17111160
Video Histogram: A Novel Video Signature for Efficient Web Video Duplicate Detection
The explosive growth of information technology and digital content industry stimulates various video applications over the Internet. Since it is quite easy to copy, reformat, modify and republish video files on the websites, similarity/duplicate detection and measurement is essential to identify the excessive content duplication, so as to facilitate effective video search and intelligence propriety protection as well. In this paper, we propose a novel signature-based approach for duplicate video comparison. The so-called video histogram scheme counts the numbers of video’s frames that are closest to a set of representative seed vectors chosen from the feature space of the training data set in advance. Then all the numbers are normalized to generate the signature of the video for further comparison. As our signature is a compact fixed-size vector with low dimension for each video, it requires less storage and computation cost than previous methods. The experiments show that our approach is both efficient and effective for web video duplicate detection.
449203
Topic mining on web-shared videos
Internet videos have grown exponentially with the help from video sharing Websites. Automatic topic mining is therefore increasingly important for organizing and navigating such large video databases. Most of current solutions of topic detection and mining were done on news videos and cannot be directly applied on Web videos, because of their limited and noisy semantic information. In this paper, we will try to address this problem and propose an automatic topic mining framework on Web videos. We develop an iterative weight-updated co-clustering scheme to filter "noisy" tags and mine the "hot" topics. We then propose a visual-based clustering approach to further group the videos with similar content, and rank the visual-similar groups by their similarity to the topic center. Experiments on a large Web video database demonstrate the superior performance of our weight-updated co-clustering to both of the traditional co-clustering and k-means. The experiments also demonstrate significant improvement of users' experience by our visual-based clustering and ranking.
17142920
Service-Oriented Integration of Systems for Military Capability
Service oriented architecture (SOA) is becoming established in computing as a means to integrate processing and data across organisations. This paper proposes that system-level integration can benefit from service oriented architectural descriptions and loose coupling between the problem domain requirements and different system solutions. The problem domain is exemplified as military capability, from the UK Ministry of Defence (MoD), in particular, network enabled capability (NEC). Representations of military capability in the problem domain can be described in terms of processes. The processes are sequences of functions that can be described as services. Then different types of system solutions can implement the described services. Firstly, the paper presents an overview of conceptual SOA and in the context of military capability compares three levels of service integration: business services, systems services and computing services. Secondly, the paper presents a framework for evaluating the performance and effectiveness of service integration to compare different solutions in delivering military capability.
3090205
Challenges for End-to-End Quality of Service in Heterogeneous Networks
To guarantee the end-to-end QoS (E2E Quality of Service) in heterogeneous network is an important issue to provide pervasive and trusted service. Currently, the different networks are optimized for different services, but it becomes urgent to provide pervasive service across different networks. This paper gives an overview on how to design a dynamic and integrated QoS architecture under heterogeneous networks. This paper firstly analyzes the actuality of the E2E quality of service in heterogeneous networks, and then discusses the requirements of the E2E QoS of two types of heterogeneous networks, finally points out the five key issues to solve the practical problem in the current network.
7638849
Efficient and scalable search on scale-free P2P networks
Unstructured peer-to-peer (P2P) systems (e.g. Gnutella) are characterized by uneven distributions of node connectivity and file sharing. The existence of “hub” nodes that have a large number of connections and “generous” nodes that share many files significantly influences performance of information search over P2P file-sharing networks. In this paper, we present a novel Scalable Peer-to-Peer Search (SP2PS) method with low maintenance overhead for resource discovery in scale-free P2P networks. Different from existing search methods which employ one heuristic to direct searches, SP2PS achieves better performance by considering both of the number of shared files and the connectivity of each neighbouring node. SP2PS enables peer nodes to forward queries to the neighbours that are more likely to have the requested files and also can help in finding the requested files in the future hops. The proposed method has been simulated in different power-law networks with different forwarding degrees and distances. From our analytic and simulation results, SP2PS achieves better performance when compared to other related methods.
34356811
Reduce Feature Based NN for Transient Stability Analysis of Large-Scale Power Systems
Aiming at the existence of relativity between repeat or similar samples and character parameters during diagnosis of character data, this paper presents an effective data analysis approach for character data compression from bi-direction, which can reduce the burden of learning machine without losing the connotative character knowledge of character data. At the first step of the algorithm, basing on the theory of component analysis, the paper adopt a principal component analysis approach to reduce the dimension of data horizontally, then after comparison of existing clustering algorithms, put forward an immune clustering algorithm based on similarity measurement of principle component core for vertical reduction by using related mechanism of clone selection as well as immune network self-stabilization in organism natural immune system for reference. Finally, to analyze machine behavior quantitatively, a pattern discrimination model based on a cerebellar model articulation controller neural network ( NN ) was developed. Simulation experiments proved the effectiveness of this algorithm.
16432148
Delivering Sustainable Capability on Evolutionary Service-oriented Architecture
Network Enabled Capability (NEC) is the U.K. Ministry of Defence’s response to the quickly changing conflict environment in which its forces must operate. In NEC, systems need to be integrated in context, to assist in human activity and provide dependable inter-operation. In order to provide reliable and sustainable military capability, fast paced changes must be conducted without halting the operation of a capability. In this paper we present the concept of evolutionary service-oriented architecture for delivering sustainable capability. The reliability of the architecture is evaluated by simulations using a computer-based model. The simulation results indicate that the evolutionary service-oriented architecture can provide higher reliability and sustainability in the provision of capability in a dynamic environment.