Formal Relationships Between Geometrical and Classical Models for Concurrency

Abstract: A wide variety of models for concurrent programs has been proposed during the past decades, each one focusing on various aspects of computations: trace equivalence, causality between events, conflicts and schedules due to resource accesses, etc. More recently, models with a geometrical flavor have been introduced, based on the notion of cubical set. These models are very rich and expressive since they can represent commutation between any bunch of events, thus generalizing the principle of true concurrency. While they seem to be very promising - because they make possible the use of techniques from algebraic topology in order to study concurrent computations - they have not yet been precisely related to the previous models, and the purpose of this paper is to fill this gap. In particular, we describe an adjunction between Petri nets and cubical sets which extends the previously known adjunction between Petri nets and asynchronous transition systems by Nielsen and Winskel.


Approximation Algorithms for the Capacitated Domination Problem

Abstract: We consider the {\em Capacitated Domination} problem, which models a service-requirement assignment scenario and is also a generalization of the well-known {\em Dominating Set} problem. In this problem, given a graph with three parameters defined on each vertex, namely cost, capacity, and demand, we want to find an assignment of demands to vertices of least cost such that the demand of each vertex is satisfied subject to the capacity constraint of each vertex providing the service. In terms of polynomial time approximations, we present logarithmic approximation algorithms with respect to different demand assignment models for this problem on general graphs, which also establishes the corresponding approximation results to the well-known approximations of the traditional {\em Dominating Set} problem. Together with our previous work, this closes the problem of generally approximating the optimal solution. On the other hand, from the perspective of parameterization, we prove that this problem is {\it W[1]}-hard when parameterized by a structure of the graph called treewidth. Based on this hardness result, we present exact fixed-parameter tractable algorithms when parameterized by treewidth and maximum capacity of the vertices. This algorithm is further extended to obtain pseudo-polynomial time approximation schemes for planar graphs.


Experimenting with Innate Immunity

Abstract: In a previous paper the authors argued the case for incorporating ideas from innate immunity into artificial immune systems (AISs) and presented an outline for a conceptual framework for such systems. A number of key general properties observed in the biological innate and adaptive immune systems were highlighted, and how such properties might be instantiated in artificial systems was discussed in detail. The next logical step is to take these ideas and build a software system with which AISs with these properties can be implemented and experimentally evaluated. This paper reports on the results of that step - the libtissue system.


Behavioural Correlation for Detecting P2P Bots

Abstract: In the past few years, IRC bots, malicious programs which are remotely controlled by the attacker through IRC servers, have become a major threat to the Internet and users. These bots can be used in different malicious ways such as issuing distributed denial of services attacks to shutdown other networks and services, keystrokes logging, spamming, traffic sniffing cause serious disruption on networks and users. New bots use peer to peer (P2P) protocols start to appear as the upcoming threat to Internet security due to the fact that P2P bots do not have a centralized point to shutdown or traceback, thus making the detection of P2P bots is a real challenge. In response to these threats, we present an algorithm to detect an individual P2P bot running on a system by correlating its activities. Our evaluation shows that correlating different activities generated by P2P bots within a specified time period can detect these kind of bots.


Nurse Rostering with Genetic Algorithms

Abstract: In recent years genetic algorithms have emerged as a useful tool for the heuristic solution of complex discrete optimisation problems. In particular there has been considerable interest in their use in tackling problems arising in the areas of scheduling and timetabling. However, the classical genetic algorithm paradigm is not well equipped to handle constraints and successful implementations usually require some sort of modification to enable the search to exploit problem specific knowledge in order to overcome this shortcoming. This paper is concerned with the development of a family of genetic algorithms for the solution of a nurse rostering problem at a major UK hospital. The hospital is made up of wards of up to 30 nurses. Each ward has its own group of nurses whose shifts have to be scheduled on a weekly basis. In addition to fulfilling the minimum demand for staff over three daily shifts, nurses' wishes and qualifications have to be taken into account. The schedules must also be seen to be fair, in that unpopular shifts have to be spread evenly amongst all nurses, and other restrictions, such as team nursing and special conditions for senior staff, have to be satisfied. The basis of the family of genetic algorithms is a classical genetic algorithm consisting of n-point crossover, single-bit mutation and a rank-based selection. The solution space consists of all schedules in which each nurse works the required number of shifts, but the remaining constraints, both hard and soft, are relaxed and penalised in the fitness function. The talk will start with a detailed description of the problem and the initial implementation and will go on to highlight the shortcomings of such an approach, in terms of the key element of balancing feasibility, i.e. covering the demand and work regulations, and quality, as measured by the nurses' preferences. A series of experiments involving parameter adaptation, niching, intelligent weights, delta coding, local hill climbing, migration and special selection rules will then be outlined and it will be shown how a series of these enhancements were able to eradicate these difficulties. Results based on several months' real data will be used to measure the impact of each modification, and to show that the final algorithm is able to compete with a tabu search approach currently employed at the hospital. The talk will conclude with some observations as to the overall quality of this approach to this and similar problems.


SMT-based Verification of LTL Specifications with Integer Constraints and its Application to Runtime Checking of Service Substitutability

Abstract: An important problem that arises during the execution of service-based applications concerns the ability to determine whether a running service can be substituted with one with a different interface, for example if the former is no longer available. Standard Bounded Model Checking techniques can be used to perform this check, but they must be able to provide answers very quickly, lest the check hampers the operativeness of the application, instead of aiding it. The problem becomes even more complex when conversational services are considered, i.e., services that expose operations that have Input/Output data dependencies among them. In this paper we introduce a formal verification technique for an extension of Linear Temporal Logic that allows users to include in formulae constraints on integer variables. This technique applied to the substitutability problem for conversational services is shown to be considerably faster and with smaller memory footprint than existing ones.


GRASP for the Coalition Structure Formation Problem

Abstract: The coalition structure formation problem represents an active research area in multi-agent systems. A coalition structure is defined as a partition of the agents involved in a system into disjoint coalitions. The problem of finding the optimal coalition structure is NP-complete. In order to find the optimal solution in a combinatorial optimization problem it is theoretically possible to enumerate the solutions and evaluate each. But this approach is infeasible since the number of solutions often grows exponentially with the size of the problem. In this paper we present a greedy adaptive search procedure (GRASP) to efficiently search the space of coalition structures in order to find an optimal one. Experiments and comparisons to other algorithms prove the validity of the proposed method in solving this hard combinatorial problem.


HMC: Verifying Functional Programs Using Abstract Interpreters

Abstract: We present Hindley-Milner-Cousots (HMC), an algorithm that allows any interprocedural analysis for first-order imperative programs to be used to verify safety properties of typed higher-order functional programs. HMC works as follows. First, it uses the type structure of the functional program to generate a set of logical refinement constraints whose satisfaction implies the safety of the source program. Next, it transforms the logical refinement constraints into a simple first-order imperative program that is safe iff the constraints are satisfiable. Thus, in one swoop, HMC makes tools for invariant generation, e.g., based on abstract domains, predicate abstraction, counterexample-guided refinement, and Craig interpolation be directly applicable to verify safety properties of modern functional languages in a fully automatic manner. We have implemented HMC and describe preliminary experimental results using two imperative checkers -- ARMC and InterProc -- to verify OCaml programs. Thus, by composing type-based reasoning grounded in program syntax and state-based reasoning grounded in abstract interpretation, HMC opens the door to automatic verification of programs written in modern programming languages.


Approximately Optimal Mechanism Design via Differential Privacy

Abstract: In this paper we study the implementation challenge in an abstract interdependent values model and an arbitrary objective function. We design a mechanism that allows for approximate optimal implementation of insensitive objective functions in ex-post Nash equilibrium. If, furthermore, values are private then the same mechanism is strategy proof. We cast our results onto two specific models: pricing and facility location. The mechanism we design is optimal up to an additive factor of the order of magnitude of one over the square root of the number of agents and involves no utility transfers. Underlying our mechanism is a lottery between two auxiliary mechanisms: with high probability we actuate a mechanism that reduces players' influence on the choice of the social alternative, while choosing the optimal outcome with high probability. This is where the recent notion of differential privacy is employed. With the complementary probability we actuate a mechanism that is typically far from optimal but is incentive compatible. The joint mechanism inherits the desired properties from both.


Code forking in open-source software: a requirements perspective

Abstract: To fork a project is to copy the existing code base and move in a direction different than that of the erstwhile project leadership. Forking provides a rapid way to address new requirements by adapting an existing solution. However, it can also create a plethora of similar tools, and fragment the developer community. Hence, it is not always clear whether forking is the right strategy. In this paper, we describe a mixed-methods exploratory case study that investigated the process of forking a project. The study concerned the forking of an open-source tool for managing software projects, Trac. Trac was forked to address differing requirements in an academic setting. The paper makes two contributions to our understanding of code forking. First, our exploratory study generated several theories about code forking in open source projects, for further research. Second, we investigated one of these theories in depth, via a quantitative study. We conjectured that the features of the OSS forking process would allow new requirements to be addressed. We show that the forking process in this case was successful at fulfilling the new projects requirements.


On the approximability of robust spanning tree problems

Abstract: In this paper the minimum spanning tree problem with uncertain edge costs is discussed. In order to model the uncertainty a discrete scenario set is specified and a robust framework is adopted to choose a solution. The min-max, min-max regret and 2-stage min-max versions of the problem are discussed. The complexity and approximability of all these problems are explored. It is proved that the min-max and min-max regret versions with nonnegative edge costs are hard to approximate within $O(\log^{1-\epsilon} n)$ for any $\epsilon>0$ unless the problems in NP have quasi-polynomial time algorithms. Similarly, the 2-stage min-max problem cannot be approximated within $O(\log n)$ unless the problems in NP have quasi-polynomial time algorithms. In this paper randomized LP-based approximation algorithms with performance ratio of $O(\log^2 n)$ for min-max and 2-stage min-max problems are also proposed.


Streaming Graph Computations with a Helpful Advisor

Abstract: Motivated by the trend to outsource work to commercial cloud computing services, we consider a variation of the streaming paradigm where a streaming algorithm can be assisted by a powerful helper that can provide annotations to the data stream. We extend previous work on such {\em annotation models} by considering a number of graph streaming problems. Without annotations, streaming algorithms for graph problems generally require significant memory; we show that for many standard problems, including all graph problems that can be expressed with totally unimodular integer programming formulations, only a constant number of hash values are needed for single-pass algorithms given linear-sized annotations. We also obtain a protocol achieving \textit{optimal} tradeoffs between annotation length and memory usage for matrix-vector multiplication; this result contributes to a trend of recent research on numerical linear algebra in streaming models.


The Online Laboratory: Conducting Experiments in a Real Labor Market

Abstract: Online labor markets have great potential as platforms for conducting experiments, as they provide immediate access to a large and diverse subject pool and allow researchers to conduct randomized controlled trials. We argue that online experiments can be just as valid---both internally and externally---as laboratory and field experiments, while requiring far less money and time to design and to conduct. In this paper, we first describe the benefits of conducting experiments in online labor markets; we then use one such market to replicate three classic experiments and confirm their results. We confirm that subjects (1) reverse decisions in response to how a decision-problem is framed, (2) have pro-social preferences (value payoffs to others positively), and (3) respond to priming by altering their choices. We also conduct a labor supply field experiment in which we confirm that workers have upward sloping labor supply curves. In addition to reporting these results, we discuss the unique threats to validity in an online setting and propose methods for coping with these threats. We also discuss the external validity of results from online domains and explain why online results can have external validity equal to or even better than that of traditional methods, depending on the research question. We conclude with our views on the potential role that online experiments can play within the social sciences, and then recommend software development priorities and best practices.


Clustering with diversity

Abstract: We consider the {\em clustering with diversity} problem: given a set of colored points in a metric space, partition them into clusters such that each cluster has at least $\ell$ points, all of which have distinct colors. We give a 2-approximation to this problem for any $\ell$ when the objective is to minimize the maximum radius of any cluster. We show that the approximation ratio is optimal unless $\mathbf{P=NP}$, by providing a matching lower bound. Several extensions to our algorithm have also been developed for handling outliers. This problem is mainly motivated by applications in privacy-preserving data publication.


Subset feedback vertex set is fixed parameter tractable

Abstract: The classical Feedback Vertex Set problem asks, for a given undirected graph G and an integer k, to find a set of at most k vertices that hits all the cycles in the graph G. Feedback Vertex Set has attracted a large amount of research in the parameterized setting, and subsequent kernelization and fixed-parameter algorithms have been a rich source of ideas in the field. In this paper we consider a more general and difficult version of the problem, named Subset Feedback Vertex Set (SUBSET-FVS in short) where an instance comes additionally with a set S ? V of vertices, and we ask for a set of at most k vertices that hits all simple cycles passing through S. Because of its applications in circuit testing and genetic linkage analysis SUBSET-FVS was studied from the approximation algorithms perspective by Even et al. [SICOMP'00, SIDMA'00]. The question whether the SUBSET-FVS problem is fixed-parameter tractable was posed independently by Kawarabayashi and Saurabh in 2009. We answer this question affirmatively. We begin by showing that this problem is fixed-parameter tractable when parametrized by |S|. Next we present an algorithm which reduces the given instance to 2^k n^O(1) instances with the size of S bounded by O(k^3), using kernelization techniques such as the 2-Expansion Lemma, Menger's theorem and Gallai's theorem. These two facts allow us to give a 2^O(k log k) n^O(1) time algorithm solving the Subset Feedback Vertex Set problem, proving that it is indeed fixed-parameter tractable.


Hybrid CDN structure with a P2P based streaming protocol

Abstract: Over the last decade, internet has seen an exponential increase in its growth.With more and more people using it, efficient data delivery over the internet has become a key issue. Peer-to-peer (P2P)/seed sharing based networks have several desirable features for content distribution, such as low costs, scalability, and fault tolerance. While the invention of each of such specialized systems has improved the user experience, some fundamental shortcomings of these systems have often been neglected. These shortcomings of content distribution systems have become severe bottlenecks in scalability of the internet.In order to combine the desired features of classical Content Distribution Networks (CDNs) and P2P/seed sharing based networks, we propose a hybrid CDN structure with a P2P/seed sharing based streaming protocol in the access network . In this work, we focus on the problem of data redundancy (at each node) and show how severely it impacts the network economics and the experience of end-user and hence leads to low traffic load and redundancy


Enumeration of the Monomials of a Polynomial and Related Complexity Classes

Abstract: We study the problem of generating monomials of a polynomial in the context of enumeration complexity. In this setting, the complexity measure is the delay between two solutions and the total time. We present two new algorithms for restricted classes of polynomials, which have a good delay and the same global running time as the classical ones. Moreover they are simple to describe, use little evaluation points and one of them is parallelizable. We introduce three new complexity classes, TotalPP, IncPP and DelayPP, which are probabilistic counterparts of the most common classes for enumeration problems, hoping that randomization will be a tool as strong for enumeration as it is for decision. Our interpolation algorithms proves that a lot of interesting problems are in these classes like the enumeration of the spanning hypertrees of a 3-uniform hypergraph. Finally we give a method to interpolate a degree 2 polynomials with an acceptable (incremental) delay. We also prove that finding a specified monomial in a degree 2 polynomial is hard unless RP = NP. It suggests that there is no algorithm with a delay as good (polynomial) as the one we achieve for multilinear polynomials.


Persistent Asymmetric Password-Based Key Exchange

Abstract: Asymmetric password based key exchange is a key exchange protocol where a client and a server share a low entropic password while the server additionally owns a high entropic secret for a public key. There are simple solutions for this (e.g. Halevi and Krawczyk (ACM TISSEC 1999) and its improvement by Boyarsky (CCS 1999)). In this paper, we consider a new threat to this type of protocol: if a server's high entropic secret gets compromised (e.g., due to cryptanalysis, virus attack or a poor management), the adversary might {\em quickly} break lots of passwords and cause uncountable damage. In this case, one should not expect the protocol to be secure against an off-line dictionary attack since, otherwise, the protocol is in fact a secure password-only key exchange where the server also only has a password (by making the server high entropic secret public). Of course a password-only key exchange does not suffer from this threat as the server does not have a high entropic secret at all. However, known password-only key exchange are not very efficient (note: we only consider protocols without random oracles). This motivates us to study efficient and secure asymmetric password key exchange that avoids the new threat. In this paper, we first provide a formal model for the new threat, where essentially we require that the active adversary can break $\ell$ passwords in $\alpha\ell |{\cal D}|$ steps (for $\alpha<1/2$) only with a probability negligibly close to $\exp(-\beta\ell)$ for some $\beta>0$. Then, we construct a framework of asymmetric password based key exchange. We prove that our protocol is secure in the usual sense. We also show that it prevents the new threat. To do this, we introduce a new technique by abstracting a probabilistic experiment from the main proof and providing a neat analysis of it.


Prizing on Paths: A PTAS for the Highway Problem

Abstract: In the highway problem, we are given an n-edge line graph (the highway), and a set of paths (the drivers), each one with its own budget. For a given assignment of edge weights (the tolls), the highway owner collects from each driver the weight of the associated path, when it does not exceed the budget of the driver, and zero otherwise. The goal is choosing weights so as to maximize the profit. A lot of research has been devoted to this apparently simple problem. The highway problem was shown to be strongly NP-hard only recently [Elbassioni,Raman,Ray-'09]. The best-known approximation is O(\log n/\log\log n) [Gamzu,Segev-'10], which improves on the previous-best O(\log n) approximation [Balcan,Blum-'06]. In this paper we present a PTAS for the highway problem, hence closing the complexity status of the problem. Our result is based on a novel randomized dissection approach, which has some points in common with Arora's quadtree dissection for Euclidean network design [Arora-'98]. The basic idea is enclosing the highway in a bounding path, such that both the size of the bounding path and the position of the highway in it are random variables. Then we consider a recursive O(1)-ary dissection of the bounding path, in subpaths of uniform optimal weight. Since the optimal weights are unknown, we construct the dissection in a bottom-up fashion via dynamic programming, while computing the approximate solution at the same time. Our algorithm can be easily derandomized. We demonstrate the versatility of our technique by presenting PTASs for two variants of the highway problem: the tollbooth problem with a constant number of leaves and the maximum-feasibility subsystem problem on interval matrices. In both cases the previous best approximation factors are polylogarithmic [Gamzu,Segev-'10,Elbassioni,Raman,Ray,Sitters-'09].


Accountable Anonymous Group Messaging

Abstract: Users often wish to participate in online groups anonymously, but misbehaving users may abuse this anonymity to spam or disrupt the group. Messaging protocols such as Mix-nets and DC-nets leave online groups vulnerable to denial-of-service and Sybil attacks, while accountable voting protocols are unusable or inefficient for general anonymous messaging. We present the first general messaging protocol that offers provable anonymity with accountability for moderate-size groups, and efficiently handles unbalanced loads where few members have much data to transmit in a given round. The N group members first cooperatively shuffle an NxN matrix of pseudorandom seeds, then use these seeds in N "pre-planned" DC-nets protocol runs. Each DC-nets run transmits the variable-length bulk data comprising one member's message, using the minimum number of bits required for anonymity under our attack model. The protocol preserves message integrity and one-to-one correspondence between members and messages, makes denial-of-service attacks by members traceable to the culprit, and efficiently handles large and unbalanced message loads. A working prototype demonstrates the protocol's practicality for anonymous messaging in groups of 40+ member nodes.


Applying Stochastic Network Calculus to 802.11 Backlog and Delay Analysis

Abstract: Stochastic network calculus provides an elegant way to characterize traffic and service processes. However, little effort has been made on applying it to multi-access communication systems such as 802.11. In this paper, we take the first step to apply it to the backlog and delay analysis of an 802.11 wireless local network. In particular, we address the following questions: In applying stochastic network calculus, under what situations can we derive stable backlog and delay bounds? How to derive the backlog and delay bounds of an 802.11 wireless node? And how tight are these bounds when compared with simulations? To answer these questions, we first derive the general stability condition of a wireless node (not restricted to 802.11). From this, we give the specific stability condition of an 802.11 wireless node. Then we derive the backlog and delay bounds of an 802.11 node based on an existing model of 802.11. We observe that the derived bounds are loose when compared with ns-2 simulations, indicating that improvements are needed in the current version of stochastic network calculus.


Focusing in Asynchronous Games

Abstract: Game semantics provides an interactive point of view on proofs, which enables one to describe precisely their dynamical behavior during cut elimination, by considering formulas as games on which proofs induce strategies. We are specifically interested here in relating two such semantics of linear logic, of very different flavor, which both take in account concurrent features of the proofs: asynchronous games and concurrent games. Interestingly, we show that associating a concurrent strategy to an asynchronous strategy can be seen as a semantical counterpart of the focusing property of linear logic.


Genetic Algorithms for Multiple-Choice Problems

Abstract: This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.


Secure Localization in Wireless Sensor Networks: A Survey

Abstract: Wireless sensor networks (WSNs) have gained researchers' attention in the last several years. Small sensors powered by miniaturized microprocessors are capable of supporting several applications for civil and military domains. Determining the location of sensors is a basic and essential knowledge for most WSN algorithms and protocols including data tagging, routing, node identification, among others. This paper surveys the different algorithms that have been proposed to securely determine the location of a sensor node. By "secure", we mean that adversaries cannot easily affect the accuracy of the localized sensors. In other words, the localization algorithm must be robust under several attacks. We provide a taxonomy for classifying different secure localization schemes and describe possible attacks that can harm localization. In addition, we survey different secure localization schemes and show how they map to the proposed taxonomy. We also give a comparison between the different schemes, showing the attacks addressed by each.


GAC: Energy-Efficient Hybrid GPS-Accelerometer-Compass GSM Localization

Abstract: Adding location to the available information enables a new category of applications. With the constrained battery on cell phones, energy-efficient localization becomes an important challenge. In this paper we introduce a low-energy calibration-free localization scheme based on the available internal sensors in many of today's phones. We start by energy profiling the different sensors that can be used for localization. Based on that, we propose GAC: a hybrid GPS/accelerometer/compass scheme that depends mainly on using the low-energy accelerometer and compass sensors and uses the GPS infrequently for synchronization. We implemented our system on Android-enabled cell phones and evaluated it in both highways and intra-city driving environments. Our results show that the proposed hybrid scheme has an exponential saving in energy, with a linear loss in accuracy compared to the GPS accuracy. We also evaluate the effect of the different parameters on the energy-accuracy tradeoff.


CellSense: A Probabilistic RSSI-based GSM Positioning System

Abstract: Context-aware applications have been gaining huge interest in the last few years. With cell phones becoming ubiquitous computing devices, cell phone localization has become an important research problem. In this paper, we present CellSense, a prob- abilistic RSSI-based fingerprinting location determina- tion system for GSM phones.We discuss the challenges of implementing a probabilistic fingerprinting local- ization technique in GSM networks and present the details of the CellSense system and how it addresses the challenges. To evaluate our proposed system, we implemented CellSense on Android-based phones. Re- sults for two different testbeds, representing urban and rural environments, show that CellSense provides at least 23.8% enhancement in accuracy in rural areas and at least 86.4% in urban areas compared to other RSSI-based GSMlocalization systems. This comes with a minimal increase in computational requirements. We also evaluate the effect of changing the different system parameters on the accuracy-complexity tradeoff.


Introducing Dendritic Cells as a Novel Immune-Inspired Algorithm for Anomoly Detection

Abstract: Dendritic cells are antigen presenting cells that provide a vital link between the innate and adaptive immune system. Research into this family of cells has revealed that they perform the role of coordinating T-cell based immune responses, both reactive and for generating tolerance. We have derived an algorithm based on the functionality of these cells, and have used the signals and differentiation pathways to build a control mechanism for an artificial immune system. We present our algorithmic details in addition to some preliminary results, where the algorithm was applied for the purpose of anomaly detection. We hope that this algorithm will eventually become the key component within a large, distributed immune system, based on sound immunological concepts.


Differential Privacy and the Fat-Shattering Dimension of Linear Queries

Abstract: In this paper, we consider the task of answering linear queries under the constraint of differential privacy. This is a general and well-studied class of queries that captures other commonly studied classes, including predicate queries and histogram queries. We show that the accuracy to which a set of linear queries can be answered is closely related to its fat-shattering dimension, a property that characterizes the learnability of real-valued functions in the agnostic-learning setting.


Causality and the semantics of provenance

Abstract: Provenance, or information about the sources, derivation, custody or history of data, has been studied recently in a number of contexts, including databases, scientific workflows and the Semantic Web. Many provenance mechanisms have been developed, motivated by informal notions such as influence, dependence, explanation and causality. However, there has been little study of whether these mechanisms formally satisfy appropriate policies or even how to formalize relevant motivating concepts such as causality. We contend that mathematical models of these concepts are needed to justify and compare provenance techniques. In this paper we review a theory of causality based on structural models that has been developed in artificial intelligence, and describe work in progress on a causal semantics for provenance graphs.


The Complexity of Finding Reset Words in Finite Automata

Abstract: We study several problems related to finding reset words in deterministic finite automata. In particular, we establish that the problem of deciding whether a shortest reset word has length k is complete for the complexity class DP. This result answers a question posed by Volkov. For the search problems of finding a shortest reset word and the length of a shortest reset word, we establish membership in the complexity classes FP^NP and FP^NP[log], respectively. Moreover, we show that both these problems are hard for FP^NP[log]. Finally, we observe that computing a reset word of a given length is FNP-complete.


Watermarking Java Programs using Dummy Methods with Dynamically Opaque Predicates

Abstract: Software piracy, the illegal using, copying, and resale of applications is a major concern for anyone develops software. Software developers also worry about their applications being reverse engineered by extracting data structures and algorithms from an application and incorporated into competitor's code. A defense against software piracy is watermarking, a process that embeds a secret message in a cover software. Watermarking is a method that does not aim to stop piracy copying, but to prove ownership of the software and possibly even the data structures and algorithms used in the software. The language Java was designed to be compiled into a platform independent bytecode format. Much of the information contained in the source code remains in the bytecode, which means that decompilation is easier than with traditional native codes. In this thesis, we present a technique for watermarking Java programs by using a never-executed dummy method (Monden et.al., 2000) combined with opaque predicates (Collberg et.al., 1998; Arboit, 2002) and improved with dynamically opaque predicates (Palsberg et.al., 2000). This work presents a method to construct a dynamic opaque predicates by grouping two or more opaque predicates according to predefined rules. Any software watermarking technique will exhibit a trade-off between resilience, data rate, cost, and stealth. To evaluate the quality of a watermarking scheme we must also know how well it stands up to different types of attacks. Ideally, we would like our watermarks to survive translation (compilation, decompilation, and binary translation), optimization, and obfuscation. Add a single watermark will increasing source code approximate 3.854 bytes with dummy method that cover up to 15 characters, two dynamic data structures, two threads and two opaque predicates. Application loading-time increase approximate 6108 milliseconds.


Automatic Mapping Tasks to Cores - Evaluating AMTHA Algorithm in Multicore Architectures

Abstract: The AMTHA (Automatic Mapping Task on Heterogeneous Architectures) algorithm for task-to-processors assignment and the MPAHA (Model of Parallel Algorithms on Heterogeneous Architectures) model are presented. The use of AMTHA is analyzed for multicore processor-based architectures, considering the communication model among processes in use. The results obtained in the tests carried out are presented, comparing the real execution times on multicores of a set of synthetic applications with the predictions obtained with AMTHA. Finally current lines of research are presented, focusing on clusters of multicores and hybrid programming paradigms.


A model-driven approach for composing SAWSDL semantic Web services

Abstract: Composing Web services is a convenient means of dealing with complex requests. However, the number of Web services on the Internet is increasing. This explains the growing interest in composing Web services automatically. Nevertheless, the Web services' semantics is necessary for any dynamic composition process. In this article, we present an MDA approach to develop and compose SAWSDL semantic Web services. To model Web services, we use a UML profile which is independent of the description standards. The SAWSDL interface files are generated by using transformation rules. To model the behavior of a composite Web service and generate its executable BPEL file, we use the BPMN notation in a platform of modeling and implementing business process. The main contribution of this work is the easy and extensible solution to a model-driven development of the semantic atomic and composite Web services.


Offline Handwriting Recognition using Genetic Algorithm

Abstract: Handwriting Recognition enables a person to scribble something on a piece of paper and then convert it into text. If we look into the practical reality there are enumerable styles in which a character may be written. These styles can be self combined to generate more styles. Even if a small child knows the basic styles a character can be written, he would be able to recognize characters written in styles intermediate between them or formed by their mixture. This motivates the use of Genetic Algorithms for the problem. In order to prove this, we made a pool of images of characters. We converted them to graphs. The graph of every character was intermixed to generate styles intermediate between the styles of parent character. Character recognition involved the matching of the graph generated from the unknown character image with the graphs generated by mixing. Using this method we received an accuracy of 98.44%.


Multiple Criteria Decision-Making Preprocessing Using Data Mining Tools

Abstract: Real-life engineering optimization problems need Multiobjective Optimization (MOO) tools. These problems are highly nonlinear. As the process of Multiple Criteria Decision-Making (MCDM) is much expanded most MOO problems in different disciplines can be classified on the basis of it. Thus MCDM methods have gained wide popularity in different sciences and applications. Meanwhile the increasing number of involved components, variables, parameters, constraints and objectives in the process, has made the process very complicated. However the new generation of MOO tools has made the optimization process more automated, but still initializing the process and setting the initial value of simulation tools and also identifying the effective input variables and objectives in order to reach the smaller design space are still complicated. In this situation adding a preprocessing step into the MCDM procedure could make a huge difference in terms of organizing the input variables according to their effects on the optimization objectives of the system. The aim of this paper is to introduce the classification task of data mining as an effective option for identifying the most effective variables of the MCDM systems. To evaluate the effectiveness of the proposed method an example has been given for 3D wing design.


Decision Support Systems (DSS) in Construction Tendering Processes

Abstract: The successful execution of a construction project is heavily impacted by making the right decision during tendering processes. Managing tender procedures is very complex and uncertain involving coordination of many tasks and individuals with different priorities and objectives. Bias and inconsistent decision are inevitable if the decision-making process is totally depends on intuition, subjective judgement or emotion. In making transparent decision and healthy competition tendering, there exists a need for flexible guidance tool for decision support. Aim of this paper is to give a review on current practices of Decision Support Systems (DSS) technology in construction tendering processes. Current practices of general tendering processes as applied to the most countries in different regions such as United States, Europe, Middle East and Asia are comprehensively discussed. Applications of Web-based tendering processes is also summarised in terms of its properties. Besides that, a summary of Decision Support System (DSS) components is included in the next section. Furthermore, prior researches on implementation of DSS approaches in tendering processes are discussed in details. Current issues arise from both of paper-based and Web-based tendering processes are outlined. Finally, conclusion is included at the end of this paper.


A Computational Algorithm for Metrical Classification of Verse

Abstract: The science of versification and analysis of verse in Sanskrit is governed by rules of metre or chandas. Such metre-wise classification of verses has numerous uses for scholars and researchers alike, such as in the study of poets and their style of Sanskrit poetical works. This paper presents a comprehensive computational scheme and set of algorithms to identify the metre of verses given as Sanskrit (Unicode) or English E-text (Latin Unicode). The paper also demonstrates the use of euphonic conjunction rules to correct verses in which these conjunctions, which are compulsory in verse, have erroneously not been implemented.


DRMS Co-design by F4MS

Abstract: In this paper, we present Digital Rights Management systems (DRMS) which are becoming more and more complex due to technology revolution in relation with telecommunication networks, multimedia applications and the reading equipments (Mobile Phone, IPhone, PDA, DVD Player,..). The complexity of the DRMS, involves the use of new tools and methodologies that support software components and hardware components coupled design. The traditional systems design approach has been somewhat hardware first in that the software components are designed after the hardware has been designed and prototyped. This leaves little flexibility in evaluating different design options and hardware-software mappings. The key of codesign is to avoid isolation between hardware and software designs to proceed in parallel, with feedback and interaction between the two as the design progresses, in order to achieve high quality designs with a reduced design time. In this paper, we present the F4MS (Framework for Mixed Systems) which is a unified framework for software and hardware design environment, simulation and aided execution of mixed systems. To illustrate this work we propose an implementation of DRMS business model based on F4MS framework.


Modelling of Human Glottis in VLSI for Low Power Architectures

Abstract: The Glottal Source is an important component of voice as it can be considered as the excitation signal to the voice apparatus. Nowadays, new techniques of speech processing such as speech recognition and speech synthesis use the glottal closure and opening instants. Current models of the glottal waves derive their shape from approximate information rather than from exactly measured data. General method concentrate on assessment of the glottis opening using optical, acoustical methods, or on visualization of the larynx position using ultrasound, computer tomography or magnetic resonance imaging techniques. In this work, circuit model of Human Glottis using MOS is designed by exploiting fluid volume velocity to current, fluid pressure to voltage, and linear and nonlinear mechanical impedances to linear and nonlinear electrical impedances. The glottis modeled as current source includes linear, non-linear impedances to represent laminar and turbulent flow respectively, in vocal tract. The MOS modelling and simulation results of glottal circuit has been carried out on BSIM 3v3 model in TSMC 0.18 micrometer technology using ELDO simulator.


Adaptive Neuro-Fuzzy Extended Kalman Filtering for Robot Localization

Abstract: Extended Kalman Filter (EKF) has been a popular approach to localization a mobile robot. However, the performance of the EKF and the quality of the estimation depends on the correct a priori knowledge of process and measurement noise covariance matrices (Qk and Rk, respectively). Imprecise knowledge of these statistics can cause significant degradation in performance. This paper proposed the development of an Adaptive Neuro- Fuzzy Extended Kalman Filtering (ANFEKF) for localization of robot. The Adaptive Neuro-Fuzzy attempts to estimate the elements of Qk and Rk matrices of the EKF algorithm, at each sampling instant when measurement update step is carried out. The ANFIS supervises the performance of the EKF with the aim of reducing the mismatch between the theoretical and actual covariance of the innovation sequences. The free parameters of ANFIS are trained using the steepest gradient descent (SD) to minimize the differences of the actual value of the covariance of the residual with its theoretical value as much possible. The simulation results show the effectiveness of the proposed algorithm.


DBSR: Dynamic base station Repositioning using Genetic algorithm in wireless sensor network

Abstract: Wireless sensor networks (WSNs) are commonly used in various ubiquitous and pervasive applications. Due to limited power resources, the optimal dynamic base station (BS) replacement could be Prolong the sensor network lifetime. In this paper we'll present a dynamic optimum method for base station replacement so that can save energy in sensors and increases network lifetime. Because positioning problem is a NPhard problem [1], therefore we'll use genetic algorithm to solve positioning problem. We've considered energy and distance parameters for finding BS optimized position. In our represented algorithm base station position is fixed just during each round and its positioning is done at the start of next round then it'll be placed in optimized position. Evaluating our proposed algorithm, we'll execute DBSR algorithm on LEACH & HEED Protocols.


Optimized Fuzzy Logic Based Framework for Effort Estimation in Software Development

Abstract: Software effort estimation at early stages of project development holds great significance for the industry to meet the competitive demands of today's world. Accuracy, reliability and precision in the estimates of effort are quite desirable. The inherent imprecision present in the inputs of the algorithmic models like Constructive Cost Model (COCOMO) yields imprecision in the output, resulting in erroneous effort estimation. Fuzzy logic based cost estimation models are inherently suitable to address the vagueness and imprecision in the inputs, to make reliable and accurate estimates of effort. In this paper, we present an optimized fuzzy logic based framework for software development effort prediction. The said framework tolerates imprecision, incorporates experts knowledge, explains prediction rationale through rules, offers transparency in the prediction system, and could adapt to changing environments with the availability of new data. The traditional cost estimation model COCOMO is extended in the proposed study by incorporating the concept of fuzziness into the measurements of size, mode of development for projects and the cost drivers contributing to the overall development effort.


A General Simulation Framework for Supply Chain Modeling: State of the Art and Case Study

Abstract: Nowadays there is a large availability of discrete event simulation software that can be easily used in different domains: from industry to supply chain, from healthcare to business management, from training to complex systems design. Simulation engines of commercial discrete event simulation software use specific rules and logics for simulation time and events management. Difficulties and limitations come up when commercial discrete event simulation software are used for modeling complex real world-systems (i.e. supply chains, industrial plants). The objective of this paper is twofold: first a state of the art on commercial discrete event simulation software and an overview on discrete event simulation models development by using general purpose programming languages are presented; then a Supply Chain Order Performance Simulator (SCOPS, developed in C++) for investigating the inventory management problem along the supply chain under different supply chain scenarios is proposed to readers.


Database Reverse Engineering based on Association Rule Mining

Abstract: Maintaining a legacy database is a difficult task especially when system documentation is poor written or even missing. Database reverse engineering is an attempt to recover high-level conceptual design from the existing database instances. In this paper, we propose a technique to discover conceptual schema using the association mining technique. The discovered schema corresponds to the normalization at the third normal form, which is a common practice in many business organizations. Our algorithm also includes the rule filtering heuristic to solve the problem of exponential growth of discovered rules inherited with the association mining technique.


A New Approach to Keyphrase Extraction Using Neural Networks

Abstract: Keyphrases provide a simple way of describing a document, giving the reader some clues about its contents. Keyphrases can be useful in a various applications such as retrieval engines, browsing interfaces, thesaurus construction, text mining etc.. There are also other tasks for which keyphrases are useful, as we discuss in this paper. This paper describes a neural network based approach to keyphrase extraction from scientific articles. Our results show that the proposed method performs better than some state-of-the art keyphrase extraction approaches.


C Implementation & comparison of companding & silence audio compression techniques

Abstract: Just about all the newest living room audio-video electronics and PC multimedia products being designed today will incorporate some form of compressed digitized-audio processing capability. Audio compression reduces the bit rate required to represent an analog audio signal while maintaining the perceived audio quality. Discarding inaudible data reduces the storage, transmission and compute requirements of handling high-quality audio files. This paper covers wave audio file format & algorithm of silence compression method and companding method to compress and decompress wave audio file. Then it compares the result of these two methods.


Color Image Compression Based On Wavelet Packet Best Tree

Abstract: In Image Compression, the researchers' aim is to reduce the number of bits required to represent an image by removing the spatial and spectral redundancies. Recently discrete wavelet transform and wavelet packet has emerged as popular techniques for image compression. The wavelet transform is one of the major processing components of image compression. The result of the compression changes as per the basis and tap of the wavelet used. It is proposed that proper selection of mother wavelet on the basis of nature of images, improve the quality as well as compression ratio remarkably. We suggest the novel technique, which is based on wavelet packet best tree based on Threshold Entropy with enhanced run-length encoding. This method reduces the time complexity of wavelet packets decomposition as complete tree is not decomposed. Our algorithm selects the sub-bands, which include significant information based on threshold entropy. The enhanced run length encoding technique is suggested provides better results than RLE. The result when compared with JPEG-2000 proves to be better.


A Pedagogical Evaluation and Discussion about the Lack of Cohesion in Method (LCOM) Metric Using Field Experiment

Abstract: Chidamber and Kemerer first defined a cohesion measure for object-oriented software - the Lack of Cohesion in Methods (LCOM) metric. This paper presents a pedagogic evaluation and discussion about the LCOM metric using field data from three industrial systems. System 1 has 34 classes, System 2 has 383 classes and System 3 has 1055 classes. The main objectives of the study were to determine if the LCOM metric was appropriate in the measurement of class cohesion and the determination of properly and improperly designed classes in the studied systems. Chidamber and Kemerer's suite of metric was used as metric tool. Descriptive statistics was used to analyze results. The result of the study showed that in System 1, 78.8% (26 classes) were cohesive; System 2 54% (207 classes) were cohesive; System 3 30% (317 classes) were cohesive. We suggest that the LCOM metric measures class cohesiveness and was appropriate in the determination of properly and improperly designed classes in the studied system.


Information Cost Tradeoffs for Augmented Index and Streaming Language Recognition

Abstract: This paper makes three main contributions to the theory of communication complexity and stream computation. First, we present new bounds on the information complexity of AUGMENTED-INDEX. In contrast to analogous results for INDEX by Jain, Radhakrishnan and Sen [J. ACM, 2009], we have to overcome the significant technical challenge that protocols for AUGMENTED-INDEX may violate the "rectangle property" due to the inherent input sharing. Second, we use these bounds to resolve an open problem of Magniez, Mathieu and Nayak [STOC, 2010] that asked about the multi-pass complexity of recognizing Dyck languages. This results in a natural separation between the standard multi-pass model and the multi-pass model that permits reverse passes. Third, we present the first passive memory checkers that verify the interaction transcripts of priority queues, stacks, and double-ended queues. We obtain tight upper and lower bounds for these problems, thereby addressing an important sub-class of the memory checking framework of Blum et al. [Algorithmica, 1994].


Approximation Algorithms for Dominating Set in Disk Graphs

Abstract: We consider the problem of finding a lowest cost dominating set in a given disk graph containing $n$ disks. The problem has been extensively studied on subclasses of disk graphs, yet the best known approximation for disk graphs has remained $O(\log n)$ -- a bound that is asymptotically no better than the general case. We improve the status quo in two ways: for the unweighted case, we show how to obtain a PTAS using the framework recently proposed (independently)by Mustafa and Ray [SoCG 09] and by Chan and Har-Peled [SoCG 09]; for the weighted case where each input disk has an associated rational weight with the objective of finding a minimum cost dominating set, we give a randomized algorithm that obtains a dominating set whose weight is within a factor $2^{O(\log^* n)}$ of a minimum cost solution, with high probability -- the technique follows the framework proposed recently by Varadarajan [STOC 10].


An Efficient Hybrid Data Gathering Scheme in Wireless Sensor Networks

Abstract: This paper has been withdrawn by the author due to a crucial sign error in equation 1


Generation and Interpretation of Temporal Decision Rules

Abstract: We present a solution to the problem of understanding a system that produces a sequence of temporally ordered observations. Our solution is based on generating and interpreting a set of temporal decision rules. A temporal decision rule is a decision rule that can be used to predict or retrodict the value of a decision attribute, using condition attributes that are observed at times other than the decision attribute's time of observation. A rule set, consisting of a set of temporal decision rules with the same decision attribute, can be interpreted by our Temporal Investigation Method for Enregistered Record Sequences (TIMERS) to signify an instantaneous, an acausal or a possibly causal relationship between the condition attributes and the decision attribute. We show the effectiveness of our method, by describing a number of experiments with both synthetic and real temporal data.


Citing for High Impact

Abstract: The question of citation behavior has always intrigued scientists from various disciplines. While general citation patterns have been widely studied in the literature we develop the notion of citation projection graphs by investigating the citations among the publications that a given paper cites. We investigate how patterns of citations vary between various scientific disciplines and how such patterns reflect the scientific impact of the paper. We find that idiosyncratic citation patterns are characteristic for low impact papers; while narrow, discipline-focused citation patterns are common for medium impact papers. Our results show that crossing-community, or bridging citation patters are high risk and high reward since such patterns are characteristic for both low and high impact papers. Last, we observe that recently citation networks are trending toward more bridging and interdisciplinary forms.


Faster Algorithms for Semi-Matching Problems

Abstract: We consider the problem of finding \textit{semi-matching} in bipartite graphs which is also extensively studied under various names in the scheduling literature. We give faster algorithms for both weighted and unweighted case. For the weighted case, we give an $O(nm\log n)$-time algorithm, where $n$ is the number of vertices and $m$ is the number of edges, by exploiting the geometric structure of the problem. This improves the classical $O(n^3)$ algorithms by Horn [Operations Research 1973] and Bruno, Coffman and Sethi [Communications of the ACM 1974]. For the unweighted case, the bound could be improved even further. We give a simple divide-and-conquer algorithm which runs in $O(\sqrt{n}m\log n)$ time, improving two previous $O(nm)$-time algorithms by Abraham [MSc thesis, University of Glasgow 2003] and Harvey, Ladner, Lov\'asz and Tamir [WADS 2003 and Journal of Algorithms 2006]. We also extend this algorithm to solve the \textit{Balance Edge Cover} problem in $O(\sqrt{n}m\log n)$ time, improving the previous $O(nm)$-time algorithm by Harada, Ono, Sadakane and Yamashita [ISAAC 2008].


Improving Update Summarization by Revisiting the MMR Criterion

Abstract: This paper describes a method for multi-document update summarization that relies on a double maximization criterion. A Maximal Marginal Relevance like criterion, modified and so called Smmr, is used to select sentences that are close to the topic and at the same time, distant from sentences used in already read documents. Summaries are then generated by assembling the high ranked material and applying some ruled-based linguistic post-processing in order to obtain length reduction and maintain coherency. Through a participation to the Text Analysis Conference (TAC) 2008 evaluation campaign, we have shown that our method achieves promising results.


Faster Approximation Schemes and Parameterized Algorithms on H-Minor-Free and Odd-Minor-Free Graphs

Abstract: We improve the running time of the general algorithmic technique known as Baker's approach (1994) on H-minor-free graphs from O(n^{f(|H|)}) to O(f(|H|) n^{O(1)}). The numerous applications include e.g. a 2-approximation for coloring and PTASes for various problems such as dominating set and max-cut, where we obtain similar improvements. On classes of odd-minor-free graphs, which have gained significant attention in recent time, we obtain a similar acceleration for a variant of the structural decomposition theorem proved by Demaine et al. (2010). We use these algorithms to derive faster 2-approximations; furthermore, we present the first PTASes and subexponential FPT-algorithms for independent set and vertex cover on these graph classes using a novel dynamic programming technique. We also introduce a technique to derive (nearly) subexponential parameterized algorithms on H-minor-free graphs. Our technique applies, in particular, to problems such as Steiner tree, (directed) subgraph with a property, (directed) longest path, and (connected/independent) dominating set, on some or all proper minor-closed graph classes. We obtain as a corollary that all problems with a minor-monotone subexponential kernel and amenable to our technique can be solved in subexponential FPT-time on H-minor free graphs. This results in a general methodology for subexponential parameterized algorithms outside the framework of bidimensionality.


Passively Mobile Communicating Logarithmic Space Machines

Abstract: We propose a new theoretical model for passively mobile Wireless Sensor Networks. We call it the PALOMA model, standing for PAssively mobile LOgarithmic space MAchines. The main modification w.r.t. the Population Protocol model is that agents now, instead of being automata, are Turing Machines whose memory is logarithmic in the population size n. Note that the new model is still easily implementable with current technology. We focus on complete communication graphs. We define the complexity class PLM, consisting of all symmetric predicates on input assignments that are stably computable by the PALOMA model. We assume that the agents are initially identical. Surprisingly, it turns out that the PALOMA model can assign unique consecutive ids to the agents and inform them of the population size! This allows us to give a direct simulation of a Deterministic Turing Machine of O(nlogn) space, thus, establishing that any symmetric predicate in SPACE(nlogn) also belongs to PLM. We next prove that the PALOMA model can simulate the Community Protocol model, thus, improving the previous lower bound to all symmetric predicates in NSPACE(nlogn). Going one step further, we generalize the simulation of the deterministic TM to prove that the PALOMA model can simulate a Nondeterministic TM of O(nlogn) space. Although providing the same lower bound, the important remark here is that the bound is now obtained in a direct manner, in the sense that it does not depend on the simulation of a TM by a Pointer Machine. Finally, by showing that a Nondeterministic TM of O(nlogn) space decides any language stably computable by the PALOMA model, we end up with an exact characterization for PLM: it is precisely the class of all symmetric predicates in NSPACE(nlogn).


Control Complexity in Fallback Voting

Abstract: We study the control complexity of fallback voting. Like manipulation and bribery, electoral control describes ways of changing the outcome of an election; unlike manipulation or bribery attempts, control actions---such as adding/deleting/partitioning either candidates or voters---modify the participative structure of an election. Via such actions one can try to either make a favorite candidate win ("constructive control") or prevent a despised candidate from winning ("destructive control"). Computational complexity can be used to protect elections from control attempts, i.e., proving an election system resistant to some type of control shows that the success of the corresponding control action, though not impossible, is computationally prohibitive. We show that fallback voting, an election system combining approval with majority voting, is resistant to each of the common types of candidate control and to each common type of constructive control. Among natural election systems with a polynomial-time winner problem, only plurality and sincere-strategy preference-based approval voting (SP-AV) were previously known to be fully resistant to candidate control, and only Copeland voting and SP-AV were previously known to be fully resistant to constructive control. However, plurality has fewer resistances to voter control, Copeland voting has fewer resistances to destructive control, and SP-AV (which like fallback voting has 19 out of 22 proven control resistances) is arguably less natural a system than fallback voting.


An Optimized Lifetime Enhancement Scheme for Data Gathering in Wireless Sensor Networks


An Energy Efficient Scheme for Data Gathering in Wireless Sensor Networks Using Particle Swarm Optimization


Towards Providing Low-Risk and Economically Feasible Network Data Transfer Services

Abstract: In the first part of this paper we present the first steps towards providing low-risk and economically feasible network data transfer services. We introduce three types of data transfer services and present general guidelines and algorithms for managing service prices, risks and schedules. In the second part of the paper we solve two packet scheduling cost optimization problems and present efficient algorithms for identifying maximum weight (k-level-) caterpillar subtrees in tree networks.


PCA 4 DCA: The Application Of Principal Component Analysis To The Dendritic Cell Algorithm

Abstract: As one of the newest members in the field of artificial immune systems (AIS), the Dendritic Cell Algorithm (DCA) is based on behavioural models of natural dendritic cells (DCs). Unlike other AIS, the DCA does not rely on training data, instead domain or expert knowledge is required to predetermine the mapping between input signals from a particular instance to the three categories used by the DCA. This data preprocessing phase has received the criticism of having manually over-?tted the data to the algorithm, which is undesirable. Therefore, in this paper we have attempted to ascertain if it is possible to use principal component analysis (PCA) techniques to automatically categorise input data while still generating useful and accurate classication results. The integrated system is tested with a biometrics dataset for the stress recognition of automobile drivers. The experimental results have shown the application of PCA to the DCA for the purpose of automated data preprocessing is successful.


Learning Better Context Characterizations: An Intelligent Information Retrieval Approach

Abstract: This paper proposes an incremental method that can be used by an intelligent system to learn better descriptions of a thematic context. The method starts with a small number of terms selected from a simple description of the topic under analysis and uses this description as the initial search context. Using these terms, a set of queries are built and submitted to a search engine. New documents and terms are used to refine the learned vocabulary. Evaluations performed on a large number of topics indicate that the learned vocabulary is much more effective than the original one at the time of constructing queries to retrieve relevant material.


Communication and Round Efficient Information Checking Protocol

Abstract: In this paper, we present a very important primitive called Information Checking Protocol (ICP) which plays an important role in constructing statistical Verifiable Secret Sharing (VSS) and Weak Secret Sharing (WSS) protocols. Informally, ICP is a tool for authenticating messages in the presence of computationally unbounded corrupted parties. Here we extend the basic bare-bone definition of ICP, introduced by Rabin et al. and then present an ICP that attains the best communication complexity and round complexity among all the existing ICPs in the literature. We also show that our ICP satisfies several interesting properties such as linearity property which is an important requirement in many applications of ICP. Though not presented in this paper, we can design communication and round efficient statistical (i.e involves negligible error probability in computation) VSS and Multiparty Computation (MPC) protocol using our new ICP.


Cryptanalysis of an Elliptic Curve-based Signcryption Scheme

Abstract: The signcryption is a relatively new cryptographic technique that is supposed to fulfill the functionalities of encryption and digital signature in a single logical step. Although several signcryption schemes are proposed over the years, some of them are proved to have security problems. In this paper, the security of Han et al.'s signcryption scheme is analyzed, and it is proved that it has many security flaws and shortcomings. Several devastating attacks are also introduced to the mentioned scheme whereby it fails all the desired and essential security attributes of a signcryption scheme.


Propose a Fuzzy Queuing Maximal Benefit Location Problem

Abstract: This paper presents a fuzzy queuing location model for congested system. In a queuing system there are different criteria that are not constant such as service rate, service rate demand, queue length, the occupancy probability of a service center and Probability of joining the queue line. In this paper with fuzzifying all of these variables, will try to reach an accurate real problem. Finally we change the problem to a single objective function and as far as this model is in NP-Hard classification we will use genetic algorithm for solving it and ant colony for comparison is used for their results and run time.


Signature Region of Interest using Auto cropping

Abstract: A new approach for signature region of interest pre-processing was presented. It used new auto cropping preparation on the basis of the image content, where the intensity value of pixel is the source of cropping. This approach provides both the possibility of improving the performance of security systems based on signature images, and also the ability to use only the region of interest of the used image to suit layout design of biometric systems. Underlying the approach is a novel segmentation method which identifies the exact region of foreground of signature for feature extraction usage. Evaluation results of this approach shows encouraging prospects by eliminating the need for false region isolating, reduces the time cost associated with signature false points detection, and addresses enhancement issues. A further contribution of this paper is an automated cropping stage in bio-secure based systems.


Comparative Performance Investigations of different scenarios for 802.15.4 WPAN

Abstract: This paper investigates the performance of WPAN based on various topological scenarios like: cluster, star and ring. The comparative results have been reported for the performance metrics like: Throughput, Traffic sent, Traffic received and Packets dropped. Cluster topology is best in comparison with star and ring topologies as it has been shown that the throughput in case of cluster topology (79.887 kbits / sec) as compared to star (31.815 kbits / sec) and ring (1.179 kbits / sec).


Policies and Economics of Digital Multimedia Transmission

Abstract: There are different Standards of digital multimedia transmission, for example DVB in Europe and ISDB in Japan and DMB in Korea, with different delivery system (example MPEG-2, MPEG-4).This paper describe an overview of Digital Multimedia Transmission (DMT) technologies. The economic aspects of digital content & software solution industry as a strategic key in the future will be discussed. The study then focuses on some important policy and technology issues, such S-DMB, T-DMB, Digital Video Broadcasting Handheld (DVB-H) and concludes DMT policies for convergence of telecommunications and broadcasting.


Neuroevolutionary optimization

Abstract: This paper presents an application of evolutionary search procedures to artificial neural networks. Here, we can distinguish among three kinds of evolution in artificial neural networks, i.e. the evolution of connection weights, of architectures, and of learning rules. We review each kind of evolution in detail and analyse critical issues related to different evolutions. This article concentrates on finding the suitable way of using evolutionary algorithms for optimizing the artificial neural network parameters.


Comparison of the Performance of Two Service Disciplines for a Shared Bus Multiprocessor with Private Caches

Abstract: In this paper, we compare two analytical models for evaluation of cache coherence overhead of a shared bus multiprocessor with private caches. The models are based on a closed queuing network with different service disciplines. We find that the priority discipline can be used as a lower-level bound. Some numerical results are shown graphically.


A QoS Provisioning Recurrent Neural Network based Call Admission Control for beyond 3G Networks

Abstract: The Call admission control (CAC) is one of the Radio Resource Management (RRM) techniques that plays influential role in ensuring the desired Quality of Service (QoS) to the users and applications in next generation networks. This paper proposes a fuzzy neural approach for making the call admission control decision in multi class traffic based Next Generation Wireless Networks (NGWN). The proposed Fuzzy Neural call admission control (FNCAC) scheme is an integrated CAC module that combines the linguistic control capabilities of the fuzzy logic controller and the learning capabilities of the neural networks. The model is based on recurrent radial basis function networks which have better learning and adaptability that can be used to develop intelligent system to handle the incoming traffic in an heterogeneous network environment. The simulation results are optimistic and indicates that the proposed FNCAC algorithm performs better than the other two methods and the call blocking probability is minimal when compared to other two methods.


An Optimized Weighted Association Rule Mining On Dynamic Content

Abstract: Association rule mining aims to explore large transaction databases for association rules. Classical Association Rule Mining (ARM) model assumes that all items have the same significance without taking their weight into account. It also ignores the difference between the transactions and importance of each and every itemsets. But, the Weighted Association Rule Mining (WARM) does not work on databases with only binary attributes. It makes use of the importance of each itemset and transaction. WARM requires each item to be given weight to reflect their importance to the user. The weights may correspond to special promotions on some products, or the profitability of different items. This research work first focused on a weight assignment based on a directed graph where nodes denote items and links represent association rules. A generalized version of HITS is applied to the graph to rank the items, where all nodes and links are allowed to have weights. This research then uses enhanced HITS algorithm by developing an online eigenvector calculation method that can compute the results of mutual reinforcement voting in case of frequent updates. For Example in Share Market Shares price may go down or up. So we need to carefully watch the market and our association rule mining has to produce the items that have undergone frequent changes. These are done by estimating the upper bound of perturbation and postponing of the updates whenever possible. Next we prove that enhanced algorithm is more efficient than the original HITS under the context of dynamic data.


An Economic-based Resource Management and Scheduling for Grid Computing Applications

Abstract: Resource management and scheduling plays a crucial role in achieving high utilization of resources in grid computing environments. Due to heterogeneity of resources, scheduling an application is significantly complicated and challenging task in grid system. Most of the researches in this area are mainly focused on to improve the performance of the grid system. There were some allocation model has been proposed based on divisible load theory with different type of workloads and a single originating processor. In this paper we introduce a new resource allocation model with multiple load originating processors as an economic model. Solutions for an optimal allocation of fraction of loads to nodes obtained to minimize the cost of the grid users via linear programming approach. It is found that the resource allocation model can efficiently and effectively allocate workloads to proper resources. Experimental results showed that the proposed model obtained the better solution in terms of cost and time.


Integrating User's Domain Knowledge with Association Rule Mining

Abstract: This paper presents a variation of Apriori algorithm that includes the role of domain expert to guide and speed up the overall knowledge discovery task. Usually, the user is interested in finding relationships between certain attributes instead of the whole dataset. Moreover, he can help the mining algorithm to select the target database which in turn takes less time to find the desired association rules. Variants of the standard Apriori and Interactive Apriori algorithms have been run on artificial datasets. The results show that incorporating user's preference in selection of target attribute helps to search the association rules efficiently both in terms of space and time.


Computer Aided Design Modeling for Heterogeneous Objects

Abstract: Heterogeneous object design is an active research area in recent years. The conventional CAD modeling approaches only provide geometry and topology of the object, but do not contain any information with regard to the materials of the object and so can not be used for the fabrication of heterogeneous objects (HO) through rapid prototyping. Current research focuses on computer-aided design issues in heterogeneous object design. A new CAD modeling approach is proposed to integrate the material information into geometric regions thus model the material distributions in the heterogeneous object. The gradient references are used to represent the complex geometry heterogeneous objects which have simultaneous geometry intricacies and accurate material distributions. The gradient references helps in flexible manipulability and control to heterogeneous objects, which guarantees the local control over gradient regions of developed heterogeneous objects. A systematic approach on data flow, processing, computer visualization, and slicing of heterogeneous objects for rapid prototyping is also presented.


Simultaneous Bayesian inference of motion velocity fields and probabilistic models in successive video-frames described by spatio-temporal MRFs

Abstract: We numerically investigate a mean-field Bayesian approach with the assistance of the Markov chain Monte Carlo method to estimate motion velocity fields and probabilistic models simultaneously in consecutive digital images described by spatio-temporal Markov random fields. Preliminary to construction of our procedure, we find that mean-field variables in the iteration diverge due to improper normalization factor of regularization terms appearing in the posterior. To avoid this difficulty, we rescale the regularization term by introducing a scaling factor and optimizing it by means of minimization of the mean-square error. We confirm that the optimal scaling factor stabilizes the mean-field iterative process of the motion velocity estimation. We next attempt to estimate the optimal values of hyper-parameters including the regularization term, which define our probabilistic model macroscopically, by using the Boltzmann-machine type learning algorithm based on gradient descent of marginal likelihood (type-II likelihood) with respect to the hyper-parameters. In our framework, one can estimate both the probabilistic model (hyper-parameters) and motion velocity fields simultaneously. We find that our motion estimation is much better than the result obtained by Zhang and Hanouer (1995) in which the hyper-parameters are set to some ad-hoc values without any theoretical justification.


Truthful Mechanisms with Implicit Payment Computation

Abstract: It is widely believed that computing payments needed to induce truthful bidding is somehow harder than simply computing the allocation. We show that the opposite is true: creating a randomized truthful mechanism is essentially as easy as a single call to a monotone allocation rule. Our main result is a general procedure to take a monotone allocation rule for a single-parameter domain and transform it (via a black-box reduction) into a randomized mechanism that is truthful in expectation and individually rational for every realization. The mechanism implements the same outcome as the original allocation rule with probability arbitrarily close to 1, and requires evaluating that allocation rule only once. We also provide an extension of this result to multi-parameter domains and cycle-monotone allocation rules, under mild star-convexity and non-negativity hypotheses on the type space and allocation rule, respectively. Because our reduction is simple, versatile, and general, it has many applications to mechanism design problems in which re-evaluating the allocation rule is either burdensome or informationally impossible. Applying our result to the multi-armed bandit problem, we obtain truthful randomized mechanisms whose regret matches the information-theoretic lower bound up to logarithmic factors, even though prior work showed this is impossible for truthful deterministic mechanisms. We also present applications to offline mechanism design, showing that randomization can circumvent a communication complexity lower bound for deterministic payments computation, and that it can also be used to create truthful shortest path auctions that approximate the welfare of the VCG allocation arbitrarily well, while having the same running time complexity as Dijkstra's algorithm.


Comparison of Two Context-Free Rewriting Systems with Simple Context-Checking Mechanisms

Abstract: This paper solves an open problem concerning the generative power of nonerasing context-free rewriting systems using a simple mechanism for checking for context dependencies, in the literature known as semi-conditional grammars of degree (1,1). In these grammars, two nonterminal symbols are attached to each context-free production, and such a production is applicable if one of the two attached symbols occurs in the current sentential form, while the other does not. Specifically, this paper demonstrates that the family of languages generated by semi-conditional grammars of degree (1,1) coincides with the family of random context languages. In addition, it shows that the normal form proved by Mayer for random context grammars with erasing productions holds for random context grammars without erasing productions, too. It also discusses two possible definitions of the relation of the direct derivation step used in the literature.


An Object-Oriented Metamodel for Bunge-Wand-Weber Ontology

Abstract: A UML based metamodel for Bunge-Wand-Weber (BWW) ontology is presented. BWW ontology is a generic framework for analysis and conceptualization of real world objects. It includes categories that can be applied to analyze and classify objects found in an information system. In the context of BWW ontology, the metamodel is a representation of the ontological categories and relationships among them. An objective behind developing an object-oriented metamodel has been to model BWW ontology in terms of widely used notions in software development. The main contributions of this paper are a classification for ontological categories, a description template, and representations through UML and typed based models.


Parameterized Control Complexity in Fallback Voting

Abstract: We study the parameterized control complexity of fallback voting, a voting system that combines preference-based with approval voting. Electoral control is one of many different ways for an external agent to tamper with the outcome of an election. We show that adding and deleting candidates in fallback voting are W[2]-hard for both the constructive and destructive case, parameterized by the amount of action taken by the external agent. Furthermore, we show that adding and deleting voters in fallback voting are W[2]-hard for the constructive case, parameterized by the amount of action taken by the external agent, and are in FPT for the destructive case.


Scheduling Multi-Mode Real-Time Systems upon Uniform Multiprocessor Platforms

Abstract: In this paper, we address the scheduling problem of multi-mode real-time systems upon uniform multiprocessor platforms. We propose two transition protocols, specified together with their schedulability test, and provide the reader with two distinct upper bounds for the length of the transient phases during mode transitions, respectively for the cases where jobs priorities are known and unknown beforehand.


A Polynomial time Algorithm for Hamilton Cycle with maximum Degree 3, 3SAT

Abstract: Based on the famous Rotation-Extension technique, by creating the new concepts and methods: broad cycle, main segment, useful cut and insert, destroying edges for a main segment, main goal Hamilton cycle, depth-first search tree, we develop a polynomial time algorithm for a famous NPC: the Hamilton cycle problem. Thus we proved that NP=P. The key points of this paper are: 1) there are two ways to get a Hamilton cycle in exponential time: a full permutation of n vertices; or, chose n edges from all k edges, and check all possible combinations. The main problem is: how to avoid checking all combinations of n edges from all edges. My algorithm can avoid this. Lemma 1 and lemma 2 are very important. They are the foundation that we always can get a good branch in the depth-first search tree and can get a series of destroying edges (all are bad edges) for this good branch in polynomial time. The extraordinary insights are: destroying edges, a tree contains each main segment at most one time at the same time, and dynamic combinations. The difficult part is to understand how to construct a main segment's series of destroying edges by dynamic combinations. The proof logic is: if there is at least on Hamilton cycle in the graph, we always can do useful cut and inserts until a Hamilton cycle is got. The times of useful cut and inserts are polynomial. So if at any step we cannot have a useful cut and insert, this means that there are no Hamilton cycles in the graph. In this version, I add a detailed polynomial time algorithm and proof for 3SAT


Parcellation of fMRI Datasets with ICA and PLS-A Data Driven Approach

Abstract: Inter-subject parcellation of functional Magnetic Resonance Imaging (fMRI) data based on a standard General Linear Model (GLM)and spectral clustering was recently proposed as a means to alleviate the issues associated with spatial normalization in fMRI. However, for all its appeal, a GLM-based parcellation approach introduces its own biases, in the form of a priori knowledge about the shape of Hemodynamic Response Function (HRF) and task-related signal changes, or about the subject behaviour during the task. In this paper, we introduce a data-driven version of the spectral clustering parcellation, based on Independent Component Analysis (ICA) and Partial Least Squares (PLS) instead of the GLM. First, a number of independent components are automatically selected. Seed voxels are then obtained from the associated ICA maps and we compute the PLS latent variables between the fMRI signal of the seed voxels (which covers regional variations of the HRF) and the principal components of the signal across all voxels. Finally, we parcellate all subjects data with a spectral clustering of the PLS latent variables. We present results of the application of the proposed method on both single-subject and multi-subject fMRI datasets. Preliminary experimental results, evaluated with intra-parcel variance of GLM t-values and PLS derived t-values, indicate that this data-driven approach offers improvement in terms of parcellation accuracy over GLM based techniques.


Multi-Criteria Evaluation of Partitioning Schemes for Real-Time Systems

Abstract: In this paper we study the partitioning approach for multiprocessor real-time scheduling. This approach seems to be the easiest since, once the partitioning of the task set has been done, the problem reduces to well understood uniprocessor issues. Meanwhile, there is no optimal and polynomial solution to partition tasks on processors. In this paper we analyze partitioning algorithms from several points of view such that for a given task set and specific constraints (processor number, task set type, etc.) we should be able to identify the best heuristic and the best schedulability test. We also analyze the influence of the heuristics on the performance of the uniprocessor tests and the impact of a specific task order on the schedulability. A study on performance difference between Fixed Priority schedulers and EDF in the case of partitioning scheduling is also considered.


Exact Sparse Matrix-Vector Multiplication on GPU's and Multicore Architectures

Abstract: We propose different implementations of the sparse matrix--dense vector multiplication (\spmv{}) for finite fields and rings $\Zb/m\Zb$. We take advantage of graphic card processors (GPU) and multi-core architectures. Our aim is to improve the speed of \spmv{} in the \linbox library, and henceforth the speed of its black box algorithms. Besides, we use this and a new parallelization of the sigma-basis algorithm in a parallel block Wiedemann rank implementation over finite fields.


A Gibbs distribution that learns from GA dynamics

Abstract: A general procedure of average-case performance evaluation for population dynamics such as genetic algorithms (GAs) is proposed and its validity is numerically examined. We introduce a learning algorithm of Gibbs distributions from training sets which are gene configurations (strings) generated by GA in order to figure out the statistical properties of GA from the view point of thermodynamics. The learning algorithm is constructed by means of minimization of the Kullback-Leibler information between a parametric Gibbs distribution and the empirical distribution of gene configurations. The formulation is applied to the solvable probabilistic models having multi-valley energy landscapes, namely, the spin glass chain and the Sherrington-Kirkpatrick model. By using computer simulations, we discuss the asymptotic behaviour of the effective temperature scheduling and the residual energy induced by the GA dynamics.


Improved Inapproximability For Submodular Maximization

Abstract: We show that it is Unique Games-hard to approximate the maximum of a submodular function to within a factor 0.695, and that it is Unique Games-hard to approximate the maximum of a symmetric submodular function to within a factor 0.739. These results slightly improve previous results by Feige, Mirrokni and Vondr\'ak (FOCS 2007) who showed that these problems are NP-hard to approximate to within $3/4 + \epsilon \approx 0.750$ and $5/6 + \epsilon \approx 0.833$, respectively.


Automated Verification of Practical Garbage Collectors

Abstract: Garbage collectors are notoriously hard to verify, due to their low-level interaction with the underlying system and the general difficulty in reasoning about reachability in graphs. Several papers have presented verified collectors, but either the proofs were hand-written or the collectors were too simplistic to use on practical applications. In this work, we present two mechanically verified garbage collectors, both practical enough to use for real-world C# benchmarks. The collectors and their associated allocators consist of x86 assembly language instructions and macro instructions, annotated with preconditions, postconditions, invariants, and assertions. We used the Boogie verification generator and the Z3 automated theorem prover to verify this assembly language code mechanically. We provide measurements comparing the performance of the verified collector with that of the standard Bartok collectors on off-the-shelf C# benchmarks, demonstrating their competitiveness.


Artificial Immune Systems Metaphor for Agent Based Modeling of Crisis Response Operations

Abstract: Crisis response requires information intensive efforts utilized for reducing uncertainty, calculating and comparing costs and benefits, and managing resources in a fashion beyond those regularly available to handle routine problems. This paper presents an Artificial Immune Systems (AIS) metaphor for agent based modeling of crisis response operations. The presented model proposes integration of hybrid set of aspects (multi-agent systems, built-in defensive model of AIS, situation management, and intensity-based learning) for crisis response operations. In addition, the proposed response model is applied on the spread of pandemic influenza in Egypt as a case study.


Resolving the Complexity of Some Data Privacy Problems

Abstract: We formally study two methods for data sanitation that have been used extensively in the database community: k-anonymity and l-diversity. We settle several open problems concerning the difficulty of applying these methods optimally, proving both positive and negative results: 1. 2-anonymity is in P. 2. The problem of partitioning the edges of a triangle-free graph into 4-stars (degree-three vertices) is NP-hard. This yields an alternative proof that 3-anonymity is NP-hard even when the database attributes are all binary. 3. 3-anonymity with only 27 attributes per record is MAX SNP-hard. 4. For databases with n rows, k-anonymity is in O(4^n poly(n)) time for all k > 1. 5. For databases with n rows and l <= log_{2c+2} log n attributes over an alphabet of cardinality c = O(1), k-anonymity is in P. Assuming c, l = O(1), k-anonymity is in O(n). 6. 3-diversity with binary attributes is NP-hard, with one sensitive attribute. 7. 2-diversity with binary attributes is NP-hard, with three sensitive attributes.


Bregman Distance to L1 Regularized Logistic Regression

Abstract: In this work we investigate the relationship between Bregman distances and regularized Logistic Regression model. We present a detailed study of Bregman Distance minimization, a family of generalized entropy measures associated with convex functions. We convert the L1-regularized logistic regression into this more general framework and propose a primal-dual method based algorithm for learning the parameters. We pose L1-regularized logistic regression into Bregman distance minimization and then apply non-linear constrained optimization techniques to estimate the parameters of the logistic model.


Oil Price Trackers Inspired by Immune Memory

Abstract: We outline initial concepts for an immune inspired algorithm to evaluate and predict oil price time series data. The proposed solution evolves a short term pool of trackers dynamically, with each member attempting to map trends and anticipate future price movements. Successful trackers feed into a long term memory pool that can generalise across repeating trend patterns. The resulting sequence of trackers, ordered in time, can be used as a forecasting tool. Examination of the pool of evolving trackers also provides valuable insight into the properties of the crude oil market.


A Study of Medium Access Control Protocols for Wireless Body Area Networks

Abstract: The seamless integration of low-power, miniaturised, invasive/non-invasive lightweight sensor nodes have contributed to the development of a proactive and unobtrusive Wireless Body Area Network (WBAN). A WBAN provides long-term health monitoring of a patient without any constraint on his/her normal dailylife activities. This monitoring requires low-power operation of invasive/non-invasive sensor nodes. In other words, a power-efficient Medium Access Control (MAC) protocol is required to satisfy the stringent WBAN requirements including low-power consumption. In this paper, we first outline the WBAN requirements that are important for the design of a low-power MAC protocol. Then we study low-power MAC protocols proposed/investigated for WBAN with emphasis on their strengths and weaknesses. We also review different power-efficient mechanisms for WBAN. In addition, useful suggestions are given to help the MAC designers to develop a low-power MAC protocol that will satisfy the stringent WBAN requirements.


Performance Evaluation of DCA and SRC on a Single Bot Detection

Abstract: Malicious users try to compromise systems using new techniques. One of the recent techniques used by the attacker is to perform complex distributed attacks such as denial of service and to obtain sensitive data such as password information. These compromised machines are said to be infected with malicious software termed a "bot". In this paper, we investigate the correlation of behavioural attributes such as keylogging and packet flooding behaviour to detect the existence of a single bot on a compromised machine by applying (1) Spearman's rank correlation (SRC) algorithm and (2) the Dendritic Cell Algorithm (DCA). We also compare the output results generated from these two methods to the detection of a single bot. The results show that the DCA has a better performance in detecting malicious activities.