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
Background:Interpersonal alienation has increasingly been recognized as a salient risk factor affecting university students’psychological adjustment and life satisfaction.Guided by Social Identity and Self-Categoriza...Background:Interpersonal alienation has increasingly been recognized as a salient risk factor affecting university students’psychological adjustment and life satisfaction.Guided by Social Identity and Self-Categorization theories,this study examines how alienation influences life satisfaction through the mediating roles of social identity and hope.Methods:This study surveyed 492 Taiwan resident,China undergraduate students(53.7 percent female,mean age 21.08 years)from 60 universities using convenience sampling in May 2023.Data were collected through an online questionnaire distributed via faculty-managed teaching media platforms.Measures included perceived social identity,state hope,interpersonal alienation,and life satisfaction.All instrumentswere adapted from validated scales,translated into traditional Chinese through back-translation,and reviewed by experts to ensure content validity and cultural relevance.Statistical analyses were conducted using SPSS 20 and SmartPLS 4.0.Results:Harman’s single-factor test indicated no significant common method bias.Measurement model analyses demonstrated satisfactory reliability,convergent validity,and absence of multicollinearity.All four hypothesized paths were supported:interpersonal alienation negatively predicted life satisfaction,with perceived social identity and hope serving as individual and sequential mediators.The model explained 10.5%of the variance in social identity,25.3%in hope,and 49.6%in life satisfaction.Group comparisons revealed that male students reported significantly higher hope and life satisfaction than females,and first-year students experienced greater alienation than upper-level peers.Conclusion:This study elucidates how interpersonal alienation undermines life satisfaction among university students and highlights the protective roles of social identity and hope.Findings underscore the importance of fostering psychological resources that promote resilience and well-being.The results offer practical implications for designing educational programs that enhance students’sense of belonging,optimism,and emotional strength.These insights contribute to a deeper theoretical understanding of the mechanisms linking alienation and life satisfaction and inform strategies to support student adaptation and flourishing in higher education.展开更多
In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading...In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.展开更多
Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using d...Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.展开更多
People started posting textual tweets on Twitter as soon as the novel coronavirus(COVID-19)emerged.Analyzing these tweets can assist institutions in better decision-making and prioritizing their tasks.Therefore,this s...People started posting textual tweets on Twitter as soon as the novel coronavirus(COVID-19)emerged.Analyzing these tweets can assist institutions in better decision-making and prioritizing their tasks.Therefore,this study aimed to analyze 43 million tweets collected between March 22 and March 30,2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering analysis.The results indicated that unigram terms were trended more frequently than bigram and trigram terms.A large number of tweets about the COVID-19 were disseminated and received widespread public attention during the epidemic.The high-frequency words such as“death”,“test”,“spread”,and“lockdown”suggest that people fear of being infected,and those who got infection are afraid of death.The results also showed that people agreed to stay at home due to the fear of the spread,and they were calling for social distancing since they become aware of the COVID-19.It can be suggested that social media posts may affect human psychology and behavior.These results may help governments and health organizations to better understand the psychology of the public,and thereby,better communicate with them to prevent and manage the panic.展开更多
The management of early stage hepatocellular carcinoma(HCC)presents significant challenges.While radiofrequency ablation(RFA)has shown safety and effectiveness in treating HCC,with lower mortality rates and shorter ho...The management of early stage hepatocellular carcinoma(HCC)presents significant challenges.While radiofrequency ablation(RFA)has shown safety and effectiveness in treating HCC,with lower mortality rates and shorter hospital stays,its high recurrence rate remains a significant impediment.Consequently,achieving improved survival solely through RFA is challenging,particularly in retrospective studies with inherent biases.Ultrasound is commonly used for guiding percutaneous RFA,but its low contrast can lead to missed tumors and the risk of HCC recurrence.To enhance the efficiency of ultrasound-guided percutaneous RFA,various techniques such as artificial ascites and contrast-enhanced ultrasound have been developed to facilitate complete tumor ablation.Minimally invasive surgery(MIS)offers advantages over open surgery and has gained traction in various surgical fields.Recent studies suggest that laparoscopic intraoperative RFA(IORFA)may be more effective than percutaneous RFA in terms of survival for HCC patients unsuitable for surgery,highlighting its significance.Therefore,combining MIS-IORFA with these enhanced percutaneous RFA techniques may hold greater significance for HCC treatment using the MIS-IORFA approach.This article reviews liver resection and RFA in HCC treatment,comparing their merits and proposing a trajectory involving their combination in future therapy.展开更多
An increasing number of social media and networking platforms have been widely used. People usually post the online comments to share their own opinions on the networking platforms with social media. Business companie...An increasing number of social media and networking platforms have been widely used. People usually post the online comments to share their own opinions on the networking platforms with social media. Business companies are increasingly seeking effective ways to mine what people think and feel regarding their products and services. How to correctly understand the online customers’ reviews becomes an important issue. This study aims to propose a method with the aspect-oriented Petri nets(AOPN) to improve the examination correctness without changing any process and program. We collect those comments from the online reviews with Scrapy tools, perform sentiment analysis using SnowNLP, and examine the analysis results to improve the correctness. In this paper, we apply our method for a case of the online movie comments. The experimental results have shown that AOPN is helpful for the sentiment analysis and verifying its correctness.展开更多
In recent years, the application of the Internet of Things （IoT） has become an emerging business. The most important concept of next-generation network for providing a common global IT platform is combining seamless...In recent years, the application of the Internet of Things （IoT） has become an emerging business. The most important concept of next-generation network for providing a common global IT platform is combining seamless networks and networked things, objects or sensors. Also, wireless body area networks （WBANs） are becoming mature with the widespread usage of the IoT. In order to support WBAN, the platform, scenario and emergency service are necessary due to the sensors in WBAN being related to wearer＆#39;s life. The sensors on the body detect a lot of information about bioinformatics and medical signals, such as heartbeat and blood. Thus, the integration of IoT and network communication in daily life is important. However, there is not only a lack of common fabric for integrating IoT with current Internet and but also no emergency call process in the current network communication envi-ronment. To overcome such situations, the prototype of integrating IoT and emergency call process is discussed. A simulated boot-strap platform to provide the discussion of open challenges and solutions for deploying IoT in Internet and the emergency commu-nication system are analyzed by using a service of 3GPP IP multimedia subsystem. Finally, the prototype for supporting WBAN with emergence service is also addressed and the performance results are useful to service providers and network operators that they can estimate their migration to IoT by referring to this experience and experiment results. Furthermore, the queuing model used to achieve the performance of emergency service in IMS and the delay time of the proposed model is analyzed.展开更多
In this study,vector quantization and hidden Markov models were used to achieve speech command recognition.Pre-emphasis,a hamming window,and Mel-frequency cepstral coefficients were first adopted to obtain feature val...In this study,vector quantization and hidden Markov models were used to achieve speech command recognition.Pre-emphasis,a hamming window,and Mel-frequency cepstral coefficients were first adopted to obtain feature values.Subsequently,vector quantization and HMMs(hidden Markov models)were employed to achieve speech command recognition.The recorded speech length was three Chinese characters,which were used to test the method.Five phrases pronounced mixing various human voices were recorded and used to test the models.The recorded phrases were then used for speech command recognition to demonstrate whether the experiment results were satisfactory.展开更多
The interaction between human and physical devices and devices in the real world is gaining more attention, and requires a natural and intuitive methodology to employ. According to this idea and living well, life has ...The interaction between human and physical devices and devices in the real world is gaining more attention, and requires a natural and intuitive methodology to employ. According to this idea and living well, life has been a growing demand. Thus, how to raise pets in an easy way has been the main issue recently. This study examines the ability of computation, communication, and control technologies to improve human interaction with pets by the technology of the Internet of Things. This work addresses the improvement through the pet application of the ability of location-awareness, and to help the pet owners raise their pet on the activity and eating control easily. Extensive experiment results demonstrate that our proposed system performs significantly help on the kidney disease and reduce the symptoms. Our study not only presents the key improvement of the pet monitor system involved in the ideas of the Internet of Things, but also meets the demands of pet owners, who are out for works without any trouble.展开更多
The secondary flow driven by the primary vortex in a cylinder,generating the so called"tea leaf paradox",is fundamental for understanding many natural phenomena,industrial applications and scientific researc...The secondary flow driven by the primary vortex in a cylinder,generating the so called"tea leaf paradox",is fundamental for understanding many natural phenomena,industrial applications and scientific researches.In this work,the effect of wettability on the primary vortex and secondary flow is investigated by the three-dimensional multiphase lattice Boltzmann method based on a chemical potential.We find that the surface wettability strongly affects the shape of the primary vortex.With the increase of the contact angle of the cylinder,the sectional plane of the primary vortex gradually changes from a steep valley into a saddle with two raised parts.Because the surface friction is reduced correspondingly,the core of the secondary vortex moves to the centerline of the cylinder and the vortex intensity also increases.The stirring force has stronger effects to enhance the secondary flow and push the vortex up than the surface wettability.Interestingly,a small secondary vortex is discovered near the three-phase contact line when the surface has a moderate wettability,owing to the interaction between the secondary flow and the curved gas/liquid interface.展开更多
In this study,we classify the genera of COVID-19 and provide brief information about the root of the spread and the transmission from animal(natural host)to humans.We establish a model of fractional-order differential...In this study,we classify the genera of COVID-19 and provide brief information about the root of the spread and the transmission from animal(natural host)to humans.We establish a model of fractional-order differential equations to discuss the spread of the infection from the natural host to the intermediate one,and from the intermediate one to the human host.At the same time,we focus on the potential spillover of bat-borne coronaviruses.We consider the local stability of the co-existing critical point of the model by using the Routh–Hurwitz Criteria.Moreover,we analyze the existence and uniqueness of the constructed initial value problem.We focus on the control parameters to decrease the outbreak from pandemic form to the epidemic by using both strong and weak Allee Effect at time t.Furthermore,the discretization process shows that the system undergoes Neimark–Sacker Bifurcation under specific conditions.Finally,we conduct a series of numerical simulations to enhance the theoretical findings.展开更多
Reversible data hiding is a confidential communication technique that takes advantage of image file characteristics,which allows us to hide sensitive data in image files.In this paper,we propose a novel high-fidelity ...Reversible data hiding is a confidential communication technique that takes advantage of image file characteristics,which allows us to hide sensitive data in image files.In this paper,we propose a novel high-fidelity reversible data hiding scheme.Based on the advantage of the multipredictor mechanism,we combine two effective prediction schemes to improve prediction accuracy.In addition,the multihistogram technique is utilized to further improve the image quality of the stego image.Moreover,a model of the grouped knapsack problem is used to speed up the search for the suitable embedding bin in each sub-histogram.Experimental results show that the quality of the stego image of our scheme outperforms state-of-the-art schemes in most cases.展开更多
A square particle suspended in a Poiseuille flow is investigated by using the lattice Boltzmann method with the Galilean-invariant momentum exchange method. The lateral migration of Segré-Silberberg effect is obs...A square particle suspended in a Poiseuille flow is investigated by using the lattice Boltzmann method with the Galilean-invariant momentum exchange method. The lateral migration of Segré-Silberberg effect is observed for the square particle, accompanied by the nonuniform rotation and regular wave. To compare with the circular particle, its circumscribed and inscribed squares are used in the simulations. Because the circumscribed square takes up a greater difference between the upper and lower flow rates, it reaches the equilibrium position earlier than the inscribed one. The trajectories of the latter are much closer to those of circle;this indicates that the circle and its inscribed square have a similar hydrodynamic radius in a Poiseuille flow. The equilibrium positions of the square particles change with Reynolds number and show a shape of saddle, whereas those of the circular particles are virtually not affected by Reynolds number. The regular wave and nonuniform rotation are owing to the interactions of the square shape and the parabolic velocity distribution of Poiseuille flow, and high Reynolds number makes the square rotating faster and decrease its oscillating amplitude. A series of contours illustrate the dynamic flow fields when the square particle has successive postures in a half rotating period. This study is beneficial to understand the motion of anisotropic particles and the dendrite growth in dynamic environment.展开更多
In this paper, we propose a low-cost posture recognition scheme using a single webcam for the signaling hand with nature sways and possible oc-clusions. It goes for developing the untouchable low-complexity utility ba...In this paper, we propose a low-cost posture recognition scheme using a single webcam for the signaling hand with nature sways and possible oc-clusions. It goes for developing the untouchable low-complexity utility based on friendly hand-posture signaling. The scheme integrates the dominant temporal-difference detection, skin color detection and morphological filtering for efficient cooperation in constructing the hand profile molds. Those molds provide representative hand profiles for more stable posture recognition than accurate hand shapes with in effect trivial details. The resultant bounding box of tracking the signaling molds can be treated as a regular-type object-matched ROI to facilitate the stable extraction of robust HOG features. With such commonly applied features on hand, the prototype SVM is adequately capable of obtaining fast and stable hand postures recognition under natural hand movement and non-hand object occlusion. Experimental results demonstrate that our scheme can achieve hand-posture recognition with enough accuracy under background clutters that the targeted hand can be allowed with medium movement and palm-grasped object. Hence, the proposed method can be easily embedded in the mobile phone as application software.展开更多
The aging society will become a serious problem for most countries in the world. Under the constraint of limited medical resource, the self-health management becomes important. In this paper, a mobile healthcare syste...The aging society will become a serious problem for most countries in the world. Under the constraint of limited medical resource, the self-health management becomes important. In this paper, a mobile healthcare system is implemented. One can easily monitor his/her physiological data through the using of a smartphone that is wirelessly connected to different medical detection devices. A cloud database is established for storing and analysing these physiological data. The guidance of suitable physical exercises to individuals is then given in the system. This paper shows the details of the system implementation.展开更多
Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present wi...Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.展开更多
As is known,centralized federated learning faces risks of a single point of failure and privacy breaches,and blockchain-based federated learning frameworks can address these challenges to a certain extent in recent wo...As is known,centralized federated learning faces risks of a single point of failure and privacy breaches,and blockchain-based federated learning frameworks can address these challenges to a certain extent in recent works.However,malicious clients may still illegally access the blockchain to upload malicious data or steal on-chain data.In addition,blockchain-based federated training suffers from a heavy storage burden and excessive network communication overhead.To address these issues,we propose an asynchronous,tiered federated learning storage scheme based on blockchain and IPFS.It manages the execution of federated learning tasks through smart contracts deployed on the blockchain,decentralizing the entire training process.Additionally,the scheme employs a secure and efficient blockchain-based asynchronous tiered architecture,integrating attribute-based access control technology for resource exchange between the clients and the blockchain network.It dynamically manages access control policies during training and adopts a hybrid data storage strategy combining blockchain and IPFS.Experiments with multiple sets of image classification tasks are conducted,indicating that the storage strategy used in this scheme saves nearly 50 percent of the communication overhead and significantly reduces the on-chain storage burden compared to the traditional blockchain-only storage strategy.In terms of training effectiveness,it maintains similar accuracy as centralized training and minimizes the probability of being attacked.展开更多
To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installa...To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installation location greatly impact the whole network.For the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the network.Moreover,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the network.To address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different environments.The algorithm adds an allocation mechanism to optimize the subnetwork in the SSA.APSSA is evaluated under three different areas and compared with other DAP placement algorithms.The experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.展开更多
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.
基金support of the National Science and Technology Council,Taiwan,grant number NSTC 113-2410-H-018-027,granted to the corresponding author,Dr.Yao-Chung Cheng.
文摘Background:Interpersonal alienation has increasingly been recognized as a salient risk factor affecting university students’psychological adjustment and life satisfaction.Guided by Social Identity and Self-Categorization theories,this study examines how alienation influences life satisfaction through the mediating roles of social identity and hope.Methods:This study surveyed 492 Taiwan resident,China undergraduate students(53.7 percent female,mean age 21.08 years)from 60 universities using convenience sampling in May 2023.Data were collected through an online questionnaire distributed via faculty-managed teaching media platforms.Measures included perceived social identity,state hope,interpersonal alienation,and life satisfaction.All instrumentswere adapted from validated scales,translated into traditional Chinese through back-translation,and reviewed by experts to ensure content validity and cultural relevance.Statistical analyses were conducted using SPSS 20 and SmartPLS 4.0.Results:Harman’s single-factor test indicated no significant common method bias.Measurement model analyses demonstrated satisfactory reliability,convergent validity,and absence of multicollinearity.All four hypothesized paths were supported:interpersonal alienation negatively predicted life satisfaction,with perceived social identity and hope serving as individual and sequential mediators.The model explained 10.5%of the variance in social identity,25.3%in hope,and 49.6%in life satisfaction.Group comparisons revealed that male students reported significantly higher hope and life satisfaction than females,and first-year students experienced greater alienation than upper-level peers.Conclusion:This study elucidates how interpersonal alienation undermines life satisfaction among university students and highlights the protective roles of social identity and hope.Findings underscore the importance of fostering psychological resources that promote resilience and well-being.The results offer practical implications for designing educational programs that enhance students’sense of belonging,optimism,and emotional strength.These insights contribute to a deeper theoretical understanding of the mechanisms linking alienation and life satisfaction and inform strategies to support student adaptation and flourishing in higher education.
基金This project was funded by Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah underGrant No.(IFPIP-1127-611-1443)the authors,therefore,acknowledge with thanks DSR technical and financial support.
文摘In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.
基金The work described in this paper was fully supported by a grant from Hong Kong Metropolitan University(RIF/2021/05).
文摘Parkinson’s disease(PD)is a debilitating neurological disorder affecting over 10 million people worldwide.PD classification models using voice signals as input are common in the literature.It is believed that using deep learning algorithms further enhances performance;nevertheless,it is challenging due to the nature of small-scale and imbalanced PD datasets.This paper proposed a convolutional neural network-based deep support vector machine(CNN-DSVM)to automate the feature extraction process using CNN and extend the conventional SVM to a DSVM for better classification performance in small-scale PD datasets.A customized kernel function reduces the impact of biased classification towards the majority class(healthy candidates in our consideration).An improved generative adversarial network(IGAN)was designed to generate additional training data to enhance the model’s performance.For performance evaluation,the proposed algorithm achieves a sensitivity of 97.6%and a specificity of 97.3%.The performance comparison is evaluated from five perspectives,including comparisons with different data generation algorithms,feature extraction techniques,kernel functions,and existing works.Results reveal the effectiveness of the IGAN algorithm,which improves the sensitivity and specificity by 4.05%–4.72%and 4.96%–5.86%,respectively;and the effectiveness of the CNN-DSVM algorithm,which improves the sensitivity by 1.24%–57.4%and specificity by 1.04%–163%and reduces biased detection towards the majority class.The ablation experiments confirm the effectiveness of individual components.Two future research directions have also been suggested.
文摘People started posting textual tweets on Twitter as soon as the novel coronavirus(COVID-19)emerged.Analyzing these tweets can assist institutions in better decision-making and prioritizing their tasks.Therefore,this study aimed to analyze 43 million tweets collected between March 22 and March 30,2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering analysis.The results indicated that unigram terms were trended more frequently than bigram and trigram terms.A large number of tweets about the COVID-19 were disseminated and received widespread public attention during the epidemic.The high-frequency words such as“death”,“test”,“spread”,and“lockdown”suggest that people fear of being infected,and those who got infection are afraid of death.The results also showed that people agreed to stay at home due to the fear of the spread,and they were calling for social distancing since they become aware of the COVID-19.It can be suggested that social media posts may affect human psychology and behavior.These results may help governments and health organizations to better understand the psychology of the public,and thereby,better communicate with them to prevent and manage the panic.
基金Supported by Feng Chia University/Chung Shan Medical University,No.FCU/CSMU 112-001(to Peng CM and Liu YJ)Taiwan National Science and Technology Council,No.111-2314-B-035-001-MY3Taichung Armed Forces General Hospital,No.107A42.
文摘The management of early stage hepatocellular carcinoma(HCC)presents significant challenges.While radiofrequency ablation(RFA)has shown safety and effectiveness in treating HCC,with lower mortality rates and shorter hospital stays,its high recurrence rate remains a significant impediment.Consequently,achieving improved survival solely through RFA is challenging,particularly in retrospective studies with inherent biases.Ultrasound is commonly used for guiding percutaneous RFA,but its low contrast can lead to missed tumors and the risk of HCC recurrence.To enhance the efficiency of ultrasound-guided percutaneous RFA,various techniques such as artificial ascites and contrast-enhanced ultrasound have been developed to facilitate complete tumor ablation.Minimally invasive surgery(MIS)offers advantages over open surgery and has gained traction in various surgical fields.Recent studies suggest that laparoscopic intraoperative RFA(IORFA)may be more effective than percutaneous RFA in terms of survival for HCC patients unsuitable for surgery,highlighting its significance.Therefore,combining MIS-IORFA with these enhanced percutaneous RFA techniques may hold greater significance for HCC treatment using the MIS-IORFA approach.This article reviews liver resection and RFA in HCC treatment,comparing their merits and proposing a trajectory involving their combination in future therapy.
基金supported by project under Grants No.MOST 107-2221-E-845-001-MY3 and No.MOST 110-2221-E-845-002
文摘An increasing number of social media and networking platforms have been widely used. People usually post the online comments to share their own opinions on the networking platforms with social media. Business companies are increasingly seeking effective ways to mine what people think and feel regarding their products and services. How to correctly understand the online customers’ reviews becomes an important issue. This study aims to propose a method with the aspect-oriented Petri nets(AOPN) to improve the examination correctness without changing any process and program. We collect those comments from the online reviews with Scrapy tools, perform sentiment analysis using SnowNLP, and examine the analysis results to improve the correctness. In this paper, we apply our method for a case of the online movie comments. The experimental results have shown that AOPN is helpful for the sentiment analysis and verifying its correctness.
基金partly funded by Ministry of Science and Technology of R.O.C. under grants no.NSC 101-2221-E-197008-MY3
文摘In recent years, the application of the Internet of Things （IoT） has become an emerging business. The most important concept of next-generation network for providing a common global IT platform is combining seamless networks and networked things, objects or sensors. Also, wireless body area networks （WBANs） are becoming mature with the widespread usage of the IoT. In order to support WBAN, the platform, scenario and emergency service are necessary due to the sensors in WBAN being related to wearer＆#39;s life. The sensors on the body detect a lot of information about bioinformatics and medical signals, such as heartbeat and blood. Thus, the integration of IoT and network communication in daily life is important. However, there is not only a lack of common fabric for integrating IoT with current Internet and but also no emergency call process in the current network communication envi-ronment. To overcome such situations, the prototype of integrating IoT and emergency call process is discussed. A simulated boot-strap platform to provide the discussion of open challenges and solutions for deploying IoT in Internet and the emergency commu-nication system are analyzed by using a service of 3GPP IP multimedia subsystem. Finally, the prototype for supporting WBAN with emergence service is also addressed and the performance results are useful to service providers and network operators that they can estimate their migration to IoT by referring to this experience and experiment results. Furthermore, the queuing model used to achieve the performance of emergency service in IMS and the delay time of the proposed model is analyzed.
基金This research work was supported by the Ministry of Science and Technology of the Republic of China under contract MOST 108-2221-E-390-018.
文摘In this study,vector quantization and hidden Markov models were used to achieve speech command recognition.Pre-emphasis,a hamming window,and Mel-frequency cepstral coefficients were first adopted to obtain feature values.Subsequently,vector quantization and HMMs(hidden Markov models)were employed to achieve speech command recognition.The recorded speech length was three Chinese characters,which were used to test the method.Five phrases pronounced mixing various human voices were recorded and used to test the models.The recorded phrases were then used for speech command recognition to demonstrate whether the experiment results were satisfactory.
文摘The interaction between human and physical devices and devices in the real world is gaining more attention, and requires a natural and intuitive methodology to employ. According to this idea and living well, life has been a growing demand. Thus, how to raise pets in an easy way has been the main issue recently. This study examines the ability of computation, communication, and control technologies to improve human interaction with pets by the technology of the Internet of Things. This work addresses the improvement through the pet application of the ability of location-awareness, and to help the pet owners raise their pet on the activity and eating control easily. Extensive experiment results demonstrate that our proposed system performs significantly help on the kidney disease and reduce the symptoms. Our study not only presents the key improvement of the pet monitor system involved in the ideas of the Internet of Things, but also meets the demands of pet owners, who are out for works without any trouble.
基金Supported by the National Natural Science Foundation of China under Grant Nos.11862003,81860635,11462003the Key Project of Guangxi Natural Science Foundation under Grant No.2017GXNSF DA198038Guangxi “Bagui Scholar” Teams for Innovation and Research Project
文摘The secondary flow driven by the primary vortex in a cylinder,generating the so called"tea leaf paradox",is fundamental for understanding many natural phenomena,industrial applications and scientific researches.In this work,the effect of wettability on the primary vortex and secondary flow is investigated by the three-dimensional multiphase lattice Boltzmann method based on a chemical potential.We find that the surface wettability strongly affects the shape of the primary vortex.With the increase of the contact angle of the cylinder,the sectional plane of the primary vortex gradually changes from a steep valley into a saddle with two raised parts.Because the surface friction is reduced correspondingly,the core of the secondary vortex moves to the centerline of the cylinder and the vortex intensity also increases.The stirring force has stronger effects to enhance the secondary flow and push the vortex up than the surface wettability.Interestingly,a small secondary vortex is discovered near the three-phase contact line when the surface has a moderate wettability,owing to the interaction between the secondary flow and the curved gas/liquid interface.
文摘In this study,we classify the genera of COVID-19 and provide brief information about the root of the spread and the transmission from animal(natural host)to humans.We establish a model of fractional-order differential equations to discuss the spread of the infection from the natural host to the intermediate one,and from the intermediate one to the human host.At the same time,we focus on the potential spillover of bat-borne coronaviruses.We consider the local stability of the co-existing critical point of the model by using the Routh–Hurwitz Criteria.Moreover,we analyze the existence and uniqueness of the constructed initial value problem.We focus on the control parameters to decrease the outbreak from pandemic form to the epidemic by using both strong and weak Allee Effect at time t.Furthermore,the discretization process shows that the system undergoes Neimark–Sacker Bifurcation under specific conditions.Finally,we conduct a series of numerical simulations to enhance the theoretical findings.
基金funded by National Science Council,Taiwan,the Grant Number is NSC 111-2410-H-167-005-MY2.
文摘Reversible data hiding is a confidential communication technique that takes advantage of image file characteristics,which allows us to hide sensitive data in image files.In this paper,we propose a novel high-fidelity reversible data hiding scheme.Based on the advantage of the multipredictor mechanism,we combine two effective prediction schemes to improve prediction accuracy.In addition,the multihistogram technique is utilized to further improve the image quality of the stego image.Moreover,a model of the grouped knapsack problem is used to speed up the search for the suitable embedding bin in each sub-histogram.Experimental results show that the quality of the stego image of our scheme outperforms state-of-the-art schemes in most cases.
文摘A square particle suspended in a Poiseuille flow is investigated by using the lattice Boltzmann method with the Galilean-invariant momentum exchange method. The lateral migration of Segré-Silberberg effect is observed for the square particle, accompanied by the nonuniform rotation and regular wave. To compare with the circular particle, its circumscribed and inscribed squares are used in the simulations. Because the circumscribed square takes up a greater difference between the upper and lower flow rates, it reaches the equilibrium position earlier than the inscribed one. The trajectories of the latter are much closer to those of circle;this indicates that the circle and its inscribed square have a similar hydrodynamic radius in a Poiseuille flow. The equilibrium positions of the square particles change with Reynolds number and show a shape of saddle, whereas those of the circular particles are virtually not affected by Reynolds number. The regular wave and nonuniform rotation are owing to the interactions of the square shape and the parabolic velocity distribution of Poiseuille flow, and high Reynolds number makes the square rotating faster and decrease its oscillating amplitude. A series of contours illustrate the dynamic flow fields when the square particle has successive postures in a half rotating period. This study is beneficial to understand the motion of anisotropic particles and the dendrite growth in dynamic environment.
文摘In this paper, we propose a low-cost posture recognition scheme using a single webcam for the signaling hand with nature sways and possible oc-clusions. It goes for developing the untouchable low-complexity utility based on friendly hand-posture signaling. The scheme integrates the dominant temporal-difference detection, skin color detection and morphological filtering for efficient cooperation in constructing the hand profile molds. Those molds provide representative hand profiles for more stable posture recognition than accurate hand shapes with in effect trivial details. The resultant bounding box of tracking the signaling molds can be treated as a regular-type object-matched ROI to facilitate the stable extraction of robust HOG features. With such commonly applied features on hand, the prototype SVM is adequately capable of obtaining fast and stable hand postures recognition under natural hand movement and non-hand object occlusion. Experimental results demonstrate that our scheme can achieve hand-posture recognition with enough accuracy under background clutters that the targeted hand can be allowed with medium movement and palm-grasped object. Hence, the proposed method can be easily embedded in the mobile phone as application software.
文摘The aging society will become a serious problem for most countries in the world. Under the constraint of limited medical resource, the self-health management becomes important. In this paper, a mobile healthcare system is implemented. One can easily monitor his/her physiological data through the using of a smartphone that is wirelessly connected to different medical detection devices. A cloud database is established for storing and analysing these physiological data. The guidance of suitable physical exercises to individuals is then given in the system. This paper shows the details of the system implementation.
基金the“Intelligent Recognition Industry Service Center”as part of the Featured Areas Research Center Program under the Higher Education Sprout Project by the Ministry of Education(MOE)in Taiwan,and the National Science and Technology Council,Taiwan,under grants 113-2221-E-224-041 and 113-2622-E-224-002.Additionally,partial support was provided by Isuzu Optics Corporation.
文摘Liver cancer remains a leading cause of mortality worldwide,and precise diagnostic tools are essential for effective treatment planning.Liver Tumors(LTs)vary significantly in size,shape,and location,and can present with tissues of similar intensities,making automatically segmenting and classifying LTs from abdominal tomography images crucial and challenging.This review examines recent advancements in Liver Segmentation(LS)and Tumor Segmentation(TS)algorithms,highlighting their strengths and limitations regarding precision,automation,and resilience.Performance metrics are utilized to assess key detection algorithms and analytical methods,emphasizing their effectiveness and relevance in clinical contexts.The review also addresses ongoing challenges in liver tumor segmentation and identification,such as managing high variability in patient data and ensuring robustness across different imaging conditions.It suggests directions for future research,with insights into technological advancements that can enhance surgical planning and diagnostic accuracy by comparing popular methods.This paper contributes to a comprehensive understanding of current liver tumor detection techniques,provides a roadmap for future innovations,and improves diagnostic and therapeutic outcomes for liver cancer by integrating recent progress with remaining challenges.
基金supported by the National Natural Science Foundation of China(Grant No.52331012)the Natural Science Foundation of Shanghai Municipality(Grant No.21ZR1426500)the Program for Cultivation of Graduate Students’Top-notch Innovative Talents of Shanghai Maritime University(Grant No.2023YBR007).
文摘As is known,centralized federated learning faces risks of a single point of failure and privacy breaches,and blockchain-based federated learning frameworks can address these challenges to a certain extent in recent works.However,malicious clients may still illegally access the blockchain to upload malicious data or steal on-chain data.In addition,blockchain-based federated training suffers from a heavy storage burden and excessive network communication overhead.To address these issues,we propose an asynchronous,tiered federated learning storage scheme based on blockchain and IPFS.It manages the execution of federated learning tasks through smart contracts deployed on the blockchain,decentralizing the entire training process.Additionally,the scheme employs a secure and efficient blockchain-based asynchronous tiered architecture,integrating attribute-based access control technology for resource exchange between the clients and the blockchain network.It dynamically manages access control policies during training and adopts a hybrid data storage strategy combining blockchain and IPFS.Experiments with multiple sets of image classification tasks are conducted,indicating that the storage strategy used in this scheme saves nearly 50 percent of the communication overhead and significantly reduces the on-chain storage burden compared to the traditional blockchain-only storage strategy.In terms of training effectiveness,it maintains similar accuracy as centralized training and minimizes the probability of being attacked.
基金supported by the Fujian University of Technology under Grant GYZ20016,GY-Z18183,and GY-Z19005partially supported by the National Science and Technology Council under Grant NSTC 113-2221-E-224-056-.
文摘To transmit customer power data collected by smart meters(SMs)to utility companies,data must first be transmitted to the corresponding data aggregation point(DAP)of the SM.The number of DAPs installed and the installation location greatly impact the whole network.For the traditional DAP placement algorithm,the number of DAPs must be set in advance,but determining the best number of DAPs is difficult,which undoubtedly reduces the overall performance of the network.Moreover,the excessive gap between the loads of different DAPs is also an important factor affecting the quality of the network.To address the above problems,this paper proposes a DAP placement algorithm,APSSA,based on the improved affinity propagation(AP)algorithm and sparrow search(SSA)algorithm,which can select the appropriate number of DAPs to be installed and the corresponding installation locations according to the number of SMs and their distribution locations in different environments.The algorithm adds an allocation mechanism to optimize the subnetwork in the SSA.APSSA is evaluated under three different areas and compared with other DAP placement algorithms.The experimental results validated that the method in this paper can reduce the network cost,shorten the average transmission distance,and reduce the load gap.
