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.
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.
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.
In environmental monitoring, deep learning models are used where we can either use past observations or extrapolated values with high uncertainty as input. The lag scheme is commonly applied during the modeling and construction process, in the application of deep learning models in multivariate time series prediction. For an adaptive feature engineering, an automated lag scheme is essential for improving the training efficiency. In multivariate time series (MTS) models, the predictive accuracy of artificial neural network ANN-type models can be improved by including more features. It is assumed that when processing a certain number of multivariate features, the timeliness and lag time of the inter-influencing between any pair of elements are different. This research aims to adopt an adaptive approach to solve it, namely, multi-level lag scheme. The research methods include literature review, searching for relevant technology frontiers, feasibility studies, selection and design solutions, modeling, data collection and pre-processing, experiments, evaluation, comprehensive analysis and conclusions. In proof of concept, we demonstrated a practical case of seasonal ANN type MTS model and public service on air quality. In terms of models, ANN type models were attempted with ARIMA as the comparing baseline. We used public data set of more than two base stations with pollution varying from low to high and including southern to northern district of a small city. Conclusions can be drawn from the analysis of multiple experimental results, proving that the proposed solution can effectively improve the training efficiency of the model. This is of great significance, so that most such models can be implemented to adaptively use lagged past measured data as input, instead of synchronously inputting future prediction values, which can greatly improve the practical application of the model in predictive ability.
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.
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.
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.
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.
Digital Library
A key challenge of visible-infrared person re-identification (VI-ReID) comes from the modality difference between visible and infrared images, which further causes large intra-person and small inter-person distances. Most existing methods design feature extractors and loss functions to bridge the modality gap. However, the unpaired-images constrain the VI-ReID model's ability to learn instance-level alignment features. Different from these methods, in this paper, we propose a pose attention-guided paired-images generation network (PAPG) from the standpoint of data augmentation. PAPG can generate cross-modality paired-images with shape and appearance consistency with the real image to perform instance-level feature alignment by minimizing the distances of every pair of images. Furthermore, our method alleviates data insufficient and reduces the risk of VI-ReID model overfitting. Comprehensive experiments conducted on two publicly available datasets validate the effectiveness and generalizability of PAPG. Especially, on the SYSU-MM01 dataset, our method accomplishes 7.76% and 5.87% gains in Rank-1 and mAP. The code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/qyhsxdx/PAPG.</uri>
The rapid digitization of the real estate and architectural design industries has created a high demand for automated tools capable of parsing 2D raster floor plans. Traditional manual measurement and visual inspection are not only time-consuming but also highly susceptible to human error. In this paper, we propose a comprehensive, end-to-end deep learning framework designed to automatically extract rich semantic information from unstructured 2D floor plan images and provide professional design guidance via Large Language Models (LLMs). Our integrated pipeline employs the state-of-the-art YOLOv8 object detection model to accurately localize and classify 18 distinct architectural symbols and furniture items (e.g., doors, windows, beds, cupboards). Simultaneously, a U-Net architecture with a ResNet34 encoder is utilized for the precise semantic segmentation of structural elements, specifically walls and interior room spaces. To translate pixel-level predictions into actionable real-world metrics, we introduce a robust area calculation algorithm based on user-defined reference scale calibration. Furthermore, to bridge the gap between raw geometric data and actionable architectural intelligence, we introduce an LLM-driven evaluation module utilizing a local Ollama deployment and a Retrieval-Augmented Generation (RAG) pipeline to assess design compliance and quality. To overcome the scarcity of annotated architectural datasets, we implement a systematic data augmentation strategy, expanding a core dataset of 101 manually annotated floor plans to 303 varied instances, thereby significantly enhancing model generalization. Experimental results indicate that our YOLOv8-based detection module achieves a mean Average Precision (mAP50) of 92.3%, while the U-Net segmentation module achieves a mean Intersection over Union (mIoU) of 95.71%. Furthermore, the integrated system is deployed as a user-friendly, interactive web application, acting as an intelligent architectural assistant and demonstrating its practical viability and high efficiency for real-world engineering and architectural applications.
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.
