Early detection of anomalous events in automated processes within industrial scenarios helps to improve service smoothness, thus becoming critical and urgent. Despite this vision, prior works face challenges in convergence on noisy training materials and insufficient construction of spatial-temporal dependencies, leading to performance limitations. In this work, we propose Spectra, a flexible framework for time-series anomaly detection in industrial scenarios. We employ a pair of parallel memory modules in the generative model to store and purify spatial and temporal knowledge in latent embeddings. As such, Spectra offsets the impact of noise and anomalous components in training materials, and signifies the difference between normals and anomalies. To dynamically integrate cross-domain information, we design an embedding fusion mechanism that comprises an agent attention module and a contrastive embedding alignment technique. This mechanism bridges embeddings from instantiated memory modules, aligns dependencies, and improves the organization of the latent space. Extensive experiments on three large-scale industrial datasets demonstrate Spectra’s effectiveness, with an average F1-Score of 0.9083 outperforming the baselines.

The construction of high-precision urban rail maps is crucial for the safe and efficient operation of railway transportation systems. However, the repetitive features and sparse textures in urban rail environments pose challenges for map construction with high-precision. Motivated by this, this paper proposes a high-precision urban rail map construction algorithm based on multi-sensor fusion. The algorithm integrates laser radar and Inertial Measurement Unit (IMU) data to construct the geometric structure map of the urban rail. It utilizes image point-line features and color information to improve map accuracy by minimizing photometric errors and incorporating color information, thus generating high-precision maps. Experimental results on a real urban rail dataset demonstrate that the proposed algorithm achieves root mean square errors of 0.345 and 1.033 m for ground and tunnel scenes, respectively, representing a 19.31 % and 56.80 % improvement compared to state-of-the-art methods.

With cloud computing technology becoming more mature, it is essential to combine the big data processing tool Hadoop with the Infrastructure as a Service (IaaS) cloud platform. In this study, we first propose a new Dynamic Hadoop Cluster on IaaS (DHCI) architecture, which includes four key modules: monitoring, scheduling, Virtual Machine (VM) management, and VM migration modules. The load of both physical hosts and VMs is collected by the monitoring module and can be used to design resource scheduling and data locality solutions. Second, we present a simple load feedback-based resource scheduling scheme. The resource allocation can be avoided on overburdened physical hosts or the strong scalability of virtual cluster can be achieved by fluctuating the number of VMs. To improve the flexibility, we adopt the separated deployment of the computation and storage VMs in the DHCI architecture, which negatively impacts the data locality. Third, we reuse the method of VM migration and propose a dynamic migration-based data locality scheme using parallel computing entropy. We migrate the computation nodes to different host(s) or rack(s) where the corresponding storage nodes are deployed to satisfy the requirement of data locality. We evaluate our solutions in a realistic scenario based on OpenStack. Substantial experimental results demonstrate the effectiveness of our solutions that contribute to balance the workload and performance improvement, even under heavy-loaded cloud system conditions.

With respect to security, the use of various terminals in the mobile Internet environment is problematic. Traditional terminal testing methods cannot simulate actual testing environments; thus, the test results do not accurately reflect the security of terminals. To address this problem, we designed and developed a cloud platform based automated testing system for the mobile Internet. In this system, virtualization and automation technology are utilized to integrate mobile terminals into the cloud platform as a resource, to achieve a novel cloud service called Testing as a Service (TaaS). The system consists of three functional modules: web front-end module, testing environment module, and automated testing module. We adopted the permeable automated testing tool Metasploit to perform security testing. In our test experiments, we selected 100 apps with diverse vulnerability levels, ranging from secure to vulnerable, to perform a series of functional tests. The experimental results show that this system can correctly test both the number of vulnerable apps and their corresponding vulnerability levels. As such, the designed system can flexibly configure various testing environments for different testing cases or projects, and thereby perform security testing automatically.