Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled...Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data.The framework integrates Kalman filtering and Q-learning.Unlike smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error.Unlike traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads.For evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art approach.Hence,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.展开更多
Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy....Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy.Due to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC server.In this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks.Furthermore,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted cost.The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution.Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.展开更多
Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation sector.However,the exchange of beacons and messages ov...Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation sector.However,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy violations.To address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed recently.However,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many attacks.As such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)operations.The formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably secure.The proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and unlinkability.Moreover,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and impersonations.From the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security features.Therefore,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.展开更多
Mobile ad hoc network(MANET)is a dynamically reconfigurable wireless network with time-variable infrastructure.Given that nodes are highly mobile,MANET’s topology often changes.These changes increase the difficulty i...Mobile ad hoc network(MANET)is a dynamically reconfigurable wireless network with time-variable infrastructure.Given that nodes are highly mobile,MANET’s topology often changes.These changes increase the difficulty in finding the routes that the packets use when they are routed.This study proposes an algorithm called genetic algorithm-based location-aided routing(GALAR)to enhance the MANET routing protocol efficiency.The GALAR algorithm maintains an adaptive update of the node location information by adding the transmitting node location information to the routing packet and selecting the transmitting node to carry the packets to their destination.The GALAR was constructed based on a genetic optimization scheme that considers all contributing factors in the delivery behavior using criterion function optimization.Simulation results showed that the GALAR algorithm can make the probability of packet delivery ratio more than 99%with less network overhead.Moreover,GALAR was compared to other algorithms in terms of different network evaluation measures.The GALAR algorithm significantly outperformed the other algorithms that were used in the study.展开更多
A study of wireless technologies for IoT applications in terms of power consumption has been presented in this paper. The study focuses on the importance of using low power wireless techniques and modules in IoT appli...A study of wireless technologies for IoT applications in terms of power consumption has been presented in this paper. The study focuses on the importance of using low power wireless techniques and modules in IoT applications by introducing a comparative between different low power wireless communication techniques such as ZigBee, Low Power Wi-Fi, 6LowPAN, LPWA and their modules to conserve power and longing the life for the IoT network sensors. The approach of the study is in term of protocol used and the particular module that achieve that protocol. The candidate protocols are classified according to the range of connectivity between sensor nodes. For short ranges connectivity the candidate protocols are ZigBee, 6LoWPAN and low power Wi-Fi. For long connectivity the candidate is LoRaWAN protocol. The results of the study demonstrate that the choice of module for each protocol plays a vital role in battery life due to the difference of power consumption for each module/protocol. So, the evaluation of protocols with each other depends on the module used.展开更多
Deep learning(DL)is a subdivision of machine learning(ML)that employs numerous algorithms,each of which provides various explanations of the data it consumes;mobile ad-hoc networks(MANET)are growing in promi-nence.For...Deep learning(DL)is a subdivision of machine learning(ML)that employs numerous algorithms,each of which provides various explanations of the data it consumes;mobile ad-hoc networks(MANET)are growing in promi-nence.For reasons including node mobility,due to MANET’s potential to provide small-cost solutions for real-world contact challenges,decentralized management,and restricted bandwidth,MANETs are more vulnerable to security threats.When protecting MANETs from attack,encryption and authentication schemes have their limits.However,deep learning(DL)approaches in intrusion detection sys-tems(IDS)can adapt to the changing environment of MANETs and allow a sys-tem to make intrusion decisions while learning about its mobility in the environment.IDSs are a secondary defiance system for mobile ad-hoc networks vs.attacks since they monitor network traffic and report anything unusual.Recently,many scientists have employed deep neural networks(DNNs)to address intrusion detection concerns.This paper used MANET to recognize com-plex patterns by focusing on security standards through efficiency determination and identifying malicious nodes,and mitigating network attacks using the three algorithms presented Cascading Back Propagation Neural Network(CBPNN),Feedforward-Neural-Network(FNN),and Cascading-Back-Propagation-Neural-Network(CBPNN)(FFNN).In addition to Convolutional-Neural-Network(CNN),these primary forms of deep neural network(DNN)building designs are widely used to improve the performance of intrusion detection systems(IDS)and the use of IDS in conjunction with machine learning(ML).Further-more,machine learning(ML)techniques than their statistical and logical methods provide MANET network learning capabilities and encourage adaptation to differ-ent environments.Compared with another current model,The proposed model has better average receiving packet(ARP)and end-to-end(E2E)performance.The results have been obtained from CBP,FFNN and CNN 74%,82%and 85%,respectively,by the time(27,18,and 17 s).展开更多
Histopathology is the investigation of tissues to identify the symptom of abnormality.The histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining th...Histopathology is the investigation of tissues to identify the symptom of abnormality.The histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining them.The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge.At the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data representation.With this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)model.The proposed EHCDLHIC technique initially performs Weiner filtering based noise removal technique.Once the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are extracted.For the classification process,the bidirectional gated recurrent unit(BGRU)model can be employed.At the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the work.For validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is performed.The experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.展开更多
Recent economic growth and development have considerably raised energy consumption over the globe.Electric load prediction approaches become essential for effective planning,decision-making,and contract evaluation of ...Recent economic growth and development have considerably raised energy consumption over the globe.Electric load prediction approaches become essential for effective planning,decision-making,and contract evaluation of the power systems.In order to achieve effective forecasting outcomes with minimumcomputation time,this study develops an improved whale optimization with deep learning enabled load prediction(IWO-DLELP)scheme for energy storage systems(ESS)in smart grid platform.The major intention of the IWO-DLELP technique is to effectually forecast the electric load in SG environment for designing proficient ESS.The proposed IWO-DLELP model initially undergoes pre-processing in two stages namely min-max normalization and feature selection.Besides,partition clustering approach is applied for the decomposition of data into distinct clusters with respect to distance and objective functions.Moreover,IWO with bidirectional gated recurrent unit(BiGRU)model is applied for the prediction of load and the hyperparameters are tuned by the use of IWO algorithm.The experiment analysis reported the enhanced results of the IWO-DLELP model over the recent methods interms of distinct evaluation measures.展开更多
As the amount of medical images transmitted over networks and kept on online servers continues to rise,the need to protect those images digitally is becoming increasingly important.However,due to the massive amounts o...As the amount of medical images transmitted over networks and kept on online servers continues to rise,the need to protect those images digitally is becoming increasingly important.However,due to the massive amounts of multimedia and medical pictures being exchanged,low computational complexity techniques have been developed.Most commonly used algorithms offer very little security and require a great deal of communication,all of which add to the high processing costs associated with using them.First,a deep learning classifier is used to classify records according to the degree of concealment they require.Medical images that aren’t needed can be saved by using this method,which cuts down on security costs.Encryption is one of the most effective methods for protecting medical images after this step.Confusion and dispersion are two fundamental encryption processes.A new encryption algorithm for very sensitive data is developed in this study.Picture splitting with image blocks is nowdeveloped by using Zigzag patterns,rotation of the image blocks,and random permutation for scrambling the blocks.After that,this research suggests a Region of Interest(ROI)technique based on selective picture encryption.For the first step,we use an active contour picture segmentation to separate the ROI from the Region of Background(ROB).Permutation and diffusion are then carried out using a Hilbert curve and a Skew Tent map.Once all of the blocks have been encrypted,they are combined to create encrypted images.The investigational analysis is carried out to test the competence of the projected ideal with existing techniques.展开更多
This paper is directed to study the isotope effects of some superconducting materials that have a strong coupling coefficient <i>λ</i> > 1.5, and focuses on new superconducting materials whose critical...This paper is directed to study the isotope effects of some superconducting materials that have a strong coupling coefficient <i>λ</i> > 1.5, and focuses on new superconducting materials whose critical temperature is close to room temperature, specifically LaH<sub>10</sub>-LaD<sub>10</sub> and H<sub>3</sub>S-D<sub>3</sub>S systems. The Eliashberg-McMillan (EM) model and the recent Gor’kov-Kresin (GK) model for evaluating the isotope effects coefficient α were examined for these systems. The predicted values of α as a function of pressure, as compared to experimental values led to inference that these two models, despite their importance and simplicity, cannot be considered complete. These models can be used to calculate isotope effect of most superconducting materials with strong coupling coefficients but with critical reliability. The significance of studying the isotope effect lies in the possibility of identifying the interatomic forces that control the properties of superconducting materials such as electrons-mediated phonons and Coulomb interactions.展开更多
Trust, as a major part of human interactions, plays an important role in helping users collect reliable infor-mation and make decisions. However, in reality, user-specified trust relations are often very sparse and fo...Trust, as a major part of human interactions, plays an important role in helping users collect reliable infor-mation and make decisions. However, in reality, user-specified trust relations are often very sparse and follow a power law distribution; hence inferring unknown trust relations attracts increasing attention in recent years. Social theories are frameworks of empirical evidence used to study and interpret social phenomena from a sociological perspective, while social networks reflect the correlations of users in real world; hence, making the principle, rules, ideas and methods of social theories into the analysis of social networks brings new opportunities for trust prediction. In this paper, we investigate how to exploit homophily and social status in trust prediction by modeling social theories. We first give several methods to compute homophily coe？cient and status coe？cient, then provide a principled way to model trust prediction mathe-matically, and propose a novel framework, hsTrust, which incorporates homophily theory and status theory. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of homophily theory and status theory in trust prediction.展开更多
User-specified trust relations are often very sparse and dynamic, making them difficult to accurately predict from online social media. In addition, trust relations are usually unavailable for most social media platfo...User-specified trust relations are often very sparse and dynamic, making them difficult to accurately predict from online social media. In addition, trust relations are usually unavailable for most social media platforms.These issues pose a great challenge for predicting trust relations and further building trust networks. In this study,we investigate whether we can predict trust relations via a sparse learning model, and propose to build a trust network without trust relations using only pervasively available interaction data and homophily effect in an online world. In particular, we analyze the reliability of predicting trust relations by interaction behaviors, and provide a principled way to mathematically incorporate interaction behaviors and homophily effect in a novel framework,b Trust. Results of experiments on real-world datasets from Epinions and Ciao demonstrated the effectiveness of the proposed framework. Further experiments were conducted to understand the importance of interaction behaviors and homophily effect in building trust networks.展开更多
Chaotic behavior can be observed in continuous and discrete-time systems.This behavior can appear in one-dimensional nonlinear maps;however,having at least three state variables in flows is necessary.Due to the lower ...Chaotic behavior can be observed in continuous and discrete-time systems.This behavior can appear in one-dimensional nonlinear maps;however,having at least three state variables in flows is necessary.Due to the lower mathematical complexity and computational cost of maps,lots of research has been conducted based on them.This paper aims to present a novel one-dimensional trigonometric chaotic map that is multi-stable and can act attractively.The proposed chaotic map is first analyzed using a single sinusoidal function;then,its abilities are expanded to a map with a combination of two sinusoidal functions.The stability conditions of both maps are investigated,and their different behaviors are validated through time series,state space,and cobweb diagrams.Eventually,the influence of parameter variations on the maps’outputs is examined by one-dimensional and two-dimensional bifurcation diagrams and Lyapunov exponent spectra.Besides,the diversity of outputs with varying initial conditions reveals this map’s multi-stability.The newly designed chaotic map can be employed in encryption applications.展开更多
文摘Intelligent traffic control requires accurate estimation of the road states and incorporation of adaptive or dynamically adjusted intelligent algorithms for making the decision.In this article,these issues are handled by proposing a novel framework for traffic control using vehicular communications and Internet of Things data.The framework integrates Kalman filtering and Q-learning.Unlike smoothing Kalman filtering,our data fusion Kalman filter incorporates a process-aware model which makes it superior in terms of the prediction error.Unlike traditional Q-learning,our Q-learning algorithm enables adaptive state quantization by changing the threshold of separating low traffic from high traffic on the road according to the maximum number of vehicles in the junction roads.For evaluation,the model has been simulated on a single intersection consisting of four roads:east,west,north,and south.A comparison of the developed adaptive quantized Q-learning(AQQL)framework with state-of-the-art and greedy approaches shows the superiority of AQQL with an improvement percentage in terms of the released number of vehicles of AQQL is 5%over the greedy approach and 340%over the state-of-the-art approach.Hence,AQQL provides an effective traffic control that can be applied in today’s intelligent traffic system.
基金supported by Jilin Provincial Science and Technology Department Natural Science Foundation of China(20210101415JC)Jilin Provincial Science and Technology Department Free exploration research project of China(YDZJ202201ZYTS642).
文摘Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and energy.Due to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC server.In this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by tasks.Furthermore,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted cost.The proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized execution.Systematic and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.
基金supported by Teaching Reform Project of Shenzhen University of Technology under Grant No.20231016.
文摘Vehicular ad hoc networks(VANETs)provide intelligent navigation and efficient route management,resulting in time savings and cost reductions in the transportation sector.However,the exchange of beacons and messages over public channels among vehicles and roadside units renders these networks vulnerable to numerous attacks and privacy violations.To address these challenges,several privacy and security preservation protocols based on blockchain and public key cryptography have been proposed recently.However,most of these schemes are limited by a long execution time and massive communication costs,which make them inefficient for on-board units(OBUs).Additionally,some of them are still susceptible to many attacks.As such,this study presents a novel protocol based on the fusion of elliptic curve cryptography(ECC)and bilinear pairing(BP)operations.The formal security analysis is accomplished using the Burrows–Abadi–Needham(BAN)logic,demonstrating that our scheme is verifiably secure.The proposed scheme’s informal security assessment also shows that it provides salient security features,such as non-repudiation,anonymity,and unlinkability.Moreover,the scheme is shown to be resilient against attacks,such as packet replays,forgeries,message falsifications,and impersonations.From the performance perspective,this protocol yields a 37.88%reduction in communication overheads and a 44.44%improvement in the supported security features.Therefore,the proposed scheme can be deployed in VANETs to provide robust security at low overheads.
基金funded by the Deanship of Scientific Research at Princess Nourah bint Abdulrahman University through the Fast-track Research Funding Program.
文摘Mobile ad hoc network(MANET)is a dynamically reconfigurable wireless network with time-variable infrastructure.Given that nodes are highly mobile,MANET’s topology often changes.These changes increase the difficulty in finding the routes that the packets use when they are routed.This study proposes an algorithm called genetic algorithm-based location-aided routing(GALAR)to enhance the MANET routing protocol efficiency.The GALAR algorithm maintains an adaptive update of the node location information by adding the transmitting node location information to the routing packet and selecting the transmitting node to carry the packets to their destination.The GALAR was constructed based on a genetic optimization scheme that considers all contributing factors in the delivery behavior using criterion function optimization.Simulation results showed that the GALAR algorithm can make the probability of packet delivery ratio more than 99%with less network overhead.Moreover,GALAR was compared to other algorithms in terms of different network evaluation measures.The GALAR algorithm significantly outperformed the other algorithms that were used in the study.
文摘A study of wireless technologies for IoT applications in terms of power consumption has been presented in this paper. The study focuses on the importance of using low power wireless techniques and modules in IoT applications by introducing a comparative between different low power wireless communication techniques such as ZigBee, Low Power Wi-Fi, 6LowPAN, LPWA and their modules to conserve power and longing the life for the IoT network sensors. The approach of the study is in term of protocol used and the particular module that achieve that protocol. The candidate protocols are classified according to the range of connectivity between sensor nodes. For short ranges connectivity the candidate protocols are ZigBee, 6LoWPAN and low power Wi-Fi. For long connectivity the candidate is LoRaWAN protocol. The results of the study demonstrate that the choice of module for each protocol plays a vital role in battery life due to the difference of power consumption for each module/protocol. So, the evaluation of protocols with each other depends on the module used.
文摘Deep learning(DL)is a subdivision of machine learning(ML)that employs numerous algorithms,each of which provides various explanations of the data it consumes;mobile ad-hoc networks(MANET)are growing in promi-nence.For reasons including node mobility,due to MANET’s potential to provide small-cost solutions for real-world contact challenges,decentralized management,and restricted bandwidth,MANETs are more vulnerable to security threats.When protecting MANETs from attack,encryption and authentication schemes have their limits.However,deep learning(DL)approaches in intrusion detection sys-tems(IDS)can adapt to the changing environment of MANETs and allow a sys-tem to make intrusion decisions while learning about its mobility in the environment.IDSs are a secondary defiance system for mobile ad-hoc networks vs.attacks since they monitor network traffic and report anything unusual.Recently,many scientists have employed deep neural networks(DNNs)to address intrusion detection concerns.This paper used MANET to recognize com-plex patterns by focusing on security standards through efficiency determination and identifying malicious nodes,and mitigating network attacks using the three algorithms presented Cascading Back Propagation Neural Network(CBPNN),Feedforward-Neural-Network(FNN),and Cascading-Back-Propagation-Neural-Network(CBPNN)(FFNN).In addition to Convolutional-Neural-Network(CNN),these primary forms of deep neural network(DNN)building designs are widely used to improve the performance of intrusion detection systems(IDS)and the use of IDS in conjunction with machine learning(ML).Further-more,machine learning(ML)techniques than their statistical and logical methods provide MANET network learning capabilities and encourage adaptation to differ-ent environments.Compared with another current model,The proposed model has better average receiving packet(ARP)and end-to-end(E2E)performance.The results have been obtained from CBP,FFNN and CNN 74%,82%and 85%,respectively,by the time(27,18,and 17 s).
文摘Histopathology is the investigation of tissues to identify the symptom of abnormality.The histopathological procedure comprises gathering samples of cells/tissues,setting them on the microscopic slides,and staining them.The investigation of the histopathological image is a problematic and laborious process that necessitates the expert’s knowledge.At the same time,deep learning(DL)techniques are able to derive features,extract data,and learn advanced abstract data representation.With this view,this paper presents an ensemble of handcrafted with deep learning enabled histopathological image classification(EHCDL-HIC)model.The proposed EHCDLHIC technique initially performs Weiner filtering based noise removal technique.Once the images get smoothened,an ensemble of deep features and local binary pattern(LBP)features are extracted.For the classification process,the bidirectional gated recurrent unit(BGRU)model can be employed.At the final stage,the bacterial foraging optimization(BFO)algorithm is utilized for optimal hyperparameter tuning process which leads to improved classification performance,shows the novelty of the work.For validating the enhanced execution of the proposed EHCDL-HIC method,a set of simulations is performed.The experimentation outcomes highlighted the betterment of the EHCDL-HIC approach over the existing techniques with maximum accuracy of 94.78%.Therefore,the EHCDL-HIC model can be applied as an effective approach for histopathological image classification.
文摘Recent economic growth and development have considerably raised energy consumption over the globe.Electric load prediction approaches become essential for effective planning,decision-making,and contract evaluation of the power systems.In order to achieve effective forecasting outcomes with minimumcomputation time,this study develops an improved whale optimization with deep learning enabled load prediction(IWO-DLELP)scheme for energy storage systems(ESS)in smart grid platform.The major intention of the IWO-DLELP technique is to effectually forecast the electric load in SG environment for designing proficient ESS.The proposed IWO-DLELP model initially undergoes pre-processing in two stages namely min-max normalization and feature selection.Besides,partition clustering approach is applied for the decomposition of data into distinct clusters with respect to distance and objective functions.Moreover,IWO with bidirectional gated recurrent unit(BiGRU)model is applied for the prediction of load and the hyperparameters are tuned by the use of IWO algorithm.The experiment analysis reported the enhanced results of the IWO-DLELP model over the recent methods interms of distinct evaluation measures.
文摘As the amount of medical images transmitted over networks and kept on online servers continues to rise,the need to protect those images digitally is becoming increasingly important.However,due to the massive amounts of multimedia and medical pictures being exchanged,low computational complexity techniques have been developed.Most commonly used algorithms offer very little security and require a great deal of communication,all of which add to the high processing costs associated with using them.First,a deep learning classifier is used to classify records according to the degree of concealment they require.Medical images that aren’t needed can be saved by using this method,which cuts down on security costs.Encryption is one of the most effective methods for protecting medical images after this step.Confusion and dispersion are two fundamental encryption processes.A new encryption algorithm for very sensitive data is developed in this study.Picture splitting with image blocks is nowdeveloped by using Zigzag patterns,rotation of the image blocks,and random permutation for scrambling the blocks.After that,this research suggests a Region of Interest(ROI)technique based on selective picture encryption.For the first step,we use an active contour picture segmentation to separate the ROI from the Region of Background(ROB).Permutation and diffusion are then carried out using a Hilbert curve and a Skew Tent map.Once all of the blocks have been encrypted,they are combined to create encrypted images.The investigational analysis is carried out to test the competence of the projected ideal with existing techniques.
文摘This paper is directed to study the isotope effects of some superconducting materials that have a strong coupling coefficient <i>λ</i> > 1.5, and focuses on new superconducting materials whose critical temperature is close to room temperature, specifically LaH<sub>10</sub>-LaD<sub>10</sub> and H<sub>3</sub>S-D<sub>3</sub>S systems. The Eliashberg-McMillan (EM) model and the recent Gor’kov-Kresin (GK) model for evaluating the isotope effects coefficient α were examined for these systems. The predicted values of α as a function of pressure, as compared to experimental values led to inference that these two models, despite their importance and simplicity, cannot be considered complete. These models can be used to calculate isotope effect of most superconducting materials with strong coupling coefficients but with critical reliability. The significance of studying the isotope effect lies in the possibility of identifying the interatomic forces that control the properties of superconducting materials such as electrons-mediated phonons and Coulomb interactions.
基金This work is supported by the National Natural Science Foundation of China under Grant No. 61300148, the Scientific and Technological Break-Through Program of Jilin Province of China under Grant No. 20130206051GX, and the Science and Technology Development Program of Jilin Province of China under Grant No. 20130522112JH.
文摘Trust, as a major part of human interactions, plays an important role in helping users collect reliable infor-mation and make decisions. However, in reality, user-specified trust relations are often very sparse and follow a power law distribution; hence inferring unknown trust relations attracts increasing attention in recent years. Social theories are frameworks of empirical evidence used to study and interpret social phenomena from a sociological perspective, while social networks reflect the correlations of users in real world; hence, making the principle, rules, ideas and methods of social theories into the analysis of social networks brings new opportunities for trust prediction. In this paper, we investigate how to exploit homophily and social status in trust prediction by modeling social theories. We first give several methods to compute homophily coe？cient and status coe？cient, then provide a principled way to model trust prediction mathe-matically, and propose a novel framework, hsTrust, which incorporates homophily theory and status theory. Experimental results on real-world datasets demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of homophily theory and status theory in trust prediction.
基金supported by the National Natural Science Foundation of China(Nos.61602057 and 11690012)the China Postdoctoral Science Foundation(No.2017M611301)+3 种基金the Science and Technology Department of Jilin Province,China(No.20170520059JH)the Education Department of Jilin Province,China(No.2016311)the Key Laboratory of Symbolic Computation and Knowledge Engineering(No.93K172016K13)the Guangxi Key Laboratory of Trusted Software(No.kx201533)
文摘User-specified trust relations are often very sparse and dynamic, making them difficult to accurately predict from online social media. In addition, trust relations are usually unavailable for most social media platforms.These issues pose a great challenge for predicting trust relations and further building trust networks. In this study,we investigate whether we can predict trust relations via a sparse learning model, and propose to build a trust network without trust relations using only pervasively available interaction data and homophily effect in an online world. In particular, we analyze the reliability of predicting trust relations by interaction behaviors, and provide a principled way to mathematically incorporate interaction behaviors and homophily effect in a novel framework,b Trust. Results of experiments on real-world datasets from Epinions and Ciao demonstrated the effectiveness of the proposed framework. Further experiments were conducted to understand the importance of interaction behaviors and homophily effect in building trust networks.
基金funded by the Centre for Nonlinear Systems,Chennai Institute of Technology,India[grant number CIT/CNS/2023/RP/008].
文摘Chaotic behavior can be observed in continuous and discrete-time systems.This behavior can appear in one-dimensional nonlinear maps;however,having at least three state variables in flows is necessary.Due to the lower mathematical complexity and computational cost of maps,lots of research has been conducted based on them.This paper aims to present a novel one-dimensional trigonometric chaotic map that is multi-stable and can act attractively.The proposed chaotic map is first analyzed using a single sinusoidal function;then,its abilities are expanded to a map with a combination of two sinusoidal functions.The stability conditions of both maps are investigated,and their different behaviors are validated through time series,state space,and cobweb diagrams.Eventually,the influence of parameter variations on the maps’outputs is examined by one-dimensional and two-dimensional bifurcation diagrams and Lyapunov exponent spectra.Besides,the diversity of outputs with varying initial conditions reveals this map’s multi-stability.The newly designed chaotic map can be employed in encryption applications.
