Generalized Adaptive Network Coded Cooperation (GANCC): A Unified Framework for Network Coding and Channel Coding

Abstract: This paper considers distributed coding for multi-source single-sink data collection wireless networks. A unified framework for network coding and channel coding, termed "generalized adaptive network coded cooperation" (GANCC), is proposed. Key ingredients of GANCC include: matching code graphs with the dynamic network graphs on-the-fly, and integrating channel coding with network coding through circulant low-density parity-check codes. Several code constructing methods and several families of sparse-graph codes are proposed, and information theoretical analysis is performed. It is shown that GANCC is simple to operate, adaptive in real time, distributed in nature, and capable of providing remarkable coding gains even with a very limited number of cooperating users.


A PCP Characterization of AM

Abstract: We introduce a 2-round stochastic constraint-satisfaction problem, and show that its approximation version is complete for (the promise version of) the complexity class AM. This gives a `PCP characterization' of AM analogous to the PCP Theorem for NP. Similar characterizations have been given for higher levels of the Polynomial Hierarchy, and for PSPACE; however, we suggest that the result for AM might be of particular significance for attempts to derandomize this class. To test this notion, we pose some `Randomized Optimization Hypotheses' related to our stochastic CSPs that (in light of our result) would imply collapse results for AM. Unfortunately, the hypotheses appear over-strong, and we present evidence against them. In the process we show that, if some language in NP is hard-on-average against circuits of size 2^{Omega(n)}, then there exist hard-on-average optimization problems of a particularly elegant form. All our proofs use a powerful form of PCPs known as Probabilistically Checkable Proofs of Proximity, and demonstrate their versatility. We also use known results on randomness-efficient soundness- and hardness-amplification. In particular, we make essential use of the Impagliazzo-Wigderson generator; our analysis relies on a recent Chernoff-type theorem for expander walks.


Privacy-Preserving Protocols for Eigenvector Computation

Abstract: In this paper, we present a protocol for computing the principal eigenvector of a collection of data matrices belonging to multiple semi-honest parties with privacy constraints. Our proposed protocol is based on secure multi-party computation with a semi-honest arbitrator who deals with data encrypted by the other parties using an additive homomorphic cryptosystem. We augment the protocol with randomization and obfuscation to make it difficult for any party to estimate properties of the data belonging to other parties from the intermediate steps. The previous approaches towards this problem were based on expensive QR decomposition of correlation matrices, we present an efficient algorithm using the power iteration method. We analyze the protocol for correctness, security, and efficiency.


Theory of Regulatory Compliance for Requirements Engineering

Abstract: Regulatory compliance is increasingly being addressed in the practice of requirements engineering as a main stream concern. This paper points out a gap in the theoretical foundations of regulatory compliance, and presents a theory that states (i) what it means for requirements to be compliant, (ii) the compliance problem, i.e., the problem that the engineer should resolve in order to verify whether requirements are compliant, and (iii) testable hypotheses (predictions) about how compliance of requirements is verified. The theory is instantiated by presenting a requirements engineering framework that implements its principles, and is exemplified on a real-world case study.


Fast Flooding over Manhattan

Abstract: We consider a Mobile Ad-hoc NETwork (MANET) formed by n agents that move at speed V according to the Manhattan Random-Way Point model over a square region of side length L. The resulting stationary (agent) spatial probability distribution is far to be uniform: the average density over the "central zone" is asymptotically higher than that over the "suburb". Agents exchange data iff they are at distance at most R within each other. We study the flooding time of this MANET: the number of time steps required to broadcast a message from one source agent to all agents of the network in the stationary phase. We prove the first asymptotical upper bound on the flooding time. This bound holds with high probability, it is a decreasing function of R and V, and it is tight for a wide and relevant range of the network parameters (i.e. L, R and V). A consequence of our result is that flooding over the sparse and highly-disconnected suburb can be as fast as flooding over the dense and connected central zone. Rather surprisingly, this property holds even when R is exponentially below the connectivity threshold of the MANET and the speed V is very low.


Improved bounds for stochastic matching

Abstract: This results in this paper have been merged with the result in arXiv:1003.0167. The authors would like to withdraw this version. Please see arXiv:1008.5356 for the merged version.


Polyominoes Simulating Arbitrary-Neighborhood Zippers and Tilings

Abstract: This paper provides a bridge between the classical tiling theory and the complex neighborhood self-assembling situations that exist in practice. The neighborhood of a position in the plane is the set of coordinates which are considered adjacent to it. This includes classical neighborhoods of size four, as well as arbitrarily complex neighborhoods. A generalized tile system consists of a set of tiles, a neighborhood, and a relation which dictates which are the "admissible" neighboring tiles of a given tile. Thus, in correctly formed assemblies, tiles are assigned positions of the plane in accordance to this relation. We prove that any validly tiled path defined in a given but arbitrary neighborhood (a zipper) can be simulated by a simple "ribbon" of microtiles. A ribbon is a special kind of polyomino, consisting of a non-self-crossing sequence of tiles on the plane, in which successive tiles stick along their adjacent edge. Finally, we extend this construction to the case of traditional tilings, proving that we can simulate arbitrary-neighborhood tilings by simple-neighborhood tilings, while preserving some of their essential properties.


Extended Range Telepresence for Evacuation Training in Pedestrian Simulations

Abstract: In this contribution, we propose a new framework to evaluate pedestrian simula-tions by using Extended Range Telepresence. Telepresence is used as a virtual reality walking simulator, which provides the user with a realistic impression of being present and walking in a virtual environment that is much larger than the real physical environment, in which the user actually walks. The validation of the simulation is performed by comparing motion data of the telepresent user with simulated data at some points of the simulation. The use of haptic feedback from the simulation makes the framework suitable for training in emergency situations.


The lattice of embedded subsets

Abstract: In cooperative game theory, games in partition function form are real-valued function on the set of so-called embedded coalitions, that is, pairs $(S,\pi)$ where $S$ is a subset (coalition) of the set $N$ of players, and $\pi$ is a partition of $N$ containing $S$. Despite the fact that many studies have been devoted to such games, surprisingly nobody clearly defined a structure (i.e., an order) on embedded coalitions, resulting in scattered and divergent works, lacking unification and proper analysis. The aim of the paper is to fill this gap, thus to study the structure of embedded coalitions (called here embedded subsets), and the properties of games in partition function form.


Limits of Approximation Algorithms: PCPs and Unique Games (DIMACS Tutorial Lecture Notes)

Abstract: These are the lecture notes for the DIMACS Tutorial "Limits of Approximation Algorithms: PCPs and Unique Games" held at the DIMACS Center, CoRE Building, Rutgers University on 20-21 July, 2009. This tutorial was jointly sponsored by the DIMACS Special Focus on Hardness of Approximation, the DIMACS Special Focus on Algorithmic Foundations of the Internet, and the Center for Computational Intractability with support from the National Security Agency and the National Science Foundation. The speakers at the tutorial were Matthew Andrews, Sanjeev Arora, Moses Charikar, Prahladh Harsha, Subhash Khot, Dana Moshkovitz and Lisa Zhang. The sribes were Ashkan Aazami, Dev Desai, Igor Gorodezky, Geetha Jagannathan, Alexander S. Kulikov, Darakhshan J. Mir, Alantha Newman, Aleksandar Nikolov, David Pritchard and Gwen Spencer.


The power of randomness in Bayesian optimal mechanism design

Abstract: We investigate the power of randomness in the context of a fundamental Bayesian optimal mechanism design problem--a single seller aims to maximize expected revenue by allocating multiple kinds of resources to "unit-demand" agents with preferences drawn from a known distribution. When the agents' preferences are single-dimensional Myerson's seminal work [Myerson '81] shows that randomness offers no benefit--the optimal mechanism is always deterministic. In the multi-dimensional case, where each agent's preferences are given by different values for each of the available services, Briest et al. [Briest, Chawla, Kleinberg, and Weinberg '10] recently showed that the gap between the expected revenue obtained by an optimal randomized mechanism and an optimal deterministic mechanism can be unbounded even when a single agent is offered only 4 services. However, this large gap is attained through unnatural instances where values of the agent for different services are correlated in a specific way. We show that when the agent's values involve no correlation or a specific kind of positive correlation, the benefit of randomness is only a small constant factor (4 and 8 respectively). Our model of positively correlated values (that we call additive values) is a natural model for unit-demand agents and items that are substitutes. Our results extend to multiple agent settings as well.


Partitionability to two trees is NP-complete

Abstract: We show that P2T - the problem of deciding whether the edge set of a simple graph can be partitioned into two trees or not - is NP-complete.


Effect of Embedding Watermark on Compression of the Digital Images

Abstract: Image Compression plays a very important role in image processing especially when we are to send the image on the internet. The threat to the information on the internet increases and image is no exception. Generally the image is sent on the internet as the compressed image to optimally use the bandwidth of the network. But as we are on the network, at any intermediate level the image can be changed intentionally or unintentionally. To make sure that the correct image is being delivered at the other end we embed the water mark to the image. The watermarked image is then compressed and sent on the network. When the image is decompressed at the other end we can extract the watermark and make sure that the image is the same that was sent by the other end. Though watermarking the image increases the size of the uncompressed image but that has to done to achieve the high degree of robustness i.e. how an image sustains the attacks on it. The present paper is an attempt to make transmission of the images secure from the intermediate attacks by applying the generally used compression transforms.


Supervised Learning of Digital image restoration based on Quantization Nearest Neighbor algorithm

Abstract: In this paper, an algorithm is proposed for Image Restoration. Such algorithm is different from the traditional approaches in this area, by utilizing priors that are learned from similar images. Original images and their degraded versions by the known degradation operators are utilized for designing the Quantization. The code vectors are designed using the blurred images. For each such vector, the high frequency information obtained from the original images is also available. During restoration, the high frequency information of a given degraded image is estimated from its low frequency information based on the artificial noise. For the restoration problem, a number of techniques are designed corresponding to various versions of the blurring function. Given a noisy and blurred image, one of the techniques is chosen based on a similarity measure, therefore providing the identification of the blur. To make the restoration process computationally efficient, the Quantization Nearest Neighborhood approaches are utilized.


Performance analysis of Zone Routing Protocol in respect of Genetic Algorithm and Estimation of Distribution Algorithm

Abstract: In this paper, Estimation of Distribution Algorithm (EDA) is used for Zone Routing Protocol (ZRP) in Mobile Ad-hoc Network instead of Genetic Algorithm (GA). It is an evolutionary approach, it is used when the network size grows and the search space increases. When the destination is outside the zone, EDA is applied to find the route with minimum cost and time. Finally, the implementation of proposed method is compared with Genetic ZRP, i.e., GZRP and the result demonstrates better performance for the proposed method. Since the method provides a set of paths to the destination, it results in load balance to the network. As both EDA and GA use random search method to reach the optimal point, the searching cost reduced significantly, especially when the number of data is large.


The government of state's power bodies by means of the Internet

Abstract: The electronic government involves developing the informational society, which refers to an economy and a society in which the access, acquisition, memorizing, taking, transmitting, spreading and using the knowledge accede to a decisive role. The informational society involves changes in the domains of administration (e-Government), business (electronic commerce and e-business), education (long distance education), culture (multimedia centers and virtual libraries), mass- media (TV, video advertising panels), and in the labor manner (tele-work and virtual commuting).The e-government refers to the interaction between the Government, Parliament and other public institutions with the citizens by the electronic means.


Optimized reversible BCD adder using new reversible logic gates

Abstract: Reversible logic has received great attention in the recent years due to their ability to reduce the power dissipation which is the main requirement in low power digital design. It has wide applications advanced computing, low power CMOS design, Optical information processing, DNA computing, bio information, quantum computation and nanotechnology. This paper presents an optimized reversible BCD adder using a new reversible gate. A comparative result is presented which shows that the proposed design is more optimized in terms of number of gates, number of garbage outputs and quantum cost than the existing designs.


Determining the quality evaluation procedures using the expert systems

Abstract: At this time, quality is a strategic instrument of the entities' global management, but it is also a determining element of their competitive spirit. The importance given to quality is abundantly found in the preoccupations of the European Union's Minister Board, by elaborating documents with a high impact over the quality of products/ services in special, and organizations in general. We live in an era, when the evolution of the social life puts the accent more and more on quality, resulted from various processes, at the level of various domains of the economical and social development.


Improvement in RUP Project Management via Service Monitoring: Best Practice of SOA

Abstract: Management of project planning, monitoring, scheduling, estimation and risk management are critical issues faced by a project manager during development life cycle of software. In RUP, project management is considered as core discipline whose activities are carried in all phases during development of software products. On other side service monitoring is considered as best practice of SOA which leads to availability, auditing, debugging and tracing process. In this paper, authors define a strategy to incorporate the service monitoring of SOA into RUP to improve the artifacts of project management activities. Moreover, the authors define the rules to implement the features of service monitoring, which help the project manager to carry on activities in well define manner. Proposed frame work is implemented on RB (Resuming Bank) application and obtained improved results on PM (Project Management) work.


The Role of the XBRL Standard in Optimizing the Financial Reporting

Abstract: When the financial information is difficult to produce, interpret, compare and analyze, we are put in the situation to face inconvenient consequences with negative repercussions, such as: the investor can give up the investment (with negative consequences on the risk equity market), the banks may not give loans, an auditor may not consider the financial statements as being credible etc. These facts allow the introduction of this paper's main objective, the eXtensible Business Reporting Language (XBRL) which is an open standard, independent and international for the treatments, opportunity, correctness, efficiency and minor costs of the financial and economical information. The XBRL will be analyzed in the second part of the paper, the history of this electronic communication language will be described, as there will also be described the promoting organizations, the base technology (the WEB and XML architecture which will be the next stage of the internet programming), and the role it has within the chain of reporting between the XBRL consortium and the international accounting organizations IASB-CI. This taxonomy serves clearly every accounting and extra- accounting information made by the company. This information which is treated in present by resorting to various formats or structures (most times incompatible between them and the owners) will be standardized with the XBRL.


E-Courseware Design and Implementation Issues and Strategies

Abstract: Over the last few years electronic learning has been in use mostly by corporate institutes in the form of computer aided instructions and computer based training. The scope of such use has not only been limited to introductory courses for beginners and working people but also to impart knowledge in higher education sector. Due to increasing market demands and current prevailing law and order situation of this area (during which the University remain closed for uncertain period of time on many occasions) Gomal University D.I.Khan, Pakistan is planning to introduce e-learning at undergraduate and post graduate level in computer and management sciences for smooth and uninterrupted delivery of quality education to local and distant students. Obvious result of elearning will be two fold. First it will meet market demands along with smooth uninterrupted delivery of quality education and secondly will solve the growing problem of shortage of experts raised by the current law and order situation. This paper investigates the main issues involved in designing and implementing an effective electronic courseware for students with diverse backgrounds belonging to this remote area. Some effective strategies for electronic delivery of courses to local and distant students are also presented along with some examples of implementation.


FPGA Implementation of LS Code Generator for CDM Based MIMO Channel Sounder

Abstract: MIMO (Multi Input Multi Output) wireless communication system is an innovative solution to improve the bandwidth efficiency by exploiting multipath-richness of the propagation environment. The degree of multipath-richness of the channel will determine the capacity gain attainable by MIMO deployment. Therefore, it is very important to have accurate knowledge of the propagation environment/radio channel before MIMO implement. The radio channel behavior can be estimated by channel measurement or channel sounding. CDM (Code Division multiplexing) is one of the channel sounding techniques that allow accurate measurement at the cost of hardware complexity. CDM based channel sounder, requires code with excellent autocorrelation and cross-correlation properties which generally difficult to achieve simultaneously. Theoretical analysis and computer simulation result demonstrated that, having excellent correlation propertied Loosely Synchronous (LS) code sequence perform efficiently. Finally, the an efficient LS code generator as a data source for transmitter implemented in Xilinx FPGA that can be integrated into CDM based 2x2 MIMO complete channel sounder.


A Modified ck-Secure Sum Protocol for Multi-Party Computation

Abstract: Secure Multi-Party Computation (SMC) allows multiple parties to compute some function of their inputs without disclosing the actual inputs to one another. Secure sum computation is an easily understood example and the component of the various SMC solutions. Secure sum computation allows parties to compute the sum of their individual inputs without disclosing the inputs to one another. In this paper, we propose a modified version of our ck-Secure Sum protocol with more security when a group of the computing parties conspire to know the data of some party.


Multi-Objective Geometric Programming Problem Being Cost Coefficients as Continous Function with Weighted Mean Method

Abstract: Geometric programming problems occur frequently in engineering design and management. In multiobjective optimization, the trade-off information between different objective functions is probably the most important piece of information in a solution process to reach the most preferred solution. In this paper we have discussed the basic concepts and principles of multiple objective optimization problems and developed a solution procedure to solve this optimization problem where the cost coefficients are continuous functions using weighted method to obtain the non-inferior solutions.


A Cluster-based Approach for Outlier Detection in Dynamic Data Streams (KORM: k-median OutlieR Miner)

Abstract: Outlier detection in data streams has gained wide importance presently due to the increasing cases of fraud in various applications of data streams. The techniques for outlier detection have been divided into either statistics based, distance based, density based or deviation based. Till now, most of the work in the field of fraud detection was distance based but it is incompetent from computational point of view. In this paper we introduced a new clustering based approach, which divides the stream in chunks and clusters each chunk using kmedian into variable number of clusters. Instead of storing complete data stream chunk in memory, we replace it with the weighted medians found after mining a data stream chunk and pass that information along with the newly arrived data chunk to the next phase. The weighted medians found in each phase are tested for outlierness and after a given number of phases, it is either declared as a real outlier or an inlier. Our technique is theoretically better than the k-means as it does not fix the number of clusters to k rather gives a range to it and provides a more stable and better solution which runs in poly-logarithmic space.


Nature inspired artificial intelligence based adaptive traffic flow distribution in computer network

Abstract: Because of the stochastic nature of traffic requirement matrix, it is very difficult to get the optimal traffic distribution to minimize the delay even with adaptive routing protocol in a fixed connection network where capacity already defined for each link. Hence there is a requirement to define such a method, which could generate the optimal solution very quickly and efficiently. This paper presenting a new concept to provide the adaptive optimal traffic distribution for dynamic condition of traffic matrix using nature based intelligence methods. With the defined load and fixed capacity of links, average delay for packet has minimized with various variations of evolutionary programming and particle swarm optimization. Comparative study has given over their performance in terms of converging speed. Universal approximation capability, the key feature of feed forward neural network has applied to predict the flow distribution on each link to minimize the average delay for a total load available at present on the network. For any variation in the total load, the new flow distribution can be generated by neural network immediately, which could generate minimum delay in the network. With the inclusion of this information, performance of routing protocol will be improved very much.


Improved NSGA-II Based on a Novel Ranking Scheme

Abstract: Non-dominated Sorting Genetic Algorithm (NSGA) has established itself as a benchmark algorithm for Multiobjective Optimization. The determination of pareto-optimal solutions is the key to its success. However the basic algorithm suffers from a high order of complexity, which renders it less useful for practical applications. Among the variants of NSGA, several attempts have been made to reduce the complexity. Though successful in reducing the runtime complexity, there is scope for further improvements, especially considering that the populations involved are frequently of large size. We propose a variant which reduces the run-time complexity using the simple principle of space-time trade-off. The improved algorithm is applied to the problem of classifying types of leukemia based on microarray data. Results of comparative tests are presented showing that the improved algorithm performs well on large populations.


Text/Graphics Separation and Skew Correction of Text Regions of Business Card Images for Mobile Devices

Abstract: Separation of the text regions from background texture and graphics is an important step of any optical character recognition system for the images containing both texts and graphics. In this paper, we have presented a novel text/graphics separation technique and a method for skew correction of text regions extracted from business card images captured with a cell-phone camera. At first, the background is eliminated at a coarse level based on intensity variance. This makes the foreground components distinct from each other. Then the non-text components are removed using various characteristic features of text and graphics. Finally, the text regions are skew corrected for further processing. Experimenting with business card images of various resolutions, we have found an optimum performance of 98.25% (recall) with 0.75 MP images, that takes 0.17 seconds processing time and 1.1 MB peak memory on a moderately powerful computer (DualCore 1.73 GHz Processor, 1 GB RAM, 1 MB L2 Cache). The developed technique is computationally efficient and consumes low memory so as to be applicable on mobile devices.


Word level Script Identification from Bangla and Devanagri Handwritten Texts mixed with Roman Script

Abstract: India is a multi-lingual country where Roman script is often used alongside different Indic scripts in a text document. To develop a script specific handwritten Optical Character Recognition (OCR) system, it is therefore necessary to identify the scripts of handwritten text correctly. In this paper, we present a system, which automatically separates the scripts of handwritten words from a document, written in Bangla or Devanagri mixed with Roman scripts. In this script separation technique, we first, extract the text lines and words from document pages using a script independent Neighboring Component Analysis technique. Then we have designed a Multi Layer Perceptron (MLP) based classifier for script separation, trained with 8 different wordlevel holistic features. Two equal sized datasets, one with Bangla and Roman scripts and the other with Devanagri and Roman scripts, are prepared for the system evaluation. On respective independent text samples, word-level script identification accuracies of 99.29% and 98.43% are achieved.


On constant factor approximation for earth mover distance over doubling metrics

Abstract: Given a metric space $(X,d_X)$, the earth mover distance between two distributions over $X$ is defined as the minimum cost of a bipartite matching between the two distributions. The doubling dimension of a metric $(X, d_X)$ is the smallest value $\alpha$ such that every ball in $X$ can be covered by $2^\alpha$ ball of half the radius. We study efficient algorithms for approximating earth mover distance over metrics with bounded doubling dimension. Given a metric $(X, d_X)$, with $|X| = n$, we can use $\tilde O(n^2)$ preprocessing time to create a data structure of size $\tilde O(n^{1 + \e})$, such that subsequently queried EMDs can be $O(\alpha_X/\e)$-approximated in $\tilde O(n)$ time. We also show a weaker form of sketching scheme, which we call "encoding scheme". Given $(X, d_X)$, by using $\tilde O(n^2)$ preprocessing time, every subsequent distribution $\mu$ over $X$ can be encoded into $F(\mu)$ in $\tilde O(n^{1 + \e})$ time. Given $F(\mu)$ and $F(\nu)$, the EMD between $\mu$ and $\nu$ can be $O(\alpha_X/\e)$-approximated in $\tilde O(n^\e)$ time.


Handwritten Bangla Basic and Compound character recognition using MLP and SVM classifier

Abstract: A novel approach for recognition of handwritten compound Bangla characters, along with the Basic characters of Bangla alphabet, is presented here. Compared to English like Roman script, one of the major stumbling blocks in Optical Character Recognition (OCR) of handwritten Bangla script is the large number of complex shaped character classes of Bangla alphabet. In addition to 50 basic character classes, there are nearly 160 complex shaped compound character classes in Bangla alphabet. Dealing with such a large varieties of handwritten characters with a suitably designed feature set is a challenging problem. Uncertainty and imprecision are inherent in handwritten script. Moreover, such a large varieties of complex shaped characters, some of which have close resemblance, makes the problem of OCR of handwritten Bangla characters more difficult. Considering the complexity of the problem, the present approach makes an attempt to identify compound character classes from most frequently to less frequently occurred ones, i.e., in order of importance. This is to develop a frame work for incrementally increasing the number of learned classes of compound characters from more frequently occurred ones to less frequently occurred ones along with Basic characters. On experimentation, the technique is observed produce an average recognition rate of 79.25 after three fold cross validation of data with future scope of improvement and extension.


Improving Term Extraction Using Particle Swarm Optimization Techniques

Abstract: Term extraction is one of the layers in the ontology development process which has the task to extract all the terms contained in the input document automatically. The purpose of this process is to generate list of terms that are relevant to the domain of the input document. In the literature there are many approaches, techniques and algorithms used for term extraction. In this paper we propose a new approach using particle swarm optimization techniques in order to improve the accuracy of term extraction results. We choose five features to represent the term score. The approach has been applied to the domain of religious document. We compare our term extraction method precision with TFIDF, Weirdness, GlossaryExtraction and TermExtractor. The experimental results show that our propose approach achieve better precision than those four algorithm.


Equal Power Distribution and Dynamic Subcarrier Assignment in OFDM Using Minimum Channel Gain Flow with Robust Optimization Uncertain Demand

Abstract: In this paper, the minimum channel gain flow with uncertainty in the demand vector is examined. The approach is based on a transformation of uncertainty in the demand vector to uncertainty in the gain vector. OFDM systems are known to overcome the impairment of the wireless channel by splitting the given system bandwidth into parallel sub-carriers, on which data-symbols can be transmitted simultaneously. This enables the possibility of enhancing the system's performance by deploying adaptive mechanisms, namely power distribution and dynamic sub-carrier assignments. The performances of maximizing the minimum throughput have been analyzed by MATLAB codes.


Supervised Classification Performance of Multispectral Images

Abstract: Nowadays government and private agencies use remote sensing imagery for a wide range of applications from military applications to farm development. The images may be a panchromatic, multispectral, hyperspectral or even ultraspectral of terra bytes. Remote sensing image classification is one amongst the most significant application worlds for remote sensing. A few number of image classification algorithms have proved good precision in classifying remote sensing data. But, of late, due to the increasing spatiotemporal dimensions of the remote sensing data, traditional classification algorithms have exposed weaknesses necessitating further research in the field of remote sensing image classification. So an efficient classifier is needed to classify the remote sensing images to extract information. We are experimenting with both supervised and unsupervised classification. Here we compare the different classification methods and their performances. It is found that Mahalanobis classifier performed the best in our classification.


Intrusion Detection System: Overview

Abstract: Network Intrusion Detection (NID) is the process of identifying network activity that can lead to the compromise of a security policy. In this paper, we will look at four intrusion detection approaches, which include ANN or Artificial Neural Network, SOM, Fuzzy Logic and SVM. ANN is one of the oldest systems that have been used for Intrusion Detection System (IDS), which presents supervised learning methods. However, in this research, we also came across SOM or Self Organizing Map, which is an ANN-based system, but applies unsupervised methods. Another approach is Fuzzy Logic (IDS-based), which also applies unsupervised learning methods. Lastly, we will look at the SVM system or Support Vector Machine for IDS. The goal of this paper is to draw an image for hybrid approaches using these supervised and unsupervised methods.


A Hough Transform based Technique for Text Segmentation

Abstract: Text segmentation is an inherent part of an OCR system irrespective of the domain of application of it. The OCR system contains a segmentation module where the text lines, words and ultimately the characters must be segmented properly for its successful recognition. The present work implements a Hough transform based technique for line and word segmentation from digitized images. The proposed technique is applied not only on the document image dataset but also on dataset for business card reader system and license plate recognition system. For standardization of the performance of the system the technique is also applied on public domain dataset published in the website by CMATER, Jadavpur University. The document images consist of multi-script printed and hand written text lines with variety in script and line spacing in single document image. The technique performs quite satisfactorily when applied on mobile camera captured business card images with low resolution. The usefulness of the technique is verified by applying it in a commercial project for localization of license plate of vehicles from surveillance camera images by the process of segmentation itself. The accuracy of the technique for word segmentation, as verified experimentally, is 85.7% for document images, 94.6% for business card images and 88% for surveillance camera images.


Optimization Digital Image Watermarking Technique for Patent Protection

Abstract: The rapid development of multimedia and internet allows for wide distribution of digital media data. It becomes much easier to edit, modify and duplicate digital information besides that, digital documents are also easy to copy and distribute, therefore it will be faced by many threats. It is a big security and privacy issue. Another problem with digital document and video is that undetectable modifications can be made with very simple and widely available equipment, which put the digital material for evidential purposes under question With the large flood of information and the development of the digital format, it become necessary to find appropriate protection because of the significance, accuracy and sensitivity of the information, therefore multimedia technology and popularity of internet communications they have great interest in using digital watermarks for the purpose of copy protection and content authentication. Digital watermarking is a technique used to embed a known piece of digital data within another piece of digital data .A digital data may represent a digital signature or digital watermark that is embedded in the host media. The signature or watermark is hidden such that it's perceptually and statistically undetectable. Then this signature or watermark can be extracted from the host media and used to identify the owner of the media.


Towards an Efficient Tile Matrix Inversion of Symmetric Positive Definite Matrices on Multicore Architectures

Abstract: The algorithms in the current sequential numerical linear algebra libraries (e.g. LAPACK) do not parallelize well on multicore architectures. A new family of algorithms, the tile algorithms, has recently been introduced. Previous research has shown that it is possible to write efficient and scalable tile algorithms for performing a Cholesky factorization, a (pseudo) LU factorization, and a QR factorization. In this extended abstract, we attack the problem of the computation of the inverse of a symmetric positive definite matrix. We observe that, using a dynamic task scheduler, it is relatively painless to translate existing LAPACK code to obtain a ready-to-be-executed tile algorithm. However we demonstrate that non trivial compiler techniques (array renaming, loop reversal and pipelining) need then to be applied to further increase the parallelism of our application. We present preliminary experimental results.


Contextual Bandit Algorithms with Supervised Learning Guarantees

Abstract: We address the problem of learning in an online, bandit setting where the learner must repeatedly select among $K$ actions, but only receives partial feedback based on its choices. We establish two new facts: First, using a new algorithm called Exp4.P, we show that it is possible to compete with the best in a set of $N$ experts with probability $1-\delta$ while incurring regret at most $O(\sqrt{KT\ln(N/\delta)})$ over $T$ time steps. The new algorithm is tested empirically in a large-scale, real-world dataset. Second, we give a new algorithm called VE that competes with a possibly infinite set of policies of VC-dimension $d$ while incurring regret at most $O(\sqrt{T(d\ln(T) + \ln (1/\delta))})$ with probability $1-\delta$. These guarantees improve on those of all previous algorithms, whether in a stochastic or adversarial environment, and bring us closer to providing supervised learning type guarantees for the contextual bandit setting.


Properties of Pseudo-Primitive Words and their Applications

Abstract: A pseudo-primitive word with respect to an antimorphic involution \theta is a word which cannot be written as a catenation of occurrences of a strictly shorter word t and \theta(t). Properties of pseudo-primitive words are investigated in this paper. These properties link pseudo-primitive words with essential notions in combinatorics on words such as primitive words, (pseudo)-palindromes, and (pseudo)-commutativity. Their applications include an improved solution to the extended Lyndon-Sch\"utzenberger equation u_1 u_2 ... u_l = v_1 ... v_n w_1 ... w_m, where u_1, ..., u_l \in {u, \theta(u)}, v_1, ..., v_n \in {v, \theta(v)}, and w_1, ..., w_m \in {w, \theata(w)} for some words u, v, w, integers l, n, m \ge 2, and an antimorphic involution \theta. We prove that for l \ge 4, n,m \ge 3, this equation implies that u, v, w can be expressed in terms of a common word t and its image \theta(t). Moreover, several cases of this equation where l = 3 are examined.


Optimal Control Strategies in Delayed Sharing Information Structures

Abstract: The $n$-step delayed sharing information structure is investigated. This information structure comprises of $K$ controllers that share their information with a delay of $n$ time steps. This information structure is a link between the classical information structure, where information is shared perfectly between the controllers, and a non-classical information structure, where there is no "lateral" sharing of information among the controllers. Structural results for optimal control strategies for systems with such information structures are presented. A sequential methodology for finding the optimal strategies is also derived. The solution approach provides an insight for identifying structural results and sequential decomposition for general decentralized stochastic control problems.


Design of a Smart Unmanned Ground Vehicle for Hazardous Environments

Abstract: A smart Unmanned Ground Vehicle (UGV) is designed and developed for some application specific missions to operate predominantly in hazardous environments. In our work, we have developed a small and lightweight vehicle to operate in general cross-country terrains in or without daylight. The UGV can send visual feedbacks to the operator at a remote location. Onboard infrared sensors can detect the obstacles around the UGV and sends signals to the operator.


Window-Based Greedy Contention Management for Transactional Memory

Abstract: We consider greedy contention managers for transactional memory for M x N execution windows of transactions with M threads and N transactions per thread. Assuming that each transaction conflicts with at most C other transactions inside the window, a trivial greedy contention manager can schedule them within CN time. In this paper, we show that there are much better schedules. We present and analyze two new randomized greedy contention management algorithms. The first algorithm Offline-Greedy produces a schedule of length O(C + N log(MN)) with high probability, and gives competitive ratio O(log(MN)) for C <= N log(MN). The offline algorithm depends on knowing the conflict graph. The second algorithm Online-Greedy produces a schedule of length O(C log(MN) + N log^2(MN)) with high probability which is only a O(log(NM)) factor worse, but does not require knowledge of the conflict graph. We also give an adaptive version which achieves similar worst-case performance and C is determined on the fly under execution. Our algorithms provide new tradeoffs for greedy transaction scheduling that parameterize window sizes and transaction conflicts within the window.


Mergeable Dictionaries

Abstract: A data structure is presented for the Mergeable Dictionary abstract data type, which supports the following operations on a collection of disjoint sets of totally ordered data: Predecessor-Search, Split and Merge. While Predecessor-Search and Split work in the normal way, the novel operation is Merge. While in a typical mergeable dictionary (e.g. 2-4 Trees), the Merge operation can only be performed on sets that span disjoint intervals in keyspace, the structure here has no such limitation, and permits the merging of arbitrarily interleaved sets. Tarjan and Brown present a data structure which can handle arbitrary Merge operations in O(log n) amortized time per operation if the set of operations is restricted to exclude the Split operation. In the presence of Split operations, the amortized time complexity of their structure becomes \Omega(n). A data structure which supports both Split and Merge operations in O(log^2 n) amortized time per operation was given by Farach and Thorup. In contrast, our data structure supports all operations, including Split and Merge, in O(log n) amortized time, thus showing that interleaved Merge operations can be supported at no additional cost vis-a-vis disjoint Merge operations.


A Quantile-Based Sequential Feedback Scheme via Overhearing in Multicarrier Access Networks

Abstract: We propose a scheme to reduce the overhead associated with channel state information (CSI) feedback required for opportunistic scheduling in multicarrier access networks. We study the case where CSI is partially overheard by mobiles and one can suppress transmitting CSI reports for time varying channel of inferior quality. As a means to assess channel quality and exploit multiuser diversity we adopt maximum quantile (MQ) scheduling. We show that the problem of minimizing the average feedback overhead can be formulated as a Bayesian network problem. A greedy heuristic using probabilistic inference is proposed to deal with the NP-hardness of the problem. Leveraging properties of MQ scheduling we first show that networks having tree-like overhearing graphs admit simple inference. We then present a class of more general network structures for which exact inference is computationally tractable. Simulation results are provided to demonstrate the improvements offered by the proposed heuristic.


Automatic Performance Debugging of SPMD Parallel Programs

Abstract: Automatic performance debugging of parallel applications usually involves two steps: automatic detection of performance bottlenecks and uncovering their root causes for performance optimization. Previous work fails to resolve this challenging issue in several ways: first, several previous efforts automate analysis processes, but present the results in a confined way that only identifies performance problems with apriori knowledge; second, several tools take exploratory or confirmatory data analysis to automatically discover relevant performance data relationships. However, these efforts do not focus on locating performance bottlenecks or uncovering their root causes. In this paper, we design and implement an innovative system, AutoAnalyzer, to automatically debug the performance problems of single program multi-data (SPMD) parallel programs. Our system is unique in terms of two dimensions: first, without any apriori knowledge, we automatically locate bottlenecks and uncover their root causes for performance optimization; second, our method is lightweight in terms of size of collected and analyzed performance data. Our contribution is three-fold. First, we propose a set of simple performance metrics to represent behavior of different processes of parallel programs, and present two effective clustering and searching algorithms to locate bottlenecks. Second, we propose to use the rough set algorithm to automatically uncover the root causes of bottlenecks. Third, we design and implement the AutoAnalyzer system, and use two production applications to verify the effectiveness and correctness of our methods. According to the analysis results of AutoAnalyzer, we optimize two parallel programs with performance improvements by minimally 20% and maximally 170%.


Redundancy, Deduction Schemes, and Minimum-Size Bases for Association Rules

Abstract: Association rules are among the most widely employed data analysis methods in the field of Data Mining. An association rule is a form of partial implication between two sets of binary variables. In the most common approach, association rules are parameterized by a lower bound on their confidence, which is the empirical conditional probability of their consequent given the antecedent, and/or by some other parameter bounds such as "support" or deviation from independence. We study here notions of redundancy among association rules from a fundamental perspective. We see each transaction in a dataset as an interpretation (or model) in the propositional logic sense, and consider existing notions of redundancy, that is, of logical entailment, among association rules, of the form "any dataset in which this first rule holds must obey also that second rule, therefore the second is redundant". We discuss several existing alternative definitions of redundancy between association rules and provide new characterizations and relationships among them. We show that the main alternatives we discuss correspond actually to just two variants, which differ in the treatment of full-confidence implications. For each of these two notions of redundancy, we provide a sound and complete deduction calculus, and we show how to construct complete bases (that is, axiomatizations) of absolutely minimum size in terms of the number of rules. We explore finally an approach to redundancy with respect to several association rules, and fully characterize its simplest case of two partial premises.


A weakly universal cellular automaton in the hyperbolic 3D space with three states

Abstract: In this paper, we significantly improve a previous result by the same author showing the existence of a weakly universal cellular automaton with five states living in the hyperbolic 3D-space. Here, we get such a cellular automaton with three states only.


Signaling games with pattern recognition

Abstract: The classical model of signaling games assumes that the receiver exactly know the type space (private information) of the sender and be able to discriminate each type of the sender distinctly. However, the justification of this assumption is questionable. It is more reasonable to let the receiver recognize the pattern of the sender. In this paper, we investigate what happens if the assumption is relaxed. A framework of signaling games with pattern recognition and an example are given.


What are suspicious VoIP delays?

Abstract: Voice over IP (VoIP) is unquestionably the most popular real-time service in IP networks today. Recent studies have shown that it is also a suitable carrier for information hiding. Hidden communication may pose security concerns as it can lead to confidential information leakage. In VoIP, RTP (Real-time Transport Protocol) in particular, which provides the means for the successful transport of voice packets through IP networks, is suitable for steganographic purposes. It is characterised by a high packet rate compared to other protocols used in IP telephony, resulting in a potentially high steganographic bandwidth. The modification of an RTP packet stream provides many opportunities for hidden communication as the packets may be delayed, reordered or intentionally lost. In this paper, to enable the detection of steganographic exchanges in VoIP, we examined real RTP traffic traces to answer the questions, what do the "normal" delays in RTP packet streams look like? and, is it possible to detect the use of known RTP steganographic methods based on this knowledge?


Load Balancing via Random Local Search in Closed and Open systems

Abstract: In this paper, we analyze the performance of random load resampling and migration strategies in parallel server systems. Clients initially attach to an arbitrary server, but may switch server independently at random instants of time in an attempt to improve their service rate. This approach to load balancing contrasts with traditional approaches where clients make smart server selections upon arrival (e.g., Join-the-Shortest-Queue policy and variants thereof). Load resampling is particularly relevant in scenarios where clients cannot predict the load of a server before being actually attached to it. An important example is in wireless spectrum sharing where clients try to share a set of frequency bands in a distributed manner.


Mining Statistically Significant Substrings Based on the Chi-Square Measure

Abstract: Given the vast reservoirs of data stored worldwide, efficient mining of data from a large information store has emerged as a great challenge. Many databases like that of intrusion detection systems, web-click records, player statistics, texts, proteins etc., store strings or sequences. Searching for an unusual pattern within such long strings of data has emerged as a requirement for diverse applications. Given a string, the problem then is to identify the substrings that differs the most from the expected or normal behavior, i.e., the substrings that are statistically significant. In other words, these substrings are less likely to occur due to chance alone and may point to some interesting information or phenomenon that warrants further exploration. To this end, we use the chi-square measure. We propose two heuristics for retrieving the top-k substrings with the largest chi-square measure. We show that the algorithms outperform other competing algorithms in the runtime, while maintaining a high approximation ratio of more than 0.96.


CLD-shaped Brushstrokes in Non-Photorealistic Rendering

Abstract: Rendering techniques based on a random grid can be improved by adapting brushstrokes to the shape of different areas of the original picture. In this paper, the concept of Coherence Length Diagram is applied to determine the adaptive brushstrokes, in order to simulate an impressionist painting. Some examples are provided to instance the proposed algorithm.


Defining and Computing Alternative Routes in Road Networks

Abstract: Every human likes choices. But today's fast route planning algorithms usually compute just a single route between source and target. There are beginnings to compute alternative routes, but this topic has not been studied thoroughly. Often, the aspect of meaningful alternative routes is neglected from a human point of view. We fill in this gap by suggesting mathematical definitions for such routes. As a second contribution we propose heuristics to compute them, as this is NP-hard in general.


On Semantic Generalizations of the Bernays-Sch\"onfinkel-Ramsey Class with Finite or Co-finite Spectra

Abstract: Motivated by model-theoretic properties of the BSR class, we present a family of semantic classes of FO formulae with finite or co-finite spectra over a relational vocabulary \Sigma. A class in this family is denoted EBS_\Sigma(\sigma), where \sigma is a subset of \Sigma. Formulae in EBS_\Sigma(\sigma) are preserved under substructures modulo a bounded core and modulo re-interpretation of predicates outside \sigma. We study properties of the family EBS_\Sigma = {EBS_\Sigma(\sigma) | \sigma \subseteq \Sigma}, e.g. classes in EBS_\Sigma are spectrally indistinguishable, EBS_\Sigma(\Sigma) is semantically equivalent to BSR over \Sigma, and EBS_\Sigma(\emptyset) is the set of all FO formulae over \Sigma with finite or co-finite spectra. Furthermore, (EBS_\Sigma, \subseteq) forms a lattice isomorphic to the powerset lattice (\wp({\Sigma}), \subseteq). This gives a natural semantic generalization of BSR as ascending chains in (EBS_\Sigma, \subseteq). Many well-known FO classes are semantically subsumed by EBS_\Sigma(\Sigma) or EBS_\Sigma(\emptyset). Our study provides alternative proofs of interesting results like the Lo\'s-Tarski Theorem and the semantic subsumption of the L\"owenheim class with equality by BSR. We also present a syntactic sub-class of EBS_\Sigma(\sigma) called EDP_\Sigma(\sigma) and give an expression for the size of the bounded cores of models of EDP_\Sigma(\sigma) formulae. We show that the EDP_\Sigma(\sigma) classes also form a lattice structure. Finally, we study some closure properties and applications of the classes presented.


On the Number of Higher Order Delaunay Triangulations

Abstract: Higher order Delaunay triangulations are a generalization of the Delaunay triangulation which provides a class of well-shaped triangulations, over which extra criteria can be optimized. A triangulation is order-$k$ Delaunay if the circumcircle of each triangle of the triangulation contains at most $k$ points. In this paper we study lower and upper bounds on the number of higher order Delaunay triangulations, as well as their expected number for randomly distributed points. We show that arbitrarily large point sets can have a single higher order Delaunay triangulation, even for large orders, whereas for first order Delaunay triangulations, the maximum number is $2^{n-3}$. Next we show that uniformly distributed points have an expected number of at least $2^{\rho_1 n(1+o(1))}$ first order Delaunay triangulations, where $\rho_1$ is an analytically defined constant ($\rho_1 \approx 0.525785$), and for $k > 1$, the expected number of order-$k$ Delaunay triangulations (which are not order-$i$ for any $i < k$) is at least $2^{\rho_k n(1+o(1))}$, where $\rho_k$ can be calculated numerically.


Complete Context Calculus Design and Implementation in GIPSY

Abstract: This paper presents the integration into the GIPSY of Lucx's context calculus defined in Wan's PhD thesis. We start by defining different types of tag sets, then we explain the concept of context, the types of context and the context calculus operators. Finally, we present how context entities have been abstracted into Java classes and embedded into the GIPSY system.


Deterministic Sample Sort For GPUs

Abstract: We present and evaluate GPU Bucket Sort, a parallel deterministic sample sort algorithm for many-core GPUs. Our method is considerably faster than Thrust Merge (Satish et.al., Proc. IPDPS 2009), the best comparison-based sorting algorithm for GPUs, and it is as fast as the new randomized sample sort for GPUs by Leischner et.al. (to appear in Proc. IPDPS 2010). Our deterministic sample sort has the advantage that bucket sizes are guaranteed and therefore its running time does not have the input data dependent fluctuations that can occur for randomized sample sort.


Exploring the Limits of GPUs With Parallel Graph Algorithms

Abstract: In this paper, we explore the limits of graphics processors (GPUs) for general purpose parallel computing by studying problems that require highly irregular data access patterns: parallel graph algorithms for list ranking and connected components. Such graph problems represent a worst case scenario for coalescing parallel memory accesses on GPUs which is critical for good GPU performance. Our experimental study indicates that PRAM algorithms are a good starting point for developing efficient parallel GPU methods but require non-trivial modifications to ensure good GPU performance. We present a set of guidelines that help algorithm designers adapt PRAM graph algorithms for parallel GPU computation. We point out that the study of parallel graph algorithms for GPUs is of wider interest for discrete and combinatorial problems in general because many of these problems require similar irregular data access patterns.


Extended gcd of quadratic integers

Abstract: Computation of the extended gcd of two quadratic integers. The ring of integers considered is principal but could be euclidean or not euclidean ring. This method rely on principal ideal ring and reduction of binary quadratic forms.


Feature Importance in Bayesian Assessment of Newborn Brain Maturity from EEG

Abstract: The methodology of Bayesian Model Averaging (BMA) is applied for assessment of newborn brain maturity from sleep EEG. In theory this methodology provides the most accurate assessments of uncertainty in decisions. However, the existing BMA techniques have been shown providing biased assessments in the absence of some prior information enabling to explore model parameter space in details within a reasonable time. The lack in details leads to disproportional sampling from the posterior distribution. In case of the EEG assessment of brain maturity, BMA results can be biased because of the absence of information about EEG feature importance. In this paper we explore how the posterior information about EEG features can be used in order to reduce a negative impact of disproportional sampling on BMA performance. We use EEG data recorded from sleeping newborns to test the efficiency of the proposed BMA technique.


A Novel Scheme for Secured Data Transfer Over Computer Networks

Abstract: This paper presents a novel encryption-less algorithm to enhance security in transmission of data in networks. The algorithm uses an intuitively simple idea of a "jigsaw puzzle" to break the transformed data into multiple parts where these parts form the pieces of the puzzle. Then these parts are packaged into packets and sent to the receiver. A secure and efficient mechanism is provided to convey the information that is necessary for obtaining the original data at the receiver-end from its parts in the packets, that is, for solving the "jigsaw puzzle". The algorithm is designed to provide information-theoretic (that is, unconditional) security by the use of a one-time pad like scheme so that no intermediate or unintended node can obtain the entire data. A parallelizable design has been adopted for the implementation. An authentication code is also used to ensure authenticity of every packet.


Proceedings of the 19th Workshop on Logic-based methods in Programming Environments (WLPE 2009)

Abstract: This volume contains the papers presented at the 19th Workshop on Logic- based methods in Programming Environments (WLPE'09), which was held in Pasadena, USA, on July 14th, 2009. WLPE aims at providing an informal meeting for researchers working on logic-based methods and tools which support program development and analy- sis. This year, we have continued and consolidated the shift in focus from en- vironmental tools for logic programming to logic-based environmental tools for programming in general, so that this workshop can be possibly interesting for a wider scientific community. All the papers submitted to WLPE'09 have gone through a careful process of peer reviewing, with at least three reviews for each paper and a subsequent in-depth discussion in the Program Committee.


Breaking the O(n^2) Bit Barrier: Scalable Byzantine agreement with an Adaptive Adversary

Abstract: We describe an algorithm for Byzantine agreement that is scalable in the sense that each processor sends only $\tilde{O}(\sqrt{n})$ bits, where $n$ is the total number of processors. Our algorithm succeeds with high probability against an \emph{adaptive adversary}, which can take over processors at any time during the protocol, up to the point of taking over arbitrarily close to a 1/3 fraction. We assume synchronous communication but a \emph{rushing} adversary. Moreover, our algorithm works in the presence of flooding: processors controlled by the adversary can send out any number of messages. We assume the existence of private channels between all pairs of processors but make no other cryptographic assumptions. Finally, our algorithm has latency that is polylogarithmic in $n$. To the best of our knowledge, ours is the first algorithm to solve Byzantine agreement against an adaptive adversary, while requiring $o(n^{2})$ total bits of communication.


Atomicity Improvement for Elliptic Curve Scalar Multiplication

Abstract: In this paper we address the problem of protecting elliptic curve scalar multiplication implementations against side-channel analysis by using the atomicity principle. First of all we reexamine classical assumptions made by scalar multiplication designers and we point out that some of them are not relevant in the context of embedded devices. We then describe the state-of-the-art of atomic scalar multiplication and propose an atomic pattern improvement method. Compared to the most efficient atomic scalar multiplication published so far, our technique shows an average improvement of up to 10.6%.


Bounded Rationality, Strategy Simplification, and Equilibrium

Abstract: It is frequently suggested that predictions made by game theory could be improved by considering computational restrictions when modeling agents. Under the supposition that players in a game may desire to balance maximization of payoff with minimization of strategy complexity, Rubinstein and co-authors studied forms of Nash equilibrium where strategies are maximally simplified in that no strategy can be further simplified without sacrificing payoff. Inspired by this line of work, we introduce a notion of equilibrium whereby strategies are also maximally simplified, but with respect to a simplification procedure that is more careful in that a player will not simplify if the simplification incents other players to deviate. We study such equilibria in two-player machine games in which players choose finite automata that succinctly represent strategies for repeated games; in this context, we present techniques for establishing that an outcome is at equilibrium and present results on the structure of equilibria.


Robust Vacuity for Branching Temporal Logic

Abstract: There is a growing interest in techniques for detecting whether a logic specification is satisfied too easily, or vacuously. For example, the specification "every request is eventually followed by an acknowledgment" is satisfied vacuously by a system that never generates any requests. Vacuous satisfaction misleads users of model-checking into thinking that a system is correct. There are several existing definitions of vacuity. Originally, Beer et al. formalized vacuity as insensitivity to syntactic perturbation. However, this definition is only reasonable for vacuity in a single occurrence. Armoni et al. argued that vacuity must be robust -- not affected by semantically invariant changes, such as extending a model with additional atomic propositions. They show that syntactic vacuity is not robust for LTL, and propose an alternative definition -- trace vacuity. In this article, we continue this line of research. We show that trace vacuity is not robust for branching time logic. We refine it to apply uniformly to linear and branching time logic and to not suffer from the common pitfalls of prior definitions. Our new definition -- bisimulation vacuity -- is a proper non-trivial extension of both syntactic and trace vacuity. We discuss the complexity of detecting bisimulation vacuity, and give efficient algorithms to detect vacuity for several practically-relevant subsets of CTL*.


Accelerating sequential programs using FastFlow and self-offloading

Abstract: FastFlow is a programming environment specifically targeting cache-coherent shared-memory multi-cores. FastFlow is implemented as a stack of C++ template libraries built on top of lock-free (fence-free) synchronization mechanisms. In this paper we present a further evolution of FastFlow enabling programmers to offload part of their workload on a dynamically created software accelerator running on unused CPUs. The offloaded function can be easily derived from pre-existing sequential code. We emphasize in particular the effective trade-off between human productivity and execution efficiency of the approach.


Pebbling and Branching Programs Solving the Tree Evaluation Problem

Abstract: We study restricted computation models related to the Tree Evaluation Problem}. The TEP was introduced in earlier work as a simple candidate for the (*very*) long term goal of separating L and LogDCFL. The input to the problem is a rooted, balanced binary tree of height h, whose internal nodes are labeled with binary functions on [k] = {1,...,k} (each given simply as a list of k^2 elements of [k]), and whose leaves are labeled with elements of [k]. Each node obtains a value in [k] equal to its binary function applied to the values of its children, and the output is the value of the root. The first restricted computation model, called Fractional Pebbling, is a generalization of the black/white pebbling game on graphs, and arises in a natural way from the search for good upper bounds on the size of nondeterministic branching programs (BPs) solving the TEP - for any fixed h, if the binary tree of height h has fractional pebbling cost at most p, then there are nondeterministic BPs of size O(k^p) solving the height h TEP. We prove a lower bound on the fractional pebbling cost of d-ary trees that is tight to within an additive constant for each fixed d. The second restricted computation model we study is a semantic restriction on (non)deterministic BPs solving the TEP - Thrifty BPs. Deterministic (resp. nondeterministic) thrifty BPs suffice to implement the best known algorithms for the TEP, based on black (resp. fractional) pebbling. In earlier work, for each fixed h a lower bound on the size of deterministic thrifty BPs was proved that is tight for sufficiently large k. We give an alternative proof that achieves the same bound for all k. We show the same bound still holds in a less-restricted model, and also that gradually weaker lower bounds can be obtained for gradually weaker restrictions on the model.


Challenges of Next-Generation Wireless Sensor Networks and its impact on Society

Abstract: Wireless Sensor Networks (WSNs) have gained worldwide attention in recent years, particularly with the proliferation in Micro-Electro-Mechanical Systems (MEMS) technology which has facilitated the development of smart sensors. The paper discusses about classification of WSN and challenges of the Next Generation WSN. One of the major challenges of Next Generation WSN is reduction of power consumption. The two approaches are discussed: Ultra-Low-Power Networks and Energy Harvesting. The paper also discusses about some major applications as designing low cost secured Intelligent Buildings, In-Home Health care and Agriculture.


Low-complexity Fusion Filtering for Continuous-Discrete Systems

Abstract: In this paper, low-complexity distributed fusion filtering algorithm for mixed continuous-discrete multisensory dynamic systems is proposed. To implement the algorithm a new recursive equations for local cross-covariances are derived. To achieve an effective fusion filtering the covariance intersection (CI) algorithm is used. The CI algorithm is useful due to its low-computational complexity for calculation of a big number of cross-covariances between local estimates and matrix weights. Theoretical and numerical examples demonstrate the effectiveness of the covariance intersection algorithm in distributed fusion filtering.


Performance Analysis of Uplink & Downlink Transmission in CDMA System

Abstract: CDMA is a multiple access method in which the user's uses spread spectrum techniques and occupy the entire spectrum whenever they transmit. In wireless communication signal-to-noise ratio (SNR) is the very important parameter that influences the system performance. Any mode of mobile transmission is not free from channel impairment such as noise, interference and fading. This channel impairment caused signal distortion and degradation in SNR.Also there are differences between uplink (forward channel) and downlink (reverse channel).Along with these differences, both the links use different codes for chanellizing the individual users. This paper simulates the expressions for the pdfs of the SNR for both uplink and downlink transmission assuming that the system is operating at an average signal-to-noise ratio is 6dB per information bit.


An Approach to Ad hoc Cloud Computing

Abstract: We consider how underused computing resources within an enterprise may be harnessed to improve utilization and create an elastic computing infrastructure. Most current cloud provision involves a data center model, in which clusters of machines are dedicated to running cloud infrastructure software. We propose an additional model, the ad hoc cloud, in which infrastructure software is distributed over resources harvested from machines already in existence within an enterprise. In contrast to the data center cloud model, resource levels are not established a priori, nor are resources dedicated exclusively to the cloud while in use. A participating machine is not dedicated to the cloud, but has some other primary purpose such as running interactive processes for a particular user. We outline the major implementation challenges and one approach to tackling them.


Triangular Decomposition of Semi-algebraic Systems

Abstract: Regular chains and triangular decompositions are fundamental and well-developed tools for describing the complex solutions of polynomial systems. This paper proposes adaptations of these tools focusing on solutions of the real analogue: semi-algebraic systems. We show that any such system can be decomposed into finitely many {\em regular semi-algebraic systems}. We propose two specifications of such a decomposition and present corresponding algorithms. Under some assumptions, one type of decomposition can be computed in singly exponential time w.r.t.\ the number of variables. We implement our algorithms and the experimental results illustrate their effectiveness.


A Trustability Metric for Code Search based on Developer Karma

Abstract: The promise of search-driven development is that developers will save time and resources by reusing external code in their local projects. To efficiently integrate this code, users must be able to trust it, thus trustability of code search results is just as important as their relevance. In this paper, we introduce a trustability metric to help users assess the quality of code search results and therefore ease the cost-benefit analysis they undertake trying to find suitable integration candidates. The proposed trustability metric incorporates both user votes and cross-project activity of developers to calculate a "karma" value for each developer. Through the karma value of all its developers a project is ranked on a trustability scale. We present JBender, a proof-of-concept code search engine which implements our trustability metric and we discuss preliminary results from an evaluation of the prototype.


SLAM : Solutions lexicales automatique pour m\'etaphores

Abstract: This article presents SLAM, an Automatic Solver for Lexical Metaphors like ?d\'eshabiller* une pomme? (to undress* an apple). SLAM calculates a conventional solution for these productions. To carry on it, SLAM has to intersect the paradigmatic axis of the metaphorical verb ?d\'eshabiller*?, where ?peler? (?to peel?) comes closer, with a syntagmatic axis that comes from a corpus where ?peler une pomme? (to peel an apple) is semantically and syntactically regular. We test this model on DicoSyn, which is a ?small world? network of synonyms, to compute the paradigmatic axis and on Frantext.20, a French corpus, to compute the syntagmatic axis. Further, we evaluate the model with a sample of an experimental corpus of the database of Flexsem


Nonlinear System Identification and Behavioral Modeling

Abstract: This paper has been withdrawn by the author


On Analysis and Evaluation of Multi-Sensory Cognitive Learning of a Mathematical Topic Using Artificial Neural Networks

Abstract: This piece of research belongs to the field of educational assessment issue based upon the cognitive multimedia theory. Considering that theory; visual and auditory material should be presented simultaneously to reinforce the retention of a mathematical learned topic, a carefully computer-assisted learning (CAL) module is designed for development of a multimedia tutorial for our suggested mathematical topic. The designed CAL module is a multimedia tutorial computer package with visual and/or auditory material. So, via suggested computer package, Multi-Sensory associative memories and classical conditioning theories are practically applicable at an educational field (a children classroom). It is noticed that comparative practical results obtained are interesting for field application of CAL package with and without associated teacher's voice. Finally, the presented study highly recommends application of a novel teaching trend aiming to improve quality of children mathematical learning performance.


Nash equilibria in Fisher market

Abstract: Much work has been done on the computation of market equilibria. However due to strategic play by buyers, it is not clear whether these are actually observed in the market. Motivated by the observation that a buyer may derive a better payoff by feigning a different utility function and thereby manipulating the Fisher market equilibrium, we formulate the {\em Fisher market game} in which buyers strategize by posing different utility functions. We show that existence of a {\em conflict-free allocation} is a necessary condition for the Nash equilibria (NE) and also sufficient for the symmetric NE in this game. There are many NE with very different payoffs, and the Fisher equilibrium payoff is captured at a symmetric NE. We provide a complete polyhedral characterization of all the NE for the two-buyer market game. Surprisingly, all the NE of this game turn out to be symmetric and the corresponding payoffs constitute a piecewise linear concave curve. We also study the correlated equilibria of this game and show that third-party mediation does not help to achieve a better payoff than NE payoffs.


Analytical Evaluation of Unfairness Problem in Wireless LANs

Abstract: The number of users using wireless Local Area Network is increasing exponentially and their behavior is changing day after day. Nowadays, users of wireless LAN are using huge amount of bandwidth because of the explosive growth of some services and applications such as video sharing. This situation imposes massive pressure on the wireless LAN performance especially in term of fairness among wireless stations. The limited resources are not distributed fairly in saturated conditions. The most important resource is the access point buffer space. This importance is a result of access point being the bottleneck between two different types of networks. These two types are wired network with relatively huge bandwidth and wireless network with much smaller bandwidth. Also the unfairness problem is keep getting worse because of the greedy nature Transmission Control Protocol (TCP). In this paper, we conduct a comprehensive study on wireless LAN dynamics and proposed a new mathematical model that describes the performance and effects of its behavior. We validate the proposed model by using the simulation technique. The proposed model was able to produce very good approximation in most of the cases. It also gave us a great insight into the effective variables in the wireless LAN behavior and what are the dimensions of the unfairness problem.


Hardware Implementation of TDES Crypto System with On Chip Verification in FPGA

Abstract: Security issues are playing dominant role in today's high speed communication systems. A fast and compact FPGA based implementation of the Data Encryption Standard (DES) and Triple DES algorithm is presented in this paper that is widely used in cryptography for securing the Internet traffic in modern day communication systems. The design of the digital cryptographic circuit was implemented in a Vertex 5 series (XCVLX5110T) target device with the use of VHDL as the hardware description language. In order to confirm the expected behavior of these algorithms, the proposed design was extensively simulated, synthesized for different FPGA devices both in Spartan and Virtex series from Xilinx viz. Spartan 3, Spartan 3AN, Virtex 5, Virtex E device families. The novelty and contribution of this work is in three folds: (i) Extensive simulation and synthesis of the proposed design targeted for various FPGA devices, (ii) Complete hardware implementation of encryption and decryption algorithms onto Virtex 5 series device (XCVLX5110T) based FPGA boards and, (iii) Generation of ICON and VIO core for the design and on chip verification and analyzing using Chipscope Pro. The experimental as well as implementation results compared to the implementations reported so far are quite encouraging.


Processing of Communication Signal Using Operational Transconductance Amplifier

Abstract: This paper proposes a signal processing methodology of communication system and realized that circuits using operational transconductance amplifier (OTA). Two important classes of communication circuit, delta modulator and compander have been designed using that procedure. In the first implementation coded pulse modulation system is demonstrated which employ sampling, quantizing and coding to convert analog waveforms to digital signals while the second gives data compression and expansion in digital communication system. The proposed compander circuit is realized with operational transconductance amplifier and diode. Required power supply to operate the circuit is 3.5V. Performance of the circuits realized with OTAs has been demonstrated through SPICE simulation.


Analyzing the Low Power Wireless Links for Wireless Sensor Networks

Abstract: There is now an increased understanding of the need for realistic link layer models in the wireless sensor networks. In this paper, we have used mathematical techniques from communication theory to model and analyze low power wireless links. Our work provides theoretical models for the link layer showing how Packet Reception Rate vary with Signal to Noise Ratio and distance for different modulation schemes and a comparison between MICA2 and TinyNode in terms of PRR.


Less Regret via Online Conditioning

Abstract: We analyze and evaluate an online gradient descent algorithm with adaptive per-coordinate adjustment of learning rates. Our algorithm can be thought of as an online version of batch gradient descent with a diagonal preconditioner. This approach leads to regret bounds that are stronger than those of standard online gradient descent for general online convex optimization problems. Experimentally, we show that our algorithm is competitive with state-of-the-art algorithms for large scale machine learning problems.


Network Coding-Aware Queue Management for TCP Flows over Coded Wireless Networks

Abstract: We are interested in unicast traffic over wireless networks that employ constructive inter-session network coding, including single-hop and multi-hop schemes. In this setting, TCP flows do not fully exploit the network coding opportunities due to their bursty behavior and due to the fact that TCP is agnostic to the underlying network coding. In order to improve the performance of TCP flows over coded wireless networks, we take the following steps. First, we formulate the problem as network utility maximization and we present a distributed solution. Second, mimicking the structure of the optimal solution, we propose a "network-coding aware" queue management scheme (NCAQM) at intermediate nodes; we make no changes to TCP or to the MAC protocol (802.11). We demonstrate, via simulation, that NCAQM significantly improves TCP performance compared to TCP over baseline schemes.


Adaptive Bound Optimization for Online Convex Optimization

Abstract: We introduce a new online convex optimization algorithm that adaptively chooses its regularization function based on the loss functions observed so far. This is in contrast to previous algorithms that use a fixed regularization function such as L2-squared, and modify it only via a single time-dependent parameter. Our algorithm's regret bounds are worst-case optimal, and for certain realistic classes of loss functions they are much better than existing bounds. These bounds are problem-dependent, which means they can exploit the structure of the actual problem instance. Critically, however, our algorithm does not need to know this structure in advance. Rather, we prove competitive guarantees that show the algorithm provides a bound within a constant factor of the best possible bound (of a certain functional form) in hindsight.


Proceedings Third Workshop From Biology To Concurrency and back

Abstract: This volume contains the papers presented at the 3rd Workshop "From Biology To Concurrency and back", FBTC 2010, held in Paphos, Cyprus, on March 27, 2010, as satellite event of the Joint European Conference on Theory and Practice of Software, ETAPS 2010. The Workshop aimed at gathering together researchers with special interest at the convergence of life and computer science, with particular focus on the application of techniques and methods from concurrency. The papers contained in this volume present works on modelling, analysis, and validation of biological behaviours using concurrency-inspired methods and platforms, and bio-inspired models and tools for describing distributed interactions.


Triangular Self-Assembly

Abstract: We discuss the self-assembly system of triangular tiles instead of square tiles, in particular right triangular tiles and equilateral triangular tiles. We show that the triangular tile assembly system, either deterministic or non-deterministic, has the same power to the square tile assembly system in computation, which is Turing universal. By providing counter-examples, we show that the triangular tile assembly system and the square tile assembly system are not comparable in general. More precisely, there exists square tile assembly system S such that no triangular tile assembly system is a division of S and produces the same shape; there exists triangular tile assembly system T such that no square tile assembly system produces the same compatible shape with border glues. We also discuss the assembly of triangles by triangular tiles and obtain results similar to the assembly of squares, that is to assemble a triangular of size O(N^2), the minimal number of tiles required is in O(log N/log log N).


Threshold rules for online sample selection

Abstract: We consider the following sample selection problem. We observe in an online fashion a sequence of samples, each endowed by a quality. Our goal is to either select or reject each sample, so as to maximize the aggregate quality of the subsample selected so far. There is a natural trade-off here between the rate of selection and the aggregate quality of the subsample. We show that for a number of such problems extremely simple and oblivious "threshold rules" for selection achieve optimal tradeoffs between rate of selection and aggregate quality in a probabilistic sense. In some cases we show that the same threshold rule is optimal for a large class of quality distributions and is thus oblivious in a strong sense.


Asymptotic Analysis of Generative Semi-Supervised Learning

Abstract: Semisupervised learning has emerged as a popular framework for improving modeling accuracy while controlling labeling cost. Based on an extension of stochastic composite likelihood we quantify the asymptotic accuracy of generative semi-supervised learning. In doing so, we complement distribution-free analysis by providing an alternative framework to measure the value associated with different labeling policies and resolve the fundamental question of how much data to label and in what manner. We demonstrate our approach with both simulation studies and real world experiments using naive Bayes for text classification and MRFs and CRFs for structured prediction in NLP.


A New Understanding of Prediction Markets Via No-Regret Learning

Abstract: We explore the striking mathematical connections that exist between market scoring rules, cost function based prediction markets, and no-regret learning. We show that any cost function based prediction market can be interpreted as an algorithm for the commonly studied problem of learning from expert advice by equating trades made in the market with losses observed by the learning algorithm. If the loss of the market organizer is bounded, this bound can be used to derive an O(sqrt(T)) regret bound for the corresponding learning algorithm. We then show that the class of markets with convex cost functions exactly corresponds to the class of Follow the Regularized Leader learning algorithms, with the choice of a cost function in the market corresponding to the choice of a regularizer in the learning problem. Finally, we show an equivalence between market scoring rules and prediction markets with convex cost functions. This implies that market scoring rules can also be interpreted naturally as Follow the Regularized Leader algorithms, and may be of independent interest. These connections provide new insight into how it is that commonly studied markets, such as the Logarithmic Market Scoring Rule, can aggregate opinions into accurate estimates of the likelihood of future events.


Energy Optimal Transmission Scheduling in Wireless Sensor Networks

Abstract: One of the main issues in the design of sensor networks is energy efficient communication of time-critical data. Energy wastage can be caused by failed packet transmission attempts at each node due to channel dynamics and interference. Therefore transmission control techniques that are unaware of the channel dynamics can lead to suboptimal channel use patterns. In this paper we propose a transmission controller that utilizes different "grades" of channel side information to schedule packet transmissions in an optimal way, while meeting a deadline constraint for all packets waiting in the transmission queue. The wireless channel is modeled as a finite-state Markov channel. We are specifically interested in the case where the transmitter has low-grade channel side information that can be obtained based solely on the ACK/NAK sequence for the previous transmissions. Our scheduler is readily implementable and it is based on the dynamic programming solution to the finite-horizon transmission control problem. We also calculate the information theoretic capacity of the finite state Markov channel with feedback containing different grades of channel side information including that, obtained through the ACK/NAK sequence. We illustrate that our scheduler achieves a given throughput at a power level that is fairly close to the fundamental limit achievable over the channel.


A Concrete Representation of Observational Equivalence for PCF

Abstract: The full abstraction result for PCF using game semantics requires one to identify all innocent strategies that are innocently indistinguishable. This involves a quantification over all innocent tests, cf. quantification over all innocent contexts. Here we present a representation of innocent strategies that equates innocently indistinguishable ones, yielding a representation of PCF terms that equates precisely those terms that are observational equivalent.


Qualitative Reachability in Stochastic BPA Games

Abstract: We consider a class of infinite-state stochastic games generated by stateless pushdown automata (or, equivalently, 1-exit recursive state machines), where the winning objective is specified by a regular set of target configurations and a qualitative probability constraint `>0' or `=1'. The goal of one player is to maximize the probability of reaching the target set so that the constraint is satisfied, while the other player aims at the opposite. We show that the winner in such games can be determined in PTIME for the `>0' constraint, and both in NP and coNP for the `=1' constraint. Further, we prove that the winning regions for both players are regular, and we design algorithms which compute the associated finite-state automata. Finally, we show that winning strategies can be synthesized effectively.


Learning from Logged Implicit Exploration Data

Abstract: We provide a sound and consistent foundation for the use of \emph{nonrandom} exploration data in "contextual bandit" or "partially labeled" settings where only the value of a chosen action is learned. The primary challenge in a variety of settings is that the exploration policy, in which "offline" data is logged, is not explicitly known. Prior solutions here require either control of the actions during the learning process, recorded random exploration, or actions chosen obliviously in a repeated manner. The techniques reported here lift these restrictions, allowing the learning of a policy for choosing actions given features from historical data where no randomization occurred or was logged. We empirically verify our solution on two reasonably sized sets of real-world data obtained from Yahoo!.


An O(loglog n)-Competitive Binary Search Tree with Optimal Worst-Case Access Times

Abstract: We present the zipper tree, an $O(\log \log n)$-competitive online binary search tree that performs each access in $O(\log n)$ worst-case time. This shows that for binary search trees, optimal worst-case access time and near-optimal amortized access time can be guaranteed simultaneously.


A Contextual-Bandit Approach to Personalized News Article Recommendation

Abstract: Personalized web services strive to adapt their services (advertisements, news articles, etc) to individual users by making use of both content and user information. Despite a few recent advances, this problem remains challenging for at least two reasons. First, web service is featured with dynamically changing pools of content, rendering traditional collaborative filtering methods inapplicable. Second, the scale of most web services of practical interest calls for solutions that are both fast in learning and computation. In this work, we model personalized recommendation of news articles as a contextual bandit problem, a principled approach in which a learning algorithm sequentially selects articles to serve users based on contextual information about the users and articles, while simultaneously adapting its article-selection strategy based on user-click feedback to maximize total user clicks. The contributions of this work are three-fold. First, we propose a new, general contextual bandit algorithm that is computationally efficient and well motivated from learning theory. Second, we argue that any bandit algorithm can be reliably evaluated offline using previously recorded random traffic. Finally, using this offline evaluation method, we successfully applied our new algorithm to a Yahoo! Front Page Today Module dataset containing over 33 million events. Results showed a 12.5% click lift compared to a standard context-free bandit algorithm, and the advantage becomes even greater when data gets more scarce.


When LP is the Cure for Your Matching Woes: Approximating Stochastic Matchings

Abstract: This results in this paper have been merged with the result in arXiv:1002.3763v1 The authors would like to withdraw this version. Please see arXiv:1008.5356v1 for the merged version.


Generating Function For Network Delay

Abstract: In this paper correspondence between experimental data for packet delay and two theoretical types of distribution is investigated. Statistical tests have shown that only exponential distribution can be used for the description of packet delays in global network. Precision experimental data to within microseconds are gathered by means of the RIPE Test Box. Statistical verification of hypothesis has shown that distribution parameters remain constants during 500 second intervals at least. In paper cumulative distribution function and generating function for packet delay in network are in an explicit form written down, the algorithm of search of parameters of distribution is resulted.


Why has (reasonably accurate) Automatic Speech Recognition been so hard to achieve?

Abstract: Hidden Markov models (HMMs) have been successfully applied to automatic speech recognition for more than 35 years in spite of the fact that a key HMM assumption -- the statistical independence of frames -- is obviously violated by speech data. In fact, this data/model mismatch has inspired many attempts to modify or replace HMMs with alternative models that are better able to take into account the statistical dependence of frames. However it is fair to say that in 2010 the HMM is the consensus model of choice for speech recognition and that HMMs are at the heart of both commercially available products and contemporary research systems. In this paper we present a preliminary exploration aimed at understanding how speech data depart from HMMs and what effect this departure has on the accuracy of HMM-based speech recognition. Our analysis uses standard diagnostic tools from the field of statistics -- hypothesis testing, simulation and resampling -- which are rarely used in the field of speech recognition. Our main result, obtained by novel manipulations of real and resampled data, demonstrates that real data have statistical dependency and that this dependency is responsible for significant numbers of recognition errors. We also demonstrate, using simulation and resampling, that if we `remove' the statistical dependency from data, then the resulting recognition error rates become negligible. Taken together, these results suggest that a better understanding of the structure of the statistical dependency in speech data is a crucial first step towards improving HMM-based speech recognition.


Central Force Optimization Applied to the PBM Suite of Antenna Benchmarks

Abstract: Central Force Optimization (CFO) is a new nature-inspired deterministic multi-dimensional search and optimization metaheuristic based on the metaphor of gravitational kinematics. CFO is applied to the PBM antenna benchmark suite and the results compared to published performance data for other optimization algorithms. CFO acquits itself quite well. CFO's gradient-like nature is discussed, and it is speculated that a "generalized hyperspace derivative" might be defined for optimization problems as a new mathematical construct based on the Unit Step function. What appears to be a sufficient but not necessary condition for local trapping, oscillation in the probe average distance curve, is discussed in the context of the theory of gravitational "resonant returns" that gives rise to strikingly similar oscillatory curves. It is suggested that the theory may be applicable to CFO as an aid to understanding trapping and to developing effective mitigation techniques, possibly based on a concept of "energy" in CFO space. It also is suggested that CFO may be re-formulated as a "total energy" model by analogizing conservation of energy for orbiting masses in physical space.


Further Exploration of the Dendritic Cell Algorithm: Antigen Multiplier and Time Windows

Abstract: As an immune-inspired algorithm, the Dendritic Cell Algorithm (DCA), produces promising performances in the field of anomaly detection. This paper presents the application of the DCA to a standard data set, the KDD 99 data set. The results of different implementation versions of the DXA, including the antigen multiplier and moving time windows are reported. The real-valued Negative Selection Algorithm (NSA) using constant-sized detectors and the C4.5 decision tree algorithm are used, to conduct a baseline comparison. The results suggest that the DCA is applicable to KDD 99 data set, and the antigen multiplier and moving time windows have the same effect on the DCA for this particular data set. The real-valued NSA with constant-sized detectors is not applicable to the data set, and the C4.5 decision tree algorithm provides a benchmark of the classification performance for this data set.