Containers have become the standard for deploying applications in many cloud systems due to its convenience. However, this convenience leads to significant container bloat, i.e., unused files that inflate container image sizes, increase provisioning times, waste resources and introduce security vulnerabilities. Bloat is particularly problematic in serverless and edge computing scenarios, where resources are constrained, and performance is critical, and for microservice applications where rapid scaling is key to meet performance targets. However, existing container debloating tools are often limited in both effectiveness and robustness. In this paper, we propose BLAFS, a bloat-aware container filesystem that removes bloat while guaranteeing the correct operation of the debloated containers. BLAFS addresses bloat at the filesystem level by introducing new layers in the filesystem to enable debloating. During runtime, accessed files are moved to the debloating layers, and then similar to garbage collection mechanisms, BLAFS removes files that are not accessed during runtime. An optional reloading layer fetches files from a remote cloud cache on-demand if the files are mistakenly removed. We discuss how BLAFS can be used in different deployment scenarios and for different use-cases including container security-hardened and a dynamic deployment mode where the target is improved provisioning performance. We evaluate BLAFS performance using the top 20 downloaded containers from DockerHub, four ML containers, and SEBS, a Serverless Benchmark containing 10 serverless functions and compare its performance against two state-of-the-art debloating tools. Our evaluation shows that BLAFS reduces container sizes by up to 95% and cold-starts by up to 68%. In the security-hardened mode, BLAFS removes up to 89% of CVEs while the two state-of-the-art debloating tools fail on most of the workloads. We identify their limitations, and show how BLAFS provides a more principled approach to debloating. Additionally, when combined with lazy-loading snapshotters, BLAFS improves provisioning efficiency, reducing conversion times by up to 93% and provisioning times by up to 19%.

Enterprise data mining applications often involve complex data such as multiple large heterogeneous data sources, user preferences, and business impact. In such situations, a single method or one-step mining is often limited in discovering informative knowledge. It would also be very time and space consuming, if not impossible, to join relevant large data sources for mining patterns consisting of multiple aspects of information. It is crucial to develop effective approaches for mining patterns combining necessary information from multiple relevant business lines, catering for real business settings and decision-making actions rather than just providing a single line of patterns. The recent years have seen increasing efforts on mining more informative patterns, e.g., integrating frequent pattern mining with classifications to generate frequent pattern-based classifiers. Rather than presenting a specific algorithm, this paper builds on our existing works and proposes combined mining as a general approach to mining for informative patterns combining components from either multiple data sets or multiple features or by multiple methods on demand. We summarize general frameworks, paradigms, and basic processes for multifeature combined mining, multisource combined mining, and multimethod combined mining. Novel types of combined patterns, such as incremental cluster patterns, can result from such frameworks, which cannot be directly produced by the existing methods. A set of real-world case studies has been conducted to test the frameworks, with some of them briefed in this paper. They identify combined patterns for informing government debt prevention and improving government service objectives, which show the flexibility and instantiation capability of combined mining in discovering informative knowledge in complex data.

Most data mining algorithms and tools stop at the mining and delivery of patterns satisfying expected technical interestingness. There are often many patterns mined but business people either are not interested in them or do not know what follow-up actions to take to support their business decisions. This issue has seriously affected the widespread employment of advanced data mining techniques in greatly promoting enterprise operational quality and productivity. In this paper, we present a formal view of actionable knowledge discovery (AKD) from the system and decision-making perspectives. AKD is a closed optimization problem-solving process from problem definition, framework/model design to actionable pattern discovery, and is designed to deliver operable business rules that can be seamlessly associated or integrated with business processes and systems. To support such processes, we correspondingly propose, formalize, and illustrate four types of generic AKD frameworks: Postanalysis-based AKD, Unified-Interestingness-based AKD, Combined-Mining-based AKD, and Multisource Combined-Mining-based AKD (MSCM-AKD). A real-life case study of MSCM-based AKD is demonstrated to extract debt prevention patterns from social security data. Substantial experiments show that the proposed frameworks are sufficiently general, flexible, and practical to tackle many complex problems and applications by extracting actionable deliverables for instant decision making.

Traditional sequential pattern mining deals with positive sequential patterns only, that is, only frequent sequential patterns with the appearance of items are discovered. However, it is often interesting in many applications to find frequent sequential patterns with the nonoccurrence of some items, which are referred to as negative sequential patterns. This paper analyzes three types of negative sequential rules and presents a new technique to find event-oriented negative sequential rules. Its effectiveness and efficiency are shown in our experiments.

Camera calibration is to estimate the intrinsic and extrinsic parameters of a camera. Most of object-based calibration methods used 3D or 2D pattern. A novel and more flexible 1D object-based calibration was introduced only a couple of years ago, but merely for estimation of intrinsic parameters. The estimation of extrinsic papers is essential when multiple cameras are involved for simultaneously taking images from different view angles and when the knowledge of relative locations between the cameras is required. Though it is relatively simple using 2D or 3D calibration pattern, the estimation of extrinsic parameters is not obvious using 1D pattern. In this paper, we will perform a 1D camera calibration involving both intrinsic and extrinsic parameters.

Using colour histogram as a stable representation over change in view has been widely used for object recognition. In this paper, three newly proposed histogram-based methods are compared with other three popular methods, including conventional histogram intersection (HI) method, Wong and Cheung's merged palette histogram matching (MPHM) method, and Gevers' colour ratio gradient (CRG) method. These methods are tested on vehicle number plate images for number plate classification. Experimental results disclose that, the CRG method is the best choice in terms of speed, and the GWHI method can give the best classification results. Overall, the CECH method produces the best performance when both speed and classification performance are concerned.

Car plate detection is a key component in automatic license plate recognition system. This paper adopts an enhanced cascaded tree style learner framework for car plate detection using the hybrid object features including the simple statistical features and Harr-like features. The statistical features are useful for simplifying the process on cascade classifier. The cascaded tree-style detector design will further reduce the false alarm and the false dismissal while retaining a high detection ratio. The experimental results obtained by the proposed algorithm exhibit the encouraging performance.

In this paper, a novel colour edge cooccurrence histogram (CECH) method is proposed to match images by measuring similarities between their CECH histograms. Unlike the previous colour edge cooccurrence histogram proposed by Crandall and Luo (2004 ) we only investigate those pixels which are located at the two sides of edge points in their gradient direction lines and at a distance away from the edge points. When measuring similarities between two CECH histograms, a newly proposed Gaussian weighted histogram intersection (GWHI) method is extended for this purpose. Both identical colour pairs and similar colour pairs are taken into account in our algorithm, and the weights are decided by the larger distance between two colour pairs involved in matching. The proposed algorithm is tested for matching vehicle number plate images captured under various illumination conditions. Experimental results demonstrate that the proposed algorithm can be used to compare images in real-time, and is robust to illumination variations and insensitive to the model images selected.

This paper proposes a fast algorithm detecting license plates in various conditions. There are three main contributions in this paper. The first contribution is that we define a new vertical edge map, with which the license plate detection algorithm is extremely fast. The second contribution is that we construct a cascade classifier which is composed of two kinds of classifiers. The classifiers based on statistical features decrease the complexity of the system. They are followed by the classifiers based on Haar-features, which make it possible to detect license plate in various conditions. Our algorithm is robust to the variance of the illumination, view angle, the position, size and color of the license plates when working in complex environment. The third contribution is that we experimentally analyze the relations of the scaling factor with detection rate and processing time. On the basis of the analysis, we select the optimal scaling factor in our algorithm. In the experiments, both high detection rate (with low false positive rate) and high speed are achieved when the algorithm is used to detect license plates in various complex conditions.

Color ratio gradient is an efficient method used for color image retrieval and object recognition, which is shown to be illumination-independent and geometry-insensitive when tested on scenery images. However, color ratio gradient produces unsatisfied matching result while dealing with relatively uniform objects without rich color texture. In addition, performance of color ratio gradient degenerates while processing unsaturated color image objects. In this paper, a scheme with modified color ratio gradient is presented, which addresses the two problems above. Experimental results using the proposed method in this paper exhibit more robust performance

In this paper we propose and study a simple trinocular rectification method in which stratification to projective and affine components gives the rectifying homographies in a closed form. The class of trinocular rectifications which has 6 DOF is parametrized by an independent set of parameters with a geometric meaning. This offers the possibility to minimize rectification distortion in a natural way. It is shown experimentally on real data that our algorithm performs the rectification task correctly. As shown on groundtruth data using Confidently Stable Matching, trinocular matching significantly improves disparity map density and mismatch error, both depending on texture strength. Matching results on real complex scenes are reported. 1