Strongly universal string hashing is fast

Abstract: We present fast strongly universal string hashing families: they can process data at a rate of 0.2 CPU cycle per byte. Maybe surprisingly, we find that these families---though they require a large buffer of random numbers---are often faster than popular hash functions with weaker theoretical guarantees. Moreover, conventional wisdom is that hash functions with fewer multiplications are faster. Yet we find that they may fail to be faster due to operation pipelining. We present experimental results on several processors including low-powered processors. Our tests include hash functions designed for processors with the Carry-Less Multiplication (CLMUL) instruction set. We also prove, using accessible proofs, the strong universality of our families.


Crowdsourcing with Endogenous Entry

Abstract: We investigate the design of mechanisms to incentivize high quality in crowdsourcing environments with strategic agents, when entry is an endogenous, strategic choice. Modeling endogenous entry in crowdsourcing is important because there is a nonzero cost to making a contribution of any quality which can be avoided by not participating, and indeed many sites based on crowdsourced content do not have adequate participation. We use a mechanism with monotone, rank-based, rewards in a model where agents strategically make participation and quality choices to capture a wide variety of crowdsourcing environments, ranging from conventional crowdsourcing contests to crowdsourced content as in online Q&A forums. We first explicitly construct the unique mixed-strategy equilibrium for such monotone rank-order mechanisms, and use these participation probabilities and quality distribution to address the design of incentives for two kinds of rewards that arise in crowdsourcing. We first show that for attention rewards as in crowdsourced content, the entire equilibrium distribution improves when the rewards for every rank but the last are as high as possible. In particular, when producing the lowest quality content has low cost, the optimal mechanism displays all but the worst contribution. We next investigate settings where there is a total reward that can be arbitrarily distributed amongst all participants, as in crowdsourcing contests. Unlike with exogenous entry, here the expected number of participants can be increased by subsidizing entry, which could potentially improve the expected quality of the best contribution. However, we show that free entry is dominated by taxing entry- making all entrants pay a small fee, which is rebated to the winner along with whatever rewards were already assigned, can improve the quality of the best contribution over a winner-take-all contest with no taxes.


A Planarity Test via Construction Sequences

Abstract: Optimal linear-time algorithms for testing the planarity of a graph are well-known for over 35 years. However, these algorithms are quite involved and recent publications still try to give simpler linear-time tests. We give a simple reduction from planarity testing to the problem of computing a certain construction of a 3-connected graph. The approach is different from previous planarity tests; as key concept, we maintain a planar embedding that is 3-connected at each point in time. The algorithm runs in linear time and computes a planar embedding if the input graph is planar and a Kuratowski-subdivision otherwise.


Asynchronous Signal Passing for Tile Self-Assembly: Fuel Efficient Computation and Efficient Assembly of Shapes

Abstract: In this paper we demonstrate the power of a model of tile self-assembly based on active glues which can dynamically change state. We formulate the Signal-passing Tile Assembly Model (STAM), based on the model of Padilla, Liu, and Seeman to be asynchronous, allowing any action of turning a glue on or off, attaching a new tile, or breaking apart an assembly to happen in any order. Within this highly generalized model we provide three new solutions to tile self-assembly problems that have been addressed within the abstract Tile Assembly Model and its variants, showing that signal passing tiles allow for substantial improvement across multiple complexity metrics. Our first result utilizes a recursive assembly process to achieve tile-type efficient assembly of linear structures, using provably fewer tile types than what is possible in standard tile assembly models. Our second system of signal-passing tiles simulates any Turing machine with high fuel efficiency by using only a constant number of tiles per computation step. Our third system assembles the discrete Sierpinski triangle, demonstrating that this pattern can be strictly self-assembled within the STAM. This result is of particular interest in that it is known that this pattern cannot self-assemble within a number of well studied tile self-assembly models. Notably, all of our constructions are at temperature 1, further demonstrating that signal-passing confers the power to bypass many restrictions found in standard tile assembly models.


The Price of Anarchy for Network Formation in an Adversary Model

Abstract: We study network formation with n players and link cost \alpha > 0. After the network is built, an adversary randomly deletes one link according to a certain probability distribution. Cost for player v incorporates the expected number of players to which v will become disconnected. We show existence of equilibria and a price of stability of 1+o(1) under moderate assumptions on the adversary and n \geq 9. As the main result, we prove bounds on the price of anarchy for two special adversaries: one removes a link chosen uniformly at random, while the other removes a link that causes a maximum number of player pairs to be separated. For unilateral link formation we show a bound of O(1) on the price of anarchy for both adversaries, the constant being bounded by 10+o(1) and 8+o(1), respectively. For bilateral link formation we show O(1+\sqrt{n/\alpha}) for one adversary (if \alpha > 1/2), and \Theta(n) for the other (if \alpha > 2 considered constant and n \geq 9). The latter is the worst that can happen for any adversary in this model (if \alpha = \Omega(1)). This points out substantial differences between unilateral and bilateral link formation.


Efficient Algorithms for Solving Hypergraphic Steiner Tree Relaxations in Quasi-Bipartite Instances

Abstract: We consider the Steiner tree problem in quasi-bipartite graphs, where no two Steiner vertices are connected by an edge. For this class of instances, we present an efficient algorithm to exactly solve the so called directed component relaxation (DCR), a specific form of hypergraphic LP relaxation that was instrumental in the recent break-through result by Byrka et al. [BGRS10] (STOC 2010). Our algorithm hinges on an efficiently computable map from extreme points of the bidirected cut relaxation to feasible solutions of (DCR). As a consequence, together with [BGRS10] we immediately obtain an efficient 73/60-approximation for quasi-bipartite Steiner tree instances. We also present a particularly simple (BCR)-based random sampling algorithm that achieves a performance guarantee slightly better than 77/60.


Solving Single-digit Sudoku Subproblems

Abstract: We show that single-digit "Nishio" subproblems in nxn Sudoku puzzles may be solved in time o(2^n), faster than previous solutions such as the pattern overlay method. We also show that single-digit deduction in Sudoku is NP-hard.


Analysis and implementation of the Large Scale Video-on-Demand System

Abstract: Next Generation Network (NGN) provides multimedia services over broadband based networks, which supports high definition TV (HDTV), and DVD quality video-on-demand content. The video services are thus seen as merging mainly three areas such as computing, communication, and broadcasting. It has numerous advantages and more exploration for the large-scale deployment of video-on-demand system is still needed. This is due to its economic and design constraints. It's need significant initial investments for full service provision. This paper presents different estimation for the different topologies and it require efficient planning for a VOD system network. The methodology investigates the network bandwidth requirements of a VOD system based on centralized servers, and distributed local proxies. Network traffic models are developed to evaluate the VOD system's operational bandwidth requirements for these two network architectures. This paper present an efficient estimation of the of the bandwidth requirement for the different architectures.


The Stretch Factor of $L_1$- and $L_\infty$-Delaunay Triangulations

Abstract: In this paper we determine the stretch factor of the $L_1$-Delaunay and $L_\infty$-Delaunay triangulations, and we show that this stretch is $\sqrt{4+2\sqrt{2}} \approx 2.61$. Between any two points $x,y$ of such triangulations, we construct a path whose length is no more than $\sqrt{4+2\sqrt{2}}$ times the Euclidean distance between $x$ and $y$, and this bound is best possible. This definitively improves the 25-year old bound of $\sqrt{10}$ by Chew (SoCG '86). To the best of our knowledge, this is the first time the stretch factor of the well-studied $L_p$-Delaunay triangulations, for any real $p\ge 1$, is determined exactly.


Path ORAM: An Extremely Simple Oblivious RAM Protocol

Abstract: We present Path ORAM, an extremely simple Oblivious RAM protocol with a small amount of client storage. Partly due to its simplicity, Path ORAM is the most practical ORAM scheme known to date with small client storage. We formally prove that Path ORAM has a O(log N) bandwidth cost for blocks of size B = Omega(log^2 N) bits. For such block sizes, Path ORAM is asymptotically better than the best known ORAM schemes with small client storage. Due to its practicality, Path ORAM has been adopted in the design of secure processors since its proposal.


Vertical representation of $C^{\infty}$-words

Abstract: We present a new framework for dealing with $C^{\infty}$-words, based on their left and right frontiers. This allows us to give a compact representation of them, and to describe the set of $C^{\infty}$-words through an infinite directed acyclic graph $G$. This graph is defined by a map acting on the frontiers of $C^{\infty}$-words. We show that this map can be defined recursively and with no explicit references to $C^{\infty}$-words. We then show that some important conjectures on $C^{\infty}$-words follow from analogous statements on the structure of the graph $G$.


Some results on more flexible versions of Graph Motif

Abstract: The problems studied in this paper originate from Graph Motif, a problem introduced in 2006 in the context of biological networks. Informally speaking, it consists in deciding if a multiset of colors occurs in a connected subgraph of a vertex-colored graph. Due to the high rate of noise in the biological data, more flexible definitions of the problem have been outlined. We present in this paper two inapproximability results for two different optimization variants of Graph Motif: one where the size of the solution is maximized, the other when the number of substitutions of colors to obtain the motif from the solution is minimized. We also study a decision version of Graph Motif where the connectivity constraint is replaced by the well known notion of graph modularity. While the problem remains NP-complete, it allows algorithms in FPT for biologically relevant parameterizations.


Fabrication of Message Digest to Authenticate Audio Signals with Alternation of Coefficients of Harmonics in Multi-Stages (MDAC)

Abstract: Providing security to audio songs for maintaining its intellectual property right (IPR) is one of chanllenging fields in commercial world especially in creative industry. In this paper, an effective approach has been incorporated to fabricate authentication of audio song through application of message digest method with alternation of coefficients of harmonics in multi-stages of higher frequency domain without affecting its audible quality. Decomposing constituent frequency components of song signal using Fourier transform with generating secret code via applying message digest followed by alternating coefficients of specific harmonics in multi-stages generates a secret code and this unique code is utilized to detect the originality of the song. A comparative study has been made with similar existing techniques and experimental results are also supported with mathematical formula based on Microsoft WAVE (".wav") stereo sound file.


Identifying Discriminating Network Motifs in YouTube Spam

Abstract: Like other social media websites, YouTube is not immune from the attention of spammers. In particular, evidence can be found of attempts to attract users to malicious third-party websites. As this type of spam is often associated with orchestrated campaigns, it has a discernible network signature, based on networks derived from comments posted by users to videos. In this paper, we examine examples of different YouTube spam campaigns of this nature, and use a feature selection process to identify network motifs that are characteristic of the corresponding campaign strategies. We demonstrate how these discriminating motifs can be used as part of a network motif profiling process that tracks the activity of spam user accounts over time, enabling the process to scale to larger networks.


Triadic Measures on Graphs: The Power of Wedge Sampling

Abstract: Graphs are used to model interactions in a variety of contexts, and there is a growing need to quickly assess the structure of a graph. Some of the most useful graph metrics, especially those measuring social cohesion, are based on triangles. Despite the importance of these triadic measures, associated algorithms can be extremely expensive. We propose a new method based on wedge sampling. This versatile technique allows for the fast and accurate approximation of all current variants of clustering coefficients and enables rapid uniform sampling of the triangles of a graph. Our methods come with provable and practical time-approximation tradeoffs for all computations. We provide extensive results that show our methods are orders of magnitude faster than the state-of-the-art, while providing nearly the accuracy of full enumeration. Our results will enable more wide-scale adoption of triadic measures for analysis of extremely large graphs, as demonstrated on several real-world examples.


Computing Lempel-Ziv Factorization Online

Abstract: We present an algorithm which computes the Lempel-Ziv factorization of a word $W$ of length $n$ on an alphabet $\Sigma$ of size $\sigma$ online in the following sense: it reads $W$ starting from the left, and, after reading each $r = O(\log_{\sigma} n)$ characters of $W$, updates the Lempel-Ziv factorization. The algorithm requires $O(n \log \sigma)$ bits of space and O(n \log^2 n) time. The basis of the algorithm is a sparse suffix tree combined with wavelet trees.


On the Power of Manifold Samples in Exploring Configuration Spaces and the Dimensionality of Narrow Passages

Abstract: We extend our study of Motion Planning via Manifold Samples (MMS), a general algorithmic framework that combines geometric methods for the exact and complete analysis of low-dimensional configuration spaces with sampling-based approaches that are appropriate for higher dimensions. The framework explores the configuration space by taking samples that are entire low-dimensional manifolds of the configuration space capturing its connectivity much better than isolated point samples. The contributions of this paper are as follows: (i) We present a recursive application of MMS in a six-dimensional configuration space, enabling the coordination of two polygonal robots translating and rotating amidst polygonal obstacles. In the adduced experiments for the more demanding test cases MMS clearly outperforms PRM, with over 20-fold speedup in a coordination-tight setting. (ii) A probabilistic completeness proof for the most prevalent case, namely MMS with samples that are affine subspaces. (iii) A closer examination of the test cases reveals that MMS has, in comparison to standard sampling-based algorithms, a significant advantage in scenarios containing high-dimensional narrow passages. This provokes a novel characterization of narrow passages which attempts to capture their dimensionality, an attribute that had been (to a large extent) unattended in previous definitions.


Explicit Optimal Hardness via Gaussian stability results

Abstract: The results of Raghavendra (2008) show that assuming Khot's Unique Games Conjecture (2002), for every constraint satisfaction problem there exists a generic semi-definite program that achieves the optimal approximation factor. This result is existential as it does not provide an explicit optimal rounding procedure nor does it allow to calculate exactly the Unique Games hardness of the problem. Obtaining an explicit optimal approximation scheme and the corresponding approximation factor is a difficult challenge for each specific approximation problem. An approach for determining the exact approximation factor and the corresponding optimal rounding was established in the analysis of MAX-CUT (KKMO 2004) and the use of the Invariance Principle (MOO 2005). However, this approach crucially relies on results explicitly proving optimal partitions in Gaussian space. Until recently, Borell's result (Borell 1985) was the only non-trivial Gaussian partition result known. In this paper we derive the first explicit optimal approximation algorithm and the corresponding approximation factor using a new result on Gaussian partitions due to Isaksson and Mossel (2012). This Gaussian result allows us to determine exactly the Unique Games Hardness of MAX-3-EQUAL. In particular, our results show that Zwick algorithm for this problem achieves the optimal approximation factor and prove that the approximation achieved by the algorithm is $\approx 0.796$ as conjectured by Zwick. We further use the previously known optimal Gaussian partitions results to obtain a new Unique Games Hardness factor for MAX-k-CSP : Using the well known fact that jointly normal pairwise independent random variables are fully independent, we show that the the UGC hardness of Max-k-CSP is $\frac{\lceil (k+1)/2 \rceil}{2^{k-1}}$, improving on results of Austrin and Mossel (2009).


Belief Consensus Algorithms for Fast Distributed Target Tracking in Wireless Sensor Networks

Abstract: In distributed target tracking for wireless sensor networks, agreement on the target state can be achieved by the construction and maintenance of a communication path, in order to exchange information regarding local likelihood functions. Such an approach lacks robustness to failures and is not easily applicable to ad-hoc networks. To address this, several methods have been proposed that allow agreement on the global likelihood through fully distributed belief consensus (BC) algorithms, operating on local likelihoods in distributed particle filtering (DPF). However, a unified comparison of the convergence speed and communication cost has not been performed. In this paper, we provide such a comparison and propose a novel BC algorithm based on belief propagation (BP). According to our study, DPF based on metropolis belief consensus (MBC) is the fastest in loopy graphs, while DPF based on BP consensus is the fastest in tree graphs. Moreover, we found that BC-based DPF methods have lower communication overhead than data flooding when the network is sufficiently sparse.


How to Bypass Verified Boot Security in Chromium OS

Abstract: Verified boot is an interesting feature of Chromium OS that supposedly can detect any modification in the root file system (rootfs) by a dedicated adversary. However, by exploiting a design flaw in verified boot, we show that an adversary can replace the original rootfs by a malicious rootfs containing exploits such as a spyware or keylogger and still pass the verified boot process. The exploit is based on the fact that a dedicated adversary can replace the rootfs and the corresponding verification information in the bootloader. We experimentally demonstrate an attack using both the base and developer version of Chromium OS in which the adversary installs a spyware in the target system to send cached user data to the attacker machine in plain text which are otherwise encrypted, and thus inaccessible. We also demonstrate techniques to mitigate this vulnerability.


Elitism Levels Traverse Mechanism For The Derivation of Upper Bounds on Unimodal Functions

Abstract: In this article we present an Elitism Levels Traverse Mechanism that we designed to find bounds on population-based Evolutionary algorithms solving unimodal functions. We prove its efficiency theoretically and test it on OneMax function deriving bounds c{\mu}n log n - O({\mu} n). This analysis can be generalized to any similar algorithm using variants of tournament selection and genetic operators that flip or swap only 1 bit in each string.


Development Trends in Steganography

Abstract: Steganography is a general term referring to all methods for the embedding of additional secret content into some form of carrier, with the aim of concealment of the introduced alterations. The choice of the carrier is nearly unlimited, it may be an ancient piece of parchment, as well as a network protocol header. Inspired by biological phenomena, adopted by man in the ancient times, it has been developed over the ages. Present day steganographic methods are far more sophisticated than their ancient predecessors, but the main principles have remained unchanged. They typically rely on the utilization of digital media files or network protocols as a carrier, in which secret data is embedded. This paper presents the evolution of the hidden data carrier from the ancient times till the present day and pinpoints the observed development trends, with special emphasis on network steganography.


Min Max Generalization for Two-stage Deterministic Batch Mode Reinforcement Learning: Relaxation Schemes

Abstract: We study the minmax optimization problem introduced in [22] for computing policies for batch mode reinforcement learning in a deterministic setting. First, we show that this problem is NP-hard. In the two-stage case, we provide two relaxation schemes. The first relaxation scheme works by dropping some constraints in order to obtain a problem that is solvable in polynomial time. The second relaxation scheme, based on a Lagrangian relaxation where all constraints are dualized, leads to a conic quadratic programming problem. We also theoretically prove and empirically illustrate that both relaxation schemes provide better results than those given in [22].


DPCube: Differentially Private Histogram Release through Multidimensional Partitioning

Abstract: Differential privacy is a strong notion for protecting individual privacy in privacy preserving data analysis or publishing. In this paper, we study the problem of differentially private histogram release for random workloads. We study two multidimensional partitioning strategies including: 1) a baseline cell-based partitioning strategy for releasing an equi-width cell histogram, and 2) an innovative 2-phase kd-tree based partitioning strategy for releasing a v-optimal histogram. We formally analyze the utility of the released histograms and quantify the errors for answering linear queries such as counting queries. We formally characterize the property of the input data that will guarantee the optimality of the algorithm. Finally, we implement and experimentally evaluate several applications using the released histograms, including counting queries, classification, and blocking for record linkage and show the benefit of our approach.


Efficient and Effective Volume Visualization with Enhanced Isosurface Rendering

Abstract: Compared with full volume rendering, isosurface rendering has several well recognized advantages in efficiency and accuracy. However, standard isosurface rendering has some limitations in effectiveness. First, it uses a monotone colored approach and can only visualize the geometry features of an isosurface. The lack of the capability to illustrate the material property and the internal structures behind an isosurface has been a big limitation of this method in applications. Another limitation of isosurface rendering is the difficulty to reveal physically meaningful structures, which are hidden in one or multiple isosurfaces. As such, the application requirements of extract and recombine structures of interest can not be implemented effectively with isosurface rendering. In this work, we develop an enhanced isosurface rendering technique to improve the effectiveness while maintaining the performance efficiency of the standard isosurface rendering. First, an isosurface color enhancement method is proposed to illustrate the neighborhood density and to reveal some of the internal structures. Second, we extend the structure extraction capability of isosurface rendering by enabling explicit scene exploration within a 3D-view, using surface peeling, voxel-selecting, isosurface segmentation, and multi-surface-structure visualization. Our experiments show that the color enhancement not only improves the visual fidelity of the rendering, but also reveals the internal structures without significant increase of the computational cost. Explicit scene exploration is also demonstrated as a powerful tool in some application scenarios, such as displaying multiple abdominal organs.


Synthesis of Succinct Systems

Abstract: Synthesis of correct by design systems from specification has recently attracted much attention. The theoretical results imply that this problem is highly intractable, e.g., synthesizing a system is 2EXPTIME-complete for an LTL specification, and EXPTIME-complete for a CTL specification. However, an argument against it is that the temporal specification is highly compact, and the complexity reflects the large size of the system constructed. In that respect, the complexity should, perhaps, be specified relative to the size of the minimal satisfying system. A careful observation reveals that the size of the system is presented in such arguments as the size of its state space. This view is a bit nonstandard, in the sense that the state space can be exponentially larger than the size of a reasonable implementation such as a circuit or a program. Although this alternative measure of the size of the synthesized system is more intuitive (e.g., this is the standard way model checking problems are measured), research on synthesis has so far stayed with measuring the system in terms of the explicit state space. This raises the question of whether or not there always exists a small system. In this paper, we show that this is the case if, and only if, PSPACE = EXPTIME.


Enhancing Navigation on Wikipedia with Social Tags

Abstract: Social tagging has become an interesting approach to improve search and navigation over the actual Web, since it aggregates the tags added by different users to the same resource in a collaborative way. This way, it results in a list of weighted tags describing its resource. Combined to a classical taxonomic classification system such as that by Wikipedia, social tags can enhance document navigation and search. On the one hand, social tags suggest alternative navigation ways, including pivot-browsing, popularity-driven navigation, and filtering. On the other hand, it provides new metadata, sometimes uncovered by documents' content, that can substantially improve document search. In this work, the inclusion of an interface to add user-defined tags describing Wikipedia articles is proposed, as a way to improve article navigation and retrieval. As a result, a prototype on applying tags over Wikipedia is proposed in order to evaluate its effectiveness.


Analyzing Tag Distributions in Folksonomies for Resource Classification

Abstract: Recent research has shown the usefulness of social tags as a data source to feed resource classification. Little is known about the effect of settings on folksonomies created on social tagging systems. In this work, we consider the settings of social tagging systems to further understand tag distributions in folksonomies. We analyze in depth the tag distributions on three large-scale social tagging datasets, and analyze the effect on a resource classification task. To this end, we study the appropriateness of applying weighting schemes based on the well-known TF-IDF for resource classification. We show the great importance of settings as to altering tag distributions. Among those settings, tag suggestions produce very different folksonomies, which condition the success of the employed weighting schemes. Our findings and analyses are relevant for researchers studying tag-based resource classification, user behavior in social networks, the structure of folksonomies and tag distributions, as well as for developers of social tagging systems in search of an appropriate setting.


CMS Workflow Execution using Intelligent Job Scheduling and Data Access Strategies

Abstract: Complex scientific workflows can process large amounts of data using thousands of tasks. The turnaround times of these workflows are often affected by various latencies such as the resource discovery, scheduling and data access latencies for the individual workflow processes or actors. Minimizing these latencies will improve the overall execution time of a workflow and thus lead to a more efficient and robust processing environment. In this paper, we propose a pilot job based infrastructure that has intelligent data reuse and job execution strategies to minimize the scheduling, queuing, execution and data access latencies. The results have shown that significant improvements in the overall turnaround time of a workflow can be achieved with this approach. The proposed approach has been evaluated, first using the CMS Tier0 data processing workflow, and then simulating the workflows to evaluate its effectiveness in a controlled environment.


Risk-Driven Compliant Access Controls for Clouds

Abstract: There is widespread agreement that cloud computing have proven cost cutting and agility benefits. However, security and regulatory compliance issues are continuing to challenge the wide acceptance of such technology both from social and commercial stakeholders. An important facture behind this is the fact that clouds and in particular public clouds are usually deployed and used within broad geographical or even international domains. This implies that the exchange of private and other protected data within the cloud environment would be governed by multiple jurisdictions. These jurisdictions have a great degree of harmonisation; however, they present possible conflicts that are hard to negotiate at run time. So far, important efforts were played in order to deal with regulatory compliance management for large distributed systems. However, measurable solutions are required for the context of cloud. In this position paper, we are suggesting an approach that starts with a conceptual model of explicit regulatory requirements for exchanging private data on a multijurisdictional environment and build on it in order to define metrics for non-compliance or, in other terms, risks to compliance. These metrics will be integrated within usual data access-control policies and will be checked at policy analysis time before a decision to allow/deny the data access is made.


An Architecture for Integrated Intelligence in Urban Management using Cloud Computing

Abstract: With the emergence of new methodologies and technologies it has now become possible to manage large amounts of environmental sensing data and apply new integrated computing models to acquire information intelligence. This paper advocates the application of cloud capacity to support the information, communication and decision making needs of a wide variety of stakeholders in the complex business of the management of urban and regional development. The complexity lies in the interactions and impacts embodied in the concept of the urban-ecosystem at various governance levels. This highlights the need for more effective integrated environmental management systems. This paper offers a user-orientated approach based on requirements for an effective management of the urban-ecosystem and the potential contributions that can be supported by the cloud computing community. Furthermore, the commonality of the influence of the drivers of change at the urban level offers the opportunity for the cloud computing community to develop generic solutions that can serve the needs of hundreds of cities from Europe and indeed globally.


Organizing the Aggregate: Languages for Spatial Computing

Abstract: As the number of computing devices embedded into engineered systems continues to rise, there is a widening gap between the needs of the user to control aggregates of devices and the complex technology of individual devices. Spatial computing attempts to bridge this gap for systems with local communication by exploiting the connection between physical locality and device connectivity. A large number of spatial computing domain specific languages (DSLs) have emerged across diverse domains, from biology and reconfigurable computing, to sensor networks and agent-based systems. In this chapter, we develop a framework for analyzing and comparing spatial computing DSLs, survey the current state of the art, and provide a roadmap for future spatial computing DSL investigation.


A Fault Tolerant, Dynamic and Low Latency BDII Architecture for Grids

Abstract: The current BDII model relies on information gathering from agents that run on each core node of a Grid. This information is then published into a Grid wide information resource known as Top BDII. The Top level BDIIs are updated typically in cycles of a few minutes each. A new BDDI architecture is proposed and described in this paper based on the hypothesis that only a few attribute values change in each BDDI information cycle and consequently it may not be necessary to update each parameter in a cycle. It has been demonstrated that significant performance gains can be achieved by exchanging only the information about records that changed during a cycle. Our investigations have led us to implement a low latency and fault tolerant BDII system that involves only minimal data transfer and facilitates secure transactions in a Grid environment.


Reusable Services from the neuGRID Project for Grid-Based Health Applications

Abstract: By abstracting Grid middleware specific considerations from clinical research applications, re-usable services should be developed that will provide generic functionality aimed specifically at medical applications. In the scope of the neuGRID project, generic services are being designed and developed which will be applied to satisfy the requirements of neuroscientists. These services will bring together sources of data and computing elements into a single view as far as applications are concerned, making it possible to cope with centralised, distributed or hybrid data and provide native support for common medical file formats. Services will include querying, provenance, portal, anonymization and pipeline services together with a 'glueing' service for connection to Grid services. Thus lower-level services will hide the peculiarities of any specific Grid technology from upper layers, provide application independence and will enable the selection of 'fit-for-purpose' infrastructures. This paper outlines the design strategy being followed in neuGRID using the glueing and pipeline services as examples.


Research Traceability using Provenance Services for Biomedical Analysis

Abstract: We outline the approach being developed in the neuGRID project to use provenance management techniques for the purposes of capturing and preserving the provenance data that emerges in the specification and execution of workflows in biomedical analyses. In the neuGRID project a provenance service has been designed and implemented that is intended to capture, store, retrieve and reconstruct the workflow information needed to facilitate users in conducting user analyses. We describe the architecture of the neuGRID provenance service and discuss how the CRISTAL system from CERN is being adapted to address the requirements of the project and then consider how a generalised approach for provenance management could emerge for more generic application to the (Health)Grid community.


Context-Aware Service Utilisation in the Clouds and Energy Conservation

Abstract: Ubiquitous computing environments are characterised by smart, interconnected artefacts embedded in our physical world that are projected to provide useful services to human inhabitants unobtrusively. Mobile devices are becoming the primary tools of human interaction with these embedded artefacts and utilisation of services available in smart computing environments such as clouds. Advancements in capabilities of mobile devices allow a number of user and environment related context consumers to be hosted on these devices. Without a coordinating component, these context consumers and providers are a potential burden on device resources; specifically the effect of uncoordinated computation and communication with cloud-enabled services can negatively impact the battery life. Therefore energy conservation is a major concern in realising the collaboration and utilisation of mobile device based context-aware applications and cloud based services. This paper presents the concept of a context-brokering component to aid in coordination and communication of context information between mobile devices and services deployed in a cloud infrastructure. A prototype context broker is experimentally analysed for effects on energy conservation when accessing and coordinating with cloud services on a smart device, with results signifying reduction in energy consumption.


Register Allocation By Model Transformer Semantics

Abstract: Register allocation has long been formulated as a graph coloring problem, coloring the conflict graph with physical registers. Such a formulation does not fully capture the goal of the allocation, which is to minimize the traffic between registers and memory. Linear scan has been proposed as an alternative to graph coloring, but in essence, it can be viewed as a greedy algorithm for graph coloring: coloring the vertices not in the order of their degrees, but in the order of their occurence in the program. Thus it suffers from almost the same constraints as graph coloring. In this article, I propose a new method of register allocation based on the ideas of model transformer semantics (MTS) and static cache replacement (SCR). Model transformer semantics captures the semantics of registers and the stack. Static cache replacement relaxes the assumptions made by graph coloring and linear scan, aiming directly at reducing register-memory traffic. The method explores a much larger solution space than that of graph coloring and linear scan, thus providing more opportunities of optimization. It seamlessly performs live range splitting, an optimization found in extensions to graph coloring and linear scan. Also, it simplifies the compiler, and its semantics-based approach provides possibilities of simplifying the formal verification of compilers.


Hybrid Batch Bayesian Optimization

Abstract: Bayesian Optimization aims at optimizing an unknown non-convex/concave function that is costly to evaluate. We are interested in application scenarios where concurrent function evaluations are possible. Under such a setting, BO could choose to either sequentially evaluate the function, one input at a time and wait for the output of the function before making the next selection, or evaluate the function at a batch of multiple inputs at once. These two different settings are commonly referred to as the sequential and batch settings of Bayesian Optimization. In general, the sequential setting leads to better optimization performance as each function evaluation is selected with more information, whereas the batch setting has an advantage in terms of the total experimental time (the number of iterations). In this work, our goal is to combine the strength of both settings. Specifically, we systematically analyze Bayesian optimization using Gaussian process as the posterior estimator and provide a hybrid algorithm that, based on the current state, dynamically switches between a sequential policy and a batch policy with variable batch sizes. We provide theoretical justification for our algorithm and present experimental results on eight benchmark BO problems. The results show that our method achieves substantial speedup (up to %78) compared to a pure sequential policy, without suffering any significant performance loss.


A Framework Studio for Component Reusability

Abstract: The deployment of a software product requires considerable amount of time and effort. In order to increase the productivity of the software products, reusability strategies were proposed in the literature. However effective reuse is still a challenging issue. This paper presents a framework studio for effective components reusability which provides the selection of components from framework studio and generation of source code based on stakeholders needs. The framework studio is implemented using swings which are integrated onto the Net Beans IDE which help in faster generation of the source code.


Fr\'echet Distance Revisited and Extended

Abstract: Given two simplicial complexes in R^d, and start and end vertices in each complex, we show how to compute curves (in each complex) between these vertices, such that the Fr\'echet distance between these curves is minimized. As a polygonal curve is a complex, this generalizes the regular notion of weak Fr\'echet distance between curves. We also generalize the algorithm to handle an input of k simplicial complexes. Using this new algorithm we can solve a slew of new problems, from computing a mean curve for a given collection of curves, to various motion planning problems. Additionally, we show that for the mean curve problem, when the k input curves are c-packed, one can (1+epsilon)-approximate the mean curve in near linear time, for fixed k and epsilon. Additionally, we present an algorithm for computing the strong Fr\'echet distance between two curves, which is simpler than previous algorithms, and avoids using parametric search.


Persistent Monitoring in Discrete Environments: Minimizing the Maximum Weighted Latency Between Observations

Abstract: In this paper, we consider the problem of planning a path for a robot to monitor a known set of features of interest in an environment. We represent the environment as a graph with vertex weights and edge lengths. The vertices represent regions of interest, edge lengths give travel times between regions, and the vertex weights give the importance of each region. As the robot repeatedly performs a closed walk on the graph, we define the weighted latency of a vertex to be the maximum time between visits to that vertex, weighted by the importance (vertex weight) of that vertex. Our goal is to find a closed walk that minimizes the maximum weighted latency of any vertex. We show that there does not exist a polynomial time algorithm for the problem. We then provide two approximation algorithms; an $O(\log n)$-approximation algorithm and an $O(\log \rho_G)$-approximation algorithm, where $\rho_G$ is the ratio between the maximum and minimum vertex weights. We provide simulation results which demonstrate that our algorithms can be applied to problems consisting of thousands of vertices, and a case study for patrolling a city for crime.


Fractional Order Phase Shaper Design with Routh's Criterion for Iso-damped Control System

Abstract: Phase curve of an open loop system is flat in nature if the derivative of phase with respect to frequency is zero. With a flat phase curve, the corresponding closed-loop system exhibits an iso-damped property i.e. maintains constant overshoot with the change of gain and with other parametric variations. In recent past application, fractional order (FO) phase shapers have been proposed by contemporary researchers to achieve enhanced parametric robustness. In this paper, a simple Routh tabulation based methodology is proposed to design an appropriate FO phase shaper to achieve phase flattening in a control loop, comprising a system, controlled by a classical PID controller. The method is demonstrated using MATLAB simulation of a second order DC motor plant and also a first order with time delay system.


(Really) Tight bounds for dispatching binary methods

Abstract: We consider binary dispatching problem originating from object oriented programming. We want to preprocess a hierarchy of classes and collection of methods so that given a function call in the run-time we are able to retrieve the most specialized implementation which can be invoked with the actual types of the arguments. For the binary dispatching, where the methods take exactly two arguments, logarithmic query time is possible, even if the structure is allowed to take linear space. Unfortunately, known solutions achieving such complexity require superlinear time for constructing the structure. Using a different idea we are able to construct in (deterministic) linear time and space a structure allowing dispatching binary methods in the same logarithmic time. Then we show how to improve the query time to slightly sublogarithmic, which is easily seen to be optimal as a consequence of some already known lower bounds if we want to keep the size of the resulting structure close to linear.


Handling Packet Dropouts and Random Delays for Unstable Delayed Processes in NCS by Optimal Tuning of PI{\lambda}D{\mu} Controllers with Evolutionary Algorithms

Abstract: The issues of stochastically varying network delays and packet dropouts in Networked Control System (NCS) applications have been simultaneously addressed by time domain optimal tuning of fractional order (FO) PID controllers. Different variants of evolutionary algorithms are used for the tuning process and their performances are compared. Also the effectiveness of the fractional order PI{\lambda}D{\mu} controllers over their integer order counterparts is looked into. Two standard test bench plants with time delay and unstable poles which are encountered in process control applications are tuned with the proposed method to establish the validity of the tuning methodology. The proposed tuning methodology is independent of the specific choice of plant and is also applicable for less complicated systems. Thus it is useful in a wide variety of scenarios. The paper also shows the superiority of FOPID controllers over their conventional PID counterparts for NCS applications.


On the Selection of Tuning Methodology of FOPID Controllers for the Control of Higher Order Processes

Abstract: In this paper, a comparative study is done on the time and frequency domain tuning strategies for fractional order (FO) PID controllers to handle higher order processes. A new fractional order template for reduced parameter modeling of stable minimum/non-minimum phase higher order processes is introduced and its advantage in frequency domain tuning of FOPID controllers is also presented. The time domain optimal tuning of FOPID controllers have also been carried out to handle these higher order processes by performing optimization with various integral performance indices. The paper highlights on the practical control system implementation issues like flexibility of online autotuning, reduced control signal and actuator size, capability of measurement noise filtration, load disturbance suppression, robustness against parameter uncertainties etc. in light of the above tuning methodologies.


A Novel Fractional Order Fuzzy PID Controller and Its Optimal Time Domain Tuning Based on Integral Performance Indices

Abstract: A novel fractional order (FO) fuzzy Proportional-Integral-Derivative (PID) controller has been proposed in this paper which works on the closed loop error and its fractional derivative as the input and has a fractional integrator in its output. The fractional order differ-integrations in the proposed fuzzy logic controller (FLC) are kept as design variables along with the input-output scaling factors (SF) and are optimized with Genetic Algorithm (GA) while minimizing several integral error indices along with the control signal as the objective function. Simulations studies are carried out to control a delayed nonlinear process and an open loop unstable process with time delay. The closed loop performances and controller efforts in each case are compared with conventional PID, fuzzy PID and PI{\lambda}D{\mu} controller subjected to different integral performance indices. Simulation results show that the proposed fractional order fuzzy PID controller outperforms the others in most cases.


Improved Model Reduction and Tuning of Fractional Order PI{\lambda}D{\mu} Controllers for Analytical Rule Extraction with Genetic Programming

Abstract: Genetic Algorithm (GA) has been used in this paper for a new approach of sub-optimal model reduction in the Nyquist plane and optimal time domain tuning of PID and fractional order (FO) PI{\lambda}D{\mu} controllers. Simulation studies show that the Nyquist based new model reduction technique outperforms the conventional H2 norm based reduced parameter modeling technique. With the tuned controller parameters and reduced order model parameter data-set, optimum tuning rules have been developed with a test-bench of higher order processes via Genetic Programming (GP). The GP performs a symbolic regression on the reduced process parameters to evolve a tuning rule which provides the best analytical expression to map the data. The tuning rules are developed for a minimum time domain integral performance index described by weighted sum of error index and controller effort. From the reported Pareto optimal front of GP based optimal rule extraction technique a trade-off can be made between the complexity of the tuning formulae and the control performance. The efficacy of the single-gene and multi-gene GP based tuning rules has been compared with original GA based control performance for the PID and PI{\lambda}D{\mu} controllers, handling four different class of representative higher order processes. These rules are very useful for process control engineers as they inherit the power of the GA based tuning methodology, but can be easily calculated without the requirement for running the computationally intensive GA every time. Three dimensional plots of the required variation in PID/FOPID controller parameters with reduced process parameters have been shown as a guideline for the operator. Parametric robustness of the reported GP based tuning rules has also been shown with credible simulation examples.


Fractional Order Modeling of a PHWR Under Step-Back Condition and Control of Its Global Power with a Robust PI{\lambda}D{\mu} Controller

Abstract: Bulk reduction of reactor power within a small finite time interval under abnormal conditions is referred to as step-back. In this paper, a 500MWe Canadian Deuterium Uranium (CANDU) type Pressurized Heavy Water Reactor (PHWR) is modeled using few variants of Least Square Estimator (LSE) from practical test data under a control rod drop scenario in order to design a control system to achieve a dead-beat response during a stepped reduction of its global power. A new fractional order (FO) model reduction technique is attempted which increases the parametric robustness of the control loop due to lesser modeling error and ensures iso-damped closed loop response with a PI{\lambda}D{\mu} or FOPID controller. Such a controller can, therefore, be used to achieve active step-back under varying load conditions for which the system dynamics change significantly. For closed loop active control of the reduced FO reactor models, the PI{\lambda}D{\mu} controller is shown to perform better than the classical integer order PID controllers and present operating Reactor Regulating System (RRS) due to its robustness against shift in system parameters.


Genetic Algorithm Based Improved Sub-Optimal Model Reduction in Nyquist Plane for Optimal Tuning Rule Extraction of PID and PI{\lambda}D{\mu} Controllers via Genetic Programming

Abstract: Genetic Algorithm (GA) has been used in this paper for a new Nyquist based sub-optimal model reduction and optimal time domain tuning of PID and fractional order (FO) PI{\lambda}D{\mu} controllers. Comparative studies show that the new model reduction technique outperforms the conventional H2-norm based reduced order modeling techniques. Optimum tuning rule has been developed next with a test-bench of higher order processes via Genetic Programming (GP) with minimum value of weighted integral error index and control signal. From the Pareto optimal front which is a trade-off between the complexity of the formulae and control performance, an efficient set of tuning rules has been generated for time domain optimal PID and PI{\lambda}D{\mu} controllers.


Estimation, Analysis and Smoothing of Self-Similar Network Induced Delays in Feedback Control of Nuclear Reactors

Abstract: This paper analyzes a nuclear reactor power signal that suffers from network induced random delays in the shared data network while being fed-back to the Reactor Regulating System (RRS). A detailed study is carried out to investigate the self similarity of random delay dynamics due to the network traffic in shared medium. The fractionality or selfsimilarity in the network induced delay that corrupts the measured power signal coming from Self Powered Neutron Detectors (SPND) is estimated and analyzed. As any fractional order randomness is intrinsically different from conventional Gaussian kind of randomness, these delay dynamics need to be handled efficiently, before reaching the controller within the RRS. An attempt has been made to minimize the effect of the randomness in the reactor power transient data with few classes of smoothing filters. The performance measure of the smoothers with fractional order noise consideration is also investigated into.


Embedded Network Test-Bed for Validating Real-Time Control Algorithms to Ensure Optimal Time Domain Performance

Abstract: The paper presents a Stateflow based network test-bed to validate real-time optimal control algorithms. Genetic Algorithm (GA) based time domain performance index minimization is attempted for tuning of PI controller to handle a balanced lag and delay type First Order Plus Time Delay (FOPTD) process over network. The tuning performance is validated on a real-time communication network with artificially simulated stochastic delay, packet loss and out-of order packets characterizing the network.


Adaptive Gain and Order Scheduling of Optimal Fractional Order PI{\lambda}D{\mu} Controllers with Radial Basis Function Neural-Network

Abstract: Gain and order scheduling of fractional order (FO) PI{\lambda}D{\mu} controllers are studied in this paper considering four different classes of higher order processes. The mapping between the optimum PID/FOPID controller parameters and the reduced order process models are done using Radial Basis Function (RBF) type Artificial Neural Network (ANN). Simulation studies have been done to show the effectiveness of the RBFNN for online scheduling of such controllers with random change in set-point and process parameters.


Optimizing Continued Fraction Expansion Based IIR Realization of Fractional Order Differ-Integrators with Genetic Algorithm

Abstract: Rational approximation of fractional order (FO) differ-integrators via Continued Fraction Expansion (CFE) is a well known technique. In this paper, the nominal structures of various generating functions are optimized using Genetic Algorithm (GA) to minimize the deviation in magnitude and phase response between the original FO element and the rationalized discrete time filter in Infinite Impulse Response (IIR) structure. The optimized filter based realizations show better approximation of the FO elements in comparison with the existing methods and is demonstrated by the frequency response of the IIR filters.


The warm-start bias of Yelp ratings

Abstract: Yelp ratings are often viewed as a reputation metric for local businesses. In this paper we study how Yelp ratings evolve over time. Our main finding is that on average the first ratings that businesses receive overestimate their eventual reputation. In particular, the first review that a business receives in our dataset averages 4.1 stars, while the 20th review averages just 3.69 stars. This significant warm-start bias which may be attributed to the limited exposure of a business in its first steps may mask analysis performed on ratings and reputational ramifications. Therefore, we study techniques to identify and correct for this bias. Further, we perform a case study to explore the effect of a Groupon deal on the merchant's subsequent ratings and show both that previous research has overestimated Groupon's effect to merchants' reputation and that average ratings anticorrelate with the number of reviews received. Our analysis points to the importance of identifying and removing biases from Yelp reviews.


Computing L1 Shortest Paths among Polygonal Obstacles in the Plane

Abstract: Given a point $s$ and a set of $h$ pairwise disjoint polygonal obstacles of totally $n$ vertices in the plane, we present a new algorithm for building an $L_1$ shortest path map of size O(n) in $O(T)$ time and O(n) space such that for any query point $t$, the length of the $L_1$ shortest obstacle-avoiding path from $s$ to $t$ can be reported in $O(\log n)$ time and the actual shortest path can be found in additional time proportional to the number of edges of the path, where $T$ is the time for triangulating the free space. It is currently known that $T=O(n+h\log^{1+\epsilon}h)$ for an arbitrarily small constant $\epsilon>0$. If the triangulation can be done optimally (i.e., $T=O(n+h\log h)$), then our algorithm is optimal. Previously, the best algorithm computes such an $L_1$ shortest path map in $O(n\log n)$ time and O(n) space. Our techniques can be extended to obtain improved results for other related problems, e.g., computing the $L_1$ geodesic Voronoi diagram for a set of point sites in a polygonal domain, finding shortest paths with fixed orientations, finding approximate Euclidean shortest paths, etc.


S3A: Secure System Simplex Architecture for Enhanced Security of Cyber-Physical Systems

Abstract: Until recently, cyber-physical systems, especially those with safety-critical properties that manage critical infrastructure (e.g. power generation plants, water treatment facilities, etc.) were considered to be invulnerable against software security breaches. The recently discovered 'W32.Stuxnet' worm has drastically changed this perception by demonstrating that such systems are susceptible to external attacks. Here we present an architecture that enhances the security of safety-critical cyber-physical systems despite the presence of such malware. Our architecture uses the property that control systems have deterministic execution behavior, to detect an intrusion within 0.6 {\mu}s while still guaranteeing the safety of the plant. We also show that even if an attack is successful, the overall state of the physical system will still remain safe. Even if the operating system's administrative privileges have been compromised, our architecture will still be able to protect the physical system from coming to harm.


Fixed-parameter tractability of multicut in directed acyclic graphs

Abstract: The MULTICUT problem, given a graph G, a set of terminal pairs T={(s_i,t_i) | 1 <= i <= r} and an integer p, asks whether one can find a cutset consisting of at most p non-terminal vertices that separates all the terminal pairs, i.e., after removing the cutset, t_i is not reachable from s_i for each 1 <= i <= r. The fixed-parameter tractability of MULTICUT in undirected graphs, parameterized by the size of the cutset only, has been recently proven by Marx and Razgon (STOC'11) and, independently, by Bousquet et al. (STOC'11), after resisting attacks as a long-standing open problem. In this paper we prove that MULTICUT is fixed-parameter tractable on directed acyclic graphs, when parameterized both by the size of the cutset and the number of terminal pairs. We complement this result by showing that this is implausible for parameterization by the size of the cutset only, as this version of the problem remains W[1]-hard.


Balancing Work and Size with Bounded Buffers

Abstract: We consider the fundamental problem of managing a bounded size queue buffer where traffic consists of packets of varying size, where each packet requires several rounds of processing before it can be transmitted from the queue buffer. The goal in such an environment is to maximize the overall size of packets that are successfully transmitted. This model is motivated by the ever-growing ubiquity of network processors architectures, which must deal with heterogeneously-sized traffic, with heterogeneous processing requirements. Our work addresses the tension between two conflicting algorithmic approaches in such settings: the tendency to favor packets with fewer processing requirements, thus leading to fast contributions to the accumulated throughput, as opposed to preferring packets of larger size, which imply a large increase in throughput at each step. We present a model for studying such systems, and present competitive algorithms whose performance depend on the maximum size a packet may have, and maximum amount of processing a packet may require. We further provide lower bounds on algorithms performance in such settings.


Stochastic Vehicle Routing with Recourse

Abstract: We study the classic Vehicle Routing Problem in the setting of stochastic optimization with recourse. StochVRP is a two-stage optimization problem, where demand is satisfied using two routes: fixed and recourse. The fixed route is computed using only a demand distribution. Then after observing the demand instantiations, a recourse route is computed -- but costs here become more expensive by a factor lambda. We present an O(log^2 n log(n lambda))-approximation algorithm for this stochastic routing problem, under arbitrary distributions. The main idea in this result is relating StochVRP to a special case of submodular orienteering, called knapsack rank-function orienteering. We also give a better approximation ratio for knapsack rank-function orienteering than what follows from prior work. Finally, we provide a Unique Games Conjecture based omega(1) hardness of approximation for StochVRP, even on star-like metrics on which our algorithm achieves a logarithmic approximation.


Tag-Aware Recommender Systems: A State-of-the-art Survey

Abstract: In the past decade, Social Tagging Systems have attracted increasing attention from both physical and computer science communities. Besides the underlying structure and dynamics of tagging systems, many efforts have been addressed to unify tagging information to reveal user behaviors and preferences, extract the latent semantic relations among items, make recommendations, and so on. Specifically, this article summarizes recent progress about tag-aware recommender systems, emphasizing on the contributions from three mainstream perspectives and approaches: network-based methods, tensor-based methods, and the topic-based methods. Finally, we outline some other tag-related works and future challenges of tag-aware recommendation algorithms.


Proceedings 7th Workshop on Model-Based Testing

Abstract: This volume contains the proceedings of the Seventh Workshop on Model-Based Testing (MBT 2012), which was held on 25 March, 2012 in Tallinn, Estonia, as a satellite event of the European Joint Conferences on Theory and Practice of Software, ETAPS 2012. The workshop is devoted to model-based testing of both software and hardware. Model-based testing uses models describing the required behavior of the system under consideration to guide such efforts as test selection and test results evaluation. Testing validates the real system behavior against models and checks that the implementation conforms to them, but is capable also to find errors in the models themselves. The first MBT workshop was held in 2004, in Barcelona. At that time MBT already had become a hot topic, but the MBT workshop was the first event devoted mostly to this topic. Since that time the area has generated enormous scientific interest, and today there are several specialized workshops and more broad conferences on software and hardware design and quality assurance covering model based testing. MBT has become one of the most powerful system analysis tools, one of the latest hot topic related is applying MBT in security analysis and testing. MBT workshop tries to keep up with current trends. In 2012 "industrial paper" category was added to the program and two industrial papers were accepted by the program committee.


Divide-and-Conquer Method for L1 Norm Matrix Factorization in the Presence of Outliers and Missing Data

Abstract: The low-rank matrix factorization as a L1 norm minimization problem has recently attracted much attention due to its intrinsic robustness to the presence of outliers and missing data. In this paper, we propose a new method, called the divide-and-conquer method, for solving this problem. The main idea is to break the original problem into a series of smallest possible sub-problems, each involving only unique scalar parameter. Each of these subproblems is proved to be convex and has closed-form solution. By recursively optimizing these small problems in an analytical way, efficient algorithm, entirely avoiding the time-consuming numerical optimization as an inner loop, for solving the original problem can naturally be constructed. The computational complexity of the proposed algorithm is approximately linear in both data size and dimensionality, making it possible to handle large-scale L1 norm matrix factorization problems. The algorithm is also theoretically proved to be convergent. Based on a series of experiment results, it is substantiated that our method always achieves better results than the current state-of-the-art methods on $L1$ matrix factorization calculation in both computational time and accuracy, especially on large-scale applications such as face recognition and structure from motion.


The Cost of Parameterized Reachability in Mobile Ad Hoc Networks

Abstract: We investigate the impact of spontaneous movement in the complexity of verification problems for an automata-based protocol model of networks with selective broadcast communication. We first consider reachability of an error state and show that parameterized verification is decidable with polynomial complexity. We then move to richer queries and show how the complexity changes when considering properties with negation or cardinality constraints.


Hierarchical Identity-Based Lossy Trapdoor Functions

Abstract: Lossy trapdoor functions, introduced by Peikert and Waters (STOC'08), have received a lot of attention in the last years, because of their wide range of applications in theoretical cryptography. The notion has been recently extended to the identity-based setting by Bellare et al. (Eurocrypt'12). We provide one more step in this direction, by considering the notion of hierarchical identity-based (lossy) trapdoor functions (HIB-TDFs). Hierarchical identity-based cryptography has proved very useful both for practical applications and to establish theoretical relations with other cryptographic primitives. The notion of security for IB-TDFs put forward by Bellare et al. easily extends to the hierarchical scenario, but an (H)IB-TDF secure in this sense is not known to generically imply other related primitives with security against adaptive-id adversaries, not even IND-ID-CPA secure encryption. Our first contribution is to define a new security property for (H)IB-TDFs. We show that functions satisfying this property imply secure cryptographic primitives in the adaptive identity-based setting: these include encryption schemes with semantic security under chosen-plaintext attacks, deterministic encryption schemes, and (non-adaptive) hedged encryption schemes that maintain some security when messages are encrypted using randomness of poor quality. Then, we describe the first pairing-based HIB-TDF realization. Our HIB-TDF construction is based on techniques that differ from those of Bellare et al. in that it uses a hierarchical predicate encryption scheme as a key ingredient. The resulting HIB-TDF is proved to satisfy the new security definition, against either selective or, for hierarchies of constant depth, adaptive adversaries.


Approximate Counting of Matchings in Sparse Hypergraphs

Abstract: In this paper we give a fully polynomial randomized approximation scheme (FPRAS) for the number of all matchings in hypergraphs belonging to a class of sparse, uniform hypergraphs. Our method is based on a generalization of the canonical path method to the case of uniform hypergraphs.


FLOW-Methode - Methodenbeschreibung zur Anwendung von FLOW

Abstract: Information of many kinds is flowing in software projects and organizations. Requirements have to flow from the customer to the developers. Testers need to know the requirements as well. Boundary conditions and design decisions have to be at the right place at the right time. Information flow analysis with FLOW facilitates modeling of mode and route of the flow of information and experience independent of the development methodology. Experience often acts as a control factor, because experienced developers can process and route information more efficiently. Therefore, experience needs to be at the right place at the right time, too. However, most valuable experiences never get documented. Since information and experience is flowing in agile as well as in traditional environments, the FLOW method does not distinguish between agile and traditional, but only between how the flows are shaped. ---- In Softwareprojekten flie{\ss}en vielerlei Informationen. Anforderungen m\"ussen vom Kunden zu den Entwicklern gelangen. Auch Tester m\"ussen die Anforderungen kennen. Randbedingungen und Entwurfsentscheidungen m\"ussen zur rechten Zeit am rechten Ort sein. Die Informationsflussanalyse mit FLOW erm\"oglicht es, unabh\"angig von der Entwicklungsmethode zu modellieren, wie und auf welchem Wege Informationen und Erfahrungen flie{\ss}en. Erfahrungen spielen dabei oft die Rolle von Steuergr\"o{\ss}en, denn erfahrene Mitarbeiter k\"onnen Informationen kompetenter bearbeiten und weiterleiten. Auch die Erfahrungen m\"ussen in geeigneter Form zur rechten Zeit am rechten Ort sein. Viele Erfahrungen werden aber nie dokumentiert. Da Informationen und Erfahrungen sowohl in agilen als auch in traditionellen Umgebungen flie{\ss}en m\"ussen, wird in FLOW ein Modell aufgebaut, das nicht nach agil, traditionell oder anderen Bezeichnungen unterscheidet, sondern einzig danach, wie die Fl\"usse gestaltet sind.


Estimators in Cryptography

Abstract: One of the main problems in cryptography is to give criteria to provide good comparators of cipher systems. The security of a cipher system must include the security of the algorithm, the security of the key generator and management module (see [BM94], [CM97],[Mau92a]) and the security of the cryptographic key agreement protocol (see [Mau93a],[MC94],[Mau93b],[Mau92b]). This paper gives show the necessary mathematical background to estimate the most important cryptographic measures of the key generators and of the unconditionally key agreement protocols. These cryptographic measures are the Shannon entropy (for the key generator module) and Renyi entropy of order alpha for the key agreement protocol.


Intelligent Car System

Abstract: In modern life the road safety has becomes the core issue. One single move of a driver can cause horrifying accident. The main goal of intelligent car system is to make communication with other cars on the road. The system is able to control to speed, direction and the distance between the cars the intelligent car system is able to recognize traffic light and is able to take decision according to it. This paper presents a framework of the intelligent car system. I validate several aspect of our system using simulation.


TCP over IEEE 802.11

Abstract: IEEE 802.11 is a widely used wireless LAN standard for medium access control. TCP is a prominent transport protocol originally designed for wired networks. TCP treats packet loss as congestion and reduces the data rate. In wireless networks packets are lost not only due to congestion but also due to various other reasons. Hence there is need for making TCP adaptable to wireless networks. Various parameters of TCP and IEEE 802.11 can be set to appropriate values to achieve optimum performance results. In this paper optimum values for various parameters of IEEE 802.11 are determined. Network simulator NS2 is used for simulation.


Computer applications in clinical psychology

Abstract: The computer-assisted analysis is not currently a novelty, but a necessity in all areas of psychology. A number of studies that examine the limits of the computer assisted and analyzed interpretations, also its advantages. A series of studies aim to assess how the computer assisting programs are able to establish a diagnosis referring to the presence of certain mental disorders. We will present the results of one computer application in clinical psychology regarding the assessment of Theory of Mind capacity by animation.


A Note on Interference in Random Point Sets

Abstract: The (maximum receiver-centric) interference of a geometric graph (von Rickenbach etal (2005)) is studied. It is shown that, with high probability, the following results hold for a set, V, of n points independently and uniformly distributed in the unit d-cube, for constant dimension d: (1) there exists a connected graph with vertex set V that has interference O((log n)^{1/3}); (2) no connected graph with vertex set V has interference o((log n)^{1/4}); and (3) the minimum spanning tree of $V$ has interference Theta((\log n)^{1/2}).


On an Ethical Use of Neural Networks: A Case Study on a North Indian Raga

Abstract: The paper gives an artificial neural network (ANN) approach to time series modeling, the data being instance versus notes (characterized by pitch) depicting the structure of a North Indian raga, namely, Bageshree. Respecting the sentiments of the artists' community, the paper argues why it is more ethical to model a structure than try and "manufacture" an artist by training the neural network to copy performances of artists. Indian Classical Music centers on the ragas, where emotion and devotion are both important and neither can be substituted by such "calculated artistry" which the ANN generated copies are ultimately up to.


Game Theoretic Network Coding-aided MAC for Data Dissemination towards Energy Efficiency

Abstract: In this paper we propose game theoretic Medium Access Control (MAC) strategies for data dissemination scenarios. In particular, we use energy-based utility functions that inherently imply power-awareness, while we consider network coding techniques to eliminate the necessity of exchanging acknowledgement control packets. Simulation results show that our proposed strategies enhance the energy efficiency of the system and reduce the dissemination completion time compared to an optimized standard protocol.


Normal Form Bisimulations for Delimited-Control Operators

Abstract: We define a notion of normal form bisimilarity for the untyped call-by-value lambda calculus extended with the delimited-control operators shift and reset. Normal form bisimilarities are simple, easy-to-use behavioral equivalences which relate terms without having to test them within all contexts (like contextual equivalence), or by applying them to function arguments (like applicative bisimilarity). We prove that the normal form bisimilarity for shift and reset is sound but not complete w.r.t. contextual equivalence and we define up-to techniques that aim at simplifying bisimulation proofs. Finally, we illustrate the simplicity of the techniques we develop by proving several equivalences on terms.


Technique detection software for Sparse Matrices

Abstract: Sparse storage formats are techniques for storing and processing the sparse matrix data efficiently. The performance of these storage formats depend upon the distribution of non-zeros, within the matrix in different dimensions. In order to have better results we need a technique that suits best the organization of data in a particular matrix. So the decision of selecting a better technique is the main step towards improving the system's results otherwise the efficiency can be decreased. The purpose of this research is to help identify the best storage format in case of reduced storage size and high processing efficiency for a sparse matrix.


Fast computation of the performance evaluation of biometric systems: application to multibiometric

Abstract: The performance evaluation of biometric systems is a crucial step when designing and evaluating such systems. The evaluation process uses the Equal Error Rate (EER) metric proposed by the International Organization for Standardization (ISO/IEC). The EER metric is a powerful metric which allows easily comparing and evaluating biometric systems. However, the computation time of the EER is, most of the time, very intensive. In this paper, we propose a fast method which computes an approximated value of the EER. We illustrate the benefit of the proposed method on two applications: the computing of non parametric confidence intervals and the use of genetic algorithms to compute the parameters of fusion functions. Experimental results show the superiority of the proposed EER approximation method in term of computing time, and the interest of its use to reduce the learning of parameters with genetic algorithms. The proposed method opens new perspectives for the development of secure multibiometrics systems by speeding up their computation time.


Marginality: a numerical mapping for enhanced treatment of nominal and hierarchical attributes

Abstract: The purpose of statistical disclosure control (SDC) of microdata, a.k.a. data anonymization or privacy-preserving data mining, is to publish data sets containing the answers of individual respondents in such a way that the respondents corresponding to the released records cannot be re-identified and the released data are analytically useful. SDC methods are either based on masking the original data, generating synthetic versions of them or creating hybrid versions by combining original and synthetic data. The choice of SDC methods for categorical data, especially nominal data, is much smaller than the choice of methods for numerical data. We mitigate this problem by introducing a numerical mapping for hierarchical nominal data which allows computing means, variances and covariances on them.


Multiflow Transmission in Delay Constrained Cooperative Wireless Networks

Abstract: This paper considers the problem of energy-efficient transmission in multi-flow multihop cooperative wireless networks. Although the performance gains of cooperative approaches are well known, the combinatorial nature of these schemes makes it difficult to design efficient polynomial-time algorithms for joint routing, scheduling and power control. This becomes more so when there is more than one flow in the network. It has been conjectured by many authors, in the literature, that the multiflow problem in cooperative networks is an NP-hard problem. In this paper, we formulate the problem, as a combinatorial optimization problem, for a general setting of $k$-flows, and formally prove that the problem is not only NP-hard but it is $o(n^{1/7-\epsilon})$ inapproxmiable. To our knowledge*, these results provide the first such inapproxmiablity proof in the context of multiflow cooperative wireless networks. We further prove that for a special case of k = 1 the solution is a simple path, and devise a polynomial time algorithm for jointly optimizing routing, scheduling and power control. We then use this algorithm to establish analytical upper and lower bounds for the optimal performance for the general case of $k$ flows. Furthermore, we propose a polynomial time heuristic for calculating the solution for the general case and evaluate the performance of this heuristic under different channel conditions and against the analytical upper and lower bounds.


On the computational complexity of a game of cops and robbers

Abstract: We study the computational complexity of a perfect-information two-player game proposed by Aigner and Fromme. The game takes place on an undirected graph where n simultaneously moving cops attempt to capture a single robber, all moving at the same speed. The players are allowed to pick their starting positions at the first move. The question of the computational complexity of deciding this game was raised in the '90s by Goldstein and Reingold. We prove that the game is hard for PSPACE.


Constant Factor Lasserre Integrality Gaps for Graph Partitioning Problems

Abstract: Partitioning the vertices of a graph into two roughly equal parts while minimizing the number of edges crossing the cut is a fundamental problem (called Balanced Separator) that arises in many settings. For this problem, and variants such as the Uniform Sparsest Cut problem where the goal is to minimize the fraction of pairs on opposite sides of the cut that are connected by an edge, there are large gaps between the known approximation algorithms and non-approximability results. While no constant factor approximation algorithms are known, even APX-hardness is not known either. In this work we prove that for balanced separator and uniform sparsest cut, semidefinite programs from the Lasserre hierarchy (which are the most powerful relaxations studied in the literature) have an integrality gap bounded away from $1$, even for $\Omega(n)$ levels of the hierarchy. This complements recent algorithmic results in Guruswami and Sinop (2011) which used the Lasserre hierarchy to give an approximation scheme for these problems (with runtime depending on the spectrum of the graph). Along the way, we make an observation that simplifies the task of lifting "polynomial constraints" (such as the global balance constraint in balanced separator) to higher levels of the Lasserre hierarchy.


Iterative Approximate Byzantine Consensus in Arbitrary Directed Graphs - Part II: Synchronous and Asynchronous Systems

Abstract: This report contains two related sets of results with different assumptions on synchrony. The first part is about iterative algorithms in synchronous systems. Following our previous work on synchronous iterative approximate Byzantine consensus (IABC) algorithms, we provide a more intuitive tight necessary and sufficient condition for the existence of such algorithms in synchronous networks1. We believe this condition and the previous results also hold in partially asynchronous algorithmic model. In the second part of the report, we explore the problem in asynchronous networks. While the traditional Byzantine consensus is not solvable in asynchronous systems, approximate Byzantine consensus can be solved using iterative algorithms.


Maximum Inner-Product Search using Tree Data-structures

Abstract: The problem of {\em efficiently} finding the best match for a query in a given set with respect to the Euclidean distance or the cosine similarity has been extensively studied in literature. However, a closely related problem of efficiently finding the best match with respect to the inner product has never been explored in the general setting to the best of our knowledge. In this paper we consider this general problem and contrast it with the existing best-match algorithms. First, we propose a general branch-and-bound algorithm using a tree data structure. Subsequently, we present a dual-tree algorithm for the case where there are multiple queries. Finally we present a new data structure for increasing the efficiency of the dual-tree algorithm. These branch-and-bound algorithms involve novel bounds suited for the purpose of best-matching with inner products. We evaluate our proposed algorithms on a variety of data sets from various applications, and exhibit up to five orders of magnitude improvement in query time over the naive search technique.


On the Convergence of Collaboration and Knowledge Management

Abstract: Collaboration technology typically focuses on collaboration and group processes (cooperation, communication, coordination and coproduction). Knowledge Management (KM) technology typically focuses on content (creation, storage, sharing and use of data, information and knowledge). Yet, to achieve their common goals, teams and organizations need both KM and collaboration technology to make that more effective and efficient. This paper is interested in knowledge management and collaboration regarding their convergence and their integration. First, it contributes to a better understanding of the knowledge management and collaboration concepts. Second, it focuses on KM and collaboration convergence by presenting the different interpretation of this convergence. Third, this paper proposes a generic framework of collaborative knowledge management.


SpeechJammer: A System Utilizing Artificial Speech Disturbance with Delayed Auditory Feedback

Abstract: In this paper we report on a system, "SpeechJammer", which can be used to disturb people's speech. In general, human speech is jammed by giving back to the speakers their own utterances at a delay of a few hundred milliseconds. This effect can disturb people without any physical discomfort, and disappears immediately by stop speaking. Furthermore, this effect does not involve anyone but the speaker. We utilize this phenomenon and implemented two prototype versions by combining a direction-sensitive microphone and a direction-sensitive speaker, enabling the speech of a specific person to be disturbed. We discuss practical application scenarios of the system, such as facilitating and controlling discussions. Finally, we argue what system parameters should be examined in detail in future formal studies based on the lessons learned from our preliminary study.


Model-Based Security Testing

Abstract: Security testing aims at validating software system requirements related to security properties like confidentiality, integrity, authentication, authorization, availability, and non-repudiation. Although security testing techniques are available for many years, there has been little approaches that allow for specification of test cases at a higher level of abstraction, for enabling guidance on test identification and specification as well as for automated test generation. Model-based security testing (MBST) is a relatively new field and especially dedicated to the systematic and efficient specification and documentation of security test objectives, security test cases and test suites, as well as to their automated or semi-automated generation. In particular, the combination of security modelling and test generation approaches is still a challenge in research and of high interest for industrial applications. MBST includes e.g. security functional testing, model-based fuzzing, risk- and threat-oriented testing, and the usage of security test patterns. This paper provides a survey on MBST techniques and the related models as well as samples of new methods and tools that are under development in the European ITEA2-project DIAMONDS.


Reusing Test-Cases on Different Levels of Abstraction in a Model Based Development Tool

Abstract: Seamless model based development aims to use models during all phases of the development process of a system. During the development process in a component-based approach, components of a system are described at qualitatively differing abstraction levels: during requirements engineering component models are rather abstract high-level and underspecified, while during implementation the component models are rather concrete and fully specified in order to enable code generation. An important issue that arises is assuring that the concrete models correspond to abstract models. In this paper, we propose a method to assure that concrete models for system components refine more abstract models for the same components. In particular we advocate a framework for reusing testcases at different abstraction levels. Our approach, even if it cannot completely prove the refinement, can be used to ensure confidence in the development process. In particular we are targeting the refinement of requirements which are represented as very abstract models. Besides a formal model of our approach, we discuss our experiences with the development of an Adaptive Cruise Control (ACC) system in a model driven development process. This uses extensions which we implemented for our model-based development tool and which are briefly presented in this paper.


Applying SMT Solvers to the Test Template Framework

Abstract: The Test Template Framework (TTF) is a model-based testing method for the Z notation. In the TTF, test cases are generated from test specifications, which are predicates written in Z. In turn, the Z notation is based on first-order logic with equality and Zermelo-Fraenkel set theory. In this way, a test case is a witness satisfying a formula in that theory. Satisfiability Modulo Theory (SMT) solvers are software tools that decide the satisfiability of arbitrary formulas in a large number of built-in logical theories and their combination. In this paper, we present the first results of applying two SMT solvers, Yices and CVC3, as the engines to find test cases from TTF's test specifications. In doing so, shallow embeddings of a significant portion of the Z notation into the input languages of Yices and CVC3 are provided, given that they do not directly support Zermelo-Fraenkel set theory as defined in Z. Finally, the results of applying these embeddings to a number of test specifications of eight cases studies are analysed.


Exact Gap Computation for Code Coverage Metrics in ISO-C

Abstract: Test generation and test data selection are difficult tasks for model based testing. Tests for a program can be meld to a test suite. A lot of research is done to quantify the quality and improve a test suite. Code coverage metrics estimate the quality of a test suite. This quality is fine, if the code coverage value is high or 100%. Unfortunately it might be impossible to achieve 100% code coverage because of dead code for example. There is a gap between the feasible and theoretical maximal possible code coverage value. Our review of the research indicates, none of current research is concerned with exact gap computation. This paper presents a framework to compute such gaps exactly in an ISO-C compatible semantic and similar languages. We describe an efficient approximation of the gap in all the other cases. Thus, a tester can decide if more tests might be able or necessary to achieve better coverage.


Using Built-In Domain-Specific Modeling Support to Guide Model-Based Test Generation

Abstract: We present a model-based testing approach to support automated test generation with domain-specific concepts. This includes a language expert who is an expert at building test models and domain experts who are experts in the domain of the system under test. First, we provide a framework to support the language expert in building test models using a full (Java) programming language with the help of simple but powerful modeling elements of the framework. Second, based on the model built with this framework, the toolset automatically forms a domain-specific modeling language that can be used to further constrain and guide test generation from these models by a domain expert. This makes it possible to generate a large set of test cases covering the full model, chosen (constrained) parts of the model, or manually define specific test cases on top of the model while using concepts familiar to the domain experts.


Towards Symbolic Model-Based Mutation Testing: Combining Reachability and Refinement Checking

Abstract: Model-based mutation testing uses altered test models to derive test cases that are able to reveal whether a modelled fault has been implemented. This requires conformance checking between the original and the mutated model. This paper presents an approach for symbolic conformance checking of action systems, which are well-suited to specify reactive systems. We also consider nondeterminism in our models. Hence, we do not check for equivalence, but for refinement. We encode the transition relation as well as the conformance relation as a constraint satisfaction problem and use a constraint solver in our reachability and refinement checking algorithms. Explicit conformance checking techniques often face state space explosion. First experimental evaluations show that our approach has potential to outperform explicit conformance checkers.


Talking quiescence: a rigorous theory that supports parallel composition, action hiding and determinisation

Abstract: The notion of quiescence - the absence of outputs - is vital in both behavioural modelling and testing theory. Although the need for quiescence was already recognised in the 90s, it has only been treated as a second-class citizen thus far. This paper moves quiescence into the foreground and introduces the notion of quiescent transition systems (QTSs): an extension of regular input-output transition systems (IOTSs) in which quiescence is represented explicitly, via quiescent transitions. Four carefully crafted rules on the use of quiescent transitions ensure that our QTSs naturally capture quiescent behaviour. We present the building blocks for a comprehensive theory on QTSs supporting parallel composition, action hiding and determinisation. In particular, we prove that these operations preserve all the aforementioned rules. Additionally, we provide a way to transform existing IOTSs into QTSs, allowing even IOTSs as input that already contain some quiescent transitions. As an important application, we show how our QTS framework simplifies the fundamental model-based testing theory formalised around ioco.


Rule-based Test Generation with Mind Maps

Abstract: This paper introduces basic concepts of rule based test generation with mind maps, and reports experiences learned from industrial application of this technique in the domain of smart card testing by Giesecke & Devrient GmbH over the last years. It describes the formalization of test selection criteria used by our test generator, our test generation architecture and test generation framework.


Constraint-Based Heuristic On-line Test Generation from Non-deterministic I/O EFSMs

Abstract: We are investigating on-line model-based test generation from non-deterministic output-observable Input/Output Extended Finite State Machine (I/O EFSM) models of Systems Under Test (SUTs). We propose a novel constraint-based heuristic approach (Heuristic Reactive Planning Tester (xRPT)) for on-line conformance testing non-deterministic SUTs. An indicative feature of xRPT is the capability of making reasonable decisions for achieving the test goals in the on-line testing process by using the results of off-line bounded static reachability analysis based on the SUT model and test goal specification. We present xRPT in detail and make performance comparison with other existing search strategies and approaches on examples with varying complexity.


Model-Based Testing of Safety Critical Real-Time Control Logic Software

Abstract: The paper presents the experience of the authors in model based testing of safety critical real-time control logic software. It describes specifics of the corresponding industrial settings and discusses technical details of usage of UniTESK model based testing technology in these settings. Finally, we discuss possible future directions of safety critical software development processes and a place of model based testing techniques in it.


High Volume Computing: Identifying and Characterizing Throughput Oriented Workloads in Data Centers

Abstract: For the first time, this paper systematically identifies three categories of throughput oriented workloads in data centers: services, data processing applications, and interactive real-time applications, whose targets are to increase the volume of throughput in terms of processed requests or data, or supported maximum number of simultaneous subscribers, respectively, and we coin a new term high volume computing (in short HVC) to describe those workloads and data center computer systems designed for them. We characterize and compare HVC with other computing paradigms, e.g., high throughput computing, warehouse-scale computing, and cloud computing, in terms of levels, workloads, metrics, coupling degree, data scales, and number of jobs or service instances. We also preliminarily report our ongoing work on the metrics and benchmarks for HVC systems, which is the foundation of designing innovative data center computer systems for HVC workloads.


Instance Based Methods --- A Brief Overview

Abstract: Instance-based methods are a specific class of methods for automated proof search in first-order logic. This article provides an overview of the major methods in the area and discusses their properties and relations to the more established resolution methods. It also discusses some recent trends on refinements and applications. This overview is rather brief and informal, but we provide a comprehensive literature list to follow-up on the details.