Storm surge elevates the water level, resulting in floods that cause significant harm to our lives and property. Predicting possible occurrences of storm surge using machine learning technology has long piqued the interest of the different communities. However, it is observed that the accuracy rate of predictions is insufficient. This paper aims to develop a framework for evaluating the performance of various machine learning models. Various algorithms are being used to forecast storm surge incidents in Hong Kong and Kaohsiung. The inclusion of trend changes in atmosphere pressure and wind components throughout the implementation of the algorithms to the dataset gathered over the past is one of the innovative aspects of this work. These inclination characteristics are very vital and crucial to capture important correlations and features for the potential occurrence of storm surges. This study yields two major findings. First, even the basic method of an ensemble algorithm produces better results than any single algorithm. Second, determining the best performing ensemble models for those storm surge events is achievable based on the technique findings for the data analyzed.
Blockchain exposes all users' transaction data to the public, including account balances, asset holdings, trading history, etc. Such data exposure leads to potential security and personal privacy risks that restrict blockchain from broader adoption. Although some existing projects focus on single-chain confidential payment, no existing cross-chain system supports private transactions yet, which is incompatible with privacy regulations such as GDPR. Also, current confidential payment systems require users to pay high extra fees. However, a private and anonymous protocol encrypting all transaction data raises concerns about malicious and illegal activities since the protocol is difficult to audit. We need to balance privacy and auditability in blockchain.We propose an auditable and affordable protocol for cross-chain and single-chain transactions. This protocol leverages zero-knowledge proofs to encrypt transactions and perform validation without disclosing sensitive users' data. To meet regulations, each auditor from an auditing committee will have an encrypted secret share of the transaction data. Auditors may view the private transaction data only if a majority of the committee agrees to decrypt the data. We employ a ZK-rollup scheme by processing multiple transactions in batches, which reduces private transaction costs of 90\% compared with solutions that do not employ the ZK-rollup. The proposed scheme has been implemented using Zokrates and Solidity and evaluated running on the Ethereum test network. The results show that the total one-to-one private transactions latency is about 5 seconds. Moreover, the security of the protocol is analyzed by mean of the standard real/ideal world paradigm.
The quality of translation work mainly depends on the understanding of the words in their domain. If machine translation can accurately translate the words in a domain in different languages, it can even avoid any human communication error. To achieve this, a high-quality bilingual corpus is crucial as they are always the basis of state-of-the-art machine translation system. However, it is complicated to construct the corpus with large amount of parallel data. In this paper, a new crawling architecture, called Hybrid Crawling Architecture (HCA), will be proposed, which efficiently and effectively collects parallel data from the Web for the bilingual corpus. HCA aims at targeted websites, which contains articles in at least two different languages. As it is a mixture of Focused crawling architecture and Parallel crawling architecture, HCA takes advantages over both architectures. In intensive experiments on crawling parallel data of relevance topics, HCA significantly outperforms Focused crawling architecture and Parallel crawling architecture for 30% and 200% respectively, in terms of quantity.
Today, most students are with their smartphone. In classroom, teachers often ask questions to evaluate the students' understanding on a topic. An easy-to-use interactive response system with a companion mobile app will be useful for improving the teaching and learning experience. There are some voting systems in the market. However, most of them are not convenient to use and configure, making students and teachers reluctant to use. The system proposed in this paper includes three major functions - voting/quiz, countdown, and real-time statistics. The voting/quiz function provides a simple interactive approach that can be used in any subject. The system is designed with simplicity in mind. Teachers will find it easy to create a voting or question, and students will find it easy to do a voting or answer a quiz. The students can login question or voting by scanning the corresponding Quick Response (QR) code or entering the Randomly Generated Code, without a complicated login system for the students.
The Building Safe Water Use Plan promoted by the Macao Marine and Water Bureau aims to encourage property management entities to regularly maintain building water supply systems to ensure the safety and stability of drinking water. However, traditional laboratory testing methods are often time-consuming and labor-intensive, making real-time and efficient water quality monitoring challenging. To address this issue, this study proposes a Raspberry Pi-based multi-sensor system for rapid water quality detection and improved monitoring efficiency. This system integrates multiple sensors to measure key water quality parameters, such as pH, total dissolved solids (TDSs), temperature, and turbidity, while recording data in real-time. The data were continuously collected over a period of five months (July to November 2024). The collected data were analyzed and validated using machine learning algorithms, including Isolation Forest, Random Forest, Logistic Regression, and Local Outlier Factor. Among these models, Random Forest exhibited the best overall performance, achieving an accuracy of 98.10% and an F1 score of 98.99%. These results show that the dataset demonstrates high reliability in anomaly detection and classification tasks, accurately identifying deviations in water quality. This approach not only enhances the efficiency of water quality monitoring but also provides technological support for urban drinking water safety management.
Modality differences and intra-modality variations make the visible-infrared person re-identification (VI-ReID) task highly challenging. Most existing methods f
Triangle routing is one of the serious attacks to the Internet infrastructure. It can be caused by malicious routers which misroute packets to wrong directions. This kind of attacks creates network problems such as network congestion, denial of service and network partition and results in degrade of network performance. This paper gives a comprehensive study on how the path analysis combats the triangle routing attacks. We discuss the method, implementation and limitation of path analysis to detect triangle routing in IPv4 network. We also discuss the implementation of path analysis in IPv6 by proposing a new extension header, called Record Path Header.
Image-based deep learning method for plant disease diagnosing is promising but relies on large-scale dataset. Currently, the shortage of data has become an obstacle to leverage deep learning methods. Few-shot learning can generalize to new categories with the supports of few samples, which is very helpful for those plant disease categories where only few samples are available. However, two challenging problems are existing in few-shot learning: (1) the feature extracted from few shots is very limited; (2) generalizing to new categories, especially to another domain is very tough. In response to the two issues, we propose a network based on the Meta-Baseline few-shot learning method, and combine cascaded multi-scale features and channel attention. The network takes advantage of multi-scale features to rich the feature representation, uses channel attention as a compensation module efficiently to learn more from the significant channels of the fused features. Meanwhile, we propose a group of training strategies from data configuration perspective to match various generalization requirements. Through extensive experiments, it is verified that the combination of multi-scale feature fusion and channel attention can alleviate the problem of limited features caused by few shots. To imitate different generalization scenarios, we set different data settings and suggest the optimal training strategies for intra-domain case and cross-domain case, respectively. The effects of important factors in few-shot learning paradigm are analyzed. With the optimal configuration, the accuracy of 1-shot task and 5-shot task achieve at 61.24% and 77.43% respectively in the task targeting to single-plant, and achieve at 82.52% and 92.83% in the task targeting to multi-plants. Our results outperform the existing related works. It demonstrates that the few-shot learning is a feasible potential solution for plant disease recognition in the future application.
This study examines the application of deep learning (DL) in predicting multivariate time-series (MTS), emphasizing the significance of novel feature exploration for intensive model re-training. This approach is essential for adaptive feature engineering, as the numerical properties of the foundational features differ from their statistical attributes. Elements such as the population density, resident count and land area, as well as foreign exchange rates, goods and hedging indicators, illustrate this disparity. The augmentation of feature space aims to improve the model’s efficiency without compromising performance by unnecessarily expanding the dataset dimension. This objective can be achieved by implementing an adaptive screening approach, specifically a multi-aspect feature dependency screening (MFDS), for the augmentation of derivative feature space (DFS). This can be accomplished through an adaptive screening process, an MFDS for augmentation of DFS. The methodology includes literature review and experimental modelling. The application of the Variance Inflation Factor (VIF) as an adaptive weighting method to evaluate redundancy is crucial for establishing effective screening mechanisms enabling them to self-adjust to subtle changes and emerging patterns that may otherwise go unnoticed, and for ensuring sustainable advancement and enhancing dynamic training processes within the DL domain. Those multi statistical indicators include: cross-correlation for monitoring movements, Granger causality for evaluating predictor-predictand effectiveness, R-squared for assessing coincidence, amplitude-square coherence for estimating response degree, and Pearson coefficient for measuring linear dependence. In a seasonal context, formulating air temperature and humidity into water vapour pressure deficit facilitates a comprehensive analysis of critical features. Furthermore, the efficiency of predictive models is enhanced through the use of this adaptive screening and auto augmentation, as these methodologies incorporate new essential feature spaces. This research effectively combines physical knowledge with statistical screening techniques of feature dependency weighted mean (FDWM), resulting in a significant reduction in loss for DL models. Additionally, the distinct temporal-spatial and physical properties of the six air pollutants exhibit considerable variation, yet to a certain extents the commonalities.
It is crucial to speed up the training process of multivariate deep learning models for forecasting time series data in a real-time adaptive computing service with automated feature engineering. Multivariate time series decomposition and recombining (MTS-DR) is proposed for this purpose with better accuracy. A proposed MTS-DR model was built to prove that not only the training time is shortened but also the error loss is slightly reduced. A case study is for demonstrating air quality forecasting in sub-tropical urban cities. Since MTS decomposition reduces complexity and makes the features to be explored easier, the speed of deep learning models as well as their accuracy are improved. The experiments show it is easier to train the trend component, and there is no need to train the seasonal component with zero MSE. All forecast results are visualized to show that the total training time has been shortened greatly and that the forecast is ideal for changing trends. The proposed method is also suitable for other time series MTS with seasonal oscillations since it was applied to the datasets of six different kinds of air pollutants individually. Thus, this proposed method has some commonality and could be applied to other datasets with obvious seasonality.
In this paper, we propose a Multiple Disjoint Path (MDP) routing protocol to maximize network throughput and minimize the protocol overheads in wireless ad hoc networks. MDP consists of two components: virtual source routing and core heuristic. Virtual source routing constructs virtual paths that do not suffer from scalability, privacy and efficiency problems caused by long paths for DSR in large networks. Core heuristic is to ensure the efficiency of request packet propagation in route discovery operation. Our simulation results reveal that MDP performs at a satisfactory level in dense networks in terms of connectivity and transmission efficiency. Network resources can be utilized so that network throughput can be significantly increased at a cost of minimal protocol overheads comparatively.
