Mobile networks possess significant information and thus are considered a gold mine for the researcher’s *** call detail records(CDR)of a mobile network are used to identify the network’s efficacy and the mobile use...
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Mobile networks possess significant information and thus are considered a gold mine for the researcher’s *** call detail records(CDR)of a mobile network are used to identify the network’s efficacy and the mobile user’s *** is evident from the recent literature that cyber-physical systems(CPS)were used in the analytics and modeling of telecom *** addition,CPS is used to provide valuable services in smart *** general,a typical telecom company hasmillions of subscribers and thus generatesmassive amounts of *** this aspect,data storage,analysis,and processing are the key *** solve these issues,herein we propose a multilevel cyber-physical social system(CPSS)for the analysis and modeling of large internet *** proposed multilevel system has three levels and each level has a specific ***,raw Call Detail Data(CDR)was collected at the first ***,the data preprocessing,cleaning,and error removal operations were *** the second level,data processing,cleaning,reduction,integration,processing,and storage were ***,suggested internet activity record measures were *** proposed system initially constructs a graph and then performs network *** proposed CPSS system accurately identifies different areas of internet peak usage in a city(Milan city).Our research is helpful for the network operators to plan effective network configuration,management,and optimization of resources.
By leveraging the high maneuverability of the unmanned aerial vehicle(UAV) and the expansive coverage of the intelligent reflecting surface(IRS), a multi-IRS-assisted UAV communication system aimed at maximizing the s...
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By leveraging the high maneuverability of the unmanned aerial vehicle(UAV) and the expansive coverage of the intelligent reflecting surface(IRS), a multi-IRS-assisted UAV communication system aimed at maximizing the sum data rate of all users was investigated in this paper. This is achieved through the joint optimization of the trajectory and transmit beamforming of the UAV, as well as the passive phase shift of the IRS. Nevertheless, the initial problem exhibits a high degree of non-convexity, posing challenges for conventional mathematical optimization techniques in delivering solutions that are both quick and efficient while maintaining low complexity. To address this issue, a novel scheme called the deep reinforcement learning(DRL)-based enhanced cooperative reflection network(DCRN) was proposed. This scheme effectively identifies optimal strategies, with the long short-term memory(LSTM) network improving algorithm convergence by extracting hidden state information. Simulation results demonstrate that the proposed scheme outperforms the baseline scheme, manifesting substantial enhancements in sum rate and superior performance.
Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Lan...
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Sign language recognition is vital for enhancing communication accessibility among the Deaf and hard-of-hearing *** Japan,approximately 360,000 individualswith hearing and speech disabilities rely on Japanese Sign Language(JSL)for ***,existing JSL recognition systems have faced significant performance limitations due to inherent *** response to these challenges,we present a novel JSL recognition system that employs a strategic fusion approach,combining joint skeleton-based handcrafted features and pixel-based deep learning *** system incorporates two distinct streams:the first stream extracts crucial handcrafted features,emphasizing the capture of hand and body movements within JSL ***,a deep learning-based transfer learning stream captures hierarchical representations of JSL gestures in the second ***,we concatenated the critical information of the first stream and the hierarchy of the second stream features to produce the multiple levels of the fusion features,aiming to create a comprehensive representation of the JSL *** reducing the dimensionality of the feature,a feature selection approach and a kernel-based support vector machine(SVM)were used for the *** assess the effectiveness of our approach,we conducted extensive experiments on our Lab JSL dataset and a publicly available Arabic sign language(ArSL)*** results unequivocally demonstrate that our fusion approach significantly enhances JSL recognition accuracy and robustness compared to individual feature sets or traditional recognition methods.
Although Convolutional Neural Networks(CNNs)have achieved remarkable success in image classification,most CNNs use image datasets in the Red-Green-Blue(RGB)color space(one of the most commonly used color spaces).The e...
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Although Convolutional Neural Networks(CNNs)have achieved remarkable success in image classification,most CNNs use image datasets in the Red-Green-Blue(RGB)color space(one of the most commonly used color spaces).The existing literature regarding the influence of color space use on the performance of CNNs is *** paper explores the impact of different color spaces on image classification using *** compare the performance of five CNN models with different convolution operations and numbers of layers on four image datasets,each converted to nine color *** find that color space selection can significantly affect classification accuracy,and that some classes are more sensitive to color space changes than *** color spaces may have different expression abilities for different image features,such as brightness,saturation,hue,*** leverage the complementary information from different color spaces,we propose a pseudo-Siamese network that fuses two color spaces without modifying the network *** experiments show that our proposed model can outperform the single-color-space models on most *** also find that our method is simple,flexible,and compatible with any CNN and image dataset.
Cognitive diagnosis is the judgment of the student’s cognitive ability, is a wide-spread concern in educational science. The cognitive diagnosis model (CDM) is an essential method to realize cognitive diagnosis measu...
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Cognitive diagnosis is the judgment of the student’s cognitive ability, is a wide-spread concern in educational science. The cognitive diagnosis model (CDM) is an essential method to realize cognitive diagnosis measurement. This paper presents new research on the cognitive diagnosis model and introduces four individual aspects of probability-based CDM and deep learning-based CDM. These four aspects are higher-order latent trait, polytomous responses, polytomous attributes, and multilevel latent traits. The paper also sorts on the contained ideas, model structures and respective characteristics, and provides direction for developing cognitive diagnosis in the future.
With the arrival of 5G,latency-sensitive applications are becoming increasingly *** Edge Computing(MEC)technology has the characteristics of high bandwidth,low latency and low energy consumption,and has attracted much...
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With the arrival of 5G,latency-sensitive applications are becoming increasingly *** Edge Computing(MEC)technology has the characteristics of high bandwidth,low latency and low energy consumption,and has attracted much attention among *** improve the Quality of Service(QoS),this study focuses on computation offloading in *** consider the QoS from the perspective of computational cost,dimensional disaster,user privacy and catastrophic forgetting of new *** QoS model is established based on the delay and energy consumption and is based on DDQN and a Federated Learning(FL)adaptive task offloading algorithm in *** proposed algorithm combines the QoS model and deep reinforcement learning algorithm to obtain an optimal offloading policy according to the local link and node state information in the channel coherence time to address the problem of time-varying transmission channels and reduce the computing energy consumption and task processing *** solve the problems of privacy and catastrophic forgetting,we use FL to make distributed use of multiple users’data to obtain the decision model,protect data privacy and improve the model *** the process of FL iteration,the communication delay of individual devices is too large,which affects the overall delay ***,we adopt a communication delay optimization algorithm based on the unary outlier detection mechanism to reduce the communication delay of *** simulation results indicate that compared with existing schemes,the proposed method significantly reduces the computation cost on a device and improves the QoS when handling complex tasks.
Website Fingerprinting(WF)attacks can extract side channel information from encrypted traffic to form a fingerprint that identifies the victim’s destination website,even if traffic is sophisticatedly anonymized by **...
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Website Fingerprinting(WF)attacks can extract side channel information from encrypted traffic to form a fingerprint that identifies the victim’s destination website,even if traffic is sophisticatedly anonymized by *** offline defenses have been proposed and claimed to have achieved good ***,such work is more of a theoretical optimization study than a technology that can be applied to real-time traffic in the practical *** defenders generate optimized defense schemes only if the complete traffic traces are *** practicality and effectiveness are *** this paper,we provide an in-depth analysis of the difficulties faced in porting existing offline defenses to the online *** then the online WF defense based on the non-targeted adversarial patch is *** reduce the overhead,we use the Gradient-weighted Class Activation Mapping(Grad-CAM)algorithm to identify critical segments that have high contribution to the *** addition,we optimize the adversarial patch generation process by splitting patches and limiting the values,so that the pre-trained patches can be injected and discarded in real-time *** experiments are carried out to evaluate the effectiveness of our *** bandwidth overhead is set to 20%,the accuracies of the two state-of-the-art attacks,DF and Var-CNN,drop to 10.83%and 15.49%,***,we implement the real-time patch traffic injection based on WFPadTools framework in the online scenario,and achieve a defense accuracy of 95.50%with 12.57%time overhead.
Traditional autonomous driving usually requires a large number of vehicles to upload data to a central server for training. However, collecting data from vehicles may violate personal privacy as road environmental inf...
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Three-dimensional(3D)surface geometry provides elemental information in various sciences and precision *** Projection Profilometry(FPP)is one of the most powerful non-contact(thus non-destructive)and non-interferometr...
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Three-dimensional(3D)surface geometry provides elemental information in various sciences and precision *** Projection Profilometry(FPP)is one of the most powerful non-contact(thus non-destructive)and non-interferometric(thus less restrictive)3D measurement techniques,featuring at its high ***,the measurement precision of FPP is currently evaluated experimentally,lacking a complete theoretical model for *** propose the first complete FPP precision model chain including four stage models(camera intensity,fringe intensity,phase and 3D geometry)and two transfer models(from fringe intensity to phase and from phase to 3D geometry).The most significant contributions include the adoption of a non-Gaussian camera noise model,which,for the first time,establishes the connection between camera’s electronics parameters(known in advance from the camera manufacturer)and the phase precision,and the formulation of the phase to geometry transfer,which makes the precision of the measured geometry representable in an explicit and concise *** a result,we not only establish the full precision model of the 3D geometry to characterize the performance of an FPP system that has already been set up,but also explore the expression of the highest possible precision limit to guide the error distribution of an FPP system that is yet to *** theoretical models make FPP a more designable technique to meet the challenges from various measurement demands concerning different object sizes from macro to micro and requiring different measurement precisions from a few millimeters to a few micrometers.
Code review is a critical process in software development, contributing to the overall quality of the product by identifying errors early. A key aspect of this process is the selection of appropriate reviewers to scru...
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Code review is a critical process in software development, contributing to the overall quality of the product by identifying errors early. A key aspect of this process is the selection of appropriate reviewers to scrutinize changes made to source code. However, in large-scale open-source projects, selecting the most suitable reviewers for a specific change can be a challenging task. To address this, we introduce the Code Context Based Reviewer Recommendation (CCB-RR), a model that leverages information from changesets to recommend the most suitable reviewers. The model takes into consideration the paths of modified files and the context derived from the changesets, including their titles and descriptions. Additionally, CCB-RR employs KeyBERT to extract the most relevant keywords and compare the semantic similarity across changesets. The model integrates the paths of modified files, keyword information, and the context of code changes to form a comprehensive picture of the changeset. We conducted extensive experiments on four open-source projects, demonstrating the effectiveness of CCB-RR. The model achieved a Top-1 accuracy of 60%, 55%, 51%, and 45% on the Android, OpenStack, QT, and LibreOffice projects respectively. For Mean Reciprocal Rank (MRR), CCB achieved 71%, 62%, 52%, and 68% on the same projects respectively, thereby highlighting its potential for practical application in code reviewer recommendation.
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