In federated learning, a parameter server may actively infer sensitive data of users and a user may arbitrarily drop out of a learning process. Bonawitz et al. propose a secure aggregation protocol for federated learn...
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In federated learning, a parameter server may actively infer sensitive data of users and a user may arbitrarily drop out of a learning process. Bonawitz et al. propose a secure aggregation protocol for federated learning against a semi-honest adversary and a security enhancement method against an active adversary at ACM CCS 2017. The purpose of this paper is to analyze their security enhancement method and to design an alternative. We point out that their security enhancement method has the risk of Eclipse attack and that the consistency check round in their method could be removed. We give a new efficient security enhancement method by redesigning an authentication message and by adjusting the authentication timing. The new method produces an secure aggregation protocol against an active adversary with less communication and computation costs.
From the onset of the COVID-19 pandemic, many researchers rushed to design Machine Learning (ML)-assisted diagnostic tools that could, supposedly, detect COVID-19 fast and reliably. ML seemed perfect for this job sinc...
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From the onset of the COVID-19 pandemic, many researchers rushed to design Machine Learning (ML)-assisted diagnostic tools that could, supposedly, detect COVID-19 fast and reliably. ML seemed perfect for this job since we had access to many COVID-19 datasets, so a datadriven approach should have quickly yielded such diagnostic tools that could then be distributed to the masses. Unfortunately, the reality fell way short of the expectations. In an extensive study, Wynants and colleagues screened 126,978 relevant titles in the literature and found 412 studies describing 731 such ML-based COVID-19 diagnostic tools, but their conclusion was that “most published prediction model studies were poorly reported and at high risk of bias such that their reported predictive performances are probably optimistic” [1]. Only 29 models had low risk of bias and “should be validated before clinical implementation.” This was confirmed by another study that identified 2,212 such tools, of which 415 were included after initial screening, and 62 were systematically reviewed. The result? “Our review finds that none of the models identified are of potential clinical use due to methodological flaws and/or underlying biases” [2]. There were several problems with the proposed tools, but the one that relates to our article is summarized in the following remedial recommendation of the authors: “When reporting results, it is important to include confidence intervals to reflect the uncertainty in the estimate, especially when training models on the small sample sizes commonly seen with COVID-19 data.”
PM2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate,and it is an evaluation indicator of air pollut...
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PM2.5 has a non-negligible impact on visibility and air quality as an important component of haze and can affect cloud formation and rainfall and thus change the climate,and it is an evaluation indicator of air pollution *** PM2.5 concentration prediction based on relevant historical data mining can effectively improve air pollution forecasting ability and guide air pollution prevention and *** past methods neglected the impact caused by PM2.5 flow between cities when analyzing the impact of inter-city PM2.5 concentrations,making it difficult to further improve the prediction ***,factors including geographical information such as altitude and distance and meteorological information such as wind speed and wind direction affect the flow of PM2.5 between cities,leading to the change of PM2.5 concentration in *** a PM2.5 directed flow graph is constructed in this *** and meteorological data is introduced into the graph structure to simulate the spatial PM2.5 flow transmission relationship between *** introduction of meteorological factors like wind direction depicts the unequal flow relationship of PM2.5 between *** on this,a PM2.5 concentration prediction method integrating spatial-temporal factors is proposed in this paper.A spatial feature extraction method based on weight aggregation graph attention network(WGAT)is proposed to extract the spatial correlation features of PM2.5 in the flow graph,and a multi-step PM2.5 prediction method based on attention gate control loop unit(AGRU)is *** PM2.5 concentration prediction model WGAT-AGRU with fused spatiotemporal features is constructed by combining the two methods to achieve multi-step PM2.5 concentration ***,accuracy and validity experiments are conducted on the KnowAir dataset,and the results show that the WGAT-AGRU model proposed in the paper has good performance in terms of prediction accuracy and validates the effectiveness
The classification of breast cancer has emerged as a significant concern in the healthcare sector in recent times. This is primarily due to its status as the second leading cause of cancer-related fatalities among wom...
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The telegrapher’s equations constitute a set of linear partial differential equations that establish a mathematical correspondence between the electrical current and voltage within transmission lines, taking into acc...
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Anemia detection using multimodal approaches leverages the integration of multiple data sources, such as imaging, clinical records, and hematological parameters, to improve diagnostic accuracy. Such methods can captur...
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A backward wave oscillator with parallel multiple beams and multi-pin slow-wave structure(SWS)operating at the frequency above 500 GHz is studied. Both the cold-cavity dispersion characteristics and CST Particle Studi...
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A backward wave oscillator with parallel multiple beams and multi-pin slow-wave structure(SWS)operating at the frequency above 500 GHz is studied. Both the cold-cavity dispersion characteristics and CST Particle Studio simulation results reveal that there are obvious mode competition problems in this kind of terahertz *** that the structure of the multi-pin SWS is similar to that of two-dimensional photonic crystals, we introduce the defects of photonic crystal with the property of filtering into the SWS to suppress high-order ***, a detailed study of the effect of suppressing higher-order modes is carried out in the process of changing location and arrangement pattern of the point defects. The stable, single-mode operation of the terahertz source is realized. The simulation results show that the ratio of the output peak power of the higher-order modes to that of the fundamental mode is less than 1.9%. Also, the source can provide the output peak power of 44.8 m W at the frequency of 502.2 GHz in the case of low beam voltage of 4.7 kV.
In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scal...
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In foggy traffic scenarios, existing object detection algorithms face challenges such as low detection accuracy, poor robustness, occlusion, missed detections, and false detections. To address this issue, a multi-scale object detection algorithm based on an improved YOLOv8 has been proposed. Firstly, a lightweight attention mechanism, Triplet Attention, is introduced to enhance the algorithm’s ability to extract multi-dimensional and multi-scale features, thereby improving the receptive capability of the feature maps. Secondly, the Diverse Branch Block (DBB) is integrated into the CSP Bottleneck with two Convolutions (C2F) module to strengthen the fusion of semantic information across different layers. Thirdly, a new decoupled detection head is proposed by redesigning the original network head based on the Diverse Branch Block module to improve detection accuracy and reduce missed and false detections. Finally, the Minimum Point Distance based Intersection-over-Union (MPDIoU) is used to replace the original YOLOv8 Complete Intersection-over-Union (CIoU) to accelerate the network’s training convergence. Comparative experiments and dehazing pre-processing tests were conducted on the RTTS and VOC-Fog datasets. Compared to the baseline YOLOv8 model, the improved algorithm achieved mean Average Precision (mAP) improvements of 4.6% and 3.8%, respectively. After defogging pre-processing, the mAP increased by 5.3% and 4.4%, respectively. The experimental results demonstrate that the improved algorithm exhibits high practicality and effectiveness in foggy traffic scenarios.
Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this ***-aided d...
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Pneumonia is an acute lung infection that has caused many fatalitiesglobally. Radiologists often employ chest X-rays to identify pneumoniasince they are presently the most effective imaging method for this ***-aided diagnosis of pneumonia using deep learning techniques iswidely used due to its effectiveness and performance. In the proposed method,the Synthetic Minority Oversampling Technique (SMOTE) approach is usedto eliminate the class imbalance in the X-ray dataset. To compensate forthe paucity of accessible data, pre-trained transfer learning is used, and anensemble Convolutional Neural Network (CNN) model is developed. Theensemble model consists of all possible combinations of the MobileNetv2,Visual Geometry Group (VGG16), and DenseNet169 models. MobileNetV2and DenseNet169 performed well in the Single classifier model, with anaccuracy of 94%, while the ensemble model (MobileNetV2+DenseNet169)achieved an accuracy of 96.9%. Using the data synchronous parallel modelin Distributed Tensorflow, the training process accelerated performance by98.6% and outperformed other conventional approaches.
Human Activity Recognition(HAR)has always been a difficult task to *** is mainly used in security surveillance,human-computer interaction,and health care as an assistive or diagnostic technology in combination with ot...
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Human Activity Recognition(HAR)has always been a difficult task to *** is mainly used in security surveillance,human-computer interaction,and health care as an assistive or diagnostic technology in combination with other technologies such as the Internet of Things(IoT).Human Activity Recognition data can be recorded with the help of sensors,images,or *** daily routine-based human activities such as walking,standing,sitting,etc.,could be a difficult statistical task to classify into categories and hence 2-dimensional Convolutional Neural Network(2D CNN)MODEL,Long Short Term Memory(LSTM)Model,Bidirectional long short-term memory(Bi-LSTM)are used for the *** has been demonstrated that recognizing the daily routine-based on human activities can be extremely accurate,with almost all activities accurately getting recognized over 90%of the ***,because all the examples are generated from only 20 s of data,these actions can be recognised *** from classification,the work extended to verify and investigate the need for wearable sensing devices in individually walking patients with Cerebral Palsy(CP)for the evaluation of chosen Spatio-temporal features based on 3D foot ***-control research was conducted with 35 persons with CP ranging in weight from 25 to 65 *** Motion Capture(OMC)equipment was used as the referral method to assess the functionality and quality of the foot-worn *** average accuracy±precision for stride length,cadence,and step length was 3.5±4.3,4.1±3.8,and 0.6±2.7 cm *** cadence,stride length,swing,and step length,people with CP had considerably high inter-stride ***-worn sensing devices made it easier to examine Gait Spatio-temporal data even without a laboratory set up with high accuracy and precision about gait abnormalities in people who have CP during linear walking.
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