little inherent knowledge of StarCraft II beyond what is provided by our training process. By using LoRA (Low-rank Adaptation) and quantization, our model can be trained on a single GPU. We demonstrate that our model performs well at build order prediction, an important StarCraft macromanagement task. Conclusions: Our research on the usage of small models is a step towards reducing the carbon footprint of AI agents. Full article

The improvements in collecting and processing data using machine learning algorithms have increased the interest in data mining. This trend has led to the development of real-life decision support systems (DSSs) in diverse areas such as biomedical informatics, fraud detection, natural language processing, face recognition, autonomous vehicles, image processing, and each part of the real production environment. The imbalanced datasets in some of these studies, which result in low performance measures, have highlighted the need for additional efforts to address this issue. The proposed method (HOUM) is used to address the issue of imbalanced datasets for classification problems in this study. The aim of the model is to prevent the overfitting problem caused by oversampling and valuable data loss caused by undersampling in imbalanced data and obtain successful classification results. The HOUM is a hybrid approach that tackles imbalanced class distribution challenges, refines datasets, and improves model robustness. In the first step, majority-class data points that are distant from the decision boundary obtained via SVM are reduced. If the data are not balanced, SLS is employed to augment the minority-class data. This loop continues until the dataset becomes balanced. The main contribution of the proposed method is reproducing informative minority data using SLS and diminishing non-informative majority data using the SVM before applying classification techniques. Firstly, the efficiency of the proposed method, the HOUM, is verified by comparison with the SMOTE, SMOTEENN, and SMOTETomek techniques using eight datasets. Then, the results of the W-SIMO and RusAda algorithms, which were developed for imbalanced datasets, are compared with those of the HOUM. The strength of the HOUM is revealed through this comparison. The proposed HOUM algorithm utilizes a real dataset obtained from a project endorsed by The Scientific and Technical Research Council of Turkey. The collected data include quality control and processing parameters of yarn data. The aim of this project is to prevent yarn breakage errors during the weaving process on looms. This study introduces a decision support system (DSS) designed to prevent yarn breakage during fabric weaving. The high performance of the algorithm may encourage producers to manage yarn flow and enhance the HOUM’s efficiency as a DSS. Full article

The past few decades have witnessed an enormous research growth in the field of natural language processing. In this regard, numerous machine learning (ML) algorithms have been applied in different sub-domains of NLP such as speech recognition, text classification, sentiment analysis, etc. Furthermore, their performances have been evaluated using diverse performance metrics. However, a comparative analysis of various ML algorithms in the aforementioned field is a feasible research area to explore. This may efficiently guide future research to precisely focus on the improvement of those particular algorithms that have been found to be more effective based on previous research. Thus, this article provides a comparative analysis regarding the application and effectiveness of different ML algorithms in the field of NLP. Additionally, it highlights the future research direction to be adopted for enhancing the ability of the natural language processing domain. Full article