A well-known use of the blockchain technology is Decentralized Finance (DeFi). DeFi makes financial information accessible to the public but raises potential privacy and security issues. In this study, we implemented a DeFi protocol that protects privacy, which is based on the Mystiko.Network protocol. As a proxy between the user and DeFi platforms, the Mystiko.Network protocol offers an auditable confidentiality mechanism for blockchain transactions. Via the new system, users may submit anonymous DeFi transactions and get income back into a shielded tokens pool. Moreover, we implemented a rollup approach to handle anonymous DeFi transactions in groups. The evaluation results suggest that the protocol is both practical and affordable, in fact it is able to save around 90% of the cost for DeFi transactions.
This research intends to show how an analytical cyclic division of a dataset can improve ANN-type models in predicting future situation of different air pollutants in small-sized urban cities. Similar to other sub-tropical cities, the four seasons are not significant...
A novel Neural Offset Min-Sum(NOMS) Belief Propagation(BP) decoding algorithm based on model-driven is proposed which applied to LDPC decoding. NOMS is improved multiplication in Neural Normalized Min-Sum(NNMS) into addition operation to reduce the complexity of calculation., a better Bit Error Rate (BER) performance is simultaneously achieved in the same condition. Secondly, considering that there are still many multiplication operations in NOMS, we propose a novel Shared Offset Min-Sum(SNOMS) to reduce the number of weights in the network by sharing parameters. Finally, codebook-based quantization is used to further reduce the memory consumption. Simulation experimental results show that the proposed method has a better BER performance, and the decoding accuracy of the decoder is 0.65dB higher than that of the NNMS after 5 iterations. In addition, SNOMS decoding method achieves almost the same decoding performance comparable to that of NOMS, but requires less complex calculation. Proposed quantization of code-book method reduces memory requirement significantly with slight performance loss.
Processing ancient text images presents significant challenges due to severe visual degradation, missing glyph structures, and various types of noise caused by aging. These issues are particularly prominent in Chinese historical documents and stone inscriptions, where diverse writing styles, multi-angle capturing, uneven lighting, and low contrast further hinder the performance of traditional OCR techniques. In this paper, we propose a unified neural framework, UniText, for the detection, recognition, and glyph restoration of Chinese characters in images of historical documents and inscriptions. UniText operates at the character level and processes full-page inputs, making it robust to multi-scale, multi-oriented, and noise-corrupted text. The model adopts a multi-task architecture that integrates spatial localization, semantic recognition, and visual restoration through stroke-aware supervision and multi-scale feature aggregation. Experimental results on our curated dataset of ancient Chinese texts demonstrate that UniText achieves a competitive performance in detection and recognition while producing visually faithful restorations under challenging conditions. This work provides a technically scalable and generalizable framework for image-based document analysis, with potential applications in historical document processing, digital archiving, and broader tasks in text image understanding.
As the number of vehicles running on the road increase, the traffic control becomes a serious problem in many historical cities, like Macao. Public transportation would hardly run and manage. In this research project, we propose the Electronic Vehicle Identification (EVI), which is a smart device with low-energy Bluetooth connection that exchanges the vehicle information and allows external communication with other control systems. As an ID for vehicle, it can be uniquely identified a vehicle and communication with vehicle is allowed. To illustrate the correctness of EVI, we conducted an experiment by using a Bluetooth reader to connect to EVI to discover the movement of bus vehicle departing or arriving at the bus terminal. The result shows that the bus vehicles can be captured on the road effectively, which can be used to support the Macao traffic department in optimizing road resources, managing traffic and making development plan.
Accurate prediction of storm surges is crucial for mitigating the impact of extreme weather events. This paper introduces the Bidirectional Attention-based Long Short-Term Memory (LSTM) Storm Surge Architecture, BALSSA, addressing limitations in traditional physical models. By leveraging machine learning techniques and extensive historical and real-time data, BALSSA significantly enhances prediction accuracy. Utilizing a bidirectional attention-based LSTM framework, it captures complex, non-linear relationships and long-term dependencies, improving the accuracy of storm surge predictions. The enhanced model, D-BALSSA, further amplifies predictive capability through a doubled bidirectional attention-based structure. Training and evaluation involve a comprehensive dataset from over 70 typhoon incidents in Macao between 2017 and 2022. The results showcase the outstanding performance of BALSSA, delivering highly accurate storm surge forecasts with a lead time of up to 72 h. Notably, the model exhibits a low Mean Absolute Error (MAE) of 0.0287 m and Root Mean Squared Error (RMSE) of 0.0357 m, crucial indicators measuring the accuracy of storm surge predictions in water level anomalies. These metrics comprehensively evaluate the model’s accuracy within the specified timeframe, enabling timely evacuation and early warnings for effective disaster mitigation. An adaptive system, integrating real-time alerts, tropical cyclone (TC) chaser, and prospective visualizations of meteorological and tidal measurements, enhances BALSSA’s capabilities for improved storm surge prediction. Positioned as a comprehensive tool for risk management, BALSSA supports decision makers, civil protection agencies, and governments involved in disaster preparedness and response. By leveraging advanced machine learning techniques and extensive data, BALSSA enables precise and timely predictions, empowering coastal communities to proactively prepare and respond to extreme weather events. This enhanced accuracy strengthens the resilience of coastal communities and protects lives and infrastructure from the escalating threats of climate change.
Accurate storm surge forecasting is vital for saving lives and avoiding economic and infrastructural damage. Failure to accurately predict storm surge can have catastrophic repercussions. Advances in machine learning models show the ability to improve accuracy of storm surge prediction by leveraging vast amounts of historical and realtime data such as weather and tide patterns. This paper proposes a bidirectional attention-based LSTM storm surge architecture (BALSSA) to improve prediction accuracy. Training and evaluation utilized extensive meteorological and tide level data from 77 typhoon incidents in Hong Kong and Macao between 2017 and 2022. The proposed methodology is able to model complex non-linearities between large amounts of data from different sources and identify complex relationships between variables that are typically not captured by traditional physical methods. BALSSA effectively resolves the problem of long-term dependencies in storm surge prediction by the incorporation of an attention mechanism. It enables selective emphasis on significant features and boosts the prediction accuracy. Evaluation has been conducted using real-world datasets from Macao to validate our storm surge prediction model. Results show that accuracy and robustness of predictions were significantly improved by the incorporation of attention mechanisms in our models. BALSSA captures temporal dynamics effectively, providing highly accurate storm surge forecasts (MAE: 0.0126, RMSE: 0.0003) up to 72 h in advance. These findings have practical significance for disaster risk reduction strategies, saving lives through timely evacuation and early warnings. Experiments comparing BALSSA variations with other machine learning algorithms consistently validate BALSSA’s superior predictive performance. It offers an additional risk management tool for civil-protection agencies and governments, as well as an ideal solution for enhancing storm surge prediction accuracy, benefiting coastal communities.
In this article, we consider in improving the throughput of existing multicast routing protocols by using multiple multicast trees in ad hoc wireless networks. To achieve our goal, we propose virtual layer, a multi-layer container for multicast trees, that provides an scalable routing structure with transmission gain in multicast routing. Without modification of multicast routing protocols, we enable a layer concept into the protocols so that multiple multicast trees can be constructed in a multicast group. Although virtual layer improves throughput, excessive data traffic will be introduced into a network that may cause network congestion. To resolve this situation, we consider a random coding scheme, a practical network coding implementation that encodes data packets for routing in disjoint-path tree while avoiding congestion. We observed in our simulations that a transmission gain trades off the excessive traffic that causes network congestion.
Visible infrared person re-identification (VI Re-ID) is of particular importance for an intelligent safe-guard system, aiming to retrieve the same pedestrian from non-overlapping visible and infrared cameras. The VI Re-ID task is extremely challenging due to significant modality differences, high-sample noise, occlusions, etc. To address these issues, we explore a dual-space aggregation learning (DSAL) method that combines instance batch normalization (IBN) and residual shrinkage (RS) into a baseline model for feature learning and compression at the channel-level. The random erasing (RE) data augmentation method has been applied to preprocess the data. Experiments on two datasets demonstrate that: 1) IBN reduces shallow layer appearance differences and can bridge the gap between heterogeneous modalities; 2) The RS adaptive soft threshold sets the zero-domain features to zero to eliminate noise and clutter information, thereby enhancing the robustness of the network to noise; 3) RE data augmentation method significantly improves the model's generalization ability. Particularly, the design of DSAL can be seamlessly embedded into other CNN frameworks as a bottleneck variant without additional computation costs. Compared with the strong baseline, on SYSU-MM01, Rank-1, mAP, and mINP significantly improved by 10.66%, 7.78%, and 5.91%, respectively. On RegDB, Rank-1, mAP, and mINP significantly improved by 16.40%, 13.83%, and 19.07%, respectively.
Robots with computer vision and text recognition functions are widely used in industrial production, especially in highly automated factories. However, most robots have an excellent ability to recognize printed characters and show low accuracy in recognition of handwritten characters. Therefore, this paper considers recognizing handwritten text in the intelligent processing of handwritten documents. Its high accuracy prediction results are closely related to the effectiveness of manuscript input, intelligent translation, and intelligent scoring. Handwritten text is more difficult to recognize because it contains sequential information, and the images are more complex than single-character images. This paper proposes a new handwritten Chinese text recognition (HCTR) framework based on existing classical convolutional neural network (CNN) and recurrent neural network (RNN) algorithms. We use a handwritten Chinese text dataset from CASIA-HWDB containing numbers and symbols close to real application scenarios to train the model and compare the performance of various models, such as MobileNetV1 and MobileNetV2, with the proposed model. From the analysis of experimental results, it can be found that the proposed method can achieve higher performance with fewer parameters. In addition, we optimize the dropout rates of input blocks and obtain the best CER of our method is 6.11%.
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In coastal regions, accurate storm surge prediction is crucial for effective disaster management and risk mitigation. This study presents a comparative analysis between BALSSA (Bidirectional Attention-based LSTM for Storm Surge Architecture) and the Japan Meteorological Agency (JMA) numerical storm surge model, focusing on the Saola-induced storm surge in Macao, September 2023. To train and assess the model, we leverage an extensive dataset comprising meteorological and tide level information from more than 80 typhoon occurrences in Macao spanning the period from 2017 to 2023. The results provide evidence of BALSSA’s effectiveness in capturing the complex spatio-temporal dynamics of storm surges, with a lead time of up to 72 h, as reflected by its MAE of 0.019 and RMSE of 0.024. It demonstrates reliable accuracy in predicting storm surge magnitude, timing, and spatial extent, potentially contributing to more precise and timely warnings for coastal communities. Furthermore, the real-time data assimilation feature of BALSSA ensures up-to-date information, aligned with the latest observations, which is essential for effective emergency preparedness and response. The high-resolution grids enhance risk assessment, highlighting BALSSA’s potential for early warnings, emergency planning, and coastal risk management. This study contributes valuable insights to the broader field of storm surge prediction, guiding decision-making processes and supporting the development of effective strategies to enhance coastal resilience.
Embedded Artificial Intelligence (EAI) integrates AI technologies with resource-constrained embedded systems, overcoming the limitations of cloud AI in aspects such as latency and energy consumption, thereby empowering edge devices with autonomous decision-making and real-time intelligence. This review provides a comprehensive overview of this rapidly evolving field, systematically covering its definition, hardware platforms, software frameworks and tools, core algorithms (including lightweight models), and detailed deployment processes. It also discusses its widespread applications in key areas like autonomous driving and smart Internet of Things (IoT), as well as emerging directions. By analyzing its core challenges and innovative opportunities in algorithms, hardware, and frameworks, this review aims to provide relevant researchers and developers with a practical guidance framework, promoting technological innovation and adoption.
Embedded Artificial Intelligence (EAI) integrates AI technologies with resource-constrained embedded systems, overcoming the limitations of cloud AI in aspects such as latency and energy consumption, thereby empowering edge devices with autonomous decision-making and real-time intelligence. This review provides a comprehensive overview of this rapidly evolving field, systematically covering its definition, hardware platforms, software frameworks and tools, core algorithms (including lightweight models), and detailed deployment processes. It also discusses its widespread applications in key areas like autonomous driving and smart Internet of Things (IoT), as well as emerging directions. By analyzing its core challenges and innovative opportunities in algorithms, hardware, and frameworks, this review aims to provide relevant researchers and developers with a practical guidance framework, promoting technological innovation and adoption.
