In mobile crowd computing(MCC),people’s smart mobile devices(SMDs)are utilized as computing resources.Considering the ever-growing computing capabilities of today’s SMDs,a collection of them can offer significantly ...In mobile crowd computing(MCC),people’s smart mobile devices(SMDs)are utilized as computing resources.Considering the ever-growing computing capabilities of today’s SMDs,a collection of them can offer significantly high-performance computing services.In a localMCC,the SMDs are typically connected to a local Wi-Fi network.Organizations and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational burden.Though it offers an economical and sustainable computing solution,users’mobility poses a serious issue in the QoS of MCC.To address this,before submitting a job to an SMD,we suggest estimating that particular SMD’s availability in the network until the job is finished.For this,we propose a convolutional GRU-based prediction model to assess how long an SMD is likely to be available in the network from any given point of time.For experimental purposes,we collected real users’mobility data(in-time and outtime)with respect to a Wi-Fi access point.To build the prediction model,we presented a novel feature extraction method to be applied to the time-series data.The experimental results prove that the proposed convolutional GRU model outperforms the conventional GRU model.展开更多
A collaborative filtering-based recommendation system has been an integral part of e-commerce and e-servicing.To keep the recommendation systems reliable,authentic,and superior,the security of these systems is very cr...A collaborative filtering-based recommendation system has been an integral part of e-commerce and e-servicing.To keep the recommendation systems reliable,authentic,and superior,the security of these systems is very crucial.Though the existing shilling attack detection methods in collaborative filtering are able to detect the standard attacks,in this paper,we prove that they fail to detect a new or unknown attack.We develop a new attack model,named Obscure attack,with unknown features and observed that it has been successful in biasing the overall top-N list of the target users as intended.The Obscure attack is able to push target items to the top-N list as well as remove the actual rated items from the list.Our proposed attack is more effective at a smaller number of k in top-k similar user as compared to other existing attacks.The effectivity of the proposed attack model is tested on the MovieLens dataset,where various classifiers like SVM,J48,random forest,and naïve Bayes are utilized.展开更多
With the popularity of e-learning,personalization and ubiquity have become important aspects of online learning.To make learning more personalized and ubiquitous,we propose a learner model for a query-based personaliz...With the popularity of e-learning,personalization and ubiquity have become important aspects of online learning.To make learning more personalized and ubiquitous,we propose a learner model for a query-based personalized learning recommendation system.Several contextual attributes characterize a learner,but considering all of them is costly for a ubiquitous learning system.In this paper,a set of optimal intrinsic and extrinsic contexts of a learner are identified for learner modeling.A total of 208 students are surveyed.DEMATEL(Decision Making Trial and Evaluation Laboratory)technique is used to establish the validity and importance of the identified contexts and find the interdependency among them.The acquiring methods of these contexts are also defined.On the basis of these contexts,the learner model is designed.A layered architecture is presented for interfacing the learner model with a query-based personalized learning recommendation system.In a ubiquitous learning scenario,the necessary adaptive decisions are identified to make a personalized recommendation to a learner.展开更多
基金This research was supported by Taif University Researchers Supporting Project Number(TURSP-2020/10),Taif University,Taif,Saudi Arabia.
文摘In mobile crowd computing(MCC),people’s smart mobile devices(SMDs)are utilized as computing resources.Considering the ever-growing computing capabilities of today’s SMDs,a collection of them can offer significantly high-performance computing services.In a localMCC,the SMDs are typically connected to a local Wi-Fi network.Organizations and institutions can leverage the SMDs available within the campus to form local MCCs to cater to their computing needs without any financial and operational burden.Though it offers an economical and sustainable computing solution,users’mobility poses a serious issue in the QoS of MCC.To address this,before submitting a job to an SMD,we suggest estimating that particular SMD’s availability in the network until the job is finished.For this,we propose a convolutional GRU-based prediction model to assess how long an SMD is likely to be available in the network from any given point of time.For experimental purposes,we collected real users’mobility data(in-time and outtime)with respect to a Wi-Fi access point.To build the prediction model,we presented a novel feature extraction method to be applied to the time-series data.The experimental results prove that the proposed convolutional GRU model outperforms the conventional GRU model.
基金Funding is provided by Taif University Researchers Supporting Project number(TURSP-2020/10),Taif University,Taif,Saudi Arabia.
文摘A collaborative filtering-based recommendation system has been an integral part of e-commerce and e-servicing.To keep the recommendation systems reliable,authentic,and superior,the security of these systems is very crucial.Though the existing shilling attack detection methods in collaborative filtering are able to detect the standard attacks,in this paper,we prove that they fail to detect a new or unknown attack.We develop a new attack model,named Obscure attack,with unknown features and observed that it has been successful in biasing the overall top-N list of the target users as intended.The Obscure attack is able to push target items to the top-N list as well as remove the actual rated items from the list.Our proposed attack is more effective at a smaller number of k in top-k similar user as compared to other existing attacks.The effectivity of the proposed attack model is tested on the MovieLens dataset,where various classifiers like SVM,J48,random forest,and naïve Bayes are utilized.
基金This work was supported by the College of Computer and Information Sciences,Prince Sultan University,Saudi Arabia.
文摘With the popularity of e-learning,personalization and ubiquity have become important aspects of online learning.To make learning more personalized and ubiquitous,we propose a learner model for a query-based personalized learning recommendation system.Several contextual attributes characterize a learner,but considering all of them is costly for a ubiquitous learning system.In this paper,a set of optimal intrinsic and extrinsic contexts of a learner are identified for learner modeling.A total of 208 students are surveyed.DEMATEL(Decision Making Trial and Evaluation Laboratory)technique is used to establish the validity and importance of the identified contexts and find the interdependency among them.The acquiring methods of these contexts are also defined.On the basis of these contexts,the learner model is designed.A layered architecture is presented for interfacing the learner model with a query-based personalized learning recommendation system.In a ubiquitous learning scenario,the necessary adaptive decisions are identified to make a personalized recommendation to a learner.
