Ventilator
发表于 2025-3-25 06:50:20
Rim Haidar,Irena Koprinska,Bryn Jeffrieses for detecting faults in such embedded systems..In this paper, a novel cross-platform verification framework including automated test-case generation by model checking is introduced. Comparing the execution behavior of a program instance running on a certain platform to the execution behavior of t
ostracize
发表于 2025-3-25 08:33:35
Jinting Chen,Zhaocheng Zhu,Cheng Li,Yuming Zhaoon those small pieces in an efficient, timely manner. MapReduce was created and popularized by Google, and is widely used as a means of processing large amounts of textual data for the purpose of indexing it for search later on. This paper examines the feasibility of using smart mobile devices in a
Aspirin
发表于 2025-3-25 12:45:58
Chunfeng Liu,Yan Zhang,Mei Yu,Ruiguo Yu,Xuewei Li,Mankun Zhao,Tianyi Xu,Hongwei Liu,Jian Yuich is undecidable in general. In contrast to other work about attributed graph transformation, we do not impose syntactic restrictions on the rules except for left-linearity. Our checking method relies on constructing the symbolic critical pairs of a rule set using an E-unification algorithm and su
Demulcent
发表于 2025-3-25 18:02:47
Xuan Cuong Pham,Thi Thu Thuy Nguyen,Alan Wee-Chung Liewich is undecidable in general. In contrast to other work about attributed graph transformation, we do not impose syntactic restrictions on the rules except for left-linearity. Our checking method relies on constructing the symbolic critical pairs of a rule set using an E-unification algorithm and su
规范要多
发表于 2025-3-25 21:32:07
FH-GAN: Face Hallucination and Recognition Using Generative Adversarial Networkmportant problem is low resolution face images which can result in bad performance on face recognition. The modern face hallucination models demonstrate reasonable performance to reconstruct high-resolution images from its corresponding low resolution images. However, they do not consider identity l
Rankle
发表于 2025-3-26 01:28:54
Adversarial Learning for Cross-Modal Retrieval with Wasserstein Distance modalities in a GAN framework. The generator projects the image and the text features into an aligned representation space, while the discriminator ensures that the image and text features are not too far from each other, in a way which would maintain the semantic relation between the input samples
genuine
发表于 2025-3-26 08:15:10
Reducing the Subject Variability of EEG Signals with Adversarial Domain Generalizationaphy (EEG) signals. To deal with this problem, the existing methods focus on domain adaptation with subject-specific EEG data, which are expensive and time consuming to collect. In this paper, domain generalization methods are introduced to reduce the influence of subject variability in BCI systems
BUCK
发表于 2025-3-26 08:52:06
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LAVA
发表于 2025-3-26 15:05:39
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MILL
发表于 2025-3-26 20:02:39
A Natural Scene Text Extraction Approach Based on Generative Adversarial Learning recognition and understanding. It can be seen as an image-to-image conversion task, in which we transform the front text in each natural image into a specified color and the background into black. After that, we use the connected component algorithm to extract text from the two-color image. Based o