In this commentary,we respond to Zhao et al’s recent paper which focuses on mechanisms underlying insomnia sufferers’engagement with acupuncture.Insomnia,a prevalent condition characterized by difficulty falling asl...In this commentary,we respond to Zhao et al’s recent paper which focuses on mechanisms underlying insomnia sufferers’engagement with acupuncture.Insomnia,a prevalent condition characterized by difficulty falling asleep and poor sleep quality,is associated with increased risk of cardiovascular disease,diabetes,and psychiatric illness.Acupuncture,a method involving the therapeutic placement of needles,has been widely accepted as a treatment for insomnia with minimal side effects.In fact,clinical trials suggest auricular acupuncture may improve sleep duration more than cognitive behavioral therapy.However,responses to acupuncture vary.Some patients find it extremely beneficial,while others view it as a routine treatment—or avoid it altogether due to needle phobia.Patient engagement is influenced by cultural beliefs,encouragement,motivation,prior experiences,and recommendations from peers or clinicians.Trust in the physician and testimonials from recovered patients are particularly important facilitators.Looking ahead,a holistic approach-integrating acupuncture with meditation,pranayama,yoga,and other restorative practices-may enhance treatment effectiveness and help patients achieve restorative sleep.展开更多
Ransomware is malware that encrypts data without permission,demanding payment for access.Detecting ransomware on Android platforms is challenging due to evolving malicious techniques and diverse application behaviors....Ransomware is malware that encrypts data without permission,demanding payment for access.Detecting ransomware on Android platforms is challenging due to evolving malicious techniques and diverse application behaviors.Traditional methods,such as static and dynamic analysis,suffer from polymorphism,code obfuscation,and high resource demands.This paper introduces a multi-stage approach to enhance behavioral analysis for Android ransomware detection,focusing on a reduced set of distinguishing features.The approach includes ransomware app collection,behavioral profile generation,dataset creation,feature identification,reduction,and classification.Experiments were conducted on∼3300 Android-based ransomware samples,despite the challenges posed by their evolving nature and complexity.The feature reduction strategy successfully reduced features by 80%,with only a marginal loss of detection accuracy(0.59%).Different machine learning algorithms are employed for classification and achieve 96.71%detection accuracy.Additionally,10-fold cross-validation demonstrated robustness,yielding an AUC-ROC of 99.3%.Importantly,latency and memory evaluations revealed that models using the reduced feature set achieved up to a 99%reduction in inference time and significant memory savings across classifiers.The proposed approach outperforms existing techniques by achieving high detection accuracy with a minimal feature set,also suitable for deployment in resource-constrained environments.Future work may extend datasets and include iOS-based ransomware applications.展开更多
Drosophila melanogaster has been a popular model organism in the study of sleep and circadian rhythm.The Drosophila activity monitoring(DAM)system is one of the many tools developed for investigating sleep behavior in...Drosophila melanogaster has been a popular model organism in the study of sleep and circadian rhythm.The Drosophila activity monitoring(DAM)system is one of the many tools developed for investigating sleep behavior in fruit flies and has been acknowledged by researchers around the world for its simplicity and cost-effectiveness.Based on the simple activity data collected by the DAM system,a wide range of parameters can be generated for sleep and circadian studies.However,current programs that analyze DAM data cover a limited number of metrics and fail to provide individual data for the user to plot graphs and conduct analysis using other software.Therefore,we have developed SleepyFlyR,an R package that:(1)is simple and easy to use with a user-friendly user interface script;(2)provides a comprehensive analysis of sleep and activity parameters;(3)generates double-plotted graphs for sleep and activity patterns;(4)offers visualization of sleep and activity profiles across multiple days or within a single day;(5)calculates the changes of sleep and activity parameters between baseline and experiment;(6)stores both summary data and individual data in files with unique title.展开更多
Advancements in animal behavior quantification methods have driven the development of computational ethology,enabling fully automated behavior analysis.Existing multianimal pose estimation workflows rely on tracking-b...Advancements in animal behavior quantification methods have driven the development of computational ethology,enabling fully automated behavior analysis.Existing multianimal pose estimation workflows rely on tracking-bydetection frameworks for either bottom-up or top-down approaches,requiring retraining to accommodate diverse animal appearances.This study introduces InteBOMB,an integrated workflow that enhances top-down approaches by incorporating generic object tracking,eliminating the need for prior knowledge of target animals while maintaining broad generalizability.InteBOMB includes two key strategies for tracking and segmentation in laboratory environments and two techniques for pose estimation in natural settings.The“background enhancement”strategy optimizesforeground-backgroundcontrastiveloss,generating more discriminative correlation maps.The“online proofreading”strategy stores human-in-the-loop long-term memory and dynamic short-term memory,enabling adaptive updates to object visual features.The“automated labeling suggestion”technique reuses the visual features saved during tracking to identify representative frames for training set labeling.Additionally,the“joint behavior analysis”technique integrates these features with multimodal data,expanding the latent space for behavior classification and clustering.To evaluate the framework,six datasets of mice and six datasets of nonhuman primates were compiled,covering laboratory and natural scenes.Benchmarking results demonstrated a24%improvement in zero-shot generic tracking and a 21%enhancement in joint latent space performance across datasets,highlighting the effectiveness of this approach in robust,generalizable behavior analysis.展开更多
With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis o...With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis of these data and insight into user behavior patterns and preferences.This paper elaborates on the application of big data technology in the analysis of user behavior on e-commerce platforms,including the technical methods of data collection,storage,processing and analysis,as well as the specific applications in the construction of user profiles,precision marketing,personalized recommendation,user retention and churn analysis,etc.,and discusses the challenges and countermeasures faced in the application.Through the study of actual cases,it demonstrates the remarkable effectiveness of big data technology in enhancing the competitiveness of e-commerce platforms and user experience.展开更多
With the rapid development ofmobile Internet,spatial crowdsourcing has becomemore andmore popular.Spatial crowdsourcing consists of many different types of applications,such as spatial crowd-sensing services.In terms ...With the rapid development ofmobile Internet,spatial crowdsourcing has becomemore andmore popular.Spatial crowdsourcing consists of many different types of applications,such as spatial crowd-sensing services.In terms of spatial crowd-sensing,it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction models.Besides collecting sensing data,spatial crowdsourcing also includes spatial delivery services like DiDi and Uber.Appropriate task assignment and worker selection dominate the service quality for spatial crowdsourcing applications.Previous research conducted task assignments via traditional matching approaches or using simple network models.However,advanced mining methods are lacking to explore the relationship between workers,task publishers,and the spatio-temporal attributes in tasks.Therefore,in this paper,we propose a Deep Double Dueling Spatial-temporal Q Network(D3SQN)to adaptively learn the spatialtemporal relationship between task,task publishers,and workers in a dynamic environment to achieve optimal allocation.Specifically,D3SQNis revised through reinforcement learning by adding a spatial-temporal transformer that can estimate the expected state values and action advantages so as to improve the accuracy of task assignments.Extensive experiments are conducted over real data collected fromDiDi and ELM,and the simulation results verify the effectiveness of our proposed models.展开更多
As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain ...As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.展开更多
The advent of the digital era has provided unprecedented opportunities for businesses to collect and analyze customer behavior data. Precision marketing, as a key means to improve marketing efficiency, highly depends ...The advent of the digital era has provided unprecedented opportunities for businesses to collect and analyze customer behavior data. Precision marketing, as a key means to improve marketing efficiency, highly depends on a deep understanding of customer behavior. This study proposes a theoretical framework for multi-dimensional customer behavior analysis, aiming to comprehensively capture customer behavioral characteristics in the digital environment. This framework integrates concepts of multi-source data including transaction history, browsing trajectories, social media interactions, and location information, constructing a theoretically more comprehensive customer profile. The research discusses the potential applications of this theoretical framework in precision marketing scenarios such as personalized recommendations, cross-selling, and customer churn prevention. Through analysis, the study points out that multi-dimensional analysis may significantly improve the targeting and theoretical conversion rates of marketing activities. However, the research also explores theoretical challenges that may be faced in the application process, such as data privacy and information overload, and proposes corresponding conceptual coping strategies. This study provides a new theoretical perspective on how businesses can optimize marketing decisions using big data thinking while respecting customer privacy, laying a foundation for future empirical research.展开更多
In recent years,cyber threats have escalated across diverse sectors,with cybercrime syndicates increasingly exploiting system vulnerabilities.Traditional passive defense mechanisms have proven insufficient,particularl...In recent years,cyber threats have escalated across diverse sectors,with cybercrime syndicates increasingly exploiting system vulnerabilities.Traditional passive defense mechanisms have proven insufficient,particularly as Linux platforms—historically overlooked in favor of Windows—have emerged as frequent targets.According to Trend Micro,there has been a substantial increase in Linux-targeted malware,with ransomware attacks on Linux surpassing those on macOS.This alarming trend underscores the need for detection strategies specifically designed for Linux environments.To address this challenge,this study proposes a comprehensive malware detection framework tailored for Linux systems,integrating dynamic behavioral analysis with the semantic reasoning capabilities of large language models(LLMs).Malware samples are executed within sandbox environments to extract behavioral features such as system calls and command-line executions.These features are then systematically mapped to the MITRE ATT&CK framework,incorporating its defined data sources,data components,and Tactics,Techniques,and Procedures(TTPs).Two mapping constructs—Conceptual Definition Mapping and TTP Technical Keyword Mapping—are developed from official MITRE documentation.These resources are utilized to fine-tune an LLM,enabling it to semantically interpret complex behavioral patterns and infer associated attack techniques,including those employed by previously unknown malware variants.The resulting detection pipeline effectively bridges raw behavioral data with structured threat intelligence.Experimental evaluations confirm the efficacy of the proposed system,with the fine-tuned Gemma 2B model demonstrating significantly enhanced accuracy in associating behavioral features with ATT&CK-defined techniques.This study contributes a fully integrated Linux-specific detection framework,a novel approach for transforming unstructured behavioral data into actionable intelligence,improved interpretability of malicious behavior,and a scalable training process for future applications of LLMs in cybersecurity.展开更多
SUMMARIES OF TOP NEWS STORIES CHINA Safeguarding Gaokao Fairness This year’s gaokao,China’s national college entrance exam,began on 7 June,with a record 13.35 million students taking part.As one of the country’s mo...SUMMARIES OF TOP NEWS STORIES CHINA Safeguarding Gaokao Fairness This year’s gaokao,China’s national college entrance exam,began on 7 June,with a record 13.35 million students taking part.As one of the country’s most important annual events,the 2025 gaokao has seen extensive nation-wide efforts to ensure fairness,safety,and support for all candidates.Authorities have implemented a range of security and logistical measures to safeguard exam integrity.AI-powered monitoring systems have been rolled out in provinces like Jiangxi,Hubei,and Guangdong,enabling real-time behaviour analysis and early warnings without human intervention.These tools reduce pressure on staff and strengthen fairness.展开更多
This paper presents an innovative investigation on prototyping a digital twin(DT)as the platform for human-robot interactive welding and welder behavior analysis.This humanrobot interaction(HRI)working style helps to ...This paper presents an innovative investigation on prototyping a digital twin(DT)as the platform for human-robot interactive welding and welder behavior analysis.This humanrobot interaction(HRI)working style helps to enhance human users'operational productivity and comfort;while data-driven welder behavior analysis benefits to further novice welder training.This HRI system includes three modules:1)a human user who demonstrates the welding operations offsite with her/his operations recorded by the motion-tracked handles;2)a robot that executes the demonstrated welding operations to complete the physical welding tasks onsite;3)a DT system that is developed based on virtual reality(VR)as a digital replica of the physical human-robot interactive welding environment.The DT system bridges a human user and robot through a bi-directional information flow:a)transmitting demonstrated welding operations in VR to the robot in the physical environment;b)displaying the physical welding scenes to human users in VR.Compared to existing DT systems reported in the literatures,the developed one provides better capability in engaging human users in interacting with welding scenes,through an augmented VR.To verify the effectiveness,six welders,skilled with certain manual welding training and unskilled without any training,tested the system by completing the same welding job;three skilled welders produce satisfied welded workpieces,while the other three unskilled do not.A data-driven approach as a combination of fast Fourier transform(FFT),principal component analysis(PCA),and support vector machine(SVM)is developed to analyze their behaviors.Given an operation sequence,i.e.,motion speed sequence of the welding torch,frequency features are firstly extracted by FFT and then reduced in dimension through PCA,which are finally routed into SVM for classification.The trained model demonstrates a 94.44%classification accuracy in the testing dataset.The successful pattern recognition in skilled welder operations should benefit to accelerate novice welder training.展开更多
In this paper three types of dual- chamber shock- struts are considered in dynamic analyses of landing-gear behavior during impact and taxi. Their dynamic characteristics are compared with each other according to calc...In this paper three types of dual- chamber shock- struts are considered in dynamic analyses of landing-gear behavior during impact and taxi. Their dynamic characteristics are compared with each other according to calculation results, and some conclusions are presented.It is very helpful for selecting a suitable type of dual-chamber shock-strut in landing-gear design.展开更多
Due to the increasing demand for security, the development of intelligent surveillance systems has attracted considerable attention in recent years. This study aims to develop a system that is able to identify whether...Due to the increasing demand for security, the development of intelligent surveillance systems has attracted considerable attention in recent years. This study aims to develop a system that is able to identify whether or not the people need help in a public place. Different from previous work, our work considers not only the behaviors of the target person but also the interaction between him and nearby people. In the paper, we propose an event alarm system which can detect the human behaviors and recognize the happening event through integrating the results generated from the single and group behavior analysis. Several new effective features are proposed in the study. Besides, a mechanism capable of extracting one-to-one and multiple-to-one relations is also developed. Experimental results show that the proposed approach can correctly detect human behaviors and provide the alarm messages when emergency events occur.展开更多
Recently,online learning platforms have proven to help people gain knowledge more conveniently.Since the outbreak of COVID-19 in 2020,online learning has become a mainstream mode,as many schools have adopted its forma...Recently,online learning platforms have proven to help people gain knowledge more conveniently.Since the outbreak of COVID-19 in 2020,online learning has become a mainstream mode,as many schools have adopted its format.The platforms are able to capture substantial data relating to the students’learning activities,which could be analyzed to determine relationships between learning behaviors and study habits.As such,an intelligent analysis method is needed to process efficiently this high volume of information.Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data.This study proposes a clustering algorithm based on brain storm optimization(CBSO)to categorize students according to their learning behaviors and determine their characteristics.This enables teaching to be tailored to taken into account those results,thereby,improving the education quality over time.Specifically,we use the individual of CBSO to represent the distribution of students and find the optimal one by the operations of convergence and divergence.The experiments are performed on the 104 students’online learning data,and the results show that CBSO is feasible and efficient.展开更多
This paper reviews the current state of knowledge on psychological interventions with empirical evidence of efficacy in treating common psychiatric and behavioral disorders in people with intellectual disability(ID)at...This paper reviews the current state of knowledge on psychological interventions with empirical evidence of efficacy in treating common psychiatric and behavioral disorders in people with intellectual disability(ID)at all stages of their life.We begin with a brief presentation of what is meant by psychiatric and behavioral disorders in this population,along with an explanation of some of the factors that contribute to the increased psychosocial vulnerability of this group to present with these problems.We then conduct a review of empirically supported psychological therapies used to treat psychiatric and behavioral disorders in people with ID.The review is structured around the three generations of therapies:Applied behavior analysis(e.g.,positive behavior support),cognitive behavioral therapies(e.g.,mindfulness-based cognitive therapy),and contextual therapies(e.g.,dialectical behavior therapy).We conclude with some recommendations for professional practice in the fields of ID and psychiatry.展开更多
Analysis method for the dynamic behavior of viscoelastically damped structures is studied.A finite element model of sandwich beams with eight degrees of freedom is set up and the finite element formulation of the equa...Analysis method for the dynamic behavior of viscoelastically damped structures is studied.A finite element model of sandwich beams with eight degrees of freedom is set up and the finite element formulation of the equations of motion is given for the viscoelastically damped structures.An iteration method for solving nonlinear eigenvalue problems is suggested to analyze the dynamic behavior of viscoelastically damped structures. The method has been applied to the complex model analysis of a sandwich cantilever beam with viscoelastic damping material core.展开更多
BACKGROUND During the coronavirus disease 2019(COVID-19)pandemic,traditional teaching methods were disrupted and online teaching became a new topic in education reform and informatization.In this context,it is importa...BACKGROUND During the coronavirus disease 2019(COVID-19)pandemic,traditional teaching methods were disrupted and online teaching became a new topic in education reform and informatization.In this context,it is important to investigate the necessity and effectiveness of online teaching methods for medical students.This study explored stomatology education in China to evaluate the development and challenges facing the field using massive open online courses(MOOCs)for oral medicine education during the pandemic.AIM To investigate the current situation and challenges facing stomatology education in China,and to assess the necessity and effectiveness of online teaching methods among medical students.METHODS Online courses were developed and offered on personal computers and mobile terminals.Behavioral analysis and formative assessments were conducted to evaluate the learning status of students.RESULTS The results showed that most learners had already completed MOOCs and achieved better results.Course behavior analysis and student surveys indicated that students enjoyed the learning experience.However,the development of oral MOOCs during the COVID-19 pandemic faced significant challenges.CONCLUSION This study provides insights into the potential of using MOOCs to support online professional learning and future teaching innovation,but emphasizes the need for careful design and positive feedback to ensure their success.展开更多
Due to polymorphic nature of malware attack,a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature ofmalware attacks.On the other hand,state-of-the-art methods like deep learning re...Due to polymorphic nature of malware attack,a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature ofmalware attacks.On the other hand,state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model.This is unlikely to be the case in production network as the dataset is unstructured and has no label.Hence an unsupervised learning is recommended.Behavioral study is one of the techniques to elicit traffic pattern.However,studies have shown that existing behavioral intrusion detection model had a few issues which had been parameterized into its common characteristics,namely lack of prior information(p(θ)),and reduced parameters(θ).Therefore,this study aims to utilize the previously built Feature Selection Model subsequently to design a Predictive Analytics Model based on Bayesian Network used to improve the analysis prediction.Feature Selection Model is used to learn significant label as a target and Bayesian Network is a sophisticated probabilistic approach to predict intrusion.Finally,the results are extended to evaluate detection,accuracy and false alarm rate of the model against the subject matter expert model,Support Vector Machine(SVM),k nearest neighbor(k-NN)using simulated and ground-truth dataset.The ground-truth dataset from the production traffic of one of the largest healthcare provider in Malaysia is used to promote realism on the real use case scenario.Results have shown that the proposed model consistently outperformed other models.展开更多
The utility of public goods vary with the behaviors of stakeholders （players）, and it is appropriate to study effective supply and management of public goods with game modeling and analysis. The comparison effect is...The utility of public goods vary with the behaviors of stakeholders （players）, and it is appropriate to study effective supply and management of public goods with game modeling and analysis. The comparison effect is the key issue of public good provision both in theoretical analysis and in practice. One major contribution of the paper is the extension of Clarke-Groves mechanism, to achieve which strategic behavior analysis is applied through the analysis and the comparison effect among various stakeholders in different stages is created and highly emphasized. In the first section of this paper, the definition of integrated water resources management （IWRM）, the importance of stakeholder participation as well as some models and methods that have been applied are illustrated. Following this, the framework of analysis is elaborated, in which the scenario and aims are shown, and it is claimed that game theory is the main approach, which includes both cooperative games and non-cooperative games. To achieve the aims of the public project, five approaches from game theory are able to cover the entire process of the project, and the fourth approach on interest compensation mechanism is the highlight of the research. After this, the interest compensation mechanism is demonstrated in the model section, and is proved to be an incentive compatible mechanism that makes each stakeholder choose to behave in accordance with the interest of the entire project. The Clarke-Groves mechanism is applied and extended in establishing the model, and the utility change by the comparison among stakeholders （defined as the comparison effect） is involved. In the application section, a water project is analyzed in consideration of various stakeholders, and other possible applications are also indicated.展开更多
文摘In this commentary,we respond to Zhao et al’s recent paper which focuses on mechanisms underlying insomnia sufferers’engagement with acupuncture.Insomnia,a prevalent condition characterized by difficulty falling asleep and poor sleep quality,is associated with increased risk of cardiovascular disease,diabetes,and psychiatric illness.Acupuncture,a method involving the therapeutic placement of needles,has been widely accepted as a treatment for insomnia with minimal side effects.In fact,clinical trials suggest auricular acupuncture may improve sleep duration more than cognitive behavioral therapy.However,responses to acupuncture vary.Some patients find it extremely beneficial,while others view it as a routine treatment—or avoid it altogether due to needle phobia.Patient engagement is influenced by cultural beliefs,encouragement,motivation,prior experiences,and recommendations from peers or clinicians.Trust in the physician and testimonials from recovered patients are particularly important facilitators.Looking ahead,a holistic approach-integrating acupuncture with meditation,pranayama,yoga,and other restorative practices-may enhance treatment effectiveness and help patients achieve restorative sleep.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1I1A3049788).
文摘Ransomware is malware that encrypts data without permission,demanding payment for access.Detecting ransomware on Android platforms is challenging due to evolving malicious techniques and diverse application behaviors.Traditional methods,such as static and dynamic analysis,suffer from polymorphism,code obfuscation,and high resource demands.This paper introduces a multi-stage approach to enhance behavioral analysis for Android ransomware detection,focusing on a reduced set of distinguishing features.The approach includes ransomware app collection,behavioral profile generation,dataset creation,feature identification,reduction,and classification.Experiments were conducted on∼3300 Android-based ransomware samples,despite the challenges posed by their evolving nature and complexity.The feature reduction strategy successfully reduced features by 80%,with only a marginal loss of detection accuracy(0.59%).Different machine learning algorithms are employed for classification and achieve 96.71%detection accuracy.Additionally,10-fold cross-validation demonstrated robustness,yielding an AUC-ROC of 99.3%.Importantly,latency and memory evaluations revealed that models using the reduced feature set achieved up to a 99%reduction in inference time and significant memory savings across classifiers.The proposed approach outperforms existing techniques by achieving high detection accuracy with a minimal feature set,also suitable for deployment in resource-constrained environments.Future work may extend datasets and include iOS-based ransomware applications.
基金the National Natural Science Foundation of China(No.81970999)the Shanghai Rising Star Project(No.19QA1404900)。
文摘Drosophila melanogaster has been a popular model organism in the study of sleep and circadian rhythm.The Drosophila activity monitoring(DAM)system is one of the many tools developed for investigating sleep behavior in fruit flies and has been acknowledged by researchers around the world for its simplicity and cost-effectiveness.Based on the simple activity data collected by the DAM system,a wide range of parameters can be generated for sleep and circadian studies.However,current programs that analyze DAM data cover a limited number of metrics and fail to provide individual data for the user to plot graphs and conduct analysis using other software.Therefore,we have developed SleepyFlyR,an R package that:(1)is simple and easy to use with a user-friendly user interface script;(2)provides a comprehensive analysis of sleep and activity parameters;(3)generates double-plotted graphs for sleep and activity patterns;(4)offers visualization of sleep and activity profiles across multiple days or within a single day;(5)calculates the changes of sleep and activity parameters between baseline and experiment;(6)stores both summary data and individual data in files with unique title.
基金supported by the STI 2030-Major Projects(2022ZD0211900,2022ZD0211902)STI 2030-Major Projects(2021ZD0204500,2021ZD0204503)+1 种基金National Natural Science Foundation of China(32171461)National Key Research and Development Program of China(2023YFC3208303)。
文摘Advancements in animal behavior quantification methods have driven the development of computational ethology,enabling fully automated behavior analysis.Existing multianimal pose estimation workflows rely on tracking-bydetection frameworks for either bottom-up or top-down approaches,requiring retraining to accommodate diverse animal appearances.This study introduces InteBOMB,an integrated workflow that enhances top-down approaches by incorporating generic object tracking,eliminating the need for prior knowledge of target animals while maintaining broad generalizability.InteBOMB includes two key strategies for tracking and segmentation in laboratory environments and two techniques for pose estimation in natural settings.The“background enhancement”strategy optimizesforeground-backgroundcontrastiveloss,generating more discriminative correlation maps.The“online proofreading”strategy stores human-in-the-loop long-term memory and dynamic short-term memory,enabling adaptive updates to object visual features.The“automated labeling suggestion”technique reuses the visual features saved during tracking to identify representative frames for training set labeling.Additionally,the“joint behavior analysis”technique integrates these features with multimodal data,expanding the latent space for behavior classification and clustering.To evaluate the framework,six datasets of mice and six datasets of nonhuman primates were compiled,covering laboratory and natural scenes.Benchmarking results demonstrated a24%improvement in zero-shot generic tracking and a 21%enhancement in joint latent space performance across datasets,highlighting the effectiveness of this approach in robust,generalizable behavior analysis.
文摘With the rapid development of the Internet and e-commerce,e-commerce platforms have accumulated huge amounts of user behavior data.The emergence of big data technology provides a powerful means for in-depth analysis of these data and insight into user behavior patterns and preferences.This paper elaborates on the application of big data technology in the analysis of user behavior on e-commerce platforms,including the technical methods of data collection,storage,processing and analysis,as well as the specific applications in the construction of user profiles,precision marketing,personalized recommendation,user retention and churn analysis,etc.,and discusses the challenges and countermeasures faced in the application.Through the study of actual cases,it demonstrates the remarkable effectiveness of big data technology in enhancing the competitiveness of e-commerce platforms and user experience.
基金supported in part by the Pioneer and Leading Goose R&D Program of Zhejiang Province under Grant 2022C01083 (Dr.Yu Li,https://zjnsf.kjt.zj.gov.cn/)Pioneer and Leading Goose R&D Program of Zhejiang Province under Grant 2023C01217 (Dr.Yu Li,https://zjnsf.kjt.zj.gov.cn/).
文摘With the rapid development ofmobile Internet,spatial crowdsourcing has becomemore andmore popular.Spatial crowdsourcing consists of many different types of applications,such as spatial crowd-sensing services.In terms of spatial crowd-sensing,it collects and analyzes traffic sensing data from clients like vehicles and traffic lights to construct intelligent traffic prediction models.Besides collecting sensing data,spatial crowdsourcing also includes spatial delivery services like DiDi and Uber.Appropriate task assignment and worker selection dominate the service quality for spatial crowdsourcing applications.Previous research conducted task assignments via traditional matching approaches or using simple network models.However,advanced mining methods are lacking to explore the relationship between workers,task publishers,and the spatio-temporal attributes in tasks.Therefore,in this paper,we propose a Deep Double Dueling Spatial-temporal Q Network(D3SQN)to adaptively learn the spatialtemporal relationship between task,task publishers,and workers in a dynamic environment to achieve optimal allocation.Specifically,D3SQNis revised through reinforcement learning by adding a spatial-temporal transformer that can estimate the expected state values and action advantages so as to improve the accuracy of task assignments.Extensive experiments are conducted over real data collected fromDiDi and ELM,and the simulation results verify the effectiveness of our proposed models.
文摘As social media and online activity continue to pervade all age groups, it serves as a crucial platform for sharing personal experiences and opinions as well as information about attitudes and preferences for certain interests or purchases. This generates a wealth of behavioral data, which, while invaluable to businesses, researchers, policymakers, and the cybersecurity sector, presents significant challenges due to its unstructured nature. Existing tools for analyzing this data often lack the capability to effectively retrieve and process it comprehensively. This paper addresses the need for an advanced analytical tool that ethically and legally collects and analyzes social media data and online activity logs, constructing detailed and structured user profiles. It reviews current solutions, highlights their limitations, and introduces a new approach, the Advanced Social Analyzer (ASAN), that bridges these gaps. The proposed solutions technical aspects, implementation, and evaluation are discussed, with results compared to existing methodologies. The paper concludes by suggesting future research directions to further enhance the utility and effectiveness of social media data analysis.
文摘The advent of the digital era has provided unprecedented opportunities for businesses to collect and analyze customer behavior data. Precision marketing, as a key means to improve marketing efficiency, highly depends on a deep understanding of customer behavior. This study proposes a theoretical framework for multi-dimensional customer behavior analysis, aiming to comprehensively capture customer behavioral characteristics in the digital environment. This framework integrates concepts of multi-source data including transaction history, browsing trajectories, social media interactions, and location information, constructing a theoretically more comprehensive customer profile. The research discusses the potential applications of this theoretical framework in precision marketing scenarios such as personalized recommendations, cross-selling, and customer churn prevention. Through analysis, the study points out that multi-dimensional analysis may significantly improve the targeting and theoretical conversion rates of marketing activities. However, the research also explores theoretical challenges that may be faced in the application process, such as data privacy and information overload, and proposes corresponding conceptual coping strategies. This study provides a new theoretical perspective on how businesses can optimize marketing decisions using big data thinking while respecting customer privacy, laying a foundation for future empirical research.
基金supported by the National Science and Technology Council under grant number 113-2221-E-027-126-MY3.
文摘In recent years,cyber threats have escalated across diverse sectors,with cybercrime syndicates increasingly exploiting system vulnerabilities.Traditional passive defense mechanisms have proven insufficient,particularly as Linux platforms—historically overlooked in favor of Windows—have emerged as frequent targets.According to Trend Micro,there has been a substantial increase in Linux-targeted malware,with ransomware attacks on Linux surpassing those on macOS.This alarming trend underscores the need for detection strategies specifically designed for Linux environments.To address this challenge,this study proposes a comprehensive malware detection framework tailored for Linux systems,integrating dynamic behavioral analysis with the semantic reasoning capabilities of large language models(LLMs).Malware samples are executed within sandbox environments to extract behavioral features such as system calls and command-line executions.These features are then systematically mapped to the MITRE ATT&CK framework,incorporating its defined data sources,data components,and Tactics,Techniques,and Procedures(TTPs).Two mapping constructs—Conceptual Definition Mapping and TTP Technical Keyword Mapping—are developed from official MITRE documentation.These resources are utilized to fine-tune an LLM,enabling it to semantically interpret complex behavioral patterns and infer associated attack techniques,including those employed by previously unknown malware variants.The resulting detection pipeline effectively bridges raw behavioral data with structured threat intelligence.Experimental evaluations confirm the efficacy of the proposed system,with the fine-tuned Gemma 2B model demonstrating significantly enhanced accuracy in associating behavioral features with ATT&CK-defined techniques.This study contributes a fully integrated Linux-specific detection framework,a novel approach for transforming unstructured behavioral data into actionable intelligence,improved interpretability of malicious behavior,and a scalable training process for future applications of LLMs in cybersecurity.
文摘SUMMARIES OF TOP NEWS STORIES CHINA Safeguarding Gaokao Fairness This year’s gaokao,China’s national college entrance exam,began on 7 June,with a record 13.35 million students taking part.As one of the country’s most important annual events,the 2025 gaokao has seen extensive nation-wide efforts to ensure fairness,safety,and support for all candidates.Authorities have implemented a range of security and logistical measures to safeguard exam integrity.AI-powered monitoring systems have been rolled out in provinces like Jiangxi,Hubei,and Guangdong,enabling real-time behaviour analysis and early warnings without human intervention.These tools reduce pressure on staff and strengthen fairness.
文摘This paper presents an innovative investigation on prototyping a digital twin(DT)as the platform for human-robot interactive welding and welder behavior analysis.This humanrobot interaction(HRI)working style helps to enhance human users'operational productivity and comfort;while data-driven welder behavior analysis benefits to further novice welder training.This HRI system includes three modules:1)a human user who demonstrates the welding operations offsite with her/his operations recorded by the motion-tracked handles;2)a robot that executes the demonstrated welding operations to complete the physical welding tasks onsite;3)a DT system that is developed based on virtual reality(VR)as a digital replica of the physical human-robot interactive welding environment.The DT system bridges a human user and robot through a bi-directional information flow:a)transmitting demonstrated welding operations in VR to the robot in the physical environment;b)displaying the physical welding scenes to human users in VR.Compared to existing DT systems reported in the literatures,the developed one provides better capability in engaging human users in interacting with welding scenes,through an augmented VR.To verify the effectiveness,six welders,skilled with certain manual welding training and unskilled without any training,tested the system by completing the same welding job;three skilled welders produce satisfied welded workpieces,while the other three unskilled do not.A data-driven approach as a combination of fast Fourier transform(FFT),principal component analysis(PCA),and support vector machine(SVM)is developed to analyze their behaviors.Given an operation sequence,i.e.,motion speed sequence of the welding torch,frequency features are firstly extracted by FFT and then reduced in dimension through PCA,which are finally routed into SVM for classification.The trained model demonstrates a 94.44%classification accuracy in the testing dataset.The successful pattern recognition in skilled welder operations should benefit to accelerate novice welder training.
文摘In this paper three types of dual- chamber shock- struts are considered in dynamic analyses of landing-gear behavior during impact and taxi. Their dynamic characteristics are compared with each other according to calculation results, and some conclusions are presented.It is very helpful for selecting a suitable type of dual-chamber shock-strut in landing-gear design.
基金supported by the“MOST”under Grant No.104-2221-E-259-024-MY2
文摘Due to the increasing demand for security, the development of intelligent surveillance systems has attracted considerable attention in recent years. This study aims to develop a system that is able to identify whether or not the people need help in a public place. Different from previous work, our work considers not only the behaviors of the target person but also the interaction between him and nearby people. In the paper, we propose an event alarm system which can detect the human behaviors and recognize the happening event through integrating the results generated from the single and group behavior analysis. Several new effective features are proposed in the study. Besides, a mechanism capable of extracting one-to-one and multiple-to-one relations is also developed. Experimental results show that the proposed approach can correctly detect human behaviors and provide the alarm messages when emergency events occur.
基金This work was partially supported by the National Natural Science Foundation of China(61876089,61876185,61902281,61375121)the Opening Project of Jiangsu Key Laboratory of Data Science and Smart Software(No.2019DS301)+1 种基金the Engineering Research Center of Digital Forensics,Ministry of Education,the Key Research and Development Program of Jiangsu Province(BE2020633)the Priority Academic Program Development of Jiangsu Higher Education Institutions.
文摘Recently,online learning platforms have proven to help people gain knowledge more conveniently.Since the outbreak of COVID-19 in 2020,online learning has become a mainstream mode,as many schools have adopted its format.The platforms are able to capture substantial data relating to the students’learning activities,which could be analyzed to determine relationships between learning behaviors and study habits.As such,an intelligent analysis method is needed to process efficiently this high volume of information.Clustering is an effect data mining method which discover data distribution and hidden characteristic from uncharacterized online learning data.This study proposes a clustering algorithm based on brain storm optimization(CBSO)to categorize students according to their learning behaviors and determine their characteristics.This enables teaching to be tailored to taken into account those results,thereby,improving the education quality over time.Specifically,we use the individual of CBSO to represent the distribution of students and find the optimal one by the operations of convergence and divergence.The experiments are performed on the 104 students’online learning data,and the results show that CBSO is feasible and efficient.
基金Supported by Ministry of Science,Innovation and Universities,and the State Research Agency,No.PID2019-105737RBI00/AEI/10.13039/501100011033。
文摘This paper reviews the current state of knowledge on psychological interventions with empirical evidence of efficacy in treating common psychiatric and behavioral disorders in people with intellectual disability(ID)at all stages of their life.We begin with a brief presentation of what is meant by psychiatric and behavioral disorders in this population,along with an explanation of some of the factors that contribute to the increased psychosocial vulnerability of this group to present with these problems.We then conduct a review of empirically supported psychological therapies used to treat psychiatric and behavioral disorders in people with ID.The review is structured around the three generations of therapies:Applied behavior analysis(e.g.,positive behavior support),cognitive behavioral therapies(e.g.,mindfulness-based cognitive therapy),and contextual therapies(e.g.,dialectical behavior therapy).We conclude with some recommendations for professional practice in the fields of ID and psychiatry.
文摘Analysis method for the dynamic behavior of viscoelastically damped structures is studied.A finite element model of sandwich beams with eight degrees of freedom is set up and the finite element formulation of the equations of motion is given for the viscoelastically damped structures.An iteration method for solving nonlinear eigenvalue problems is suggested to analyze the dynamic behavior of viscoelastically damped structures. The method has been applied to the complex model analysis of a sandwich cantilever beam with viscoelastic damping material core.
基金National Natural Science Foundation of China,No.31870971Zhejiang Medical and Health Science and Technology Plan,No.2023KY155.
文摘BACKGROUND During the coronavirus disease 2019(COVID-19)pandemic,traditional teaching methods were disrupted and online teaching became a new topic in education reform and informatization.In this context,it is important to investigate the necessity and effectiveness of online teaching methods for medical students.This study explored stomatology education in China to evaluate the development and challenges facing the field using massive open online courses(MOOCs)for oral medicine education during the pandemic.AIM To investigate the current situation and challenges facing stomatology education in China,and to assess the necessity and effectiveness of online teaching methods among medical students.METHODS Online courses were developed and offered on personal computers and mobile terminals.Behavioral analysis and formative assessments were conducted to evaluate the learning status of students.RESULTS The results showed that most learners had already completed MOOCs and achieved better results.Course behavior analysis and student surveys indicated that students enjoyed the learning experience.However,the development of oral MOOCs during the COVID-19 pandemic faced significant challenges.CONCLUSION This study provides insights into the potential of using MOOCs to support online professional learning and future teaching innovation,but emphasizes the need for careful design and positive feedback to ensure their success.
基金The work is fully sponsored by the research project grant FRGS/1/2021/ICT07/UITM/02/3。
文摘Due to polymorphic nature of malware attack,a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature ofmalware attacks.On the other hand,state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model.This is unlikely to be the case in production network as the dataset is unstructured and has no label.Hence an unsupervised learning is recommended.Behavioral study is one of the techniques to elicit traffic pattern.However,studies have shown that existing behavioral intrusion detection model had a few issues which had been parameterized into its common characteristics,namely lack of prior information(p(θ)),and reduced parameters(θ).Therefore,this study aims to utilize the previously built Feature Selection Model subsequently to design a Predictive Analytics Model based on Bayesian Network used to improve the analysis prediction.Feature Selection Model is used to learn significant label as a target and Bayesian Network is a sophisticated probabilistic approach to predict intrusion.Finally,the results are extended to evaluate detection,accuracy and false alarm rate of the model against the subject matter expert model,Support Vector Machine(SVM),k nearest neighbor(k-NN)using simulated and ground-truth dataset.The ground-truth dataset from the production traffic of one of the largest healthcare provider in Malaysia is used to promote realism on the real use case scenario.Results have shown that the proposed model consistently outperformed other models.
文摘The utility of public goods vary with the behaviors of stakeholders （players）, and it is appropriate to study effective supply and management of public goods with game modeling and analysis. The comparison effect is the key issue of public good provision both in theoretical analysis and in practice. One major contribution of the paper is the extension of Clarke-Groves mechanism, to achieve which strategic behavior analysis is applied through the analysis and the comparison effect among various stakeholders in different stages is created and highly emphasized. In the first section of this paper, the definition of integrated water resources management （IWRM）, the importance of stakeholder participation as well as some models and methods that have been applied are illustrated. Following this, the framework of analysis is elaborated, in which the scenario and aims are shown, and it is claimed that game theory is the main approach, which includes both cooperative games and non-cooperative games. To achieve the aims of the public project, five approaches from game theory are able to cover the entire process of the project, and the fourth approach on interest compensation mechanism is the highlight of the research. After this, the interest compensation mechanism is demonstrated in the model section, and is proved to be an incentive compatible mechanism that makes each stakeholder choose to behave in accordance with the interest of the entire project. The Clarke-Groves mechanism is applied and extended in establishing the model, and the utility change by the comparison among stakeholders （defined as the comparison effect） is involved. In the application section, a water project is analyzed in consideration of various stakeholders, and other possible applications are also indicated.
