Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal ...Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal alignment,temporal consistency,and robust handling of noisy or incomplete inputs across multiple modalities.We propose Multi Agent-Chain of Thought(CoT),a novel multi-agent chain-of-thought reasoning framework where specialized agents for text,vision,and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms.Our architecture incorporates self-reflection modules,conflict resolution protocols,and dynamic rationale alignment to enhance consistency,factual accuracy,and user engagement.The framework employs a hierarchical attention mechanism with cross-modal fusion and implements adaptive reasoning depth based on dialogue complexity.Comprehensive evaluations on Situated Interactive Multi-Modal Conversations(SIMMC)2.0,VisDial v1.0,and newly introduced challenging scenarios demonstrate statistically significant improvements in grounding accuracy(p<0.01),chain-of-thought interpretability,and robustness to adversarial inputs compared to state-of-the-art monolithic transformer baselines and existing multi-agent approaches.展开更多
The multi-objective optimization problems,especially in constrained environments such as power distribution planning,demand robust strategies for discovering effective solutions.This work presents the improved variant...The multi-objective optimization problems,especially in constrained environments such as power distribution planning,demand robust strategies for discovering effective solutions.This work presents the improved variant of the Multi-population Cooperative Constrained Multi-Objective Optimization(MCCMO)Algorithm,termed Adaptive Diversity Preservation(ADP).This enhancement is primarily focused on the improvement of constraint handling strategies,local search integration,hybrid selection approaches,and adaptive parameter control.Theimproved variant was experimented on with the RWMOP50 power distribution systemplanning benchmark.As per the findings,the improved variant outperformed the original MCCMO across the eleven performance metrics,particularly in terms of convergence speed,constraint handling efficiency,and solution diversity.The results also establish that MCCMOADP consistently delivers substantial performance gains over the baseline MCCMO,demonstrating its effectiveness across performancemetrics.The new variant also excels atmaintaining the balanced trade-off between exploration and exploitation throughout the search process,making it especially suitable for complex optimization problems in multiconstrained power systems.These enhancements make MCCMO-ADP a valuable and promising candidate for handling problems such as renewable energy scheduling,logistics planning,and power system optimization.Future work will benchmark the MCCMO-ADP against widely recognized algorithms such as NSGA-Ⅱ,NSGA-Ⅲ,and MOEA/D and will also extend its validation to large-scale real-world optimization domains to further consolidate its generalizability.展开更多
Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted featur...Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.展开更多
This paper investigates the teaching reform of the Program Comprehension and Analysis course in the context of industry-education integration and AI empowerment.To align with the evolving needs of the software industr...This paper investigates the teaching reform of the Program Comprehension and Analysis course in the context of industry-education integration and AI empowerment.To align with the evolving needs of the software industry,the course content has been updated to incorporate AI techniques such as large language models and deep learning.The reform enriches educational resources and introduces innovative instructional approaches.In addition,high-quality practical teaching cases have been developed,and immersive,hands-on learning experiences have been designed based on industrial platforms and real-world applications.These initiatives aim to enhance the practical skills and innovative thinking of professional degree graduate students,fostering high-caliber talent that aligns with industry demands.A survey of 90 graduate students revealed high levels of satisfaction regarding course content,teaching methodology,and skill development.The reform has proven effective in cultivating interdisciplinary professionals with solid foundations in software engineering and AI-driven innovation.展开更多
With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent ...With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent customer service,and medical diagnosis,is constructed based on complex technologies like machine learning and deep learning.Its uncertain behavior and data dependence pose unprecedented challenges to software testing.However,existing software testing courses mainly focus on conventional contents and are unable to meet the requirements of intelligent software testing.Therefore,this work deeply analyzed the relevant technologies of intelligent software testing,including reliability evaluation indicator system,neuron coverage,and test case generation.It also systematically designed an intelligent software testing course,covering teaching objectives,teaching content,teaching methods,and a teaching case.Verified by the practical teaching in four classes,this course has achieved remarkable results,providing practical experience for the reform of software testing courses.展开更多
Wave energy is a promising form of marine renewable energy that offers a sustainable pathway for electricity generation in coastal regions.Despite Malaysia’s extensive coastline,the exploration of wave energy in Sara...Wave energy is a promising form of marine renewable energy that offers a sustainable pathway for electricity generation in coastal regions.Despite Malaysia’s extensive coastline,the exploration of wave energy in Sarawak remains limited due to economic,technical,and environmental challenges that hinder its implementation.Compared to other renewable energy sources,wave energy is underutilized largely because of cost uncertainties and the lack of local performance data.This research aims to identify themost suitable coastal zone in Sarawak that achieves an optimal balance between energy potential,cost-effectiveness,and environmental impact,particularly in relation to infrastructure and regional development.The findings indicate that wave energy generation in Sarawak is technically feasible based on MOGA analysis.Among the studied sites,Bintulu emerged as the most balanced option,with a levelized cost of electricity(LCOE)of 0.778–0.864 USD/kWh and a CO_(2) emission factor as low as 0.019–0.020 CO_(2)/k Wh.Miri,while producing lower emissions than Sematan,recorded a higher LCOE of 1.045 USD/kWh with moderate emissions at 0.029 CO_(2)/kWh.Sematan,characterized by weaker wave conditions and higher installation penalties,resulted in the least favorable outcome,with an LCOE of 3.735 USD/kWh.Bintulu’s strategic location reduces CAPEX requirements,making it the most suitable site for large-scale wave energy deployment in Sarawak.展开更多
This paper delves into effective pathways for transforming course ecosystems from resource provision to knowledge service and competency development through university-enterprise collaboration in co-building knowledge...This paper delves into effective pathways for transforming course ecosystems from resource provision to knowledge service and competency development through university-enterprise collaboration in co-building knowledge graphs and intelligent shared courses.This approach enables personalized,learning-driven teaching.Based on knowledge graphs and integrated teacher-machine-student smart teaching scenarios,it not only innovates autonomous learning environments and human-computer interaction models while optimizing teaching experiences for both instructors and students,but also effectively addresses the issues of students’“scattered,superficial,and fragmented learning”.This establishes the foundation for personalized teaching tailored to individual aptitudes.展开更多
The exponential growth of Internet of Things(IoT)devices,autonomous systems,and digital services is generating massive volumes of big data,projected to exceed 291 zettabytes by 2027.Conventional cloud computing,despit...The exponential growth of Internet of Things(IoT)devices,autonomous systems,and digital services is generating massive volumes of big data,projected to exceed 291 zettabytes by 2027.Conventional cloud computing,despite its high processing and storage capacity,suffers from increased network latency,network congestion,and high operational costs,making it unsuitable for latency-sensitive applications.Edge computing addresses these issues by processing data near the source but faces scalability challenges and elevated Total Cost of Ownership(TCO).Hybrid solutions,such as fog computing,cloudlets,and Mobile Edge Computing(MEC),attempt to balance cost and performance;however,they still struggle with limited resource sharing and high deployment expenses.This paper proposes Public Edge as a Service(PEaaS),a novel paradigm that utilizes idle resources contributed by universities,enterprises,cellular operators,and individuals under a collaborative service model.By decentralizing computation and enabling multi-tenant resource sharing,PEaaS reduces reliance on centralized cloud infrastructure,minimizes communication costs,and enhances scalability.The proposed framework is evaluated using EdgeCloudSim under varying workloads,for keymetrics such as latency,communication cost,server utilization,and task failure rate.Results reveal that while cloud has a task failure rate rising sharply to 12.3%at 2000 devices,PEaaS maintains a low rate of 2.5%,closely matching edge computing.Furthermore,communication costs remain 25% lower than cloud and latency remains below 0.3,even under peak load.These findings demonstrate that PEaaS achieves near-edge performance with reduced costs and enhanced scalability,offering a sustainable and economically viable solution for next-generation computing environments.展开更多
With the widespread adoption of digital equipment in intelligent substations,testing digital signals in power systems has become an important role for relay protection test equipment.Testing and calibrating digital si...With the widespread adoption of digital equipment in intelligent substations,testing digital signals in power systems has become an important role for relay protection test equipment.Testing and calibrating digital signals require high accuracy.However,existing methods have low precision,cannot be calibrated at full range for all indexes,and have complex configuration,making them unsuitable for routine calibration work.To solve the above problems,a novel calibration method is designed and implemented using field programmable gate array(FPGA)to achieve accurate input and output time control.Accurate calibration relies on multiple forms of traceability including theoretical value traceability based on waveform comparison,time scale value traceability based on accurate time stamps,and algorithm traceability based on typical algorithms.Compared with other existing methods,the proposed approach reduces the mean absolute error of action time and time measurement by 92.88%,effectively addressing a key industry challenge and offering a valuable reference for further research,application,and standardization.展开更多
Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their dia...Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their diagnostic reliability.This review presents a structured and comprehensive analysis of advanced histogram equalization(HE)-based techniques for medical image enhancement.Our review methodology encompasses:(1)classical HE approaches and related limitations in medical domains;(2)adaptive schemes like Adaptive Histogram Equalization(AHE)and Contrast Limited Adaptive Histogrma Equalization(CLAHE)and their advance variants;(3)brightnesspreserving schemes like BBHE and MMBEBHE and related algorithms;(4)dynamic and recursive histogram equalization methods incorporating DHE and RMSHE;(5)fuzzy logic-based enhancement methodologies addressing uncertainty and noise in medical images;and(6)hybrid optimization methodologies through the application of metaheuristic algorithms(World Cup Optimization,Particle Swarm Optimization,Genetic Algorithms,along with histogram-based methodologies.)There is also a comparative discussion given based on contrast improvement,image brightness preservation,noise management,and computational efficiency.Such advancements have better capabilities of improving image quality,which is more important for improved diagnosis and image analysis.展开更多
The main idea behind the present research is to design a state-feedback controller for an underactuated nonlinear rotary inverted pendulum module by employing the linear quadratic regulator(LQR)technique using local a...The main idea behind the present research is to design a state-feedback controller for an underactuated nonlinear rotary inverted pendulum module by employing the linear quadratic regulator(LQR)technique using local approximation.The LQR is an excellent method for developing a controller for nonlinear systems.It provides optimal feedback to make the closed-loop system robust and stable,rejecting external disturbances.Model-based optimal controller for a nonlinear system such as a rotatory inverted pendulum has not been designed and implemented using Newton-Euler,Lagrange method,and local approximation.Therefore,implementing LQR to an underactuated nonlinear system was vital to design a stable controller.A mathematical model has been developed for the controller design by utilizing the Newton-Euler,Lagrange method.The nonlinear model has been linearized around an equilibrium point.Linear and nonlinear models have been compared to find the range in which linear and nonlinear models’behaviour is similar.MATLAB LQR function and system dynamics have been used to estimate the controller parameters.For the performance evaluation of the designed controller,Simulink has been used.Linear and nonlinear models have been simulated along with the designed controller.Simulations have been performed for the designed controller over the linear and nonlinear system under different conditions through varying system variables.The results show that the system is stable and robust enough to act against external disturbances.The controller maintains the rotary inverted pendulum in an upright position and rejects disruptions like falling under gravitational force or any external disturbance by adjusting the rotation of the horizontal link in both linear and nonlinear environments in a specific range.The controller has been practically designed and implemented.It is vivid from the results that the controller is robust enough to reject the disturbances in milliseconds and keeps the pendulum arm deflection angle to zero degrees.展开更多
In light of the coronavirus disease 2019(COVID-19)outbreak caused by the novel coronavirus,companies and institutions have instructed their employees to work from home as a precautionary measure to reduce the risk of ...In light of the coronavirus disease 2019(COVID-19)outbreak caused by the novel coronavirus,companies and institutions have instructed their employees to work from home as a precautionary measure to reduce the risk of contagion.Employees,however,have been exposed to different security risks because of working from home.Moreover,the rapid global spread of COVID-19 has increased the volume of data generated from various sources.Working from home depends mainly on cloud computing(CC)applications that help employees to efficiently accomplish their tasks.The cloud computing environment(CCE)is an unsung hero in the COVID-19 pandemic crisis.It consists of the fast-paced practices for services that reflect the trend of rapidly deployable applications for maintaining data.Despite the increase in the use of CC applications,there is an ongoing research challenge in the domains of CCE concerning data,guaranteeing security,and the availability of CC applications.This paper,to the best of our knowledge,is the first paper that thoroughly explains the impact of the COVID-19 pandemic on CCE.Additionally,this paper also highlights the security risks of working from home during the COVID-19 pandemic.展开更多
In recent years,task offloading and its scheduling optimization have emerged as widely discussed and signif-icant topics.The multi-objective optimization problems inherent in this domain,particularly those related to ...In recent years,task offloading and its scheduling optimization have emerged as widely discussed and signif-icant topics.The multi-objective optimization problems inherent in this domain,particularly those related to resource allocation,have been extensively investigated.However,existing studies predominantly focus on matching suitable computational resources for task offloading requests,often overlooking the optimization of the task data transmission process.This inefficiency in data transmission leads to delays in the arrival of task data at computational nodes within the edge network,resulting in increased service times due to elevated network transmission latencies and idle computational resources.To address this gap,we propose an Asynchronous Data Transmission Policy(ADTP)for optimizing data transmission for task offloading in edge-computing enabled ultra-dense IoT.ADTP dynamically generates data transmission scheduling strategies by jointly considering task offloading decisions and the fluctuating operational states of edge computing-enabled IoT networks.In contrast to existing methods,the Deep Deterministic Policy Gradient(DDPG)based task data transmission scheduling module works asynchronously with the Deep Q-Network(DQN)based Virtual Machine(VM)selection module in ADTP.This significantly reduces the computational space required for the scheduling algorithm.The continuous dynamic adjustment of data transmission bandwidth ensures timely delivery of task data and optimal utilization of network bandwidth resources.This reduces the task completion time and minimizes the failure rate caused by timeouts.Moreover,the VM selection module only performs the next inference step when a new task arrives or when a task finishes its computation.As a result,the wastage of computational resources is further reduced.The simulation results indicate that the proposed ADTP reduced average data transmission delay and service time by 7.11%and 8.09%,respectively.Furthermore,the task failure rate due to network congestion decreased by 68.73%.展开更多
In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computi...In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end devices.Edge computing is considered a method of forwarding the processing and communication resources in the cloud towards the edge.One of the considerations of the edge computing environment is resource management that involves resource scheduling,load balancing,task scheduling,and quality of service(QoS)to accomplish improved performance.With this motivation,this paper presents new soft computing based metaheuristic algorithms for resource scheduling(RS)in the edge computing environment.The SCBMARS model involves the hybridization of the Group Teaching Optimization Algorithm(GTOA)with rat swarm optimizer(RSO)algorithm for optimal resource allocation.The goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way,that the client’s necessities are satisfied with the minimum number of possible resources and optimal energy consumption.The problem is formulated based on the availability of VMs,task characteristics,and queue dynamics.The integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data center.For experimental validation,a comprehensive set of simulations were performed using the CloudSim tool.The experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.展开更多
The development of human-robot interaction has been continu-ously increasing for the last decades.Through this development,it has become simpler and safe interactions using a remotely controlled telepresence robot in ...The development of human-robot interaction has been continu-ously increasing for the last decades.Through this development,it has become simpler and safe interactions using a remotely controlled telepresence robot in an insecure and hazardous environment.The audio-video communication connection or data transmission stability has already been well handled by fast-growing technologies such as 5G and 6G.However,the design of the phys-ical parameters,e.g.,maneuverability,controllability,and stability,still needs attention.Therefore,the paper aims to present a systematic,controlled design and implementation of a telepresence mobile robot.The primary focus of this paper is to perform the computational analysis and experimental implementa-tion design with sophisticated position control,which autonomously controls the robot’s position and speed when reaching an obstacle.A system model and a position controller design are developed with root locus points.The design robot results are verified experimentally,showing the robot’s agreement and control in the desired position.The robot was tested by considering various parameters:driving straight ahead,right turn,self-localization and complex path.The results prove that the proposed approach is flexible and adaptable and gives a better alternative.The experimental results show that the proposed method significantly minimizes the obstacle hits.展开更多
The human ear has been substantiated as a viable nonintrusive biometric modality for identification or verification.Among many feasible techniques for ear biometric recognition,convolutional neural network(CNN)models ...The human ear has been substantiated as a viable nonintrusive biometric modality for identification or verification.Among many feasible techniques for ear biometric recognition,convolutional neural network(CNN)models have recently offered high-performance and reliable systems.However,their performance can still be further improved using the capabilities of soft biometrics,a research question yet to be investigated.This research aims to augment the traditional CNN-based ear recognition performance by adding increased discriminatory ear soft biometric traits.It proposes a novel framework of augmented ear identification/verification using a group of discriminative categorical soft biometrics and deriving new,more perceptive,comparative soft biometrics for feature-level fusion with hard biometric deep features.It conducts several identification and verification experiments for performance evaluation,analysis,and comparison while varying ear image datasets,hard biometric deep-feature extractors,soft biometric augmentation methods,and classifiers used.The experimental work yields promising results,reaching up to 99.94%accuracy and up to 14%improvement using the AMI and AMIC datasets,along with their corresponding soft biometric label data.The results confirm the proposed augmented approaches’superiority over their standard counterparts and emphasize the robustness of the new ear comparative soft biometrics over their categorical peers.展开更多
In view of the deficiencies in aspects such as failure rate requirements and analysis assumptions of advisory circular,this paper investigates the sources of high safety requirements,and the top-down design method for...In view of the deficiencies in aspects such as failure rate requirements and analysis assumptions of advisory circular,this paper investigates the sources of high safety requirements,and the top-down design method for the flight control system life cycle.Correspondingly,measures are proposed,including enhancing the safety target value to 10^(−10)per flight hour and implementing development assurance.In view of the shortcomings of mainstream aircraft flight control systems,such as weak backup capability and complex fault reconfiguration logic,improvements have been made to the system’s operating modes,control channel allocation,and common mode failure mitigation schemes based on the existing flight control architecture.The flight control design trends and philosophies have been analyzed.A flight control system architecture scheme is proposed,which includes three operating modes and multi-level voters/monitors,three main control channels,and a backup system independent of the main control system,which has been confirmed through functional modeling simulations.The proposed method plays an important role in the architecture design of safety-critical flight control system.展开更多
The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent require...The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent requirements for ultra-low latency,high reliability,and robust privacy present significant challenges.Conventional centralized Federated Learning(FL)architectures struggle with latency and privacy constraints,while fully distributed FL(DFL)faces scalability and non-IID data issues as client populations expand and datasets become increasingly heterogeneous.To address these limitations,we propose a Clustered Distributed Federated Learning(CDFL)architecture tailored for a 6G-enabled TIoT environment.Clients are grouped into clusters based on data similarity and/or geographical proximity,enabling local intra-cluster aggregation before inter-cluster model sharing.This hierarchical,peer-to-peer approach reduces communication overhead,mitigates non-IID effects,and eliminates single points of failure.By offloading aggregation to the network edge and leveraging dynamic clustering,CDFL enhances both computational and communication efficiency.Extensive analysis and simulation demonstrate that CDFL outperforms both centralized FL and DFL as the number of clients grows.Specifically,CDFL demonstrates up to a 30%reduction in training time under highly heterogeneous data distributions,indicating faster convergence.It also reduces communication overhead by approximately 40%compared to DFL.These improvements and enhanced network performance metrics highlight CDFL’s effectiveness for practical TIoT deployments.These results validate CDFL as a scalable,privacy-preserving solution for next-generation TIoT applications.展开更多
Globally,liver cancer ranks as the sixth most frequent malignancy cancer.The importance of early detection is undeniable,as liver cancer is the fifth most common disease in men and the ninth most common cancer in wome...Globally,liver cancer ranks as the sixth most frequent malignancy cancer.The importance of early detection is undeniable,as liver cancer is the fifth most common disease in men and the ninth most common cancer in women.Recent advances in imaging,biomarker discovery,and genetic profiling have greatly enhanced the ability to diagnose liver cancer.Early identification is vital since liver cancer is often asymptomatic,making diagnosis difficult.Imaging techniques such as Magnetic Resonance Imaging(MRI),Computed Tomography(CT),and ultrasonography can be used to identify liver cancer once a sample of liver tissue is taken.In recent research,reliable detection of liver cancer with minimal computing computational complexity and time has remained a serious difficulty.This paper employs the DenseNet model to enhance the detection of liver nodules with tumors by segmenting them using UNet and VGG using Fastai(UVF)in CT images.Its dense interconnections distinguish the DenseNet between layers.These dense connections facilitate the propagation of gradients and the flow of information throughout the network,thereby enhancing the efficacy and performance of training.DenseNet’s architecture combines dense blocks,bottleneck layers,and transition layers,allowing it to achieve a compromise between expressiveness and computing efficiency.Finally,the 3D liver nodular models were created using a raycasting volume rendering approach.Compared to other state-of-the-art deep neural networks,it is suitable for clinical applications to assist doctors in diagnosing liver cancer.The proposed approach was tested on a 3Dircadb dataset.According to experiments,UVF segmentation on the 3Dircadb dataset is 97.9%accurate.According to the study,the DenseNet and UVF segment liver cancer better than prior methods.The system proposes automated 3D liver cancer tumor visualization.展开更多
Due to uncertainties in seismic pipeline damage and post-earthquake recovery processes,probabilistic characteristics such as mean value,standard deviation,probability density function,and cumulative distribution funct...Due to uncertainties in seismic pipeline damage and post-earthquake recovery processes,probabilistic characteristics such as mean value,standard deviation,probability density function,and cumulative distribution function provide valuable information.In this study,a simulation-based framework to evaluate these probabilistic characteristics in water distribution systems(WDSs)during post-earthquake recovery is developed.The framework first calculates pipeline failure probabilities using seismic fragility models and then generates damage samples through quasi-Monte Carlo simulations with Sobol’s sequence for faster convergence.System performance is assessed using a hydraulic model,and recovery simulations produce time-varying performance curves,where the dynamic importance of unrepaired damage determines repair sequences.Finally,the probabilistic characteristics of seismic performance indicators,resilience index,resilience loss,and recovery time are evaluated.The framework is applied in two benchmark WDSs with different layouts to investigate the probabilistic characteristics of their seismic performance and resilience.Application results show that the cumulative distribution function reveals the variations in resilience indicators for different exceedance probabilities,and there are dramatic differences among the recovery times corresponding to the system performance recovery targets of 80%,90%,and 100%.展开更多
文摘Multimodal dialogue systems often fail to maintain coherent reasoning over extended conversations and suffer from hallucination due to limited context modeling capabilities.Current approaches struggle with crossmodal alignment,temporal consistency,and robust handling of noisy or incomplete inputs across multiple modalities.We propose Multi Agent-Chain of Thought(CoT),a novel multi-agent chain-of-thought reasoning framework where specialized agents for text,vision,and speech modalities collaboratively construct shared reasoning traces through inter-agent message passing and consensus voting mechanisms.Our architecture incorporates self-reflection modules,conflict resolution protocols,and dynamic rationale alignment to enhance consistency,factual accuracy,and user engagement.The framework employs a hierarchical attention mechanism with cross-modal fusion and implements adaptive reasoning depth based on dialogue complexity.Comprehensive evaluations on Situated Interactive Multi-Modal Conversations(SIMMC)2.0,VisDial v1.0,and newly introduced challenging scenarios demonstrate statistically significant improvements in grounding accuracy(p<0.01),chain-of-thought interpretability,and robustness to adversarial inputs compared to state-of-the-art monolithic transformer baselines and existing multi-agent approaches.
文摘The multi-objective optimization problems,especially in constrained environments such as power distribution planning,demand robust strategies for discovering effective solutions.This work presents the improved variant of the Multi-population Cooperative Constrained Multi-Objective Optimization(MCCMO)Algorithm,termed Adaptive Diversity Preservation(ADP).This enhancement is primarily focused on the improvement of constraint handling strategies,local search integration,hybrid selection approaches,and adaptive parameter control.Theimproved variant was experimented on with the RWMOP50 power distribution systemplanning benchmark.As per the findings,the improved variant outperformed the original MCCMO across the eleven performance metrics,particularly in terms of convergence speed,constraint handling efficiency,and solution diversity.The results also establish that MCCMOADP consistently delivers substantial performance gains over the baseline MCCMO,demonstrating its effectiveness across performancemetrics.The new variant also excels atmaintaining the balanced trade-off between exploration and exploitation throughout the search process,making it especially suitable for complex optimization problems in multiconstrained power systems.These enhancements make MCCMO-ADP a valuable and promising candidate for handling problems such as renewable energy scheduling,logistics planning,and power system optimization.Future work will benchmark the MCCMO-ADP against widely recognized algorithms such as NSGA-Ⅱ,NSGA-Ⅲ,and MOEA/D and will also extend its validation to large-scale real-world optimization domains to further consolidate its generalizability.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant no.(GPIP:1074-612-2024).
文摘Log anomaly detection is essential for maintaining the reliability and security of large-scale networked systems.Most traditional techniques rely on log parsing in the reprocessing stage and utilize handcrafted features that limit their adaptability across various systems.In this study,we propose a hybrid model,BertGCN,that integrates BERT-based contextual embedding with Graph Convolutional Networks(GCNs)to identify anomalies in raw system logs,thereby eliminating the need for log parsing.TheBERT module captures semantic representations of log messages,while the GCN models the structural relationships among log entries through a text-based graph.This combination enables BertGCN to capture both the contextual and semantic characteristics of log data.BertGCN showed excellent performance on the HDFS and BGL datasets,demonstrating its effectiveness and resilience in detecting anomalies.Compared to multiple baselines,our proposed BertGCN showed improved precision,recall,and F1 scores.
基金supported by Project of Higher Education Teaching Reform Research in Heilongjiang Province(Graduate Education)(Grant No.SJGYY2024030).
文摘This paper investigates the teaching reform of the Program Comprehension and Analysis course in the context of industry-education integration and AI empowerment.To align with the evolving needs of the software industry,the course content has been updated to incorporate AI techniques such as large language models and deep learning.The reform enriches educational resources and introduces innovative instructional approaches.In addition,high-quality practical teaching cases have been developed,and immersive,hands-on learning experiences have been designed based on industrial platforms and real-world applications.These initiatives aim to enhance the practical skills and innovative thinking of professional degree graduate students,fostering high-caliber talent that aligns with industry demands.A survey of 90 graduate students revealed high levels of satisfaction regarding course content,teaching methodology,and skill development.The reform has proven effective in cultivating interdisciplinary professionals with solid foundations in software engineering and AI-driven innovation.
基金Computer Basic Education Teaching Research Project of Association of Fundamental Computing Education in Chinese Universities(Nos.2025-AFCEC-527 and 2024-AFCEC-088)Research on the Reform of Public Course Teaching at Nantong College of Science and Technology(No.2024JGG015).
文摘With the rapid development of artificial intelligence,the intelligence level of software is increasingly improving.Intelligent software,which is widely applied in crucial fields such as autonomous driving,intelligent customer service,and medical diagnosis,is constructed based on complex technologies like machine learning and deep learning.Its uncertain behavior and data dependence pose unprecedented challenges to software testing.However,existing software testing courses mainly focus on conventional contents and are unable to meet the requirements of intelligent software testing.Therefore,this work deeply analyzed the relevant technologies of intelligent software testing,including reliability evaluation indicator system,neuron coverage,and test case generation.It also systematically designed an intelligent software testing course,covering teaching objectives,teaching content,teaching methods,and a teaching case.Verified by the practical teaching in four classes,this course has achieved remarkable results,providing practical experience for the reform of software testing courses.
基金supported by Swinburne University of Technology Sarawak Campus and Birmingham City University.
文摘Wave energy is a promising form of marine renewable energy that offers a sustainable pathway for electricity generation in coastal regions.Despite Malaysia’s extensive coastline,the exploration of wave energy in Sarawak remains limited due to economic,technical,and environmental challenges that hinder its implementation.Compared to other renewable energy sources,wave energy is underutilized largely because of cost uncertainties and the lack of local performance data.This research aims to identify themost suitable coastal zone in Sarawak that achieves an optimal balance between energy potential,cost-effectiveness,and environmental impact,particularly in relation to infrastructure and regional development.The findings indicate that wave energy generation in Sarawak is technically feasible based on MOGA analysis.Among the studied sites,Bintulu emerged as the most balanced option,with a levelized cost of electricity(LCOE)of 0.778–0.864 USD/kWh and a CO_(2) emission factor as low as 0.019–0.020 CO_(2)/k Wh.Miri,while producing lower emissions than Sematan,recorded a higher LCOE of 1.045 USD/kWh with moderate emissions at 0.029 CO_(2)/kWh.Sematan,characterized by weaker wave conditions and higher installation penalties,resulted in the least favorable outcome,with an LCOE of 3.735 USD/kWh.Bintulu’s strategic location reduces CAPEX requirements,making it the most suitable site for large-scale wave energy deployment in Sarawak.
基金supported by Harbin Institute of Technology High-level Teaching Achievement Award(National Level)Cultivation Project(256709).
文摘This paper delves into effective pathways for transforming course ecosystems from resource provision to knowledge service and competency development through university-enterprise collaboration in co-building knowledge graphs and intelligent shared courses.This approach enables personalized,learning-driven teaching.Based on knowledge graphs and integrated teacher-machine-student smart teaching scenarios,it not only innovates autonomous learning environments and human-computer interaction models while optimizing teaching experiences for both instructors and students,but also effectively addresses the issues of students’“scattered,superficial,and fragmented learning”.This establishes the foundation for personalized teaching tailored to individual aptitudes.
文摘The exponential growth of Internet of Things(IoT)devices,autonomous systems,and digital services is generating massive volumes of big data,projected to exceed 291 zettabytes by 2027.Conventional cloud computing,despite its high processing and storage capacity,suffers from increased network latency,network congestion,and high operational costs,making it unsuitable for latency-sensitive applications.Edge computing addresses these issues by processing data near the source but faces scalability challenges and elevated Total Cost of Ownership(TCO).Hybrid solutions,such as fog computing,cloudlets,and Mobile Edge Computing(MEC),attempt to balance cost and performance;however,they still struggle with limited resource sharing and high deployment expenses.This paper proposes Public Edge as a Service(PEaaS),a novel paradigm that utilizes idle resources contributed by universities,enterprises,cellular operators,and individuals under a collaborative service model.By decentralizing computation and enabling multi-tenant resource sharing,PEaaS reduces reliance on centralized cloud infrastructure,minimizes communication costs,and enhances scalability.The proposed framework is evaluated using EdgeCloudSim under varying workloads,for keymetrics such as latency,communication cost,server utilization,and task failure rate.Results reveal that while cloud has a task failure rate rising sharply to 12.3%at 2000 devices,PEaaS maintains a low rate of 2.5%,closely matching edge computing.Furthermore,communication costs remain 25% lower than cloud and latency remains below 0.3,even under peak load.These findings demonstrate that PEaaS achieves near-edge performance with reduced costs and enhanced scalability,offering a sustainable and economically viable solution for next-generation computing environments.
基金supported by the Key Technologies R&D Program of Henan Province(No.242102211065)Postgraduate Education Reform and Quality Im-provement Project of Henan Province(No.YJS2025GZZ36).
文摘With the widespread adoption of digital equipment in intelligent substations,testing digital signals in power systems has become an important role for relay protection test equipment.Testing and calibrating digital signals require high accuracy.However,existing methods have low precision,cannot be calibrated at full range for all indexes,and have complex configuration,making them unsuitable for routine calibration work.To solve the above problems,a novel calibration method is designed and implemented using field programmable gate array(FPGA)to achieve accurate input and output time control.Accurate calibration relies on multiple forms of traceability including theoretical value traceability based on waveform comparison,time scale value traceability based on accurate time stamps,and algorithm traceability based on typical algorithms.Compared with other existing methods,the proposed approach reduces the mean absolute error of action time and time measurement by 92.88%,effectively addressing a key industry challenge and offering a valuable reference for further research,application,and standardization.
基金funded by the Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah,under grant No.(IFPDP-261-22).
文摘Medical imaging is essential in modern health care,allowing accurate diagnosis and effective treatment planning.These images,however,often demonstrate low contrast,noise,and brightness distortion that reduce their diagnostic reliability.This review presents a structured and comprehensive analysis of advanced histogram equalization(HE)-based techniques for medical image enhancement.Our review methodology encompasses:(1)classical HE approaches and related limitations in medical domains;(2)adaptive schemes like Adaptive Histogram Equalization(AHE)and Contrast Limited Adaptive Histogrma Equalization(CLAHE)and their advance variants;(3)brightnesspreserving schemes like BBHE and MMBEBHE and related algorithms;(4)dynamic and recursive histogram equalization methods incorporating DHE and RMSHE;(5)fuzzy logic-based enhancement methodologies addressing uncertainty and noise in medical images;and(6)hybrid optimization methodologies through the application of metaheuristic algorithms(World Cup Optimization,Particle Swarm Optimization,Genetic Algorithms,along with histogram-based methodologies.)There is also a comparative discussion given based on contrast improvement,image brightness preservation,noise management,and computational efficiency.Such advancements have better capabilities of improving image quality,which is more important for improved diagnosis and image analysis.
文摘The main idea behind the present research is to design a state-feedback controller for an underactuated nonlinear rotary inverted pendulum module by employing the linear quadratic regulator(LQR)technique using local approximation.The LQR is an excellent method for developing a controller for nonlinear systems.It provides optimal feedback to make the closed-loop system robust and stable,rejecting external disturbances.Model-based optimal controller for a nonlinear system such as a rotatory inverted pendulum has not been designed and implemented using Newton-Euler,Lagrange method,and local approximation.Therefore,implementing LQR to an underactuated nonlinear system was vital to design a stable controller.A mathematical model has been developed for the controller design by utilizing the Newton-Euler,Lagrange method.The nonlinear model has been linearized around an equilibrium point.Linear and nonlinear models have been compared to find the range in which linear and nonlinear models’behaviour is similar.MATLAB LQR function and system dynamics have been used to estimate the controller parameters.For the performance evaluation of the designed controller,Simulink has been used.Linear and nonlinear models have been simulated along with the designed controller.Simulations have been performed for the designed controller over the linear and nonlinear system under different conditions through varying system variables.The results show that the system is stable and robust enough to act against external disturbances.The controller maintains the rotary inverted pendulum in an upright position and rejects disruptions like falling under gravitational force or any external disturbance by adjusting the rotation of the horizontal link in both linear and nonlinear environments in a specific range.The controller has been practically designed and implemented.It is vivid from the results that the controller is robust enough to reject the disturbances in milliseconds and keeps the pendulum arm deflection angle to zero degrees.
文摘In light of the coronavirus disease 2019(COVID-19)outbreak caused by the novel coronavirus,companies and institutions have instructed their employees to work from home as a precautionary measure to reduce the risk of contagion.Employees,however,have been exposed to different security risks because of working from home.Moreover,the rapid global spread of COVID-19 has increased the volume of data generated from various sources.Working from home depends mainly on cloud computing(CC)applications that help employees to efficiently accomplish their tasks.The cloud computing environment(CCE)is an unsung hero in the COVID-19 pandemic crisis.It consists of the fast-paced practices for services that reflect the trend of rapidly deployable applications for maintaining data.Despite the increase in the use of CC applications,there is an ongoing research challenge in the domains of CCE concerning data,guaranteeing security,and the availability of CC applications.This paper,to the best of our knowledge,is the first paper that thoroughly explains the impact of the COVID-19 pandemic on CCE.Additionally,this paper also highlights the security risks of working from home during the COVID-19 pandemic.
文摘In recent years,task offloading and its scheduling optimization have emerged as widely discussed and signif-icant topics.The multi-objective optimization problems inherent in this domain,particularly those related to resource allocation,have been extensively investigated.However,existing studies predominantly focus on matching suitable computational resources for task offloading requests,often overlooking the optimization of the task data transmission process.This inefficiency in data transmission leads to delays in the arrival of task data at computational nodes within the edge network,resulting in increased service times due to elevated network transmission latencies and idle computational resources.To address this gap,we propose an Asynchronous Data Transmission Policy(ADTP)for optimizing data transmission for task offloading in edge-computing enabled ultra-dense IoT.ADTP dynamically generates data transmission scheduling strategies by jointly considering task offloading decisions and the fluctuating operational states of edge computing-enabled IoT networks.In contrast to existing methods,the Deep Deterministic Policy Gradient(DDPG)based task data transmission scheduling module works asynchronously with the Deep Q-Network(DQN)based Virtual Machine(VM)selection module in ADTP.This significantly reduces the computational space required for the scheduling algorithm.The continuous dynamic adjustment of data transmission bandwidth ensures timely delivery of task data and optimal utilization of network bandwidth resources.This reduces the task completion time and minimizes the failure rate caused by timeouts.Moreover,the VM selection module only performs the next inference step when a new task arrives or when a task finishes its computation.As a result,the wastage of computational resources is further reduced.The simulation results indicate that the proposed ADTP reduced average data transmission delay and service time by 7.11%and 8.09%,respectively.Furthermore,the task failure rate due to network congestion decreased by 68.73%.
基金This research was supported by Hankuk University of Foreign Studies Research Fund of 2021.Also,This research was supported by the MIST(Ministry of Science,ICT),Korea,under the National Program for Excellence in SW),supervised by the IITP(Institute of Information&communications Technology Planing&Evaluation)in 2021”(2019-0-01816).
文摘In recent times,internet of things(IoT)applications on the cloud might not be the effective solution for every IoT scenario,particularly for time sensitive applications.A significant alternative to use is edge computing that resolves the problem of requiring high bandwidth by end devices.Edge computing is considered a method of forwarding the processing and communication resources in the cloud towards the edge.One of the considerations of the edge computing environment is resource management that involves resource scheduling,load balancing,task scheduling,and quality of service(QoS)to accomplish improved performance.With this motivation,this paper presents new soft computing based metaheuristic algorithms for resource scheduling(RS)in the edge computing environment.The SCBMARS model involves the hybridization of the Group Teaching Optimization Algorithm(GTOA)with rat swarm optimizer(RSO)algorithm for optimal resource allocation.The goal of the SCBMA-RS model is to identify and allocate resources to every incoming user request in such a way,that the client’s necessities are satisfied with the minimum number of possible resources and optimal energy consumption.The problem is formulated based on the availability of VMs,task characteristics,and queue dynamics.The integration of GTOA and RSO algorithms assist to improve the allocation of resources among VMs in the data center.For experimental validation,a comprehensive set of simulations were performed using the CloudSim tool.The experimental results showcased the superior performance of the SCBMA-RS model interms of different measures.
基金supported by the Deanship of Scientific Research at Prince Sattam bin Abdulaziz University under the research project (PSAU/2023/01/23001).
文摘The development of human-robot interaction has been continu-ously increasing for the last decades.Through this development,it has become simpler and safe interactions using a remotely controlled telepresence robot in an insecure and hazardous environment.The audio-video communication connection or data transmission stability has already been well handled by fast-growing technologies such as 5G and 6G.However,the design of the phys-ical parameters,e.g.,maneuverability,controllability,and stability,still needs attention.Therefore,the paper aims to present a systematic,controlled design and implementation of a telepresence mobile robot.The primary focus of this paper is to perform the computational analysis and experimental implementa-tion design with sophisticated position control,which autonomously controls the robot’s position and speed when reaching an obstacle.A system model and a position controller design are developed with root locus points.The design robot results are verified experimentally,showing the robot’s agreement and control in the desired position.The robot was tested by considering various parameters:driving straight ahead,right turn,self-localization and complex path.The results prove that the proposed approach is flexible and adaptable and gives a better alternative.The experimental results show that the proposed method significantly minimizes the obstacle hits.
基金funded by WAQF at King Abdulaziz University,Jeddah,Saudi Arabia.
文摘The human ear has been substantiated as a viable nonintrusive biometric modality for identification or verification.Among many feasible techniques for ear biometric recognition,convolutional neural network(CNN)models have recently offered high-performance and reliable systems.However,their performance can still be further improved using the capabilities of soft biometrics,a research question yet to be investigated.This research aims to augment the traditional CNN-based ear recognition performance by adding increased discriminatory ear soft biometric traits.It proposes a novel framework of augmented ear identification/verification using a group of discriminative categorical soft biometrics and deriving new,more perceptive,comparative soft biometrics for feature-level fusion with hard biometric deep features.It conducts several identification and verification experiments for performance evaluation,analysis,and comparison while varying ear image datasets,hard biometric deep-feature extractors,soft biometric augmentation methods,and classifiers used.The experimental work yields promising results,reaching up to 99.94%accuracy and up to 14%improvement using the AMI and AMIC datasets,along with their corresponding soft biometric label data.The results confirm the proposed augmented approaches’superiority over their standard counterparts and emphasize the robustness of the new ear comparative soft biometrics over their categorical peers.
文摘In view of the deficiencies in aspects such as failure rate requirements and analysis assumptions of advisory circular,this paper investigates the sources of high safety requirements,and the top-down design method for the flight control system life cycle.Correspondingly,measures are proposed,including enhancing the safety target value to 10^(−10)per flight hour and implementing development assurance.In view of the shortcomings of mainstream aircraft flight control systems,such as weak backup capability and complex fault reconfiguration logic,improvements have been made to the system’s operating modes,control channel allocation,and common mode failure mitigation schemes based on the existing flight control architecture.The flight control design trends and philosophies have been analyzed.A flight control system architecture scheme is proposed,which includes three operating modes and multi-level voters/monitors,three main control channels,and a backup system independent of the main control system,which has been confirmed through functional modeling simulations.The proposed method plays an important role in the architecture design of safety-critical flight control system.
基金supported by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under grant No.GPIP:2040-611-2024。
文摘The Tactile Internet of Things(TIoT)promises transformative applications—ranging from remote surgery to industrial robotics—by incorporating haptic feedback into traditional IoT systems.Yet TIoT’s stringent requirements for ultra-low latency,high reliability,and robust privacy present significant challenges.Conventional centralized Federated Learning(FL)architectures struggle with latency and privacy constraints,while fully distributed FL(DFL)faces scalability and non-IID data issues as client populations expand and datasets become increasingly heterogeneous.To address these limitations,we propose a Clustered Distributed Federated Learning(CDFL)architecture tailored for a 6G-enabled TIoT environment.Clients are grouped into clusters based on data similarity and/or geographical proximity,enabling local intra-cluster aggregation before inter-cluster model sharing.This hierarchical,peer-to-peer approach reduces communication overhead,mitigates non-IID effects,and eliminates single points of failure.By offloading aggregation to the network edge and leveraging dynamic clustering,CDFL enhances both computational and communication efficiency.Extensive analysis and simulation demonstrate that CDFL outperforms both centralized FL and DFL as the number of clients grows.Specifically,CDFL demonstrates up to a 30%reduction in training time under highly heterogeneous data distributions,indicating faster convergence.It also reduces communication overhead by approximately 40%compared to DFL.These improvements and enhanced network performance metrics highlight CDFL’s effectiveness for practical TIoT deployments.These results validate CDFL as a scalable,privacy-preserving solution for next-generation TIoT applications.
文摘Globally,liver cancer ranks as the sixth most frequent malignancy cancer.The importance of early detection is undeniable,as liver cancer is the fifth most common disease in men and the ninth most common cancer in women.Recent advances in imaging,biomarker discovery,and genetic profiling have greatly enhanced the ability to diagnose liver cancer.Early identification is vital since liver cancer is often asymptomatic,making diagnosis difficult.Imaging techniques such as Magnetic Resonance Imaging(MRI),Computed Tomography(CT),and ultrasonography can be used to identify liver cancer once a sample of liver tissue is taken.In recent research,reliable detection of liver cancer with minimal computing computational complexity and time has remained a serious difficulty.This paper employs the DenseNet model to enhance the detection of liver nodules with tumors by segmenting them using UNet and VGG using Fastai(UVF)in CT images.Its dense interconnections distinguish the DenseNet between layers.These dense connections facilitate the propagation of gradients and the flow of information throughout the network,thereby enhancing the efficacy and performance of training.DenseNet’s architecture combines dense blocks,bottleneck layers,and transition layers,allowing it to achieve a compromise between expressiveness and computing efficiency.Finally,the 3D liver nodular models were created using a raycasting volume rendering approach.Compared to other state-of-the-art deep neural networks,it is suitable for clinical applications to assist doctors in diagnosing liver cancer.The proposed approach was tested on a 3Dircadb dataset.According to experiments,UVF segmentation on the 3Dircadb dataset is 97.9%accurate.According to the study,the DenseNet and UVF segment liver cancer better than prior methods.The system proposes automated 3D liver cancer tumor visualization.
基金National Key R&D Program of China under Grant No.2022YFC3003600National Natural Science Foundation of China(NSFC)under Grant No.51978023。
文摘Due to uncertainties in seismic pipeline damage and post-earthquake recovery processes,probabilistic characteristics such as mean value,standard deviation,probability density function,and cumulative distribution function provide valuable information.In this study,a simulation-based framework to evaluate these probabilistic characteristics in water distribution systems(WDSs)during post-earthquake recovery is developed.The framework first calculates pipeline failure probabilities using seismic fragility models and then generates damage samples through quasi-Monte Carlo simulations with Sobol’s sequence for faster convergence.System performance is assessed using a hydraulic model,and recovery simulations produce time-varying performance curves,where the dynamic importance of unrepaired damage determines repair sequences.Finally,the probabilistic characteristics of seismic performance indicators,resilience index,resilience loss,and recovery time are evaluated.The framework is applied in two benchmark WDSs with different layouts to investigate the probabilistic characteristics of their seismic performance and resilience.Application results show that the cumulative distribution function reveals the variations in resilience indicators for different exceedance probabilities,and there are dramatic differences among the recovery times corresponding to the system performance recovery targets of 80%,90%,and 100%.
