Computational Complexity Analysis of Multi-Objective Genetic Programming

Abstract: The computational complexity analysis of genetic programming (GP) has been started recently by analyzing simple (1+1) GP algorithms for the problems ORDER and MAJORITY. In this paper, we study how taking the complexity as an additional criteria influences the runtime behavior. We consider generalizations of ORDER and MAJORITY and present a computational complexity analysis of (1+1) GP using multi-criteria fitness functions that take into account the original objective and the complexity of a syntax tree as a secondary measure. Furthermore, we study the expected time until population-based multi-objective genetic programming algorithms have computed the Pareto front when taking the complexity of a syntax tree as an equally important objective.


Single pass sparsification in the streaming model with edge deletions

Abstract: In this paper we give a construction of cut sparsifiers of Benczur and Karger in the {\em dynamic} streaming setting in a single pass over the data stream. Previous constructions either required multiple passes or were unable to handle edge deletions. We use $\tilde{O}(1/\e^2)$ time for each stream update and $\tilde{O}(n/\e^2)$ time to construct a sparsifier. Our $\e$-sparsifiers have $O(n\log^3 n/\e^2)$ edges. The main tools behind our result are an application of sketching techniques of Ahn et al.[SODA'12] to estimate edge connectivity together with a novel application of sampling with limited independence and sparse recovery to produce the edges of the sparsifier.


A survey of proof nets and matrices for substructural logics

Abstract: This paper is a survey of two kinds of "compressed" proof schemes, the \emph{matrix method} and \emph{proof nets}, as applied to a variety of logics ranging along the substructural hierarchy from classical all the way down to the nonassociative Lambek system. A novel treatment of proof nets for the latter is provided. Descriptions of proof nets and matrices are given in a uniform notation based on sequents, so that the properties of the schemes for the various logics can be easily compared.


Combined Channel Aggregation and Fragmentation Strategy in Cognitive Radio Networks

Abstract: In cognitive radio networks, channel aggregation (CA) and channel fragmentation (CF) techniques have been proposed to enhance the spectrum utilization. While most of the literature studies CA and CF independently, in this paper we combine CA and CF innovatively and present a new spectrum sharing strategy named CAF (Channel Aggregation and Fragmentation). We elaborate on the proposed CAF strategy and derive the balance equation by a continuous time Markov chain (CTMC) model. Then various system performance metrics including blocking probability, dropping probability, spectrum utilization and throughput of the secondary network are evaluated. Both analytical and simulation results show that our strategy lowers the blocking and dropping probabilities and enhances the spectrum utilization and throughput effectively. Moreover, by tuning the bandwidth requirement of each secondary user, different system performance can be achieved.


Work function algorithm can forget history without losing competitiveness

Abstract: The Work Function Algorithm is the most effective deterministic on-line algorithm for the k-server problem. Koutsoupias and Papadimitriou proved WFA is (2k-1) competitive. However the best known implementation of WFA requires time O(i^2) to process request r_i and this makes WFA impractical for long sequences of requests. The O(i^2) time is spent to compute the work function on the whole history of past requests. In order to make constant the time to process a request, Rudec and Menger proposed to restrict the history to a moving window of fixed size. However WFA restricted to a moving window loses its competitiveness. Here we give a condition that allows WFA to forget the whole previous history and restart from scratch without losing competitiveness. Moreover for most of the metric spaces of practical interest (finite or bounded spaces) there is a constant bound on the length of the history before the condition is verified and this makes O(1) the time to process each request.


Kernels for linear time invariant system identification

Abstract: In this paper, we study the problem of identifying the impulse response of a linear time invariant (LTI) dynamical system from the knowledge of the input signal and a finite set of noisy output observations. We adopt an approach based on regularization in a Reproducing Kernel Hilbert Space (RKHS) that takes into account both continuous and discrete time systems. The focus of the paper is on designing spaces that are well suited for temporal impulse response modeling. To this end, we construct and characterize general families of kernels that incorporate system properties such as stability, relative degree, absence of oscillatory behavior, smoothness, or delay. In addition, we discuss the possibility of automatically searching over these classes by means of kernel learning techniques, so as to capture different modes of the system to be identified.


Reduplicated MWE (RMWE) helps in improving the CRF based Manipuri POS Tagger

Abstract: This paper gives a detail overview about the modified features selection in CRF (Conditional Random Field) based Manipuri POS (Part of Speech) tagging. Selection of features is so important in CRF that the better are the features then the better are the outputs. This work is an attempt or an experiment to make the previous work more efficient. Multiple new features are tried to run the CRF and again tried with the Reduplicated Multiword Expression (RMWE) as another feature. The CRF run with RMWE because Manipuri is rich of RMWE and identification of RMWE becomes one of the necessities to bring up the result of POS tagging. The new CRF system shows a Recall of 78.22%, Precision of 73.15% and F-measure of 75.60%. With the identification of RMWE and considering it as a feature makes an improvement to a Recall of 80.20%, Precision of 74.31% and F-measure of 77.14%.


Advanced Programming Platform for efficient use of Data Parallel Hardware

Abstract: Graphics processing units (GPU) had evolved from a specialized hardware capable to render high quality graphics in games to a commodity hardware for effective processing blocks of data in a parallel schema. This evolution is particularly interesting for scientific groups, which traditionally use mainly CPU as a work horse, and now can profit of the arrival of GPU hardware to HPC clusters. This new GPU hardware promises a boost in peak performance, but it is not trivial to use. In this article a programming platform designed to promote a direct use of this specialized hardware is presented. This platform includes a visual editor of parallel data flows and it is oriented to the execution in distributed clusters with GPUs. Examples of application in two characteristic problems, Fast Fourier Transform and Image Compression, are also shown.


CUDA implementation of Wagener's 2D convex hull PRAM algorithm

Abstract: This paper describes a CUDA implementation of Wagener's PRAM convex hull algorithm in two dimensions. It is presented in Knuth's literate programming style.


Hybridizing PSM and RSM Operator for Solving NP-Complete Problems: Application to Travelling Salesman Problem

Abstract: In this paper, we present a new mutation operator, Hybrid Mutation (HPRM), for a genetic algorithm that generates high quality solutions to the Traveling Salesman Problem (TSP). The Hybrid Mutation operator constructs an offspring from a pair of parents by hybridizing two mutation operators, PSM and RSM. The efficiency of the HPRM is compared as against some existing mutation operators; namely, Reverse Sequence Mutation (RSM) and Partial Shuffle Mutation (PSM) for BERLIN52 as instance of TSPLIB. Experimental results show that the new mutation operator is better than the RSM and PSM.


Minimizing Slowdown in Heterogeneous Size-Aware Dispatching Systems (full version)

Abstract: We consider a system of parallel queues where tasks are assigned (dispatched) to one of the available servers upon arrival. The dispatching decision is based on the full state information, i.e., on the sizes of the new and existing jobs. We are interested in minimizing the so-called mean slowdown criterion corresponding to the mean of the sojourn time divided by the processing time. Assuming no new jobs arrive, the shortest-processing-time-product (SPTP) schedule is known to minimize the slowdown of the existing jobs. The main contribution of this paper is three-fold: 1) To show the optimality of SPTP with respect to slowdown in a single server queue under Poisson arrivals; 2) to derive the so-called size-aware value functions for M/G/1-FIFO/LIFO/SPTP/SPT/SRPT with general holding costs of which the slowdown criterion is a special case; and 3) to utilize the value functions to derive efficient dispatching policies so as to minimize the mean slowdown in a heterogeneous server system. The derived policies offer a significantly better performance than e.g., the size-aware-task-assignment with equal load (SITA-E) and least-work-left (LWL) policies.


Analysing Temporally Annotated Corpora with CAVaT

Abstract: We present CAVaT, a tool that performs Corpus Analysis and Validation for TimeML. CAVaT is an open source, modular checking utility for statistical analysis of features specific to temporally-annotated natural language corpora. It provides reporting, highlights salient links between a variety of general and time-specific linguistic features, and also validates a temporal annotation to ensure that it is logically consistent and sufficiently annotated. Uniquely, CAVaT provides analysis specific to TimeML-annotated temporal information. TimeML is a standard for annotating temporal information in natural language text. In this paper, we present the reporting part of CAVaT, and then its error-checking ability, including the workings of several novel TimeML document verification methods. This is followed by the execution of some example tasks using the tool to show relations between times, events, signals and links. We also demonstrate inconsistencies in a TimeML corpus (TimeBank) that have been detected with CAVaT.


Using Signals to Improve Automatic Classification of Temporal Relations

Abstract: Temporal information conveyed by language describes how the world around us changes through time. Events, durations and times are all temporal elements that can be viewed as intervals. These intervals are sometimes temporally related in text. Automatically determining the nature of such relations is a complex and unsolved problem. Some words can act as "signals" which suggest a temporal ordering between intervals. In this paper, we use these signal words to improve the accuracy of a recent approach to classification of temporal links.


USFD2: Annotating Temporal Expresions and TLINKs for TempEval-2

Abstract: We describe the University of Sheffield system used in the TempEval-2 challenge, USFD2. The challenge requires the automatic identification of temporal entities and relations in text. USFD2 identifies and anchors temporal expressions, and also attempts two of the four temporal relation assignment tasks. A rule-based system picks out and anchors temporal expressions, and a maximum entropy classifier assigns temporal link labels, based on features that include descriptions of associated temporal signal words. USFD2 identified temporal expressions successfully, and correctly classified their type in 90% of cases. Determining the relation between an event and time expression in the same sentence was performed at 63% accuracy, the second highest score in this part of the challenge.


An Annotation Scheme for Reichenbach's Verbal Tense Structure

Abstract: In this paper we present RTMML, a markup language for the tenses of verbs and temporal relations between verbs. There is a richness to tense in language that is not fully captured by existing temporal annotation schemata. Following Reichenbach we present an analysis of tense in terms of abstract time points, with the aim of supporting automated processing of tense and temporal relations in language. This allows for precise reasoning about tense in documents, and the deduction of temporal relations between the times and verbal events in a discourse. We define the syntax of RTMML, and demonstrate the markup in a range of situations.


A Corpus-based Study of Temporal Signals

Abstract: Automatic temporal ordering of events described in discourse has been of great interest in recent years. Event orderings are conveyed in text via va rious linguistic mechanisms including the use of expressions such as "before", "after" or "during" that explicitly assert a temporal relation -- temporal signals. In this paper, we investigate the role of temporal signals in temporal relation extraction and provide a quantitative analysis of these expres sions in the TimeBank annotated corpus.


USFD at KBP 2011: Entity Linking, Slot Filling and Temporal Bounding

Abstract: This paper describes the University of Sheffield's entry in the 2011 TAC KBP entity linking and slot filling tasks. We chose to participate in the monolingual entity linking task, the monolingual slot filling task and the temporal slot filling tasks. We set out to build a framework for experimentation with knowledge base population. This framework was created, and applied to multiple KBP tasks. We demonstrated that our proposed framework is effective and suitable for collaborative development efforts, as well as useful in a teaching environment. Finally we present results that, while very modest, provide improvements an order of magnitude greater than our 2010 attempt.


Massively Increasing TIMEX3 Resources: A Transduction Approach

Abstract: Automatic annotation of temporal expressions is a research challenge of great interest in the field of information extraction. Gold standard temporally-annotated resources are limited in size, which makes research using them difficult. Standards have also evolved over the past decade, so not all temporally annotated data is in the same format. We vastly increase available human-annotated temporal expression resources by converting older format resources to TimeML/TIMEX3. This task is difficult due to differing annotation methods. We present a robust conversion tool and a new, large temporal expression resource. Using this, we evaluate our conversion process by using it as training data for an existing TimeML annotation tool, achieving a 0.87 F1 measure -- better than any system in the TempEval-2 timex recognition exercise.


Kernel Density Feature Points Estimator for Content-Based Image Retrieval

Abstract: Research is taking place to find effective algorithms for content-based image representation and description. There is a substantial amount of algorithms available that use visual features (color, shape, texture). Shape feature has attracted much attention from researchers that there are many shape representation and description algorithms in literature. These shape image representation and description algorithms are usually not application independent or robust, making them undesirable for generic shape description. This paper presents an object shape representation using Kernel Density Feature Points Estimator (KDFPE). In this method, the density of feature points within defined rings around the centroid of the image is obtained. The KDFPE is then applied to the vector of the image. KDFPE is invariant to translation, scale and rotation. This method of image representation shows improved retrieval rate when compared to Density Histogram Feature Points (DHFP) method. Analytic analysis is done to justify our method, which was compared with the DHFP to prove its robustness.


A Data Driven Approach to Query Expansion in Question Answering

Abstract: Automated answering of natural language questions is an interesting and useful problem to solve. Question answering (QA) systems often perform information retrieval at an initial stage. Information retrieval (IR) performance, provided by engines such as Lucene, places a bound on overall system performance. For example, no answer bearing documents are retrieved at low ranks for almost 40% of questions. In this paper, answer texts from previous QA evaluations held as part of the Text REtrieval Conferences (TREC) are paired with queries and analysed in an attempt to identify performance-enhancing words. These words are then used to evaluate the performance of a query expansion method. Data driven extension words were found to help in over 70% of difficult questions. These words can be used to improve and evaluate query expansion methods. Simple blind relevance feedback (RF) was correctly predicted as unlikely to help overall performance, and an possible explanation is provided for its low value in IR for QA.


Bayesian Optimal Auctions via Multi- to Single-agent Reduction

Abstract: We study an abstract optimal auction problem for a single good or service. This problem includes environments where agents have budgets, risk preferences, or multi-dimensional preferences over several possible configurations of the good (furthermore, it allows an agent's budget and risk preference to be known only privately to the agent). These are the main challenge areas for auction theory. A single-agent problem is to optimize a given objective subject to a constraint on the maximum probability with which each type is allocated, a.k.a., an allocation rule. Our approach is a reduction from multi-agent mechanism design problem to collection of single-agent problems. We focus on maximizing revenue, but our results can be applied to other objectives (e.g., welfare). An optimal multi-agent mechanism can be computed by a linear/convex program on interim allocation rules by simultaneously optimizing several single-agent mechanisms subject to joint feasibility of the allocation rules. For single-unit auctions, Border \citeyearpar{B91} showed that the space of all jointly feasible interim allocation rules for $n$ agents is a $\NumTypes$-dimensional convex polytope which can be specified by $2^\NumTypes$ linear constraints, where $\NumTypes$ is the total number of all agents' types. Consequently, efficiently solving the mechanism design problem requires a separation oracle for the feasibility conditions and also an algorithm for ex-post implementation of the interim allocation rules. We show that the polytope of jointly feasible interim allocation rules is the projection of a higher dimensional polytope which can be specified by only $O(\NumTypes^2)$ linear constraints. Furthermore, our proof shows that finding a preimage of the interim allocation rules in the higher dimensional polytope immediately gives an ex-post implementation.


A Reduction-Preserving Completion for Proving Confluence of Non-Terminating Term Rewriting Systems

Abstract: We give a method to prove confluence of term rewriting systems that contain non-terminating rewrite rules such as commutativity and associativity. Usually, confluence of term rewriting systems containing such rules is proved by treating them as equational term rewriting systems and considering E-critical pairs and/or termination modulo E. In contrast, our method is based solely on usual critical pairs and it also (partially) works even if the system is not terminating modulo E. We first present confluence criteria for term rewriting systems whose rewrite rules can be partitioned into a terminating part and a possibly non-terminating part. We then give a reduction-preserving completion procedure so that the applicability of the criteria is enhanced. In contrast to the well-known Knuth-Bendix completion procedure which preserves the equivalence relation of the system, our completion procedure preserves the reduction relation of the system, by which confluence of the original system is inferred from that of the completed system.


Acceleration of the shiftable O(1) algorithm for bilateral filtering and non-local means

Abstract: A direct implementation of the bilateral filter [1] requires O(\sigma_s^2) operations per pixel, where \sigma_s is the (effective) width of the spatial kernel. A fast implementation of the bilateral filter was recently proposed in [2] that required O(1) operations per pixel with respect to \sigma_s. This was done by using trigonometric functions for the range kernel of the bilateral filter, and by exploiting their so-called shiftability property. In particular, a fast implementation of the Gaussian bilateral filter was realized by approximating the Gaussian range kernel using raised cosines. Later, it was demonstrated in [3] that this idea could be extended to a larger class of filters, including the popular non-local means filter [4]. As already observed in [2], a flip side of this approach was that the run time depended on the width \sigma_r of the range kernel. For an image with (local) intensity variations in the range [0,T], the run time scaled as O(T^2/\sigma^2_r) with \sigma_r. This made it difficult to implement narrow range kernels, particularly for images with large dynamic range. We discuss this problem in this note, and propose some simple steps to accelerate the implementation in general, and for small \sigma_r in particular. [1] C. Tomasi and R. Manduchi, "Bilateral filtering for gray and color images", Proc. IEEE International Conference on Computer Vision, 1998. [2] K.N. Chaudhury, Daniel Sage, and M. Unser, "Fast O(1) bilateral filtering using trigonometric range kernels", IEEE Transactions on Image Processing, 2011. [3] K.N. Chaudhury, "Constant-time filtering using shiftable kernels", IEEE Signal Processing Letters, 2011. [4] A. Buades, B. Coll, and J.M. Morel, "A review of image denoising algorithms, with a new one", Multiscale Modeling and Simulation, 2005.


Bayesian Games and the Smoothness Framework

Abstract: We consider a general class of Bayesian Games where each players utility depends on his type (possibly multidimensional) and on the strategy profile and where players' types are distributed independently. We show that if their full information version for any fixed instance of the type profile is a smooth game then the Price of Anarchy bound implied by the smoothness property, carries over to the Bayes-Nash Price of Anarchy. We show how some proofs from the literature (item bidding auctions, greedy auctions) can be cast as smoothness proofs or be simplified using smoothness. For first price item bidding with fractionally subadditive bidders we actually manage to improve by much the existing result \cite{Hassidim2011a} from 4 to $\frac{e}{e-1}\approx 1.58$. This also shows a very interesting separation between first and second price item bidding since second price item bidding has PoA at least 2 even under complete information. For a larger class of Bayesian Games where the strategy space of a player also changes with his type we are able to show that a slightly stronger definition of smoothness also implies a Bayes-Nash PoA bound. We show how weighted congestion games actually satisfy this stronger definition of smoothness. This allows us to show that the inefficiency bounds of weighted congestion games known in the literature carry over to incomplete versions where the weights of the players are private information. We also show how an incomplete version of a natural class of monotone valid utility games, called effort market games are universally $(1,1)$-smooth. Hence, we show that incomplete versions of effort market games where the abilities of the players and their budgets are private information has Bayes-Nash PoA at most 2.


Multiple Frequency Selection in DVFS-Enabled Processors to Minimize Energy Consumption

Abstract: In this chapter we focus on slack reclamation and propose a new slack reclamation technique, Multiple Frequency Selection DVFS (MFS-DVFS). The key idea is to execute each task with a linear combination of more than one frequency such that this combination results in using the lowest energy by covering the whole slack time of the task. We have tested our algorithm with both random and real-world application task graphs and compared with the results in previous researches in [9] and [12-13]. The experimental results show that our approach can achieve energy almost identical to the optimum energy saving.


Semi-Automatically Extracting FAQs to Improve Accessibility of Software Development Knowledge

Abstract: Frequently asked questions (FAQs) are a popular way to document software development knowledge. As creating such documents is expensive, this paper presents an approach for automatically extracting FAQs from sources of software development discussion, such as mailing lists and Internet forums, by combining techniques of text mining and natural language processing. We apply the approach to popular mailing lists and carry out a survey among software developers to show that it is able to extract high-quality FAQs that may be further improved by experts.


Market-Oriented Cloud Computing and the Cloudbus Toolkit

Abstract: Cloud computing has penetrated the Information Technology industry deep enough to influence major companies to adopt it into their mainstream business. A strong thrust on the use of virtualization technology to realize Infrastructure-as-a-Service (IaaS) has led enterprises to leverage subscription-oriented computing capabilities of public Clouds for hosting their application services. In parallel, research in academia has been investigating transversal aspects such as security, software frameworks, quality of service, and standardization. We believe that the complete realization of the Cloud computing vision will lead to the introduction of a virtual market where Cloud brokers, on behalf of end users, are in charge of selecting and composing the services advertised by different Cloud vendors. In order to make this happen, existing solutions and technologies have to be redesigned and extended from a market-oriented perspective and integrated together, giving rise to what we term Market-Oriented Cloud Computing. In this paper, we will assess the current status of Cloud computing by providing a reference model, discuss the challenges that researchers and IT practitioners are facing and will encounter in the near future, and present the approach for solving them from the perspective of the Cloudbus toolkit, which comprises of a set of technologies geared towards the realization of Market Oriented Cloud Computing vision. We provide experimental results demonstrating market-oriented resource provisioning and brokering within a Cloud and across multiple distributed resources. We also include an application illustrating the hosting of ECG analysis as SaaS on Amazon IaaS (EC2 and S3) services.


A linear time algorithm for the next-to-shortest path problem on undirected graphs with nonnegative edge lengths

Abstract: For two vertices $s$ and $t$ in a graph $G=(V,E)$, the next-to-shortest path is an $st$-path which length is minimum amongst all $st$-paths strictly longer than the shortest path length. In this paper we show that, when the graph is undirected and all edge lengths are nonnegative, the problem can be solved in linear time if the distances from $s$ and $t$ to all other vertices are given. This result generalizes the previous work (DOI 10.1007/s00453-011-9601-7) to allowing zero-length edges.


Post-Editing Error Correction Algorithm for Speech Recognition using Bing Spelling Suggestion

Abstract: ASR short for Automatic Speech Recognition is the process of converting a spoken speech into text that can be manipulated by a computer. Although ASR has several applications, it is still erroneous and imprecise especially if used in a harsh surrounding wherein the input speech is of low quality. This paper proposes a post-editing ASR error correction method and algorithm based on Bing's online spelling suggestion. In this approach, the ASR recognized output text is spell-checked using Bing's spelling suggestion technology to detect and correct misrecognized words. More specifically, the proposed algorithm breaks down the ASR output text into several word-tokens that are submitted as search queries to Bing search engine. A returned spelling suggestion implies that a query is misspelled; and thus it is replaced by the suggested correction; otherwise, no correction is performed and the algorithm continues with the next token until all tokens get validated. Experiments carried out on various speeches in different languages indicated a successful decrease in the number of ASR errors and an improvement in the overall error correction rate. Future research can improve upon the proposed algorithm so much so that it can be parallelized to take advantage of multiprocessor computers.


Autonomic Model for Self-Configuring C#.NET Applications

Abstract: With the advances in computational technologies over the last decade, large organizations have been investing in Information Technology to automate their internal processes to cut costs and efficiently support their business projects. However, this comes to a price. Business requirements always change. Likewise, IT systems constantly evolves as developers make new versions of them, which require endless administrative manual work to customize and configure them, especially if they are being used in different contexts, by different types of users, and for different requirements. Autonomic computing was conceived to provide an answer to these ever-changing requirements. Essentially, autonomic systems are self-configuring, self-healing, self-optimizing, and self-protecting; hence, they can automate all complex IT processes without human intervention. This paper proposes an autonomic model based on Venn diagram and set theory for self-configuring C#.NET applications, namely the self-customization of their GUI, event-handlers, and security permissions. The proposed model does not require altering the source-code of the original application; rather, it uses an XML-based customization file to turn on and off the internal attributes of the application. Experiments conducted on the proposed model, showed a successful automatic customization for C# applications and an effective self-adaption based on dynamic business requirements. As future work, other programming languages such as Java and C++ are to be supported, in addition to other operating systems such as Linux and Mac so as to provide a standard platform-independent autonomic self-configuring model.


ASR Context-Sensitive Error Correction Based on Microsoft N-Gram Dataset

Abstract: At the present time, computers are employed to solve complex tasks and problems ranging from simple calculations to intensive digital image processing and intricate algorithmic optimization problems to computationally-demanding weather forecasting problems. ASR short for Automatic Speech Recognition is yet another type of computational problem whose purpose is to recognize human spoken speech and convert it into text that can be processed by a computer. Despite that ASR has many versatile and pervasive real-world applications,it is still relatively erroneous and not perfectly solved as it is prone to produce spelling errors in the recognized text, especially if the ASR system is operating in a noisy environment, its vocabulary size is limited, and its input speech is of bad or low quality. This paper proposes a post-editing ASR error correction method based on MicrosoftN-Gram dataset for detecting and correcting spelling errors generated by ASR systems. The proposed method comprises an error detection algorithm for detecting word errors; a candidate corrections generation algorithm for generating correction suggestions for the detected word errors; and a context-sensitive error correction algorithm for selecting the best candidate for correction. The virtue of using the Microsoft N-Gram dataset is that it contains real-world data and word sequences extracted from the web which canmimica comprehensive dictionary of words having a large and all-inclusive vocabulary. Experiments conducted on numerous speeches, performed by different speakers, showed a remarkable reduction in ASR errors. Future research can improve upon the proposed algorithm so much so that it can be parallelized to take advantage of multiprocessor and distributed systems.


Social Media and the Social Good: How Nonprofits Use Facebook to Communicate with the Public

Abstract: In this study, we examine the social networking practices of the 100 largest nonprofit organizations in the United States. More specifically, we develop a comprehensive classification scheme to delineate these organizations' use of Facebook as a stakeholder engagement tool. We find that there are 5 primary categories of Facebook "statuses", which can be aggregated into three key dimensions - "information", "community", and "action". Our analysis reveals that, though the "informational" use of Facebook is still significant, nonprofit organizations are better at using Facebook to strategically engage their stakeholders via "dialogic" and "community-building" practices than they have been with traditional websites. The adoption of social media seems to have engendered new paradigms of public engagement.


Bound Analysis of Imperative Programs with the Size-change Abstraction (extended version)

Abstract: The size-change abstraction (SCA) is an important program abstraction for termination analysis, which has been successfully implemented in many tools for functional and logic programs. In this paper, we demonstrate that SCA is also a highly effective abstract domain for the bound analysis of imperative programs. We have implemented a bound analysis tool based on SCA for imperative programs. We abstract programs in a pathwise and context dependent manner, which enables our tool to analyze real-world programs effectively. Our work shows that SCA captures many of the essential ideas of previous termination and bound analysis and goes beyond in a conceptually simpler framework.


Parameterized Proof Complexity and W[1]

Abstract: We initiate a program of parameterized proof complexity that aims to provide evidence that FPT is different from W[1]. A similar program already exists for the classes W[2] and W[SAT]. We contrast these programs and prove upper and lower bounds for W[1]-parameterized Resolution.


Improving an Hybrid Literary Book Recommendation System through Author Ranking

Abstract: Literary reading is an important activity for individuals and choosing to read a book can be a long time commitment, making book choice an important task for book lovers and public library users. In this paper we present an hybrid recommendation system to help readers decide which book to read next. We study book and author recommendation in an hybrid recommendation setting and test our approach in the LitRec data set. Our hybrid book recommendation approach purposed combines two item-based collaborative filtering algorithms to predict books and authors that the user will like. Author predictions are expanded in to a book list that is subsequently aggregated with the former list generated through the initial collaborative recommender. Finally, the resulting book list is used to yield the top-n book recommendations. By means of various experiments, we demonstrate that author recommendation can improve overall book recommendation.


LOCKE Detailed Specification Tables

Abstract: This document shows the detailed specification of LOCKE coherence protocol for each cache controller, using a table-based technique. This representation provides clear, concise visual information yet includes sufficient detail (e.g., transient states) arguably lacking in the traditional, graphical form of state diagrams.


Public-Key Cryptography Based on Modular Lattices

Abstract: We present an approach to generalization of practical Identity-Based Encryption scheme of Boneh and Franklin. In particular we show how the protocol could be used on finite modular lattices and as a special case on vector spaces over finite field. The original proof of security for this protocol does not hold in this general algebraic structure, thus this is still a work in progress.


The state complexity of star-complement-star

Abstract: We resolve an open question by determining matching (asymptotic) upper and lower bounds on the state complexity of the operation that sends a language L to (c(L*))*, where c() denotes complement.


Finding Connected Components on Map-reduce in Logarithmic Rounds

Abstract: Given a large graph G = (V,E) with millions of nodes and edges, how do we compute its connected components efficiently? Recent work addresses this problem in map-reduce, where a fundamental trade-off exists between the number of map-reduce rounds and the communication of each round. Denoting d the diameter of the graph, and n the number of nodes in the largest component, all prior map-reduce techniques either require d rounds, or require about n|V| + |E| communication per round. We propose two randomized map-reduce algorithms -- (i) Hash-Greater-To-Min, which provably requires at most 3log(n) rounds with high probability, and at most 2(|V| + |E|) communication per round, and (ii) Hash-to-Min, which has a worse theoretical complexity, but in practice completes in at most 2log(d) rounds and 3(|V| + |E|) communication per rounds. Our techniques for connected components can be applied to clustering as well. We propose a novel algorithm for agglomerative single linkage clustering in map-reduce. This is the first algorithm that can provably compute a clustering in at most O(log(n)) rounds, where n is the size of the largest cluster. We show the effectiveness of all our algorithms through detailed experiments on large synthetic as well as real-world datasets.


Agent-time Epistemics and Coordination

Abstract: A minor change to the standard epistemic logical language, replacing $K_{i}$ with $K_{\node{i,t}}$ where $t$ is a time instance, gives rise to a generalized and more expressive form of knowledge and common knowledge operators. We investigate the communication structures that are necessary for such generalized epistemic states to arise, and the inter-agent coordination tasks that require such knowledge. Previous work has established a relation between linear event ordering and nested knowledge, and between simultaneous event occurrences and common knowledge. In the new, extended, formalism, epistemic necessity is decoupled from temporal necessity. Nested knowledge and event ordering are shown to be related even when the nesting order does not match the temporal order of occurrence. The generalized form of common knowledge does {\em not} correspond to simultaneity. Rather, it corresponds to a notion of tight coordination, of which simultaneity is an instance.


Distributed, Cross-Platform, and Regression Testing Architecture for Service-Oriented Architecture

Abstract: As per leading IT experts, today's large enterprises are going through business transformations. They are adopting service-based IT models such as SOA to develop their enterprise information systems and applications. In fact, SOA is an integration of loosely-coupled interoperable components, possibly built using heterogeneous software technologies and hardware platforms. As a result, traditional testing architectures are no more adequate for verifying and validating the quality of SOA systems and whether they are operating to specifications. This paper first discusses the various state-of-the-art methods for testing SOA applications, and then it proposes a novel automated, distributed, cross-platform, and regression testing architecture for SOA systems. The proposed testing architecture consists of several testing units which include test engine, test code generator, test case generator, test executer, and test monitor units. Experiments conducted showed that the proposed testing architecture managed to use parallel agents to test heterogeneous web services whose technologies were incompatible with the testing framework. As future work, testing non-functional aspects of SOA applications are to be investigated so as to allow the testing of such properties as performance, security, availability, and scalability.


Clique problem, cutting plane proofs and communication complexity

Abstract: Motivated by its relation to the length of cutting plane proofs for the Maximum Biclique problem, we consider the following communication game on a given graph G, known to both players. Let K be the maximal number of vertices in a complete bipartite subgraph of G, which is not necessarily an induced subgraph if G is not bipartite. Alice gets a set A of vertices, and Bob gets a disjoint set B of vertices such that |A|+|B|>K. The goal is to find a nonedge of G between A and B. We show that O(\log n) bits of communication are enough for every n-vertex graph.


Incremental Collaborative Filtering Considering Temporal Effects

Abstract: Recommender systems require their recommendation algorithms to be accurate, scalable and should handle very sparse training data which keep changing over time. Inspired by ant colony optimization, we propose a novel collaborative filtering scheme: Ant Collaborative Filtering that enjoys those favorable characteristics above mentioned. With the mechanism of pheromone transmission between users and items, our method can pinpoint most relative users and items even in face of the sparsity problem. By virtue of the evaporation of existing pheromone, we capture the evolution of user preference over time. Meanwhile, the computation complexity is comparatively small and the incremental update can be done online. We design three experiments on three typical recommender systems, namely movie recommendation, book recommendation and music recommendation, which cover both explicit and implicit rating data. The results show that the proposed algorithm is well suited for real-world recommendation scenarios which have a high throughput and are time sensitive.


Proceedings 6th Workshop on Logical and Semantic Frameworks with Applications

Abstract: This volume contains the proceedings of the Sixth Workshop on Logical and Semantic Frameworks with Applications (LSFA 2011). The workshop will be hold in Belo Horizonte, on August 27th 2011. Logical and semantic frameworks are formal languages used to represent logics, languages and systems. These frameworks provide foundations for formal specification of systems and programming languages, supporting tool development and reasoning. The objective of this one-day workshop is to put together theoreticians and practitioners to promote new techniques and results, from the theoretical side, and feedback on the implementation and the use of such techniques and results, from the practical side.


Transfer Learning, Soft Distance-Based Bias, and the Hierarchical BOA

Abstract: An automated technique has recently been proposed to transfer learning in the hierarchical Bayesian optimization algorithm (hBOA) based on distance-based statistics. The technique enables practitioners to improve hBOA efficiency by collecting statistics from probabilistic models obtained in previous hBOA runs and using the obtained statistics to bias future hBOA runs on similar problems. The purpose of this paper is threefold: (1) test the technique on several classes of NP-complete problems, including MAXSAT, spin glasses and minimum vertex cover; (2) demonstrate that the technique is effective even when previous runs were done on problems of different size; (3) provide empirical evidence that combining transfer learning with other efficiency enhancement techniques can often yield nearly multiplicative speedups.


Multiple faults diagnosis using causal graph

Abstract: This work proposes to put up a tool for diagnosing multi faults based on model using techniques of detection and localization inspired from the community of artificial intelligence and that of automatic. The diagnostic procedure to be integrated into the supervisory system must therefore be provided with explanatory features. Techniques based on causal reasoning are a pertinent approach for this purpose. Bond graph modeling is used to describe the cause effect relationship between process variables. Experimental results are presented and discussed in order to compare performance of causal graph technique and classic methods inspired from artificial intelligence (DX) and control theory (FDI).


Modeling of Mixed Decision Making Process

Abstract: Decision making whenever and wherever it is happened is key to organizations success. In order to make correct decision, individuals, teams and organizations need both knowledge management (to manage content) and collaboration (to manage group processes) to make that more effective and efficient. In this paper, we explain the knowledge management and collaboration convergence. Then, we propose a formal description of mixed and multimodal decision making (MDM) process where decision may be made by three possible modes: individual, collective or hybrid. Finally, we explicit the MDM process based on UML-G profile.


Optimal Private Halfspace Counting via Discrepancy

Abstract: A range counting problem is specified by a set $P$ of size $|P| = n$ of points in $\mathbb{R}^d$, an integer weight $x_p$ associated to each point $p \in P$, and a range space ${\cal R} \subseteq 2^{P}$. Given a query range $R \in {\cal R}$, the target output is $R(\vec{x}) = \sum_{p \in R}{x_p}$. Range counting for different range spaces is a central problem in Computational Geometry. We study $(\epsilon, \delta)$-differentially private algorithms for range counting. Our main results are for the range space given by hyperplanes, that is, the halfspace counting problem. We present an $(\epsilon, \delta)$-differentially private algorithm for halfspace counting in $d$ dimensions which achieves $O(n^{1-1/d})$ average squared error. This contrasts with the $\Omega(n)$ lower bound established by the classical result of Dinur and Nissim [PODS 2003] for arbitrary subset counting queries. We also show a matching lower bound on average squared error for any $(\epsilon, \delta)$-differentially private algorithm for halfspace counting. Both bounds are obtained using discrepancy theory. For the lower bound, we use a modified discrepancy measure and bound approximation of $(\epsilon, \delta)$-differentially private algorithms for range counting queries in terms of this discrepancy. We also relate the modified discrepancy measure to classical combinatorial discrepancy, which allows us to exploit known discrepancy lower bounds. This approach also yields a lower bound of $\Omega((\log n)^{d-1})$ for $(\epsilon, \delta)$-differentially private orthogonal range counting in $d$ dimensions, the first known superconstant lower bound for this problem. For the upper bound, we use an approach inspired by partial coloring methods for proving discrepancy upper bounds, and obtain $(\epsilon, \delta)$-differentially private algorithms for range counting with polynomially bounded shatter function range spaces.


A Novel Fault Detection Approach combining Adaptive Thresholding and Fuzzy Reasoning

Abstract: Fault detection methods have their pros and cons. Thus, it is possible that some methods can complement each other and offer consequently better diagnostic systems. The integration of various characteristics is a way to develop "hybrid" systems to overcome the limitations of individual strategies of each method. In this paper a novel detection module combining the use of adaptive threshold and fuzzy logic reasoning inspired by the Evsukoff's approach is proposed in order to reduce the rate of false alarms, guarantee more robustness to disturbances and assist the operator in making decisions. The proposed approach can be used in case of multiple faults detection. This approach is applied to a benchmark in diagnosis domain: the three-tank system. The results of the proposed detection module are then presented through a gradual palette of colors in the graphical interface of the system.


A note on triangle partitions

Abstract: Koivisto studied the partitioning of sets of bounded cardinality. We improve his time analysis somewhat, for the special case of triangle partitions, and obtain a slight improvement.


Breaking a novel colour image encryption algorithm based on chaos

Abstract: Recently, a colour image encryption algorithm based on chaos was proposed by cascading two position permutation operations and one substitution operation, which are all determined by some pseudo-random number sequences generated by iterating the Logistic map. This paper evaluates the security level of the encryption algorithm and finds that the position permutation-only part and the substitution part can be separately broken with only $\lceil (\log_2(3MN))/8 \rceil$ and 2 chosen plain-images, respectively, where $MN$ is the size of the plain-image. Concise theoretical analyses are provided to support the chosen-plaintext attack, which are verified by experimental results also.


BlinkDB: Queries with Bounded Errors and Bounded Response Times on Very Large Data

Abstract: In this paper, we present BlinkDB, a massively parallel, sampling-based approximate query engine for running ad-hoc, interactive SQL queries on large volumes of data. The key insight that BlinkDB builds on is that one can often make reasonable decisions in the absence of perfect answers. For example, reliably detecting a malfunctioning server using a distributed collection of system logs does not require analyzing every request processed by the system. Based on this insight, BlinkDB allows one to trade-off query accuracy for response time, enabling interactive queries over massive data by running queries on data samples and presenting results annotated with meaningful error bars. To achieve this, BlinkDB uses two key ideas that differentiate it from previous work in this area: (1) an adaptive optimization framework that builds and maintains a set of multi-dimensional, multi-resolution samples from original data over time, and (2) a dynamic sample selection strategy that selects an appropriately sized sample based on a query's accuracy and/or response time requirements. We have built an open-source version of BlinkDB and validated its effectiveness using the well-known TPC-H benchmark as well as a real-world analytic workload derived from Conviva Inc. Our experiments on a 100 node cluster show that BlinkDB can answer a wide range of queries from a real-world query trace on up to 17 TBs of data in less than 2 seconds (over 100\times faster than Hive), within an error of 2 - 10%.


Wireless Video Transmission with Over-the-Air Packet Mixing

Abstract: In this paper, we propose a system for wireless video transmission with a wireless physical layer (PHY) that supports cooperative forwarding of interfered/superimposed packets. Our system model considers multiple and independent unicast transmissions between network nodes while a number of them serve as relays of the interfered/superimposed signals. For this new PHY the average transmission rate that each node can achieve is estimated first. Next, we formulate a utility optimization framework for the video transmission problem and we show that it can be simplified due to the features of the new PHY. Simulation results reveal the system operating regions for which superimposing wireless packets is a better choice than a typical cooperative PHY.


On the Use of Non-Stationary Policies for Infinite-Horizon Discounted Markov Decision Processes

Abstract: We consider infinite-horizon $\gamma$-discounted Markov Decision Processes, for which it is known that there exists a stationary optimal policy. We consider the algorithm Value Iteration and the sequence of policies $\pi_1,...,\pi_k$ it implicitely generates until some iteration $k$. We provide performance bounds for non-stationary policies involving the last $m$ generated policies that reduce the state-of-the-art bound for the last stationary policy $\pi_k$ by a factor $\frac{1-\gamma}{1-\gamma^m}$. In particular, the use of non-stationary policies allows to reduce the usual asymptotic performance bounds of Value Iteration with errors bounded by $\epsilon$ at each iteration from $\frac{\gamma}{(1-\gamma)^2}\epsilon$ to $\frac{\gamma}{1-\gamma}\epsilon$, which is significant in the usual situation when $\gamma$ is close to 1. Given Bellman operators that can only be computed with some error $\epsilon$, a surprising consequence of this result is that the problem of "computing an approximately optimal non-stationary policy" is much simpler than that of "computing an approximately optimal stationary policy", and even slightly simpler than that of "approximately computing the value of some fixed policy", since this last problem only has a guarantee of $\frac{1}{1-\gamma}\epsilon$.


Graph-Theoretic Characterizations of Structural Controllability for Multi-Agent System with Switching Topology

Abstract: This paper considers the controllability problem for multi-agent systems. In particular, the structural controllability of multi-agent systems under switching topologies is investigated. The structural controllability of multi-agent systems is a generalization of the traditional controllability concept for dynamical systems, and purely based on the communication topologies among agents. The main contributions of the paper are graph-theoretic characterizations of the structural controllability for multi-agent systems. It turns out that the multi-agent system with switching topology is structurally controllable if and only if the union graph G of the underlying communication topologies is connected (single leader) or leader-follower connected (multi-leader). Finally, the paper concludes with several illustrative examples and discussions of the results and future work.


Memory Hierarchy Sensitive Graph Layout

Abstract: Mining large graphs for information is becoming an increasingly important workload due to the plethora of graph structured data becoming available. An aspect of graph algorithms that has hitherto not received much interest is the effect of memory hierarchy on accesses. A typical system today has multiple levels in the memory hierarchy with differing units of locality; ranging across cache lines, TLB entries and DRAM pages. We postulate that it is possible to allocate graph structured data in main memory in a way as to improve the spatial locality of the data. Previous approaches to improving cache locality have focused only on a single unit of locality, either the cache line or virtual memory page. On the other hand cache oblivious algorithms can optimise layout for all levels of the memory hierarchy but unfortunately need to be specially designed for individual data structures. In this paper we explore hierarchical blocking as a technique for closing this gap. We require as input a specification of the units of locality in the memory hierarchy and lay out the input graph accordingly by copying its nodes using a hierarchy of breadth first searches. We start with a basic algorithm that is limited to trees and then extend it to arbitrary graphs. Our most efficient version requires only a constant amount of additional space. We have implemented versions of the algorithm in various environments: for C programs interfaced with macros, as an extension to the Boost object oriented graph library and finally as a modification to the traversal phase of the semispace garbage collector in the Jikes Java virtual machine. Our results show significant improvements in the access time to graphs of various structure.


Time-Constrained Temporal Logic Control of Multi-Affine Systems

Abstract: In this paper, we consider the problem of controlling a dynamical system such that its trajectories satisfy a temporal logic property in a given amount of time. We focus on multi-affine systems and specifications given as syntactically co-safe linear temporal logic formulas over rectangular regions in the state space. The proposed algorithm is based on the estimation of time bounds for facet reachability problems and solving a time optimal reachability problem on the product between a weighted transition system and an automaton that enforces the satisfaction of the specification. A random optimization algorithm is used to iteratively improve the solution.


Building Custom Term Suggestion Web Services with OAI-Harvested Open Data

Abstract: The problem that the same information need can be expressed in a variety of ways is especially true for scientific literature. Each scientific discipline has its own domain-specific language and vocabulary. This language is coded into documentary tools like thesauri or classifications that are used to document and describe scientific documents. When we think of information retrieval as "fundamentally a linguistic process" (Blair, 2003) users have to be aware of the most relevant search terms - which are the controlled thesauri terms the documents are described with. This can be achieved with so-called search-term-recommenders (STR) that map free search terms of a lay user to controlled vocabulary terms which can then be used as a term suggestion or to do an automatic query expansion (Hienert, Schaer, Schaible, & Mayr, 2011). State-of-the-art repository software systems like DSpace or EPrints already offer some kind of term suggestion features in search or input forms but these implementations only work as simple auto completion mechanisms that don't incorporate any kind of semantic mapping. Such software systems would gain a lot in terms of usability and data consistency if tools like the proposed domain-specific STRs would be freely available. We aim to implement a rich toolbox of web services (like the mentioned domain-specific STRs) to support users and providers of online Digital Library (DL) or repository systems.


Network Formation: Heterogeneous Traffic, Bilateral Contracting and Myopic Dynamics

Abstract: We study a network formation game where nodes wish to send traffic to other nodes. Nodes can contract bilaterally other nodes to form bidirectional links as well as nodes can break unilaterally contracts to eliminate the corresponding links. Our model is an extension of the model considered in Arcaute et al. The novelty is that we do no require the traffic to be uniform all-to-all. Each node specifies the amount of traffic that it wants to send to any other node. We characterize stable topologies under a static point of view and we also study the game under a myopic dynamics. We show its convergence to stable networks under some natural assumptions on the contracting functions. Finally we consider the efficiency of pairwise Nash topologies from a social point of view and we show that the problem of deciding the existence stable topologies of a given price is $\NP$-complete.


Credal Classification based on AODE and compression coefficients

Abstract: Bayesian model averaging (BMA) is an approach to average over alternative models; yet, it usually gets excessively concentrated around the single most probable model, therefore achieving only sub-optimal classification performance. The compression-based approach (Boulle, 2007) overcomes this problem, averaging over the different models by applying a logarithmic smoothing over the models' posterior probabilities. This approach has shown excellent performances when applied to ensembles of naive Bayes classifiers. AODE is another ensemble of models with high performance (Webb, 2005), based on a collection of non-naive classifiers (called SPODE) whose probabilistic predictions are aggregated by simple arithmetic mean. Aggregating the SPODEs via BMA rather than by arithmetic mean deteriorates the performance; instead, we aggregate the SPODEs via the compression coefficients and we show that the resulting classifier obtains a slight but consistent improvement over AODE. However, an important issue in any Bayesian ensemble of models is the arbitrariness in the choice of the prior over the models. We address this problem by the paradigm of credal classification, namely by substituting the unique prior with a set of priors. Credal classifier automatically recognize the prior-dependent instances, namely the instances whose most probable class varies, when different priors are considered; in these cases, credal classifiers remain reliable by returning a set of classes rather than a single class. We thus develop the credal version of both the BMA-based and the compression-based ensemble of SPODEs, substituting the single prior over the models by a set of priors. Experiments show that both credal classifiers provide higher classification reliability than their determinate counterparts; moreover the compression-based credal classifier compares favorably to previous credal classifiers.


A Pseudo Random Number Generator from Chaos

Abstract: A random number generator is proposed based on a theorem about existence of chaos in fixed point iteration of x= cot2(x). Digital computer simulation of this function iteration exhibits random behavior. A method is proposed to extract random bytes from this simulation. Diehard and NIST test suite for randomness detection is run on this bytes, and it is found to pass all the tests in the suite. Thus, this method qualifies even for cryptographic quality random number generation.


Adaptive Row-grouped CSR Format for Storing of Sparse Matrices on GPU

Abstract: We present new adaptive format for storing sparse matrices on GPU. We compare it with several other formats including CUSPARSE which is today probably the best choice for processing of sparse matrices on GPU in CUDA. Contrary to CUSPARSE which works with common CSR format, our new format requires conversion. However, multiplication of sparse-matrix and vector is significantly faster for many atrices. We demonstrate it on set of 1 600 matrices and we show for what types of matrices our format is profitable.


Self-Healing by Means of Runtime Execution Profiling

Abstract: A self-healing application brings itself into a stable state after a failure put the software into an unstable state. For such self-healing software application, finding fix for a previously unseen fault is a grand challenge. Asking the user to provide fixes for every fault is bad for productivity, especially when the users are non-savvy in technical aspect of computing. If failure scenarios come into existence, the user wants the runtime environment to handle those situations autonomically. This paper presents a new technique of finding self-healing actions by matching a fault scenario to already established fault models. By profiling and capturing runtime parameters and execution pathWays, stable execution models are established and later are used to match with an unstable execution scenario. Experimentation and results are presented that showed that even with additional overheads; this technique can prove beneficial for autonomically healing faults and reliving system administrators from mundane troubleshooting situations.


Polynomial Interpretations for Higher-Order Rewriting

Abstract: The termination method of weakly monotonic algebras, which has been defined for higher-order rewriting in the HRS formalism, offers a lot of power, but has seen little use in recent years. We adapt and extend this method to the alternative formalism of algebraic functional systems, where the simply-typed lambda-calculus is combined with algebraic reduction. Using this theory, we define higher-order polynomial interpretations, and show how the implementation challenges of this technique can be tackled. A full implementation is provided in the termination tool WANDA.


A 100 mA Low Voltage Linear Regulators for Systems on Chip Applications Using 0.18 {\mu}m CMOS Technology

Abstract: A novel design for a low dropout (LDO) voltage regulator is presented and dedicated to power many sections of a typical cellular handset. However, these baseband, RF, and audio sections have different requirements that influence which LDO is most appropriate. After discussion of the specific requirements, different LDOs are recommended. Also, some LDO design techniques are briefly discussed to demonstrate how an LDO may be optimized for a specific level of performance. Cellular phone designs require linear regulators with lowdropout, low-noise, high PSRR, low quiescent current (Iq), and low-cost. They need to deliver a stable output and use smallvalue output capacitors. Ideally, one device would have all these characteristics and one low-dropout linear regulator (LDO) could be used anywhere in the phone without worry. But in practice, the various cell phone blocks are best powered by LDOs with different performance characteristics. This paper provides a new design methodology to choosing the right LDO to power each cell phone and especially for the Voltage Phase-Locked loops (VPLLs) blocks. Fabricated in a 0.18 {\mu}m CMOS process, the measured results show the adopted topology achieves a better phase noise than the conventional saturation current source. and the spread of the current limitation (without matching) is 100mA, the VPLLs system demonstrates a phase noise of 782 nv/sqrtHz at 100-kHz, and 33 nv/sqrtHz at 1 MHz, while quiescent current 33 {\mu}A from a 2.6 V supply voltage.


An Independent Validation of Vulnerability Discovery Models

Abstract: Having a precise vulnerability discovery model (VDM) would provide a useful quantitative insight to assess software security. Thus far, several models have been proposed with some evidence supporting their goodness-of-fit. In this work we describe an independent validation of the applicability of six existing VDMs in seventeen releases of the three popular browsers Firefox, Google Chrome and Internet Explorer. We have collected five different kinds of data sets based on different definitions of a vulnerability. We introduce two quantitative metrics, goodness-of-fit entropy and goodness-of-fit quality, to analyze the impact of vulnerability data sets to the stability as well as quality of VDMs in the software life cycles. The experiment result shows that the "confirmed-by-vendors' advisories" data sets apparently yields more stable and better results for VDMs. And the performance of the s-shape logistic model (AML) seems to be superior performance in overall. Meanwhile, Anderson thermodynamic model (AT) is indeed not suitable for modeling the vulnerability discovery process. This means that the discovery process of vulnerabilities and normal bugs are different because the interests of people in finding security vulnerabilities are more than finding normal programming bugs.


Series Crimes

Abstract: Puiseux series are power series in which the exponents can be fractional and/or negative rational numbers. Several computer algebra systems have one or more built-in or loadable functions for computing truncated Puiseux series. Some are generalized to allow coefficients containing functions of the series variable that are dominated by any power of that variable, such as logarithms and nested logarithms of the series variable. Some computer algebra systems also have built-in or loadable functions that compute infinite Puiseux series. Unfortunately, there are some little-known pitfalls in computing Puiseux series. The most serious of these is expansions within branch cuts or at branch points that are incorrect for some directions in the complex plane. For example with each series implementation accessible to you: Compare the value of (z^2 + z^3)^(3/2) with that of its truncated series expansion about z = 0, approximated at z = -0.01. Does the series converge to a value that is the negative of the correct value? Compare the value of ln(z^2 + z^3) with its truncated series expansion about z = 0, approximated at z = -0.01 + 0.1i. Does the series converge to a value that is incorrect by 2pi i? Compare arctanh(-2 + ln(z)z) with its truncated series expansion about z = 0, approximated at z = -0.01. Does the series converge to a value that is incorrect by about pi i? At the time of this writing, most implementations that accommodate such series exhibit such errors. This article describes how to avoid these errors both for manual derivation of series and when implementing series packages.


Optimizing Channel Access for Event-Driven Wireless Sensor Networks: Analysis and Enhancements

Abstract: We study the problem of medium access control in domain of event-driven wireless sensor networks (WSNs). In this kind of WSN, sensor nodes send data to sink node only when an event occurs in the monitoring area. The nodes in this kind of WSNs encounter correlated traffic as a subset of nodes start sending data by sensing a common event simultaneously. We wish to rethink of medium access control (MAC) for this type of traffic characteristics. For WSNs, many existing MAC protocols utilize the basic CSMA/CA strategy such as IEEE 802.11 Binary Exponential Backoff (BEB) algorithm to handle the collisions among packets when more than one node need to access the channel. We show that this BEB algorithm does not work well without incurring access delay or performance degradation due to increased number of collisions and retransmissions when nodes encounter correlated traffic. Based on above observations in mind, We present a Adaptive Random Backoff (ARB) algorithm that is capable of mitigating the impact of correlated traffic and capable of minimizing the chance of collisions. ARB is based on minor modifications of BEB. We show using numerical analysis that our proposals improve the channel access in terms of latency, throughput, and frame dropping probability as compared with IEEE 802.11 DCF. Simulations using NS-2 network simulator are conducted to validate the analytical results.


Switching strategy based on homotopy continuation for non-regular affine systems with application in induction motor control

Abstract: In the article the problem of output setpoint tracking for affine non-linear system is considered. Presented approach combines state feedback linearization and homotopy numerical continuation in subspaces of phase space where feedback linearization fails. The method of numerical parameter continuation for solving systems of nonlinear equations is generalized to control affine non-linear dynamical systems. The illustrative example of control of MIMO system which is not static feedback linearizable is given. Application of proposed method demonstrated on the speed and rotor magnetic flux control in the three-phase asynchronous motor.


Bayesian Auctions with Friends and Foes

Abstract: We study auctions whose bidders are embedded in a social or economic network. As a result, even bidders who do not win the auction themselves might derive utility from the auction, namely, when a friend wins. On the other hand, when an enemy or competitor wins, a bidder might derive negative utility. Such spite and altruism will alter the bidding strategies. A simple and natural model for bidders' utilities in these settings posits that the utility of a losing bidder i as a result of bidder j winning is a constant (positive or negative) fraction of bidder j's utility.


TSET: Token based Secure Electronic Transaction

Abstract: Security and trust are the most important factors in online transaction, this paper introduces TSET a Token based Secure Electronic Transaction which is an improvement over the existing SET, Secure Electronic Transaction protocol. We take the concept of tokens in the TSET protocol to provide end to end security. It also provides trust evaluation mechanism so that trustworthiness of the merchants can be known by customers before being involved in the transaction. Moreover, we also propose a grading mechanism so that quality of service in the transactions improves.


The Kernel Quantum Probabilities (KQP) Library

Abstract: In this document, we show how the different quantities necessary to compute kernel quantum probabilities can be computed. This document form the basis of the implementation of the Kernel Quantum Probability (KQP) open source project


How to solve kSAT in polynomial time

Abstract: With using of multi-nary logic analytic formulas proposition that "kSAT is in P and could be solved in $O(n^{3.5})$" was proved


A "Hybrid" Approach for Synthesizing Optimal Controllers of Hybrid Systems: A Case Study of the Oil Pump Industrial Example

Abstract: In this paper, we propose an approach to reduce the optimal controller synthesis problem of hybrid systems to quantifier elimination; furthermore, we also show how to combine quantifier elimination with numerical computation in order to make it more scalable but at the same time, keep arising errors due to discretization manageable and within bounds. A major advantage of our approach is not only that it avoids errors due to numerical computation, but it also gives a better optimal controller. In order to illustrate our approach, we use the real industrial example of an oil pump provided by the German company HYDAC within the European project Quasimodo as a case study throughout this paper, and show that our method improves (up to 7.5%) the results reported in [3] based on game theory and model checking.


A Multi-Agent Prediction Market based on Partially Observable Stochastic Game

Abstract: We present a novel, game theoretic representation of a multi-agent prediction market using a partially observable stochastic game with information (POSGI). We then describe a correlated equilibrium (CE)-based solution strategy for this game which enables each agent to dynamically calculate the prices at which it should trade a security in the prediction market. We have extended our results to risk averse traders and shown that a Pareto optimal correlated equilibrium strategy can be used to incentively truthful revelations from risk averse agents. Simulation results comparing our CE strategy with five other strategies commonly used in similar markets, with both risk neutral and risk averse agents, show that the CE strategy improves price predictions and provides higher utilities to the agents as compared to other existing strategies.


Proposal of a New Block Cipher reasonably Non-Vulnerable against Cryptanalytic Attacks

Abstract: This paper proposes a new block cipher termed as "Modular Arithmetic based Block Cipher with Varying Key-Spaces (MABCVK)" that uses private key-spaces of varying lengths to encrypt data files. There is a simple but intelligent use of theory of modular arithmetic in the scheme of the cipher. Based on observed implementation of the proposed cipher on a set of real data files of several types, all results are tabulated and analyzed.The schematic strength of the cipher and the freedom of using a long key-space expectedly can make it reasonably nonvulnerable against possible cryptanalytic attacks. As a part of the future scope of the work, it is also intended to formulate and implement an enhanced scheme that will use a carrier image to have a secure transmission of the private key.


MDCC: Multi-Data Center Consistency

Abstract: Replicating data across multiple data centers not only allows moving the data closer to the user and, thus, reduces latency for applications, but also increases the availability in the event of a data center failure. Therefore, it is not surprising that companies like Google, Yahoo, and Netflix already replicate user data across geographically different regions. However, replication across data centers is expensive. Inter-data center network delays are in the hundreds of milliseconds and vary significantly. Synchronous wide-area replication is therefore considered to be unfeasible with strong consistency and current solutions either settle for asynchronous replication which implies the risk of losing data in the event of failures, restrict consistency to small partitions, or give up consistency entirely. With MDCC (Multi-Data Center Consistency), we describe the first optimistic commit protocol, that does not require a master or partitioning, and is strongly consistent at a cost similar to eventually consistent protocols. MDCC can commit transactions in a single round-trip across data centers in the normal operational case. We further propose a new programming model which empowers the application developer to handle longer and unpredictable latencies caused by inter-data center communication. Our evaluation using the TPC-W benchmark with MDCC deployed across 5 geographically diverse data centers shows that MDCC is able to achieve throughput and latency similar to eventually consistent quorum protocols and that MDCC is able to sustain a data center outage without a significant impact on response times while guaranteeing strong consistency.


Asynchrony from Synchrony

Abstract: We consider synchronous dynamic networks which like radio networks may have asymmetric communication links, and are affected by communication rather than processor failures. In this paper we investigate the minimal message survivability in a per round basis that allows for the minimal global cooperation, i.e., allows to solve any task that is wait-free read-write solvable. The paper completely characterizes this survivability requirement. Message survivability is formalized by considering adversaries that have a limited power to remove messages in a round. Removal of a message on a link in one direction does not necessarily imply the removal of the message on that link in the other direction. Surprisingly there exist a single strongest adversary which solves any wait-free read/write task. Any different adversary that solves any wait-free read/write task is weaker, and any stronger adversary will not solve any wait-free read/write task. ABD \cite{ABD} who considered processor failure, arrived at an adversary that is $n/2$ resilient, consequently can solve tasks, such as $n/2$-set-consensus, which are not read/write wait-free solvable. With message adversaries, we arrive at an adversary which has exactly the read-write wait-free power. Furthermore, this adversary allows for a considerably simpler (simplest that we know of) proof that the protocol complex of any read/write wait-free task is a subdivided simplex, finally making this proof accessible for students with no algebraic-topology prerequisites, and alternatively dispensing with the assumption that the Immediate Snapshot complex is a subdivided simplex.


A Programmer-Centric Approach to Program Verification in ATS

Abstract: Formal specification is widely employed in the construction of high-quality software. However, there is often a huge gap between formal specification and actual implementation. While there is already a vast body of work on software testing and verification, the task to ensure that an implementation indeed meets its specification is still undeniably of great difficulty. ATS is a programming language equipped with a highly expressive type system that allows the programmer to specify and implement and then verify within the language itself that an implementation meets its specification. In this paper, we present largely through examples a programmer-centric style of program verification that puts emphasis on requesting the programmer to explain in a literate fashion why his or her code works. This is a solid step in the pursuit of software construction that is verifiably correct according to specification.


Tree Transducers, Machine Translation, and Cross-Language Divergences

Abstract: Tree transducers are formal automata that transform trees into other trees. Many varieties of tree transducers have been explored in the automata theory literature, and more recently, in the machine translation literature. In this paper I review T and xT transducers, situate them among related formalisms, and show how they can be used to implement rules for machine translation systems that cover all of the cross-language structural divergences described in Bonnie Dorr's influential article on the topic. I also present an implementation of xT transduction, suitable and convenient for experimenting with translation rules.


The FO^2 alternation hierarchy is decidable

Abstract: We consider the two-variable fragment FO^2[<] of first-order logic over finite words. Numerous characterizations of this class are known. Th\'erien and Wilke have shown that it is decidable whether a given regular language is definable in FO^2[<]. From a practical point of view, as shown by Weis, FO^2[<] is interesting since its satisfiability problem is in NP. Restricting the number of quantifier alternations yields an infinite hierarchy inside the class of FO^2[<]-definable languages. We show that each level of this hierarchy is decidable. For this purpose, we relate each level of the hierarchy with a decidable variety of finite monoids. Our result implies that there are many different ways of climbing up the FO^2[<]-quantifier alternation hierarchy: deterministic and co-deterministic products, Mal'cev products with definite and reverse definite semigroups, iterated block products with J-trivial monoids, and some inductively defined omega-term identities. A combinatorial tool in the process of ascension is that of condensed rankers, a refinement of the rankers of Weis and Immerman and the turtle programs of Schwentick, Th\'erien, and Vollmer.


A Logical Framework for Set Theories

Abstract: Axiomatic set theory is almost universally accepted as the basic theory which provides the foundations of mathematics, and in which the whole of present day mathematics can be developed. As such, it is the most natural framework for Mathematical Knowledge Management. However, in order to be used for this task it is necessary to overcome serious gaps that exist between the "official" formulations of set theory (as given e.g. by formal set theory ZF) and actual mathematical practice. In this work we present a new unified framework for formalizations of axiomatic set theories of different strength, from rudimentary set theory to full ZF. It allows the use of set terms, but provides a static check of their validity.


Mendler-style Iso-(Co)inductive predicates: a strongly normalizing approach

Abstract: We present an extension of the second-order logic AF2 with iso-style inductive and coinductive definitions specifically designed to extract programs from proofs a la Krivine-Parigot by means of primitive (co)recursion principles. Our logic includes primitive constructors of least and greatest fixed points of predicate transformers, but contrary to the common approach, we do not restrict ourselves to positive operators to ensure monotonicity, instead we use the Mendler-style, motivated here by the concept of monotonization of an arbitrary operator on a complete lattice. We prove an adequacy theorem with respect to a realizability semantics based on saturated sets and saturated-valued functions and as a consequence we obtain the strong normalization property for the proof-term reduction, an important feature which is absent in previous related work.


On Graph Refutation for Relational Inclusions

Abstract: We introduce a graphical refutation calculus for relational inclusions: it reduces establishing a relational inclusion to establishing that a graph constructed from it has empty extension. This sound and complete calculus is conceptually simpler and easier to use than the usual ones.


A Formalization of the Theorem of Existence of First-Order Most General Unifiers

Abstract: This work presents a formalization of the theorem of existence of most general unifiers in first-order signatures in the higher-order proof assistant PVS. The distinguishing feature of this formalization is that it remains close to the textbook proofs that are based on proving the correctness of the well-known Robinson's first-order unification algorithm. The formalization was applied inside a PVS development for term rewriting systems that provides a complete formalization of the Knuth-Bendix Critical Pair theorem, among other relevant theorems of the theory of rewriting. In addition, the formalization methodology has been proved of practical use in order to verify the correctness of unification algorithms in the style of the original Robinson's unification algorithm.


On Distance Function among Finite Set of Points

Abstract: In practical purposes for some geometrical problems in computer science we have as information the coordinates of some finite points in surface instead of the whole body of a surface. The problem arised here is: "How to define a distance function in a finite space?" as we will show the appropriate function for this purpose is not a metric function. Here we try to define this distance function in order to apply it in further proposes, specially in the field setting of transportation theory and vehicle routing problem. More precisely in this paper we consider VRP problem for two dimensional manifolds in R3.


Integrated Solution Modeling Software: A New Paradigm on Information Security Review and Assessment

Abstract: Actually Information security becomes a very important part for the organization's intangible assets, so level of confidence and stakeholder trusted are performance indicator as successes organization. Since information security has a very important role in supporting the activities of the organization, we need a standard or benchmark which regulates governance over information security. The main objective of this paper is to implement a novel practical approach framework to the development of information security management system (ISMS) assessment and monitoring software, called by I-SolFramework. System / software is expected to assist stakeholders in assessing the level of their ISO27001 compliance readiness, the software could help stakeholders understood security control or called by compliance parameters, being shorter and more structured. The case study illustrated provided to the reader with a set of guidelines, that aims easy understood and applicable as measuring tools for ISMS standards (ISO27001) compliance.


Optimal Pruning for Multi-Step Sensor Scheduling

Abstract: In the considered linear Gaussian sensor scheduling problem, only one sensor out of a set of sensors performs a measurement. To minimize the estimation error over multiple time steps in a computationally tractable fashion, the so-called information-based pruning algorithm is proposed. It utilizes the information matrices of the sensors and the monotonicity of the Riccati equation. This allows ordering sensors according to their information contribution and excluding many of them from scheduling. Additionally, a tight lower is calculated for branch-and-bound search, which further improves the pruning performance.


Circle separability queries in logarithmic time

Abstract: Let $P$ be a set of $n$ points in the plane. In this paper we study a new variant of the circular separability problem in which a point set $P$ is preprocessed so that one can quickly answer queries of the following form: Given a geometric object $Q$, report the minimum circle containing $P$ and exluding $Q$. Our data structure can be constructed in $O(n\log n)$ time using O(n) space, and can be used to answer the query when $Q$ is either a circle or a convex $m$-gon in $O(\log n)$ or $O(\log n + \log m)$ time, respectively.


Small $\ell$-edge-covers in $k$-connected graphs

Abstract: Let $G=(V,E)$ be a $k$-edge-connected graph with edge costs $\{c(e):e \in E\}$ and let $1 \leq \ell \leq k-1$. We show by a simple and short proof, that $G$ contains an $\ell$-edge cover $I$ such that: $c(I) \leq \frac{\ell}{k}c(E)$ if $G$ is bipartite, or if $\ell |V|$ is even, or if $|E| \geq \frac{k|V|}{2} +\frac{k}{2\ell}$; otherwise, $c(I) \leq (\frac{\ell}{k}+\frac{1}{k|V|})c(E)$. The particular case $\ell=k-1$ and unit costs already includes a result of Cheriyan and Thurimella, that $G$ contains a $(k-1)$-edge-cover of size $|E|-\lfloor |V|/2 \rfloor$. Using our result, we slightly improve the approximation ratios for the {\sf $k$-Connected Subgraph} problem (the node-connectivity version) with uniform and $\beta$-metric costs. We then consider the dual problem of finding a spanning subgraph of maximum connectivity $k^*$ with a prescribed number of edges. We give an algorithm that computes a $(k^*-1)$-connected subgraph, which is tight, since the problem is NP-hard.


Fuzzy Time in LTL

Abstract: In the last years, the adoption of active systems has increased in many fields of computer science, such as databases, sensor networks, and software engineering. These systems are able to automatically react to events, by collecting information from outside and internally generating new events. However, the collection of data is often hampered by uncertainty and vagueness that can arise from the imprecision of the monitoring infrastructure, unreliable data sources, and networks. The decision making mechanism used to produce a reaction is also imprecise, and cannot be evaluated in a crisp way. It depends on the evaluation of vague temporal constraints, which are expressed on the collected data by humans. Despite fuzzy logic has been mainly conceived as a mathematical abstraction to express vagueness, no attempt has been made to fuzzify the temporal modalities. Existing fuzzy languages do not allow us to represent temporal properties, such as "almost always" and "soon". Indeed, the semantics of existing fuzzy temporal operators is based on the idea of replacing classical connectives or propositions with their fuzzy counterparts. To overcome these limitations, we propose a temporal framework, FTL (Fuzzy-time Temporal Logic), to express vagueness on time. This framework formally defines a set of fuzzy temporal modalities, which can be customized by choosing a specific semantics for the connectives. The semantics of the language is sound, and the introduced modalities respect a set of expected mutual relations. We also prove that under the assumption that all events are crisp, FTL reduces to LTL. Finally, for some of the possible fuzzy interpretations of the connectives, we identify adequate sets of temporal operators, from which it is possible to derive all the others.


On the Easiest and Hardest Fitness Functions

Abstract: The hardness of fitness functions is an important research topic in the field of evolutionary computation. In theory, the study can help understanding the ability of evolutionary algorithms. In practice, the study may provide a guideline to the design of benchmarks. The aim of this paper is to answer the following research questions: Given a fitness function class, which functions are the easiest with respect to an evolutionary algorithm? Which are the hardest? How are these functions constructed? The paper provides theoretical answers to these questions. The easiest and hardest fitness functions are constructed for an elitist (1+1) evolutionary algorithm to maximise a class of fitness functions with the same optima. It is demonstrated that the unimodal functions are the easiest and deceptive functions are the hardest in terms of the time-fitness landscape. The paper also reveals that the easiest fitness function to one algorithm may become the hardest to another algorithm, and vice versa.


Performance Optimization of Multiple Interconnected Heterogeneous Sensor Networks via Collaborative Information Sharing

Abstract: Interconnecting multiple sensor networks is a relatively new research field which has emerged in the Wireless Sensor Network domain. Wireless Sensor Networks (WSNs) have typically been seen as logically separate, and few works have considered interconnection and interaction between them. Interconnecting multiple heterogeneous sensor networks therefore opens up a new field besides more traditional research on, e.g., routing, self organization, or MAC layer development. Up to now, some approaches have been proposed for interconnecting multiple sensor networks with goals like information sharing or monitoring multiple sensor networks. In this paper, we propose to utilize inter-WSN communication to enable Collaborative Performance Optimization, i.e., our approach aims to optimize the performance of individual WSNs by taking into account measured information from others. The parameters to be optimized are energy consumption on the one hand and sensing quality on the other.


Analysis of Magnification in Depth from Defocus

Abstract: In depth from defocus (DFD), when images are captured with different camera parameters, a relative magnification is induced between them. Image warping is a simpler solution to account for magnification than seemingly more accurate optical approaches. This work is an investigation into the effects of magnification on the accuracy of DFD. We comment on issues regarding scaling effect on relative blur computation. We statistically analyze accountability of scale factor, commenting on the bias and efficiency of the estimator that does not consider scale. We also discuss the effect of interpolation errors on blur estimation in a warping based solution to handle magnification and carry out experimental analysis to comment on the blur estimation accuracy.


Intelligent Interface Architectures for Folksonomy Driven Structure Network

Abstract: The folksonomy is the result of free personal information or assignment of tags to an object (determined by the URI) in order to find them. The practice of tagging is done in a collective environment. Folksonomies are self constructed, based on co-occurrence of definitions, rather than a hierarchical structure of the data. The downside of this was that a few sites and applications are able to successfully exploit the sharing of bookmarks. The need for tools that are able to resolve the ambiguity of the definitions is becoming urgent as the need of simple instruments for their visualization, editing and exploitation in web applications still hinders their diffusion and wide adoption. An intelligent interactive interface design for folksonomies should consider the contextual design and inquiry based on a concurrent interaction for a perceptual user interfaces. To represent folksonomies a new concept structure called "Folksodriven" is used in this paper. While it is presented the Folksodriven Structure Network (FSN) to resolve the ambiguity of definitions of folksonomy tags suggestions for the user. On this base a Human-Computer Interactive (HCI) systems is developed for the visualization, navigation, updating and maintenance of folksonomies Knowledge Bases - the FSN - through the web. System functionalities as well as its internal architecture will be introduced.


An Analysis of One-Dimensional Schelling Segregation

Abstract: We analyze the Schelling model of segregation in which a society of n individuals live in a ring. Each individual is one of two races and is only satisfied with his location so long as at least half his 2w nearest neighbors are of the same race as him. In the dynamics, randomly-chosen unhappy individuals successively swap locations. We consider the average size of monochromatic neighborhoods in the final stable state. Our analysis is the first rigorous analysis of the Schelling dynamics. We note that, in contrast to prior approximate analyses, the final state is nearly integrated: the average size of monochromatic neighborhoods is independent of n and polynomial in w.


Max-Sum Diversification, Monotone Submodular Functions and Dynamic Updates

Abstract: Result diversification is an important aspect in web-based search, document summarization, facility location, portfolio management and other applications. Given a set of ranked results for a set of objects (e.g. web documents, facilities, etc.) with a distance between any pair, the goal is to select a subset $S$ satisfying the following three criteria: (a) the subset $S$ satisfies some constraint (e.g. bounded cardinality); (b) the subset contains results of high "quality"; and (c) the subset contains results that are "diverse" relative to the distance measure. The goal of result diversification is to produce a diversified subset while maintaining high quality as much as possible. We study a broad class of problems where the distances are a metric, where the constraint is given by independence in a matroid, where quality is determined by a monotone submodular function, and diversity is defined as the sum of distances between objects in $S$. Our problem is a generalization of the {\em max sum diversification} problem studied in \cite{GoSh09} which in turn is a generaliztion of the {\em max sum $p$-dispersion problem} studied extensively in location theory. It is NP-hard even with the triangle inequality. We propose two simple and natural algorithms: a greedy algorithm for a cardinality constraint and a local search algorithm for an arbitary matroid constraint. We prove that both algorithms achieve constant approximation ratios.


PerfXplain: Debugging MapReduce Job Performance

Abstract: While users today have access to many tools that assist in performing large scale data analysis tasks, understanding the performance characteristics of their parallel computations, such as MapReduce jobs, remains difficult. We present PerfXplain, a system that enables users to ask questions about the relative performances (i.e., runtimes) of pairs of MapReduce jobs. PerfXplain provides a new query language for articulating performance queries and an algorithm for generating explanations from a log of past MapReduce job executions. We formally define the notion of an explanation together with three metrics, relevance, precision, and generality, that measure explanation quality. We present the explanation-generation algorithm based on techniques related to decision-tree building. We evaluate the approach on a log of past executions on Amazon EC2, and show that our approach can generate quality explanations, outperforming two naive explanation-generation methods.


Uncertain Centroid based Partitional Clustering of Uncertain Data

Abstract: Clustering uncertain data has emerged as a challenging task in uncertain data management and mining. Thanks to a computational complexity advantage over other clustering paradigms, partitional clustering has been particularly studied and a number of algorithms have been developed. While existing proposals differ mainly in the notions of cluster centroid and clustering objective function, little attention has been given to an analysis of their characteristics and limits. In this work, we theoretically investigate major existing methods of partitional clustering, and alternatively propose a well-founded approach to clustering uncertain data based on a novel notion of cluster centroid. A cluster centroid is seen as an uncertain object defined in terms of a random variable whose realizations are derived based on all deterministic representations of the objects to be clustered. As demonstrated theoretically and experimentally, this allows for better representing a cluster of uncertain objects, thus supporting a consistently improved clustering performance while maintaining comparable efficiency with existing partitional clustering algorithms.