Empowered by edge computing, resources and computation capabilities provided by edge devices can be encapsulated as containerized services, and domain applications can be achieved through service compositions. When burst requests are coming to be satisfied, there may exist edge devices which are overloaded, since requests are mostly spatially and temporally constrained, and edge devices are resource-scarceness and capacity-limited. In this setting, overloaded devices should be relieved through optimally migrating one or more activated services to contiguous edge devices. Besides, sensory data gathered by original edge devices should be periodically transmitted to migrated devices for data analysis purpose. To mitigate this issue, this paper proposes an Energy-efficient Online Service Migration (EOSM) mechanism to conduct the migration of multiple services simultaneously. Specifically, a light service sharing strategy is developed to only transmit the top container layer, and a modified NSGA-II algorithm is adopted to generate one or multiple paths for the container layer and time-series sensory data migration of each migrated service. Extensive experimental results show that our EOSM strategy outperforms the state of arts techniques in mitigating overloading devices in terms of access latency, energy consumption, and request success rate.
This paper proposes a fast graph convolution network (FGCNet) to match two sets of sparse features. FGCNet has three new modules connected in sequence: (i) a local graph convolution block takes point-wise features as inputs and encodes local contextual infor-mation to extract local features; (ii) a fast graph message-passing network takes local features as inputs, encodes two-view global contextual information, to improve the discriminativeness of point-wise features; (iii) a preemptive optimal matching layer takes point-wise features as inputs, regress point-wise matchedness scores and es-timate a 2D joint probability matrix, with each item describes the matchedness of a feature correspondence. We validate the proposed method on three AR/VR related tasks: two-view matching, 3D re-construction and visual localization. Experiments show that our method significantly reduces the computational complexity compared with state-of-the-art methods, while achieving competitive or better performance.
In this paper, we propose an adversarial learning network for the task of multi-style image captioning (MSCap) with a standard factual image caption dataset and a multi-stylized language corpus without paired images. How to learn a single model for multi-stylized image captioning with unpaired data is a challenging and necessary task, whereas rarely studied in previous works. The proposed framework mainly includes four contributive modules following a typical image encoder. First, a style dependent caption generator to output a sentence conditioned on an encoded image and a specified style. Second, a caption discriminator is presented to distinguish the input sentence to be real or not. The discriminator and the generator are trained in an adversarial manner to enable more natural and human-like captions. Third, a style classifier is employed to discriminate the specific style of the input sentence. Besides, a back-translation module is designed to enforce the generated stylized captions are visually grounded, with the intuition of the cycle consistency for factual caption and stylized caption. We enable an end-to-end optimization of the whole model with differentiable softmax approximation. At last, we conduct comprehensive experiments using a combined dataset containing four caption styles to demonstrate the outstanding performance of our proposed method.
In this paper, we present a robust feature set to detect human hands in still images having simple as well as complex backgrounds. Our method relies on using a blend of existing and new shape-based, color-based and texture-based features. First, we identify the shortcomings of two existing features: Histograms of Oriented Gradient (HOG) and Color Name (CN). For HOG, we investigate the scenarios where the traditional block normalization schemes generate noisy results in near uniform regions in the image background and impede the accurate detection of human hands. We offer a more effective block normalization scheme for our new shape-based feature, αHOG, which results in considerably improved detection. Our new color-based feature, Clipped Color Name (CCN), caters for the noise induced color labels encountered in the CN feature, by modifying the probability assignment method for the basic colors in each pixel. For capturing the texture cues, we employ Local Binary Patterns (LBP) and Local Trinary Patterns (LTP). We compare the relative performance of the individual features in isolation and in different feature sets. For feature sets' comparison, the issue of high dimensional feature space generated as a result of feature fusion is addressed by using Partial Least Squares (PLS) for dimensionality reduction. Subsequently, we employ the non-linear Radial Basis Function Support Vector Machine (RBF SVM) classifier on PLS reduced feature sets. In our experiments, we use two different image datasets, namely the benchmark Cambridge Gesture Dataset (having simple backgrounds) and our own dataset (having a wider variety of complex backgrounds). Based on the experimental results, we find that out of the four feature sets we use, the feature set consisting of αHOG, CCN and LTP gives the best results in terms of the combined criteria of classification accuracy and computation time, and also offers improvement over the feature set proposed by Hussain and Triggs [1].
Image captioning attempts to generate a sentence composed of several linguistic words, which are used to describe objects, attributes, and interactions in an image, denoted as visual semantic units in this paper. Based on this view, we propose to explicitly model the object interactions in semantics and geometry based on Graph Convolutional Networks (GCNs), and fully exploit the alignment between linguistic words and visual semantic units for image captioning. Particularly, we construct a semantic graph and a geometry graph, where each node corresponds to a visual semantic unit, i.e., an object, an attribute, or a semantic (geometrical) interaction between two objects. Accordingly, the semantic (geometrical) context-aware embeddings for each unit are obtained through the corresponding GCN learning processers. At each time step, a context gated attention module takes as inputs the embeddings of the visual semantic units and hierarchically align the current word with these units by first deciding which type of visual semantic unit (object, attribute, or interaction) the current word is about, and then finding the most correlated visual semantic units under this type. Extensive experiments are conducted on the challenging MS-COCO image captioning dataset, and superior results are reported when comparing to state-of-the-art approaches.
Abstract In order to solve the problem that the process of WAPI terminal connection is troublesome, this paper proposes an intelligent support and command system of WAPI terminal equipment based on centralized control AC + AP architecture. The system stores WAPI certificate on AC (centralized controller), WAPI terminal equipment is associated with AP (wireless access point), and AP equipment notifies AC of the associated events of the terminal, AC equipment and terminal equipment enter authentication and complete certificate authentication. Complete access authentication through AC and terminal equipment, and store the unicast and multicast keys generated by authentication negotiation on AC. This method is more convenient to maintain the certificate. At the same time, on the premise of ensuring the security of the certificate, it also improves the encryption and decryption efficiency and improves the user’s business experience.
A smart transmission grid fault diagnosis and analysis system (FDAS) is presented based on the complex event processing (CEP) technology. Firstly, the basic concept of the CEP technology is introduced. Secondly, a systematic hierarchical structure of fault diagnosis and analysis system based on CEP technology is illustrated. Thirdly, the events and patterns are modeled on the extensible markup language (XML) files. Finally, case analysis shows that the FDAS is a system good at fast identifying cause during cascading failure, supplying optimum dispatching strategy, and avoiding blackouts effectively.
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