OpenRaiser/Intern-Atlas huggingface dataset:
Simultaneous Distributed Sensor Self-Localization and Target Tracking Using Belief Propagation and Likelihood Consensus
Venue: arXiv cs.NI 2012
Year: 2012

We introduce the framework of cooperative simultaneous localization and tracking (CoSLAT), which provides a consistent combination of cooperative self-localization (CSL) and distributed target tracking (DTT) in sensor networks without a fusion center. CoSLAT extends simultaneous localization and tracking (SLAT) in that it uses also intersensor measurements. Starting from a factor graph formulation of the CoSLAT problem, we develop a particle-based, distributed message passing algorithm for CoSLAT that combines nonparametric belief propagation with the likelihood consensus scheme. The proposed CoSLAT algorithm improves on state-of-the-art CSL and DTT algorithms by exchanging probabilistic information between CSL and DTT. Simulation results demonstrate substantial improvements in both self-localization and tracking performance.
Virtual Simulation Objects Concept as a Framework for System-Level Simulation
Venue: arXiv cs.SY 2012
Year: 2012

This paper presents Virtual Simulation Objects (VSO) concept which forms theoretical basis for building tools and framework that is developed for system-level simulations using existing software modules available within cyber-infrastructure. Presented concept is implemented by the software tool for building composite solutions using VSO-based GUI and running them using CLAVIRE simulation environment.
Multislice Modularity Optimization in Community Detection and Image Segmentation
Venue: arXiv cs.SI 2012
Year: 2012

Because networks can be used to represent many complex systems, they have attracted considerable attention in physics, computer science, sociology, and many other disciplines. One of the most important areas of network science is the algorithmic detection of cohesive groups (i.e., "communities") of nodes. In this paper, we algorithmically detect communities in social networks and image data by optimizing multislice modularity. A key advantage of modularity optimization is that it does not require prior knowledge of the number or sizes of communities, and it is capable of finding network partitions that are composed of communities of different sizes. By optimizing multislice modularity and subsequently calculating diagnostics on the resulting network partitions, it is thereby possible to obtain information about network structure across multiple system scales. We illustrate this method on data from both social networks and images, and we find that optimization of multislice modularity performs well on these two tasks without the need for extensive problem-specific adaptation. However, improving the computational speed of this method remains a challenging open problem.
JooFlux : modification de code à chaud et injection d'aspects directement dans une JVM 7
Venue: arXiv cs.SE 2012
Year: 2012

Changing functional and non-functional software implementation at runtime is useful and even sometimes critical both in development and production environments. JooFlux is a JVM agent that allows both the dynamic replacement of method implementations and the application of aspect advices. It works by doing bytecode transformation to take advantage of the new invokedynamic instruction added in Java SE 7 to help implementing dynamic languages for the JVM. JooFlux can be managed using a JMX agent so as to operate dynamic modifications at runtime, without resorting to a dedicated domain-specific language. We compared JooFlux with existing AOP platforms and dynamic languages. Results demonstrate that JooFlux performances are close to the Java ones --- with most of the time a marginal overhead, and sometimes a gain --- where AOP platforms and dynamic languages present significant overheads. This paves the way for interesting future evolutions and applications of JooFlux.
Secure voice based authentication for mobile devices: Vaulted Voice Verification
Venue: arXiv cs.CR 2012
Year: 2012

As the use of biometrics becomes more wide-spread, the privacy concerns that stem from the use of biometrics are becoming more apparent. As the usage of mobile devices grows, so does the desire to implement biometric identification into such devices. A large majority of mobile devices being used are mobile phones. While work is being done to implement different types of biometrics into mobile phones, such as photo based biometrics, voice is a more natural choice. The idea of voice as a biometric identifier has been around a long time. One of the major concerns with using voice as an identifier is the instability of voice. We have developed a protocol that addresses those instabilities and preserves privacy. This paper describes a novel protocol that allows a user to authenticate using voice on a mobile/remote device without compromising their privacy. We first discuss the \vv protocol, which has recently been introduced in research literature, and then describe its limitations. We then introduce a novel adaptation and extension of the vaulted verification protocol to voice, dubbed $V^3$. Following that we show a performance evaluation and then conclude with a discussion of security and future work.
From the decoding of cortical activities to the control of a JACO robotic arm: a whole processing chain
Venue: arXiv cs.NE 2012
Year: 2012

This paper presents a complete processing chain for decoding intracranial data recorded in the cortex of a monkey and replicates the associated movements on a JACO robotic arm by Kinova. We developed specific modules inside the OpenViBE platform in order to build a Brain-Machine Interface able to read the data, compute the position of the robotic finger and send this position to the robotic arm. More pre- cisely, two client/server protocols have been tested to transfer the finger positions: VRPN and a light protocol based on TCP/IP sockets. According to the requested finger position, the server calls the associ- ated functions of an API by Kinova to move the fin- gers properly. Finally, we monitor the gap between the requested and actual fingers positions. This chain can be generalized to any movement of the arm or wrist.
Towards semantic and affective coupling in emotionally annotated databases
Venue: arXiv cs.HC 2012
Year: 2012

Emotionally annotated databases are repositories of multimedia documents with annotated affective content that elicit emotional responses in exposed human subjects. They are primarily used in research of human emotions, attention and development of stress-related mental disorders. This can be successfully exploited in larger processes like selection, evaluation and training of personnel for occupations involving high stress levels. Emotionally annotated databases are also used in multimodal affective user interfaces to facilitate richer and more intuitive human-computer interaction. Multimedia documents in emotionally annotated databases must have maximum personal ego relevance to be the most effective in all these applications. For this reason flexible construction of subject-specific of emotionally annotated databases is imperative. But current construction process is lengthy and labor intensive because it inherently includes an elaborate tagging experiment involving a team of human experts. This is unacceptable since the creation of new databases or modification of the existing ones becomes slow and difficult. We identify a positive correlation between the affect and semantics in the existing emotionally annotated databases and propose to exploit this feature with an interactive relevance feedback for a more efficient construction of emotionally annotated databases. Automatic estimation of affective annotations from existing semantics enhanced with information refinement processes may lead to an efficient construction of high-quality emotionally annotated databases.
An Evolution Strategy Approach toward Rule-set Generation for Network Intrusion Detection Systems (IDS)
Venue: arXiv cs.CR 2012
Year: 2012

With the increasing number of intrusions in system and network infrastructures, Intrusion Detection Systems (IDS) have become an active area of research to develop reliable and effective solutions to detect and counter them. The use of Evolutionary Algorithms in IDS has proved its maturity over the times. Although most of the research works have been based on the use of genetic algorithms in IDS, this paper presents an approach toward the generation of rules for the identification of anomalous connections using evolution strategies . The emphasis is given on how the problem can be modeled into ES primitives and how the fitness of the population can be evaluated in order to find the local optima, therefore resulting in optimal rules that can be used for detecting intrusions in intrusion detection systems.
Artificial Neural Network for Performance Modeling and Optimization of CMOS Analog Circuits
Venue: arXiv cs.NE 2012
Year: 2012

This paper presents an implementation of multilayer feed forward neural networks (NN) to optimize CMOS analog circuits. For modeling and design recently neural network computational modules have got acceptance as an unorthodox and useful tool. To achieve high performance of active or passive circuit component neural network can be trained accordingly. A well trained neural network can produce more accurate outcome depending on its learning capability. Neural network model can replace empirical modeling solutions limited by range and accuracy.[2] Neural network models are easy to obtain for new circuits or devices which can replace analytical methods. Numerical modeling methods can also be replaced by neural network model due to their computationally expansive behavior.[2][10][20]. The pro- posed implementation is aimed at reducing resource requirement, without much compromise on the speed. The NN ensures proper functioning by assigning the appropriate inputs, weights, biases, and excitation function of the layer that is currently being computed. The concept used is shown to be very effective in reducing resource requirements and enhancing speed.
DBGen User Manual
Venue: arXiv cs.LO 2012
Year: 2012

DBGen is a tool for Coq developers. It takes as input the definition of a term structure with bindings annotations and generates definitions and properties for lifting and substitution in the De Bruijn setting, up to the substitution lemma. It provides also a named syntax and a translation function to the De Bruijn syntax.
A Non-Monetary Protocol for Peer-to-Peer Content Distribution in Wireless Broadcast Networks with Network Coding
Venue: arXiv cs.NI 2012
Year: 2012

This paper studies the problem of content distribution in wireless peer-to-peer networks where all nodes are selfish and non-cooperative. We propose a model that considers both the broadcast nature of wireless channels and the incentives of nodes, where each node aims to increase its own download rate and reduces its upload rate through the course of content distribution. We then propose a protocol for these selfish nodes to exchange contents. Our protocol is distributed and does not require the exchange of money, reputation, etc., and hence can be easily implemented without additional infrastructure. Moreover, we show that our protocol can be easily modified to employ network coding. The performance of our protocol is studied. We derive a closed-form expression of Nash Equilibriums when there are only two files in the system. The prices of anarchy, both from each node's perspective and the whole system's perspective, are also characterized. Moreover, we propose a distributed mechanism where each node adjusts its strategies only based on local information and show that the mechanism converges to a Nash Equilibrium. We also introduce an approach for calculating Nash Equilibriums for systems that incorporate network coding when there are more than two files.
Exploring Relay Cooperation for Secure and Reliable Transmission in Two-Hop Wireless Networks
Venue: arXiv cs.CR 2012
Year: 2012

This work considers the problem of secure and reliable information transmission via relay cooperation in two-hop relay wireless networks without the information of both eavesdropper channels and locations. While previous work on this problem mainly studied infinite networks and their asymptotic behavior and scaling law results, this papers focuses on a more practical network with finite number of system nodes and explores the corresponding exact result on the number of eavesdroppers one network can tolerant to ensure desired secrecy and reliability. We first study the scenario where path-loss is equal between all pairs of nodes and consider two transmission protocols there, one adopts an optimal but complex relay selection process with less load balance capacity while the other adopts a random but simple relay selection process with good load balance capacity. Theoretical analysis is then provided to determine the maximum number of eavesdroppers one network can tolerate to ensure a desired performance in terms of the secrecy outage probability and transmission outage probability. We further extend our study to the more general scenario where path-loss between each pair of nodes also depends the distance between them, for which a new transmission protocol with both preferable relay selection and good load balance as well as the corresponding theoretical analysis are presented.
The Geometry of Differential Privacy: the Sparse and Approximate Cases
Venue: arXiv cs.DS 2012
Year: 2012

In this work, we study trade-offs between accuracy and privacy in the context of linear queries over histograms. This is a rich class of queries that includes contingency tables and range queries, and has been a focus of a long line of work. For a set of $d$ linear queries over a database $x \in \R^N$, we seek to find the differentially private mechanism that has the minimum mean squared error. For pure differential privacy, an $O(\log^2 d)$ approximation to the optimal mechanism is known. Our first contribution is to give an $O(\log^2 d)$ approximation guarantee for the case of $(\eps,δ)$-differential privacy. Our mechanism is simple, efficient and adds correlated Gaussian noise to the answers. We prove its approximation guarantee relative to the hereditary discrepancy lower bound of Muthukrishnan and Nikolov, using tools from convex geometry. We next consider this question in the case when the number of queries exceeds the number of individuals in the database, i.e. when $d > n \triangleq \|x\|_1$. It is known that better mechanisms exist in this setting. Our second main contribution is to give an $(\eps,δ)$-differentially private mechanism which is optimal up to a $\polylog(d,N)$ factor for any given query set $A$ and any given upper bound $n$ on $\|x\|_1$. This approximation is achieved by coupling the Gaussian noise addition approach with a linear regression step. We give an analogous result for the $\eps$-differential privacy setting. We also improve on the mean squared error upper bound for answering counting queries on a database of size $n$ by Blum, Ligett, and Roth, and match the lower bound implied by the work of Dinur and Nissim up to logarithmic factors. The connection between hereditary discrepancy and the privacy mechanism enables us to derive the first polylogarithmic approximation to the hereditary discrepancy of a matrix $A$.
Enhanced Cluster Based Routing Protocol for MANETS
Venue: arXiv cs.NI 2012
Year: 2012

Mobile ad-hoc networks (MANETs) are a set of self organized wireless mobile nodes that works without any predefined infrastructure. For routing data in MANETs, the routing protocols relay on mobile wireless nodes. In general, any routing protocol performance suffers i) with resource constraints and ii) due to the mobility of the nodes. Due to existing routing challenges in MANETs clustering based protocols suffers frequently with cluster head failure problem, which degrades the cluster stability. This paper proposes, Enhanced CBRP, a schema to improve the cluster stability and in-turn improves the performance of traditional cluster based routing protocol (CBRP), by electing better cluster head using weighted clustering algorithm and considering some crucial routing challenges. Moreover, proposed protocol suggests a secondary cluster head for each cluster, to increase the stability of the cluster and implicitly the network infrastructure in case of sudden failure of cluster head.
Software Reuse in Medical Database for Cardiac Patients using Pearson Family Equations
Venue: arXiv cs.SE 2012
Year: 2012

Software reuse is a subfield of software engineering that is used to adopt the existing software for similar purposes. Reuse Metrics determine the extent to which an existing software component is reused in new software with an objective to minimize the errors and cost of the new project. In this paper, medical database related to cardiology is considered. The Pearson Type I Distribution is used to calculate the probability density function (pdf) and thereby utilizing it for clustering the data. Further, coupling methodology is used to bring out the similarity of the new patient data by comparing it with the existing data. By this, the concerned treatment to be followed for the new patient is deduced by comparing with that of the previous patients case history. The metrics proposed by Chidamber and Kemerer are utilized for this purpose. This model will be useful for the medical field through software, particularly in remote areas.
Compositional Stochastic Modeling and Probabilistic Programming
Venue: arXiv cs.AI 2012
Year: 2012

Probabilistic programming is related to a compositional approach to stochastic modeling by switching from discrete to continuous time dynamics. In continuous time, an operator-algebra semantics is available in which processes proceeding in parallel (and possibly interacting) have summed time-evolution operators. From this foundation, algorithms for simulation, inference and model reduction may be systematically derived. The useful consequences are potentially far-reaching in computational science, machine learning and beyond. Hybrid compositional stochastic modeling/probabilistic programming approaches may also be possible.
Greedy is good: An experimental study on minimum clique cover and maximum independent set problems for randomly generated rectangles
Venue: arXiv cs.CG 2012
Year: 2012

Given a set ${\cal R}=\{R_1,R_2,..., R_n\}$ of $n$ randomly positioned axis parallel rectangles in 2D, the problem of computing the minimum clique cover (MCC) and maximum independent set (MIS) for the intersection graph $G({\cal R})$ of the members in $\cal R$ are both computationally hard \cite{CC05}. For the MCC problem, it is proved that polynomial time constant factor approximation is impossible to obtain \cite{PT11}. Though such a result is not proved yet for the MIS problem, no polynomial time constant factor approximation algorithm exists in the literature. We study the performance of greedy algorithms for computing these two parameters of $G({\cal R})$. Experimental results shows that for each of the MCC and MIS problems, the corresponding greedy algorithm produces a solution that is very close to its optimum solution. Scheinerman \cite{Scheinerman80} showed that the size of MIS is tightly bounded by $\sqrt{n}$ for a random instance of the 1D version of the problem, (i.e., for the interval graph). Our experiment shows that the size of independent set and the clique cover produced by the greedy algorithm is at least $2\sqrt{n}$ and at most $3\sqrt{n}$, respectively. Thus the experimentally obtained approximation ratio of the greedy algorithm for MIS problem is at most 3/2 and the same for the MCC problem is at least 2/3. Finally we will provide refined greedy algorithms based on a concept of {\it simplicial rectangle}. The characteristics of this algorithm may be of interest in getting a provably constant factor approximation algorithm for random instance of both the problems. We believe that the result also holds true for any finite dimension.
Training Support Vector Machines Using Frank-Wolfe Optimization Methods
Venue: arXiv cs.LG 2012
Year: 2012

Training a Support Vector Machine (SVM) requires the solution of a quadratic programming problem (QP) whose computational complexity becomes prohibitively expensive for large scale datasets. Traditional optimization methods cannot be directly applied in these cases, mainly due to memory restrictions. By adopting a slightly different objective function and under mild conditions on the kernel used within the model, efficient algorithms to train SVMs have been devised under the name of Core Vector Machines (CVMs). This framework exploits the equivalence of the resulting learning problem with the task of building a Minimal Enclosing Ball (MEB) problem in a feature space, where data is implicitly embedded by a kernel function. In this paper, we improve on the CVM approach by proposing two novel methods to build SVMs based on the Frank-Wolfe algorithm, recently revisited as a fast method to approximate the solution of a MEB problem. In contrast to CVMs, our algorithms do not require to compute the solutions of a sequence of increasingly complex QPs and are defined by using only analytic optimization steps. Experiments on a large collection of datasets show that our methods scale better than CVMs in most cases, sometimes at the price of a slightly lower accuracy. As CVMs, the proposed methods can be easily extended to machine learning problems other than binary classification. However, effective classifiers are also obtained using kernels which do not satisfy the condition required by CVMs and can thus be used for a wider set of problems.
Parameters of Two-Prover-One-Round Game and The Hardness of Connectivity Problems
Venue: arXiv cs.CC 2012
Year: 2012

Optimizing parameters of Two-Prover-One-Round Game (2P1R) is an important task in PCPs literature as it would imply a smaller PCP with the same or stronger soundness. While this is a basic question in PCPs community, the connection between the parameters of PCPs and hardness of approximations is sometime obscure to approximation algorithm community. In this paper, we investigate the connection between the parameters of 2P1R and the hardness of approximating the class of so-called connectivity problems, which includes as subclasses the survivable network design and (multi)cut problems. Based on recent development on 2P1R by Chan (ECCC 2011) and several techniques in PCPs literature, we improve hardness results of some connectivity problems that are in the form $k^σ$, for some (very) small constant $σ>0$, to hardness results of the form $k^c$ for some explicit constant $c$, where $k$ is a connectivity parameter. In addition, we show how to convert these hardness into hardness results of the form $D^{c'}$, where $D$ is the number of demand pairs (or the number of terminals). Thus, we give improved hardness results of k^{1/2-ε} and k^{1/10-ε} for the root $k$-connectivity problem on directed and undirected graphs, k^{1/6-ε} for the vertex-connectivity survivable network design problem on undirected graphs, and k^{1/6-ε} for the vertex-connectivity $k$-route cut problem on undirected graphs.