Role-based access control (RBAC) has significantly simplified the management of users and permissions in computing systems. In dynamic environments, systems are subject to changes, so that the associated configuration...
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Spatiograms were generalization of histograms, which can harvest spatial information of images. The similarity measure is important when applying spatiograms to various computer vision problems such as tracking and im...
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Spatiograms were generalization of histograms, which can harvest spatial information of images. The similarity measure is important when applying spatiograms to various computer vision problems such as tracking and image retrieval. The original proposed measures use Mahalanobis distance of coordinate mean to measure spatial information in spatiograms. However, spatial information which is described by spatiograms does not lie on vector space. Measures for vector space such as Mahalanobis distance are not effective measures for them. In this paper, We model spatial information as Gaussian approximation of coordinate distributions. Then we parameterize them as a Lie group. Based on Lie group theory, we analyze function space structure of Gaussian pdfs (probability density function) and propose an effective spatiogram similarity measure. We test our measure in object tracking scenarios. Experiments show better tracking results compared with previously proposed measures.
In this paper, a fast and robust video copy detection scheme is proposed, which is suitable for the DCT-coded video sequences. To address the efficiency and effectiveness issue, we extract the video signature directly...
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In this paper, a fast and robust video copy detection scheme is proposed, which is suitable for the DCT-coded video sequences. To address the efficiency and effectiveness issue, we extract the video signature directly from the compressed domain. The video sequence clusters are constructed with a fixed length. Each cluster consists of several fictional key-frames. For each key-frame, some low-middle frequency full DCT coefficients are obtained directly from block DCT coefficients, and their ordinal measure is computed and acts as video signature. A rotation compensational strategy is further employed to resist the rotation attacks. The experimental results show that the proposed scheme can be resilient to various types of video transformations, including scaling, rotation, speed change, text insertion, and subsequence insertion/deletion etc.. The most important thing is that the proposed approach not only handles geometric distortion perfectly, but also reduces the computation costs substantially.
Detecting human in still images is one of the most challenging object detection problems. In this paper we apply the scale theory to human detection. By integrating Gaussian Pyramids multi-scale object representation ...
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Detecting human in still images is one of the most challenging object detection problems. In this paper we apply the scale theory to human detection. By integrating Gaussian Pyramids multi-scale object representation approach we present a Learned Multi-scale Mid-level Feature (LMMF) based human detection algorithm. Firstly multiscale low-level features are extracted by Gaussian Pyramid decomposition and gradient computation. Then LMMFs are learned from multi-scale low-level features using AdaBoost algorithm. The final human/non-human decision is made by classification on the LMMFs. Using LMMF descriptors, our method attempts to harvest more information than using uni-scale feature descriptors. Experiments on INRIA person dataset demonstrate that our method outperforms the previous state of the art detector.
Traditional access control disciplines such as RBAC has difficulty in covering open and decentralized multi-centric systems because it has focused on a closed system where all users are known and primarily utilizes a ...
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Traditional access control disciplines such as RBAC has difficulty in covering open and decentralized multi-centric systems because it has focused on a closed system where all users are known and primarily utilizes a server-side reference monitor within the system. Trust management has relaxed this known user restriction and allowed authorize for strangers based on their credentials. However, trust management has also been found to be lacking because of certain inherent drawbacks with the notion of credential. In this work, a new access control model T&RBAC is presented in this paper. It integrates RBAC and TM. User can be assigned to local roles, also can be assigned to foreign roles based on his credential and local roles. We proof that there is no security constraints in T&RBAC. To some extends, T&RBAC is only a core model and can be extended for specific requirement.
Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are su...
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Microservice architectures are increasingly used to modularize IoT applications and deploy them in distributed and heterogeneous edge computing environments. Over time, these microservice-based IoT applications are susceptible to performance anomalies caused by resource hogging (e.g., CPU or memory), resource contention, etc., which can negatively impact their Quality of Service and violate their Service Level Agreements. Existing research on performance anomaly detection for edge computing environments focuses on model training approaches that either achieve high accuracy at the expense of a time-consuming and resource-intensive training process or prioritize training efficiency at the cost of lower accuracy. To address this gap, while considering the resource constraints and the large number of devices in modern edge platforms, we propose two clustering-based model training approaches: (1) intra-cluster parameter transfer learning-based model training (ICPTL) and (2) cluster-level model training (CM). These approaches aim to find a trade-off between the training efficiency of anomaly detection models and their accuracy. We compared the models trained under ICPTL and CM to models trained for specific devices (most accurate, least efficient) and a single general model trained for all devices (least accurate, most efficient). Our findings show that ICPTL’s model accuracy is comparable to that of the model per device approach while requiring only 40% of the training time. In addition, CM further improves training efficiency by requiring 23% less training time and reducing the number of trained models by approximately 66% compared to ICPTL, yet achieving a higher accuracy than a single general model.
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