High-Confidence Computing最新文献

Compared to 2D imaging data, the 4D light field (LF) data retains richer scene’s structure information, which can significantly improve the computer’s perception capability, including depth estimation, semantic segmentation, and LF rendering. However, there is a contradiction between spatial and angular resolution during the LF image acquisition period. To overcome the above problem, researchers have gradually focused on the light field super-resolution (LFSR). In the traditional solutions, researchers achieved the LFSR based on various optimization frameworks, such as Bayesian and Gaussian models. Deep learning-based methods are more popular than conventional methods because they have better performance and more robust generalization capabilities. In this paper, the present approach can mainly divided into conventional methods and deep learning-based methods. We discuss these two branches in light field spatial super-resolution (LFSSR), light field angular super-resolution (LFASR), and light field spatial and angular super-resolution (LFSASR), respectively. Subsequently, this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these datasets. Finally, we discuss the potential innovations of the LFSR to propose the progress of our research field.

The Connected Sensor Problem (CSP) presents a prevalent challenge in the realms of communication and Internet of Things (IoT) applications. Its primary aim is to maximize the coverage of users while maintaining connectivity among K sensors. Addressing the challenge of managing a large user base alongside a finite number of candidate locations, this paper proposes an extension to the CSP: the h-hop independently submodular maximization problem characterized by curvature $\alpha$. We have developed an approximation algorithm that achieves a ratio of $\frac{1-e^{-\alpha }}{(2h+3)\alpha }$. The efficacy of this algorithm is demonstrated on the CSP, where it shows superior performance over existing algorithms, marked by an average enhancement of 8.4%.

Recently, vehicles have experienced a rise in networking and informatization, leading to increased security concerns. As the most widely used automotive bus network, the Controller Area Network (CAN) bus is vulnerable to attacks, as security was not considered in its original design. This paper proposes SIDuBzip2, a traffic anomaly detection method for the CAN bus based on the bzip2 compression algorithm. The proposed method utilizes the pseudo-periodic characteristics of CAN bus traffic, constructing time series of CAN IDs and calculating the similarity between adjacent time series to identify abnormal traffic. The method consists of three parts: the conversion of CAN ID values to characters, the calculation of similarity based on bzip2 compression, and the optimal solution of model parameters. The experimental results demonstrate that the proposed SIDuBzip2 method effectively detects various attacks, including Denial of Service , replay, basic injection, mixed injection, and suppression attacks. In addition, existing CAN bus traffic anomaly detection methods are compared with the proposed method in terms of performance and delay, demonstrating the feasibility of the proposed method.

In recent years, with the development of blockchain, electronic bidding auction has received more and more attention. Aiming at the possible problems of privacy leakage in the current electronic bidding and auction, this paper proposes an electronic bidding auction system based on blockchain against malicious adversaries, which uses the secure multi-party computation to realize secure bidding auction protocol without any trusted third party. The protocol proposed in this paper is an electronic bidding auction scheme based on the threshold elliptic curve cryptography. It can be implemented without any third party to complete the bidding auction for some malicious behaviors of the participants, which can solve the problem of resisting malicious adversary attacks. The security of the protocol is proved by the real/ideal model paradigm, and the efficiency of the protocol is analyzed. The efficiency of the protocol is verified by simulating experiments, and the protocol has practical value.

As the communication needs in the smart distribution grid continue to rise, using existing resources to meet this growing demand poses a significant challenge. This paper researches on spectrum allocation strategies utilizing cognitive radio (CR) technology. We consider a model containing strong time-sensitive and regular communication service requirements such as distribution terminal communication services, which can be seen as a user with primary data (PD) and weak time-sensitive services such as power quality monitoring, which can be seen as a user with secondary data (SD). To fit the diversity of services in smart distribution grids (SDGs), we formulate an optimization problem with two indicators, including the sum of SD transmission rates and the maximum latency of them. Then, we analyze the two convex sub-problems and utilize convex optimization methods to obtain the optimal power and frequency bandwidth allocation for the users with SD. The simulation results indicate that, when the available transmission power of SD is low, Maximization of Transmission Sum Rate (MTSR) achieves lower maximum transmit time. Conversely, when the available transmission power is high, the performance of Minimization of the Maximum Latency (MML) is better, compared with MTSR.

Wireless networks have become integral to modern communication systems, enabling the seamless exchange of information across a myriad of applications. However, the inherent characteristics of wireless channels, such as fading, interference, and openness, pose significant challenges to achieving fault-tolerant consensus within these networks. Fault-tolerant consensus, a critical aspect of distributed systems, ensures that network nodes collectively agree on a consistent value even in the presence of faulty or compromised components. This survey paper provides a comprehensive overview of fault-tolerant consensus mechanisms specifically tailored for wireless networks. We explore the diverse range of consensus protocols and techniques that have been developed to address the unique challenges of wireless environments. The paper systematically categorizes these consensus mechanisms based on their underlying principles, communication models, and fault models. It investigates how these mechanisms handle various types of faults, including communication errors, node failures, and malicious attacks. It highlights key use cases, such as sensor networks, Internet of Things (IoT) applications, wireless blockchain, and vehicular networks, where fault-tolerant consensus plays a pivotal role in ensuring reliable and accurate data dissemination.

Bundle recommendation offers users more holistic insights by recommending multiple compatible items at once. However, the intricate correlations between items, varied user preferences, and the pronounced data sparsity in combinations present significant challenges for bundle recommendation algorithms. Furthermore, current bundle recommendation methods fail to identify mismatched items within a given set, a process termed as “outlier item detection”. These outlier items are those with the weakest correlations within a bundle. Identifying them can aid users in refining their item combinations. While the correlation among items can predict the detection of such outliers, the adaptability of combinations might not be adequately responsive to shifts in individual items during the learning phase. This limitation can hinder the algorithm’s performance. To tackle these challenges, we introduce an encoder–decoder architecture tailored for outlier item detection. The encoder learns potential item correlations through a self-attention mechanism. Concurrently, the decoder garners efficient inference frameworks by directly assessing item anomalies. We have validated the efficacy and efficiency of our proposed algorithm using real-world datasets.