A Wu‘s Method Based Parallelization Design and Implementation for Algebraic Cryptanalysis

2014 ◽  
Vol 530-531 ◽  
pp. 641-645
Author(s):  
Huai Zhe Zhou ◽  
Hai He Ba ◽  
Jiang Chun Ren ◽  
Tao Xie ◽  
Min Zhu ◽  
...  
Keyword(s):  
Large Scale ◽  
Computing Time ◽  
Polynomial Equation ◽  
Wu’S Method ◽  
Algebraic Cryptanalysis ◽  
Wu's Method ◽  
Efficient Data ◽  
Computing Performance ◽  
Balance Mechanism ◽  
Serial Algorithm

Wus method is one of the effective methods for solving large-scale polynomial equation systems in algebraic cryptanalysis. But it will take a lot of time to solve polynomial equations with a serial algorithm of Wu’s method. In order to eliminate the bottleneck of computing time overheads, we proposes an efficient data parallelization scheme based on Wu‘s method. And a load balance mechanism applied to our scheme, which greatly improves computing-performance of Wu’s method and enhances its ability to meet challenges of algebraic cryptanalysis.

scholarly journals Selecting Optimal Combination of Data Channels for Semantic Segmentation in City Information Modelling (CIM)

2021 ◽  
Vol 13 (7) ◽  
pp. 1367
Author(s):  
Yuanzhi Cai ◽  
Hong Huang ◽  
Kaiyang Wang ◽  
Cheng Zhang ◽  
Lei Fan ◽  
...  
Keyword(s):  
Large Scale ◽  
Semantic Segmentation ◽  
Optimal Combination ◽  
Reconstruction Technique ◽  
Process Data ◽  
Redundant Data ◽  
Multiple Data ◽  
Information Models ◽  
Efficient Data

Over the last decade, a 3D reconstruction technique has been developed to present the latest as-is information for various objects and build the city information models. Meanwhile, deep learning based approaches are employed to add semantic information to the models. Studies have proved that the accuracy of the model could be improved by combining multiple data channels (e.g., XYZ, Intensity, D, and RGB). Nevertheless, the redundant data channels in large-scale datasets may cause high computation cost and time during data processing. Few researchers have addressed the question of which combination of channels is optimal in terms of overall accuracy (OA) and mean intersection over union (mIoU). Therefore, a framework is proposed to explore an efficient data fusion approach for semantic segmentation by selecting an optimal combination of data channels. In the framework, a total of 13 channel combinations are investigated to pre-process data and the encoder-to-decoder structure is utilized for network permutations. A case study is carried out to investigate the efficiency of the proposed approach by adopting a city-level benchmark dataset and applying nine networks. It is found that the combination of IRGB channels provide the best OA performance, while IRGBD channels provide the best mIoU performance.

A Fast and Scalable Heuristic for the Solution of Large-Scale Capacitated Vehicle Routing Problems

2021 ◽  
Author(s):  
Luca Accorsi ◽  
Daniele Vigo
Keyword(s):  
Vehicle Routing ◽  
Large Scale ◽  
Computing Time ◽  
Search Space ◽  
Iterated Local Search ◽  
Solution Quality ◽  
Routing Problem ◽  
Acceptance Criterion ◽  
Acceleration Techniques ◽  
Capacitated Vehicle

In this paper, we propose a fast and scalable, yet effective, metaheuristic called FILO to solve large-scale instances of the Capacitated Vehicle Routing Problem. Our approach consists of a main iterative part, based on the Iterated Local Search paradigm, which employs a carefully designed combination of existing acceleration techniques, as well as novel strategies to keep the optimization localized, controlled, and tailored to the current instance and solution. A Simulated Annealing-based neighbor acceptance criterion is used to obtain a continuous diversification, to ensure the exploration of different regions of the search space. Results on extensively studied benchmark instances from the literature, supported by a thorough analysis of the algorithm’s main components, show the effectiveness of the proposed design choices, making FILO highly competitive with existing state-of-the-art algorithms, both in terms of computing time and solution quality. Finally, guidelines for possible efficient implementations, algorithm source code, and a library of reusable components are open-sourced to allow reproduction of our results and promote further investigations.

Energy Efficient Data Collection in Large-Scale Internet of Things via Computation Offloading

2019 ◽  
Vol 6 (3) ◽  
pp. 4176-4187 ◽  
Author(s):  
Guorui Li ◽  
Jingsha He ◽  
Sancheng Peng ◽  
Weijia Jia ◽  
Cong Wang ◽  
...  
Keyword(s):  
Data Collection ◽  
Internet Of Things ◽  
Energy Efficient ◽  
Large Scale ◽  
Computation Offloading ◽  
Efficient Data ◽  
Efficient Data Collection

Secure and Privacy Preserving Keyword Search over the Large Scale Cloud Data

2016 ◽  
pp. 200-215
Author(s):  
Wei Zhang ◽  
Jie Wu ◽  
Yaping Lin
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Keyword Search ◽  
Data Encryption ◽  
Privacy Preserving ◽  
Security And Privacy ◽  
Data Utilization ◽  
Cloud Data ◽  
Efficient Resource ◽  
Efficient Data

Cloud computing has attracted a lot of interests from both the academics and the industries, since it provides efficient resource management, economical cost, and fast deployment. However, concerns on security and privacy become the main obstacle for the large scale application of cloud computing. Encryption would be an alternative way to relief the concern. However, data encryption makes efficient data utilization a challenging problem. To address this problem, secure and privacy preserving keyword search over large scale cloud data is proposed and widely developed. In this paper, we make a thorough survey on the secure and privacy preserving keyword search over large scale cloud data. We investigate existing research arts category by category, where the category is classified according to the search functionality. In each category, we first elaborate on the key idea of existing research works, then we conclude some open and interesting problems.

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