Rotation Invariant Face Detection Using Wavelet, PCA and Radial Basis Function Networks

Abstract: This paper introduces a novel method for human face detection with its orientation by using wavelet, principle component analysis (PCA) and redial basis networks. The input image is analyzed by two-dimensional wavelet and a two-dimensional stationary wavelet. The common goals concern are the image clearance and simplification, which are parts of de-noising or compression. We applied an effective procedure to reduce the dimension of the input vectors using PCA. Radial Basis Function (RBF) neural network is then used as a function approximation network to detect where either the input image is contained a face or not and if there is a face exists then tell about its orientation. We will show how RBF can perform well then back-propagation algorithm and give some solution for better regularization of the RBF (GRNN) network. Compared with traditional RBF networks, the proposed network demonstrates better capability of approximation to underlying functions, faster learning speed, better size of network, and high robustness to outliers.


Text Classification using Association Rule with a Hybrid Concept of Naive Bayes Classifier and Genetic Algorithm

Abstract: Text classification is the automated assignment of natural language texts to predefined categories based on their content. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data. Now a day the demand of text classification is increasing tremendously. Keeping this demand into consideration, new and updated techniques are being developed for the purpose of automated text classification. This paper presents a new algorithm for text classification. Instead of using words, word relation i.e. association rules is used to derive feature set from pre-classified text documents. The concept of Naive Bayes Classifier is then used on derived features and finally a concept of Genetic Algorithm has been added for final classification. A system based on the proposed algorithm has been implemented and tested. The experimental results show that the proposed system works as a successful text classifier.


Universal Numeric Segmented Display

Abstract: Segmentation display plays a vital role to display numerals. But in today's world matrix display is also used in displaying numerals. Because numerals has lots of curve edges which is better supported by matrix display. But as matrix display is costly and complex to implement and also needs more memory, segment display is generally used to display numerals. But as there is yet no proposed compact display architecture to display multiple language numerals at a time, this paper proposes uniform display architecture to display multiple language digits and general mathematical expressions with higher accuracy and simplicity by using a 18-segment display, which is an improvement over the 16 segment display.


Extracting Symbolic Rules for Medical Diagnosis Problem

Abstract: Neural networks (NNs) have been successfully applied to solve a variety of application problems involving classification and function approximation. Although backpropagation NNs generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions cannot be explained as those of decision trees. In many applications, it is desirable to extract knowledge from trained NNs for the users to gain a better understanding of how the networks solve the problems. An algorithm is proposed and implemented to extract symbolic rules for medical diagnosis problem. Empirical study on three benchmarks classification problems, such as breast cancer, diabetes, and lenses demonstrates that the proposed algorithm generates high quality rules from NNs comparable with other methods in terms of number of rules, average number of conditions for a rule, and predictive accuracy.


Smart Bengali Cell Phone Keypad Layout

Abstract: Nowadays cell phone is the most common communicating used by mass people. SMS based communication is a cheap and popular communication method. It is human tendency to have the opportunity to write SMS in their mother language. Text input in mother language is more flexible when the alphabets of that language are printed on the keypad. Bangla mobile keypad based on phonetics has been proposed earlier. But the keypad is not scientific from frequency and flexibility point of view. Since it is not a feasible solution in this paper we have proposed an efficient Bengali keypad for cell phone and other cellular device. The proposed keypad is based on the frequency of the alphabets in Bengali language and also with the view of structure of human finger movements. We took the two points in count to provide a flexible and fast cell phone keypad.


Completely Enhanced Cell Phone Keypad

Abstract: The enhanced frequency based keypad is designed to speed up the typing process. This paper will show that the proposed layout will increase the typing speed and be flexible for thumb. Traditional cell phone keypad is not a scientific keypad from the frequency point of view. Approaches have been explored to speed up the typing process. We found that no manufacturer has considered the frequency of the alphabet. The current architecture does not provide flexibility although the users are accustomed to the currently available multi-tapping keypad. Since the currently available keypad layouts are not best suited for users, this paper will suggest a keypad for cell phone and other cellular device based on the frequency of the alphabet in English language and also with the view of structure of human finger movements to provide a flexible and fast cell phone keypad. It also takes into consideration the key jamming problem that was available in typewriter. At first we identified those keys of cell phone, which are easily reachable and create less pressure on the thumb. Thus the key frequency order is calculated from anatomical point of view. In our proposed layout we arranged the alphabet in the frequent keys based on the frequency of the alphabet.


Optimal Bangla Keyboard Layout using Data Mining Technique

Abstract: This paper presents an optimal Bangla Keyboard Layout, which distributes the load equally on both hands so that maximizing the ease and minimizing the effort. Bangla alphabet has a large number of letters, for this it is difficult to type faster using Bangla keyboard. Our proposed keyboard will maximize the speed of operator as they can type with both hands parallel. Here we use the association rule of data mining to distribute the Bangla characters in the keyboard. First, we analyze the frequencies of data consisting of monograph, digraph and trigraph, which are derived from data wire-house, and then used association rule of data mining to distribute the Bangla characters in the layout. Experimental results on several data show the effectiveness of the proposed approach with better performance.


Pattern Classification using Simplified Neural Networks

Abstract: In recent years, many neural network models have been proposed for pattern classification, function approximation and regression problems. This paper presents an approach for classifying patterns from simplified NNs. Although the predictive accuracy of ANNs is often higher than that of other methods or human experts, it is often said that ANNs are practically "black boxes", due to the complexity of the networks. In this paper, we have an attempted to open up these black boxes by reducing the complexity of the network. The factor makes this possible is the pruning algorithm. By eliminating redundant weights, redundant input and hidden units are identified and removed from the network. Using the pruning algorithm, we have been able to prune networks such that only a few input units, hidden units and connections left yield a simplified network. Experimental results on several benchmarks problems in neural networks show the effectiveness of the proposed approach with good generalization ability.


Rule Extraction using Artificial Neural Networks

Abstract: Artificial neural networks have been successfully applied to a variety of business application problems involving classification and regression. Although backpropagation neural networks generally predict better than decision trees do for pattern classification problems, they are often regarded as black boxes, i.e., their predictions are not as interpretable as those of decision trees. In many applications, it is desirable to extract knowledge from trained neural networks so that the users can gain a better understanding of the solution. This paper presents an efficient algorithm to extract rules from artificial neural networks. We use two-phase training algorithm for backpropagation learning. In the first phase, the number of hidden nodes of the network is determined automatically in a constructive fashion by adding nodes one after another based on the performance of the network on training data. In the second phase, the number of relevant input units of the network is determined using pruning algorithm. The pruning process attempts to eliminate as many connections as possible from the network. Relevant and irrelevant attributes of the data are distinguished during the training process. Those that are relevant will be kept and others will be automatically discarded. From the simplified networks having small number of connections and nodes we may easily able to extract symbolic rules using the proposed algorithm. Extensive experimental results on several benchmarks problems in neural networks demonstrate the effectiveness of the proposed approach with good generalization ability.


Text Classification using Data Mining

Abstract: Text classification is the process of classifying documents into predefined categories based on their content. It is the automated assignment of natural language texts to predefined categories. Text classification is the primary requirement of text retrieval systems, which retrieve texts in response to a user query, and text understanding systems, which transform text in some way such as producing summaries, answering questions or extracting data. Existing supervised learning algorithms to automatically classify text need sufficient documents to learn accurately. This paper presents a new algorithm for text classification using data mining that requires fewer documents for training. Instead of using words, word relation i.e. association rules from these words is used to derive feature set from pre-classified text documents. The concept of Naive Bayes classifier is then used on derived features and finally only a single concept of Genetic Algorithm has been added for final classification. A system based on the proposed algorithm has been implemented and tested. The experimental results show that the proposed system works as a successful text classifier.


REx: An Efficient Rule Generator

Abstract: This paper describes an efficient algorithm REx for generating symbolic rules from artificial neural network (ANN). Classification rules are sought in many areas from automatic knowledge acquisition to data mining and ANN rule extraction. This is because classification rules possess some attractive features. They are explicit, understandable and verifiable by domain experts, and can be modified, extended and passed on as modular knowledge. REx exploits the first order information in the data and finds shortest sufficient conditions for a rule of a class that can differentiate it from patterns of other classes. It can generate concise and perfect rules in the sense that the error rate of the rules is not worse than the inconsistency rate found in the original data. An important feature of rule extraction algorithm, REx, is its recursive nature. They are concise, comprehensible, order insensitive and do not involve any weight values. Extensive experimental studies on several benchmark classification problems, such as breast cancer, iris, season, and golf-playing, demonstrate the effectiveness of the proposed approach with good generalization ability.


Web Page Categorization Using Artificial Neural Networks

Abstract: Web page categorization is one of the challenging tasks in the world of ever increasing web technologies. There are many ways of categorization of web pages based on different approach and features. This paper proposes a new dimension in the way of categorization of web pages using artificial neural network (ANN) through extracting the features automatically. Here eight major categories of web pages have been selected for categorization; these are business & economy, education, government, entertainment, sports, news & media, job search, and science. The whole process of the proposed system is done in three successive stages. In the first stage, the features are automatically extracted through analyzing the source of the web pages. The second stage includes fixing the input values of the neural network; all the values remain between 0 and 1. The variations in those values affect the output. Finally the third stage determines the class of a certain web page out of eight predefined classes. This stage is done using back propagation algorithm of artificial neural network. The proposed concept will facilitate web mining, retrievals of information from the web and also the search engines.


A System for Smart Home Control of Appliances based on Timer and Speech Interaction

Abstract: The main objective of this work is to design and construct a microcomputer based system: to control electric appliances such as light, fan, heater, washing machine, motor, TV, etc. The paper discusses two major approaches to control home appliances. The first involves controlling home appliances using timer option. The second approach is to control home appliances using voice command. Moreover, it is also possible to control appliances using Graphical User Interface. The parallel port is used to transfer data from computer to the particular device to be controlled. An interface box is designed to connect the high power loads to the parallel port. This system will play an important role for the elderly and physically disable people to control their home appliances in intuitive and flexible way. We have developed a system, which is able to control eight electric appliances properly in these three modes.


Text Categorization using Association Rule and Naive Bayes Classifier

Abstract: As the amount of online text increases, the demand for text categorization to aid the analysis and management of text is increasing. Text is cheap, but information, in the form of knowing what classes a text belongs to, is expensive. Automatic categorization of text can provide this information at low cost, but the classifiers themselves must be built with expensive human effort, or trained from texts which have themselves been manually classified. Text categorization using Association Rule and Na\"ive Bayes Classifier is proposed here. Instead of using words word relation i.e association rules from these words is used to derive feature set from pre-classified text documents. Naive Bayes Classifier is then used on derived features for final categorization.


Demonstrating a Service-Enhanced Retrieval System

Abstract: This paper is a short description of an information retrieval system enhanced by three model driven retrieval services: (1) co-word analysis based query expansion, re-ranking via (2) Bradfordizing and (3) author centrality. The different services each favor quite other - but still relevant - documents than pure term-frequency based rankings. Each service can be interactively combined with each other to allow an iterative retrieval refinement.


On reverse-engineering the KUKA Robot Language

Abstract: Most commercial manufacturers of industrial robots require their robots to be programmed in a proprietary language tailored to the domain - a typical domain-specific language (DSL). However, these languages oftentimes suffer from shortcomings such as controller-specific design, limited expressiveness and a lack of extensibility. For that reason, we developed the extensible Robotics API for programming industrial robots on top of a general-purpose language. Although being a very flexible approach to programming industrial robots, a fully-fledged language can be too complex for simple tasks. Additionally, legacy support for code written in the original DSL has to be maintained. For these reasons, we present a lightweight implementation of a typical robotic DSL, the KUKA Robot Language (KRL), on top of our Robotics API. This work deals with the challenges in reverse-engineering the language and mapping its specifics to the Robotics API. We introduce two different approaches of interpreting and executing KRL programs: tree-based and bytecode-based interpretation.


Bulk File Download Throughput in a Single Station WLAN with Nonzero Propagation Delay

Abstract: We analyze TCP-controlled bulk file transfers in a single station (STA) WLAN with nonzero propagation delay between the file server and the WLAN. Our approach is to model the flow of packets as a closed queueing network (BCMP network) with 3 service centres, one each for the Access Point (AP) and the STA, and the third for the propagation delay. The service rates of the first two are obtained by analyzing the WLAN MAC. Simulations show a very close match with the theory.


The Complexity of Counting Eulerian Tours in 4-Regular Graphs

Abstract: We investigate the complexity of counting Eulerian tours ({\sc #ET}) and its variations from two perspectives---the complexity of exact counting and the complexity w.r.t. approximation-preserving reductions (AP-reductions \cite{MR2044886}). We prove that {\sc #ET} is #P-complete even for planar 4-regular graphs. A closely related problem is that of counting A-trails ({\sc #A-trails}) in graphs with rotational embedding schemes (so called maps). Kotzig \cite{MR0248043} showed that {\sc #A-trails} can be computed in polynomial time for 4-regular plane graphs (embedding in the plane is equivalent to giving a rotational embedding scheme). We show that for 4-regular maps the problem is #P-hard. Moreover, we show that from the approximation viewpoint {\sc #A-trails} in 4-regular maps captures the essence of {\sc #ET}, that is, we give an AP-reduction from {\sc #ET} in general graphs to {\sc #A-trails} in 4-regular maps. The reduction uses a fast mixing result for a card shuffling problem \cite{MR2023023}. In order to understand whether #{\sc A-trails} in 4-regular maps can AP-reduce to #{\sc ET} in 4-regular graphs, we investigate a problem in which transitions in vertices are weighted (this generalizes both #{\sc A-trails} and #{\sc ET}). In the 4-regular case we show that {\sc A-trails} can be used to simulate any vertex weights and provide evidence that {\sc ET} can simulate only a limited set of vertex weights.


On the complexity of the multiple stack TSP, kSTSP

Abstract: The multiple Stack Travelling Salesman Problem, STSP, deals with the collect and the deliverance of n commodities in two distinct cities. The two cities are represented by means of two edge-valued graphs (G1,d2) and (G2,d2). During the pick-up tour, the commodities are stored into a container whose rows are subject to LIFO constraints. As a generalisation of standard TSP, the problem obviously is NP-hard; nevertheless, one could wonder about what combinatorial structure of STSP does the most impact its complexity: the arrangement of the commodities into the container, or the tours themselves? The answer is not clear. First, given a pair (T1,T2) of pick-up and delivery tours, it is polynomial to decide whether these tours are or not compatible. Second, for a given arrangement of the commodities into the k rows of the container, the optimum pick-up and delivery tours w.r.t. this arrangement can be computed within a time that is polynomial in n, but exponential in k. Finally, we provide instances on which a tour that is optimum for one of three distances d1, d2 or d1+d2 lead to solutions of STSP that are arbitrarily far to the optimum STSP.


Approximability of the Multiple Stack TSP

Abstract: STSP seeks a pair of pickup and delivery tours in two distinct networks, where the two tours are related by LIFO contraints. We address here the problem approximability. We notably establish that asymmetric MaxSTSP and MinSTSP12 are APX, and propose a heuristic that yields to a 1/2, 3/4 and 3/2 standard approximation for respectively Max2STSP, Max2STSP12 and Min2STSP12.


A Genetic Algorithm for the Multi-Pickup and Delivery Problem with time windows

Abstract: In This paper we present a genetic algorithm for the multi-pickup and delivery problem with time windows (m-PDPTW). The m-PDPTW is an optimization vehicles routing problem which must meet requests for transport between suppliers and customers satisfying precedence, capacity and time constraints. This paper purposes a brief literature review of the PDPTW, present our approach based on genetic algorithms to minimizing the total travel distance and thereafter the total travel cost, by showing that an encoding represents the parameters of each individual.


Buyback Problem - Approximate matroid intersection with cancellation costs

Abstract: In the buyback problem, an algorithm observes a sequence of bids and must decide whether to accept each bid at the moment it arrives, subject to some constraints on the set of accepted bids. Decisions to reject bids are irrevocable, whereas decisions to accept bids may be canceled at a cost that is a fixed fraction of the bid value. Previous to our work, deterministic and randomized algorithms were known when the constraint is a matroid constraint. We extend this and give a deterministic algorithm for the case when the constraint is an intersection of $k$ matroid constraints. We further prove a matching lower bound on the competitive ratio for this problem and extend our results to arbitrary downward closed set systems. This problem has applications to banner advertisement, semi-streaming, routing, load balancing and other problems where preemption or cancellation of previous allocations is allowed.


The Most Advantageous Bangla Keyboard Layout Using Data Mining Technique

Abstract: Bangla alphabet has a large number of letters, for this it is complicated to type faster using Bangla keyboard. The proposed keyboard will maximize the speed of operator as they can type with both hands parallel. Association rule of data mining to distribute the Bangla characters in the keyboard is used here. The frequencies of data consisting of monograph, digraph and trigraph are analyzed, which are derived from data wire-house, and then used association rule of data mining to distribute the Bangla characters in the layout. Experimental results on several data show the effectiveness of the proposed approach with better performance. This paper presents an optimal Bangla Keyboard Layout, which distributes the load equally on both hands so that maximizing the ease and minimizing the effort.


Bus Protocols: MSC-Based Specifications and Translation into Program of Verification Tool for Formal Verification

Abstract: Message Sequence Charts (MSCs) are an appealing visual formalism mainly used in the early stages of system design to capture the system requirements. However, if we move towards an implementation, an executable specifications related in some fashion to the MSC-based requirements must be obtained. The MSCs can be used effectively to specify the bus protocol in the way where high-level transition systems is used to capture the control flow of the system components of the protocol and MSCs to describe the non-atomic component interactions. This system of specification is amenable to formal verification. In this paper, we present the way how we can specify the bus protocols using MSCs and how these specifications can be translated into program of verification tool (we have used Symbolic Model Verifier (SMV)) for the use of formal verification. We have contributed to the following tasks in this respect. Firstly, the way to specify the protocol using MSC has been presented. Secondly, a translator that translates the specifications (described in a textual input file) into SMV programs has been constructed. Finally, we have presented the verification result of the AMBA bus protocol using the SMV program found through the translation process. The SMV program found through the translation process can be used in order to automatically verify various properties of any bus protocol specified.


Modelling Variability for System Families

Abstract: In this paper, an approach to facilitate the treatment with variabilities in system families is presented by explicitly modelling variants. The proposed method of managing variability consists of a variant part, which models variants and a decision table to depict the customisation decision regarding each variant. We have found that it is easy to implement and has advantage over other methods. We present this model as an integral part of modelling system families.


Testing of Bridging Faults in AND-EXOR based Reversible Logic Circuits

Abstract: Reversible circuits find applications in many areas of Computer Science including Quantum Computation. This paper examines the testability of an important subclass of reversible logic circuits that are composed of k-wire controlled NOT (k-CNOT with k >/- 1) gates. A reversible k-CNOT gate can be implemented using an irreversible k-input AND gate and an EXOR gate. A reversible k-CNOT circuit where each k-CNOT gate is realized using irreversible k-input AND and EXOR gate, has been considered. One of the most commonly used Single Bridging Fault model (both wired-AND and wired-OR) has been assumed to be type of fault for such circuits. It has been shown that an (n+p)-input AND-EXOR based reversible logic circuit with p observable outputs, can be tested for single bridging faults (SBF) using (3n + \lefthalfcap log2p \righthalfcap + 2) tests.


Improving the Space-Bounded Version of Muchnik's Conditional Complexity Theorem via "Naive" Derandomization

Abstract: Many theorems about Kolmogorov complexity rely on existence of combinatorial objects with specific properties. Usually the probabilistic method gives such objects with better parameters than explicit constructions do. But the probabilistic method does not give "effective" variants of such theorems, i.e. variants for resource-bounded Kolmogorov complexity. We show that a "naive derandomization" approach of replacing these objects by the output of Nisan-Wigderson pseudo-random generator may give polynomial-space variants of such theorems. Specifically, we improve the preceding polynomial-space analogue of Muchnik's conditional complexity theorem. I.e., for all $a$ and $b$ there exists a program $p$ of least possible length that transforms $a$ to $b$ and is simple conditional on $b$. Here all programs work in polynomial space and all complexities are measured with logarithmic accuracy instead of polylogarithmic one in the previous work.


FAST: Kernelization based on Graph Modular Decomposition

Abstract: Kernelization algorithms, usually a preprocessing step before other more traditional algorithms, are very special in the sense that they return (reduced) instances, instead of final results. This characteristic excludes the freedom of applying a kernelization algorithm for the weighted version of a problem to its unweighted instances. Thus with only very few special cases, kernelization algorithms have to be studied separately for weigthed and unweighted versions of a single problem. {\sc feedback arc set on tournament} is currently a very popular problem in recent research of parameterized, as well as approximation computation, and its wide applications in many areas make it appear in all top conferences. The theory of graph modular decompositions is a general approach in the study of graph structures, which only had its surfaces touched in previous work on kernelization algorithms of {\sc feedback arc set on tournament}. In this paper, we study further properties of graph modular decompositions and apply them to obtain the first linear kernel for the unweighted {\sc feedback arc set on tournament} problem, which only admits linear kernel in its weighted version, while quadratic kernel for the unweighted.


Towards an incremental maintenance of cyclic association rules

Abstract: Recently, the cyclic association rules have been introduced in order to discover rules from items characterized by their regular variation over time. In real life situations, temporal databases are often appended or updated. Rescanning the whole database every time is highly expensive while existing incremental mining techniques can efficiently solve such a problem. In this paper, we propose an incremental algorithm for cyclic association rules maintenance. The carried out experiments of our proposal stress on its efficiency and performance.


Efficient Clustering with Limited Distance Information

Abstract: Given a point set S and an unknown metric d on S, we study the problem of efficiently partitioning S into k clusters while querying few distances between the points. In our model we assume that we have access to one versus all queries that given a point s in S return the distances between s and all other points. We show that given a natural assumption about the structure of the instance, we can efficiently find an accurate clustering using only O(k) distance queries. Our algorithm uses an active selection strategy to choose a small set of points that we call landmarks, and considers only the distances between landmarks and other points to produce a clustering. We use our algorithm to cluster proteins by sequence similarity. This setting nicely fits our model because we can use a fast sequence database search program to query a sequence against an entire dataset. We conduct an empirical study that shows that even though we query a small fraction of the distances between the points, we produce clusterings that are close to a desired clustering given by manual classification.


A Framework for an Ego-centered and Time-aware Visualization of Relations in Arbitrary Data Repositories

Abstract: Understanding constellations in large data collections has become a common task. One obstacle a user has to overcome is the internal complexity of these repositories. For example, extracting connected data from a normalized relational database requires knowledge of the table structure which might not be available for the casual user. In this paper we present a visualization framework which presents the collection as a set of entities and relations (on the data level). Using rating functions, we divide large relation networks into small graphs which resemble ego-centered networks. These graphs are connected so the user can browse from one to another. To further assist the user, we present two views which embed information on the evolution of the relations into the graphs. Each view emphasizes another aspect of temporal development. The framework can be adapted to any repository by a flexible data interface and a graph configuration file. We present some first web-based applications including a visualization of the DBLP data set. We use the DBLP visualization to evaluate our approach.


The complexity of linear-time temporal logic over the class of ordinals

Abstract: We consider the temporal logic with since and until modalities. This temporal logic is expressively equivalent over the class of ordinals to first-order logic by Kamp's theorem. We show that it has a PSPACE-complete satisfiability problem over the class of ordinals. Among the consequences of our proof, we show that given the code of some countable ordinal alpha and a formula, we can decide in PSPACE whether the formula has a model over alpha. In order to show these results, we introduce a class of simple ordinal automata, as expressive as B\"uchi ordinal automata. The PSPACE upper bound for the satisfiability problem of the temporal logic is obtained through a reduction to the nonemptiness problem for the simple ordinal automata.


The Straight-Line RAC Drawing Problem is NP-Hard

Abstract: Recent cognitive experiments have shown that the negative impact of an edge crossing on the human understanding of a graph drawing, tends to be eliminated in the case where the crossing angles are greater than 70 degrees. This motivated the study of RAC drawings, in which every pair of crossing edges intersects at right angle. In this work, we demonstrate a class of graphs with unique RAC combinatorial embedding and we employ members of this class in order to show that it is NP-hard to decide whether a graph admits a straight-line RAC drawing.


A Simple Abstraction for Data Modeling

Abstract: The problems that scientists face in creating well designed databases intersect with the concerns of data curation. Entity-relationship modeling and its variants have been the basis of most relational data modeling for decades. However, these abstractions and the relational model itself are intricate and have proved not to be very accessible among scientists with limited resources for data management. This paper explores one aspect of relational data models, the meaning of foreign key relationships. We observe that a foreign key produces a table relationship that generally references either an entity or repeating attributes. This paper proposes constructing foreign keys based on these two cases, and suggests that the method promotes intuitive data modeling and normalization.


General Scaled Support Vector Machines

Abstract: Support Vector Machines (SVMs) are popular tools for data mining tasks such as classification, regression, and density estimation. However, original SVM (C-SVM) only considers local information of data points on or over the margin. Therefore, C-SVM loses robustness. To solve this problem, one approach is to translate (i.e., to move without rotation or change of shape) the hyperplane according to the distribution of the entire data. But existing work can only be applied for 1-D case. In this paper, we propose a simple and efficient method called General Scaled SVM (GS-SVM) to extend the existing approach to multi-dimensional case. Our method translates the hyperplane according to the distribution of data projected on the normal vector of the hyperplane. Compared with C-SVM, GS-SVM has better performance on several data sets.


Measuring Similarity of Graphs and their Nodes by Neighbor Matching

Abstract: The problem of measuring similarity of graphs and their nodes is important in a range of practical problems. There is a number of proposed measures, some of them being based on iterative calculation of similarity between two graphs and the principle that two nodes are as similar as their neighbors are. In our work, we propose one novel method of that sort, with a refined concept of similarity of two nodes that involves matching of their neighbors. We prove convergence of the proposed method and show that it has some additional desirable properties that, to our knowledge, the existing methods lack. We illustrate the method on two specific problems and empirically compare it to other methods.


Application Delay Modelling for Variable Length Packets in Single Cell IEEE 802.11 WLANs

Abstract: In this paper, we consider the problem of modelling the average delay experienced by an application packets of variable length in a single cell IEEE 802.11 DCF wireless local area network. The packet arrival process at each node i is assumed to be a stationary and independent increment random process with mean ai and second moment a(2) i . The packet lengths at node i are assumed to be i.i.d random variables Pi with finite mean and second moment. A closed form expression has been derived for the same. We assume the input arrival process across queues to be uncorrelated Poison processes. As the nodes share a single channel, they have to contend with one another for a successful transmission. The mean delay for a packet has been approximated by modelling the system as a 1-limited Random Polling system with zero switchover times. Extensive simulations are conducted to verify the analytical results.


Vast Educational Mobile Content Broadcasting using ARMrayan Multimedia Mobile CMS

Abstract: The huge information flow currently available to young generation makes it difficult for educational centers to train them as needed. Most of these information flows occur in transportation time or while on public areas. Competing with commercial information streams is far out of educational centers time and budget. For creating enough mobile applications for vast educational mobile content broadcasting that can match young spirits as well, we designed and developed the ARMrayan Multimedia Mobile CMS as the software that helps communities, educational, cultural or marketing centers in a way that ordinary operators be able to create a variety of fully functional multimedia mobile applications such as tutorials, catalogues, books, and guides in minutes without writing even a line of code. In this paper, we present the role of our developed software in our proposed vast educational content broadcasting system using kiosks and Bluetooth advertising, which will lead to a great leap in M-commerce marketing and public education solutions. Related experiences are described and diagrams are used to illustrate the solution. Upon release of the software, it achieved two titles and prizes in different festivals and various cultural and commercial centers became its customers.


Canonizable Partial Order Generators and Regular Slice Languages

Abstract: In a previous work we introduced slice graphs as a way to specify both infinite languages of directed acyclic graphs (DAGs) and infinite languages of partial orders. Therein we focused on the study of Hasse diagram generators, i.e., slice graphs that generate only transitive reduced DAGs, and showed that they could be used to solve several problems related to the partial order behavior of p/t-nets. In the present work we show that both slice graphs and Hasse diagram generators are worth studying on their own. First, we prove that any slice graph SG can be effectively transformed into a Hasse diagram generator HG representing the same set of partial orders. Thus from an algorithmic standpoint we introduce a method of transitive reducing infinite families of DAGs specified by slice graphs. Second, we identify the class of saturated slice graphs. By using our transitive reduction algorithm, we prove that the class of partial order languages representable by saturated slice graphs is closed under union, intersection and even under a suitable notion of complementation (cut-width complementation). Furthermore partial order languages belonging to this class can be tested for inclusion and admit canonical representatives in terms of Hasse diagram generators. As an application of our results, we give stronger forms of some results in our previous work, and establish some unknown connections between the partial order behavior of $p/t$-nets and other well known formalisms for the specification of infinite families of partial orders, such as Mazurkiewicz trace languages and message sequence chart (MSC) languages.


A Novel Approach for Cardiac Disease Prediction and Classification Using Intelligent Agents

Abstract: The goal is to develop a novel approach for cardiac disease prediction and diagnosis using intelligent agents. Initially the symptoms are preprocessed using filter and wrapper based agents. The filter removes the missing or irrelevant symptoms. Wrapper is used to extract the data in the data set according to the threshold limits. Dependency of each symptom is identified using dependency checker agent. The classification is based on the prior and posterior probability of the symptoms with the evidence value. Finally the symptoms are classified in to five classes namely absence, starting, mild, moderate and serious. Using the cooperative approach the cardiac problem is solved and verified.


ARMrayan Multimedia Mobile CMS: a Simplified Approach towards Content-Oriented Mobile Application Designing

Abstract: The ARMrayan Multimedia Mobile CMS (Content Management System) is the first mobile CMS that gives the opportunity to users for creating multimedia J2ME mobile applications with their desired content, design and logo; simply, without any need for writing even a line of code. The low-level programming and compatibility problems of the J2ME, along with UI designing difficulties, makes it hard for most people -even programmers- to broadcast their content to the widespread mobile phones used by nearly all people. This system provides user-friendly, PC-based tools for creating a tree index of pages and inserting multiple multimedia contents (e.g. sound, video and picture) in each page for creating a J2ME mobile application. The output is a stand-alone Java mobile application that has a user interface, shows texts and pictures and plays music and videos regardless of the type of devices used as long as the devices support the J2ME platform. Bitmap fonts have also been used thus Middle Eastern languages can be easily supported on all mobile phone devices. We omitted programming concepts for users in order to simplify multimedia content-oriented mobile application designing for use in educational, cultural or marketing centers. Ordinary operators can now create a variety of multimedia mobile applications such as tutorials, catalogues, books, and guides in minutes rather than months. Simplicity and power has been the goal of this CMS. In this paper, we present the software engineered-designed concepts of the ARMrayan MCMS along with the implementation challenges faces and solutions adapted.


Establishing a Multi-Thesauri-Scenario based on SKOS and Cross-Concordances

Abstract: This case study proposes a scenario with three topic-related thesauri, which have been connected with bilateral cross-concordances as part of a major terminology mapping initiative in the project KoMoHe (Mayr & Petras, 2008). The thesauri have already been or will be converted to SKOS and in order to not omit the relevant crosswalks, the mapping properties of SKOS will be used for modeling them adequately.


A Scenario-Based Mobile Application for Robot-Assisted Smart Digital Homes

Abstract: Smart homes are becoming more popular, as every day a new home appliance can be digitally controlled. Smart Digital Homes are using a server to make interaction with all the possible devices in one place, on a computer or webpage. In this paper we designed and implemented a mobile application using Windows Mobile platform that can connect to the controlling server of a Smart Home and grants the access to the Smart Home devices and robots everywhere possible. UML diagrams are presented to illustrate the application design process. Robots are also considered as devices that are able to interact to other object and devices. Scenarios are defined as a set of sequential actions to help manage different tasks all in one place. The mobile application can connect to the server using GPRS mobile internet and Short Message System (SMS). Interactive home map is also designed for easier status-checking and interacting with the devices using the mobile phones.


Portfolio Allocation for Bayesian Optimization

Abstract: Bayesian optimization with Gaussian processes has become an increasingly popular tool in the machine learning community. It is efficient and can be used when very little is known about the objective function, making it popular in expensive black-box optimization scenarios. It uses Bayesian methods to sample the objective efficiently using an acquisition function which incorporates the model's estimate of the objective and the uncertainty at any given point. However, there are several different parameterized acquisition functions in the literature, and it is often unclear which one to use. Instead of using a single acquisition function, we adopt a portfolio of acquisition functions governed by an online multi-armed bandit strategy. We propose several portfolio strategies, the best of which we call GP-Hedge, and show that this method outperforms the best individual acquisition function. We also provide a theoretical bound on the algorithm's performance.


The Need to Support of Data Flow Graph Visualization of Forensic Lucid Programs, Forensic Evidence, and their Evaluation by GIPSY

Abstract: Lucid programs are data-flow programs and can be visually represented as data flow graphs (DFGs) and composed visually. Forensic Lucid, a Lucid dialect, is a language to specify and reason about cyberforensic cases. It includes the encoding of the evidence (representing the context of evaluation) and the crime scene modeling in order to validate claims against the model and perform event reconstruction, potentially within large swaths of digital evidence. To aid investigators to model the scene and evaluate it, instead of typing a Forensic Lucid program, we propose to expand the design and implementation of the Lucid DFG programming onto Forensic Lucid case modeling and specification to enhance the usability of the language and the system and its behavior. We briefly discuss the related work on visual programming an DFG modeling in an attempt to define and select one approach or a composition of approaches for Forensic Lucid based on various criteria such as previous implementation, wide use, formal backing in terms of semantics and translation. In the end, we solicit the readers' constructive, opinions, feedback, comments, and recommendations within the context of this short discussion.


Determining All Maximum Uniquely Restricted Matching in Bipartite Graphs

Abstract: The approach mapping from a matching of bipartite graphs to digraphs has been successfully used for forcing set problem, in this paper, it is extended to uniquely restricted matching problem. We show to determine a uniquely restricted matching in a bipartite graph is equivalent to recognition a acyclic digraph. Based on these results, it proves that determine the bipartite graphs with all maximum matching are uniquely restricted is polynomial time. This answers an open question of Levit and Mandrescu(Discrete Applied Mathematics 132(2004) 163-164).


Priority Queues with Multiple Time Fingers

Abstract: A priority queue is presented that supports the operations insert and find-min in worst-case constant time, and delete and delete-min on element x in worst-case O(lg(min{w_x, q_x}+2)) time, where w_x (respectively q_x) is the number of elements inserted after x (respectively before x) and are still present at the time of the deletion of x. Our priority queue then has both the working-set and the queueish properties, and more strongly it satisfies these properties in the worst-case sense. We also define a new distribution-sensitive property---the time-finger property, which encapsulates and generalizes both the working-set and queueish properties, and present a priority queue that satisfies this property. In addition, we prove a strong implication that the working-set property is equivalent to the unified bound (which is the minimum per operation among the static finger, static optimality, and the working-set bounds). This latter result is of tremendous interest by itself as it had gone unnoticed since the introduction of such bounds by Sleater and Tarjan [JACM 1985]. Accordingly, our priority queue satisfies other distribution-sensitive properties as the static finger, static optimality, and the unified bound.


A Mobile Application for Smart House Remote Control System

Abstract: At the start of the second decade of 21th century, the time has come to make the Smart Houses a reality for regular use. The different parts of a Smart House are researched but there are still distances from an applicable system, using the modern technology. In this paper we present an overview of the Smart House subsystems necessary for controlling the house using a mobile application efficiently and securely. The sequence diagram of the mobile application connecting to the server application and also the use-cases possible are presented. The challenges faced in designing the mobile application and illustrating the updated house top plane view in that application, are discussed and solutions are adapted for it. Finally the designed mobile application was implemented and the important sections of it were described, such as the interactive house top view map which indicates the status of the devices using predefined icons. The facilities to manage the scheduled tasks and defined rules are also implemented in this mobile application that was developed for use in Windows Mobile platform. This application has the capability of connecting to the main server using GPRS mobile internet and SMS. This system is expected to be an important step towards a unified system structure that can be used efficiently in near future regular houses.


Second Moment Method on k-SAT: a General Framework

Abstract: We give a general framework implementing the Second Moment Method on k-SAT and discuss the conditions making the Second Moment Method work in this framework. As applications, we make the Second Moment Method work on boolean solutions and implicants. We extend this to the distributional model of k-SAT.


Clandestine Simulations in Cellular Automata

Abstract: This paper studies two kinds of simulation between cellular automata: simulations based on factor and simulations based on sub-automaton. We show that these two kinds of simulation behave in two opposite ways with respect to the complexity of attractors and factor subshifts. On the one hand, the factor simulation preserves the complexity of limits sets or column factors (the simulator CA must have a higher complexity than the simulated CA). On the other hand, we show that any CA is the sub-automaton of some CA with a simple limit set (NL-recognizable) and the sub-automaton of some CA with a simple column factor (finite type). As a corollary, we get intrinsically universal CA with simple limit sets or simple column factors. Hence we are able to 'hide' the simulation power of any CA under simple dynamical indicators.


Integer Point Sets Minimizing Average Pairwise L1-Distance: What is the Optimal Shape of a Town?

Abstract: An n-town, for a natural number n, is a group of n buildings, each occupying a distinct position on a 2-dimensional integer grid. If we measure the distance between two buildings along the axis-parallel street grid, then an n-town has optimal shape if the sum of all pairwise Manhattan distances is minimized. This problem has been studied for cities, i.e., the limiting case of very large n. For cities, it is known that the optimal shape can be described by a differential equation, for which no closed-form is known. We show that optimal n-towns can be computed in O(n^7.5) time. This is also practically useful, as it allows us to compute optimal solutions up to n=80.


One-Counter Stochastic Games

Abstract: We study the computational complexity of basic decision problems for one-counter simple stochastic games (OC-SSGs), under various objectives. OC-SSGs are 2-player turn-based stochastic games played on the transition graph of classic one-counter automata. We study primarily the termination objective, where the goal of one player is to maximize the probability of reaching counter value 0, while the other player wishes to avoid this. Partly motivated by the goal of understanding termination objectives, we also study certain "limit" and "long run average" reward objectives that are closely related to some well-studied objectives for stochastic games with rewards. Examples of problems we address include: does player 1 have a strategy to ensure that the counter eventually hits 0, i.e., terminates, almost surely, regardless of what player 2 does? Or that the liminf (or limsup) counter value equals infinity with a desired probability? Or that the long run average reward is >0 with desired probability? We show that the qualitative termination problem for OC-SSGs is in NP intersection coNP, and is in P-time for 1-player OC-SSGs, or equivalently for one-counter Markov Decision Processes (OC-MDPs). Moreover, we show that quantitative limit problems for OC-SSGs are in NP intersection coNP, and are in P-time for 1-player OC-MDPs. Both qualitative limit problems and qualitative termination problems for OC-SSGs are already at least as hard as Condon's quantitative decision problem for finite-state SSGs.


Defending the future: An MSc module in End User Computing Risk Management

Abstract: This paper describes the rationale, curriculum and subject matter of a new MSc module being taught on an MSc Finance and Information Management course at the University of Wales Institute in Cardiff. Academic research on spreadsheet risks now has some penetration in academic literature and there is a growing body of knowledge on the subjects of spreadsheet error, human factors, spreadsheet engineering, "best practice", spreadsheet risk management and various techniques used to mitigate spreadsheet errors. This new MSc module in End User Computing Risk Management is an attempt to pull all of this research and practitioner experience together to arm the next generation of finance spreadsheet champions with the relevant knowledge, techniques and critical perspective on an emerging discipline.


Changing User Attitudes to Reduce Spreadsheet Risk

Abstract: A business case study on how three simple guidelines: 1. Make it easy to check (and maintain) 2. Make it safe to use 3. Keep business logic out of code changed user attitudes and improved spreadsheet quality in a financial services organisation.


Spreadsheets - the Good, the Bad and the Downright Ugly

Abstract: Spreadsheets are ubiquitous, heavily relied on throughout vast swathes of finance, commerce, industry, academia and Government. They are also acknowledged to be extraordinarily and unacceptably prone to error. If these two points are accepted, it has to follow that their uncontrolled use has the potential to inflict considerable damage. One approach to controlling such error should be to define as "good practice" a set of characteristics that a spreadsheet must possess and as "bad practice" another set that it must avoid. Defining such characteristics should, in principle, perfectly do-able. However, being able to say with authority at a definite moment that any particular spreadsheet complies with these characteristics is very much more difficult. The author asserts that the use of automated spreadsheet development could markedly help in ensuring and demonstrating such compliance.


Monitoring wild animal communities with arrays of motion sensitive camera traps

Abstract: Studying animal movement and distribution is of critical importance to addressing environmental challenges including invasive species, infectious diseases, climate and land-use change. Motion sensitive camera traps offer a visual sensor to record the presence of a broad range of species providing location -specific information on movement and behavior. Modern digital camera traps that record video present new analytical opportunities, but also new data management challenges. This paper describes our experience with a terrestrial animal monitoring system at Barro Colorado Island, Panama. Our camera network captured the spatio-temporal dynamics of terrestrial bird and mammal activity at the site - data relevant to immediate science questions, and long-term conservation issues. We believe that the experience gained and lessons learned during our year long deployment and testing of the camera traps as well as the developed solutions are applicable to broader sensor network applications and are valuable for the advancement of the sensor network research. We suggest that the continued development of these hardware, software, and analytical tools, in concert, offer an exciting sensor-network solution to monitoring of animal populations which could realistically scale over larger areas and time spans.


A secure email login system using virtual password

Abstract: In today's world password compromise by some adversaries is common for different purpose. In ICC 2008 Lei et al. proposed a new user authentication system based on the virtual password system. In virtual password system they have used linear randomized function to be secure against identity theft attacks, phishing attacks, keylogging attack and shoulder surfing system. In ICC 2010 Li's given a security attack on the Lei's work. This paper gives modification on Lei's work to prevent the Li's attack with reducing the server overhead. This paper also discussed the problems with current password recovery system and gives the better approach.


Approximability of Capacitated Network Design

Abstract: In the {\em capacitated} survivable network design problem (Cap-SNDP), we are given an undirected multi-graph where each edge has a capacity and a cost. The goal is to find a minimum cost subset of edges that satisfies a given set of pairwise minimum-cut requirements. Unlike its classical special case of SNDP when all capacities are unit, the approximability of Cap-SNDP is not well understood; even in very restricted settings no known algorithm achieves a $o(m)$ approximation, where $m$ is the number of edges in the graph. In this paper, we obtain several new results and insights into the approximability of Cap-SNDP.


Face Detection with Effective Feature Extraction

Abstract: There is an abundant literature on face detection due to its important role in many vision applications. Since Viola and Jones proposed the first real-time AdaBoost based face detector, Haar-like features have been adopted as the method of choice for frontal face detection. In this work, we show that simple features other than Haar-like features can also be applied for training an effective face detector. Since, single feature is not discriminative enough to separate faces from difficult non-faces, we further improve the generalization performance of our simple features by introducing feature co-occurrences. We demonstrate that our proposed features yield a performance improvement compared to Haar-like features. In addition, our findings indicate that features play a crucial role in the ability of the system to generalize.


Approximate Maximum A Posteriori Inference with Entropic Priors

Abstract: In certain applications it is useful to fit multinomial distributions to observed data with a penalty term that encourages sparsity. For example, in probabilistic latent audio source decomposition one may wish to encode the assumption that only a few latent sources are active at any given time. The standard heuristic of applying an L1 penalty is not an option when fitting the parameters to a multinomial distribution, which are constrained to sum to 1. An alternative is to use a penalty term that encourages low-entropy solutions, which corresponds to maximum a posteriori (MAP) parameter estimation with an entropic prior. The lack of conjugacy between the entropic prior and the multinomial distribution complicates this approach. In this report I propose a simple iterative algorithm for MAP estimation of multinomial distributions with sparsity-inducing entropic priors.


Fast Reinforcement Learning for Energy-Efficient Wireless Communications

Abstract: We consider the problem of energy-efficient point-to-point transmission of delay-sensitive data (e.g. multimedia data) over a fading channel. Existing research on this topic utilizes either physical-layer centric solutions, namely power-control and adaptive modulation and coding (AMC), or system-level solutions based on dynamic power management (DPM); however, there is currently no rigorous and unified framework for simultaneously utilizing both physical-layer centric and system-level techniques to achieve the minimum possible energy consumption, under delay constraints, in the presence of stochastic and a priori unknown traffic and channel conditions. In this report, we propose such a framework. We formulate the stochastic optimization problem as a Markov decision process (MDP) and solve it online using reinforcement learning. The advantages of the proposed online method are that (i) it does not require a priori knowledge of the traffic arrival and channel statistics to determine the jointly optimal power-control, AMC, and DPM policies; (ii) it exploits partial information about the system so that less information needs to be learned than when using conventional reinforcement learning algorithms; and (iii) it obviates the need for action exploration, which severely limits the adaptation speed and run-time performance of conventional reinforcement learning algorithms. Our results show that the proposed learning algorithms can converge up to two orders of magnitude faster than a state-of-the-art learning algorithm for physical layer power-control and up to three orders of magnitude faster than conventional reinforcement learning algorithms.


List Factoring and Relative Worst Order Analysis

Abstract: Relative worst order analysis is a supplement or alternative to competitive analysis which has been shown to give results more in accordance with observed behavior of online algorithms for a range of different online problems. The contribution of this paper is twofold. First, it adds the static list accessing problem to the collection of online problems where relative worst order analysis gives better results. Second, and maybe more interesting, it adds the non-trivial supplementary proof technique of list factoring to the theoretical toolbox for relative worst order analysis.


Fast Pseudo-Random Fingerprints

Abstract: We propose a method to exponentially speed up computation of various fingerprints, such as the ones used to compute similarity and rarity in massive data sets. Rather then maintaining the full stream of $b$ items of a universe $[u]$, such methods only maintain a concise fingerprint of the stream, and perform computations using the fingerprints. The computations are done approximately, and the required fingerprint size $k$ depends on the desired accuracy $\epsilon$ and confidence $\delta$. Our technique maintains a single bit per hash function, rather than a single integer, thus requiring a fingerprint of length $k = O(\frac{\ln \frac{1}{\delta}}{\epsilon^2})$ bits, rather than $O(\log u \cdot \frac{\ln \frac{1}{\delta}}{\epsilon^2})$ bits required by previous approaches. The main advantage of the fingerprints we propose is that rather than computing the fingerprint of a stream of $b$ items in time of $O(b \cdot k)$, we can compute it in time $O(b \log k)$. Thus this allows an exponential speedup for the fingerprint construction, or alternatively allows achieving a much higher accuracy while preserving computation time. Our methods rely on a specific family of pseudo-random hashes for which we can quickly locate hashes resulting in small values.


Synthesis of Binary k-Stage Machines

Abstract: An algorithm for constructing a shortest binary k-stage machine generating a given binary sequence is presented. This algorithm can be considered as an extension of Berlekamp-Massey algorithm to the non-linear case.


Simultaneous Event Execution in Heterogeneous Wireless Sensor Networks

Abstract: We present a synchronization algorithm to let nodes in a sensor network simultaneously execute a task at a given point in time. In contrast to other time synchronization algorithms we do not provide a global time basis that is shared on all nodes. Instead, any node in the network can spontaneously initiate a process that allows the simultaneous execution of arbitrary tasks. We show that our approach is beneficial in scenarios where a global time is not needed, as it requires little communication compared with other time synchronization algorithms. We also show that our algorithm works in heterogeneous systems where the hardware provides highly varying clock accuracy. Moreover, heterogeneity does not only affect the hardware, but also the communication channels. We deal with different connection types---from highly unreliable and fluctuating wireless channels to reliable and fast wired connections.


LRM-Trees: Compressed Indices, Adaptive Sorting, and Compressed Permutations

Abstract: LRM-Trees are an elegant way to partition a sequence of values into sorted consecutive blocks, and to express the relative position of the first element of each block within a previous block. They were used to encode ordinal trees and to index integer arrays in order to support range minimum queries on them. We describe how they yield many other convenient results in a variety of areas, from data structures to algorithms: some compressed succinct indices for range minimum queries; a new adaptive sorting algorithm; and a compressed succinct data structure for permutations supporting direct and indirect application in time all the shortest as the permutation is compressible.


Performance analysis of Xen virtual machines in real-world scenarios

Abstract: This paper presents results of the performance benchmarks of the Open Source hypervisor Xen. The study focuses on the network related performance as well as on the application related performance of multiple virtual machines that were running on the same Xen hypervisor. The comparison was carried out using a self-developed benchmark suite that consists of easily available Open Source tools. The goal is to measure the performance of the hypervisor in typical real-world application scenarios when used for "mass virtual hosting", such as hosting solutions of so called virtual private servers for small-to-medium sized businesses environments. The results of the benchmarks show, that the tested Xen setup offers good performance with respect to network traffic stress tests, but only 75% of the performance of the non-virtualized reference environment. This application performance score decreases as more virtual machines are running simultaneously.


Usability testing: a review of some methodological and technical aspects of the method

Abstract: The aim of this paper is to review some work conducted in the field of user testing that aims at specifying or clarifying the test procedures and at defining and developing tools to help conduct user tests. The topics that have been selected were considered relevant for evaluating applications in the field of medical and health care informatics. These topics are: the number of participants that should take part in a user test, the test procedure, remote usability evaluation, usability testing tools, and evaluating mobile applications.


The Attentive Perceptron

Abstract: We propose a focus of attention mechanism to speed up the Perceptron algorithm. Focus of attention speeds up the Perceptron algorithm by lowering the number of features evaluated throughout training and prediction. Whereas the traditional Perceptron evaluates all the features of each example, the Attentive Perceptron evaluates less features for easy to classify examples, thereby achieving significant speedups and small losses in prediction accuracy. Focus of attention allows the Attentive Perceptron to stop the evaluation of features at any interim point and filter the example. This creates an attentive filter which concentrates computation at examples that are hard to classify, and quickly filters examples that are easy to classify.


A proposed "osi based" network troubles identification model

Abstract: The OSI model, developed by ISO in 1984, attempts to summarize complicated network cases on layers. Moreover, network troubles are expressed by taking the model into account. However, there has been no standardization for network troubles up to now. Network troubles have only been expressed by the name of the related layer. In this paper, it is pointed out that possible troubles on the related layer vary and possible troubles on each layer are categorized for functional network administration and they are standardized in an eligible way. The proposed model for network trouble shooting was developed considering the OSI model.


A Low Overhead Reachability Guaranteed Dynamic Route Discovery Mechanism for Dense MANETs

Abstract: A crucial issue for a mobile ad hoc network is the handling of a large number of nodes. As more nodes join the mobile ad hoc network, contention and congestion are more likely. The on demand routing protocols which broadcasts control packets to discover routes to the destination nodes, generate a high number of broadcast packets in a larger networks causing contention and collision. We propose an efficient route discovery protocol, which reduces the number of broadcast packet, using controlled flooding technique. The simulation results show that the proposed probabilistic flooding decreases the number of control packets floating in the network during route discovery phase, without lowering the success ratio of path discoveries. Furthermore, the proposed method adapts to the normal network conditions. The results show that up to 70% of control packet traffic is saved in route discovery phase when the network is denser.


A Fair and Efficient Packet Scheduling Scheme for IEEE 802.16 Broadband Wireless Access Systems

Abstract: This paper proposes a fair and efficient QoS scheduling scheme for IEEE 802.16 BWA systems that satisfies both throughput and delay guarantee to various real and non-real time applications. The proposed QoS scheduling scheme is compared with an existing QoS scheduling scheme proposed in literature in recent past. Simulation results show that the proposed scheduling scheme can provide a tight QoS guarantee in terms of delay, delay violation rate and throughput for all types of traffic as defined in the WiMAX standard, thereby maintaining the fairness and helps to eliminate starvation of lower priority class services. Bandwidth utilization of the system and fairness index of the resources are also encountered to validate the QoS provided by our proposed scheduling scheme.


Sensor Activation and Radius Adaptation (SARA) in Heterogeneous Sensor Networks

Abstract: In this paper we address the problem of prolonging the lifetime of wireless sensor networks (WSNs) deployed to monitor an area of interest. In this scenario, a helpful approach is to reduce coverage redundancy and therefore the energy expenditure due to coverage. We introduce the first algorithm which reduces coverage redundancy by means of Sensor Activation and sensing Radius Adaptation (SARA)in a general applicative scenario with two classes of devices: sensors that can adapt their sensing range (adjustable sensors) and sensors that cannot (fixed sensors). In particular, SARA activates only a subset of all the available sensors and reduces the sensing range of the adjustable sensors that have been activated. In doing so, SARA also takes possible heterogeneous coverage capabilities of sensors belonging to the same class into account. It specifically addresses device heterogeneity by modeling the coverage problem in the Laguerre geometry through Voronoi-Laguerre diagrams. SARA executes quickly and is guaranteed to terminate. It provides a configuration of the active set of sensors that lifetime and coverage requirements of demanding WSN applications, not met by current solutions. By means of extensive simulations we show that SARA achieves a network lifetime that is significantly superior to that obtained by previous algorithms in all the considered scenarios.


The Baire partial quasi-metric space: A mathematical tool for asymptotic complexity analysis in Computer Science

Abstract: In 1994, S.G. Matthews introduced the notion of partial metric space in order to obtain a suitable mathematical tool for program verification [Ann. New York Acad. Sci. 728 (1994), 183-197]. He gave an application of this new structure to parallel computing by means of a partial metric version of the celebrated Banach fixed point theorem [Theoret. Comput. Sci. 151 (1995), 195-205]. Later on, M.P. Schellekens introduced the theory of complexity (quasi-metric) spaces as a part of the development of a topological foundation for the asymptotic complexity analysis of programs and algorithms [Elec- tronic Notes in Theoret. Comput. Sci. 1 (1995), 211-232]. The applicability of this theory to the asymptotic complexity analysis of Divide and Conquer algorithms was also illustrated by Schellekens. In particular, he gave a new proof, based on the use of the aforenamed Banach fixed point theorem, of the well-known fact that Mergesort al- gorithm has optimal asymptotic average running time of computing. In this paper, motivated by the utility of partial metrics in Computer Science, we discuss whether the Matthews fixed point theorem is a suitable tool to analyze the asymptotic complexity of algorithms in the spirit of Schellekens. Specifically, we show that a slight modification of the well-known Baire partial metric on the set of all words over an alphabet constitutes an appropriate tool to carry out the asymptotic complexity analysis of algorithms via fixed point methods without the need for assuming the convergence condition inherent to the defini- tion of the complexity space in the Shellekens framework. Finally, in order to illustrate and to validate the developed theory we apply our results to analyze the asymptotic complexity of Quicksort, Mergesort and Largesort algorithms.


Exact Analysis of Pattern Matching Algorithms with Probabilistic Arithmetic Automata

Abstract: We propose a framework for the exact probabilistic analysis of window-based pattern matching algorithms, such as Boyer-Moore, Horspool, Backward DAWG Matching, Backward Oracle Matching, and more. In particular, we show how to efficiently obtain the distribution of such an algorithm's running time cost for any given pattern in a random text model, which can be quite general, from simple uniform models to higher-order Markov models or hidden Markov models (HMMs). Furthermore, we provide a technique to compute the exact distribution of \emph{differences} in running time cost of two algorithms. In contrast to previous work, our approach is neither limited to simple text models, nor to asymptotic statements, nor to moment computations such as expectation and variance. Methodically, we use extensions of finite automata which we call deterministic arithmetic automata (DAAs) and probabilistic arithmetic automata (PAAs) [13]. To our knowledge, this is the first time that substring- or suffix-based pattern matching algorithms are analyzed exactly. Experimentally, we compare Horspool's algorithm, Backward DAWG Matching, and Backward Oracle Matching on prototypical patterns of short length and provide statistics on the size of minimal DAAs for these computations.


A Comprehensive Survey of Data Mining-based Fraud Detection Research

Abstract: This survey paper categorises, compares, and summarises from almost all published technical and review articles in automated fraud detection within the last 10 years. It defines the professional fraudster, formalises the main types and subtypes of known fraud, and presents the nature of data evidence collected within affected industries. Within the business context of mining the data to achieve higher cost savings, this research presents methods and techniques together with their problems. Compared to all related reviews on fraud detection, this survey covers much more technical articles and is the only one, to the best of our knowledge, which proposes alternative data and solutions from related domains.


Efficient Knowledge Base Management in DCSP

Abstract: DCSP (Distributed Constraint Satisfaction Problem) has been a very important research area in AI (Artificial Intelligence). There are many application problems in distributed AI that can be formalized as DSCPs. With the increasing complexity and problem size of the application problems in AI, the required storage place in searching and the average searching time are increasing too. Thus, to use a limited storage place efficiently in solving DCSP becomes a very important problem, and it can help to reduce searching time as well. This paper provides an efficient knowledge base management approach based on general usage of hyper-resolution-rule in consistence algorithm. The approach minimizes the increasing of the knowledge base by eliminate sufficient constraint and false nogood. These eliminations do not change the completeness of the original knowledge base increased. The proofs are given as well. The example shows that this approach decrease both the new nogoods generated and the knowledge base greatly. Thus it decreases the required storage place and simplify the searching process.


Multi-standard programmable baseband modulator for next generation wireless communication

Abstract: Considerable research has taken place in recent times in the area of parameterization of software defined radio (SDR) architecture. Parameterization decreases the size of the software to be downloaded and also limits the hardware reconfiguration time. The present paper is based on the design and development of a programmable baseband modulator that perform the QPSK modulation schemes and as well as its other three commonly used variants to satisfy the requirement of several established 2G and 3G wireless communication standards. The proposed design has been shown to be capable of operating at a maximum data rate of 77 Mbps on Xilinx Virtex 2-Pro University field programmable gate array (FPGA) board. The pulse shaping root raised cosine (RRC) filter has been implemented using distributed arithmetic (DA) technique in the present work in order to reduce the computational complexity, and to achieve appropriate power reduction and enhanced throughput. The designed multiplier-less programmable 32-tap FIR-based RRC filter has been found to withstand a peak inter-symbol interference (ISI) distortion of -41 dBs


NetInf Mobile Node Architecture and Mobility Management based on LISP Mobile Node

Abstract: In this paper, we propose an architecture for Network of Information mobile node (NetInf MN). It bears characteristics and features of basic NetInf node architecture with features introduced in the LISP MN architecture. We also introduce a virtual node layer for mobility management in the Network of Information. Therefore, by adopting this architecture no major changes in the contemporary network topologies is required. Thus, making our approach more practical.


Cut Elimination for a Logic with Induction and Co-induction

Abstract: Proof search has been used to specify a wide range of computation systems. In order to build a framework for reasoning about such specifications, we make use of a sequent calculus involving induction and co-induction. These proof principles are based on a proof theoretic (rather than set-theoretic) notion of definition. Definitions are akin to logic programs, where the left and right rules for defined atoms allow one to view theories as "closed" or defining fixed points. The use of definitions and free equality makes it possible to reason intentionally about syntax. We add in a consistent way rules for pre and post fixed points, thus allowing the user to reason inductively and co-inductively about properties of computational system making full use of higher-order abstract syntax. Consistency is guaranteed via cut-elimination, where we give the first, to our knowledge, cut-elimination procedure in the presence of general inductive and co-inductive definitions.


Heuristic approach to optimize the number of test cases for simple circuits

Abstract: In this paper a new solution is proposed for testing simple stwo stage electronic circuits. It minimizes the number of tests to be performed to determine the genuinity of the circuit. The main idea behind the present research work is to identify the maximum number of indistinguishable faults present in the given circuit and minimize the number of test cases based on the number of faults that has been detected. Heuristic approach is used for test minimization part, which identifies the essential tests from overall test cases. From the results it is observed that, test minimization varies from 50% to 99% with the lowest one corresponding to a circuit with four gates .Test minimization is low in case of circuits with lesser input leads in gates compared to greater input leads in gates for the boolean expression with same number of symbols. Achievement of 99% reduction is due to the fact that the large number of tests find the same faults. The new approach is implemented for simple circuits. The results show potential for both smaller test sets and lower cpu times.


How to Extract the Geometry and Topology from Very Large 3D Segmentations

Abstract: Segmentation is often an essential intermediate step in image analysis. A volume segmentation characterizes the underlying volume image in terms of geometric information--segments, faces between segments, curves in which several faces meet--as well as a topology on these objects. Existing algorithms encode this information in designated data structures, but require that these data structures fit entirely in Random Access Memory (RAM). Today, 3D images with several billion voxels are acquired, e.g. in structural neurobiology. Since these large volumes can no longer be processed with existing methods, we present a new algorithm which performs geometry and topology extraction with a runtime linear in the number of voxels and log-linear in the number of faces and curves. The parallelizable algorithm proceeds in a block-wise fashion and constructs a consistent representation of the entire volume image on the hard drive, making the structure of very large volume segmentations accessible to image analysis. The parallelized C++ source code, free command line tools and MATLAB mex files are avilable from http://hci.iwr.uni-heidelberg.de/software.php


Low Power Reversible Parallel Binary Adder/Subtractor

Abstract: In recent years, Reversible Logic is becoming more and more prominent technology having its applications in Low Power CMOS, Quantum Computing, Nanotechnology, and Optical Computing. Reversibility plays an important role when energy efficient computations are considered. In this paper, Reversible eight-bit Parallel Binary Adder/Subtractor with Design I, Design II and Design III are proposed. In all the three design approaches, the full Adder and Subtractors are realized in a single unit as compared to only full Subtractor in the existing design. The performance analysis is verified using number reversible gates, Garbage input/outputs and Quantum Cost. It is observed that Reversible eight-bit Parallel Binary Adder/Subtractor with Design III is efficient compared to Design I, Design II and existing design.


An Embarrassingly Simple Speed-Up of Belief Propagation with Robust Potentials

Abstract: We present an exact method of greatly speeding up belief propagation (BP) for a wide variety of potential functions in pairwise MRFs and other graphical models. Specifically, our technique applies whenever the pairwise potentials have been {\em truncated} to a constant value for most pairs of states, as is commonly done in MRF models with robust potentials (such as stereo) that impose an upper bound on the penalty assigned to discontinuities; for each of the $M$ possible states in one node, only a smaller number $m$ of compatible states in a neighboring node are assigned milder penalties. The computational complexity of our method is $O(mM)$, compared with $O(M^2)$ for standard BP, and we emphasize that the method is {\em exact}, in contrast with related techniques such as pruning; moreover, the method is very simple and easy to implement. Unlike some previous work on speeding up BP, our method applies both to sum-product and max-product BP, which makes it useful in any applications where marginal probabilities are required, such as maximum likelihood estimation. We demonstrate the technique on a stereo MRF example, confirming that the technique speeds up BP without altering the solution.


Mantis: Predicting System Performance through Program Analysis and Modeling

Abstract: We present Mantis, a new framework that automatically predicts program performance with high accuracy. Mantis integrates techniques from programming language and machine learning for performance modeling, and is a radical departure from traditional approaches. Mantis extracts program features, which are information about program execution runs, through program instrumentation. It uses machine learning techniques to select features relevant to performance and creates prediction models as a function of the selected features. Through program analysis, it then generates compact code slices that compute these feature values for prediction. Our evaluation shows that Mantis can achieve more than 93% accuracy with less than 10% training data set, which is a significant improvement over models that are oblivious to program features. The system generates code slices that are cheap to compute feature values.


A non-cooperative Pareto-efficient solution to a one-shot Prisoner's Dilemma

Abstract: The Prisoner's Dilemma is a simple model that captures the essential contradiction between individual rationality and global rationality. Although the one-shot Prisoner's Dilemma is usually viewed simple, in this paper we will categorize it into five different types. For the type-4 Prisoner's Dilemma game, we will propose a self-enforcing algorithmic model to help non-cooperative agents obtain Pareto-efficient payoffs. The algorithmic model is based on an algorithm using complex numbers and can work in macro applications.


Rule-based Generation of Diff Evolution Mappings between Ontology Versions

Abstract: Ontologies such as taxonomies, product catalogs or web directories are heavily used and hence evolve frequently to meet new requirements or to better reflect the current instance data of a domain. To effectively manage the evolution of ontologies it is essential to identify the difference (Diff) between two ontology versions. We propose a novel approach to determine an expressive and invertible diff evolution mapping between given versions of an ontology. Our approach utilizes the result of a match operation to determine an evolution mapping consisting of a set of basic change operations (insert/update/delete). To semantically enrich the evolution mapping we adopt a rule-based approach to transform the basic change operations into a smaller set of more complex change operations, such as merge, split, or changes of entire subgraphs. The proposed algorithm is customizable in different ways to meet the requirements of diverse ontologies and application scenarios. We evaluate the proposed approach by determining and analyzing evolution mappings for real-world life science ontologies and web directories.


L1 Projections with Box Constraints

Abstract: We study the L1 minimization problem with additional box constraints. We motivate the problem with two different views of optimality considerations. We look into imposing such constraints in projected gradient techniques and propose a worst case linear time algorithm to perform such projections. We demonstrate the merits and effectiveness of our algorithms on synthetic as well as real experiments.


Multi-Agent Programming Contest 2010 - The Jason-DTU Team

Abstract: We provide a brief description of the Jason-DTU system, including the methodology, the tools and the team strategy that we plan to use in the agent contest.


Implementing Lego Agents Using Jason

Abstract: Since many of the currently available multi-agent frameworks are generally mostly intended for research, it can be difficult to built multi-agent systems using physical robots. In this report I describe a way to combine the multi-agent framework Jason, an extended version of the agent-oriented programming language AgentSpeak, with Lego robots to address this problem. By extending parts of the Jason reasoning cycle I show how Lego robots are able to complete tasks such as following lines on a floor and communicating to be able to avoid obstacles with minimal amount of coding. The final implementation is a functional extension that is able to built multi-agent systems using Lego agents, however there are some issues that have not been addressed. If the agents are highly dependent on percepts from their sensors, they are required to move quite slowly, because there currently is a high delay in the reasoning cycle, when it is combined with a robot. Overall the system is quite robust and can be used to make simple Lego robots perform tasks of an advanced agent in a multi-agent environment.


An Investigation of the Advantages of Organization-Centered Multi-Agent Systems

Abstract: Whereas classical multi-agent systems have the agent in center, there have recently been a development towards focusing more on the organization of the system. This allows the designer to focus on what the system goals are, without considering how the goals should be fulfilled. This paper investigates whether taking this approach has any clear advantages to the classical way of implementing multi-agent systems. The investigation is done by implementing each type of system in the same environment in order to realize what advantages and disadvantages each approach has.


Comparative Performance of Tabu Search and Simulated Annealing Heuristics for the Quadratic Assignment Problem

Abstract: For almost two decades the question of whether tabu search (TS) or simulated annealing (SA) performs better for the quadratic assignment problem has been unresolved. To answer this question satisfactorily, we compare performance at various values of targeted solution quality, running each heuristic at its optimal number of iterations for each target. We find that for a number of varied problem instances, SA performs better for higher quality targets while TS performs better for lower quality targets.


Survey of trust models in different network domains

Abstract: This paper introduces the security and trust concepts in wireless sensor networks and explains the difference between them, stating that even though both terms are used interchangeably when defining a secure system, they are not the same. The difference between reputation and trust is also explained, highlighting that reputation partially affects trust. A survey of trust and reputation systems in various domains is conducted, with more details given to models in ad-hoc and sensor networks as they are closely related to each other and to our research interests. The methodologies used to model trust and their references are presented. The factors affecting trust updating are summarised and some examples of the systems in which these factors have been implemented are given. The survey states that, even though researchers have started to explore the issue of trust in wireless sensor networks, they are still examining the trust associated with routing messages between nodes (binary events). However, wireless sensor networks are mainly deployed to monitor events and report data, both continuous and discrete. This leads to the development of new trust models addressing the continuous data issue and also to combine the data trust and the communication trust to infer the total trust.


A Novel and Highly Efficient AES Implementation Robust against Differential Power Analysis

Abstract: Developed by Paul Kocher, Joshua Jaffe, and Benjamin Jun in 1999, Differential Power Analysis (DPA) represents a unique and powerful cryptanalysis technique. Insight into the encryption and decryption behavior of a cryptographic device can be determined by examining its electrical power signature. This paper describes a novel approach for implementation of the AES algorithm which provides a significantly improved strength against differential power analysis with a minimal additional hardware overhead. Our method is based on randomization in composite field arithmetic which entails an area penalty of only 7% while does not decrease the working frequency, does not alter the algorithm and keeps perfect compatibility with the published standard. The efficiency of the proposed technique was verified by practical results obtained from real implementation on a Xilinx Spartan-II FPGA.


Review on the Advancements of DNA Cryptography

Abstract: Since security is one of the most important issues, the evolve of cryptography and cryptographic analysis are considered as the fields of on-going research. The latest development on this field is DNA cryptography. It has emerged after the disclosure of computational ability of Deoxyribo Nucleic Acid (DNA). DNA cryptography uses DNA as the computational tool along with several molecular techniques to manipulate it. Due to very high storage capacity of DNA, this field is becoming very promising. Currently it is in the development phase and it requires a lot of work and research to reach a mature stage. By reviewing all the potential and cutting edge technology of current research, this paper shows the directions that need to be addressed further in the field of DNA cryptography.


The complexity of global cardinality constraints

Abstract: In a constraint satisfaction problem (CSP) the goal is to find an assignment of a given set of variables subject to specified constraints. A global cardinality constraint is an additional requirement that prescribes how many variables must be assigned a certain value. We study the complexity of the problem CCSP(G), the constraint satisfaction problem with global cardinality constraints that allows only relations from the set G. The main result of this paper characterizes sets G that give rise to problems solvable in polynomial time, and states that the remaining such problems are NP-complete.


Characterizing perfect recall using next-step temporal operators in S5 and sub-S5 Epistemic Temporal Logic

Abstract: We review the notion of perfect recall in the literature on interpreted systems, game theory, and epistemic logic. In the context of Epistemic Temporal Logic (ETL), we give a (to our knowledge) novel frame condition for perfect recall, which is local and can straightforwardly be translated to a defining formula in a language that only has next-step temporal operators. This frame condition also gives rise to a complete axiomatization for S5 ETL frames with perfect recall. We then consider how to extend and consolidate the notion of perfect recall in sub-S5 settings, where the various notions discussed are no longer equivalent.


Queue-Aware Distributive Resource Control for Delay-Sensitive Two-Hop MIMO Cooperative Systems

Abstract: In this paper, we consider a queue-aware distributive resource control algorithm for two-hop MIMO cooperative systems. We shall illustrate that relay buffering is an effective way to reduce the intrinsic half-duplex penalty in cooperative systems. The complex interactions of the queues at the source node and the relays are modeled as an average-cost infinite horizon Markov Decision Process (MDP). The traditional approach solving this MDP problem involves centralized control with huge complexity. To obtain a distributive and low complexity solution, we introduce a linear structure which approximates the value function of the associated Bellman equation by the sum of per-node value functions. We derive a distributive two-stage two-winner auction-based control policy which is a function of the local CSI and local QSI only. Furthermore, to estimate the best fit approximation parameter, we propose a distributive online stochastic learning algorithm using stochastic approximation theory. Finally, we establish technical conditions for almost-sure convergence and show that under heavy traffic, the proposed low complexity distributive control is global optimal.


Steepest Ascent Hill Climbing For A Mathematical Problem

Abstract: The paper proposes artificial intelligence technique called hill climbing to find numerical solutions of Diophantine Equations. Such equations are important as they have many applications in fields like public key cryptography, integer factorization, algebraic curves, projective curves and data dependency in super computers. Importantly, it has been proved that there is no general method to find solutions of such equations. This paper is an attempt to find numerical solutions of Diophantine equations using steepest ascent version of Hill Climbing. The method, which uses tree representation to depict possible solutions of Diophantine equations, adopts a novel methodology to generate successors. The heuristic function used help to make the process of finding solution as a minimization process. The work illustrates the effectiveness of the proposed methodology using a class of Diophantine equations given by a1. x1 p1 + a2. x2 p2 + ...... + an . xn pn = N where ai and N are integers. The experimental results validate that the procedure proposed is successful in finding solutions of Diophantine Equations with sufficiently large powers and large number of variables.


A Microwave Imaging and Enhancement Technique from Noisy Synthetic Data

Abstract: An inverse iterative algorithm for microwave imaging based on moment method solution is presented here. The iterative scheme has been developed on constrained optimization technique and is certain to converge. Different mesh size for the model has been used here to overcome the Inverse Crime. The synthetic data at the receivers is contaminated with different percentage of noise. The ill-posedness of the problem is solved by Levenberg-Marquardt method. The algorithm is applied to synthetic data and the reconstructed image is then further enhanced through the Image enhancement technique


Routing Protocols for Cognitive Radio Networks: A Survey

Abstract: This article has been withdrawn by arXiv administrators because it plagiarises http://www2.ece.ohio-state.edu/~ekici/papers/crnroutingsurvey.pdf


Reflection-based language support for the heterogeneous capture and restoration of running computations

Abstract: This work is devoted to the study of the problem of user-level capture and restoration of running computations in heterogeneous environments. Support for those operations has traditionally been offered through ready-made solutions for specific applications, which are difficult to tailor or adapt to different needs. We believe that a more promising approach would be to build specific solutions as needed, over a more general framework for capture and restoration. In this work, in order to explore the basic mechanisms a language should provide to support the implementation of different policies, we extend the Lua programming language with an API that allows the programmer to reify the internal structures of execution into fine-grained language values.