Considering dynamical disease spreading network consisting of moving individuals,a new double-layer network is constructed,one where the information dissemination process takes place and the other where the dynamics o...Considering dynamical disease spreading network consisting of moving individuals,a new double-layer network is constructed,one where the information dissemination process takes place and the other where the dynamics of disease spreading evolves.On the basis of Markov chains theory,a new model characterizing the coupled dynamics between information dissemination and disease spreading in populations of moving agents is established and corresponding state probability equations are formulated to describe the probability in each state of every node at each moment.Monte Carlo simulations are performed to characterize the interaction process between information and disease spreading and investigate factors that influence spreading dynamics.Simulation results show that the increasing of information transmission rate can reduce the scale of disease spreading in some degree.Shortening infection period and strengthening consciousness for self-protection by decreasing individual’s scope of activity both can effectively reduce the final refractory density for the disease but have less effect on the information dissemination.In addition,the increasing of vaccination rate or decreasing of long-range travel can also reduce the scale of disease spreading.展开更多
Aims: This study aims at designing and implementing syllabus-oriented question-bank system that is capable of producing paper-based exams with multiple forms along with answer keys. The developed software tool is nam...Aims: This study aims at designing and implementing syllabus-oriented question-bank system that is capable of producing paper-based exams with multiple forms along with answer keys. The developed software tool is named Χ(Chi)-Pro Milestone and supports four types of questions, namely: Multiple-choice, True/False, Short-Answer and Free-Response Essay questions. The study is motivated by the fact that student number in schools and universities is continuously growing at high, non-linear, and uncontrolled rates. This growth, however, is not accompanied by an equivalent growth of educational resources (mainly: instructors, classrooms, and labs). A direct result of this situation is having relatively large number of students in each classroom. It is observed that providing and using online-examining systems could be intractable and expensive. As an alternative, paper-based exams can be used. One main issue is that manually produced paper-based exams are of low quality because of some human factors such as instability and relatively narrow range of topics [1]. Further, it is observed that instructors usually need to spend a lot of time and energy in composing paper-based exams with multiple forms. Therefore, the use of computers for automatic production of paper-based exams from question banks is becoming more and more important. Methodology: The design and evaluation of X-Pro Milestone are done by considering a basic set of design principles that are based on a list of identified Functional and Non-Functional Requirements. Deriving those requirements is made possible by developing X-Pro Milestone using the Iterative and Incremental model from software engineering domain. Results: We demonstrate that X-Pro Milestone has a number of excellent characteristics compared to the exam-preparation and question banks tools available in market. Some of these characteristics are: ease of use and operation, user-friendly interface and good usability, high security and protection of the question bank-items, high stability, and reliability. Further, X-Pro Milestone makes initiating, maintaining and archiving Question-Banks and produced exams possible. Putting X-Pro Milestone into real use has showed that X-Pro Milestone is easy to be learned and effectively used. We demonstrate that X-Pro Milestone is a cost-effective alternative to online examining systems with more and richer features and with low infrastructure requirements.展开更多
This research paper analyzes data breaches in the human service sector. The hypothesis for the solution to this problem is that there will be a significant reduction in data breaches in the human service sector due to...This research paper analyzes data breaches in the human service sector. The hypothesis for the solution to this problem is that there will be a significant reduction in data breaches in the human service sector due to an increase in information assurance. The hypothesis is tested using data from the United States Department of Health and Human Services data breach notification repository during January 2018-December 2020. Our result shows that without the increased mitigation of information assurance, data breaches in the human service sector will continue to increase.展开更多
In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe ...In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints inWSNs.This paper presents an Adaptive Enhanced GreyWolf Optimizer(AEGWO)for energy-efficient cluster head(CH)selection that mitigates the exploration–exploitation imbalance,preserves population diversity,and avoids premature convergence inherent in baseline GWO.The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation,a hybrid velocity-momentum update based on the dynamics of PSO,and an intelligent mutation operator to maintain the diversity of the population.The search is guided by a multi-objective fitness,which aims at maximizing the residual energy,equal distribution of CH,minimizing the intra-cluster distance,desirable proximity to sinks,and enhancing the coverage.Simulations on 100 nodes homogeneousWSN Tested the proposed AEGWO under the same conditions with LEACH,GWO,IGWO,PSO,WOA,and GA,AEGWO significantly increases stability and lifetime compared to LEACHand other tested algorithms;it has the best first,half,and last node dead,and higher residual energy and smaller communication overhead.The findings prove that AEGWO provides sustainable energy management and better lifetime extension,which makes it a robust,flexible clustering protocol of large-scaleWSNs.展开更多
Leaf area index(LAI) is a key factor that determines a forest ecosystem's net primary production and energy exchange between the atmosphere and land surfaces.LAI can be measured in many ways, but there has been li...Leaf area index(LAI) is a key factor that determines a forest ecosystem's net primary production and energy exchange between the atmosphere and land surfaces.LAI can be measured in many ways, but there has been little research to compare LAI estimated by different methods. In this study, we compared the LAI results from two different approaches, i.e., the dimidiate pixel model(DPM) and an empirical statistic model(ESM) using ZY-3 high-accuracy satellite images validated by field data. We explored the relationship of LAI of Larix principis-rupprechtii Mayr plantations with topographic conditions. The results show that DPM improves the simulation of LAI(r = 0.86,RMSE = 0.57) compared with ESM(r = 0.62, RMSE =0.79). We further concluded that elevation and slope significantly affect the distribution of LAI. The maximum peak of LAI appeared at an aspect of east and southeast at an elevation of 1700–2000 m. Our results suggest that ZY-3 can satisfy the needs of quantitative monitoring of leaf area indices in small-scale catchment areas. DPM provides a simple and accurate method to obtain forest vegetation parameters in the case of non-ground measurement points.展开更多
In recent years,the infrastructure,instruments,and resources of network systems are becoming more complex and heterogeneous,with the rapid development of current internet and mobile communication technologies.In order...In recent years,the infrastructure,instruments,and resources of network systems are becoming more complex and heterogeneous,with the rapid development of current internet and mobile communication technologies.In order to efficaciously prepare,control,hold and optimize networking systems,greater intelligence needs to be deployed.However,due to the inherently dispensed characteristic of conventional networks,Machine Learning(ML)techniques are hard to implement and deployed to govern and operate networks.Software-Defined Networking(SDN)brings us new possibilities to offer intelligence in the networks.SDN’s characteristics(e.g.,logically centralized control,global network view,software-based site visitor analysis,and dynamic updating of forwarding rules)make it simpler to apply machine learning strategies.Various perspectives of fiber-optic communications including fiber nonlinearity coverage,optical performance checking,cognitive shortcoming detection/anticipation,and arranging and improvement of softwaredefined networks are examined in Machine Learning(ML)applications.This research paper has presented an imaginative framework concept called Intelligent Software Defined Network(ISDN)for Cognitive Routing Optimization(CRO)using Deep Extreme Learning Machine(DELM)approach(ISDN-CRO-DELM)in light of the new challenges in the development and operation of communication systems,and capturing motivation from how living creatures deal with difficulty and usability.The proposed methodology develops around the planned applications of progressive DELM methods and,specifically,probabilistic generative models for framework wide learning,demonstrating,improvement,and information description.Furthermore,ISDN-CRO-DELM,suggest to integrate this learning framework with the ISDN for CRO and reconfiguration approaches at the system level.MATLAB 2019a is used for DELM simulation and superior results show the effectiveness of the proposed framework.展开更多
With the frequent occurrence of telecommunications and networkfraud crimes in recent years, new frauds have emerged one after another whichhas caused huge losses to the people. However, due to the lack of an effective...With the frequent occurrence of telecommunications and networkfraud crimes in recent years, new frauds have emerged one after another whichhas caused huge losses to the people. However, due to the lack of an effectivepreventive mechanism, the police are often in a passive position. Usingtechnologies such as web crawlers, feature engineering, deep learning, andartificial intelligence, this paper proposes a user portrait fraudwarning schemebased on Weibo public data. First, we perform preliminary screening andcleaning based on the keyword “defrauded” to obtain valid fraudulent userIdentity Documents (IDs). The basic information and account information ofthese users is user-labeled to achieve the purpose of distinguishing the typesof fraud. Secondly, through feature engineering technologies such as avatarrecognition, Artificial Intelligence (AI) sentiment analysis, data screening,and follower blogger type analysis, these pictures and texts will be abstractedinto user preferences and personality characteristics which integrate multidimensionalinformation to build user portraits. Third, deep neural networktraining is performed on the cube. 80% percent of the data is predicted basedon the N-way K-shot problem and used to train the model, and the remaining20% is used for model accuracy evaluation. Experiments have shown thatFew-short learning has higher accuracy compared with Long Short TermMemory (LSTM), Recurrent Neural Networks (RNN) and ConvolutionalNeural Network (CNN). On this basis, this paper develops a WeChat smallprogram for early warning of telecommunications network fraud based onuser portraits. When the user enters some personal information on the frontend, the back-end database can perform correlation analysis by itself, so as tomatch the most likely fraud types and give relevant early warning information.The fraud warning model is highly scaleable. The data of other Applications(APPs) can be extended to further improve the efficiency of anti-fraud whichhas extremely high public welfare value.展开更多
Novel Coronavirus-19(COVID-19)is a newer type of coronavirus that has not been formally detected in humans.It is established that this disease often affects people of different age groups,particularly those with body ...Novel Coronavirus-19(COVID-19)is a newer type of coronavirus that has not been formally detected in humans.It is established that this disease often affects people of different age groups,particularly those with body disorders,blood pressure,diabetes,heart problems,or weakened immune systems.The epidemic of this infection has recently had a huge impact on people around the globe with rising mortality rates.Rising levels of mortality are attributed to their transmitting behavior through physical contact between humans.It is extremely necessary to monitor the transmission of the infection and also to anticipate the early stages of the disease in such a way that the appropriate timing of effective precautionary measures can be taken.The latest global coronavirus epidemic(COVID-19)has brought new challenges to the scientific community.Artificial Intelligence(AI)-motivated methodologies may be useful in predicting the conditions,consequences,and implications of such an outbreak.These forecasts may help to monitor and prevent the spread of these outbreaks.This article proposes a predictive framework incorporating Support Vector Machines(SVM)in the forecasting of a potential outbreak of COVID-19.The findings indicate that the suggested system outperforms cutting-edge approaches.The method could be used to predict the long-term spread of such an outbreak so that we can implement proactive measures in advance.The findings of the analyses indicate that the SVM forecasting framework outperformed the Neural Network methods in terms of accuracy and computational complexity.The proposed SVM system model exhibits 98.88%and 96.79%result in terms of accuracy during training and validation respectively.展开更多
COVID-19 turned out to be an infectious and life-threatening viral disease,and its swift and overwhelming spread has become one of the greatest challenges for the world.As yet,no satisfactory vaccine or medication has...COVID-19 turned out to be an infectious and life-threatening viral disease,and its swift and overwhelming spread has become one of the greatest challenges for the world.As yet,no satisfactory vaccine or medication has been developed that could guarantee its mitigation,though several efforts and trials are underway.Countries around the globe are striving to overcome the COVID-19 spread and while they are finding out ways for early detection and timely treatment.In this regard,healthcare experts,researchers and scientists have delved into the investigation of existing as well as new technologies.The situation demands development of a clinical decision support system to equip the medical staff ways to timely detect this disease.The state-of-the-art research in Artificial intelligence(AI),Machine learning(ML)and cloud computing have encouraged healthcare experts to find effective detection schemes.This study aims to provide a comprehensive review of the role of AI&ML in investigating prediction techniques for the COVID-19.A mathematical model has been formulated to analyze and detect its potential threat.The proposed model is a cloud-based smart detection algorithm using support vector machine(CSDC-SVM)with cross-fold validation testing.The experimental results have achieved an accuracy of 98.4%with 15-fold cross-validation strategy.The comparison with similar state-of-the-art methods reveals that the proposed CSDC-SVM model possesses better accuracy and efficiency.展开更多
Pervasive schemes are the significant techniques that allow intelligent communication among the devices without any human intervention.Recently Internet of Vehicles(IoVs)has been introduced as one of the applications ...Pervasive schemes are the significant techniques that allow intelligent communication among the devices without any human intervention.Recently Internet of Vehicles(IoVs)has been introduced as one of the applications of pervasive computing that addresses the road safety challenges.Vehicles participating within the IoV are embedded with a wide range of sensors which operate in a real time environment to improve the road safety issues.Various mechanisms have been proposed which allow automatic actions based on uncertainty of sensory and managed data.Due to the lack of existing transportation integration schemes,IoV has not been completely explored by business organizations.In order to tackle this problem,we have proposed a novel trusted mechanism in IoV during communication,sensing,and record storing.Our proposed method uses trust based analysis and subjective logic functions with the aim of creating a trust environment for vehicles to communicate.In addition,the subjective logic function is integrated with multi-attribute SAW scheme to improve the decision metrics of authenticating nodes.The trust analysis depends on a variety of metrics to ensure an accurate identification of legitimate vehicles embedded with IoT devices ecosystem.The proposed scheme is determined and verified rigorously through various IoT devices and decision making metrics against a baseline solution.The simulation results show that the proposed scheme leads to 88%improvement in terms of better identification of legitimate nodes,road accidents and message alteration records during data transmission among vehicles as compared to the baseline approach.展开更多
In recent years, the Internet of Things (IoT) technology has developedby leaps and bounds. However, the large and heterogeneous networkstructure of IoT brings high management costs. In particular, the low costof IoT d...In recent years, the Internet of Things (IoT) technology has developedby leaps and bounds. However, the large and heterogeneous networkstructure of IoT brings high management costs. In particular, the low costof IoT devices exposes them to more serious security concerns. First, aconvolutional neural network intrusion detection system for IoT devices isproposed. After cleaning and preprocessing the NSL-KDD dataset, this paperuses feature engineering methods to select appropriate features. Then, basedon the combination of DCNN and machine learning, this paper designs acloud-based loss function, which adopts a regularization method to preventoverfitting. The model consists of one input layer, two convolutional layers,two pooling layers and three fully connected layers and one output layer.Finally, a framework that can fully consider the user’s privacy protection isproposed. The framework can only exchange model parameters or intermediateresults without exchanging local individuals or sample data. This paperfurther builds a global model based on virtual fusion data, so as to achievea balance between data privacy protection and data sharing computing. Theperformance indicators such as accuracy, precision, recall, F1 score, and AUCof the model are verified by simulation. The results show that the model ishelpful in solving the problem that the IoT intrusion detection system cannotachieve high precision and low cost at the same time.展开更多
Autonomic software recovery enables software to automatically detect and recover software faults. This feature makes the software to run more efficiently, actively, and reduces the maintenance time and cost. This pape...Autonomic software recovery enables software to automatically detect and recover software faults. This feature makes the software to run more efficiently, actively, and reduces the maintenance time and cost. This paper proposes an automated approach for Software Fault Detection and Recovery (SFDR). The SFDR detects the cases if a fault occurs with software components such as component deletion, replacement or modification, and recovers the component to enable the software to continue its intended operation. The SFDR is analyzed and implemented in parallel as a standalone software at the design phase of the target software. The practical applicability of the proposed approach has been tested by implementing an application demonstrating the performance and effectiveness of the SFDR. The experimental results and the comparisons with other works show the effectiveness of the proposed approach.展开更多
A network analyzer can often comprehend many protocols, which enables it to display talks taking place between hosts over a network. A network analyzer analyzes the device or network response and measures for the oper...A network analyzer can often comprehend many protocols, which enables it to display talks taking place between hosts over a network. A network analyzer analyzes the device or network response and measures for the operator to keep an eye on the network’s or object’s performance in an RF circuit. The purpose of the following research includes analyzing the capabilities of NetFlow analyzer to measure various parts, including filters, mixers, frequency sensitive networks, transistors, and other RF-based instruments. NetFlow Analyzer is a network traffic analyzer that measures the network parameters of electrical networks. Although there are other types of network parameter sets including Y, Z, & H-parameters, these instruments are typically employed to measure S-parameters since transmission & reflection of electrical networks are simple to calculate at high frequencies. These analyzers are widely employed to distinguish between two-port networks, including filters and amplifiers. By allowing the user to view the actual data that is sent over a network, packet by packet, a network analyzer informs you of what is happening there. Also, this research will contain the design model of NetFlow Analyzer that Measurements involving transmission and reflection use. Gain, insertion loss, and transmission coefficient are measured in transmission measurements, whereas return loss, reflection coefficient, impedance, and other variables are measured in reflection measurements. These analyzers’ operational frequencies vary from 1 Hz to 1.5 THz. These analyzers can also be used to examine stability in measurements of open loops, audio components, and ultrasonics.展开更多
Campus network provides a critical stage to student service and campus administration,which assumes a paramount part in the strategy of‘Rejuvenating the Country through Science and Education’and‘Revitalizing China ...Campus network provides a critical stage to student service and campus administration,which assumes a paramount part in the strategy of‘Rejuvenating the Country through Science and Education’and‘Revitalizing China through Talented Persons’.However,with the rapid development and continuous expansion of campus network,network security needs to be an essential issue that could not be overlooked in campus network construction.In order to ensure the normal operation of various functions of the campus network,the security risk level of the campus network is supposed to be controlled within a reasonable range at any moment.Through literature research,theory analysis and other methods,this paper systematically combs the research on campus network security at home and abroad,analyzing and researching the campus network security issues from a theoretical perspective.A series of efficient solutions accordingly were also put forward.展开更多
The Internet of Things(IoT)is where almost anything can be controlled and managed remotely by means of sensors.Although the IoT evolution led to quality of life enhancement,many of its devices are insecure.The lack of...The Internet of Things(IoT)is where almost anything can be controlled and managed remotely by means of sensors.Although the IoT evolution led to quality of life enhancement,many of its devices are insecure.The lack of robust key management systems,efficient identity authentication,low fault tolerance,and many other issues lead to IoT devices being easily targeted by attackers.In this paper we propose a new authentication protocol called Authenblue that improve the authentication process of IoT devices and Coordinators of Personal Area Network(CPANs)in an Industrial IoT(IIoT)environment.This study proposed Authenblue protocol as a new Blockchainbased authentication protocol.To enhance the authentication process and make it more secure,Authenblue modified the way of generating IIoT identifiers and the shared secret keys used by the IIoT devices to raise the efficiency of the authentication protocol.Authenblue enhance the authentication protocol that other models rely on by enhancing the approach used to generate the User Identifier(UI).The UI values changed from being static values,sensors MAC addresses,to be generated values in the inception phase.This approach makes the process of renewing the sensor keys more secure by renewing their UI values instead of changing the secret key.In this study,Authenblue has been simulated in the Network Simulator 3(NS3).Simulation results show an improved performance compared to the related work.展开更多
Driver state sensing technologies,such as vehicular systems,start to be widely considered by automotive manufacturers.To reduce the cost and minimize the intrusiveness towards driving,the majority of these systems rel...Driver state sensing technologies,such as vehicular systems,start to be widely considered by automotive manufacturers.To reduce the cost and minimize the intrusiveness towards driving,the majority of these systems rely on the in-cabin camera(s)and other optical sensors.With their great capabilities in detecting and intervening of driver distraction and inattention,these technologies may become key components in future vehicle safety and control systems.However,to the best of our knowledge,currently,there is no common standard available to objectively compare the performance of these technologies.Thus,it is imperative to develop one standardized process for evaluation purposes.In this paper,we propose one systematic and standardized evaluation process after successfully addressing three difficulties:1)defining and selecting the important influential individual and environmental factors,2)countering the effects of individual differences and randomness in driver behaviors,and 3)building a reliable in-vehicle driver head motion tracking tool to collect ground-truth motion data.We have collected data on a large scale on a commercial driver state-sensing platform.For each subject,30 to 40 minutes of head motion data was collected and included variables,such as lighting conditions,head/face features,and camera locations.The collected data was analyzed based on a proposed performance measure.The results show that the developed process can efficiently evaluate an individual camerabased driver state sensing product,which builds a common base for comparing the performance of different systems.展开更多
Network robustness is one of the core contents of complex network security research.This paper focuses on the robustness of community networks with respect to cascading failures,considering the nodes influence and com...Network robustness is one of the core contents of complex network security research.This paper focuses on the robustness of community networks with respect to cascading failures,considering the nodes influence and community heterogeneity.A novel node influence ranking method,community-based Clustering-LeaderRank(CCL)algorithm,is first proposed to identify influential nodes in community networks.Simulation results show that the CCL method can effectively identify the influence of nodes.Based on node influence,a new cascading failure model with heterogeneous redistribution strategy is proposed to describe and analyze node fault propagation in community networks.Analytical and numerical simulation results on cascading failure show that the community attribute has an important influence on the cascading failure process.The network robustness against cascading failures increases when the load is more distributed to neighbors of the same community instead of different communities.When the initial load distribution and the load redistribution strategy based on the node influence are the same,the network shows better robustness against node failure.展开更多
In this paper,considering the cost of base station,coverage,call quality,and other practical factors,a multi-objective optimal site planning scheme is proposed.Firstly,based on practical needs,mathematical modeling me...In this paper,considering the cost of base station,coverage,call quality,and other practical factors,a multi-objective optimal site planning scheme is proposed.Firstly,based on practical needs,mathematical modeling methods were used to establish mathematical expressions for the three sub-objectives of cost objectives,coverage objectives,and quality objectives.Then,a multi-objective optimization model was established by combining threshold and traffic volume constraints.In order to reduce the time complexity of optimization,a non-dominated sorting genetic algorithm(NSGA)is used to solve the multi-objective optimization problem of site planning.Finally,a strategy for clustering and optimizing weak coverage areas was proposed.In order to avoid redundant neighborhood retrieval during cluster expansion,the Fast Density-Based Spatial Clustering of Applications with Noise(FDBSCAN)clustering method was adopted.With different sub-objectives as the main objectives,this paper obtained the distribution map of weak coverage areas before and after the establishment of new base stations,as well as relevant site planning maps,and provided three planning schemes for different main objectives.The simulation results show that the traffic coverage of the three station planning schemes is above 90%.The change in the main optimization objective will result in a significant difference between the cost of the three solutions and the coverage of weak coverage points.展开更多
Electromagnetic pulse(EMP)is a kind of transient electromagnetic phenomenon with short rise time of the leading edge and wide spectrum,which usually disrupts communications and damages electronic equipment and system....Electromagnetic pulse(EMP)is a kind of transient electromagnetic phenomenon with short rise time of the leading edge and wide spectrum,which usually disrupts communications and damages electronic equipment and system.It is challenging for an EMP sensor to measure a wideband electromagnetic pulse without distortion for the whole spectrum.Therefore,analyzing the distortion of EMP measurement is crucial to evaluating the sensor distortion characteristics and correcting the measurement results.Waveform fidelity is usually employed to evaluate the distortion of an antenna.However,this metric depends on specific signal waveforms,thus is unsuitable for evaluating and analyzing the distortion of EMP sensors.In this paper,an associated-hermite-function based distortion analysis method including system transfer matrices and distortion rates is proposed,which is general and independent from individual waveforms.The system transfer matrix and distortion rate can be straightforwardly calculated by the signal orthogonal transformation coefficients using associated-hermite functions.Distortion of a sensor vs.frequency is then visualized via the system transfer matrix,which is convenient in quantitative analysis of the distortion.Measurement of a current probe,a coaxial pulse voltage probe and a B-field sensor were performed,based on which the feasibility and effectiveness of the proposed distortion analysis method is successfully verified.展开更多
基金This research has been supported by the National Natural Science Foundation of China(Grant No.61672298 and 61373136)the National Social Science Foundation of China(Grant No.13BTQ046)+3 种基金the High-level Introduction of Talent Scientific Research Start-up Fund of Jiangsu Police Institute(Grant No.JSPI17GKZL403)the Scientific Research Program of Jiangsu Police Institute(Grant No.2017SJYZQ01)the Science and Technology Plan Projects of Jiangsu Province(Grant No.BE2017067)and the Research Foundation for Humanities and Social Sciences of Ministry of Education of China(Grant No.15YJAZH016).
文摘Considering dynamical disease spreading network consisting of moving individuals,a new double-layer network is constructed,one where the information dissemination process takes place and the other where the dynamics of disease spreading evolves.On the basis of Markov chains theory,a new model characterizing the coupled dynamics between information dissemination and disease spreading in populations of moving agents is established and corresponding state probability equations are formulated to describe the probability in each state of every node at each moment.Monte Carlo simulations are performed to characterize the interaction process between information and disease spreading and investigate factors that influence spreading dynamics.Simulation results show that the increasing of information transmission rate can reduce the scale of disease spreading in some degree.Shortening infection period and strengthening consciousness for self-protection by decreasing individual’s scope of activity both can effectively reduce the final refractory density for the disease but have less effect on the information dissemination.In addition,the increasing of vaccination rate or decreasing of long-range travel can also reduce the scale of disease spreading.
文摘Aims: This study aims at designing and implementing syllabus-oriented question-bank system that is capable of producing paper-based exams with multiple forms along with answer keys. The developed software tool is named Χ(Chi)-Pro Milestone and supports four types of questions, namely: Multiple-choice, True/False, Short-Answer and Free-Response Essay questions. The study is motivated by the fact that student number in schools and universities is continuously growing at high, non-linear, and uncontrolled rates. This growth, however, is not accompanied by an equivalent growth of educational resources (mainly: instructors, classrooms, and labs). A direct result of this situation is having relatively large number of students in each classroom. It is observed that providing and using online-examining systems could be intractable and expensive. As an alternative, paper-based exams can be used. One main issue is that manually produced paper-based exams are of low quality because of some human factors such as instability and relatively narrow range of topics [1]. Further, it is observed that instructors usually need to spend a lot of time and energy in composing paper-based exams with multiple forms. Therefore, the use of computers for automatic production of paper-based exams from question banks is becoming more and more important. Methodology: The design and evaluation of X-Pro Milestone are done by considering a basic set of design principles that are based on a list of identified Functional and Non-Functional Requirements. Deriving those requirements is made possible by developing X-Pro Milestone using the Iterative and Incremental model from software engineering domain. Results: We demonstrate that X-Pro Milestone has a number of excellent characteristics compared to the exam-preparation and question banks tools available in market. Some of these characteristics are: ease of use and operation, user-friendly interface and good usability, high security and protection of the question bank-items, high stability, and reliability. Further, X-Pro Milestone makes initiating, maintaining and archiving Question-Banks and produced exams possible. Putting X-Pro Milestone into real use has showed that X-Pro Milestone is easy to be learned and effectively used. We demonstrate that X-Pro Milestone is a cost-effective alternative to online examining systems with more and richer features and with low infrastructure requirements.
文摘This research paper analyzes data breaches in the human service sector. The hypothesis for the solution to this problem is that there will be a significant reduction in data breaches in the human service sector due to an increase in information assurance. The hypothesis is tested using data from the United States Department of Health and Human Services data breach notification repository during January 2018-December 2020. Our result shows that without the increased mitigation of information assurance, data breaches in the human service sector will continue to increase.
基金The Open Access publication fee for this article was fully covered by Abu Dhabi University.
文摘In Wireless Sensor Networks(WSNs),survivability is a crucial issue that is greatly impacted by energy efficiency.Solutions that satisfy application objectives while extending network life are needed to address severe energy constraints inWSNs.This paper presents an Adaptive Enhanced GreyWolf Optimizer(AEGWO)for energy-efficient cluster head(CH)selection that mitigates the exploration–exploitation imbalance,preserves population diversity,and avoids premature convergence inherent in baseline GWO.The AEGWO combines adaptive control of the parameter of the search pressure to accelerate convergence without stagnation,a hybrid velocity-momentum update based on the dynamics of PSO,and an intelligent mutation operator to maintain the diversity of the population.The search is guided by a multi-objective fitness,which aims at maximizing the residual energy,equal distribution of CH,minimizing the intra-cluster distance,desirable proximity to sinks,and enhancing the coverage.Simulations on 100 nodes homogeneousWSN Tested the proposed AEGWO under the same conditions with LEACH,GWO,IGWO,PSO,WOA,and GA,AEGWO significantly increases stability and lifetime compared to LEACHand other tested algorithms;it has the best first,half,and last node dead,and higher residual energy and smaller communication overhead.The findings prove that AEGWO provides sustainable energy management and better lifetime extension,which makes it a robust,flexible clustering protocol of large-scaleWSNs.
基金supported by the National Forestry Public Welfare Professional Scientific Research Project(No.201404213)the National Key Research and Development Program of China(No.2016YFD0600205)
文摘Leaf area index(LAI) is a key factor that determines a forest ecosystem's net primary production and energy exchange between the atmosphere and land surfaces.LAI can be measured in many ways, but there has been little research to compare LAI estimated by different methods. In this study, we compared the LAI results from two different approaches, i.e., the dimidiate pixel model(DPM) and an empirical statistic model(ESM) using ZY-3 high-accuracy satellite images validated by field data. We explored the relationship of LAI of Larix principis-rupprechtii Mayr plantations with topographic conditions. The results show that DPM improves the simulation of LAI(r = 0.86,RMSE = 0.57) compared with ESM(r = 0.62, RMSE =0.79). We further concluded that elevation and slope significantly affect the distribution of LAI. The maximum peak of LAI appeared at an aspect of east and southeast at an elevation of 1700–2000 m. Our results suggest that ZY-3 can satisfy the needs of quantitative monitoring of leaf area indices in small-scale catchment areas. DPM provides a simple and accurate method to obtain forest vegetation parameters in the case of non-ground measurement points.
基金supported by Data and Artificial Intelligence Scientific Chair at Umm AlQura University.
文摘In recent years,the infrastructure,instruments,and resources of network systems are becoming more complex and heterogeneous,with the rapid development of current internet and mobile communication technologies.In order to efficaciously prepare,control,hold and optimize networking systems,greater intelligence needs to be deployed.However,due to the inherently dispensed characteristic of conventional networks,Machine Learning(ML)techniques are hard to implement and deployed to govern and operate networks.Software-Defined Networking(SDN)brings us new possibilities to offer intelligence in the networks.SDN’s characteristics(e.g.,logically centralized control,global network view,software-based site visitor analysis,and dynamic updating of forwarding rules)make it simpler to apply machine learning strategies.Various perspectives of fiber-optic communications including fiber nonlinearity coverage,optical performance checking,cognitive shortcoming detection/anticipation,and arranging and improvement of softwaredefined networks are examined in Machine Learning(ML)applications.This research paper has presented an imaginative framework concept called Intelligent Software Defined Network(ISDN)for Cognitive Routing Optimization(CRO)using Deep Extreme Learning Machine(DELM)approach(ISDN-CRO-DELM)in light of the new challenges in the development and operation of communication systems,and capturing motivation from how living creatures deal with difficulty and usability.The proposed methodology develops around the planned applications of progressive DELM methods and,specifically,probabilistic generative models for framework wide learning,demonstrating,improvement,and information description.Furthermore,ISDN-CRO-DELM,suggest to integrate this learning framework with the ISDN for CRO and reconfiguration approaches at the system level.MATLAB 2019a is used for DELM simulation and superior results show the effectiveness of the proposed framework.
文摘With the frequent occurrence of telecommunications and networkfraud crimes in recent years, new frauds have emerged one after another whichhas caused huge losses to the people. However, due to the lack of an effectivepreventive mechanism, the police are often in a passive position. Usingtechnologies such as web crawlers, feature engineering, deep learning, andartificial intelligence, this paper proposes a user portrait fraudwarning schemebased on Weibo public data. First, we perform preliminary screening andcleaning based on the keyword “defrauded” to obtain valid fraudulent userIdentity Documents (IDs). The basic information and account information ofthese users is user-labeled to achieve the purpose of distinguishing the typesof fraud. Secondly, through feature engineering technologies such as avatarrecognition, Artificial Intelligence (AI) sentiment analysis, data screening,and follower blogger type analysis, these pictures and texts will be abstractedinto user preferences and personality characteristics which integrate multidimensionalinformation to build user portraits. Third, deep neural networktraining is performed on the cube. 80% percent of the data is predicted basedon the N-way K-shot problem and used to train the model, and the remaining20% is used for model accuracy evaluation. Experiments have shown thatFew-short learning has higher accuracy compared with Long Short TermMemory (LSTM), Recurrent Neural Networks (RNN) and ConvolutionalNeural Network (CNN). On this basis, this paper develops a WeChat smallprogram for early warning of telecommunications network fraud based onuser portraits. When the user enters some personal information on the frontend, the back-end database can perform correlation analysis by itself, so as tomatch the most likely fraud types and give relevant early warning information.The fraud warning model is highly scaleable. The data of other Applications(APPs) can be extended to further improve the efficiency of anti-fraud whichhas extremely high public welfare value.
文摘Novel Coronavirus-19(COVID-19)is a newer type of coronavirus that has not been formally detected in humans.It is established that this disease often affects people of different age groups,particularly those with body disorders,blood pressure,diabetes,heart problems,or weakened immune systems.The epidemic of this infection has recently had a huge impact on people around the globe with rising mortality rates.Rising levels of mortality are attributed to their transmitting behavior through physical contact between humans.It is extremely necessary to monitor the transmission of the infection and also to anticipate the early stages of the disease in such a way that the appropriate timing of effective precautionary measures can be taken.The latest global coronavirus epidemic(COVID-19)has brought new challenges to the scientific community.Artificial Intelligence(AI)-motivated methodologies may be useful in predicting the conditions,consequences,and implications of such an outbreak.These forecasts may help to monitor and prevent the spread of these outbreaks.This article proposes a predictive framework incorporating Support Vector Machines(SVM)in the forecasting of a potential outbreak of COVID-19.The findings indicate that the suggested system outperforms cutting-edge approaches.The method could be used to predict the long-term spread of such an outbreak so that we can implement proactive measures in advance.The findings of the analyses indicate that the SVM forecasting framework outperformed the Neural Network methods in terms of accuracy and computational complexity.The proposed SVM system model exhibits 98.88%and 96.79%result in terms of accuracy during training and validation respectively.
文摘COVID-19 turned out to be an infectious and life-threatening viral disease,and its swift and overwhelming spread has become one of the greatest challenges for the world.As yet,no satisfactory vaccine or medication has been developed that could guarantee its mitigation,though several efforts and trials are underway.Countries around the globe are striving to overcome the COVID-19 spread and while they are finding out ways for early detection and timely treatment.In this regard,healthcare experts,researchers and scientists have delved into the investigation of existing as well as new technologies.The situation demands development of a clinical decision support system to equip the medical staff ways to timely detect this disease.The state-of-the-art research in Artificial intelligence(AI),Machine learning(ML)and cloud computing have encouraged healthcare experts to find effective detection schemes.This study aims to provide a comprehensive review of the role of AI&ML in investigating prediction techniques for the COVID-19.A mathematical model has been formulated to analyze and detect its potential threat.The proposed model is a cloud-based smart detection algorithm using support vector machine(CSDC-SVM)with cross-fold validation testing.The experimental results have achieved an accuracy of 98.4%with 15-fold cross-validation strategy.The comparison with similar state-of-the-art methods reveals that the proposed CSDC-SVM model possesses better accuracy and efficiency.
基金funded by the Abu Dhabi University,Faculty Research Incentive Grant(19300483–Adel Khelifi),United Arab Emirates.Link to Sponsor website:https://www.adu.ac.ae/research/research-at-adu/overview.
文摘Pervasive schemes are the significant techniques that allow intelligent communication among the devices without any human intervention.Recently Internet of Vehicles(IoVs)has been introduced as one of the applications of pervasive computing that addresses the road safety challenges.Vehicles participating within the IoV are embedded with a wide range of sensors which operate in a real time environment to improve the road safety issues.Various mechanisms have been proposed which allow automatic actions based on uncertainty of sensory and managed data.Due to the lack of existing transportation integration schemes,IoV has not been completely explored by business organizations.In order to tackle this problem,we have proposed a novel trusted mechanism in IoV during communication,sensing,and record storing.Our proposed method uses trust based analysis and subjective logic functions with the aim of creating a trust environment for vehicles to communicate.In addition,the subjective logic function is integrated with multi-attribute SAW scheme to improve the decision metrics of authenticating nodes.The trust analysis depends on a variety of metrics to ensure an accurate identification of legitimate vehicles embedded with IoT devices ecosystem.The proposed scheme is determined and verified rigorously through various IoT devices and decision making metrics against a baseline solution.The simulation results show that the proposed scheme leads to 88%improvement in terms of better identification of legitimate nodes,road accidents and message alteration records during data transmission among vehicles as compared to the baseline approach.
文摘In recent years, the Internet of Things (IoT) technology has developedby leaps and bounds. However, the large and heterogeneous networkstructure of IoT brings high management costs. In particular, the low costof IoT devices exposes them to more serious security concerns. First, aconvolutional neural network intrusion detection system for IoT devices isproposed. After cleaning and preprocessing the NSL-KDD dataset, this paperuses feature engineering methods to select appropriate features. Then, basedon the combination of DCNN and machine learning, this paper designs acloud-based loss function, which adopts a regularization method to preventoverfitting. The model consists of one input layer, two convolutional layers,two pooling layers and three fully connected layers and one output layer.Finally, a framework that can fully consider the user’s privacy protection isproposed. The framework can only exchange model parameters or intermediateresults without exchanging local individuals or sample data. This paperfurther builds a global model based on virtual fusion data, so as to achievea balance between data privacy protection and data sharing computing. Theperformance indicators such as accuracy, precision, recall, F1 score, and AUCof the model are verified by simulation. The results show that the model ishelpful in solving the problem that the IoT intrusion detection system cannotachieve high precision and low cost at the same time.
文摘Autonomic software recovery enables software to automatically detect and recover software faults. This feature makes the software to run more efficiently, actively, and reduces the maintenance time and cost. This paper proposes an automated approach for Software Fault Detection and Recovery (SFDR). The SFDR detects the cases if a fault occurs with software components such as component deletion, replacement or modification, and recovers the component to enable the software to continue its intended operation. The SFDR is analyzed and implemented in parallel as a standalone software at the design phase of the target software. The practical applicability of the proposed approach has been tested by implementing an application demonstrating the performance and effectiveness of the SFDR. The experimental results and the comparisons with other works show the effectiveness of the proposed approach.
文摘A network analyzer can often comprehend many protocols, which enables it to display talks taking place between hosts over a network. A network analyzer analyzes the device or network response and measures for the operator to keep an eye on the network’s or object’s performance in an RF circuit. The purpose of the following research includes analyzing the capabilities of NetFlow analyzer to measure various parts, including filters, mixers, frequency sensitive networks, transistors, and other RF-based instruments. NetFlow Analyzer is a network traffic analyzer that measures the network parameters of electrical networks. Although there are other types of network parameter sets including Y, Z, & H-parameters, these instruments are typically employed to measure S-parameters since transmission & reflection of electrical networks are simple to calculate at high frequencies. These analyzers are widely employed to distinguish between two-port networks, including filters and amplifiers. By allowing the user to view the actual data that is sent over a network, packet by packet, a network analyzer informs you of what is happening there. Also, this research will contain the design model of NetFlow Analyzer that Measurements involving transmission and reflection use. Gain, insertion loss, and transmission coefficient are measured in transmission measurements, whereas return loss, reflection coefficient, impedance, and other variables are measured in reflection measurements. These analyzers’ operational frequencies vary from 1 Hz to 1.5 THz. These analyzers can also be used to examine stability in measurements of open loops, audio components, and ultrasonics.
基金sponsored by the JSPIGKZ Project (No.2911121110)Innovative and Entrepreneurial Doctor of Jiangsu Province (No.JSSCBS20210598)+1 种基金Jiangsu Provincial University Nature Science Foundation Project (No.2020KX007Z)Jiangsu Provincial Science and Technology Research Project (No.20KJB413002)。
文摘Campus network provides a critical stage to student service and campus administration,which assumes a paramount part in the strategy of‘Rejuvenating the Country through Science and Education’and‘Revitalizing China through Talented Persons’.However,with the rapid development and continuous expansion of campus network,network security needs to be an essential issue that could not be overlooked in campus network construction.In order to ensure the normal operation of various functions of the campus network,the security risk level of the campus network is supposed to be controlled within a reasonable range at any moment.Through literature research,theory analysis and other methods,this paper systematically combs the research on campus network security at home and abroad,analyzing and researching the campus network security issues from a theoretical perspective.A series of efficient solutions accordingly were also put forward.
文摘The Internet of Things(IoT)is where almost anything can be controlled and managed remotely by means of sensors.Although the IoT evolution led to quality of life enhancement,many of its devices are insecure.The lack of robust key management systems,efficient identity authentication,low fault tolerance,and many other issues lead to IoT devices being easily targeted by attackers.In this paper we propose a new authentication protocol called Authenblue that improve the authentication process of IoT devices and Coordinators of Personal Area Network(CPANs)in an Industrial IoT(IIoT)environment.This study proposed Authenblue protocol as a new Blockchainbased authentication protocol.To enhance the authentication process and make it more secure,Authenblue modified the way of generating IIoT identifiers and the shared secret keys used by the IIoT devices to raise the efficiency of the authentication protocol.Authenblue enhance the authentication protocol that other models rely on by enhancing the approach used to generate the User Identifier(UI).The UI values changed from being static values,sensors MAC addresses,to be generated values in the inception phase.This approach makes the process of renewing the sensor keys more secure by renewing their UI values instead of changing the secret key.In this study,Authenblue has been simulated in the Network Simulator 3(NS3).Simulation results show an improved performance compared to the related work.
基金supported by Ford Motor Company Research and Innovation Center
文摘Driver state sensing technologies,such as vehicular systems,start to be widely considered by automotive manufacturers.To reduce the cost and minimize the intrusiveness towards driving,the majority of these systems rely on the in-cabin camera(s)and other optical sensors.With their great capabilities in detecting and intervening of driver distraction and inattention,these technologies may become key components in future vehicle safety and control systems.However,to the best of our knowledge,currently,there is no common standard available to objectively compare the performance of these technologies.Thus,it is imperative to develop one standardized process for evaluation purposes.In this paper,we propose one systematic and standardized evaluation process after successfully addressing three difficulties:1)defining and selecting the important influential individual and environmental factors,2)countering the effects of individual differences and randomness in driver behaviors,and 3)building a reliable in-vehicle driver head motion tracking tool to collect ground-truth motion data.We have collected data on a large scale on a commercial driver state-sensing platform.For each subject,30 to 40 minutes of head motion data was collected and included variables,such as lighting conditions,head/face features,and camera locations.The collected data was analyzed based on a proposed performance measure.The results show that the developed process can efficiently evaluate an individual camerabased driver state sensing product,which builds a common base for comparing the performance of different systems.
基金the National Natural Science Foundation of China(Grant Nos.62203229,61672298,61873326,and 61802155)the Philosophy and Social Sciences Research of Universities in Jiangsu Province(Grant No.2018SJZDI142)+2 种基金the Natural Science Research Projects of Universities in Jiangsu Province(Grant No.20KJB120007)the Jiangsu Natural Science Foundation Youth Fund Project(Grant No.BK20200758)Qing Lan Project and the Science and Technology Project of Market Supervision Administration of Jiangsu Province(Grant No.KJ21125027)。
文摘Network robustness is one of the core contents of complex network security research.This paper focuses on the robustness of community networks with respect to cascading failures,considering the nodes influence and community heterogeneity.A novel node influence ranking method,community-based Clustering-LeaderRank(CCL)algorithm,is first proposed to identify influential nodes in community networks.Simulation results show that the CCL method can effectively identify the influence of nodes.Based on node influence,a new cascading failure model with heterogeneous redistribution strategy is proposed to describe and analyze node fault propagation in community networks.Analytical and numerical simulation results on cascading failure show that the community attribute has an important influence on the cascading failure process.The network robustness against cascading failures increases when the load is more distributed to neighbors of the same community instead of different communities.When the initial load distribution and the load redistribution strategy based on the node influence are the same,the network shows better robustness against node failure.
基金The work is supported by Jiangsu Higher Education“Qinglan Project”,an Open Project of Criminal Inspection Laboratory in Key Laboratories of Sichuan Provincial Universities(2023YB03)Major Project of Basic Science(Natural Science)Research in Higher Education Institutions in Jiangsu Province(23KJA520004)+4 种基金Jiangsu Higher Education Philosophy and Social Sciences Research General Project(2023SJYB0467)Action Plan of the National Engineering Research Center for Cybersecurity Level Protection and Security Technology(KJ-24-004)Jiangsu Province Degree and Postgraduate Education and Teaching ReformProject(JGKT24_B036)Digital Forensics Engineering Research Center of the Ministry of Education Open Project(DF20-010)the Youth Fund of Nanjing Railway Vocational and Technical College(Yq220012).
文摘In this paper,considering the cost of base station,coverage,call quality,and other practical factors,a multi-objective optimal site planning scheme is proposed.Firstly,based on practical needs,mathematical modeling methods were used to establish mathematical expressions for the three sub-objectives of cost objectives,coverage objectives,and quality objectives.Then,a multi-objective optimization model was established by combining threshold and traffic volume constraints.In order to reduce the time complexity of optimization,a non-dominated sorting genetic algorithm(NSGA)is used to solve the multi-objective optimization problem of site planning.Finally,a strategy for clustering and optimizing weak coverage areas was proposed.In order to avoid redundant neighborhood retrieval during cluster expansion,the Fast Density-Based Spatial Clustering of Applications with Noise(FDBSCAN)clustering method was adopted.With different sub-objectives as the main objectives,this paper obtained the distribution map of weak coverage areas before and after the establishment of new base stations,as well as relevant site planning maps,and provided three planning schemes for different main objectives.The simulation results show that the traffic coverage of the three station planning schemes is above 90%.The change in the main optimization objective will result in a significant difference between the cost of the three solutions and the coverage of weak coverage points.
基金Research Project of High-Level Talents of Jiangsu Police Institute(No.2911118010).
文摘Electromagnetic pulse(EMP)is a kind of transient electromagnetic phenomenon with short rise time of the leading edge and wide spectrum,which usually disrupts communications and damages electronic equipment and system.It is challenging for an EMP sensor to measure a wideband electromagnetic pulse without distortion for the whole spectrum.Therefore,analyzing the distortion of EMP measurement is crucial to evaluating the sensor distortion characteristics and correcting the measurement results.Waveform fidelity is usually employed to evaluate the distortion of an antenna.However,this metric depends on specific signal waveforms,thus is unsuitable for evaluating and analyzing the distortion of EMP sensors.In this paper,an associated-hermite-function based distortion analysis method including system transfer matrices and distortion rates is proposed,which is general and independent from individual waveforms.The system transfer matrix and distortion rate can be straightforwardly calculated by the signal orthogonal transformation coefficients using associated-hermite functions.Distortion of a sensor vs.frequency is then visualized via the system transfer matrix,which is convenient in quantitative analysis of the distortion.Measurement of a current probe,a coaxial pulse voltage probe and a B-field sensor were performed,based on which the feasibility and effectiveness of the proposed distortion analysis method is successfully verified.
