Existing studies on extractive summarization have primarily focused on scoring and selecting summary sentences independently. However, these models are limited to sentence-level extraction and tend to select highly generalized sentences while overlooking the overall content of a document. To effectively consider the semantics of a document, in this study, we introduce a novel machine reading comprehension (MRC) framework for extractive summarization (MRCSum) by setting a query as the title. Our framework enables MRCSum to consider the semantic coherence and relevance of summary sentences in relation to the overall content. In particular, when a title is not available, we generate a title-like query, which is expected to achieve the same effect as a title. Our title-like query consists of the topic and keywords to serve as information on the main topic or theme of the document. We conduct experiments in both Korean and English languages, evaluating the performance of MRCSum on datasets comprising both long and short summaries. Our results demonstrate the effectiveness of MRCSum in extractive summarization, showcasing its ability to generate concise and informative summaries with or without explicit titles. Furthermore, our MRCSum outperforms existing models by capturing the essence of the document content and producing more coherent summaries.

Nested Named Entity Recognition (NER) poses a significant challenge in Natural Language Processing (NLP), demanding sophisticated techniques to identify entities within entities.This research investigates the application of Large Language Models (LLMs) to nested NER, exploring methodologies from prior work and introducing specific reasoning techniques and instructions to improve LLM efficacy.Through experiments conducted on the ACE 2004, ACE 2005, and GENIA datasets, we evaluate the impact of these approaches on nested NER performance.Results indicate that output format critically influences nested NER performance, methodologies from previous works are less effective, and our nested NER-tailored instructions significantly enhance performance.Additionally, we find that label information and descriptions of nested cases are crucial in eliciting the capabilities of LLMs for nested NER, especially in specific domains (i.e., the GENIA dataset).However, these methods still do not outperform BERT-based models, highlighting the ongoing need for innovative approaches in nested NER with LLMs.

In color-independent visual multiple-input multiple-output (hereinafter, “visual-MIMO”) systems that use color-space-based modulation techniques, determining the color of the received symbols using image sensors is difficult. In this study, we apply the K-means based color quantization method at the receiving end to minimize the influence of the wireless channel noise. In addition, we propose a method to determine the symbol color using a dynamic palette that was generated based on the characteristics of a color-independent visual-MIMO system. Finally, by comparing the position of the received symbol with the position of the transmitted symbol in the color coordinate space, we prove that the proposed method improves the accuracy of symbol decision (i.e., symbol error rate performance).

Because LEDs offer flexible expressions such as brightness, color control, and various patterns, they are popularly used in multidevice interactions. Moreover, LEDs have excellent physical characteristics. However, existing LED light-based wearable interactions are designed for interest and attention. So, LED can be used in fashion as it can give new look to our style and at the same time also as an interaction device. Therefore, in this paper, we present the design guideline for regulating the technical implementation, design strategies, and directions of interactive LED devices. The technology and design concepts are demonstrated through a case study (analysis) of an existing LED light-based wearable interaction. We also design a scenario-based iterative collaborative design process model. Finally, we develop a smart fashion of modular strapped cuffs and zipper slider types that can be attached and detached according to the user’s preference as the interactive smart fashion using user-oriented visible light communication, ultimately pursuing a visual-MIMO (Multiple-Input Multiple-Output) product through stepwise strategy.

In this paper, we consider color gamut multi-dimensionally for optimal constellation design in the color-space-based modulation methods. If points of some symbols are outside the three-dimensional color gamut, this can be a serious problem such as non-uniformity of the target color and the degradation of SER performance. Therefore, we propose the constraints in three-dimensional color gamut essential in the constellation design. Finally, through simulation, we demonstrate that the accurate and valid SER performance is obtained by considering the color gamut constraints. Finally, through simulation, we analyze the SER performance of color-space-based modulation under the color gamut constraints.