Hierarchical Multiresolution Feature- and Prior-based Graphs for Classification

To incorporate spatial (neighborhood) and bidirectional hierarchical relationships as well as features and priors of the samples into their classification, we formulated the classification problem on three variants of multiresolution neighborhood graphs and the graph of a hierarchical conditional random field. Each of these graphs was weighted and undirected and could thus incorporate the spatial or hierarchical relationships in all directions. In addition, each variant of the proposed neighborhood graphs was composed of a spatial feature-based subgraph and an aspatial prior-based subgraph. It expanded on a random walker graph by using novel mechanisms to derive the edge weights of its spatial feature-based subgraph. These mechanisms included implicit and explicit edge detection to enhance detection of weak boundaries between different classes in spatial domain. The implicit edge detection relied on the outlier detection capability of the Tukey's function and the classification reliabilities of the samples estimated by a hierarchical random forest classifier. Similar mechanism was used to derive the edge weights and thus the energy function of the hierarchical conditional random field. This way, the classification problem boiled down to a system of linear equations and a minimization of the energy function which could be done via fast and efficient techniques

Securing Edge Computing: A Hierarchical IoT Service Framework

Title: Securing Edge Computing: A Hierarchical IoT Service Framework Authors: Nishar Miya, Sajan Poudel, Faculty Advisor: Rasib Khan, Ph.D. Department: School of Computing and Analytics, College of Informatics, Northern Kentucky University Abstract: Edge computing, a paradigm shift in data processing, faces a critical challenge: ensuring security in a landscape marked by decentralization, distributed nodes, and a myriad of devices. These factors make traditional security measures inadequate, as they cannot effectively address the unique vulnerabilities of edge environments. Our research introduces a hierarchical framework that excels in securing IoT-based edge services against these inherent risks. Our secure by design approach prioritizes the establishment of a robust security infrastructure from the outset. By incorporating an Intrusion Detection System and leveraging blockchain for immutable data storage, we create a formidable barrier against security threats. The feasibility of the proposed model for the secure hierarchical architecture is justified by integrating the design with the open-source platform EdgeX Foundry, streamlining device management and data flow among interconnected nodes. A central monitoring device orchestrates system operations, while an acclaimed threat detection model within the server scrutinizes network activities for anomalies. When suspicious actions are detected, the system swiftly alerts other clusters, facilitating a prompt and unified response. The use of blockchain technology not only enhances the intrusion detection capabilities but also guarantees secure and verifiable data storage. Our findings confirm that the framework delivers secure, optimized IoT services with robust intrusion detection and effective data management. The secure-by-design strategy is both advantageous and practical, offering a superior solution to the pressing security challenges in edge computing

HCGMNET: A Hierarchical Change Guiding Map Network For Change Detection

Very-high-resolution (VHR) remote sensing (RS) image change detection (CD) has been a challenging task for its very rich spatial information and sample imbalance problem. In this paper, we have proposed a hierarchical change guiding map network (HCGMNet) for change detection. The model uses hierarchical convolution operations to extract multiscale features, continuously merges multi-scale features layer by layer to improve the expression of global and local information, and guides the model to gradually refine edge features and comprehensive performance by a change guide module (CGM), which is a self-attention with changing guide map. Extensive experiments on two CD datasets show that the proposed HCGMNet architecture achieves better CD performance than existing state-of-the-art (SOTA) CD methods