86 publications from this institution
An essential part of a city’s transportation infrastructure, taxis allow for regular encounters between drivers and customers. Nevertheless, there are issues with efficiency since there is an imbalance in the supply and demand for taxis. This study describes the creation of a platform that serves both customers and taxi drivers by offering immediate forecasts of demand and fare. Root mean squared error (RMSE) of 3.31 and a negative log-likelihood of −3.84, the long short-term memory recurrent neural network (LSTM-RNN) with the mixture density network (MDN) is employed to forecast taxi demand. The best RMSE of 3.24 is obtained for fare prediction via an ensemble learning model that integrates linear regression (LR), ridge regression (RR), and multilayer perceptron (MLP). To ensure peak performance, the models are systematically created, implemented, trained, and improved. By integrating these models into a web application interface, the taxi service system offers a better overall user experience, which improves urban mobility.
Few-shot learning is good solution for plant disease recognition which can generalize to new categories by using few samples. However, the features extracted from few shots are limited. Attention is a technique for focusing on the significant features which can help to obtain better feature representation. In this work, we use a naive metric-based few-shot learning network as the baseline method, exploit the effect of different kinds of attention module: channel attention, spatial attention and hybrid attention. In experiments, we choose the representative modules of each attention category to show their effects in few-shot learning paradigm: SE, ASPP, CBAM and Triplet Attention. We conduct experiments with two data settings of PlantVillage, and illustrate the usage these attention modules in Residual Networks. The results indicate that the different attention modules can improve recognition accuracy to varying degrees. Attention can be used as effective improvement of feature representation under few-shot condition.
Environmental engineering plays a critical role in managing air quality services, which are of daily concern to the public, particularly as climate change alters the factors affecting air quality. Within this context, our study introduces a comprehensive approach that emphasizes predictive models relying on multivariate time-series data. By integrating data from various sources and modalities, we propose a multimodal deep learning method to enhance traditional unimodal models. This study includes a review of existing literature, the preparation of relevant datasets, the development of robust models, and extensive evaluations. The experiments feature a case study focused on air quality services in a subtropical city, aiming to provide insights for improving prediction models. The integrated multimodal approach offers a better understanding of environmental conditions by combining data from automatic air quality monitors, meteorological stations, the European Centre for Medium-Range Weather Forecasts reanalysis data, as well as public welfare information and societal disruption reports. The analysis also considers weather-related alerts, such as typhoon and rainstorm warnings, which lead to school closures and city-wide suspensions. The model incorporates emission sources and upwind areas. Preliminary causality tests confirm that augmented feature space to encompass upstream areas enhances the model analytical capability. Downstream pollution and environmental conditions are significantly influenced by socio-economic activities in upwind areas. Granger causality and Diebold-Mariano tests highlight the importance of public welfare information and societal disruption reports, addressing a critical gap in this field.
Tobacco is a valuable plant in agricultural and commercial industry. Any disease infection to the plant may lower the harvest and interfere the operation of supply chain in the market. Image-based deep learning methods are cutting-edge technologies that can facilitate the diagnosis of diseases efficiently and effectively when large-scale dataset is available for training. However, there is not a public dataset about tobacco currently. A comprehensive dataset is appealed to take advantage of deep learning methods in tobacco cultivation urgently. In this paper, we propose to create a specific dataset for tobacco diseases, called Tobacco Plant Disease Dataset (TPDD). 2721 tobacco leaf images are taken in field. The dataset serves for two purposes: disease classification and leaf detection. For classification, we identify 12 classes and provide two types of disease annotations: 1) Whole Leaf Section; 2) Disease Fragment Section. For leaf detection, we provide two kinds of bounding box: rectangle bounding box and polygon bounding box. In addition, we conduct baseline experiments to illustrate the usefulness of TPDD: 1) using deep learning model to detect single disease and multiple diseases; 2) using YOLO-v3 and Mask-RCNN to detect leaves. We hope that the dataset could support the tobacco industry, also be a benchmark in fine-grained vision classification.
With the rapid development of mobile technology, teaching and learning become location-less and time-less. The increasing popularity of mobile technologies provides more opportunities for educators and developers to create a wider range of educational tools. Gamification in the field of education is to motivate students to enjoy learning by applying game design. This project seeks to apply gamification techniques in Portuguese learning for Chinese learners using mobile technology. Gamification elements, progression and leaderboard are used to encourage and motivate students to achieve better results. Finally, an evaluation survey was conducted to verify the effectiveness of beginners' language learning and building their confidence. Further studies could design experimental studies and adopt more qualitative research methods. Some different individual characters of students, like confidence level and cognitive style, could be considered in the future research. An idea to increase the interests and motivation of Portuguese learning is worth studying.
An edge server acts as a data gateway in an IoT network between IoT devices and backend servers. If the edge server is under the ransomware attack, the server's operation would be interfered by locking its critical files, leading to a single point of failure in the IoT network. This paper proposes a blockchain solution, called Blockchain-enabled Recovery Service (BRS), to tackle the malware injection attack in edge servers. In BRS, edge servers are implemented by nodes in a blockchain network which allows IoT data distributed in edge servers over multiple data connections. It ensures data availability and consistency while improving the network throughput. A simulation test has been conducted to evaluate the performance of edge servers in the blockchain network. The result showed that the size of data record in a ledger is crucial when maximizing the efficiency of a ledger and minimizing data loss in ransomware attack are considered. Moreover, even though the blockchain network is under the attack, data availability and data recovery can be achieved.
Digital Library
The implementation of blockchain technology is becoming popular among cyber-physical systems. However, the current solutions suffer from scalability and privacy issues. In this position paper, we leverage zero-knowledge proof and multichain technology to propose an efficient system for data transferring across different components. Each component may maintain a private chain storing its data, and the system acts as a relayer between different chains, in which multiple private chains are efficient for appending new data. Only encrypted data is transferred from a source chain to a destination chain. The relayer handles data transferring in two phases: send and receive, and the relayer keeps a Merkle tree of all sent data. In fact, it only transfers the data if the receiver can submit a valid zero-knowledge proof that proves the ownership of the data. The zero-knowledge proof discloses nothing but the statement is true; therefore it protects anonymity for the data owners. This system is secure and satisfies relevant properties such as ledger indistinguishability, transaction non-malleability, and matchability.
To secure the operations of address auto-configuration protocols in IPv6 networks, a solution called Secure Address Configuration for IPv6 (SAC6) is proposed in this paper. Unlike the previous solutions that mainly use the cryptographic approach, SAC6 eliminates the threats to configuration protocols for the network nodes by acting like a Neighbor Discovery Protocol agent. The major merit of SAC6 is that its operations are transparent to the network and do not require the modification of existing protocols. Therefore, it can be seamlessly deployed in existing IPv6 networks. To demonstrate the viability of SAC6, we implemented SAC6 as a kernel module in a Linux bridge. In our experiments, SAC6 have successfully blocked various kinds of spoofing configuration protocol messages and prevent the network nodes from being attacked during the address configuration.
Digital Library
Attracting and engaging computer science students to enhance their thinking skills are challenging tasks. In the past 10 years, the Computing Program at the Macao Polytechnic Institute encouraged our undergraduate students in doing research work, striving to enhance their mathematical and algorithmic thinking skills as well as to engage them in their study. We are convinced that these experiences change students' perception of computer science and how they can be part of the innovation engine throughout their career in science and technology. This paper discusses the MPI Collaborative Undergraduate Research Engagement model which consists of three main building blocks: team work and interpersonal skills, domain knowledge building, and technical communications. Besides computer science, we believe that our model can also be applied to other disciplines.
This paper presented a case study on an agile shift from brick-and-mortar to online distance learning as a measure to sustain teaching and learning in higher education in response to the interruption and impact brought by the outbreak of Novel Coronavirus Pneumonia (COVID-19) to the society. This case study provided a practical example demonstrating possible practices that can be adopted to deliver different types of modules and carry out assessment under force majeure circumstances. We have applied several methods in module delivery and reflected on their strengths and weaknesses. Our approaches have taken into consideration the different needs of taught and non-taught modules, robustness of online assessment, and quick orientation for students who were unfamiliar with online education. The consequent effectiveness of teaching and learning was evaluated in terms of instructor participation and student participation. The findings indicated a relatively slow start due to the series of necessary adjustments to be made to encounter the shift. However, once the adaptation process was completed, an enhancing trend was observed in students and instructors' engagement in various online teaching and learning practices. Our study highlighted that pastoral support is essential to maintain the motivation and engagement of students. In addition, adjustments to module delivery and assessment methods are highly recommended.
This article explores the feasibility of developing cloud storage dApps on the Internet Computer (IC) blockchain, which can hold full-stack applications entirely on-chain and run at web speed. We analyze cloud storage services' security, stability, cost, and performance issues and propose a multi-canister system design scheme for building cloud storage dApps on the IC. Our quantitative tests demonstrate that the blockchain-secured data I/O operations on the IC can result in ten times time savings and up to a million times cost savings compared to those on Ethereum; deploying small-scale data storage applications on the IC can even be more resource-efficient than on traditional servers. We also discuss the trade-offs and decision-making considerations in our conclusion.
