Steady Marginality: A Uniform Approach to Shapley Value for Games with Externalities

Abstract: The Shapley value is one of the most important solution concepts in cooperative game theory. In coalitional games without externalities, it allows to compute a unique payoff division that meets certain desirable fairness axioms. However, in many realistic applications where externalities are present, Shapley's axioms fail to indicate such a unique division. Consequently, there are many extensions of Shapley value to the environment with externalities proposed in the literature built upon additional axioms. Two important such extensions are "externality-free" value by Pham Do and Norde and value that "absorbed all externalities" by McQuillin. They are good reference points in a space of potential payoff divisions for coalitional games with externalities as they limit the space at two opposite extremes. In a recent, important publication, De Clippel and Serrano presented a marginality-based axiomatization of the value by Pham Do Norde. In this paper, we propose a dual approach to marginality which allows us to derive the value of McQuillin. Thus, we close the picture outlined by De Clippel and Serrano.


Multiple-Source Multiple-Sink Maximum Flow in Directed Planar Graphs in Near-Linear Time

Abstract: We give an O(n log^3 n) algorithm that, given an n-node directed planar graph with arc capacities, a set of source nodes, and a set of sink nodes, finds a maximum flow from the sources to the sinks. Previously, the fastest algorithms known for this problem were those for general graphs.


More about Base Station Location Games

Abstract: This paper addresses the problem of locating base stations in a certain area which is highly populated by mobile stations; each mobile station is assumed to select the closest base station. Base stations are modeled by players who choose their best location for maximizing their uplink throughput. The approach of this paper is to make some simplifying assumptions in order to get interpretable analytical results and insights to the problem under study. Specifically, a relatively complete Nash equilibrium (NE) analysis is conducted (existence, uniqueness, determination, and efficiency). Then, assuming that the base station location can be adjusted dynamically, the best-response dynamics and reinforcement learning algorithm are applied, discussed, and illustrated through numerical results.


On the Limitations of Provenance for Queries With Difference

Abstract: The annotation of the results of database transformations was shown to be very effective for various applications. Until recently, most works in this context focused on positive query languages. The provenance semirings is a particular approach that was proven effective for these languages, and it was shown that when propagating provenance with semirings, the expected equivalence axioms of the corresponding query languages are satisfied. There have been several attempts to extend the framework to account for relational algebra queries with difference. We show here that these suggestions fail to satisfy some expected equivalence axioms (that in particular hold for queries on "standard" set and bag databases). Interestingly, we show that this is not a pitfall of these particular attempts, but rather every such attempt is bound to fail in satisfying these axioms, for some semirings. Finally, we show particular semirings for which an extension for supporting difference is (im)possible.


Distributed Semantic Web Data Management in HBase and MySQL Cluster

Abstract: Various computing and data resources on the Web are being enhanced with machine-interpretable semantic descriptions to facilitate better search, discovery and integration. This interconnected metadata constitutes the Semantic Web, whose volume can potentially grow the scale of the Web. Efficient management of Semantic Web data, expressed using the W3C's Resource Description Framework (RDF), is crucial for supporting new data-intensive, semantics-enabled applications. In this work, we study and compare two approaches to distributed RDF data management based on emerging cloud computing technologies and traditional relational database clustering technologies. In particular, we design distributed RDF data storage and querying schemes for HBase and MySQL Cluster and conduct an empirical comparison of these approaches on a cluster of commodity machines using datasets and queries from the Third Provenance Challenge and Lehigh University Benchmark. Our study reveals interesting patterns in query evaluation, shows that our algorithms are promising, and suggests that cloud computing has a great potential for scalable Semantic Web data management.


Data-Distributed Weighted Majority and Online Mirror Descent

Abstract: In this paper, we focus on the question of the extent to which online learning can benefit from distributed computing. We focus on the setting in which $N$ agents online-learn cooperatively, where each agent only has access to its own data. We propose a generic data-distributed online learning meta-algorithm. We then introduce the Distributed Weighted Majority and Distributed Online Mirror Descent algorithms, as special cases. We show, using both theoretical analysis and experiments, that compared to a single agent: given the same computation time, these distributed algorithms achieve smaller generalization errors; and given the same generalization errors, they can be $N$ times faster.


Analysis of Power-aware Buffering Schemes in Wireless Sensor Networks

Abstract: We study the power-aware buffering problem in battery-powered sensor networks, focusing on the fixed-size and fixed-interval buffering schemes. The main motivation is to address the yet poorly understood size variation-induced effect on power-aware buffering schemes. Our theoretical analysis elucidates the fundamental differences between the fixed-size and fixed-interval buffering schemes in the presence of data size variation. It shows that data size variation has detrimental effects on the power expenditure of the fixed-size buffering in general, and reveals that the size variation induced effects can be either mitigated by a positive skewness or promoted by a negative skewness in size distribution. By contrast, the fixed-interval buffering scheme has an obvious advantage of being eminently immune to the data-size variation. Hence the fixed-interval buffering scheme is a risk-averse strategy for its robustness in a variety of operational environments. In addition, based on the fixed-interval buffering scheme, we establish the power consumption relationship between child nodes and parent node in a static data collection tree, and give an in-depth analysis of the impact of child bandwidth distribution on parent's power consumption. This study is of practical significance: it sheds new light on the relationship among power consumption of buffering schemes, power parameters of radio module and memory bank, data arrival rate and data size variation, thereby providing well-informed guidance in determining an optimal buffer size (interval) to maximize the operational lifespan of sensor networks.


Parallel and Distributed Simulation from Many Cores to the Public Cloud (Extended Version)

Abstract: In this tutorial paper, we will firstly review some basic simulation concepts and then introduce the parallel and distributed simulation techniques in view of some new challenges of today and tomorrow. More in particular, in the last years there has been a wide diffusion of many cores architectures and we can expect this trend to continue. On the other hand, the success of cloud computing is strongly promoting the everything as a service paradigm. Is parallel and distributed simulation ready for these new challenges? The current approaches present many limitations in terms of usability and adaptivity: there is a strong need for new evaluation metrics and for revising the currently implemented mechanisms. In the last part of the paper, we propose a new approach based on multi-agent systems for the simulation of complex systems. It is possible to implement advanced techniques such as the migration of simulated entities in order to build mechanisms that are both adaptive and very easy to use. Adaptive mechanisms are able to significantly reduce the communication cost in the parallel/distributed architectures, to implement load-balance techniques and to cope with execution environments that are both variable and dynamic. Finally, such mechanisms will be used to build simulations on top of unreliable cloud services.


Hierarchical Complexity: Measures of High Level Modularity

Abstract: Software is among the most complex endeavors of the human mind; large scale systems can have tens of millions of lines of source code. However, seldom is complexity measured above the lowest level of code, and sometimes source code files or low level modules. In this paper a hierarchical approach is explored in order to find a set of metrics that can measure higher levels of organization. These metrics are then used on a few popular free software packages (totaling more than 25 million lines of code) to check their efficiency and coherency.


Learning content similarity for music recommendation

Abstract: Many tasks in music information retrieval, such as recommendation, and playlist generation for online radio, fall naturally into the query-by-example setting, wherein a user queries the system by providing a song, and the system responds with a list of relevant or similar song recommendations. Such applications ultimately depend on the notion of similarity between items to produce high-quality results. Current state-of-the-art systems employ collaborative filter methods to represent musical items, effectively comparing items in terms of their constituent users. While collaborative filter techniques perform well when historical data is available for each item, their reliance on historical data impedes performance on novel or unpopular items. To combat this problem, practitioners rely on content-based similarity, which naturally extends to novel items, but is typically out-performed by collaborative filter methods. In this article, we propose a method for optimizing contentbased similarity by learning from a sample of collaborative filter data. The optimized content-based similarity metric can then be applied to answer queries on novel and unpopular items, while still maintaining high recommendation accuracy. The proposed system yields accurate and efficient representations of audio content, and experimental results show significant improvements in accuracy over competing content-based recommendation techniques.


LP-Based Approximation Algorithms for Traveling Salesman Path Problems

Abstract: This paper has been merged into 1110.4604.


Workflows for the Management of Change in Science, Technologies, Engineering and Mathematics

Abstract: Mathematical knowledge is a central component in science, engineering, and technology (documentation). Most of it is represented informally, and -- in contrast to published research mathematics -- subject to continual change. Unfortunately, machine support for change management has either been very coarse grained and thus barely useful, or restricted to formal languages, where automation is possible. In this paper, we report on an effort to extend change management to collections of semi-formal documents which flexibly intermix mathematical formulas and natural language and to integrate it into a semantic publishing system for mathematical knowledge. We validate the long-standing assumption that the semantic annotations in these flexiformal documents that drive the machine-supported interaction with documents can support semantic impact analyses at the same time. But in contrast to the fully formal setting, where adaptations of impacted documents can be automated to some degree, the flexiformal setting requires much more user interaction and thus a much tighter integration into document management workflows.


Incremental Cycle Detection, Topological Ordering, and Strong Component Maintenance

Abstract: We present two on-line algorithms for maintaining a topological order of a directed $n$-vertex acyclic graph as arcs are added, and detecting a cycle when one is created. Our first algorithm handles $m$ arc additions in $O(m^{3/2})$ time. For sparse graphs ($m/n = O(1)$), this bound improves the best previous bound by a logarithmic factor, and is tight to within a constant factor among algorithms satisfying a natural {\em locality} property. Our second algorithm handles an arbitrary sequence of arc additions in $O(n^{5/2})$ time. For sufficiently dense graphs, this bound improves the best previous bound by a polynomial factor. Our bound may be far from tight: we show that the algorithm can take $\Omega(n^2 2^{\sqrt{2\lg n}})$ time by relating its performance to a generalization of the $k$-levels problem of combinatorial geometry. A completely different algorithm running in $\Theta(n^2 \log n)$ time was given recently by Bender, Fineman, and Gilbert. We extend both of our algorithms to the maintenance of strong components, without affecting the asymptotic time bounds.


Selfishness Level of Strategic Games

Abstract: We introduce a new measure of the discrepancy in strategic games between the social welfare in a Nash equilibrium and in a social optimum, that we call selfishness level. It is the smallest fraction of the social welfare that needs to be offered to each player to achieve that a social optimum is realized in a pure Nash equilibrium. The selfishness level is unrelated to the price of stability and the price of anarchy and is invariant under positive linear transformations of the payoff functions. Also, it naturally applies to other solution concepts and other forms of games. We study the selfishness level of several well-known strategic games. This allows us to quantify the implicit tension within a game between players' individual interests and the impact of their decisions on the society as a whole. Our analyses reveal that the selfishness level often provides a deeper understanding of the characteristics of the underlying game that influence the players' willingness to cooperate. In particular, the selfishness level of finite ordinal potential games is finite, while that of weakly acyclic games can be infinite. We derive explicit bounds on the selfishness level of fair cost sharing games and linear congestion games, which depend on specific parameters of the underlying game but are independent of the number of players. Further, we show that the selfishness level of the $n$-players Prisoner's Dilemma is $c/(b(n-1)-c)$, where $b$ and $c$ are the benefit and cost for cooperation, respectively, that of the $n$-players public goods game is $(1-\frac{c}{n})/(c-1)$, where $c$ is the public good multiplier, and that of the Traveler's Dilemma game is $\frac{1}{2}(b-1)$, where $b$ is the bonus. Finally, the selfishness level of Cournot competition (an example of an infinite ordinal potential game, Tragedy of the Commons, and Bertrand competition is infinite.


Science Models as Value-Added Services for Scholarly Information Systems

Abstract: The paper introduces scholarly Information Retrieval (IR) as a further dimension that should be considered in the science modeling debate. The IR use case is seen as a validation model of the adequacy of science models in representing and predicting structure and dynamics in science. Particular conceptualizations of scholarly activity and structures in science are used as value-added search services to improve retrieval quality: a co-word model depicting the cognitive structure of a field (used for query expansion), the Bradford law of information concentration, and a model of co-authorship networks (both used for re-ranking search results). An evaluation of the retrieval quality when science model driven services are used turned out that the models proposed actually provide beneficial effects to retrieval quality. From an IR perspective, the models studied are therefore verified as expressive conceptualizations of central phenomena in science. Thus, it could be shown that the IR perspective can significantly contribute to a better understanding of scholarly structures and activities.


LUNES: Agent-based Simulation of P2P Systems (Extended Version)

Abstract: We present LUNES, an agent-based Large Unstructured NEtwork Simulator, which allows to simulate complex networks composed of a high number of nodes. LUNES is modular, since it splits the three phases of network topology creation, protocol simulation and performance evaluation. This permits to easily integrate external software tools into the main software architecture. The simulation of the interaction protocols among network nodes is performed via a simulation middleware that supports both the sequential and the parallel/distributed simulation approaches. In the latter case, a specific mechanism for the communication overhead-reduction is used; this guarantees high levels of performance and scalability. To demonstrate the efficiency of LUNES, we test the simulator with gossip protocols executed on top of networks (representing peer-to-peer overlays), generated with different topologies. Results demonstrate the effectiveness of the proposed approach.


Mobile Computing in Digital Ecosystems: Design Issues and Challenges

Abstract: In this paper we argue that the set of wireless, mobile devices (e.g., portable telephones, tablet PCs, GPS navigators, media players) commonly used by human users enables the construction of what we term a digital ecosystem, i.e., an ecosystem constructed out of so-called digital organisms (see below), that can foster the development of novel distributed services. In this context, a human user equipped with his/her own mobile devices, can be though of as a digital organism (DO), a subsystem characterized by a set of peculiar features and resources it can offer to the rest of the ecosystem for use from its peer DOs. The internal organization of the DO must address issues of management of its own resources, including power consumption. Inside the DO and among DOs, peer-to-peer interaction mechanisms can be conveniently deployed to favor resource sharing and data dissemination. Throughout this paper, we show that most of the solutions and technologies needed to construct a digital ecosystem are already available. What is still missing is a framework (i.e., mechanisms, protocols, services) that can support effectively the integration and cooperation of these technologies. In addition, in the following we show that that framework can be implemented as a middleware subsystem that enables novel and ubiquitous forms of computation and communication. Finally, in order to illustrate the effectiveness of our approach, we introduce some experimental results we have obtained from preliminary implementations of (parts of) that subsystem.


Optimal grid exploration by asynchronous oblivious robots

Abstract: We consider a team of {\em autonomous weak robots} that are endowed with visibility sensors and motion actuators. Autonomous means that the team cannot rely on any kind of central coordination mechanism or scheduler. By weak we mean that the robots are devoid of (1) any (observable) IDs allowing to differentiate them (anonymous), (2) means of communication allowing them to communicate directly, and (3) any way to remember any previous observation nor computation performed in any previous step (oblivious). Robots asynchronously operate in cycles of three phases: Look, Compute, and Move. Furthermore, the network is an anonymous unoriented grid. In such settings, the robots must collaborate to solve a collective task, here the terminating grid exploration (exploration for short), despite being limited with respect to input from the environment, asymmetry, memory, etc. Exploration requires that robots explore the grid and stop when the task is complete. We propose optimal (w.r.t. the number of robots) solutions for the deterministic terminating exploration of a grid shaped network by a team of $k$ asynchronous oblivious robots in the fully asynchronous and non-atomic model, so called CORDA. In more details, we first assume the ATOM model in which each Look-Compute-Move cycle execution is executed atomically, ie every robot that is activated at instant t instantaneously executes a full cycle between t and t+1. ATOM being strictly stronger than CORDA, all impossibility results in ATOM also hold in CORDA. We show that it is impossible to explore a grid of at least three nodes with less than three robots in ATOM. (This first result holds for both deterministic and probabilistic settings.) Next, we show that it is impossible to deterministically explore a (2,2)-Grid with less than 4 robots, and a (3,3)-Grid with less than 5 robots, respectively. Then, we propose deterministic algorithms in CORDA to exhibit the optimal number of robots allowing to explore of a given grid. Our results show that except in two particular cases, 3 robots are necessary and sufficient to deterministically explore a grid of at least three nodes. The optimal number of robots for the two remaining cases is: 4 for the (2,2)-Grid and 5 for the (3,3)-Grid.


A Multiple Component Matching Framework for Person Re-Identification

Abstract: Person re-identification consists in recognizing an individual that has already been observed over a network of cameras. It is a novel and challenging research topic in computer vision, for which no reference framework exists yet. Despite this, previous works share similar representations of human body based on part decomposition and the implicit concept of multiple instances. Building on these similarities, we propose a Multiple Component Matching (MCM) framework for the person re-identification problem, which is inspired by Multiple Component Learning, a framework recently proposed for object detection. We show that previous techniques for person re-identification can be considered particular implementations of our MCM framework. We then present a novel person re-identification technique as a direct, simple implementation of our framework, focused in particular on robustness to varying lighting conditions, and show that it can attain state of the art performances.


A Maximal Large Deviation Inequality for Sub-Gaussian Variables

Abstract: In this short note we prove a maximal concentration lemma for sub-Gaussian random variables stating that for independent sub-Gaussian random variables we have \[P<(\max_{1\le i\le N}S_{i}>\epsilon>) \le\exp<(-\frac{1}{N^2}\sum_{i=1}^{N}\frac{\epsilon^{2}}{2\sigma_{i}^{2}}>), \] where $S_i$ is the sum of $i$ zero mean independent sub-Gaussian random variables and $\sigma_i$ is the variance of the $i$th random variable.


Garbage Collection for Multicore NUMA Machines

Abstract: Modern high-end machines feature multiple processor packages, each of which contains multiple independent cores and integrated memory controllers connected directly to dedicated physical RAM. These packages are connected via a shared bus, creating a system with a heterogeneous memory hierarchy. Since this shared bus has less bandwidth than the sum of the links to memory, aggregate memory bandwidth is higher when parallel threads all access memory local to their processor package than when they access memory attached to a remote package. This bandwidth limitation has traditionally limited the scalability of modern functional language implementations, which seldom scale well past 8 cores, even on small benchmarks. This work presents a garbage collector integrated with our strict, parallel functional language implementation, Manticore, and shows that it scales effectively on both a 48-core AMD Opteron machine and a 32-core Intel Xeon machine.


TRX: A Formally Verified Parser Interpreter

Abstract: Parsing is an important problem in computer science and yet surprisingly little attention has been devoted to its formal verification. In this paper, we present TRX: a parser interpreter formally developed in the proof assistant Coq, capable of producing formally correct parsers. We are using parsing expression grammars (PEGs), a formalism essentially representing recursive descent parsing, which we consider an attractive alternative to context-free grammars (CFGs). From this formalization we can extract a parser for an arbitrary PEG grammar with the warranty of total correctness, i.e., the resulting parser is terminating and correct with respect to its grammar and the semantics of PEGs; both properties formally proven in Coq.


Workload Classification & Software Energy Measurement for Efficient Scheduling on Private Cloud Platforms

Abstract: At present there are a number of barriers to creating an energy efficient workload scheduler for a Private Cloud based data center. Firstly, the relationship between different workloads and power consumption must be investigated. Secondly, current hardware-based solutions to providing energy usage statistics are unsuitable in warehouse scale data centers where low cost and scalability are desirable properties. In this paper we discuss the effect of different workloads on server power consumption in a Private Cloud platform. We display a noticeable difference in energy consumption when servers are given tasks that dominate various resources (CPU, Memory, Hard Disk and Network). We then use this insight to develop CloudMonitor, a software utility that is capable of >95% accurate power predictions from monitoring resource consumption of workloads, after a "training phase" in which a dynamic power model is developed.


A Flexible LDPC code decoder with a Network on Chip as underlying interconnect architecture

Abstract: LDPC (Low Density Parity Check) codes are among the most powerful and widely adopted modern error correcting codes. The iterative decoding algorithms required for these codes involve high computational complexity and high processing throughput is achieved by allocating a sufficient number of processing elements (PEs). Supporting multiple heterogeneous LDPC codes on a parallel decoder poses serious problems in the design of the interconnect structure for such PEs. The aim of this work is to explore the feasibility of NoC (Network on Chip) based decoders, where full flexibility in terms of supported LDPC codes is obtained resorting to an NoC to connect PEs. NoC based LDPC decoders have been previously considered unfeasible because of the cost overhead associated to packet management and routing. On the contrary, the designed NoC adopts a low complexity routing, which introduces a very limited cost overhead with respect to architectures dedicated to specific classes of codes. Moreover the paper proposes an efficient configuration technique, which allows for fast on--the--fly switching among different codes. The decoder architecture is scalable and VLSI synthesis results are presented for several cases of study, including the whole set of WiMAX LDPC codes, WiFi codes and DVB-S2 standard.


Broadcast Strategies with Probabilistic Delivery Guarantee in Multi-Channel Multi-Interface Wireless Mesh Networks

Abstract: Multi-channel multi-interface Wireless Mesh Networks permit to spread the load across orthogonal channels to improve network capacity. Although broadcast is vital for many layer-3 protocols, proposals for taking advantage of multiple channels mostly focus on unicast transmissions. In this paper, we propose broadcast algorithms that fit any channel and interface assignment strategy. They guarantee that a broadcast packet is delivered with a minimum probability to all neighbors. Our simulations show that the proposed algorithms efficiently limit the overhead.


Dynamic Backward Slicing of Rewriting Logic Computations

Abstract: Trace slicing is a widely used technique for execution trace analysis that is effectively used in program debugging, analysis and comprehension. In this paper, we present a backward trace slicing technique that can be used for the analysis of Rewriting Logic theories. Our trace slicing technique allows us to systematically trace back rewrite sequences modulo equational axioms (such as associativity and commutativity) by means of an algorithm that dynamically simplifies the traces by detecting control and data dependencies, and dropping useless data that do not influence the final result. Our methodology is particularly suitable for analyzing complex, textually-large system computations such as those delivered as counter-example traces by Maude model-checkers.


Smoothed Performance Guarantees for Local Search

Abstract: We study popular local search and greedy algorithms for scheduling. The performance guarantee of these algorithms is well understood, but the worst-case lower bounds seem somewhat contrived and it is questionable if they arise in practical applications. To find out how robust these bounds are, we study the algorithms in the framework of smoothed analysis, in which instances are subject to some degree of random noise. While the lower bounds for all scheduling variants with restricted machines are rather robust, we find out that the bounds are fragile for unrestricted machines. In particular, we show that the smoothed performance guarantee of the jump and the lex-jump algorithm are (in contrast to the worst case) independent of the number of machines. They are Theta(phi) and Theta(log(phi)), respectively, where 1/phi is a parameter measuring the magnitude of the perturbation. The latter immediately implies that also the smoothed price of anarchy is Theta(log(phi)) for routing games on parallel links. Additionally we show that for unrestricted machines also the greedy list scheduling algorithm has an approximation guarantee of Theta(log(phi)).


Hitting and Harvesting Pumpkins

Abstract: The "c-pumpkin" is the graph with two vertices linked by c>0 parallel edges. A c-pumpkin-model in a graph G is a pair A,B of disjoint subsets of vertices of G, each inducing a connected subgraph of G, such that there are at least c edges in G between A and B. We focus on covering and packing c-pumpkin-models in a given graph: On the one hand, we provide an FPT algorithm running in time 2^O(k) n^O(1) deciding, for any fixed c>0, whether all c-pumpkin-models can be covered by at most k vertices. This generalizes known single-exponential FPT algorithms for Vertex Cover and Feedback Vertex Set, which correspond to the cases c=1,2 respectively. On the other hand, we present a O(log n)-approximation algorithm for both the problems of covering all c-pumpkin-models with a smallest number of vertices, and packing a maximum number of vertex-disjoint c-pumpkin-models.


A Foundational View on Integration Problems

Abstract: The integration of reasoning and computation services across system and language boundaries is a challenging problem of computer science. In this paper, we use integration for the scenario where we have two systems that we integrate by moving problems and solutions between them. While this scenario is often approached from an engineering perspective, we take a foundational view. Based on the generic declarative language MMT, we develop a theoretical framework for system integration using theories and partial theory morphisms. Because MMT permits representations of the meta-logical foundations themselves, this includes integration across logics. We discuss safe and unsafe integration schemes and devise a general form of safe integration.


Computer certified efficient exact reals in Coq

Abstract: Floating point operations are fast, but require continuous effort on the part of the user in order to ensure that the results are correct. This burden can be shifted away from the user by providing a library of exact analysis in which the computer handles the error estimates. We provide an implementation of the exact real numbers in the Coq proof assistant. This improves on the earlier Coq-implementation by O'Connor in two ways: we use dyadic rationals built from the machine integers and we optimize computation of power series by using approximate division. Moreover, we use type classes for clean mathematical interfaces. This appears to be the first time that type classes are used in heavy computation. We obtain over a 100 times speed up of the basic operations and indications for improving the Coq system.


Improving Performance of Speaker Identification System Using Complementary Information Fusion

Abstract: Feature extraction plays an important role as a front-end processing block in speaker identification (SI) process. Most of the SI systems utilize like Mel-Frequency Cepstral Coefficients (MFCC), Perceptual Linear Prediction (PLP), Linear Predictive Cepstral Coefficients (LPCC), as a feature for representing speech signal. Their derivations are based on short term processing of speech signal and they try to capture the vocal tract information ignoring the contribution from the vocal cord. Vocal cord cues are equally important in SI context, as the information like pitch frequency, phase in the residual signal, etc could convey important speaker specific attributes and are complementary to the information contained in spectral feature sets. In this paper we propose a novel feature set extracted from the residual signal of LP modeling. Higher-order statistical moments are used here to find the nonlinear relationship in residual signal. To get the advantages of complementarity vocal cord based decision score is fused with the vocal tract based score. The experimental results on two public databases show that fused mode system outperforms single spectral features.


View subspaces for indexing and retrieval of 3D models

Abstract: View-based indexing schemes for 3D object retrieval are gaining popularity since they provide good retrieval results. These schemes are coherent with the theory that humans recognize objects based on their 2D appearances. The viewbased techniques also allow users to search with various queries such as binary images, range images and even 2D sketches. The previous view-based techniques use classical 2D shape descriptors such as Fourier invariants, Zernike moments, Scale Invariant Feature Transform-based local features and 2D Digital Fourier Transform coefficients. These methods describe each object independent of others. In this work, we explore data driven subspace models, such as Principal Component Analysis, Independent Component Analysis and Nonnegative Matrix Factorization to describe the shape information of the views. We treat the depth images obtained from various points of the view sphere as 2D intensity images and train a subspace to extract the inherent structure of the views within a database. We also show the benefit of categorizing shapes according to their eigenvalue spread. Both the shape categorization and data-driven feature set conjectures are tested on the PSB database and compared with the competitor view-based 3D shape retrieval algorithms


Salient Local 3D Features for 3D Shape Retrieval

Abstract: In this paper we describe a new formulation for the 3D salient local features based on the voxel grid inspired by the Scale Invariant Feature Transform (SIFT). We use it to identify the salient keypoints (invariant points) on a 3D voxelized model and calculate invariant 3D local feature descriptors at these keypoints. We then use the bag of words approach on the 3D local features to represent the 3D models for shape retrieval. The advantages of the method are that it can be applied to rigid as well as to articulated and deformable 3D models. Finally, this approach is applied for 3D Shape Retrieval on the McGill articulated shape benchmark and then the retrieval results are presented and compared to other methods.


Face Recognition using 3D Facial Shape and Color Map Information: Comparison and Combination

Abstract: In this paper, we investigate the use of 3D surface geometry for face recognition and compare it to one based on color map information. The 3D surface and color map data are from the CAESAR anthropometric database. We find that the recognition performance is not very different between 3D surface and color map information using a principal component analysis algorithm. We also discuss the different techniques for the combination of the 3D surface and color map information for multi-modal recognition by using different fusion approaches and show that there is significant improvement in results. The effectiveness of various techniques is compared and evaluated on a dataset with 200 subjects in two different positions.


Retrieval and Clustering from a 3D Human Database based on Body and Head Shape

Abstract: In this paper, we describe a framework for similarity based retrieval and clustering from a 3D human database. Our technique is based on both body and head shape representation and the retrieval is based on similarity of both of them. The 3D human database used in our study is the CAESAR anthropometric database which contains approximately 5000 bodies. We have developed a web-based interface for specifying the queries to interact with the retrieval system. Our approach performs the similarity based retrieval in a reasonable amount of time and is a practical approach.


Optimal Upper and Lower Bounds for Boolean Expressions by Dissociation

Abstract: This paper develops upper and lower bounds for the probability of Boolean expressions by treating multiple occurrences of variables as independent and assigning them new individual probabilities. Our technique generalizes and extends the underlying idea of a number of recent approaches which are varyingly called node splitting, variable renaming, variable splitting, or dissociation for probabilistic databases. We prove that the probabilities we assign to new variables are the best possible in some sense.


Pattern avoidance with involution

Abstract: We give the avoidance indices (morphic and antimorphic) for all unary patterns with involution.


Semantic Vector Machines

Abstract: We first present our work in machine translation, during which we used aligned sentences to train a neural network to embed n-grams of different languages into an $d$-dimensional space, such that n-grams that are the translation of each other are close with respect to some metric. Good n-grams to n-grams translation results were achieved, but full sentences translation is still problematic. We realized that learning semantics of sentences and documents was the key for solving a lot of natural language processing problems, and thus moved to the second part of our work: sentence compression. We introduce a flexible neural network architecture for learning embeddings of words and sentences that extract their semantics, propose an efficient implementation in the Torch framework and present embedding results comparable to the ones obtained with classical neural language models, while being more powerful.


Clique Separator Decomposition of Hole- and Diamond-Free Graphs and Algorithmic Consequences

Abstract: Clique separator decomposition introduced by Tarjan and Whitesides is one of the most important graph decompositions. A graph is an {\em atom} if it has no clique separator. A {\em hole} is a chordless cycle with at least five vertices, and an {\em antihole} is the complement graph of a hole. A graph is {\em weakly chordal} if it is hole- and antihole-free. $K_4-e$ is also called {\em diamond}. {\em Paraglider} has five vertices four of which induce a diamond, and the fifth vertex sees exactly the two vertices of degree two in the diamond. In this paper we show that atoms of hole- and diamond-free graphs (of hole- and paraglider-free graphs, respectively) are either weakly chordal or of a very specific structure. Hole- and paraglider-free graphs are perfect graphs. The structure of their atoms leads to efficient algorithms for various problems.


Improving Usability of Interactive Graphics Specification and Implementation with Picking Views and Inverse Transformations

Abstract: Specifying and programming graphical interactions are difficult tasks, notably because designers have difficulties to express the dynamics of the interaction. This paper shows how the MDPC architecture improves the usability of the specification and the implementation of graphical interaction. The architecture is based on the use of picking views and inverse transforms from the graphics to the data. With three examples of graphical interaction, we show how to express them with the architecture, how to implement them, and how this improves programming usability. Moreover, we show that it enables implementing graphical interaction without a scene graph. This kind of code prevents from errors due to cache consistency management.


Ant Colony Optimization and Hypergraph Covering Problems

Abstract: Ant Colony Optimization (ACO) is a very popular metaheuristic for solving computationally hard combinatorial optimization problems. Runtime analysis of ACO with respect to various pseudo-boolean functions and different graph based combinatorial optimization problems has been taken up in recent years. In this paper, we investigate the runtime behavior of an MMAS*(Max-Min Ant System) ACO algorithm on some well known hypergraph covering problems that are NP-Hard. In particular, we have addressed the Minimum Edge Cover problem, the Minimum Vertex Cover problem and the Maximum Weak- Independent Set problem. The influence of pheromone values and heuristic information on the running time is analysed. The results indicate that the heuristic information has greater impact towards improving the expected optimization time as compared to pheromone values. For certain instances of hypergraphs, we show that the MMAS* algorithm gives a constant order expected optimization time when the dominance of heuristic information is suitably increased.


Fast restoration of natural images corrupted by high-density impulse noise

Abstract: In this paper, we suggest a general model for the fixed-valued impulse noise and propose a two-stage method for high density noise suppression while preserving the image details. In the first stage, we apply an iterative impulse detector, exploiting the image entropy, to identify the corrupted pixels and then employ an Adaptive Iterative Mean filter to restore them. The filter is adaptive in terms of the number of iterations, which is different for each noisy pixel, according to the Euclidean distance from the nearest uncorrupted pixel. Experimental results show that the proposed filter is fast and outperforms the best existing techniques in both objective and subjective performance measures.


A Multi-Purpose Scenario-based Simulator for Smart House Environments

Abstract: Developing smart house systems has been a great challenge for researchers and engineers in this area because of the high cost of implementation and evaluation process of these systems, while being very time consuming. Testing a designed smart house before actually building it is considered as an obstacle towards an efficient smart house project. This is because of the variety of sensors, home appliances and devices available for a real smart environment. In this paper, we present the design and implementation of a multi-purpose smart house simulation system for designing and simulating all aspects of a smart house environment. This simulator provides the ability to design the house plan and different virtual sensors and appliances in a two dimensional model of the virtual house environment. This simulator can connect to any external smart house remote controlling system, providing evaluation capabilities to their system much easier than before. It also supports detailed adding of new emerging sensors and devices to help maintain its compatibility with future simulation needs. Scenarios can also be defined for testing various possible combinations of device states; so different criteria and variables can be simply evaluated without the need of experimenting on a real environment.


Interactive Overlays: A New Method for Generating Global Journal Maps from Web-of-Science Data

Abstract: Recent advances in methods and techniques enable us to develop an interactive overlay to the global map of science based on aggregated citation relations among the 9,162 journals contained in the Science Citation Index and Social Science Citation Index 2009 combined. The resulting mapping is provided by VOSViewer. We first discuss the pros and cons of the various options: cited versus citing, multidimensional scaling versus spring-embedded algorithms, VOSViewer versus Gephi, and the various clustering algorithms and similarity criteria. Our approach focuses on the positions of journals in the multidimensional space spanned by the aggregated journal-journal citations. A number of choices can be left to the user, but we provide default options reflecting our preferences. Some examples are also provided; for example, the potential of using this technique to assess the interdisciplinarity of organizations and/or document sets.


Invitation to Algorithmic Uses of Inclusion-Exclusion

Abstract: I give an introduction to algorithmic uses of the principle of inclusion-exclusion. The presentation is intended to be be concrete and accessible, at the expense of generality and comprehensiveness.


Feature Selection for MAUC-Oriented Classification Systems

Abstract: Feature selection is an important pre-processing step for many pattern classification tasks. Traditionally, feature selection methods are designed to obtain a feature subset that can lead to high classification accuracy. However, classification accuracy has recently been shown to be an inappropriate performance metric of classification systems in many cases. Instead, the Area Under the receiver operating characteristic Curve (AUC) and its multi-class extension, MAUC, have been proved to be better alternatives. Hence, the target of classification system design is gradually shifting from seeking a system with the maximum classification accuracy to obtaining a system with the maximum AUC/MAUC. Previous investigations have shown that traditional feature selection methods need to be modified to cope with this new objective. These methods most often are restricted to binary classification problems only. In this study, a filter feature selection method, namely MAUC Decomposition based Feature Selection (MDFS), is proposed for multi-class classification problems. To the best of our knowledge, MDFS is the first method specifically designed to select features for building classification systems with maximum MAUC. Extensive empirical results demonstrate the advantage of MDFS over several compared feature selection methods.


Multi-Amdahl: Optimal Resource Sharing with Multiple Program Execution Segments

Abstract: This paper presents Multi-Amdahl, a resource allocation analytical tool for heterogeneous systems. Our model includes multiple program execution segments, where each one is accelerated by a specific hardware unit. The acceleration speedup of the specific hardware unit is a function of a limited resource, such as the unit area, power, or energy. Using the Lagrange theorem we discover the optimal resource distribution between all specific units. We then illustrate this general Multi-Amdahl technique using several examples of area and power allocation among several cores and accelerators.


An Energy Efficient Protocol for Gateway-Centric Federated Residential Access Networks

Abstract: The proliferation of overlapping, always-on IEEE 802.11 Access Points (APs) in urban areas can cause spectrum sharing conflicts, inefficient bandwidth usage and power waste. Cooperation among APs could address these problems (i) by allowing under-used devices to hand over their clients to nearby APs and temporarily switch off, (ii) by balancing the load of clients among APs and thus offloading congested APs. The federated houses model provides an appealing backdrop to implement cooperation among APs. In this paper, we outline a framework that, assuming the presence of a multipurpose gateway with AP capabilities in every household, allows such cooperation through the monitoring of local wireless resources and the triggering of offloading requests toward other federated gateways. We then present simulation results in realistic settings that provide some insight on the capabilities of our framework.


Learning to Place New Objects

Abstract: The ability to place objects in the environment is an important skill for a personal robot. An object should not only be placed stably, but should also be placed in its preferred location/orientation. For instance, a plate is preferred to be inserted vertically into the slot of a dish-rack as compared to be placed horizontally in it. Unstructured environments such as homes have a large variety of object types as well as of placing areas. Therefore our algorithms should be able to handle placing new object types and new placing areas. These reasons make placing a challenging manipulation task. In this work, we propose a supervised learning algorithm for finding good placements given the point-clouds of the object and the placing area. It learns to combine the features that capture support, stability and preferred placements using a shared sparsity structure in the parameters. Even when neither the object nor the placing area is seen previously in the training set, our algorithm predicts good placements. In extensive experiments, our method enables the robot to stably place several new objects in several new placing areas with 98% success-rate; and it placed the objects in their preferred placements in 92% of the cases.


On the number of Dejean words over alphabets of 5, 6, 7, 8, 9 and 10 letters

Abstract: We give lower bounds on the growth rate of Dejean words, i.e. minimally repetitive words, over a k-letter alphabet, for k=5, 6, 7, 8, 9, 10. Put together with the known upper bounds, we estimate these growth rates with the precision of 0,005. As an consequence, we establish the exponential growth of the number of Dejean words over a k-letter alphabet, for k=5, 6, 7, 8, 9, 10.


A Novel Method for Calculating Demand Not Served for Transmission Expansion Planning

Abstract: Restructuring of the power market introduced demand uncertainty in transmission expansion planning (TEP), which in turn also requires an accurate estimation of demand not served (DNS). Unfortunately, the graph theory based minimum-cut maximum-flow (MCMF) approach does not ensure that electrical laws are followed. Nor can it be used for calculating DNS at individual buses. In this letter, we propose a generalized load flow based methodology for calculating DNS. This procedure is able to calculate simultaneously generation not served (GNS) and wheeling loss (WL). Importantly, the procedure is able to incorporate the effect of I2R losses, excluded in MCMF approach. Case study on a 5-bus IEEE system shows the effectiveness of the proposed approach over existing method.


A recursive field-normalized bibliometric performance indicator: An application to the field of library and information science

Abstract: Two commonly used ideas in the development of citation-based research performance indicators are the idea of normalizing citation counts based on a field classification scheme and the idea of recursive citation weighing (like in PageRank-inspired indicators). We combine these two ideas in a single indicator, referred to as the recursive mean normalized citation score indicator, and we study the validity of this indicator. Our empirical analysis shows that the proposed indicator is highly sensitive to the field classification scheme that is used. The indicator also has a strong tendency to reinforce biases caused by the classification scheme. Based on these observations, we advise against the use of indicators in which the idea of normalization based on a field classification scheme and the idea of recursive citation weighing are combined.


Unleashing the Power of Mobile Cloud Computing using ThinkAir

Abstract: Smartphones have exploded in popularity in recent years, becoming ever more sophisticated and capable. As a result, developers worldwide are building increasingly complex applications that require ever increasing amounts of computational power and energy. In this paper we propose ThinkAir, a framework that makes it simple for developers to migrate their smartphone applications to the cloud. ThinkAir exploits the concept of smartphone virtualization in the cloud and provides method level computation offloading. Advancing on previous works, it focuses on the elasticity and scalability of the server side and enhances the power of mobile cloud computing by parallelizing method execution using multiple Virtual Machine (VM) images. We evaluate the system using a range of benchmarks starting from simple micro-benchmarks to more complex applications. First, we show that the execution time and energy consumption decrease two orders of magnitude for the N-queens puzzle and one order of magnitude for a face detection and a virus scan application, using cloud offloading. We then show that if a task is parallelizable, the user can request more than one VM to execute it, and these VMs will be provided dynamically. In fact, by exploiting parallelization, we achieve a greater reduction on the execution time and energy consumption for the previous applications. Finally, we use a memory-hungry image combiner tool to demonstrate that applications can dynamically request VMs with more computational power in order to meet their computational requirements.


Double Blind Comparisons using Groups with Infeasible Inversion

Abstract: Double Blind Comparison is a new cryptographic primitive that allows a user who is in possession of a ciphertext to determine if the corresponding plaintext is identical to the plaintext for a different ciphertext held by a different user, but only if both users co-operate. Neither user knows anything about the plaintexts corresponding to either ciphertext, and neither user learns anything about the plaintexts as a result of the comparison, other than whether the two plaintexts are identical. Neither user can determine whether the plaintexts are equal without the other user's co-operation. Double Blind Comparisons have potential application in Anonymous Credentials and the Database Aggregation Problem. This paper shows how Double Blind Comparisons can be implemented using a Strong Associative One-Way Function (SAOWF). Proof of security is given, making an additional assumption that the SAOWF is implemented on a Group with Infeasible Inversion (GII), whose existence was postulated by Hohenberger and Molnar.


Double Blind Comparisons: A New Approach to the Database Aggregation Problem

Abstract: The Data Aggregation Problem occurs when a large collection of data takes on a higher security level than any of its individual component records. Traditional approaches of breaking up the data and restricting access on a "need to know" basis take away one of the great advantages of collecting the data in the first place. This paper introduces a new cryptographic primitive, Double Blind Comparisons, which allows two co-operating users, who each have an encrypted secret, to determine the equality or inequality of those two secrets, even though neither user can discover any information about what the secret is. This paper also introduces a new problem in bilinear groups, conjectured to be a hard problem. Assuming this conjecture, it is shown that neither user can discover any information about whether the secrets are equal, without the other user's co-operation. We then look at how Double Blind Comparisons can be used to mitigate the Data Aggregation Problem. Finally, the paper concludes with some suggested possibilities for future research and some other potential uses for Double Blind Comparisons.


Scholarly Communication

Abstract: The chapter tackles the role of scholarly publication in the research process (quality, preservation) and looks at the consequences of new information technologies in the organization of the scholarly communication ecology. It will then show how new technologies have had an impact on the scholarly communication process and made it depart from the traditional publishing environment. Developments will address new editorial processes, dissemination of new content and services, as well as the development of publication archives. This last aspect will be covered on all levels (open access, scientific, technical and legal aspects). A view on the possible evolutions of the scientific publishing environment will be provided.


Hierarchies in Dependence Logic

Abstract: We study fragments of dependence logic defined either by restricting the number k of universal quantifiers or the width of dependence atoms in formulas. We find the sublogics of existential second-order logic corresponding to these fragments of dependence logic. We also show that these both ways of defining fragments of dependence logic give rise to a hierarchy in expressive power with respect to k.


Randomly Roving Agents in Wireless Sensor Networks

Abstract: Quantitative characterization of randomly roving agents in wireless sensor networks (WSN) is studied. Below the formula simplifications, regarding the known results and publications, it is shown that the basic agent model is probabilistically equivalent to a similar simpler model and then a formula for frequencies is achieved in terms of combinatorial second kind Stirling numbers. Stirling numbers are well studied and different estimates are known for them letting to justify the roving agents quantitative characteristics.


The block cipher NSABC (public domain)

Abstract: We introduce NSABC/w -- Nice-Structured Algebraic Block Cipher using w-bit word arithmetic, a 4w-bit analogous of Skipjack [NSA98] with 5w-bit key. The Skipjack's internal 4-round Feistel structure is replaced with a w-bit, 2-round cascade of a binary operation (x,z)\mapsto(x\boxdot z)\lll(w/2) that permutes a text word x under control of a key word z. The operation \boxdot, similarly to the multiplication in IDEA [LM91, LMM91], bases on an algebraic group over w-bit words, so it is also capable of decrypting by means of the inverse element of z in the group. The cipher utilizes a secret 4w-bit tweak -- an easily changeable parameter with unique value for each block encrypted under the same key [LRW02] -- that is derived from the block index and an additional 4w -bit key. A software implementation for w=64 takes circa 9 clock cycles per byte on x86-64 processors.


Relating Weight Constraint and Aggregate Programs: Semantics and Representation

Abstract: Weight constraint and aggregate programs are among the most widely used logic programs with constraints. In this paper, we relate the semantics of these two classes of programs, namely the stable model semantics for weight constraint programs and the answer set semantics based on conditional satisfaction for aggregate programs. Both classes of programs are instances of logic programs with constraints, and in particular, the answer set semantics for aggregate programs can be applied to weight constraint programs. We show that the two semantics are closely related. First, we show that for a broad class of weight constraint programs, called strongly satisfiable programs, the two semantics coincide. When they disagree, a stable model admitted by the stable model semantics may be circularly justified. We show that the gap between the two semantics can be closed by transforming a weight constraint program to a strongly satisfiable one, so that no circular models may be generated under the current implementation of the stable model semantics. We further demonstrate the close relationship between the two semantics by formulating a transformation from weight constraint programs to logic programs with nested expressions which preserves the answer set semantics. Our study on the semantics leads to an investigation of a methodological issue, namely the possibility of compact representation of aggregate programs by weight constraint programs. We show that almost all standard aggregates can be encoded by weight constraints compactly. This makes it possible to compute the answer sets of aggregate programs using the ASP solvers for weight constraint programs. This approach is compared experimentally with the ones where aggregates are handled more explicitly, which show that the weight constraint encoding of aggregates enables a competitive approach to answer set computation for aggregate programs.


Substructuring domain decomposition scheme for unsteady problems

Abstract: Domain decomposition methods are used for approximate solving boundary problems for partial differential equations on parallel computing systems. Specific features of unsteady problems are taken into account in the most complete way in iteration-free schemes of domain decomposition. Regionally-additive schemes are based on different classes of splitting schemes. In this paper we highlight a class of domain decomposition schemes which is based on the partition of the initial domain into subdomains with common boundary nodes. Using the partition of unit we have constructed and studied unconditionally stable schemes of domain decomposition based on two-component splitting: the problem within subdomain and the problem at their boundaries. As an example there is considered the Cauchy problem for evolutionary equations of first and second order with non-negative self-adjoint operator in a finite Hilbert space. The theoretical consideration is supplemented with numerical solving a model problem for the two-dimensional parabolic equation.


Computing in the fractal cloud: modular generic solvers for SAT and Q-SAT variants

Abstract: Abstract geometrical computation can solve hard combinatorial problems efficiently: we showed previously how Q-SAT can be solved in bounded space and time using instance-specific signal machines and fractal parallelization. In this article, we propose an approach for constructing a particular generic machine for the same task. This machine deploies the Map/Reduce paradigm over a fractal structure. Moreover our approach is modular: the machine is constructed by combining modules. In this manner, we can easily create generic machines for solving satifiability variants, such as SAT, #SAT, MAX-SAT.


Analyzing the Persistence of Referenced Web Resources with Memento

Abstract: In this paper we present the results of a study into the persistence and availability of web resources referenced from papers in scholarly repositories. Two repositories with different characteristics, arXiv and the UNT digital library, are studied to determine if the nature of the repository, or of its content, has a bearing on the availability of the web resources cited by that content. Memento makes it possible to automate discovery of archived resources and to consider the time between the publication of the research and the archiving of the referenced URLs. This automation allows us to process more than 160000 URLs, the largest known such study, and the repository metadata allows consideration of the results by discipline. The results are startling: 45% (66096) of the URLs referenced from arXiv still exist, but are not preserved for future generations, and 28% of resources referenced by UNT papers have been lost. Moving forwards, we provide some initial recommendations, including that repositories should publish URL lists extracted from papers that could be used as seeds for web archiving systems.


Book review: Katy B\"orner, Atlas of Science: Visualizing What We Know. Cambridge, MA/ London UK: The MIT Press, 2010

Abstract: Katy B\"orner has written a wonderful book about visualization that makes our field of scientometrics accessible to much larger audiences. The book is to be read in relation to the ongoing series of exhibitions entitled "Places & Spaces: Mapping Science" currently touring the world. The book also provides the scholarly background to the exhibitions. It celebrates scientometrics as the discipline in the background that enables us to visualize the evolution of knowledge as the acumen of human civilization.


Xapagy: a cognitive architecture for narrative reasoning

Abstract: We introduce the Xapagy cognitive architecture: a software system designed to perform narrative reasoning. The architecture has been designed from scratch to model and mimic the activities performed by humans when witnessing, reading, recalling, narrating and talking about stories.


The Exact Schema Theorem

Abstract: A schema is a naturally defined subset of the space of fixed-length binary strings. The Holland Schema Theorem gives a lower bound on the expected fraction of a population in a schema after one generation of a simple genetic algorithm. This paper gives formulas for the exact expected fraction of a population in a schema after one generation of the simple genetic algorithm. Holland's schema theorem has three parts, one for selection, one for crossover, and one for mutation. The selection part is exact, whereas the crossover and mutation parts are approximations. This paper shows how the crossover and mutation parts can be made exact. Holland's schema theorem follows naturally as a corollary. There is a close relationship between schemata and the representation of the population in the Walsh basis. This relationship is used in the derivation of the results, and can also make computation of the schema averages more efficient. This paper gives a version of the Vose infinite population model where crossover and mutation are separated into two functions rather than a single "mixing" function.


Invariant Representative Cocycles of Cohomology Generators using Irregular Graph Pyramids

Abstract: Structural pattern recognition describes and classifies data based on the relationships of features and parts. Topological invariants, like the Euler number, characterize the structure of objects of any dimension. Cohomology can provide more refined algebraic invariants to a topological space than does homology. It assigns `quantities' to the chains used in homology to characterize holes of any dimension. Graph pyramids can be used to describe subdivisions of the same object at multiple levels of detail. This paper presents cohomology in the context of structural pattern recognition and introduces an algorithm to efficiently compute representative cocycles (the basic elements of cohomology) in 2D using a graph pyramid. An extension to obtain scanning and rotation invariant cocycles is given.


First-order query evaluation on structures of bounded degree

Abstract: We consider the enumeration problem of first-order queries over structures of bounded degree. It was shown that this problem is in the Constant-Delaylin class. An enumeration problem belongs to Constant-Delaylin if for an input of size n it can be solved by: - an O(n) precomputation phase building an index structure, - followed by a phase enumerating the answers with no repetition and a constant delay between two consecutive outputs. In this article we give a different proof of this result based on Gaifman's locality theorem for first-order logic. Moreover, the constants we obtain yield a total evaluation time that is triply exponential in the size of the input formula, matching the complexity of the best known evaluation algorithms.


Face Shape and Reflectance Acquisition using a Multispectral Light Stage

Abstract: In this thesis, we discuss the design and calibration (geometric and radiometric) of a novel shape and reflectance acquisition device called the "Multispectral Light Stage". This device can capture highly detailed facial geometry (down to the level of skin pores detail) and Multispectral reflectance map which can be used to estimate biophysical skin parameters such as the distribution of pigmentation and blood beneath the surface of the skin. We extend the analysis of the original spherical gradient photometric stereo method to study the effects of deformed diffuse lobes on the quality of recovered surface normals. Based on our modified radiance equations, we develop a minimal image set method to recover high quality photometric normals using only four, instead of six, spherical gradient images. Using the same radiance equations, we explore a Quadratic Programming (QP) based algorithm for correction of surface normals obtained using spherical gradient photometric stereo. Based on the proposed minimal image sets method, we present a performance capture sequence that significantly reduces the data capture requirement and post-processing computational cost of existing photometric stereo based performance geometry capture methods. Furthermore, we explore the use of images captured in our Light Stage to generate stimuli images for a psychology experiment exploring the neural representation of 3D shape and texture of a human face.


Chain Homotopies for Object Topological Representations

Abstract: This paper presents a set of tools to compute topological information of simplicial complexes, tools that are applicable to extract topological information from digital pictures. A simplicial complex is encoded in a (non-unique) algebraic-topological format called AM-model. An AM-model for a given object K is determined by a concrete chain homotopy and it provides, in particular, integer (co)homology generators of K and representative (co)cycles of these generators. An algorithm for computing an AM-model and the cohomological invariant HB1 (derived from the rank of the cohomology ring) with integer coefficients for a finite simplicial complex in any dimension is designed here. A concept of generators which are "nicely" representative cycles is also presented. Moreover, we extend the definition of AM-models to 3D binary digital images and we design algorithms to update the AM-model information after voxel set operations (union, intersection, difference and inverse).


Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians

Abstract: This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of finite mixture models, conjugate families and factorization. Both the joint probability density of the variables and the likelihood function of the (objective or subjective) observation are approximated by a special mixture model, in such a way that any desired conditional distribution can be directly obtained without numerical integration. We have developed an extended version of the expectation maximization (EM) algorithm to estimate the parameters of mixture models from uncertain training examples (indirect observations). As a consequence, any piece of exact or uncertain information about both input and output values is consistently handled in the inference and learning stages. This ability, extremely useful in certain situations, is not found in most alternative methods. The proposed framework is formally justified from standard probabilistic principles and illustrative examples are provided in the fields of nonparametric pattern classification, nonlinear regression and pattern completion. Finally, experiments on a real application and comparative results over standard databases provide empirical evidence of the utility of the method in a wide range of applications.


TorrentGuard: stopping scam and malware distribution in the BitTorrent ecosystem

Abstract: In this paper we conduct a large scale measurement study in order to analyse the fake content publishing phenomenon in the BitTorrent Ecosystem. Our results reveal that fake content represents an important portion (35%) of those files shared in BitTorrent and just a few tens of users are responsible for 90% of this content. Furthermore, more than 99% of the analysed fake files are linked to either malware or scam websites. This creates a serious threat for the BitTorrent ecosystem. To address this issue, we present a new detection tool named TorrentGuard for the early detection of fake content. Based on our evaluation this tool may prevent the download of more than 35 millions of fake files per year. This could help to reduce the number of computer infections and scams suffered by BitTorrent users. TorrentGuard is already available and it can be accessed through both a webpage or a Vuze plugin.


Benchmarks, Performance Evaluation and Contests for 3D Shape Retrieval

Abstract: Benchmarking of 3D Shape retrieval allows developers and researchers to compare the strengths of different algorithms on a standard dataset. Here we describe the procedures involved in developing a benchmark and issues involved. We then discuss some of the current 3D shape retrieval benchmarks efforts of our group and others. We also review the different performance evaluation measures that are developed and used by researchers in the community. After that we give an overview of the 3D shape retrieval contest (SHREC) tracks run under the EuroGraphics Workshop on 3D Object Retrieval and give details of tracks that we organized for SHREC 2010. Finally we demonstrate some of the results based on the different SHREC contest tracks and the NIST shape benchmark.


Tecnologia M\'ovel: Uma Tend\^encia, Uma Realidade

Abstract: Currently, mobility presents itself as a major innovation in historic technological revolution. From the first decade of this century, nothing compares to what happened in the field of Information Technology (IT), which is adding to market a range of news relating to infrastructure such as mobile computing hardware, software, computer networks, etc.. From a handheld as a wireless device, iPad, Smartphone, you can connect to the digital world, people, customers, business partners, etc.. Given the infinite range of information, services and resources available in the electronic world, it is considered that few are those, markets and people who want to be left behind. The interest in this channel of communication becomes not only a new strategy of marketing and communications. Mobile devices are becoming more sophisticated and allows access to the web. Thinking in this context is that manufacturers of electronic components face each other in a war over the disputed technology to a competitive environment that is open to those who put on the market the product more attractive, interactive and versatile.


Personal Marks and Community Certificates: Detecting Clones in Mobile Wireless Networks of Smart-Phones

Abstract: We consider the problem of detecting clones in wireless mobile adhoc networks. We assume that one of the devices of the network has been cloned. Everything, including certificates and secret keys. This can happen quite easily, because of a virus that immediately after sending all the content of the infected device to the adversary destroys itself, or just because the owner has left his device unattended for a few minutes in a hostile environment. The problem is to detect this attack. We propose a solution in networks of mobile devices carried by individuals. These networks are composed by nodes that have the capability of using short-range communication technology like blue-tooth or Wi-Fi, where nodes are carried by mobile users, and where links appear and disappear according to the social relationships between the users. Our idea is to use social physical contacts, securely collected by wireless personal smart-phones, as a biometric way to authenticate the legitimate owner of the device and detect the clone attack. We introduce two mechanisms: Personal Marks and Community Certificates. Personal Marks is a simple cryptographic protocol that works very well when the adversary is an insider, a malicious node in the network that is part, or not very far, from the social community of the original device that has been cloned. Community Certificates work very well when the adversary is an outsider, a node that has the goal of using the stolen credentials when interacting with other nodes that are far in the social network from the original device. When combined, these mechanisms provide an excellent protection against this very strong attack. We prove our ideas and solutions with extensive simulations in a real world scenario-with mobility traces collected in a real life experiment


Scalably Scheduling Power-Heterogeneous Processors

Abstract: We show that a natural online algorithm for scheduling jobs on a heterogeneous multiprocessor, with arbitrary power functions, is scalable for the objective function of weighted flow plus energy.


A Secure RFID Deactivation/Activation Mechanism for Supporting Customer Service and Consumer Shopping

Abstract: RFID has been regarded as a time and money-saving solution for a wide variety of applications, such as manufacturing, supply chain management, and inventory control, etc. However, there are some security problems on RFID in the product managements. The most concerned issues are the tracking and the location privacy. Numerous scholars tried to solve these problems, but their proposals do not include the after-sales service. In this paper, we propose a purchase and after-sales service RFID scheme for shopping mall. The location privacy, confidentiality, data integrity, and some security protection are hold in this propose mechanism.


Ontological Crises in Artificial Agents' Value Systems

Abstract: Decision-theoretic agents predict and evaluate the results of their actions using a model, or ontology, of their environment. An agent's goal, or utility function, may also be specified in terms of the states of, or entities within, its ontology. If the agent may upgrade or replace its ontology, it faces a crisis: the agent's original goal may not be well-defined with respect to its new ontology. This crisis must be resolved before the agent can make plans towards achieving its goals. We discuss in this paper which sorts of agents will undergo ontological crises and why we may want to create such agents. We present some concrete examples, and argue that a well-defined procedure for resolving ontological crises is needed. We point to some possible approaches to solving this problem, and evaluate these methods on our examples.


An Algorithmic Solution to the Five-Point Pose Problem Based on the Cayley Representation of Rotations

Abstract: We give a new algorithmic solution to the well-known five-point relative pose problem. Our approach does not deal with the famous cubic constraint on an essential matrix. Instead, we use the Cayley representation of rotations in order to obtain a polynomial system from epipolar constraints. Solving that system, we directly get relative rotation and translation parameters of the cameras in terms of roots of a 10th degree polynomial.


Hierarchical Recursive Running Median

Abstract: To date, the histogram-based running median filter of Perreault and H\'ebert is considered the fastest for 8-bit images, being roughly O(1) in average case. We present here another approximately constant time algorithm which further improves the aforementioned one and exhibits lower associated constant, being at the time of writing the lowest theoretical complexity algorithm for calculation of 2D and higher dimensional median filters. The algorithm scales naturally to higher precision (e.g. 16-bit) integer data without any modifications. Its adaptive version offers additional speed-up for images showing compact modes in gray-value distribution. The experimental comparison to the previous constant-time algorithm defines the application domain of this new development, besides theoretical interest, as high bit depth data and/or hardware without SIMD extensions. The C/C++ implementation of the algorithm is available under GPL for research purposes.


Typical models: minimizing false beliefs

Abstract: A knowledge system S describing a part of real world does in general not contain complete information. Reasoning with incomplete information is prone to errors since any belief derived from S may be false in the present state of the world. A false belief may suggest wrong decisions and lead to harmful actions. So an important goal is to make false beliefs as unlikely as possible. This work introduces the notions of "typical atoms" and "typical models", and shows that reasoning with typical models minimizes the expected number of false beliefs over all ways of using incomplete information. Various properties of typical models are studied, in particular, correctness and stability of beliefs suggested by typical models, and their connection to oblivious reasoning.


A Multiple-Choice Test Recognition System based on the Gamera Framework

Abstract: This article describes JECT-OMR, a system that analyzes digital images representing scans of multiple-choice tests compiled by students. The system performs a structural analysis of the document in order to get the chosen answer for each question, and it also contains a bar-code decoder, used for the identification of additional information encoded in the document. JECT-OMR was implemented using the Python programming language, and leverages the power of the Gamera framework in order to accomplish its task. The system exhibits an accuracy of over 99% in the recognition of marked and non-marked squares representing answers, thus making it suitable for real world applications


Fast Distributed Process Creation with the XMOS XS1 Architecture

Abstract: The provision of mechanisms for processor allocation in current distributed parallel programming models is very limited. This makes difficult, or even prohibits, the expression of a large class of programs which require a run-time assessment of their required resources. This includes programs whose structure is irregular, composite or unbounded. Efficient allocation of processors requires a process creation mechanism able to initiate and terminate remote computations quickly. This paper presents the design, demonstration and analysis of an explicit mechanism to do this, implemented on the XMOS XS1 architecture, as a foundation for a more dynamic scheme. It shows that process creation can be made efficient so that it incurs only a fractional overhead of the total runtime and that it can be combined naturally with recursion to enable rapid distribution of computations over a system.


Component Based Clustering in Wireless Sensor Networks

Abstract: Clustering is an important research topic for wireless sensor networks (WSNs). A large variety of approaches has been presented focusing on different performance metrics. Even though all of them have many practical applications, an extremely limited number of software implementations is available to the research community. Furthermore, these very few techniques are implemented for specific WSN systems or are integrated in complex applications. Thus it is very difficult to comparatively study their performance and almost impossible to reuse them in future applications under a different scope. In this work we study a large body of well established algorithms. We identify their main building blocks and propose a component-based architecture for developing clustering algorithms that (a) promotes exchangeability of algorithms thus enabling the fast prototyping of new approaches, (b) allows cross-layer implementations to realize complex applications, (c) offers a common platform to comparatively study the performance of different approaches, (d) is hardware and OS independent. We implement 5 well known algorithms and discuss how to implement 11 more. We conduct an extended simulation study to demonstrate the faithfulness of our implementations when compared to the original implementations. Our simulations are at very large scale thus also demonstrating the scalability of the original algorithms beyond their original presentations. We also conduct experiments to assess their practicality in real WSNs. We demonstrate how the implemented clustering algorithms can be combined with routing and group key establishment algorithms to construct WSN applications. Our study clearly demonstrates the applicability of our approach and the benefits it offers to both research & development communities.


STiCMAC: A MAC Protocol for Robust Space-Time Coding in Cooperative Wireless LANs

Abstract: Relay-assisted cooperative wireless communication has been shown to have significant performance gains over the legacy direct transmission scheme. Compared with single relay based cooperation schemes, utilizing multiple relays further improves the reliability and rate of transmissions. Distributed space-time coding (DSTC), as one of the schemes to utilize multiple relays, requires tight coordination between relays and does not perform well in a distributed environment with mobility. In this paper, a cooperative medium access control (MAC) layer protocol, called \emph{STiCMAC}, is designed to allow multiple relays to transmit at the same time in an IEEE 802.11 network. The transmission is based on a novel DSTC scheme called \emph{randomized distributed space-time coding} (\emph{R-DSTC}), which requires minimum coordination. Unlike conventional cooperation schemes that pick nodes with good links, \emph{STiCMAC} picks a \emph{transmission mode} that could most improve the end-to-end data rate. Any station that correctly receives from the source can act as a relay and participate in forwarding. The MAC protocol is implemented in a fully decentralized manner and is able to opportunistically recruit relays on the fly, thus making it \emph{robust} to channel variations and user mobility. Simulation results show that the network capacity and delay performance are greatly improved, especially in a mobile environment.


Compressed k2-Triples for Full-In-Memory RDF Engines

Abstract: Current "data deluge" has flooded the Web of Data with very large RDF datasets. They are hosted and queried through SPARQL endpoints which act as nodes of a semantic net built on the principles of the Linked Data project. Although this is a realistic philosophy for global data publishing, its query performance is diminished when the RDF engines (behind the endpoints) manage these huge datasets. Their indexes cannot be fully loaded in main memory, hence these systems need to perform slow disk accesses to solve SPARQL queries. This paper addresses this problem by a compact indexed RDF structure (called k2-triples) applying compact k2-tree structures to the well-known vertical-partitioning technique. It obtains an ultra-compressed representation of large RDF graphs and allows SPARQL queries to be full-in-memory performed without decompression. We show that k2-triples clearly outperforms state-of-the-art compressibility and traditional vertical-partitioning query resolution, remaining very competitive with multi-index solutions.


Logical Modelling of Physarum Polycephalum

Abstract: We propose a novel model of unconventional computing where a structural part of computation is presented by dynamics of plasmodium of Physarum polycephalum, a large single cell. We sketch a new logical approach combining conventional logic with process calculus to demonstrate how to employ formal methods in design of unconventional computing media presented by Physarum polycephalum.


Emergent velocity agreement in robot networks

Abstract: In this paper we propose and prove correct a new self-stabilizing velocity agreement (flocking) algorithm for oblivious and asynchronous robot networks. Our algorithm allows a flock of uniform robots to follow a flock head emergent during the computation whatever its direction in plane. Robots are asynchronous, oblivious and do not share a common coordinate system. Our solution includes three modules architectured as follows: creation of a common coordinate system that also allows the emergence of a flock-head, setting up the flock pattern and moving the flock. The novelty of our approach steams in identifying the necessary conditions on the flock pattern placement and the velocity of the flock-head (rotation, translation or speed) that allow the flock to both follow the exact same head and to preserve the flock pattern. Additionally, our system is self-healing and self-stabilizing. In the event of the head leave (the leading robot disappears or is damaged and cannot be recognized by the other robots) the flock agrees on another head and follows the trajectory of the new head. Also, robots are oblivious (they do not recall the result of their previous computations) and we make no assumption on their initial position. The step complexity of our solution is O(n).


Privacy-Preserving Group Data Access via Stateless Oblivious RAM Simulation

Abstract: We study the problem of providing privacy-preserving access to an outsourced honest-but-curious data repository for a group of trusted users. We show that such privacy-preserving data access is possible using a combination of probabilistic encryption, which directly hides data values, and stateless oblivious RAM simulation, which hides the pattern of data accesses. We give simulations that have only an $O(\log n)$ amortized time overhead for simulating a RAM algorithm, $\cal A$, that has a memory of size $n$, using a scheme that is data-oblivious with very high probability assuming the simulation has access to a private workspace of size $O(n^\nu)$, for any given fixed constant $\nu>0$. This simulation makes use of pseudorandom hash functions and is based on a novel hierarchy of cuckoo hash tables that all share a common stash. We also provide results from an experimental simulation of this scheme, showing its practicality. In addition, in a result that may be of some theoretical interest, we also show that one can eliminate the dependence on pseudorandom hash functions in our simulation while having the overhead rise to be $O(\log^2 n)$.


On 2-Site Voronoi Diagrams under Geometric Distance Functions

Abstract: We revisit a new type of a Voronoi diagram, in which distance is measured from a point to a pair of points. We consider a few more such distance functions, based on geometric primitives, and analyze the structure and complexity of the nearest- and furthest-neighbor Voronoi diagrams of a point set with respect to these distance functions.


Cubical Cohomology Ring of 3D Photographs

Abstract: Cohomology and cohomology ring of three-dimensional (3D) objects are topological invariants that characterize holes and their relations. Cohomology ring has been traditionally computed on simplicial complexes. Nevertheless, cubical complexes deal directly with the voxels in 3D images, no additional triangulation is necessary, facilitating efficient algorithms for the computation of topological invariants in the image context. In this paper, we present formulas to directly compute the cohomology ring of 3D cubical complexes without making use of any additional triangulation. Starting from a cubical complex $Q$ that represents a 3D binary-valued digital picture whose foreground has one connected component, we compute first the cohomological information on the boundary of the object, $\partial Q$ by an incremental technique; then, using a face reduction algorithm, we compute it on the whole object; finally, applying the mentioned formulas, the cohomology ring is computed from such information.


Fast O(1) bilateral filtering using trigonometric range kernels

Abstract: It is well-known that spatial averaging can be realized (in space or frequency domain) using algorithms whose complexity does not depend on the size or shape of the filter. These fast algorithms are generally referred to as constant-time or O(1) algorithms in the image processing literature. Along with the spatial filter, the edge-preserving bilateral filter [Tomasi1998] involves an additional range kernel. This is used to restrict the averaging to those neighborhood pixels whose intensity are similar or close to that of the pixel of interest. The range kernel operates by acting on the pixel intensities. This makes the averaging process non-linear and computationally intensive, especially when the spatial filter is large. In this paper, we show how the O(1) averaging algorithms can be leveraged for realizing the bilateral filter in constant-time, by using trigonometric range kernels. This is done by generalizing the idea in [Porikli2008] of using polynomial range kernels. The class of trigonometric kernels turns out to be sufficiently rich, allowing for the approximation of the standard Gaussian bilateral filter. The attractive feature of our approach is that, for a fixed number of terms, the quality of approximation achieved using trigonometric kernels is much superior to that obtained in [Porikli2008] using polynomials.


On A Semi-Automatic Method for Generating Composition Tables

Abstract: Originating from Allen's Interval Algebra, composition-based reasoning has been widely acknowledged as the most popular reasoning technique in qualitative spatial and temporal reasoning. Given a qualitative calculus (i.e. a relation model), the first thing we should do is to establish its composition table (CT). In the past three decades, such work is usually done manually. This is undesirable and error-prone, given that the calculus may contain tens or hundreds of basic relations. Computing the correct CT has been identified by Tony Cohn as a challenge for computer scientists in 1995. This paper addresses this problem and introduces a semi-automatic method to compute the CT by randomly generating triples of elements. For several important qualitative calculi, our method can establish the correct CT in a reasonable short time. This is illustrated by applications to the Interval Algebra, the Region Connection Calculus RCC-8, the INDU calculus, and the Oriented Point Relation Algebras. Our method can also be used to generate CTs for customised qualitative calculi defined on restricted domains.


Voronoi Diagram: The Generator Recognition Problem

Abstract: For the analysis of systems consisting of small, regular objects, the methods of mathematical morphology applied to images of these systems are well-suited. One of these methods is the use of Voronoi polygons. It was found that the Voronoi tessellation method represents a powerful tool for the analysis of thin film morphology and provides nanostructural information to many multi-particle assemblies. In these notes, several morphological algorithms are analyzed and we study how to join all of them to design a graphical user interface (GUI) that provides as input for the system the "AFM image" and interprets the output of the system in terms of errors and generators coordinates.


Approximating subset $k$-connectivity problems

Abstract: A subset $T \subseteq V$ of terminals is $k$-connected to a root $s$ in a directed/undirected graph $J$ if $J$ has $k$ internally-disjoint $vs$-paths for every $v \in T$; $T$ is $k$-connected in $J$ if $T$ is $k$-connected to every $s \in T$. We consider the {\sf Subset $k$-Connectivity Augmentation} problem: given a graph $G=(V,E)$ with edge/node-costs, node subset $T \subseteq V$, and a subgraph $J=(V,E_J)$ of $G$ such that $T$ is $k$-connected in $J$, find a minimum-cost augmenting edge-set $F \subseteq E \setminus E_J$ such that $T$ is $(k+1)$-connected in $J \cup F$. The problem admits trivial ratio $O(|T|^2)$. We consider the case $|T|>k$ and prove that for directed/undirected graphs and edge/node-costs, a $\rho$-approximation for {\sf Rooted Subset $k$-Connectivity Augmentation} implies the following ratios for {\sf Subset $k$-Connectivity Augmentation}: (i) $b(\rho+k) + {(\frac{3|T|}{|T|-k})}^2 H(\frac{3|T|}{|T|-k})$; (ii) $\rho \cdot O(\frac{|T|}{|T|-k} \log k)$, where b=1 for undirected graphs and b=2 for directed graphs, and $H(k)$ is the $k$th harmonic number. The best known values of $\rho$ on undirected graphs are $\min\{|T|,O(k)\}$ for edge-costs and $\min\{|T|,O(k \log |T|)\}$ for node-costs; for directed graphs $\rho=|T|$ for both versions. Our results imply that unless $k=|T|-o(|T|)$, {\sf Subset $k$-Connectivity Augmentation} admits the same ratios as the best known ones for the rooted version. This improves the ratios in \cite{N-focs,L}.


Synthesizing Products for Online Catalogs

Abstract: A high-quality, comprehensive product catalog is essential to the success of Product Search engines and shopping sites such as Yahoo! Shopping, Google Product Search or Bing Shopping. But keeping catalogs up-to-date becomes a challenging task, calling for the need of automated techniques. In this paper, we introduce the problem of product synthesis, a key component of catalog creation and maintenance. Given a set of offers advertised by merchants, the goal is to identify new products and add them to the catalog together with their (structured) attributes. A fundamental challenge is the scale of the problem: a Product Search engine receives data from thousands of merchants and millions of products; the product taxonomy contains thousands of categories, where each category comes in a different schema; and merchants use representations for products that are different from the ones used in the catalog of the Product Search engine. We propose a system that provides an end-to-end solution to the product synthesis problem, and includes components for extraction, and addresses issues involved in data extraction from offers, schema reconciliation, and data fusion. We developed a novel and scalable technique for schema matching which leverages knowledge about previously-known instance-level associations between offers and products; and it is trained using automatically created training sets (no manually-labeled data is needed). We present an experimental evaluation of our system using data from Bing Shopping for more than 800K offers, a thousand merchants, and 400 categories. The evaluation confirms that our approach is able to automatically generate a large number of accurate product specifications, and that our schema reconciliation component outperforms state-of-the-art schema matching techniques in terms of precision and recall.