18897982
An Efficient SAN-Level Caching Method Based on Chunk-Aging
SAN-level caching can manage caching within a global view so that global hot data can be identified and cached. However, two problems may be encountered in the existing SAN-level caching methods: first, too many migrations from disks to cache resulting from cache thrashing; second, too few cache hits resulting from insufficient caching. This paper proposes a SAN-level caching method employing chunk-aging to control the migrations of chunks of data from disks to cache, which takes the temporal distribution of chunk accesses into account and solves the two problems above. Then by employing two LRU lists to manage the cache replacement, this method separates and manages two types of hot data chunks, one of which have burst accesses and the other have long-term frequent accesses, thus preventing them from interfering with each other in cache. The simulation results demonstrate that our method can achieve a high hit ratio while maintaining a relatively low migration rate.
23190531
Algorithm for yield driven correction of layout
As the development of VLSI technique, the critical dimension of IC has become smaller than the exposure wavelength. Due to the diffraction and interaction of optical waves, deformations between the image on wafer and the feature on layout are undeniable. This results in bad performance or even invalid circuits of the chips. OPC is critical compensation technique to correct the deformations on wafer images. This work presents a layout correction and optimization algorithm called MOPC; it's a flexible and efficient core for the model-based OPC system. Since we divide the target features into different types before correction, the OPE between the target features and the environment features and the OPE between the neighboring segments of the inside feature are both considered during the correction.
18765129
Security Analysis of Enterprise Network Based on Stochastic Game Nets Model
In this paper, we propose a novel modeling method, Stochastic Game Nets (SGN), and use it to model and analyze the security issues in enterprise networks. Firstly, the definition and modeling algorithm of Stochastic Game Nets are given. And then we apply the Stochastic Game Nets method to describe the attack and defense course in the enterprise networks successfully, and find a Nash equilibrium. Finally we analyze the confidentiality and integrity of the enterprise network quantificationally based on the model. The method can also be applied to other areas with respect to a game.
8746552
A queueing analysis for the denial of service (DoS) attacks in computer networks
In most network security analysis, researchers mainly focus on qualitative studies on security schemes and possible attacks, and there are few papers on quantitative analysis in the current literature. In this paper, we propose one queueing model for the evaluation of the denial of service (DoS) attacks in computer networks. The network under DoS attacks is characterized by a two-dimensional embedded Markov chain model. With this model, we can develop a memory-efficient algorithm for finding the stationary probability distribution which can be used to find other interesting performance metrics such as the connection loss probability and buffer occupancy percentages of half-open connections for regular traffic and attack traffic. Different from previous works in the literature, this paper gives a more general analytical approach to the study of security measures of a computer network under DoS attacks. We hope that our approach opens a new avenue to the quantitative evaluation of more complicated security schemes in computer networks.
10093847
The Evil Twin
A girl falls in love with a man at her mother's funeral. After the funeral, she finds the best way to see that man again and realises who the man is ...
6225988
A global optimal algorithm for class-dependent discretization of continuous data
This paper presents a new method to convert continuous variables into discrete variables for inductive machine learning. The method can be applied to pattern classification problems in machine learning and data mining. The discretization process is formulated as an optimization problem. We first use the normalized mutual information that measures the interdependence between the class labels and the variable to be discretized as the objective function, and then use fractional programming (iterative dynamic programming) to find its optimum. Unlike the majority of class-dependent discretization methods in the literature which only find the local optimum of the objective functions, the proposed method, OCDD, or Optimal Class-Dependent Discretization, finds the global optimum. The experimental results demonstrate that this algorithm is very effective in classification when coupled with popular learning systems such as C4.5 decision trees and Naive-Bayes classifier. It can be used to discretize continuous variables for many existing inductive learning systems.
18718248
It is time for a computer application degree
Contemporary computer technology has spread computing activity to almost every field. The computing industry's need for application software developers who have not only proficient programming skills but also a good understanding of subjects in a specific application field has grown rapidly. Continuing to use existing degree programs in computer science to train expected application software developers would not only be a disservice to students but also hamper the further development of computer science programs themselves. Thus, a new degree program is called for in the sub-discipline of computer applications.
33302534
A space-efficient algorithm for sequence alignment with inversions and reversals
A dynamic programming algorithm to find an optimal alignment for a pair of DNA sequences has been described by Schoniger and Waterman. The alignments use not only substitutions, insertions, and deletions of single nucleotides, but also inversions, which are the reversed complements, of substrings of the sequences. With the restriction that the inversions are pairwise non-intersecting, their proposed algorithm runs in O(n2m2) time and consumes O(n2m2) space, where n and m are the lengths of the input sequences, respectively. We develop a space-efficient algorithm to compute such an optimal alignment which consumes only O(nm) space within the same amount of time. Our algorithm enables the computation for a pair of DNA sequences of length up to 10,000 to be carried out on an ordinary desktop computer. Simulation study is conducted to verify some biological facts about gene shuffling across species.
2882341
Effective Criterion Functions for Efficient Agglomerative Clustering on Very Large Networks
As the agglomerative clustering algorithm is widely used in data mining, image processing, bioinformatics and pattern recognition. it has attracted great interests from both academical and industrial communities. However, existing studies neglect the decisive factor of the efficiency of the agglomerative clustering algorithm for large complex networks and usually use criterion functions which lead to inefficiency. In this paper, we propose three effective criterion functions for improving performance of agglomerative clustering algorithm. We note that clustering efficiency is determined by two factors: a) the number of neighbors of two merged clusters in each merge step; b) the number of neighbors shared by the two clusters. Based on these observations, we propose a framework for designing criterion functions in order to efficiently find clusters in very large networks. We devise three criterion functions that can effectively control the number of neighbors of clusters, and they can efficiently produce high-quality clusters. We have implemented our method and compared with existing studies on real networks, and our method outperforms state-of-the-art approaches significantly on large networks.