Interval total colorings of graphs

Abstract: A total coloring of a graph $G$ is a coloring of its vertices and edges such that no adjacent vertices, edges, and no incident vertices and edges obtain the same color. An \emph{interval total $t$-coloring} of a graph $G$ is a total coloring of $G$ with colors $1,2,\...,t$ such that at least one vertex or edge of $G$ is colored by $i$, $i=1,2,\...,t$, and the edges incident to each vertex $v$ together with $v$ are colored by $d_{G}(v)+1$ consecutive colors, where $d_{G}(v)$ is the degree of the vertex $v$ in $G$. In this paper we investigate some properties of interval total colorings. We also determine exact values of the least and the greatest possible number of colors in such colorings for some classes of graphs.


A Stackelberg Strategy for Routing Flow over Time

Abstract: Routing games are used to to understand the impact of individual users' decisions on network efficiency. Most prior work on routing games uses a simplified model of network flow where all flow exists simultaneously, and users care about either their maximum delay or their total delay. Both of these measures are surrogates for measuring how long it takes to get all of a user's traffic through the network. We attempt a more direct study of how competition affects network efficiency by examining routing games in a flow over time model. We give an efficiently computable Stackelberg strategy for this model and show that the competitive equilibrium under this strategy is no worse than a small constant times the optimal, for two natural measures of optimality.


The Emerging Web of Social Machines

Abstract: We define a notion of social machine and envisage an algebra that can describe networks of such. To start with, social machines are defined as tuples of input, output, processes, constraints, state, requests and responses; apart from defining the machines themselves, the algebra defines a set of connectors and conditionals that can be used to describe the interactions between any number of machines in a multitude of ways, as a means to represent real machines interacting in the real web, such as Twitter, Twitter running on top of Amazon AWS, mashups built using Twitter and, obviously, other social machines. This work is not a theoretical paper as yet; but, in more than one sense, we think we have found a way to describe web based information systems and are starting to work on what could be a practical way of dealing with the complexity of this emerging web of social machines that is all around us. This version should be read as work in progress and comments, observations, bugs... are most welcome and should be sent to the email of the first, corresponding author.


Parallel Sorted Neighborhood Blocking with MapReduce

Abstract: Cloud infrastructures enable the efficient parallel execution of data-intensive tasks such as entity resolution on large datasets. We investigate challenges and possible solutions of using the MapReduce programming model for parallel entity resolution. In particular, we propose and evaluate two MapReduce-based implementations for Sorted Neighborhood blocking that either use multiple MapReduce jobs or apply a tailored data replication.


Rank-1 Bi-matrix Games: A Homeomorphism and a Polynomial Time Algorithm

Abstract: Given a rank-1 bimatrix game (A,B), i.e., where rank(A+B)=1, we construct a suitable linear subspace of the rank-1 game space and show that this subspace is homeomorphic to its Nash equilibrium correspondence. Using this homeomorphism, we give the first polynomial time algorithm for computing an exact Nash equilibrium of a rank-1 bimatrix game. This settles an open question posed in Kannan and Theobald (SODA 2007) and Theobald (2007). In addition, we give a novel algorithm to enumerate all the Nash equilibria of a rank-1 game and show that a similar technique may also be applied for finding a Nash equilibrium of any bimatrix game. This technique also proves the existence, oddness and the index theorem of Nash equilibria in a bimatrix game. Further, we extend the rank-1 homeomorphism result to a fixed rank game space, and give a fixed point formulation on $[0,1]^k$ for solving a rank-k game. The homeomorphism and the fixed point formulation are piece-wise linear and considerably simpler than the classical constructions.


Near-Optimal Bayesian Active Learning with Noisy Observations

Abstract: We tackle the fundamental problem of Bayesian active learning with noise, where we need to adaptively select from a number of expensive tests in order to identify an unknown hypothesis sampled from a known prior distribution. In the case of noise-free observations, a greedy algorithm called generalized binary search (GBS) is known to perform near-optimally. We show that if the observations are noisy, perhaps surprisingly, GBS can perform very poorly. We develop EC2, a novel, greedy active learning algorithm and prove that it is competitive with the optimal policy, thus obtaining the first competitiveness guarantees for Bayesian active learning with noisy observations. Our bounds rely on a recently discovered diminishing returns property called adaptive submodularity, generalizing the classical notion of submodular set functions to adaptive policies. Our results hold even if the tests have non-uniform cost and their noise is correlated. We also propose EffECXtive, a particularly fast approximation of EC2, and evaluate it on a Bayesian experimental design problem involving human subjects, intended to tease apart competing economic theories of how people make decisions under uncertainty.


On Object-Orientation

Abstract: Although object-orientation has been around for several decades, its key concept abstraction has not been exploited for proper application of object-orientation in other phases of software development than the implementation phase. We mention some issues that lead to a lot of confusion and obscurity with object-orientation and its application in software development. We describe object-orientation as abstract as possible such that it can be applied to all phases of software development.


The Digital Signature Scheme MQQ-SIG

Abstract: This document contains the Intellectual Property Statement and the technical description of the MQQ-SIG - a new public key digital signature scheme. The complete scientific publication covering the design rationale and the security analysis will be given in a separate publication. MQQ-SIG consists of $n - \frac{n}{4}$ quadratic polynomials with $n$ Boolean variables where n=160, 196, 224 or 256.


Introduction to the iDian

Abstract: The iDian (previously named as the Operation Agent System) is a framework designed to enable computer users to operate software in natural language. Distinct from current speech-recognition systems, our solution supports format-free combinations of orders, and is open to both developers and customers. We used a multi-layer structure to build the entire framework, approached rule-based natural language processing, and implemented demos narrowing down to Windows, text-editing and a few other applications. This essay will firstly give an overview of the entire system, and then scrutinize the functions and structure of the system, and finally discuss the prospective de-velopment, esp. on-line interaction functions.


QoS-Aware Joint Policies in Cognitive Radio Networks

Abstract: One of the most challenging problems in Opportunistic Spectrum Access (OSA) is to design channel sensing-based protocol in multi secondary users (SUs) network. Quality of Service (QoS) requirements for SUs have significant implications on this protocol design. In this paper, we propose a new method to find joint policies for SUs which not only guarantees QoS requirements but also maximizes network throughput. We use Decentralized Partially Observable Markov Decision Process (Dec-POMDP) to formulate interactions between SUs. Meanwhile, a tractable approach for Dec-POMDP is utilized to extract sub-optimum joint policies for large horizons. Among these policies, the joint policy which guarantees QoS requirements is selected as the joint sensing strategy for SUs. To show the efficiency of the proposed method, we consider two SUs trying to access two-channel primary users (PUs) network modeled by discrete Markov chains. Simulations demonstrate three interesting findings: 1- Optimum joint policies for large horizons can be obtained using the proposed method. 2- There exists a joint policy for the assumed QoS constraints. 3- Our method outperforms other related works in terms of network throughput.


A Survey of Virtualization Technologies With Performance Testing

Abstract: Virtualization has rapidly become a go-to technology for increasing efficiency in the data center. With virtualization technologies providing tremendous flexibility, even disparate architectures may be deployed on a single machine without interference. Awareness of limitations and requirements of physical hosts to be used for virtualization is important. This paper reviews the present virtualization methods, virtual computing software, and provides a brief analysis of the performance issues inherent to each. In the end we present testing results of KVM-QEMU on two current Multi-Core CPU Architectures and System Configurations.


Syntactic Complexity of Ideal and Closed Languages

Abstract: The state complexity of a regular language is the number of states in the minimal deterministic automaton accepting the language. The syntactic complexity of a regular language is the cardinality of its syntactic semigroup. The syntactic complexity of a subclass of regular languages is the worst-case syntactic complexity taken as a function of the state complexity $n$ of languages in that class. We study the syntactic complexity of the class of regular ideal languages and their complements, the closed languages. We prove that $n^{n-1}$ is a tight upper bound on the complexity of right ideals and prefix-closed languages, and that there exist left ideals and suffix-closed languages of syntactic complexity $n^{n-1}+n-1$, and two-sided ideals and factor-closed languages of syntactic complexity $n^{n-2}+(n-2)2^{n-2}+1$.


Wireless Sensor Network based Future of Telecom Applications

Abstract: A system and method for enabling human beings to communicate by way of their monitored brain activity. The brain activity of an individual is monitored and transmitted to a remote location (e.g. by satellite). At the remote location, the monitored brain activity is compared with pre-recorded normalized brain activity curves, waveforms, or patterns to determine if a match or substantial match is found. If such a match is found, then the computer at the remote location determines that the individual was attempting to communicate the word, phrase, or thought corresponding to the matched stored normalized signal.


A Hidden Markov Model for Localization Using Low-End GSM Cell Phones

Abstract: Research in location determination for GSM phones has gained interest recently as it enables a wide set of location based services. RSSI-based techniques have been the preferred method for GSM localization on the handset as RSSI information is available in all cell phones. Although the GSM standard allows for a cell phone to receive signal strength information from up to seven cell towers, many of today's cell phones are low-end phones, with limited API support, that gives only information about the associated cell tower. In addition, in many places in the world, the density of cell towers is very small and therefore, the available cell tower information for localization is very limited. This raises the challenge of accurately determining the cell phone location with very limited information, mainly the RSSI of the associated cell tower. In this paper we propose a Hidden Markov Model based solution that leverages the signal strength history from only the associated cell tower to achieve accurate GSM localization. We discuss the challenges of implementing our system and present the details of our system and how it addresses the challenges. To evaluate our proposed system, we implemented it on Androidbased phones. Results for two different testbeds, representing urban and rural environments, show that our system provides at least 156% enhancement in median error in rural areas and at least 68% enhancement in median error in urban areas compared to current RSSI-based GSM localization systems


Wireless Scheduling with Power Control

Abstract: We consider the scheduling of arbitrary wireless links in the physical model of interference to minimize the time for satisfying all requests. We study here the combined problem of scheduling and power control, where we seek both an assignment of power settings and a partition of the links so that each set satisfies the signal-to-interference-plus-noise (SINR) constraints. We give an algorithm that attains an approximation ratio of $O(\log n \cdot \log\log \Delta)$, where $n$ is the number of links and $\Delta$ is the ratio between the longest and the shortest link length. Under the natural assumption that lengths are represented in binary, this gives the first approximation ratio that is polylogarithmic in the size of the input. The algorithm has the desirable property of using an oblivious power assignment, where the power assigned to a sender depends only on the length of the link. We give evidence that this dependence on $\Delta$ is unavoidable, showing that any reasonably-behaving oblivious power assignment results in a $\Omega(\log\log \Delta)$-approximation. These results hold also for the (weighted) capacity problem of finding a maximum (weighted) subset of links that can be scheduled in a single time slot. In addition, we obtain improved approximation for a bidirectional variant of the scheduling problem, give partial answers to questions about the utility of graphs for modeling physical interference, and generalize the setting from the standard 2-dimensional Euclidean plane to doubling metrics. Finally, we explore the utility of graph models in capturing wireless interference.


Fast Inference in Sparse Coding Algorithms with Applications to Object Recognition

Abstract: Adaptive sparse coding methods learn a possibly overcomplete set of basis functions, such that natural image patches can be reconstructed by linearly combining a small subset of these bases. The applicability of these methods to visual object recognition tasks has been limited because of the prohibitive cost of the optimization algorithms required to compute the sparse representation. In this work we propose a simple and efficient algorithm to learn basis functions. After training, this model also provides a fast and smooth approximator to the optimal representation, achieving even better accuracy than exact sparse coding algorithms on visual object recognition tasks.


Hardness Results for Agnostically Learning Low-Degree Polynomial Threshold Functions

Abstract: Hardness results for maximum agreement problems have close connections to hardness results for proper learning in computational learning theory. In this paper we prove two hardness results for the problem of finding a low degree polynomial threshold function (PTF) which has the maximum possible agreement with a given set of labeled examples in $\R^n \times \{-1,1\}.$ We prove that for any constants $d\geq 1, \eps > 0$, {itemize} Assuming the Unique Games Conjecture, no polynomial-time algorithm can find a degree-$d$ PTF that is consistent with a $(\half + \eps)$ fraction of a given set of labeled examples in $\R^n \times \{-1,1\}$, even if there exists a degree-$d$ PTF that is consistent with a $1-\eps$ fraction of the examples. It is $\NP$-hard to find a degree-2 PTF that is consistent with a $(\half + \eps)$ fraction of a given set of labeled examples in $\R^n \times \{-1,1\}$, even if there exists a halfspace (degree-1 PTF) that is consistent with a $1 - \eps$ fraction of the examples. {itemize} These results immediately imply the following hardness of learning results: (i) Assuming the Unique Games Conjecture, there is no better-than-trivial proper learning algorithm that agnostically learns degree-$d$ PTFs under arbitrary distributions; (ii) There is no better-than-trivial learning algorithm that outputs degree-2 PTFs and agnostically learns halfspaces (i.e. degree-1 PTFs) under arbitrary distributions.


Scalable XML Collaborative Editing with Undo short paper

Abstract: Commutative Replicated Data-Type (CRDT) is a new class of algorithms that ensures scalable consistency of replicated data. It has been successfully applied to collaborative editing of texts without complex concurrency control. In this paper, we present a CRDT to edit XML data. Compared to existing approaches for XML collaborative editing, our approach is more scalable and handles all the XML editing aspects : elements, contents, attributes and undo. Indeed, undo is recognized as an important feature for collaborative editing that allows to overcome system complexity through error recovery or collaborative conflict resolution.


Fixed-parameter tractability of multicut parameterized by the size of the cutset

Abstract: Given an undirected graph $G$, a collection $\{(s_1,t_1),..., (s_k,t_k)\}$ of pairs of vertices, and an integer $p$, the Edge Multicut problem ask if there is a set $S$ of at most $p$ edges such that the removal of $S$ disconnects every $s_i$ from the corresponding $t_i$. Vertex Multicut is the analogous problem where $S$ is a set of at most $p$ vertices. Our main result is that both problems can be solved in time $2^{O(p^3)}... n^{O(1)}$, i.e., fixed-parameter tractable parameterized by the size $p$ of the cutset in the solution. By contrast, it is unlikely that an algorithm with running time of the form $f(p)... n^{O(1)}$ exists for the directed version of the problem, as we show it to be W[1]-hard parameterized by the size of the cutset.


On the Iterated Hairpin Completion

Abstract: The (bounded) hairpin completion and its iterated versions are operations on formal lan- guages which have been inspired by the hairpin formation in DNA-biochemistry. The paper answers two questions asked in the literature about the iterated hairpin completion. The first question is whether the class of regular languages is closed under iterated bounded hairpin completion. Here we show that this is true by providing a more general result which applies to all the classes of languages which are closed under finite union, intersection with regular sets, and concatenation with regular sets. In particular, all Chomsky classes and all standard complexity classes are closed under iterated bounded hairpin completion. In the second part of the paper we address the question whether the iterated hairpin completion of a singleton is always regular. In contrast to the first question, this one has a negative answer. We exhibit an example of a singleton language whose iterated hairpin completion is not regular, actually it is not context-free, but context-sensitive.


The set of realizations of a max-plus linear sequence is semi-polyhedral

Abstract: We show that the set of realizations of a given dimension of a max-plus linear sequence is a finite union of polyhedral sets, which can be computed from any realization of the sequence. This yields an (expensive) algorithm to solve the max-plus minimal realization problem. These results are derived from general facts on rational expressions over idempotent commutative semirings: we show more generally that the set of values of the coefficients of a commutative rational expression in one letter that yield a given max-plus linear sequence is a semi-algebraic set in the max-plus sense. In particular, it is a finite union of polyhedral sets.


Unifying the Landscape of Cell-Probe Lower Bounds

Abstract: We show that a large fraction of the data-structure lower bounds known today in fact follow by reduction from the communication complexity of lopsided (asymmetric) set disjointness. This includes lower bounds for: * high-dimensional problems, where the goal is to show large space lower bounds. * constant-dimensional geometric problems, where the goal is to bound the query time for space O(n polylog n). * dynamic problems, where we are looking for a trade-off between query and update time. (In this case, our bounds are slightly weaker than the originals, losing a lglg n factor.) Our reductions also imply the following new results: * an Omega(lg n / lglg n) bound for 4-dimensional range reporting, given space O(n polylog n). This is quite timely, since a recent result solved 3D reporting in O(lglg n) time, raising the prospect that higher dimensions could also be easy. * a tight space lower bound for the partial match problem, for constant query time. * the first lower bound for reachability oracles. In the process, we prove optimal randomized lower bounds for lopsided set disjointness.


A Logical Foundation for Environment Classifiers

Abstract: Taha and Nielsen have developed a multi-stage calculus {\lambda}{\alpha} with a sound type system using the notion of environment classifiers. They are special identifiers, with which code fragments and variable declarations are annotated, and their scoping mechanism is used to ensure statically that certain code fragments are closed and safely runnable. In this paper, we investigate the Curry-Howard isomorphism for environment classifiers by developing a typed {\lambda}-calculus {\lambda}|>. It corresponds to multi-modal logic that allows quantification by transition variables---a counterpart of classifiers---which range over (possibly empty) sequences of labeled transitions between possible worlds. This interpretation will reduce the "run" construct---which has a special typing rule in {\lambda}{\alpha}---and embedding of closed code into other code fragments of different stages---which would be only realized by the cross-stage persistence operator in {\lambda}{\alpha}---to merely a special case of classifier application. {\lambda}|> enjoys not only basic properties including subject reduction, confluence, and strong normalization but also an important property as a multi-stage calculus: time-ordered normalization of full reduction. Then, we develop a big-step evaluation semantics for an ML-like language based on {\lambda}|> with its type system and prove that the evaluation of a well-typed {\lambda}|> program is properly staged. We also identify a fragment of the language, where erasure evaluation is possible. Finally, we show that the proof system augmented with a classical axiom is sound and complete with respect to a Kripke semantics of the logic.


A New Non Linear, Time Stamped & Feed Back Model Based Encryption Mechanism with Acknowledgement Support

Abstract: In this work a model is going to be used which develops data distributed over a identified value which is used as nonce (IV). The model considers an equilibrium equation which is a function of non linear relationships, time variant and nonce variant values and takes the feed back of earlier round as input to the present round. The process is repeated for different timings which are used as time stamps in the encryption mechanism. Thus this model generates a distributed sequence which is used as sub key. This model supports very important parameters in symmetric data encryption schemes like non linear relationships between different values used in the model, variable key length, timeliness of encryption mechanism and also acknowledgement between the participating parties. It also supports feed back mode which provides necessary strength against crypto analysis.


Joint interpretation of on-board vision and static GPS cartography for determination of correct speed limit

Abstract: We present here a first prototype of a "Speed Limit Support" Advance Driving Assistance System (ADAS) producing permanent reliable information on the current speed limit applicable to the vehicle. Such a module can be used either for information of the driver, or could even serve for automatic setting of the maximum speed of a smart Adaptive Cruise Control (ACC). Our system is based on a joint interpretation of cartographic information (for static reference information) with on-board vision, used for traffic sign detection and recognition (including supplementary sub-signs) and visual road lines localization (for detection of lane changes). The visual traffic sign detection part is quite robust (90% global correct detection and recognition for main speed signs, and 80% for exit-lane sub-signs detection). Our approach for joint interpretation with cartography is original, and logic-based rather than probability-based, which allows correct behaviour even in cases, which do happen, when both vision and cartography may provide the same erroneous information.


A Survey of Green Networking Research

Abstract: Reduction of unnecessary energy consumption is becoming a major concern in wired networking, because of the potential economical benefits and of its expected environmental impact. These issues, usually referred to as "green networking", relate to embedding energy-awareness in the design, in the devices and in the protocols of networks. In this work, we first formulate a more precise definition of the "green" attribute. We furthermore identify a few paradigms that are the key enablers of energy-aware networking research. We then overview the current state of the art and provide a taxonomy of the relevant work, with a special focus on wired networking. At a high level, we identify four branches of green networking research that stem from different observations on the root causes of energy waste, namely (i) Adaptive Link Rate, (ii) Interface proxying, (iii) Energy-aware infrastructures and (iv) Energy-aware applications. In this work, we do not only explore specific proposals pertaining to each of the above branches, but also offer a perspective for research.


Advancements in scientific data searching, sharing and retrieval

Abstract: The Open Archive Initiative Protocol for Metadata Handling (OAI-PMHiii) is a standard that is seeing increased use as a means for exchanging structured metadata. OAI-PMH implementations must support Dublin Core as a metadata standard, with other metadata formats as optional. We have developed tools which enable Mercury to consume metadata from OAI-PMH services in any of the metadata formats we support (Dublin Core, Darwin Core, FCDC CSDGM, GCMD DIF, EML, and ISO 19115/19137). We are also making ORNL DAAC metadata available through OAI-PMH for other metadata tools to utilize. This paper describes Mercury capabilities with multiple metadata formats, in general, and, more specifically, the results of our OAI-PMH implementations and the lessons learned.


Diffieties and Liouvillian Systems

Abstract: Liouvillian systems were initially introduced within the framework of differential algebra. They can be seen as a natural extension of differential flat systems. Many physical non flat systems seem to be Liouvillian. We present in this paper an alternative definition to this class of systems using the language of diffieties and infinite prolongation theory.


3-D Rigid Models from Partial Views - Global Factorization

Abstract: The so-called factorization methods recover 3-D rigid structure from motion by factorizing an observation matrix that collects 2-D projections of features. These methods became popular due to their robustness - they use a large number of views, which constrains adequately the solution - and computational simplicity - the large number of unknowns is computed through an SVD, avoiding non-linear optimization. However, they require that all the entries of the observation matrix are known. This is unlikely to happen in practice, due to self-occlusion and limited field of view. Also, when processing long videos, regions that become occluded often appear again later. Current factorization methods process these as new regions, leading to less accurate estimates of 3-D structure. In this paper, we propose a global factorization method that infers complete 3-D models directly from the 2-D projections in the entire set of available video frames. Our method decides whether a region that has become visible is a region that was seen before, or a previously unseen region, in a global way, i.e., by seeking the simplest rigid object that describes well the entire set of observations. This global approach increases significantly the accuracy of the estimates of the 3-D shape of the scene and the 3-D motion of the camera. Experiments with artificial and real videos illustrate the good performance of our method.


Exact Analysis of Rate Adaptation Algorithms in Wireless LANs

Abstract: Rate adaptation plays a key role in determining the performance of wireless LANs. In this paper, we introduce a semi-Markovian framework to analyze the performance of two of the most popular rate adaptation algorithms used in wireless LANs, namely Automatic Rate Fallback (ARF) and Adaptive Automatic Rate Fallback (AARF). Given our modeling assumptions, the analysis is exact and provides closed form expressions for the achievable throughput of ARF and AARF. We illustrate the benefit of our analysis by numerically comparing the throughput performance of ARF and AARF in two different channel regimes. The results show that neither of these algorithms consistently outperforms the other. We thus propose and analyze a new variant to AARF, called Persistent AARF (or PAARF), and show that it achieves a good compromise between the two algorithms, often performing close to the best algorithm in each of the studied regimes. The numerical results also shed light into the impact of MAC overhead on the performance of the three algorithms. In particular, they show that the more conservative strategies AARF and PAARF scale better as the bit rate increases.


Maximum Likelihood Mosaics

Abstract: The majority of the approaches to the automatic recovery of a panoramic image from a set of partial views are suboptimal in the sense that the input images are aligned, or registered, pair by pair, e.g., consecutive frames of a video clip. These approaches lead to propagation errors that may be very severe, particularly when dealing with videos that show the same region at disjoint time intervals. Although some authors have proposed a post-processing step to reduce the registration errors in these situations, there have not been attempts to compute the optimal solution, i.e., the registrations leading to the panorama that best matches the entire set of partial views}. This is our goal. In this paper, we use a generative model for the partial views of the panorama and develop an algorithm to compute in an efficient way the Maximum Likelihood estimate of all the unknowns involved: the parameters describing the alignment of all the images and the panorama itself.


Alternatives to speech in low bit rate communication systems

Abstract: This paper describes a framework and a method with which speech communication can be analyzed. The framework consists of a set of low bit rate, short-range acoustic communication systems, such as speech, but that are quite different from speech. The method is to systematically compare these systems according to different objective functions such as data rate, computational overhead, psychoacoustic effects and semantics. One goal of this study is to better understand the nature of human communication. Another goal is to identify acoustic communication systems that are more efficient than human speech for some specific purposes.


Combating False Reports for Secure Networked Control in Smart Grid via Trustiness Evaluation

Abstract: Smart grid, equipped with modern communication infrastructures, is subject to possible cyber attacks. Particularly, false report attacks which replace the sensor reports with fraud ones may cause the instability of the whole power grid or even result in a large area blackout. In this paper, a trustiness system is introduced to the controller, who computes the trustiness of different sensors by comparing its prediction, obtained from Kalman filtering, on the system state with the reports from sensor. The trustiness mechanism is discussed and analyzed for the Linear Quadratic Regulation (LQR) controller. Numerical simulations show that the trustiness system can effectively combat the cyber attacks to smart grid.


On Graph Crossing Number and Edge Planarization

Abstract: Given an n-vertex graph G, a drawing of G in the plane is a mapping of its vertices into points of the plane, and its edges into continuous curves, connecting the images of their endpoints. A crossing in such a drawing is a point where two such curves intersect. In the Minimum Crossing Number problem, the goal is to find a drawing of G with minimum number of crossings. The value of the optimal solution, denoted by OPT, is called the graph's crossing number. This is a very basic problem in topological graph theory, that has received a significant amount of attention, but is still poorly understood algorithmically. The best currently known efficient algorithm produces drawings with $O(\log^2 n)(n + OPT)$ crossings on bounded-degree graphs, while only a constant factor hardness of approximation is known. A closely related problem is Minimum Edge Planarization, in which the goal is to remove a minimum-cardinality subset of edges from G, such that the remaining graph is planar. Our main technical result establishes the following connection between the two problems: if we are given a solution of cost k to the Minimum Edge Planarization problem on graph G, then we can efficiently find a drawing of G with at most $\poly(d)\cdot k\cdot (k+OPT)$ crossings, where $d$ is the maximum degree in G. This result implies an $O(n\cdot \poly(d)\cdot \log^{3/2}n)$-approximation for Minimum Crossing Number, as well as improved algorithms for special cases of the problem, such as, for example, k-apex and bounded-genus graphs.


Colour Guided Colour Image Steganography

Abstract: Information security has become a cause of concern because of the electronic eavesdropping. Capacity, robustness and invisibility are important parameters in information hiding and are quite difficult to achieve in a single algorithm. This paper proposes a novel steganography technique for digital color image which achieves the purported targets. The professed methodology employs a complete random scheme for pixel selection and embedding of data. Of the three colour channels (Red, Green, Blue) in a given colour image, the least two significant bits of any one of the channels of the color image is used to channelize the embedding capacity of the remaining two channels. We have devised three approaches to achieve various levels of our desired targets. In the first approach, Red is the default guide but it results in localization of MSE in the remaining two channels, which makes it slightly vulnerable. In the second approach, user gets the liberty to select the guiding channel (Red, Green or Blue) to guide the remaining two channels. It will increase the robustness and imperceptibility of the embedded image however the MSE factor will still remain as a drawback. The third approach improves the performance factor as a cyclic methodology is employed and the guiding channel is selected in a cyclic fashion. This ensures the uniform distribution of MSE, which gives better robustness and imperceptibility along with enhanced embedding capacity. The imperceptibility has been enhanced by suitably adapting optimal pixel adjustment process (OPAP) on the stego covers.


A Paradigm for Channel Assignment and Data Migration in Distributed Systems

Abstract: In this manuscript, we consider the problems of channel assignment in wireless networks and data migration in heterogeneous storage systems. We show that a soft edge coloring approach to both problems gives rigorous approximation guarantees. In the channel assignment problem arising in wireless networks a pair of edges incident to a vertex are said to be conflicting if the channels assigned to them are the same. Our goal is to assign channels (color edges) so that the number of conflicts is minimized. The problem is NP-hard by a reduction from Edge coloring and we present two combinatorial algorithms for this case. The first algorithm is based on a distributed greedy method and gives a solution with at most $2(1-\frac{1}{k})|E|$ more conflicts than the optimal solution.The approximation ratio if the second algorithm is $1 + \frac{|V|}{|E|}$, which gives a ($1 + o(1)$)-factor for dense graphs and is the best possible unless P = NP. We also consider the data migration problem in heterogeneous storage systems. In such systems, data layouts may need to be reconfigured over time for load balancing or in the event of system failure/upgrades. It is critical to migrate data to their target locations as quickly as possible to obtain the best performance of the system. Most of the previous results on data migration assume that each storage node can perform only one data transfer at a time. However, storage devices tend to have heterogeneous capabilities as devices may be added over time due to storage demand increase. We develop algorithms to minimize the data migration time. We show that it is possible to find an optimal migration schedule when all $c_v$'s are even. Furthermore, though the problem is NP-hard in general, we give an efficient soft edge coloring algorithm that offers a rigorous $(1 + o(1))$-approximation guarantee.


ANSIG - An Analytic Signature for Arbitrary 2D Shapes (or Bags of Unlabeled Points)

Abstract: In image analysis, many tasks require representing two-dimensional (2D) shape, often specified by a set of 2D points, for comparison purposes. The challenge of the representation is that it must not only capture the characteristics of the shape but also be invariant to relevant transformations. Invariance to geometric transformations, such as translation, rotation, and scale, has received attention in the past, usually under the assumption that the points are previously labeled, i.e., that the shape is characterized by an ordered set of landmarks. However, in many practical scenarios, the points describing the shape are obtained from automatic processes, e.g., edge or corner detection, thus without labels or natural ordering. Obviously, the combinatorial problem of computing the correspondences between the points of two shapes in the presence of the aforementioned geometrical distortions becomes a quagmire when the number of points is large. We circumvent this problem by representing shapes in a way that is invariant to the permutation of the landmarks, i.e., we represent bags of unlabeled 2D points. Within our framework, a shape is mapped to an analytic function on the complex plane, leading to what we call its analytic signature (ANSIG). To store an ANSIG, it suffices to sample it along a closed contour in the complex plane. We show that the ANSIG is a maximal invariant with respect to the permutation group, i.e., that different shapes have different ANSIGs and shapes that differ by a permutation (or re-labeling) of the landmarks have the same ANSIG. We further show how easy it is to factor out geometric transformations when comparing shapes using the ANSIG representation. Finally, we illustrate these capabilities with shape-based image classification experiments.


Efficient Matrix Completion with Gaussian Models

Abstract: A general framework based on Gaussian models and a MAP-EM algorithm is introduced in this paper for solving matrix/table completion problems. The numerical experiments with the standard and challenging movie ratings data show that the proposed approach, based on probably one of the simplest probabilistic models, leads to the results in the same ballpark as the state-of-the-art, at a lower computational cost.


Maxwell-independence: a new rank estimate for 3D rigidity matroids

Abstract: The problem of combinatorially determining the rank of the 3-dimensional bar-joint {\em rigidity matroid} of a graph is an important open problem in combinatorial rigidity theory. Maxwell's condition states that the edges of a graph $G=(V, E)$ are {\em independent} in its $d$-dimensional generic rigidity matroid only if $(a)$ the number of edges $|E|$ $\le$ $d|V| - {d+1\choose 2}$, and $(b)$ this holds for every induced subgraph with at least $d$ vertices. We call such graphs {\em Maxwell-independent} in $d$ dimensions. Laman's theorem shows that the converse holds for $d=2$ and thus every maximal Maxwell-independent set of $G$ has size equal to the rank of the 2-dimensional generic rigidity matroid. While this is false for $d=3$, we show that every maximal, Maxwell-independent set of a graph $G$ has size at least the rank of the 3-dimensional generic rigidity matroid of $G$. This answers a question posed by Tib\'or Jord\'an at the 2008 rigidity workshop at BIRS \cite{bib:birs}. Along the way, we construct subgraphs (1) that yield alternative formulae for a rank upper bound for Maxwell-independent graphs and (2) that contain a maximal (true) independent set. We extend this bound to special classes of non-Maxwell-independent graphs. One further consequence is a simpler proof of correctness for existing algorithms that give rank bounds.


Multiplierless Modules for Forward and Backward Integer Wavelet Transform

Abstract: This article is about the architecture of a lossless wavelet filter bank with reprogrammable logic. It is based on second generation of wavelets with a reduced of number of operations. A new basic structure for parallel architecture and modules to forward and backward integer discrete wavelet transform is proposed.


Model-Based Development of Distributed Embedded Systems by the Example of the Scicos/SynDEx Framework

Abstract: The embedded systems engineering industry faces increasing demands for more functionality, rapidly evolving components, and shrinking schedules. Abilities to quickly adapt to changes, develop products with safe design, minimize project costs, and deliver timely are needed. Model-based development (MBD) follows a separation of concerns by abstracting systems with an appropriate intensity. MBD promises higher comprehension by modeling on several abstraction-levels, formal verification, and automated code generation. This thesis demonstrates MBD with the Scicos/SynDEx framework on a distributed embedded system. Scicos is a modeling and simulation environment for hybrid systems. SynDEx is a rapid prototyping integrated development environment for distributed systems. Performed examples implement well-known control algorithms on a target system containing several networked microcontrollers, sensors, and actuators. The addressed research question tackles the feasibility of MBD for medium-sized embedded systems. In the case of single-processor applications experiments show that the comforts of tool-provided simulation, verification, and code-generation have to be weighed against an additional memory consumption in dynamic and static memory compared to a hand-written approach. Establishing a near-seamless modeling-framework with Scicos/SynDEx is expensive. An increased development effort indicates a high price for developing single applications, but might pay off for product families. A further drawback was that the distributed code generated with SynDEx could not be adapted to microcontrollers without a significant alteration of the scheduling tables. The Scicos/SynDEx framework forms a valuable tool set that, however, still needs many improvements. Therefore, its usage is only recommended for experimental purposes.


Haar Wavelet Based Approach for Image Compression and Quality Assessment of Compressed Image

Abstract: With the increasing growth of technology and the entrance into the digital age, we have to handle a vast amount of information every time which often presents difficulties. So, the digital information must be stored and retrieved in an efficient and effective manner, in order for it to be put to practical use. Wavelets provide a mathematical way of encoding information in such a way that it is layered according to level of detail. This layering facilitates approximations at various intermediate stages. These approximations can be stored using a lot less space than the original data. Here a low complex 2D image compression method using wavelets as the basis functions and the approach to measure the quality of the compressed image are presented. The particular wavelet chosen and used here is the simplest wavelet form namely the Haar Wavelet. The 2D discret wavelet transform (DWT) has been applied and the detail matrices from the information matrix of the image have been estimated. The reconstructed image is synthesized using the estimated detail matrices and information matrix provided by the Wavelet transform. The quality of the compressed images has been evaluated using some factors like Compression Ratio (CR), Peak Signal to Noise Ratio (PSNR), Mean Opinion Score (MOS), Picture Quality Scale (PQS) etc.


Predicting Bugs' Components via Mining Bug Reports

Abstract: The number of bug reports in complex software increases dramatically. Now bugs are triaged manually, bug triage or assignment is a labor-intensive and time-consuming task. Without knowledge about the structure of the software, testers often specify the component of a new bug wrongly. Meanwhile, it is difficult for triagers to determine the component of the bug only by its description. We dig out the components of 28,829 bugs in Eclipse bug project have been specified wrongly and modified at least once. It results in these bugs have to be reassigned and delays the process of bug fixing. The average time of fixing wrongly-specified bugs is longer than that of correctly-specified ones. In order to solve the problem automatically, we use historical fixed bug reports as training corpus and build classifiers based on support vector machines and Na\"ive Bayes to predict the component of a new bug. The best prediction accuracy reaches up to 81.21% on our validation corpus of Eclipse project. Averagely our predictive model can save about 54.3 days for triagers and developers to repair a bug. Keywords: bug reports; bug triage; text classification; predictive model


Towards an SDP-based Approach to Spectral Methods: A Nearly-Linear-Time Algorithm for Graph Partitioning and Decomposition

Abstract: In this paper, we consider the following graph partitioning problem: The input is an undirected graph $G=(V,E),$ a balance parameter $b \in (0,1/2]$ and a target conductance value $\gamma \in (0,1).$ The output is a cut which, if non-empty, is of conductance at most $O(f),$ for some function $f(G, \gamma),$ and which is either balanced or well correlated with all cuts of conductance at most $\gamma.$ Spielman and Teng gave an $\tilde{O}(|E|/\gamma^{2})$-time algorithm for $f= \sqrt{\gamma \log^{3}|V|}$ and used it to decompose graphs into a collection of near-expanders. We present a new spectral algorithm for this problem which runs in time $\tilde{O}(|E|/\gamma)$ for $f=\sqrt{\gamma}.$ Our result yields the first nearly-linear time algorithm for the classic Balanced Separator problem that achieves the asymptotically optimal approximation guarantee for spectral methods. Our method has the advantage of being conceptually simple and relies on a primal-dual semidefinite-programming SDP approach. We first consider a natural SDP relaxation for the Balanced Separator problem. While it is easy to obtain from this SDP a certificate of the fact that the graph has no balanced cut of conductance less than $\gamma,$ somewhat surprisingly, we can obtain a certificate for the stronger correlation condition. This is achieved via a novel separation oracle for our SDP and by appealing to Arora and Kale's framework to bound the running time. Our result contains technical ingredients that may be of independent interest.


Energy-Efficient Multiprocessor Scheduling for Flow Time and Makespan

Abstract: We consider energy-efficient scheduling on multiprocessors, where the speed of each processor can be individually scaled, and a processor consumes power $s^{\alpha}$ when running at speed $s$, for $\alpha>1$. A scheduling algorithm needs to decide at any time both processor allocations and processor speeds for a set of parallel jobs with time-varying parallelism. The objective is to minimize the sum of the total energy consumption and certain performance metric, which in this paper includes total flow time and makespan. For both objectives, we present instantaneous parallelism clairvoyant (IP-clairvoyant) algorithms that are aware of the instantaneous parallelism of the jobs at any time but not their future characteristics, such as remaining parallelism and work. For total flow time plus energy, we present an $O(1)$-competitive algorithm, which significantly improves upon the best known non-clairvoyant algorithm and is the first constant competitive result on multiprocessor speed scaling for parallel jobs. In the case of makespan plus energy, which is considered for the first time in the literature, we present an $O(\ln^{1-1/\alpha}P)$-competitive algorithm, where $P$ is the total number of processors. We show that this algorithm is asymptotically optimal by providing a matching lower bound. In addition, we also study non-clairvoyant scheduling for total flow time plus energy, and present an algorithm that achieves $O(\ln P)$-competitive for jobs with arbitrary release time and $O(\ln^{1/\alpha}P)$-competitive for jobs with identical release time. Finally, we prove an $\Omega(\ln^{1/\alpha}P)$ lower bound on the competitive ratio of any non-clairvoyant algorithm, matching the upper bound of our algorithm for jobs with identical release time.


Sparse and silent coding in neural circuits

Abstract: Sparse coding algorithms are about finding a linear basis in which signals can be represented by a small number of active (non-zero) coefficients. Such coding has many applications in science and engineering and is believed to play an important role in neural information processing. However, due to the computational complexity of the task, only approximate solutions provide the required efficiency (in terms of time). As new results show, under particular conditions there exist efficient solutions by minimizing the magnitude of the coefficients (`$l_1$-norm') instead of minimizing the size of the active subset of features (`$l_0$-norm'). Straightforward neural implementation of these solutions is not likely, as they require \emph{a priori} knowledge of the number of active features. Furthermore, these methods utilize iterative re-evaluation of the reconstruction error, which in turn implies that final sparse forms (featuring `population sparseness') can only be reached through the formation of a series of non-sparse representations, which is in contrast with the overall sparse functioning of the neural systems (`lifetime sparseness'). In this article we present a novel algorithm which integrates our previous `$l_0$-norm' model on spike based probabilistic optimization for sparse coding with ideas coming from novel `$l_1$-norm' solutions. The resulting algorithm allows neurally plausible implementation and does not require an exactly defined sparseness level thus it is suitable for representing natural stimuli with a varying number of features. We also demonstrate that the combined method significantly extends the domain where optimal solutions can be found by `$l_1$-norm' based algorithms.


Revisiting Complex Moments For 2D Shape Representation and Image Normalization

Abstract: When comparing 2D shapes, a key issue is their normalization. Translation and scale are easily taken care of by removing the mean and normalizing the energy. However, defining and computing the orientation of a 2D shape is not so simple. In fact, although for elongated shapes the principal axis can be used to define one of two possible orientations, there is no such tool for general shapes. As we show in the paper, previous approaches fail to compute the orientation of even noiseless observations of simple shapes. We address this problem. In the paper, we show how to uniquely define the orientation of an arbitrary 2D shape, in terms of what we call its Principal Moments. We show that a small subset of these moments suffice to represent the underlying 2D shape and propose a new method to efficiently compute the shape orientation: Principal Moment Analysis. Finally, we discuss how this method can further be applied to normalize grey-level images. Besides the theoretical proof of correctness, we describe experiments demonstrating robustness to noise and illustrating the method with real images.


No dimension independent Core-Sets for Containment under Homothetics

Abstract: This paper deals with the containment problem under homothetics which has the minimal enclosing ball (MEB) problem as a prominent representative. We connect the problem to results in classic convex geometry and introduce a new series of radii, which we call core-radii. For the MEB problem, these radii have already been considered from a different point of view and sharp inequalities between them are known. In this paper sharp inequalities between core-radii for general containment under homothetics are obtained. Moreover, the presented inequalities are used to derive sharp upper bounds on the size of core-sets for containment under homothetics. In the MEB case, this yields a tight (dimension independent) bound for the size of such core-sets. In the general case, we show that there are core-sets of size linear in the dimension and that this bound stays sharp even if the container is required to be symmetric.


Wireless Capacity with Oblivious Power in General Metrics

Abstract: The capacity of a wireless network is the maximum possible amount of simultaneous communication, taking interference into account. Formally, we treat the following problem. Given is a set of links, each a sender-receiver pair located in a metric space, and an assignment of power to the senders. We seek a maximum subset of links that are feasible in the SINR model: namely, the signal received on each link should be larger than the sum of the interferences from the other links. We give a constant-factor approximation that holds for any length-monotone, sub-linear power assignment and any distance metric. We use this to give essentially tight characterizations of capacity maximization under power control using oblivious power assignments. Specifically, we show that the mean power assignment is optimal for capacity maximization of bi-directional links, and give a tight $\theta(\log n)$-approximation of scheduling bi-directional links with power control using oblivious power. For uni-directional links we give a nearly optimal $O(\log n + \log \log \Delta)$-approximation to the power control problem using mean power, where $\Delta$ is the ratio of longest and shortest links. Combined, these results clarify significantly the centralized complexity of wireless communication problems.


Large-Scale Clustering Based on Data Compression

Abstract: This paper considers the clustering problem for large data sets. We propose an approach based on distributed optimization. The clustering problem is formulated as an optimization problem of maximizing the classification gain. We show that the optimization problem can be reformulated and decomposed into small-scale sub optimization problems by using the Dantzig-Wolfe decomposition method. Generally speaking, the Dantzig-Wolfe method can only be used for convex optimization problems, where the duality gaps are zero. Even though, the considered optimization problem in this paper is non-convex, we prove that the duality gap goes to zero, as the problem size goes to infinity. Therefore, the Dantzig-Wolfe method can be applied here. In the proposed approach, the clustering problem is iteratively solved by a group of computers coordinated by one center processor, where each computer solves one independent small-scale sub optimization problem during each iteration, and only a small amount of data communication is needed between the computers and center processor. Numerical results show that the proposed approach is effective and efficient.


Rational Convex Programs, Their Feasibility, and the Arrow-Debreu Nash Bargaining Game

Abstract: Over the last decade, combinatorial algorithms have been obtained for exactly solving several nonlinear convex programs. We first provide a formal context to this activity by introducing the notion of {\em rational convex programs} -- this also enables us to identify a number of questions for further study. So far, such algorithms were obtained for total problems only. Our main contribution is developing the methodology for handling non-total problems, i.e., their associated convex programs may be infeasible for certain settings of the parameters. The specific problem we study pertains to a Nash bargaining game, called ADNB, which is derived from the linear case of the Arrow-Debreu market model. We reduce this game to computing an equilibrium in a new market model called {\em flexible budget market}, and we obtain primal-dual algorithms for determining feasibility, as well as giving a proof of infeasibility and finding an equilibrium. We give an application of our combinatorial algorithm for ADNB to an important "fair" throughput allocation problem on a wireless channel.


Non-Separable, Quasiconcave Utilities are Easy -- in a Perfect Price Discrimination Market Model

Abstract: Recent results, establishing evidence of intractability for such restrictive utility functions as additively separable, piecewise-linear and concave, under both Fisher and Arrow-Debreu market models, have prompted the question of whether we have failed to capture some essential elements of real markets, which seem to do a good job of finding prices that maintain parity between supply and demand. The main point of this paper is to show that even non-separable, quasiconcave utility functions can be handled efficiently in a suitably chosen, though natural, realistic and useful, market model; our model allows for perfect price discrimination. Our model supports unique equilibrium prices and, for the restriction to concave utilities, satisfies both welfare theorems.


Statistical Compressive Sensing of Gaussian Mixture Models

Abstract: A new framework of compressive sensing (CS), namely statistical compressive sensing (SCS), that aims at efficiently sampling a collection of signals that follow a statistical distribution and achieving accurate reconstruction on average, is introduced. For signals following a Gaussian distribution, with Gaussian or Bernoulli sensing matrices of O(k) measurements, considerably smaller than the O(k log(N/k)) required by conventional CS, where N is the signal dimension, and with an optimal decoder implemented with linear filtering, significantly faster than the pursuit decoders applied in conventional CS, the error of SCS is shown tightly upper bounded by a constant times the k-best term approximation error, with overwhelming probability. The failure probability is also significantly smaller than that of conventional CS. Stronger yet simpler results further show that for any sensing matrix, the error of Gaussian SCS is upper bounded by a constant times the k-best term approximation with probability one, and the bound constant can be efficiently calculated. For signals following Gaussian mixture models, SCS with a piecewise linear decoder is introduced and shown to produce for real images better results than conventional CS based on sparse models.


A Protocol for Self-Synchronized Duty-Cycling in Sensor Networks: Generic Implementation in Wiselib

Abstract: In this work we present a protocol for self-synchronized duty-cycling in wireless sensor networks with energy harvesting capabilities. The protocol is implemented in Wiselib, a library of generic algorithms for sensor networks. Simulations are conducted with the sensor network simulator Shawn. They are based on the specifications of real hardware known as iSense sensor nodes. The experimental results show that the proposed mechanism is able to adapt to changing energy availabilities. Moreover, it is shown that the system is very robust against packet loss.


Sublinear Optimization for Machine Learning

Abstract: We give sublinear-time approximation algorithms for some optimization problems arising in machine learning, such as training linear classifiers and finding minimum enclosing balls. Our algorithms can be extended to some kernelized versions of these problems, such as SVDD, hard margin SVM, and L2-SVM, for which sublinear-time algorithms were not known before. These new algorithms use a combination of a novel sampling techniques and a new multiplicative update algorithm. We give lower bounds which show the running times of many of our algorithms to be nearly best possible in the unit-cost RAM model. We also give implementations of our algorithms in the semi-streaming setting, obtaining the first low pass polylogarithmic space and sublinear time algorithms achieving arbitrary approximation factor.


Revisiting deadlock prevention: a probabilistic approach

Abstract: We revisit the deadlock-prevention problem by focusing on priority digraphs instead of the traditional wait-for digraphs. This has allowed us to formulate deadlock prevention in terms of prohibiting the occurrence of directed cycles even in the most general of wait models (the so-called AND-OR model, in which prohibiting wait-for directed cycles is generally overly restrictive). For a particular case in which the priority digraphs are somewhat simplified, we introduce a Las Vegas probabilistic mechanism for resource granting and analyze its key aspects in detail.


Efficient Interpolant Generation in Satisfiability Modulo Linear Integer Arithmetic

Abstract: The problem of computing Craig interpolants in SAT and SMT has recently received a lot of interest, mainly for its applications in formal verification. Efficient algorithms for interpolant generation have been presented for some theories of interest ---including that of equality and uninterpreted functions, linear arithmetic over the rationals, and their combination--- and they are successfully used within model checking tools. For the theory of linear arithmetic over the integers (LA(Z)), however, the problem of finding an interpolant is more challenging, and the task of developing efficient interpolant generators for the full theory LA(Z) is still the objective of ongoing research. In this paper we try to close this gap. We build on previous work and present a novel interpolation algorithm for SMT(LA(Z)), which exploits the full power of current state-of-the-art SMT(LA(Z)) solvers. We demonstrate the potential of our approach with an extensive experimental evaluation of our implementation of the proposed algorithm in the MathSAT SMT solver.


Towards A Shape Analysis for Graph Transformation Systems

Abstract: Graphs and graph transformation systems are a frequently used modelling technique for a wide range of different domains, cover- ing areas as diverse as refactorings, network topologies or reconfigurable software. Being a formal method, graph transformation systems lend themselves to a formal analysis. This has inspired the development of various verification methods, in particular also model checking tools. In this paper, we present a verification technique for infinite-state graph transformation systems. The technique employs the abstraction principle used in shape analysis of programs, summarising possibly infinitely many nodes thus giving shape graphs. The technique has been implemented using the 3-valued logical foundations of standard shape analysis. We exemplify the approach on an example from the railway domain.


On the Foundations of Adversarial Single-Class Classification

Abstract: Motivated by authentication, intrusion and spam detection applications we consider single-class classification (SCC) as a two-person game between the learner and an adversary. In this game the learner has a sample from a target distribution and the goal is to construct a classifier capable of distinguishing observations from the target distribution from observations emitted from an unknown other distribution. The ideal SCC classifier must guarantee a given tolerance for the false-positive error (false alarm rate) while minimizing the false negative error (intruder pass rate). Viewing SCC as a two-person zero-sum game we identify both deterministic and randomized optimal classification strategies for different game variants. We demonstrate that randomized classification can provide a significant advantage. In the deterministic setting we show how to reduce SCC to two-class classification where in the two-class problem the other class is a synthetically generated distribution. We provide an efficient and practical algorithm for constructing and solving the two class problem. The algorithm distinguishes low density regions of the target distribution and is shown to be consistent.


State Dependent Attempt Rate Modeling of Single Cell IEEE~802.11 WLANs with Homogeneous Nodes and Poisson Packet Arrivals

Abstract: Analytical models for IEEE 802.11-based WLANs are invariably based on approximations, such as the well-known \textit{decoupling approximation} proposed by Bianchi for modeling single cell WLANs consisting of saturated nodes. In this paper, we provide a new approach to model the situation when the nodes are not saturated. We study a State Dependent Attempt Rate (SDAR) approximation to model $M$ queues (one queue per node) served by the CSMA/CA protocol as standardized in the IEEE 802.11 DCF MAC protocol. The approximation is that, when $n$ of the $M$ queues are non-empty, the transmission attempt probability of the $n$ non-empty nodes is given by the long-term transmission attempt probability of $n$ "saturated" nodes as provided by Bianchi's model. The SDAR approximation reduces a single cell WLAN with non-saturated nodes to a "coupled queue system". When packets arrive to the $M$ queues according to independent Poisson processes, we provide a Markov model for the coupled queue system with SDAR service. \textit{The main contribution of this paper is to provide an analysis of the coupled queue process by studying a lower dimensional process, and by introducing a certain conditional independence approximation}. We show that the SDAR model of contention provides an accurate model for the DCF MAC protocol in single cells, and report the simulation speed-ups thus obtained by our \textit{model-based simulation}.


Online Square Packing

Abstract: We analyze the problem of packing squares in an online fashion: Given a semi-infinite strip of width 1 and an unknown sequence of squares of side length in [0,1] that arrive from above, one at a time. The objective is to pack these items as they arrive, minimizing the resulting height. Just like in the classical game of Tetris, each square must be moved along a collision-free path to its final destination. In addition, we account for gravity in both motion (squares must never move up) and position (any final destination must be supported from below). A similar problem has been considered before; the best previous result is by Azar and Epstein, who gave a 4-competitive algorithm in a setting without gravity (i.e., with the possibility of letting squares "hang in the air") based on ideas of shelf-packing: Squares are assigned to different horizontal levels, allowing an analysis that is reminiscent of some bin-packing arguments. We apply a geometric analysis to establish a competitive factor of 3.5 for the bottom-left heuristic and present a 34/13=2.615...-competitive algorithm.


The Last Paper on the Halpern-Shoham Interval Temporal Logic

Abstract: The Halpern-Shoham logic is a modal logic of time intervals. Some effort has been put in last ten years to classify fragments of this beautiful logic with respect to decidability of its satisfiability problem. We contribute to this effort by showing - what we believe is quite an unexpected result - that the logic of subintervals, the fragment of the Halpern-Shoham where only the operator "during", or D, is allowed, is undecidable over discrete structures. This is surprising as this logic is decidable over dense orders and its reflexive variant is known to be decidable over discrete structures.


Certificate size reduction in Abstraction-Carrying Code

Abstract: Carrying Code (ACC) has recently been proposed as a framework for mobile code safety in which the code supplier provides a program together with an abstraction (or abstract model of the program) whose validity entails compliance with a predefined safety policy. The advantage of providing a (fixpoint) abstraction to the code consumer is that its validity is checked in a single pass (i.e., one iteration) of an abstract interpretation-based checker. A main challenge to make ACC useful in practice is to reduce the size of certificates as much as possible while at the same time not increasing checking time. The intuitive idea is to only include in the certificate information that the checker is unable to reproduce without iterating. We introduce the notion of reduced certificate which characterizes the subset of the abstraction which a checker needs in order to validate (and re-construct) the full certificate in a single pass. Based on this notion, we instrument a generic analysis algorithm with the necessary extensions in order to identify the information relevant to the checker. Interestingly, the fact that the reduced certificate omits (parts of) the abstraction has implications in the design of the checker. We provide the sufficient conditions which allow us to ensure that 1) if the checker succeeds in validating the certificate, then the certificate is valid for the program (correctness) and 2) the checker will succeed for any reduced certificate which is valid (completeness). Our approach has been implemented and benchmarked within the ciaopp system. The experimental results show that our proposal is able to greatly reduce the size of certificates in practice.To appear in Theory and Practice of Logic Programming (TPLP).


New S-norm and T-norm Operators for Active Learning Method

Abstract: Active Learning Method (ALM) is a soft computing method used for modeling and control based on fuzzy logic. All operators defined for fuzzy sets must serve as either fuzzy S-norm or fuzzy T-norm. Despite being a powerful modeling method, ALM does not possess operators which serve as S-norms and T-norms which deprive it of a profound analytical expression/form. This paper introduces two new operators based on morphology which satisfy the following conditions: First, they serve as fuzzy S-norm and T-norm. Second, they satisfy Demorgans law, so they complement each other perfectly. These operators are investigated via three viewpoints: Mathematics, Geometry and fuzzy logic.


A Partial Taxonomy of Substitutability and Interchangeability

Abstract: Substitutability, interchangeability and related concepts in Constraint Programming were introduced approximately twenty years ago and have given rise to considerable subsequent research. We survey this work, classify, and relate the different concepts, and indicate directions for future work, in particular with respect to making connections with research into symmetry breaking. This paper is a condensed version of a larger work in progress.


Parallel Sparse Matrix Solver on the GPU Applied to Simulation of Electrical Machines

Abstract: Nowadays, several industrial applications are being ported to parallel architectures. In fact, these platforms allow acquire more performance for system modelling and simulation. In the electric machines area, there are many problems which need speed-up on their solution. This paper examines the parallelism of sparse matrix solver on the graphics processors. More specifically, we implement the conjugate gradient technique with input matrix stored in CSR, and Symmetric CSR and CSC formats. This method is one of the most efficient iterative methods available for solving the finite-element basis functions of Maxwell's equations. The GPU (Graphics Processing Unit), which is used for its implementation, provides mechanisms to parallel the algorithm. Thus, it increases significantly the computation speed in relation to serial code on CPU based systems.


Navigating the Topology of 2x2 Games: An Introductory Note on Payoff Families, Normalization, and Natural Order

Abstract: The Robinson-Goforth topology of swaps in adjoining payoffs elegantly arranges 2x2 ordinal games in accordance with important properties including symmetry, number of dominant strategies and Nash Equilibria, and alignment of interests. Adding payoff families based on Nash Equilibria illustrates an additional aspect of this order and aids visualization of the topology. Making ties through half-swaps not only creates simpler games within the topology, but, in reverse, breaking ties shows the evolution of preferences, yielding a natural ordering for the topology of 2x2 games with ties. An ordinal game not only represents an equivalence class of games with real values, but also a discrete equivalent of the normalized version of those games. The topology provides coordinates which could be used to identify related games in a semantic web ontology and facilitate comparative analysis of agent-based simulations and other research in game theory, as well as charting relationships and potential moves between games as a tool for institutional analysis and design.


A Short Decidability Proof for DPDA Language Equivalence via First-Order Grammars

Abstract: The main aim of the paper is to give a short self-contained proof of the decidability of language equivalence for deterministic pushdown automata, which is the famous problem solved by G. Senizergues, for which C. Stirling has derived a primitive recursive complexity upper bound. The proof here is given in the framework of first-order grammars, which seems to be particularly apt for the aim. An appendix presents a modification of Stirling's approach, yielding a complexity bound of the form tetr(2,g(n)) where tetr is the (nonelementary) operator of iterated exponentiation (tetration) and g is an elementary function of the input size.


Learning under Concept Drift: an Overview

Abstract: Concept drift refers to a non stationary learning problem over time. The training and the application data often mismatch in real life problems. In this report we present a context of concept drift problem 1. We focus on the issues relevant to adaptive training set formation. We present the framework and terminology, and formulate a global picture of concept drift learners design. We start with formalizing the framework for the concept drifting data in Section 1. In Section 2 we discuss the adaptivity mechanisms of the concept drift learners. In Section 3 we overview the principle mechanisms of concept drift learners. In this chapter we give a general picture of the available algorithms and categorize them based on their properties. Section 5 discusses the related research fields and Section 5 groups and presents major concept drift applications. This report is intended to give a bird's view of concept drift research field, provide a context of the research and position it within broad spectrum of research fields and applications.


Blocking Underhand Attacks by Hidden Coalitions (Extended Version)

Abstract: Similar to what happens between humans in the real world, in open multi-agent systems distributed over the Internet, such as online social networks or wiki technologies, agents often form coalitions by agreeing to act as a whole in order to achieve certain common goals. However, agent coalitions are not always a desirable feature of a system, as malicious or corrupt agents may collaborate in order to subvert or attack the system. In this paper, we consider the problem of hidden coalitions, whose existence and the purposes they aim to achieve are not known to the system, and which carry out so-called underhand attacks. We give a first approach to hidden coalitions by introducing a deterministic method that blocks the actions of potentially dangerous agents, i.e. possibly belonging to such coalitions. We also give a non-deterministic version of this method that blocks the smallest set of potentially dangerous agents. We calculate the computational cost of our two blocking methods, and prove their soundness and completeness.


Polynomial Bottleneck Congestion Games with Optimal Price of Anarchy

Abstract: We study {\em bottleneck congestion games} where the social cost is determined by the worst congestion of any resource. These games directly relate to network routing problems and also job-shop scheduling problems. In typical bottleneck congestion games, the utility costs of the players are determined by the worst congested resources that they use. However, the resulting Nash equilibria are inefficient, since the price of anarchy is proportional on the number of resources which can be high. Here we show that we can get smaller price of anarchy with the bottleneck social cost metric. We introduce the {\em polynomial bottleneck games} where the utility costs of the players are polynomial functions of the congestion of the resources that they use. In particular, the delay function for any resource $r$ is $C_{r}^\M$, where $C_r$ is the congestion measured as the number of players that use $r$, and $\M \geq 1$ is an integer constant that defines the degree of the polynomial. The utility cost of a player is the sum of the individual delays of the resources that it uses. The social cost of the game remains the same, namely, it is the worst bottleneck resource congestion: $\max_{r} C_r$. We show that polynomial bottleneck games are very efficient and give price of anarchy $O(|R|^{1/(\M+1)})$, where $R$ is the set of resources. This price of anarchy is tight, since we demonstrate a game with price of anarchy $\Omega(|R|^{1/(\M+1)})$, for any $\M \geq 1$. We obtain our tight bounds by using two proof techniques: {\em transformation}, which we use to convert arbitrary games to simpler games, and {\em expansion}, which we use to bound the price of anarchy in a simpler game.


Cluster based Key Management in Wireless Sensor Networks

Abstract: Wireless sensor networks consist of sensor nodes with limited computational and communication capabilities. In this paper, the whole network of sensor nodes is divided into clusters based on their physical locations. In addition, efficient ways of key distribution among the nodes within the cluster and among controllers of each cluster are discussed. Also, inter and intra cluster communications are presented in detail. The security of the entire network through efficient key management by taking into consideration the network's power capabilities is discussed. A graphical representation of the simulation on the scheme is also presented.


A Unifying Probabilistic Perspective for Spectral Dimensionality Reduction: Insights and New Models

Abstract: We introduce a new perspective on spectral dimensionality reduction which views these methods as Gaussian Markov random fields (GRFs). Our unifying perspective is based on the maximum entropy principle which is in turn inspired by maximum variance unfolding. The resulting model, which we call maximum entropy unfolding (MEU) is a nonlinear generalization of principal component analysis. We relate the model to Laplacian eigenmaps and isomap. We show that parameter fitting in the locally linear embedding (LLE) is approximate maximum likelihood MEU. We introduce a variant of LLE that performs maximum likelihood exactly: Acyclic LLE (ALLE). We show that MEU and ALLE are competitive with the leading spectral approaches on a robot navigation visualization and a human motion capture data set. Finally the maximum likelihood perspective allows us to introduce a new approach to dimensionality reduction based on L1 regularization of the Gaussian random field via the graphical lasso.


Securing data transfer in the cloud through introducing identification packet and UDT-authentication option field: a characterization

Abstract: The emergence of various technologies has since pushed researchers to develop new protocols that support high density data transmissions in Wide Area Networks. Many of these protocols are TCP protocol variants, which have demonstrated better performance in simulation and several limited network experiments but have limited practical applications because of implementation and installation difficulties. On the other hand, users who need to transfer bulk data (e.g., in grid/cloud computing) usually turn to application level solutions where these variants do not fair well. Among protocols considered in the application level solutions are UDP-based protocols, such as UDT (UDP-based Data Transport Protocol) for cloud /grid computing. Despite the promising development of protocols like UDT, what remains to be a major challenge that current and future network designers face is to achieve survivability and security of data and networks. Our previous research surveyed various security methodologies which led to the development of a framework for UDT. In this paper we present lowerlevel security by introducing an Identity Packet (IP) and Authentication Option (AO) for UDT.


Treillis des concepts skylines : Analyse multidimensionnelle des skylines fond\'ee sur les ensembles en accord

Abstract: The skyline concept has been introduced in order to exhibit the best objects according to all the criterion combinations and makes it possible to analyse the relationships between skyline objects. Like the data cube, the skycube is so voluminous that reduction approaches are really necessary. In this paper, we define an approach which partially materializes the skycube. The underlying idea is to discard from the representation the skycuboids which can be computed again the most easily. To meet this reduction objective, we characterize a formal framework: the agree concept lattice. From this structure, we derive the skyline concept lattice which is one of its constrained instances. The strong points of our approach are: (i) it is attribute oriented; (ii) it provides a boundary for the number of lattice nodes; (iii) it facilitates the navigation within the Skycuboids.


Suggested Rules for Designing Secure Communication Systems Utilizing Chaotic Lasers: A Survey

Abstract: Chaotic communications based on semiconductor lasers have aroused great research interest since 1990s. Physical-layer encryption using chaotic lasers is an alternative to transmit message rapidly and confidentially. There are some practical devices and setups for optical chaotic communications, which are intuitively considered to be secure. However, there is lack of a set of security evaluation rules for these communication setups. According to the recent literature, we summarize several criteria for optical chaotic communications to evaluate the security and point out some methods to enhance the security. These criteria and suggested rules are very helpful in designing secure communication systems using chaotic lasers. Finally we propose some possible hot topics on security analysis of optical chaotic communications in future.


Emergency Response Communications and Associated Security Challenges

Abstract: The natural or man-made disaster demands an efficient communication and coordination among first responders to save life and other community resources. Normally, the traditional communication infrastructures such as land line or cellular networks are damaged and don't provide adequate communication services to first responders for exchanging emergency related information. Wireless ad hoc networks such as mobile ad hoc networks, wireless sensor networks and wireless mesh networks are the promising alternatives in such type of situations. The security requirements for emergency response communications include privacy, data integrity, authentication, key management, access control and availability. Various ad hoc communication frameworks have been proposed for emergency response situations. The majority of the proposed frameworks don't provide adequate security services for reliable and secure information exchange. This paper presents a survey of the proposed emergency response communication frameworks and the potential security services required by them to provide reliable and secure information exchange during emergency situations.


Mayavi: a package for 3D visualization of scientific data

Abstract: Mayavi is an open-source, general-purpose, 3D scientific visualization package. It seeks to provide easy and interactive tools for data visualization that fit with the scientific user's workflow. For this purpose, Mayavi provides several entry points: a full-blown interactive application; a Python library with both a MATLAB-like interface focused on easy scripting and a feature-rich object hierarchy; widgets associated with these objects for assembling in a domain-specific application, and plugins that work with a general purpose application-building framework. In this article, we present an overview of the various features of Mayavi, we then provide insight on the design and engineering decisions made in implementing Mayavi, and finally discuss a few novel applications.


Collaborative Sources Identification in Mixed Signals via Hierarchical Sparse Modeling

Abstract: A collaborative framework for detecting the different sources in mixed signals is presented in this paper. The approach is based on C-HiLasso, a convex collaborative hierarchical sparse model, and proceeds as follows. First, we build a structured dictionary for mixed signals by concatenating a set of sub-dictionaries, each one of them learned to sparsely model one of a set of possible classes. Then, the coding of the mixed signal is performed by efficiently solving a convex optimization problem that combines standard sparsity with group and collaborative sparsity. The present sources are identified by looking at the sub-dictionaries automatically selected in the coding. The collaborative filtering in C-HiLasso takes advantage of the temporal/spatial redundancy in the mixed signals, letting collections of samples collaborate in identifying the classes, while allowing individual samples to have different internal sparse representations. This collaboration is critical to further stabilize the sparse representation of signals, in particular the class/sub-dictionary selection. The internal sparsity inside the sub-dictionaries, as naturally incorporated by the hierarchical aspects of C-HiLasso, is critical to make the model consistent with the essence of the sub-dictionaries that have been trained for sparse representation of each individual class. We present applications from speaker and instrument identification and texture separation. In the case of audio signals, we use sparse modeling to describe the short-term power spectrum envelopes of harmonic sounds. The proposed pitch independent method automatically detects the number of sources on a recording.


Property Testing via Set-Theoretic Operations

Abstract: Given two testable properties $\mathcal{P}_{1}$ and $\mathcal{P}_{2}$, under what conditions are the union, intersection or set-difference of these two properties also testable? We initiate a systematic study of these basic set-theoretic operations in the context of property testing. As an application, we give a conceptually different proof that linearity is testable, albeit with much worse query complexity. Furthermore, for the problem of testing disjunction of linear functions, which was previously known to be one-sided testable with a super-polynomial query complexity, we give an improved analysis and show it has query complexity $O(1/\eps^2)$, where $\eps$ is the distance parameter.


Design-on-demand or how to create a target-oriented social web-site

Abstract: We describe an informal methodology for developing on-line applications, which is, to some extent, complementary to the Web 2.0 aspects of web development. The presented methodology is suitable for developing low-cost and non-cost web sites targeted at medium-sized communities. We present basic building blocks used in the described strategy. To achieve a better understanding of the discussed concepts we comment on their application during the realization of two web projects. We focus on the role of community-driven development, which is crucial for projects of the discussed type.


Local Component Analysis for Nonparametric Bayes Classifier

Abstract: The decision boundaries of Bayes classifier are optimal because they lead to maximum probability of correct decision. It means if we knew the prior probabilities and the class-conditional densities, we could design a classifier which gives the lowest probability of error. However, in classification based on nonparametric density estimation methods such as Parzen windows, the decision regions depend on the choice of parameters such as window width. Moreover, these methods suffer from curse of dimensionality of the feature space and small sample size problem which severely restricts their practical applications. In this paper, we address these problems by introducing a novel dimension reduction and classification method based on local component analysis. In this method, by adopting an iterative cross-validation algorithm, we simultaneously estimate the optimal transformation matrices (for dimension reduction) and classifier parameters based on local information. The proposed method can classify the data with complicated boundary and also alleviate the course of dimensionality dilemma. Experiments on real data show the superiority of the proposed algorithm in term of classification accuracies for pattern classification applications like age, facial expression and character recognition. Keywords: Bayes classifier, curse of dimensionality dilemma, Parzen window, pattern classification, subspace learning.


Towards Constraint-based High Performance Cloud System in the Process of Cloud Computing Adoption in an Organization

Abstract: Cloud computing is penetrating into various domains and environments, from theoretical computer science to economy, from marketing hype to educational curriculum and from R&D lab to enterprise IT infrastructure. Yet, the currently developing state of cloud computing leaves several issues to address and also affects cloud computing adoption by organizations. In this paper, we explain how the transition into the cloud can occur in an organization and describe the mechanism for transforming legacy infrastructure into a virtual infrastructure-based cloud. We describe the state of the art of infrastructural cloud, which is essential in the decision making on cloud adoption, and highlight the challenges that can limit the scale and speed of the adoption. We then suggest a strategic framework for designing a high performance cloud system. This framework is applicable when transformation cloudbased deployment model collides with some constraints. We give an example of the implementation of the framework in a design of a budget-constrained high availability cloud system.


Equivalence Checking in Embedded Systems Design Verification using PRES+ model

Abstract: In this paper we focus on some aspects related to modeling and formal verification of embedded systems. Many models have been proposed to represent embedded systems. These models encompass a broad range of styles, characteristics, and application domains and include the extensions of finite state machines, data flow graphs, communication processes and Petri nets. In this report, we have used a PRES+ model (Petri net based Representation for Embedded Systems) as an extension of classical Petri net model that captures concurrency, timing behaviour of embedded systems; it allows systems to be representative in different levels of abstraction and improves expressiveness by allowing the token to carry information. Modeling using PRES+, as discussed above, may be convenient for specifying the input behaviour because it supports concurrency. However, there is no equivalence checking method reported in the literature for PRES+ models to the best of our knowledge. In contrast, equivalence checking of FSMD models exist. As a first step, therefore, we seek to devise an algorithm to translate PRES+ models to FSMD models and we seek to hand execute our algorithm on a real life example and we have to translate two versions of PRES+ models to FSMD models. Then using existing equivalence checker we have checked the equivalence between two FSMD models.


A Testbed Implementation for Securing OLSR in Mobile Ad hoc Networks

Abstract: Contemporary personal computing devices are increasingly required to be portable and mobile enabling user's wireless access, to wired network infrastructures and services. This approach to mobile computing and communication is only appropriate in situations where a coherent infrastructure is available. There are many situations where these requirements are not fulfilled such as; developing nations, rural areas, natural disasters, and military conflicts to name but a few. A practical solution is to use mobile devices interconnected via a wireless medium to form a network, known as a Mobile Ad-hoc Network (MANET), and provide the services normally found in wired networks. Security in MANETs is an issue of paramount importance due to the wireless nature of the communication links. Additionally due to the lack of central administration security issues are different from conventional networks. For the purposes of this article we have used the "WMN test-bed" to enable secure routing in MANETs. The use of cryptography is an efficient proven way of securing data in communications, but some cryptographic algorithms are not as efficient as others and require more processing power, which is detrimental to MANETs. In this article we have assessed different cryptographic approaches to securing the OLSR (Optimised Link State Routing) protocol to provide a basis for research. We conclude the paper with a series of performance evaluation results regarding different cryptographic and hashing schemes. Our findings clearly show that the most efficient combination of algorithms used for authentication and encryption are SHA-1 and AES respectively. Using this combination over their counterparts will lead to a considerable reduction in processing time and delay on the network, creating an efficient transaction moving towards satisfying resource constraints and security requirements.


Yacc is dead

Abstract: We present two novel approaches to parsing context-free languages. The first approach is based on an extension of Brzozowski's derivative from regular expressions to context-free grammars. The second approach is based on a generalization of the derivative to parser combinators. The payoff of these techniques is a small (less than 250 lines of code), easy-to-implement parsing library capable of parsing arbitrary context-free grammars into lazy parse forests. Implementations for both Scala and Haskell are provided. Preliminary experiments with S-Expressions parsed millions of tokens per second, which suggests this technique is efficient enough for use in practice.


Authentication from matrix conjugation

Abstract: We propose an authentication scheme where forgery (a.k.a. impersonation) seems infeasible without finding the prover's long-term private key. The latter would follow from solving the conjugacy search problem in the platform (noncommutative) semigroup, i.e., to recovering X from X^{-1}AX and A. The platform semigroup that we suggest here is the semigroup of nxn matrices over truncated multivariable polynomials over a ring.


Dynamic Multi Layer Signature based Intrusion Detection system Using Mobile Agents

Abstract: Intrusion detection systems have become a key component in ensuring the safety of systems and networks. As networks grow in size and speed continues to increase, it is crucial that efficient scalable techniques should be developed for IDS systems. Signature based detection is the most extensively used threat detection technique for Intrusion Detection Systems (IDS). One of the foremost challenges for signaturebased IDS systems is how to keep up with large volume of incoming traffic when each packet needs to be compared with every signature in the database. When an IDS cannot keep up with the traffic flood, all it can do is to drop packets, therefore, may miss potential attacks. This paper proposes a new model called Dynamic Multi-Layer Signature based IDS using Mobile Agents, which can detect imminent threats with very high success rate by dynamically and automatically creating and using small and efficient multiple databases, and at the same time, provide mechanism to update these small signature databases at regular intervals using Mobile Agents


Server Consolidation: An Approach to make Data Centers Energy Efficient and Green

Abstract: Data centers are the building blocks of IT business organizations providing the capabilities of centralized repository for storage, management, networking and dissemination of data. With the rapid increase in the capacity and size of data centers, there is a continuous increase in the demand for energy consumption. These data centers not only consume a tremendous amount of energy but are riddled with IT inefficiencies. All data center are plagued with thousands of servers as major components. These servers consume huge energy without performing useful work. In an average server environment, 30% of the servers are "dead" only consuming energy, without being properly utilized. Their utilization ratio is only 5 to 10 percent. This paper focuses on the use of an emerging technology called virtualization to achieve energy efficient data centers by providing a solution called server consolidation. It increases the utilization ratio up to 50% saving huge amount of energy. Server consolidation helps in implementing green data centers to ensure that IT infrastructure contributes as little as possible to the emission of green house gases, and helps to regain power and cooling capacity, recapture resilience and dramatically reducing energy costs and total cost of ownership.