Panagiotis Karampelas

Cloud Computing learning

Cloud Computing is evolving as a key technology for sharing resources. Grid Computing, distributed computing, parallel computing and virtualization technologies define the shape of a new era. Traditional distance learning systems lack reusability, portability and interoperability. This paper sees cloud computing ecosystem as a new opportunity in designing cloud computing educational platforms where learning actors can reuse learning resources handled by cloud educational operating systems. To enhance learning objects portability and interoperability not only cloud computing API standards should be advocated by the key cloud providers but also learning resources standards should be defined by the Open Cloud Computing Education Federation as proposed by this paper.

Notice of Violation of IEEE Publication Principles Detecting New Trends in Terrorist Networks

This paper discusses new trends in terrorist networks. We investigate a new case study regarding the recent Denmark terror plan and present analysis of the thwarted plot. Analyzing covert networks after an incident is practically easy for trial purposes. Mapping clandestine networks to thwarted terrorist activities is much more complicated. The network surrounding the recent Denmark terror plan is studied through publicly available information. We are able to map a piece of the network centered on David Headley, who recently confessed to have planned a terrorist attack to take place on Danish soil. The map gives us an insight into the organizations and people involved.

Analyzing very large time series using suffix arrays

Suffix arrays form a powerful data structure for pattern detection and matching. In a previous work, we presented a novel algorithm (COV) which is the only algorithm that allows the detection of all repeated patterns in a time series by using the actual suffix array. However, the requirements for storing the actual suffix strings even on external media makes the use of suffix arrays impossible for very large time series. We have already proved that using the concept of Longest Expected Repeated Pattern (LERP) allows the actual suffices to be stored in linear capacity O(n) on external media. The repeated pattern detection using LERP has analogous time complexity, and thus makes the analysis of large time series feasible and limited only to the size of the external media and not memory. Yet, there are cases when hardware limitations might be an obstacle for the analysis of very larger time series of size comparable to hard disk capacity. With the Moving LERP (MLERP) method introduced in this paper, it is possible to analyze very large time series (of size tens or hundreds thousands times larger than what the LERP can analyze) by maximal utilization of the available hardware. Further, when empirical knowledge related to the distribution of repeated pattern’s length is available, the proposed method (MLERP) can achieve better time performance compared to the standard LERP method and definitely much better than using any other pattern matching algorithm and applying brute force techniques which are unfeasible in logical (human) time frame. Thus, we may argue that MLERP is a very useful tool for detecting all repeated patterns in a time series regardless of its size and hardware limitations.

Modeling of the grounding resistance variation using ARMA models

Abstract This study addresses the problem of modeling the variation of the grounding resistance during the year. An AutoRegressive Moving Average (ARMA) model is fitted (off-line) on the provided actual data using the Corrected Akaike Information Criterion (AICC). The developed model is shown to fit the data in a successful manner. Difficulties occur when the provided data includes noise or errors and also when an on line/adaptive modeling is required. In both cases, and under the assumption that the provided data can be represented by an ARMA model, simultaneous order and parameter estimation of ARMA models under the presence of noise is necessary. In this paper, a new method based on the multi-model partitioning theory which is also applicable to on line/adaptive operation, is used for the solution of the above mentioned problem. The simulations show that the proposed method succeeds in selecting the correct ARMA model order and estimates the parameters accurately in very few steps and even with a small sample size. For validation purposes the method introduced is compared with three other established order selection criteria presenting very good results. The proposed method can be extremely useful in the studies of electrical engineer designers, since the variation of the grounding resistance during the year affects significantly power systems performance and must be definitely considered.

Approximation Formula for Easy Calculation of Signal-to-Noise Ratio of Sigma-Delta Modulators

The signal-to-noise ratio (SNR) is one of the most significant measures of performance of the sigma-delta modulators. An approximate formula for calculation of signal-to-noise ratio of an arbitrary sigma-delta modulator (SDM) has been proposed. Our approach for signal-to-noise ratio computation does not require modulator modeling and simulation. The proposed formula is compared with SNR calculations based on output bitstream obtained by simulations, and the reasons for small discrepancies are explained. The proposed approach is suitable for fast and precise signal-to-noise ratio computation. It is very useful in the modulator design stage, where multiple performance estimates are required.

CCM: A Text Classification Model by Clustering

In this paper, a new Cluster based Classification Model (CCM) for suspicious email detection and other text classification tasks, is presented. Comparative experiments of the proposed model against traditional classification models and the boosting algorithm are also discussed. Experimental results show that the CCM outperforms traditional classification models as well as the boosting algorithm for the task of suspicious email detection on terrorism domain email dataset and topic categorization on the Reuters-21578 and 20 Newsgroups datasets. The overall finding is that applying a cluster based approach to text classification tasks simplifies the model and at the same time increases the accuracy.

Repeated patterns detection in big data using classification and parallelism on LERP Reduced Suffix Arrays

Suffix array is a powerful data structure, used mainly for pattern detection in strings. The main disadvantage of a full suffix array is its quadratic O(n2) space capacity when the actual suffixes are needed. In our previous work [39], we introduced the innovative All Repeated Patterns Detection (ARPaD) algorithm and the Moving Longest Expected Repeated Pattern (MLERP) process. The former detects all repeated patterns in a string using a partition of the full Suffix Array and the latter is capable of analyzing large strings regardless of their size. Furthermore, the notion of Longest Expected Repeated Pattern (LERP), also introduced by the authors in a previous work, significantly reduces to linear O(n) the space capacity needed for the full suffix array. However, so far the LERP value has to be specified in ad hoc manner based on experimental or empirical values. In order to overcome this problem, the Probabilistic Existence of LERP theorem has been proven in this paper and, furthermore, a formula for an accurate upper bound estimation of the LERP value has been introduced using only the length of the string and the size of the alphabet used in constructing the string. The importance of this method is the optimum upper bounding of the LERP value without any previous preprocess or knowledge of string characteristics. Moreover, the new data structure LERP Reduced Suffix Array is defined; it is a variation of the suffix array, and has the advantage of permitting the classification and parallelism to be implemented directly on the data structure. All other alternative methodologies deal with the very common problem of fitting any kind of data structure in a computer memory or disk in order to apply different time efficient methods for pattern detection. The current advanced and elegant proposed methodology allows us to alter the above-mentioned problem such that smaller classes of the problem can be distributed on different systems and then apply current, state-of-the-art, techniques such as parallelism and cloud computing using advanced DBMSs which are capable of handling the storage and analysis of big data. The implementation of the above-described methodology can be achieved by invoking our innovative ARPaD algorithm. Extensive experiments have been conducted on small, comparable strings of Champernowne Constant and DNA as well as on extremely large strings of π with length up to 68 billion digits. Furthermore, the novelty and superiority of our methodology have been also tested on real life application such as a Distributed Denial of Service (DDoS) attack early warning system.

Experimental Analysis on the Normality of Using Advanced Data-Mining Techniques (Experimental Analysis on the Normality of pi, e, phi, and square root of 2 Using Advanced Data-Mining Techniques)

The main focus of the work described in this paper is to examine whether the famous mathematical constants are normal numbers. We have conducted extensive experiments with different attributes for each constant using advanced data-mining techniques, and we have tried to express a theoretical model that can help to determine with high probability whether the numbers are normal in base ten. We have expanded and generalized the experimental results so as to formulate conjectures about the attributes of a normal number, and we have presented conjectures that can lead to determining whether a number is normal. The experimental results and analysis have shown that indeed, satisfy the definition of a normal number. Not only does the distribution of each of the base-10 digits occur with frequency approximately one-tenth, as is known already for very large sequences, but we have also shown for the first time that all arrangements with repetition of digits up to a specific length, depending on the number of decimals examined, occur also with the expected frequencies. As a result, we establish a new process to examine not only whether these constants are just simply normal but normal as well.

Periodicity data mining in time series using Suffix Arrays

This research paper focuses on data mining in time series and its applications on financial data. Data-mining attempts to analyze time series and extract valuable information about pattern periodicity, which might be concealed by substantial amounts of unformatted, random information. Such information, however, is of great importance as it can be used to forecast future behavior. In this paper, a new methodology is introduced aiming to utilize Suffix Arrays in data mining instead of the commonly used data structure Suffix Trees. Although Suffix Arrays, normally, require high storage capacity, the algorithm proposed allows them to be constructed in linear time. The methodology is also extended to detect repeated patterns in time series with time complexity of. This, combined with the capability of external storage, creates a critical advantage, for an overall efficient data mining and analysis regarding the construction of time series data structure and periodicity detection. The test results, presented below demonstrate the applicability and effectiveness of the proposed technique.

Finding Hidden Links in Terrorist Networks by Checking Indirect Links of Different Sub-Networks

Modeling and analyzing criminal and terrorists networks is a challenging problem that has attracted considerable attention in the academia, industry and government institutions, especially intelligence services. Criminals try to keep their communications and interactions uncovered as much as possible in order not to be discovered and resolved. Their success is our society failure and vice versa. Hence, it is essential to thoroughly study such networks to investigate their details. However, incompleteness of criminal networks is one of the main problems facing investigators, due to missing links in the network; and social network methods could be effectively used to analyze and hopefully prevent, avoid or stop criminal activities. Social network analysis can be applied to criminal networks in order to elaborate on good strategies to prosecute or prevent criminal activities. Having all this in mind, our research provides a method to identify hidden links between nodes in a network using the current information available to investigators. The method presented generates networks that represent all the possible hidden links, and the links of these generated networks represent the number of times the two entities are indirectly connected in each relationship type. The method was tested on multiple small terrorism data sets and the results demonstrate that the proposed method is capable of finding interesting hidden links. This is a valuable technique in criminal network analysis, because it can assist investigators in finding hidden links in the network and reduce the amount of missing data.