Marine Object Detection, leveraging computer vision, plays a vital role in detecting objects in marine environments ranging from marine organisms to marine surveillance. However, marine environment presents ...
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Deep Learning has recently been in trend when it comes to medical image analysis as it uses Convolution Neural Network (CNN), which utilizes multi-layer processing to extract intricate and complex features from the da...
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In the realm of medical diagnostics, particularly in differential diagnosis, where differentiating between illnesses or ailments with comparable symptoms is essential, deep learning has gained importance. Recent devel...
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Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical...
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Anomaly detection(AD) has been extensively studied and applied across various scenarios in recent years. However, gaps remain between the current performance and the desired recognition accuracy required for practical *** paper analyzes two fundamental failure cases in the baseline AD model and identifies key reasons that limit the recognition accuracy of existing approaches. Specifically, by Case-1, we found that the main reason detrimental to current AD methods is that the inputs to the recovery model contain a large number of detailed features to be recovered, which leads to the normal/abnormal area has not/has been recovered into its original state. By Case-2, we surprisingly found that the abnormal area that cannot be recognized in image-level representations can be easily recognized in the feature-level representation. Based on the above observations, we propose a novel recover-then-discriminate(ReDi) framework for *** takes a self-generated feature map(e.g., histogram of oriented gradients) and a selected prompted image as explicit input information to address the identified in Case-1. Additionally, a feature-level discriminative network is introduced to amplify abnormal differences between the recovered and input representations. Extensive experiments on two widely used yet challenging AD datasets demonstrate that ReDi achieves state-of-the-art recognition accuracy.
Iris biometrics allow contactless authentication, which makes it widely deployed human recognition mechanisms since the couple of years. Susceptibility of iris identification systems remains a challenging task due to ...
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Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inher...
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Matrix minimization techniques that employ the nuclear norm have gained recognition for their applicability in tasks like image inpainting, clustering, classification, and reconstruction. However, they come with inherent biases and computational burdens, especially when used to relax the rank function, making them less effective and efficient in real-world scenarios. To address these challenges, our research focuses on generalized nonconvex rank regularization problems in robust matrix completion, low-rank representation, and robust matrix regression. We introduce innovative approaches for effective and efficient low-rank matrix learning, grounded in generalized nonconvex rank relaxations inspired by various substitutes for the ?0-norm relaxed functions. These relaxations allow us to more accurately capture low-rank structures. Our optimization strategy employs a nonconvex and multi-variable alternating direction method of multipliers, backed by rigorous theoretical analysis for complexity and *** algorithm iteratively updates blocks of variables, ensuring efficient convergence. Additionally, we incorporate the randomized singular value decomposition technique and/or other acceleration strategies to enhance the computational efficiency of our approach, particularly for large-scale constrained minimization problems. In conclusion, our experimental results across a variety of image vision-related application tasks unequivocally demonstrate the superiority of our proposed methodologies in terms of both efficacy and efficiency when compared to most other related learning methods.
Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data r...
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Recently, redactable blockchain has been proposed and leveraged in a wide range of real systems for its unique properties of decentralization, traceability, and transparency while ensuring controllable on-chain data redaction. However, the development of redactable blockchain is now obstructed by three limitations, which are data privacy breaches, high communication overhead, and low searching efficiency, respectively. In this paper, we propose PriChain, the first efficient privacy-preserving fine-grained redactable blockchain in decentralized settings. PriChain provides data owners with rights to control who can read and redact on-chain data while maintaining downward compatibility, ensuring the one who can redact will be able to read. Specifically, inspired by the concept of multi-authority attribute-based encryption, we utilize the isomorphism of the access control tree, realizing fine-grained redaction mechanism, downward compatibility, and collusion resistance. With the newly designed structure, PriChain can realize O(n) communication and storage overhead compared to prior O(n2) schemes. Furthermore, we integrate multiple access trees into a tree-based dictionary, optimizing searching efficiency. Theoretical analysis proves that PriChain is secure against the chosen-plaintext attack and has competitive complexity. The experimental evaluations show that PriChain realizes 10× efficiency improvement of searching and 100× lower communication and storage overhead on average compared with existing schemes.
Constructing an effective common latent embedding by aligning the latent spaces of cross-modal variational autoencoders(VAEs) is a popular strategy for generalized zero-shot learning(GZSL). However, due to the lac...
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Constructing an effective common latent embedding by aligning the latent spaces of cross-modal variational autoencoders(VAEs) is a popular strategy for generalized zero-shot learning(GZSL). However, due to the lack of fine-grained instance-wise annotations, existing VAE methods can easily suffer from the posterior collapse problem. In this paper, we propose an innovative asymmetric VAE network by aligning enhanced feature representation(AEFR) for GZSL. Distinguished from general VAE structures, we designed two asymmetric encoders for visual and semantic observations and one decoder for visual reconstruction. Specifically, we propose a simple yet effective gated attention mechanism(GAM) in the visual encoder for enhancing the information interaction between observations and latent variables, alleviating the possible posterior collapse problem effectively. In addition, we propose a novel distributional decoupling-based contrastive learning(D2-CL) to guide learning classification-relevant information while aligning the representations at the taxonomy level in the latent representation space. Extensive experiments on publicly available datasets demonstrate the state-of-the-art performance of our method. The source code is available at https://***/seeyourmind/AEFR.
The prime goal of parallel computing is the simultaneous parallel execution of several program instructions. Consequently, to accomplish this, the program should be divided into independent sets so that each processor...
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Agriculture is important in emerging nations like India, but food security is still a serious problem. Plant diseases, inadequate storage facilities, and poor transportation cause the majority of harvests to be squand...
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Agriculture is important in emerging nations like India, but food security is still a serious problem. Plant diseases, inadequate storage facilities, and poor transportation cause the majority of harvests to be squandered. Since illnesses cause almost 15% of India’s crop yield to be lost, this is a big issue that needs to be addressed. This proposed model is an automated system that can identify the diseases and assist farmers to take the necessary action to cure the crop losses. Farmers have been using the traditional method of using their own eyes to detect plant illnesses, but not all farmers can detect these diseases in the same way. computer vision capabilities must be incorporated into agriculture given the advancements in artificial intelligence. The proposed model uses a convolutional neural network (CNN) with Recurrent Neural Network (RNN) for PlantVillage dataset, the greatest publicly accessible dataset. The proposed model has a 99.37% prediction accuracy for the condition. The proposed approach can identify 14 different plant classes out of the 38 and other moderate in the Plant Village dataset shows how versatile it is. Farmers may decrease crop loss and enhance crop quality and output using this automated and user-friendly technique. In this study, we present the use of a deep recurrent neural network to automatically detect plant diseases. The resulting algorithm is used to identify the bacterial blight of rice during the growing season with a detection accuracy of 99.16%, a classification accuracy of 99.17%, and a sensor-based detection accuracy of 98.98%. Recurrent networks have made great advances in various sequence modeling, such as speech recognition, language modeling, image captioning, and many other applications in recent years. We detect the bacterial blight of rice leaves in this study with a deep recurrent network. We use a stacked LSTM-CNN network to train representations for the radio signal data collected during the lifespan of the rice
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