Reaching an Optimal Consensus: Dynamical Systems that Compute Intersections of Convex Sets

Abstract: In this paper, multi-agent systems minimizing a sum of objective functions, where each component is only known to a particular node, is considered for continuous-time dynamics with time-varying interconnection topologies. Assuming that each node can observe a convex solution set of its optimization component, and the intersection of all such sets is nonempty, the considered optimization problem is converted to an intersection computation problem. By a simple distributed control rule, the considered multi-agent system with continuous-time dynamics achieves not only a consensus, but also an optimal agreement within the optimal solution set of the overall optimization objective. Directed and bidirectional communications are studied, respectively, and connectivity conditions are given to ensure a global optimal consensus. In this way, the corresponding intersection computation problem is solved by the proposed decentralized continuous-time algorithm. We establish several important properties of the distance functions with respect to the global optimal solution set and a class of invariant sets with the help of convex and non-smooth analysis.


Connectivity and Set Tracking of Multi-agent Systems Guided by Multiple Moving Leaders

Abstract: In this paper, we investigate distributed multi-agent tracking of a convex set specified by multiple moving leaders with unmeasurable velocities. Various jointly-connected interaction topologies of the follower agents with uncertainties are considered in the study of set tracking. Based on the connectivity of the time-varying multi-agent system, necessary and sufficient conditions are obtained for set input-to-state stability and set integral input-to-state stability for a nonlinear neighbor-based coordination rule with switching directed topologies. Conditions for asymptotic set tracking are also proposed with respect to the polytope spanned by the leaders.


Consensus over Random Graph Processes: Network Borel-Cantelli Lemmas for Almost Sure Convergence

Abstract: Distributed consensus computation over random graph processes is considered. The random graph process is defined as a sequence of random variables which take values from the set of all possible digraphs over the node set. At each time step, every node updates its state based on a Bernoulli trial, independent in time and among different nodes: either averaging among the neighbor set generated by the random graph, or sticking with its current state. Connectivity-independence and arc-independence are introduced to capture the fundamental influence of the random graphs on the consensus convergence. Necessary and/or sufficient conditions are presented on the success probabilities of the Bernoulli trials for the network to reach a global almost sure consensus, with some sharp threshold established revealing a consensus zero-one law. Convergence rates are established by lower and upper bounds of the $\epsilon$-computation time. We also generalize the concepts of connectivity/arc independence to their analogues from the $*$-mixing point of view, so that our results apply to a very wide class of graphical models, including the majority of random graph models in the literature, e.g., Erd\H{o}s-R\'{e}nyi, gossiping, and Markovian random graphs. We show that under $*$-mixing, our convergence analysis continues to hold and the corresponding almost sure consensus conditions are established. Finally, we further investigate almost sure finite-time convergence of random gossiping algorithms, and prove that the Bernoulli trials play a key role in ensuring finite-time convergence. These results add to the understanding of the interplay between random graphs, random computations, and convergence probability for distributed information processing.


The Role of Persistent Graphs in the Agreement Seeking of Social Networks

Abstract: This paper investigates the role persistent arcs play for a social network to reach a global belief agreement under discrete-time or continuous-time evolution. Each (directed) arc in the underlying communication graph is assumed to be associated with a time-dependent weight function which describes the strength of the information flow from one node to another. An arc is said to be persistent if its weight function has infinite $\mathscr{L}_1$ or $\ell_1$ norm for continuous-time or discrete-time belief evolutions, respectively. The graph that consists of all persistent arcs is called the persistent graph of the underlying network. Three necessary and sufficient conditions on agreement or $\epsilon$-agreement are established, by which we prove that the persistent graph fully determines the convergence to a common opinion in social networks. It is shown how the convergence rates explicitly depend on the diameter of the persistent graph. The results adds to the understanding of the fundamentals behind global agreements, as it is only persistent arcs that contribute to the convergence.


Discovering novel computer music techniques by exploring the space of short computer programs

Abstract: Very short computer programs, sometimes consisting of as few as three arithmetic operations in an infinite loop, can generate data that sounds like music when output as raw PCM audio. The space of such programs was recently explored by dozens of individuals within various on-line communities. This paper discusses the programs resulting from this exploratory work and highlights some rather unusual methods they use for synthesizing sound and generating musical structure.


An Identity for Kernel Ridge Regression

Abstract: This paper derives an identity connecting the square loss of ridge regression in on-line mode with the loss of the retrospectively best regressor. Some corollaries about the properties of the cumulative loss of on-line ridge regression are also obtained.


A Study on Hierarchical Floorplans of Order k

Abstract: A floorplan is a rectangular dissection which describes the relative placement of electronic modules on the chip. It is called a mosaic floorplan if there are no empty rooms or cross junctions in the rectangular dissection. We study a subclass of mosaic floorplans called hierarchical floorplans of order $k$ (abbreviated HFO-${k}$). A floorplan is HFO-$k$ if it can be obtained by starting with a single rectangle and recursively embedding mosaic floorplans of at most $k$ rooms inside the rooms of intermediate floorplans. When $k=2$ this is exactly the class of slicing floorplans as the only distinct floorplans with two rooms are a room with a vertical slice and a room with a horizontal slice respectdeively. And embedding such a room is equivalent to slicing the parent room vertically/horizontally. In this paper we characterize permutations corresponding to the Abe-labeling of HFO-$k$ floorplans and also give an algorithm for identification of such permutations in linear time for any particular $k$. We give a recurrence relation for exact number of HFO-5 floorplans with $n$ rooms which can be easily extended to any $k$ also. Based on this recurrence we provide a polynomial time algorithm to generate the number of HFO-$k$ floorplans with $n$ rooms. Considering its application in VLSI design we also give moves on HFO-$k$ family of permutations for combinatorial optimization using simulated annealing etc. We also explore some interesting properties of Baxter permutations which have a bijective correspondence with mosaic floorplans.


On the complexity of strongly connected components in directed hypergraphs

Abstract: We study the complexity of some algorithmic problems on directed hypergraphs and their strongly connected components (SCCs). The main contribution is an almost linear time algorithm computing the terminal strongly connected components (i.e. SCCs which do not reach any components but themselves). "Almost linear" here means that the complexity of the algorithm is linear in the size of the hypergraph up to a factor alpha(n), where alpha is the inverse of Ackermann function, and n is the number of vertices. Our motivation to study this problem arises from a recent application of directed hypergraphs to computational tropical geometry. We also discuss the problem of computing all SCCs. We establish a superlinear lower bound on the size of the transitive reduction of the reachability relation in directed hypergraphs, showing that it is combinatorially more complex than in directed graphs. Besides, we prove a linear time reduction from the well-studied problem of finding all minimal sets among a given family to the problem of computing the SCCs. Only subquadratic time algorithms are known for the former problem. These results strongly suggest that the problem of computing the SCCs is harder in directed hypergraphs than in directed graphs.


Intelligent Paging Strategy for Multi-Carrier CDMA System

Abstract: Subscriber satisfaction and maximum radio resource utilization are the pivotal criteria in communication system design. In multi-Carrier CDMA system, different paging algorithms are used for locating user within the shortest possible time and best possible utilization of radio resources. Different paging algorithms underscored different techniques based on the different purposes. However, low servicing time of sequential search and better utilization of radio resources of concurrent search can be utilized simultaneously by swapping of the algorithms. In this paper, intelligent mechanism has been developed for dynamic algorithm assignment basing on time-varying traffic demand, which is predicted by radial basis neural network; and its performance has been analyzed are based on prediction efficiency of different types of data. High prediction efficiency is observed with a good correlation coefficient (0.99) and subsequently better performance is achieved by dynamic paging algorithm assignment. This claim is substantiated by the result of proposed intelligent paging strategy.


Multiuser Cellular Network

Abstract: Modern radio communication is faced with a problem about how to distribute restricted frequency to users in a certain space. Since our task is to minimize the number of repeaters, a natural idea is enlarging coverage area. However, coverage has restrictions. First, service area has to be divided economically as repeater's coverage is limited. In this paper, our fundamental method is to adopt seamless cellular network division. Second, underlying physics content in frequency distribution problem is interference between two close frequencies. Consequently, we choose a proper frequency width of 0.1MHz and a relevantly reliable setting to apply one frequency several times. We make a few general assumptions to simplify real situation. For instance, immobile users yield to homogenous distribution; repeaters can receive and transmit information in any given frequency in duplex operation; coverage is mainly decided by antenna height. Two models are built up to solve 1000 users and 10000 users situations respectively. In order to utilize restricted frequency and PL code, three stratified terms - "cell", "cluster", "group" - are introduced to describe the models in detail. Under our analysis, 91 repeaters for 1000 users and 469 repeaters for 10000 users are viable results. Next, to test stability and sensitivity of models, we give total consideration to the variation of sum of users, antenna height, and frequency width and service radius. Evaluation about models is offered qualitatively. Finally, two practical cases are put forward to gain a partial knowledge of mountainous area. The brief method in dealing with mountains is classified discussion in two ideal conditions. It may provide some constructive suggestions to avoid shortcomings or take proper measures in similar locations.


POCS Based Super-Resolution Image Reconstruction Using an Adaptive Regularization Parameter

Abstract: Crucial information barely visible to the human eye is often embedded in a series of low-resolution images taken of the same scene. Super-resolution enables the extraction of this information by reconstructing a single image, at a high resolution than is present in any of the individual images. This is particularly useful in forensic imaging, where the extraction of minute details in an image can help to solve a crime. Super-resolution image restoration has been one of the most important research areas in recent years which goals to obtain a high resolution (HR) image from several low resolutions (LR) blurred, noisy, under sampled and displaced images. Relation of the HR image and LR images can be modeled by a linear system using a transformation matrix and additive noise. However, a unique solution may not be available because of the singularity of transformation matrix. To overcome this problem, POCS method has been used. However, their performance is not good because the effect of noise energy has been ignored. In this paper, we propose an adaptive regularization approach based on the fact that the regularization parameter should be a linear function of noise variance. The performance of the proposed approach has been tested on several images and the obtained results demonstrate the superiority of our approach compared with existing methods.


Multi-granular Perspectives on Covering

Abstract: Covering model provides a general framework for granular computing in that overlapping among granules are almost indispensable. For any given covering, both intersection and union of covering blocks containing an element are exploited as granules to form granular worlds at different abstraction levels, respectively, and transformations among these different granular worlds are also discussed. As an application of the presented multi-granular perspective on covering, relational interpretation and axiomization of four types of covering based rough upper approximation operators are investigated, which can be dually applied to lower ones.


Re-initialization Free Level Set Evolution via Reaction Diffusion

Abstract: This paper presents a novel reaction-diffusion (RD) method for implicit active contours, which is completely free of the costly re-initialization procedure in level set evolution (LSE). A diffusion term is introduced into LSE, resulting in a RD-LSE equation, to which a piecewise constant solution can be derived. In order to have a stable numerical solution of the RD based LSE, we propose a two-step splitting method (TSSM) to iteratively solve the RD-LSE equation: first iterating the LSE equation, and then solving the diffusion equation. The second step regularizes the level set function obtained in the first step to ensure stability, and thus the complex and costly re-initialization procedure is completely eliminated from LSE. By successfully applying diffusion to LSE, the RD-LSE model is stable by means of the simple finite difference method, which is very easy to implement. The proposed RD method can be generalized to solve the LSE for both variational level set method and PDE-based level set method. The RD-LSE method shows very good performance on boundary anti-leakage, and it can be readily extended to high dimensional level set method. The extensive and promising experimental results on synthetic and real images validate the effectiveness of the proposed RD-LSE approach.


Pure Strategy or Mixed Strategy?

Abstract: Mixed strategy EAs aim to integrate several mutation operators into a single algorithm. However few theoretical analysis has been made to answer the question whether and when the performance of mixed strategy EAs is better than that of pure strategy EAs. In theory, the performance of EAs can be measured by asymptotic convergence rate and asymptotic hitting time. In this paper, it is proven that given a mixed strategy (1+1) EAs consisting of several mutation operators, its performance (asymptotic convergence rate and asymptotic hitting time)is not worse than that of the worst pure strategy (1+1) EA using one mutation operator; if these mutation operators are mutually complementary, then it is possible to design a mixed strategy (1+1) EA whose performance is better than that of any pure strategy (1+1) EA using one mutation operator.


On an Approach to the Design of a Logical Model of Innovation Project Data

Abstract: Questions concerning the development of a logical model of innovation project data, as well as those concerning the design of information systems for decision-making support in the management of innovation projects, are discussed.


A program logic for higher-order procedural variables and non-local jumps

Abstract: Relying on the formulae-as-types paradigm for classical logic, we define a program logic for an imperative language with higher-order procedural variables and non-local jumps. Then, we show how to derive a sound program logic for this programming language. As a by-product, we obtain a non-dependent type system which is more permissive than what is usually found in statically typed imperative languages. As a generic example, we encode imperative versions of delimited continuations operators shift and reset.


Improved hardness results for unique shortest vector problem

Abstract: We give several improvements on the known hardness of the unique shortest vector problem. - We give a deterministic reduction from the shortest vector problem to the unique shortest vector problem. As a byproduct, we get deterministic NP-hardness for unique shortest vector problem in the $\ell_\infty$ norm. - We give a randomized reduction from SAT to uSVP_{1+1/poly(n)}. This shows that uSVP_{1+1/poly(n)} is NP-hard under randomized reductions. - We show that if GapSVP_\gamma \in coNP (or coAM) then uSVP_{\sqrt{\gamma}} \in coNP (coAM respectively). This simplifies previously known uSVP_{n^{1/4}} \in coAM proof by Cai \cite{Cai98} to uSVP_{(n/\log n)^{1/4}} \in coAM, and additionally generalizes it to uSVP_{n^{1/4}} \in coNP. - We give a deterministic reduction from search-uSVP_\gamma to the decision-uSVP_{\gamma/2}. We also show that the decision-uSVP is {\bf NP}-hard for randomized reductions, which does not follow from Kumar-Sivakumar \cite{KS01}.


SLA Establishment with Guaranteed QoS in the Interdomain Network: A Stock Model

Abstract: The new model that we present in this paper is introduced in the context of guaranteed QoS and resources management in the inter-domain routing framework. This model, called the stock model, is based on a reverse cascade approach and is applied in a distributed context. So transit providers have to learn the right capacities to buy and to stock and, therefore learning theory is applied through an iterative process. We show that transit providers manage to learn how to strategically choose their capacities on each route in order to maximize their benefits, despite the very incomplete information. Finally, we provide and analyse some simulation results given by the application of the model in a simple case where the model quickly converges to a stable state.


Identification of the Risk Related to a Process on Hospital Emergency Service: a Case Study

Abstract: This paper, framed in a vast investigation, describes the application of techniques and methodologies in Organizational Engineering connected to the associated risk to the processes developed in an Emergency Service of an important Portuguese Hospital. The transactions performed in an emergency service and the consequent risk identification (negative behaviour associated to those transactions) is done based on static and dynamic models, developed during the business modelling. Any non-trivial system is better portrayed trough a small number of reasonably independent models. From this point of view it is important to look at the systems from a "micro" perspective, which allows us to analyse the system at the transaction level. All processes have some associated risk (inherent risk). Its identification will be decisive for future analysis and for the consequent decision over the need, or not, to study internal control mechanisms. This decision will depend on the risk level that the organization considers acceptable.


Urgency/Emergency Health Processes' Modelling: A Case Study

Abstract: The growing complexity and sophistication of the organizational information systems, and hospital ones particularly, render difficult their comprehension and, consequently, the implementation of control mechanisms that may assure, at all times, the auditability of the above mentioned systems, without having to use models. This paper, framed in a wider investigation, aims to describe the application of techniques and methodologies, in the sphere of action of Organizational Engineering, in the modelling of business processes developed in the main Operating Theatre of the Coimbra's University Hospital Emergency Service, as a support for the implementation of an information system architecture, using for that purpose the CEO framework, developed and suggested by the Centre for Organizational Engineering (CEO), based on the UML language.


Data Mining and Electronic Health Records: Selecting Optimal Clinical Treatments in Practice

Abstract: Electronic health records (EHR's) are only a first step in capturing and utilizing health-related data - the problem is turning that data into useful information. Models produced via data mining and predictive analysis profile inherited risks and environmental/behavioral factors associated with patient disorders, which can be utilized to generate predictions about treatment outcomes. This can form the backbone of clinical decision support systems driven by live data based on the actual population. The advantage of such an approach based on the actual population is that it is "adaptive". Here, we evaluate the predictive capacity of a clinical EHR of a large mental healthcare provider (~75,000 distinct clients a year) to provide decision support information in a real-world clinical setting. Initial research has achieved a 70% success rate in predicting treatment outcomes using these methods.


Data Mining Session-Based Patient Reported Outcomes (PROs) in a Mental Health Setting: Toward Data-Driven Clinical Decision Support and Personalized Treatment

Abstract: The CDOI outcome measure - a patient-reported outcome (PRO) instrument utilizing direct client feedback - was implemented in a large, real-world behavioral healthcare setting in order to evaluate previous findings from smaller controlled studies. PROs provide an alternative window into treatment effectiveness based on client perception and facilitate detection of problems/symptoms for which there is no discernible measure (e.g. pain). The principal focus of the study was to evaluate the utility of the CDOI for predictive modeling of outcomes in a live clinical setting. Implementation factors were also addressed within the framework of the Theory of Planned Behavior by linking adoption rates to implementation practices and clinician perceptions. The results showed that the CDOI does contain significant capacity to predict outcome delta over time based on baseline and early change scores in a large, real-world clinical setting, as suggested in previous research. The implementation analysis revealed a number of critical factors affecting successful implementation and adoption of the CDOI outcome measure, though there was a notable disconnect between clinician intentions and actual behavior. Most importantly, the predictive capacity of the CDOI underscores the utility of direct client feedback measures such as PROs and their potential use as the basis for next generation clinical decision support tools and personalized treatment approaches.


Steganography: a Class of Algorithms having Secure Properties

Abstract: Chaos-based approaches are frequently proposed in information hiding, but without obvious justification. Indeed, the reason why chaos is useful to tackle with discretion, robustness, or security, is rarely elucidated. This research work presents a new class of non-blind information hidingalgorithms based on some finite domains iterations that are Devaney's topologically chaotic. The approach is entirely formalized and reasons to take place into the mathematical theory of chaos are explained. Finally, stego-security and chaos security are consequently proven for a large class of algorithms.


A literature review: What exactly should we preserve? How scholars address this question and where is the gap

Abstract: This review addresses the question of what exactly should we preserve, and how the digital preservation community and scholars address this question. The paper first introduces the much-abused-term "significant properties," before revealing how some scholars are of the opinion that characteristics of digital objects to be preserved (i.e., significant properties) can be identified and should be expressed formally, while others are not of that opinion. The digital preservation community's attempt to expound on the general characteristics of digital objects and significant properties will then be discussed. Finally, the review shows that while there may be ways to identify the technical makeup or general characteristics of a digital object, there is currently no formal and objective methodology to help stakeholders identify and decide what the significant properties of the objects are. This review thus helps open questions and generates a formative recommendation based on expert opinion that expressing an object's functions in an explicit and formal way (using didactic guides from the archives community) could be the solution to help stakeholders decide what characteristics/ elements exactly we should preserve.


On the Link Between Strongly Connected Iteration Graphs and Chaotic Boolean Discrete-Time Dynamical Systems

Abstract: Chaotic functions are characterized by sensitivity to initial conditions, transitivity, and regularity. Providing new functions with such properties is a real challenge. This work shows that one can associate with any Boolean network a continuous function, whose discrete-time iterations are chaotic if and only if the iteration graph of the Boolean network is strongly connected. Then, sufficient conditions for this strong connectivity are expressed on the interaction graph of this network, leading to a constructive method of chaotic function computation. The whole approach is evaluated in the chaos-based pseudo-random number generation context.


Using Taxonomies to Facilitate the Analysis of the Association Rules

Abstract: The Data Mining process enables the end users to analyze, understand and use the extracted knowledge in an intelligent system or to support in the decision-making processes. However, many algorithms used in the process encounter large quantities of patterns, complicating the analysis of the patterns. This fact occurs with association rules, a Data Mining technique that tries to identify intrinsic patterns in large data sets. A method that can help the analysis of the association rules is the use of taxonomies in the step of post-processing knowledge. In this paper, the GART algorithm is proposed, which uses taxonomies to generalize association rules, and the RulEE-GAR computational module, that enables the analysis of the generalized rules.


HandsInAir: A Wearable System for Remote Collaboration

Abstract: We present HandsInAir, a real-time collaborative wearable system for remote collaboration. The system is developed to support real world scenarios in which a remote mobile helper guides a local mobile worker performing a physical task. HandsInAir implements a novel approach to support mobility of remote collaborators. This approach allows the helper to perform gestures without having to touch tangible objects, requiring little environment support. The system consists of two parts: the helper part and the worker part. The two parts are connected via a wireless network and the collaboration partners communicate with each other via audio and visual links. In this paper, we review related work, describe technical implementation of the system and envision future work for further improvements.


Unicode in Domain-Specific Programming Languages for Modeling & Simulation: ScalaTion as a Case Study

Abstract: As recent programming languages provide improved conciseness and flexibility of syntax, the development of embedded or internal Domain-Specific Languages has increased. The field of Modeling and Simulation has had a long history of innovation in programming languages (e.g. Simula-67, GPSS). Much effort has gone into the development of Simulation Programming Languages. The ScalaTion project is working to develop an embedded or internal Domain-Specific Language for Modeling and Simulation which could streamline language innovation in this domain. One of its goals is to make the code concise, readable, and in a form familiar to experts in the domain. In some cases the code looks very similar to textbook formulas. To enhance readability by domain experts, a version of ScalaTion is provided that heavily utilizes Unicode. This paper discusses the development of the ScalaTion DSL and the underlying features of Scala that make this possible. It then provides an overview of ScalaTion highlighting some uses of Unicode. Statistical analysis capabilities needed for Modeling and Simulation are presented in some detail. The notation developed is clear and concise which should lead to improved usability and extendibility.


Distributed Priority Synthesis and its Applications

Abstract: Given a set of interacting components with non-deterministic variable update and given safety requirements, the goal of priority synthesis is to restrict, by means of priorities, the set of possible interactions in such a way as to guarantee the given safety conditions for all possible runs. In distributed priority synthesis we are interested in obtaining local sets of priorities, which are deployed in terms of local component controllers sharing intended next moves between components in local neighborhoods only. These possible communication paths between local controllers are specified by means of a communication architecture. We formally define the problem of distributed priority synthesis in terms of a multi-player safety game between players for (angelically) selecting the next transition of the components and an environment for (demonically) updating uncontrollable variables; this problem is NP-complete. We propose several optimizations including a solution-space exploration based on a diagnosis method using a nested extension of the usual attractor computation in games together with a reduction to corresponding SAT problems. When diagnosis fails, the method proposes potential candidates to guide the exploration. These optimized algorithms for solving distributed priority synthesis problems have been integrated into our VissBIP framework. An experimental validation of this implementation is performed using a range of case studies including scheduling in multicore processors and modular robotics.


Minimum Average Distance Triangulations

Abstract: We study the problem of finding a triangulation T of a planar point set S such as to minimize the expected distance between two points x and y chosen uniformly at random from S. By distance we mean the length of the shortest path between x and y along edges of T. The length of a path is the sum of the weights of its edges. Edge weights are assumed to be given as part of the problem for every pair of distinct points (x,y) in S^2. In a different variant of the problem, the points are vertices of a simple polygon and we look for a triangulation of the interior of the polygon that is optimal in the same sense. We prove that a general formulation of the problem in which the weights are arbitrary positive numbers is strongly NP-complete. For the case when all the weights are equal we give polynomial-time algorithms. In the end we mention several open problems.


A Formally Specified Program Logic for Higher-Order Procedural Variables and non-local Jumps

Abstract: We formally specified a program logic for higher-order procedural variables and non-local jumps with Ott and Twelf. Moreover, the dependent type systems and the translation are both executable specifications thanks to Twelf's logic programming engine. In particular, relying on Filinski's encoding of shift/reset using callcc/throw and a global meta-continuation (simulated in state passing style), we have mechanically checked the correctness of a few examples (all source files are available on request).


(MC2)2: A Generic Decision-Making Framework and its Application to Cloud Computing

Abstract: Cloud computing is a disruptive technology, representing a new model for information technology (IT) solution engineering and management that promises to introduce significant cost savings and other benefits. The adoption of Cloud computing requires a detailed comparison of infrastructure alternatives, taking a number of aspects into careful consideration. Existing methods of evaluation, however, limit decision making to the relative costs of cloud computing, but do not take a broader range of criteria into account. In this paper, we introduce a generic, multi-criteria-based decision framework and an application for Cloud Computing, the Multi-Criteria Comparison Method for Cloud Computing ((MC2)2). The framework and method allow organizations to determine what infrastructure best suits their needs by evaluating and ranking infrastructure alternatives using multiple criteria. Therefore, (MC2)2 offers a way to differentiate infrastructures not only by costs, but also in terms of benefits, opportunities and risks. (MC2)2 can be adapted to facilitate a wide array of decision-making scenarios within the domain of information technology infrastructures, depending on the criteria selected to support the framework.


Combined Base Station Association and Power Control in Multi-channel Cellular Networks

Abstract: A combined base station association and power control problem is studied for the uplink of multichannel multicell cellular networks, in which each channel is used by exactly one cell (i.e., base station). A distributed association and power update algorithm is proposed and shown to converge to a Nash equilibrium of a noncooperative game. We consider network models with discrete mobiles (yielding an atomic congestion game), as well as a continuum of mobiles (yielding a population game). We find that the equilibria need not be Pareto efficient, nor need they be system optimal. To address the lack of system optimality, we propose pricing mechanisms. It is shown that these mechanisms can be implemented in a distributed fashion.


Equilibria of Channel Selection Games in Parallel Multiple Access Channel

Abstract: In this paper, we study the decentralized parallel multiple access channel (MAC) when transmitters selfishly maximize their individual spectral efficiency by selecting a single channel to transmit. More specifically, we investigate the set of Nash equilibria (NE) of decentralized networks comprising several transmitters communicating with a single receiver that implements single user decoding. This scenario is modeled as a one-shot game where the players (the transmitters) have discrete action sets (the channels). We show that the corresponding game has always at least one NE in pure strategies, but, depending on certain parameters, the game might possess several NE. We provide an upper bound for the maximum number of NE as a function of the number of transmitters and available channels. The main contribution of this paper is a mathematical proof of the existence of a Braess-type paradox. In particular, it is shown that under the assumption of a fully loaded network, when transmitters are allowed to use all the available channels, the corresponding sum spectral efficiency achieved at the NE is lower or equal than the sum spectral efficiency achieved when transmitters can use only one channel. A formal proof of this observation is provided in the case of small networks. For general scenarios, we provide numerical examples that show that the same effect holds as long as the network is kept fully loaded. We conclude the paper by considering the case of successive interference cancellation at the receiver. In this context, we show that the power allocation vectors at the NE are capacity maximizers. Finally, simulations are presented to verify our theoretical results.


A Multipath TCP model for ns-3 simulator

Abstract: We present an implementation of Multipath TCP (MPTCP) under the NS-3 open source network simulator. MPTCP is a promising extension of TCP currently considered by the recent eponymous IETF working group, with the objective of improving the performance of TCP, especially its robustness to variable network conditions. We describe this new protocol, its main functions and our implementation in NS-3. Besides this implementation compliant to the current versions of the IETF drafts, we have also added and compared various packet reordering mechanisms. We indeed notice that such mechanisms highly improve the performance of MPTCP. We believe that our implementation could be useful for future works in MPTCP performance evaluation, especially to compare packet reordering algorithms or coupling congestion control mechanisms between subfows.


Bootstrapping Intrinsically Motivated Learning with Human Demonstrations

Abstract: This paper studies the coupling of internally guided learning and social interaction, and more specifically the improvement owing to demonstrations of the learning by intrinsic motivation. We present Socially Guided Intrinsic Motivation by Demonstration (SGIM-D), an algorithm for learning in continuous, unbounded and non-preset environments. After introducing social learning and intrinsic motivation, we describe the design of our algorithm, before showing through a fishing experiment that SGIM-D efficiently combines the advantages of social learning and intrinsic motivation to gain a wide repertoire while being specialised in specific subspaces.


Approximation Algorithms for Edge Partitioned Vertex Cover Problems

Abstract: We consider a natural generalization of the Partial Vertex Cover problem. Here an instance consists of a graph G = (V,E), a positive cost function c: V-> Z^{+}, a partition $P_1,..., P_r$ of the edge set $E$, and a parameter $k_i$ for each partition $P_i$. The goal is to find a minimum cost set of vertices which cover at least $k_i$ edges from the partition $P_i$. We call this the Partition Vertex Cover problem. In this paper, we give matching upper and lower bound on the approximability of this problem. Our algorithm is based on a novel LP relaxation for this problem. This LP relaxation is obtained by adding knapsack cover inequalities to a natural LP relaxation of the problem. We show that this LP has integrality gap of $O(log r)$, where $r$ is the number of sets in the partition of the edge set. We also extend our result to more general settings.


Bipartite ranking algorithm for classification and survival analysis

Abstract: Unsupervised aggregation of independently built univariate predictors is explored as an alternative regularization approach for noisy, sparse datasets. Bipartite ranking algorithm Smooth Rank implementing this approach is introduced. The advantages of this algorithm are demonstrated on two types of problems. First, Smooth Rank is applied to two-class problems from bio-medical field, where ranking is often preferable to classification. In comparison against SVMs with radial and linear kernels, Smooth Rank had the best performance on 8 out of 12 benchmark benchmarks. The second area of application is survival analysis, which is reduced here to bipartite ranking in a way which allows one to use commonly accepted measures of methods performance. In comparison of Smooth Rank with Cox PH regression and CoxPath methods, Smooth Rank proved to be the best on 9 out of 10 benchmark datasets.


A discrepancy lower bound for information complexity

Abstract: This paper provides the first general technique for proving information lower bounds on two-party unbounded-rounds communication problems. We show that the discrepancy lower bound, which applies to randomized communication complexity, also applies to information complexity. More precisely, if the discrepancy of a two-party function $f$ with respect to a distribution $\mu$ is $Disc_\mu f$, then any two party randomized protocol computing $f$ must reveal at least $\Omega(\log (1/Disc_\mu f))$ bits of information to the participants. As a corollary, we obtain that any two-party protocol for computing a random function on $\{0,1\}^n\times\{0,1\}^n$ must reveal $\Omega(n)$ bits of information to the participants. In addition, we prove that the discrepancy of the Greater-Than function is $\Omega(1/\sqrt{n})$, which provides an alternative proof to the recent proof of Viola \cite{Viola11} of the $\Omega(\log n)$ lower bound on the communication complexity of this well-studied function and, combined with our main result, proves the tight $\Omega(\log n)$ lower bound on its information complexity. The proof of our main result develops a new simulation procedure that may be of an independent interest. In a very recent breakthrough work of Kerenidis et al. \cite{kerenidis2012lower}, this simulation procedure was the main building block for proving that almost all known lower bound techniques for communication complexity (and not just discrepancy) apply to information complexity.


A Framework for Picture Extraction on Search Engine Improved and Meaningful Result

Abstract: Searching is an important tool of information gathering, if information is in the form of picture than it play a major role to take quick action and easy to memorize. This is a human tendency to retain more picture than text. The complexity and the occurrence of variety of query can give variation in result and provide the humans to learn something new or get confused. This paper presents a development of a framework that will focus on recourse identification for the user so that they can get faster access with accurate & concise results on time and analysis of the change that is evident as the scenario changes from text to picture retrieval. This paper also provides a glimpse how to get accurate picture information in advance and extended technologies searching framework. The new challenges and design techniques of picture retrieval systems are also suggested in this paper.


Differentially Private Trajectory Data Publication

Abstract: With the increasing prevalence of location-aware devices, trajectory data has been generated and collected in various application domains. Trajectory data carries rich information that is useful for many data analysis tasks. Yet, improper publishing and use of trajectory data could jeopardize individual privacy. However, it has been shown that existing privacy-preserving trajectory data publishing methods derived from partition-based privacy models, for example k-anonymity, are unable to provide sufficient privacy protection. In this paper, motivated by the data publishing scenario at the Societe de transport de Montreal (STM), the public transit agency in Montreal area, we study the problem of publishing trajectory data under the rigorous differential privacy model. We propose an efficient data-dependent yet differentially private sanitization algorithm, which is applicable to different types of trajectory data. The efficiency of our approach comes from adaptively narrowing down the output domain by building a noisy prefix tree based on the underlying data. Moreover, as a post-processing step, we make use of the inherent constraints of a prefix tree to conduct constrained inferences, which lead to better utility. This is the first paper to introduce a practical solution for publishing large volume of trajectory data under differential privacy. We examine the utility of sanitized data in terms of count queries and frequent sequential pattern mining. Extensive experiments on real-life trajectory data from the STM demonstrate that our approach maintains high utility and is scalable to large trajectory datasets.


Channel Estimation for MIMO MC-CDMA Systems

Abstract: The concepts of MIMO MC-CDMA are not new but the new technologies to improve their functioning are an emerging area of research. In general, most mobile communication systems transmit bits of information in the radio space to the receiver. The radio channels in mobile radio systems are usually multipath fading channels, which cause inter-symbol interference (ISI) in the received signal. To remove ISI from the signal, there is a need of strong equalizer. In this thesis we have focused on simulating the MIMO MC-CDMA systems in MATLAB and designed the channel estimation for them.


PC-Cluster based Storage System Architecture for Cloud Storage

Abstract: Design and architecture of cloud storage system plays a vital role in cloud computing infrastructure in order to improve the storage capacity as well as cost effectiveness. Usually cloud storage system provides users to efficient storage space with elasticity feature. One of the challenges of cloud storage system is difficult to balance the providing huge elastic capacity of storage and investment of expensive cost for it. In order to solve this issue in the cloud storage infrastructure, low cost PC cluster based storage server is configured to be activated for large amount of data to provide cloud users. Moreover, one of the contributions of this system is proposed an analytical model using M/M/1 queuing network model, which is modeled on intended architecture to provide better response time, utilization of storage as well as pending time when the system is running. According to the analytical result on experimental testing, the storage can be utilized more than 90% of storage space. In this paper, two parts have been described such as (i) design and architecture of PC cluster based cloud storage system. On this system, related to cloud applications, services configurations are explained in detailed. (ii) Analytical model has been enhanced to be increased the storage utilization on the target architecture.


Future Robotics Database Management System along with Cloud TPS

Abstract: This paper deals with memory management issues of robotics. In our proposal we break one of the major issues in creating humanoid. . Database issue is the complicated thing in robotics schema design here in our proposal we suggest new concept called NOSQL database for the effective data retrieval, so that the humanoid robots will get the massive thinking ability in searching each items using chained instructions. For query transactions in robotics we need an effective consistency transactions so by using latest technology called CloudTPS which guarantees full ACID properties so that the robot can make their queries using multi-item transactions through this we obtain data consistency in data retrievals. In addition we included map reduce concepts it can splits the job to the respective workers so that it can process the data in a parallel way.


Automatic Classification of X-rated Videos using Obscene Sound Analysis based on a Repeated Curve-like Spectrum Feature

Abstract: This paper addresses the automatic classification of X-rated videos by analyzing its obscene sounds. In this paper, obscene sounds refer to audio signals generated from sexual moans and screams during sexual scenes. By analyzing various sound samples, we determined the distinguishable characteristics of obscene sounds and propose a repeated curve-like spectrum feature that represents the characteristics of such sounds. We constructed 6,269 audio clips to evaluate the proposed feature, and separately constructed 1,200 X-rated and general videos for classification. The proposed feature has an F1-score, precision, and recall rate of 96.6%, 98.2%, and 95.2%, respectively, for the original dataset, and 92.6%, 97.6%, and 88.0% for a noisy dataset of 5dB SNR. And, in classifying videos, the feature has more than a 90% F1-score, 97% precision, and an 84% recall rate. From the measured performance, X-rated videos can be classified with only the audio features and the repeated curve-like spectrum feature is suitable to detect obscene sounds.


Document Classification Using Expectation Maximization with Semi Supervised Learning

Abstract: As the amount of online document increases, the demand for document classification to aid the analysis and management of document is increasing. Text is cheap, but information, in the form of knowing what classes a document belongs to, is expensive. The main purpose of this paper is to explain the expectation maximization technique of data mining to classify the document and to learn how to improve the accuracy while using semi-supervised approach. Expectation maximization algorithm is applied with both supervised and semi-supervised approach. It is found that semi-supervised approach is more accurate and effective. The main advantage of semi supervised approach is "Dynamically Generation of New Class". The algorithm first trains a classifier using the labeled document and probabilistically classifies the unlabeled documents. The car dataset for the evaluation purpose is collected from UCI repository dataset in which some changes have been done from our side.


Learning Context for Text Categorization

Abstract: This paper describes our work which is based on discovering context for text document categorization. The document categorization approach is derived from a combination of a learning paradigm known as relation extraction and an technique known as context discovery. We demonstrate the effectiveness of our categorization approach using reuters 21578 dataset and synthetic real world data from sports domain. Our experimental results indicate that the learned context greatly improves the categorization performance as compared to traditional categorization approaches.


ECAKP: Encrypt Collect Authenticate Kill Play

Abstract: We are taught from a young age that plagiarism (copying other's work) is wrong. However, the problem of Illegal copies of multimedia data is exacerbated by the widespread availability of circumvention devices, which enable people to make infringing copies of multimedia data. Recently, Joint Video Compression and Encryption (JVCE) has gained increased attention to reduce the computational complexity of video compression, as well as provide encryption of multimedia data. In this paper, a novel protection method for multimedia data (ECAKP) is proposed. It combines encryption process and compression with authenticating process. The method had been implemented and the results are discussed in detail.


Recent Trends and Research Issues in Video Association Mining

Abstract: With the ever-growing digital libraries and video databases, it is increasingly important to understand and mine the knowledge from video database automatically. Discovering association rules between items in a large video database plays a considerable role in the video data mining research areas. Based on the research and development in the past years, application of association rule mining is growing in different domains such as surveillance, meetings, broadcast news, sports, archives, movies, medical data, as well as personal and online media collections. The purpose of this paper is to provide general framework of mining the association rules from video database. This article is also represents the research issues in video association mining followed by the recent trends.


Statistical Information of the Increased Demand for Watch the VOD with the Increased Sophistication in the Mobile Devices,Communications and Internet Penetration in Asia

Abstract: As the rapid progress of the media streaming applications such as video streaming can be classified into two types of streaming, Live video streaming, Video on Demand (VoD). Live video streaming is a service which allows the clients to watch many TV channels over the internet and the clients able to use one operation to perform is to switch the channels. Video on Demand (VoD) is one of the most important applications for the internet of the future and has become an interactive multimedia service which allows the users to start watching the video of their choice at anytime and anywhere, especially after the rapid deployment of the wireless networks and mobile devices. In this paper provide statistical information about the Internet, communications and mobile devices etc. This has led to an increased demand for the development, communication and computational powers of many of the mobile wireless subscribers/mobile devices such as laptops, PDAs, smart phones and notebook. These techniques are utilized to obtain a video on demand service with higher resolution and quality. Another objective in this paper is to see Malaysia ranked as a fully developed country by the year 2020.


Modelling Gesture Based Ubiquitous Applications

Abstract: A cost effective, gesture based modelling technique called Virtual Interactive Prototyping (VIP) is described in this paper. Prototyping is implemented by projecting a virtual model of the equipment to be prototyped. Users can interact with the virtual model like the original working equipment. For capturing and tracking the user interactions with the model image and sound processing techniques are used. VIP is a flexible and interactive prototyping method that has much application in ubiquitous computing environments. Different commercial as well as socio-economic applications and extension to interactive advertising of VIP are also discussed.


Improving TCP Performance over Wireless Network with Frequent Disconnections

Abstract: Presented in this paper is the solution to the problem that arises when the TCP/IP protocol suite is used to provide Internet connectivity through mobile terminals over emerging 802.11 wireless links. Taking into consideration the strong drive towards wireless Internet access through mobile terminals, the problem of frequent disconnections causing serial timeouts is examined and analyzed, with the help of extensive simulations. After a detailed review of wireless link loss recovery mechanism and identification of related problems, a new scheme with modifications at link layer and transport layer is proposed. The proposed modifications which depend on interaction between two layers (i) reduce the idle time before transmission at TCP by preventing timeout occurrences and (ii) decouple the congestion control from recovery of the losses due to link failure. Results of simulation based experiments demonstrate considerable performance improvement with the proposed modifications over the conventional TCP, when a wireless sender is experiencing frequent link failures.


Symmetrical Dispersion Compensation For High Speed Optical Links

Abstract: In this paper, the performance of high speed optical fiber based network is analysed by using dispersion compensating module (DCM). The optimal operating condition of the DCM is obtained by considering dispersion management configurations for the symmetrical system i.e Pre-compensation & Post-compensation. The dispersion compensating fiber (DCF) is tested for a single span, single channel system operating at a speed of 10 Gb/s with a transmitting wavelength of 1550 nm, over 120 km single mode fibre by using the compensating fiber for 24 km,30km and 35Km. So far, most of the investigations for single mode fiber (SMF) transmission at high amplifier spacings in the order of 90 km to 120 km is focused on conventional Non Return to Zero(NRZ) format. The simulation results are validated by analysing the Q-factor and Bit error rate (BER) in the numerical simulator OptSim.


Ontology-Based Emergency Management System in a Social Cloud

Abstract: The need for Emergency Management continually grows as the population and exposure to catastrophic failures increase. The ability to offer appropriate services at these emergency situations can be tackled through group communication mechanisms. The entities involved in the group communication include people, organizations, events, locations and essential services. Cloud computing is a "as a service" style of computing that enables on-demand network access to a shared pool of resources. So this work focuses on proposing a social cloud constituting group communication entities using an open source platform, Eucalyptus. The services are exposed as semantic web services, since the availability of machine-readable metadata (Ontology) will enable the access of these services more intelligently. The objective of this paper is to propose an Ontology-based Emergency Management System in a social cloud and demonstrate the same using emergency healthcare domain.


Thematic Analysis and Visualization of Textual Corpus

Abstract: The semantic analysis of documents is a domain of intense research at present. The works in this domain can take several directions and touch several levels of granularity. In the present work we are exactly interested in the thematic analysis of the textual documents. In our approach, we suggest studying the variation of the theme relevance within a text to identify the major theme and all the minor themes evoked in the text. This allows us at the second level of analysis to identify the relations of thematic associations in a textual corpus. Through the identification and the analysis of these association relations we suggest generating thematic paths allowing users, within the frame work of information search system, to explore the corpus according to their themes of interest and to discover new knowledge by navigating in the thematic association relations.


Real-time face swapping as a tool for understanding infant self-recognition

Abstract: To study the preference of infants for contingency of movements and familiarity of faces during self-recognition task, we built, as an accurate and instantaneous imitator, a real-time face- swapper for videos. We present a non-constraint face-swapper based on 3D visual tracking that achieves real-time performance through parallel computing. Our imitator system is par- ticularly suited for experiments involving children with Autistic Spectrum Disorder who are often strongly disturbed by the constraints of other methods.


MC CDMA PAPR Reduction Techniques using Discrete Transforms and Companding

Abstract: High Peak to Average Power Ratio (PAPR) of the transmitted signal is a serious problem in multicarrier modulation systems. In this paper a new technique for reduction in PAPR of the Multicarrier Code Division Multiple Access (MC CDMA) signals based on combining the Discrete Transform either Discrete Cosine Transform (DCT) or multi-resolution Discrete Wavelet Transform (DWT) with companding is proposed. It is analyzed and implemented using MATLAB. Simulation results of reduction in PAPR and power Spectral Density (PSD) of the MC CDMA with companding and without companding are compared with the MC CDMA with DCT and companding, DWT and companding systems. The new technique proposed is to make use of multi-resolution DWT in combination with companding in order to achieve a very substantial reduction in PAPR of the MC CDMA signal


Incremental Slow Feature Analysis: Adaptive and Episodic Learning from High-Dimensional Input Streams

Abstract: Slow Feature Analysis (SFA) extracts features representing the underlying causes of changes within a temporally coherent high-dimensional raw sensory input signal. Our novel incremental version of SFA (IncSFA) combines incremental Principal Components Analysis and Minor Components Analysis. Unlike standard batch-based SFA, IncSFA adapts along with non-stationary environments, is amenable to episodic training, is not corrupted by outliers, and is covariance-free. These properties make IncSFA a generally useful unsupervised preprocessor for autonomous learning agents and robots. In IncSFA, the CCIPCA and MCA updates take the form of Hebbian and anti-Hebbian updating, extending the biological plausibility of SFA. In both single node and deep network versions, IncSFA learns to encode its input streams (such as high-dimensional video) by informative slow features representing meaningful abstract environmental properties. It can handle cases where batch SFA fails.


Satisfiability thresholds beyond k-XORSAT

Abstract: We consider random systems of equations x_1 + ... + x_k = a; 0 <= a <= 2 which are interpreted as equations modulo 3: We show for k >= 15 that the satisfiability threshold of such systems occurs where the 2-core has density 1: We show a similar result for random uniquely extendible constraints over 4 elements. Our results extend previous results of Dubois/Mandler for equations mod 2 and k = 3 and Connamacher/Molloy for uniquely extendible constraints over a domain of 4 elements with k = 3 arguments. Our proof technique is based on variance calculations, using a technique introduced Dubois/Mandler. However, several additional observations (of independent interest) are necessary.


Efficiency Theory: a Unifying Theory for Information, Computation and Intelligence

Abstract: The paper serves as the first contribution towards the development of the theory of efficiency: a unifying framework for the currently disjoint theories of information, complexity, communication and computation. Realizing the defining nature of the brute force approach in the fundamental concepts in all of the above mentioned fields, the paper suggests using efficiency or improvement over the brute force algorithm as a common unifying factor necessary for the creation of a unified theory of information manipulation. By defining such diverse terms as randomness, knowledge, intelligence and computability in terms of a common denominator we are able to bring together contributions from Shannon, Levin, Kolmogorov, Solomonoff, Chaitin, Yao and many others under a common umbrella of the efficiency theory.


A Novel Multipath Approach to Security in Mobile Ad Hoc Networks (MANETs)

Abstract: In this paper, we present a novel encryption-less algorithm to enhance security in transmission of data packets across mobile ad hoc networks. The paper hinges on the paradigm of multipath routing and exploits the properties of polynomials. The first step in the algorithm is to transform the data such that it is impossible to obtain any information without possessing the entire transformed data. The algorithm then uses an intuitively simple idea of a jigsaw puzzle to break the transformed data into multiple packets where these packets form the pieces of the puzzle. Then these packets are sent along disjoint paths to reach the receiver. A secure and efficient mechanism is provided to convey the information that is necessary for obtaining the original data at the receiver-end from its fragments in the packets, that is, for solving the jigsaw puzzle. The algorithm is designed to be secure so that no intermediate or unintended node can obtain the entire data. An authentication code is also used to ensure authenticity of every packet.


Compact Weighted Class Association Rule Mining using Information Gain

Abstract: Weighted association rule mining reflects semantic significance of item by considering its weight. Classification constructs the classifier and predicts the new data instance. This paper proposes compact weighted class association rule mining method, which applies weighted association rule mining in the classification and constructs an efficient weighted associative classifier. This proposed associative classification algorithm chooses one non class informative attribute from dataset and all the weighted class association rules are generated based on that attribute. The weight of the item is considered as one of the parameter in generating the weighted class association rules. This proposed algorithm calculates the weight using the HITS model. Experimental results show that the proposed system generates less number of high quality rules which improves the classification accuracy.


Experiments on Density-Constrained Graph Clustering

Abstract: Clustering a graph means identifying internally dense subgraphs which are only sparsely interconnected. Formalizations of this notion lead to measures that quantify the quality of a clustering and to algorithms that actually find clusterings. Since, most generally, corresponding optimization problems are hard, heuristic clustering algorithms are used in practice, or other approaches which are not based on an objective function. In this work we conduct a comprehensive experimental evaluation of the qualitative behavior of greedy bottom-up heuristics driven by cut-based objectives and constrained by intracluster density, using both real-world data and artificial instances. Our study documents that a greedy strategy based on local movement is superior to one based on merging. We further reveal that the former approach generally outperforms alternative setups and reference algorithms from the literature in terms of its own objective, while a modularity-based algorithm competes surprisingly well. Finally, we exhibit which combinations of cut-based inter- and intracluster measures are suitable for identifying a hidden reference clustering in synthetic random graphs.


A Concurrency Control Method Based on Commitment Ordering in Mobile Databases

Abstract: Disconnection of mobile clients from server, in an unclear time and for an unknown duration, due to mobility of mobile clients, is the most important challenges for concurrency control in mobile database with client-server model. Applying pessimistic common classic methods of concurrency control (like 2pl) in mobile database leads to long duration blocking and increasing waiting time of transactions. Because of high rate of aborting transactions, optimistic methods aren`t appropriate in mobile database. In this article, OPCOT concurrency control algorithm is introduced based on optimistic concurrency control method. Reducing communications between mobile client and server, decreasing blocking rate and deadlock of transactions, and increasing concurrency degree are the most important motivation of using optimistic method as the basis method of OPCOT algorithm. To reduce abortion rate of transactions, in execution time of transactions` operators a timestamp is assigned to them. In other to checking commitment ordering property of scheduler, the assigned timestamp is used in server on time of commitment. In this article, serializability of OPCOT algorithm scheduler has been proved by using serializability graph. Results of evaluating simulation show that OPCOT algorithm decreases abortion rate and waiting time of transactions in compare to 2pl and optimistic algorithms.


The Expert System Designed to Improve Customer Satisfaction

Abstract: Customer Relationship Management becomes a leading business strategy in highly competitive business environment. It aims to enhance the performance of the businesses by improving the customer satisfaction and loyalty. The objective of this paper is to improve customer satisfaction on product's colors and design with the help of the expert system developed by using Artificial Neural Networks. The expert system's role is to capture the knowledge of the experts and the data from the customer requirements, and then, process the collected data and form the appropriate rules for choosing product's colors and design. In order to identify the hidden pattern of the customer's needs, the Artificial Neural Networks technique has been applied to classify the colors and design based upon a list of selected information. Moreover, the expert system has the capability to make decisions in ranking the scores of the colors and design presented in the selection. In addition, the expert system has been validated with a different customer types.


Chinese Restaurant Game - Part II: Applications to Wireless Networking, Cloud Computing, and Online Social Networking

Abstract: In Part I of this two-part paper [1], we proposed a new game, called Chinese restaurant game, to analyze the social learning problem with negative network externality. The best responses of agents in the Chinese restaurant game with imperfect signals are constructed through a recursive method, and the influence of both learning and network externality on the utilities of agents is studied. In Part II of this two-part paper, we illustrate three applications of Chinese restaurant game in wireless networking, cloud computing, and online social networking. For each application, we formulate the corresponding problem as a Chinese restaurant game and analyze how agents learn and make strategic decisions in the problem. The proposed method is compared with four common-sense methods in terms of agents' utilities and the overall system performance through simulations. We find that the proposed Chinese restaurant game theoretic approach indeed helps agents make better decisions and improves the overall system performance. Furthermore, agents with different decision orders have different advantages in terms of their utilities, which also verifies the conclusions drawn in Part I of this two-part paper.


Chinese Restaurant Game - Part I: Theory of Learning with Negative Network Externality

Abstract: In a social network, agents are intelligent and have the capability to make decisions to maximize their utilities. They can either make wise decisions by taking advantages of other agents' experiences through learning, or make decisions earlier to avoid competitions from huge crowds. Both these two effects, social learning and negative network externality, play important roles in the decision process of an agent. While there are existing works on either social learning or negative network externality, a general study on considering both these two contradictory effects is still limited. We find that the Chinese restaurant process, a popular random process, provides a well-defined structure to model the decision process of an agent under these two effects. By introducing the strategic behavior into the non-strategic Chinese restaurant process, in Part I of this two-part paper, we propose a new game, called Chinese Restaurant Game, to formulate the social learning problem with negative network externality. Through analyzing the proposed Chinese restaurant game, we derive the optimal strategy of each agent and provide a recursive method to achieve the optimal strategy. How social learning and negative network externality influence each other under various settings is also studied through simulations.


ANDaNA: Anonymous Named Data Networking Application

Abstract: Content-centric networking -- also known as information-centric networking (ICN) -- shifts emphasis from hosts and interfaces (as in today's Internet) to data. Named data becomes addressable and routable, while locations that currently store that data become irrelevant to applications. Named Data Networking (NDN) is a large collaborative research effort that exemplifies the content-centric approach to networking. NDN has some innate privacy-friendly features, such as lack of source and destination addresses on packets. However, as discussed in this paper, NDN architecture prompts some privacy concerns mainly stemming from the semantic richness of names. We examine privacy-relevant characteristics of NDN and present an initial attempt to achieve communication privacy. Specifically, we design an NDN add-on tool, called ANDaNA, that borrows a number of features from Tor. As we demonstrate via experiments, it provides comparable anonymity with lower relative overhead.


Faked states attack and quantum cryptography protocols

Abstract: Leveraging quantum mechanics, cryptographers have devised provably secure key sharing protocols. Despite proving the security in theory, real-world application falls short of the ideal. Last year, cryptanalysts completed an experiment demonstrating a successful eavesdropping attack on commercial quantum key distribution (QKD) systems. This attack exploits a weakness in the typical real-world implementation of quantum cryptosystems. Cryptanalysts have successfully attacked several protocols. In this paper, we examine the Kak quantum cryptography protocol and how it may perform under such attacks.


Enhancing Data Security by Making Data Disappear in a P2P Systems

Abstract: This paper describes the problem of securing data by making it disappear after some time limit, making it impossible for it to be recovered by an unauthorized party. This method is in response to the need to keep the data secured and to protect the privacy of archived data on the servers, Cloud and Peer-to-Peer architectures. Due to the distributed nature of these architectures, it is impossible to destroy the data completely. So, we store the data by applying encryption and then manage the key, which is easier to do as the key is small and it can be hidden in the DHT (Distributed hash table). Even if the keys in the DHT and the encrypted data were compromised, the data would still be secure. This paper describes existing solutions, points to their limitations and suggests improvements with a new secure architecture. We evaluated and executed this architecture on the Java platform and proved that it is more secure than other architectures.


Decryptable to Your Eyes: Visualization of Security Protocols at the User Interface

Abstract: The design of authentication protocols, for online banking services in particular and any service that is of sensitive nature in general, is quite challenging. Indeed, enforcing security guarantees has overhead thus imposing additional computation and design considerations that do not always meet usability and user requirements. On the other hand, relaxing assumptions and rigorous security design to improve the user experience can lead to security breaches that can harm the users' trust in the system. In this paper, we demonstrate how careful visualization design can enhance not only the security but also the usability of the authentication process. To that end, we propose a family of visualized authentication protocols, a visualized transaction verification, and a "decryptable to your eyes only" protocol. Through rigorous analysis, we verify that our protocols are immune to many of the challenging authentication attacks applicable in the literature. Furthermore, using an extensive case study on a prototype of our protocols, we highlight the potential of our approach for real-world deployment: we were able to achieve a high level of usability while satisfying stringent security requirements.


A Survey on Cooperative Diversity and Its Applications in Various Wireless Networks

Abstract: Cooperative diversity is a technique in which various radio terminals relay signals for each other. Cooperative diversity results when cooperative communications is used primarily to leverage the spatial diversity available among distributed radios. In this paper different cooperative diversity schemes and their applications in various wireless networks are discussed. In this paper the impact of cooperative diversity on the energy consumption and lifetime of sensor network and the impact of cooperation in cognitive radio are discussed. Here, user scheduling and radio resource allocation techniques are also discussed which are developed in order to efficiently integrate various cooperative diversity schemes for the emerging IEEE 802.16j based systems.


Being, space and time in the Web

Abstract: The Web initially emerged as an "antidote" to accumulated scientific knowledge since it enables global representation and communication with minimum costs. Its gigantic scale and interdependence incommode our ability to find relevant information and develop trustworthy contexts. It is time for science to compensate by providing an epistemological "antidote" to Web issues. Philosophy should be in the front line by forming the salient questions and analysis. The scope of our research is to provide a theory about the Web being that will bridge philosophical thinking and engineering. We analyze existence and spatiotemporality in the Web and how it transforms the traditional actualities. The Web space is specified by incoming and outgoing links. The primordial role of visiting durations in Web's existence is approximated by Bergsonian time. The physical space becomes more discoverable. The human activity can be asynchronous, synchronous and continuous. Networked individuals operate in a flexible and spatially dispersed environment. The resulting issues concern the self-determination of a being and the way in which the Web could be a free and open platform for innovation and participation.


SocialCloud: Using Social Networks for Building Distributed Computing Services

Abstract: In this paper we investigate a new computing paradigm, called SocialCloud, in which computing nodes are governed by social ties driven from a bootstrapping trust-possessing social graph. We investigate how this paradigm differs from existing computing paradigms, such as grid computing and the conventional cloud computing paradigms. We show that incentives to adopt this paradigm are intuitive and natural, and security and trust guarantees provided by it are solid. We propose metrics for measuring the utility and advantage of this computing paradigm, and using real-world social graphs and structures of social traces; we investigate the potential of this paradigm for ordinary users. We study several design options and trade-offs, such as scheduling algorithms, centralization, and straggler handling, and show how they affect the utility of the paradigm. Interestingly, we conclude that whereas graphs known in the literature for high trust properties do not serve distributed trusted computing algorithms, such as Sybil defenses---for their weak algorithmic properties, such graphs are good candidates for our paradigm for their self-load-balancing features.


Efficient Detection of Sybil Attack Based on Cryptography in Vanet

Abstract: Vehicular communications play a substantial role in providing safety transportation by means of safety message exchange. Researchers have proposed several solutions for securing safety messages. Protocols based on a fixed key infrastructure are more efficient in implementation and maintain stronger security in comparison with dynamic structures. The purpose of this paper present a method based on a fixed key infrastructure for detection impersonation attack, in other words, Sybil attack, in the vehicular ad hoc network. This attack, puts a great impact on performance of the network. The proposed method, using an cryptography mechanism to detection Sybil attack. Finally, using Mat lab simulator the results of this approach are reviewed, This method it has low delay for detection Sybil attack, because most operations are done in Certification Authority, so this proposed schema is a efficient method for detection Sybil attack.


Lossless Digital Image Compression Method for Bitmap Images

Abstract: In this research paper, the authors propose a new approach to digital image compression using crack coding This method starts with the original image and develop crack codes in a recursive manner, marking the pixels visited earlier and expanding the entropy in four directions. The proposed method is experimented with sample bitmap images and results are tabulated. The method is implemented in uni-processor machine using C language source code.


A Novel Approach for Password Authentication Using Bidirectional Associative Memory

Abstract: Password authentication is a very important system security procedure to gain access to user resources. In the Traditional password authentication methods a server has check the authenticity of the users. In our proposed method users can freely select their passwords from a predefined character set. They can also use a graphical image as password. The password may be a character or an image it will be converted into binary form and the binary values will be normalized. Associative memories have been used recently for password authentication in order to overcome drawbacks of the traditional password authentication methods. In this paper we proposed a method using Bidirectional Associative Memory algorithm for both alphanumeric (Text) and graphical password. By doing so the amount of security what we provide for the user can be enhanced. This paper along with test results show that converting user password in to Probabilistic values and giving them as input for BAM improves the security of the system


Steiner Forest Orientation Problems

Abstract: We consider connectivity problems with orientation constraints. Given a directed graph $D$ and a collection of ordered node pairs $P$ let $P[D]=\{(u,v) \in P: D {contains a} uv{-path}}$. In the {\sf Steiner Forest Orientation} problem we are given an undirected graph $G=(V,E)$ with edge-costs and a set $P \subseteq V \times V$ of ordered node pairs. The goal is to find a minimum-cost subgraph $H$ of $G$ and an orientation $D$ of $H$ such that $P[D]=P$. We give a 4-approximation algorithm for this problem. In the {\sf Maximum Pairs Orientation} problem we are given a graph $G$ and a multi-collection of ordered node pairs $P$ on $V$. The goal is to find an orientation $D$ of $G$ such that $|P[D]|$ is maximum. Generalizing the result of Arkin and Hassin [DAM'02] for $|P|=2$, we will show that for a mixed graph $G$ (that may have both directed and undirected edges), one can decide in $n^{O(|P|)}$ time whether $G$ has an orientation $D$ with $P[D]=P$ (for undirected graphs this problem admits a polynomial time algorithm for any $P$, but it is NP-complete on mixed graphs). For undirected graphs, we will show that one can decide whether $G$ admits an orientation $D$ with $|P[D]| \geq k$ in $O(n+m)+2^{O(k\cdot \log \log k)}$ time; hence this decision problem is fixed-parameter tractable, which answers an open question from Dorn et al. [AMB'11]. We also show that {\sf Maximum Pairs Orientation} admits ratio $O(\log |P|/\log\log |P|)$, which is better than the ratio $O(\log n/\log\log n)$ of Gamzu et al. [WABI'10] when $|P|<n$. Finally, we show that the following node-connectivity problem can be solved in polynomial time: given a graph $G=(V,E)$ with edge-costs, $s,t \in V$, and an integer $\ell$, find a min-cost subgraph $H$ of $G$ with an orientation $D$ such that $D$ contains $\ell$ internally-disjoint $st$-paths, and $\ell$ internally-disjoint $ts$-paths.


On Problems as Hard as CNFSAT

Abstract: The field of exact exponential time algorithms for NP-hard problems has thrived over the last decade. While exhaustive search remains asymptotically the fastest known algorithm for some basic problems, difficult and non-trivial exponential time algorithms have been found for a myriad of problems, including Graph Coloring, Hamiltonian Path, Dominating Set and 3-CNF-Sat. In some instances, improving these algorithms further seems to be out of reach. The CNF-Sat problem is the canonical example of a problem for which the trivial exhaustive search algorithm runs in time O(2^n), where n is the number of variables in the input formula. While there exist non-trivial algorithms for CNF-Sat that run in time o(2^n), no algorithm was able to improve the growth rate 2 to a smaller constant, and hence it is natural to conjecture that 2 is the optimal growth rate. The strong exponential time hypothesis (SETH) by Impagliazzo and Paturi [JCSS 2001] goes a little bit further and asserts that, for every epsilon<1, there is a (large) integer k such that that k-CNF-Sat cannot be computed in time 2^{epsilon n}. In this paper, we show that, for every epsilon < 1, the problems Hitting Set, Set Splitting, and NAE-Sat cannot be computed in time O(2^{epsilon n}) unless SETH fails. Here n is the number of elements or variables in the input. For these problems, we actually get an equivalence to SETH in a certain sense. We conjecture that SETH implies a similar statement for Set Cover, and prove that, under this assumption, the fastest known algorithms for Steinter Tree, Connected Vertex Cover, Set Partitioning, and the pseudo-polynomial time algorithm for Subset Sum cannot be significantly improved. Finally, we justify our assumption about the hardness of Set Cover by showing that the parity of the number of set covers cannot be computed in time O(2^{epsilon n}) for any epsilon<1 unless SETH fails.


A comparative study of aggregate TCP retransmission rates

Abstract: Segment retransmissions are an essential tool in assuring reliable end-to-end communication in the Internet. Their crucial role in TCP design and operation has been studied extensively, in particular with respect to identifying non-conformant, buggy, or underperforming behaviour. However, TCP segment retransmissions are often overlooked when examining and analyzing large traffic traces. In fact, some have come to believe that retransmissions are a rare oddity, characteristically associated with faulty network paths, which, typically, tend to disappear as networking technology advances and link capacities grow. We find that this may be far from the reality experienced by TCP flows. We quantify aggregate TCP segment retransmission rates using publicly available network traces from six passive monitoring points attached to the egress gateways at large sites. In virtually half of the traces examined we observed aggregate TCP retransmission rates exceeding 1%, and of these, about half again had retransmission rates exceeding 2%. Even for sites with low utilization and high capacity gateway links, retransmission rates of 1%, and sometimes higher, were not uncommon. Our results complement, extend and bring up to date partial and incomplete results in previous work, and show that TCP retransmissions continue to constitute a non-negligible percentage of the overall traffic, despite significant advances across the board in telecommunications technologies and network protocols. The results presented are pertinent to end-to-end protocol designers and evaluators as they provide a range of "realistic" scenarios under which, and a "marker" against which, simulation studies can be configured and calibrated, and future protocols evaluated.


A Door into Another World

Abstract: Is it possible to design programs which each user can change according to his preferences? Not an illusion of such a thing that adaptive interface provides but really an interface ruled by users. What is the main problem of such design and what is the solution to this problem? This short article gives a glimpse into the theory discussed in the book "The World of Movable Objects".


Towards Optimal and Expressive Kernelization for d-Hitting Set

Abstract: d-Hitting Set is the NP-hard problem of selecting at most k vertices of a hypergraph so that each hyperedge, all of which have cardinality at most d, contains at least one selected vertex. The applications of d-Hitting Set are, for example, fault diagnosis, automatic program verification, and the noise-minimizing assignment of frequencies to radio transmitters. We show a linear-time algorithm that transforms an instance of d-Hitting Set into an equivalent instance comprising at most O(k^d) hyperedges and vertices. In terms of parameterized complexity, this is a problem kernel. Our kernelization algorithm is based on speeding up the well-known approach of finding and shrinking sunflowers in hypergraphs, which yields problem kernels with structural properties that we condense into the concept of expressive kernelization. We conduct experiments to show that our kernelization algorithm can kernelize instances with more than 10^7 hyperedges in less than five minutes. Finally, we show that the number of vertices in the problem kernel can be further reduced to O(k^{d-1}) with additional O(k^{1.5 d}) processing time by nontrivially combining the sunflower technique with d-Hitting Set problem kernels due to Abu-Khzam and Moser.


QBF-Based Boolean Function Bi-Decomposition

Abstract: Boolean function bi-decomposition is ubiquitous in logic synthesis. It entails the decomposition of a Boolean function using two-input simple logic gates. Existing solutions for bi-decomposition are often based on BDDs and, more recently, on Boolean Satisfiability. In addition, the partition of the input set of variables is either assumed, or heuristic solutions are considered for finding good partitions. In contrast to earlier work, this paper proposes the use of Quantified Boolean Formulas (QBF) for computing bi- decompositions. These bi-decompositions are optimal in terms of the achieved disjointness and balancedness of the input set of variables. Experimental results, obtained on representative benchmarks, demonstrate clear improvements in the quality of computed decompositions, but also the practical feasibility of QBF-based bi-decomposition.


The Spatial Nearest Neighbor Skyline Queries

Abstract: User preference queries are very important in spatial databases. With the help of these queries, one can found best location among points saved in database. In many situation users evaluate quality of a location with its distance from its nearest neighbor among a special set of points. There has been less attention about evaluating a location with its distance to nearest neighbors in spatial user preference queries. This problem has application in many domains such as service recommendation systems and investment planning. Related works in this field are based on top-k queries. The problem with top-k queries is that user must set weights for attributes and a function for aggregating them. This is hard for him in most cases. In this paper a new type of user preference queries called spatial nearest neighbor skyline queries will be introduced in which user has some sets of points as query parameters. For each point in database attributes are its distances to the nearest neighbors from each set of query points. By separating this query as a subset of dynamic skyline queries N2S2 algorithm is provided for computing it. This algorithm has good performance compared with the general branch and bound algorithm for skyline queries.


Performance Analysis of Bidirectional Relay Selection with Imperfect Channel State Information

Abstract: In this paper, we investigate the performance of bidirectional relay selection using amplify-and-forward protocol with imperfect channel state information, i.e., delay effect and channel estimation error. The asymptotic expression of end-to-end SER in high SNR regime is derived in a closed form, which indicates that the delay effect causes the loss of both coding gain and diversity order, while the channel estimation error merely affects the coding gain. Finally, analytical results are verified by Monte-Carlo simulations.


Improvement of BM3D Algorithm and Employment to Satellite and CFA Images Denoising

Abstract: This paper proposes a new procedure in order to improve the performance of block matching and 3-D filtering (BM3D) image denoising algorithm. It is demonstrated that it is possible to achieve a better performance than that of BM3D algorithm in a variety of noise levels. This method changes BM3D algorithm parameter values according to noise level, removes prefiltering, which is used in high noise level; therefore Peak Signal-to-Noise Ratio (PSNR) and visual quality get improved, and BM3D complexities and processing time are reduced. This improved BM3D algorithm is extended and used to denoise satellite and color filter array (CFA) images. Output results show that the performance has upgraded in comparison with current methods of denoising satellite and CFA images. In this regard this algorithm is compared with Adaptive PCA algorithm, that has led to superior performance for denoising CFA images, on the subject of PSNR and visual quality. Also the processing time has decreased significantly.


A Real-Time Database QoS-aware Service Selection Protocol for MANET

Abstract: The real-time database service selection depends typically to the system stability in order to handle the time-constrained transactions within their deadline. However, applying the real-time database system in the mobile ad hoc networks requires considering the mobile nodes limited capacities. In this paper, we propose cross-layer service selection which combines performance metrics measured in the real-time database system to those used by the routing protocol in order to make the best selection decision. It ensures both timeliness and energy efficiency by avoiding low-power and busy service provider node. A multicast packet is used in order to reduce the transmission cost and network load when sending the same packet to multiple service providers. In this paper, we evaluate the performance of our proposed protocol. Simulation results, using the Network Simulator NS2, improve that the protocol decreases the deadline miss ratio of packets, increases the service availability and reduces the service response time.


Performance Evaluation and Impact of Weighting Factors on an Energy and Delay Aware Dynamic Source Routing Protocol

Abstract: Typical applications of the mobile ad-hoc network, MANET, are in disaster recovery operations which have to respect time constraint needs. Since MANET is affected by limited resources such as power constraints, it is a challenge to respect the deadline of a real-time data. This paper proposes the Energy and Delay aware based on Dynamic Source Routing protocol, ED-DSR. ED-DSR efficiently utilizes the network resources such as the intermediate mobile nodes energy and load. It ensures both timeliness and energy efficiency by avoiding low-power and overloaded intermediate mobile nodes. Through simulations, we compare our proposed routing protocol with the basic routing protocol Dynamic Source Routing, DSR. Weighting factors are introduced to improve the route selection. Simulation results, using the NS-2 simulator, show that the proposed protocol prolongs the network lifetime (up to 66%), increases the volume of packets delivered while meeting the data flows real-time constraints and shortens the endto- end delay.


Maximum Production of Transmission Messages Rate for Service Discovery Protocols

Abstract: Minimizing the number of dropped User Datagram Protocol (UDP) messages in a network is regarded as a challenge by researchers. This issue represents serious problems for many protocols particularly those that depend on sending messages as part of their strategy, such us service discovery protocols. This paper proposes and evaluates an algorithm to predict the minimum period of time required between two or more consecutive messages and suggests the minimum queue sizes for the routers, to manage the traffic and minimise the number of dropped messages that has been caused by either congestion or queue overflow or both together. The algorithm has been applied to the Universal Plug and Play (UPnP) protocol using ns2 simulator. It was tested when the routers were connected in two configurations; as a centralized and de centralized. The message length and bandwidth of the links among the routers were taken in the consideration. The result shows Better improvement in number of dropped messages `among the routers.


Networks Utilization Improvements for Service Discovery Performance

Abstract: Service discovery requests' messages have a vital role in sharing and locating resources in many of service discovery protocols. Sending more messages than a link can handle may cause congestion and loss of messages which dramatically influences the performance of these protocols. Re-send the lost messages result in latency and inefficiency in performing the tasks which user(s) require from the connected nodes. This issue become a serious problem in two cases: first, when the number of clients which performs a service discovery request is increasing, as this result in increasing in the number of sent discovery messages; second, when the network resources such as bandwidth capacity are consumed by other applications. These two cases lead to network congestion and loss of messages. This paper propose an algorithm to improve the services discovery protocols performance by separating each consecutive burst of messages with a specific period of time which calculated regarding the available network resources. It was tested when the routers were connected in two configurations; decentralised and centralised .In addition, this paper explains the impact of increasing the number of clients and the consumed network resources on the proposed algorithm.


Distributed Particle Filter Implementation with Intermittent/Irregular Consensus Convergence

Abstract: Motivated by non-linear, non-Gaussian, distributed multi-sensor/agent navigation and tracking applications, we propose a multi-rate consensus/fusion based framework for distributed implementation of the particle filter (CF/DPF). The CF/DPF framework is based on running localized particle filters to estimate the overall state vector at each observation node. Separate fusion filters are designed to consistently assimilate the local filtering distributions into the global posterior by compensating for the common past information between neighbouring nodes. The CF/DPF offers two distinct advantages over its counterparts. First, the CF/DPF framework is suitable for scenarios where network connectivity is intermittent and consensus can not be reached between two consecutive observations. Second, the CF/DPF is not limited to the Gaussian approximation for the global posterior density. A third contribution of the paper is the derivation of the exact expression for computing the posterior Cramer-Rao lower bound (PCRLB) for the distributed architecture based on a recursive procedure involving the local Fisher information matrices (FIM) of the distributed estimators. The performance of the CF/DPF algorithm closely follows the centralized particle filter approaching the PCRLB at the signal to noise ratios that we tested.