What is semantic in research qualitative?5 answersSemantic technologies play a crucial role in enhancing qualitative research by providing structured metadata, machine-based qualitative analysis, and supporting collaborative documentation. These technologies focus on understanding unstructured content, enabling researchers to navigate complex fields effectively. The Semantic Web, a key component of semantic technologies, offers vast amounts of structured data for automatic processing, aiding in answering research questions. Additionally, semantic networks derived from the Information Processing Model help represent students' responses in science education research, facilitating the analysis of answering strategies and knowledge representation. Overall, semantic technologies offer a means to comprehend and analyze qualitative data in research, supporting researchers in various disciplines.
What is semantic change?4 answersSemantic change refers to the process in which the meaning of a word evolves or alters over time. It can occur at various levels, such as the meanings of individual words, phrases, sentences, or even larger units of discourse. Semantic change can be influenced by factors like borrowing words from other languages, metaphorical usage, or changes in cultural and societal contexts. Different types of semantic changes have been identified, including generalization (broadening of meaning), specialization (narrowing of meaning), pejoration (negative shift in meaning), and amelioration (positive shift in meaning). The study of semantic change is important in fields like linguistics and natural language processing, as it helps understand how language evolves and how meanings can shift over time.
How to use text to enhance semantics of images in the feature space?5 answersTo enhance the semantics of images in the feature space using text, a multimodal data augmentation technique can be employed. This technique works by creating new videos and captions in the feature space by mixing semantically similar samples. Another approach is to use an Action-aware Memory-Enhanced (AME) method, which integrates action prediction and an action-aware memory bank to enrich image and text features with action-similar text features. Additionally, a Transformer-Enhanced visual-semantic Representation Model (TERM) can be used, which incorporates a Transformer-Enhanced (TE) module to mine context relationships between local regions in images or between words in sentences, providing more fine-grained clues for image and text matching. These methods have been shown to improve the performance of text-image retrieval and achieve state-of-the-art results.
What is the definition of semantic data science?4 answersSemantic data science refers to the analysis and understanding of data in the context of decision-making processes, using techniques from knowledge representation and program analysis. It involves the creation of semantic representations of computer programs and data through the enrichment of dataflow graphs, which reveal the connections between code and its subject matter. Semantic data science also utilizes semantic methods to connect and interpret large amounts of metadata collected in research projects, enabling the mapping of content and context using ontologies. The goal is to provide intelligent tools and systems that facilitate the analysis, interpretation, and utilization of data in various domains, such as combating the COVID-19 pandemic. By incorporating semantic-based interconnected knowledge graphs, data science efforts can be transformed, allowing for the interrelation of knowledge and analysis findings to meet diverse information requirements.
How can semantic communications be used to improve the performance of IoT systems?5 answersSemantic communications can improve the performance of IoT systems by focusing on the meaning behind transmitted information and reducing bandwidth requirements. This approach ensures that only relevant information for the underlying task is communicated to the receiver, leading to more efficient communication. Traditional wireless communication security techniques for bit transmission cannot be directly applied to semantic IoT (SIoT) due to the lack of new security performance indicators. Therefore, it is necessary to rethink wireless communication security in the SIoT. In addition, the adoption of deep learning techniques for joint source-channel coding (JSCC) in semantic communications has shown remarkable performance gains, particularly in low-latency and low-power scenarios encountered in edge intelligence applications. Furthermore, the use of edge computing and semantic caching models can reduce the time and resources required to establish individual knowledge bases, making semantic communication more accessible and efficient.
How can semantic communication be used to improve communication between humans and machines?4 answersSemantic communication can improve communication between humans and machines by focusing on the meaning behind transmitted information. It seeks to ensure that only relevant information for the task at hand is communicated to the receiver, reducing bandwidth requirements. In semantic communication, both the transmitter and receiver share common knowledge, which can be used to extract small-size information at the transmitter and recover the original information at the receiver. By using deep learning techniques and knowledge graphs, semantic communication systems can improve the accuracy of semantic representation and remove semantic ambiguity. Additionally, the use of edge computing can aid in the semantic encoding/decoding process, which requires significant computing power and storage capabilities. Overall, semantic communication enables more efficient and accessible communication between humans and machines by focusing on the intended meaning of the information being transmitted.