Reviewerly/exHarmony huggingface dataset:
Developing a Game-Based Speech Recognition System to Facilitate Oral Training Performance for Hearing Loss Children

Oral training of hearing loss children is an important issue since hearing loss children are difficult to hear what people are saying and also difficult to correct their oral expression. Although hearing aids or cochlear implants can assist hearing loss children in learning language and communicating, they still cannot own healthy hearing. Therefore, hearing loss children still needs long-term listening and speech therapy to improve oral expression ability. However, speech-language pathologists are significantly insufficient to support the therapy of hearing loss children in Taiwan. Therefore, this study applied speech recognition technology to develop a game-based oral training system to assist hearing loss children in conducting oral training. To evaluate the effectiveness of the proposed system, three hearing loss children were invited to use the system for a week to conduct oral training, and the effectiveness of the system was measured by recording the game process and a questionnaire. The results indicated that the proposed system can improve the children’ pronunciation abilities. Moreover, the children have high motivation and satisfaction to use the system to conduct oral training.
Facilitating Geometry Learning Through Real-Time Collaborative Activities with Augmented Reality in Authentic Context

In this study, a smart Ubiquitous Geometry (Smart-UG) system with smart mechanisms support was developed to facilitate learners’ geometry learning through measuring objects in authentic context. Further, we designed a real-time collaborative measuring function based on the augmented reality (AR) in the mobile device. Learners were required to measure objects collaboratively with their peers, calculate the surface area and perimeter of the objects in authentic contexts, and discuss. In order to facilitate meaningful collaboration, interaction and socialization among peers, we designed the smart mechanisms that provided learners with a way to ask others for help if they face difficulty. Participants were fifth-grade learners from two classes that were assigned into different groups. The experimental group (EG) consists of 26 learners and the control group (CG) consists of 26 learners with/without smart mechanisms support, respectively. The results revealed that EG with assistance of smart mechanisms like peer help significantly improved geometry ability and got higher scores than CG. In addition, EG who completed more practices, made fewer mistakes, and asked others for help had a better understanding of geometry concepts. Moreover, learners showed a positive attitude and high intention toward Smart-UG. Therefore, our Smart-UG with the smart mechanism support could facilitate learners to learn geometry smartly in authentic context and enhance their geometry ability.
Virtual Assets: A Legal, Causal, and Case Study Analysis of the Influence of COVID-19 on the Use of Virtual Assets to Launder Money and of the European Union Regulatory Reaction on It

During COVID-19, the use of virtual assets to launder money surged. Because of COVID-19 consequences, including the recent migration to the digital sphere, money laundering criminals now find it easier to use virtual assets to launder illicit money. In response to this phenomenon, FATF published several recommendations and guidelines on virtual assets, and the European Union proposed a few important legal frameworks which effectively mitigate money laundering using virtual assets and its adverse effects on the economy.This thesis aims to measure the efficiency of the European Union’s post-COVID-19 legal frameworks on money laundering through the use of virtual assets. To this end, this thesis provides a causal analysis of the impact of COVID-19 on the use of virtual assets to launder money. The thesis also introduces FATF and briefly examines its recommendations on virtual assets. Following this, a comparative legal analysis of the European Union’s legal frameworks pre-COVID-19 compared to during COVID-19 was conducted. The focal frameworks included the AMLD5 and the proposals for the AMLD6, AMLR, the reform on the Regulation 2015/847, and MiCA.A case study was conducted to test the efficiency of these frameworks. This case study used a hypothetical crypto-asset service provider currently under investigation. The service provider needed advice from a consultant firm to establish the company in Italy and to improve its compliance procedures. Through the report provided by the consultant firm, the application of the discussed EU legal frameworks was tested in a hypothetical scenario. This case study provided a practical measure of the efficiency of these frameworks in combating money laundering. The results suggest that the current proposals discussed by the European Union will mitigate money laundering using virtual assets and alleviate the adverse effects on the economy.
A Proposed Framework for Learning Assessment Ontology Generator

Different institutions have shown interest in standardizing the learning result. It may be used in the same way to assess students’ learning status. The teacher must quantify the learning outcomes for evaluation purposes. It often requires a great deal of time and effort to do paper tasks. Additionally, this activity prevents instructors from concentrating on the learning process. Teachers are continuously burdened with administrative responsibilities that should be alleviated using technology that adheres to the current framework. The Bloom Taxonomy, a widely used framework for defining learning outcomes, allows for the assessment of learning outcomes at several levels. The purpose of this research is to provide a framework that will assist the instructor in completing the evaluation more quickly and accurately. This study provided an algorithm for adapting ontology and text classification technologies to detect correlations between words and keywords to aid in evaluation. It is anticipated that the categorization findings will assist in shortening the time required to complete the evaluation.
The Development of Augmented Reality Book to Promote Analytical Thinking on the Basic of Life Units for Secondary School

This study aims to 1) Develop an augmented reality (AR) book to help students develop basic analytical thinking abilities on the basic life units. 2) Examine how well students use analytical thinking when studying AR books. 3) Compare the learning outcomes of students before and after using the AR book. 4) Examine the students’ satisfaction with the AR book. 40 students from Thailand’s secondary school as the research participants. Analytical thinking tests, pre-tests and post-tests, and satisfaction assessments all are part of data collection method. The results showed that AR book “the basic of life units” was developed using unity 3D and designed using Adobe InDesign for 4 chapters. (1) the ability to identify elements of something or a particular subject (2) the capacity to recognize linked reasons, and (3) the capacity to judge the worth of items. The result found that the students’ Analytical thinking score were higher than the specified criterion as 70% of 37 people, representing 92.50%. (4) The test performance of the students’ achievement scores because of their study with the AR book exceeded the stipulated requirement as 70% of 36 people, or 90.00%, achieved such. At the level of.01 on the t-test scale, the post-test scores were significantly higher than those obtained during the initial testing. Most students gave the AR book a high rating for its ability to help them develop their analytical thinking skills ( $$\overline{x }$$ 4.56, S.D. = 0.90).
Single Image Denoising Based on Interlacing Dual-Domain Network

Deep learning (DL)-based-image denoising methods have recently achieved excellent performance by learning non-linear mapping in the spatial domain. However, these methods fail to address the noise without specific distribution because they only use features of the spatial domain. Meanwhile, existing methods that utilize features in the frequency domain fail to combine the detailed information of both domains properly for effective reconstruction and demonstrate poor generalization. Therefore, we introduce a novel interlacing dual-domain network (IDN) for single image restoration. Different from DL-based methods, which operate on the spatial or dual-domains in a certain order, our proposed IDN combine the spatial-domain image and corresponding frequency-domain image as the input and use the interlacing dual-domain module with flexible adaptability to learn the relationship between spatial and frequency domains. In experimental results, we have compared our IDN with several state-of-the-art restoration methods. Quantitative results of IDN showed that our IDN achieves enhanced effects and high index values.
An Alternative Proof of Minimum Trace Reconciliation

Minimum trace reconciliation, developed by Wickramasuriya et. al. (2019), is an innovation in the literature on forecast reconciliation. The proof, however, has a gap and the idea is not easy to be extended to more general situations. This paper fills the gap by providing an alternative proof based on the first-order condition in the space of non-square matrix and arguing that it is not only simpler but also can be extended to incorporate more general results on minimum weighted trace reconciliation in Panagiotelis et. al. (2021). Thus, our alternative proof not only has pedagogical value but also connects the results in the literature from a unified perspective.
The Effectiveness of Incorporating Augmented Reality Board Game into Temple Culture

In human civilization, religious belief is the accumulation of spirit and even the attribution of spirit. At the same time, temples are an important cultural property inherited from ancient Taiwan, with rich beliefs, history, and values. To improve students’ learning effectiveness and understanding of temple culture, this study designs a set of augmented reality table games to integrate temple culture and use table games as the media to enable students to understand temple culture knowledge in game learning. The participants were 39 students from a university in Tainan. The results show that the AR board games developed in this study can improve students’ learning effectiveness. Through the analysis of the interview results, we know that this board game has a good usability and teaching significance so that learning is no longer boring.
A New Local Search for the Bees Algorithm to Optimize Multiple Travelling Salesman Problem

This paper presents a new local search operator with the Bees Algorithm (BA) to solve a Multiple Travelling Salesman Problem (MTSP) which is a kind of combinatorial optimization problems. The Bees Algorithm is adopted to solve this problem using local search operators. These operators are used to exploit the neighbors for local solutions. In this paper, many local search operators are described thoroughly. A number of them are used with the Bees Algorithm (BA). Then, a new local search operator called SBESTSO is proposed to improve the performance of the Algorithm. The efficiency of the Bees Algorithm with the new local search operator was evaluated using different MTSP benchmark datasets. The results obtained were compared with those of other optimization algorithms for the same datasets. The comparison proved the ability and robustness of the Bees Algorithm to find optimal solutions of Multiple Travelling Salesman Problems.
Optronic convolutional neural network for SAR automatic target recognition

Deep learning technology has been widely used in synthetic aperture radar automatic target recognition (SAR ATR) tasks due to its good performance and high efficiency. However, the development of semiconductor technology cannot completely satisfy the demand for electronic hardware with high computational capability to conduct deep neural networks. To solve this dilemma, we propose an optronic convolutional neural network (OPCNN) that can perform SAR ATR tasks directly in optical platform. In OPCNN, all computational operations are implemented in optics with high speed and low energy cost. Electronic platforms are used only to control devices and transmit data information without a massive computational burden. As in digital CNNs, the convolutional layer, downsampling layer, nonlinear activation layer, and fully connected layer are all contained in OPCNN. The simulations demonstrate the feasibility of our OPCNN in solving SAR ATR problems. The good performance in experiments, which achieves 87.4% and 93.8% recognition accuracy on original and denoised moving and stationary target acquisition and recognition dataset, validates the application ability of OPCNN in practical scenario. In one-time recognition tasks, the processing time of our OPCNN is only 0.26 s with the speed of light and the power consumption of our prototype is also far less than digital processer, which is 954 W. Through analysis, our OPCNN obtains the higher processing speed and lower energy cost than digital CNNs with the same structure due to the advantages of optical technology. Also, the scalability of optical structure contributes to build more complex networks to solve complicated dataset without the demand of advanced electronic hardware.
The Application of Vision Transformer in Image Classification

This project aims to study the different performance between the Vision Transformer and a Convolu- tional Nerual Network. Google Colab will be used as the environment in this project. The dataset will use CIFAR-100 image dataset to train vision transformer and Convolutional Neural Network (CNN) separately, which are both built by Keras and Tensorflow in Python, and compare the performance of these two models through the training results. The experiment of this project has found that at the scale of 60,000 images, CNN has a slight better performance than vision transformer in general. The CNN's top-5 accuracy can reach 82.38% when using test set to evaluate the model, while the top-5 accuracy of vision transformer is 82.24%.
Text-guided Attention Mechanism Fine-grained Image Classification

Scene texts with explicit semantic information in natural images can provide important clues to solve the corresponding computer vision problems. In the text, we usually focus on using multimodal content in the form of visual and text prompts to solve the task of fine-grained image classification and retrieval. In this paper, graph convolution network is used to perform multimodal reasoning, and the features of relationship enhancement are obtained by learning the common semantic space between salient objects and texts found in images. By obtaining a set of enhanced visual and textual functions, the proposed model is highly superior to the existing technologies in two different tasks (fine-grained classification and image retrieval in contextual texts).
Recommender System Using LDA Topic Modeling Approach

Recommender systems are used to offer items to users who need assistance choosing an item from a large selection. These systems' high efficiency in areas such as e-commerce will lead to increased utility and success. One of the most common approaches used in recommender systems is to present a user with an item based on collaborative filtering (CF). So far, the traditional methods based on this approach have placed a greater emphasis on estimating the item rate based on similarity measures and calculating the similarity of neighborhood users. However, in the proposed method of this study, the latent Dirichlet allocation (LDA) has been used to extract the user's latent interests and estimate the probability of his interest in the item in combination with the values obtained through similarity measures. The proposed method was tested on three datasets from the MovieLens and Jester datasets. The experimental results show that the evaluated measures have improved.
A Survey on Relation Extraction

With the advent of the Internet, the daily production of digital text in the form of social media, emails, blogs, news items, books, research papers, and Q&A forums has increased significantly. This unstructured or semi-structured text contains a huge amount of information. Information Extraction (IE) can extract meaningful information from text sources and present it in a structured format. The sub-tasks of IE include Named Entity Recognition (NER), Event Extraction, Relation Extraction (RE), Sentiment Extraction, Opinion Extraction, Terminology Extraction, Reference Extraction, and so on.One way to represent information in the text is in the form of entities and relations representing links between entities. The Entity Extraction task identifies entities from the text, and the Relation Extraction (RE) task can identify relationships between those entities. Many NLP applications can benefit from relational information derived from natural language, including Structured Search, Knowledge Base (KB) population, Information Retrieval, Question-Answering, Language Understanding, Ontology Learning, etc. This survey covers (1) basic concepts of Relation Extraction; (2) various Relation Extraction methodologies; (3) Deep Learning techniques for Relation Extraction; and (4) different datasets that can be used to evaluate the RE system.
A News Recommendation Algorithm Based on Word2vec and Convolutional Neural Network

The information overload of news makes it difficult for users to find news they are interested in. How to obtain the news that users are interested in among tens of thousands of news has become an urgent need in the current news recommendation field. Therefore, this paper proposes a news recommendation algorithm based on Word2vec and convolutional neural network. Firstly, the news content is modeled, and Word2vec is used to train the news word vector model, and then a convolutional neural network model is used to classify news; secondly, the user interest is modeled to obtain a user-news topic preference matrix; finally, a collaborative filtering algorithm is used to recommend news based on the user-news topic preference matrix. The experiments show that the news recommendation algorithm based on Word2vec and convolutional neural network has better recommendation performance.
The Promise and Prospects of Blockchain-Based Decentralized Business Models

The emergence of Bitcoin and the underlying blockchain technology have enabled novel solutions for creating, transacting, and validating digital assets. In this vein, inspired by Bitcoin, a variety of projects has been building blockchain-based “decentralized” business models (BDBM), market places, and organizations. However, there has been ambiguity and confusion around the meaning of “decentralized” in the context of blockchain technology-based business models in both research and practice. Moreover, the implications for mainstream adoption of BDBM from a mass customers’ perspective are insufficiently understood. This article discusses the notion of decentralization in BDBM and develops a two-dimensional framework explaining the decentralization focus of BDBM. Based on this typology, the article analyzes the implications, prerequisites, and desirability of decentralization for BDBMs’ mainstream adoption.
Semantic Segmentation Model of Fluorescent Neuronal Cells in Mouse Brain Slices Under Few Samples.

Image of mouse neuronal cells is an important tool for studying mice. Examining mouse neuronal cells is an essential step in both pharmacological and toxicological tests. The morphology of individual neurons and the state of connections between multiple neurons are essential measures of the physiological state of the mouse. Staining neuronal cells samples and observation through microscopy is the mainstay of this field. However, this step is tedious, monotonous, and requires a high level of practical experience from the researcher. In recent years, the analysis of cell morphology using computer vision has proven to be an efficient and accurate solution. This paper presents a deep neural network-based semantic segmentation model; it does not use the popular attention mechanism but breaks down the process into two steps to achieve satisfactory performance while maintaining the number of parameters at a low level.
Count Roy model with finite mixtures

Summary This paper develops the Finite Mixture Roy model for count variables and uses this semiparametric model to analyze the effect of supplemental Medigap private insurance on the demand for prescription drugs for the U.S. elderly unemployed Medicare population. The model is an extension of the Count Roy model, which produces unrealistic treatment effects when observed count patterns are consistent with finite mixtures. To estimate the numbers of components in the mixtures for individuals with and without Medigap, this paper adopts the random permutation sampler. The considered application motivates two additional features of the model. Specifically, the smoothly mixing regression approach is utilized to model the probabilities of the components, and a continuous instrumental variable is allowed to enter the treatment equation nonparametrically. Strong evidence is found that there are two components both in the treated and untreated states. These lower and higher utilization components are interpreted as relatively healthy and unhealthy groups. The estimated treatment effects show that Medigap insurance provides incentives to increase prescription drug utilization by 2%. The results are consistent with adverse selection.
Assessing the Resistance of Internet of Things Applications Against Memory Corruption Attacks: A Case Study for Contiki and Tizen

The Internet of Things helps objects to interact with each other and function more efficiently. However, one of the major challenges in this technology is security. Research on the security of the Internet of Things mostly revolves around network security, but IoT software security has not garnered adequate attention. Due to the need for high-speed applications and the real-time structure of operating systems in the Internet of Things, their applications are often coded in languages such as C and C++ and may be vulnerable to memory corruption attacks. In this article, we study the resistance of the applications running in two well-known operating systems in IoT, namely Contiki and Tizen, against memory corruption attacks and show how memory corruption vulnerabilities arise in these applications. We demonstrate the possibility of exploiting these vulnerabilities in Contiki and Tizen and the weakness or loss of memory protection mechanisms in these operating systems. By this analysis, we hope to notify the developers of Tizen and Contiki applications of the feasibility of exploiting memory corruption vulnerabilities in these operating systems and the importance of avoiding these vulnerabilities in their codes by paying enough attention to the secure coding principles.
Natural Scenes’ Text Detection and Recognition Using CNN and Pytesseract

The problem of one of the long-existing research topics being sequence recognition for natural images and videos is taken into account in the paper. The aim is to achieve efficiency comparable to that of human vision. This work focuses on recognizing text from natural scene images without using complex model architecture. In order to get the objective to come to fruition, we have integrated and implemented an Efficient and Accurate Scene Text Detector (EAST) with Pytesseract to gain the best possible outcomes. The EAST model helps to detect the region of interest in the image with the most possibility of having text. This is further supported by Pytesseract, which is a python OCR (Optical Character Recognition) tool, to perceive the output. Also, the former architecture is compared with the results gained from recognizing sequences using Pytesseract only. Testing is done over the street view text (SVT) dataset and the ICDAR-2015 dataset. All the results are tabularized and compared while discussing the gaps and future directions for research.