Reviewerly/exHarmony huggingface dataset:
Feature Construction, Feature Reduction and Search Space Reduction Using Genetic Programming

Feature construction and feature selection are essential pre-processing techniques in data mining, especially for high-dimensional data. The principal goals of such techniques are to increase accuracy in classification tasks and reduce runtime in the learning process. Genetic programming is used to construct a new high-level feature space. Additionally, the feature selection process, immersed in the task, is seized. Therefore, a set of features with relevant information is obtained. This paper presents an approach to reducing the features of high-dimensional data throughout genetic programming. Moreover, reducing the search space eliminates features that do not have considerable information over the generations of the search process. Although the approach is simple, competitive results are achieved. In the implementation, the wrapper approach is used for the classifier to lead the searching process.
Overview on Risk of Earthquake Early Warning for Transportation Systems

Earthquake is a natural disaster that cannot be controlled by human beings currently. It will make major impacts on transportation, such as hindering traffic, causing fatality, and damaging properties etc. Therefore, transportation should be protected by an earthquake early warning (EEW) system. This paper envisions the application of EEW in various transportation modes and scenarios, such as highways, railways, bridges, tunnels, natural gas lines, etc. False alerts and missed-alerts are two types of risk events involved with EEW, which were frequently reported over the years. This paper presents a risk evaluation model of a false alert and a missed-alert of EEW for the transportation mode, and a method to calculate false alert rates and missed-alert rates.
A Fast System for Person Description Search in Videos

Security CCTV cameras are important for public safety. These cameras record continuously 24/7 and produce a large amount of video data. If the videos are not reviewed immediately after an incident, it can be difficult and timeconsuming to find a specific person out of many hours of recording. In this work we present a system that can search for people that fit a textual description in a video. It utilizes a imagetext multimodal deep learning model to calculate the similarity between an image of a person against a text description and find the top matches. Normally this would require calculating the textimage similarity scores between one text description and every person in the video, which is $O(n)$ in the number of people in the video and therefore impractical for real-time search. We propose a solution to this by pre-calculating embeddings of person images and applying approximate nearest neighbor vector search. At inference time, only one forward pass through the deep learning model is needed, the computational cost is therefore the time to embed a text description $O (1)$, plus the time to perform an approximate nearest neighbor search $O( \log (n))$. This makes realtime interactive search possible.
A Study of Comparative Methods for Closed-Price Cryptocurrency Prediction

This work aims to present a comparative study method of closed-price prediction for cryptocurrencies namely machine learning-based and deep learning-based methods. Three data normalization techniques in the data pre-processing stage were also compared. They are log scaling, min-max, and z-score normalization. In the machine learning-based method, support vector regression (SVR) was used to develop the predictive model, whereas long-short term memory (LSTM) was used in the deep learning-based method to develop the predictive model. In addition, three datasets are used in this study namely Bitcoin (BTC), Ethereum (ETH), and Litecoin (LTC). The results of evaluating the predictive models using RMSE and MAPE revealed that SRV with RBF kernel produced slightly better results than LSTM. Compared to other data normalization methods, log scaling normalization produced outcomes that are more satisfactory.
Permutation Invariant Agent-Specific Centralized Critic in Multi-Agent Reinforcement Learning

We proposed a permutation invariant agent-specific centralized critic using graph convolutional networks in multiagent reinforcement learning. We consider an environment with partial observability where a joint observation of homogeneous agents is used as a state information in centralized training. A joint observation of homogeneous agents is permutation invariant, meaning that different permutations must be treated as the same. However, a traditional deep network like multilayer perceptron (MLP) outputs different values to different permutations, despite being the same data. A centralized critic using MLPs to represent joint observation of homogeneous agents suffers from data inefficiency because it only learns a single permutation instead of all permutations. Previous work has addressed this problem using graph convolutional networks (GCN) for “agent-agnostic’' centralized critics. Our work extends the use of GCNs to an “agent-specific’' centralized critic such as the critic used in Counterfactual Multi-Agent Policy Gradients (COMA) algorithm. We introduce three GCN variants of agentspecific critic architectures. Our experimental results on the multi-agent particle environment with COMA algorithm show that all GCN critics outperform the MLP baseline critics. Finally, we concluded that as the number of agents increases, the critic that takes advantage of agent homogeneity by separating global and local feature representation is the most scalable in terms of time complexity.
Smart Contracts on the Management of EHR: Review, Challenges, and Future Directions

Computer systems at the security and management level play a role in the era called Digital health. Thus, security processes in the medical field benefit health service providers, their administrators, and registered patients with an electronic medical record (EHR) since the information belongs to them. Smart contracts, in conjunction with blockchain technology, are a way to work and improve these security components associated with EHR. This manuscript describes different proposals presented by researchers where smart contracts are used for authentication and entry into a system, as well as a brief comparison between the proposed algorithms, their performance in terms of time and concurrency, and the security tests carried out to confirm system security. This paper also verifies the open issues and proposed future works described by the different authors.
Machine Learning Techniques to Detect Failure in Hard Disk Drive Test Process

This paper presents machine learning techniques to detect servo track read back failure in hard disk drive manufacturing test process. The experiments are performed on real hard disk drive manufacturing data with 359 parameters. The data is high-dimensional and highly imbalanced. Feature selection techniques with filter method and embedded method with light gradient boost are used to reduce the dimension of data. We apply two machine learning algorithms: Support Vector Machine (SVM) and Extreme Gradient Boosting (XGBoost). Each algorithm applied with three different imbalanced data handling methods: Synthetic Minority Oversampling Technique (SMOTE), Different Cost (DC) and SMOTE with Different Cost (SDC). XGBoost algorithm shows good performance on both Receiver Operating Characteristic area under curve (ROC-AUC) and Precision-Recall area under curve (PRC-AUC) metrics. The XGBoost with SMOTE achieved the best performance with 97% of accuracy.
Specification of Incremental Requirements Upstream of System Design

Drones and autonomous robots have started to be widely used in various application domains like military and real-life operations such as transportation, logistics, infrastructure inspection, disaster relief, rescue operation, etc. Therefore, industries have started designing and modeling these drones and autonomous robots to address general operations such as surveillance and infrastructure inspection of inaccessible environments. Though, these drones are still quite limited to relatively simple operations due to its immature level of missions. However, an extension of the drone operation generally leads to equip the drone with additional components such as camera, storage device, lidar, sonar, sensors, etc. In this article, a focus is placed on the deployment of a pre-requirements and post-requirements specification approach while dealing with requirements and constraints of the designed system. This is done in order to maintain control over the overall stakeholder needs and mitigates the risk of an useless system. More precisely, a high attention must be paid to the payload of the drone, the volume and the dimension dedicated to the payload.
Out of Distribution Reasoning by Weakly-Supervised Disentangled Logic Variational Autoencoder

Out-of-distribution (OOD) detection, i.e., finding test samples derived from a different distribution than the training set, as well as reasoning about such samples (OOD reasoning), are necessary to ensure the safety of results generated by machine learning models. Recently there have been promising results for OOD detection in the latent space of variational autoencoders (VAEs). However, without disentanglement, VAEs cannot perform OOD reasoning. Disentanglement ensures a one-to-many mapping between generative factors of OOD (e.g., rain in image data) and the latent variables to which they are encoded. Although previous literature has focused on weakly-supervised disentanglement on simple datasets with known and independent generative factors. In practice, achieving full disentanglement through weak supervision is impossible for complex datasets, such as Carla, with unknown and abstract generative factors. As a result, we propose an OOD reasoning framework that learns a partially disentangled VAE to reason about complex datasets. Our framework consists of three steps: partitioning data based on observed generative factors, training a VAE as a logic tensor network that satisfies disentanglement rules, and run-time OOD reasoning. We evaluate our approach on the Carla dataset and compare the results against three state-of-the-art methods. We found that our framework outperformed these methods in terms of disentanglement and end-to-end OOD reasoning.
An Innovate Noise Extinguish Technique Ground on Iterative Median Filter for Low- Density Fix-Valued Impulse Noise

In noise extinguish inquisition commonality, exclusively the impulse noise, the noise extinguish technique is a conspicuousness mechanism, which is originally achieved earlier than alternative enrichment techniques. Consequently, this research paper proposes the performance analysis of an innovate noise extinguish technique ground on iterative median filter, which is originated from median filter in order to accomplish the frailty of the aforementioned noise extinguish inquisition, for low scattering fix-valued impulse noise (5%-20%). In the simulation inquisition, generous noisy photographs that are gathered together of F16, Girl, Lena, Pepper and House at low scattering of noise consistency, are simulated in order to testify the functioning of the noise extinguish technique ground on iterative median filter. From these aftermaths, the noise extinguish technique ground on iterative median filter has high-caliber functioning, which is compared with SMF, MF, AMF, and IMF.
The Finding of Factors to Motivate Stakeholders in the Coffee Supply Chain Towards the Use of Blockchain Technology: Case of Chiang Rai Coffee Supply Chain

Using a blockchain e-commerce platform for transparent traceability and promoting coffee trade between stakeholders in the coffee supply chain is one proposed approach to identifying the value of coffee. However, the efficient work process of the Blockchain Ecommerce Platform (BEP). Studying the factors that motivate stakeholders to use the BEP system is necessary. This research applied The unified theory of acceptance and use of technology (UTAUT) to study the factors that motivate stakeholders in the coffee supply chain toward using blockchain technology. The result shows that social influence (SI), facilitating conditions (FC), performance expectancy (PE), and effort expectancy (EE) are other factors affecting the adoption of BEP technology among coffee supply chain stakeholders in Chiang Rai. Furthermore, this study reveals that users over 45 years of age tend to be unacceptable in using BEP technology due to their relatively less ability and readiness to use information technology comparing with the other populations. Training to promote awareness of the blockchain benefits provides appropriate information technology equipment, will be one way to encourage acceptance of technology for those users.
The effect of data anonymization on a data science project

Personal data is protected by Personal Data Protection Act (PDPA). Data Science directly has a considerable impact on collecting and analyzing personal data. Data anonymization is a new technique in compliance with the Thai Personal Data Protection Act (PDPA) and cyber security. On the same token, the meaning of data analytics is also lost while we gain data privacy. This paper aims to demonstrate the effect of implementing data privacy on a data science project. A classification model is used to measure the effect of Kanonymity. A healthcare dataset is used for a demonstration. With k range from 10 to 10000, our lab result shows information loss compared to k values in the conclusion section.
Designing Augmented Learning Platform with IoT and Chatbot-based Learning Technology for Primary School Students in Northern Thailand

Rural students such as those living in Northernmost regions of Thailand, tend to have lower learning performance compared to native Thai students due to the lack of access to educational resources, and learning technologies. To empower the knowledge, and 21st century skills of this user group, promoting active learning, and connecting subjects with students’ everyday lives is crucial. This paper presents an educational platform that integrates IoT (Internet of Things), and Chatbot-based learning system called ZiZy-BOT to facilitate learning for primary school students in Northern Thailand. This platform was designed to enable young learners to gain a more meaningful learning experience with an interactive agent and the real world information, anywhere anytime, and when they needed. The user context study was conducted, and observed to understand how 12 rural-students design, and interact with their paper-prototype. The results of the study contributed to a conceptual framework, and the key findings will lead to developing the platform, including augmented real-world information, conversation-based interface with friendly design, and a collaborative learning space.
Research on Self-driving Based on Dynamic Recognition of Traffic Signs

Traffic sign recognition is important for self-driving cars, and several approaches use deep neural networks to solve the problem. However, most of them use pre-collected datasets and are fed into cluster computing for testing traffic sign recognition, which we call traffic sign detection in a static environment. These approaches ignore the noise interference of the realworld environment on traffic signs and reduce the difficulty of implementing the task. Therefore, we built one agent which detects real-world traffic signs in realtime. We call it dynamic detection. To implement the real-time traffic sign recognition task, we chose the lightweight MobilenetV2 as the baseline model and filtered the optimal activation function and optimizer for MobilenetV2. The experiments show that our method achieves similar results to existing methods in terms of Accuracy-Static. However, our proposed MAMobilenetV2 achieves 95.9% in terms of AccuracyDynamic, which is significantly higher than existing models.
Disambiguation of Web Search Terms based on Clustering using Page Rank and Distance between Words

Web search is an indispensable activity in our daily lives. However, when users are not familiar with search operations, there is a problem that they cannot input appropriate search terms and cannot reach the desired information. In this study, we construct a system that extracts important words from search results, divides the words into several groups using clustering, and presents the groups to users. Experimental results show that clustering performance is improved by reflecting the rank of documents in the search results and the distance of words in the search results in judging the similarity between words. The rank weights of the proposed method showed somewhat better results than the conventional method for words that are polysemous but whose meanings are relatively easy to distinguish. As for the index weights, good results were obtained when the search phrase was an abbreviation of a word or when it was often accompanied by other words to form a noun phrase.
Utilization of Augmented Reality-Based Application to Promote Digital Citizenship

The purpose of this paper is to describe the process of creating an Android-based application with Augmented Reality feature as well as the user’s evaluation of the application. The application was created to equip students with some knowledge on digital citizenship. As the users of digital technologies, college students have to have the ability to peruse technology and participate in online activities in a responsible, safe, and legal manner. The augmented reality-based application was designed to teach students the nine elements of digital citizenship. This paper also explains the feedback from the users of the application to further improve the functionalities of the application for future use.
An Online Gap Analysis on Cyber Security Principles for Thailand Organizations Based on ISO/IEC 27001:2013 Standard

The cybersecurity act enforcement in Thailand has been promulgated since 2019. Especially the Personal Data Protection Act (PDPA) forces all organizations to comply. However, there is no security standard relying on PDPA. The most well-known security standard is ISO/IEC 27001:2013, which can use as a security guideline for various organization sizes. Unfortunately, ISO/IEC 27001:2013 requires a high budget to analyze and adapt it to the organization. Therefore, we proposed an online ISO/IEC 27001:2013 gap analysis to provide the preliminary guidance for the organizations to sketch up their cyber security policy. This proposed web application enables users to evaluate their organization based on the ISO/IEC 27001:2013, Annex A. The online gap analysis covers a1114 security standard domains, generating the average score and security safety level for the organizations. We evaluated this online system with twelve organizations in various sectors both from industry and government. The result reveals that the industrial sectors scored higher than the governmental sectors in overall security level. Therefore, the proposed system is practical to be implemented as one part of the Information Security Management System (ISMS).
Improving the ID3 Algorithm By Filtering Out Attributes With Values Of 0 or 1

The iterative Dichotomiser 3 (ID3) algorithm is a classification algorithm that generates a decision tree, and it is one of the most simple and well-known tools. However, there is still room for improvement. This paper aims to reduce the running time of generating a decision tree by ignoring attributes that have an information gain value of 0 or 1 and the experiment result shows that the improved ID3 algorithm can significantly reduce the running time by more than 10 percent when compared with the traditional ID3 algorithm.
Pre-Impact Fall Detection Based on Wearable Inertial Sensors using Hybrid Deep Residual Neural Network

Falls are the primary cause of fatal and nonfatal injuries among the elderly. Consequently, pre-impact fall detection that identifies a fall before the body’s collision with the ground is of essential importance. In the latest days, academics have moved their attention from post-impact fall detection to pre-impact fall detection. Pre-impact fall detection systems typically use either a threshold-based or machine learning-based approach, and the thresholding is challenging to determine accurately for threshold-based methods. In addition, while additional features can sometimes assist in categorizing falls and non-falls more precisely, the value computation of prominent features will be too time-intensive, using too much of the algorithm’s operating time. We proposed a pre-impact fall detection method employing wearable inertial sensors and a deep residual model to address the limitations of feature extraction, threshold definition, and algorithm sophistication. After training on a large-scale motion dataset known as the KFall, the suggested deep learning model could identify with 91.87% accuracy in 0.5 second.
Linguistic Resources Construction for Food and Beverages in Thai Texts for Product Storytelling Text Generation

Due to the impact of COVID-19, most people have to quit their jobs. As a result of the epidemic, many people are turning to open online stores on e-commerce platforms, and people new to this market are inexperienced in competing with other stores. One of the critical points that can help them is to present their stories through merchandising. The presentation of a story through sales can be like telling a story about their store or the product. However, writing a good story is not easy. So, this research aims to help the sellers by constructing linguistic resources that sellers can use for writing a story about their products. In this paper, 3378 token product description texts were collected and ranked by the Text Ranking method to determine the frequency of frequently-used words. The 1,853 words from the Text Ranking were applied for text modeling using Latent Dirichlet Allocation (LDA). The result shows that these words can be categorized into seven food and beveragerelated topics. Moreover, sellers can use these words for writing product descriptions.
Beauty Face: An Android Application for Cosmetic Consumers to Try On and Receive Product Recommendation

Nowadays, the number of smartphone users shopping online over the internet is increasing due to the COVID-19 pandemic situation. People need to stay safe at home and work from home according to the social distancing policy. It is difficult for people to go shopping onsite at a physical store. Especially, cosmetic consumers cannot try on cosmetic products, and they are not sure which product is suitable for them. Therefore, to solve these problems, this research project proposes “Beauty Face”, which is an android application for cosmetic consumers who would like to try on products and receive recommendations. The Beauty Face application allows users to find the desired products and view product information. Also, it provides a useful feature for users to try on products by applying augmented reality (AR) technology to simulate cosmetic and beauty products on the user’s face. Moreover, the Beauty Face application can recommend products that are appropriate for users by applying machine learning (ML) technology. The Beauty Face application can be helpful for cosmetic users to be more convenient in buying cosmetic and beauty products online and reducing chances of being infected or spreading COVID-19.