Effective incident management in large-scale microservice systems demands both accurate anomaly detection (AD) and precise root cause localization (RCL) across heterogeneous data modalities. However, existing approaches often treat these tasks in isolation, resulting in redundant maintenance, delayed response, and the absence of shared diagnostic context. While recent efforts have explored unified frameworks to support both tasks, these approaches often suffer from high falsealarm rates due to cross-modal interference. To address these issues, we propose DeST, an unsupervised decoupled spatiotemporal framework that jointly performs anomaly detection and root cause localization. DeST proposes a multi-stage fusion strategy that decouples temporal and spatial feature learning to mitigate cross-modal interference and prevent cross-modal interference. Furthermore, it incorporates task-specific modal routing to direct learned representations to different tasks, enhancing both detection and localization accuracy. To ensure robustness against transient noise, DeST designs a Differential Multi-Scale Convolutional Network (DMCN) for noise-resistant temporal feature representation. We evaluate DeST on two real-world microservice benchmarks, where it achieves a perfect F1-score of $\mathbf{1. 0 0}$ for anomaly detection and outperforms existing methods in root cause localization accuracy. Ablation studies highlight the effectiveness of key components. Our unified framework reduces false alarms in anomaly detection and streamlines root cause localization, providing a robust and practical solution for microservice incident management.

Accurate human tracking in surveillance scenes is one of the preliminary requirements for other tasks. However, when the human target is small, the extracted features may not be prominent and thus the tracking performance is unsatisfactory. The colour feature is relatively robust to the change of target size and shape, but it is prone to be affected by the background information. For the above reasons, the authors introduce random walker segmentation into human tracking and determine the background region according to the distribution characteristics of segmentation results. Even if the colour of the target is very similar to that of the background, this algorithm can segment the target. Furthermore, the principal component analysis method is used to distinguish human targets from the background as well. During tracking, the authors prevent the degradation of the target model by adding new target information. In order to overcome the mean‐shift local optimisation problem, the authors search for the candidate target region with the largest weight according to the sum of all probability in each region. Experimental results further show that the authors’ tracking algorithm demonstrates better performance on tracking small human target under some challenging scenes compared with several existing tracking methods.

This study proposes a novel digital video stabilisation scheme based on modelling of motion imaging (MI). The modelling of MI eliminates the speed motion as a result of a moving car, which is ignored in other models such as rotation + translation model, and estimates movement parameters of the background in video sequences captured from cameras mounted on moving cars. The authors first analyse the MI to understand the principle of the effects of car motion on MI, and select the matching method according to the proposed model. Then, they employ symmetric points to remove the speed motion. Finally, unwanted motion vector is stimulated by employing adaptive step‐length filter, and the boundary compensating approach is employed to suppress the image jitter effectively. Their major contribution is the elimination of the effect of carrier's speed in motion estimation. Other contributions include new robust block matching approach and adaptive‐step selection for motion filtering. They conduct experiments on real videos and artificial data. Experiments on real videos show that the proposed model can remove the effect of car motion, whereas the experiments on artificial data are conducted for theoretical analysis.

For Face Recognition (FR) task, local feature based approaches have proven to be more robust to variations than the holistic approaches. As one of the state-of-the-art local descriptors with excellent performance for matching different images of an object or a scene, Scale Invariant Feature Transform (SIFT) has sparingly been used in FR, and whether it is a good descriptor for face images needs to be analyzed more. In this paper we propose a face representation approach based on SIFT and Spatial Pyramid Representation (SPR), to enhance the recognition performance of SIFT based method. Firstly it employs SIFT to extract discriminative local features and then constructs Spatial Pyramid to form a local-holistic representation of a face image. Finally Classifiers such as Nearest Neighbor (NN) and Support Vector Machine (SVM) could be performed on representation vectors of equal length. The comparative experimental results on ORL and Yale databases indicate that our approach achieves better performance than other SIFT based methods. In addition, it shows great robustness against environmental variations such as pose mismatches, imperfect face alignment, various facial expressions and accessory configuration variations, demonstrating a new alternative method for FR task.

Intrusion detection remains an important and challenging task in current and next generation networks (NGN). Emerging technologies such as multi-core processors and virtualization have changed the architecture of the building elements of NGN significantly, thus call for rethinking of how network processing is done. In this paper, we propose distributed intrusion detection using intelligent network interfaces where additional processing capabilities are available. We design and implement a prototype to perform pattern matching using network processors since pattern matching is one of the important workloads in intrusion detection. Through the experimental results, we show the feasibility and performance of distributed intrusion detection in next generation networks.

Traffic classification remains a hot research problem, especially when facing new traffic trends and new hardware architectures. We propose a classification tree search method called explicit range search, motivated by the characteristics of machine learning based classification approaches. Our method differs from previously known algorithms such as HiCut and HyperCut in how to cut the ranges within a dimension and how to search within the ranges. By storing explicit marks and performing hardware supported parallel comparison, the explicit range search can reduce the worst-case number of memory accesses from 26 to 5 on a number of realistic rule sets generated from a well-known machine learning algorithm (C4.5). We also describe in this paper the proposed design based on FPGA devices.