Solving the Resource Constrained Project Scheduling Problem with Generalized Precedences by Lazy Clause Generation

Abstract: The technical report presents a generic exact solution approach for minimizing the project duration of the resource-constrained project scheduling problem with generalized precedences (Rcpsp/max). The approach uses lazy clause generation, i.e., a hybrid of finite domain and Boolean satisfiability solving, in order to apply nogood learning and conflict-driven search on the solution generation. Our experiments show the benefit of lazy clause generation for finding an optimal solutions and proving its optimality in comparison to other state-of-the-art exact and non-exact methods. The method is highly robust: it matched or bettered the best known results on all of the 2340 instances we examined except 3, according to the currently available data on the PSPLib. Of the 631 open instances in this set it closed 573 and improved the bounds of 51 of the remaining 58 instances.


Dichotomy for tree-structured trigraph list homomorphism problems

Abstract: Trigraph list homomorphism problems (also known as list matrix partition problems) have generated recent interest, partly because there are concrete problems that are not known to be polynomial time solvable or NP-complete. Thus while digraph list homomorphism problems enjoy dichotomy (each problem is NP-complete or polynomial time solvable), such dichotomy is not necessarily expected for trigraph list homomorphism problems. However, in this paper, we identify a large class of trigraphs for which list homomorphism problems do exhibit a dichotomy. They consist of trigraphs with a tree-like structure, and, in particular, include all trigraphs whose underlying graphs are trees. In fact, we show that for these tree-like trigraphs, the trigraph list homomorphism problem is polynomially equivalent to a related digraph list homomorphism problem. We also describe a few examples illustrating that our conditions defining tree-like trigraphs are not unnatural, as relaxing them may lead to harder problems.


Discovering potential user browsing behaviors using custom-built apriori algorithm

Abstract: Most of the organizations put information on the web because they want it to be seen by the world. Their goal is to have visitors come to the site, feel comfortable and stay a while and try to know completely about the running organization. As educational system increasingly requires data mining, the opportunity arises to mine the resulting large amounts of student information for hidden useful information (patterns like rule, clustering, and classification, etc). The education domain offers ground for many interesting and challenging data mining applications like astronomy, chemistry, engineering, climate studies, geology, oceanography, ecology, physics, biology, health sciences and computer science. Collecting the interesting patterns using the required interestingness measures, which help us in discovering the sophisticated patterns that are ultimately used for developing the site. We study the application of data mining to educational log data collected from Guru Nanak Institute of Technology, Ibrahimpatnam, India. We have proposed a custom-built apriori algorithm to find the effective pattern analysis. Finally, analyzing web logs for usage and access trends can not only provide important information to web site developers and administrators, but also help in creating adaptive web sites.


A Unified Mechanism Design Framework for Networked Systems

Abstract: Mechanisms such as auctions and pricing schemes are utilized to design strategic (noncooperative) games for networked systems. Although the participating players are selfish, these mechanisms ensure that the game outcome is optimal with respect to a global criterion (e.g. maximizing a social welfare function), preference-compatible, and strategy-proof, i.e. players have no reason to deceive the designer. The mechanism designer achieves these objectives by introducing specific rules and incentives to the players; in this case by adding resource prices to their utilities. In auction-based mechanisms, the mechanism designer explicitly allocates the resources based on bids of the participants in addition to setting prices. Alternatively, pricing mechanisms enforce global objectives only by charging the players for the resources they have utilized. In either setting, the player preferences represented by utility functions may be coupled or decoupled, i.e. they depend on other player's actions or only on player's own actions, respectively. The unified framework and its information structures are illustrated through multiple example resource allocation problems from wireless and wired networks.


Clustering high dimensional data using subspace and projected clustering algorithms

Abstract: Problem statement: Clustering has a number of techniques that have been developed in statistics, pattern recognition, data mining, and other fields. Subspace clustering enumerates clusters of objects in all subspaces of a dataset. It tends to produce many over lapping clusters. Approach: Subspace clustering and projected clustering are research areas for clustering in high dimensional spaces. In this research we experiment three clustering oriented algorithms, PROCLUS, P3C and STATPC. Results: In general, PROCLUS performs better in terms of time of calculation and produced the least number of un-clustered data while STATPC outperforms PROCLUS and P3C in the accuracy of both cluster points and relevant attributes found. Conclusions/Recommendations: In this study, we analyze in detail the properties of different data clustering method.


Analyzing the performance of probabilistic algorithm in noisy manets

Abstract: Probabilistic broadcast has been widely used as a flooding optimization mechanism to alleviate the effect of broadcast storm problem (BSP) in mobile ad hoc networks (MANETs). Many research studies have been carried-out to develop and evaluate the performance of this mechanism in an error-free (noiseless) environment. In reality, wireless communication channels in MANETs are an error-prone and suffer from high packet-loss due to presence of noise, i.e., noisy environment. In this paper, we propose a simulation model that can be used to evaluate the performance of probabilistic broadcast for flooding in noisy environment. In the proposed model, the noise-level is represented by a generic name, probability of reception (pc) (0<=pc<=1), where pc=1 for noiseless and <1 for noisy environment. The effect of noise is determined randomly by generating a random number \zeta (0<=\zeta<1); if \zeta<=pc means the packet is successfully delivered to the receiving node, otherwise, unsuccessful delivery occurs. The proposed model is implemented on a MANET simulator, namely, MANSim. The effect of noise on the performance of probabilistic algorithm was investigated in four scenarios. The main conclusions of these scenarios are: the performance of probabilistic algorithm suffers in presence of noise. However, this suffering is less in high density networks, or if the nodes characterized by high retransmission probability or large radio transmission range. The nodes' speed has no or insignificant effect on the performance.


Performance Evaluation of an OMPR Algorithm for Route Discovery in Noisy MANETs

Abstract: It has been revealed in the literature that pure multipoint relaying (MPR) algorithms demonstrate both simplicity and outstanding performance, as compared to other flooding algorithms in wireless networks. One drawback of pure MPR algorithms is that the selected forwarding set may not represent the optimum selection. In addition, little efforts have been carried-out to investigate the performance of such algorithms in noisy mobile ad hoc networks (MANETs) suffering from high packet-loss and node mobility. In this paper, we develop and evaluate the performance of an optimal MPR (OMPR) algorithm for route discovery in noisy MANETs. The main feature of this new algorithm is that it calculates all possible sets of multipoint relays (MPRs) and then selects the set with minimum number of nodes. The algorithm demonstrates an excellent performance when it is compared with other route discovery algorithms as it achieves the highest cost-effective reachability.


Acdmcp: An adaptive and completely distributed multi-hop clustering protocol for wireless sensor networks

Abstract: Clustering is a very popular network structuring technique which mainly addresses the issue of scalability in large scale Wireless Sensor Networks. Additionally, it has been shown to improve the energy efficiency and prolong the life of the network. The suggested protocols mostly base their clustering criteria on some grouping attribute(s) of the nodes. One important attribute that is largely ignored by most of the existing multi-hop clustering protocols is the reliability of the communication links between the nodes. In this paper, we suggest an adaptive and completely distributed multi-hop clustering protocol that incorporates different notions of reliability of the communication links, among other things, into a composite metric and uses it in all phases of the clustering process. The joining criteria for the nodes, which lie at one hop from the elected cluster heads, to a particular cluster not only consider the reliability of their communication link with their cluster head but also other important attributes. The nodes that lie outside the communication range of cluster heads become cluster members transitively through existing cluster members utilizing the end-to-end notion of link reliability, between the nodes and the cluster heads, along with other important attributes. Similarly, inter-cluster communication paths are selected using a set of criteria that includes the end-to-end communication link reliability with the sink node along with other important node and network attributes. We believe that incorporating link reliability in all phases of clustering process results in an efficient multi-hop communication hierarchy that has the potential of bringing down the total communication costs in the network.


Mobile Information Collectors' Trajectory Data Warehouse Design

Abstract: To analyze complex phenomena which involve moving objects, Trajectory Data Warehouse (TDW) seems to be an answer for many recent decision problems related to various professions (physicians, commercial representatives, transporters, ecologists ...) concerned with mobility. This work aims to make trajectories as a first class concept in the trajectory data conceptual model and to design a TDW, in which data resulting from mobile information collectors' trajectory are gathered. These data will be analyzed, according to trajectory characteristics, for decision making purposes, such as new products commercialization, new commerce implementation, etc.


An Applied Study on Educational Use of Facebook as a Web 2.0 Tool: The Sample Lesson of Computer Networks and Communication

Abstract: The main aim of the research was to examine educational use of Facebook. The Computer Networks and Communication lesson was taken as the sample and the attitudes of the students included in the study group towards Facebook were measured in a semi-experimental setup. The students on Facebook platform were examined for about three months and they continued their education interactively in that virtual environment. After the-three-month-education period, observations for the students were reported and the attitudes of the students towards Facebook were measured by three different measurement tools. As a result, the attitudes of the students towards educational use of Facebook and their views were heterogeneous. When the average values of the group were examined, it was reported that the attitudes towards educational use of Facebook was above a moderate level. Therefore, it might be suggested that social networks in virtual environments provide continuity in life long learning.


Experimental Evaluation of Branching Schemes for the CSP

Abstract: The search strategy of a CP solver is determined by the variable and value ordering heuristics it employs and by the branching scheme it follows. Although the effects of variable and value ordering heuristics on search effort have been widely studied, the effects of different branching schemes have received less attention. In this paper we study this effect through an experimental evaluation that includes standard branching schemes such as 2-way, d-way, and dichotomic domain splitting, as well as variations of set branching where branching is performed on sets of values. We also propose and evaluate a generic approach to set branching where the partition of a domain into sets is created using the scores assigned to values by a value ordering heuristic, and a clustering algorithm from machine learning. Experimental results demonstrate that although exponential differences between branching schemes, as predicted in theory between 2-way and d-way branching, are not very common, still the choice of branching scheme can make quite a difference on certain classes of problems. Set branching methods are very competitive with 2-way branching and outperform it on some problem classes. A statistical analysis of the results reveals that our generic clustering-based set branching method is the best among the methods compared.


Automatic Recommendation for Online Users Using Web Usage Mining

Abstract: A real world challenging task of the web master of an organization is to match the needs of user and keep their attention in their web site. So, only option is to capture the intuition of the user and provide them with the recommendation list. Most specifically, an online navigation behavior grows with each passing day, thus extracting information intelligently from it is a difficult issue. Web master should use web usage mining method to capture intuition. A WUM is designed to operate on web server logs which contain user's navigation. Hence, recommendation system using WUM can be used to forecast the navigation pattern of user and recommend those to user in a form of recommendation list. In this paper, we propose a two tier architecture for capturing users intuition in the form of recommendation list containing pages visited by user and pages visited by other user's having similar usage profile. The practical implementation of proposed architecture and algorithm shows that accuracy of user intuition capturing is improved.


Delay Modelling for a Single-hop Wireless Mesh Network under Light Aggregate Traffic

Abstract: In this paper, we consider the problem of modelling the average delay in an IEEE 802.11 DCF wireless mesh network with a single root node under light traffic. We derive expression for mean delay for a co-located wireless mesh network, when packet generation is homogeneous Poisson process with rate \lambda. We also show how our analysis can be extended for non-homogeneous Poisson packet generation. We model mean delay by decoupling queues into independent M/M/1 queues. Extensive simulations are conducted to verify the analytical results.


The Challenge of Believability in Video Games: Definitions, Agents Models and Imitation Learning

Abstract: In this paper, we address the problem of creating believable agents (virtual characters) in video games. We consider only one meaning of believability, ``giving the feeling of being controlled by a player'', and outline the problem of its evaluation. We present several models for agents in games which can produce believable behaviours, both from industry and research. For high level of believability, learning and especially imitation learning seems to be the way to go. We make a quick overview of different approaches to make video games' agents learn from players. To conclude we propose a two-step method to develop new models for believable agents. First we must find the criteria for believability for our application and define an evaluation method. Then the model and the learning algorithm can be designed.


Automatable Evaluation Method Oriented toward Behaviour Believability for Video Games

Abstract: Classic evaluation methods of believable agents are time-consuming because they involve many human to judge agents. They are well suited to validate work on new believable behaviours models. However, during the implementation, numerous experiments can help to improve agents' believability. We propose a method which aim at assessing how much an agent's behaviour looks like humans' behaviours. By representing behaviours with vectors, we can store data computed for humans and then evaluate as many agents as needed without further need of humans. We present a test experiment which shows that even a simple evaluation following our method can reveal differences between quite believable agents and humans. This method seems promising although, as shown in our experiment, results' analysis can be difficult.


Optimizing Selective Search in Chess

Abstract: In this paper we introduce a novel method for automatically tuning the search parameters of a chess program using genetic algorithms. Our results show that a large set of parameter values can be learned automatically, such that the resulting performance is comparable with that of manually tuned parameters of top tournament-playing chess programs.


Basic Performance Limits and Tradeoffs in Energy Harvesting Sensor Nodes with Finite Data and Energy Storage

Abstract: As many sensor network applications require deployment in remote and hard-to-reach areas, it is critical to ensure that such networks are capable of operating unattended for long durations. Consequently, the concept of using nodes with energy replenishment capabilities has been gaining popularity. However, new techniques and protocols must be developed to maximize the performance of sensor networks with energy replenishment. Here, we analyze limits of the performance of sensor nodes with limited energy, being replenished at a variable rate. We provide a simple localized energy management scheme that achieves a performance close to that with an unlimited energy source, and at the same time keeps the probability of complete battery discharge low. Based on the insights developed, we address the problem of energy management for energy-replenishing nodes with finite battery and finite data buffer capacities. To this end, we give an energy management scheme that achieves the optimal utility asymptotically while keeping both the battery discharge and data loss probabilities low.


Lombardi Drawings of Graphs

Abstract: We introduce the notion of Lombardi graph drawings, named after the American abstract artist Mark Lombardi. In these drawings, edges are represented as circular arcs rather than as line segments or polylines, and the vertices have perfect angular resolution: the edges are equally spaced around each vertex. We describe algorithms for finding Lombardi drawings of regular graphs, graphs of bounded degeneracy, and certain families of planar graphs.


Drawing Trees with Perfect Angular Resolution and Polynomial Area

Abstract: We study methods for drawing trees with perfect angular resolution, i.e., with angles at each node v equal to 2{\pi}/d(v). We show: 1. Any unordered tree has a crossing-free straight-line drawing with perfect angular resolution and polynomial area. 2. There are ordered trees that require exponential area for any crossing-free straight-line drawing having perfect angular resolution. 3. Any ordered tree has a crossing-free Lombardi-style drawing (where each edge is represented by a circular arc) with perfect angular resolution and polynomial area. Thus, our results explore what is achievable with straight-line drawings and what more is achievable with Lombardi-style drawings, with respect to drawings of trees with perfect angular resolution.


Secured Greedy Perimeter Stateless Routing for Wireless Sensor Networks

Abstract: Wireless sensor networks are collections of large number of sensor nodes. The sensor nodes are featured with limited energy, computation and transmission power. Each node in the network coordinates with every other node in forwarding their packets to reach the destination. Since these nodes operate in a physically insecure environment; they are vulnerable to different types of attacks such as selective forwarding and sinkhole. These attacks can inject malicious packets by compromising the node. Geographical routing protocols of wireless sensor networks have been developed without considering the security aspects against these attacks. In this paper, a secure routing protocol named secured greedy perimeter stateless routing protocol (S-GPSR) is proposed for mobile sensor networks by incorporating trust based mechanism in the existing greedy perimeter stateless routing protocol (GPSR). Simulation results prove that S-GPSR outperforms the GPSR by reducing the overhead and improving the delivery ratio of the networks.


Gaussian Process Bandits for Tree Search: Theory and Application to Planning in Discounted MDPs

Abstract: We motivate and analyse a new Tree Search algorithm, GPTS, based on recent theoretical advances in the use of Gaussian Processes for Bandit problems. We consider tree paths as arms and we assume the target/reward function is drawn from a GP distribution. The posterior mean and variance, after observing data, are used to define confidence intervals for the function values, and we sequentially play arms with highest upper confidence bounds. We give an efficient implementation of GPTS and we adapt previous regret bounds by determining the decay rate of the eigenvalues of the kernel matrix on the whole set of tree paths. We consider two kernels in the feature space of binary vectors indexed by the nodes of the tree: linear and Gaussian. The regret grows in square root of the number of iterations T, up to a logarithmic factor, with a constant that improves with bigger Gaussian kernel widths. We focus on practical values of T, smaller than the number of arms. Finally, we apply GPTS to Open Loop Planning in discounted Markov Decision Processes by modelling the reward as a discounted sum of independent Gaussian Processes. We report similar regret bounds to those of the OLOP algorithm.


Field Reconstruction in Sensor Networks with Coverage Holes and Packet Losses

Abstract: Environmental monitoring is often performed through a wireless sensor network, whose nodes are randomly deployed over the geographical region of interest. Sensors sample a physical phenomenon (the so-called field) and send their measurements to a {\em sink} node, which is in charge of reconstructing the field from such irregular samples. In this work, we focus on scenarios of practical interest where the sensor deployment is unfeasible in certain areas of the geographical region, e.g., due to terrain asperities, and the delivery of sensor measurements to the sink may fail due to fading or to transmission collisions among sensors simultaneously accessing the wireless medium. Under these conditions, we carry out an asymptotic analysis and evaluate the quality of the estimation of a d-dimensional field when the sink uses linear filtering as a reconstruction technique. Specifically, given the matrix representing the sampling system, V, we derive both the moments and an expression of the limiting spectral distribution of VV*, as the size of V goes to infinity and its aspect ratio has a finite limit bounded away from zero. By using such asymptotic results, we approximate the mean square error on the estimated field through the eta-transform of VV*, and derive the sensor network performance under the conditions described above.


Weighted Attribute Fusion Model for Face Recognition

Abstract: Recognizing a face based on its attributes is an easy task for a human to perform as it is a cognitive process. In recent years, Face Recognition is achieved with different kinds of facial features which were used separately or in a combined manner. Currently, Feature fusion methods and parallel methods are the facial features used and performed by integrating multiple feature sets at different levels. However, this integration and the combinational methods do not guarantee better result. Hence to achieve better results, the feature fusion model with multiple weighted facial attribute set is selected. For this feature model, face images from predefined data set has been taken from Olivetti Research Laboratory (ORL) and applied on different methods like Principal Component Analysis (PCA) based Eigen feature extraction technique, Discrete Cosine Transformation (DCT) based feature extraction technique, Histogram Based Feature Extraction technique and Simple Intensity based features. The extracted feature set obtained from these methods were compared and tested for accuracy. In this work we have developed a model which will use the above set of feature extraction techniques with different levels of weights to attain better accuracy. The results show that the selection of optimum weight for a particular feature will lead to improvement in recognition rate.


Power-efficient Routing & Increased Yield Approach For WSNs

Abstract: The sensor nodes in a Wireless Sensor Network are generally constrained with limited power supply. Efficient power management is a must for any sensor network to keep the sensor nodes in the network to be operational for a longer period of time this increasing the lifetime of the sensor network. Hierarchy based routing enables the sensor networks to be deployed in larger areas. In this paper we present a hierarchical cluster based routing protocol which improves the scalability as the data travels from one cluster level to another covering a greater amount of distance and increases the lifetime of the wireless sensor network by distributing the power dissipation load evenly among all the sensor nodes within the network. Also the time delay in case of critical data to be received by the Base Station has also been lowered.


The E-net model for the Risk Analysis and Assessment System for the Information Security of Communication and Information Systems ("Defining" Subsystem)

Abstract: This paper presents one suggestion that comprises the authors' experience in development and implementation of systems for information security in the Automated Information Systems of the Bulgarian Armed Forces. The architecture of risk analysis and assessment system for the communication and information system's information security (CIS IS) has been presented. E-net model of "Defining" Subsystem as a tool that allows to examine the subsystems is proposed as well. Such approach can be applied successfully for communication and information systems in the business field.


Grammar-Based Geodesics in Semantic Networks

Abstract: A geodesic is the shortest path between two vertices in a connected network. The geodesic is the kernel of various network metrics including radius, diameter, eccentricity, closeness, and betweenness. These metrics are the foundation of much network research and thus, have been studied extensively in the domain of single-relational networks (both in their directed and undirected forms). However, geodesics for single-relational networks do not translate directly to multi-relational, or semantic networks, where vertices are connected to one another by any number of edge labels. Here, a more sophisticated method for calculating a geodesic is necessary. This article presents a technique for calculating geodesics in semantic networks with a focus on semantic networks represented according to the Resource Description Framework (RDF). In this framework, a discrete "walker" utilizes an abstract path description called a grammar to determine which paths to include in its geodesic calculation. The grammar-based model forms a general framework for studying geodesic metrics in semantic networks.


System Description: H-PILoT (Version 1.9)

Abstract: This system description provides an overview of H-PILoT (Hierarchical Proving by Instantiation in Local Theory extensions), a program for hierarchical reasoning in extensions of logical theories. H-PILoT reduces deduction problems in the theory extension to deduction problems in the base theory. Specialized provers and standard SMT solvers can be used for testing the satisfiability of the formulae obtained after the reduction. For a certain type of theory extension (namely for local theory extensions) this hierarchical reduction is sound and complete and -- if the formulae obtained this way belong to a fragment decidable in the base theory -- H-PILoT provides a decision procedure for testing satisfiability of ground formulae, and can also be used for model generation.


Extended h-Index Parameterized Data Structures for Computing Dynamic Subgraph Statistics

Abstract: We present techniques for maintaining subgraph frequencies in a dynamic graph, using data structures that are parameterized in terms of h, the h-index of the graph. Our methods extend previous results of Eppstein and Spiro for maintaining statistics for undirected subgraphs of size three to directed subgraphs and to subgraphs of size four. For the directed case, we provide a data structure to maintain counts for all 3-vertex induced subgraphs in O(h) amortized time per update. For the undirected case, we maintain the counts of size-four subgraphs in O(h^2) amortized time per update. These extensions enable a number of new applications in Bioinformatics and Social Networking research.


Layered Depth-Normal Images: a Sparse Implicit Representation of Solid Models

Abstract: This paper presents a novel implicit representation of solid models. With this representation, every solid model can be effectively presented by three layered depth-normal images (LDNIs) that are perpendicular to three orthogonal axes respectively. The layered depth-normal images for a solid model, whose boundary is presented by a polygonal mesh, can be generated efficiently with help of the graphics hardware accelerated sampling. Based on this implicit representation - LDNIs, solid modeling operations including the Boolean operations and the offsetting operation have been developed. A contouring algorithm is also introduced in this paper to generate thin structure and sharp feature preserved mesh surfaces from the layered depth-normal images. Comparisons between LDNIs and other implicit representation of solid models are given at the end of the paper to demonstrate the advantages of LDNIs.


Towards Autopoietic Computing

Abstract: A key challenge in modern computing is to develop systems that address complex, dynamic problems in a scalable and efficient way, because the increasing complexity of software makes designing and maintaining efficient and flexible systems increasingly difficult. Biological systems are thought to possess robust, scalable processing paradigms that can automatically manage complex, dynamic problem spaces, possessing several properties that may be useful in computer systems. The biological properties of self-organisation, self-replication, self-management, and scalability are addressed in an interesting way by autopoiesis, a descriptive theory of the cell founded on the concept of a system's circular organisation to define its boundary with its environment. In this paper, therefore, we review the main concepts of autopoiesis and then discuss how they could be related to fundamental concepts and theories of computation. The paper is conceptual in nature and the emphasis is on the review of other people's work in this area as part of a longer-term strategy to develop a formal theory of autopoietic computing.


A Quartic Kernel for Pathwidth-One Vertex Deletion

Abstract: The pathwidth of a graph is a measure of how path-like the graph is. Given a graph G and an integer k, the problem of finding whether there exist at most k vertices in G whose deletion results in a graph of pathwidth at most one is NP- complete. We initiate the study of the parameterized complexity of this problem, parameterized by k. We show that the problem has a quartic vertex-kernel: We show that, given an input instance (G = (V, E), k); |V| = n, we can construct, in polynomial time, an instance (G', k') such that (i) (G, k) is a YES instance if and only if (G', k') is a YES instance, (ii) G' has O(k^{4}) vertices, and (iii) k' \leq k. We also give a fixed parameter tractable (FPT) algorithm for the problem that runs in O(7^{k} k \cdot n^{2}) time.


Towards a Property Preserving Transformation from IEC 61131-3 to BIP

Abstract: We report on a transformation from Sequential Function Charts of the IEC 61131-3 standard to BIP. Our presentation features a description of formal syntax and semantics representation of the involved languages and transformation rules. Furthermore, we present a formalism for describing invariants of IEC 61131-3 systems and establish a notion of invariant preservation between the two languages. For a subset of our transformation rules we sketch a proof showing invariant preservation during the transformation of IEC 61131-3 to BIP and vice versa.


A Novel Watermarking Scheme for Detecting and Recovering Distortions in Database Tables

Abstract: In this paper a novel fragile watermarking scheme is proposed to detect, localize and recover malicious modifications in relational databases. In the proposed scheme, all tuples in the database are first securely divided into groups. Then watermarks are embedded and verified group-by-group independently. By using the embedded watermark, we are able to detect and localize the modification made to the database and even we recover the true data from the database modified locations. Our experimental results show that this scheme is so qualified; i.e. distortion detection and true data recovery both are performed successfully.


Going Green: A Holistic Approach to Transform Business

Abstract: In recent years environmental and energy conservation issues have taken the central theme in the global business arena. The reality of rising energy cost and their impact on international affairs coupled with the different kinds of environmental issues has shifted the social and economic consciousness of the business community. Hence, the business community is now in search of an eco-friendly business model. This paper highlights the concept of green business and their needs in the current global scenario.


Fast Color Space Transformations Using Minimax Approximations

Abstract: Color space transformations are frequently used in image processing, graphics, and visualization applications. In many cases, these transformations are complex nonlinear functions, which prohibits their use in time-critical applications. In this paper, we present a new approach called Minimax Approximations for Color-space Transformations (MACT).We demonstrate MACT on three commonly used color space transformations. Extensive experiments on a large and diverse image set and comparisons with well-known multidimensional lookup table interpolation methods show that MACT achieves an excellent balance among four criteria: ease of implementation, memory usage, accuracy, and computational speed.


Brief Announcement: Decentralized Construction of Multicast Trees Embedded into P2P Overlay Networks based on Virtual Geometric Coordinates

Abstract: In this paper we consider the problem of efficiently constructing in a fully distributed manner multicast trees which are embedded into P2P overlays using virtual geometric node coordinates. We consider two objectives: to minimize the number of messages required for constructing a multicast tree by using the geometric properties of the P2P overlay, and to construct stable multicast trees when the lifetime durations of the peers are known.


Knowledge Recognition Algorithm enables P = NP

Abstract: This paper introduces a knowledge recognition algorithm (KRA) that is both a Turing machine algorithm and an Oracle Turing machine algorithm. By definition KRA is a non-deterministic language recognition algorithm. Simultaneously it can be implemented as a deterministic Turing machine algorithm. KRA applies mirrored perceptual-conceptual languages to learn member-class relations between the two languages iteratively and retrieve information through deductive and reductive recognition from one language to another. The novelty of KRA is that the conventional concept of relation is adjusted. The computation therefore becomes efficient bidirectional string mapping.


Effective Pedestrian Detection Using Center-symmetric Local Binary/Trinary Patterns

Abstract: Accurately detecting pedestrians in images plays a critically important role in many computer vision applications. Extraction of effective features is the key to this task. Promising features should be discriminative, robust to various variations and easy to compute. In this work, we present novel features, termed dense center-symmetric local binary patterns (CS-LBP) and pyramid center-symmetric local binary/ternary patterns (CS-LBP/LTP), for pedestrian detection. The standard LBP proposed by Ojala et al. \cite{c4} mainly captures the texture information. The proposed CS-LBP feature, in contrast, captures the gradient information and some texture information. Moreover, the proposed dense CS-LBP and the pyramid CS-LBP/LTP are easy to implement and computationally efficient, which is desirable for real-time applications. Experiments on the INRIA pedestrian dataset show that the dense CS-LBP feature with linear supporct vector machines (SVMs) is comparable with the histograms of oriented gradients (HOG) feature with linear SVMs, and the pyramid CS-LBP/LTP features outperform both HOG features with linear SVMs and the start-of-the-art pyramid HOG (PHOG) feature with the histogram intersection kernel SVMs. We also demonstrate that the combination of our pyramid CS-LBP feature and the PHOG feature could significantly improve the detection performance-producing state-of-the-art accuracy on the INRIA pedestrian dataset.


Memristor Crossbar-based Hardware Implementation of Fuzzy Membership Functions

Abstract: In May 1, 2008, researchers at Hewlett Packard (HP) announced the first physical realization of a fundamental circuit element called memristor that attracted so much interest worldwide. This newly found element can easily be combined with crossbar interconnect technology which this new structure has opened a new field in designing configurable or programmable electronic systems. These systems in return can have applications in signal processing and artificial intelligence. In this paper, based on the simple memristor crossbar structure, we propose new and simple circuits for hardware implementation of fuzzy membership functions. In our proposed circuits, these fuzzy membership functions can have any shapes and resolutions. In addition, these circuits can be used as a basis in the construction of evolutionary systems.


Comparing Pedigree Graphs

Abstract: Pedigree graphs, or family trees, are typically constructed by an expensive process of examining genealogical records to determine which pairs of individuals are parent and child. New methods to automate this process take as input genetic data from a set of extant individuals and reconstruct ancestral individuals. There is a great need to evaluate the quality of these methods by comparing the estimated pedigree to the true pedigree. In this paper, we consider two main pedigree comparison problems. The first is the pedigree isomorphism problem, for which we present a linear-time algorithm for leaf-labeled pedigrees. The second is the pedigree edit distance problem, for which we present 1) several algorithms that are fast and exact in various special cases, and 2) a general, randomized heuristic algorithm. In the negative direction, we first prove that the pedigree isomorphism problem is as hard as the general graph isomorphism problem, and that the sub-pedigree isomorphism problem is NP-hard. We then show that the pedigree edit distance problem is APX-hard in general and NP-hard on leaf-labeled pedigrees. We use simulated pedigrees to compare our edit-distance algorithms to each other as well as to a branch-and-bound algorithm that always finds an optimal solution.


Results of Evolution Supervised by Genetic Algorithms

Abstract: A series of results of evolution supervised by genetic algorithms with interest to agricultural and horticultural fields are reviewed. New obtained original results from the use of genetic algorithms on structure-activity relationships are reported.


Aggregate AP Throughputs for Long File Transfers in a WLAN controlled by Inhomogeneous TCP Connections

Abstract: The performance analysis of long file TCP controlled transfers in a WLAN in infrastructure mode is available in the present literature with one of the main assumptions being equal window size for all TCP connections. In this paper, we extend the analysis to TCP-controlled long file uploads and downloads with different TCP windows. Our approach is based on simple Markov chain given in the paper [1], [2] with arbitrary window sizes. We presented simulation results to show the accuracy of the analytical model.


A Note on the Membrane Computer

Abstract: Inspired by the emergent membrane computing (P Systems) concepts, some efforts are carried out introducing simulation models, some are software oriented, and others are hardware, yet all are applied with the current vision of the conventional computers, based on "Von Neumann architecture", which is a sequential design in its essence. We think that these models will need "as a consequent result" to a new architecture exposing a true parallel design, in this paper; we try to investigate and introduce a global view for how it would be like to have such architecture. The main goal is to point out to this direction broadly, suggesting that it might be useful considering some aspects, like the need for a new definition of an operating system and its programs, which will eventually lead to a higher scope: the membrane computer.


Mining Target-Oriented Sequential Patterns with Time-Intervals

Abstract: A target-oriented sequential pattern is a sequential pattern with a concerned itemset in the end of pattern. A time-interval sequential pattern is a sequential pattern with time-intervals between every pair of successive itemsets. In this paper we present an algorithm to discover target-oriented sequential pattern with time-intervals. To this end, the original sequences are reversed so that the last itemsets can be arranged in front of the sequences. The contrasts between reversed sequences and the concerned itemset are then used to exclude the irrelevant sequences. Clustering analysis is used with typical sequential pattern mining algorithm to extract the sequential patterns with time-intervals between successive itemsets. Finally, the discovered time-interval sequential patterns are reversed again to the original order for searching the target patterns.


Distance Measures for Reduced Ordering Based Vector Filters

Abstract: Reduced ordering based vector filters have proved successful in removing long-tailed noise from color images while preserving edges and fine image details. These filters commonly utilize variants of the Minkowski distance to order the color vectors with the aim of distinguishing between noisy and noise-free vectors. In this paper, we review various alternative distance measures and evaluate their performance on a large and diverse set of images using several effectiveness and efficiency criteria. The results demonstrate that there are in fact strong alternatives to the popular Minkowski metrics.


Real-Time Implementation of Order-Statistics Based Directional Filters

Abstract: Vector filters based on order-statistics have proved successful in removing impulsive noise from color images while preserving edges and fine image details. Among these filters, the ones that involve the cosine distance function (directional filters) have particularly high computational requirements, which limits their use in time critical applications. In this paper, we introduce two methods to speed up these filters. Experiments on a diverse set of color images show that the proposed methods provide substantial computational gains without significant loss of accuracy.


Cost-Effective Implementation of Order-Statistics Based Vector Filters Using Minimax Approximations

Abstract: Vector operators based on robust order statistics have proved successful in digital multichannel imaging applications, particularly color image filtering and enhancement, in dealing with impulsive noise while preserving edges and fine image details. These operators often have very high computational requirements which limits their use in time-critical applications. This paper introduces techniques to speed up vector filters using the minimax approximation theory. Extensive experiments on a large and diverse set of color images show that proposed approximations achieve an excellent balance among ease of implementation, accuracy, and computational speed.


A Fast Switching Filter for Impulsive Noise Removal from Color Images

Abstract: In this paper, we present a fast switching filter for impulsive noise removal from color images. The filter exploits the HSL color space, and is based on the peer group concept, which allows for the fast detection of noise in a neighborhood without resorting to pairwise distance computations between each pixel. Experiments on large set of diverse images demonstrate that the proposed approach is not only extremely fast, but also gives excellent results in comparison to various state-of-the-art filters.


Nonlinear Vector Filtering for Impulsive Noise Removal from Color Images

Abstract: In this paper, a comprehensive survey of 48 filters for impulsive noise removal from color images is presented. The filters are formulated using a uniform notation and categorized into 8 families. The performance of these filters is compared on a large set of images that cover a variety of domains using three effectiveness and one efficiency criteria. In order to ensure a fair efficiency comparison, a fast and accurate approximation for the inverse cosine function is introduced. In addition, commonly used distance measures (Minkowski, angular, and directional-distance) are analyzed and evaluated. Finally, suggestions are provided on how to choose a filter given certain requirements.


ETP-Mine: An Efficient Method for Mining Transitional Patterns

Abstract: A Transaction database contains a set of transactions along with items and their associated timestamps. Transitional patterns are the patterns which specify the dynamic behavior of frequent patterns in a transaction database. To discover transitional patterns and their significant milestones, first we have to extract all frequent patterns and their supports using any frequent pattern generation algorithm. These frequent patterns are used in the generation of transitional patterns. The existing algorithm (TP-Mine) generates frequent patterns, some of which cannot be used in generation of transitional patterns. In this paper, we propose a modification to the existing algorithm, which prunes the candidate items to be used in the generation of frequent patterns. This method drastically reduces the number of frequent patterns which are used in discovering transitional patterns. Extensive simulation test is done to evaluate the proposed method.


Automatic Detection of Blue-White Veil and Related Structures in Dermoscopy Images

Abstract: Dermoscopy is a non-invasive skin imaging technique, which permits visualization of features of pigmented melanocytic neoplasms that are not discernable by examination with the naked eye. One of the most important features for the diagnosis of melanoma in dermoscopy images is the blue-white veil (irregular, structureless areas of confluent blue pigmentation with an overlying white "ground-glass" film). In this article, we present a machine learning approach to the detection of blue-white veil and related structures in dermoscopy images. The method involves contextual pixel classification using a decision tree classifier. The percentage of blue-white areas detected in a lesion combined with a simple shape descriptor yielded a sensitivity of 69.35% and a specificity of 89.97% on a set of 545 dermoscopy images. The sensitivity rises to 78.20% for detection of blue veil in those cases where it is a primary feature for melanoma recognition.


An Improved Objective Evaluation Measure for Border Detection in Dermoscopy Images

Abstract: Background: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, dermoscopy image analysis has become an important research area. One of the most important steps in dermoscopy image analysis is the automated detection of lesion borders. Although numerous methods have been developed for the detection of lesion borders, very few studies were comprehensive in the evaluation of their results. Methods: In this paper, we evaluate five recent border detection methods on a set of 90 dermoscopy images using three sets of dermatologist-drawn borders as the ground-truth. In contrast to previous work, we utilize an objective measure, the Normalized Probabilistic Rand Index, which takes into account the variations in the ground-truth images. Conclusion: The results demonstrate that the differences between four of the evaluated border detection methods are in fact smaller than those predicted by the commonly used XOR measure.


JIT Spraying and Mitigations

Abstract: With the discovery of new exploit techniques, novel protection mechanisms are needed as well. Mitigations like DEP (Data Execution Prevention) or ASLR (Address Space Layout Randomization) created a significantly more difficult environment for exploitation. Attackers, however, have recently researched new exploitation methods which are capable of bypassing the operating system's memory mitigations. One of the newest and most popular exploitation techniques to bypass both of the aforementioned security protections is JIT memory spraying, introduced by Dion Blazakis. In this article we will present a short overview of the JIT spraying technique and also novel mitigation methods against this innovative class of attacks. An anti-JIT spraying library was created as part of our shellcode execution prevention system.


The General Vector Addition System Reachability Problem by Presburger Inductive Invariants

Abstract: The reachability problem for Vector Addition Systems (VASs) is a central problem of net theory. The general problem is known to be decidable by algorithms exclusively based on the classical Kosaraju-Lambert-Mayr-Sacerdote-Tenney decomposition. This decomposition is used in this paper to prove that the Parikh images of languages recognized by VASs are semi-pseudo-linear; a class that extends the semi-linear sets, a.k.a. the sets definable in Presburger arithmetic. We provide an application of this result; we prove that a final configuration is not reachable from an initial one if and only if there exists a semi-linear inductive invariant that contains the initial configuration but not the final one. Since we can decide if a Presburger formula denotes an inductive invariant, we deduce that there exist checkable certificates of non-reachability. In particular, there exists a simple algorithm for deciding the general VAS reachability problem based on two semi-algorithms. A first one that tries to prove the reachability by enumerating finite sequences of actions and a second one that tries to prove the non-reachability by enumerating Presburger formulas.


Constructions d\'efinitoires des tables du Lexique-Grammaire

Abstract: Lexicon-Grammar tables are a very rich syntactic lexicon for the French language. This linguistic database is nevertheless not directly suitable for use by computer programs, as it is incomplete and lacks consistency. Tables are defined on the basis of features which are not explicitly recorded in the lexicon. These features are only described in literature. Our aim is to define for each tables these essential properties to make them usable in various Natural Language Processing (NLP) applications, such as parsing.


Analyzing Web 2.0 Integration with Next Generation Networks for Services Rendering

Abstract: The Next Generation Networks (NGN) aims to integrate for IP-based telecom infrastructures and provide most advance & high speed emerging value added services. NGN capable to provide higher innovative services, these services will able to integrate communication and Web service into a single platform. IP Multimedia Subsystem, a NGN leading technology, enables a variety of NGN-compliant communications services to interoperate while being accessed through different kinds of access networks, preferably broadband. IMS-NGN services essential by both consumer and corporate users are by now used to access services, even communications services through the web and web-based communities and social networks, It is key for success of IMS-based services to be provided with efficient web access, so users can benefit from those new services by using web-based applications and user interfaces, not only NGN-IMS User Equipments and SIP protocol. Many Service are under planning which provided only under convergence of IMS & Web 2.0. Convergence between Web 2.0 and NGN-IMS creates and serves new invented innovative, entertainment and information appealing as well as user centric services and applications. These services merge features from WWW and Communication worlds. On the one hand, interactivity, ubiquity, social orientation, user participation and content generation, etc. are relevant characteristics coming from Web 2.0 services. Parallel IMS enables services including multimedia telephony, media sharing (video-audio), instant messaging with presence and context, online directory, etc. all of them applicable to mobile, fixed or convergent telecom networks. With this paper, this paper brings out the benefits of adopting web 2.0 technologies for telecom services. As the services are today mainly driven by the user's needs, and proposed the concept of unique customizable service interface.


Design & Deploy Web 2.0 enable services over Next Generation Network Platform


Efficient Collaborative Application Monitoring Scheme for Mobile Networks

Abstract: New operating systems for mobile devices allow their users to download millions of applications created by various individual programmers, some of which may be malicious or flawed. In order to detect that an application is malicious, monitoring its operation in a real environment for a significant period of time is often required. Mobile devices have limited computation and power resources and thus are limited in their monitoring capabilities. In this paper we propose an efficient collaborative monitoring scheme that harnesses the collective resources of many mobile devices, "vaccinating" them against potentially unsafe applications. We suggest a new local information flooding algorithm called "TTL Probabilistic Propagation" (TPP). The algorithm periodically monitors one or more application and reports its conclusions to a small number of other mobile devices, who then propagate this information onwards. The algorithm is analyzed, and is shown to outperform existing state of the art information propagation algorithms, in terms of convergence time as well as network overhead. The maximal "load" of the algorithm (the fastest arrival rate of new suspicious applications, that can still guarantee complete monitoring), is analytically calculated and shown to be significantly superior compared to any non-collaborative approach. Finally, we show both analytically and experimentally using real world network data that implementing the proposed algorithm significantly reduces the number of infected mobile devices. In addition, we analytically prove that the algorithm is tolerant to several types of Byzantine attacks where some adversarial agents may generate false information, or abuse the algorithm in other ways.


M-Learning: A New Paradigm of Learning Mathematics in Malaysia

Abstract: M-Learning is a new learning paradigm of the new social structure with mobile and wireless technologies.Smart school is one of the four flagship applications for Multimedia Super Corridor (MSC) under Malaysian government initiative to improve education standard in the country. With the advances of mobile devices technologies, mobile learning could help the government in realizing the initiative. This paper discusses the prospect of implementing mobile learning for primary school students. It indicates significant and challenges and analysis of user perceptions on potential mobile applications through a survey done in primary school context. The authors propose the m-Learning for mathematics by allowing the extension of technology in the traditional classroom in term of learning and teaching.


Parameterized Complexity Results in Symmetry Breaking

Abstract: Symmetry is a common feature of many combinatorial problems. Unfortunately eliminating all symmetry from a problem is often computationally intractable. This paper argues that recent parameterized complexity results provide insight into that intractability and help identify special cases in which symmetry can be dealt with more tractably


E2XLRADR (Energy Efficient Cross Layer Routing Algorithm with Dynamic Retransmission for Wireless Sensor Networks)

Abstract: The main focus of this article is to achieve prolonged network lifetime with overall energy efficiency in wireless sensor networks through controlled utilization of limited energy. Major percentage of energy in wireless sensor network is consumed during routing from source to destination, retransmission of data on packet loss. For improvement, cross layered algorithm is proposed for routing and retransmission scheme. Simulation and results shows that this approach can save the overall energy consumption


Complexity classifications for different equivalence and audit problems for Boolean circuits

Abstract: We study Boolean circuits as a representation of Boolean functions and consider different equivalence, audit, and enumeration problems. For a number of restricted sets of gate types (bases) we obtain efficient algorithms, while for all other gate types we show these problems are at least NP-hard.


LinBox founding scope allocation, parallel building blocks, and separate compilation

Abstract: To maximize efficiency in time and space, allocations and deallocations, in the exact linear algebra library \linbox, must always occur in the founding scope. This provides a simple lightweight allocation model. We present this model and its usage for the rebinding of matrices between different coefficient domains. We also present automatic tools to speed-up the compilation of template libraries and a software abstraction layer for the introduction of transparent parallelism at the algorithmic level.


Component Specification in the Cactus Framework: The Cactus Configuration Language

Abstract: Component frameworks are complex systems that rely on many layers of abstraction to function properly. One essential requirement is a consistent means of describing each individual component and how it relates to both other components and the whole framework. As component frameworks are designed to be flexible by nature, the description method should be simultaneously powerful, lead to efficient code, and be easy to use, so that new users can quickly adapt their own code to work with the framework. In this paper, we discuss the Cactus Configuration Language (CCL) which is used to describe components ("thorns'') in the Cactus Framework. The CCL provides a description language for the variables, parameters, functions, scheduling and compilation of a component and includes concepts such as interface and implementation which allow thorns providing the same capabilities to be easily interchanged. We include several application examples which illustrate how community toolkits use the CCL and Cactus and identify needed additions to the language.


Simplifying Complex Software Assembly: The Component Retrieval Language and Implementation

Abstract: Assembling simulation software along with the associated tools and utilities is a challenging endeavor, particularly when the components are distributed across multiple source code versioning systems. It is problematic for researchers compiling and running the software across many different supercomputers, as well as for novices in a field who are often presented with a bewildering list of software to collect and install. In this paper, we describe a language (CRL) for specifying software components with the details needed to obtain them from source code repositories. The language supports public and private access. We describe a tool called GetComponents which implements CRL and can be used to assemble software. We demonstrate the tool for application scenarios with the Cactus Framework on the NSF TeraGrid resources. The tool itself is distributed with an open source license and freely available from our web page.


On Scheduling and Redundancy for P2P Backup

Abstract: An online backup system should be quick and reliable in both saving and restoring users' data. To do so in a peer-to-peer implementation, data transfer scheduling and the amount of redundancy must be chosen wisely. We formalize the problem of exchanging multiple pieces of data with intermittently available peers, and we show that random scheduling completes transfers nearly optimally in terms of duration as long as the system is sufficiently large. Moreover, we propose an adaptive redundancy scheme that improves performance and decreases resource usage while keeping the risks of data loss low. Extensive simulations show that our techniques are effective in a realistic trace-driven scenario with heterogeneous bandwidth.


Approximate Lesion Localization in Dermoscopy Images

Abstract: Background: Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma and other pigmented skin lesions. Due to the difficulty and subjectivity of human interpretation, automated analysis of dermoscopy images has become an important research area. Border detection is often the first step in this analysis. Methods: In this article, we present an approximate lesion localization method that serves as a preprocessing step for detecting borders in dermoscopy images. In this method, first the black frame around the image is removed using an iterative algorithm. The approximate location of the lesion is then determined using an ensemble of thresholding algorithms. Results: The method is tested on a set of 428 dermoscopy images. The localization error is quantified by a metric that uses dermatologist determined borders as the ground truth. Conclusion: The results demonstrate that the method presented here achieves both fast and accurate localization of lesions in dermoscopy images.


A Branch-and-Reduce Algorithm for Finding a Minimum Independent Dominating Set

Abstract: An independent dominating set D of a graph G = (V,E) is a subset of vertices such that every vertex in V \ D has at least one neighbor in D and D is an independent set, i.e. no two vertices of D are adjacent in G. Finding a minimum independent dominating set in a graph is an NP-hard problem. Whereas it is hard to cope with this problem using parameterized and approximation algorithms, there is a simple exact O(1.4423^n)-time algorithm solving the problem by enumerating all maximal independent sets. In this paper we improve the latter result, providing the first non trivial algorithm computing a minimum independent dominating set of a graph in time O(1.3569^n). Furthermore, we give a lower bound of \Omega(1.3247^n) on the worst-case running time of this algorithm, showing that the running time analysis is almost tight.


A Practical Approach to Managing Spreadsheet Risk in a Global Business

Abstract: Spreadsheets are used extensively within today's organisations. Although spreadsheets have many benefits, they can also present a significant risk exposure, requiring appropriate management. Protiviti has worked with a number of organisations, ranging in size up to huge multi-nationals, to help them build appropriate spreadsheet governance frameworks, including the design and implementation of policies, minimum design standards, control processes, training and awareness programmes and the consideration and implementation of spreadsheet management tools. This paper presents a case-study explaining the practical and pragmatic approach that was recently taken to control spreadsheet risk at one of Protiviti's clients - a global energy firm.


Transforming Critical Spreadsheets into Web Applications at Zurich Financial

Abstract: In the insurance industry, spreadsheets have emerged as an invaluable tool to for product pricing, because it is relatively straightforward to create and maintain complex pricing models using Excel. In fact, Excel is often preferred to "hard-code" whenever there are frequent changes to the calculations and business logic which under-pin the pricing of an insurance product. However, problems arise as soon as spreadsheets are deployed to end-users: version control, security of intellectual property, and ensuring correct usage are obvious issues; frequently, integration with other systems is also a requirement. Zurich Financial Services Group is a leading financial services provider; several possible solutions to these problems have been evaluated, and EASA has been selected as the preferred technology. Other spreadsheet collaboration approaches which were considered include Excel Services, and/or custom-built software; however, EASA has provided clear benefits over these strategies.


Spreadsheet Refactoring

Abstract: Refactoring is a change made to the internal structure of software to make it easier to understand and cheaper to modify without changing its observable behaviour. A database refactoring is a small change to the database schema which improves its design without changing its semantics. This paper presents example 'spreadsheet refactorings', derived from the above and taking into account the unique characteristics of spreadsheet formulas and VBA code. The techniques are constrained by the tightly coupled data and code in spreadsheets.


The Development of the Journal Environment of Leonardo

Abstract: We present animations based on the aggregated journal-journal citations of Leonardo during the period 1974-2008. Leonardo is mainly cited by journals outside the arts domain for cultural reasons, for example, in neuropsychology and physics. Articles in Leonardo itself cite a large number of journals, but with a focus on the arts. Animations at this level of aggregation enable us to show the history of the journal from a network perspective.


A Block Based Scheme for Enhancing Low Luminated Images

Abstract: In this paper the background detection in images in poor lighting can be done by the use of morphological filters. Lately contrast image enhancement technique is used to detect the background in image which uses Weber's Law. The proposed technique is more effective one in which the background detection in image can be done in color images. The given image obtained in this method is very effective one. More enhancement can be obtained while comparing the results. In this technique compressed domain enhancement has been used for better result.


Artificial Neural Networks, Symmetries and Differential Evolution

Abstract: Neuroevolution is an active and growing research field, especially in times of increasingly parallel computing architectures. Learning methods for Artificial Neural Networks (ANN) can be divided into two groups. Neuroevolution is mainly based on Monte-Carlo techniques and belongs to the group of global search methods, whereas other methods such as backpropagation belong to the group of local search methods. ANN's comprise important symmetry properties, which can influence Monte-Carlo methods. On the other hand, local search methods are generally unaffected by these symmetries. In the literature, dealing with the symmetries is generally reported as being not effective or even yielding inferior results. In this paper, we introduce the so called Minimum Global Optimum Proximity principle derived from theoretical considerations for effective symmetry breaking, applied to offline supervised learning. Using Differential Evolution (DE), which is a popular and robust evolutionary global optimization method, we experimentally show significant global search efficiency improvements by symmetry breaking.


An Application-oriented Model for Wireless Sensor Networks integrated with Telecom Infra

Abstract: This paper aims to propose a significant way of remote access and real time monitoring of a particular geographic area by integrating wireless sensor clouds with existing Telecom infrastructure and applications built around them through a gateway. This utility is very potent for environment monitoring in harsh and inaccessible places like mines, nuclear reactors, etc. We demonstrate a scaled down version of multi-hop network of wireless sensor nodes and its integration with existing telecom network infrastructure via a gateway. The kind of results achieved like temperature monitoring etc. gives a glimpse of an enormous step ahead in mine safety.


Pan-private Algorithms: When Memory Does Not Help

Abstract: Consider updates arriving online in which the $t$th input is $(i_t,d_t)$, where $i_t$'s are thought of as IDs of users. Informally, a randomized function $f$ is {\em differentially private} with respect to the IDs if the probability distribution induced by $f$ is not much different from that induced by it on an input in which occurrences of an ID $j$ are replaced with some other ID $k$ Recently, this notion was extended to {\em pan-privacy} where the computation of $f$ retains differential privacy, even if the internal memory of the algorithm is exposed to the adversary (say by a malicious break-in or by fiat by the government). This is a strong notion of privacy, and surprisingly, for basic counting tasks such as distinct counts, heavy hitters and others, Dwork et al~\cite{dwork-pan} present pan-private algorithms with reasonable accuracy. The pan-private algorithms are nontrivial, and rely on sampling. We reexamine these basic counting tasks and show improved bounds. In particular, we estimate the distinct count $\Dt$ to within $(1\pm \eps)\Dt \pm O(\polylog m)$, where $m$ is the number of elements in the universe. This uses suitably noisy statistics on sketches known in the streaming literature. We also present the first known lower bounds for pan-privacy with respect to a single intrusion. Our lower bounds show that, even if allowed to work with unbounded memory, pan-private algorithms for distinct counts can not be significantly more accurate than our algorithms. Our lower bound uses noisy decoding. For heavy hitter counts, we present a pan private streaming algorithm that is accurate to within $O(k)$ in worst case; previously known bound for this problem is arbitrarily worse. An interesting aspect of our pan-private algorithms is that, they deliberately use very small (polylogarithmic) space and tend to be streaming algorithms, even though using more space is not forbidden.


Stack-Summarizing Control-Flow Analysis of Higher-Order Programs

Abstract: Two sinks drain precision from higher-order flow analyses: (1) merging of argument values upon procedure call and (2) merging of return values upon procedure return. To combat the loss of precision, these two sinks have been addressed independently. In the case of procedure calls, abstract garbage collection reduces argument merging; while in the case of procedure returns, context-free approaches eliminate return value merging. It is natural to expect a combined analysis could enjoy the mutually beneficial interaction between the two approaches. The central contribution of this work is a direct product of abstract garbage collection with context-free analysis. The central challenge to overcome is the conflict between the core constraint of a pushdown system and the needs of garbage collection: a pushdown system can only see the top of the stack, yet garbage collection needs to see the entire stack during a collection. To make the direct product computable, we develop "stack summaries," a method for tracking stack properties at each control state in a pushdown analysis of higher-order programs.


DynaChanAl: Dynamic Channel Allocation with Minimal End-to-end Delay for Wireless Sensor Networks

Abstract: With recent advances in wireless communication, networking, and low power sensor technology, wireless sensor network (WSN) systems have begun to take significant roles in various applications ranging from environmental sensing to mobile healthcare sensing. While some WSN applications only require a lim- ited amount of bandwidth, new emerging applications operate with a notice- ably large amount of data transfers. One way to deal with such applications is to maximize the available capacity by utilizing the use of multiple wireless channels. This work proposes DynaChannAl, a distributed dynamic wireless channel algorithm with the goal of effectively distributing nodes on multiple wireless channels in WSN systems. Specifically, DynaChannAl targets applica- tions where mobile nodes connect to a pre-existing wireless backbone and takes the expected end-to-end queuing delay as its core metric. We use the link qual- ity indicator (LQI) values provided by IEEE 802.15.4 radios white-list potential links with good link quality and evaluate such links with the aggregated packet transmission latency at each hop. Our approach is useful for applications that require minimal end-to-end delay (i.e., healthcare applications). DynaChannAl is a light weight and highly adoptable scheme that can be easily incorporated with various pre-developed components and pre-deployed applications. We eval- uate DynaChannAl in on a 45 node WSN testbed. As the first study to consider end-to-end latency as the core metric for channel allocation in WSN systems, the experimental results indicate that DynaChannAl successfully distributes multi- ple (mobile) source nodes on different wireless channels and enables the nodes to select wireless channel and links that can minimize the end-to-end latency.


Protocols for Bio-Inspired Resource Discovery and Erasure Coded Replication in P2P Networks

Abstract: Efficient resource discovery and availability improvement are very important issues in unstructured P2P networks. In this paper, a bio-inspired resource discovery scheme inspired by the principle of elephants migration is proposed. A replication scheme based on Q-learning and erasure codes is also introduced. Simulation results show that the proposed schemes significantly increases query success rate and availability, and reduces the network traffic as the resources are effectively distributed to well-performing nodes.


One method of storing information

Abstract: Formulate the problem as follows. Split a file into n pieces so that it can be restored without any m parts (1<=m<=n). Such problems are called problems secret sharing. There exists a set of methods for solving such problems, but they all require a fairly large number of calculations applied to the problem posed above. The proposed method does not require calculations, and requires only the operations of the division of the file into equal (nearly equal) parts and gluing them in a certain order in one or more files.


On the Performance Evaluation and Analysis of the Hybridised Bittorrent Protocol with Partial Mobility Characteristics

Abstract: Engaging mobility with file sharing is considered very promising in today's run Anywhere, Anytime, Anything (3As) environments. The Bittorrent file sharing protocol can be rarely combined with the mobility scenario framework since resources are not available due to the dynamically changing topology network. As a result, mobility in P2P-oriented file sharing platforms, degrades the end-to-end efficiency and the system's performance. This work proposes a new hybridized model, which takes into account the mobility characteristics of the combined Bittorrent protocol in a centralized manner enabling partial mobility characteristics, where the clients of the network use a distinct technique to differentiate between mobile and static nodes. Many parameters were taken into consideration like the round trip delays, the diffusion process, and the seeding techniques, targeting the maximization of the average throughput in the clustered swarms containing mobile peers. Partial mobility characteristics are set in a peer-tracker and peer-peer communication enhancement schema with partial mobility, allowing an optimistic approach to attain high availability and throughput response as simulation results show.


On the Impact of Caching and a Model for Storage-Capacity Measurements for Energy Conservation in Asymmetrical Wireless Devices

Abstract: Traffic and channel-data rate combined with the stream oriented methodology can provide a scheme for offering optimized and guaranteed QoS. In this work a stream oriented modeled scheme is proposed based on each node's self-scheduling energy management. This scheme is taking into account the overall packet loss in order to form the optimal effective -for the end-to-end connection- throughput response. The scheme also -quantitatively- takes into account the asymmetrical nature of wireless links and the caching activity that is used for data revocation in the ad-hoc based connectivity scenario. Through the designed middleware and the architectural layering and through experimental simulation, the proposed energy-aware management scheme is thoroughly evaluated in order to meet the parameters' values where the optimal throughput response for each device/user is achieved.


Conductance and Eigenvalue

Abstract: We show the following. \begin{theorem} Let $M$ be an finite-state ergodic time-reversible Markov chain with transition matrix $P$ and conductance $\phi$. Let $\lambda \in (0,1)$ be an eigenvalue of $P$. Then, $$\phi^2 + \lambda^2 \leq 1$$ \end{theorem} This strengthens the well-known~\cite{HLW,Dod84, AM85, Alo86, JS89} inequality $\lambda \leq 1- \phi^2/2$. We obtain our result by a slight variation in the proof method in \cite{JS89, HLW}; the same method was used earlier in \cite{RS06} to obtain the same inequality for random walks on regular undirected graphs.


Power optimized programmable embedded controller

Abstract: Now a days, power has become a primary consideration in hardware design, and is critical in computer systems especially for portable devices with high performance and more functionality. Clock-gating is the most common technique used for reducing processor's power. In this work clock gating technique is applied to optimize the power of fully programmable Embedded Controller (PEC) employing RISC architecture. The CPU designed supports i) smart instruction set, ii) I/O port, UART iii) on-chip clocking to provide a range of frequencies , iv) RISC as well as controller concepts. The whole design is captured using VHDL and is implemented on FPGA chip using Xilinx .The architecture and clock gating technique together is found to reduce the power consumption by 33.33% of total power consumed by this chip.


Refinement Types for Logical Frameworks and Their Interpretation as Proof Irrelevance

Abstract: Refinement types sharpen systems of simple and dependent types by offering expressive means to more precisely classify well-typed terms. We present a system of refinement types for LF in the style of recent formulations where only canonical forms are well-typed. Both the usual LF rules and the rules for type refinements are bidirectional, leading to a straightforward proof of decidability of typechecking even in the presence of intersection types. Because we insist on canonical forms, structural rules for subtyping can now be derived rather than being assumed as primitive. We illustrate the expressive power of our system with examples and validate its design by demonstrating a precise correspondence with traditional presentations of subtyping. Proof irrelevance provides a mechanism for selectively hiding the identities of terms in type theories. We show that LF refinement types can be interpreted as predicates using proof irrelevance, establishing a uniform relationship between two previously studied concepts in type theory. The interpretation and its correctness proof are surprisingly complex, lending support to the claim that refinement types are a fundamental construct rather than just a convenient surface syntax for certain uses of proof irrelevance.


Fat Polygonal Partitions with Applications to Visualization and Embeddings

Abstract: Let $\mathcal{T}$ be a rooted and weighted tree, where the weight of any node is equal to the sum of the weights of its children. The popular Treemap algorithm visualizes such a tree as a hierarchical partition of a square into rectangles, where the area of the rectangle corresponding to any node in $\mathcal{T}$ is equal to the weight of that node. The aspect ratio of the rectangles in such a rectangular partition necessarily depends on the weights and can become arbitrarily high. We introduce a new hierarchical partition scheme, called a polygonal partition, which uses convex polygons rather than just rectangles. We present two methods for constructing polygonal partitions, both having guarantees on the worst-case aspect ratio of the constructed polygons; in particular, both methods guarantee a bound on the aspect ratio that is independent of the weights of the nodes. We also consider rectangular partitions with slack, where the areas of the rectangles may differ slightly from the weights of the corresponding nodes. We show that this makes it possible to obtain partitions with constant aspect ratio. This result generalizes to hyper-rectangular partitions in $\mathbb{R}^d$. We use these partitions with slack for embedding ultrametrics into $d$-dimensional Euclidean space: we give a $\mathop{\rm polylog}(\Delta)$-approximation algorithm for embedding $n$-point ultrametrics into $\mathbb{R}^d$ with minimum distortion, where $\Delta$ denotes the spread of the metric, i.e., the ratio between the largest and the smallest distance between two points. The previously best-known approximation ratio for this problem was polynomial in $n$. This is the first algorithm for embedding a non-trivial family of weighted-graph metrics into a space of constant dimension that achieves polylogarithmic approximation ratio.


Privacy-Preserving Data-Oblivious Geometric Algorithms for Geographic Data

Abstract: We give efficient data-oblivious algorithms for several fundamental geometric problems that are relevant to geographic information systems, including planar convex hulls and all-nearest neighbors. Our methods are "data-oblivious" in that they don't perform any data-dependent operations, with the exception of operations performed inside low-level blackbox circuits having a constant number of inputs and outputs. Thus, an adversary who observes the control flow of one of our algorithms, but who cannot see the inputs and outputs to the blackbox circuits, cannot learn anything about the input or output. This behavior makes our methods applicable to secure multiparty computation (SMC) protocols for geographic data used in location-based services. In SMC protocols, multiple parties wish to perform a computation on their combined data without revealing individual data to the other parties. For instance, our methods can be used to solve a problem posed by Du and Atallah, where Alice has a set, A, of m private points in the plane, Bob has another set, B, of n private points in the plane, and Alice and Bob want to jointly compute the convex hull of A u B without disclosing any more information than what can be derived from the answer. In particular, neither Alice nor Bob want to reveal any of their respective points that are in the interior of the convex hull of A u B.


A Transformation-based Implementation for CLP with Qualification and Proximity

Abstract: Uncertainty in logic programming has been widely investigated in the last decades, leading to multiple extensions of the classical LP paradigm. However, few of these are designed as extensions of the well-established and powerful CLP scheme for Constraint Logic Programming. In a previous work we have proposed the SQCLP (proximity-based qualified constraint logic programming) scheme as a quite expressive extension of CLP with support for qualification values and proximity relations as generalizations of uncertainty values and similarity relations, respectively. In this paper we provide a transformation technique for transforming SQCLP programs and goals into semantically equivalent CLP programs and goals, and a practical Prolog-based implementation of some particularly useful instances of the SQCLP scheme. We also illustrate, by showing some simple-and working-examples, how the prototype can be effectively used as a tool for solving problems where qualification values and proximity relations play a key role. Intended use of SQCLP includes flexible information retrieval applications.


Fixpoint & Proof-theoretic Semantics for CLP with Qualification and Proximity

Abstract: Uncertainty in Logic Programming has been investigated during the last decades, dealing with various extensions of the classical LP paradigm and different applications. Existing proposals rely on different approaches, such as clause annotations based on uncertain truth values, qualification values as a generalization of uncertain truth values, and unification based on proximity relations. On the other hand, the CLP scheme has established itself as a powerful extension of LP that supports efficient computation over specialized domains while keeping a clean declarative semantics. In this report we propose a new scheme SQCLP designed as an extension of CLP that supports qualification values and proximity relations. We show that several previous proposals can be viewed as particular cases of the new scheme, obtained by partial instantiation. We present a declarative semantics for SQCLP that is based on observables, providing fixpoint and proof-theoretical characterizations of least program models as well as an implementation-independent notion of goal solutions.


Evolutionary Computational Method of Facial Expression Analysis for Content-based Video Retrieval using 2-Dimensional Cellular Automata

Abstract: In this paper, Deterministic Cellular Automata (DCA) based video shot classification and retrieval is proposed. The deterministic 2D Cellular automata model captures the human facial expressions, both spontaneous and posed. The determinism stems from the fact that the facial muscle actions are standardized by the encodings of Facial Action Coding System (FACS) and Action Units (AUs). Based on these encodings, we generate the set of evolutionary update rules of the DCA for each facial expression. We consider a Person-Independent Facial Expression Space (PIFES) to analyze the facial expressions based on Partitioned 2D-Cellular Automata which capture the dynamics of facial expressions and classify the shots based on it. Target video shot is retrieved by comparing the similar expression is obtained for the query frame's face with respect to the key faces expressions in the database video. Consecutive key face expressions in the database that are highly similar to the query frame's face, then the key faces are used to generate the set of retrieved video shots from the database. A concrete example of its application which realizes an affective interaction between the computer and the user is proposed. In the affective interaction, the computer can recognize the facial expression of any given video shot. This interaction endows the computer with certain ability to adapt to the user's feedback.


Complexity of Non-Monotonic Logics

Abstract: Over the past few decades, non-monotonic reasoning has developed to be one of the most important topics in computational logic and artificial intelligence. Different ways to introduce non-monotonic aspects to classical logic have been considered, e.g., extension with default rules, extension with modal belief operators, or modification of the semantics. In this survey we consider a logical formalism from each of the above possibilities, namely Reiter's default logic, Moore's autoepistemic logic and McCarthy's circumscription. Additionally, we consider abduction, where one is not interested in inferences from a given knowledge base but in computing possible explanations for an observation with respect to a given knowledge base. Complexity results for different reasoning tasks for propositional variants of these logics have been studied already in the nineties. In recent years, however, a renewed interest in complexity issues can be observed. One current focal approach is to consider parameterized problems and identify reasonable parameters that allow for FPT algorithms. In another approach, the emphasis lies on identifying fragments, i.e., restriction of the logical language, that allow more efficient algorithms for the most important reasoning tasks. In this survey we focus on this second aspect. We describe complexity results for fragments of logical languages obtained by either restricting the allowed set of operators (e.g., forbidding negations one might consider only monotone formulae) or by considering only formulae in conjunctive normal form but with generalized clause types. The algorithmic problems we consider are suitable variants of satisfiability and implication in each of the logics, but also counting problems, where one is not only interested in the existence of certain objects (e.g., models of a formula) but asks for their number.


Home Automation

Abstract: In this paper I briefly discuss the importance of home automation system. Going in to the details I briefly present a real time designed and implemented software and hardware oriented house automation research project, capable of automating house's electricity and providing a security system to detect the presence of unexpected behavior.


Aero Fighter - 2D Gaming

Abstract: Designing and developing quality based computer game is always a challenging task for developers. In this paper I briefly discuss aero fighting war game based on simple 2D gaming concepts and developed in C & C++ programming languages, using old bitmapping concepts. Going into the details of the game development, I discuss the designed strategies, flow of game and implemented prototype version of game, especially for beginners of game programming.


AI 3D Cybug Gaming

Abstract: In this short paper I briefly discuss 3D war Game based on artificial intelligence concepts called AI WAR. Going in to the details, I present the importance of CAICL language and how this language is used in AI WAR. Moreover I also present a designed and implemented 3D War Cybug for AI WAR using CAICL and discus the implemented strategy to defeat its enemies during the game life.


The Complexity of Causality and Responsibility for Query Answers and non-Answers

Abstract: An answer to a query has a well-defined lineage expression (alternatively called how-provenance) that explains how the answer was derived. Recent work has also shown how to compute the lineage of a non-answer to a query. However, the cause of an answer or non-answer is a more subtle notion and consists, in general, of only a fragment of the lineage. In this paper, we adapt Halpern, Pearl, and Chockler's recent definitions of causality and responsibility to define the causes of answers and non-answers to queries, and their degree of responsibility. Responsibility captures the notion of degree of causality and serves to rank potentially many causes by their relative contributions to the effect. Then, we study the complexity of computing causes and responsibilities for conjunctive queries. It is known that computing causes is NP-complete in general. Our first main result shows that all causes to conjunctive queries can be computed by a relational query which may involve negation. Thus, causality can be computed in PTIME, and very efficiently so. Next, we study computing responsibility. Here, we prove that the complexity depends on the conjunctive query and demonstrate a dichotomy between PTIME and NP-complete cases. For the PTIME cases, we give a non-trivial algorithm, consisting of a reduction to the max-flow computation problem. Finally, we prove that, even when it is in PTIME, responsibility is complete for LOGSPACE, implying that, unlike causality, it cannot be computed by a relational query.


Multi-Agent Only-Knowing Revisited

Abstract: Levesque introduced the notion of only-knowing to precisely capture the beliefs of a knowledge base. He also showed how only-knowing can be used to formalize non-monotonic behavior within a monotonic logic. Despite its appeal, all attempts to extend only-knowing to the many agent case have undesirable properties. A belief model by Halpern and Lakemeyer, for instance, appeals to proof-theoretic constructs in the semantics and needs to axiomatize validity as part of the logic. It is also not clear how to generalize their ideas to a first-order case. In this paper, we propose a new account of multi-agent only-knowing which, for the first time, has a natural possible-world semantics for a quantified language with equality. We then provide, for the propositional fragment, a sound and complete axiomatization that faithfully lifts Levesque's proof theory to the many agent case. We also discuss comparisons to the earlier approach by Halpern and Lakemeyer.


Ontology Temporal Evolution for Multi-Entity Bayesian Networks under Exogenous and Endogenous Semantic Updating

Abstract: It is a challenge for any Knowledge Base reasoning to manage ubiquitous uncertain ontology as well as uncertain updating times, while achieving acceptable service levels at minimum computational cost. This paper proposes an application-independent merging ontologies for any open interaction system. A solution that uses Multi-Entity Bayesan Networks with SWRL rules, and a Java program is presented to dynamically monitor Exogenous and Endogenous temporal evolution on updating merging ontologies on a probabilistic framework for the Semantic Web.


Maximizing the Total Resolution of Graphs

Abstract: A major factor affecting the readability of a graph drawing is its resolution. In the graph drawing literature, the resolution of a drawing is either measured based on the angles formed by consecutive edges incident to a common node (angular resolution) or by the angles formed at edge crossings (crossing resolution). In this paper, we evaluate both by introducing the notion of "total resolution", that is, the minimum of the angular and crossing resolution. To the best of our knowledge, this is the first time where the problem of maximizing the total resolution of a drawing is studied. The main contribution of the paper consists of drawings of asymptotically optimal total resolution for complete graphs (circular drawings) and for complete bipartite graphs (2-layered drawings). In addition, we present and experimentally evaluate a force-directed based algorithm that constructs drawings of large total resolution.


Clustering, Encoding and Diameter Computation Algorithms for Multidimensional Data

Abstract: In this paper we present novel algorithms for several multidimensional data processing problems. We consider problems related to the computation of restricted clusters and of the diameter of a set of points using a new distance function. We also consider two string (1D data) processing problems, regarding an optimal encoding method and the computation of the number of occurrences of a substring within a string generated by a grammar. The algorithms have been thoroughly analyzed from a theoretical point of view and some of them have also been evaluated experimentally.


Simplifying Negative Goals Using Typed Existence Properties

Abstract: A method for extracting positive information from negative goals is proposed. It makes use of typed existence properties between arguments of a predicate to rewrite negative goals in a logic program. A typed existence property is a generalization of functional dependencies in that an input value maps to a fixed number of output values. Types are used to specify the domains of the input and output values. An implementation of the simplification method is presented and its complexity is analyzed. A key algorithm of the implementation checks if an atom in a negative goal can be extracted using a given typed existence property. A digraph links an atom to the quantified variables occurring in the atom and is used to quickly retrieve atoms in the negative goal that may become extractable after some other atom is extracted.


Proceedings 5th International Workshop on Logical Frameworks and Meta-languages: Theory and Practice

Abstract: Type theories, logical frameworks and meta-languages form a common foundation for designing, implementing, and reasoning about formal languages and their semantics. They are central to the design of modern programming languages, certified software, and domain specific logics. More generally, they continue to influence applications in many areas in mathematics, logic and computer science. The Logical Frameworks and Meta-languages: Theory and Practice workshop aims to bring together designers, implementers, and practitioners working on these areas, and in particular about: the automation and implementation of the meta-theory of programming languages and related calculi; the design of proof assistants, automated theorem provers, and formal digital libraries building upon logical framework technology; theoretical and practical issues concerning the encoding of variable binding and fresh name generation, especially the representation of, and reasoning about, datatypes defined from binding signatures; case studies of meta-programming, and the mechanization of the (meta) theory of descriptions of programming languages and other calculi. This volume contains the final and revised versions of the papers presented at LFMTP 2010, which was held on July 14, 2010 in Edinburgh (UK). LFMTP 2010 was part of the Federated Logic Conference (FLoC 2010), and affilated with LICS 2010.