Brain-computer interface (BCI) technology has promising applications as an intuitive communication tool and in fields such as language rehabilitation. This study aims to decode human speech intentions by analyzing EEG...
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Blockchain technology has emerged as a promising solution to address key challenges in public transport, such as interoperability, privacy and transaction transparency. Despite these advances, the lack of a unified pl...
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Approximately 70 million individuals globally utilize sign languages as a means of communication due to their hearing impairment. The study undertaken in sign languages is extensive and fruitful. However, there are ov...
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ISBN:
(纸本)9781510688278
Approximately 70 million individuals globally utilize sign languages as a means of communication due to their hearing impairment. The study undertaken in sign languages is extensive and fruitful. However, there are over 300 sign languages worldwide, but most research focuses on a single language [1]. Creating AI models for sign language challenges sometimes requires large datasets, which can be difficult to produce. Various research has produced datasets for sign language using various methodologies;nevertheless, they often focus on specific sign languages. Developing hand skeleton templates for sign languages provides a more efficient method than creating numerous instances of distinct signs. By creating a basic framework or structure, it becomes much simpler to utilize generative models, like GANs[2], to generate a wide range of different versions of the framework. These generative models can effectively reproduce and adjust the fundamental structures into many sign language forms, capturing the diversity in hand shapes, orientations, and movements necessary for precise sign representation. The main objective of our research is to develop a conditional generative adversarial network (cGAN) model that can generate hand images based on hand skeletons;this approach not only improves the capacity to generate sign language data on a larger scale, but also guarantees uniformity across different versions of signs. This makes it easier to create sign language recognition systems that are more reliable and flexible. To train this model, we devised a web scraping technique that produced a significant collection of hand photos taken from TED lecture recordings, together with their corresponding skeletons. Our created cGAN-based model allows researchers to generate artificial hand images by employing target skeleton inputs. This enables the creation of extensive datasets for sign language. Our contribution is expected to streamline the exploration of additional sign languages
Liveness properties are traditionally proven using a ranking function that maps system states to some well-founded set. Carrying out such proofs in first-order logic enables automation by SMT solvers. However, reasoni...
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With recent advancements in industrial robots, educating students in new technologies and preparing them for the future is imperative. However, access to industrial robots for teaching poses challenges, such as the hi...
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Deductive verification is often more efficient than alternative techniques like model checking at reasoning about functional properties of programs. This is especially true when the program under verification contains...
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Soldering irons are a hand tool that is indispensable in the process of making small series of electronic devices. Soldering irons have evolved from very simple devices without temperature control to devices with comp...
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[Context and motivation] software requirement patterns (SRPs) is one of the many techniques that contribute to requirements elicitation. At this respect, the emergence of large language models (LLMs) opens the door to...
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This paper presents a comparative study of key metrics for OCR engines in Bangla language processing. PyTesseract (a Python wrapper for Tesseract OCR) and EasyOCR were benchmarked on a novel dataset, "Bangla-Cros...
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Bio-inspired event sensors are gaining popularity at the edge, such as in robots and wearable electronics. This trend necessitates learning vast amounts of sensory data on the edge, often in few-shot or even zero-shot...
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