Spike cameras, a novel neuromorphic visual sensor, can capture full-time spatial information through spike stream, offering ultra-high temporal resolution and an extensive dy-namic range. Autofocus control (AC) plays a pivotal role in a camera to efficiently capture information in challenging real-world scenarios. Nevertheless, due to disparities in data modality and information characteristics compared to frame stream and event stream, the current lack of effi-cient AC methods has made it challenging for spike cam-eras to adapt to intricate real-world conditions. To ad-dress this challenge, we introduce a spike-based autofo-cus framework that includes a spike-specific focus measure called spike dispersion (SD), which effectively mitigates the influence of variations in scene light intensity during the focusing process by leveraging the spike camera's ability to record full-time spatial light intensity. Additionally, the framework integrates a fast search strategy called spike-based goldenfast search (SGFS), allowing rapidfocal positioning without the need for a complete focus range traver-sal. To validate the performance of our method, we have collected a spike-based autofocus dataset (SAD) containing synthetic data and real-world data under varying scene brightness and motion scenarios. Experimental results on these datasets demonstrate that our method offers state-of-the-art accuracy and efficiency. Furthermore, experiments with data captured under varying scene brightness levels illustrate the robustness of our method to changes in light intensity during the focusing process.

The exponential growth of data generated from increasing smart meters and smart appliances brings about huge potentials for more efficient energy production, pricing, and personalized energy services in smart grids. However, it also causes severe concerns due to improper use of individuals' private data, as well as the lack of transparency and auditability for data usage. To bridge this gap, in this article, we propose a secure and auditable private data sharing (SPDS) scheme under data processing-as-a-service mode in smart grid. Specifically, we first present a novel blockchain-based framework for trust-free private data computation and data usage tracking, where smart contracts are employed to specify fine-grained data usage policies (i.e., who can access what kinds of data, for what purposes, at what price) while the distributed ledgers keep an immutable and transparent record of data usage. A trusted execution environment based off-chain smart contract execution mechanism is exploited as well to process confidential user datasets and relieve the computation overhead in blockchain systems. A two-phase atomic delivery protocol is designed to ensure the atomicity of data transactions in computing result release and payment. Furthermore, based on contract theory, the optimal contracts are designed under information asymmetry to stimulate user's participation and high-quality data sharing while optimizing the payoff of the energy service provider. Extensive simulation results demonstrate that the proposed SPDS can effectively improve the payoffs of participants, compared with conventional schemes.

Improving the reliability of the Power Communication Network (PCN) has become an important research content. It is known that the existing algorithm analyzes the characteristics of the network from the dimension of the network topology, ignoring the alternative association characteristics between the network nodes, resulting in low reliability of the PCN. In order to solve this problem, this paper first analyzes the characteristics of network resource association from three aspects: resource characteristics, resource performance association analysis and resource fault correlation analysis. Then, the reliability analysis and optimization algorithm of PCN based on resource association features is proposed. Through simulation experiments, it is verified that the proposed algorithm effectively improves the reliability of the PCN.

Drive-by download attack is one of the most severe threats to Internet users. Typically, only visiting a malicious page will result in compromise of the client and infection of malware. By the end of 2008, drive-by download had already become the number one infection vector of malware [5]. The downloaded malware may steal the users' personal identification and password. They may also join botnet to send spams, host phishing site or launch distributed denial of service attacks.