Search

This paper explores the potential developments of self-directed learning in the metaverse in response to Education 4.0 and the Fourth Industrial Revolution. It highlights the importance of education keeping up with technological advancements and adopting learner-centered approaches. Additionally, it focuses on exploring value exchange systems through natural interaction, text mining, and analysis. The metaverse concept extends beyond extended reality (XR) technologies, encompassing digital avatars and shared ecological value. The role of educators in exploring new technologies and leveraging text-mining techniques to enhance learning efficiency is emphasized. The metaverse is presented as a platform for value exchange, necessitating meaningful and valuable content to attract users. Integrating virtual and real-world experiences within the metaverse offers practical applications and contributes to its essence. This paper sheds light on the metaverse's potential to create a learner-centered educational environment and adapt to the evolving landscape of Education 4.0. Its findings, supported by text mining analysis, contribute to understanding the metaverse's role in shaping education in the Fourth Industrial Revolution., Comment: 15 pages, 9 figures, 1 table

The ChatGPT Python API plays a crucial role in promoting Learner-Centered Instruction (LCI) and aligns with the principles of Tinker Learning, allowing students to discover their learning strategies. LCI emphasizes the importance of active, hands-on learning experiences and encourages students to take responsibility for their learning journey. By integrating the ChatGPT Python API into the educational process, students can explore various resources, generate new ideas, and create content in a more personalized manner. This innovative approach enables students to engage with the learning material deeper, fostering a sense of ownership and motivation. As they work through the Creative Learning Spiral, students develop essential skills such as critical thinking, problem-solving, and creativity. The ChatGPT Python API is a valuable tool for students to explore different solutions, evaluate alternatives, and make informed decisions, all while encouraging self-directed learning. In Tinker Learning environments, the integration of ChatGPT Python API empowers students to experiment and iterate, allowing them to find the most effective learning strategies that cater to their individual needs and preferences. This personalized approach helps students to become more confident in their abilities, leading to tremendous academic success and long-term skill development. By leveraging the capabilities of the ChatGPT Python API, educational institutions can create a more engaging, supportive, and dynamic learning environment. This approach aligns with the principles of Learner-Centered Instruction and Tinker Learning, promoting a culture of curiosity, exploration, and creativity among students while preparing them for the challenges of the fast-paced, ever-changing world., Comment: 10 pages, 4 figures, 1 table

As blockchain technology continues to gain attention, there is a growing need to make it more accessible to young learners in K-12 education. However, the technical complexity and lack of accessible tools have been identified as significant barriers to adoption. Our paper proposes a new method for empowering NFTs by continuously updating their metadata using an API layer. The approach aims to reduce the barriers to entry and enable K-12 students to explore blockchain technology in the same way they learn computational thinking through visual programming tools like Scratch. Our method utilizes Google Blockly, a visual programming language, to make updating NFT metadata more accessible and engaging for young learners. By leveraging a familiar and engaging visual programming language, students can develop their computational thinking skills and explore blockchain technology in a fun and intuitive way. The paper discusses the benefits of using NFTs as a learning tool, including how they can help students understand the concept of digital ownership and value. Overall, our proposed method has the potential to promote student engagement and understanding of blockchain technology, which could have significant implications for the future of education., Comment: 7 pages, 7 figures

Since the end of 2021, the Metaverse has been booming. Many unknown possibilities are gradually being realized, but many people only determined that they use Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) in the Metaverse. It is even considered that as long as the above realities (VR, AR, MR) are used, it is equal to the Metaverse. However, this is not true, for Reality-based display tools are only one of the presentation methods of the Metaverse. If we cannot return to the three main characteristics of the Metaverse: "digital avatars," a decentralized "consensus value system," and "Immersive experience," the practice and imagination of the Metaverse will become very narrow. Since 2022, the concept of Metaverse has also been widely used in classroom teaching to integrate into teaching activities. Therefore, to prevent teachers and students from understanding the Metaverse not only in the "Using VR, AR, MR is equivalent to Metaverse" but also pay more attention to the other two characteristics of the Metaverse: "digital avatars" and a decentralized "consensus value system."

Recent advances in artificial intelligence (AI) for quantitative trading have led to its general superhuman performance in significant trading performance. However, the potential risk of AI trading is a "black box" decision. Some AI computing mechanisms are complex and challenging to understand. If we use AI without proper supervision, AI may lead to wrong choices and make huge losses. Hence, we need to ask about the AI "black box", including why did AI decide to do this or not? Why can people trust AI or not? How can people fix their mistakes? These problems also highlight the challenges that AI technology can explain in the trading field., Comment: arXiv admin note: text overlap with arXiv:2104.07715

Candlestick pattern is one of the most fundamental and valuable graphical tools in financial trading that supports traders observing the current market conditions to make the proper decision. This task has a long history and, most of the time, human experts. Recently, efforts have been made to automatically classify these patterns with the deep learning models. The GAF-CNN model is a well-suited way to imitate how human traders capture the candlestick pattern by integrating spatial features visually. However, with the great potential of the GAF encoding, this classification task can be extended to a more complicated object detection level. This work presents an innovative integration of modern object detection techniques and GAF time-series encoding on candlestick pattern tasks. We make crucial modifications to the representative yet straightforward YOLO version 1 model based on our time-series encoding method and the property of such data type. Powered by the deep neural networks and the unique architectural design, the proposed model performs pretty well in candlestick classification and location recognition. The results show tremendous potential in applying modern object detection techniques on time-series tasks in a real-time manner., Comment: 11 pages, 9 figures, 2 tables

The importance of deep learning data privacy has gained significant attention in recent years. It is probably to suffer data breaches when applying deep learning to cryptocurrency that lacks supervision of financial regulatory agencies. However, there is little relative research in the financial area to our best knowledge. We apply two representative deep learning privacy-privacy frameworks proposed by Google to financial trading data. We designed the experiments with several different parameters suggested from the original studies. In addition, we refer the degree of privacy to Google and Apple companies to estimate the results more reasonably. The results show that DP-SGD performs better than the PATE framework in financial trading data. The tradeoff between privacy and accuracy is low in DP-SGD. The degree of privacy also is in line with the actual case. Therefore, we can obtain a strong privacy guarantee with precision to avoid potential financial loss., Comment: 11 pages, 10 figures

Deep learning (DL) has been applied extensively in a wide range of fields. However, it has been shown that DL models are susceptible to a certain kinds of perturbations called \emph{adversarial attacks}. To fully unlock the power of DL in critical fields such as financial trading, it is necessary to address such issues. In this paper, we present a method of constructing perturbed examples and use these examples to boost the robustness of the model. Our algorithm increases the stability of DL models for candlestick classification with respect to perturbations in the input data., Comment: arXiv admin note: text overlap with arXiv:2005.06731

To successfully build a deep learning model, it will need a large amount of labeled data. However, labeled data are hard to collect in many use cases. To tackle this problem, a bunch of data augmentation methods have been introduced recently and have demonstrated successful results in computer vision, natural language and so on. For financial trading data, to our best knowledge, successful data augmentation framework has rarely been studied. Here we propose a Modified Local Search Attack Sampling method to augment the candlestick data, which is a very important tool for professional trader. Our results show that the proposed method can generate high-quality data which are hard to distinguish by human and will open a new way for finance community to employ existing machine learning techniques even if the dataset is small., Comment: 12 pages, 9 figures, 2 tables, 1 algorithm

Candlesticks are graphical representations of price movements for a given period. The traders can discovery the trend of the asset by looking at the candlestick patterns. Although deep convolutional neural networks have achieved great success for recognizing the candlestick patterns, their reasoning hides inside a black box. The traders cannot make sure what the model has learned. In this contribution, we provide a framework which is to explain the reasoning of the learned model determining the specific candlestick patterns of time series. Based on the local search adversarial attacks, we show that the learned model perceives the pattern of the candlesticks in a way similar to the human trader., Comment: Accepted by The 32nd International Conference on Software Engineering & Knowledge Engineering (SEKE 2020), KSIR Virtual Conference Cener, Pittsburgh, USA, July 9--July 19, 2020

Reinforcement learning can interact with the environment and is suitable for applications in decision control systems. Therefore, we used the reinforcement learning method to establish a foreign exchange transaction, avoiding the long-standing problem of unstable trends in deep learning predictions. In the system design, we optimized the Sure-Fire statistical arbitrage policy, set three different actions, encoded the continuous price over a period of time into a heat-map view of the Gramian Angular Field (GAF) and compared the Deep Q Learning (DQN) and Proximal Policy Optimization (PPO) algorithms. To test feasibility, we analyzed three currency pairs, namely EUR/USD, GBP/USD, and AUD/USD. We trained the data in units of four hours from 1 August 2018 to 30 November 2018 and tested model performance using data between 1 December 2018 and 31 December 2018. The test results of the various models indicated that favorable investment performance was achieved as long as the model was able to handle complex and random processes and the state was able to describe the environment, validating the feasibility of reinforcement learning in the development of trading strategies.

Candlestick charts display the high, low, opening, and closing prices in a specific period. Candlestick patterns emerge because human actions and reactions are patterned and continuously replicate. These patterns capture information on the candles. According to Thomas Bulkowski's Encyclopedia of Candlestick Charts, there are 103 candlestick patterns. Traders use these patterns to determine when to enter and exit. Candlestick pattern classification approaches take the hard work out of visually identifying these patterns. To highlight its capabilities, we propose a two-steps approach to recognize candlestick patterns automatically. The first step uses the Gramian Angular Field (GAF) to encode the time series as different types of images. The second step uses the Convolutional Neural Network (CNN) with the GAF images to learn eight critical kinds of candlestick patterns. In this paper, we call the approach GAF-CNN. In the experiments, our method can identify the eight types of candlestick patterns with 90.7% average accuracy automatically in real-world data, outperforming the LSTM model., Comment: 18 pages, 20 figures. Accepted by Financial Innovation, June 2020. (Social Sciences Citation Index)

Deep learning is an effective approach to solving image recognition problems. People draw intuitive conclusions from trading charts; this study uses the characteristics of deep learning to train computers in imitating this kind of intuition in the context of trading charts. The three steps involved are as follows: 1. Before training, we pre-process the input data from quantitative data to images. 2. We use a convolutional neural network (CNN), a type of deep learning, to train our trading model. 3. We evaluate the model's performance in terms of the accuracy of classification. A trading model is obtained with this approach to help devise trading strategies. The main application is designed to help clients automatically obtain personalized trading strategies., Comment: 30 pages, 41 figures

With the rapid evolution of cross-strait situation, "Mainland China" as a subject of social science study has evoked the voice of "Rethinking China Study" among intelligentsia recently. This essay tried to apply an automatic content analysis tool (CATAR) to the journal "Mainland China Studies" (1998-2015) in order to observe the research trends based on the clustering of text from the title and abstract of each paper in the journal. The results showed that the 473 articles published by the journal were clustered into seven salient topics. From the publication number of each topic over time (including "volume of publications", "percentage of publications"), there are two major topics of this journal while other topics varied over time widely. The contribution of this study includes: 1. We could group each "independent" study into a meaningful topic, as a small scale experiment verified that this topic clustering is feasible. 2. This essay reveals the salient research topics and their trends for the Taiwan journal "Mainland China Studies". 3. Various topical keywords were identified, providing easy access to the past study. 4. The yearly trends of the identified topics could be viewed as signature of future research directions., Comment: 10 pages, 8 figures, 1 table