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- Semantic Communication Meets Edge Intelligence
Abstract—The development of emerging applications, such as autonomous transportation systems, are expected to result in an explosive growth in mobile data traffic As the available spectrum resource becomes more and more scarce, there is a growing need for a paradigm shift from Shannon’s Classical Information Theory (CIT) to semantic communication (SemCom) Specifically, the former adopts
- Smart data operations for better data-informed decision-making
Data preparation and semantic enrichment refer to the set of processes used to clean and transform the raw data prior to processing and analysis in the following stage They involve detecting errors and making corrections to the data (e g , removing outliers), combining data sets if needed, reformatting data, assigning (and standardizing) data
- Abstract - ResearchGate
able 1) This highlights the effectiveness of synthetic data generation in bridging data gaps in semantic caching, allow-ing models to excel in specialized domains without direct reliance on large
- Introduction - School of Engineering Applied Science
Loader: The loader performs the following tasks: Reads the executable and create an address space large enough for the program and its data Copies the instructions and data into memory Initializes the machines registers and sets the stack pointer Load the Program Counter with the address of the entry point of the program (main method in Java
- Data Recombination for Neural Semantic Parsing - ACL Anthology
In this paper, we introduce data recom- bination, a novel framework for inject- ing such prior knowledge into a model From the training data, we induce a high- precision synchronous context-free gram- mar, which captures important conditional independence properties commonly found in semantic parsing
- Paper Title (use style: paper title) - Cardiff University
Abstract— Of a number of ML (Machine Learning) algorithms, k-nearest neighbour (KNN) is among the most common for data classification research, and classifying diseases and faults, which is essential due to frequent alterations in the training dataset, in which it would be expensive using most methods to construct a different classifier every time this happens Therefore, KNN can be used
- Semantic embedding based online cross‐modal hashing method
To exploit semantic information, we map semantic labels to a latent semantic space and construct a semantic similar-ity matrix to preserve the similarity between new and existing data in the
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