Yain Whar Si

University of Macau · Department of Computer and Information Science

Force-directed (FD) algorithms can be used to explore relationships in social networks, visualize money markets, and analyze transaction networks. However, FD algorithms are mainly designed for visualizing static graphs in which the topology of the networks remains constant throughout the calculation. In contrast to static graphs, nodes and edges i...

Patterns with varying numbers of candlestick-shaped features are commonly used by analysts to predict future price trends in financial markets. Although general descriptions of candlestick patterns have been reported in literature, they are usually described in natural languages. Such descriptions are prone to ambiguity and misinterpretation by use...

Complex non-convex ad hoc networks (CNCAH) contain intersecting polygons and edges. In many instances, the layouts of these networks are not entirely convex in shape. In this article, we propose a Kamada-Kawai-based algorithm called W-KK-MS for boundary node detection problems, which is capable of aligning node positions while achieving high sensit...

Classifying chart patterns from input subsequences is a crucial pre-processing step in technical analysis. In this paper, we compile comprehensive formal specifications of 53 chart patterns reported in the literature. A first-order logic representation is chosen to describe the shape and corresponding constraints of each pattern. These formal speci...

Force-directed algorithms such as the Kamada-Kawai algorithm have shown promising results for solving the boundary detection problem in a mobile ad hoc network. However, the classical Kamada-Kawai algorithm does not scale well when it is used in networks with large numbers of nodes. It also produces poor results in non-convex networks. To address t...

Trading robots, meticulously crafted programs, are designed to execute trades automatically. However, stock trading presents a unique challenge. Unlike finite game tasks, stock markets operate perpetually, making it arduous for traders to design appropriate reward functions for training Reinforcement Learning models. For stock trading tasks that ca...

Deep neural networks often experience performance degradation when evaluated on testing (target) data that exhibit different distributions compared to the training (source) data. To solve the issue, Domain Generalization (DG) approaches were proposed by researchers to learn models that demonstrate robustness to domain shift. These models were train...

In a permissionless social network environment, it is difficult for a user to assess the trustworthiness of other users. Thus, trust management becomes a crucial issue for securing social activities. The conventional method for assessing trust involves an authority to identify fakes. However, generative AI is gradually reducing the ability to disti...

With the rapid advancement of intelligent devices and cloud services, a novel edge-cloud computing paradigm is emerging, finding widespread adoption in numerous advanced applications. Despite its considerable convenience and benefits, edge-cloud computing raises security and privacy concerns. Although many cryptographic solutions have been proposed...

Decentralized Finance (DeFi), propelled by Blockchain technology, has revolutionized traditional financial systems, improving transparency, reducing costs, and fostering financial inclusion. However, transaction activities in these systems fluctuate significantly and the throughput can be effected. To address this issue, we propose a Dynamic Mining...

Training automated trading agents is a long-standing topic that has been widely discussed in artificial intelligence for the quantitative finance. Reinforcement learning (RL) is designed to solve the sequential decision-making tasks, like the stock trading. The output of the RL is the policy which can be presented as the probability values of the p...

As coin-based rewards dwindle, transaction fees play an important role as mining incentives in Bitcoin. In this paper, we propose a novel mechanism called efficient dynamic transaction storage (EDTS) for dynamically allocating transactions among blocks to achieve efficient storage utilization. By leveraging a combination of Cuckoo Filter and dynami...

Increasing popularity of trading digital assets can lead to significant delays in Blockchain networks when processing transactions. When transaction fees become miners' primary revenue, an imbalance in reward may lead to miners adopting deviant mining strategies. Scaling the block capacity is one of the potential approaches to alleviate the problem...

Blockchain-enabled smart contracts have revolutionized the insurance industry due to their potential to streamline backend operations, mitigate fraudulent claims, and enhance data security and transparency. Guided by the design science methodology, the authors propose two specific smart contract frameworks to enhance insurance claims processing rel...

Reinforcement learning is one of the promising approaches for algorithmic trading in financial markets. However, in certain situations, buy or sell orders issued by an algorithmic trading program may not be fulfilled entirely. By considering the actual scenarios from the financial markets, in this paper, we propose a novel framework named Supervise...

The key challenge of Unsupervised Domain Adaptation (UDA) for analyzing time series data is to learn domain-invariant representations by capturing complex temporal dependencies. In addition, existing unsupervised domain adaptation methods for time series data are designed to align marginal distribution between source and target domains. However, ex...

Recognizing potential defaulters is a crucial problem for financial institutions. Therefore, many credit scoring methods have been proposed in the past to address this issue. However, these methods rarely consider the interaction among customers such as bank transfer and remittance. With rapid growth in the number of customers adopting online banki...

This paper proposes a novel deep learning-based approach for financial chart patterns classification. Convolutional neural networks (CNNs) have made notable achievements in image recognition and computer vision applications. These networks are usually based on two-dimensional convolutional neural networks (2D CNNs). In this paper, we describe the d...

Time-series classification approaches based on deep neural networks easily overfit UCR datasets, which is caused by the few-shot problem of those datasets. Therefore, to alleviate the overfitting phenomenon to further improve accuracy, we first propose label smoothing for InceptionTime (LSTime), which adopts the soft label information compared to o...

Stock movement prediction is one of the most challenging problems in time series analysis due to the stochastic nature of financial markets. In recent years, a plethora of statistical methods and machine learning algorithms were proposed for stock movement prediction. Specifically, deep learning models are increasingly applied for the prediction of...

Force-directed algorithms have been developed over the last 50 years and used in many application fields, including information visualisation, biological network visualisation, sensor networks, routing algorithms, scheduling, and graph drawing. Our survey provides a comprehensive summary of developments and a full roadmap for state-of-the-art force...

Hole detection is a crucial task for monitoring the status of wireless sensor networks (WSN) which often consist of low-capability sensors. Holes can form in WSNs due to the problems during placement of the sensors or power/hardware failure. In these situations, sensing or transmitting data could be affected and can interrupt the normal operation o...

Complex non-convex ad hoc networks (CNCAH) contain intersecting polygons and edges. In many instances, the layouts of these networks are not entirely convex in shape. In this article, we propose a Kamada-Kawai-based algorithm called W-KK-MS for boundary node detection problems, which is capable of aligning node positions while achieving high sensit...

Lifeline is a group of systems designed for mobile phones and battery powered wireless routers for forming emergency Ad hoc networks. Devices installed with Lifeline program can automatically form Ad hoc networks when cellular signal is unavailable or disrupted during natural disasters. For instance, large scale earthquakes can cause extensive dama...

This paper aims to propose a system for automatically forming ad hoc networks using mobile phones and battery-powered wireless routers for emergency situations. The system also provides functions to send emergency messages and identify the location of victims based on the network topology information. Optimized link state routing protocol is used t...

Force-directed algorithms are widely used for visualizing graphs. However, these algorithms are computationally expensive in producing good quality layouts for complex graphs. The layout quality is largely influenced by execution time and methods' input parameters especially for large complex graphs. The snapshots of visualization generated from th...

Hole detection is a crucial task for monitoring the status of wireless sensor networks (WSN) which often consist of low-capability sensors. Holes can form in WSNs due to the problems during placement of the sensors or power/hardware failure. In these situations, sensing or transmitting data could be affected and can interrupt the normal operation o...

Force-directed (FD) algorithms can be used to explore relationships in social networks, visualize money markets, and analyze transaction networks. However, FD algorithms are mainly designed for visualizing static graphs in which the topology of the networks remains constant throughout the calculation. In contrast to static graphs, nodes and edges i...

Coverage hole detection is an important research problem in wireless sensor network research community. However, distributed approaches proposed in recent years for coverage hole detection problem have high computational complexity. In this paper, we propose a novel approach for coverage hole detection in wireless sensor networks called FD-TL (Forc...

Efficient message forwarding in mobile ad hoc network in disaster scenarios is challenging because location information on the boundary and interior nodes is often unavailable. Information related to boundary nodes can be used to design efficient routing protocols as well as to prolong the battery power of devices along the boundary of an ad hoc ne...

Coverage hole detection is an important research problem in wireless sensor network research community. However, distributed approaches proposed in recent years for coverage hole detection problem have high computational complexity. In this paper, we propose a novel approach for coverage hole detection in wireless sensor networks called FD-TL (Forc...

Nowadays, academic certificates are still widely issued in paper format. Traditional certificate verification is a lengthy, manually intensive, and sometimes expensive process. In this paper, we propose a novel NFT-based certificate framework called NFTCert, which enables the establishment of links between a legitimate certificate and its owner thr...

As the core technology behind Bitcoin, Blockchain's decentralized, tamper-proof, and traceable features make it the preferred platform for organizational innovation. In current Bitcoin, block reward is halved every four years, and transaction fees are expected to become the majority of miner revenues around 2140. When transaction fee dominates mini...

Sales volume forecasting is of great significance to E-commerce companies. Accurate sales forecasting enables managers to make reasonable resource allocation in advance. In this paper, we propose a novel approach based on Long Short-Term Memory with Particle Swam Optimization (LSTM-PSO) for sale forecasting in E-commerce companies. In the proposed...

Time-series classification approaches based on deep neural networks are easy to be overfitting on UCR datasets, which is caused by the few-shot problem of those datasets. Therefore, in order to alleviate the overfitting phenomenon for further improving the accuracy, we first propose Label Smoothing for InceptionTime (LSTime), which adopts the infor...

As safety is one of the most important properties of drugs, chemical toxicology prediction has received increasing attentions in the drug discovery research. Traditionally, researchers rely on in vitro and in vivo experiments to test the toxicity of chemical compounds. However, not only are these experiments time consuming and costly, but experimen...

Chatbots are intelligent conversational agents that can interact with users through natural languages. As chatbots can perform a variety of tasks, many companies have committed numerous resources to develop and deploy chatbots to enhance various business processes. However, we lack an up‐to‐date critical review that thoroughly examines both state‐o...

As safety is one of the most important properties of drugs, chemical toxicology prediction has received increasing attentions in the drug discovery research. Traditionally, researchers rely on in vitro and in vivo experiments to test the toxicity of chemical compounds. However, not only are these experiments time consuming and costly, but experimen...

Extracting shape-related features from a given query subsequence is a crucial preprocessing step for chart pattern matching in rule-based, template-based and hybrid pattern classification methods. The extracted features can significantly influence the accuracy of pattern recognition tasks during the data mining process. Although shape-related featu...

In financial markets, appearances of chart patterns in time series are commonly considered as potential signals for imminent change in the direction of price movement. To identify chart patterns, time series data is usually segmented before it can be processed by different classification methods. However, existing segmentation methods are less effe...

Although transfer learning is proven to be effective in computer vision and natural language processing applications, it is rarely investigated in forecasting financial time series. Majority of existing works on transfer learning are based on single-source transfer learning due to the availability of open-access large-scale datasets. However, in fi...

This article aims to explore how could distance affect news diffusion and polarity of the sentiment. Understanding the estimation potential point of origin of news diffusion can allow time to control or monitor the potential of fake news to continue to disperse. In this case, we collect a total of 10,427 English tweets posted 1 hour after the real...

Iterative control structures allow the repeated execution of tasks, activities or sub-processes according to the given conditions in a process model. Iterative control structures can significantly increase the risk of triggering temporal exceptions since activities within the scope of these control structures could be repeatedly executed until a pr...

Force-directed algorithm is one of the most commonly used methods for visualization of 2D graphs. These algorithms can be applied to a plethora of applications such as data visualization, social network analysis, crypto-currency transactions, and wireless sensor networks. Due to their effectiveness in visualization of topological data, various forc...

Purpose The purpose of this research is to investigate the usage characteristics and the information propagation patterns of Chinese microblogs in different stages of an epidemic, given that the microblogging in China is different from other parts of the world. In addition, the authors aim to conceptualize the roles of different users and provide i...

Segmentation is an important preprocessing step for pattern classification in financial time series. In this paper, we propose a novel segmentation method called Perceptually Important Point with Binary tree (PIP-Btree) for efficient preprocessing of financial time series for classifying chart patterns. PIP-Btree takes advantage of a standard binar...

Given the advancement in algorithmic trading, the needs for real-time monitoring of patterns and execution of trades in stock exchanges become increasing important for investors. However, real-time monitoring of patterns from a vast number of markets become inefficient when tens of thousands of time series are required to be processed. In order to...

Chart patterns are one of the important tools used by the financial analysts for predicting future price trends (subsequent trends) in stock markets. Although many works related to the descriptions of chart patterns and several effective methods to identify chart patterns from the financial time series have been proposed in recent years, there is n...

Treemaps have been used in information visualisation for over two decades. They make use of nested filled areas to represent information hierarchies such as file systems, library catalogues, etc. Recent years have witnessed the emergence of visualisations that resemble geographic maps. In this paper we present a study that compares the performance...

Time-series are widely used for representing non-stationary data such as weather information, health related data, economic and stock market indexes. Many statistical methods and traditional machine learning techniques are commonly used for forecasting time series. With the development of deep learning in artificial intelligence, many researchers h...

Force-directed algorithms have been developed over the last 50 years and used in many application fields, including information visualisation, biological network visualisation, sensor networks, routing algorithms, scheduling, and graph drawing. Our survey provides a comprehensive summary of developments and a full roadmap for state-of-the-art force...

Many pattern matching approaches have been applied in financial time series to detect chart patterns and predict price trends. In this paper, we propose an extended hidden semi-Markov model for chart pattern matching (HSMM-CP). In our approach, a hidden semi-Markov model is trained and a Viterbi algorithm is used to detect chart patterns. The propo...

Efficient message forwarding in mobile ad hoc network in disaster scenarios is challenging because location information on the boundary and interior nodes is often unavailable. Information related to boundary nodes can be used to design efficient routing protocols as well as to prolong the battery power of devices along the boundary of an ad hoc ne...

Purpose This paper aims to propose a system for automatically forming ad hoc networks using mobile phones and battery-powered wireless routers for emergency situations. The system also provides functions to send emergency messages and identify the location of victims based on the network topology information. Design/methodology/approach Optimize...

Subsequence matching algorithms have many applications on time-series, such as detecting specific patterns on Electrocardiogram (ECG) and temperature data. To the best of author's knowledge, there are relatively few research studies on time-series fuzzy subsequence matching yet, which better expresses the logic in real life compared to exact subseq...

In stock markets around the world, financial analysts continuously monitor and screen chart patterns (technical patterns) to predict future price trends. Although a plethora of methods have been proposed for classification of these patterns, there is no uniform standard in defining their shapes. To facilitate the classification and discovery of cha...

Business process simulation (BPS) enables detailed analysis of resource allocation schemes prior to actually deploying and executing the processes. Although BPS has been widely researched in recent years, less attention has been devoted to intelligent optimization of resource allocation in business processes by exploiting simulation outputs. This p...

Streaming time-series has drawn unprecedented interests from the computer science researchers. It requires faster execution time and less memory space than traditional approaches in processing historical time-series. Given the real-time constraint in the analysis over streaming time-series, a proper pre-processing step may not even be applicable. S...

Trend following (TF) is an investment strategy based on the technical analysis of market prices. Trend followers do not aim to forecast nor predict specific price levels. They simply jump on the uptrend and ride on it until the end of this uptrend. Most of the trend followers determine the establishment and termination of uptrend based on their own...

This paper mainly addresses the heterogeneous fleet capacitated vehicle routing problem with two-dimensional loading constrains (2L-HFCVRP). The 2L-HFCVRP is a combination of two NP-hard problems and has a wide range of applications in transportation and logistics fields. In this paper, we propose a hybrid swarm algorithm, which is a combination of...

In technical analysis, the appearance of chart patterns in financial time series is considered as one of the crucial signals in predicting future price trend. In recent years, various classification methods have been proposed by researchers to locate and identify potential chart patterns from input time series. This paper presents a novel applicati...

This paper mainly addresses the heterogeneous fleet capacitated vehicle routing problem with two-dimensional loading constrains (2L-HFCVRP). The 2L-HFCVRP is a combination of two NP-hard problems and has a wide range of applications in transportation and logistics fields. In this paper, we propose a hybrid swarm algorithm, which is a combination of...