Globally,skin cancer is a prevalent form of malignancy,and its early and accurate diagnosis is critical for patient survival.Clinical evaluation of skin lesions is essential,but several challenges,such as long waiting...Globally,skin cancer is a prevalent form of malignancy,and its early and accurate diagnosis is critical for patient survival.Clinical evaluation of skin lesions is essential,but several challenges,such as long waiting times and subjective interpretations,make this task difficult.The recent advancement of deep learning in healthcare has shownmuch success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics.Deep learning improves the speed and precision of skin cancer diagnosis,leading to earlier prediction and treatment.In this work,we proposed a novel deep architecture for skin cancer classification in innovative healthcare.The proposed framework performed data augmentation at the first step to resolve the imbalance issue in the selected dataset.The proposed architecture is based on two customized,innovative Convolutional neural network(CNN)models based on small depth and filter sizes.In the first model,four residual blocks are added in a squeezed fashion with a small filter size.In the second model,five residual blocks are added with smaller depth and more useful weight information of the lesion region.To make models more useful,we selected the hyperparameters through Bayesian Optimization,in which the learning rate is selected.After training the proposed models,deep features are extracted and fused using a novel information entropy-controlled Euclidean Distance technique.The final features are passed on to the classifiers,and classification results are obtained.Also,the proposed trained model is interpreted through LIME-based localization on the HAM10000 dataset.The experimental process of the proposed architecture is performed on two dermoscopic datasets,HAM10000 and ISIC2019.We obtained an improved accuracy of 90.8%and 99.3%on these datasets,respectively.Also,the proposed architecture returned 91.6%for the cancer localization.In conclusion,the proposed architecture accuracy is compared with several pre-trained and state-of-the-art(SOTA)techniques and shows improved performance.展开更多
Real-time surveillance is attributed to recognizing the variety of actions performed by humans.Human Action Recognition(HAR)is a technique that recognizes human actions from a video stream.A range of variations in hum...Real-time surveillance is attributed to recognizing the variety of actions performed by humans.Human Action Recognition(HAR)is a technique that recognizes human actions from a video stream.A range of variations in human actions makes it difficult to recognize with considerable accuracy.This paper presents a novel deep neural network architecture called Attention RB-Net for HAR using video frames.The input is provided to the model in the form of video frames.The proposed deep architecture is based on the unique structuring of residual blocks with several filter sizes.Features are extracted from each frame via several operations with specific parameters defined in the presented novel Attention-based Residual Bottleneck(Attention-RB)DCNN architecture.A fully connected layer receives an attention-based features matrix,and final classification is performed.Several hyperparameters of the proposed model are initialized using Bayesian Optimization(BO)and later utilized in the trained model for testing.In testing,features are extracted from the self-attention layer and passed to neural network classifiers for the final action classification.Two highly cited datasets,HMDB51 and UCF101,were used to validate the proposed architecture and obtained an average accuracy of 87.70%and 97.30%,respectively.The deep convolutional neural network(DCNN)architecture is compared with state-of-the-art(SOTA)methods,including pre-trained models,inside blocks,and recently published techniques,and performs better.展开更多
Scenario planning is a powerful tool for cities to navigate uncertainties and mitigate the impacts of adverse scenarios by projecting future outcomes based on present-day decisions.This approach is becoming increasing...Scenario planning is a powerful tool for cities to navigate uncertainties and mitigate the impacts of adverse scenarios by projecting future outcomes based on present-day decisions.This approach is becoming increasingly important given the growing call for building resilient cities to face adverse future scenarios posed by emerging disruptive technologies and climate change.However,conventional scenario planning practices predominantly rely on expert knowledge and judgment,which may be limited in accounting for the complexity of future scenarios.Therefore,we explored the potential integration of artificial intelligence(AI)techniques to assist scenario planning practices.We synthesized related studies from various disciplines(e.g.,engineering,computer science,and urban planning)to identify the potential applications of AI in the three key components of scenario planning:plan generation,scenario generation,and plan evaluation.We then discuss the challenges and possible solutions for integrating AI into the scenario planning process and highlight the critical role of planning experts in this process.We conclude by outlining future research opportunities in this context.Ultimately,this study contributes to the advancement of scenario planning practices and aids the creation of more resilient cities that can thrive in an uncertain future.展开更多
The network dismantling problem asks the minimum separate node set of a graph whose removal will break the graph into connected components with the size not larger than the one percentage of the original graph.This pr...The network dismantling problem asks the minimum separate node set of a graph whose removal will break the graph into connected components with the size not larger than the one percentage of the original graph.This problem has attracted much attention recently and a lot of algorithms have been proposed. However, most of the network dismantling algorithms mainly focus on which nodes are included in the minimum separate set but overlook how to order them for removal, which will lead to low general efficiency during the dismantling process. In this paper,we reformulate the network dismantling problem by taking the order of nodes’ removal into consideration. An efficient dismantling sequence will break the network quickly during the dismantling processes. We take the belief-propagation guided decimation(BPD) dismantling algorithm, a state-of-the-art algorithm, as an example, and employ the node explosive percolation(NEP) algorithm to reorder the early part of the dismantling sequence given by the BPD. The proposed method is denoted as the NEP-BPD algorithm(NBA) here. The numerical results on Erd¨os-R′enyi graphs,random-regular graphs, scale-free graphs, and some real networks show the high general efficiency of NBA during the entire dismantling process. In addition, numerical computations on random graph ensembles with the size from 210 to219 exhibit that the NBA is in the same complexity class with the BPD algorithm. It is clear that the NEP method we used to improve the general efficiency could also be applied to other dismantling algorithms, such as Min-Sum algorithm,equal graph partitioning algorithm and so on.展开更多
Tumor detection has been an active research topic in recent years due to the high mortality rate.Computer vision(CV)and image processing techniques have recently become popular for detecting tumors inMRI images.The au...Tumor detection has been an active research topic in recent years due to the high mortality rate.Computer vision(CV)and image processing techniques have recently become popular for detecting tumors inMRI images.The automated detection process is simpler and takes less time than manual processing.In addition,the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for clinicians.We proposed a newframework for tumor detection aswell as tumor classification into relevant categories in this paper.For tumor segmentation,the proposed framework employs the Particle Swarm Optimization(PSO)algorithm,and for classification,the convolutional neural network(CNN)algorithm.Popular preprocessing techniques such as noise removal,image sharpening,and skull stripping are used at the start of the segmentation process.Then,PSO-based segmentation is applied.In the classification step,two pre-trained CNN models,alexnet and inception-V3,are used and trained using transfer learning.Using a serial approach,features are extracted from both trained models and fused features for final classification.For classification,a variety of machine learning classifiers are used.Average dice values on datasets BRATS-2018 and BRATS-2017 are 98.11 percent and 98.25 percent,respectively,whereas average jaccard values are 96.30 percent and 96.57%(Segmentation Results).The results were extended on the same datasets for classification and achieved 99.0%accuracy,sensitivity of 0.99,specificity of 0.99,and precision of 0.99.Finally,the proposed method is compared to state-of-the-art existingmethods and outperforms them.展开更多
The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a network.In a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles...The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a network.In a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion.Therefore,optimal path selection to route traffic between the origin and destination is vital.This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access.Firstly,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal routes.Considering the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal path.The model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective path.Thus,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.展开更多
Comparative space-time thinking lies at the heart of spatiotemporally integrated social sciences. The multiple dimensions and scales of socioeconomic dynamics pose numerous challenges for the application and evaluatio...Comparative space-time thinking lies at the heart of spatiotemporally integrated social sciences. The multiple dimensions and scales of socioeconomic dynamics pose numerous challenges for the application and evaluation of public policies in the comparative context. At the same time, social scientists have been slow to adopt and implement new spatiotemporally explicit methods of data analysis due to the lack of extensible software packages, which becomes a major impediment to the promotion of spatiotemporal thinking. The proposed framework will address this need by developing a set of research questions based on space-time-distributional features of socioeconomic datasets. The authors aim to develop, evaluate, and implement this framework in an open source toolkit to comprehensively quantify the changes and level of hidden variation of space-time datasets across scales and dimensions. Free access to the source code allows a broader community to incorporate additional advances in perspectives and methods, thus facilitating interdisciplinary collaboration. Being written in Python, it is entirely cross-platform, lowering transmission costs in research and education.展开更多
Due to rapid development in Artificial Intelligence(AI)and Deep Learning(DL),it is difficult to maintain the security and robustness of these techniques and algorithms due to emergence of novel term adversary sampling...Due to rapid development in Artificial Intelligence(AI)and Deep Learning(DL),it is difficult to maintain the security and robustness of these techniques and algorithms due to emergence of novel term adversary sampling.Such technique is sensitive to these models.Thus,fake samples cause AI and DL model to produce diverse results.Adversarial attacks that successfully implemented in real world scenarios highlight their applicability even further.In this regard,minor modifications of input images cause“Adversarial Attacks”that altered the performance of competing attacks dramatically.Recently,such attacks and defensive strategies are gaining lot of attention by the machine learning and security researchers.Doctors use different kinds of technologies to examine the patient abnormalities including Wireless Capsule Endoscopy(WCE).However,using WCE it is very difficult for doctors to detect an abnormality within images since it takes enough time while inspection and deciding abnormality.As a result,it took weeks to generate patients test report,which is tiring and strenuous for them.Therefore,researchers come out with the solution to adopt computerized technologies,which are more suitable for the classification and detection of such abnormalities.As far as the classification is concern,the adversarial attacks generate problems in classified images.Now days,to handle this issue machine learning is mainstream defensive approach against adversarial attacks.Hence,this research exposes the attacks by altering the datasets with noise including salt and pepper and Fast Gradient Sign Method(FGSM)and then reflects that how machine learning algorithms work fine to handle these noises in order to avoid attacks.Results obtained on the WCE images which are vulnerable to adversarial attack are 96.30%accurate and prove that the proposed defensive model is robust when compared to competitive existing methods.展开更多
Human action recognition(HAR)attempts to understand a subject’sbehavior and assign a label to each action performed.It is more appealingbecause it has a wide range of applications in computer vision,such asvideo surv...Human action recognition(HAR)attempts to understand a subject’sbehavior and assign a label to each action performed.It is more appealingbecause it has a wide range of applications in computer vision,such asvideo surveillance and smart cities.Many attempts have been made in theliterature to develop an effective and robust framework for HAR.Still,theprocess remains difficult and may result in reduced accuracy due to severalchallenges,such as similarity among actions,extraction of essential features,and reduction of irrelevant features.In this work,we proposed an end-toendframework using deep learning and an improved tree seed optimizationalgorithm for accurate HAR.The proposed design consists of a fewsignificantsteps.In the first step,frame preprocessing is performed.In the second step,two pre-trained deep learning models are fine-tuned and trained throughdeep transfer learning using preprocessed video frames.In the next step,deeplearning features of both fine-tuned models are fused using a new ParallelStandard Deviation Padding Max Value approach.The fused features arefurther optimized using an improved tree seed algorithm,and select the bestfeatures are finally classified by using the machine learning classifiers.Theexperiment was carried out on five publicly available datasets,including UTInteraction,Weizmann,KTH,Hollywood,and IXAMS,and achieved higheraccuracy than previous techniques.展开更多
The content-centric networking(CCN)architecture allows access to the content through name,instead of the physical location where the content is stored,which makes it a more robust and flexible content-based architectu...The content-centric networking(CCN)architecture allows access to the content through name,instead of the physical location where the content is stored,which makes it a more robust and flexible content-based architecture.Nevertheless,in CCN,the broadcast nature of vehicles on the Internet of Vehicles(IoV)results in latency and network congestion.The IoVbased content distribution is an emerging concept in which all the vehicles are connected via the internet.Due to the high mobility of vehicles,however,IoV applications have different network requirements that differ from those of many other networks,posing new challenges.Considering this,a novel strategy mediator framework is presented in this paper for managing the network resources efficiently.Software-defined network(SDN)controller is deployed for improving the routing flexibility and facilitating in the interinteroperability of heterogeneous devices within the network.Due to the limited memory of edge devices,the delectable bloom filters are used for caching and storage.Finally,the proposed scheme is compared with the existing variants for validating its effectiveness.展开更多
Breast cancer(BC)is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year.The BC positive newly diagnosed cases in ...Breast cancer(BC)is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year.The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6%of total cases.Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths.The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis.Manual diagnosis of BC is a complex and challenging task.This work proposed a deep learning-based(DL)solution for the early detection of this deadly disease from histopathology images.To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized.The proposed automatic diagnosis of BC detection and classification mainly involves three steps.Initially,a DL model is proposed for feature extraction.Secondly,the extracted feature vector(FV)is passed to the proposed novel feature selection(FS)framework for the best FS.Finally,for the classification of BC into invasive ductal carcinoma(IDC)and normal class different machine learning(ML)algorithms are used.Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7%which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.展开更多
Software-defined networking(SDN)represents a paradigm shift in network traffic management.It distinguishes between the data and control planes.APIs are then used to communicate between these planes.The controller is c...Software-defined networking(SDN)represents a paradigm shift in network traffic management.It distinguishes between the data and control planes.APIs are then used to communicate between these planes.The controller is central to the management of an SDN network and is subject to security concerns.This research shows how a deep learning algorithm can detect intrusions in SDN-based IoT networks.Overfitting,low accuracy,and efficient feature selection is all discussed.We propose a hybrid machine learning-based approach based on Random Forest and Long Short-Term Memory(LSTM).In this study,a new dataset based specifically on Software Defined Networks is used in SDN.To obtain the best and most relevant features,a feature selection technique is used.Several experiments have revealed that the proposed solution is a superior method for detecting flow-based anomalies.The performance of our proposed model is also measured in terms of accuracy,recall,and precision.F1 rating and detection time Furthermore,a lightweight model for training is proposed,which selects fewer features while maintaining the model’s performance.Experiments show that the adopted methodology outperforms existing models.展开更多
Despite the planned installation and operations of the traditional IEEE 802.11 networks,they still experience degraded performance due to the number of inefficiencies.One of the main reasons is the received signal str...Despite the planned installation and operations of the traditional IEEE 802.11 networks,they still experience degraded performance due to the number of inefficiencies.One of the main reasons is the received signal strength indicator(RSSI)association problem,in which the user remains connected to the access point(AP)unless the RSSI becomes too weak.In this paper,we propose a multi-criterion association(WiMA)scheme based on software defined networking(SDN)in Wi-Fi networks.An association solution based on multi-criterion such as AP load,RSSI,and channel occupancy is proposed to satisfy the quality of service(QoS).SDNhaving an overall view of the network takes the association and reassociation decisions making the handoffs smooth in throughput performance.To implementWiMA extensive simulations runs are carried out on Mininet-NS3-Wi-Fi network simulator.The performance evaluation shows that the WiMA significantly reduces the average number of retransmissions by 5%–30%and enhances the throughput by 20%–50%,hence maintaining user fairness and accommodating more wireless devices and traffic load in the network,when compared to traditional client-driven(CD)approach and state of the art Wi-Balance approach.展开更多
This study aims to quantitatively explore the multifaceted determinants that influence earnings in the translation industry.Using a dataset comprising 45000 translator profiles,the study focusses on delineating dispar...This study aims to quantitatively explore the multifaceted determinants that influence earnings in the translation industry.Using a dataset comprising 45000 translator profiles,the study focusses on delineating disparities correlated with demographic variables such as age,gender,home country wealth,and language pairs.The study uses a random forest regression model to delineate the complex interaction between gender,the economic standing of a translator’s domicile country,age,and linguistic proficiency,as they relate to earnings.Our findings substantiate and,in many ways,extend existing qualitative and anecdotal evidence that has shaped the discourse in this sector.The rigorous empirical framework employed here can be replicated or adapted to study other sectors within the gig economy,thus contributing to a more comprehensive understanding of labour dynamics in the digital age.展开更多
In recent years,as China has grappled with rising debt and broad economic restructure,the prevalence of zombie firms has become a critical problem.This paper provides a theoretical framework illustrating the rationale...In recent years,as China has grappled with rising debt and broad economic restructure,the prevalence of zombie firms has become a critical problem.This paper provides a theoretical framework illustrating the rationale behind the occurrence of zombie firms from the perspective of banks.We develop differential equations to model a bank s expectation and the ex ante estimate that underlies its decision to refinance an insolvent borrower.An optimistic expectation is essential in zombie lending and is intrinsic to the countercyclical pattern of zombie firms.Our model also predicts that debt can build up to an unsustainable level if recovery ofprofitability is sluggish or the initial debt burden is too high.Examining the Chinese experience of zombie firms over 2007-2017,this paper highlights two findings.First,the share of zombie firms among Shanghai and Shenzhen A-share listed companies demonstrates a countercyclical pattern.Second,the positive correlation between zombie share and debt accumulation across manufacturing sectors sheds light on the link between zombie firms and the rising corporate debt in China.To deal with the zombie"problem,the government should carefully weigh its policies to avoid further distortions because the occurrence of zombie firms may be inevitable and impossible to eliminate.展开更多
On behalf of the Editorial Board,it is our privilege to present the first issue of the Journal of Social Computing,affectionately shortened JoSoCo.Social computing concerns the intersection of social behavior and comp...On behalf of the Editorial Board,it is our privilege to present the first issue of the Journal of Social Computing,affectionately shortened JoSoCo.Social computing concerns the intersection of social behavior and computational systems.Historically focused on recreating human social conventions and contexts through software and technology,we propose its expansion to the full interface between social interaction and computation.展开更多
Introduction:Minimizing the importation and exportation risks of coronavirus disease 2019(COVID-19)is a primary concern for sustaining the“Dynamic COVID-zero”strategy in China.Risk estimation is essential for cities...Introduction:Minimizing the importation and exportation risks of coronavirus disease 2019(COVID-19)is a primary concern for sustaining the“Dynamic COVID-zero”strategy in China.Risk estimation is essential for cities to conduct before relaxing border control measures.Methods:Informed by the daily number of passengers traveling between 367 prefectures(cities)in China,this study used a stochastic metapopulation model parameterized with COVID-19 epidemic characteristics to estimate the importation and exportation risks.Results:Under the transmission scenario(R0=5.49),this study estimated the cumulative case incidence of Changchun City,Jilin Province as 3,233(95%confidence interval:1,480,4,986)before a lockdown on March 14,2022,which is close to the 3,168 cases reported in real life by March 16,2022.In a total of 367 prefectures(cities),127(35%)had high exportation risks according to the simulation and could transmit the disease to 50%of all other regions within a period from 17 to 94 days.The average time until a new infection arrives in a location in 1 of the 367 prefectures(cities)ranged from 26 to 101 days.Conclusions:Estimating COVID-19 importation and exportation risks is necessary for preparedness,prevention,and control measures of COVID-19—especially when new variants emerge.展开更多
基金supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(*MSIT)(No.2018R1A5A7059549)supported through Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R508)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘Globally,skin cancer is a prevalent form of malignancy,and its early and accurate diagnosis is critical for patient survival.Clinical evaluation of skin lesions is essential,but several challenges,such as long waiting times and subjective interpretations,make this task difficult.The recent advancement of deep learning in healthcare has shownmuch success in diagnosing and classifying skin cancer and has assisted dermatologists in clinics.Deep learning improves the speed and precision of skin cancer diagnosis,leading to earlier prediction and treatment.In this work,we proposed a novel deep architecture for skin cancer classification in innovative healthcare.The proposed framework performed data augmentation at the first step to resolve the imbalance issue in the selected dataset.The proposed architecture is based on two customized,innovative Convolutional neural network(CNN)models based on small depth and filter sizes.In the first model,four residual blocks are added in a squeezed fashion with a small filter size.In the second model,five residual blocks are added with smaller depth and more useful weight information of the lesion region.To make models more useful,we selected the hyperparameters through Bayesian Optimization,in which the learning rate is selected.After training the proposed models,deep features are extracted and fused using a novel information entropy-controlled Euclidean Distance technique.The final features are passed on to the classifiers,and classification results are obtained.Also,the proposed trained model is interpreted through LIME-based localization on the HAM10000 dataset.The experimental process of the proposed architecture is performed on two dermoscopic datasets,HAM10000 and ISIC2019.We obtained an improved accuracy of 90.8%and 99.3%on these datasets,respectively.Also,the proposed architecture returned 91.6%for the cancer localization.In conclusion,the proposed architecture accuracy is compared with several pre-trained and state-of-the-art(SOTA)techniques and shows improved performance.
基金Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.supported by the National Research Foundation of Korea(NRF)grant funded by the Korea government(*MSIT)(No.2018R1A5A7059549)the Competitive Research Fund of The University of Aizu,Japan.
文摘Real-time surveillance is attributed to recognizing the variety of actions performed by humans.Human Action Recognition(HAR)is a technique that recognizes human actions from a video stream.A range of variations in human actions makes it difficult to recognize with considerable accuracy.This paper presents a novel deep neural network architecture called Attention RB-Net for HAR using video frames.The input is provided to the model in the form of video frames.The proposed deep architecture is based on the unique structuring of residual blocks with several filter sizes.Features are extracted from each frame via several operations with specific parameters defined in the presented novel Attention-based Residual Bottleneck(Attention-RB)DCNN architecture.A fully connected layer receives an attention-based features matrix,and final classification is performed.Several hyperparameters of the proposed model are initialized using Bayesian Optimization(BO)and later utilized in the trained model for testing.In testing,features are extracted from the self-attention layer and passed to neural network classifiers for the final action classification.Two highly cited datasets,HMDB51 and UCF101,were used to validate the proposed architecture and obtained an average accuracy of 87.70%and 97.30%,respectively.The deep convolutional neural network(DCNN)architecture is compared with state-of-the-art(SOTA)methods,including pre-trained models,inside blocks,and recently published techniques,and performs better.
基金supported by the University Development Fund(UDF01003238)provided by the Chinese University of Hong Kong(Shenzhen)graduate school fellowship program at the University of Florida。
文摘Scenario planning is a powerful tool for cities to navigate uncertainties and mitigate the impacts of adverse scenarios by projecting future outcomes based on present-day decisions.This approach is becoming increasingly important given the growing call for building resilient cities to face adverse future scenarios posed by emerging disruptive technologies and climate change.However,conventional scenario planning practices predominantly rely on expert knowledge and judgment,which may be limited in accounting for the complexity of future scenarios.Therefore,we explored the potential integration of artificial intelligence(AI)techniques to assist scenario planning practices.We synthesized related studies from various disciplines(e.g.,engineering,computer science,and urban planning)to identify the potential applications of AI in the three key components of scenario planning:plan generation,scenario generation,and plan evaluation.We then discuss the challenges and possible solutions for integrating AI into the scenario planning process and highlight the critical role of planning experts in this process.We conclude by outlining future research opportunities in this context.Ultimately,this study contributes to the advancement of scenario planning practices and aids the creation of more resilient cities that can thrive in an uncertain future.
基金Supported by the 2016 Basic Operational Outlays for the Research Activities of Centric University,Civil Aviation University of China under Grant No.3122016L010the National Natural Science Foundation of China under Grant No.11705279+1 种基金supported by the Zhejiang Provincial Natural Science Foundation of China under Grant No.LR16A050001the National Natural Science Foundation of China under Grant Nos.11622538 and 61673150
文摘The network dismantling problem asks the minimum separate node set of a graph whose removal will break the graph into connected components with the size not larger than the one percentage of the original graph.This problem has attracted much attention recently and a lot of algorithms have been proposed. However, most of the network dismantling algorithms mainly focus on which nodes are included in the minimum separate set but overlook how to order them for removal, which will lead to low general efficiency during the dismantling process. In this paper,we reformulate the network dismantling problem by taking the order of nodes’ removal into consideration. An efficient dismantling sequence will break the network quickly during the dismantling processes. We take the belief-propagation guided decimation(BPD) dismantling algorithm, a state-of-the-art algorithm, as an example, and employ the node explosive percolation(NEP) algorithm to reorder the early part of the dismantling sequence given by the BPD. The proposed method is denoted as the NEP-BPD algorithm(NBA) here. The numerical results on Erd¨os-R′enyi graphs,random-regular graphs, scale-free graphs, and some real networks show the high general efficiency of NBA during the entire dismantling process. In addition, numerical computations on random graph ensembles with the size from 210 to219 exhibit that the NBA is in the same complexity class with the BPD algorithm. It is clear that the NEP method we used to improve the general efficiency could also be applied to other dismantling algorithms, such as Min-Sum algorithm,equal graph partitioning algorithm and so on.
基金This work was supported by“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP)granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea.(No.20204010600090).
文摘Tumor detection has been an active research topic in recent years due to the high mortality rate.Computer vision(CV)and image processing techniques have recently become popular for detecting tumors inMRI images.The automated detection process is simpler and takes less time than manual processing.In addition,the difference in the expanding shape of brain tumor tissues complicates and complicates tumor detection for clinicians.We proposed a newframework for tumor detection aswell as tumor classification into relevant categories in this paper.For tumor segmentation,the proposed framework employs the Particle Swarm Optimization(PSO)algorithm,and for classification,the convolutional neural network(CNN)algorithm.Popular preprocessing techniques such as noise removal,image sharpening,and skull stripping are used at the start of the segmentation process.Then,PSO-based segmentation is applied.In the classification step,two pre-trained CNN models,alexnet and inception-V3,are used and trained using transfer learning.Using a serial approach,features are extracted from both trained models and fused features for final classification.For classification,a variety of machine learning classifiers are used.Average dice values on datasets BRATS-2018 and BRATS-2017 are 98.11 percent and 98.25 percent,respectively,whereas average jaccard values are 96.30 percent and 96.57%(Segmentation Results).The results were extended on the same datasets for classification and achieved 99.0%accuracy,sensitivity of 0.99,specificity of 0.99,and precision of 0.99.Finally,the proposed method is compared to state-of-the-art existingmethods and outperforms them.
基金supported by“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea.(No.20204010600090).
文摘The Internet of Vehicles(IoV)is a networking paradigm related to the intercommunication of vehicles using a network.In a dynamic network,one of the key challenges in IoV is traffic management under increasing vehicles to avoid congestion.Therefore,optimal path selection to route traffic between the origin and destination is vital.This research proposed a realistic strategy to reduce traffic management service response time by enabling real-time content distribution in IoV systems using heterogeneous network access.Firstly,this work proposed a novel use of the Ant Colony Optimization(ACO)algorithm and formulated the path planning optimization problem as an Integer Linear Program(ILP).This integrates the future estimation metric to predict the future arrivals of the vehicles,searching the optimal routes.Considering the mobile nature of IOV,fuzzy logic is used for congestion level estimation along with the ACO to determine the optimal path.The model results indicate that the suggested scheme outperforms the existing state-of-the-art methods by identifying the shortest and most cost-effective path.Thus,this work strongly supports its use in applications having stringent Quality of Service(QoS)requirements for the vehicles.
基金Under the auspices of Humanities and Social Science Research,Major Project of Chinese Ministry of Education(No.13JJD790008)Basic Research Funds of National Higher Education Institutions of China(No.2722013JC030)+2 种基金Zhongnan University of Economics and Law 2012 Talent Grant(No.31541210702)Key Research Program of Chinese Academy of Sciences(No.KZZD-EW-06-03,KSZD-EW-Z-021-03)National Key Science and Technology Support Program of China(No.2012BAH35B03)
文摘Comparative space-time thinking lies at the heart of spatiotemporally integrated social sciences. The multiple dimensions and scales of socioeconomic dynamics pose numerous challenges for the application and evaluation of public policies in the comparative context. At the same time, social scientists have been slow to adopt and implement new spatiotemporally explicit methods of data analysis due to the lack of extensible software packages, which becomes a major impediment to the promotion of spatiotemporal thinking. The proposed framework will address this need by developing a set of research questions based on space-time-distributional features of socioeconomic datasets. The authors aim to develop, evaluate, and implement this framework in an open source toolkit to comprehensively quantify the changes and level of hidden variation of space-time datasets across scales and dimensions. Free access to the source code allows a broader community to incorporate additional advances in perspectives and methods, thus facilitating interdisciplinary collaboration. Being written in Python, it is entirely cross-platform, lowering transmission costs in research and education.
基金This work was supported by“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea.(No.20204010600090).
文摘Due to rapid development in Artificial Intelligence(AI)and Deep Learning(DL),it is difficult to maintain the security and robustness of these techniques and algorithms due to emergence of novel term adversary sampling.Such technique is sensitive to these models.Thus,fake samples cause AI and DL model to produce diverse results.Adversarial attacks that successfully implemented in real world scenarios highlight their applicability even further.In this regard,minor modifications of input images cause“Adversarial Attacks”that altered the performance of competing attacks dramatically.Recently,such attacks and defensive strategies are gaining lot of attention by the machine learning and security researchers.Doctors use different kinds of technologies to examine the patient abnormalities including Wireless Capsule Endoscopy(WCE).However,using WCE it is very difficult for doctors to detect an abnormality within images since it takes enough time while inspection and deciding abnormality.As a result,it took weeks to generate patients test report,which is tiring and strenuous for them.Therefore,researchers come out with the solution to adopt computerized technologies,which are more suitable for the classification and detection of such abnormalities.As far as the classification is concern,the adversarial attacks generate problems in classified images.Now days,to handle this issue machine learning is mainstream defensive approach against adversarial attacks.Hence,this research exposes the attacks by altering the datasets with noise including salt and pepper and Fast Gradient Sign Method(FGSM)and then reflects that how machine learning algorithms work fine to handle these noises in order to avoid attacks.Results obtained on the WCE images which are vulnerable to adversarial attack are 96.30%accurate and prove that the proposed defensive model is robust when compared to competitive existing methods.
基金supported by“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea.(No.20204010600090).
文摘Human action recognition(HAR)attempts to understand a subject’sbehavior and assign a label to each action performed.It is more appealingbecause it has a wide range of applications in computer vision,such asvideo surveillance and smart cities.Many attempts have been made in theliterature to develop an effective and robust framework for HAR.Still,theprocess remains difficult and may result in reduced accuracy due to severalchallenges,such as similarity among actions,extraction of essential features,and reduction of irrelevant features.In this work,we proposed an end-toendframework using deep learning and an improved tree seed optimizationalgorithm for accurate HAR.The proposed design consists of a fewsignificantsteps.In the first step,frame preprocessing is performed.In the second step,two pre-trained deep learning models are fine-tuned and trained throughdeep transfer learning using preprocessed video frames.In the next step,deeplearning features of both fine-tuned models are fused using a new ParallelStandard Deviation Padding Max Value approach.The fused features arefurther optimized using an improved tree seed algorithm,and select the bestfeatures are finally classified by using the machine learning classifiers.Theexperiment was carried out on five publicly available datasets,including UTInteraction,Weizmann,KTH,Hollywood,and IXAMS,and achieved higheraccuracy than previous techniques.
基金supported by“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea(No.20204010600090).
文摘The content-centric networking(CCN)architecture allows access to the content through name,instead of the physical location where the content is stored,which makes it a more robust and flexible content-based architecture.Nevertheless,in CCN,the broadcast nature of vehicles on the Internet of Vehicles(IoV)results in latency and network congestion.The IoVbased content distribution is an emerging concept in which all the vehicles are connected via the internet.Due to the high mobility of vehicles,however,IoV applications have different network requirements that differ from those of many other networks,posing new challenges.Considering this,a novel strategy mediator framework is presented in this paper for managing the network resources efficiently.Software-defined network(SDN)controller is deployed for improving the routing flexibility and facilitating in the interinteroperability of heterogeneous devices within the network.Due to the limited memory of edge devices,the delectable bloom filters are used for caching and storage.Finally,the proposed scheme is compared with the existing variants for validating its effectiveness.
基金This work was supported by the“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea.(No.20204010600090).
文摘Breast cancer(BC)is a most spreading and deadly cancerous malady which is mostly diagnosed in middle-aged women worldwide and effecting beyond a half-million people every year.The BC positive newly diagnosed cases in 2018 reached 2.1 million around the world with a death rate of 11.6%of total cases.Early diagnosis and detection of breast cancer disease with proper treatment may reduce the number of deaths.The gold standard for BC detection is biopsy analysis which needs an expert for correct diagnosis.Manual diagnosis of BC is a complex and challenging task.This work proposed a deep learning-based(DL)solution for the early detection of this deadly disease from histopathology images.To evaluate the robustness of the proposed method a large publically available breast histopathology image database containing a total of 277524 histopathology images is utilized.The proposed automatic diagnosis of BC detection and classification mainly involves three steps.Initially,a DL model is proposed for feature extraction.Secondly,the extracted feature vector(FV)is passed to the proposed novel feature selection(FS)framework for the best FS.Finally,for the classification of BC into invasive ductal carcinoma(IDC)and normal class different machine learning(ML)algorithms are used.Experimental outcomes of the proposed methodology achieved the highest accuracy of 92.7%which shows that the proposed technique can successfully be implemented for BC detection to aid the pathologists in the early and accurate diagnosis of BC.
基金supported by“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea(No.20204010600090).
文摘Software-defined networking(SDN)represents a paradigm shift in network traffic management.It distinguishes between the data and control planes.APIs are then used to communicate between these planes.The controller is central to the management of an SDN network and is subject to security concerns.This research shows how a deep learning algorithm can detect intrusions in SDN-based IoT networks.Overfitting,low accuracy,and efficient feature selection is all discussed.We propose a hybrid machine learning-based approach based on Random Forest and Long Short-Term Memory(LSTM).In this study,a new dataset based specifically on Software Defined Networks is used in SDN.To obtain the best and most relevant features,a feature selection technique is used.Several experiments have revealed that the proposed solution is a superior method for detecting flow-based anomalies.The performance of our proposed model is also measured in terms of accuracy,recall,and precision.F1 rating and detection time Furthermore,a lightweight model for training is proposed,which selects fewer features while maintaining the model’s performance.Experiments show that the adopted methodology outperforms existing models.
基金supported by the“Human Resources Program in Energy Technology”of the Korea Institute of Energy Technology Evaluation and Planning(KETEP),granted financial resources from the Ministry of Trade,Industry&Energy,Republic of Korea(No.20204010600090).
文摘Despite the planned installation and operations of the traditional IEEE 802.11 networks,they still experience degraded performance due to the number of inefficiencies.One of the main reasons is the received signal strength indicator(RSSI)association problem,in which the user remains connected to the access point(AP)unless the RSSI becomes too weak.In this paper,we propose a multi-criterion association(WiMA)scheme based on software defined networking(SDN)in Wi-Fi networks.An association solution based on multi-criterion such as AP load,RSSI,and channel occupancy is proposed to satisfy the quality of service(QoS).SDNhaving an overall view of the network takes the association and reassociation decisions making the handoffs smooth in throughput performance.To implementWiMA extensive simulations runs are carried out on Mininet-NS3-Wi-Fi network simulator.The performance evaluation shows that the WiMA significantly reduces the average number of retransmissions by 5%–30%and enhances the throughput by 20%–50%,hence maintaining user fairness and accommodating more wireless devices and traffic load in the network,when compared to traditional client-driven(CD)approach and state of the art Wi-Balance approach.
文摘This study aims to quantitatively explore the multifaceted determinants that influence earnings in the translation industry.Using a dataset comprising 45000 translator profiles,the study focusses on delineating disparities correlated with demographic variables such as age,gender,home country wealth,and language pairs.The study uses a random forest regression model to delineate the complex interaction between gender,the economic standing of a translator’s domicile country,age,and linguistic proficiency,as they relate to earnings.Our findings substantiate and,in many ways,extend existing qualitative and anecdotal evidence that has shaped the discourse in this sector.The rigorous empirical framework employed here can be replicated or adapted to study other sectors within the gig economy,thus contributing to a more comprehensive understanding of labour dynamics in the digital age.
文摘In recent years,as China has grappled with rising debt and broad economic restructure,the prevalence of zombie firms has become a critical problem.This paper provides a theoretical framework illustrating the rationale behind the occurrence of zombie firms from the perspective of banks.We develop differential equations to model a bank s expectation and the ex ante estimate that underlies its decision to refinance an insolvent borrower.An optimistic expectation is essential in zombie lending and is intrinsic to the countercyclical pattern of zombie firms.Our model also predicts that debt can build up to an unsustainable level if recovery ofprofitability is sluggish or the initial debt burden is too high.Examining the Chinese experience of zombie firms over 2007-2017,this paper highlights two findings.First,the share of zombie firms among Shanghai and Shenzhen A-share listed companies demonstrates a countercyclical pattern.Second,the positive correlation between zombie share and debt accumulation across manufacturing sectors sheds light on the link between zombie firms and the rising corporate debt in China.To deal with the zombie"problem,the government should carefully weigh its policies to avoid further distortions because the occurrence of zombie firms may be inevitable and impossible to eliminate.
文摘On behalf of the Editorial Board,it is our privilege to present the first issue of the Journal of Social Computing,affectionately shortened JoSoCo.Social computing concerns the intersection of social behavior and computational systems.Historically focused on recreating human social conventions and contexts through software and technology,we propose its expansion to the full interface between social interaction and computation.
基金Supported by AIR@InnoHK programme from The Innovation and Technology Commission of the Hong Kong Special Administrative Region,National Natural Science Foundation of China(72104208)JSPS KAKENHI(JP21H04595)National Nature Science Foundation of China(72025405,91846301,72088101,and 71790615).
文摘Introduction:Minimizing the importation and exportation risks of coronavirus disease 2019(COVID-19)is a primary concern for sustaining the“Dynamic COVID-zero”strategy in China.Risk estimation is essential for cities to conduct before relaxing border control measures.Methods:Informed by the daily number of passengers traveling between 367 prefectures(cities)in China,this study used a stochastic metapopulation model parameterized with COVID-19 epidemic characteristics to estimate the importation and exportation risks.Results:Under the transmission scenario(R0=5.49),this study estimated the cumulative case incidence of Changchun City,Jilin Province as 3,233(95%confidence interval:1,480,4,986)before a lockdown on March 14,2022,which is close to the 3,168 cases reported in real life by March 16,2022.In a total of 367 prefectures(cities),127(35%)had high exportation risks according to the simulation and could transmit the disease to 50%of all other regions within a period from 17 to 94 days.The average time until a new infection arrives in a location in 1 of the 367 prefectures(cities)ranged from 26 to 101 days.Conclusions:Estimating COVID-19 importation and exportation risks is necessary for preparedness,prevention,and control measures of COVID-19—especially when new variants emerge.
