The Polymorphic Network (PINet) is a future network architecture that can dynamically load multiple network modalities (NM), solving the rigidity and single IP bearer problem. However, due to the different design concepts and protocol stacks of each NM, it is challenging to provide compatible data access control for different NMs. In this paper, we propose a unified attribute-based data access control framework within PINet. This framework ensures consistent access control across NMs by managing user attribute identifiers and seamlessly integrating access policies. We design Span-hiding Cipher-policy Attribute-based Encryption (SHCP-ABE). The design of span attribute cuckoo filter and two-round encryption in SHCP-ABE implements access control that supports comparing attribute values and policy hiding. We further modified the linear secret sharing scheme in SHCP-ABE to achieve isolation of access policy information among NMs. The proposed SHCP-ABE scheme is unified, fine-grained, privacy-preserving, and can effectively adapt to heterogeneous future network architectures such as PINet. Security analysis shows that SHCP-ABE is secure against the adaptive chosen plaintext attack under the generic group model. Experimental results show that SHCP-ABE reduces the time overhead of key generation and decryption and the key storage overhead by about 50% over the existing comparable CP-ABE scheme, providing excellent efficiency.

Road damage represents a serious challenge to the health of road infrastructure and driving safety, making deep learning-based image analysis for road damage detection (RDD) an important research focus. The limited diversity in road damage types, road image collection, size, environment, and imperfect damage definitions within current RDD datasets restrict the real-world applications of RDD. To address this issue, this paper constructs PCL-RDD, a new and extensive RDD dataset. It comprises 24,765 road images, 54,732 instances, and 19 types of road damage. Compared to the existing datasets that mainly include common road damage, the proposed dataset contains a variety of rare and urgent road damages. Besides, we collect road facility-related damages, which also affect traffic safety. We evaluate eight well-established object detection algorithms on the dataset, highlighting the limitations of state-of-the-art detection algorithms under complex conditions. This study contributes a significant dataset to the RDD field and can advance artificial intelligence in both city infrastructure management and environmental perception for autonomous driving. The dataset is available at https://github.com/humh-c/PCL-RDD.

Dataset distillation compresses large-scale datasets into compact, highly informative synthetic data, significantly reducing storage and training costs. However, existing research primarily focuses on balanced datasets and struggles to perform under real-world long-tailed distributions. In this work, we emphasize the critical role of soft labels in long-tailed dataset distillation and uncover the underlying mechanisms contributing to performance degradation. Specifically, we derive an imbalance-aware generalization bound for model trained on distilled dataset. We then identify two primary sources of soft-label bias, which originate from the distillation model and the distilled images, through systematic perturbation of the data imbalance levels. To address this, we propose ADSA, an Adaptive Soft-label Alignment module that calibrates the entangled biases. This lightweight module integrates seamlessly into existing distillation pipelines and consistently improves performance. On ImageNet-1k-LT with EDC and IPC=50, ADSA improves tail-class accuracy by up to 11.8\% and raises overall accuracy to 41.4\%. Extensive experiments demonstrate that ADSA provides a robust and generalizable solution under limited label budgets and across a range of distillation techniques.

As agriculture and food industries evolve toward smarter and more efficient practices, uncrewed aerial vehicles (UAVs) are playing an increasingly vital role in tasks, such as data collection and precision spraying, and the reliable identity authentication has emerged as a critical issue for ensuring operational security and data privacy. Traditional authentication mechanisms, including public-key infrastructure-based and ID-based, have provided mutual trust and security of UAVs. However, when deployed on a large scale of UAVs, they suffer from a single point of failure, a heavy computational overhead, and challenges in managing UAVs at a large scale. In this article, we propose a lightweight decentralized authentication scheme with dynamic management for UAVs. Specifically, by leveraging the self-sovereign property of decentralized identifiers (DIDs), we enable each UAV to autonomously generate its own identifier, thereby realizing the decentralized authentication. And for the key pairs required in authentication, we design a secure and lightweight secret key generator based on the physical unclonable function (PUF), which prevents key leakage. In terms of identity management, we propose a cryptographic accumulator-based dynamic lightweight management module (DLMM), which is endowed with dynamic scalability and provides efficient dynamic management with low communication and computation overhead. We evaluate the performance of the proposed scheme, and the results show that the proposed scheme has significant advantages in security and computation overhead compared with the existing schemes.

With the development of the Internet of Things (IoT) and cloud computing, various multimedia data such as audio, video, and images have experienced explosive growth, ushering in the era of big data. Large-scale computing tasks in the Multimedia Internet of Things (M-IoT), such as mathematical optimization problems, have begun to be outsourced from IoT devices with limited computing power to cloud servers for execution. However, outsourcing computation brings security concerns, because the behaviors of clouds are invisible to users. The leakage of privacy data in outsourced optimization problems leads to immeasurable losses. The mutual distrust between clouds and users causes that the correctness of the optimal decisions and the fairness of the payment activities are not guaranteed. Blockchain technology has the characteristic of immutability and has become a new security paradigm for eliminating multi-party trust concerns. In this article, we propose a Bitcoin-based secure outsourcing scheme to address the aforementioned security concerns. To prevent confidential data leakage, the proposed scheme designs a computable privacy-preserving method for the outsourced optimization problems. To judge the correctness of the optimal decision and reduce verification costs, the proposed scheme designs a low-cost two-layer verification mechanism based on dual theory and blockchain technology. Blockchain nodes reach a consensus on the problem solutions and trigger an automatic fair payment protocol-based Bitcoin. Security analysis and experimental results demonstrate that our scheme guarantees privacy, fairness, and computational efficiency.

Knowledge graph (KG), integrating complex information and containing rich semantics, is widely considered as side information to enhance the recommendation systems. However, most of the existing KG-based methods concentrate on encoding the structural information in the graph, without utilizing the collaborative signals in user-item interaction data, which are important for understanding user preferences. Therefore, the representations learned by these models are insufficient for representing semantic information of users and items in the recommendation environment. The combination of both kinds of data provides a good chance to solve this problem, but it faces the following challenges: i) the inner correlations in user-item interaction data are difficult to capture from one side of the user or item; ii) capturing the knowledge associations on the whole KG would introduce noises and variously influence the recommendation results; iii) the semantic gap between both kinds of data is hard to alleviate.

As the global pandemic of the COVID-19 continues, the statistical modeling and analysis of the spreading process of COVID-19 have attracted widespread attention. Various propagation simulation models have been proposed to predict the spread of the epidemic and the effectiveness of related control measures. These models play an indispensable role in understanding the complex dynamic situation of the epidemic. Most existing work studies the spread of epidemic at two levels including population and agent. However, there is no comprehensive statistical analysis of community lockdown measures and corresponding control effects. This paper performs a statistical analysis of the effectiveness of community lockdown based on the Agent-Level Pandemic Simulation (ALPS) model. We propose a statistical model to analyze multiple variables affecting the COVID-19 pandemic, which include the timings of implementing and lifting lockdown, the crowd mobility, and other factors. Specifically, a motion model followed by ALPS and related basic assumptions is discussed first. Then the model has been evaluated using the real data of COVID-19. The simulation study and comparison with real data have validated the effectiveness of our model.

This article aims to implement trend forecasting of stock time series based on candlestick patterns and sequence similarity. Financial time series forecasting plays a central role in hedging market risks and optimizing investment portfolios. This is a challenging task, as financial engineering requires the proposed approach to be interpretable, robust, and compatible. It is noted that many published research studies are based on multi-modal data, which makes the prediction approaches increasingly complex, difficult to interpret, and does not allow the migration across different data. Given this situation, it is believed that a candlestick data-based approach is promising. It is already recognized by the technical analyses, prevalent in financial markets, more readily available, and has better interpretability. In this paper, the forecasting approach is divided into two steps. In the first step, sequential pattern mining is used to obtain candlestick patterns from multidimensional candlestick data, and the correlation between different patterns and the corresponding future trends are calculated. In the second step, a new sequence similarity is proposed to match the diverse candlestick sequences with the existing patterns. The method is validated on real data from 800 stocks in the Chinese stock market, which are divided into two groups of experiments, and the average accuracy achieved by the proposed method is 56.04% and 55.56%, which is higher than the SVM model (50.83% and 51.32%) and the LSTM model (50.71% and 50.68%) used for comparison, proving that our work is more stable and accurate. This work is instructive for further research around candlestick data to follow.

The prediction of stock price has always been a main challenge. The time series of stock price tends to exhibit very strong nonlinear characteristics. In recent years, with the rapid development of deep learning, the ability to automatically extract nonlinear features has significantly attracted scholars’ attention. However, the majority of the relevant studies have concentrated on prediction of the changes of stock market based on the data of the specific stock (e.g., transaction data, financial data, etc.), while those studies ignored the interaction between stocks of different industries, especially the interaction between the stocks of upstream enterprises and downstream enterprises in the industrial chain. This paper aims to propose a combination of transfer learning of industrial chain information and deep learning models, including multilayer perceptron (MLP), recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), for stock market prediction. These models are used to predict the trend of the 379 stock market indices by industry in China, and the DM test was employed for validation of the prediction results. It can be concluded that RNNs are not necessarily such an optimal choice for the prediction when dealing with specific time series data, and it could be justified by using the local interpretable model-agnostic explanations (LIME) algorithm. Hence, the MLP was selected to effectively improve the accuracy of the prediction of the stock market indices based on the transfer learning of industrial chain information. The investment strategy is constructed according to the prediction results, and the yield of maturity exceeds that of the buy-and-hold strategy.

Accurate prediction of the stock market trend can assist efficient portfolio and risk management. In recent years, with the rapid development of deep learning, it can make the classifiers more robust, which can be used for solving nonlinear problems. In our previous research, we proposed a numerical model for predicting the stock market via combination of discrete wavelet transform (DWT) denoising and extreme learning machine (ELM), and promising outcomes are achieved. The current research presents a hybrid framework using DWT, ELM, and autoencoder (AE) with feature penalty. Firstly, the backpropagation of the AE with feature penalty was deduced theoretically. Then, the raw data were denoised by DWT. The denoised data were used to train the AE with feature penalty after feature preprocessing and utilization of the labeling method. Afterward, the encoder part of the well-trained AE was utilized as the feature extraction model to train ELM model, and the hybrid framework named DAELM (DWT-AE-ELM) could be successfully developed. We also carried out experiments on the corresponding dataset of 400 stocks, and the prediction accuracy of the current study was higher than that of our previous research. According to the predicted labels, we presented an investment strategy, and the yield-to-maturity of 400 stocks was significantly higher than that of the buy-and-hold (BAH) strategy. The results confirmed the superiority of the proposed hybrid framework.