Fuzzing is the most widely used method for uncovering software security vulnerabilities, and many fuzzing implementations (fuzzers) are available on Linux. On Windows, however, only a few fuzzers are available;in part...
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Images are used widely nowadays. Images are used in many fields such as medicine to terrain mapping. There is a need to compress the images and represent them in shorter form for effective transmission. Several techni...
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Emotion recognition is crucial in human-computer interaction and psychological research, utilizing modalities such as facial expressions, voice intonations, and EEG signals. This research investigates AI-driven techni...
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The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized ***,how to protect the p...
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The past decades have witnessed a wide application of federated learning in crowd sensing,to handle the numerous data collected by the sensors and provide the users with precise and customized ***,how to protect the private information of users in federated learning has become an important research *** with the differential privacy(DP)technique and secure multiparty computation(SMC)strategy,the covert communication mechanism in federated learning is more efficient and energy-saving in training the ma-chine learning *** this paper,we study the covert communication problem for federated learning in crowd sensing Internet-of-Things *** from the previous works about covert communication in federated learning,most of which are considered in a centralized framework and experimental-based,we firstly proposes a centralized covert communication mechanism for federated learning among n learning agents,the time complexity of which is O(log n),approximating to the optimal ***,for the federated learning without parameter server,which is a harder case,we show that solving such a problem is NP-hard and prove the existence of a distributed covert communication mechanism with O(log logΔlog n)times,approximating to the optimal solution.Δis the maximum distance between any pair of learning *** analysis and nu-merical simulations are presented to show the performance of our covert communication *** hope that our covert communication work can shed some light on how to protect the privacy of federated learning in crowd sensing from the view of communications.
The limited energy and high mobility of unmanned aerial vehicles(UAVs)lead to drastic topology changes in UAV *** existing routing protocols necessitate a large number of messages for route discovery and maintenance,g...
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The limited energy and high mobility of unmanned aerial vehicles(UAVs)lead to drastic topology changes in UAV *** existing routing protocols necessitate a large number of messages for route discovery and maintenance,greatly increasing network delay and control overhead.A energyefficient routing method based on the discrete timeaggregated graph(TAG)theory is proposed since UAV formation is a defined time-varying *** network is characterized using the TAG,which utilizes the prior knowledge in UAV *** energyefficient routing algorithm is designed based on TAG,considering the link delay,relative mobility,and residual energy of *** routing path is determined with global network information before requesting *** results demonstrate that the routing method can improve the end-to-end delay,packet delivery ratio,routing control overhead,and residual ***,introducing timevarying graphs to design routing algorithms is more effective for UAV formation.
Skin cancer is a serious and potentially life-threatening condition caused by DNA damage in the skin cells, leading to genetic mutations and abnormal cell growth. These mutations can cause the cells to divide and grow...
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Skin cancer is a serious and potentially life-threatening condition caused by DNA damage in the skin cells, leading to genetic mutations and abnormal cell growth. These mutations can cause the cells to divide and grow uncontrollably, forming a tumor on the skin. To prevent skin cancer from spreading and potentially leading to serious complications, it's critical to identify and treat it as early as possible. An innovative two-fold deep learning based skin cancer detection model is presented in this research work. Five main stages make up the proposed model: Preprocessing, segmentation, feature extraction, feature selection, and skin cancer detection. Initially, the Min–max contrast stretching and median filtering used to pre-process the collected raw image. From the pre-processed image, the Region of Intertest (ROI) is identified via optimized mask Region-based Convolutional Neural Network (R-CNN). Then, from the identified ROI areas, the texture features like Illumination-invariant Binary Gabor Pattern (II-BGP), Local Binary Pattern (LBP), Gray-Level Co-occurrence Matrix (GLCM), Color feature such as Color Correlogram and Histogram Intersection, and Shape feature including Moments, Area, Perimeter, Eccentricity, Average bending energy are extracted. To choose the optimal features from the extracted ones, the Golden Eagle Mutated Leader Optimization (GEMLO) is used. The proposed Golden Eagle Mutated Leader Optimization (GEMLO) is the conceptual amalgamation of the standard Mutated Leader Algorithm (MLA) and Golden Eagle Optimizer are used to select best features (GEO). The skin cancer detection is accomplished via two-fold-deep-learning-classifiers, that includes the Fully Convolutional Neural Networks (FCNs) and Multi-Layer Perception (MLP). The final outcome is the combination of the outcomes acquired from Fully Convolutional Neural Networks (FCNs) and Multi-Layer Perception (MLP). The PYTHON platform is being used to implement the suggested model. Using the curre
Knowledge distillation(KD) enhances student network generalization by transferring dark knowledge from a complex teacher network. To optimize computational expenditure and memory utilization, self-knowledge distillati...
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Knowledge distillation(KD) enhances student network generalization by transferring dark knowledge from a complex teacher network. To optimize computational expenditure and memory utilization, self-knowledge distillation(SKD) extracts dark knowledge from the model itself rather than an external teacher network. However, previous SKD methods performed distillation indiscriminately on full datasets, overlooking the analysis of representative samples. In this work, we present a novel two-stage approach to providing targeted knowledge on specific samples, named two-stage approach self-knowledge distillation(TOAST). We first soften the hard targets using class medoids generated based on logit vectors per class. Then, we iteratively distill the under-trained data with past predictions of half the batch size. The two-stage knowledge is linearly combined, efficiently enhancing model performance. Extensive experiments conducted on five backbone architectures show our method is model-agnostic and achieves the best generalization ***, TOAST is strongly compatible with existing augmentation-based regularization methods. Our method also obtains a speedup of up to 2.95x compared with a recent state-of-the-art method.
The manual process of evaluating answer scripts is strenuous. Evaluators use the answer key to assess the answers in the answer scripts. Advancements in technology and the introduction of new learning paradigms need a...
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With the development of the miniaturization of electronic equipment and lightweight weapon equipment,there are new requirements for electromagnetic wave absorption material(EMWAM).EMWAM has outstanding electromagnetic...
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With the development of the miniaturization of electronic equipment and lightweight weapon equipment,there are new requirements for electromagnetic wave absorption material(EMWAM).EMWAM has outstanding electromagnetic wave absorption properties and lightweight characteristics become an important direction of *** this study,graphene/g-C_(3)N_(4)(GGCN)EMWAM was first synthesized in situ by simple heat treatment,in which the g-C_(3)N_(4) had a porous structure and dispersed on the surface of *** impedance matching of the GGCN was well adjusted by decreasing the dielectric constant and attenuation constant due to the g-C_(3)N_(4) semiconductor property and the graphite-like *** EMW loss mechanism of GGCN was also analyzed by simulating GGCN’s electric field mode distribution and resistance loss power *** analysis result shows that the distribution of g-C_(3)N_(4) among GGCN sheets can produce more polarization effects and relaxation effects by increasing the lamellar ***,the polarization loss of GGCN could be increased successfully by porous g-C_(3)N_(4).Ultimately,the EMW absorption property of GGCN is optimized significantly,and GGCN exhibits excellent EMW absorption *** the thickness is 2 mm,the effective absorption bandwidth(EAB)can reach 4.6 GHz,and when the thickness is 4.5 mm,the minimum reflection loss(RLmin)at 4.56 GHz can reach-34.69 ***,the practical application of EMWAM was studied by radar cross-section(RCS)simulation,showing that GGCN has a good application prospect.
Data cleaning is considered as an effective approach of improving data quality in order to help practitioners and researchers be devoted to downstream analysis and decision-making without worrying about data *** paper...
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Data cleaning is considered as an effective approach of improving data quality in order to help practitioners and researchers be devoted to downstream analysis and decision-making without worrying about data *** paper provides a systematic summary of the two main stages of data cleaning for Internet of Things(IoT)data with time series characteristics,including error data detection and data *** respect to error data detection techniques,it categorizes an overview of quantitative data error detection methods for detecting single-point errors,continuous errors,and multidimensional time series data errors and qualitative data error detection methods for detecting rule-violating ***,it provides a detailed description of error data repairing techniques,involving statistics-based repairing,rule-based repairing,and human-involved *** review the strengths and the limitations of the current data cleaning techniques under IoT data applications and conclude with an outlook on the future of IoT data cleaning.
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