Social media outlets deliver customers a medium for communication,exchange,and expression of their thoughts with others.The advent of social networks and the fast escalation of the quantity of data have created opport...Social media outlets deliver customers a medium for communication,exchange,and expression of their thoughts with others.The advent of social networks and the fast escalation of the quantity of data have created opportunities for textual evaluation.Utilising the user corpus,characteristics of social platform users,and other data,academic research may accurately discern the personality traits of users.This research examines the traits of consumer personalities.Usually,personality tests administered by psychological experts via interviews or self-report questionnaires are costly,time-consuming,complex,and labour-intensive.Currently,academics in computational linguistics are increasingly focused on predicting personality traits from social media data.An individual’s personality comprises their traits and behavioral habits.To address this distinction,we propose a novel LSTMapproach(BERT-LIWC-LSTM)that simultaneously incorporates users’enduring and immediate personality characteristics for textual personality recognition.Long-termPersonality Encoding in the proposed paradigmcaptures and represents persisting personality traits.Short-termPersonality Capturing records changing personality states.Experimental results demonstrate that the designed BERT-LIWC-LSTM model achieves an average improvement in accuracy of 3.41% on the Big Five dataset compared to current methods,thereby justifying the efficacy of encoding both stable and dynamic personality traits simultaneously through long-and short-term feature interaction.展开更多
Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalize...Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalized Learning Ecosystem,which integrates 3D/VR environments,as well as machine learning algorithms,and business intelligence frameworks to enhance learner-centered education and inferenced decision-making.This Learning System makes use of immersive,analytically assessed virtual learning spaces,therefore facilitating real-time monitoring of not just learning performance,but also overall engagement and behavioral patterns,via a comprehensive set of sustainability-oriented ESG-aligned Key Performance Indicators(KPIs).Machine learning models support predictive analysis,personalized feedback,and hybrid recommendation mechanisms,whilst dedicated dashboards translate complex educational data into actionable insights for all Use Cases of the System(Educational Institutions,Educators and Learners).Additionally,the presented Learning System introduces a structured Mentoring and Consulting Subsystem,thence reinforcing human-centered guidance alongside automated intelligence.The Platform’s modular architecture and simulation-centered evaluation approach actively support personalized,and continuously optimized learning pathways.Thence,it exemplifies a mature,adaptive Learning Ecosystem,supporting immersive technologies,analytics,and pedagogical support,hence,contributing to contemporary digital learning innovation and sociotechnical transformation in education.展开更多
Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitiv...Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption.展开更多
Evaluating individuals＇ personality traits and intelligence from their faces plays a crucial role in interpersonal relationship and important social events such as elections and court sentences. To assess the possibl...Evaluating individuals＇ personality traits and intelligence from their faces plays a crucial role in interpersonal relationship and important social events such as elections and court sentences. To assess the possible correlations between personality traits （also measured intelligence） and face images, we first construct a dataset consisting of face photographs, personality measurements, and intelligence measurements. Then, we build an end-to-end convolutional neural network for prediction of personality traits and intelligence to investigate whether self-reported personality traits and intelligence can be predicted reliably from a face image. To our knowledge, it is the first work where deep learning is applied to this problem. Experimental results show the following three points： 1） ＂Rule-consciousness＂ and ＂Tension＂ can be reliably predicted from face images. 2） It is difficult, if not impossible, to predict intelligence from face images, a finding in accord with previous studies. 3） Convolutional neural network （CNN） features outperform traditional handcrafted features in predicting traits.展开更多
Knowing each other is obligatory in a multi-agent collaborative environment.Collaborators may develop the desired know-how of each other in various aspects such as habits,job roles,status,and behaviors.Among different...Knowing each other is obligatory in a multi-agent collaborative environment.Collaborators may develop the desired know-how of each other in various aspects such as habits,job roles,status,and behaviors.Among different distinguishing characteristics related to a person,personality traits are an effective predictive tool for an individual’s behavioral pattern.It has been observed that when people are asked to share their details through questionnaires,they intentionally or unintentionally become biased.They knowingly or unknowingly provide enough information in much-unbiased comportment in open writing about themselves.Such writings can effectively assess an individual’s personality traits that may yield enormous possibilities for applications such as forensic departments,job interviews,mental health diagnoses,etc.Stream of consciousness,collected by James Pennbaker and Laura King,is one such way of writing,referring to a narrative technique where the emotions and thoughts of the writer are presented in a way that brings the reader to the fluid through the mental states of the narrator.More-over,computationally,various attempts have been made in an individual’s personality traits assessment through deep learning algorithms;however,the effectiveness and reliability of results vary with varying word embedding techniques.This article proposes an empirical approach to assessing personality by applying convolutional networks to text documents.Bidirectional Encoder Representations from Transformers(BERT)word embedding technique is used for word vector generation to enhance the contextual meanings.展开更多
Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-...Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their performance.This study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality recognition.These models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term dependencies.This makes them suitable to classify multi-label personality traits from reviews while mitigating computational costs.The focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from texts.Moreover,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook users.These status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality traits.To test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted averages.The results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of F1-score.The findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality recognition.These findings represent substantial advancements in personality recognition,making them appropriate for the development of user-centric applications.展开更多
While artificial intelligence(AI)shows promise in education,its real-world effectiveness in specific settings like blended English as a Foreign Language(EFL)learning needs closer examination.This study investigated th...While artificial intelligence(AI)shows promise in education,its real-world effectiveness in specific settings like blended English as a Foreign Language(EFL)learning needs closer examination.This study investigated the impact of a blended teaching model incorporating AI tools on the Superstar Learning Platform for Chinese university EFL students.Using a mixed-methods approach,60 first-year students were randomized into an experimental group(using the AI-enhanced model)and a control group(traditional instruction)for 16 weeks.Data included test scores,learning behaviors(duration,task completion),satisfaction surveys,and interviews.Results showed the experimental group significantly outperformed the control group on post-tests and achieved larger learning gains.These students also demonstrated greater engagement through longer study times and higher task completion rates,and reported significantly higher satisfaction.Interviews confirmed these findings,with students attributing benefits to the model’s personalized guidance,structured content presentation(knowledge graphs),immediate responses,flexibility,and varied interaction methods.However,limitations were noted,including areas where the platform’s AI could be improved(e.g.,for assessing speaking/translation)and ongoing challenges with student self-discipline.The study concludes that this AI-enhanced blended model significantly improved student performance,engagement,and satisfaction in this EFL context.The findings offer practical insights for educators and platform developers,suggesting AI integration holds significant potential while highlighting areas for refinement.展开更多
Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients a...Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients and the server.However,the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability.Meanwhile,data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks,and standalone personalization tasks may cause severe overfitting issues.To address these limitations,we introduce a federated learning dual optimization model based on clustering and personalization strategy(FedCPS).FedCPS adopts a decentralized approach,where clients identify their cluster membership locally without relying on a centralized clustering algorithm.Building on this,FedCPS introduces personalized training tasks locally,adding a regularization term to control deviations between local and cluster models.This improves the generalization ability of the final model while mitigating overfitting.The use of weight-sharing techniques also reduces the computational cost of central machines.Experimental results on MNIST,FMNIST,CIFAR10,and CIFAR100 datasets demonstrate that our method achieves better personalization effects compared to other personalized federated learning methods,with an average test accuracy improvement of 0.81%–2.96%.Meanwhile,we adjusted the proportion of few-shot clients to evaluate the impact on accuracy across different methods.The experiments show that FedCPS reduces accuracy by only 0.2%–3.7%,compared to 2.1%–10%for existing methods.Our method demonstrates its advantages across diverse data environments.展开更多
With the rapid development of artificial intelligence(AI)technology,the teaching mode in the field of education is undergoing profound changes.Especially the design and implementation of personalized learning paths ha...With the rapid development of artificial intelligence(AI)technology,the teaching mode in the field of education is undergoing profound changes.Especially the design and implementation of personalized learning paths have become an important direction of intelligent teaching reform.The traditional“one-size-fits-all”teaching model has gradually failed to meet the individualized learning needs of students.However,through the advantages of data analysis and real-time feedback,AI technology can provide tailor-made teaching content and learning paths based on students’learning progress,interests,and abilities.This study explores the innovation of the personalized learning path model based on AI technology,and analyzes the potential and challenges of this model in improving teaching effectiveness,promoting the all-round development of students,and optimizing the interaction between teachers and students.Through case analysis and empirical research,this paper summarizes the implementation methods of the AI-driven personalized learning path,the innovation of teaching models,and their application prospects in educational reform.Meanwhile,the research also discussed the ethical issues of AI technology in education,data privacy protection,and its impact on the teacher-student relationship,and proposed corresponding solutions.展开更多
Personality prediction on social network has become a hot topic.At present,most studies are using single-task classification/regression machine learning.However,this method ignores the potential association between mu...Personality prediction on social network has become a hot topic.At present,most studies are using single-task classification/regression machine learning.However,this method ignores the potential association between multiple tasks.Also an ideal prediction result is difficult to achieve based on the small scale training data,since it is not easy to get a lot of social network data with personality label samples.In this paper,a robust multi-task learning method(RMTL)is proposed to predict Big-Five personality on Micro-blog.We aim to learn five tasks simultaneously by extracting and utilizing appropriate shared information among multiple tasks as well as identifying irrelevant tasks.For a set of Sina Micro-blog users’information and personality labeled data retrieved by questionnaire,we validate the RMTL method by comparing it with 4 single-task learning methods and the mere multi-task learning.Our experiment demonstrates that the proposed RMTL can improve the precision rate,recall rate of the prediction and F value.展开更多
Network architectures assisted by Generative Artificial Intelligence(GAI)are envisioned as foundational elements of sixth-generation(6G)communication system.To deliver ubiquitous intelligent services and meet diverse ...Network architectures assisted by Generative Artificial Intelligence(GAI)are envisioned as foundational elements of sixth-generation(6G)communication system.To deliver ubiquitous intelligent services and meet diverse service requirements,6G network architecture should offer personalized services to various mobile devices.Federated learning(FL)with personalized local training,as a privacypreserving machine learning(ML)approach,can be applied to address these challenges.In this paper,we propose a meta-learning-based personalized FL(PFL)method that improves both communication and computation efficiency by utilizing over-the-air computations.Its“pretraining-and-fine-tuning”principle makes it particularly suitable for enabling edge nodes to access personalized GAI services while preserving local privacy.Experiment results demonstrate the outperformance and efficacy of the proposed algorithm,and notably indicate enhanced communication efficiency without compromising accuracy.展开更多
Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transforma...Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.展开更多
The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these systems.One of the key developments is the integration of d...The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these systems.One of the key developments is the integration of deep learning techniques in biometric systems.However,despite these advancements,certain challenges persist.One of the most significant challenges is scalability over growing complexity.Traditional methods either require maintaining and securing a growing database,introducing serious security challenges,or relying on retraining the entiremodelwhen new data is introduced-a process that can be computationally expensive and complex.This challenge underscores the need for more efficient methods to scale securely.To this end,we introduce a novel approach that addresses these challenges by integrating multimodal biometrics,cancelable biometrics,and incremental learning techniques.This work is among the first attempts to seamlessly incorporate deep cancelable biometrics with dynamic architectural updates,applied incrementally to the deep learning model as new users are enrolled,achieving high performance with minimal catastrophic forgetting.By leveraging a One-Dimensional Convolutional Neural Network(1D-CNN)architecture combined with a hybrid incremental learning approach,our system achieves high recognition accuracy,averaging 98.98% over incrementing datasets,while ensuring user privacy through cancelable templates generated via a pre-trained CNN model and random projection.The approach demonstrates remarkable adaptability,utilizing the least intrusive biometric traits like facial features and fingerprints,ensuring not only robust performance but also long-term serviceability.展开更多
Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure ...Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.展开更多
With the continuous advancement of artificial intelligence(AI)technology,personalized learning systems are increasingly applied in higher education.Particularly within STEM(Science,Technology,Engineering,and Mathemati...With the continuous advancement of artificial intelligence(AI)technology,personalized learning systems are increasingly applied in higher education.Particularly within STEM(Science,Technology,Engineering,and Mathematics)education,AI demonstrates significant advantages through adaptive learning pathways,instant feedback,and individualized resource allocation.However,current research predominantly focuses on the technical architecture and application effectiveness of such systems,with insufficient exploration of how AI-enabled personalized learning systems influence university students’learning motivation and academic achievement through educational psychological mechanisms.This paper adopts an educational psychology perspective to construct a causal mechanism model linking“learning motivation-learning behavior-academic achievement.”Findings indicate that AI-powered personalized learning systems enhance learning autonomy,boost self-efficacy,and optimize feedback mechanisms.These effects collectively stimulate university students’learning motivation in STEM disciplines,thereby promoting academic achievement.Building upon empirical research,this paper proposes implications for educational practice and policy formulation,emphasizing the necessity of advancing higher education reform through the dual influence of technology and psychological mechanisms.展开更多
The nutritional management of patients with esophageal cancer(EC)presents significant complexities,with traditional approaches facing inherent limitations in data collection,real-time decision-making,and personalized ...The nutritional management of patients with esophageal cancer(EC)presents significant complexities,with traditional approaches facing inherent limitations in data collection,real-time decision-making,and personalized care.This narrative review explores the transformative potential of artificial intelligence(AI)and machine learning(ML),particularly deep learning(DL)and reinforcement learning(RL),in revolutionizing nutritional support for this vulnerable patient population.DL has demonstrated remarkable capabilities in enhancing the accuracy and objectivity of nutritional assessment through precise,automated body composition analysis from medical imaging,offering valuable prognostic insights.Concurrently,RL enables the dynamic optimization of nutritional interventions,adapting them in real time to individual patient responses,paving the way for truly personalized care paradigms.Although AI/ML offers potential advantages in efficiency,precision,and personalization by integrating multidimensional data for superior clinical decision support,its widespread adoption is accompanied by critical challenges.These include safeguarding data privacy and security,mitigating algorithmic bias,ensuring transparency and accountability,and establishing rigorous clinical validation.Early evidence suggests the feasibility of applying AI/ML in nutritional risk stratification and workflow optimization,but highquality prospective studies are needed to demonstrate the direct impact on clinical outcomes,including complications,readmissions,and survival.Overcoming these hurdles necessitates robust ethical governance,interdisciplinary collaboration,and continuous education.Ultimately,the strategic integration of AI/ML holds immense promise to profoundly improve patient outcomes,enhance quality of life,and optimize health care resource utilization in the nutritional management of esophageal cancer.展开更多
Depression is a prevalent mental health disorder characterized by high relapse rates,highlighting the need for effective preventive interventions.This paper reviews the potential of reinforcement learning(RL)in preven...Depression is a prevalent mental health disorder characterized by high relapse rates,highlighting the need for effective preventive interventions.This paper reviews the potential of reinforcement learning(RL)in preventing depression relapse.RL,a subset of artificial intelligence,utilizes machine learning algorithms to analyze behavioral data,enabling early detection of relapse risk and optimization of personalized interventions.RL's ability to tailor treatment in real-time by adapting to individual needs and responses offers a dynamic alternative to traditional therapeutic approaches.Studies have demonstrated the efficacy of RL in customizing e-Health interventions and integrating mobile sensing with machine learning for adaptive mental health systems.Despite these advantages,challenges remain in algorithmic complexity,ethical considerations,and clinical implementation.Addressing these issues is crucial for the successful integration of RL into mental health care.This paper concludes with recommendations for future research directions,emphasizing the need for larger-scale studies and interdisciplinary collaboration to fully realize RL’s potential in improving mental health outcomes and preventing depression relapse.展开更多
In federated learning(FL),the distribution of data across different clients leads to the degradation of global model performance in training.Personalized Federated Learning(pFL)can address this problem through global ...In federated learning(FL),the distribution of data across different clients leads to the degradation of global model performance in training.Personalized Federated Learning(pFL)can address this problem through global model personalization.Researches over the past few years have calibrated differences in weights across the entire model or optimized only individual layers of the model without considering that different layers of the whole neural network have different utilities,resulting in lagged model convergence and inadequate personalization in non-IID data.In this paper,we propose model layered optimization for feature extractor and classifier(pFedEC),a novel pFL training framework personalized for different layers of the model.Our study divides the model layers into the feature extractor and classifier.We initialize the model's classifiers during model training,while making the local model's feature extractors learn the representation of the global model's feature extractors to correct each client's local training,integrating the utilities of the different layers in the entire model.Our extensive experiments show that pFedEC achieves 92.95%accuracy on CIFAR-10,outperforming existing pFL methods by approximately 1.8%.On CIFAR-100 and Tiny-ImageNet,pFedEC improves the accuracy by at least 4.2%,reaching 73.02%and 28.39%,respectively.展开更多
With the rapid development of advanced networking and computing technologies such as the Internet of Things, network function virtualization, and 5G infrastructure, new development opportunities are emerging for Marit...With the rapid development of advanced networking and computing technologies such as the Internet of Things, network function virtualization, and 5G infrastructure, new development opportunities are emerging for Maritime Meteorological Sensor Networks(MMSNs). However, the increasing number of intelligent devices joining the MMSN poses a growing threat to network security. Current Artificial Intelligence(AI) intrusion detection techniques turn intrusion detection into a classification problem, where AI excels. These techniques assume sufficient high-quality instances for model construction, which is often unsatisfactory for real-world operation with limited attack instances and constantly evolving characteristics. This paper proposes an Adaptive Personalized Federated learning(APFed) framework that allows multiple MMSN owners to engage in collaborative training. By employing an adaptive personalized update and a shared global classifier, the adverse effects of imbalanced, Non-Independent and Identically Distributed(Non-IID) data are mitigated, enabling the intrusion detection model to possess personalized capabilities and good global generalization. In addition, a lightweight intrusion detection model is proposed to detect various attacks with an effective adaptation to the MMSN environment. Finally, extensive experiments on a classical network dataset show that the attack classification accuracy is improved by about 5% compared to most baselines in the global scenarios.展开更多
文摘Social media outlets deliver customers a medium for communication,exchange,and expression of their thoughts with others.The advent of social networks and the fast escalation of the quantity of data have created opportunities for textual evaluation.Utilising the user corpus,characteristics of social platform users,and other data,academic research may accurately discern the personality traits of users.This research examines the traits of consumer personalities.Usually,personality tests administered by psychological experts via interviews or self-report questionnaires are costly,time-consuming,complex,and labour-intensive.Currently,academics in computational linguistics are increasingly focused on predicting personality traits from social media data.An individual’s personality comprises their traits and behavioral habits.To address this distinction,we propose a novel LSTMapproach(BERT-LIWC-LSTM)that simultaneously incorporates users’enduring and immediate personality characteristics for textual personality recognition.Long-termPersonality Encoding in the proposed paradigmcaptures and represents persisting personality traits.Short-termPersonality Capturing records changing personality states.Experimental results demonstrate that the designed BERT-LIWC-LSTM model achieves an average improvement in accuracy of 3.41% on the Big Five dataset compared to current methods,thereby justifying the efficacy of encoding both stable and dynamic personality traits simultaneously through long-and short-term feature interaction.
文摘Latest digital advancements have intensified the necessity for adaptive,data-driven and socially-centered learning ecosystems.This paper presents the formulation of a cross-platform,innovative,gamified and personalized Learning Ecosystem,which integrates 3D/VR environments,as well as machine learning algorithms,and business intelligence frameworks to enhance learner-centered education and inferenced decision-making.This Learning System makes use of immersive,analytically assessed virtual learning spaces,therefore facilitating real-time monitoring of not just learning performance,but also overall engagement and behavioral patterns,via a comprehensive set of sustainability-oriented ESG-aligned Key Performance Indicators(KPIs).Machine learning models support predictive analysis,personalized feedback,and hybrid recommendation mechanisms,whilst dedicated dashboards translate complex educational data into actionable insights for all Use Cases of the System(Educational Institutions,Educators and Learners).Additionally,the presented Learning System introduces a structured Mentoring and Consulting Subsystem,thence reinforcing human-centered guidance alongside automated intelligence.The Platform’s modular architecture and simulation-centered evaluation approach actively support personalized,and continuously optimized learning pathways.Thence,it exemplifies a mature,adaptive Learning Ecosystem,supporting immersive technologies,analytics,and pedagogical support,hence,contributing to contemporary digital learning innovation and sociotechnical transformation in education.
文摘Federated Learning(FL)has become a leading decentralized solution that enables multiple clients to train a model in a collaborative environment without directly sharing raw data,making it suitable for privacy-sensitive applications such as healthcare,finance,and smart systems.As the field continues to evolve,the research field has become more complex and scattered,covering different system designs,training methods,and privacy techniques.This survey is organized around the three core challenges:how the data is distributed,how models are synchronized,and how to defend against attacks.It provides a structured and up-to-date review of FL research from 2023 to 2025,offering a unified taxonomy that categorizes works by data distribution(Horizontal FL,Vertical FL,Federated Transfer Learning,and Personalized FL),training synchronization(synchronous and asynchronous FL),optimization strategies,and threat models(data leakage and poisoning attacks).In particular,we summarize the latest contributions in Vertical FL frameworks for secure multi-party learning,communication-efficient Horizontal FL,and domain-adaptive Federated Transfer Learning.Furthermore,we examine synchronization techniques addressing system heterogeneity,including straggler mitigation in synchronous FL and staleness management in asynchronous FL.The survey covers security threats in FL,such as gradient inversion,membership inference,and poisoning attacks,as well as their defense strategies that include privacy-preserving aggregation and anomaly detection.The paper concludes by outlining unresolved issues and highlighting challenges in handling personalized models,scalability,and real-world adoption.
基金supported by National Natural Science Foundation of China(Nos.61333015,61421004 and 61375042)Strategic Priority Research Program of the Chinese Academy of Sciences(No.XDB02070002)
文摘Evaluating individuals＇ personality traits and intelligence from their faces plays a crucial role in interpersonal relationship and important social events such as elections and court sentences. To assess the possible correlations between personality traits （also measured intelligence） and face images, we first construct a dataset consisting of face photographs, personality measurements, and intelligence measurements. Then, we build an end-to-end convolutional neural network for prediction of personality traits and intelligence to investigate whether self-reported personality traits and intelligence can be predicted reliably from a face image. To our knowledge, it is the first work where deep learning is applied to this problem. Experimental results show the following three points： 1） ＂Rule-consciousness＂ and ＂Tension＂ can be reliably predicted from face images. 2） It is difficult, if not impossible, to predict intelligence from face images, a finding in accord with previous studies. 3） Convolutional neural network （CNN） features outperform traditional handcrafted features in predicting traits.
文摘Knowing each other is obligatory in a multi-agent collaborative environment.Collaborators may develop the desired know-how of each other in various aspects such as habits,job roles,status,and behaviors.Among different distinguishing characteristics related to a person,personality traits are an effective predictive tool for an individual’s behavioral pattern.It has been observed that when people are asked to share their details through questionnaires,they intentionally or unintentionally become biased.They knowingly or unknowingly provide enough information in much-unbiased comportment in open writing about themselves.Such writings can effectively assess an individual’s personality traits that may yield enormous possibilities for applications such as forensic departments,job interviews,mental health diagnoses,etc.Stream of consciousness,collected by James Pennbaker and Laura King,is one such way of writing,referring to a narrative technique where the emotions and thoughts of the writer are presented in a way that brings the reader to the fluid through the mental states of the narrator.More-over,computationally,various attempts have been made in an individual’s personality traits assessment through deep learning algorithms;however,the effectiveness and reliability of results vary with varying word embedding techniques.This article proposes an empirical approach to assessing personality by applying convolutional networks to text documents.Bidirectional Encoder Representations from Transformers(BERT)word embedding technique is used for word vector generation to enhance the contextual meanings.
基金This work has been partially supported by FEDER and the State Research Agency(AEI)of the Spanish Ministry of Economy and Competition under Grant SAFER:PID2019-104735RB-C42(AEI/FEDER,UE)the General Subdirection for Gambling Regulation of the Spanish ConsumptionMinistry under the Grant Detec-EMO:SUBV23/00010the Project PLEC2021-007681 funded by MCIN/AEI/10.13039/501100011033 and by the European Union NextGenerationEU/PRTR.
文摘Personality recognition plays a pivotal role when developing user-centric solutions such as recommender systems or decision support systems across various domains,including education,e-commerce,or human resources.Tra-ditional machine learning techniques have been broadly employed for personality trait identification;nevertheless,the development of new technologies based on deep learning has led to new opportunities to improve their performance.This study focuses on the capabilities of pre-trained language models such as BERT,RoBERTa,ALBERT,ELECTRA,ERNIE,or XLNet,to deal with the task of personality recognition.These models are able to capture structural features from textual content and comprehend a multitude of language facets and complex features such as hierarchical relationships or long-term dependencies.This makes them suitable to classify multi-label personality traits from reviews while mitigating computational costs.The focus of this approach centers on developing an architecture based on different layers able to capture the semantic context and structural features from texts.Moreover,it is able to fine-tune the previous models using the MyPersonality dataset,which comprises 9,917 status updates contributed by 250 Facebook users.These status updates are categorized according to the well-known Big Five personality model,setting the stage for a comprehensive exploration of personality traits.To test the proposal,a set of experiments have been performed using different metrics such as the exact match ratio,hamming loss,zero-one-loss,precision,recall,F1-score,and weighted averages.The results reveal ERNIE is the top-performing model,achieving an exact match ratio of 72.32%,an accuracy rate of 87.17%,and 84.41%of F1-score.The findings demonstrate that the tested models substantially outperform other state-of-the-art studies,enhancing the accuracy by at least 3%and confirming them as powerful tools for personality recognition.These findings represent substantial advancements in personality recognition,making them appropriate for the development of user-centric applications.
基金supported by the 2024“Special Research Project on the Application of Artificial Intelligence in Empowering Teaching and Education”of Zhejiang Province Association of Higher Education(KT2024165).
文摘While artificial intelligence(AI)shows promise in education,its real-world effectiveness in specific settings like blended English as a Foreign Language(EFL)learning needs closer examination.This study investigated the impact of a blended teaching model incorporating AI tools on the Superstar Learning Platform for Chinese university EFL students.Using a mixed-methods approach,60 first-year students were randomized into an experimental group(using the AI-enhanced model)and a control group(traditional instruction)for 16 weeks.Data included test scores,learning behaviors(duration,task completion),satisfaction surveys,and interviews.Results showed the experimental group significantly outperformed the control group on post-tests and achieved larger learning gains.These students also demonstrated greater engagement through longer study times and higher task completion rates,and reported significantly higher satisfaction.Interviews confirmed these findings,with students attributing benefits to the model’s personalized guidance,structured content presentation(knowledge graphs),immediate responses,flexibility,and varied interaction methods.However,limitations were noted,including areas where the platform’s AI could be improved(e.g.,for assessing speaking/translation)and ongoing challenges with student self-discipline.The study concludes that this AI-enhanced blended model significantly improved student performance,engagement,and satisfaction in this EFL context.The findings offer practical insights for educators and platform developers,suggesting AI integration holds significant potential while highlighting areas for refinement.
基金supported by the Foundation of President of Hebei University(XZJJ202303).
文摘Federated learning is a machine learning framework designed to protect privacy by keeping training data on clients’devices without sharing private data.It trains a global model through collaboration between clients and the server.However,the presence of data heterogeneity can lead to inefficient model training and even reduce the final model’s accuracy and generalization capability.Meanwhile,data scarcity can result in suboptimal cluster distributions for few-shot clients in centralized clustering tasks,and standalone personalization tasks may cause severe overfitting issues.To address these limitations,we introduce a federated learning dual optimization model based on clustering and personalization strategy(FedCPS).FedCPS adopts a decentralized approach,where clients identify their cluster membership locally without relying on a centralized clustering algorithm.Building on this,FedCPS introduces personalized training tasks locally,adding a regularization term to control deviations between local and cluster models.This improves the generalization ability of the final model while mitigating overfitting.The use of weight-sharing techniques also reduces the computational cost of central machines.Experimental results on MNIST,FMNIST,CIFAR10,and CIFAR100 datasets demonstrate that our method achieves better personalization effects compared to other personalized federated learning methods,with an average test accuracy improvement of 0.81%–2.96%.Meanwhile,we adjusted the proportion of few-shot clients to evaluate the impact on accuracy across different methods.The experiments show that FedCPS reduces accuracy by only 0.2%–3.7%,compared to 2.1%–10%for existing methods.Our method demonstrates its advantages across diverse data environments.
基金The 2024 Guangdong University of Science and Technology Teaching,Science and Innovation Project(GKJXXZ2024028)。
文摘With the rapid development of artificial intelligence(AI)technology,the teaching mode in the field of education is undergoing profound changes.Especially the design and implementation of personalized learning paths have become an important direction of intelligent teaching reform.The traditional“one-size-fits-all”teaching model has gradually failed to meet the individualized learning needs of students.However,through the advantages of data analysis and real-time feedback,AI technology can provide tailor-made teaching content and learning paths based on students’learning progress,interests,and abilities.This study explores the innovation of the personalized learning path model based on AI technology,and analyzes the potential and challenges of this model in improving teaching effectiveness,promoting the all-round development of students,and optimizing the interaction between teachers and students.Through case analysis and empirical research,this paper summarizes the implementation methods of the AI-driven personalized learning path,the innovation of teaching models,and their application prospects in educational reform.Meanwhile,the research also discussed the ethical issues of AI technology in education,data privacy protection,and its impact on the teacher-student relationship,and proposed corresponding solutions.
文摘Personality prediction on social network has become a hot topic.At present,most studies are using single-task classification/regression machine learning.However,this method ignores the potential association between multiple tasks.Also an ideal prediction result is difficult to achieve based on the small scale training data,since it is not easy to get a lot of social network data with personality label samples.In this paper,a robust multi-task learning method(RMTL)is proposed to predict Big-Five personality on Micro-blog.We aim to learn five tasks simultaneously by extracting and utilizing appropriate shared information among multiple tasks as well as identifying irrelevant tasks.For a set of Sina Micro-blog users’information and personality labeled data retrieved by questionnaire,we validate the RMTL method by comparing it with 4 single-task learning methods and the mere multi-task learning.Our experiment demonstrates that the proposed RMTL can improve the precision rate,recall rate of the prediction and F value.
基金supported in part by the National Key R&D Program of China under Grant 2024YFE0200700in part by the National Natural Science Foundation of China under Grant 62201504.
文摘Network architectures assisted by Generative Artificial Intelligence(GAI)are envisioned as foundational elements of sixth-generation(6G)communication system.To deliver ubiquitous intelligent services and meet diverse service requirements,6G network architecture should offer personalized services to various mobile devices.Federated learning(FL)with personalized local training,as a privacypreserving machine learning(ML)approach,can be applied to address these challenges.In this paper,we propose a meta-learning-based personalized FL(PFL)method that improves both communication and computation efficiency by utilizing over-the-air computations.Its“pretraining-and-fine-tuning”principle makes it particularly suitable for enabling edge nodes to access personalized GAI services while preserving local privacy.Experiment results demonstrate the outperformance and efficacy of the proposed algorithm,and notably indicate enhanced communication efficiency without compromising accuracy.
文摘Diabetic retinopathy(DR)remains a leading cause of vision impairment and blindness among individuals with diabetes,necessitating innovative approaches to screening and management.This editorial explores the transformative potential of artificial intelligence(AI)and machine learning(ML)in revolutionizing DR care.AI and ML technologies have demonstrated remarkable advancements in enhancing the accuracy,efficiency,and accessibility of DR screening,helping to overcome barriers to early detection.These technologies leverage vast datasets to identify patterns and predict disease progression with unprecedented precision,enabling clinicians to make more informed decisions.Furthermore,AI-driven solutions hold promise in personalizing management strategies for DR,incorpo-rating predictive analytics to tailor interventions and optimize treatment path-ways.By automating routine tasks,AI can reduce the burden on healthcare providers,allowing for a more focused allocation of resources towards complex patient care.This review aims to evaluate the current advancements and applic-ations of AI and ML in DR screening,and to discuss the potential of these techno-logies in developing personalized management strategies,ultimately aiming to improve patient outcomes and reduce the global burden of DR.The integration of AI and ML in DR care represents a paradigm shift,offering a glimpse into the future of ophthalmic healthcare.
基金the Deputyship for Research&Innovation,Ministry of Education in Saudi Arabia for funding this research work through project number RI-44-0833.
文摘The field of biometric identification has seen significant advancements over the years,with research focusing on enhancing the accuracy and security of these systems.One of the key developments is the integration of deep learning techniques in biometric systems.However,despite these advancements,certain challenges persist.One of the most significant challenges is scalability over growing complexity.Traditional methods either require maintaining and securing a growing database,introducing serious security challenges,or relying on retraining the entiremodelwhen new data is introduced-a process that can be computationally expensive and complex.This challenge underscores the need for more efficient methods to scale securely.To this end,we introduce a novel approach that addresses these challenges by integrating multimodal biometrics,cancelable biometrics,and incremental learning techniques.This work is among the first attempts to seamlessly incorporate deep cancelable biometrics with dynamic architectural updates,applied incrementally to the deep learning model as new users are enrolled,achieving high performance with minimal catastrophic forgetting.By leveraging a One-Dimensional Convolutional Neural Network(1D-CNN)architecture combined with a hybrid incremental learning approach,our system achieves high recognition accuracy,averaging 98.98% over incrementing datasets,while ensuring user privacy through cancelable templates generated via a pre-trained CNN model and random projection.The approach demonstrates remarkable adaptability,utilizing the least intrusive biometric traits like facial features and fingerprints,ensuring not only robust performance but also long-term serviceability.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B 187)。
文摘Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.
文摘With the continuous advancement of artificial intelligence(AI)technology,personalized learning systems are increasingly applied in higher education.Particularly within STEM(Science,Technology,Engineering,and Mathematics)education,AI demonstrates significant advantages through adaptive learning pathways,instant feedback,and individualized resource allocation.However,current research predominantly focuses on the technical architecture and application effectiveness of such systems,with insufficient exploration of how AI-enabled personalized learning systems influence university students’learning motivation and academic achievement through educational psychological mechanisms.This paper adopts an educational psychology perspective to construct a causal mechanism model linking“learning motivation-learning behavior-academic achievement.”Findings indicate that AI-powered personalized learning systems enhance learning autonomy,boost self-efficacy,and optimize feedback mechanisms.These effects collectively stimulate university students’learning motivation in STEM disciplines,thereby promoting academic achievement.Building upon empirical research,this paper proposes implications for educational practice and policy formulation,emphasizing the necessity of advancing higher education reform through the dual influence of technology and psychological mechanisms.
文摘The nutritional management of patients with esophageal cancer(EC)presents significant complexities,with traditional approaches facing inherent limitations in data collection,real-time decision-making,and personalized care.This narrative review explores the transformative potential of artificial intelligence(AI)and machine learning(ML),particularly deep learning(DL)and reinforcement learning(RL),in revolutionizing nutritional support for this vulnerable patient population.DL has demonstrated remarkable capabilities in enhancing the accuracy and objectivity of nutritional assessment through precise,automated body composition analysis from medical imaging,offering valuable prognostic insights.Concurrently,RL enables the dynamic optimization of nutritional interventions,adapting them in real time to individual patient responses,paving the way for truly personalized care paradigms.Although AI/ML offers potential advantages in efficiency,precision,and personalization by integrating multidimensional data for superior clinical decision support,its widespread adoption is accompanied by critical challenges.These include safeguarding data privacy and security,mitigating algorithmic bias,ensuring transparency and accountability,and establishing rigorous clinical validation.Early evidence suggests the feasibility of applying AI/ML in nutritional risk stratification and workflow optimization,but highquality prospective studies are needed to demonstrate the direct impact on clinical outcomes,including complications,readmissions,and survival.Overcoming these hurdles necessitates robust ethical governance,interdisciplinary collaboration,and continuous education.Ultimately,the strategic integration of AI/ML holds immense promise to profoundly improve patient outcomes,enhance quality of life,and optimize health care resource utilization in the nutritional management of esophageal cancer.
文摘Depression is a prevalent mental health disorder characterized by high relapse rates,highlighting the need for effective preventive interventions.This paper reviews the potential of reinforcement learning(RL)in preventing depression relapse.RL,a subset of artificial intelligence,utilizes machine learning algorithms to analyze behavioral data,enabling early detection of relapse risk and optimization of personalized interventions.RL's ability to tailor treatment in real-time by adapting to individual needs and responses offers a dynamic alternative to traditional therapeutic approaches.Studies have demonstrated the efficacy of RL in customizing e-Health interventions and integrating mobile sensing with machine learning for adaptive mental health systems.Despite these advantages,challenges remain in algorithmic complexity,ethical considerations,and clinical implementation.Addressing these issues is crucial for the successful integration of RL into mental health care.This paper concludes with recommendations for future research directions,emphasizing the need for larger-scale studies and interdisciplinary collaboration to fully realize RL’s potential in improving mental health outcomes and preventing depression relapse.
基金supported in part by the National Natural Science Foundation of China(62472032)the Young Elite Scientists Sponsorship Program by CAST(2023QNRC001)the Fundamental Research Funds for the Central Universities and Research Innovation Project of China University of Political Science and Law(21ZFY52001)。
文摘In federated learning(FL),the distribution of data across different clients leads to the degradation of global model performance in training.Personalized Federated Learning(pFL)can address this problem through global model personalization.Researches over the past few years have calibrated differences in weights across the entire model or optimized only individual layers of the model without considering that different layers of the whole neural network have different utilities,resulting in lagged model convergence and inadequate personalization in non-IID data.In this paper,we propose model layered optimization for feature extractor and classifier(pFedEC),a novel pFL training framework personalized for different layers of the model.Our study divides the model layers into the feature extractor and classifier.We initialize the model's classifiers during model training,while making the local model's feature extractors learn the representation of the global model's feature extractors to correct each client's local training,integrating the utilities of the different layers in the entire model.Our extensive experiments show that pFedEC achieves 92.95%accuracy on CIFAR-10,outperforming existing pFL methods by approximately 1.8%.On CIFAR-100 and Tiny-ImageNet,pFedEC improves the accuracy by at least 4.2%,reaching 73.02%and 28.39%,respectively.
基金supported by the National Natural Science Foundation of China under Grant 62371181the Project on Excellent Postgraduate Dissertation of Hohai University (422003482)the Changzhou Science and Technology International Cooperation Program under Grant CZ20230029。
文摘With the rapid development of advanced networking and computing technologies such as the Internet of Things, network function virtualization, and 5G infrastructure, new development opportunities are emerging for Maritime Meteorological Sensor Networks(MMSNs). However, the increasing number of intelligent devices joining the MMSN poses a growing threat to network security. Current Artificial Intelligence(AI) intrusion detection techniques turn intrusion detection into a classification problem, where AI excels. These techniques assume sufficient high-quality instances for model construction, which is often unsatisfactory for real-world operation with limited attack instances and constantly evolving characteristics. This paper proposes an Adaptive Personalized Federated learning(APFed) framework that allows multiple MMSN owners to engage in collaborative training. By employing an adaptive personalized update and a shared global classifier, the adverse effects of imbalanced, Non-Independent and Identically Distributed(Non-IID) data are mitigated, enabling the intrusion detection model to possess personalized capabilities and good global generalization. In addition, a lightweight intrusion detection model is proposed to detect various attacks with an effective adaptation to the MMSN environment. Finally, extensive experiments on a classical network dataset show that the attack classification accuracy is improved by about 5% compared to most baselines in the global scenarios.
