A dynamic multi-secrets sharing threshold scheme is presented to apply to a large scale electronic voting system with many talliers (tallying authorities). Even if there exists adaptive adversaries, this scheme can gu...
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A dynamic multi-secrets sharing threshold scheme is presented to apply to a large scale electronic voting system with many talliers (tallying authorities). Even if there exists adaptive adversaries, this scheme can guard the ballot's producing, encrypting, transmitting, decrypting and final tallying in spite of the adversaries's attack, so the scheme guarantees robustness. In this paper, the verifiability of the voters' qualification and talliers' identification will be solved by a dynamic multi-secret sharing scheme without invoking more zero knowledge proof to maintain privacy, universal verifiability, and anonymity of ballots. It holds more communication efficiency and more security than the proposed schemes in early time.
Three kinds of constrained traveling salesman problems (TSP) arising from application problems, namely the open route TSP, the end-fixed TSP, and the path-constrained TSP, are proposed. The corresponding approaches ba...
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Three kinds of constrained traveling salesman problems (TSP) arising from application problems, namely the open route TSP, the end-fixed TSP, and the path-constrained TSP, are proposed. The corresponding approaches based on modified genetic algorithms (GA) for solving these constrained TSPs are presented. Numerical experiments demonstrate that the algorithm for the open route TSP shows its advantages when the open route is required, the algorithm for the end-fixed TSP can deal with route optimization with constraint of fixed ends effectively, and the algorithm for the path-constraint could benefit the traffic problems where some cities cannot be visited from each other.
The use of computational-intelligence-based techniques in the optimization of agent initial positions in land combat simulations is studied. A novel method for the reduction of support vectors in the support vector ma...
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The use of computational-intelligence-based techniques in the optimization of agent initial positions in land combat simulations is studied. A novel method for the reduction of support vectors in the support vector machine (SVM) is presented. The optimization on the width of the Gaussian kernel function and the combination of the SVM with the radial basis function neural network are performed in the proposed method. Simulation results show that the proposed method can improve the running efficiency drastically compared with that using the traditional SVM with the same precision. We also summarize and present some experiences and trends in the study on the optimization problem in land combat simulation.
An operation template is proposed in this paper for describing the mapping between operations and a subset of natural numbers. With such operation template, a job shop scheduling problem (JSSP) can be transformed into...
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A modified particle swarm optimization (PSO) algorithm is proposed. Linear constraints in the PSO are added to satisfy the normalization conditions for different problems. A hybrid algorithm based on the modified PSO ...
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A modified particle swarm optimization (PSO) algorithm is proposed. Linear constraints in the PSO are added to satisfy the normalization conditions for different problems. A hybrid algorithm based on the modified PSO and combining forecasting is presented. Combining forecasting can improve the forecasting accuracy through combining different forecasting methods. The effectiveness of the algorithm is demonstrated through the prediction on the sunspots and the stocks data. Simulated results show that the hybrid algorithm can improve the forecasting accuracy to a great extent.
The gene section ordering on solving traveling salesman problems is analyzed by numerical experiments. Some improved crossover operations are presented. Several combinations of genetic operations are examined and the ...
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The gene section ordering on solving traveling salesman problems is analyzed by numerical experiments. Some improved crossover operations are presented. Several combinations of genetic operations are examined and the functions of these operations are analyzed. The essentiality of the ordering of the gene section and the significance of the evolutionary inversion operation are discussed. Some results and conclusions are obtained and given, which provide useful information for the implementation of the genetic operations for solving the traveling salesman problem.
Computational intelligence is the computational simulation of the bio-intelligence, which includes artificial neural networks, fuzzy systems and evolutionary computations. This article summarizes the state of the art ...
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Computational intelligence is the computational simulation of the bio-intelligence, which includes artificial neural networks, fuzzy systems and evolutionary computations. This article summarizes the state of the art in the field of simulated modeling of vibration systems using methods of computational intelligence, based on some relevant subjects and the authors' own research work. First, contributions to the applications of computational intelligence to the identification of nonlinear characteristics of packaging are reviewed. Subsequently, applications of the newly developed training algorithms for feedforward neural networks to the identification of restoring forces in multi-degree-of-freedom nonlinear systems are discussed. Finally, the neural-network-based method of model reduction for the dynamic simulation of microelectromechanical systems (MEMS) using generalized Hebbian algorithm (GHA) and robust GHA is outlined. The prospects of the simulated modeling of vibration systems using techniques of computational intelligence are also indicated.
This book constitutes the refereed proceedings of the Third International Frontiers of Algorithmics Workshop, FAW 2009, held in Hefei, Anhui, China, in June 2009. The 33 revised full papers presented together with the...
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ISBN:
(数字)9783642022708
ISBN:
(纸本)9783642022692
This book constitutes the refereed proceedings of the Third International Frontiers of Algorithmics Workshop, FAW 2009, held in Hefei, Anhui, China, in June 2009. The 33 revised full papers presented together with the abstracts of 3 invited talks were carefully reviewed and selected from 87 submissions. The papers are organized in topical sections on graph algorithms; game theory with applications; graph theory, computational geometry; machine learning; parameterized algorithms, heuristics and analysis; approximation algorithms; as well as pattern recognition algorithms, large scale data mining.
In-memory KV stores have recently been migrated from traditional monolithic servers to disaggregated memory (DM) for higher resource utilization and elasticity. These works use replication-based schemes for fault tole...
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In-memory KV stores have recently been migrated from traditional monolithic servers to disaggregated memory (DM) for higher resource utilization and elasticity. These works use replication-based schemes for fault tolerance, which can be replaced with erasure coding (EC) for space efficiency. However, existing EC schemes designed in KV stores on traditional monolithic architectures encounter performance constraints when directly implemented in DM due to the challenges in EC metadata management and consistent parity updating. This paper proposes MetaEC, an erasure-coded KV store on DM with high efficiency and resilience. First, for organizing KV pairs to stripes, MetaEC logically forms data chunks and leverages lazy coding to remove the accumulating and coding latency from the critical path. Second, for efficient EC metadata management, MetaEC designs EC metadata structures based on accessing features, and employs a hybrid redundancy schema with deterministic distribution to provide fault tolerance with high storage efficiency. Third, for consistent parity updating, we design a parity updating protocol based on parity logging and co-design EC metadata structures to handle concurrent conflicts by allowing only concurrent reads or writes. Experimental results show that compared with the state-of-the-art replication-based KV stores on DM, MetaEC achieves up to 53.33% latency reduction, up to 31.01% throughput improvement, and 58.17% memory consumption savings.
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