In recent years, advancements in underwater imaging technologies have played a pivotal role in expanding our understanding of the Earth's aquatic environments. Nevertheless, underwater imagery frequently presents ...
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Channel state information (CSI) based sensing approaches have unique advantages for motion detection. However, due to the introduction of additional angle information in multi-antenna system, the mapping relationship ...
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Some markers have been proposed to determine the positional relationship between an object and a camera for XR applications. Most of markers consider only a single scale, but there are many cases where the distance to...
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The semantics of programming languages is one of the core topics in computerscience. This topic is formalism-heavy and requires the student to attempt numerous proofs for a deep understanding. We argue that modern th...
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ISBN:
(纸本)9798350322590
The semantics of programming languages is one of the core topics in computerscience. This topic is formalism-heavy and requires the student to attempt numerous proofs for a deep understanding. We argue that modern theorem provers are excellent aids to teaching and understanding programming language semantics. As pen-and-paper proofs get automated via the theorem prover, it allows an experiment-driven strategy at exploring this topic. This article provides an encoding of the semantics of the WHILE language in the most popular styles-operational, denotational, and axiomatic-within the F* proof assistant. We show that once the program and its semantics are encoded, modern proof assistants can prove exciting language features with minimal human assistance. We believe that teaching programming languages via proof assistants will not only provide a more concrete understanding of this topic but also prepare future programming language researchers to use theorem provers as fundamental tools in their research and not as an afterthought.
This examination presents a thorough examination concerning the space of distributed picture grouping on tremendous data stages, with a specific focus on using gradient-boosted trees (GBT). The audit differentiates th...
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The detection of protein interaction partners using amino acid sequences is a challenging task in bioinformatics and molecular biology. This study proposes a hybrid deep learning model combining Convolutional Neural N...
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This study explores the influence of social media marketing on consumers' decisions to purchase green software and identifies key factors affecting those decisions. The findings contribute to effective marketing s...
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Effective detection of DGA (Domain Generation Algorithm) domain names is crucial for identifying and countering Botnets, and safeguarding cyber security. In this paper, we propose a new detection method using a hybrid...
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The Graph Transformer Network (GTN) is a state-of-the-art solution for graph representation learning, which utilizes the transformer architecture to capture attention and long-range dependencies in a non-euclidean dat...
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Recently, evolutionary multi-objective optimization (EMO) algorithms have been used in various application fields. Whereas many new EMO algorithms are proposed every year, well-known EMO algorithms such as NSGA-II, MO...
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