software component selection is one of the most challenging problems for software developers to meet customer needs. Despite various methods researchers propose to aid in component selection, a gap exists in addressin...
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The advent of the Internet of Things (IoT) has revolutionized connectivity by interconnecting a vast array of devices, underscoring the critical need for robust data security, particularly at the Physical Layer Securi...
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The advent of the Internet of Things (IoT) has revolutionized connectivity by interconnecting a vast array of devices, underscoring the critical need for robust data security, particularly at the Physical Layer Security (PLS). Ensuring data confidentiality and integrity during wireless communications poses a primary challenge in IoT environments. Additionally, within the constrained frequency bands available, Cognitive Radio Networks (CRNs) has emerged as an urgent necessity to optimize spectrum utilization. This technology enables intelligent management of radio frequencies, enhancing network efficiency and adaptability to dynamic environmental changes. In this research, we focus on examining the PLS for the primary channel within the underlying CRNs. Our proposed model involves a primary source-destination pair and a secondary transmitter-receiver pair sharing the same frequency band simultaneously. In the presence of a common eavesdropper, the primary concern lies in securing the primary link communication. The secondary user (SU) acts as cooperative jamming, strategically allocating a portion of its transmission power to transmit artificial interference, thus confusing the eavesdropper and protecting the primary user's (PU) communication. The transmit power of the SU is regulated by the maximum interference power tolerated by the primary network's receiver. To evaluate the effectiveness of our proposed protocol, we develop closed-form mathematical expressions for intercept probability ((Formula presented.)) and outage probability (OP) along the primary communication link. Additionally, we derive mathematical expressions for OP along the secondary communications network. Furthermore, we investigate the impact of transmit power allocation on intercept and outage probabilities across various links. Through both simulation and theoretical analysis, our protocol aims to enhance protection and outage efficiency for the primary link while ensuring appropriate secondary
Very recent attacks like ladder leak demonstrated feasibility to recover private key with side channel attacks using just one bit of secret nonce. ECDSA nonce bias can be exploited in many ways. Some attacks on ECDSA ...
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Analysis and reaction to natural disasters have made extensive use of deep learning methods using semantic segmentation networks. These implementations’ foundation is based on convolutional neural networks (CNNs), wh...
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This paper proposes the first known approaches to locating an athletics track from GPS data of a running workout. This is done by mapping the GPS points onto an image and applying computer Vision methods, including: p...
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Blockchain technology provides transparency and reliability by sharing transactions and maintaining the same information through consensus among all ***,single-signature applications in transactions can lead to user i...
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Blockchain technology provides transparency and reliability by sharing transactions and maintaining the same information through consensus among all ***,single-signature applications in transactions can lead to user identification issues due to the reuse of public *** address this issue,group signatures can be used,where the same group public key is used to verify signatures from group members to provide anonymity to ***,in dynamic groups where membership may change,an attack can occur where a user who has left the group can disguise themselves as a group member by leaking a partial *** problem cannot be traced back to the partial key *** this paper,we propose assigning different partial keys to group members to trace partial key leakers and partially alleviate the damage caused by partial key *** schemes have shown that arbitrary tracing issues occurred when a single administrator had exclusive key generation and tracing *** paper proposes a group signature scheme that solves the synchronization problem by involving a threshold number of TMs while preventing arbitrary tracing by distributing authority among multiple TMs.
This paper situates itself within the broader medical imaging field, focusing on applying generative modelling techniques for anomaly detection in retinal images. Given the complexity of retinal structures, detecting ...
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Existing self-knowledge distillation (Self-KD) solutions usually focus on transferring historical predictions of individual instances to the current network. However, this approach tends to create overconfidence for e...
Existing self-knowledge distillation (Self-KD) solutions usually focus on transferring historical predictions of individual instances to the current network. However, this approach tends to create overconfidence for easy instances and underconfidence for hard instances. The widely used temperature-based strategies to smooth or sharpen the predicted distributions can lead to inconsistencies across instances, causing sensitivity issues. To address this, our approach views a queue of instances as an ensemble rather than treating each instance independently. We propose a novel method that distills historical knowledge from a dimensional perspective, utilizing intra class characteristics and interclass relationships within each ensemble. First, we align each dimension distribution from the current network to the historical output. Second, we ensure each dimension is closer to similar dimensions than dissimilar ones, maintaining consistent attitudes from present and historical perspectives. Our insights reveal that distilling historical knowledge from a dimensional perspective is more effective than the traditional instance-based approach, with potential applications in related tasks. Empirical results on three famous datasets and various network architectures demonstrate the superiority of our proposed method. Our code is available at https://***/WenkeHuang/DimSelfKD.
This paper presents an approach to improve medical image retrieval, particularly for brain tumors, by addressing the gap between low-level visual and high-level perceived contents in MRI, X-ray, and CT scans. Traditio...
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This paper presents an approach to improve medical image retrieval, particularly for brain tumors, by addressing the gap between low-level visual and high-level perceived contents in MRI, X-ray, and CT scans. Traditional methods based on color, shape, or texture are less effective. The proposed solution uses machine learning to handle high-dimensional image features, reducing computational complexity and mitigating issues caused by artifacts or noise. It employs a genetic algorithm for feature reduction and a hybrid residual UNet(HResUNet) model for Region-of-Interest(ROI) segmentation and classification, with enhanced image preprocessing. The study examines various loss functions, finding that a hybrid loss function yields superior results, and the GA-HResUNet model outperforms the HResUNet. Comparative analysis with state-of-the-art models shows a 4% improvement in retrieval accuracy.
Multiplayer Online Battle Arena (MOBA) games currently dominate the esports landscape, offering a concrete and vivid embodiment for team comparisons, where accurately predicting the winning team is both important and ...
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