10021622
Dynamically Maintaining Duplicate-Insensitive and Time-Decayed Sum Using Time-Decaying Bloom Filter
The duplicate-insensitive and time-decayed sum of an arbitrary subset in a stream is an important aggregation for various analyses in many distributed stream scenarios. In general, precisely providing this sum in an unbounded and high-rate stream is infeasible. Therefore, we target at this problem and introduce a sketch, namely, time-decaying Bloom Filter (TDBF). The TDBF can detect duplicates in a stream and meanwhile dynamically maintain decayed-weight of all distinct elements in the stream according to a user-specified decay function. For a query for the current decayed sum of a subset in the stream, TDBF provides an effective estimation. In our theoretical analysis, a provably approximate guarantee has been given for the error of the estimation. In addition, the experimental results on synthetic stream validate our theoretical analysis.
22570791
A Novel Biology-Inspired Virus Detection Model with RVNS
A virus detection model based on artificial immune principles with real-valued negative selection (RVNS) is proposed. Feature vectors of program code are mapped into high dimension real-valued space. The architecture of this model, the formal definitions of self, nonself, antigen, antibody, and gene library are given. Then the process of self-tolerance of variable-sized detectors in real-valued space is discussed in detail. Variable-sized detectors provide concise representation of the detectors derived from benign files, which reduces the size of detectors set and advances the detection rate. The experimental results show that the model can detect obfuscated and firstly unknown virus more effectively than traditional model.
14272122
A Dynamic Immunity-Based Model for Computer Virus Detection
Internet provides a fertile medium for new breeds of computer viruses. Many people who have access to a wealth of information via Internet are attacked by more computer viruses than they can effectively process. We present a dynamic computer virus detection model that can detect known viruses and previously unknown viruses to prevent information systems from damage. This model is inspired by biological immune systems that protect the body against damage from pathogens. The architecture of this model, the formal definitions of self, nonself, antigen, antibody, and vaccine gene library are introduced. Furthermore, the evolution of self and nonself, the generation of the antibody, the evolution of the virus vaccine gene, and the detection of the antigen are depicted. Experiment results show that this model has better capacity of self-adaptability and self-learning in detecting unknown viruses than traditional models.
18262600
Parameters optimization of support vector regression based on immune particle swarm optimization algorithm
A novel Immune Particle Swarm Optimization (IPSO) for parameters optimization of Support Vector Regression (SVR) is proposed in this article. After introduced clonal copy and mutation process of Immune Algorithm (IA), the particle of PSO is considered as antibodies. Therefore, evaluated the fitness of particles by the Leave-One-Out Cross-Validation (LOOCV) standard, the best individual mutated particle for each cloned group will be selected to compose the next generation to get better parameters of SVR. It can construct high accuracy and generalization performance regression model rapidly by optimizing the combination of three SVR parameters at the same time. Under the datasets generated from sinx function with additive noise and spectra dataset, simulation results show that the new method can determine the parameters of SVR quickly and the gotten models have superior learning accuracy and generalization performance.
35751769
CNP: A Protocol for Reducing Maintenance Cost of Structured P2P
With highly dynamic, structured P2P system needs very high maintenance cost. In this paper we propose a Clone Node Protocol to reduce the maintenance cost of structured P2P system by a mechanism of clone nodes. In order to verify the efficiency of CNP, we achieve a Clone Node Chord structure based on CNP, i.e. CNChord. Furthermore, we implement a bidirectional CNChord (BCNChord) in order to reduce query time of CNChord. Theoretical analysis and experimental results show that CNChord can greatly reduce the cost of maintaining P2P structure and BCNChord can effectively improve the query speed. In a word, CNP can effectively reduce the maintenance cost of structured P2P.
29802848
Improved Approximate Detection of Duplicates for Data Streams Over Sliding Windows
Detecting duplicates in data streams is an important problem that has a wide range of applications. In general, precisely detecting duplicates in an unbounded data stream is not feasible in most streaming scenarios, and, on the other hand, the elements in data streams are always time sensitive. These make it particular significant approximately detecting duplicates among newly arrived elements of a data stream within a fixed time frame. In this paper, we present a novel data structure, Decaying Bloom Filter (DBF), as an extension of the Counting Bloom Filter, that effectively removes stale elements as new elements continuously arrive over sliding windows. On the DBF basis we present an efficient algorithm to approximately detect duplicates over sliding windows. Our algorithm may produce false positive errors, but not false negative errors as in many previous results. We analyze the time complexity and detection accuracy, and give a tight upper bound of false positive rate. For a given space G bits and sliding window size W, our algorithm has an amortized time complexity of $ O + (\sqrt{G/W}) $. Both analytical and experimental results on synthetic data demonstrate that our algorithm is superior in both execution time and detection accuracy to the previous results.
6483143
Using Proximity in Query Focused Multi-document Extractive Summarization
The query focused multi-document summarization tasks usually tend to answer the queries in the summary. In this paper, we suggest introducing an effective feature which can represent the relation of key terms in the query. Here, we adopt the feature of term proximity commonly used in the field of information retrieval, which has improved the retrieval performance according to the relative position of terms. To resolve the problem of data sparseness and to represent the proximity in the semantic level, concept expansion is conducted based on WordNet. By leveraging the term importance, the proximity feature is further improved and weighted according to the inverse term frequency of terms. The experimental results show that our proposed feature can contribute to improving the summarization performance.
16162037
Ranking-Based Statistical Localization for Wireless Sensor Networks
This paper studies Ranking-Based localization method, which, as a special case of common Range-Based localization methods, statistically estimates node positions using the ranking-order constraints rather than the actual values of network measurements. Through extensive empirical evaluation, the advantages of Ranking-Based approach over traditional Range-Based approaches under extreme network conditions are verified. Ranking-Based approach turns out to be more robust to measurement insufficiency and more immune to measurement noise.
18909695
Ear Recognition using Improved Non-Negative Matrix Factorization
An improved non-negative matrix factorization with sparseness constraints (INMFSC) is proposed by imposing an additional constraint on the objective function of NMFSC, which can control the sparseness of both the basis vectors and the coefficient matrix simultaneously. The update rules to solve the objective function with constraints are presented. Research of ear recognition and its application is a new subject in the field of biometrics authentication. In practical application, ear is maybe partially occluded by hair etc. So the proposed INMFSC is applied on ear recognition with normal images and partially occluded images. Experiment results show that, compared with the traditional NMFSC, the proposed method not only obtains higher recognition rate, but also improves the sparseness and the orthogonality of coefficient matrix
5679790
Proactive Information Exchange During Team Cooperation
We are concerned, ultimately, with multi-agent teams involving both software and human agents in which the software agents assist in training the humans, e.g., as virtual team members. The software agents must be capable of utilizing the teamwork mechanisms used by humans. In this paper, we describe how to give agents the same implicit communication capabilities, i.e., observation, that humans use. We show how agents can use observations of the environment and teammates' actions to estimate other agents beliefs without bothering them with unnecessary messages; we also show how agents can anticipate information needs among the team and proactively exchange information, reducing the total communication volume. To achieve these goals, we add sensing capabilities to agents and present algorithms that infer teammates' mental state from the observation. Experimental data is presented that shows that the advantage of observation in proactive information exchange as well as enhanced team performance.
29942985
Poisson approximation for significance in genome-wide ChIP-chip tiling arrays
MOTIVATION
A genome-wide ChIP-chip tiling array study requires millions of simultaneous comparisons of hybridization for significance. Controlling the false positive rate in genome-wide tiling array studies is very important, because the number of computationally identified regions can easily go beyond the capability of experimental verification. No accurate and efficient method exists for evaluating statistical significance in tiling arrays. The Bonferroni method is overly conservative and the permutation test is time consuming for genome-wide studies.


RESULT
Motivated by the Poisson clumping heuristic, we propose an accurate and efficient method for evaluating statistical significance in genome-wide ChIP-chip tiling arrays. The method works accurately for any large number of multiple comparisons, and the computational cost for evaluating P-values does not increase with the total number of tests. Based on a moving window approach, we demonstrate how to combine results using various window sizes to increase the detection power while maintaining a specified type I error rate. We further introduce a new false discovery rate control that is more appropriate in measuring the false proportion of binding intervals in tiling array analysis. Our method is general and can be applied to many large-scale genomic and genetic studies.


AVAILABILITY
http://www.stat.psu.edu/~yuzhang/pass.tar