MiBoard: Metacognitive Training Through Gaming in iSTART

Abstract: MiBoard (Multiplayer Interactive Board Game) is an online, turn-based board game, which is a supplement of the iSTART (Interactive Strategy Training for Active Reading and Thinking) application. MiBoard is developed to test the hypothesis that integrating game characteristics (point rewards, game-like interaction, and peer feedback) into the iSTART trainer will significantly improve its effectiveness on students' learning. It was shown by M. Rowe that a physical board game did in fact enhance students' performance. MiBoard is a computer-based version of Rowe's board game that eliminates constraints on locality while retaining the crucial practice components that were the game's objective. MiBoard gives incentives for participation and provides a more enjoyable and social practice environment compared to the online individual practice component of the original trainer.


MiBoard: iSTART Metacognitive Training through Gaming

Abstract: MiBoard (Multiplayer Interactive Board Game) is an online, turn-based board game, which is a supplement of the iSTART (Interactive Strategy Training for Active Reading and Thinking) application. MiBoard is developed to test the hypothesis that integrating game characteristics (point rewards, game-like interaction, and peer feedback) into the iSTART trainer will significantly improve its effectiveness on students' learning. It was shown by M. Rowe that a physical board game did in fact enhance students' performance. MiBoard is a computer-based version of Rowe's board game that eliminates constraints on locality while retaining the crucial practice components that were the game's objective. MiBoard gives incentives for participation and provides a more enjoyable and social practice environment compared to the online individual practice component of the original trainer


MiBoard: Multiplayer Interactive Board Game

Abstract: Serious games have recently emerged as an avenue for curriculum delivery. Serious games incorporate motivation and entertainment while providing pointed curriculum for the user. This paper presents a serious game, called MiBoard, currently being developed from the iSTART Intelligent Tutoring System. MiBoard incorporates a multiplayer interaction that iSTART was previously unable to provide. This multiplayer interaction produces a wide variation across game trials, while also increasing the repeat playability for users. This paper presents a demonstration of the MiBoard system and the expectations for its application.


MiBoard: A Digital Game from a Physical World

Abstract: Increasing user engagement is constant challenge for Intelligent Tutoring Systems researchers. A current trend in the ITS field is to increase engagement of proven learning systems by integrating them within games, or adding in game like components. Incorporating proven learning methods within a game based environment is expected to add to the overall experience without detracting from the original goals, however, the current study demonstrates two important issues with regard to ITS design. First, effective designs from the physical world do not always translate into the digital world. Second, games do not necessarily improve engagement, and in some cases, they may have the opposite effect. The current study discusses the development and a brief assessment of MiBoard a multiplayer collaborative online board game designed to closely emulate a previously developed physical board game, iSTART: The Board Game.


Gamed-based iSTART Practice: From MiBoard to Self-Explanation Showdown

Abstract: MiBoard (Multiplayer Interactive Board Game) is an online, turnbased board game that was developed to assess the integration of game characteristics (point rewards, game-like interaction, and peer feedback) and how that might affect student engagement and learning efficacy. This online board game was designed to fit within the Extended Practice module of iSTART (Interactive Strategy Training for Active Reading and Thinking). Unfortunately, preliminary research shows that MiBoard actually reduces engagement and does not benefit the quality of student self-explanations when compared to the original Extended Practice module. Consequently the MiBoard framework has been revamped to create Self-Explanation Showdown, a faster-paced, less analytically oriented game that adds competition to the creation of self-explanations. Students are evaluated on the quality of their self-explanations using the same assessment algorithms from iSTART Extended Practice module (this includes both word-based and LSA-based assessments). The technical issues involved in development of MiBoard and Self- Explanation Showdown are described. The lessons learned from the MiBoard experience are also discussed in this paper.


On Unit Distances in a Convex Polygon

Abstract: In 1959, Erd\H{o}s and Moser asked for the maximum number of unit distances that may be formed among the vertices of a convex $n$-gon; until now, the best known upper bound has been $2\pi n \log_2 n + O(n)$, achieved by F\"uredi in 1990. In this paper, we examine two properties that any convex polygon must satisfy and use them to prove several new facts related to the question posed by Erd\H{o}s and Moser. In particular, we improve on F\"uredi's result, and instead obtain a bound of $n \log_2 n + O(n)$; we exhibit a class of `cycles' formed by unit distances that are forbidden in convex polygons; and we provide a lower bound that shows the limitations of our methods. The second result addresses a question asked by Fishburn and Reeds regarding the possible configurations of vertices that form a convex polygon.


Predicting the Impact of Measures Against P2P Networks on the Transient Behaviors

Abstract: The paper has two objectives. The first is to study rigorously the transient behavior of some P2P networks whenever information is replicated and disseminated according to epidemic-like dynamics. The second is to use the insight gained from the previous analysis in order to predict how efficient are measures taken against peer-to-peer (P2P) networks. We first introduce a stochastic model which extends a classical epidemic model and characterize the P2P swarm behavior in presence of free riding peers. We then study a second model in which a peer initiates a contact with another peer chosen randomly. In both cases the network is shown to exhibit a phase transition: a small change in the parameters causes a large change in the behavior of the network. We show, in particular, how the phase transition affects measures that content provider networks may take against P2P networks that distribute non-authorized music or books, and what is the efficiency of counter-measures.


Some New Equiprojective Polyhedra

Abstract: A convex polyhedron $P$ is $k$-equiprojective if all of its orthogonal projections, i.e., shadows, except those parallel to the faces of $P$ are $k$-gon for some fixed value of $k$. Since 1968, it is an open problem to construct all equiprojective polyhedra. Recently, Hasan and Lubiw [CGTA 40(2):148-155, 2008] have given a characterization of equiprojective polyhedra. Based on their characterization, in this paper we discover some new equiprojective polyhedra by cutting and gluing existing polyhedra.


Theory of processes

Abstract: The book gives a detailed exposition of basic concepts and results of a theory of processes. The presentation of theoretical concepts and results is accompanied with illustrations of their application to solving various problems of verification of processes. Along with well-known results there are presented author's results related to verification of processes with message passing, and there are given examples of an application of these results.


Active Integrity Constraints and Revision Programming

Abstract: We study active integrity constraints and revision programming, two formalisms designed to describe integrity constraints on databases and to specify policies on preferred ways to enforce them. Unlike other more commonly accepted approaches, these two formalisms attempt to provide a declarative solution to the problem. However, the original semantics of founded repairs for active integrity constraints and justified revisions for revision programs differ. Our main goal is to establish a comprehensive framework of semantics for active integrity constraints, to find a parallel framework for revision programs, and to relate the two. By doing so, we demonstrate that the two formalisms proposed independently of each other and based on different intuitions when viewed within a broader semantic framework turn out to be notational variants of each other. That lends support to the adequacy of the semantics we develop for each of the formalisms as the foundation for a declarative approach to the problem of database update and repair. In the paper we also study computational properties of the semantics we consider and establish results concerned with the concept of the minimality of change and the invariance under the shifting transformation.


PhishDef: URL Names Say It All

Abstract: Phishing is an increasingly sophisticated method to steal personal user information using sites that pretend to be legitimate. In this paper, we take the following steps to identify phishing URLs. First, we carefully select lexical features of the URLs that are resistant to obfuscation techniques used by attackers. Second, we evaluate the classification accuracy when using only lexical features, both automatically and hand-selected, vs. when using additional features. We show that lexical features are sufficient for all practical purposes. Third, we thoroughly compare several classification algorithms, and we propose to use an online method (AROW) that is able to overcome noisy training data. Based on the insights gained from our analysis, we propose PhishDef, a phishing detection system that uses only URL names and combines the above three elements. PhishDef is a highly accurate method (when compared to state-of-the-art approaches over real datasets), lightweight (thus appropriate for online and client-side deployment), proactive (based on online classification rather than blacklists), and resilient to training data inaccuracies (thus enabling the use of large noisy training data).


SNAP: SNowbAll multi-tree Pushing for Peer-to-Peer Media Streaming

Abstract: Given the respective advantages of the two complimentary techniques for peer-to-peer media streaming (namely tree-based push and mesh-based pull), there is a strong trend of combining them into a hybrid streaming system. Backed by recently proposed mechanisms to identify stable peers, such a hybrid system usually consists of backbone trees formed by the stable peers and other overlay structures in the second tier to accommodate the remaining peers. In this paper, we embrace the hybrid push-pull structure for peer-to-peer media streaming. Our protocol is dominated by a multi-tree push mechanism to minimize the delay in the backbone and is complemented by other overlay structures to cope with peer dynamics. What mainly distinguishes our multi-tree pushing from the conventional ones is an unbalanced tree design guided by the so called snow-ball streaming, which has a provable minimum delay and can be smoothly "melded" with virtually any other existing overlay structures lying in the second tier. We design algorithms to construct and maintain our SNowbAll multi-tree Pushing (SNAP) overlay, and we also illustrate how to smoothly weld the SNAP backbone with the second tier. Finally, we perform simulations in ns-2; the results indicate that our approach outperforms a recently proposed hybrid streaming system.


Mapping Cloud Computing onto Useful e-Governance

Abstract: Most of the services viewed in context to grid and cloud computing are mostly confined to services that are available for intellectual purposes. The grid or cloud computing are large scale distributed systems. The essence of large scale distribution can only be realized if the services are rendered to common man. The only organization which has exposure to almost every single resident is the respective governments in every country. As the size of population increases so the need for a larger purview arises. The problem of having a large purview can be solved by means of large scale grid for online services. The government services can be rendered through fully customized Service-oriented Clouds. In this paper we are presenting tight similarities between generic government functioning and the service oriented grid/cloud approach. Also, we will discuss the major issues in establishing services oriented grids for governmental organization.


Deterministic Online Call Control in Cellular Networks and Triangle-Free Cellular Networks

Abstract: Wireless Communication Networks based on Frequency Division Multiplexing (FDM in short) plays an important role in the field of communications, in which each request can be satisfied by assigning a frequency. To avoid interference, each assigned frequency must be different to the neighboring assigned frequencies. Since frequency is a scarce resource, the main problem in wireless networks is how to fully utilize the given bandwidth of frequencies. In this paper, we consider the online call control problem. Given a fixed bandwidth of frequencies and a sequence of communication requests arrive over time, each request must be either satisfied immediately after its arrival by assigning an available frequency, or rejected. The objective of call control problem is to maximize the number of accepted requests. We study the asymptotic performance of this problem, i.e., the number of requests in the sequence and the bandwidth of frequencies are very large. In this paper, we give a 7/3-competitive algorithm for call control problem in cellular network, improving the previous 2.5-competitive result. Moreover, we investigate the triangle-free cellular network, propose a 9/4-competitive algorithm and prove that the lower bound of competitive ratio is at least 5/3.


The Exponential Time Complexity of Computing the Probability That a Graph is Connected

Abstract: We show that for every probability p with 0 < p < 1, computation of all-terminal graph reliability with edge failure probability p requires time exponential in Omega(m/ log^2 m) for simple graphs of m edges under the Exponential Time Hypothesis.


Network evolution and QOS provisioning for integrated femtocell/macrocell networks

Abstract: Integrated femtocell/macrocell networks, comprising a conventional cellular network overlaid with femtocells, offer an economically appealing way to improve coverage, quality of service, and access network capacity. The key element to successful femtocells/macrocell integration lies in its self-organizing capability. Provisioning of quality of service is the main technical challenge of the femtocell/macrocell integrated networks, while the main administrative challenge is the choice of the proper evolutionary path from the existing macrocellular networks to the integrated network. In this article, we introduce three integrated network architectures which, while increasing the access capacity, they also reduce the deployment and operational costs. Then, we discuss a number of technical issues, which are key to making such integration a reality, and we offer possible approaches to their solution. These issues include efficient frequency and interference management, quality of service provisioning of the xDSL-based backhaul networks, and intelligent handover control.


Ensuring Cache Freshness in On-demand Routing Protocols for Mobile Ad Hoc Network: A Cross-layer Framework

Abstract: One of the big challenges in ad hoc network design is packet routing. Studies have shown that on-demand routing protocols perform better than table-driven routing protocols. In order to avoid route discovery for each packet, on-demand routing protocols cache routes previously learnt. A node in ad hoc network learns routing information by overhearing or forwarding packets to other nodes and keep learned routes in its route cache. However, node movement results broken links and therefore increases risk of cache pollution. Ensuring cache freshness in on-demand routing protocols, therefore, presents a serious challenge. A lot of research has been done in route cache organization, however, little effort has been done for route cache timeout policy to prevent stale routes from being used. In this paper we propose a new cross-layer framework to improve route cache performance in on-demand routing protocols. The proposed framework presents novel use of Received Signal Strength Indicator (RSSI) information to choose cache timeout of individual links in route cache.


A Model of Cooperative Threads

Abstract: We develop a model of concurrent imperative programming with threads. We focus on a small imperative language with cooperative threads which execute without interruption until they terminate or explicitly yield control. We define and study a trace-based denotational semantics for this language; this semantics is fully abstract but mathematically elementary. We also give an equational theory for the computational effects that underlie the language, including thread spawning. We then analyze threads in terms of the free algebra monad for this theory.


Adaptation of the neural network-based IDS to new attacks detection

Abstract: In this paper we report our experiment concerning new attacks detection by a neural network-based Intrusion Detection System. What is crucial for this topic is the adaptation of the neural network that is already in use to correct classification of a new "normal traffic" and of an attack representation not presented during the network training process. When it comes to the new attack it should also be easy to obtain vectors to test and to retrain the neural classifier. We describe the proposal of an algorithm and a distributed IDS architecture that could achieve the goals mentioned above.


A Protection Approach for Video Information transmitted in TCP/IP based networks

Abstract: In this paper an analysis of the existing video information protection methods is made. Analysis of current H.323 protocol stack has been made. A new encryption/decryption layer has been suggested. An approach for partial data encryption in the H.323 protocol stack is proposed and sample architecture of a Virtual Private Video Network is given.


Facility Location with Client Latencies: Linear-Programming based Techniques for Minimum-Latency Problems

Abstract: We introduce a problem that is a common generalization of the uncapacitated facility location and minimum latency (ML) problems, where facilities need to be opened to serve clients and also need to be sequentially activated before they can provide service. Formally, we are given a set \F of n facilities with facility-opening costs {f_i}, a set of m clients, and connection costs {c_{ij}} specifying the cost of assigning a client j to a facility i, a root node r denoting the depot, and a time metric d on \F\cup{r}. Our goal is to open a subset F of facilities, find a path P starting at r and spanning F to activate the open facilities, and connect each client j to a facility \phi(j)\in F, so as to minimize \sum_{i\in F}f_i +\sum_{clients j}(c_{\phi(j),j}+t_j), where t_j is the time taken to reach \phi(j) along path P. We call this the minimum latency uncapacitated facility location (MLUFL) problem. Our main result is an O(\log n\max{\log n,\log m})-approximation for MLUFL. We also show that any improvement in this approximation guarantee, implies an improvement in the (current-best) approximation factor for group Steiner tree. We obtain constant approximations for two natural special cases of the problem: (a) related MLUFL (metric connection costs that are a scalar multiple of the time metric); (b) metric uniform MLUFL (metric connection costs, unform time-metric). Our LP-based methods are versatile and easily adapted to yield approximation guarantees for MLUFL in various more general settings, such as (i) when the latency-cost of a client is a function of the delay faced by the facility to which it is connected; and (ii) the k-route version, where k vehicles are routed in parallel to activate the open facilities. Our LP-based understanding of MLUFL also offers some LP-based insights into ML, which we believe is a promising direction for obtaining improvements for ML.


One software tool for testing square s-boxes

Abstract: An encryption technique is widely used to keep data confidential. Most of the block symmetric algorithms use substitution functions. Often this functions use so called S-BOX matrix. In this paper author presents one software tool for testing and measuring square s-boxes. Based of information theory functions for testing static and dynamic criteria are presented. These criteria are mathematically defined for square s-boxes. Two new criteria "private criteria" a proposed and pseudo codes for they creation and testing are presented.


INDECT Advanced Security Requirements

Abstract: This paper reviews the requirements for the security mechanisms that are currently being developed in the framework of the European research project INDECT. An overview of features for integrated technologies such as Virtual Private Networks (VPNs), Cryptographic Algorithms, Quantum Cryptography, Federated ID Management and Secure Mobile Ad-hoc networking are described together with their expected use in INDECT.


An Improved Algorithm for Reconstructing a Simple Polygon from the Visibility Angles

Abstract: In this paper, we study the following problem of reconstructing a simple polygon: Given a cyclically ordered vertex sequence of an unknown simple polygon P of n vertices and, for each vertex v of P, the sequence of angles defined by all the visible vertices of v in P, reconstruct the polygon P (up to similarity). An O(n^3 log n) time algorithm has been proposed for this problem. We present an improved algorithm with running time O(n^2), based on new observations on the geometric structures of the problem. Since the input size (i.e., the total number of input visibility angles) is O(n^2) in the worst case, our algorithm is worst-case optimal.


Loss Rate Inference in Multi-Sources and Multicast-Based General Topology

Abstract: Loss tomography has received considerable attention in recent years and a number of estimators have been proposed. Unfortunately, almost all of them are devoted to the tree topology despite the general topology is more common in practice. In addition, most of the works presented in the literature rely on iterative approximation to search for the maximum of a likelihood function formed from observations, which have been known neither scalable nor efficient. In contrast to the tree topology, there is few paper dedicated to the general topology because of the lack of understanding the impacts created by the probes sent by different sources. We in this paper present the analytical results obtained recently for the general topology that show the correlation created by the probes sent by multiple sources to a node located in an intersection of multiple trees. The correlation is expressed by a set of polynomials of the pass rates of the paths connecting the sources to the node. In addition to the expression, a closed form solution is proposed to obtain the MLE of the pass rates of the paths connecting the sources to the node. Then, two strategies are proposed to estimate the loss rate of a link for the general topology: one is path-based and the other is link-based, depending on whether we need to obtain the pass rate of a path first. The two strategies are compared in the context of the general topology that shows each has its advantages and the link-based one is more general. Apart from proving the estimates obtained are the MLEs, we prove the estimator presented here has the optimal asymptotic property.


Reinforcement Learning by Comparing Immediate Reward

Abstract: This paper introduces an approach to Reinforcement Learning Algorithm by comparing their immediate rewards using a variation of Q-Learning algorithm. Unlike the conventional Q-Learning, the proposed algorithm compares current reward with immediate reward of past move and work accordingly. Relative reward based Q-learning is an approach towards interactive learning. Q-Learning is a model free reinforcement learning method that used to learn the agents. It is observed that under normal circumstances algorithm take more episodes to reach optimal Q-value due to its normal reward or sometime negative reward. In this new form of algorithm agents select only those actions which have a higher immediate reward signal in comparison to previous one. The contribution of this article is the presentation of new Q-Learning Algorithm in order to maximize the performance of algorithm and reduce the number of episode required to reach optimal Q-value. Effectiveness of proposed algorithm is simulated in a 20 x20 Grid world deterministic environment and the result for the two forms of Q-Learning Algorithms is given.


A New Route Maintenance in Dynamic Source Routing Protocol

Abstract: Mobile ad-hoc network (MANET) is infrastructureless, self-organizable, multi hop packet switched network. A number of routing protocols for MANETs have been proposed in recent years. Dynamic Source Routing (DSR) protocol is one of the most popular routing protocol for ad hoc networks. This paper presents a novel method to enhance route maintenance part of DSR protocol. Our proposed route maintenance significantly increases the efficiency of the protocol at the time of route failures.


On the Reduction of Broadcast Traffic in Mobile Ad Hoc Networks

Abstract: Many mobile ad hoc network protocols use simple flooding, in order to adapt to changes in time varying network topology. Most of the times, a network-wide flood results in redundant packets and increases network congestion, probability of packet collision, low utilization of available bandwidth, and most important, higher power consumption. In this paper, we propose a new cross-layer broadcast scheme to minimize broadcast traffic in mobile ad hoc networks. Our scheme is based on use of received signal strength indicator, RSSI, value to reduce the number of broadcast packets. The effectiveness of the proposed technique is verified using simulations.


A Simple Battery Aware Gossip Based Sleep Protocol for Densely Deployed Ad-hoc and Sensor Networks

Abstract: Conserving power in mobile ad-hoc and sensor networks is a big challenge. Most of the nodes in these networks, in general, are battery powered, therefore, an efficient power saving protocol is required to extend the lifetime of such networks. A lot of work has been done and several protocols have been proposed to address this problem. Gossip based protocols, which are based on the results of percolation theory, significantly reduce power consumption with very little implementation overhead. However, not much work has been done to make gossiping battery aware. In this paper we introduce a simple gossip based battery aware sleep protocol. The protocol allows low battery nodes to sleep more, therefore, improves overall network lifetime.


Small Vertex Cover makes Petri Net Coverability and Boundedness Easier

Abstract: The coverability and boundedness problems for Petri nets are known to be Expspace-complete. Given a Petri net, we associate a graph with it. With the vertex cover number k of this graph and the maximum arc weight W as parameters, we show that coverability and boundedness are in ParaPspace. This means that these problems can be solved in space O(ef(k,W)poly(n)), where ef(k,W) is some exponential function and poly(n) is some polynomial in the size of the input. We then extend the ParaPspace result to model checking a logic that can express some generalizations of coverability and boundedness.


Popularity at Minimum Cost

Abstract: We consider an extension of the {\em popular matching} problem in this paper. The input to the popular matching problem is a bipartite graph G = (A U B,E), where A is a set of people, B is a set of items, and each person a belonging to A ranks a subset of items in an order of preference, with ties allowed. The popular matching problem seeks to compute a matching M* between people and items such that there is no matching M where more people are happier with M than with M*. Such a matching M* is called a popular matching. However, there are simple instances where no popular matching exists. Here we consider the following natural extension to the above problem: associated with each item b belonging to B is a non-negative price cost(b), that is, for any item b, new copies of b can be added to the input graph by paying an amount of cost(b) per copy. When G does not admit a popular matching, the problem is to "augment" G at minimum cost such that the new graph admits a popular matching. We show that this problem is NP-hard; in fact, it is NP-hard to approximate it within a factor of sqrt{n1}/2, where n1 is the number of people. This problem has a simple polynomial time algorithm when each person has a preference list of length at most 2. However, if we consider the problem of "constructing" a graph at minimum cost that admits a popular matching that matches all people, then even with preference lists of length 2, the problem becomes NP-hard. On the other hand, when the number of copies of each item is "fixed", we show that the problem of computing a minimum cost popular matching or deciding that no popular matching exists can be solved in O(mn1) time, where m is the number of edges.


Understanding Fashion Cycles as a Social Choice

Abstract: We present a formal model for studying fashion trends, in terms of three parameters of fashionable items: (1) their innate utility; (2) individual boredom associated with repeated usage of an item; and (3) social influences associated with the preferences from other people. While there are several works that emphasize the effect of social influence in understanding fashion trends, in this paper we show how boredom plays a strong role in both individual and social choices. We show how boredom can be used to explain the cyclic choices in several scenarios such as an individual who has to pick a restaurant to visit every day, or a society that has to repeatedly `vote' on a single fashion style from a collection. We formally show that a society that votes for a single fashion style can be viewed as a single individual cycling through different choices. In our model, the utility of an item gets discounted by the amount of boredom that has accumulated over the past; this boredom increases with every use of the item and decays exponentially when not used. We address the problem of optimally choosing items for usage, so as to maximize over-all satisfaction, i.e., composite utility, over a period of time. First we show that the simple greedy heuristic of always choosing the item with the maximum current composite utility can be arbitrarily worse than the optimal. Second, we prove that even with just a single individual, determining the optimal strategy for choosing items is NP-hard. Third, we show that a simple modification to the greedy algorithm that simply doubles the boredom of each item is a provably close approximation to the optimal strategy. Finally, we present an experimental study over real-world data collected from query logs to compare our algorithms.


On the Design and Analysis of Quaternary Serial and Parallel Adders

Abstract: Optimization techniques for decreasing the time and area of adder circuits have been extensively studied for years mostly in binary logic system. In this paper, we provide the necessary equations required to design a full adder in quaternary logic system. We develop the equations for single-stage parallel adder which works as a carry look-ahead adder. We also provide the design of a logarithmic stage parallel adder which can compute the carries within log2(n) time delay for n qudits. At last, we compare the designs and finally propose a hybrid adder which combines the advantages of serial and parallel adder.


Minimization Strategies for Maximally Parallel Multiset Rewriting Systems

Abstract: Maximally parallel multiset rewriting systems (MPMRS) give a convenient way to express relations between unstructured objects. The functioning of various computational devices may be expressed in terms of MPMRS (e.g., register machines and many variants of P systems). In particular, this means that MPMRS are computationally complete; however, a direct translation leads to quite a big number of rules. Like for other classes of computationally complete devices, there is a challenge to find a universal system having the smallest number of rules. In this article we present different rule minimization strategies for MPMRS based on encodings and structural transformations. We apply these strategies to the translation of a small universal register machine (Korec, 1996) and we show that there exists a universal MPMRS with 23 rules. Since MPMRS are identical to a restricted variant of P systems with antiport rules, the results we obtained improve previously known results on the number of rules for those systems.


A Blink Tree latch method and protocol to support synchronous node deletion

Abstract: A Blink Tree latch method and protocol supports synchronous node deletion in a high concurrency environment. Full source code is available.


How do Range Names Hinder Novice Spreadsheet Debugging Performance?

Abstract: Although experts diverge on how best to improve spreadsheet quality, it is generally agreed that more time needs to be spent testing spreadsheets. Ideally, experienced and trained spreadsheet engineers would carry this out, but quite often this is neither practical nor possible. Many spreadsheets are a legacy, developed by staff that have since moved on, or indeed modified by many staff no longer employed by the organisation. When such spreadsheets fall into the hands of inexperienced, non-experts, any features that reduce error visibility may become a risk. Range names are one such feature, and this paper, building on previous research, investigates in a more structured and controlled manner the effect they have on the debugging performance of novice spreadsheet users.


Spreadsheet Risk Management in Organisations

Abstract: The paper examines in the context of financial reporting, the controls that organisations have in place to manage spreadsheet risk and errors. There has been widespread research conducted in this area, both in Ireland and internationally. This paper describes a study involving 19 participants (2 case studies and 17 by survey) from Ireland. Three areas are examined; firstly, the extent of spreadsheet usage, secondly, the level of complexity employed in spreadsheets, and finally, the controls in place regarding spreadsheets. The findings support previous findings of Panko (1998), that errors occur frequently in spreadsheets and that there is little or unenforced controls employed, however this research finds that attitudes are changing with regard to spreadsheet risk and that one organisation is implementing a comprehensive project regarding policies on the development and control of spreadsheets. Further research could be undertaken in the future to examine the development of a "best practice model" both for the reduction in errors and to minimise the risk in spreadsheet usage.


The Detection of Human Spreadsheet Errors by Humans versus Inspection (Auditing) Software

Abstract: Previous spreadsheet inspection experiments have had human subjects look for seeded errors in spreadsheets. In this study, subjects attempted to find errors in human-developed spreadsheets to avoid the potential artifacts created by error seeding. Human subject success rates were compared to the successful rates for error-flagging by spreadsheet static analysis tools (SSATs) applied to the same spreadsheets. The human error detection results were comparable to those of studies using error seeding. However, Excel Error Check and Spreadsheet Professional were almost useless for correctly flagging natural (human) errors in this study.


Teaching Spreadsheets: Curriculum Design Principles

Abstract: EuSpRIG concerns direct researchers to revisit spreadsheet education, taking into account error auditing tools, checklists, and good practices. This paper aims at elaborating principles to design a spreadsheet curriculum. It mainly focuses on two important issues. Firstly, it is necessary to establish the spreadsheet invariants to be taught, especially those concerning errors and good practices. Secondly, it is important to take into account the learners' ICT experience, and to encourage them to attitudes that foster self-learning. We suggest key principles for spreadsheet teaching, and we illustrate them with teaching guidelines.


Explicit Substitutions for Contextual Type Theory

Abstract: In this paper, we present an explicit substitution calculus which distinguishes between ordinary bound variables and meta-variables. Its typing discipline is derived from contextual modal type theory. We first present a dependently typed lambda calculus with explicit substitutions for ordinary variables and explicit meta-substitutions for meta-variables. We then present a weak head normalization procedure which performs both substitutions lazily and in a single pass thereby combining substitution walks for the two different classes of variables. Finally, we describe a bidirectional type checking algorithm which uses weak head normalization and prove soundness.


Generating Bijections between HOAS and the Natural Numbers

Abstract: A provably correct bijection between higher-order abstract syntax (HOAS) and the natural numbers enables one to define a "not equals" relationship between terms and also to have an adequate encoding of sets of terms, and maps from one term family to another. Sets and maps are useful in many situations and are preferably provided in a library of some sort. I have released a map and set library for use with Twelf which can be used with any type for which a bijection to the natural numbers exists. Since creating such bijections is tedious and error-prone, I have created a "bijection generator" that generates such bijections automatically together with proofs of correctness, all in the context of Twelf.


Closed nominal rewriting and efficiently computable nominal algebra equality

Abstract: We analyse the relationship between nominal algebra and nominal rewriting, giving a new and concise presentation of equational deduction in nominal theories. With some new results, we characterise a subclass of equational theories for which nominal rewriting provides a complete procedure to check nominal algebra equality. This subclass includes specifications of the lambda-calculus and first-order logic.


Pure Type Systems without Explicit Contexts

Abstract: We present an approach to type theory in which the typing judgments do not have explicit contexts. Instead of judgments of shape "Gamma |- A : B", our systems just have judgments of shape "A : B". A key feature is that we distinguish free and bound variables even in pseudo-terms. Specifically we give the rules of the "Pure Type System" class of type theories in this style. We prove that the typing judgments of these systems correspond in a natural way with those of Pure Type Systems as traditionally formulated. I.e., our systems have exactly the same well-typed terms as traditional presentations of type theory. Our system can be seen as a type theory in which all type judgments share an identical, infinite, typing context that has infinitely many variables for each possible type. For this reason we call our system "Gamma_infinity". This name means to suggest that our type judgment "A : B" should be read as "Gamma_infinity |- A : B", with a fixed infinite type context called "Gamma_infinity".


A Monadic Formalization of ML5

Abstract: ML5 is a programming language for spatially distributed computing, based on a Curry-Howard correspondence with the modal logic S5. Despite being designed by a correspondence with S5 modal logic, the ML5 programming language differs from the logic in several ways. In this paper, we explain these discrepancies between ML5 and S5 by translating ML5 into a slightly different logic: intuitionistic S5 extended with a lax modality that encapsulates effectful computations in a monad. This translation both explains the existing ML5 design and suggests some simplifications and generalizations. We have formalized our translation within the Agda proof assistant. Rather than formalizing lax S5 as a proof theory, we \emph{embed} it as a universe within the the dependently typed host language, with the universe elimination given by implementing the modal logic's Kripke semantics. This representation technique saves us the work of defining a proof theory for the logic and proving it correct, and additionally allows us to inherit the equational theory of the meta-language, which can be exploited in proving that the semantics validates the operational semantics of ML5.


Representing Isabelle in LF

Abstract: LF has been designed and successfully used as a meta-logical framework to represent and reason about object logics. Here we design a representation of the Isabelle logical framework in LF using the recently introduced module system for LF. The major novelty of our approach is that we can naturally represent the advanced Isabelle features of type classes and locales. Our representation of type classes relies on a feature so far lacking in the LF module system: morphism variables and abstraction over them. While conservative over the present system in terms of expressivity, this feature is needed for a representation of type classes that preserves the modular structure. Therefore, we also design the necessary extension of the LF module system.


Pattern Unification for the Lambda Calculus with Linear and Affine Types

Abstract: We define the pattern fragment for higher-order unification problems in linear and affine type theory and give a deterministic unification algorithm that computes most general unifiers.


Estimation of Infants' Cry Fundamental Frequency using a Modified SIFT algorithm

Abstract: This paper addresses the problem of infants' cry fundamental frequency estimation. The fundamental frequency is estimated using a modified simple inverse filtering tracking (SIFT) algorithm. The performance of the modified SIFT is studied using a real database of infants' cry. It is shown that the algorithm is capable of overcoming the problem of under-estimation and over-estimation of the cry fundamental frequency, with an estimation accuracy of 6.15% and 3.75%, for hyperphonated and phonated cry segments, respectively. Some typical examples of the fundamental frequency contour in typical cases of pathological and healthy cry signals are presented and discussed.


What we understand is what we get: Assessment in Spreadsheets

Abstract: In previous work we have studied how an explicit representation of background knowledge associated with a specific spreadsheet can be exploited to alleviate usability problems with spreadsheet-based applications. We have implemented this approach in the SACHS system to provide a semantic help system for spreadsheets applications. In this paper, we evaluate the (comprehension) coverage of SACHS on an Excel-based financial controlling system via a "Wizard-of-Oz" experiment. This shows that SACHS adds significant value, but systematically misses important classes of explanations. For judgements about the information contained in spreadsheets, we provide a first approach for an "assessment module" in SACHS.


Proposed System for data hiding using Cryptography and Steganography Proposed System for data hiding using Cryptography and Steganography

Abstract: Steganography and Cryptography are two popular ways of sending vital information in a secret way. One hides the existence of the message and the other distorts the message itself. There are many cryptography techniques available; among them AES is one of the most powerful techniques. In Steganography we have various techniques in different domains like spatial domain, frequency domain etc. to hide the message. It is very difficult to detect hidden message in frequency domain and for this domain we use various transformations like DCT, FFT and Wavelets etc. In this project we are developing a system where we develop a new technique in which Cryptography and Steganography are used as integrated part along with newly developed enhanced security module. In Cryptography we are using AES algorithm to encrypt a message and a part of the message is hidden in DCT of an image; remaining part of the message is used to generate two secret keys which make this system highly secured. Keyword: Cryptography, Steganography, Stego- image, Threshold Value, DCT Coefficient


Sharing Graphs

Abstract: Almost all known secret sharing schemes work on numbers. Such methods will have difficulty in sharing graphs since the number of graphs increases exponentially with the number of nodes. We propose a secret sharing scheme for graphs where we use graph intersection for reconstructing the secret which is hidden as a sub graph in the shares. Our method does not rely on heavy computational operations such as modular arithmetic or polynomial interpolation but makes use of very basic operations like assignment and checking for equality, and graph intersection can also be performed visually. In certain cases, the secret could be reconstructed using just pencil and paper by authorised parties but cannot be broken by an adversary even with unbounded computational power. The method achieves perfect secrecy for (2, n) scheme and requires far fewer operations compared to Shamir's algorithm. The proposed method could be used to share objects such as matrices, sets, plain text and even a heterogeneous collection of these. Since we do not require a previously agreed upon encoding scheme, the method is very suitable for sharing heterogeneous collection of objects in a dynamic fashion.


Tree Languages Defined in First-Order Logic with One Quantifier Alternation

Abstract: We study tree languages that can be defined in \Delta_2 . These are tree languages definable by a first-order formula whose quantifier prefix is forall exists, and simultaneously by a first-order formula whose quantifier prefix is . For the quantifier free part we consider two signatures, either the descendant relation alone or together with the lexicographical order relation on nodes. We provide an effective characterization of tree and forest languages definable in \Delta_2 . This characterization is in terms of algebraic equations. Over words, the class of word languages definable in \Delta_2 forms a robust class, which was given an effective algebraic characterization by Pin and Weil.


On the performance evaluation of wireless networks with broadcast and interference-limited channels

Abstract: In this report we propose a MultiObjective (MO) performance evaluation framework for wireless ad hoc networks where criteria such as capacity, robustness, energy and delay are optimized concurrently. Within such a framework, we can determine both the Pareto-optimal performance bounds and the networking parameters that provide these bounds. The originality of this approach is that it accounts for the inherent broadcast properties of the transmission and finely models the interference distribution. In the proposed model, the network performance can be optimized when several flows (source- destination transmissions) exist. One benefit of our approach is that the complexity does not grow with the number of flows. The other major contribution of this paper is the new analytical formulation of the performance metrics. It relies on a matrix representation of the constraints imposed by the interference- limited and broadcast wireless channel. Because of the similarity of this matrix with a Markovian transition matrix, we can exploit classical results from Markov chains theory to derive steady state performance metrics relative to capacity, robustness, energy and delay. Another very interesting feature of these new metrics is that the Pareto-optimal solutions related to them provide a tight bound on capacity, robustness, energy and delay.


On Second-Order Monadic Monoidal and Groupoidal Quantifiers

Abstract: We study logics defined in terms of second-order monadic monoidal and groupoidal quantifiers. These are generalized quantifiers defined by monoid and groupoid word-problems, equivalently, by regular and context-free languages. We give a computational classification of the expressive power of these logics over strings with varying built-in predicates. In particular, we show that ATIME(n) can be logically characterized in terms of second-order monadic monoidal quantifiers.


Linear-Logic Based Analysis of Constraint Handling Rules with Disjunction

Abstract: Constraint Handling Rules (CHR) is a declarative committed-choice programming language with a strong relationship to linear logic. Its generalization CHR with Disjunction (CHRv) is a multi-paradigm declarative programming language that allows the embedding of horn programs. We analyse the assets and the limitations of the classical declarative semantics of CHR before we motivate and develop a linear-logic declarative semantics for CHR and CHRv. We show how to apply the linear-logic semantics to decide program properties and to prove operational equivalence of CHRv programs across the boundaries of language paradigms.


Informal Control code logic

Abstract: General definitions as well as rules of reasoning regarding control code production, distribution, deployment, and usage are described. The role of testing, trust, confidence and risk analysis is considered. A rationale for control code testing is sought and found for the case of safety critical embedded control code.


Performance Analysis of an Improved Graded Precision Localization Algorithm for Wireless Sensor Networks

Abstract: In this paper an improved version of the graded precision localization algorithm GRADELOC, called IGRADELOC is proposed. The performance of GRADELOC is dependent on the regions formed by the overlapping radio ranges of the nodes of the underlying sensor network. A different region pattern could significantly alter the nature and precision of localization. In IGRADELOC, two improvements are suggested. Firstly, modifications are proposed in the radio range of the fixed-grid nodes, keeping in mind the actual radio range of commonly available nodes, to allow for routing through them. Routing is not addressed by GRADELOC, but is of prime importance to the deployment of any adhoc network, especially sensor networks. A theoretical model expressing the radio range in terms of the cell dimensions of the grid infrastructure is proposed, to help in carrying out a deployment plan which achieves the desirable precision of coarse-grained localization. Secondly, in GRADELOC it is observed that fine-grained localization does not achieve significant performance benefits over coarse-grained localization. In IGRADELOC, this factor is addressed with the introduction of a parameter that could be used to improve and fine-tune the precision of fine-grained localization.


Sensor Scheduling for Energy-Efficient Target Tracking in Sensor Networks

Abstract: In this paper we study the problem of tracking an object moving randomly through a network of wireless sensors. Our objective is to devise strategies for scheduling the sensors to optimize the tradeoff between tracking performance and energy consumption. We cast the scheduling problem as a Partially Observable Markov Decision Process (POMDP), where the control actions correspond to the set of sensors to activate at each time step. Using a bottom-up approach, we consider different sensing, motion and cost models with increasing levels of difficulty. At the first level, the sensing regions of the different sensors do not overlap and the target is only observed within the sensing range of an active sensor. Then, we consider sensors with overlapping sensing range such that the tracking error, and hence the actions of the different sensors, are tightly coupled. Finally, we consider scenarios wherein the target locations and sensors' observations assume values on continuous spaces. Exact solutions are generally intractable even for the simplest models due to the dimensionality of the information and action spaces. Hence, we devise approximate solution techniques, and in some cases derive lower bounds on the optimal tradeoff curves. The generated scheduling policies, albeit suboptimal, often provide close-to-optimal energy-tracking tradeoffs.


Minimum-Area Enclosing Triangle with a Fixed Angle

Abstract: Given a set S of n points in the plane and a fixed angle 0 < omega < pi, we show how to find in O(n log n) time all triangles of minimum area with one angle omega that enclose S. We prove that in general, the solution cannot be written without cubic roots. We also prove an Omega(n log n) lower bound for this problem in the algebraic computation tree model. If the input is a convex n-gon, our algorithm takes Theta(n) time.


Invariant Spectral Hashing of Image Saliency Graph

Abstract: Image hashing is the process of associating a short vector of bits to an image. The resulting summaries are useful in many applications including image indexing, image authentication and pattern recognition. These hashes need to be invariant under transformations of the image that result in similar visual content, but should drastically differ for conceptually distinct contents. This paper proposes an image hashing method that is invariant under rotation, scaling and translation of the image. The gist of our approach relies on the geometric characterization of salient point distribution in the image. This is achieved by the definition of a "saliency graph" connecting these points jointly with an image intensity function on the graph nodes. An invariant hash is then obtained by considering the spectrum of this function in the eigenvector basis of the Laplacian graph, that is, its graph Fourier transform. Interestingly, this spectrum is invariant under any relabeling of the graph nodes. The graph reveals geometric information of the image, making the hash robust to image transformation, yet distinct for different visual content. The efficiency of the proposed method is assessed on a set of MRI 2-D slices and on a database of faces.


Asymmetric Totally-corrective Boosting for Real-time Object Detection

Abstract: Real-time object detection is one of the core problems in computer vision. The cascade boosting framework proposed by Viola and Jones has become the standard for this problem. In this framework, the learning goal for each node is asymmetric, which is required to achieve a high detection rate and a moderate false positive rate. We develop new boosting algorithms to address this asymmetric learning problem. We show that our methods explicitly optimize asymmetric loss objectives in a totally corrective fashion. The methods are totally corrective in the sense that the coefficients of all selected weak classifiers are updated at each iteration. In contract, conventional boosting like AdaBoost is stage-wise in that only the current weak classifier's coefficient is updated. At the heart of the totally corrective boosting is the column generation technique. Experiments on face detection show that our methods outperform the state-of-the-art asymmetric boosting methods.


CloneCloud: Boosting Mobile Device Applications Through Cloud Clone Execution

Abstract: Mobile applications are becoming increasingly ubiquitous and provide ever richer functionality on mobile devices. At the same time, such devices often enjoy strong connectivity with more powerful machines ranging from laptops and desktops to commercial clouds. This paper presents the design and implementation of CloneCloud, a system that automatically transforms mobile applications to benefit from the cloud. The system is a flexible application partitioner and execution runtime that enables unmodified mobile applications running in an application-level virtual machine to seamlessly off-load part of their execution from mobile devices onto device clones operating in a computational cloud. CloneCloud uses a combination of static analysis and dynamic profiling to optimally and automatically partition an application so that it migrates, executes in the cloud, and re-integrates computation in a fine-grained manner that makes efficient use of resources. Our evaluation shows that CloneCloud can achieve up to 21.2x speedup of smartphone applications we tested and it allows different partitioning for different inputs and networks.


Quantum function computation using sublogarithmic space (abstract & poster)

Abstract: We prove that quantum Turing machines are strictly superior to probabilistic Turing machines in function computation for any space bound $ o(\log(n)) $.


D$^2$-Tree: A New Overlay with Deterministic Bounds

Abstract: We present a new overlay, called the {\em Deterministic Decentralized tree} ($D^2$-tree). The $D^2$-tree compares favourably to other overlays for the following reasons: (a) it provides matching and better complexities, which are deterministic for the supported operations; (b) the management of nodes (peers) and elements are completely decoupled from each other; and (c) an efficient deterministic load-balancing mechanism is presented for the uniform distribution of elements into nodes, while at the same time probabilistic optimal bounds are provided for the congestion of operations at the nodes. The load-balancing scheme of elements into nodes is deterministic and general enough to be applied to other hierarchical tree-based overlays. This load-balancing mechanism is based on an innovative lazy weight-balancing mechanism, which is interesting in its own right.


Sensor Management for Tracking in Sensor Networks

Abstract: We study the problem of tracking an object moving through a network of wireless sensors. In order to conserve energy, the sensors may be put into a sleep mode with a timer that determines their sleep duration. It is assumed that an asleep sensor cannot be communicated with or woken up, and hence the sleep duration needs to be determined at the time the sensor goes to sleep based on all the information available to the sensor. Having sleeping sensors in the network could result in degraded tracking performance, therefore, there is a tradeoff between energy usage and tracking performance. We design sleeping policies that attempt to optimize this tradeoff and characterize their performance. As an extension to our previous work in this area [1], we consider generalized models for object movement, object sensing, and tracking cost. For discrete state spaces and continuous Gaussian observations, we derive a lower bound on the optimal energy-tracking tradeoff. It is shown that in the low tracking error regime, the generated policies approach the derived lower bound.


Evaluating Call-By-Need on the Control Stack

Abstract: Ariola and Felleisen's call-by-need {\lambda}-calculus replaces a variable occurrence with its value at the last possible moment. To support this gradual notion of substitution, function applications-once established-are never discharged. In this paper we show how to translate this notion of reduction into an abstract machine that resolves variable references via the control stack. In particular, the machine uses the static address of a variable occurrence to extract its current value from the dynamic control stack.


Computing sparse multiples of polynomials

Abstract: We consider the problem of finding a sparse multiple of a polynomial. Given f in F[x] of degree d over a field F, and a desired sparsity t, our goal is to determine if there exists a multiple h in F[x] of f such that h has at most t non-zero terms, and if so, to find such an h. When F=Q and t is constant, we give a polynomial-time algorithm in d and the size of coefficients in h. When F is a finite field, we show that the problem is at least as hard as determining the multiplicative order of elements in an extension field of F (a problem thought to have complexity similar to that of factoring integers), and this lower bound is tight when t=2.


The Complexity of Rerouting Shortest Paths

Abstract: The Shortest Path Reconfiguration problem has as input a graph G (with unit edge lengths) with vertices s and t, and two shortest st-paths P and Q. The question is whether there exists a sequence of shortest st-paths that starts with P and ends with Q, such that subsequent paths differ in only one vertex. This is called a rerouting sequence. This problem is shown to be PSPACE-complete. For claw-free graphs and chordal graphs, it is shown that the problem can be solved in polynomial time, and that shortest rerouting sequences have linear length. For these classes, it is also shown that deciding whether a rerouting sequence exists between all pairs of shortest st-paths can be done in polynomial time. Finally, a polynomial time algorithm for counting the number of isolated paths is given.


Tableaux for the Lambek-Grishin calculus

Abstract: Categorial type logics, pioneered by Lambek, seek a proof-theoretic understanding of natural language syntax by identifying categories with formulas and derivations with proofs. We typically observe an intuitionistic bias: a structural configuration of hypotheses (a constituent) derives a single conclusion (the category assigned to it). Acting upon suggestions of Grishin to dualize the logical vocabulary, Moortgat proposed the Lambek-Grishin calculus (LG) with the aim of restoring symmetry between hypotheses and conclusions. We develop a theory of labeled modal tableaux for LG, inspired by the interpretation of its connectives as binary modal operators in the relational semantics of Kurtonina and Moortgat. As a linguistic application of our method, we show that grammars based on LG are context-free through use of an interpolation lemma. This result complements that of Melissen, who proved that LG augmented by mixed associativity and -commutativity was exceeds LTAG in expressive power.


A Unified View of Regularized Dual Averaging and Mirror Descent with Implicit Updates

Abstract: We study three families of online convex optimization algorithms: follow-the-proximally-regularized-leader (FTRL-Proximal), regularized dual averaging (RDA), and composite-objective mirror descent. We first prove equivalence theorems that show all of these algorithms are instantiations of a general FTRL update. This provides theoretical insight on previous experimental observations. In particular, even though the FOBOS composite mirror descent algorithm handles L1 regularization explicitly, it has been observed that RDA is even more effective at producing sparsity. Our results demonstrate that FOBOS uses subgradient approximations to the L1 penalty from previous rounds, leading to less sparsity than RDA, which handles the cumulative penalty in closed form. The FTRL-Proximal algorithm can be seen as a hybrid of these two, and outperforms both on a large, real-world dataset. Our second contribution is a unified analysis which produces regret bounds that match (up to logarithmic terms) or improve the best previously known bounds. This analysis also extends these algorithms in two important ways: we support a more general type of composite objective and we analyze implicit updates, which replace the subgradient approximation of the current loss function with an exact optimization.


Proceedings Fourth International Workshop on Testing, Analysis and Verification of Web Software

Abstract: This volume contains the papers presented at the fourth international workshop on Testing, Analysis and Verification of Software, which was associated with the 25th IEEE/ACM International Conference on Automated Software Engineering (ASE 2010). The collection of papers includes research on formal specification, model-checking, testing, and debugging of Web software.


Conditional Random Fields and Support Vector Machines: A Hybrid Approach

Abstract: We propose a novel hybrid loss for multiclass and structured prediction problems that is a convex combination of log loss for Conditional Random Fields (CRFs) and a multiclass hinge loss for Support Vector Machines (SVMs). We provide a sufficient condition for when the hybrid loss is Fisher consistent for classification. This condition depends on a measure of dominance between labels - specifically, the gap in per observation probabilities between the most likely labels. We also prove Fisher consistency is necessary for parametric consistency when learning models such as CRFs. We demonstrate empirically that the hybrid loss typically performs as least as well as - and often better than - both of its constituent losses on variety of tasks. In doing so we also provide an empirical comparison of the efficacy of probabilistic and margin based approaches to multiclass and structured prediction and the effects of label dominance on these results.


An evaluation of the Australian Research Council's journal ranking

Abstract: As part of its program of 'Excellence in Research for Australia' (ERA), the Australian Research Council ranked journals into four categories (A*, A, B, C) in preparation for their performance evaluation of Australian universities. The ranking is important because it likely to have a major impact on publication choices and research dissemination in Australia. The ranking is problematic because it is evident that some disciplines have been treated very differently than others. This paper reveals weaknesses in the ERA journal ranking and highlights the poor correlation between ERA rankings and other acknowledged metrics of journal standing. It highlights the need for a reasonable representation of journals ranked as A* in each scientific discipline.


Fuzzy Ontology Representation using OWL 2

Abstract: The need to deal with vague information in Semantic Web languages is rising in importance and, thus, calls for a standard way to represent such information. We may address this issue by either extending current Semantic Web languages to cope with vagueness, or by providing a procedure to represent such information within current standard languages and tools. In this work, we follow the latter approach, by identifying the syntactic differences that a fuzzy ontology language has to cope with, and by proposing a concrete methodology to represent fuzzy ontologies using OWL 2 annotation properties. We also report on the prototypical implementations.


Temporal Starvation in CSMA Wireless Networks

Abstract: It is well known that links in CSMA wireless networks are prone to starvation. Prior works focused almost exclusively on equilibrium starvation. In this paper, we show that links in CSMA wireless networks are also susceptible to temporal starvation. Specifically, although some links have good equilibrium throughputs and do not suffer from equilibrium starvation, they can still have no throughput for extended periods from time to time. Given its impact on quality of service, it is important to understand and characterize temporal starvation. To this end, we develop a "trap theory" to analyze temporal throughput fluctuation. The trap theory serves two functions. First, it allows us to derive new mathematical results that shed light on the transient behavior of CSMA networks. For example, we show that the duration of a trap, during which some links receive no throughput, is insensitive to the distributions of the backoff countdown and transmission time (packet duration) in the CSMA protocol. Second, we can develop analytical tools for computing the "degrees of starvation" for CSMA networks to aid network design. For example, given a CSMA network, we can determine whether it suffers from starvation, and if so, which links will starve. Furthermore, the likelihood and durations of temporal starvation can also be computed. We believe that the ability to identify and characterize temporal starvation as established in this paper will serve as an important first step toward the design of effective remedies for it.


Semantics of Typed Lambda-Calculus with Constructors

Abstract: We present a Curry-style second-order type system with union and intersection types for the lambda-calculus with constructors of Arbiser, Miquel and Rios, an extension of lambda-calculus with a pattern matching mechanism for variadic constructors. We then prove the strong normalisation and the absence of match failure for a restriction of this system, by adapting the standard reducibility method.


Internal Location Based System for Mobile Devices Using Passive RFID

Abstract: We have explored our own innovative work about the design & development of internal location-identification system for mobile devices based on integration of RFID and wireless technology. The function of our system is based on strategically located passive RFID tags placed on objects around building which are identified using an RFID reader attached to a mobile device. The mobile device reads the RFID tag and through the wireless network, sends the request to the server. The server resolves the request and sends the desired location-based information back to the mobile device. We had addressed that we can go through the RFID technology for internal location identification (indoor), which provides us better location accuracy because of no contact between the tag and the reader, and the system requires no line of sight. In this paper we had also focused on the issues of RFID technologies i.e. Non-line-of-sight & High inventory speeds.


A control-theoretical methodology for the scheduling problem

Abstract: This paper presents a novel methodology to develop scheduling algorithms. The scheduling problem is phrased as a control problem, and control-theoretical techniques are used to design a scheduling algorithm that meets specific requirements. Unlike most approaches to feedback scheduling, where a controller integrates a "basic" scheduling algorithm and dynamically tunes its parameters and hence its performances, our methodology essentially reduces the design of a scheduling algorithm to the synthesis of a controller that closes the feedback loop. This approach allows the re-use of control-theoretical techniques to design efficient scheduling algorithms; it frames and solves the scheduling problem in a general setting; and it can naturally tackle certain peculiar requirements such as robustness and dynamic performance tuning. A few experiments demonstrate the feasibility of the approach on a real-time benchmark.


How to obtain efficient GPU kernels: an illustration using FMM & FGT algorithms

Abstract: Computing on graphics processors is maybe one of the most important developments in computational science to happen in decades. Not since the arrival of the Beowulf cluster, which combined open source software with commodity hardware to truly democratize high-performance computing, has the community been so electrified. Like then, the opportunity comes with challenges. The formulation of scientific algorithms to take advantage of the performance offered by the new architecture requires rethinking core methods. Here, we have tackled fast summation algorithms (fast multipole method and fast Gauss transform), and applied algorithmic redesign for attaining performance on gpus. The progression of performance improvements attained illustrates the exercise of formulating algorithms for the massively parallel architecture of the gpu. The end result has been gpu kernels that run at over 500 Gigaflops on one nvidia Tesla C1060 card, thereby reaching close to practical peak. We can confidently say that gpu computing is not just a vogue, it is truly an irresistible trend in high-performance computing.


On Modelling and Analysis of Dynamic Reconfiguration of Dependable Real-Time Systems

Abstract: This paper motivates the need for a formalism for the modelling and analysis of dynamic reconfiguration of dependable real-time systems. We present requirements that the formalism must meet, and use these to evaluate well established formalisms and two process algebras that we have been developing, namely, Webpi and CCSdp. A simple case study is developed to illustrate the modelling power of these two formalisms. The paper shows how Webpi and CCSdp represent a significant step forward in modelling adaptive and dependable real-time systems.


Delay Modelling for Single Cell IEEE 802.11 WLANs Using a Random Polling System

Abstract: In this paper, we consider the problem of modelling the average delay experienced by a packet in a single cell IEEE 802.11 DCF wireless local area network. The packet arrival process at each node i is assumed to be Poisson with rate parameter \lambda_i. Since the nodes are sharing a single channel, they have to contend with one another for a successful transmission. The mean delay for a packet has been approximated by modelling the system as a 1-limited Random Polling system with zero switchover time. We show that even for non-homogeneous packet arrival processes, the mean delay of packets across the queues are same and depends on the system utilization factor and the aggregate throughput of the MAC. Extensive simulations are conducted to verify the analytical results.


Connectivity graphs of uncertainty regions

Abstract: We study connectivity relations among points, where the precise location of each input point lies in a region of uncertainty. We distinguish two fundamental scenarios under which uncertainty arises. In the favorable Best-Case Uncertainty (BU), each input point can be chosen from a given set to yield the best possible objective value. In the unfavorable Worst-Case Uncertainty (WU), the input set has worst possible objective value among all possible point locations, which are uncertain due, for example, to imprecise data. We consider these notions of uncertainty for the bottleneck spanning tree problem, giving rise to the following Best-Case Connectivity with Uncertainty (BCU) problem: Given a family of geometric regions, choose one point per region, such that the longest edge length of an associated geometric spanning tree is minimized. We show that this problem is NP-hard even for very simple scenarios in which the regions are line segments or squares. On the other hand, we give an exact solution for the case in which there are n+k regions, where k of the regions are line segments and n of the regions are fixed points. We then give approximation algorithms for cases where the regions are either all line segments or all unit discs. We also provide approximation methods for the corresponding Worst-Case Connectivity with Uncertainty (WCU) problem: Given a set of uncertainty regions, find the minimal distance r such that for any choice of points, one per region, there is a spanning tree among the points with edge length at most r.


Simultaneous Interval Graphs

Abstract: In a recent paper, we introduced the simultaneous representation problem (defined for any graph class C) and studied the problem for chordal, comparability and permutation graphs. For interval graphs, the problem is defined as follows. Two interval graphs G_1 and G_2, sharing some vertices I (and the corresponding induced edges), are said to be `simultaneous interval graphs' if there exist interval representations R_1 and R_2 of G_1 and G_2, such that any vertex of I is mapped to the same interval in both R_1 and R_2. Equivalently, G_1 and G_2 are simultaneous interval graphs if there exist edges E' between G_1-I and G_2-I such that G_1 \cup G_2 \cup E' is an interval graph. Simultaneous representation problems are related to simultaneous planar embeddings, and have applications in any situation where it is desirable to consistently represent two related graphs, for example: interval graphs capturing overlaps of DNA fragments of two similar organisms; or graphs connected in time, where one is an updated version of the other. In this paper we give an O(n^2*logn) time algorithm for recognizing simultaneous interval graphs,where n = |G_1 \cup G_2|. This result complements the polynomial time algorithms for recognizing probe interval graphs and provides an efficient algorithm for the interval graph sandwich problem for the special case where the set of optional edges induce a complete bipartite graph.


Open, Closed, and Shared Access Femtocells in the Downlink

Abstract: A fundamental choice in femtocell deployments is the set of users which are allowed to access each femtocell. Closed access restricts the set to specifically registered users, while open access allows any mobile subscriber to use any femtocell. Which one is preferable depends strongly on the distance between the macrocell base station (MBS) and femtocell. The main results of the paper are lemmas which provide expressions for the SINR distribution for various zones within a cell as a function of this MBS-femto distance. The average sum throughput (or any other SINR-based metric) of home users and cellular users under open and closed access can be readily determined from these expressions. We show that unlike in the uplink, the interests of home and cellular users are in conflict, with home users preferring closed access and cellular users preferring open access. The conflict is most pronounced for femtocells near the cell edge, when there are many cellular users and fewer femtocells. To mitigate this conflict, we propose a middle way which we term shared access in which femtocells allocate an adjustable number of time-slots between home and cellular users such that a specified minimum rate for each can be achieved. The optimal such sharing fraction is derived. Analysis shows that shared access achieves at least the overall throughput of open access while also satisfying rate requirements, while closed access fails for cellular users and open access fails for the home user.


Priority Range Trees

Abstract: We describe a data structure, called a priority range tree, which accommodates fast orthogonal range reporting queries on prioritized points. Let $S$ be a set of $n$ points in the plane, where each point $p$ in $S$ is assigned a weight $w(p)$ that is polynomial in $n$, and define the rank of $p$ to be $r(p)=\lfloor \log w(p) \rfloor$. Then the priority range tree can be used to report all points in a three- or four-sided query range $R$ with rank at least $\lfloor \log w \rfloor$ in time $O(\log W/w + k)$, and report $k$ highest-rank points in $R$ in time $O(\log\log n + \log W/w' + k)$, where $W=\sum_{p\in S}{w(p)}$, $w'$ is the smallest weight of any point reported, and $k$ is the output size. All times assume the standard RAM model of computation. If the query range of interest is three sided, then the priority range tree occupies $O(n)$ space, otherwise $O(n\log n)$ space is used to answer four-sided queries. These queries are motivated by the Weber--Fechner Law, which states that humans perceive and interpret data on a logarithmic scale.


PC 2 Phone Event Announcer

Abstract: Nowadays, mobile phones are indispensable devices; it has become a trend now that college and university students are owners of such devices and this particular factor plays a very important role behind the coming up with the proposed system. "PC 2 Phone event Announcer", is the name of the new proposed system suggested to solve the existing communication problem between the College staff and students. As the name suggests, it can be deduced that the system will involve computers and phones, more specifically mobile phones.


Mobile Testbeds with an Attitude

Abstract: There have been significant recent advances in mobile networks, specifically in multi-hop wireless networks including DTNs and sensor networks. It is critical to have a testing environment to realistically evaluate such networks and their protocols and services. Towards this goal, we propose a novel, mobile testbed of two main components. The first consists of a network of robots with personality- mimicking, human-encounter behaviors, which will be the focus of this demo. The personality is build upon behavioral profiling of mobile users based on extensive wireless-network measurements and analysis. The second component combines the testbed with the human society using a new concept that we refer to as participatory testing utilizing crowd sourcing.


Deep Self-Taught Learning for Handwritten Character Recognition

Abstract: Recent theoretical and empirical work in statistical machine learning has demonstrated the importance of learning algorithms for deep architectures, i.e., function classes obtained by composing multiple non-linear transformations. Self-taught learning (exploiting unlabeled examples or examples from other distributions) has already been applied to deep learners, but mostly to show the advantage of unlabeled examples. Here we explore the advantage brought by {\em out-of-distribution examples}. For this purpose we developed a powerful generator of stochastic variations and noise processes for character images, including not only affine transformations but also slant, local elastic deformations, changes in thickness, background images, grey level changes, contrast, occlusion, and various types of noise. The out-of-distribution examples are obtained from these highly distorted images or by including examples of object classes different from those in the target test set. We show that {\em deep learners benefit more from out-of-distribution examples than a corresponding shallow learner}, at least in the area of handwritten character recognition. In fact, we show that they beat previously published results and reach human-level performance on both handwritten digit classification and 62-class handwritten character recognition.


Center-based Clustering under Perturbation Stability

Abstract: Clustering under most popular objective functions is NP-hard, even to approximate well, and so unlikely to be efficiently solvable in the worst case. Recently, Bilu and Linial \cite{Bilu09} suggested an approach aimed at bypassing this computational barrier by using properties of instances one might hope to hold in practice. In particular, they argue that instances in practice should be stable to small perturbations in the metric space and give an efficient algorithm for clustering instances of the Max-Cut problem that are stable to perturbations of size $O(n^{1/2})$. In addition, they conjecture that instances stable to as little as O(1) perturbations should be solvable in polynomial time. In this paper we prove that this conjecture is true for any center-based clustering objective (such as $k$-median, $k$-means, and $k$-center). Specifically, we show we can efficiently find the optimal clustering assuming only stability to factor-3 perturbations of the underlying metric in spaces without Steiner points, and stability to factor $2+\sqrt{3}$ perturbations for general metrics. In particular, we show for such instances that the popular Single-Linkage algorithm combined with dynamic programming will find the optimal clustering. We also present NP-hardness results under a weaker but related condition.


Small NFAs from Regular Expressions: Some Experimental Results

Abstract: Regular expressions (res), because of their succinctness and clear syntax, are the common choice to represent regular languages. However, efficient pattern matching or word recognition depend on the size of the equivalent nondeterministic finite automata (NFA). We present the implementation of several algorithms for constructing small epsilon-free NFAss from res within the FAdo system, and a comparison of regular expression measures and NFA sizes based on experimental results obtained from uniform random generated res. For this analysis, nonredundant res and reduced res in star normal form were considered.


Geometric Decision Tree

Abstract: In this paper we present a new algorithm for learning oblique decision trees. Most of the current decision tree algorithms rely on impurity measures to assess the goodness of hyperplanes at each node while learning a decision tree in a top-down fashion. These impurity measures do not properly capture the geometric structures in the data. Motivated by this, our algorithm uses a strategy to assess the hyperplanes in such a way that the geometric structure in the data is taken into account. At each node of the decision tree, we find the clustering hyperplanes for both the classes and use their angle bisectors as the split rule at that node. We show through empirical studies that this idea leads to small decision trees and better performance. We also present some analysis to show that the angle bisectors of clustering hyperplanes that we use as the split rules at each node, are solutions of an interesting optimization problem and hence argue that this is a principled method of learning a decision tree.


On the Doubt about Margin Explanation of Boosting

Abstract: Margin theory provides one of the most popular explanations to the success of \texttt{AdaBoost}, where the central point lies in the recognition that \textit{margin} is the key for characterizing the performance of \texttt{AdaBoost}. This theory has been very influential, e.g., it has been used to argue that \texttt{AdaBoost} usually does not overfit since it tends to enlarge the margin even after the training error reaches zero. Previously the \textit{minimum margin bound} was established for \texttt{AdaBoost}, however, \cite{Breiman1999} pointed out that maximizing the minimum margin does not necessarily lead to a better generalization. Later, \cite{Reyzin:Schapire2006} emphasized that the margin distribution rather than minimum margin is crucial to the performance of \texttt{AdaBoost}. In this paper, we first present the \textit{$k$th margin bound} and further study on its relationship to previous work such as the minimum margin bound and Emargin bound. Then, we improve the previous empirical Bernstein bounds \citep{Maurer:Pontil2009,Audibert:Munos:Szepesvari2009}, and based on such findings, we defend the margin-based explanation against Breiman's doubts by proving a new generalization error bound that considers exactly the same factors as \cite{Schapire:Freund:Bartlett:Lee1998} but is sharper than \cite{Breiman1999}'s minimum margin bound. By incorporating factors such as average margin and variance, we present a generalization error bound that is heavily related to the whole margin distribution. We also provide margin distribution bounds for generalization error of voting classifiers in finite VC-dimension space.


Influence is a Matter of Degree: New Algorithms for Activation Problems

Abstract: We consider the target set selection problem. In this problem, a vertex is active either if it belongs to a set of initially activated vertices or if at some point it has at least $k$ active neighbors ($k$ is identical for all vertices of the graph). Our goal is to find a set of minimum size whose activation will result with the entire graph being activated. Call such a set \emph{contagious}. We prove that if $G=(V,E)$ is an undirected graph, the size of a contagious set is bounded by $\sum_{v\in V}{\min \{1,\frac{k}{d(v)+1}\}}$ (where $d(v)$ is the degree of $v$). We present a simple and efficient algorithm that finds a contagious set that is not larger than the aforementioned bound and discuss algorithmic applications of this algorithm to finding contagious sets in dense graphs.


A Survey on Low-cost RFID Authentication Protocols

Abstract: This paper presents a survey on several RFID authentication protocols under low cost restrictions. Low cost RFID are mainly addressed with limited security and privacy protections. In this study, we explore several protocols with various authentication mechanisms found in literature that satisfy low cost restrictions. Assessments of these protocols are based on data protection, tracking protection, forward security. Finally, it is concluded that no single low cost RFID protocol fully meets the requirement of the given assessments. While a protocol satisfies one or two assessments, it fails to fully meet the requirement of the third assessment. This study provides a new insight in RFID literature which can be used particularly by small and medium industries to choose the appropriate RFID protocol for their needs.


A Dynamic Data Middleware Cache for Rapidly-growing Scientific Repositories

Abstract: Modern scientific repositories are growing rapidly in size. Scientists are increasingly interested in viewing the latest data as part of query results. Current scientific middleware cache systems, however, assume repositories are static. Thus, they cannot answer scientific queries with the latest data. The queries, instead, are routed to the repository until data at the cache is refreshed. In data-intensive scientific disciplines, such as astronomy, indiscriminate query routing or data refreshing often results in runaway network costs. This severely affects the performance and scalability of the repositories and makes poor use of the cache system. We present Delta, a dynamic data middleware cache system for rapidly-growing scientific repositories. Delta's key component is a decision framework that adaptively decouples data objects---choosing to keep some data object at the cache, when they are heavily queried, and keeping some data objects at the repository, when they are heavily updated. Our algorithm profiles incoming workload to search for optimal data decoupling that reduces network costs. It leverages formal concepts from the network flow problem, and is robust to evolving scientific workloads. We evaluate the efficacy of Delta, through a prototype implementation, by running query traces collected from a real astronomy survey.


Efficient Indexing of the BitTorrent Distributed Hash Table

Abstract: The following paper presents various methods and implementation techniques used to harvest metadata efficiently from a Kademlia Distributed Hashtable (DHT) as used in the BitTorrent P2P network to build an index of publicly available files in the BitTorrent ecosystem. The indexer design makes various tradeoffs between throughput and fairness towards other DHT nodes while also being scaleable in a distributed environment.


3-SAT Polynomial Solution of Knowledge Recognition Algorithm

Abstract: This paper introduces a knowledge recognition algorithm (KRA) for solving the 3SAT problem in polynomial time. KRA learns member-class relations and retrieves information through deductive and reductive iterative reasoning. It applies the principle of Chinese COVA* (equivalent to a set of eight 3-variable conjunctive clauses) and eliminates the "OR" operation to solve 3-SAT problem. That is, KRA does not search the assignment directly. It recognizes the complements as rejections at each level of the set through iterative set relation recognition. KRA recognizes which conjunctive 3-variable-clause is not satisfiable. If all the eight clauses of any set of 3-variable clauses are rejected, then there is not an assignment for the formula. If there is at least one clause in each set that remains, then there is at least one assignment that is the union of clauses of each set. If there is more than one clause in each set that remains, then there are multiple assignments that are the unions of the clauses of each set respectively.


Totally Corrective Multiclass Boosting with Binary Weak Learners

Abstract: In this work, we propose a new optimization framework for multiclass boosting learning. In the literature, AdaBoost.MO and AdaBoost.ECC are the two successful multiclass boosting algorithms, which can use binary weak learners. We explicitly derive these two algorithms' Lagrange dual problems based on their regularized loss functions. We show that the Lagrange dual formulations enable us to design totally-corrective multiclass algorithms by using the primal-dual optimization technique. Experiments on benchmark data sets suggest that our multiclass boosting can achieve a comparable generalization capability with state-of-the-art, but the convergence speed is much faster than stage-wise gradient descent boosting. In other words, the new totally corrective algorithms can maximize the margin more aggressively.


Optimizing Computation of Recovery Plans for BPEL Applications

Abstract: Web service applications are distributed processes that are composed of dynamically bounded services. In our previous work [15], we have described a framework for performing runtime monitoring of web service against behavioural correctness properties (described using property patterns and converted into finite state automata). These specify forbidden behavior (safety properties) and desired behavior (bounded liveness properties). Finite execution traces of web services described in BPEL are checked for conformance at runtime. When violations are discovered, our framework automatically proposes and ranks recovery plans which users can then select for execution. Such plans for safety violations essentially involve "going back" - compensating the executed actions until an alternative behaviour of the application is possible. For bounded liveness violations, recovery plans include both "going back" and "re-planning" - guiding the application towards a desired behaviour. Our experience, reported in [16], identified a drawback in this approach: we compute too many plans due to (a) overapproximating the number of program points where an alternative behaviour is possible and (b) generating recovery plans for bounded liveness properties which can potentially violate safety properties. In this paper, we describe improvements to our framework that remedy these problems and describe their effectiveness on a case study.


Structural Learning of Attack Vectors for Generating Mutated XSS Attacks

Abstract: Web applications suffer from cross-site scripting (XSS) attacks that resulting from incomplete or incorrect input sanitization. Learning the structure of attack vectors could enrich the variety of manifestations in generated XSS attacks. In this study, we focus on generating more threatening XSS attacks for the state-of-the-art detection approaches that can find potential XSS vulnerabilities in Web applications, and propose a mechanism for structural learning of attack vectors with the aim of generating mutated XSS attacks in a fully automatic way. Mutated XSS attack generation depends on the analysis of attack vectors and the structural learning mechanism. For the kernel of the learning mechanism, we use a Hidden Markov model (HMM) as the structure of the attack vector model to capture the implicit manner of the attack vector, and this manner is benefited from the syntax meanings that are labeled by the proposed tokenizing mechanism. Bayes theorem is used to determine the number of hidden states in the model for generalizing the structure model. The paper has the contributions as following: (1) automatically learn the structure of attack vectors from practical data analysis to modeling a structure model of attack vectors, (2) mimic the manners and the elements of attack vectors to extend the ability of testing tool for identifying XSS vulnerabilities, (3) be helpful to verify the flaws of blacklist sanitization procedures of Web applications. We evaluated the proposed mechanism by Burp Intruder with a dataset collected from public XSS archives. The results show that mutated XSS attack generation can identify potential vulnerabilities.


Preventing SQL Injection through Automatic Query Sanitization with ASSIST

Abstract: Web applications are becoming an essential part of our everyday lives. Many of our activities are dependent on the functionality and security of these applications. As the scale of these applications grows, injection vulnerabilities such as SQL injection are major security challenges for developers today. This paper presents the technique of automatic query sanitization to automatically remove SQL injection vulnerabilities in code. In our technique, a combination of static analysis and program transformation are used to automatically instrument web applications with sanitization code. We have implemented this technique in a tool named ASSIST (Automatic and Static SQL Injection Sanitization Tool) for protecting Java-based web applications. Our experimental evaluation showed that our technique is effective against SQL injection vulnerabilities and has a low overhead.


Relational Constraint Driven Test Case Synthesis for Web Applications

Abstract: This paper proposes a relational constraint driven technique that synthesizes test cases automatically for web applications. Using a static analysis, servlets can be modeled as relational transducers, which manipulate backend databases. We present a synthesis algorithm that generates a sequence of HTTP requests for simulating a user session. The algorithm relies on backward symbolic image computation for reaching a certain database state, given a code coverage objective. With a slight adaptation, the technique can be used for discovering workflow attacks on web applications.


Browser-based Analysis of Web Framework Applications

Abstract: Although web applications evolved to mature solutions providing sophisticated user experience, they also became complex for the same reason. Complexity primarily affects the server-side generation of dynamic pages as they are aggregated from multiple sources and as there are lots of possible processing paths depending on parameters. Browser-based tests are an adequate instrument to detect errors within generated web pages considering the server-side process and path complexity a black box. However, these tests do not detect the cause of an error which has to be located manually instead. This paper proposes to generate metadata on the paths and parts involved during server-side processing to facilitate backtracking origins of detected errors at development time. While there are several possible points of interest to observe for backtracking, this paper focuses user interface components of web frameworks.