Attribute-based encryption provides fine-grained access control, ensuring data security in edge computing. However, existing access control schemes face issues such as high computational complexity due to bilinear pairing and key escrow with a single authority, which limit their effectiveness for lightweight terminal devices. To address these challenges, we propose a lightweight and key escrow-free access control scheme on encrypted data. This scheme replaces bilinear pairing with fast elliptic curve scalar multiplication, transferring most computational tasks to edge servers while preserving privacy, thus alleviating the computational burden on terminal devices. Additionally, we design a blockchain-based distributed key management method that allows multiple edge servers to distribute private keys collaboratively, resolving the key-escrow issue. Security analysis and performance evaluation demonstrate that our scheme effectively ensures data confidentiality, resists collusion attacks, and achieves high computational efficiency on the user side with low storage overheads, making it highly suitable for lightweight devices in edge computing.
This paper represents a perspective on the design of AI chatbots with respect to the concept of method engineering. Overall, this paper includes the following sections: First, we represent primitive findings based on a set of method fragments extracted from selected papers in the domain of AI chatbot design. Then, we discuss our future work and the limitations of our research in information systems.
Predicting traffic safety trends is of great significance for the realization of autonomous driving and other related areas. Current research focuses on using machine learning and deep learning models to predict traffic safety issues, such as whether a vehicle will collide or the severity of a collision. However, traffic safety is influenced by multiple factors, and relying solely on primary traffic feature data for predictions is far from sufficient. Predictions about traffic safety should consider as many factors as possible. This paper summarizes five main factors: besides primary traffic features, others include weather and environmental features, road design and configuration features, socio-economic features, and traffic violation records. Additionally, an improved meta-analysis method is introduced to quantify and evaluate the use of multiple factors, thereby identifying which factors have a greater impact on traffic safety. Meta-analysis can accurately integrate the methods and results of various studies to conclude. Thus, this paper provides a comprehensive, in-depth, and detailed review through the improved meta-analysis.
Grain price has a significant impact on social stable and economy development. However, the price of grain are often influenced by various complex factors, particularly the time factors. Therefore, this paper first splits the time characteristics into more fine-grained time characteristics of multiple dimensions to capture the seasonal changes, holiday effects, long-term trends, and short-term fluctuations in prices. Secondly, the isolated forest algorithm is employed to remove outliers from the dataset, ensuring the accuracy and reliability of the data set. Finally, adjust the Transformer model structure. This study removes the positional embedding layer and directly utilizes the encoding part of the Transformer model for feature extraction and representation of the input sequence, while defining the prediction layer to replace the decoder layer for numerical prediction. This design simplifies the model structure, reduces computational complexity and the number of parameters, and aligns better with the goals and requirements of regression prediction tasks. The experimental results demonstrate that the time feature decomposition method and the isolation forest algorithm significantly improve the accuracy of price prediction. Furthermore, the modified Transformer model in this study outperforms other machine learning methods and neural network algorithms in multiple evaluation metrics.
Community question answering (CQA) portals have become very popular platforms attracting numerous participants to share and acquire knowledge and information on the Internet. However, many malicious users post suggestive questions and deceptive answers to promote a target (product or service), which can extremely distort the users' decision, and make the CQA environment less credible. Although various methods have been proposed to detect the fraud content from CQA, most of them detect deceptive questions and answers separately, thus suffer the problem of interactive information missing. In this paper, we take the Question-Answer-Pair (QAP) as the detecting target and propose a GNN-based method to detect the spam activities in the CQA. Specifically, we also design a label-aware neighbor selection module based on supervised learning to select more appropriate neighbors for attributes aggregating in G NN. Extensive experiments are conducted in a real-world dataset and results demonstrate the advantage and effectiveness of the proposed model for CQA spam detection.
