Evaluation of copy number variation detection for a SNP array platform Background: Copy Number Variations (CNVs) are usually inferred from Single Nucleotide Polymorphism (SNP) arrays by use of some software packages based on given algorithms. However, there is no clear understanding of the performance of these software packages; it is therefore difficult to select one or several software packages for CNV detection based on the SNP array platform. We selected four publicly available software packages designed for CNV calling from an Affymetrix SNP array, including Birdsuite, dChip, Genotyping Console (GTC) and PennCNV. The publicly available dataset generated by Array-based Comparative Genomic Hybridization (CGH), with a resolution of 24 million probes per sample, was considered to be the "gold standard". Compared with the CGH-based dataset, the success rate, average stability rate, sensitivity, consistence and reproducibility of these four software packages were assessed compared with the "gold standard". Specially, we also compared the efficiency of detecting CNVs simultaneously by two, three and all of the software packages with that by a single software package. The objective of this research was to investigate the publicly available software algorithms for CNV calling from raw data produced by the Affymetrix 6.0 SNP array platform. For this purpose, four software packages, e.g., Birdsuite, dChip, GTC and PennCNV-Affy were evaluated. In our study, the total quantity, the mean and median sizes and the grouping of the detected CNVs were thoroughly investigated to evaluate the success rate, the detecting bias, the sensitivity and the reproducibility etc. of the four software packages.. Through our study, we found that PennCVN-Affy outperformed the other three software packages on the whole. First of all, the parameters setting for PennCNVAffy could be very easy. It were performed according to the manual on its official websites with default settings which could save time and make it general.. Secondly, PennCNV performed better than the other three ones in the success rate and bias comparison. Although the total quantity of CNVs called by PennCNV was not the largest among these four software packages, PennCNV showed less bias when calling CNVs, which enabled it to find similar amount of both known and unknown CNVs. We considered this kind of balance also reflected its high sensitivity and high specificity.. In addition, we used a Receiver Operating Characteristic (ROC) curve to graphically represent the false negative and false positive rates of each of the four tested software packages. Statistically, PennCNV outperformed the other three packages, and the performances of the other three software packages were similar.. Moreover, in the reproducibility test, CNVs were categorized into three groups, including all CNVs, CNVs larger than 15 kb, and CNVs larger than 30 kb. GTC shown no differences among these groups because all of the CNVs called by GTC were larger than 30 kb. Birdsuite, dChip, and GTC had only an approximately 55% consistency if the CNVs shorter than 30 kb were considered. However, PennCNV obtained a consistent CNV calling as high as 87% even if it used the same algorithm with dChip and GTC, which indicated that only 13% of the CNVs called by PennCNV were not found in the CGH-based CNV dataset.. Based on the above reasons, PennCNV seemed to be a reasonable and acceptable option when choosing single software package for CNV detection. But it was worth noting that the algorithms themselves might cause differences in the CNV detection [1] . Meanwhile, software packages also have different emphases when they employed different algorithms. For example, Birdsuite had a higher success rate but lower reproducibility. In contrast, GTC obtained high specificity but lower sensitivity and appears to be more conservative than other types of software.. Obviously, a large part of the matching CNVs were detected by several software packages by the same time. Also, if we used only one method, the calling effect would be certainly not so good as the combined use of multiple software packages. Besides, the concordance between the SNP 6.0 and CGH platforms was much lower than 40%, and the different algorithms of each software packages would also make the detecting result diverse.. Therefore, we proposed the combined use of multiple software packages, thus could provide us higher accuracy and reliability in the CNV detecting. Copy number variation (CNV) is a type of genetic variation that is widely found in human and other mammalian genomes. It includes genomic deletion, duplication, and complex rearrangement that range from 100 base pairs to several mega base pairs in size [1] . A substantial number of CNVs have significant impact on complex human diseases, such as cancer [2] , autism [3] , and even susceptibility to HIV [4] , due to the fact that they can disrupt gene structure and affect gene regulation [1] . Therefore, studies on CNVs can further our understanding of the genetic etiology of human diseases.. To date, approximately 180,000 CNVs have been reported in the Database of Genomic Variants (DGV, see URLs). Arising from the completion of the Human Genome Project and the HapMap Project, a large number of genetic variations associated with human phenotypes or complex diseases have been identified by SNP-based genome-wide association studies (GWAS) [5] . CNVs might assist us in finding the missing heritability in GWAS. There are many methods available for CNV detection, such as microarray and Polymerase Chain Reaction (PCR) based technologies [6] . SNP array and array-based comparative genomic hybridization (CGH) are two of the most frequently used high throughput platforms. The Affymetrix (Santa Clara, CA, USA) Genome-Wide Human SNP Array 6.0 contains more than 1,800,000 probes, including 906,600 probes to detect SNPs and 946,000 probes to detect structural variations. The Agilent 1 M CGH Array contains approximately 1 million 60-mer oligonucleotide probes for CNV detection. It remains unclear whether the widely used SNParray-based CNV calling methods can provide sufficient concordance with CGH in CNV detection.. The objective of this paper was to evaluate the performances of publicly available software packages that are used to call CNVs from SNP arrays. The CGH-based CNV detection results derived from 20 HapMap samples were used as a "gold standard" due to their high Signalto-Noise Ratio and detection accuracy, which were described in detail by Park et al. [7] . Nowadays, the same 20 HapMap samples have also been studied using SNP 6.0 arrays in the Phase II HapMap Project [8] . In order to evaluate four CNV detecting software packages specifically developed for the Affymetrix 6.0 SNP array platform, we should firstly finish parameters setting for each software package. Only when following their manuals could we start and continue the software packages, we then follow the manual issued on the official website of each software to finish the default settings (it was recommended and necessary). For example, we made all twenty-six SNP 6.0 array datasets pass the Quality Control (QC) threshold of the software package GTC as a default setting. And since dChip had no parameters setting program, we then used the output information of the genotyping results of GTC as the input data for subsequent analyses for it [9] . This was exactly instructed by the manual of dChip. For Birdsuite and PennCNV-Affy, CNV detection was performed according to the manuals on their official websites with default settings. We selected a published CNV dataset generated by CGH with a resolution of 24 million probes per sample as the "gold standard " to evaluate four CNV detecting software packages developed for the Affymetrix 6.0 SNP array platform, including GTC (version 4.0), Birdsuite (version 1.5. Compared with the CGH-based dataset, the CNVs identified by the four software packages were then divided into three groups-a matching group, an overlapping group and a non-overlapping group. The success rate, average stability rate, sensitivity, consistence and reproducibility of these four software packages were assessed compared with the "gold standard". Specially, we also compared the efficiency of detecting CNVs simultaneously by two, three and all of the software packages with that by a single software package (Figure 1 ).

B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O B B O O B O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B B B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B I O O B O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O B B O B O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O B O B O O O O O O O O O O B O O O O O O B O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O B I O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O B O O O O O O O O O B O O O O O O O B O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O B O O B O O O O O O O O O O O O O O O O O O B O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Modeling the Development of Goal-Specificity in Mirror Neurons Abstract Neurophysiological studies have shown that parietal mirror neurons encode not only actions but also the goal of these actions. Although some mirror neurons will fire whenever a certain action is perceived (goal-independently), most will only fire if the motion is perceived as part of an action with a specific goal. This result is important for the action-understanding hypothesis as it provides a potential neurological basis for such a cognitive ability. It is also relevant for the design of artificial cognitive systems, in particular robotic systems that rely on computational models of the mirror system in their interaction with other agents. Yet, to date, no computational model has explicitly addressed the mechanisms that give rise to both goal-specific and goal-independent parietal mirror neurons. In the present paper, we present a computational model based on a self-organizing map, which receives artificial inputs representing information about both the observed or executed actions and the context in which they were executed. We show that the map develops a biologically plausible organization in which goal-specific mirror neurons emerge. We further show that the fundamental cause for both the appearance and the number of goal-specific neurons can be found in geometric relationships between the different inputs to the map. The results are important to the actionunderstanding hypothesis as they provide a mechanism for the emergence of goal-specific parietal mirror neurons and lead to a number of predictions: (1) Learning of new goals may mostly reassign existing goal-specific neurons rather than recruit new ones; (2) input differences between executed and observed actions can explain observed corresponding differences in the number of goal-specific neurons; and (3) the percentage of goal-specific neurons may differ between motion primitives. We have presented a computational model detailing a developmental process that results in pools of neurons in a SOM encoding specific motion primitives as postulated by the chain model [28] . We have shown that the neurons within these pools can self-organize/develop into goalspecific neurons as discovered by [6] , and we were further able to detail the precise mechanisms underlying the emergence of such an organization and provide empirically testable predictions of the model. This work thus provides further support for the hypothesis that goal-specific mirror neurons (in parietal areas) may not have specifically evolved to support action understanding. Later cognitive processes may well have evolved to make use of the firing patterns which can facilitate action understanding, in what could be an instance of neural reuse [47] . A similar argument has been presented previously for the development of social roles of prefrontal mirror neurons [24] . Furthermore, the work presented here extends previous modeling work on premotor mirror neurons [30] and can be used in the design of robotic systems dealing with the initial processing of affordances and observed/executed motions in order to facilitate a natural emergence (rather than a hard-coded design) of a parietal mirror system complete with goalspecificity within the agent's controller.

O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Effects of coating rate on morphology of copper surfaces We have used standard fractal analysis and Markov approach to obtain further insights on roughness and multifractality of different surfaces. The effect of coating rates on generating topographic rough surfaces in copper thin films with same thickness has been studied using atomic force microscopy technique (AFM). Our results show that by increasing the coating rates, correlation length (grain sizes) and Markov length are decreased and roughness exponent is decreased and our surfaces become more multifractal. Indeed, by decreasing the coating rate, the relaxation time of embedding the particles is increased. Surface, which is the first interface of a material, has an important role in the interaction of matter with environment. Physical and chemical properties of the surfaces are not only determined by the material properties but also to a significant degree by the topography. No surface is perfectly flat and every surface depending on the scale it is observed, has a certain amount of roughness. Surface roughness is one of the important properties of the surfaces that affect many other properties of the surface like adhesion, friction and contact, reflection or scattering [1] [2] [3] [4] [5] [6] [7] . The roughness effect appears in various devices such as field emission devices [8] , sensors [9] , self-cleaning materials [10] . It should be noted that roughness is not an intrinsic property of the surface. Indeed, it depends on the scale of observation. In other words, when we observe a surface from different scales different roughness could be obtained.. In this article, we investigate the effect of coating rate on statistical properties of our prepared surfaces. These surfaces have the same composition and have been prepared in the same condition. Coating rates of these samples differ while final thickness of the samples is the same (250nm). We have considered copper thin films. Copper thin films have different applications in various technologies such as optics and laser science, because of the high reflection power of copper in red and infrared region of spectrum. Glass substrates were used in this survey because of smoothness. In Tab. I, the experimental conditions of two selected thin layers are given. AFM measurements of these samples were carried out and the exported data was used for further calculations. The topography of the samples was investigated using Park Scientific Instruments (model Autoprobe CP). The images were collected in a constant force mode and digitized into 256 × 256 pixels with scanning frequency of 0.6 Hz. A rough surface can be described mathematically as h(x), where h(x) is the surface height of a rough surface with respect to a smooth reference surface defined by a mean surface height and x is the position vector on the surface.. To study the effect of topography and scaling properties of the thin films, the standard fractal analysis is used [11, 12] . When long-range correlations are absent in h(x), shortrange correlations may exist. In this case, it can be more suitable to study multifractality by Markov analysis [13, 14] . Fazeli at al. showed how Markov processes play a fundamental role in probing rough surfaces and characterizing their topography. They explained tip convolution in AFM images by non-Markovian properties in the AFM images reported. Our results show that by increasing the coating rates, correlation length (grain sizes) and Markov length are decreased, roughness exponent is decreased and our surfaces get more multifractal which means that our probability density functions get more non-Gaussian.. The paper is organized as follows. Standard fractal method and Markov analysis are described in Section II. Data description and analysis based on these methods for two selected copper thin films are given in Section III. In Section IV, we present our conclusion.

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Improving the scaling normalization for high-density oligonucleotide GeneChip expression microarrays Background: Normalization is an important step for microarray data analysis to minimize biological and technical variations. Choosing a suitable approach can be critical. The default method in GeneChip expression microarray uses a constant factor, the scaling factor (SF), for every gene on an array. The SF is obtained from a trimmed average signal of the array after excluding the 2% of the probe sets with the highest and the lowest values.Results: Among the 76 U34A GeneChip experiments, the total signals on each array showed 25.8% variations in terms of the coefficient of variation, although all microarrays were hybridized with the same amount of biotin-labeled cRNA. The 2% of the probe sets with the highest signals that were normally excluded from SF calculation accounted for 34% to 54% of the total signals (40.7% ± 4.4%, mean ± sd). In comparison with normalization factors obtained from the median signal or from the mean of the log transformed signal, SF showed the greatest variation. The normalization factors obtained from log transformed signals showed least variation.Eliminating 40% of the signal data during SF calculation failed to show any benefit. Normalization factors obtained with log transformed signals performed the best. Thus, it is suggested to use the mean of the logarithm transformed data for normalization, rather than the arithmetic mean of signals in GeneChip gene expression microarrays. Normalization of microarray data allows direct comparison of gene expression levels among experiments. A glo-bal linear normalization, called scaling has been widely used in GeneChip microarray technology for gene expression analysis. The scaling factor (SF) is calculated from a trimmed average of gene expression level after excluding the 2% of the data points of the highest values and the lowest values. It is shown here that the 2% of the probe sets of the highest signals contained from 34% to 54% of the total signals. Elimination of the outliers did not reduce, but increased the variation among multiple arrays. Instead, normalization factors obtained from the mean of the log transformed signals had the best performance. Thus, the current scaling method, although widely used, is not optimal and needs further improvement. The mean of logarithm transformed signals is highly recommended to use for normalization factor calculation. Logarithmic transformation is a well-accepted approach for stabilizing variance and has become a common choice for data transformation and normalization for spotted microarrays [12, 16] . Much improvement has been made in GeneChip microarray technology and accompanying software during the past few years. The current version of GeneChip software has improved its performance and is better than the earlier versions that used the Average Difference to express levels of gene expression [3, 4] . However, the normalization algorithm was inherited and remains the only and default option for gene expression data processing in both MAS 5 and the newly released GeneChip Operating Software (GCOS) software. They continue to use the arithmetic mean of signals to obtain the SF in absolute analysis (single array) and the NF in comparison analysis (two arrays) [3] [4] [5] 7, 11, 22] . It is clearly shown here that the trimmed average and the resulting SF had a larger variance than the median-based NF, or the NF based on the mean of log transformed signals. Similar results were observed in other GeneChip expression arrays, such as mouse U74A and human U133A (data not shown). Elimination of the highest and the lowest 2% of the probe set signals did not stabilize the trimmed means. When intra-array variance was reduced by 40%, this approach cannot be considered to be optimal. The logarithmic transformation of signals stabilized the variation well and made the normalization process much less dependent upon the mean and less affected by the outliers.. Although simple and popular, the global linear normalization has its drawbacks, especially when the relationship among multiple experiments or genes is not linear. To address such problems, several methods have been proposed to conduct local and non-linear normalization, [12, [14] [15] [16] [17] 20, 22, 27] . Data normalization is a very critical and important step for microarray data mining process. The use of different approaches to normalization may have a profound impact on the selection of differentially expressed genes and conclusions about the underlying biological processes especially when subtle biological changes are investigated [12, 16, 28] . The statistical algorithm in current GeneChip software (MAS 5 and GCOS 1) for gene expression microarray data has eliminated the negative gene expression values, a problem present in earlier versions of the software [5, 7] . It uses a robust averaging method based on the Tukey biweight function to calculate the gene expression level from the logarithm transformed hybridization data [3] [4] [5] 11] . The reported data of a probe set is the antilog of the Tukey biweight mean multiplied by a SF and/or a normalization factor (NF affy ). When both the SF and NF affy are equal to 1, there is no normalization or manipulation of original data. Both NF affy and SF are computed in virtually the same way. NF affy is calculated in comparison analysis to compare the array average of one experiment with that of a baseline experiment, while SF is obtained from the signal average of one experiment comparing with a common value, the target signal in absolute analysis [3] [4] [5] 11, 22] . The average value used in GeneChip is a trimmed average. It is not calculated from all probe sets, but from 96% of the probe sets after the 2% of the probe sets with the highest and the 2% of the lowest signals were removed.. In this report, a total of 76 experiments with rat U34A GeneChip were analyzed. As shown in Table 1 , the total hybridization signals varied although all arrays were hybridized with the same amount of biotin-labeled cRNA and scanned with the same scanner of identical settings. The array of the highest hybridization intensities had 2.8 times more signals than that of the lowest. The average array signals had 25.8% variation in terms of coefficient of variation. The mean signals were significantly greater than the median signals on each array, indicating a non-normal distribution. The density plot showed a long-tailed and skewed distribution (not shown) and the average of such data is known to be sensitive to the larger values in the data set.. The rat U34A GeneChip contained 8799 probe sets; hence 2% was about 176 probe sets. The sum of the 2% of the probe sets with the lowest signals accounts for less than 0.1% of the total signals (0.05% ± 0.01%, mean ± SD, n = 76) and its impact on SF calculation can be ignored. However, the sum of the 2% of the probe sets with the highest signals, the TrimTotal as used in this report, was responsible for about 40% of the total signals (from 34% to 54%, Table 1 ). The remaining 96% of the probe sets used for SF calculation, produced only about 60% of the signals. Excluding 4% of the probe sets did not reduce the variation, but rather slightly increased the variation, which in turn resulted in a wider range of SFs (Table 1) . It was also found that the TrimTotal was highly correlated with total signal (R = 0.928), but less with medians (R = 0.536) and the mean of log signals (R = 0.643). The trimmed percentage (Tp) was found to be negatively associated with the median (R = 0.558, b = -1.116) and the mean of log signals (R = 0.495, b = -0.968), but not with the total signal of all probe sets.. Among other approaches to global linear normalization, one can also use the median signal or the mean of logarithm transformed signals to calculate the NF. NFLogMean showed a higher correlation with NFMedian than with SF. There were larger differences between NFLogMean and SF than those between NFLogMean and NFMedian (Fig. 1) . To test if the larger difference was a result of removing 4% of the probe sets from the calculation, another NF, the NFTrimLogMean was obtained using the same data as for SF, but with a log transformation. There is a very significant correlation between NFTrimLogMean and NFLog-Mean (R = 0.9998). The 4% of the probe sets that was removed from NFTrimLogMean calculation reduced the total data by only 4% after log transformation.. Since it is impossible to obtain the true normalization factor, an average of the four global linear NFs mentioned above was used instead to estimate the 'true' NF. To compare them with the true NF, a score (NFscore) is introduced. Each NF is calculated against the respective 'true' NF to obtain its NFscore. The average NFscore (± SD) is 7.01% (± 6.24%), 4.51% (± 3.48%), 2.25%(± 2.33%) and 1.95% (± 1.61%), and the sum of NFscore is 5.33, 3.43, 1.71 and 1.48 for SF, NFMedian, NFTrimLogMean and NFLogMean, respectively (Fig. 1) . The sum of NFscore indi- "Total signal" is the sum of all the signals on each array. "Sum of signals used for SF" is the sum of signals excluding the trimmed data and used to calculate SF. "Trimmed total" is the sum of the 2% probe sets with the highest signals on the array. "Mean of log signals" is the mean of log 2 transformed signals. "Trimmed percentage" = (Trimmed total/Total signal) × 100%. See also in Methods. The "lowest" and "highest" showed the lowest and highest number in the category among the 76 chips, respectively. The mean, standard deviation (SD) and coefficient of variation (CV) were also calculated. cated an accumulated variation from the true NF, and the larger the number, the larger the accumulated variation. An attempt to add a 5th NF obtained from the arithmetic mean of all probe sets of the array was also made to calculate and compare NFscore with each NFs, and the results showed the same conclusion (data not shown). It is fair to conclude that NFLogMean produced the least variation. The high-density oligonucleotide microarray, also known as GeneChip ® , made by Affymetrix Inc (Santa Clara, CA), has been widely used in both academic institutions and industrial companies, and is considered as the "standard" of gene expression microarrays among several platforms. A single GeneChip ® can hold more than 50,000 probe sets for every gene in human genome. A probe set is a collec-tion of probe pairs that interrogates the same sequence, or set of sequences, and typically contains 11 probe pairs of 25-mer oligonucleotides [1] [2] [3] . Each pair contains the complementary sequence to the gene of interest, the socalled perfect match (PM), and a specificity control, called the Mismatch (MM) [3] . Gene expression level is obtained from the calculation of hybridization intensity to the probe pairs and is referred to as the "signal" [4] [5] [6] [7] [8] [9] [10] . The normalization method used in GeneChip software is called scaling and is defined as an adjustment of the average signal value of all arrays to a common value, the target signal value in order to make the data from multiple arrays comparable [4, 11] .. The purpose of data normalization is to minimize the effects of experimental and/or technical variations so that meaningful biological comparisons can be made and true biological changes can be found among multiple experiments. Several approaches have been proposed and shown to be effective and beneficial. They were mostly from studies on two-color spotted microarrays [12] [13] [14] [15] [16] [17] [18] [19] . Some authors proposed normalization of the hybridization intensities, while others preferred to normalize the intensity ratios. Some used global, linear methods, while others used local, non-linear methods. Some suggested using the spike-in controls, or house-keeping genes, or invariant genes, while others preferred all the genes on the array. For GeneChip data, some have proposed different models to normalize signal values or normalize probe pair values [10, [20] [21] [22] [23] [24] . Despite the presence of other alternatives, many biologists still use the default scaling method and consider that such method is satisfactory and is useful to identify biological alterations [23, 25, 26] . With the increasing awareness and usage of GeneChip technology and willingness to continue to use GeneChip software among many biologists, it is worth improving the performance or correcting the problems of the software. In this report, the author has demonstrated that in the scaling algorithm excluding 2% of the probe sets with the highest and the lowest values did not have much benefit. However, the logarithmic transformation of signal values prior to scaling proved to be the optimum normalization strategy and is strongly recommended.

O O B B O O B B O O O B O O O O O B O O O O O O O O O O O O O O O O O O O B O B O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B B I O B O B O O O O O B I O O O O O O B O B O O O O O B B O O B I O O B O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B I O O O O O B O O O O O O O O O O O B I O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O B O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B I B O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O B O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O B O O O O O


Extended Component Importance Measures Considering Aleatory and Epistemic Uncertainties Abstract-We introduce extended component importance measures (Birnbaum importance, RAW, RRW, and Criticality importance) considering aleatory and epistemic uncertainties. The Dempster-Shafer theory, which is considered to be a less restricted extension of probability theory, is proposed as a framework for taking into account both aleatory and epistemic uncertainties. The epistemic uncertainty defined in this paper is the total lack of knowledge of the component state. The objective is to translate this epistemic uncertainty to the epistemic uncertainty of system state, and to the epistemic uncertainty of importance measures of components. Affine arithmetic allows us to provide much tighter bounds in the computing process of interval bounds of importance measures, avoiding the error explosion problem. The efficiency of the proposed measures is demonstrated using a bridge system with different types of reliability data (aleatory uncertainty, epistemic uncertainty, and experts' judgments). The influence of the epistemic uncertainty on the components' rankings is described. Finally, a case study of a fire-detector system located in a production room is provided. A comparison between the proposed measures and the probabilistic importance measures using two-stage Monte Carlo simulations is also made.

O O B I O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O B I O B I O O B I O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O


A Unifying Approach to Moment-Based Shape Orientation and Symmetry Classification Abstract-In this paper, the problem of moment-based shape orientation and symmetry classification is jointly considered. A generalization and modification of current state-of-the-art geometric moment-based functions is introduced. The properties of these functions are investigated thoroughly using Fourier series analysis and several observations and closed-form solutions are derived. We demonstrate the connection between the results presented in this work and symmetry detection principles suggested from previous complex moment-based formulations. The proposed analysis offers a unifying framework for shape orientation/symmetry detection. In the context of symmetry classification and matching, the second part of this work presents a frequency domain method, aiming at computing a robust moment-based feature set based on a true polar Fourier representation of image complex gradients and a novel periodicity detection scheme using subspace analysis. The proposed approach removes the requirement for accurate shape centroid estimation, which is the main limitation of moment-based methods, operating in the image spatial domain. The proposed framework demonstrated improved performance, compared to state-of-the-art methods. T HERE is strong evidence that shape orientation identification is an important task, performed during the preattentive stage of the human visual processing system, which activates object-oriented mechanisms and helps in scene interpretation [1] . Shape orientation is also an important object visual attribute useful in many image processing and computer vision applications, such as shape analysis and representation, image retrieval, image normalization and object recognition. Over the past years, significant effort has been made by the computer vision community to develop robust and efficient algorithms which aim at defining and computing the orientation of a 2-D shape. Unfortunately, the large shape variability hinders the formulation of a common approach for all possible shapes.. It seems reasonable that efficient orientation estimation techniques for machine vision applications should possess the following favorable properties: 1) reasonable geometric interpretation; 2) robustness to noise/outliers; 3) low computational complexity; 4) flexibility in a sense that important parameters should be automatically computed in a blind environment; 5) robust performance for a large number of shapes. In this work, moment-based shape orientation methods are pursued, as they offer a good compromise among the above requirements. Unlike any other methods (see [2] and [3] for detailed review), a moment-based formulation appears to be the only one which provides a complete solution to the shape orientation problem. Additionally, moments are useful features that can be employed to other pattern analysis tasks, such as recognition.. It was shown in [4] that the traditional approach to defining a pattern's orientation based on second-order central geometric moments [5] will fail for all shapes which are rotationally symmetric of order with . Defining and computing the orientation of symmetric shapes is the main scope of this work. More specifically, we jointly consider the problem of shape orientation and symmetry detection/classification. Such an approach appears to have a reasonable geometrical interpretation, and, therefore, it may be useful for higher level processing such as pattern analysis and classification [6] . In contrast, moment-based methods which compute a single orientation for symmetric patterns [7] - [13] have no clear geometric meaning, and, therefore, their use is mainly limited to image normalization tasks.. In the first part of this paper, the focus is on the shape orientation/symmetry classification problem based on a geometric moment-based approach. In particular, we introduce a novel modification and generalization of the objective functions proposed in [4] , [6] , such that a reflection symmetry criterion is satisfied. The properties of these functions are thoroughly investigated using Fourier series analysis which simplifies mathematical manipulations and enables the derivation of closed-form solutions. Furthermore, we demonstrate that shape orientation principles suggested in previous complex moment formulations [2] , [14] can be naturally extended to the case of the proposed geometric moment-based functions. In the light of the presented analysis, moment-based shape orientation can be unified in a single framework.. In the context of moment-based symmetry classification and matching, in the second part of this work, a novel scheme is proposed, aiming at computing moment features in a more stable and robust way than the currently considered state-of-the-art method given in [2] . The core of our method is based on the computation of a moment-based feature set in the image Fourier domain; an approach motivated by the recent development of a fast and accurate algorithm for computing a true polar Fourier representation [15] . The shift invariance property of the Fourier transform removes completely the requirement for the accurate shape centroid estimation, which is the main limitation of the method in [2] . Further robustness is achieved by considering the edge map of the original pattern solely and employing a novel periodicity detection scheme based on subspace analysis.. This work is organized as follows. In the next section, the necessary definitions, that are used throughout in this work, are provided. A brief introduction to the methods which constitute the basis of our approach is presented in Section III. In Section IV, a detailed description of the proposed novel unifying approach is provided. Section V introduces the novel polar frequency domain moment-based formulation. In Section VI, the performance of the proposed framework is evaluated. Finally, Section VII outlines the contributions and offerings of this work. An image is both -fold rotationally and reflection symmetric about , if there exist lines , satisfying (II.5). The problem of moment-based shape orientation and symmetry classification was considered. In the first part of this work, we presented a study which attempts to unify many popular moment-based approaches in a single framework. In particular, we showed that results given from a complex moment formulation also apply to the case of appropriately defined geometric moment-based functions. Analytical expressions were derived based on Fourier series analysis. In the second part of this work, we presented a moment-based symmetry classification and matching algorithm which operates on the image Fourier domain and, therefore, does not require the accurate estimation of the symmetry center which is the main limitation of current approaches. Our formulation was based on a true polar Fourier representation of the extracted image gradients. Further robustness was achieved by using a periodicity estimation scheme based on subspace analysis. Simulation results demonstrated the efficiency of our approach. The main target of this section is to draw a comparison between Shen's complex moment formulation which operates on the image spatial domain [2] and the frequency domain moment-based approach proposed in Section V. For this purpose, we have considered more than 150 patterns taken from two different databases used in [3] and in [2] and [12] . A representative sample of the images used is shown in Fig. 2 . The section is organized in a way such that performance is assessed for progressively increased shape distortions and more challenging situations.

O O O O O B I O B I O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Online Continuous Scale Estimation of Hand Gestures The style of a gesture provides significant information for communication, and thus understanding the style is of great importance in improving gestural interfaces using hand gestures. We present a novel method to estimate temporal and spatial scale-which are considered principal elements of the style-of hand gestures. Gesture synchronization is proposed for matching progression between spatio-temporally varying gestures, and scales are estimated based on the progression matching. For comparing gestures of various sizes and speeds, gesture representation is defined by adopting turning angle representation. Also, LCSS is used as a similarity measure for reliability and robustness to noise and outliers. Performance of our algorithm is evaluated with synthesized data to show the accuracy and robustness to noise and experiments are carried out using recorded hand gestures to analyze applicability under real-world situations. Using hand gestures to improve human-computer interfaces has been of great interest for an expressive and natural way of interaction [1] . As human behavior, the meaning of expression made by hand gestures can be interpreted in two main ways-what is done and how it is performed [2] . Traditional gestural interfaces have usually been for recognizing the former (a message or content).. However, style-the way in which a gesture is performed-also has significant importance in communication. For instance, stylistic differences can be used to describe characteristics of individual personality [2] , and changes in the style may imply transition in emotional status [3] , [4] . Our interest is in expanding perspectives of gesture recognition by taking into account style of gestures and improving expressiveness of interfaces for applications like 3D animation. Especially, we focus on the dynamics of hand gestures measured in temporal and spatial scale variation, which is an essential element contributing to stylistic differences [2] .. We have chosen single-handed motions as targets for estimating scale. Single-handed motions can be thought of as spatio-temporal trajectories or shapes in space. So it is less ambiguous to define scale measures on them, and various methods for representation and comparison of trajectories and shapes are introduced by many researchers [5] . Even though single-handed motions may not be complex gestures, estimating their scale is not a simple task. Comparing time series or temporal sequence with variations in size and speed requires, at the very least, representation methods which preserve scale-related information and similarity measures independent from scale variations. In addition, our method assumes real-time evaluation of scales from online continuous user input streams, which can improve interactivity by minimizing delay between interactions and providing localized scale information since many applications using gestural interfaces are required to behave in a highly interactive manner (like multimedia and entertainment applications). This requirement adds difficulties to the problem since boundaries (start and end points of gestures) are unknown for data from continuous input streams [6] . Gesture spotting [7] - [10] in continuous gesture recognition presents similar difficulties but with different requirements; we are interested in on-going local progression of gesture input for continuous scale evaluation, however gesture spotting tries to isolate single and whole gesture data for consecutive recognition without explicit intervention by users or systems. In this paper, we presented a method for scale estimation from continuous gesture input. Gesture synchronization is introduced for matching gesture progression between input and a reference. Temporal and spatial scales are estimated based on progression matching. By adopting ideas of turning angle representation, scale-aware gesture representation is defined and an LCSS-based similarity measure is used to compare temporally mismatching gestures. Evaluation with synthesized data and recorded gestures shows the accuracy and reliability of our algorithm under noisy condition, and applicability for real-world situations.. One limitation of this work is that we assume a target gesture type is known before starting estimation. In reality, gesture types are usually unknown before gestures are performed and recognized. Therefore, it is necessary to carry out gesture recognition and estimation at the same time. However, contradicting characteristics between recognition and estimation makes this problem more difficult than thought-gesture recognition needs as much data as possible for accuracy but real-time continuous scale estimation has to be performed on incomplete partial data. Dividing gestures into a preparation stage [33] (for recognition) and a performance stage (for estimation) or using gesture graph [31] may be candidate solutions.. In our future work, we are planning to extend our current work to recognize the expressiveness of human motions for controlling animation characters. Expressiveness of motions could be described in size, speed, and other factors [2] . After the relationship is modeled, it may be possible to recognize motions at the affective level such as happy or sad [4] , [14] , [15] . We expect that our approach will enable us to control expressive animations more naturally and intuitively, which is one of the important issues in computer animation. The estimation process mainly consists of the following three steps:. 1. preparing a reference gesture: Single-handed motion is recorded and smoothed (e.g. by Gaussian or kalman filters) to reduce noise. Then the recorded sequence of position vectors is converted into a sequence of displacement vectors (Eq. (2)). Scale of the reference gesture is considered as unit scale. 2. gesture synchronization: After a user starts gesture performance, amounts of progression by accumulated (and possibly incomplete) input data are evaluated. The evaluation of gesture progression is carried out by searching for corresponding segments in the reference and user input, which are spatio-temporally matching each other. 3. estimating scale: Temporal and spatial scale of user input are estimated using the progression matching given by step 2. Scale of the user input is expressed in ratio to scale of the reference gesture.. Step 2 and 3 are repeated until gesture performance is finished.. We assume the type of gesture is known before starting gesture performance since this paper focuses on estimation rather than recognition. Approaches for simultaneously applying gesture recognition and scale estimation will be discussed in Sect. 6.

O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O


Cell population balance and hybrid modeling of population dynamics for a single gene with feedback Latest research on biological systems is steadily shifting from isolated single cells to entire cell populations. The latter are inherently heterogeneous, and their modeling requires approaches that explicitly account for this property. A comprehensive such approach is the cell population balance (CPB), which, however, is computationally expensive and becomes intractable for multivariable models.In this work, we demonstrate the use of model-reduction to efficiently simulate cell population heterogeneity in a genetic network of a single gene with feedback. Starting from a 4-species model we use singular perturbation analysis to derive a single equation for the intracellular protein concentration. We subsequently incorporate this equation to a hybrid model consisting of a CPB for the cell volume, and a continuum equation for the protein concentration. We finally compare the results obtained with the hybrid model with those of the full CPB, demonstrating the accuracy and computational efficiency of the hybrid methodology. Heterogeneity in cell populations is a ubiquitous phenomenon that is observed even in the case of clonal cells (Avery, 2006; Davidson and Surette, 2008) . This inherent property of cell populations stems from numerous sources, in particular, the random occurrence of intracellular reactions, the differences in the cell growth rates, the temporal variability in the transitions through the different phases of the cell cycle as well as in the division times, and finally, the stochasticity in the partitioning of mother cell contents to the two daughters. As a result, studying the behavior of single cells is not enough to characterize the behavior of the population, thereby motivating the development of experimental and computational approaches that can capture cell population heterogeneity (Fernandes et al., 2011) .. Thus, from a modeling perspective, the aforementioned sources of heterogeneity, with the exception of the random reaction occurrence, were explicitly taken into account in the deterministic cell population balance (CPB) framework developed by Tsuchiya et al. (1966) , and Fredrickson et al. (1967) . These models describe the temporal evolution of the distribution of the population over a state variable. The latter can include the cell mass (Sinko and Streifer, 1971; Subramanian and Ramkrishna, 1971) , the age and/or volume of the cell (Bell and Anderson, 1967; Sinko and Streifer, 1967; Bell, 1968; Anderson et al., 1969; Sinko and Streifer, 1969) , and also the molar contents of intracellular components (Ataai and Shuler, 1985; Mantzaris et al., 2001a; Mantzaris et al., 2001b; Mantzaris et al., 2001c; Henson et al., 2002) . More recently Fredrickson and Mantzaris (2002) and Fredrickson (2003) expanded the CPB formulation to account for the transitions between the different phases of the cell cycle. The CPB framework has been successfully used to model cell cycle dynamics (Faraday and Kirkby, 1992; Liu et al., 2007) , capture cell population heterogeneity (Mantzaris, 2005b; Mantzaris, 2005a) , predict and control the dynamics of fermentation processes in batch or continuous bioreactors (Godin et al., 1999; Mantzaris et al., 1999; Zhu et al., 2000; Mantzaris and Daoutidis, 2004; Sharifian and Fanaei, 2009) , study aggregation dynamics in suspension cultures (Kolewe et al., 2012) , as well as investigate in vitro cell proliferation patterns (Fadda et al., 2012b; Fadda et al., 2012a) . The deterministic CPB framework just discussed does not include stochasticity in intracellular reaction occurrence. This source of randomness is however important in the spatial scales on the order of a cell, because of the small number of molecules involved in biochemical reactions. To account for such effects, Mantzaris (2007) proposed a computational algorithm that incorporates a Langevin-type description of stochastic reaction dynamics in a CPB. This description is applicable to the mesoscopic scale, in which the copy numbers of reacting molecules is on the order of thousands. More recently, Stamatakis and Zygourakis (2010) formulated a cell population master equation and a computational framework that take into account all the major sources of heterogeneity, including random reaction occurrence and DNA duplication events. Reaction events are treated as discrete point processes, making the framework generally applicable to any scale, including the microscopic one, in which the copy numbers of molecules are low. Stamatakis and Zygourakis (2011) subsequently used this approach to model cell population heterogeneity in an artificial lac operon genetic network.. Though it is desirable to have a comprehensive CPB model that includes all sources of heterogeneity, the computational expense involved in the solution thereof becomes prohibitively high for multi-variable models. To tackle this challenge, methods such as proper orthogonal decomposition have been used by Zhang et al. (2003) to construct reduced nonlinear models that capture the dynamics of full CPB simulations. Moreover, hierarchical two-tier approaches that decompose fast and slow processes have been proposed (Immanuel and Doyle III, 2003; Pinto et al., 2007) . Moving-boundary approaches have also been developed (Kavousanakis et al., 2009) to overcome the issue that the state space boundaries in which the cell population distribution is confined are not known a priori. As an alternative to solving the actual CPB equations, computational methods that employ kinetic Monte Carlo (KMC) simulation have also been developed (Smith and Matsoukas, 1998; Mantzaris, 2006) . Finally, Stamatakis (2010) investigated a set of assumptions that allowed for the transformation of a CPB to hybrid models involving ensemble or continuum equations. These assumptions call for equal partitioning of the contents of the mother cell to the two daughters, an (intensive) reaction rate that does not depend on the volume of the cell, growth and division rates that depend on the volume of the cell but not on the intracellular contents, and a partitioning probability density function in which intensive variables do not appear (in essence the concentrations of the mother and daughter cell species are assumed to be the same). Under vegetative growth of bacterial populations these conditions are expected to hold true to a reasonably good approximation. The transformation of the CPB to hybrid models essentially unifies the aforementioned three modeling approaches (CPB, ensemble and continuum models) and can be the starting point in the development of novel and highly efficient computational methods for the solution of multi-variable CPBs.. In the current work, we compare a CPB and a hybrid model capturing population heterogeneity for a genetic network consisting of a single gene with feedback. We begin our investigations by considering two variants of a 4-species model, the first variant written for extensive quantities (species contents) and the second for intensive quantities (concentrations). We use singular perturbation analysis (SPA) to derive reduced 2-variable systems of equations for the cell volume and intracellular protein levels. We subsequently incorporate the single cell equations into two different cell population frameworks: (i) a 2-variable CPB equation, and (ii) a hybrid model consisting of an ensemble equation and a single-variable CPB. We finally compare the cell population distributions obtained by the two frameworks and analyze the accuracy and efficiency of the hybrid approach. The ever-increasing interest in investigating cell population heterogeneity necessitates the development of novel and efficient approaches for simulating the dynamics of entire cell populations. CPB models are among the most comprehensive and accurate approaches for this task. However, they can quickly become intractable if multiple species participate in several reactions that need to be explicitly taken into account.. In this work, we demonstrated how one can use reduction techniques to efficiently simulate cell population heterogeneity. Thus, we showed how singular perturbation can be used to reduce the dimensionality of a system of equations pertaining to multiple species, thereby deriving a single cell model that faithfully captures the original dynamics. We contrasted this detailed approach with an ad hoc technique which entails setting the temporal derivatives of the fast species to zero. This latter approach was shown to incur large errors in the prediction of the dynamics of the system.. We further showed how a hybrid model can be used to simulate the dynamics of a cell population. This class of models was introduced in previous work (Stamatakis, 2010) , in which it was shown that under certain biologically plausible assumptions, the behavior of the hybrid models are identical to those of the full CPB. This theoretical prediction was confirmed computationally for the single gene with feedback, for which the temporal behavior of the hybrid model was found to be identical to that of the CPB. In addition, by analyzing the error and computational times of the two models, the high accuracy and efficiency of the hybrid model was demonstrated.. Even in the case that the assumptions discussed in (Stamatakis, 2010) do not hold true, hybrid models can still be used to obtain approximate solutions of the full CPB, rather than the exact solution thereof. Such investigations open opportunities for further research on improving the accuracy and expanding the applicability of hybrid models in simulating cell population heterogeneity.

B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B


Generating

attacks

in

SysML

activity

diagrams

by

detecting

attack

surfaces

In

the

development

process

of

a

secure

system

is

essential

to

detect

as

early

as

possible

the

system's

vulnerable

points,

the

so

called

attack

surfaces,

and

to

estimate

how

feasible

it

would

be

that

known

attacks

breach

through

them.

Even

if

attack

surfaces

can

be

sometimes

detected

automatically,

mapping

them

against

known

attacks

still

is

a

step

apart.

Systems

and

attacks

are

not

usually

modelled

in

compatible

formalisms.

We

develop

a

practical

framework

that

automates

the

whole

process.

We

formalize

a

system

as

SysML

activity

diagrams

and

in

the

same

formalism

we

model

libraries

of

patterns

taken

from

standard

catalogues

of

social

engineering

and

technical

attacks.

An

algorithm

that

we

define,

navigates

the

system's

diagrams

in

search

for

its

attack

surfaces;

then

it

evaluates

the

possibility

and

the

probability

that

the

detected

weak

points

host

attacks

among

those

in

the

modelled

library.

We

prove

the

correctness

and

the

completeness

of

our

approach

and

we

show

how

it

works

on

a

use

case

scenario.

It

represents

a

very

common

situation

in

the

domain

of

communication

and

data

security

for

corporations.

There

are

two

distinct

yet

related

challenges

in

the

development

of

secure

software

and

systems.

One

is

about

discovering

vulnerabilities

at

as

early

as

possible

stages

of

the

system

development's

life-cycle;

the

other

is

about

assessing

and

possibly

quantifying

the

degree

of

vulnerability

of

an

existing

system

when

this

is

exposed

to

known

attacks..

A

response

to

the

first

challenge

requires

to

check

whether

a

model

of

the

system

satisfies

a

set

of

relevant

security

properties.

This

check

is

performed

in

the

presence

of

an

attacker,

usually

a

Dolev

Yao

adversary

(Dolev

and

Yao

that

controls

all

the

system's

communication

channels

to

interfere

with

the

system's

functionalities.

This

technique

of

analysis

is

known

as

model

checking

(Clarke

et

al.

It

has

been

successfully

applied

to

discover

insidious

attacks

and

anomalies

for

the

risk

analysis

and

security

assessment

of

the

model-based

systems

(Solhaug

and

Seehusen

However,

although

efficiently

implemented

in

specific

cases

(Clarke

et

al.

model

checking's

worst-case

time

complexity

is

exponential

in

the

size

of

the

system's

and

of

the

property's

models.

Large

systems

may

be

beyond

reach

for

this

type

of

analysis.

The

response

to

the

second

challenge,

instead,

is

more

pragmatic.

Considering

only

documented

attacks-that

is,

patterns

of

actions

known

to

be

used

to

compromise

the

system's

integrity,

availability,

and

confidentiality

(Abrams

)-it

consists

in

estimating

the

system's

degree

of

vulnerability

looking

at

the

system's

attack

surfaces

(Manadhata

and

Wing

An

attack

surface

is

roughly

the

set

of

system's

actions

that

are

accessible

externally

and

the

system's

resources

which

can

be

modified

via

those

actions.

The

more

extensive

the

attack

surface

is,

the

more

vulnerable

the

system

can

be.

This

paper's

work

is

about

this

second

challenge.

It

proposes

a

formal

framework

to

detect

attack

surfaces

automatically

on

systems

modelled

in

SysML

(OMG

SysML

is

a

formalism

and

a

prominent

object-oriented

graphical

language

which

has

become

de

facto

a

standard

in

software

and

systems

modelling.

Assuming

that

a

system

is

modelled

in

SysML

is

therefore

a

pragmatic

choice,

in

order

to

be

compliant

with

the

current

engineering

practices.

SysML

reuses

a

subset

of

UML

packages

(OMG

namely

the

subset

already

extensively

used

in

modelling

large

and

complex

systems,

and

it

extends

it

with

other

features

of

quantitative

nature,

such

as

probability.

These

diagrams

can

call

and

communicate

with

other

diagrams

and

allow

for

probabilistic

behaviour

specification.

Particularly,

SysML's

activity

diagrams,

the

specific

formalism

that

this

work

adopts,

can

express

a

qualitative

and

quantitative

elements

of

a

system's

behaviour

and

at

various

levels

of

abstraction

(Holt

and

Perry

Detecting

attack

surfaces

requires

to

inspect

a

system's

model

and

to

find

out

if

known

attacks

can

reach

the

system's

core

procedures

via

the

system's

exposed

actions.

The

literature

offers

a

variety

of

ways

to

describe

attacks:

attack

tree

(Mauw

and

Oostdijk

attack

graph

(Sawilla

and

Ottawa

and

network

attack

graph

(Sheyner

to

cite

a

few.

Such

models

are

used

by

many

organizations

that

have

a

special

interest

in

collecting,

describing,

and

classifying

attack

patterns.

For

instance,

large

taxonomies

of

comprehensive

samples

of

existing

attacks

have

been

built

by

security

organizations,

such

as

the

common

attack

pattern

enumeration

and

classification

(CAPEC)

a

sponsored

by

the

National

Cyber

Security

Division

of

the

U.S.

Department

of

Homeland

Security,

and

the

web

application

security

consortium

(WASC)

But

existing

techniques

to

model

attacks

are

not

compatible

with

the

techniques

commonly

used

to

model

systems.

These

last

are

not

meant

to

be

used

to

highlight

easily

a

system's

attack

surfaces.

Systems,

on

their

side,

are

not

modelled

to

be

interfaced

with

attack

trees/graphs/

networks.

There

is

so

a

gap

between

the

way

attacks

and

the

way

systems

are

modelled.

Detecting

attack

surfaces

against

attack

models

is

therefore

not

a

process

that

can

be

done

fully

automatically.

This

paper

proposes

a

solution

to

this

problem.

We

model

both

systems

and

attack

patterns

directly

in

SysML,

and

we

design

an

algorithm

that,

by

traversing

a

system

model,

collects

all

the

system's

attack

surfaces

and

links

them

to

the

given

library

of

attack

patterns.

We

consider,

as

a

proof

of

concept,

standard

attacks

among

those

proposed

by

CAPEC,

and

we

show

how

to

model

both

technical

attacks

and

social

engineering

attacks

in

SysML:

the

result

is

a

rich

library

of

socio-technical

attack

templates

in

such

a

formalism.

The

library

can

be

of

course

extended.

This

paper

is

based

on

a

previous

conference

paper

that

the

authors

have

published

(Ouchani

and

Lenzini

However,

it

extends

considerably

that

work:

it

models

social

engineering

attacks,

not

studied

in

Ouchani

and

Lenzini

it

improves

the

algorithm

for

searching

attack

surfaces,

and

proves

the

algorithm's

correctness

and

completeness

with

respect

to

the

library

of

attacks

in

input.

The

whole

presentation

has

also

been

restyled

and

improved.

Finally,

the

current

paper

applies

the

framework

to

a

new

use

case,

more

complex

than

the

one

presented

in

Ouchani

and

Lenzini

and

more

interesting

from

a

sociotechnical

perspective.

It

is,

in

fact,

a

typical

scenario

in

the

domain

of

data

and

communication

security

for

ICT-based

corporations..

Outline

The

remainder

of

this

paper

is

organized

as

follows.

Section

reviews

the

existing

related

work.

Section

describes

and

formalizes

SysML

activity

diagrams;

and

Sect.

presents

the

concept

of

attack

patterns.

The

attack

generation

framework

is

detailed

in

Sect.

The

experimental

results

are

described

in

Sect.

Finally,

Sect.

concludes

this

paper

and

provides

the

future

works.

One

way

to

reduce

the

cost

of

system

and

software

products

is

to

detect

vulnerabilities

to

attacks,

technically

called

attack

surfaces,

at

early

stages

of

the

development

lifecycle.

We

presented

a

framework

to

detect

attack

surfaces

and

the

attacks

that

can

exploit

the

surfaces.

We

developed

a

library

of

system

attacks

that

includes

for

the

first

time

the

social

engineering

aspects.

In

addition,

we

devised

an

algorithm

that

detects

attack

surfaces

of

the

system

and

a

function

that

assigns

for

each

attach

surface

a

set

of

potentially

harmful

attacks.

We

proved

the

correctness

and

the

completeness

of

the

whole

procedure.

We

also

validate

the

effectiveness

and

the

efficiency

of

the

presented

framework

by

applying

it

on

a

real

case

which

is

a

system

of

a

research

group

from

our

institution.

The

results

show

the

potentiality

of

presented

approach..

The

presented

work

can

be

extended

in

the

following

directions.

First,

we

intend

to

apply

our

framework

on

different

real

cases.

Also,

we

would

like

to

achieve

more

complete

catalogue

that

covers

more

type

of

attacks

such

that

related

to

product

chain

and

cyber-physical

systems.

In

addition,

as

a

next

task

is

to

implement

the

proposed

algorithm

and

deliver

a

prototype.

Using

our

framework,

the

library

of

attacks,

and

the

algorithm

presented

we

have

found

the

following

attack

surfaces.

Each

represents

a

possible

attacks

on

the

model

of

the

system.

One

of

such

attack

surface,

ðn;

x;

o;

chÞ,

is

the

following:.

n

¼

fEmail?emailbody;

LoadpageðurlÞg

x

¼

fAuthentication!ðurl;

login

À

passwordÞg

o

¼

femail

À

body;

urlg

ch

¼

fðEmail?emailbody;

emailbodyÞ;

ðLoadpageðurlÞ;

urlÞ;

ðAuthentication!ðurl;

login

À

passwordÞ;

urlÞg:.

Here,

we

have

give

a

name

for

the

objects,

but

they

are

not

represented

in

Fig.

Object

emailbody

is

untrusted

because

that

object

is

the

object

of

an

input

artifact,

and

url

is

untrusted

because

it

depends

on

it:

the

url

is

taken

from

the

body

of

the

email.

Figure

shows

an

instance

of

the

attack

k

which

is

associated

to

n

and

to

x.

An

analysis

of

the

attack

surface,

reveals

that

such

an

attack

is

actually

possible.

Here

how

it

works.

To

prepare

this

attack,

the

intruder

starts

by

doing

a

web

search

(Fig.

top)

He

finds

the

project's

web

page,

and

there

the

members

of

the

project

and

their

names.

He

collects

the

emails

and

information

concerning

the

research

topics.

This

second

step

of

the

attack

is

shown

in

the

Fig.

middle.

Knowing

the

public

web

page,

the

intruder

clones

it,

creates

a

similar

url,

say

and

get

a

self-signed

certificate

for

it.

It

also

creates

a

repository

there.

Then,

it

sends

an

email

to

one

of

the

researcher

with

a

text

that

mirrows

the

text

of

an

usual

mail

with

a

link

to

the

madeup

repository

(Fig.

bottom)

The

intruder

counts

to

phish

those

researchers

that

may

not

be

warned

by

browser's

alerting

them

because

of

selfsigned

certificate

[such

as

mini

Opera,

which

only

change

the

visual

icon

of

the

lock

but

does

not

warns

explicitly

the

users

(Bella

et

al.

The

intruder

aims

at

having

the

researcher

logging

in

into

its

repository.

It

can

engineering

the

document

in

such

a

way

to

appear

corrupt

or

empty,

but

from

this

action

he

can

steal

the

researcher

login-password.

Here,

reasoning

further

from

what

the

attack

surface

shows,

we

imagine

the

intruder

accessing

the

real

repository

and

later

mirroring

the

entire

original

repository

into

its

own

repository

so

to

make

it

look

like

the

real

repository.

In

this

way

he

can

re-iterate

the

attack

and

hope

to

phish

more

researchers

from

the

consortium..

Of

course

this

analysis

just

made

on

the

basis

of

the

given

attack

surface

gives

an

argument

that

the

attack

is

feasible.

It

does

not

necessarily

happen,

or

happen

in

this

way.

As

a

final

remark,

we

note

that,

in

this

argument,

we

avoided

to

talk

about

probabilities.

They

may

be

not

easily

estimable.

In

this

section,

we

survey

the

existing

initiatives

related

to

system

attacks

modelling

and

to

attack

surfaces

detection.

O O O B I I O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O


Sequential Detection of Targets in Multichannel Systems Abstract-It is supposed that there is a multichannel sensor system which performs sequential detection of a target. Sequential detection is done by implementing a generalized Wald's sequential probability ratio test, which is based on the maximum-likelihood ratio statistic and allows one to fix the false-alarm rate and the rate of missed detections at specified levels. We present the asymptotic performance of this sequential detection procedure and show that it is asymptotically optimal in the sense of minimizing the expected sample size when the probabilities of erroneous decisions are small. We do not assume that the observations are independent and identically distributed (i.i.d.). The first-order asymptotic optimality result holds for general statistical models that are not confined to the restrictive i.i.d. assumption. However, for i.i.d. and quasi-i.i.d. cases, where the log-likelihood ratios can be represented in the form of sums of random walks and slowly changing sequences, we obtain much stronger results. Specifically, using the nonlinear renewal theory we are able to obtain both tight expressions for the error probabilities and higher order approximations for the average sample size up to a vanishing term. The performance of the multichannel sequential detection algorithm is illustrated by an example of detection of a deterministic signal in correlated (colored) Gaussian noise. In this example, we provide both the results of theoretical analysis and the results of a Monte Carlo experiment. These results allow us to conclude that the use of the sequential detection algorithm substantially reduces the required resources of the system compared to the best nonsequential algorithm.Index Terms-Asymptotically optimal procedures, generalized sequential probability ratio test, multichannel systems, nonlinear renewal theory, sequential detection.

B I O O O B I O O O O O O O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I I I I B I B I I B I


Secure and Efficient Tree-based Group Diffie-Hellman Protocol Current group key agreement protocols (often tree-based) involve unnecessary delays because members with low-performance computer systems can join group key computation. These delays are caused by the computations needed to balance a key tree after membership changes. An alternate approach to group key generation that reduces delays is the dynamic prioritizing mechanism of filtering low performance members in group key generation. This paper presents an efficient tree-based group key agreement protocol and the results of its performance evaluation. The proposed approach to filtering of low performance members in group key generation is scalable and it requires less computational overhead than conventional tree-based protocols. An Enhanced Tree-based Group Diffie-Hellman (ETGDH) protocol is proposed that improves the existing TGDH, a tree-based group key agreement protocol that is currently the most efficient such protocol. The overall efficiency of TGDH is always determined by the slowest member's performance in generating a group key, because group communication under the TGDH protocol does not start until all members have completed generation of the group key. Under ETGDH, unnecessary delays are avoided, as low performance members are not allowed to participate in group key generation. To evaluate the efficiency, the communication and computation overhead of the proposed protocol (ETGDH) were measured in terms of the total response time of generating a group key, and then compared to existing protocols. Results show that ETGDH, which uses a queue structure to determine high performance members, provides the highest efficiency in terms of the response time of generating a group key, even though it involves a relatively large number of message exchanges. Therefore, the factors which must be considered in order to improve efficiency in group key generation include tree maintenance, diversity of computing power, and heterogeneity of network environments. The results were obtained for a small LAN. However, a future research direction is to determine if the communication costs in larger networks (e.g. wide area networks) are greater than the group key generation costs, and to develop a careful experimental design to accurately assess the overhead of the proposed queue-based protocol. Group key generation is an important issue in group key management. Several attempts to enhance group key generation have been reported [11] . Group key management focuses on minimizing the computational overhead of inherently expensive cryptographic operations [10] . As a result of the complexity of group key generation, group key management needs to adopt a key tree structure in order to reduce group key generation times. Key trees have been suggested for centralized group key distribution systems in order to reduce the complexity of key calculation [12] . One efficient group key generation protocol is TGDH [13] . The TGDH protocol has two necessary preconditions, (a) the key tree is perfectly balanced and (b) all members in the group have equal computing power. If the two preconditions are not satisfied then TGDH cannot perform with an efficiency of O (log n). As mentioned earlier, the members in a distributed computing environment can use any computing machine. Conventional group key agreement protocols require all members to contribute to group key generation. Consequently, it is possible for low performance members to join, which will result in delays. To improve group key generation, (a) low performance members should not participate in generation of the group key and (b) relatively high performance members should be authorized to participate in calculation of the group key. The performance of each member's computing machine is classified as high or low.. This paper proposed an enhanced group key agreement to filter low performance members and achieve maximum efficiency in group key generation.

O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O