Statistics for the Dynamic Analysis of Scientometric Data: The evolution of the sciences in terms of trajectories and regimes

Abstract: The gap in statistics between multi-variate and time-series analysis can be bridged by using entropy statistics and recent developments in multi-dimensional scaling. For explaining the evolution of the sciences as non-linear dynamics, the configurations among variables can be important in addition to the statistics of individual variables and trend lines. Animations enable us to combine multiple perspectives (based on configurations of variables) and to visualize path-dependencies in terms of trajectories and regimes. Path-dependent transitions and systems formation can be tested using entropy statistics.


Security Measures for Black Hole Attack in MANET: An Approach

Abstract: A Mobile Ad-Hoc Network is a collection of mobile nodes that are dynamically and arbitrarily located in such a manner that the interconnections between nodes are capable of changing on continual basis. Due to security vulnerabilities of the routing protocols, wireless ad-hoc networks are unprotected to attacks of the malicious nodes. One of these attacks is the Black Hole Attack. In this paper, we give an algorithmic approach to focus on analyzing and improving the security of AODV, which is one of the popular routing protocols for MANET. Our aim is on ensuring the security against Black hole attack. The proposed solution is capable of detecting & removing Black hole node(s) in the MANET at the beginning. Also the objective of this paper is to provide a simulation study that illustrates the effects of Black hole attack on network performance.


On The Recovery Performance of Single- and Multipath OLSR in Wireless Multi-Hop Networks

Abstract: In this paper, we study and improve the recovery properties of single and multipath routing strategies when facing network failure situations. In particular, we focus our study on two MANET routing protocols: OLSR and its multipath extension MP-OLSR. In various wireless multi-hop network environments, especially in multiple chain topologies, we define and seek to evaluate the latency introduced by these protocols to find a new path after a link failure. Theoretical estimations and simulation results show that, under dual chain-topologies, this latency can be too long and incompatible with the needs of loss and delay constrained applications. As the source nodes cannot detect link failures immediately because of the delay incurred by the well-known nature of link state protocols in general, and of OLSR Topology Control (TC) messages in particular, these nodes keep sending packets along broken paths. We thus study the inconsistencies between the actual network topology and the nodes' own representation. After analyzing the consequences of this long latency, we seek to alleviate these problems with the introduction of adapted mechanisms. We propose three new different schemes and accordingly extend the original OLSR and MP-OLSR protocols in order to decrease the expected latency and improve the protocol performance. Simulation results show a steep decrease of the latency when using these new schemes in dual chain-topologies. We also discuss these results in terms of packet loss, end-to-end delay and overhead.


Cryptographic Key Management for Smart Power Grids - Approaches and Issues

Abstract: The smart power grid promises to improve efficiency and reliability of power delivery. This report introduces the logical components, associated technologies, security protocols, and network designs of the system. Undermining the potential benefits are security threats, and those threats related to cyber security are described in this report. Concentrating on the design of the smart meter and its communication links, this report describes the ZigBee technology and implementation, and the communication between the smart meter and the collector node, with emphasis on security attributes. It was observed that many of the secure features are based on keys that must be maintained; therefore, secure key management techniques become the basis to securing the entire grid. The descriptions of current key management techniques are delineated, highlighting their weaknesses. Finally some initial research directions are outlined.


Compiling Finite Domain Constraints to SAT with BEE

Abstract: We present BEE, a compiler which enables to encode finite domain constraint problems to CNF. Using BEE both eases the encoding process for the user and also performs transformations to simplify constraints and optimize their encoding to CNF. These optimizations are based primarily on equi-propagation and on partial evaluation, and also on the idea that a given constraint may have various possible CNF encodings. Often, the better encoding choice is made after constraint simplification. BEE is written in Prolog and integrates directly with a SAT solver through a suitable Prolog interface. We demonstrate that constraint simplification is often highly beneficial when solving hard finite domain constraint problems. A BEE implementation is available with this paper.


A Study on Internet of Things based Applications

Abstract: This paper gives a detail analysis of various applications based on Internet of Thing (IoT)s. This explains about how internet of things evolved from mobile computing and ubiquitous computing. It emphasises the fact that objects are connected over the internet rather than people. The properties of Internet of Things (IOT) are product information, electronic tag, standard expressed and uploading information. It utilises the Radio Frequency Identification (RFID) technology and wireless sensor networks (WSN). IOT applications are used in domains such as healthcare, supply chain management, defence and agriculture. Lastly the paper focuses on issues involved in IOT. Though it is a boon, IOT faces certain crucial issues like privacy and security.


On the Complexity of Existential Positive Queries

Abstract: We systematically investigate the complexity of model checking the existential positive fragment of first-order logic. In particular, for a set of existential positive sentences, we consider model checking where the sentence is restricted to fall into the set; a natural question is then to classify which sentence sets are tractable and which are intractable. With respect to fixed-parameter tractability, we give a general theorem that reduces this classification question to the corresponding question for primitive positive logic, for a variety of representations of structures. This general theorem allows us to deduce that an existential positive sentence set having bounded arity is fixed-parameter tractable if and only if each sentence is equivalent to one in bounded-variable logic. We then use the lens of classical complexity to study these fixed-parameter tractable sentence sets. We show that such a set can be NP-complete, and consider the length needed by a translation from sentences in such a set to bounded-variable logic; we prove superpolynomial lower bounds on this length using the theory of compilability, obtaining an interesting type of formula size lower bound. Overall, the tools, concepts, and results of this article set the stage for the future consideration of the complexity of model checking on more expressive logics.


Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (2009)

Abstract: This is the Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, which was held in Montreal, QC, Canada, June 18 - 21 2009.


The Ultrasound Visualization Pipeline - A Survey

Abstract: Ultrasound is one of the most frequently used imaging modality in medicine. The high spatial resolution, its interactive nature and non-invasiveness makes it the first choice in many examinations. Image interpretation is one of ultrasound's main challenges. Much training is required to obtain a confident skill level in ultrasound-based diagnostics. State-of-the-art graphics techniques is needed to provide meaningful visualizations of ultrasound in real-time. In this paper we present the process-pipeline for ultrasound visualization, including an overview of the tasks performed in the specific steps. To provide an insight into the trends of ultrasound visualization research, we have selected a set of significant publications and divided them into a technique-based taxonomy covering the topics pre-processing, segmentation, registration, rendering and augmented reality. For the different technique types we discuss the difference between ultrasound-based techniques and techniques for other modalities.


Visualizing Streaming Text Data with Dynamic Maps

Abstract: The many endless rivers of text now available present a serious challenge in the task of gleaning, analyzing and discovering useful information. In this paper, we describe a methodology for visualizing text streams in real time. The approach automatically groups similar messages into "countries," with keyword summaries, using semantic analysis, graph clustering and map generation techniques. It handles the need for visual stability across time by dynamic graph layout and Procrustes projection techniques, enhanced with a novel stable component packing algorithm. The result provides a continuous, succinct view of evolving topics of interest. It can be used in passive mode for overviews and situational awareness, or as an interactive data exploration tool. To make these ideas concrete, we describe their application to an online service called TwitterScope.


Cooperative localization using angle of arrival measurements: sequential algorithms and non-line-of-sight suppression

Abstract: We investigate localization of a source based on angle of arrival (AoA) measurements made at a geographically dispersed network of cooperating receivers. The goal is to efficiently compute accurate estimates despite outliers in the AoA measurements due to multipath reflections in non-line-of-sight (NLOS) environments. Maximal likelihood (ML) location estimation in such a setting requires exhaustive testing of estimates from all possible subsets of "good" measurements, which has exponential complexity in the number of measurements. We provide a randomized algorithm that approaches ML performance with linear complexity in the number of measurements. The building block for this algorithm is a low-complexity sequential algorithm for updating the source location estimates under line-of-sight (LOS) environments. Our Bayesian framework can exploit the ability to resolve multiple paths in wideband systems to provide significant performance gains over narrowband systems in NLOS environments, and easily extends to accommodate additional information such as range measurements and prior information about location.


A polynomial-time algorithm for planar multicuts with few source-sink pairs

Abstract: Given an edge-weighted undirected graph and a list of k source-sink pairs of vertices, the well-known minimum multicut problem consists in selecting a minimum-weight set of edges whose removal leaves no path between every source and its corresponding sink. We give the first polynomial-time algorithm to solve this problem in planar graphs, when k is fixed. Previously, this problem was known to remain NP-hard in general graphs with fixed k, and in trees with arbitrary k; the most noticeable tractable case known so far was in planar graphs with fixed k and sources and sinks lying on the outer face.


ConeRANK: Ranking as Learning Generalized Inequalities

Abstract: We propose a new data mining approach in ranking documents based on the concept of cone-based generalized inequalities between vectors. A partial ordering between two vectors is made with respect to a proper cone and thus learning the preferences is formulated as learning proper cones. A pairwise learning-to-rank algorithm (ConeRank) is proposed to learn a non-negative subspace, formulated as a polyhedral cone, over document-pair differences. The algorithm is regularized by controlling the `volume' of the cone. The experimental studies on the latest and largest ranking dataset LETOR 4.0 shows that ConeRank is competitive against other recent ranking approaches.


Experience on Re-engineering Applying with Software Product Line

Abstract: In this paper, we present our experience based on a reengineering project. The software project is to re-engineer the original system of a company to answer the new requirements and changed business functions. Reengineering is a process that involves not only the software system, but also underlying business model. Particularly, the new business model is designed along with new technologies to support the new system. This paper presents our experience that applies with software product line approach to develop the new system supporting original business functions and new ones.


Classification of Various Security Techniques in Databases and their Comparative Analysis

Abstract: Data security is one of the most crucial and a major challenge in the digital world. Security, privacy and integrity of data are demanded in every operation performed on internet. Whenever security of data is discussed, it is mostly in the context of secure transfer of data over the unreliable communication networks. But the security of the data in databases is also as important. In this paper we will be presenting some of the common security techniques for the data that can be implemented in fortifying and strengthening the databases.


Image based Cryptography from a distance

Abstract: An information is a message which is received and understood. Information can be sent one person to another over a long range but the process of sending information must be done in a secure way especially in case of a private message. Mathematicians and Engineers have historically relied on different algorithmic techniques to secure messages and signals. Cryptography, to most people, is concerned with keeping communications private. Indeed, the protection of sensitive communications has been the emphasis of cryptography throughout much of its history. Sometimes it is safer to send a message using an image and thus cryptography can also be done using images during an emergency. The need to extract information from images and interpret their contents has been one of the driving factors in the development of image processing and cryptography during the past decades. In this paper, a simple cryptographic method was used to decode a message which was in an image and it was done using a popular computational software.


Data Hiding Techniques using number decompositions

Abstract: Data hiding is the art of embedding data into digital media in a way such that the existence of data remains concealed from everyone except the intended recipient. In this paper, we discuss the various Least Significant Bit (LSB) data hiding techniques. We first look at the classical LSB data hiding technique and the method to embed secret data into cover media by bit manipulation. We also take a look at the data hiding technique by bit plane decomposition based on Fibonacci numbers. This method generates more bit planes which allows users to embed more data into the cover image without causing significant distortion. We also discuss the data hiding technique based on bit plane decomposition by prime numbers and natural numbers. These methods are based on mapping the sequence of image bit size to the decomposed bit number to hide the intended information. Finally we present a comparative analysis of these data hiding techniques.


Quantifying Paedophile Activity in a Large P2P System

Abstract: Increasing knowledge of paedophile activity in P2P systems is a crucial societal concern, with important consequences on child protection, policy making, and internet regulation. Because of a lack of traces of P2P exchanges and rigorous analysis methodology, however, current knowledge of this activity remains very limited. We consider here a widely used P2P system, eDonkey, and focus on two key statistics: the fraction of paedophile queries entered in the system and the fraction of users who entered such queries. We collect hundreds of millions of keyword-based queries; we design a paedophile query detection tool for which we establish false positive and false negative rates using assessment by experts; with this tool and these rates, we then estimate the fraction of paedophile queries in our data; finally, we design and apply methods for quantifying users who entered such queries. We conclude that approximately 0.25% of queries are paedophile, and that more than 0.2% of users enter such queries. These statistics are by far the most precise and reliable ever obtained in this domain.


Comparing paedophile activity in different P2P systems

Abstract: Peer-to-peer (P2P) systems are widely used to exchange content over the Internet. Knowledge on paedophile activity in such networks remains limited while it has important social consequences. Moreover, though there are different P2P systems in use, previous academic works on this topic focused on one system at a time and their results are not directly comparable. We design a methodology for comparing \kad and \edonkey, two P2P systems among the most prominent ones and with different anonymity levels. We monitor two \edonkey servers and the \kad network during several days and record hundreds of thousands of keyword-based queries. We detect paedophile-related queries with a previously validated tool and we propose, for the first time, a large-scale comparison of paedophile activity in two different P2P systems. We conclude that there are significantly fewer paedophile queries in \kad than in \edonkey (approximately 0.09% \vs 0.25%).


Clustered Bandits

Abstract: We consider a multi-armed bandit setting that is inspired by real-world applications in e-commerce. In our setting, there are a few types of users, each with a specific response to the different arms. When a user enters the system, his type is unknown to the decision maker. The decision maker can either treat each user separately ignoring the previously observed users, or can attempt to take advantage of knowing that only few types exist and cluster the users according to their response to the arms. We devise algorithms that combine the usual exploration-exploitation tradeoff with clustering of users and demonstrate the value of clustering. In the process of developing algorithms for the clustered setting, we propose and analyze simple algorithms for the setup where a decision maker knows that a user belongs to one of few types, but does not know which one.


Clustered Network Coding for Maintenance in Practical Storage Systems

Abstract: Classical erasure codes, e.g. Reed-Solomon codes, have been acknowledged as an efficient alternative to plain replication to reduce the storage overhead in reliable distributed storage systems. Yet, such codes experience high overhead during the maintenance process. In this paper we propose a novel erasure-coded framework especially tailored for networked storage systems. Our approach relies on the use of random codes coupled with a clustered placement strategy, enabling the maintenance of a failed machine at the granularity of multiple files. Our repair protocol leverages network coding techniques to reduce by half the amount of data transferred during maintenance, as several files can be repaired simultaneously. This approach, as formally proven and demonstrated by our evaluation on a public experimental testbed, enables to dramatically decrease the bandwidth overhead during the maintenance process, as well as the time to repair a failure. In addition, the implementation is made as simple as possible, aiming at a deployment into practical systems.


Ant Robotics: Covering Continuous Domains by Multi-A(ge)nt Systems

Abstract: In this work we present an algorithm for covering continuous connected domains by ant-like robots with very limited capabilities. The robots can mark visited places with pheromone marks and sense the level of the pheromone in their local neighborhood. In case of multiple robots these pheromone marks can be sensed by all robots and provide the only way of (indirect) communication between the robots. The robots are assumed to be memoryless, and to have no global information such as the domain map, their own position (either absolute or relative), total marked area percentage, maximal pheromone level, etc.. Despite the robots' simplicity, we show that they are able, by running a very simple rule of behavior, to ensure efficient covering of arbitrary connected domains, including non-planar and multidimensional ones. The novelty of our algorithm lies in the fact that, unlike previously proposed methods, our algorithm works on continuous domains without relying on some "induced" underlying graph, that effectively reduces the problem to a discrete case of graph covering. The algorithm guarantees complete coverage of any connected domain. We also prove that the algorithm is noise immune, i.e., it is able to cope with any initial pheromone profile (noise). In addition the algorithm provides a bounded constant time between two successive visits of the robot, and thus, is suitable for patrolling or surveillance applications.


Correlating Pedestrian Flows and Search Engine Queries

Abstract: An important challenge for ubiquitous computing is the development of techniques that can characterize a location vis-a-vis the richness and diversity of urban settings. In this paper we report our work on correlating urban pedestrian flows with Google search queries. Using longitudinal data we show pedestrian flows at particular locations can be correlated with the frequency of Google search terms that are semantically relevant to those locations. Our approach can identify relevant content, media, and advertisements for particular locations.


Factoring bivariate lacunary polynomials without heights

Abstract: We present an algorithm which computes the multilinear factors of bivariate lacunary polynomials. It is based on a new Gap Theorem which allows to test whether a polynomial of the form P(X,X+1) is identically zero in time polynomial in the number of terms of P(X,Y). The algorithm we obtain is more elementary than the one by Kaltofen and Koiran (ISSAC'05) since it relies on the valuation of polynomials of the previous form instead of the height of the coefficients. As a result, it can be used to find some linear factors of bivariate lacunary polynomials over a field of large finite characteristic in probabilistic polynomial time.


Succinct Representations for Abstract Interpretation

Abstract: Abstract interpretation techniques can be made more precise by distinguishing paths inside loops, at the expense of possibly exponential complexity. SMT-solving techniques and sparse representations of paths and sets of paths avoid this pitfall. We improve previously proposed techniques for guided static analysis and the generation of disjunctive invariants by combining them with techniques for succinct representations of paths and symbolic representations for transitions based on static single assignment. Because of the non-monotonicity of the results of abstract interpretation with widening operators, it is difficult to conclude that some abstraction is more precise than another based on theoretical local precision results. We thus conducted extensive comparisons between our new techniques and previous ones, on a variety of open-source packages.


Comparative Analysis of Routing Attacks in Ad Hoc Network

Abstract: In the mobile ad hoc networks the major role is played by the routing protocols in order to route the data from one mobile node to another mobile node. But in such mobile networks, routing protocols are vulnerable to various kinds of security attacks such as blackhole node attacks. The routing protocols of MANET are unprotected and hence resulted into the network with the malicious mobile nodes in the network. These malicious nodes in the network are basically acts as attacks in the network. In this paper, we modify the existing DSR protocol with the functionality of attacks detection without affecting overall performance of the network. Also, we are considering the various attacks on mobile ad hoc network called blackhole attack, flooding attack and show the comparative analysis of these attacks using network simulator ns-2.


A Novel Effective, Secure and Robust CDMA Digital Image Watermarking in YUV Color Space Using DWT2

Abstract: This paper is allocated to CDMA digital images watermarking for ownership verification and image authentication applications, which for more security, watermark W is converted to a sequence and then a random binary sequence R of size n is adopted to encrypt the watermark; where n is the size of the watermark. This adopting process uses a pseudo-random number generator to determine the pixel to be used on a given key. After converting the host image to YUV color space and then wavelet decomposition of Y channel, this adopted watermark is embedded into the selected subbands coefficients of Y channel using the correlation properties of additive pseudo- random noise patterns. The experimental results show that the proposed approach provides extra imperceptibility, security and robustness against JPEG compression and different noises attacks compared to the similar proposed methods. Moreover, the proposed approach has no need of the original image to extract watermarks.


Pricing of insurance policies against cloud storage price rises

Abstract: When a company migrates to cloud storage, the way back is neither easy nor cheap. The company is then locked up in the storage contract and exposed to upward market prices, which reduce the company's profit and may even bring it below zero. We propose a protection means based on an insurance contract, by which the cloud purchaser is indemnified when the current storage price exceeds a pre-defined threshold. By applying the financial options theory, we provide a formula for the insurance price (the premium). By using historical data on market prices for disks, we apply the formula in realistic scenarios. We show that the premium grows nearly quadratically with the length of the coverage period as long as this is below one year, but grows more slowly, though faster than linearly, over longer coverage periods.


Quasi-Succinct Indices

Abstract: Compressed inverted indices in use today are based on the idea of gap compression: documents pointers are stored in increasing order, and the gaps between successive document pointers are stored using suitable codes which represent smaller gaps using less bits. Additional data such as counts and positions is stored using similar techniques. A large body of research has been built in the last 30 years around gap compression, including theoretical modeling of the gap distribution, specialized instantaneous codes suitable for gap encoding, and ad hoc document reorderings which increase the efficiency of instantaneous codes. This paper proposes to represent an index using a different architecture based on quasi-succinct representation of monotone sequences. We show that, besides being theoretically elegant and simple, the new index provides expected constant-time operations and, in practice, significant performance improvements on conjunctive, phrasal and proximity queries.


Joint Reconstruction of Multi-view Compressed Images

Abstract: The distributed representation of correlated multi-view images is an important problem that arise in vision sensor networks. This paper concentrates on the joint reconstruction problem where the distributively compressed correlated images are jointly decoded in order to improve the reconstruction quality of all the compressed images. We consider a scenario where the images captured at different viewpoints are encoded independently using common coding solutions (e.g., JPEG, H.264 intra) with a balanced rate distribution among different cameras. A central decoder first estimates the underlying correlation model from the independently compressed images which will be used for the joint signal recovery. The joint reconstruction is then cast as a constrained convex optimization problem that reconstructs total-variation (TV) smooth images that comply with the estimated correlation model. At the same time, we add constraints that force the reconstructed images to be consistent with their compressed versions. We show by experiments that the proposed joint reconstruction scheme outperforms independent reconstruction in terms of image quality, for a given target bit rate. In addition, the decoding performance of our proposed algorithm compares advantageously to state-of-the-art distributed coding schemes based on disparity learning and on the DISCOVER.


An Improved Gauss-Newtons Method based Back-propagation Algorithm for Fast Convergence

Abstract: The present work deals with an improved back-propagation algorithm based on Gauss-Newton numerical optimization method for fast convergence. The steepest descent method is used for the back-propagation. The algorithm is tested using various datasets and compared with the steepest descent back-propagation algorithm. In the system, optimization is carried out using multilayer neural network. The efficacy of the proposed method is observed during the training period as it converges quickly for the dataset used in test. The requirement of memory for computing the steps of algorithm is also analyzed.


Allocation of Divisible Goods under Lexicographic Preferences

Abstract: We present a simple and natural non-pricing mechanism for allocating divisible goods among strategic agents having lexicographic preferences. Our mechanism has favorable properties of incentive compatibility (strategy-proofness), Pareto efficiency, envy-freeness, and time efficiency.


Upper and Lower Bounds on the Cost of a Map-Reduce Computation

Abstract: In this paper we study the tradeoff between parallelism and communication cost in a map-reduce computation. For any problem that is not "embarrassingly parallel," the finer we partition the work of the reducers so that more parallelism can be extracted, the greater will be the total communication between mappers and reducers. We introduce a model of problems that can be solved in a single round of map-reduce computation. This model enables a generic recipe for discovering lower bounds on communication cost as a function of the maximum number of inputs that can be assigned to one reducer. We use the model to analyze the tradeoff for three problems: finding pairs of strings at Hamming distance $d$, finding triangles and other patterns in a larger graph, and matrix multiplication. For finding strings of Hamming distance 1, we have upper and lower bounds that match exactly. For triangles and many other graphs, we have upper and lower bounds that are the same to within a constant factor. For the problem of matrix multiplication, we have matching upper and lower bounds for one-round map-reduce algorithms. We are also able to explore two-round map-reduce algorithms for matrix multiplication and show that these never have more communication, for a given reducer size, than the best one-round algorithm, and often have significantly less.


Gray Image extraction using Fuzzy Logic

Abstract: Fuzzy systems concern fundamental methodology to represent and process uncertainty and imprecision in the linguistic information. The fuzzy systems that use fuzzy rules to represent the domain knowledge of the problem are known as Fuzzy Rule Base Systems (FRBS). On the other hand image segmentation and subsequent extraction from a noise-affected background, with the help of various soft computing methods, are relatively new and quite popular due to various reasons. These methods include various Artificial Neural Network (ANN) models (primarily supervised in nature), Genetic Algorithm (GA) based techniques, intensity histogram based methods etc. providing an extraction solution working in unsupervised mode happens to be even more interesting problem. Literature suggests that effort in this respect appears to be quite rudimentary. In the present article, we propose a fuzzy rule guided novel technique that is functional devoid of any external intervention during execution. Experimental results suggest that this approach is an efficient one in comparison to different other techniques extensively addressed in literature. In order to justify the supremacy of performance of our proposed technique in respect of its competitors, we take recourse to effective metrics like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal to Noise Ratio (PSNR).


Inverse Modeling of Climate Responses of Monumental Buildings

Abstract: The indoor climate conditions of monumental buildings are very important for the conservation of these objects. Simplified models with physical meaning are desired that are capable of simulating temperature and relative humidity. In this paper we research state-space models as methodology for the inverse modeling of climate responses of unheated monumental buildings. It is concluded that this approach is very promising for obtaining physical models and parameters of indoor climate responses. Furthermore state space models can be simulated very efficiently: the simulation duration time of a 100 year hourly based period take less than a second on an ordinary computer.


Generalized Craig Interpolation for Stochastic Boolean Satisfiability Problems with Applications to Probabilistic State Reachability and Region Stability

Abstract: The stochastic Boolean satisfiability (SSAT) problem has been introduced by Papadimitriou in 1985 when adding a probabilistic model of uncertainty to propositional satisfiability through randomized quantification. SSAT has many applications, among them probabilistic bounded model checking (PBMC) of symbolically represented Markov decision processes. This article identifies a notion of Craig interpolant for the SSAT framework and develops an algorithm for computing such interpolants based on a resolution calculus for SSAT. As a potential application area of this novel concept of Craig interpolation, we address the symbolic analysis of probabilistic systems. We first investigate the use of interpolation in probabilistic state reachability analysis, turning the falsification procedure employing PBMC into a verification technique for probabilistic safety properties. We furthermore propose an interpolation-based approach to probabilistic region stability, being able to verify that the probability of stabilizing within some region is sufficiently large.


Terminating Calculi for Propositional Dummett Logic with Subformula Property

Abstract: In this paper we present two terminating tableau calculi for propositional Dummett logic obeying the subformula property. The ideas of our calculi rely on the linearly ordered Kripke semantics of Dummett logic. The first calculus works on two semantical levels: the present and the next possible world. The second calculus employs the usual object language of tableau systems and exploits a property of the construction of the completeness theorem to introduce a check which is an alternative to loop check mechanisms.


Modeling Languages: metrics and assessing tools

Abstract: Any traditional engineering field has metrics to rigorously assess the quality of their products. Engineers know that the output must satisfy the requirements, must comply with the production and market rules, and must be competitive. Professionals in the new field of software engineering started a few years ago to define metrics to appraise their product: individual programs and software systems. This concern motivates the need to assess not only the outcome but also the process and tools employed in its development. In this context, assessing the quality of programming languages is a legitimate objective; in a similar way, it makes sense to be concerned with models and modeling approaches, as more and more people start the software development process by a modeling phase. In this paper we introduce and motivate the assessment of models quality in the Software Development cycle. After the general discussion of this topic, we focus the attention on the most popular modeling language -- the UML -- presenting metrics. Through a Case-Study, we present and explore two tools. To conclude we identify what is still lacking in the tools side.


Parsimonious Mahalanobis Kernel for the Classification of High Dimensional Data

Abstract: The classification of high dimensional data with kernel methods is considered in this article. Exploit- ing the emptiness property of high dimensional spaces, a kernel based on the Mahalanobis distance is proposed. The computation of the Mahalanobis distance requires the inversion of a covariance matrix. In high dimensional spaces, the estimated covariance matrix is ill-conditioned and its inversion is unstable or impossible. Using a parsimonious statistical model, namely the High Dimensional Discriminant Analysis model, the specific signal and noise subspaces are estimated for each considered class making the inverse of the class specific covariance matrix explicit and stable, leading to the definition of a parsimonious Mahalanobis kernel. A SVM based framework is used for selecting the hyperparameters of the parsimonious Mahalanobis kernel by optimizing the so-called radius-margin bound. Experimental results on three high dimensional data sets show that the proposed kernel is suitable for classifying high dimensional data, providing better classification accuracies than the conventional Gaussian kernel.


Revisiting Timed Specification Theories: A Linear-Time Perspective

Abstract: We consider the setting of component-based design for real-time systems with critical timing constraints. Based on our earlier work, we propose a compositional specification theory for timed automata with I/O distinction, which supports substitutive refinement. Our theory provides the operations of parallel composition for composing components at run-time, logical conjunction/disjunction for independent development, and quotient for incremental synthesis. The key novelty of our timed theory lies in a weakest congruence preserving safety as well as bounded liveness properties. We show that the congruence can be characterised by two linear-time semantics, timed-traces and timed-strategies, the latter of which is derived from a game-based interpretation of timed interaction.


Decentralized Estimation of Laplacian Eigenvalues in Multi-Agent Systems

Abstract: In this paper we present a decentralized algorithm to estimate the eigenvalues of the Laplacian matrix that encodes the network topology of a multi-agent system. We consider network topologies modeled by undirected graphs. The basic idea is to provide a local interaction rule among agents so that their state trajectory is a linear combination of sinusoids oscillating only at frequencies function of the eigenvalues of the Laplacian matrix. In this way, the problem of decentralized estimation of the eigenvalues is mapped into a standard signal processing problem in which the unknowns are the finite number of frequencies at which the signal oscillates.


Improved DWT Based Watermarking Using JPEG-YCbCr

Abstract: In this paper a blind, Secure, imperceptible and robust watermarking algorithm based on wavelet transform domain is proposed in which for more security, the watermark W is converted to a sequence and then a random binary sequence R of size n is adopted to encrypt the watermark, where n is the size of the watermark image. Afterwards, the encrypted watermark sequence W1 is generated by executing exclusive-OR operation on W and R. This generated watermark embeds into low frequency selected coefficients of Y channel wavelet decomposition of JPEG-YCbCr using LSB insertion technique. The experimental results show that the proposed algorithm increases the security and imperceptibility of watermark and has better robustness against wavelet compression and cropping attacks compared to the earlier work in [1].


Effective Digital Image Watermarking in YCbCr Color Space Accompanied by Presenting a Novel Technique Using DWT

Abstract: In this paper, a quantization based watermark casting and blind watermark retrieval algorithm operating in YCbCr color space using discrete wavelet transform (DWT), for ownership verification and image authentication applications is implemented. This method uses implicit visual masking by inserting watermark bits into only the wavelet coefficients of high magnitude, in Y channel of YCbCr color space. A blind watermark retrieval technique that can detect the embedded watermark without the help from the original uncorrupted image is devised which is computationally efficient. The new watermarking algorithm combines and adapts various aspects from existing watermarking methods. Experimental results show that the proposed technique to embed watermark provides extra imperceptibility and robustness against various signal processing attacks in comparison with the same technique in RGB color space.


BADREX: In situ expansion and coreference of biomedical abbreviations using dynamic regular expressions

Abstract: BADREX uses dynamically generated regular expressions to annotate term definition-term abbreviation pairs, and corefers unpaired acronyms and abbreviations back to their initial definition in the text. Against the Medstract corpus BADREX achieves precision and recall of 98% and 97%, and against a much larger corpus, 90% and 85%, respectively. BADREX yields improved performance over previous approaches, requires no training data and allows runtime customisation of its input parameters. BADREX is freely available from https://github.com/philgooch/BADREX-Biomedical-Abbreviation-Expander as a plugin for the General Architecture for Text Engineering (GATE) framework and is licensed under the GPLv3.


On the Distributed Construction of a Collision-Free Schedule in Multi-Hop Packet Radio Networks

Abstract: This paper introduces a protocol that distributively constructs a collision-free schedule for multi-hop packet radio networks in the presence of hidden terminals. As a preliminary step, each wireless station computes the schedule length after gathering information about the number of flows in its neighbourhood. Then, a combination of deterministic and random backoffs are used to reach a collision-free schedule. A deterministic backoff is used after successful transmissions and a random backoff is used otherwise. It is explained that the short acknowledgement control packets can easily result in channel time fragmentation and, to avoid this, the use of link layer delayed acknowledgements is advocated and implemented. The performance results show that a collision-free protocol easily outperforms a collision-prone protocol such as Aloha. The time that is required for the network to converge to a collision-free schedule is assessed by means of simulation.


Initiality for Typed Syntax and Semantics

Abstract: We give an algebraic characterization of the syntax and semantics of a class of simply-typed languages, such as the language PCF: we characterize simply-typed binding syntax equipped with reduction rules via a universal property, namely as the initial object of some category. For this purpose, we employ techniques developed in two previous works: in [2], we model syntactic translations between languages over different sets of types as initial morphisms in a category of models. In [1], we characterize untyped syntax with reduction rules as initial object in a category of models. In the present work, we show that those techniques are modular enough to be combined: we thus characterize simply-typed syntax with reduction rules as initial object in a category. The universal property yields an operator which allows to specify translations - that are semantically faithful by construction - between languages over possibly different sets of types. We specify a language by a 2-signature, that is, a signature on two levels: the syntactic level specifies the types and terms of the language, and associates a type to each term. The semantic level specifies, through inequations, reduction rules on the terms of the language. To any given 2-signature we associate a category of models. We prove that this category has an initial object, which integrates the types and terms freely generated by the 2-signature, and the reduction relation on those terms generated by the given inequations. We call this object the (programming) language generated by the 2-signature. [1] Ahrens, B.: Modules over relative monads for syntax and semantics (2011), arXiv:1107.5252, to be published in Math. Struct. in Comp. Science [2] Ahrens, B.: Extended Initiality for Typed Abstract Syntax. Logical Methods in Computer Science 8(2), 1-35 (2012)


Abstract: In this thesis we give an algebraic characterization of the syntax and semantics of simply-typed languages. More precisely, we characterize simply-typed binding syntax equipped with reduction rules via a universal property, namely as the initial object of some category. We specify a language by a 2-signature ({\Sigma}, A), that is, a signature on two levels: the syntactic level {\Sigma} specifies the sorts and terms of the language, and associates a sort to each term. The semantic level A specifies, through inequations, reduction rules on the terms of the language. To any given 2-signature ({\Sigma}, A) we associate a category of "models" of ({\Sigma}, A). We prove that this category has an initial object, which integrates the terms freely generated by {\Sigma} and the reduction relation - on those terms - generated by A. We call this object the programming language generated by ({\Sigma}, A). Initiality provides an iteration principle which allows to specify translations on the syntax, possibly to a language over different sorts. Furthermore, translations specified via the iteration principle are by construction type-safe and faithful with respect to reduction. To illustrate our results, we consider two examples extensively: firstly, we specify a double negation translation from classical to intuitionistic propositional logic via the category-theoretic iteration principle. Secondly, we specify a translation from PCF to the untyped lambda calculus which is faithful with respect to reduction in the source and target languages. In a second part, we formalize some of our initiality theorems in the proof assistant Coq. The implementation yields a machinery which, when given a 2-signature, returns an implementation of its associated abstract syntax together with certified substitution operation, iteration operator and a reduction relation generated by the specified reduction rules.


Computational Aspects of Dependence Logic

Abstract: In this thesis (modal) dependence logic is investigated. It was introduced in 2007 by Jouko V\"a\"aan\"anen as an extension of first-order (resp. modal) logic by the dependence operator =(). For first-order (resp. propositional) variables x_1,...,x_n, =(x_1,...,x_n) intuitively states that the value of x_n is determined by those of x_1,...,x_n-1. We consider fragments of modal dependence logic obtained by restricting the set of allowed modal and propositional connectives. We classify these fragments with respect to the complexity of their satisfiability and model-checking problems. For satisfiability we obtain complexity degrees from P over NP, Sigma_P^2 and PSPACE up to NEXP, while for model-checking we only classify the fragments with respect to their tractability, i.e. we either show NP-completeness or containment in P. We then study the extension of modal dependence logic by intuitionistic implication. For this extension we again classify the complexity of the model-checking problem for its fragments. Here we obtain complexity degrees from P over NP and coNP up to PSPACE. Finally, we analyze first-order dependence logic, independence-friendly logic and their two-variable fragments. We prove that satisfiability for two-variable dependence logic is NEXP-complete, whereas for two-variable independence-friendly logic it is undecidable; and use this to prove that the latter is also more expressive than the former.


Robust statistics, hypothesis testing, and confidence intervals for persistent homology on metric measure spaces

Abstract: We study distributions of persistent homology barcodes associated to taking subsamples of a fixed size from metric measure spaces. We show that such distributions provide robust invariants of metric measure spaces, and illustrate their use in hypothesis testing and providing confidence intervals for topological data analysis.


A Comparison between Digital Image Watermarking in Tow Different Color Spaces Using DWT2

Abstract: A novel digital watermarking for ownership verification and image authentication applications using discrete wavelet transform (DWT) is proposed in this paper. Most previous proposed watermarking algorithms embed sequences of random numbers as watermarks. Here binary images are taken as watermark for embedding. In the proposed approach, the host image is converted into the YCbCr color space and then its Y channel decomposed into wavelet coefficients. The selected approximation coefficients are quantized and then their four least significant bits of the quantized coefficients are replaced by the watermark using LSB insertion technique. At last, the watermarked image is synthesized from the changed and unchanged DWT coefficients. The experiments show that the proposed approach provides extra imperceptibility and robustness against wavelet compression compared to the traditional embedding methods in RGB color space. Moreover, the proposed approach has no need of the original image to extract watermarks.


An Evolutionary Approach to Drug-Design Using Quantam Binary Particle Swarm Optimization Algorithm

Abstract: The present work provides a new approach to evolve ligand structures which represent possible drug to be docked to the active site of the target protein. The structure is represented as a tree where each non-empty node represents a functional group. It is assumed that the active site configuration of the target protein is known with position of the essential residues. In this paper the interaction energy of the ligands with the protein target is minimized. Moreover, the size of the tree is difficult to obtain and it will be different for different active sites. To overcome the difficulty, a variable tree size configuration is used for designing ligands. The optimization is done using a quantum discrete PSO. The result using fixed length and variable length configuration are compared.


Defining the symmetry of the universal semi-regular autonomous asynchronous systems

Abstract: The regular autonomous asynchronous systems are the non-deterministic Boolean dynamical systems and universality means the greatest in the sense of the inclusion. The paper gives four definitions of symmetry of these systems in a slightly more general framework, called semi-regularity and also many examples.


Latent Collaborative Retrieval

Abstract: Retrieval tasks typically require a ranking of items given a query. Collaborative filtering tasks, on the other hand, learn to model user's preferences over items. In this paper we study the joint problem of recommending items to a user with respect to a given query, which is a surprisingly common task. This setup differs from the standard collaborative filtering one in that we are given a query x user x item tensor for training instead of the more traditional user x item matrix. Compared to document retrieval we do have a query, but we may or may not have content features (we will consider both cases) and we can also take account of the user's profile. We introduce a factorized model for this new task that optimizes the top-ranked items returned for the given query and user. We report empirical results where it outperforms several baselines.


Learning the Experts for Online Sequence Prediction

Abstract: Online sequence prediction is the problem of predicting the next element of a sequence given previous elements. This problem has been extensively studied in the context of individual sequence prediction, where no prior assumptions are made on the origin of the sequence. Individual sequence prediction algorithms work quite well for long sequences, where the algorithm has enough time to learn the temporal structure of the sequence. However, they might give poor predictions for short sequences. A possible remedy is to rely on the general model of prediction with expert advice, where the learner has access to a set of $r$ experts, each of which makes its own predictions on the sequence. It is well known that it is possible to predict almost as well as the best expert if the sequence length is order of $\log(r)$. But, without firm prior knowledge on the problem, it is not clear how to choose a small set of {\em good} experts. In this paper we describe and analyze a new algorithm that learns a good set of experts using a training set of previously observed sequences. We demonstrate the merits of our approach by applying it on the task of click prediction on the web.


Clustering to Maximize the Ratio of Split to Diameter

Abstract: Given a weighted and complete graph G = (V, E), V denotes the set of n objects to be clustered, and the weight d(u, v) associated with an edge (u, v) belonging to E denotes the dissimilarity between objects u and v. The diameter of a cluster is the maximum dissimilarity between pairs of objects in the cluster, and the split of a cluster is the minimum dissimilarity between objects within the cluster and objects outside the cluster. In this paper, we propose a new criterion for measuring the goodness of clusters: the ratio of the minimum split to the maximum diameter, and the objective is to maximize the ratio. For k = 2, we present an exact algorithm. For k >= 3, we prove that the problem is NP-hard and present a factor of 2 approximation algorithm on the precondition that the weights associated with E satisfy the triangle inequality. The worst-case runtime of both algorithms is O(n^3). We compare the proposed algorithms with the Normalized Cut by applying them to image segmentation. The experimental results on both natural and synthetic images demonstrate the effectiveness of the proposed algorithms.


Exact Soft Confidence-Weighted Learning

Abstract: In this paper, we propose a new Soft Confidence-Weighted (SCW) online learning scheme, which enables the conventional confidence-weighted learning method to handle non-separable cases. Unlike the previous confidence-weighted learning algorithms, the proposed soft confidence-weighted learning method enjoys all the four salient properties: (i) large margin training, (ii) confidence weighting, (iii) capability to handle non-separable data, and (iv) adaptive margin. Our experimental results show that the proposed SCW algorithms significantly outperform the original CW algorithm. When comparing with a variety of state-of-the-art algorithms (including AROW, NAROW and NHERD), we found that SCW generally achieves better or at least comparable predictive accuracy, but enjoys significant advantage of computational efficiency (i.e., smaller number of updates and lower time cost).


Inductive Kernel Low-rank Decomposition with Priors: A Generalized Nystrom Method

Abstract: Low-rank matrix decomposition has gained great popularity recently in scaling up kernel methods to large amounts of data. However, some limitations could prevent them from working effectively in certain domains. For example, many existing approaches are intrinsically unsupervised, which does not incorporate side information (e.g., class labels) to produce task specific decompositions; also, they typically work "transductively", i.e., the factorization does not generalize to new samples, so the complete factorization needs to be recomputed when new samples become available. To solve these problems, in this paper we propose an"inductive"-flavored method for low-rank kernel decomposition with priors. We achieve this by generalizing the Nystr\"om method in a novel way. On the one hand, our approach employs a highly flexible, nonparametric structure that allows us to generalize the low-rank factors to arbitrarily new samples; on the other hand, it has linear time and space complexities, which can be orders of magnitudes faster than existing approaches and renders great efficiency in learning a low-rank kernel decomposition. Empirical results demonstrate the efficacy and efficiency of the proposed method.


Abstract: There has been a recent focus in reinforcement learning on addressing continuous state and action problems by optimizing parameterized policies. PI2 is a recent example of this approach. It combines a derivation from first principles of stochastic optimal control with tools from statistical estimation theory. In this paper, we consider PI2 as a member of the wider family of methods which share the concept of probability-weighted averaging to iteratively update parameters to optimize a cost function. We compare PI2 to other members of the same family - Cross-Entropy Methods and CMAES - at the conceptual level and in terms of performance. The comparison suggests the derivation of a novel algorithm which we call PI2-CMA for "Path Integral Policy Improvement with Covariance Matrix Adaptation". PI2-CMA's main advantage is that it determines the magnitude of the exploration noise automatically.


Optimizing F-measure: A Tale of Two Approaches

Abstract: F-measures are popular performance metrics, particularly for tasks with imbalanced data sets. Algorithms for learning to maximize F-measures follow two approaches: the empirical utility maximization (EUM) approach learns a classifier having optimal performance on training data, while the decision-theoretic approach learns a probabilistic model and then predicts labels with maximum expected F-measure. In this paper, we investigate the theoretical justifications and connections for these two approaches, and we study the conditions under which one approach is preferable to the other using synthetic and real datasets. Given accurate models, our results suggest that the two approaches are asymptotically equivalent given large training and test sets. Nevertheless, empirically, the EUM approach appears to be more robust against model misspecification, and given a good model, the decision-theoretic approach appears to be better for handling rare classes and a common domain adaptation scenario.


Multiple Kernel Learning from Noisy Labels by Stochastic Programming

Abstract: We study the problem of multiple kernel learning from noisy labels. This is in contrast to most of the previous studies on multiple kernel learning that mainly focus on developing efficient algorithms and assume perfectly labeled training examples. Directly applying the existing multiple kernel learning algorithms to noisily labeled examples often leads to suboptimal performance due to the incorrect class assignments. We address this challenge by casting multiple kernel learning from noisy labels into a stochastic programming problem, and presenting a minimax formulation. We develop an efficient algorithm for solving the related convex-concave optimization problem with a fast convergence rate of $O(1/T)$ where $T$ is the number of iterations. Empirical studies on UCI data sets verify both the effectiveness of the proposed framework and the efficiency of the proposed optimization algorithm.


Efficient Decomposed Learning for Structured Prediction

Abstract: Structured prediction is the cornerstone of several machine learning applications. Unfortunately, in structured prediction settings with expressive inter-variable interactions, exact inference-based learning algorithms, e.g. Structural SVM, are often intractable. We present a new way, Decomposed Learning (DecL), which performs efficient learning by restricting the inference step to a limited part of the structured spaces. We provide characterizations based on the structure, target parameters, and gold labels, under which DecL is equivalent to exact learning. We then show that in real world settings, where our theoretical assumptions may not completely hold, DecL-based algorithms are significantly more efficient and as accurate as exact learning.


Modeling Latent Variable Uncertainty for Loss-based Learning

Abstract: We consider the problem of parameter estimation using weakly supervised datasets, where a training sample consists of the input and a partially specified annotation, which we refer to as the output. The missing information in the annotation is modeled using latent variables. Previous methods overburden a single distribution with two separate tasks: (i) modeling the uncertainty in the latent variables during training; and (ii) making accurate predictions for the output and the latent variables during testing. We propose a novel framework that separates the demands of the two tasks using two distributions: (i) a conditional distribution to model the uncertainty of the latent variables for a given input-output pair; and (ii) a delta distribution to predict the output and the latent variables for a given input. During learning, we encourage agreement between the two distributions by minimizing a loss-based dissimilarity coefficient. Our approach generalizes latent SVM in two important ways: (i) it models the uncertainty over latent variables instead of relying on a pointwise estimate; and (ii) it allows the use of loss functions that depend on latent variables, which greatly increases its applicability. We demonstrate the efficacy of our approach on two challenging problems---object detection and action detection---using publicly available datasets.


Adaptive Regularization for Weight Matrices

Abstract: Algorithms for learning distributions over weight-vectors, such as AROW were recently shown empirically to achieve state-of-the-art performance at various problems, with strong theoretical guaranties. Extending these algorithms to matrix models pose challenges since the number of free parameters in the covariance of the distribution scales as $n^4$ with the dimension $n$ of the matrix, and $n$ tends to be large in real applications. We describe, analyze and experiment with two new algorithms for learning distribution of matrix models. Our first algorithm maintains a diagonal covariance over the parameters and can handle large covariance matrices. The second algorithm factors the covariance to capture inter-features correlation while keeping the number of parameters linear in the size of the original matrix. We analyze both algorithms in the mistake bound model and show a superior precision performance of our approach over other algorithms in two tasks: retrieving similar images, and ranking similar documents. The factored algorithm is shown to attain faster convergence rate.


Lightning Does Not Strike Twice: Robust MDPs with Coupled Uncertainty

Abstract: We consider Markov decision processes under parameter uncertainty. Previous studies all restrict to the case that uncertainties among different states are uncoupled, which leads to conservative solutions. In contrast, we introduce an intuitive concept, termed "Lightning Does not Strike Twice," to model coupled uncertain parameters. Specifically, we require that the system can deviate from its nominal parameters only a bounded number of times. We give probabilistic guarantees indicating that this model represents real life situations and devise tractable algorithms for computing optimal control policies using this concept.


Active Learning for Matching Problems

Abstract: Effective learning of user preferences is critical to easing user burden in various types of matching problems. Equally important is active query selection to further reduce the amount of preference information users must provide. We address the problem of active learning of user preferences for matching problems, introducing a novel method for determining probabilistic matchings, and developing several new active learning strategies that are sensitive to the specific matching objective. Experiments with real-world data sets spanning diverse domains demonstrate that matching-sensitive active learning


Two-Manifold Problems with Applications to Nonlinear System Identification

Abstract: Recently, there has been much interest in spectral approaches to learning manifolds---so-called kernel eigenmap methods. These methods have had some successes, but their applicability is limited because they are not robust to noise. To address this limitation, we look at two-manifold problems, in which we simultaneously reconstruct two related manifolds, each representing a different view of the same data. By solving these interconnected learning problems together, two-manifold algorithms are able to succeed where a non-integrated approach would fail: each view allows us to suppress noise in the other, reducing bias. We propose a class of algorithms for two-manifold problems, based on spectral decomposition of cross-covariance operators in Hilbert space, and discuss when two-manifold problems are useful. Finally, we demonstrate that solving a two-manifold problem can aid in learning a nonlinear dynamical system from limited data.


The Most Persistent Soft-Clique in a Set of Sampled Graphs

Abstract: When searching for characteristic subpatterns in potentially noisy graph data, it appears self-evident that having multiple observations would be better than having just one. However, it turns out that the inconsistencies introduced when different graph instances have different edge sets pose a serious challenge. In this work we address this challenge for the problem of finding maximum weighted cliques. We introduce the concept of most persistent soft-clique. This is subset of vertices, that 1) is almost fully or at least densely connected, 2) occurs in all or almost all graph instances, and 3) has the maximum weight. We present a measure of clique-ness, that essentially counts the number of edge missing to make a subset of vertices into a clique. With this measure, we show that the problem of finding the most persistent soft-clique problem can be cast either as: a) a max-min two person game optimization problem, or b) a min-min soft margin optimization problem. Both formulations lead to the same solution when using a partial Lagrangian method to solve the optimization problems. By experiments on synthetic data and on real social network data, we show that the proposed method is able to reliably find soft cliques in graph data, even if that is distorted by random noise or unreliable observations.


Modelling transition dynamics in MDPs with RKHS embeddings

Abstract: We propose a new, nonparametric approach to learning and representing transition dynamics in Markov decision processes (MDPs), which can be combined easily with dynamic programming methods for policy optimisation and value estimation. This approach makes use of a recently developed representation of conditional distributions as \emph{embeddings} in a reproducing kernel Hilbert space (RKHS). Such representations bypass the need for estimating transition probabilities or densities, and apply to any domain on which kernels can be defined. This avoids the need to calculate intractable integrals, since expectations are represented as RKHS inner products whose computation has linear complexity in the number of points used to represent the embedding. We provide guarantees for the proposed applications in MDPs: in the context of a value iteration algorithm, we prove convergence to either the optimal policy, or to the closest projection of the optimal policy in our model class (an RKHS), under reasonable assumptions. In experiments, we investigate a learning task in a typical classical control setting (the under-actuated pendulum), and on a navigation problem where only images from a sensor are observed. For policy optimisation we compare with least-squares policy iteration where a Gaussian process is used for value function estimation. For value estimation we also compare to the NPDP method. Our approach achieves better performance in all experiments.


Projection-free Online Learning

Abstract: The computational bottleneck in applying online learning to massive data sets is usually the projection step. We present efficient online learning algorithms that eschew projections in favor of much more efficient linear optimization steps using the Frank-Wolfe technique. We obtain a range of regret bounds for online convex optimization, with better bounds for specific cases such as stochastic online smooth convex optimization. Besides the computational advantage, other desirable features of our algorithms are that they are parameter-free in the stochastic case and produce sparse decisions. We apply our algorithms to computationally intensive applications of collaborative filtering, and show the theoretical improvements to be clearly visible on standard datasets.


Learning with Augmented Features for Heterogeneous Domain Adaptation

Abstract: We propose a new learning method for heterogeneous domain adaptation (HDA), in which the data from the source domain and the target domain are represented by heterogeneous features with different dimensions. Using two different projection matrices, we first transform the data from two domains into a common subspace in order to measure the similarity between the data from two domains. We then propose two new feature mapping functions to augment the transformed data with their original features and zeros. The existing learning methods (e.g., SVM and SVR) can be readily incorporated with our newly proposed augmented feature representations to effectively utilize the data from both domains for HDA. Using the hinge loss function in SVM as an example, we introduce the detailed objective function in our method called Heterogeneous Feature Augmentation (HFA) for a linear case and also describe its kernelization in order to efficiently cope with the data with very high dimensions. Moreover, we also develop an alternating optimization algorithm to effectively solve the nontrivial optimization problem in our HFA method. Comprehensive experiments on two benchmark datasets clearly demonstrate that HFA outperforms the existing HDA methods.


Bayesian Watermark Attacks

Abstract: This paper presents an application of statistical machine learning to the field of watermarking. We propose a new attack model on additive spread-spectrum watermarking systems. The proposed attack is based on Bayesian statistics. We consider the scenario in which a watermark signal is repeatedly embedded in specific, possibly chosen based on a secret message bitstream, segments (signals) of the host data. The host signal can represent a patch of pixels from an image or a video frame. We propose a probabilistic model that infers the embedded message bitstream and watermark signal, directly from the watermarked data, without access to the decoder. We develop an efficient Markov chain Monte Carlo sampler for updating the model parameters from their conjugate full conditional posteriors. We also provide a variational Bayesian solution, which further increases the convergence speed of the algorithm. Experiments with synthetic and real image signals demonstrate that the attack model is able to correctly infer a large part of the message bitstream and obtain a very accurate estimate of the watermark signal.


Unachievable Region in Precision-Recall Space and Its Effect on Empirical Evaluation

Abstract: Precision-recall (PR) curves and the areas under them are widely used to summarize machine learning results, especially for data sets exhibiting class skew. They are often used analogously to ROC curves and the area under ROC curves. It is known that PR curves vary as class skew changes. What was not recognized before this paper is that there is a region of PR space that is completely unachievable, and the size of this region depends only on the skew. This paper precisely characterizes the size of that region and discusses its implications for empirical evaluation methodology in machine learning.


Finding Botnets Using Minimal Graph Clusterings

Abstract: We study the problem of identifying botnets and the IP addresses which they comprise, based on the observation of a fraction of the global email spam traffic. Observed mailing campaigns constitute evidence for joint botnet membership, they are represented by cliques in the graph of all messages. No evidence against an association of nodes is ever available. We reduce the problem of identifying botnets to a problem of finding a minimal clustering of the graph of messages. We directly model the distribution of clusterings given the input graph; this avoids potential errors caused by distributional assumptions of a generative model. We report on a case study in which we evaluate the model by its ability to predict the spam campaign that a given IP address is going to participate in.


Marginalized Denoising Autoencoders for Domain Adaptation

Abstract: Stacked denoising autoencoders (SDAs) have been successfully used to learn new representations for domain adaptation. Recently, they have attained record accuracy on standard benchmark tasks of sentiment analysis across different text domains. SDAs learn robust data representations by reconstruction, recovering original features from data that are artificially corrupted with noise. In this paper, we propose marginalized SDA (mSDA) that addresses two crucial limitations of SDAs: high computational cost and lack of scalability to high-dimensional features. In contrast to SDAs, our approach of mSDA marginalizes noise and thus does not require stochastic gradient descent or other optimization algorithms to learn parameters ? in fact, they are computed in closed-form. Consequently, mSDA, which can be implemented in only 20 lines of MATLAB^{TM}, significantly speeds up SDAs by two orders of magnitude. Furthermore, the representations learnt by mSDA are as effective as the traditional SDAs, attaining almost identical accuracies in benchmark tasks.


Collaborative Topic Regression with Social Matrix Factorization for Recommendation Systems

Abstract: Social network websites, such as Facebook, YouTube, Lastfm etc, have become a popular platform for users to connect with each other and share content or opinions. They provide rich information for us to study the influence of user's social circle in their decision process. In this paper, we are interested in examining the effectiveness of social network information to predict the user's ratings of items. We propose a novel hierarchical Bayesian model which jointly incorporates topic modeling and probabilistic matrix factorization of social networks. A major advantage of our model is to automatically infer useful latent topics and social information as well as their importance to collaborative filtering from the training data. Empirical experiments on two large-scale datasets show that our algorithm provides a more effective recommendation system than the state-of-the art approaches. Our results reveal interesting insight that the social circles have more influence on people's decisions about the usefulness of information (e.g., bookmarking preference on Delicious) than personal taste (e.g., music preference on Lastfm). We also examine and discuss solutions on potential information leak in many recommendation systems that utilize social information.


On the serial connection of the regular asynchronous systems

Abstract: The asynchronous systems f are multi-valued functions, representing the non-deterministic models of the asynchronous circuits from the digital electrical engineering. In real time, they map an 'admissible input' function u:R\rightarrow{0,1}^{m} to a set f(u) of 'possible states' x\inf(u), where x:R\rightarrow{0,1}^{m}. When f is defined by making use of a 'generator function' {\Phi}:{0,1}^{n}\times{0,1}^{m}\rightarrow{0,1}^{n}, the system is called regular. The usual definition of the serial connection of systems as composition of multi-valued functions does not bring the regular systems into regular systems, thus the first issue in this study is to modify in an acceptable manner the definition of the serial connection in a way that matches regularity. This intention was expressed for the first time, without proving the regularity of the serial connection of systems, in a previous work. Our present purpose is to restate with certain corrections and prove that result.


On the basins of attraction of the regular autonomous asynchronous systems

Abstract: The Boolean autonomous dynamical systems, also called regular autonomous asynchronous systems are systems whose 'vector field' is a function {\Phi}:{0,1}^{n}{\to}{0,1}^{n} and time is discrete or continuous. While the synchronous systems have their coordinate functions {\Phi}_{1},...,{\Phi}_{n} computed at the same time: {\Phi},{\Phi}{\circ}{\Phi},{\Phi}{\circ}{\Phi}{\circ}{\Phi},... the asynchronous systems have {\Phi}_{1},...,{\Phi}_{n} computed independently on each other. The purpose of the paper is that of studying the basins of attraction of the fixed points, of the orbits and of the {\omega}-limit sets of the regular autonomous asynchronous systems. The bibliography consists in analogies.


Universal Regular Autonomous Asynchronous Systems: Fixed Points, Equivalencies and Dynamic Bifurcations

Abstract: The asynchronous systems are the non-deterministic models of the asynchronous circuits from the digital electrical engineering. In the autonomous version, such a system is a set of functions x:R{\to}{0,1}^{n} called states (R is the time set). If an asynchronous system is defined by making use of a so called generator function {\Phi}:{0,1}^{n}{\to}{0,1}^{n}, then it is called regular. The property of universality means the greatest in the sense of the inclusion. The purpose of the paper is that of defining and of characterizing the fixed points, the equivalencies and the dynamical bifurcations of the universal regular autonomous asynchronous systems. We use analogies with the dynamical systems theory.


The decomposition of the regular asynchronous systems as parallel connection of regular asynchronous systems

Abstract: The asynchronous systems are the non-deterministic models of the asynchronous circuits from the digital electrical engineering, where non-determinism is a consequence of the fact that modelling is made in the presence of unknown and variable parameters. Such a system is a multi-valued function f that assigns to an (admissible) input u:R{\to}{0,1}^{m} a set f(u) of (possible) states x:R{\to}{0,1}^{n}. When this assignment is defined by making use of a so-called generator function {\Phi}:{0,1}^{n}{\times}{0,1}^{m}{\to}{0,1}^{n}, then the asynchronous system f is called regular. The generator function {\Phi} acts in this asynchronous framework similarly with the next state function from a synchronous framework. The parallel connection of the asynchronous systems f' and f" is the asynchronous system (f'||f")(u)=f'(u){\times}f"(u). The purpose of the paper is to give the circumstances under which a regular asynchronous system f may be written as a parallel connection of regular asynchronous systems.


DLS: Directoryless Shared Last-level Cache

Abstract: Directory-based protocols have been the de facto solution for maintaining cache coherence in shared-memory parallel systems comprising multi/many cores, where each store instruction is eagerly made globally visible by invalidating the private cache (PC) backups of other cores. Consequently, the directory not only consumes large chip area, but also incurs considerable energy consumption and performance degradation, due to the large number of Invalidation/Ack messages transferred in the interconnection network and resulting network congestion. In this paper, we reveal the interesting fact that the directory is actually an unnecessary luxury for practical parallel systems. Because of widely deployed software/hardware techniques involving instruction reordering, most (if not all) parallel systems work under the weak consistency model, where a remote store instruction is allowed to be invisible to a core before the next synchronization of the core, instead of being made visible eagerly by invalidating PC backups of other cores. Based on this key observation, we propose a lightweight novel scheme called {\em DLS (DirectoryLess Shared last-level cache)}, which completely removes the directory and Invalidation/Ack messages, and efficiently maintains cache coherence using a novel {\em self-suspicion + speculative execution} mechanism. Experimental results over SPLASH-2 benchmarks show that on a 16-core processor, DLS not only completely removes the chip area cost of the directory, but also improves processor performance by 11.08%, reduces overall network traffic by 28.83%, and reduces energy consumption of the network by 15.65% on average (compared with traditional MESI protocol with full directory). Moreover, DLS does not involve any modification to programming languages and compilers, and hence is seamlessly compatible with legacy codes.


Bayesian Sequential Auctions

Abstract: In many natural settings agents participate in multiple different auctions that are not simultaneous. In such auctions, future opportunities affect strategic considerations of the players. The goal of this paper is to develop a quantitative understanding of outcomes of such sequential auctions. In earlier work (Paes Leme et al. 2012) we initiated the study of the price of anarchy in sequential auctions. We considered sequential first price auctions in the full information model, where players are aware of all future opportunities, as well as the valuation of all players. In this paper, we study efficiency in sequential auctions in the Bayesian environment, relaxing the informational assumption on the players. We focus on two environments, both studied in the full information model in Paes Leme et al. 2012, matching markets and matroid auctions. In the full information environment, a sequential first price cut auction for matroid settings is efficient. In Bayesian environments this is no longer the case, as we show using a simple example with three players. Our main result is a bound of $1+\frac{e}{e-1}\approx 2.58$ on the price of anarchy in both matroid auctions and single-value matching markets (even with correlated types) and a bound of $2\frac{e}{e-1}\approx 3.16$ for general matching markets with independent types. To bound the price of anarchy we need to consider possible deviations at an equilibrium. In a sequential Bayesian environment the effect of deviations is more complex than in one-shot games; early bids allow others to infer information about the player's value. We create effective deviations despite the presence of this difficulty by introducing a bluffing technique of independent interest.


Better Than Their Reputation? On the Reliability of Relevance Assessments with Students

Abstract: During the last three years we conducted several information retrieval evaluation series with more than 180 LIS students who made relevance assessments on the outcomes of three specific retrieval services. In this study we do not focus on the retrieval performance of our system but on the relevance assessments and the inter-assessor reliability. To quantify the agreement we apply Fleiss' Kappa and Krippendorff's Alpha. When we compare these two statistical measures on average Kappa values were 0.37 and Alpha values 0.15. We use the two agreement measures to drop too unreliable assessments from our data set. When computing the differences between the unfiltered and the filtered data set we see a root mean square error between 0.02 and 0.12. We see this as a clear indicator that disagreement affects the reliability of retrieval evaluations. We suggest not to work with unfiltered results or to clearly document the disagreement rates.


Indexed realizability for bounded-time programming with references and type fixpoints

Abstract: The field of implicit complexity has recently produced several bounded-complexity programming languages. This kind of language allows to implement exactly the functions belonging to a certain complexity class. We here present a realizability semantics for a higher-order functional language based on a fragment of linear logic called LAL which characterizes the complexity class PTIME. This language features recursive types and higher-order store. Our realizability is based on biorthogonality, step-indexing and is moreover quantitative. This last feature enables us not only to derive a semantical proof of termination, but also to give bounds on the number of computational steps needed by typed programs to terminate.


Constraint satisfaction parameterized by solution size

Abstract: In the constraint satisfaction problem (CSP) corresponding to a constraint language (i.e., a set of relations) $\Gamma$, the goal is to find an assignment of values to variables so that a given set of constraints specified by relations from $\Gamma$ is satisfied. The complexity of this problem has received substantial amount of attention in the past decade. In this paper we study the fixed-parameter tractability of constraint satisfaction problems parameterized by the size of the solution in the following sense: one of the possible values, say 0, is "free," and the number of variables allowed to take other, "expensive," values is restricted. A size constraint requires that exactly $k$ variables take nonzero values. We also study a more refined version of this restriction: a global cardinality constraint prescribes how many variables have to be assigned each particular value. We study the parameterized complexity of these types of CSPs where the parameter is the required number $k$ of nonzero variables. As special cases, we can obtain natural and well-studied parameterized problems such as Independent Set, Vertex Cover, d-Hitting Set, Biclique, etc. In the case of constraint languages closed under substitution of constants, we give a complete characterization of the fixed-parameter tractable cases of CSPs with size constraints, and we show that all the remaining problems are W[1]-hard. For CSPs with cardinality constraints, we obtain a similar classification, but for some of the problems we are only able to show that they are Biclique-hard. The exact parameterized complexity of the Biclique problem is a notorious open problem, although it is believed to be W[1]-hard.


Asynchronous Multi-Tape Automata Intersection: Undecidability and Approximation

Abstract: When their reading heads are allowed to move completely asynchronously, finite-state automata with multiple tapes achieve a significant expressive power, but also lose useful closure properties---closure under intersection, in particular. This paper investigates to what extent it is still feasible to use multi-tape automata as recognizer of polyadic predicates on words. On the negative side, determining whether the intersection of asynchronous multi-tape automata is expressible is not even semidecidable. On the positive side, we present an algorithm that computes under-approximations of the intersection; and discuss simple conditions under which it can construct complete intersections. A prototype implementation and a few non-trivial examples demonstrate the algorithm in practice.


An empirical analysis of the use of alphabetical authorship in scientific publishing

Abstract: There are different ways in which the authors of a scientific publication can determine the order in which their names are listed. Sometimes author names are simply listed alphabetically. In other cases, authorship order is determined based on the contribution authors have made to a publication. Contribution-based authorship can facilitate proper credit assignment, for instance by giving most credits to the first author. In the case of alphabetical authorship, nothing can be inferred about the relative contribution made by the different authors of a publication. In this paper, we present an empirical analysis of the use of alphabetical authorship in scientific publishing. Our analysis covers all fields of science. We find that the use of alphabetical authorship is declining over time. In 2011, the authors of less than 4% of all publications intentionally chose to list their names alphabetically. The use of alphabetical authorship is most common in mathematics, economics (including finance), and high energy physics. Also, the use of alphabetical authorship is relatively more common in the case of publications with either a small or a large number of authors.


Portraits of Julius Caesar: a proposal for 3D analysis

Abstract: Here I suggest the use of a 3D scanning and rendering to create some virtual copies of ancient artifacts to study and compare them. In particular, this approach could be interesting for some roman marble busts, two of which are portraits of Julius Caesar, and the third is a realistic portrait of a man recently found at Arles, France. The comparison of some images indicates that a three-dimensional visualization is necessary.


Dynamic Domain Classification for Fractal Image Compression

Abstract: Fractal image compression is attractive except for its high encoding time requirements. The image is encoded as a set of contractive affine transformations. The image is partitioned into non-overlapping range blocks, and a best matching domain block larger than the range block is identified. There are many attempts on improving the encoding time by reducing the size of search pool for range-domain matching. But these methods are attempting to prepare a static domain pool that remains unchanged throughout the encoding process. This paper proposes dynamic preparation of separate domain pool for each range block. This will result in significant reduction in the encoding time. The domain pool for a particular range block can be selected based upon a parametric value. Here we use classification based on local fractal dimension.


Multilingual Medical Documents Classification Based on MesH Domain Ontology

Abstract: This article deals with the semantic Web and ontologies. It addresses the issue of the classification of multilingual Web documents, based on domain ontology. The objective is being able, using a model, to classify documents in different languages. We will try to solve this problematic using two different approaches. The two approaches will have two elementary stages: the creation of the model using machine learning algorithms on a labeled corpus, then the classification of documents after detecting their languages and mapping their terms into the concepts of the language of reference (English). But each one will deal with the multilingualism with a different approach. One supposes the ontology is monolingual, whereas the other considers it multilingual. To show the feasibility and the importance of our work, we implemented it on a domain that attracts nowadays a lot of attention from the data mining community: the biomedical domain. The selected documents are from the biomedical benchmark corpus Ohsumed, and the associated ontology is the thesaurus MeSH (Medical Subject Headings). The main idea in our work is a new document representation, the masterpiece of all good classification, based on concept. The experimental results show that the recommended ideas are promising.


Planarizing an Unknown Surface

Abstract: It has been recently shown that any graph of genus g>0 can be stochastically embedded into a distribution over planar graphs, with distortion Olog (g+1)) [Sidiropoulos, FOCS 2010]. This embedding can be computed in polynomial time, provided that a drawing of the input graph into a genus-g surface is given. We show how to compute the above embedding without having such a drawing. This implies a general reduction for solving problems on graphs of small genus, even when the drawing into a small genus surface is unknown. To the best of our knowledge, this is the first result of this type.


Preprocessing Subgraph and Minor Problems: When Does a Small Vertex Cover Help?

Abstract: We prove a number of results around kernelization of problems parameterized by the size of a given vertex cover of the input graph. We provide three sets of simple general conditions characterizing problems admitting kernels of polynomial size. Our characterizations not only give generic explanations for the existence of many known polynomial kernels for problems like q-Coloring, Odd Cycle Transversal, Chordal Deletion, Eta Transversal, or Long Path, parameterized by the size of a vertex cover, but also imply new polynomial kernels for problems like F-Minor-Free Deletion, which is to delete at most k vertices to obtain a graph with no minor from a fixed finite set F. While our characterization captures many interesting problems, the kernelization complexity landscape of parameterizations by vertex cover is much more involved. We demonstrate this by several results about induced subgraph and minor containment testing, which we find surprising. While it was known that testing for an induced complete subgraph has no polynomial kernel unless NP is in coNP/poly, we show that the problem of testing if a graph contains a complete graph on t vertices as a minor admits a polynomial kernel. On the other hand, it was known that testing for a path on t vertices as a minor admits a polynomial kernel, but we show that testing for containment of an induced path on t vertices is unlikely to admit a polynomial kernel.


The Biased Sampling Profit Extraction Auction

Abstract: We give an auction for downward-closed environments that generalizes the random sampling profit extraction auction for digital goods of Fiat et al. (2002). The mechanism divides the agents in to a market and a sample using a biased coin and attempts to extract the optimal revenue from the sample from the market. The latter step is done with the downward-closed profit extractor of Ha and Hartline (2012). The auction is a 11-approximation to the envyfree benchmark in downward-closed permutation environments. This is an improvement on the previously best known results of 12.5 for matroid and 30.4 for downward-closed permutation environments that are due to Devanur et al. (2012) and Ha and Hartline (2012), respectively.


A Pointillism Approach for Natural Language Processing of Social Media

Abstract: The Chinese language poses challenges for natural language processing based on the unit of a word even for formal uses of the Chinese language, social media only makes word segmentation in Chinese even more difficult. In this document we propose a pointillism approach to natural language processing. Rather than words that have individual meanings, the basic unit of a pointillism approach is trigrams of characters. These grams take on meaning in aggregate when they appear together in a way that is correlated over time. Our results from three kinds of experiments show that when words and topics do have a meme-like trend, they can be reconstructed from only trigrams. For example, for 4-character idioms that appear at least 99 times in one day in our data, the unconstrained precision (that is, precision that allows for deviation from a lexicon when the result is just as correct as the lexicon version of the word or phrase) is 0.93. For longer words and phrases collected from Wiktionary, including neologisms, the unconstrained precision is 0.87. We consider these results to be very promising, because they suggest that it is feasible for a machine to reconstruct complex idioms, phrases, and neologisms with good precision without any notion of words. Thus the colorful and baroque uses of language that typify social media in challenging languages such as Chinese may in fact be accessible to machines.


An Adaptative Multi-GPU based Branch-and-Bound. A Case Study: the Flow-Shop Scheduling Problem

Abstract: Solving exactly Combinatorial Optimization Problems (COPs) using a Branch-and-Bound (B&B) algorithm requires a huge amount of computational resources. Therefore, we recently investigated designing B&B algorithms on top of graphics processing units (GPUs) using a parallel bounding model. The proposed model assumes parallelizing the evaluation of the lower bounds on pools of sub-problems. The results demonstrated that the size of the evaluated pool has a significant impact on the performance of B&B and that it depends strongly on the problem instance being solved. In this paper, we design an adaptative parallel B&B algorithm for solving permutation-based combinatorial optimization problems such as FSP (Flow-shop Scheduling Problem) on GPU accelerators. To do so, we propose a dynamic heuristic for parameter auto-tuning at runtime. Another challenge of this work is to exploit larger degrees of parallelism by using the combined computational power of multiple GPU devices. The approach has been applied to the permutation flow-shop problem. Extensive experiments have been carried out on well-known FSP benchmarks using an Nvidia Tesla S1070 Computing System equipped with two Tesla T10 GPUs. Compared to a CPU-based execution, accelerations up to 105 are achieved for large problem instances.


The Planetary Project: Towards eMath3.0

Abstract: The Planetary project develops a general framework - the Planetary system - for social semantic portals that support users in interacting with STEM (Science/Technology/Engineering/Mathematics) documents. Developed from an initial attempt to replace the aging portal of PlanetMath.org with a mashup of existing MKM technologies, the Planetary system is now in a state, where it can serve as a basis for various eMath3.0 portals, ranging from eLearning systems over scientific archives to semantic help systems.