We implement and experimentally evaluate landmark- based oracles for min-cost paths in two different types of road networks with time-dependent arc-cost functions, based on distinct real-world historic traffic data: T...
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ISBN:
(纸本)9781510819689
We implement and experimentally evaluate landmark- based oracles for min-cost paths in two different types of road networks with time-dependent arc-cost functions, based on distinct real-world historic traffic data: The road network for the metropolitan area of Berlin, and the national road network of Germany. Our first contribution is a significant improvement on the implementation of the FLAT oracle, which was proposed and experimentally tested in previous works. Regarding the implementation, we exploit parallelism to reduce preprocessing time and real-time responsiveness to live-traffic reports. We also adopt a lossless compression scheme that severely reduces preprocessing space and time requirements. As for the experimentation, apart from employing the new data set of Germany, we also construct several refinements and hybrids of the most prominent landmark sets for the city of Berlin. A significant improvement to the speedup of FLAT is observed: For Berlin, the average query time can now be as small as 83/isec, achieving a speedup (against.the time- dependent variant of Dijkstra's algorithm) of more than 1,119 in absolute running times and more than 1,570 in Dijkstra-ranks, with worst-case observed stretch less than 0.781%. For Germany, our experimental findings are analogous: The average query-response time can be 1.269msec, achieving a speedup of more than 902 in absolute running times, and 1,531 in Dijkstra-ranks, with worst-case stretch less than 1.534%. Our second contribution is the implementation and.
The Galactic magnetar SGR 1935+2154 was associated with a bright, millisecond-timescale fast radio burst (FRB) which occured in April 2020, during a flaring episode. This was the first time an FRB was unequivocally as...
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Starting from the first announcement of unequivocal detection of very high energy (VHE) emission from a gamma-ray burst (GRB) by the MAGIC telescopes (GRB 190114C), four additional detections of VHE emission from GRBs...
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This paper introduces a micro-nexus of water and energy which can be considered as one of the physical infrastructures of the future building/city/village systems. For the electricity side, an alternating current (AC)...
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Systems and control are all around us. System view, understanding systems and how they are controlled is important for everyone. The behavior of systems is determined by some fundamental principles which can be unders...
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Deep cross-modal learning has successfully demonstrated excellent performances in cross-modal multimedia retrieval, with the aim of learning joint representations between different data modalities. Unfortunately, litt...
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Deep cross-modal learning has successfully demonstrated excellent performances in cross-modal multimedia retrieval, with the aim of learning joint representations between different data modalities. Unfortunately, little research focuses on cross-modal correlation learning where temporal structures of different data modalities such as audio and lyrics are taken into account. Stemming from the characteristic of temporal structures of music in nature, we are motivated to learn the deep sequential correlation between audio and lyrics. In this work, we propose a deep cross-modal correlation learning architecture involving two-branch deep neural networks for audio modality and text modality (lyrics). Different modality data are converted to the same canonical space where inter modal canonical correlation analysis is utilized as an objective function to calculate the similarity of temporal structures. This is the first study on understanding the correlation between language and music audio through deep architectures for learning the paired temporal correlation of audio and lyrics. Pre-trained Doc2vec model followed by fully-connected layers (fully-connected deep neural network) is used to represent lyrics. Two significant contributions are made in the audio branch, as follows: i) pretrained CNN followed by fully-connected layers is investigated for representing music audio. ii) We further suggest an end-toend architecture that simultaneously trains convolutional layers and fully-connected layers to better learn temporal structures of music audio. Particularly, our end-to-end deep architecture contains two properties: simultaneously implementing feature learning and cross-modal correlation learning, and learning joint representation by considering temporal structures. Experimental results, using audio to retrieve lyrics or using lyrics to retrieve audio, verify the effectiveness of the proposed deep correlation learning architectures in cross-modal music retrieval. Copyrigh
The emerging Information-Centric Networking (ICN) paradigm is expected to facilitate content sharing among users. ICN will make it easy for users to ap-point storage nodes, in various network locations, per-haps owned...
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A technique of lossless compression via substring enumeration (CSE) attains compression ratios as well as popular lossless compressors for one-dimensional (1D) sources. The CSE utilizes a probabilistic model built fro...
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The Euclidean minimum spanning tree (EMST) is a fundamental and widely studied structure. In the approximate version we are given an n-element point set P in Rd and an error parameter ε > 0, and the objective is t...
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ISBN:
(纸本)9781510819672
The Euclidean minimum spanning tree (EMST) is a fundamental and widely studied structure. In the approximate version we are given an n-element point set P in Rd and an error parameter ε > 0, and the objective is to compute a spanning tree over P whose weight is at most (1 + ε) times that of the true minimum spanning tree. Assuming that d is a fixed constant, existing algorithms have running times that (up to logarithmic factors) grow as O(n/ε~(Ω(d))). We present an algorithm whose running time is O(n log n+(ε~(-2) log~2 1/ε)n). Thus, this is the first algorithm for approximate EMSTs that eliminates the exponential ε dependence on dimension. (Note that the O-notation conceals a constant factor of the form O(1)~d.) The algorithm is deterministic and very simple.
Mobile Ad Hoc Networks (MANETs) gained lot of importance due to fast growth of the businesses and used in different applications such as military, entertainment, commerce, emergency services etc. Because of openness a...
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ISBN:
(纸本)9781467399159
Mobile Ad Hoc Networks (MANETs) gained lot of importance due to fast growth of the businesses and used in different applications such as military, entertainment, commerce, emergency services etc. Because of openness and decentralization feature of the MANET, nodes of the MANET can be vulnerable to malicious entities. Hence, anonymity becomes one of the important aspects in MANET. In last few years, a lot of research has been carried out by different researcher to prevent the anonymity of the MANET. Different routing protocols have been evolved such as GSPR, AO2P, ALARM, PRISM and ASR to secure the anonymity of the MANET. But all of these protocols have some limitations regarding providing complete anonymity of source, destination or routes and also involving high cost. To overcome above issues ALERT protocol is evolved which is distinguished by its low cost and anonymity protection for sources, destinations, and routes. But ALERT protocol is not providing security against.the active attacks, so in the proposed work we have decided to make the strategy to prevent the DoS and Man in the middle attacks on ALERT by using Hash function with SHA-1 algorithm.
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