Reviewerly/exHarmony huggingface dataset:
Development of a Compact System to Recognize 26 Letters in American Sign Language Alphabet Based on Flexible Ionic Liquid Strain Sensors

In this paper, a sign language recognition system is presented and successfully implemented using self-developed strain sensors based on ionic liquid and uses five strain sensors attached to the fingers via a glove for detecting the relative positions of the fingers. The signals from the sensors are transmitted to a data acquisition and processing circuit system, where an algorithm decodes the data into alphabet letters. The sensitivity of the flexible ionic sensors and the functioning circuit of the system were evaluated before being integrated into the system. The experimental results showed that the ionic sensors are suitable for this application and the proposed system can recognize 26 letters in the American Sign Language alphabet (ASL) with high accuracy, up to 98.27%. A graphical user interface on a PC was also developed to convey information in the form of text and speech, enabling communication with deaf people without the need for learning sign language. This low-cost sign language recognition system has the potential to improve communication between deaf.
Two-stage Thai Misspelling Correction based on Pre-trained Language Models

Thai spelling detection and correction is a task to detect and correct mistakes in natural language. The implementation of an edit distance and dictionary mapping to identify and correct misspelled words is a widely used method for addressing this challenge in the Thai language. However, this technique excludes contextualization from the input and can yield incorrect answers. In this paper, we propose a two-stage framework that leverages pre-trained language models, such as WangchanBERTa, for correcting Thai misspellings. Our proposed method consists of a model for misspelling detection based on the token prediction task and a correction model based on masked prediction. Additionally, character edit distance (CED) can be used as a post-processing step to help improve the correction results. Our experiments were conducted on two standard datasets for Thai misspellings, namely UGWC and VISTEC-TPTH-2021. On the UGWC data, our model can help correct 3,867 out of the 6,399 misspelled words (60.43%), which is higher than the baseline's rate of 41.75%. <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sup> https://github.com/bookpanda/Two-stage-Thai-Misspelling-Correction-Based-on-Pre-trained-Language-Models
Enhancing Student Photography Skills with Web-based Photo Stock and Learning System

Digital technology has greatly increased the use of photography as a visual communication tool in social media, advertising, and education. In parallel, photography education in higher education has become more important in providing students with the technical and creative skills they need to be successful in the field of photography. However traditional university digital photography teaching methods often resulting in low-quality student work and a lack of practical skills due to instructors at universities have different experiences and skills than professional photographers. In this research, we proposed the integration of a novel web-based photography recommendation system and Google Classroom learning system into our photography courses, thereby providing students hands-on high experience and real-world training. Our experimental results show that implementing our proposed method resulted in a significant increase in the average number of accepted photos in the order of 3.8 times previously achieved using traditional methods. As well, the number of students receiving an “A” grade increased by 2.8 times compared to the traditional teaching method.
Topic Modelling using Latent Dirichlet Allocation (LDA) and Analysis of Students Sentiments

Open-ended question responses are an unstructured data source that contains extremely useful information. The idea of obtaining information using open-ended questions is particularly popular and frequently employed in various schools and digital platforms. In the field of education, universities collect and generate vast amounts of data daily, left unused or not analysed. This demonstrates the need to develop different models to promote the use of text-based data analysis techniques. In this study, surveys with open-ended questions were used to draw out analytical insights from student views on automated personalised feedback assessments. Opened-ended questions present questions that require answers that use natural language. The human language data described in text documents is made possible by the use of various Natural Language Processing (NLP) approaches. These approaches include the Latent Semantic Analysis (LSA) and Latent Dirichlet Allocation (LDA) that analyse open ended questions and surveys data. In this study, LDA is used for modelling topics to analyse students' sentiments on the feedback assessment given to them regarding their performance on the module. Providing automated personalised feedback to large student groups has many challenges, such as the type of communication delivery mode and the language used for feedback about their progress. This study offers an application of NLP in an education environment to provide meaningful and useful student feedback. The paper presents the steps, algorithm and findings so that practitioners can apply this model to their education setting. The findings reveal 87% of students have positive sentiments, 7% are neutral, 5% are confused by the personalised feedback forms. This informs practitioners about the value of the feedback but also areas for future work when offering feedback to students.
CCE-Stream: Semi-supervised Stream Clustering Using Color-based Constraints

In recent years, Numerous stream clustering techniques have recently emerged. However these techniques do not utilize the valuable background knowledge provided by domain experts. Using knowledge in stream clustering offers several advantages, including enhanced accuracy and performance of the resulting clusters. The proposed method in this research is CCE-Stream, which incorporates background knowledge as constraints for incremental stream clustering. Instance-level constraints, including Must-Link and Cannot-Link constraints, are used to guide improved clustering behaviors in various operations. Constraint operators are introduced to handle evolving constraint characteristics. CCE-Stream introduces the concept of assigning colors to constraints and a new cluster representation called Color of Cluster (CoC). Experimental results on Covertype and Electricity datasets demonstrate increased F-measure and Purity.
Modified Feature Extraction to apply PSO for solving Handwritten Digits

Handwritten Digit Recognition (HDR) is a widely used technology that has been demonstrated in various research topics. However, there are many limitations because everyone's handwriting is different, including important handwriting characteristics. The experimental results of the previously proposed methods were unsatisfactory when using limited amounts of the datasets for the training model and inefficient pre-processing. Therefore, we describe a new method for increasing the recognition rate through well-planned pre-processing and enhanced feature extraction for applying Particle Swarm Optimization (PSO). When compared to other techniques, the proposed method performed well when tested against an individual's handwritten digits and the original MNIST database. Our results are a high recognition rate and short runtime.
Analysis of the 5Rs in Thailand Medication Error Classification through Natural Language Processing

Medication errors threaten patient safety considerably, underscoring the necessity for enhanced detection and prevention techniques. A prevalent classification system in hospitals relies on the standard practice of medication administration known as the Five Rights (5R). This study seeks to develop an NLP-based tool designed to expand 5R error categorization coverage and alleviate the workload of medical professionals. The proposed method focuses on Thai medical text, incorporating Thai and English vocabulary. In this investigation, we developed a supervised learning classification framework using the Universal Sentence Encoder (USE) for sentence embedding, followed by an Artificial Neural Network (ANN) for model training. Additionally, we explored a zero-shot classification model employing pre-trained Large Language Models (PLMs). Our findings reveal that the supervised learning classification model provides the most favorable performance, albeit with the limitation of reliance on labeled datasets, which can be resource intensive. Conversely, the zero-shot classification framework's performance is less optimal. However, future advancements in Thai medical PLMs may improve efficacy and present a viable alternative for medical data analysis without dependence on labeled datasets. This initiative lays the groundwork for potential future applications and advantages within Thailand's medical domain.
A Practical Usability Study Framework Using the SUS and the Affinity Diagram: A Case Study on the Revised Online Roadshow Website

An Online Roadshow is a new model that serves the same goal as a physical roadshow. After the initial trial of the Online Roadshow website version 1.0, it was discovered that there were several aspects of the Online Roadshow website such as the website interface and functionality that might be improved. As a result, version 2.0 of the Online Roadshow website was built based on these enhancements. This study assessed the usability of the newly developed website using the proposed practical usability framework that combined the System Usability Scale (SUS) and one open-ended question, which was then analysed using the affinity diagrams. A total of 250 respondents completed the survey after experiencing the website. The result showed that the average SUS score of the new Online Roadshow website increased from 58.85 to 62.6. The overall analysis suggests that enhancements to version 1.0 using the same proposed pragmatic usability framework improves user experience.
Explainable AI for Systematic Detection of Potential Problems in Natural Language Datasets

Quality of training data is a major issue in machine learning. In some domains, data samples may be hard to obtain, and detection of problematic samples becomes essential. This work proposes to use explainable AI to explore how text misclassification occurs and how to systematically identify whether the issue arises from a dataset. The proposed method finds similar samples and uses SHAP to calculate feature attributions. This approach helps to efficiently detect conflicts between samples and highly attributed features, allowing for the identification of many root causes of misclassification. This is particularly useful for rare samples and questionable labels. As a result, dataset quality can be improved with relatively little effort, which in turn enhances the predictive performance of a model. We validated the method on two datasets: Event Planner Survey and Wisesight Sentiment Corpus (WSC). Accuracy, precision, and recall were significantly improved in both sets. Especially, the accuracy in WSC was improved from 64.4 to 70.9% through data cleaning guided by the method.
The Comparison of Password Composition Policies Among US, German, and Thailand Samples

The study by Mayer, Kirchner, and Volkamer published at SOUPS 2017 showed that the password composition policy (PCP) strength of both the US and German websites was not influenced by the security but by the usability features of the websites. Surprisingly, the PCP strength of the banking website category was the lowest, whereas the government website was the highest. Our aim in conducting the first study is to find whether 78 Thai frequently used websites in 2018 would yield the same surprising results. Our finding showed an opposite perspective, the highest PCP strength was from the banking websites, followed by university and government websites, respectively. Two more security features were added to our study: 2FA and HTTPS. Although some German websites employing 2FA allowed lower PCPs for better usability, Thai websites with 2FA did not loosen the password requirements. Also, employing HTTPS did not impact the PCP strength. The study with Thai websites was reinvestigated in 2021, two years after the Personal Data Protection Act (PDPA) was announced. The result showed that the median PCP strength of all Thailand samples had grown from 26.6 in 2018 to 31.0 in 2021. The banking websites still retained the highest PCP strength. A significant change appeared on the government websites, increasing from 29.9 to 40.4. In summary, the security features such as the size of services, and values of assets which play no part in both the US and German PCPs were heavily concerned by Thai websites. Government and university websites in Germany and USA gave much higher PCP strength than those in Thailand. The Thai government's PCP strength sharply increased in 2021 due to the privacy law. Nevertheless, it was still lower than the results in Germany and USA in 2016. Therefore, the criteria influencing PCP vary depending on the country.
Cell-key Perturbation Data Privacy Procedure for Security Operations Center Team

Data privacy laws such as GDPR in Europe and PDPA in Thailand are both laws to protect personal data. The data center task is also a data service organization that needs to do data publishing services among their stakeholders. The challenging task for the Security Operation Center (SOC) team is to analyze all security risks such as data breaches. Most cases of data breach problems are overlooked cases that occur indirectly by guessing from other prior knowledge. For example, attackers combine our dataset with other data sets to reidentify personal data. This attack is called a re-Identification attack that causes a data breach. To fix the risk, statistical noise control techniques for data anonymization are explored and implemented in this study. A Cell-key perturbation is to fix the attack without modifying an original dataset but return an answer dataset with noise addition per query instead.
BLUR &amp; TRACK: Real-time Face Detection with Immediate Blurring and Efficient Tracking

This paper proposes the BLUR & TRACK system that anonymizes detected face based on blurring algorithm and supports efficient retrieval of the specified human face recorded in the video file. The development of our system has been driven by the privacy regulations such as GDPA and PDPA that enforce the data controllers and data processors to be aware of the high protection of personal data privacy. Most CCTVs available in the markets are not initially designed to serve the face blurring of people while the video files have been recorded. This is vulnerable to privacy breaches if those files are not strictly controlled with appropriate access control mechanisms. In this paper, our BLUR & TRACK system incorporates two major functions including blurring the human face while the video is running and the efficient tracking function that supports the face query by authorized person. To this end, we used the image frame to do face and object detection, extract the area of interest for the face and object based on region of interest (ROI). Then, we applied blurring to ROI by combining every frame that was blurred into the video. Then, they are kept in the graph database for efficient retrieval. Finally, we reported the experiment results related to the precision and recall of our proposed scheme when it was implemented with RetinaFace and YOLOv5Face models.
Solving Non-IID in Federated Learning for Image Classification using GANs

Federated Learning (FL) has emerged as a powerful methodology for training centralized models while preserving data privacy by using trained parameters from local models that are distributed among decentralized sites. Despite its growing popularity in the development of cloud-based Internet of Things (IoT) applications, FL performance is significantly impacted when data is non-independently and identically distributed (non-IID). This paper proposes a novel framework, called GANs Augmented IID - Federated Learning (GAIID-FL) to tackle the diversity of data distribution among clients in FL. The GAIID-FL framework collaboratively trains Generative Adversarial Networks (GANs) and then employs the trained GANs models to generate synthetic data and distribute proportionally to each device. Using GAIID-FL effectively restores the IID data distribution for the setting. The experimental results demonstrate that our framework can achieve up to 45% improvement in accuracy. In addition, the batch collaborative training approach for GANs models can reduce communication overhead by up to 90 times when compared to the unoptimized method.
CannabisO: The Ontology-based Knowledge Model for Safe Cannabis Consumption in Thailand

In June 2022, the Ministry of Public Health of Thailand announced that cannabis can be used in healthcare, medical, research, and commerce. This announcement led to a wide discussion in social media, TV broadcasts, and public media about how to use cannabis safely, the symptoms that cannabis can relieve, side effects, and precautions of cannabis. Prior to the Ministry's announcement, cannabis was illegal and forbidden to consume since it was considered as a kind of drugs and was dangerous to consume, even privately. Most people still lack of proper knowledge about safe cannabis consumption. Also, information provided on the Internet and social media about cannabis is now often redundant, inconsistent, and unreliable. Therefore, this study gathers official and reliable published knowledge about cannabis consumption specifically for general users. CannabisO is modeled as a formal, shareable, and reusable ontology-based knowledge model to be used for a Q&A system that can help answer questions about safe edible consumption of cannabis, precautions and side effects of cannabis consumption, and lastly, symptoms that can be alleviated by cannabis. Several interesting Q&As are demonstrated in this paper.
A Study of Using GPT-3 to Generate a Thai Sentiment Analysis of COVID-19 Tweets Dataset

This study evaluated the effectiveness of using synthetic text datasets generated by GPT-3 for sentiment analysis with deep learning models, namely Bi-GRU and Bi-LSTM. The study compares the performance of these models on both synthetic text and Label Tweet datasets using GPT-3 and reveals that deep learning model performance is dependent on the dataset's nature. The results indicate that using synthetic text generated by GPT-3 significantly enhances the accuracy of both models, with Bi-LSTM achieving an accuracy of 0.84 and Bi-GRU achieving an accuracy of 0.85. The study underscores the importance of meticulous dataset selection and preparation for developing precise and effective deep learning models for various sequential data types. The findings demonstrate that synthetic text datasets generated by GPT-3 can serve as a valuable resource for developing deep learning models as they are labeled and save researchers time and effort in manual labeling of large datasets.
Effects of Speech Duration on Preserving the Identity of Synthesized Voice

This paper studied the identity preserving performance of the speech synthesized model when durations of speech samples in Thai language were varied. In particular, two experiments were designed to investigate such property of the model. The first experiment was set to reflect the identity preserving performance of the identity vector derived from speech synthesized model. The results suggest that better identity vector quality is achieved when the longer duration of a Thai speech signal is used as shorter speech signals result in identity vectors that are more dispersed. The second experiment was set to directly reflect the identity preserving performance of the synthesized voice signal generated from the speech synthesized model in independent speaker recognition systems. The results similarly suggest that a better identity-preserving voice signal is achieved when the longer duration of Thai speech signal is used as shorter speech signals result in synthesized voice signals with larger distances from the real voice signals. Therefore, the trade-off between usability and quality of synthesized voices must be carefully considered when developing applications from such models. In addition, the investigation framework used in this study could be used to evaluate the newly developed identity-preserving speech synthesized models.
Office Syndrome Treatment Using Augmented Reality Technology on Mobile Applications

Office syndrome is caused by sitting in the same posture for a long time. or in an inappropriate position, such as a table or chair that is unbalanced in height The office syndrome can cause 3 types of pain. The first is muscle pain in specific parts. the second is Nervous system symptoms such as numbness., tingling., and the last one is a suppressed nervous system. Therefore, it is necessary to strengthen the posture. or undertake physical therapy to prevent the occurrence of office syndrome by stretching the muscles and to promote posture management [1]. It is suggested that office syndrome can be prevented by exercising with stretching postures. Therefore., augmented reality technology has been applied in the treatment of office syndrome and to help create more interest in users.
Application of RBF Neural Networks for Solving the Inverse Light Scattering Problem of Platelets

In this paper, the possibility of using Radial Basis Function (RBF) neural networks to solve the inverse problem of light scattering for platelets is considered. The database of theoretical signals has been generated by the discrete dipole approximation (DDA) method and has been used to train the predictive model. The method is tested on model and experimental data. The main features of the RBF neural network solution method have been considered and compared with the DATABASE method. The error of solving the inverse problem for the proposed neural network model was 6.4-11 % with a solution time of 8ms.
ECHAIN: Securing Electronic Device Provenance through Privacy-Preserving Consortium Blockchains

A conventional electronic device provenance is vulnerable to several attacks due to the limited traceability of transactions and a lack of unclonable device identities. Although electronic chip identification (ECID) has been suggested to improve security, discussion on ECID data management and users’ privacy preservation in such a decentralized device provenance management system is insufficient. In this work, we propose a framework named ECHAIN. The novelties include: (1) it uses uniquely hashed electronic chip identification (ECIDs) to maintain the security of sensitive ECID data; (2) it allows most transactions processed in parallel with a part of blockchain nodes participating to maximize the system efficiency; and (3) it employs ECID-based PKI keyless encryption to protect users’ privacy from the blockchain system. A prototype system is implemented and deployed on a 58-node consortium blockchain. Compared with the conventional solutions, experiments show that ECHAIN incurs less than 10% overhead when registering electronic devices while delivering up to $5\times$ higher transaction throughput.