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Empowering Sentiment Analysis in Resource-Constrained Environments:Leveraging Lightweight Pre-trained Models for Optimal Performance
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作者 V.Prema V.Elavazhahan 《Journal of Harbin Institute of Technology(New Series)》 2025年第1期76-84,共9页
Sentiment analysis,a cornerstone of natural language processing,has witnessed remarkable advancements driven by deep learning models which demonstrated impressive accuracy in discerning sentiment from text across vari... Sentiment analysis,a cornerstone of natural language processing,has witnessed remarkable advancements driven by deep learning models which demonstrated impressive accuracy in discerning sentiment from text across various domains.However,the deployment of such models in resource-constrained environments presents a unique set of challenges that require innovative solutions.Resource-constrained environments encompass scenarios where computing resources,memory,and energy availability are restricted.To empower sentiment analysis in resource-constrained environments,we address the crucial need by leveraging lightweight pre-trained models.These models,derived from popular architectures such as DistilBERT,MobileBERT,ALBERT,TinyBERT,ELECTRA,and SqueezeBERT,offer a promising solution to the resource limitations imposed by these environments.By distilling the knowledge from larger models into smaller ones and employing various optimization techniques,these lightweight models aim to strike a balance between performance and resource efficiency.This paper endeavors to explore the performance of multiple lightweight pre-trained models in sentiment analysis tasks specific to such environments and provide insights into their viability for practical deployment. 展开更多
关键词 sentiment analysis light weight models resource⁃constrained environment pre⁃trained models
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Attenuate Class Imbalance Problem for Pneumonia Diagnosis Using Ensemble Parallel Stacked Pre-Trained Models
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作者 Aswathy Ravikumar Harini Sriraman 《Computers, Materials & Continua》 SCIE EI 2023年第4期891-909,共19页
Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this purpose.Com... Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this purpose.Computer-aided diagnosis of pneumonia using deep learning techniques iswidely used due to its effectiveness and performance. In the proposed method,the Synthetic Minority Oversampling Technique (SMOTE) approach is usedto eliminate the class imbalance in the X-ray dataset. To compensate forthe paucity of accessible data, pre-trained transfer learning is used, and anensemble Convolutional Neural Network (CNN) model is developed. Theensemble model consists of all possible combinations of the MobileNetv2,Visual Geometry Group (VGG16), and DenseNet169 models. MobileNetV2and DenseNet169 performed well in the Single classifier model, with anaccuracy of 94%, while the ensemble model (MobileNetV2+DenseNet169)achieved an accuracy of 96.9%. Using the data synchronous parallel modelin Distributed Tensorflow, the training process accelerated performance by98.6% and outperformed other conventional approaches. 展开更多
关键词 Pneumonia prediction distributed deep learning data parallel model ensemble deep learning class imbalance skewed data
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Multilingual Text Summarization in Healthcare Using Pre-Trained Transformer-Based Language Models
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作者 Josua Käser Thomas Nagy +1 位作者 Patrick Stirnemann Thomas Hanne 《Computers, Materials & Continua》 2025年第4期201-217,共17页
We analyze the suitability of existing pre-trained transformer-based language models(PLMs)for abstractive text summarization on German technical healthcare texts.The study focuses on the multilingual capabilities of t... We analyze the suitability of existing pre-trained transformer-based language models(PLMs)for abstractive text summarization on German technical healthcare texts.The study focuses on the multilingual capabilities of these models and their ability to perform the task of abstractive text summarization in the healthcare field.The research hypothesis was that large language models could perform high-quality abstractive text summarization on German technical healthcare texts,even if the model is not specifically trained in that language.Through experiments,the research questions explore the performance of transformer language models in dealing with complex syntax constructs,the difference in performance between models trained in English and German,and the impact of translating the source text to English before conducting the summarization.We conducted an evaluation of four PLMs(GPT-3,a translation-based approach also utilizing GPT-3,a German language Model,and a domain-specific bio-medical model approach).The evaluation considered the informativeness using 3 types of metrics based on Recall-Oriented Understudy for Gisting Evaluation(ROUGE)and the quality of results which is manually evaluated considering 5 aspects.The results show that text summarization models could be used in the German healthcare domain and that domain-independent language models achieved the best results.The study proves that text summarization models can simplify the search for pre-existing German knowledge in various domains. 展开更多
关键词 Text summarization pre-trained transformer-based language models large language models technical healthcare texts natural language processing
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GPT2-ICC:A data-driven approach for accurate ion channel identification using pre-trained large language models 被引量:1
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作者 Zihan Zhou Yang Yu +9 位作者 Chengji Yang Leyan Cao Shaoying Zhang Junnan Li Yingnan Zhang Huayun Han Guoliang Shi Qiansen Zhang Juwen Shen Huaiyu Yang 《Journal of Pharmaceutical Analysis》 2025年第8期1800-1809,共10页
Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Class... Current experimental and computational methods have limitations in accurately and efficiently classifying ion channels within vast protein spaces.Here we have developed a deep learning algorithm,GPT2 Ion Channel Classifier(GPT2-ICC),which effectively distinguishing ion channels from a test set containing approximately 239 times more non-ion-channel proteins.GPT2-ICC integrates representation learning with a large language model(LLM)-based classifier,enabling highly accurate identification of potential ion channels.Several potential ion channels were predicated from the unannotated human proteome,further demonstrating GPT2-ICC’s generalization ability.This study marks a significant advancement in artificial-intelligence-driven ion channel research,highlighting the adaptability and effectiveness of combining representation learning with LLMs to address the challenges of imbalanced protein sequence data.Moreover,it provides a valuable computational tool for uncovering previously uncharacterized ion channels. 展开更多
关键词 Ion channel Artificial intelligence Representation learning GPT2 Protein language model
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A Classification–Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models 被引量:10
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作者 Xing Deng Haijian Shao +2 位作者 Liang Shi Xia Wang Tongling Xie 《Computer Modeling in Engineering & Sciences》 SCIE EI 2020年第11期579-596,共18页
The Coronavirus Disease 2019(COVID-19)is wreaking havoc around the world,bring out that the enormous pressure on national health and medical staff systems.One of the most effective and critical steps in the fight agai... The Coronavirus Disease 2019(COVID-19)is wreaking havoc around the world,bring out that the enormous pressure on national health and medical staff systems.One of the most effective and critical steps in the fight against COVID-19,is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation imaging.In this paper,five keras-related deep learning models:ResNet50,InceptionResNetV2,Xception,transfer learning and pre-trained VGGNet16 is applied to formulate an classification-detection approaches of COVID-19.Two benchmark methods SVM(Support Vector Machine),CNN(Conventional Neural Networks)are provided to compare with the classification-detection approaches based on the performance indicators,i.e.,precision,recall,F1 scores,confusion matrix,classification accuracy and three types of AUC(Area Under Curve).The highest classification accuracy derived by classification-detection based on 5857 Chest X-rays and 767 Chest CTs are respectively 84%and 75%,which shows that the keras-related deep learning approaches facilitate accurate and effective COVID-19-assisted detection. 展开更多
关键词 COVID-19 detection deep learning transfer learning pre-trained models
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Pre-trained models for natural language processing: A survey 被引量:204
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作者 QIU XiPeng SUN TianXiang +3 位作者 XU YiGe SHAO YunFan DAI Ning HUANG XuanJing 《Science China(Technological Sciences)》 SCIE EI CAS CSCD 2020年第10期1872-1897,共26页
Recently, the emergence of pre-trained models(PTMs) has brought natural language processing(NLP) to a new era. In this survey, we provide a comprehensive review of PTMs for NLP. We first briefly introduce language rep... Recently, the emergence of pre-trained models(PTMs) has brought natural language processing(NLP) to a new era. In this survey, we provide a comprehensive review of PTMs for NLP. We first briefly introduce language representation learning and its research progress. Then we systematically categorize existing PTMs based on a taxonomy from four different perspectives. Next,we describe how to adapt the knowledge of PTMs to downstream tasks. Finally, we outline some potential directions of PTMs for future research. This survey is purposed to be a hands-on guide for understanding, using, and developing PTMs for various NLP tasks. 展开更多
关键词 deep learning neural network natural language processing pre-trained model distributed representation word embedding self-supervised learning language modelling
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Ecosystem service models are indeed being validated:A response to Pereira et al.(2025)
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作者 James M.Bullock Danny A.P.Hooftman +1 位作者 John W.Redhead Simon Willcock 《Geography and Sustainability》 2026年第1期247-248,共2页
In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation ... In their recent paper Pereira et al.(2025)claim that validation is overlooked in mapping and modelling of ecosystem services(ES).They state that“many studies lack critical evaluation of the results and no validation is provided”and that“the validation step is largely overlooked”.This assertion may have been true several years ago,for example,when Ochoa and Urbina-Cardona(2017)made a similar observation.However,there has been much work on ES model validation over the last decade. 展开更多
关键词 evaluation MAPPING modeling es model ecosystem services VALIDATION
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KitWaSor:Pioneering pre-trained model for kitchen waste sorting with an innovative million-level benchmark dataset
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作者 Leyuan Fang Shuaiyu Ding +3 位作者 Hao Feng Junwu Yu Lin Tang Pedram Ghamisi 《CAAI Transactions on Intelligence Technology》 2025年第1期94-114,共21页
Intelligent sorting is an important prerequisite for the full quantitative consumption and harmless disposal of kitchen waste.The existing object detection method based on an ImageNet pre-trained model is an effective... Intelligent sorting is an important prerequisite for the full quantitative consumption and harmless disposal of kitchen waste.The existing object detection method based on an ImageNet pre-trained model is an effective way of sorting.Owing to significant domain gaps between natural images and kitchen waste images,it is difficult to reflect the characteristics of diverse scales and dense distribution in kitchen waste based on an ImageNet pre-trained model,leading to poor generalisation.In this article,the authors propose the first pre-trained model for kitchen waste sorting called KitWaSor,which combines both contrastive learning(CL)and masked image modelling(MIM)through self-supervised learning(SSL).First,to address the issue of diverse scales,the authors propose a mixed masking strategy by introducing an incomplete masking branch based on the original random masking branch.It prevents the complete loss of small-scale objects while avoiding excessive leakage of large-scale object pixels.Second,to address the issue of dense distribution,the authors introduce semantic consistency constraints on the basis of the mixed masking strategy.That is,object semantic reasoning is performed through semantic consistency constraints to compensate for the lack of contextual information.To train KitWaSor,the authors construct the first million-level kitchen waste dataset across seasonal and regional distributions,named KWD-Million.Extensive experiments show that KitWaSor achieves state-of-the-art(SOTA)performance on the two most relevant downstream tasks for kitchen waste sorting(i.e.image classification and object detection),demonstrating the effectiveness of the proposed KitWaSor. 展开更多
关键词 contrastive learning kitchen waste masked image modeling pre-trained model self-supervised learning
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DPCIPI: A pre-trained deep learning model for predicting cross-immunity between drifted strains of Influenza A/H3N2
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作者 Yiming Du Zhuotian Li +8 位作者 Qian He Thomas Wetere Tulu Kei Hang Katie Chan Lin Wang Sen Pei Zhanwei Du Zhen Wang Xiao-Ke Xu Xiao Fan Liu 《Journal of Automation and Intelligence》 2025年第2期115-124,共10页
Predicting cross-immunity between viral strains is vital for public health surveillance and vaccine development.Traditional neural network methods,such as BiLSTM,could be ineffective due to the lack of lab data for mo... Predicting cross-immunity between viral strains is vital for public health surveillance and vaccine development.Traditional neural network methods,such as BiLSTM,could be ineffective due to the lack of lab data for model training and the overshadowing of crucial features within sequence concatenation.The current work proposes a less data-consuming model incorporating a pre-trained gene sequence model and a mutual information inference operator.Our methodology utilizes gene alignment and deduplication algorithms to preprocess gene sequences,enhancing the model’s capacity to discern and focus on distinctions among input gene pairs.The model,i.e.,DNA Pretrained Cross-Immunity Protection Inference model(DPCIPI),outperforms state-of-theart(SOTA)models in predicting hemagglutination inhibition titer from influenza viral gene sequences only.Improvement in binary cross-immunity prediction is 1.58%in F1,2.34%in precision,1.57%in recall,and 1.57%in Accuracy.For multilevel cross-immunity improvements,the improvement is 2.12%in F1,3.50%in precision,2.19%in recall,and 2.19%in Accuracy.Our study showcases the potential of pre-trained gene models to improve predictions of antigenic variation and cross-immunity.With expanding gene data and advancements in pre-trained models,this approach promises significant impacts on vaccine development and public health. 展开更多
关键词 Cross-immunity prediction pre-trained model Deep learning Influenza strains Hemagglutination inhibition
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CIT-Rec:Enhancing Sequential Recommendation System with Large Language Models
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作者 Ziyu Li Zhen Chen +2 位作者 Xuejing Fu Tong Mo Weiping Li 《Computers, Materials & Continua》 2026年第3期2328-2343,共16页
Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interact... Recommendation systems are key to boosting user engagement,satisfaction,and retention,particularly on media platforms where personalized content is vital.Sequential recommendation systems learn from user-item interactions to predict future items of interest.However,many current methods rely on unique user and item IDs,limiting their ability to represent users and items effectively,especially in zero-shot learning scenarios where training data is scarce.With the rapid development of Large Language Models(LLMs),researchers are exploring their potential to enhance recommendation systems.However,there is a semantic gap between the linguistic semantics of LLMs and the collaborative semantics of recommendation systems,where items are typically indexed by IDs.Moreover,most research focuses on item representations,neglecting personalized user modeling.To address these issues,we propose a sequential recommendation framework using LLMs,called CIT-Rec,a model that integrates Collaborative semantics for user representation and Image and Text information for item representation to enhance Recommendations.Specifically,by aligning intuitive image information with text containing semantic features,we can more accurately represent items,improving item representation quality.We focus not only on item representations but also on user representations.To more precisely capture users’personalized preferences,we use traditional sequential recommendation models to train on users’historical interaction data,effectively capturing behavioral patterns.Finally,by combining LLMs and traditional sequential recommendation models,we allow the LLM to understand linguistic semantics while capturing collaborative semantics.Extensive evaluations on real-world datasets show that our model outperforms baseline methods,effectively combining user interaction history with item visual and textual modalities to provide personalized recommendations. 展开更多
关键词 Large language models vision language models sequential recommendation instruction tuning
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Large-scale Multi-modal Pre-trained Models: A Comprehensive Survey 被引量:22
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作者 Xiao Wang Guangyao Chen +5 位作者 Guangwu Qian Pengcheng Gao Xiao-Yong Wei Yaowei Wang Yonghong Tian Wen Gao 《Machine Intelligence Research》 EI CSCD 2023年第4期447-482,共36页
With the urgent demand for generalized deep models,many pre-trained big models are proposed,such as bidirectional encoder representations(BERT),vision transformer(ViT),generative pre-trained transformers(GPT),etc.Insp... With the urgent demand for generalized deep models,many pre-trained big models are proposed,such as bidirectional encoder representations(BERT),vision transformer(ViT),generative pre-trained transformers(GPT),etc.Inspired by the success of these models in single domains(like computer vision and natural language processing),the multi-modal pre-trained big models have also drawn more and more attention in recent years.In this work,we give a comprehensive survey of these models and hope this paper could provide new insights and helps fresh researchers to track the most cutting-edge works.Specifically,we firstly introduce the background of multi-modal pre-training by reviewing the conventional deep learning,pre-training works in natural language process,computer vision,and speech.Then,we introduce the task definition,key challenges,and advantages of multi-modal pre-training models(MM-PTMs),and discuss the MM-PTMs with a focus on data,objectives,network architectures,and knowledge enhanced pre-training.After that,we introduce the downstream tasks used for the validation of large-scale MM-PTMs,including generative,classification,and regression tasks.We also give visualization and analysis of the model parameters and results on representative downstream tasks.Finally,we point out possible research directions for this topic that may benefit future works.In addition,we maintain a continuously updated paper list for large-scale pre-trained multi-modal big models:https://github.com/wangxiao5791509/MultiModal_BigModels_Survey. 展开更多
关键词 Multi-modal(MM) pre-trained model(PTM) information fusion representation learning deep learning
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Do Higher Horizontal Resolution Models Perform Better?
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作者 Shoji KUSUNOKI 《Advances in Atmospheric Sciences》 2026年第1期259-262,共4页
Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(... Climate model prediction has been improved by enhancing model resolution as well as the implementation of sophisticated physical parameterization and refinement of data assimilation systems[section 6.1 in Wang et al.(2025)].In relation to seasonal forecasting and climate projection in the East Asian summer monsoon season,proper simulation of the seasonal migration of rain bands by models is a challenging and limiting factor[section 7.1 in Wang et al.(2025)]. 展开更多
关键词 enhancing model resolution refinement data assimilation systems section climate model climate projection higher horizontal resolution seasonal forecasting simulation seasonal migration rain bands model resolution
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Secured-FL:Blockchain-Based Defense against Adversarial Attacks on Federated Learning Models
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作者 Bello Musa Yakubu Nor Shahida Mohd Jamail +1 位作者 Rabia Latif Seemab Latif 《Computers, Materials & Continua》 2026年第3期734-757,共24页
Federated Learning(FL)enables joint training over distributed devices without data exchange but is highly vulnerable to attacks by adversaries in the form of model poisoning and malicious update injection.This work pr... Federated Learning(FL)enables joint training over distributed devices without data exchange but is highly vulnerable to attacks by adversaries in the form of model poisoning and malicious update injection.This work proposes Secured-FL,a blockchain-based defensive framework that combines smart contract-based authentication,clustering-driven outlier elimination,and dynamic threshold adjustment to defend against adversarial attacks.The framework was implemented on a private Ethereum network with a Proof-of-Authority consensus algorithm to ensure tamper-resistant and auditable model updates.Large-scale simulation on the Cyber Data dataset,under up to 50%malicious client settings,demonstrates Secured-FL achieves 6%-12%higher accuracy,9%-15%lower latency,and approximately 14%less computational expense compared to the PPSS benchmark framework.Additional tests,including confusion matrices,ROC and Precision-Recall curves,and ablation tests,confirm the interpretability and robustness of the defense.Tests for scalability also show consistent performance up to 500 clients,affirming appropriateness to reasonably large deployments.These results make Secured-FL a feasible,adversarially resilient FL paradigm with promising potential for application in smart cities,medicine,and other mission-critical IoT deployments. 展开更多
关键词 Federated learning(FL) blockchain FL based privacy model defense FL model security ethereum smart contract
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Review of machine learning tight-binding models:Route to accurate and scalable electronic simulations
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作者 Jijie Zou Zhanghao Zhouyin +1 位作者 Shishir Kumar Pandey Qiangqiang Gu 《Chinese Physics B》 2026年第1期2-12,共11页
The rapid advancement of machine learning based tight-binding Hamiltonian(MLTB)methods has opened new avenues for efficient and accurate electronic structure simulations,particularly in large-scale systems and long-ti... The rapid advancement of machine learning based tight-binding Hamiltonian(MLTB)methods has opened new avenues for efficient and accurate electronic structure simulations,particularly in large-scale systems and long-time scenarios.This review begins with a concise overview of traditional tight-binding(TB)models,including both(semi-)empirical and first-principles approaches,establishing the foundation for understanding MLTB developments.We then present a systematic classification of existing MLTB methodologies,grouped into two major categories:direct prediction of TB Hamiltonian elements and inference of empirical parameters.A comparative analysis with other ML-based electronic structure models is also provided,highlighting the advancement of MLTB approaches.Finally,we explore the emerging MLTB application ecosystem,highlighting how the integration of MLTB models with a diverse suite of post-processing tools from linear-scaling solvers to quantum transport frameworks and molecular dynamics interfaces is essential for tackling complex scientific problems across different domains.The continued advancement of this integrated paradigm promises to accelerate materials discovery and open new frontiers in the predictive simulation of complex quantum phenomena. 展开更多
关键词 machine learning tight-binding model electronic simulations
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Transformation of Verbal Descriptions of Process Flows into Business Process Modelling and Notation Models Using Multimodal Artificial Intelligence:Application in Justice
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作者 Silvia Alayón Carlos Martín +3 位作者 Jesús Torres Manuel Bacallado Rosa Aguilar Guzmán Savirón 《Computer Modeling in Engineering & Sciences》 2026年第2期870-892,共23页
Business Process Modelling(BPM)is essential for analyzing,improving,and automating the flow of information within organizations,but traditional approaches based on manual interpretation are slow,error-prone,and requir... Business Process Modelling(BPM)is essential for analyzing,improving,and automating the flow of information within organizations,but traditional approaches based on manual interpretation are slow,error-prone,and require a high level of expertise.This article proposes an innovative alternative solution that overcomes these limitations by automatically generating comprehensive Business Process Modelling and Notation(BPMN)diagrams solely from verbal descriptions of the processes to be modeled,utilizing Large Language Models(LLMs)and multimodal Artificial Intelligence(AI).Experimental results,based on video recordings of process explanations provided by an expert from an organization(in this case,the Commercial Courts of a public justice administration),demonstrate that the proposed methodology successfully enables the automatic generation of complete and accurate BPMN diagrams,leading to significant improvements in the speed,accuracy,and accessibility of process modeling.This research makes a substantial contribution to the field of business process modeling,as its methodology is groundbreaking in its use of LLMs and multimodal AI capabilities to handle different types of source material(text and video),combining several tools to minimize the number of queries and reduce the complexity of the prompts required for the automatic generation of successful BPMN diagrams. 展开更多
关键词 Process modelling verbal description BPMN LLM multimodal AI
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Recent advances in animal models for pathological scar research:A comprehensive review of experimental approaches and translational relevance
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作者 Diana-Larisa Ancuța Mariana Văduva +1 位作者 Cristin Coman Iuliana Caraș 《Animal Models and Experimental Medicine》 2026年第1期59-71,共13页
Pathological scarring,manifested in the form of hypertrophic scars(HTS)and keloid scars(KS),represents a major clinical challenge due to its aesthetic and functional implications for patients.Understanding the molecul... Pathological scarring,manifested in the form of hypertrophic scars(HTS)and keloid scars(KS),represents a major clinical challenge due to its aesthetic and functional implications for patients.Understanding the molecular mechanisms involved in these types of scars and developing effective treatments requires the use of controlled ex-perimental models,especially animals,to overcome the limitations of clinical studies.The aim of this sistematic review is to critically analyze the animal models used in the last five years(2020-2025)for the study of pathological scars,highlighting their advantages,limitations and applicability in the development of new therapeutic strat-egies.Murine,rabbit and porcine models,as well as alternative models,offer varied perspectives on the formation and treatment of HTS and KS,with an emphasis on histological and molecular correlations with human pathology.By synthesizing recent data,the paper highlights the essential role of preclinical research in optimizing an-tifibrotic treatments and in advancing the translation of data into the clinical sphere.Overall,animal models remain essential for bridging mechanistic insights with clinical translation,supporting the development of more effective and personalized anti-scar therapies. 展开更多
关键词 animal model EXPERIMENT hypertrophic scar keloid scar TRANSLATION
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Red Alarm for Pre-trained Models:Universal Vulnerability to Neuron-level Backdoor Attacks 被引量:5
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作者 Zhengyan Zhang Guangxuan Xiao +6 位作者 Yongwei Li Tian Lv Fanchao Qi Zhiyuan Liu Yasheng Wang Xin Jiang Maosong Sun 《Machine Intelligence Research》 EI CSCD 2023年第2期180-193,共14页
The pre-training-then-fine-tuning paradigm has been widely used in deep learning.Due to the huge computation cost for pre-training,practitioners usually download pre-trained models from the Internet and fine-tune them... The pre-training-then-fine-tuning paradigm has been widely used in deep learning.Due to the huge computation cost for pre-training,practitioners usually download pre-trained models from the Internet and fine-tune them on downstream datasets,while the downloaded models may suffer backdoor attacks.Different from previous attacks aiming at a target task,we show that a backdoored pre-trained model can behave maliciously in various downstream tasks without foreknowing task information.Attackers can restrict the output representations(the values of output neurons)of trigger-embedded samples to arbitrary predefined values through additional training,namely neuron-level backdoor attack(NeuBA).Since fine-tuning has little effect on model parameters,the fine-tuned model will retain the backdoor functionality and predict a specific label for the samples embedded with the same trigger.To provoke multiple labels in a specific task,attackers can introduce several triggers with predefined contrastive values.In the experiments of both natural language processing(NLP)and computer vision(CV),we show that NeuBA can well control the predictions for trigger-embedded instances with different trigger designs.Our findings sound a red alarm for the wide use of pre-trained models.Finally,we apply several defense methods to NeuBA and find that model pruning is a promising technique to resist NeuBA by omitting backdoored neurons. 展开更多
关键词 pre-trained language models backdoor attacks transformers natural language processing(NLP) computer vision(CV)
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When Large Language Models and Machine Learning Meet Multi-Criteria Decision Making: Fully Integrated Approach for Social Media Moderation
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作者 Noreen Fuentes Janeth Ugang +4 位作者 Narcisan Galamiton Suzette Bacus Samantha Shane Evangelista Fatima Maturan Lanndon Ocampo 《Computers, Materials & Continua》 2026年第1期2137-2162,共26页
This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to use... This study demonstrates a novel integration of large language models,machine learning,and multicriteria decision-making to investigate self-moderation in small online communities,a topic under-explored compared to user behavior and platform-driven moderation on social media.The proposed methodological framework(1)utilizes large language models for social media post analysis and categorization,(2)employs k-means clustering for content characterization,and(3)incorporates the TODIM(Tomada de Decisão Interativa Multicritério)method to determine moderation strategies based on expert judgments.In general,the fully integrated framework leverages the strengths of these intelligent systems in a more systematic evaluation of large-scale decision problems.When applied in social media moderation,this approach promotes nuanced and context-sensitive self-moderation by taking into account factors such as cultural background and geographic location.The application of this framework is demonstrated within Facebook groups.Eight distinct content clusters encompassing safety,harassment,diversity,and misinformation are identified.Analysis revealed a preference for content removal across all clusters,suggesting a cautious approach towards potentially harmful content.However,the framework also highlights the use of other moderation actions,like account suspension,depending on the content category.These findings contribute to the growing body of research on self-moderation and offer valuable insights for creating safer and more inclusive online spaces within smaller communities. 展开更多
关键词 Self-moderation user-generated content k-means clustering TODIM large language models
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Therapeutic Potential of Fingolimod and Dimethyl Fumarate in Preclinical Pancreatic Cancer Models
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作者 Pauline Gousseau Laurie Genest +1 位作者 Guillaume Froget Tristan Rupp 《Oncology Research》 2026年第3期387-405,共19页
Objectives:The five-year survival rate for pancreatic cancer is notably low,posing a significant challenge to patient health.The primary treatments are radiotherapy and chemotherapy,sometimes combined with targeted th... Objectives:The five-year survival rate for pancreatic cancer is notably low,posing a significant challenge to patient health.The primary treatments are radiotherapy and chemotherapy,sometimes combined with targeted therapy;however,their clinical benefits are limited.Therefore,developing new models to evaluate the therapeutic potential of novel molecules is essential.Fingolimod and Dimethyl Fumarate(DMF),currently used to treat multiple sclerosis,have recently been shown to have anti-cancer effects in several preclinical tumor models.This study aims to evaluate the therapeutic potential of Fingolimod and DMF in pancreatic cancer by investigating their respective in vitro cytotoxicity and in vivo antitumor effects.Methods:In this study,we evaluated for the first time these two drugs in pancreatic preclinical models in vitro using 3D spheroid tumor models and in vivo,which are compared to two standard-of-care consisting of Gemcitabine and Erlotinib.Results:In vitro,both Fingolimod and DMF induced cytotoxicity in spheroids from two pancreatic cell lines.Additionally,Fingolimod and DMF displayed anticancer effects in two subcutaneous xenograft models using PANC-1 and CFPAC-1 cells.Conclusions:Although the responses observed with Fingolimod and DMF were similar to those of Gemcitabine and Erlotinib,these findings indicate a potential emerging interest in Fingolimod and DMF for the treatment of pancreatic cancer.However,further work is still necessary to fully characterize how these drugs affect tumor progression. 展开更多
关键词 Pancreatic cancer preclinical models tumor progression FINGOLIMOD dimethyl Fumarate
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Animal models of benign airway stenosis:Advances in construction techniques,evaluation systems,and perspectives
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作者 Wusheng Zhang Yilin Chen +4 位作者 Chengcheng Yang Yuchao Dong Haidong Huang Hui Shi Chong Bai 《Animal Models and Experimental Medicine》 2026年第2期280-297,共18页
The incidence of benign airway stenosis(BAS)is on the rise,and current treatment options are associated with a significant risk of restenosis.Therefore,there is an urgent need to explore new and effective prevention a... The incidence of benign airway stenosis(BAS)is on the rise,and current treatment options are associated with a significant risk of restenosis.Therefore,there is an urgent need to explore new and effective prevention and treatment methods.Animal models serve as essential tools for investigating disease mechanisms and assessing novel therapeutic strategies,and the scientific rigor of their construction and validation significantly impacts the reliability of research findings.This paper systematically reviews the research progress and evaluation systems of BAS animal models over the past decade,aiming to provide a robust foundation for the optimized construction of BAS models,intervention studies,and clinical translation.This effort is intended to facilitate the innovation and advancement in BAS prevention and treatment strategies. 展开更多
关键词 airway stenosis animal models benign airway stenosis evaluation systems
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