Competition and Regulation in Wireless Services Markets

Abstract: We consider a wireless services market where a set of operators compete for a large common pool of users. The latter have a reservation utility of U0 units or, equivalently, an alternative option to satisfy their communication needs. The operators must satisfy these minimum requirements in order to attract the users. We model the users decisions and interaction as an evolutionary game and the competition among the operators as a non cooperative price game which is proved to be a potential game. For each set of prices selected by the operators, the evolutionary game attains a different stationary point. We show that the outcome of both games depend on the reservation utility of the users and the amount of spectrum W the operators have at their disposal. We express the market welfare and the revenue of the operators as functions of these two parameters. Accordingly, we consider the scenario where a regulating agency is able to intervene and change the outcome of the market by tuning W and/or U0. Different regulators may have different objectives and criteria according to which they intervene. We analyze the various possible regulation methods and discuss their requirements, implications and impact on the market.


A Mediated Definite Delegation Model allowing for Certified Grid Job Submission

Abstract: Grid computing infrastructures need to provide traceability and accounting of their users" activity and protection against misuse and privilege escalation. A central aspect of multi-user Grid job environments is the necessary delegation of privileges in the course of a job submission. With respect to these generic requirements this document describes an improved handling of multi-user Grid jobs in the ALICE ("A Large Ion Collider Experiment") Grid Services. A security analysis of the ALICE Grid job model is presented with derived security objectives, followed by a discussion of existing approaches of unrestricted delegation based on X.509 proxy certificates and the Grid middleware gLExec. Unrestricted delegation has severe security consequences and limitations, most importantly allowing for identity theft and forgery of delegated assignments. These limitations are discussed and formulated, both in general and with respect to an adoption in line with multi-user Grid jobs. Based on the architecture of the ALICE Grid Services, a new general model of mediated definite delegation is developed and formulated, allowing a broker to assign context-sensitive user privileges to agents. The model provides strong accountability and long- term traceability. A prototype implementation allowing for certified Grid jobs is presented including a potential interaction with gLExec. The achieved improvements regarding system security, malicious job exploitation, identity protection, and accountability are emphasized, followed by a discussion of non- repudiation in the face of malicious Grid jobs.


Creating a Live, Public Short Message Service Corpus: The NUS SMS Corpus

Abstract: Short Message Service (SMS) messages are largely sent directly from one person to another from their mobile phones. They represent a means of personal communication that is an important communicative artifact in our current digital era. As most existing studies have used private access to SMS corpora, comparative studies using the same raw SMS data has not been possible up to now. We describe our efforts to collect a public SMS corpus to address this problem. We use a battery of methodologies to collect the corpus, paying particular attention to privacy issues to address contributors' concerns. Our live project collects new SMS message submissions, checks their quality and adds the valid messages, releasing the resultant corpus as XML and as SQL dumps, along with corpus statistics, every month. We opportunistically collect as much metadata about the messages and their sender as possible, so as to enable different types of analyses. To date, we have collected about 60,000 messages, focusing on English and Mandarin Chinese.


Errors in Improved Polynomial Algorithm For 3 Sat Proposed By Narendra Chaudhari

Abstract: There are errors in the algorithm proposed by Narendra Chaudhari [2] purporting to solve the 3-sat problem in polynomial time. The present paper present instances for which the algorithm outputs erroneous results.


Secure Querying of Recursive XML Views: A Standard XPath-based Technique

Abstract: Most state-of-the art approaches for securing XML documents allow users to access data only through authorized views defined by annotating an XML grammar (e.g. DTD) with a collection of XPath expressions. To prevent improper disclosure of confidential information, user queries posed on these views need to be rewritten into equivalent queries on the underlying documents. This rewriting enables us to avoid the overhead of view materialization and maintenance. A major concern here is that query rewriting for recursive XML views is still an open problem. To overcome this problem, some works have been proposed to translate XPath queries into non-standard ones, called Regular XPath queries. However, query rewriting under Regular XPath can be of exponential size as it relies on automaton model. Most importantly, Regular XPath remains a theoretical achievement. Indeed, it is not commonly used in practice as translation and evaluation tools are not available. In this paper, we show that query rewriting is always possible for recursive XML views using only the expressive power of the standard XPath. We investigate the extension of the downward class of XPath, composed only by child and descendant axes, with some axes and operators and we propose a general approach to rewrite queries under recursive XML views. Unlike Regular XPath-based works, we provide a rewriting algorithm which processes the query only over the annotated DTD grammar and which can run in linear time in the size of the query. An experimental evaluation demonstrates that our algorithm is efficient and scales well.


The ViP2P Platform: XML Views in P2P

Abstract: The growing volumes of XML data sources on the Web or produced by enterprises, organizations etc. raise many performance challenges for data management applications. In this work, we are concerned with the distributed, peer-to-peer management of large corpora of XML documents, based on distributed hash table (or DHT, in short) overlay networks. We present ViP2P (standing for Views in Peer-to-Peer), a distributed platform for sharing XML documents based on a structured P2P network infrastructure (DHT). At the core of ViP2P stand distributed materialized XML views, defined by arbitrary XML queries, filled in with data published anywhere in the network, and exploited to efficiently answer queries issued by any network peer. ViP2P allows user queries to be evaluated over XML documents published by peers in two modes. First, a long-running subscription mode, when a query can be registered in the system and receive answers incrementally when and if published data matches the query. Second, queries can also be asked in an ad-hoc, snapshot mode, where results are required immediately and must be computed based on the results of other long-running, subscription queries. ViP2P innovates over other similar DHT-based XML sharing platforms by using a very expressive structured XML query language. This expressivity leads to a very flexible distribution of XML content in the ViP2P network, and to efficient snapshot query execution. ViP2P has been tested in real deployments of hundreds of computers. We present the platform architecture, its internal algorithms, and demonstrate its efficiency and scalability through a set of experiments. Our experimental results outgrow by orders of magnitude similar competitor systems in terms of data volumes, network size and data dissemination throughput.


Towards a Reference Model for Open Access and Knowledge Sharing, Lessons from Systems Research

Abstract: The Open Access Movement has been striving to grant universal unrestricted access to the knowledge and data outputs of publicly funded research. leveraging the real time, virtually cost free publishing opportunities offered by the internet and the web. However, evidence suggests that in the systems engineering domain open access policies are not widely adopted. This paper presents the rationale, methodology and results of an evidence based inquiry that investigates the dichotomy between policy and practice in Open Access (OA) of systems engineering research in the UK, explores entangled dimensions of the problem space from a socio-technical perspective, and issues a set of recommendations, including a reference model outline for knowledge sharing in systems research


Fast Hybrid PSO and Tabu Search Approach for Optimization of a Fuzzy Controller

Abstract: In this paper, a fuzzy controller type Takagi_Sugeno zero order is optimized by the method of hybrid Particle Swarm Optimization (PSO) and Tabu Search (TS). The algorithm automatically adjusts the membership functions of fuzzy controller inputs and the conclusions of fuzzy rules. At each iteration of PSO, we calculate the best solution and we seek the best neighbor by Tabu search, this operation minimizes the number of iterations and computation time while maintaining accuracy and minimum response time. We apply this algorithm to optimize a fuzzy controller for a simple inverted pendulum with three rules.


Threshold Choice Methods: the Missing Link

Abstract: Many performance metrics have been introduced for the evaluation of classification performance, with different origins and niches of application: accuracy, macro-accuracy, area under the ROC curve, the ROC convex hull, the absolute error, and the Brier score (with its decomposition into refinement and calibration). One way of understanding the relation among some of these metrics is the use of variable operating conditions (either in the form of misclassification costs or class proportions). Thus, a metric may correspond to some expected loss over a range of operating conditions. One dimension for the analysis has been precisely the distribution we take for this range of operating conditions, leading to some important connections in the area of proper scoring rules. However, we show that there is another dimension which has not received attention in the analysis of performance metrics. This new dimension is given by the decision rule, which is typically implemented as a threshold choice method when using scoring models. In this paper, we explore many old and new threshold choice methods: fixed, score-uniform, score-driven, rate-driven and optimal, among others. By calculating the loss of these methods for a uniform range of operating conditions we get the 0-1 loss, the absolute error, the Brier score (mean squared error), the AUC and the refinement loss respectively. This provides a comprehensive view of performance metrics as well as a systematic approach to loss minimisation, namely: take a model, apply several threshold choice methods consistent with the information which is (and will be) available about the operating condition, and compare their expected losses. In order to assist in this procedure we also derive several connections between the aforementioned performance metrics, and we highlight the role of calibration in choosing the threshold choice method.


X-pire! - A digital expiration date for images in social networks

Abstract: The Internet and its current information culture of preserving all kinds of data cause severe problems with privacy. Most of today's Internet users, especially teenagers, publish various kinds of sensitive information, yet without recognizing that revealing this information might be detrimental to their future life and career. Unflattering images that can be openly accessed now and in the future, e.g., by potential employers, constitute a particularly important such privacy concern. We have developed a novel, fast, and scalable system called X-pire! that allows users to set an expiration date for images in social networks (e.g., Facebook and Flickr) and on static websites, without requiring any form of additional interaction with these web pages. Once the expiration date is reached, the images become unavailable. Moreover, the publishing user can dynamically prolong or shorten the expiration dates of his images later, and even enforce instantaneous expiration. Rendering the approach possible for social networks crucially required us to develop a novel technique for embedding encrypted information within JPEG files in a way that survives JPEG compression, even for highly optimized implementations of JPEG post-processing with their various idiosyncrasies as commonly used in such networks. We have implemented our system and conducted performance measurements to demonstrate its robustness and efficiency.


Location- and Time-Dependent VPD for Privacy-Preserving Wireless Accesses to Cloud Services

Abstract: The advent of smartphones in recent years has changed the wireless landscape. Smartphones have become a platform for online user interface to cloud databases. Cloud databases may provide a large set of user-private and sensitive data (i.e., objects), while smartphone users (i.e., subjects) provide location-sensitive information. Secure and private services in wireless accessing to cloud databases have been discussed actively for the past recent years. However, the previous techniques are unsatisfactory for dynamism of moving subjects' wireless accesses. In this paper, we propose a novel technique to dynamically generate virtual private databases (VPD) for each access by taking subjects' location and time information into account. The contribution of this paper includes a privacy-preserving access control mechanism for dynamism of wireless access.


Customer Data Clustering using Data Mining Technique

Abstract: Classification and patterns extraction from customer data is very important for business support and decision making. Timely identification of newly emerging trends is very important in business process. Large companies are having huge volume of data but starving for knowledge. To overcome the organization current issue, the new breed of technique is required that has intelligence and capability to solve the knowledge scarcity and the technique is called Data mining. The objectives of this paper are to identify the high-profit, high-value and low-risk customers by one of the data mining technique - customer clustering. In the first phase, cleansing the data and developed the patterns via demographic clustering algorithm using IBM I-Miner. In the second phase, profiling the data, develop the clusters and identify the high-value low-risk customers. This cluster typically represents the 10-20 percent of customers which yields 80% of the revenue.


Inference in Probabilistic Logic Programs with Continuous Random Variables

Abstract: Probabilistic Logic Programming (PLP), exemplified by Sato and Kameya's PRISM, Poole's ICL, Raedt et al's ProbLog and Vennekens et al's LPAD, is aimed at combining statistical and logical knowledge representation and inference. A key characteristic of PLP frameworks is that they are conservative extensions to non-probabilistic logic programs which have been widely used for knowledge representation. PLP frameworks extend traditional logic programming semantics to a distribution semantics, where the semantics of a probabilistic logic program is given in terms of a distribution over possible models of the program. However, the inference techniques used in these works rely on enumerating sets of explanations for a query answer. Consequently, these languages permit very limited use of random variables with continuous distributions. In this paper, we present a symbolic inference procedure that uses constraints and represents sets of explanations without enumeration. This permits us to reason over PLPs with Gaussian or Gamma-distributed random variables (in addition to discrete-valued random variables) and linear equality constraints over reals. We develop the inference procedure in the context of PRISM; however the procedure's core ideas can be easily applied to other PLP languages as well. An interesting aspect of our inference procedure is that PRISM's query evaluation process becomes a special case in the absence of any continuous random variables in the program. The symbolic inference procedure enables us to reason over complex probabilistic models such as Kalman filters and a large subclass of Hybrid Bayesian networks that were hitherto not possible in PLP frameworks. (To appear in Theory and Practice of Logic Programming).


Employees Adoption of E-Procurement System: An Empirical Study

Abstract: Today, organizations are investing a lot in their IT infrastructure and reengineering their business processes by digitizing firms. If organizational employees will not optimum utilize its IT infrastructure, the productivity gain reduced enormously. In Uttarakhand e-procurement system implemented by public sector under e-governance integrated mission mode projects. So, there is need to find the determinants which influence employee's adoption and uses of e-procurement systems. This research study assesses the organizational and individual determinants that influence the use of e-procurement system in Uttarakhand public sector. This study provides managers with the valuable information to take intervention programs to achieve greater acceptance and usage of e-procurement system. Data collected for this study by the means of a survey conducted in Uttarakhand state in 2011. A total 1200 questionnaire forms were distributed personally and online to employees using e-procurement system in Uttarakhand.


Extended Combinatorial Constructions for Peer-to-peer User-Private Information Retrieval

Abstract: We consider user-private information retrieval (UPIR), an interesting alternative to private information retrieval (PIR) introduced by Domingo-Ferrer et al. In UPIR, the database knows which records have been retrieved, but does not know the identity of the query issuer. The goal of UPIR is to disguise user profiles from the database. Domingo-Ferrer et al.\ focus on using a peer-to-peer community to construct a UPIR scheme, which we term P2P UPIR. In this paper, we establish a strengthened model for P2P UPIR and clarify the privacy goals of such schemes using standard terminology from the field of privacy research. In particular, we argue that any solution providing privacy against the database should attempt to minimize any corresponding loss of privacy against other users. We give an analysis of existing schemes, including a new attack by the database. Finally, we introduce and analyze two new protocols. Whereas previous work focuses on a special type of combinatorial design known as a configuration, our protocols make use of more general designs. This allows for flexibility in protocol set-up, allowing for a choice between having a dynamic scheme (in which users are permitted to enter and leave the system), or providing increased privacy against other users.


Hybrid Heuristic-Based Artificial Immune System for Task Scheduling

Abstract: Task scheduling problem in heterogeneous systems is the process of allocating tasks of an application to heterogeneous processors interconnected by high-speed networks, so that minimizing the finishing time of application as much as possible. Tasks are processing units of application and have precedenceconstrained, communication and also, are presented by Directed Acyclic Graphs (DAGs). Evolutionary algorithms are well suited for solving task scheduling problem in heterogeneous environment. In this paper, we propose a hybrid heuristic-based Artificial Immune System (AIS) algorithm for solving the scheduling problem. In this regard, AIS with some heuristics and Single Neighbourhood Search (SNS) technique are hybridized. Clonning and immune-remove operators of AIS provide diversity, while heuristics and SNS provide convergence of algorithm into good solutions, that is balancing between exploration and exploitation. We have compared our method with some state-of-the art algorithms. The results of the experiments show the validity and efficiency of our method.


On the Treewidth of Dynamic Graphs

Abstract: Dynamic graph theory is a novel, growing area that deals with graphs that change over time and is of great utility in modelling modern wireless, mobile and dynamic environments. As a graph evolves, possibly arbitrarily, it is challenging to identify the graph properties that can be preserved over time and understand their respective computability. In this paper we are concerned with the treewidth of dynamic graphs. We focus on metatheorems, which allow the generation of a series of results based on general properties of classes of structures. In graph theory two major metatheorems on treewidth provide complexity classifications by employing structural graph measures and finite model theory. Courcelle's Theorem gives a general tractability result for problems expressible in monadic second order logic on graphs of bounded treewidth, and Frick & Grohe demonstrate a similar result for first order logic and graphs of bounded local treewidth. We extend these theorems by showing that dynamic graphs of bounded (local) treewidth where the length of time over which the graph evolves and is observed is finite and bounded can be modelled in such a way that the (local) treewidth of the underlying graph is maintained. We show the application of these results to problems in dynamic graph theory and dynamic extensions to static problems. In addition we demonstrate that certain widely used dynamic graph classes naturally have bounded local treewidth.


Design and Implementation of a Simple Web Search Engine

Abstract: We present a simple web search engine for indexing and searching html documents using python programming language. Because python is well known for its simple syntax and strong support for main operating systems, we hope it will be beneficial for learning information retrieval techniques, especially web search engine technology.


Steganography Algorithm to Hide Secret Message inside an Image

Abstract: In this paper, the authors propose a new algorithm to hide data inside image using steganography technique. The proposed algorithm uses binary codes and pixels inside an image. The zipped file is used before it is converted to binary codes to maximize the storage of data inside the image. By applying the proposed algorithm, a system called Steganography Imaging System (SIS) is developed. The system is then tested to see the viability of the proposed algorithm. Various sizes of data are stored inside the images and the PSNR (Peak signal-to-noise ratio) is also captured for each of the images tested. Based on the PSNR value of each images, the stego image has a higher PSNR value. Hence this new steganography algorithm is very efficient to hide the data inside the image.


A Temporal Approach to Stochastic Network Calculus

Abstract: Stochastic network calculus is a newly developed theory for stochastic service guarantee analysis of computer networks. In the current stochastic network calculus literature, its fundamental models are based on the cumulative amount of traffic or cumulative amount of service. However, there are network scenarios where direct application of such models is difficult. This paper presents a temporal approach to stochastic network calculus. The key idea is to develop models and derive results from the time perspective. Particularly, we define traffic models and service models based on the cumulative packet inter-arrival time and the cumulative packet service time, respectively. Relations among these models as well as with the existing models in the literature are established. In addition, we prove the basic properties of the proposed models, such as delay bound and backlog bound, output characterization, concatenation property and superposition property. These results form a temporal stochastic network calculus and compliment the existing results.


Joint Rate Selection and Wireless Network Coding for Time Critical Applications

Abstract: In this paper, we dynamically select the transmission rate and design wireless network coding to improve the quality of services such as delay for time critical applications. With low transmission rate, and hence longer transmission range, more packets may be encoded together, which increases the coding opportunity. However, low transmission rate may incur extra transmission delay, which is intolerable for time critical applications. We design a novel joint rate selection and wireless network coding (RSNC) scheme with delay constraint, so as to minimize the total number of packets that miss their deadlines at the destination nodes. We prove that the proposed problem is NPhard, and propose a novel graph model and transmission metric which consider both the heterogenous transmission rates and the packet deadline constraints during the graph construction. Using the graph model, we mathematically formulate the problem and design an efficient algorithm to determine the transmission rate and coding strategy for each transmission. Finally, simulation results demonstrate the superiority of the RSNC scheme.


An Extension of Parikh's Theorem beyond Idempotence

Abstract: The commutative ambiguity of a context-free grammar G assigns to each Parikh vector v the number of distinct leftmost derivations yielding a word with Parikh vector v. Based on the results on the generalization of Newton's method to omega-continuous semirings, we show how to approximate the commutative ambiguity by means of rational formal power series, and give a lower bound on the convergence speed of these approximations. From the latter result we deduce that the commutative ambiguity itself is rational modulo the generalized idempotence identity k=k+1 (for k some positive integer), and, subsequently, that it can be represented as a weighted sum of linear sets. This extends Parikh's well-known result that the commutative image of context-free languages is semilinear (k=1). Based on the well-known relationship between context-free grammars and algebraic systems over semirings, our results extend the work by Green et al. on the computation of the provenance of Datalog queries over commutative omega-continuous semirings.


Large Scale Correlation Clustering Optimization

Abstract: Clustering is a fundamental task in unsupervised learning. The focus of this paper is the Correlation Clustering functional which combines positive and negative affinities between the data points. The contribution of this paper is two fold: (i) Provide a theoretic analysis of the functional. (ii) New optimization algorithms which can cope with large scale problems (>100K variables) that are infeasible using existing methods. Our theoretic analysis provides a probabilistic generative interpretation for the functional, and justifies its intrinsic "model-selection" capability. Furthermore, we draw an analogy between optimizing this functional and the well known Potts energy minimization. This analogy allows us to suggest several new optimization algorithms, which exploit the intrinsic "model-selection" capability of the functional to automatically recover the underlying number of clusters. We compare our algorithms to existing methods on both synthetic and real data. In addition we suggest two new applications that are made possible by our algorithms: unsupervised face identification and interactive multi-object segmentation by rough boundary delineation.


Multiple Traveling Salesmen in Asymmetric Metrics

Abstract: We consider some generalizations of the Asymmetric Traveling Salesman Path problem. Suppose we have an asymmetric metric G = (V,A) with two distinguished nodes s,t. We are also given a positive integer k. The goal is to find k paths of minimum total cost from s to t whose union spans all nodes. We call this the k-Person Asymmetric Traveling Salesmen Path problem (k-ATSPP). Our main result for k-ATSPP is a bicriteria approximation that, for some parameter b >= 1 we may choose, finds between k and k + k/b paths of total length O(b log |V|) times the optimum value of an LP relaxation based on the Held-Karp relaxation for the Traveling Salesman problem. On one extreme this is an O(log |V|)-approximation that uses up to 2k paths and on the other it is an O(k log |V|)-approximation that uses exactly k paths. Next, we consider the case where we have k pairs of nodes (s_1,t_1), ..., (s_k,t_k). The goal is to find an s_i-t_i path for every pair such that each node of G lies on at least one of these paths. Simple approximation algorithms are presented for the special cases where the metric is symmetric or where s_i = t_i for each i. We also show that the problem can be approximated within a factor O(log n) when k=2. On the other hand, we demonstrate that the general problem cannot be approximated within any bounded ratio unless P = NP.


Deriving a Hoare-Floyd logic for non-local jumps from a formulae-as-types notion of control

Abstract: We derive a Hoare-Floyd logic for non-local jumps and mutable higher-order procedural variables from a formul{\ae}-as-types notion of control for classical logic. The main contribution of this work is the design of an imperative dependent type system for non-local jumps which corresponds to classical logic but where the famous consequence rule is still derivable.


Synthesis of Spherical 4R Mechanism for Path Generation using Differential Evolution

Abstract: The problem of path generation for the spherical 4R mechanism is solved using the Differential Evolution algorithm (DE). Formulas for the spherical geodesics are employed in order to obtain the parametric equation for the generated trajectory. Direct optimization of the objective function gives the solution to the path generation task without prescribed timing. Therefore, there is no need to separate this task into two stages to make the optimization. Moreover, the order defect problem can be solved without difficulty by means of manipulations of the individuals in the DE algorithm. Two examples of optimum synthesis showing the simplicity and effectiveness of this approach are included.


Constraint Satisfaction with Counting Quantifiers

Abstract: We initiate the study of constraint satisfaction problems (CSPs) in the presence of counting quantifiers, which may be seen as variants of CSPs in the mould of quantified CSPs (QCSPs). We show that a single counting quantifier strictly between exists^1:=exists and exists^n:=forall (the domain being of size n) already affords the maximal possible complexity of QCSPs (which have both exists and forall), being Pspace-complete for a suitably chosen template. Next, we focus on the complexity of subsets of counting quantifiers on clique and cycle templates. For cycles we give a full trichotomy -- all such problems are in L, NP-complete or Pspace-complete. For cliques we come close to a similar trichotomy, but one case remains outstanding. Afterwards, we consider the generalisation of CSPs in which we augment the extant quantifier exists^1:=exists with the quantifier exists^j (j not 1). Such a CSP is already NP-hard on non-bipartite graph templates. We explore the situation of this generalised CSP on bipartite templates, giving various conditions for both tractability and hardness -- culminating in a classification theorem for general graphs. Finally, we use counting quantifiers to solve the complexity of a concrete QCSP whose complexity was previously open.


Supervised Generative Reconstruction: An Efficient Way To Flexibly Store and Recognize Patterns

Abstract: Matching animal-like flexibility in recognition and the ability to quickly incorporate new information remains difficult. Limits are yet to be adequately addressed in neural models and recognition algorithms. This work proposes a configuration for recognition that maintains the same function of conventional algorithms but avoids combinatorial problems. Feedforward recognition algorithms such as classical artificial neural networks and machine learning algorithms are known to be subject to catastrophic interference and forgetting. Modifying or learning new information (associations between patterns and labels) causes loss of previously learned information. I demonstrate using mathematical analysis how supervised generative models, with feedforward and feedback connections, can emulate feedforward algorithms yet avoid catastrophic interference and forgetting. Learned information in generative models is stored in a more intuitive form that represents the fixed points or solutions of the network and moreover displays similar difficulties as cognitive phenomena. Brain-like capabilities and limits associated with generative models suggest the brain may perform recognition and store information using a similar approach. Because of the central role of recognition, progress understanding the underlying principles may reveal significant insight on how to better study and integrate with the brain.


Open Source CRM Systems for SMEs

Abstract: Customer Relationship Management (CRM) systems are very common in large companies. However, CRM systems are not very common in Small and Medium Enterprises (SMEs). Most SMEs do not implement CRM systems due to several reasons, such as lack of knowledge about CRM or lack of financial resources to implement CRM systems. SMEs have to start implementing Information Systems (IS) technology into their business operations in order to improve business values and gain more competitive advantage over rivals. CRM system has the potential to help improve the business value and competitive capabilities of SMEs. Given the high fixed costs of normal activity of companies, we intend to promote free and viable solutions for small and medium businesses. In this paper, we explain the reasons why SMEs do not implement CRM system and the benefits of using open source CRM system in SMEs. We also describe the functionalities of top open source CRM systems, examining the applicability of these tools in fitting the needs of SMEs.


Estimation of the length of interactions in arena game semantics

Abstract: We estimate the maximal length of interactions between strategies in HO/N game semantics, in the spirit of the work by Schwichtenberg and Beckmann for the length of reduction in simply typed lambdacalculus. Because of the operational content of game semantics, the bounds presented here also apply to head linear reduction on lambda-terms and to the execution of programs by abstract machines (PAM/KAM), including in presence of computational effects such as non-determinism or ground type references. The proof proceeds by extracting from the games model a combinatorial rewriting rule on trees of natural numbers, which can then be analyzed independently of game semantics or lambda-calculus.


Using Provenance to support Good Laboratory Practice in Grid Environments

Abstract: Conducting experiments and documenting results is daily business of scientists. Good and traceable documentation enables other scientists to confirm procedures and results for increased credibility. Documentation and scientific conduct are regulated and termed as "good laboratory practice." Laboratory notebooks are used to record each step in conducting an experiment and processing data. Originally, these notebooks were paper based. Due to computerised research systems, acquired data became more elaborate, thus increasing the need for electronic notebooks with data storage, computational features and reliable electronic documentation. As a new approach to this, a scientific data management system (DataFinder) is enhanced with features for traceable documentation. Provenance recording is used to meet requirements of traceability, and this information can later be queried for further analysis. DataFinder has further important features for scientific documentation: It employs a heterogeneous and distributed data storage concept. This enables access to different types of data storage systems (e. g. Grid data infrastructure, file servers). In this chapter we describe a number of building blocks that are available or close to finished development. These components are intended for assembling an electronic laboratory notebook for use in Grid environments, while retaining maximal flexibility on usage scenarios as well as maximal compatibility overlap towards each other. Through the usage of such a system, provenance can successfully be used to trace the scientific workflow of preparation, execution, evaluation, interpretation and archiving of research data. The reliability of research results increases and the research process remains transparent to remote research partners.


GPU-based Image Analysis on Mobile Devices

Abstract: With the rapid advances in mobile technology many mobile devices are capable of capturing high quality images and video with their embedded camera. This paper investigates techniques for real-time processing of the resulting images, particularly on-device utilizing a graphical processing unit. Issues and limitations of image processing on mobile devices are discussed, and the performance of graphical processing units on a range of devices measured through a programmable shader implementation of Canny edge detection.


Proposing Cluster_Similarity Method in Order to Find as Much Better Similarities in Databases

Abstract: Different ways of entering data into databases result in duplicate records that cause increasing of databases' size. This is a fact that we cannot ignore it easily. There are several methods that are used for this purpose. In this paper, we have tried to increase the accuracy of operations by using cluster similarity instead of direct similarity of fields. So that clustering is done on fields of database and according to accomplished clustering on fields, similarity degree of records is obtained. In this method by using present information in database, more logical similarity is obtained for deficient information that in general, the method of cluster similarity could improve operations 24% compared with previous methods.


Higher-Order Momentum Distributions and Locally Affine LDDMM Registration

Abstract: To achieve sparse parametrizations that allows intuitive analysis, we aim to represent deformation with a basis containing interpretable elements, and we wish to use elements that have the description capacity to represent the deformation compactly. To accomplish this, we introduce in this paper higher-order momentum distributions in the LDDMM registration framework. While the zeroth order moments previously used in LDDMM only describe local displacement, the first-order momenta that are proposed here represent a basis that allows local description of affine transformations and subsequent compact description of non-translational movement in a globally non-rigid deformation. The resulting representation contains directly interpretable information from both mathematical and modeling perspectives. We develop the mathematical construction of the registration framework with higher-order momenta, we show the implications for sparse image registration and deformation description, and we provide examples of how the parametrization enables registration with a very low number of parameters. The capacity and interpretability of the parametrization using higher-order momenta lead to natural modeling of articulated movement, and the method promises to be useful for quantifying ventricle expansion and progressing atrophy during Alzheimer's disease.


A new approach to cross-bifix-free sets

Abstract: Cross-bifix-free sets are sets of words such that no prefix of any word is a suffix of any other word. In this paper, we introduce a general constructive method for the sets of cross-bifix-free binary words of fixed length. It enables us to determine a cross-bifix-free words subset which has the property to be non-expandable.


Isomorphisms of types in the presence of higher-order references

Abstract: We investigate the problem of type isomorphisms in a programming language with higher-order references. We first recall the game-theoretic model of higher-order references by Abramsky, Honda and McCusker. Solving an open problem by Laurent, we show that two finitely branching arenas are isomorphic if and only if they are geometrically the same, up to renaming of moves (Laurent's forest isomorphism). We deduce from this an equational theory characterizing isomorphisms of types in a finitary language with higher order references. We show however that Laurent's conjecture does not hold on infinitely branching arenas, yielding a non-trivial type isomorphism in the extension of this language with natural numbers.


k-Gap Interval Graphs

Abstract: We initiate the study of a new parameterization of graph problems. In a multiple interval representation of a graph, each vertex is associated to at least one interval of the real line, with an edge between two vertices if and only if an interval associated to one vertex has a nonempty intersection with an interval associated to the other vertex. A graph on n vertices is a k-gap interval graph if it has a multiple interval representation with at most n+k intervals in total. In order to scale up the nice algorithmic properties of interval graphs (where k=0), we parameterize graph problems by k, and find FPT algorithms for several problems, including Feedback Vertex Set, Dominating Set, Independent Set, Clique, Clique Cover, and Multiple Interval Transversal. The Coloring problem turns out to be W[1]-hard and we design an XP algorithm for the recognition problem.


Independence of Tabulation-Based Hash Classes

Abstract: A tabulation-based hash function maps a key into d derived characters indexing random values in tables that are then combined with bitwise xor operations to give the hash. Thorup and Zhang (2004) presented d-wise independent tabulation-based hash classes that use linear maps over finite fields to map a key, considered as a vector (a,b), to derived characters. We show that a variant where the derived characters are a+b*i for i=0,..., q-1 (using integer arithmetic) yielding (2d-1)-wise independence. Our analysis is based on an algebraic property that characterizes k-wise independence of tabulation-based hashing schemes, and combines this characterization with a geometric argument. We also prove a non-trivial lower bound on the number of derived characters necessary for k-wise independence with our and related hash classes.


Partial order approach to compute shortest paths in multimodal networks

Abstract: Many networked systems involve multiple modes of transport. Such systems are called multimodal, and examples include logistic networks, biomedical phenomena, manufacturing process and telecommunication networks. Existing techniques for determining optimal paths in multimodal networks have either required heuristics or else application-specific constraints to obtain tractable problems, removing the multimodal traits of the network during analysis. In this paper weighted coloured--edge graphs are introduced to model multimodal networks, where colours represent the modes of transportation. Optimal paths are selected using a partial order that compares the weights in each colour, resulting in a Pareto optimal set of shortest paths. This approach is shown to be tractable through experimental analyses for random and real multimodal networks without the need to apply heuristics or constraints.


An Alternative Interpretation of Linguistic Variables as Linguistic Finite Automata

Abstract: Linguistic variables represent crisp information in a form and precision appropriate for the problem. For example, to answer the question "How are you?" one may say "I am fine." the linguistic variables like "fine", so common in everyday speech. In this paper an alternative interpretation of linguistic variables is introduced with the notion of a linguistic description of a value or set of values. The use of linguistic variables in many applications reduces the overall computation complexity of the application. Linguistic variables have been shown to be particularly useful in complex non-linear applications. Here we are applying the concept of reasoning with Linguistic Quantifiers to define the Linguistic Finite Automata along with the expansion of \delta^{\box} and \lambda^{\box} over \delta and \lambda.


Max-Cut Parameterized Above the Edwards-Erd\H{o}s Bound

Abstract: We study the boundary of tractability for the Max-Cut problem in graphs. Our main result shows that Max-Cut above the Edwards-Erd\H{o}s bound is fixed-parameter tractable: we give an algorithm that for any connected graph with n vertices and m edges finds a cut of size m/2 + (n-1)/4 + k in time 2^O(k)n^4, or decides that no such cut exists. This answers a long-standing open question from parameterized complexity that has been posed several times over the past 15 years. Our algorithm is asymptotically optimal, under the Exponential Time Hypothesis, and is strengthened by a polynomial-time computable kernel of polynomial size.


Smart Grid Communications: Overview of Research Challenges, Solutions, and Standardization Activities

Abstract: Optimization of energy consumption in future intelligent energy networks (or Smart Grids) will be based on grid-integrated near-real-time communications between various grid elements in generation, transmission, distribution and loads. This paper discusses some of the challenges and opportunities of communications research in the areas of smart grid and smart metering. In particular, we focus on some of the key communications challenges for realizing interoperable and future-proof smart grid/metering networks, smart grid security and privacy, and how some of the existing networking technologies can be applied to energy management. Finally, we also discuss the coordinated standardization efforts in Europe to harmonize communications standards and protocols.


Approximating the Edge Length of 2-Edge Connected Planar Geometric Graphs on a Set of Points

Abstract: Given a set $P$ of $n$ points in the plane, we solve the problems of constructing a geometric planar graph spanning $P$ 1) of minimum degree 2, and 2) which is 2-edge connected, respectively, and has max edge length bounded by a factor of 2 times the optimal; we also show that the factor 2 is best possible given appropriate connectivity conditions on the set $P$, respectively. First, we construct in $O(n\log{n})$ time a geometric planar graph of minimum degree 2 and max edge length bounded by 2 times the optimal. This is then used to construct in $O(n\log n)$ time a 2-edge connected geometric planar graph spanning $P$ with max edge length bounded by $\sqrt{5}$ times the optimal, assuming that the set $P$ forms a connected Unit Disk Graph. Second, we prove that 2 times the optimal is always sufficient if the set of points forms a 2 edge connected Unit Disk Graph and give an algorithm that runs in $O(n^2)$ time. We also show that for $k \in O(\sqrt{n})$, there exists a set $P$ of $n$ points in the plane such that even though the Unit Disk Graph spanning $P$ is $k$-vertex connected, there is no 2-edge connected geometric planar graph spanning $P$ even if the length of its edges is allowed to be up to 17/16.


Variation principle and the universal metric of dynamic routing

Abstract: In this paper the variation principles from theoretical physics is considered that would describe the process of routing in computer networks. The total traffic which is currently served on all hops of the route has been chosen as the quantity to minimize. Universal metric function has been found for dynamic routing taking into account the packet loss effect. An attempt to derive the metric of the most popular dynamic routing protocols such as RIP, OSPF, EIGRP from universal metric was made.


Decentralized Supervisory Control of Discrete Event Systems for Bisimulation Equivalence

Abstract: In decentralized systems, branching behaviors naturally arise due to communication, unmodeled dynamics and system abstraction, which can not be adequately captured by the traditional sequencing-based language equivalence. As a finer behavior equivalence than language equivalence, bisimulation not only allows the full set of branching behaviors but also explicitly specifies the properties in terms of temporal logic such as CTL* and mu-calculus. This observation motivates us to consider the decentralized control of discrete event systems (DESs) for bisimulation equivalence in this paper, where the plant and the specification are taken to be nondeterministic and the supervisor is taken to be deterministic. An automata-based control framework is formalized, upon which we develop three architectures with respect to different decision fusion rules for the decentralized bisimilarity control, named a conjunctive architecture, a disjunctive architecture and a general architecture. Under theses three architectures, necessary and sufficient conditions for the existence of decentralized bisimilarity supervisors are derived respectively, which extend the traditional results of supervisory control from language equivalence to bisimulation equivalence. It is shown that these conditions can be verified with exponential complexity. Furthermore, the synthesis of bisimilarity supervisors is presented when the existence condition holds.


Approximation Algorithms and Hardness of the k-Route Cut Problem

Abstract: We study the k-route cut problem: given an undirected edge-weighted graph G=(V,E), a collection {(s_1,t_1),(s_2,t_2),...,(s_r,t_r)} of source-sink pairs, and an integer connectivity requirement k, the goal is to find a minimum-weight subset E' of edges to remove, such that the connectivity of every pair (s_i, t_i) falls below k. Specifically, in the edge-connectivity version, EC-kRC, the requirement is that there are at most (k-1) edge-disjoint paths connecting s_i to t_i in G \ E', while in the vertex-connectivity version, NC-kRC, the same requirement is for vertex-disjoint paths. Prior to our work, poly-logarithmic approximation algorithms have been known for the special case where k >= 3, but no non-trivial approximation algorithms were known for any value k>3, except in the single-source setting. We show an O(k log^{3/2}r)-approximation algorithm for EC-kRC with uniform edge weights, and several polylogarithmic bi-criteria approximation algorithms for EC-kRC and NC-kRC, where the connectivity requirement k is violated by a constant factor. We complement these upper bounds by proving that NC-kRC is hard to approximate to within a factor of k^{eps} for some fixed eps>0. We then turn to study a simpler version of NC-kRC, where only one source-sink pair is present. We give a simple bi-criteria approximation algorithm for this case, and show evidence that even this restricted version of the problem may be hard to approximate. For example, we prove that the single source-sink pair version of NC-kRC has no constant-factor approximation, assuming Feige's Random k-AND assumption.


The Robust Price of Anarchy of Altruistic Games

Abstract: We study the inefficiency of equilibria for various classes of games when players are (partially) altruistic. We model altruistic behavior by assuming that player i's perceived cost is a convex combination of 1-\alpha_i times his direct cost and \alpha_i times the social cost. Tuning the parameters \alpha_i allows smooth interpolation between purely selfish and purely altruistic behavior. Within this framework, we study altruistic extensions of linear congestion games, fair cost-sharing games and valid utility games. We derive (tight) bounds on the price of anarchy of these games for several solution concepts. Thereto, we suitably adapt the smoothness notion introduced by Roughgarden and show that it captures the essential properties to determine the robust price of anarchy of these games. Our bounds show that for congestion games and cost-sharing games, the worst-case robust price of anarchy increases with increasing altruism, while for valid utility games, it remains constant and is not affected by altruism. However, the increase in the price of anarchy is not a universal phenomenon: for symmetric singleton linear congestion games, we derive a bound on the pure price of anarchy that decreases as the level of altruism increases. Since the bound is also strictly lower than the robust price of anarchy, it exhibits a natural example in which Nash equilibria are more efficient than more permissive notions of equilibrium.


How Far Can Client-Only Solutions Go for Mobile Browser Speed?

Abstract: Mobile browser is known to be slow because of the bottleneck in resource loading. Client-only solutions to improve resource loading are attractive because they are immediately deployable, scalable, and secure. We present the first publicly known treatment of client-only solutions to understand how much they can improve mobile browser speed without infrastructure support. Leveraging an unprecedented set of web usage data collected from 24 iPhone users continuously over one year, we examine the three fundamental, orthogonal approaches a client-only solution can take: caching, prefetching, and speculative loading, which is first proposed and studied in this work. Speculative loading predicts and speculatively loads the subresources needed to open a web page once its URL is given. We show that while caching and prefetching are highly limited for mobile browsing, speculative loading can be significantly more effective. Empirically, we show that client-only solutions can improve the browser speed by about 1.4 second on average for web sites visited by the 24 iPhone users. We also report the design, realization, and evaluation of speculative loading in a WebKit-based browser called Tempo. On average, Tempo can reduce browser delay by 1 second (~20%).


Insights from Classifying Visual Concepts with Multiple Kernel Learning

Abstract: Combining information from various image features has become a standard technique in concept recognition tasks. However, the optimal way of fusing the resulting kernel functions is usually unknown in practical applications. Multiple kernel learning (MKL) techniques allow to determine an optimal linear combination of such similarity matrices. Classical approaches to MKL promote sparse mixtures. Unfortunately, so-called 1-norm MKL variants are often observed to be outperformed by an unweighted sum kernel. The contribution of this paper is twofold: We apply a recently developed non-sparse MKL variant to state-of-the-art concept recognition tasks within computer vision. We provide insights on benefits and limits of non-sparse MKL and compare it against its direct competitors, the sum kernel SVM and the sparse MKL. We report empirical results for the PASCAL VOC 2009 Classification and ImageCLEF2010 Photo Annotation challenge data sets. About to be submitted to PLoS ONE.


Analysis and Extension of Arc-Cosine Kernels for Large Margin Classification

Abstract: We investigate a recently proposed family of positive-definite kernels that mimic the computation in large neural networks. We examine the properties of these kernels using tools from differential geometry; specifically, we analyze the geometry of surfaces in Hilbert space that are induced by these kernels. When this geometry is described by a Riemannian manifold, we derive results for the metric, curvature, and volume element. Interestingly, though, we find that the simplest kernel in this family does not admit such an interpretation. We explore two variations of these kernels that mimic computation in neural networks with different activation functions. We experiment with these new kernels on several data sets and highlight their general trends in performance for classification.


Nonnegative Matrix Factorization for Semi-supervised Dimensionality Reduction

Abstract: We show how to incorporate information from labeled examples into nonnegative matrix factorization (NMF), a popular unsupervised learning algorithm for dimensionality reduction. In addition to mapping the data into a space of lower dimensionality, our approach aims to preserve the nonnegative components of the data that are important for classification. We identify these components from the support vectors of large-margin classifiers and derive iterative updates to preserve them in a semi-supervised version of NMF. These updates have a simple multiplicative form like their unsupervised counterparts; they are also guaranteed at each iteration to decrease their loss function---a weighted sum of I-divergences that captures the trade-off between unsupervised and supervised learning. We evaluate these updates for dimensionality reduction when they are used as a precursor to linear classification. In this role, we find that they yield much better performance than their unsupervised counterparts. We also find one unexpected benefit of the low dimensional representations discovered by our approach: often they yield more accurate classifiers than both ordinary and transductive SVMs trained in the original input space.


Web Services Non-Functional Classification to Enhance Discovery Speed

Abstract: Recently, the use and deployment of web services has dramatically increased. This is due to the easiness, interoperability, and flexibility that web services offer to the software systems, which other software structures don't support or support poorly. Web services discovery became more important and research conducted in this area became more critical. With the increasing number of published and publicly available web services, speed in web service discovery process is becoming an issue which cannot be neglected. This paper proposes a generic non-functional based web services classification algorithm. Classification algorithm depends on information supplied by web service provider at the registration time. Authors have proved mathematically and experimentally the usefulness and efficiency of proposed algorithm.


KM and WEB 2.0 methods for project-based learning. MESHAT : a monitoring and experience sharing tool

Abstract: Our work aims at studying tools offered to learners and tutors involved in face-to-face or blended project-based learning activities. To understand better the needs and expectations of each actor, we are especially interested in the specific case of project management training. The results of a course observation show that the lack of monitoring and expertise transfer tools involves important dysfunctions in the course organisation and therefore dissatisfaction for tutors and students (in particular about the acquisition of knowledge and expertise). So as to solve this problem, we propose a personalised platform (according to the actor: project group, student or tutor), which gives information to monitor activities and supports the acquisition and transfer of expertise. This platform is based on Knowledge Management (KM) and Web 2.0 concepts to support the dynamic building of knowledge. KM is used to define the learning process (based on the experiential learning theory) and the way the individual knowledge building is monitored (based on metacognitive concepts). Web 2.0 is used to define the way the experience is shared. We make the hypothesis that this approach improves the acquisition of complex skills (e.g. management, communication and collaboration), which requires a behavioural evolution. We aim at making the students become able 'to learn to learn' and evolve according to contexts. We facilitate their ability to have a critical analysis of their actions according to the situations they encounter.


Modeling Tiered Pricing in the Internet Transit Market

Abstract: ISPs are increasingly selling "tiered" contracts, which offer Internet connectivity to wholesale customers in bundles, at rates based on the cost of the links that the traffic in the bundle is traversing. Although providers have already begun to implement and deploy tiered pricing contracts, little is known about how such pricing affects ISPs and their customers. While contracts that sell connectivity on finer granularities improve market efficiency, they are also more costly for ISPs to implement and more difficult for customers to understand. In this work we present two contributions: (1) we develop a novel way of mapping traffic and topology data to a demand and cost model; and (2) we fit this model on three large real-world networks: an European transit ISP, a content distribution network, and an academic research network, and run counterfactuals to evaluate the effects of different pricing strategies on both the ISP profit and the consumer surplus. We highlight three core findings. First, ISPs gain most of the profits with only three or four pricing tiers and likely have little incentive to increase granularity of pricing even further. Second, we show that consumer surplus follows closely, if not precisely, the increases in ISP profit with more pricing tiers. Finally, the common ISP practice of structuring tiered contracts according to the cost of carrying the traffic flows (e.g., offering a discount for traffic that is local) can be suboptimal and that dividing contracts based on both traffic demand and the cost of carrying it into only three or four tiers yields near-optimal profit for the ISP.


Stochastic Geometry based Medium Access Games in Mobile Ad hoc Networks

Abstract: This paper studies the performance of Mobile Ad hoc Networks (MANETs) when the nodes, that form a Poisson point process, selfishly choose their Medium Access Probability (MAP). We consider goodput and delay as the performance metric that each node is interested in optimizing taking into account the transmission energy costs. We introduce a pricing scheme based on the transmission energy requirements and compute the symmetric Nash equilibria of the game in closed form. It is shown that by appropriately pricing the nodes, the selfish behavior of the nodes can be used to achieve the social optimum at equilibrium. The Price of Anarchy is then analyzed for these games. For the game with delay based utility, we bound the price of anarchy and study the effect of the price factor. For the game with goodput based utility, it is shown that price of anarchy is infinite at the price factor that achieves the global optima.


Probabilistic pointer analysis for multithreaded programs

Abstract: The use of pointers and data-structures based on pointers results in circular memory references that are interpreted by a vital compiler analysis, namely pointer analysis. For a pair of memory references at a program point, a typical pointer analysis specifies if the points-to relation between them may exist, definitely does not exist, or definitely exists. The "may be" case, which describes the points-to relation for most of the pairs, cannot be dealt with by most compiler optimizations. This is so to guarantee the soundness of these optimizations. However, the "may be" case can be capitalized by the modern class of speculative optimizations if the probability that two memory references alias can be measured. Focusing on multithreading, a prevailing technique of programming, this paper presents a new flow-sensitive technique for probabilistic pointer analysis of multithreaded programs. The proposed technique has the form of a type system and calculates the probability of every points-to relation at each program point. The key to our approach is to calculate the points-to information via a post-type derivation. The use of type systems has the advantage of associating each analysis results with a justification (proof) for the correctness of the results. This justification has the form of a type derivation and is very much required in applications like certified code.


Filtrations of Formal Languages by Arithmetic Progressions

Abstract: A filtration of a formal language L by a sequence s maps L to the set of words formed by taking the letters of words of L indexed only by s. We consider the languages resulting from filtering by all arithmetic progressions. If L is regular, it is easy to see that only finitely many distinct languages result. By contrast, there exist CFL's that give infinitely many distinct languages as a result. We use our technique to show that the operation diag, which extracts the diagonal of words of square length arranged in a square array, preserves regularity but does not preserve context-freeness.


The Efficiency of MapReduce in Parallel External Memory

Abstract: Since its introduction in 2004, the MapReduce framework has become one of the standard approaches in massive distributed and parallel computation. In contrast to its intensive use in practise, theoretical footing is still limited and only little work has been done yet to put MapReduce on a par with the major computational models. Following pioneer work that relates the MapReduce framework with PRAM and BSP in their macroscopic structure, we focus on the functionality provided by the framework itself, considered in the parallel external memory model (PEM). In this, we present upper and lower bounds on the parallel I/O-complexity that are matching up to constant factors for the shuffle step. The shuffle step is the single communication phase where all information of one MapReduce invocation gets transferred from map workers to reduce workers. Hence, we move the focus towards the internal communication step in contrast to previous work. The results we obtain further carry over to the BSP* model. On the one hand, this shows how much complexity can be "hidden" for an algorithm expressed in MapReduce compared to PEM. On the other hand, our results bound the worst-case performance loss of the MapReduce approach in terms of I/O-efficiency.


Single Time-Stamped Tries for Retroactive Call Subsumption

Abstract: Tabling is an evaluation strategy for Prolog programs that works by storing answers in a table space and then by using them in similar subgoals. Some tabling engines use call by subsumption, where it is determined that a subgoal will consume answers from a more general subgoal in order to reduce the search space and increase efficiency. We designed an extension, named Retroactive Call Subsumption (RCS), that implements call by subsumption independently of the call order, thus allowing a more general subgoal to force previous called subgoals to become answer consumers. For this extension, we propose a new table space design, the Single Time Stamped Trie (STST), that is organized to make answer sharing across subsumed/subsuming subgoals simple and efficient. In this paper, we present the new STST table space design and we discuss the main modifications made to the original Time Stamped Tries approach to non-retroactive call by subsumption. In experimental results, with programs that stress some deficiencies of the new STST design, some overheads may be observed, however the results achieved with more realistic programs greatly offset these overheads.


Global Trie for Subterms

Abstract: A critical component in the implementation of an efficient tabling system is the design of the table space. The most popular and successful data structure for representing tables is based on a two-level trie data structure, where one trie level stores the tabled subgoal calls and the other stores the computed answers. The Global Trie (GT) is an alternative table space organization designed with the intent to reduce the tables's memory usage, namely by storing terms in a global trie, thus preventing repeated representations of the same term in different trie data structures. In this paper, we propose an extension to the GT organization, named Global Trie for Subterms (GT-ST), where compound subterms in term arguments are represented as unique entries in the GT. Experiments results using the YapTab tabling system show that GT-ST support has potential to achieve significant reductions on memory usage, for programs with increasing compound subterms in term arguments, without compromising the execution time for other programs.


Computing with Hereditarily Finite Sequences

Abstract: e use Prolog as a flexible meta-language to provide executable specifications of some fundamental mathematical objects and their transformations. In the process, isomorphisms are unraveled between natural numbers and combinatorial objects (rooted ordered trees representing hereditarily finite sequences and rooted ordered binary trees representing G\"odel's System {\bf T} types). This paper focuses on an application that can be seen as an unexpected "paradigm shift": we provide recursive definitions showing that the resulting representations are directly usable to perform symbolically arbitrary-length integer computations. Besides the theoretically interesting fact of "breaking the arithmetic/symbolic barrier", the arithmetic operations performed with symbolic objects like trees or types turn out to be genuinely efficient -- we derive implementations with asymptotic performance comparable to ordinary bitstring implementations of arbitrary-length integer arithmetic. The source code of the paper, organized as a literate Prolog program, is available at \url{http://logic.cse.unt.edu/tarau/research/2011/pPAR.pl}


L-FLAT: Logtalk Toolkit for Formal Languages and Automata Theory

Abstract: We describe L-FLAT, a Logtalk Toolkit for teaching Formal Languages and Automata Theory. L-FLAT supports the definition of \textsl{alphabets}, the definition of \textsl{orders} over alphabet symbols, the partial definition of \textsl{languages} using unit tests, and the definition of \textsl{mechanisms}, which implement language generators or language recognizers. Supported mechanisms include \textsl{predicates}, \textsl{regular expressions}, \textsl{finite automata}, \textsl{context-free grammars}, \textsl{Turing machines}, and \textsl{push-down automata}. L-FLAT entities are implemented using the object-oriented features of Logtalk, providing a highly portable and easily extendable framework. The use of L-FLAT in educational environments is enhanced by supporting Mooshak, a web application that features automatic grading of submitted programs.


Using Constraint Handling Rules to Provide Static Type Analysis for the Q Functional Language

Abstract: We describe an application of Prolog: a type checking tool for the Q functional language. Q is a terse vector processing language, a descendant of APL, which is getting more and more popular, especially in financial applications. Q is a dynamically typed language, much like Prolog. Extending Q with static typing improves both the readability of programs and programmer productivity, as type errors are discovered by the tool at compile time, rather than through debugging the program execution. The type checker uses constraints that are handled by Prolog Constraint Handling Rules. During the analysis, we determine the possible type values for each program expression and detect inconsistencies. As most built-in function names of Q are overloaded, i.e. their meaning depends on the argument types, a quite complex system of constraints had to be implemented.


Nesting Probabilistic Inference

Abstract: When doing inference in ProbLog, a probabilistic extension of Prolog, we extend SLD resolution with some additional bookkeeping. This additional information is used to compute the probabilistic results for a probabilistic query. In Prolog's SLD, goals are nested very naturally. In ProbLog's SLD, nesting probabilistic queries interferes with the probabilistic bookkeeping. In order to support nested probabilistic inference we propose the notion of a parametrised ProbLog engine. Nesting becomes possible by suspending and resuming instances of ProbLog engines. With our approach we realise several extensions of ProbLog such as meta-calls, negation, and answers of probabilistic goals.


High-Level Multi-Threading in hProlog

Abstract: A new high-level interface to multi-threading in Prolog, implemented in hProlog, is described. Modern CPUs often contain multiple cores and through high-level multi-threading a programmer can leverage this power without having to worry about low-level details. Two common types of high-level explicit parallelism are discussed: independent and-parallelism and competitive or-parallelism. A new type of explicit parallelism, pipeline parallelism, is proposed. This new type can be used in certain cases where independent and-parallelism and competitive or-parallelism cannot be used.


Approximating Constraint Propagation in Datalog

Abstract: We present a technique exploiting Datalog with aggregates to improve the performance of programs with arithmetic (in)equalities. Our approach employs a source-to-source program transformation which approximates the propagation technique from Constraint Programming. The experimental evaluation of the approach shows good run time speed-ups on a range of non-recursive as well as recursive programs. Furthermore, our technique improves upon the previously reported in the literature constraint magic set transformation approach.


Bijective Term Encodings

Abstract: We encode/decode Prolog terms as unique natural numbers. Our encodings have the following properties: a) are bijective b) natural numbers always decode to syntactically valid terms c) they work in low polynomial time in the bitsize of the representations d) the bitsize of our encodings is within constant factor of the syntactic representation of the input. We describe encodings of term algebras with finite signature as well as algorithms that separate the "structure" of a term, a natural number encoding of a list of balanced parenthesis, from its "content", a list of atomic terms and Prolog variables. The paper is organized as a literate Prolog program available from \url{http://logic.cse.unt.edu/tarau/research/2011/bijenc.pl}.


An Implementation of Bubbling

Abstract: Non-determinism is of great importance in functional logic programming. It provides expressiveness and efficiency to functional logic computations. In this paper we describe an implementation of the multi-paradigm functional logic language Curry. The evaluation strategy employed by the implementation is based on definitional trees and needed narrowing for deterministic operations, while non-deterministic operations will depend on the graph transformation, bubbling. Bubbling preserves the completeness of non-deterministic operations and avoids unnecessary large-scale reconstruction of expressions done by other approaches.


Dependently Typed Programming based on Automated Theorem Proving

Abstract: Mella is a minimalistic dependently typed programming language and interactive theorem prover implemented in Haskell. Its main purpose is to investigate the effective integration of automated theorem provers in a pure and simple setting. Such integrations are essential for supporting program development in dependently typed languages. We integrate the equational theorem prover Waldmeister and test it on more than 800 proof goals from the TPTP library. In contrast to previous approaches, the reconstruction of Waldmeister proofs within Mella is quite robust and does not generate a significant overhead to proof search. Mella thus yields a template for integrating more expressive theorem provers in more sophisticated languages.


Cross Layer Implementation of Key Establishment and Configuration Protocols in WSN

Abstract: Security in Wireless Sensor Networks (WSN) can be achieved by establishing shared keys among the neighbor sensor nodes to create secure communication links. The protocol to be used for such a pairwise key establishment is a key factor determining the energy to be consumed by each sensor node during the secure network configuration. On the other hand, to achieve the optimum network configuration, nodes may not need to establish pairwise keys with all of their neighbors. Because, links to be established are defined by the network configuration protocol and as long as the network connectivity requirements are satisfied, number of links to be secured can be limited accordingly. In this sense, key establishment and network configuration performances are related to each other and this cross relation should be taken into consideration while implementing security for WSN. In this paper, we have investigated the cross layer relations and performance figures of the selected randomized pre-distribution and public key based key establishment protocols with the configuration protocol we proposed in a separate publication. Simulation results indicate that total network configuration energy cost can be reduced by reducing the number of links to be secured without affecting the global network connectivity performance. Results also show that the energy and resilience performances of the public key establishment can be better than the key pre-distribution for a given set of network configuration parameters.


A Classical Fuzzy Approach for Software Effort Estimation on Machine Learning Technique

Abstract: Software Cost Estimation with resounding reliability,productivity and development effort is a challenging and onerous task. This has incited the software community to give much needed thrust and delve into extensive research in software effort estimation for evolving sophisticated methods. Estimation by analogy is one of the expedient techniques in software effort estimation field. However, the methodology utilized for the estimation of software effort by analogy is not able to handle the categorical data in an explicit and precise manner. A new approach has been developed in this paper to estimate software effort for projects represented by categorical or numerical data using reasoning by analogy and fuzzy approach. The existing historical data sets, analyzed with fuzzy logic, produce accurate results in comparison to the data set analyzed with the earlier methodologies.


CloudGenius: Automated Decision Support for Migrating Multi-Component Enterprise Applications to Clouds

Abstract: One of the key problems in migrating multi-component enterprise applications to Clouds is selecting the best mix of VM images and Cloud infrastructure services. A migration process has to ensure that Quality of Service (QoS) requirements are met, while satisfying conflicting selection criteria, e.g. throughput and cost. When selecting Cloud services, application engineers must consider heterogeneous sets of criteria and complex dependencies across multiple layers impossible to resolve manually. To overcome this challenge, we present the generic recommender framework CloudGenius and an implementation that leverage well known multi-criteria decision making technique Analytic Hierarchy Process to automate the selection process based on a model, factors, and QoS requirements related to enterprise applications. In particular, we introduce a structured migration process for multi-component enterprise applications, clearly identify the most important criteria relevant to the selection problem and present a multi-criteria-based selection algorithm. Experiments with the software prototype CumulusGenius show time complexities.


Root finding with threshold circuits

Abstract: We show that for any constant d, complex roots of degree d univariate rational (or Gaussian rational) polynomials---given by a list of coefficients in binary---can be computed to a given accuracy by a uniform TC^0 algorithm (a uniform family of constant-depth polynomial-size threshold circuits). The basic idea is to compute the inverse function of the polynomial by a power series. We also discuss an application to the theory VTC^0 of bounded arithmetic.


Developing Autonomic Properties for Distributed Pattern-Recognition Systems with ASSL: A Distributed MARF Case Study

Abstract: In this paper, we discuss our research towards developing special properties that introduce autonomic behavior in pattern-recognition systems. In our approach we use ASSL (Autonomic System Specification Language) to formally develop such properties for DMARF (Distributed Modular Audio Recognition Framework). These properties enhance DMARF with an autonomic middleware that manages the four stages of the framework's pattern-recognition pipeline. DMARF is a biologically inspired system employing pattern recognition, signal processing, and natural language processing helping us process audio, textual, or imagery data needed by a variety of scientific applications, e.g., biometric applications. In that context, the notion go autonomic DMARF (ADMARF) can be employed by autonomous and robotic systems that theoretically require less-to-none human intervention other than data collection for pattern analysis and observing the results. In this article, we explain the ASSL specification models for the autonomic properties of DMARF.


On Optimal Multi-Dimensional Mechanism Design

Abstract: We efficiently solve the optimal multi-dimensional mechanism design problem for independent bidders with arbitrary demand constraints when either the number of bidders is a constant or the number of items is a constant. In the first setting, we need that each bidder's values for the items are sampled from a possibly correlated, item-symmetric distribution, allowing different distributions for each bidder. In the second setting, we allow the values of each bidder for the items to be arbitrarily correlated, but assume that the distribution of bidder types is bidder-symmetric. For all eps>0, we obtain an additive eps-approximation, when the value distributions are bounded, or a multiplicative (1-eps)-approximation when the value distributions are unbounded, but satisfy the Monotone Hazard Rate condition, covering a widely studied class of distributions in Economics. Our runtime is polynomial in max{#items,#bidders}, and not the size of the support of the joint distribution of all bidders' values for all items, which is typically exponential in both the number of items and the number of bidders. Our mechanisms are randomized, explicitly price bundles, and can sometimes accommodate budget constraints. Our results are enabled by establishing several new tools and structural properties of Bayesian mechanisms. We provide a symmetrization technique turning any truthful mechanism into one that has the same revenue and respects all symmetries in the underlying value distributions. We also prove that item-symmetric mechanisms satisfy a natural monotonicity property which, unlike cyclic-monotonicity, can be harnessed algorithmically. Finally, we provide a technique that turns any given eps-BIC mechanism (i.e. one where incentive constraints are violated by eps) into a truly-BIC mechanism at the cost of O(sqrt{eps}) revenue. We expect our tools to be used beyond the settings we consider here.


Note on fast division algorithm for polynomials using Newton iteration

Abstract: The classical division algorithm for polynomials requires $O(n^2)$ operations for inputs of size $n$. Using reversal technique and Newton iteration, it can be improved to $O({M}(n))$, where ${M}$ is a multiplication time. But the method requires that the degree of the modulo, $x^l$, should be the power of 2. If $l$ is not a power of 2 and $f(0)=1$, Gathen and Gerhard suggest to compute the inverse,$f^{-1}$, modulo $x^{\lceil l/2^r\rceil}, x^{\lceil l/2^{r-1}\rceil},..., x^{\lceil l/2\rceil}, x^l$, separately. But they did not specify the iterative step. In this note, we show that the original Newton iteration formula can be directly used to compute $f^{-1}\,{mod}\,x^{l}$ without any additional cost, when $l$ is not a power of 2.


The Study and Approach of Software Re-Engineering

Abstract: The nature of software re-engineering is to improve or transform existing software so it can be understood, controlled and reused as new software. Needs, the necessity of re-engineering software has greatly increased. The system software has become obsolete no longer used in architecture, platform they're running, stable and consistent they support the development and support needs change. Software re-engineering is vital to restore and reuse the things inherent in the existing software, put the cost of software maintenance to the lowest in the control and establish a basis for the development of software in the future.


Abstract: IT operates in dynamic environments with the need always to change and adapt. There is a need to improve performance. Many gaps were found when we conduct the IT audit and we tried to seek to close gaps in capabilities. One way to the close these gaps is the adoption of good practices in wide industry use. There are several sources for good practices including public frameworks and standards such as ITIL, COBIT, CMMI, eSCM-SP, PRINCE2, ISO 9000, ISO/IEC 20000 and ISO/IEC 27001, etc. The paper propose ITIL frameworks to ITD Company for improving capabilities in service management.


Mobile IP and protocol authentication extension

Abstract: Mobile IP is an open standard, defined by the Internet Engineering Task Force (IETF) RFC 3220. By using Mobile IP, you can keep the same IP address, stay connected, and maintain ongoing applications while roaming between IP networks. Mobile IP is scalable for the Internet because it is based on IP - any media that can support IP can support Mobile IP.


Legal Resources Information System for Information Agencies of Specialized Libraries

Abstract: In recent years, the rapid development of information technology and communication has a strong impact to industry information - the library. The mission of the industry when in fact the great social place to see the library as knowledge management. Vietnam is in the process of building the rule of law socialist orientation and improves the legal system. So in the current development process, the law library plays an important role in the retention, dissemination and provision of legal information service of legislative, executive and judiciary, particularly especially research, teaching and learning of law school. But the response of the legal information library information agencies remains limited compared to the increasing demand of users.


Clustering and Latent Semantic Indexing Aspects of the Nonnegative Matrix Factorization

Abstract: This paper provides a theoretical support for clustering aspect of the nonnegative matrix factorization (NMF). By utilizing the Karush-Kuhn-Tucker optimality conditions, we show that NMF objective is equivalent to graph clustering objective, so clustering aspect of the NMF has a solid justification. Different from previous approaches which usually discard the nonnegativity constraints, our approach guarantees the stationary point being used in deriving the equivalence is located on the feasible region in the nonnegative orthant. Additionally, since clustering capability of a matrix decomposition technique can sometimes imply its latent semantic indexing (LSI) aspect, we will also evaluate LSI aspect of the NMF by showing its capability in solving the synonymy and polysemy problems in synthetic datasets. And more extensive evaluation will be conducted by comparing LSI performances of the NMF and the singular value decomposition (SVD), the standard LSI method, using some standard datasets.


Application of Data Mining Techniques to a Selected Business Organisation with Special Reference to Buying Behaviour

Abstract: Data mining is a new concept & an exploration and analysis of large data sets, in order to discover meaningful patterns and rules. Many organizations are now using the data mining techniques to find out meaningful patterns from the database. The present paper studies how data mining techniques can be apply to the large database. These data mining techniques give certain behavioral pattern from the database. The results which come after analysis of the database are useful for organization. This paper examines the result after applying association rule mining technique, rule induction technique and Apriori algorithm. These techniques are applied to the database of shopping mall. Market basket analysis is performing by the above mentioned techniques and some important results are found such as buying behavior.


Rational Secret Sharing over an Asynchronous Broadcast Channel with Information Theoretic Security

Abstract: We consider the problem of rational secret sharing introduced by Halpern and Teague [1], where the players involved in secret sharing play only if it is to their advantage. This can be characterized in the form of preferences. Players would prefer to get the secret than to not get it and secondly with lesser preference, they would like as few other players to get the secret as possible. Several positive results have already been published to efficiently solve the problem of rational secret sharing but only a handful of papers have touched upon the use of an asynchronous broadcast channel. [2] used cryptographic primitives, [3] used an interactive dealer, and [4] used an honest minority of players in order to handle an asynchronous broadcast channel. In our paper, we propose an m-out-of-n rational secret sharing scheme which can function over an asynchronous broadcast channel without the use of cryptographic primitives and with a non-interactive dealer. This is possible because our scheme uses a small number, k+1, of honest players. The protocol is resilient to coalitions of size up to k and furthermore it is {\epsilon}-resilient to coalitions of size up to and including m-1. The protocol will have a strict Nash equilibrium with probability Pr((k+1)/n) and an {\epsilon}-Nash equilibrium with probability Pr((n-k-1)/n) . Furthermore, our protocol is immune to backward induction. Later on in the paper, we extend our results to include malicious players as well. We also show that our protocol handles the possibility of a player deviating in order to force another player to get a wrong value in what we believe to be a more time efficient manner than was done in Asharov and Lindell [5].


Testing Differences Statistically with the Leiden Ranking

Abstract: The Leiden Ranking 2011/2012 provides the Proportion top-10% publications (PP top 10%) as a new indicator. This indicator allows for testing the difference between two ranks for statistical significance.


An Adaptive Design Methodology for Reduction of Product Development Risk

Abstract: Embedded systems interaction with environment inherently complicates understanding of requirements and their correct implementation. However, product uncertainty is highest during early stages of development. Design verification is an essential step in the development of any system, especially for Embedded System. This paper introduces a novel adaptive design methodology, which incorporates step-wise prototyping and verification. With each adaptive step product-realization level is enhanced while decreasing the level of product uncertainty, thereby reducing the overall costs. The back-bone of this frame-work is the development of Domain Specific Operational (DOP) Model and the associated Verification Instrumentation for Test and Evaluation, developed based on the DOP model. Together they generate functionally valid test-sequence for carrying out prototype evaluation. With the help of a case study 'Multimode Detection Subsystem' the application of this method is sketched. The design methodologies can be compared by defining and computing a generic performance criterion like Average design-cycle Risk. For the case study, by computing Average design-cycle Risk, it is shown that the adaptive method reduces the product development risk for a small increase in the total design cycle time.


Performance Evaluation of Road Traffic Control Using a Fuzzy Cellular Model

Abstract: In this paper a method is proposed for performance evaluation of road traffic control systems. The method is designed to be implemented in an on-line simulation environment, which enables optimisation of adaptive traffic control strategies. Performance measures are computed using a fuzzy cellular traffic model, formulated as a hybrid system combining cellular automata and fuzzy calculus. Experimental results show that the introduced method allows the performance to be evaluated using imprecise traffic measurements. Moreover, the fuzzy definitions of performance measures are convenient for uncertainty determination in traffic control decisions.


A real time vehicles detection algorithm for vision based sensors

Abstract: A vehicle detection plays an important role in the traffic control at signalised intersections. This paper introduces a vision-based algorithm for vehicles presence recognition in detection zones. The algorithm uses linguistic variables to evaluate local attributes of an input image. The image attributes are categorised as vehicle, background or unknown features. Experimental results on complex traffic scenes show that the proposed algorithm is effective for a real-time vehicles detection.


Vehicles Recognition Using Fuzzy Descriptors of Image Segments

Abstract: In this paper a vision-based vehicles recognition method is presented. Proposed method uses fuzzy description of image segments for automatic recognition of vehicles recorded in image data. The description takes into account selected geometrical properties and shape coefficients determined for segments of reference image (vehicle model). The proposed method was implemented using reasoning system with fuzzy rules. A vehicles recognition algorithm was developed based on the fuzzy rules describing shape and arrangement of the image segments that correspond to visible parts of a vehicle. An extension of the algorithm with set of fuzzy rules defined for different reference images (and various vehicle shapes) enables vehicles classification in traffic scenes. The devised method is suitable for application in video sensors for road traffic control and surveillance systems.


Markov Decision Process Based Energy-Efficient On-Line Scheduling for Slice-Parallel Video Decoders on Multicore Systems

Abstract: We consider the problem of energy-efficient on-line scheduling for slice-parallel video decoders on multicore systems. We assume that each of the processors are Dynamic Voltage Frequency Scaling (DVFS) enabled such that they can independently trade off performance for power, while taking the video decoding workload into account. In the past, scheduling and DVFS policies in multi-core systems have been formulated heuristically due to the inherent complexity of the on-line multicore scheduling problem. The key contribution of this report is that we rigorously formulate the problem as a Markov decision process (MDP), which simultaneously takes into account the on-line scheduling and per-core DVFS capabilities; the power consumption of the processor cores and caches; and the loss tolerant and dynamic nature of the video decoder's traffic. In particular, we model the video traffic using a Direct Acyclic Graph (DAG) to capture the precedence constraints among frames in a Group of Pictures (GOP) structure, while also accounting for the fact that frames have different display/decoding deadlines and non-deterministic decoding complexities. The objective of the MDP is to minimize long-term power consumption subject to a minimum Quality of Service (QoS) constraint related to the decoder's throughput. Although MDPs notoriously suffer from the curse of dimensionality, we show that, with appropriate simplifications and approximations, the complexity of the MDP can be mitigated. We implement a slice-parallel version of H.264 on a multiprocessor ARM (MPARM) virtual platform simulator, which provides cycle-accurate and bus signal-accurate simulation for different processors. We use this platform to generate realistic video decoding traces with which we evaluate the proposed on-line scheduling algorithm in Matlab.


Multi databases in Health Care Networks

Abstract: E-Health is a relatively recent term for healthcare practice supported by electronic processes and communication, dating back to at least 1999. E-Health is greatly impacting on information distribution and availability within the health services, hospitals and to the public. E-health was introduced as the death of telemedicine, because - in the context of a broad availability of medical information systems that can interconnect and communicate - telemedicine will no longer exist as a specific field. The same could also be said for any other traditional field in medical informatics, including information systems and electronic patient records. E-health presents itself as a common name for all such technological fields. In this paper we focuses in multi database by determined some sites and distributed it in Homogenous way. This will be followed by an illustrative example as related works. Finally, the paper concludes with general remarks and a statement of further work.


epsilon-Samples of Kernels

Abstract: We study the worst case error of kernel density estimates via subset approximation. A kernel density estimate of a distribution is the convolution of that distribution with a fixed kernel (e.g. Gaussian kernel). Given a subset (i.e. a point set) of the input distribution, we can compare the kernel density estimates of the input distribution with that of the subset and bound the worst case error. If the maximum error is eps, then this subset can be thought of as an eps-sample (aka an eps-approximation) of the range space defined with the input distribution as the ground set and the fixed kernel representing the family of ranges. Interestingly, in this case the ranges are not binary, but have a continuous range (for simplicity we focus on kernels with range of [0,1]); these allow for smoother notions of range spaces. It turns out, the use of this smoother family of range spaces has an added benefit of greatly decreasing the size required for eps-samples. For instance, in the plane the size is O((1/eps^{4/3}) log^{2/3}(1/eps)) for disks (based on VC-dimension arguments) but is only O((1/eps) sqrt{log (1/eps)}) for Gaussian kernels and for kernels with bounded slope that only affect a bounded domain. These bounds are accomplished by studying the discrepancy of these "kernel" range spaces, and here the improvement in bounds are even more pronounced. In the plane, we show the discrepancy is O(sqrt{log n}) for these kernels, whereas for balls there is a lower bound of Omega(n^{1/4}).


Dependent Types for JavaScript

Abstract: We present Dependent JavaScript (DJS), a statically-typed dialect of the imperative, object-oriented, dynamic language. DJS supports the particularly challenging features such as run-time type-tests, higher-order functions, extensible objects, prototype inheritance, and arrays through a combination of nested refinement types, strong updates to the heap, and heap unrolling to precisely track prototype hierarchies. With our implementation of DJS, we demonstrate that the type system is expressive enough to reason about a variety of tricky idioms found in small examples drawn from several sources, including the popular book JavaScript: The Good Parts and the SunSpider benchmark suite.