One important obstruction against Thai COVID-19 recovery is fake news shared on social media that is one of the “Artificial Intelligence Open Issues against COVID-19” reported by Montreal.AI. Misinformation spread is one of the main cyber-security threats that should be filtered out as the IDS for maintaining COVID-19 information quality. To detect fake news in Thai texts, Thai-NLP techniques are necessary. This paper proposes a state-of-the-art Thai COVID-19 fake news detection among word relations using transfer learning models. For pre-training from the global open COVID-19 datasets, the source dataset is constructed by English to Thai translating. The novel feature shifting is formulated to enlarge Thai text examples in target dataset. Machine translation can be used for constructing Thai source dataset to cope with the lack of local dataset for future Thai-NLP applications. To lead the knowledge in Thai text understanding forward, feature shifting is a promising accuracy improvement in fine-tuning stage.
<span>Thai textual memes have been popular in social media, as a form of image information summarization. Unfortunately, many memes contain some hateful content that easily causes the controversy in Thailand. </span><span>For global protection, t</span><span>he </span><em><span>Hateful Memes Challenge</span></em><span> is also provided by </span><em><span>Facebook AI</span></em><span> to enable researchers to compete their algorithms for combating the hate speech on memes as one of </span><em><span>NeurIPS’20</span></em><span> competitions. As well as in Thailand, this paper introduces the Thai textual meme detection as a new research problem in Thai natural language processing (Thai-NLP) that is the settlement of transmission linkage between scene text localization, Thai optical recognition (Thai-OCR) and language understanding. From the results, both regular and irregular text position can be localized by one-stage detection pipeline. More scene text can be augmented by different resolution and rotation. The accuracy of Thai-OCR using convolutional neural network (CNN) can be improved by recurrent neural network (RNN). Since misspelling Thai words are frequently used in social, this paper categorizes them as synonyms to train on multi-task pre-trained language model. </span>
Availability is one of the biggest challenges which slow down the adoption of Cloud computing in the IT industry. A failover system can be designed to use as a backup in case of Cloud failure or unavailability. In this paper, we introduce the method for designing a local failover system for Cloud. Since a failover system has less resource and limited scalability; it cannot handle all workloads previously on the Cloud. We overcome this drawback by adapting the full operation performed in the Cloud into a light-weight operation optimized for a failover system. A light-weight operation consumes less resource but also has reduced quality of service (QoS) compared to the full operation. We also define a write type of data according to change and future use of that data. We adapt the full operation into a lightweight one by reducing the number of write types or changing one type to another. In other words, we reduce the quality of service of the system in order to serve more requests. Experimental result shows that a system with full operation can sustain an average of 472 maximum numbers of requests, and a system with light-weight operation can sustain 844 requests, an improvement of 179%.
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