OpenRaiser/Intern-Atlas huggingface dataset:
Online Deliberation Design: Choices, Criteria, and Evidence
Venue: arXiv cs.HC 2013
Year: 2013

This chapter reviews empirical evidence bearing on the design of online forums for deliberative civic engagement. Dimensions of design are defined for different aspects of the deliberation: its purpose, the target population, the spatiotemporal distance separating participants, the communication medium, and the deliberative process to be followed. After a brief overview of criteria for evaluating different design options, empirical findings are organized around design choices. Research has evolved away from treating technology for online deliberation dichotomously (either present or not) toward nuanced findings that differentiate between technological features, ways of using them, and cultural settings. The effectiveness of online deliberation depends on how well the communicative environment is matched to the deliberative task. Tradeoffs, e.g. between rich and lean media and between anonymous and identifiable participation, suggest different designs depending on the purpose and participants. Findings are limited by existing technologies, and may change as technologies and users co-evolve.
Opportunistic Interference Alignment for MIMO Interfering Multiple-Access Channels
Venue: arXiv cs.IT 2013
Year: 2013

We consider the $K$-cell multiple-input multiple-output (MIMO) interfering multiple-access channel (IMAC) with time-invariant channel coefficients, where each cell consists of a base station (BS) with $M$ antennas and $N$ users having $L$ antennas each. In this paper, we propose two opportunistic interference alignment (OIA) techniques utilizing multiple transmit antennas at each user: antenna selection-based OIA and singular value decomposition (SVD)-based OIA. Their performance is analyzed in terms of \textit{user scaling law} required to achieve $KS$ degrees-of-freedom (DoF), where $S(\le M)$ denotes the number of simultaneously transmitting users per cell. We assume that each selected user transmits a single data stream at each time-slot. It is shown that the antenna selection-based OIA does not fundamentally change the user scaling condition if $L$ is fixed, compared with the single-input multiple-output (SIMO) IMAC case, which is given by $\text{SNR}^{(K 1)S}$, where SNR denotes the signal-to-noise ratio. In addition, we show that the SVD-based OIA can greatly reduce the user scaling condition to $\text{SNR}^{(K-1)S-L+1}$ through optimizing a weight vector at each user. Simulation results validate the derived scaling laws of the proposed OIA techniques. The sum-rate performance of the proposed OIA techniques is compared with the conventional techniques in MIMO IMAC channels and it is shown that the proposed OIA techniques outperform the conventional techniques.
Reversible Logic Synthesis by Quantum Rotation Gates
Venue: arXiv cs.ET 2013
Year: 2013

A rotation-based synthesis framework for reversible logic is proposed. We develop a canonical representation based on binary decision diagrams and introduce operators to manipulate the developed representation model. Furthermore, a recursive functional bi-decomposition approach is proposed to automatically synthesize a given function. While Boolean reversible logic is particularly addressed, our framework constructs intermediate quantum states that may be in superposition, hence we combine techniques from reversible Boolean logic and quantum computation. The proposed approach results in quadratic gate count for multiple-control Toffoli gates without ancillae, linear depth for quantum carry-ripple adder, and quasilinear size for quantum multiplexer.
Blind Estimation of Primary User Traffic Parameters Under Sensing Errors
Venue: arXiv cs.PF 2013
Year: 2013

In this work we investigate the bounds on the estimation accuracy of Primary User (PU) traffic parameters with exponentially distributed busy and idle times. We derive closed-form expressions for the Cramer-Rao bounds on the mean squared estimation error for the blind joint estimation of the PU traffic parameters, specifically, the duty cycle, and the mean arrival and departure rates. Moreover, we present the corresponding maximum-likelihood estimators for the traffic parameters. In addition, we derive a modified likelihood function for the joint estimation of traffic parameters when spectrum sensing errors are considered, and we present the impact of spectrum sensing errors on the estimation error via simulations. Finally, we consider a duty cycle estimator, common in traffic estimation literature, that is based on averaging the traffic samples. We derive, in closed-form, the mean squared estimation error of the considered estimator under spectrum sensing errors.
Towards Python-based Domain-specific Languages for Self-reconfigurable Modular Robotics Research
Venue: arXiv cs.RO 2013
Year: 2013

This paper explores the role of operating system and high-level languages in the development of software and domain-specific languages (DSLs) for self-reconfigurable robotics. We review some of the current trends in self-reconfigurable robotics and describe the development of a software system for ATRON II which utilizes Linux and Python to significantly improve software abstraction and portability while providing some basic features which could prove useful when using Python, either stand-alone or via a DSL, on a self-reconfigurable robot system. These features include transparent socket communication, module identification, easy software transfer and reliable module-to-module communication. The end result is a software platform for modular robots that where appropriate builds on existing work in operating systems, virtual machines, middleware and high-level languages.
The Importance of Clipping in Neurocontrol by Direct Gradient Descent on the Cost-to-Go Function and in Adaptive Dynamic Programming
Venue: arXiv cs.LG 2013
Year: 2013

In adaptive dynamic programming, neurocontrol and reinforcement learning, the objective is for an agent to learn to choose actions so as to minimise a total cost function. In this paper we show that when discretized time is used to model the motion of the agent, it can be very important to do "clipping" on the motion of the agent in the final time step of the trajectory. By clipping we mean that the final time step of the trajectory is to be truncated such that the agent stops exactly at the first terminal state reached, and no distance further. We demonstrate that when clipping is omitted, learning performance can fail to reach the optimum; and when clipping is done properly, learning performance can improve significantly. The clipping problem we describe affects algorithms which use explicit derivatives of the model functions of the environment to calculate a learning gradient. These include Backpropagation Through Time for Control, and methods based on Dual Heuristic Dynamic Programming. However the clipping problem does not significantly affect methods based on Heuristic Dynamic Programming, Temporal Differences or Policy Gradient Learning algorithms. Similarly, the clipping problem does not affect fixed-length finite-horizon problems.
Distributed Community Detection in Dynamic Graphs
Venue: arXiv cs.SI 2013
Year: 2013

Inspired by the increasing interest in self-organizing social opportunistic networks, we investigate the problem of distributed detection of unknown communities in dynamic random graphs. As a formal framework, we consider the dynamic version of the well-studied \emph{Planted Bisection Model} $\sdG(n,p,q)$ where the node set $[n]$ of the network is partitioned into two unknown communities and, at every time step, each possible edge $(u,v)$ is active with probability $p$ if both nodes belong to the same community, while it is active with probability $q$ (with $q<<p$) otherwise. We also consider a time-Markovian generalization of this model. We propose a distributed protocol based on the popular \emph{Label Propagation Algorithm} and prove that, when the ratio $p/q$ is larger than $n^{b}$ (for an arbitrarily small constant $b>0$), the protocol finds the right "planted" partition in $O(\log n)$ time even when the snapshots of the dynamic graph are sparse and disconnected (i.e. in the case $p=Θ(1/n)$).
Primitive digraphs with large exponents and slowly synchronizing automata
Venue: arXiv cs.FL 2013
Year: 2013

We present several infinite series of synchronizing automata for which the minimum length of reset words is close to the square of the number of states. All these automata are tightly related to primitive digraphs with large exponent.
A framework for coupling flow and deformation of the porous solid
Venue: arXiv cs.NA 2013
Year: 2013

In this paper, we consider the flow of an incompressible fluid in a deformable porous solid. We present a mathematical model using the framework offered by the theory of interacting continua. In its most general form, this framework provides a mechanism for capturing multiphase flow, deformation, chemical reactions and thermal processes, as well as interactions between the various physics in a conveniently implemented fashion. To simplify the presentation of the framework, results are presented for a particular model than can be seen as an extension of Darcy's equation (which assumes that the porous solid is rigid) that takes into account elastic deformation of the porous solid. The model also considers the effect of deformation on porosity. We show that using this model one can recover identical results as in the framework proposed by Biot and Terzaghi. Some salient features of the framework are as follows: (a) It is a consistent mixture theory model, and adheres to the laws and principles of continuum thermodynamics, (b) the model is capable of simulating various important phenomena like consolidation and surface subsidence, and (c) the model is amenable to several extensions. We also present numerical coupling algorithms to obtain coupled flow-deformation response. Several representative numerical examples are presented to illustrate the capability of the mathematical model and the performance of the computational framework.
Four Side Distance: A New Fourier Shape Signature
Venue: arXiv cs.CV 2013
Year: 2013

Shape is one of the main features in content based image retrieval (CBIR). This paper proposes a new shape signature. In this technique, features of each shape are extracted based on four sides of the rectangle that covers the shape. The proposed technique is Fourier based and it is invariant to translation, scaling and rotation. The retrieval performance between some commonly used Fourier based signatures and the proposed four sides distance (FSD) signature has been tested using MPEG-7 database. Experimental results are shown that the FSD signature has better performance compared with those signatures.
Heterogeneous Dynamic Priority Scheduling in time critical applications: Mobile Wireless Sensor Networks
Venue: arXiv cs.NI 2013
Year: 2013

In the unlicensed band, the notion of primary user and secondary user (To implement cognitive radio) is not explicit. By dynamic priority assignment we propose to implement cognitive radio in the unlicensed band. In time critical events, the data which is most important, has to be given the time slots. Wireless Sensor nodes in our case are considered to be mobile, and hence make it difficult to prioritize one over another. A node may be out of the reach of the cluster head or base station by the time it is allotted a time slot and hence mobility is a constraint. With the data changing dynamically and factors such as energy and mobility, which are major constraints, assigning priority to the nodes becomes difficult. In this paper, we have discussed about how Wireless Sensor Networks are able to allocate priorities to nodes in the unlicensed band with multiple parameters being posed. We have done simulations on NS-2 and have shown the implementation results.
Compressed Sensing with Sparse Binary Matrices: Instance Optimal Error Guarantees in Near-Optimal Time
Venue: arXiv cs.IT 2013
Year: 2013

A compressed sensing method consists of a rectangular measurement matrix, $M \in \mathbbm{R}^{m \times N}$ with $m \ll N$, together with an associated recovery algorithm, $\mathcal{A}: \mathbbm{R}^m \rightarrow \mathbbm{R}^N$. Compressed sensing methods aim to construct a high quality approximation to any given input vector ${\bf x} \in \mathbbm{R}^N$ using only $M {\bf x} \in \mathbbm{R}^m$ as input. In particular, we focus herein on instance optimal nonlinear approximation error bounds for $M$ and $\mathcal{A}$ of the form $ \| {\bf x} - \mathcal{A} (M {\bf x}) \|_p \leq \| {\bf x} - {\bf x}^{\rm opt}_k \|_p + C k^{1/p - 1/q} \| {\bf x} - {\bf x}^{\rm opt}_k \|_q$ for ${\bf x} \in \mathbbm{R}^N$, where ${\bf x}^{\rm opt}_k$ is the best possible $k$-term approximation to ${\bf x}$. In this paper we develop a compressed sensing method whose associated recovery algorithm, $\mathcal{A}$, runs in $O((k \log k) \log N)$-time, matching a lower bound up to a $O(\log k)$ factor. This runtime is obtained by using a new class of sparse binary compressed sensing matrices of near optimal size in combination with sublinear-time recovery techniques motivated by sketching algorithms for high-volume data streams. The new class of matrices is constructed by randomly subsampling rows from well-chosen incoherent matrix constructions which already have a sub-linear number of rows. As a consequence, fewer random bits than previously required are needed in order to select the rows utilized by the fast reconstruction algorithms considered herein.
A Modified Riccati Transformation for Decentralized Computation of the Viability Kernel Under LTI Dynamics
Venue: arXiv cs.SY 2013
Year: 2013

Computing the viability kernel is key in providing guarantees of safety and proving existence of safety-preserving controllers for constrained dynamical systems. Current numerical techniques that approximate this construct suffer from a complexity that is exponential in the dimension of the state. We study conditions under which a linear time-invariant (LTI) system can be suitably decomposed into lower-dimensional subsystems so as to admit a conservative computation of the viability kernel in a decentralized fashion in subspaces. We then present an isomorphism that imposes these desired conditions, particularly on two-time-scale systems. Decentralized computations are performed in the transformed coordinates, yielding a conservative approximation of the viability kernel in the original state space. Significant reduction of complexity can be achieved, allowing the previously inapplicable tools to be employed for treatment of higher-dimensional systems. We show the results on two examples including a 6D system.
A Homogeneous Ensemble of Artificial Neural Networks for Time Series Forecasting
Venue: arXiv cs.NE 2013
Year: 2013

Enhancing the robustness and accuracy of time series forecasting models is an active area of research. Recently, Artificial Neural Networks (ANNs) have found extensive applications in many practical forecasting problems. However, the standard backpropagation ANN training algorithm has some critical issues, e.g. it has a slow convergence rate and often converges to a local minimum, the complex pattern of error surfaces, lack of proper training parameters selection methods, etc. To overcome these drawbacks, various improved training methods have been developed in literature; but, still none of them can be guaranteed as the best for all problems. In this paper, we propose a novel weighted ensemble scheme which intelligently combines multiple training algorithms to increase the ANN forecast accuracies. The weight for each training algorithm is determined from the performance of the corresponding ANN model on the validation dataset. Experimental results on four important time series depicts that our proposed technique reduces the mentioned shortcomings of individual ANN training algorithms to a great extent. Also it achieves significantly better forecast accuracies than two other popular statistical models.
Parallel Maximum Clique Algorithms with Applications to Network Analysis and Storage
Venue: arXiv cs.SI 2013
Year: 2013

We propose a fast, parallel maximum clique algorithm for large sparse graphs that is designed to exploit characteristics of social and information networks. The method exhibits a roughly linear runtime scaling over real-world networks ranging from 1000 to 100 million nodes. In a test on a social network with 1.8 billion edges, the algorithm finds the largest clique in about 20 minutes. Our method employs a branch and bound strategy with novel and aggressive pruning techniques. For instance, we use the core number of a vertex in combination with a good heuristic clique finder to efficiently remove the vast majority of the search space. In addition, we parallelize the exploration of the search tree. During the search, processes immediately communicate changes to upper and lower bounds on the size of maximum clique, which occasionally results in a super-linear speedup because vertices with large search spaces can be pruned by other processes. We apply the algorithm to two problems: to compute temporal strong components and to compress graphs.
Wireless Intrusion Detection Systems(WIDS)
Venue: arXiv cs.CR 2013
Year: 2013

The rapid proliferation of wireless networks and mobile computing applications has changed the landscape of network security,the wireless networks have changed the way business, organizations work and offered a new range of possibilities and flexibilities; It is clear that wireless solutions are transforming the way we work and live. Employees are able to keep in touch with their e-mail, calendar and employer from mobile devices, but on the other hands they introduced a new security threats appeared. While an attacker needs physical access to a wired network in order to gain access to the network and to accomplish his goals, a wireless network allows anyone within its range to passively monitor the traffic or even start an attack,one of the countermeasures that can be used is the intrusion detection systems in order to allow us to know both the threats affecting our wireless network and our system vulnerabilities in order to prevent those attackers, the IDS its main purpose is to manage the system and its operations and Its duty depends on .
Super-resolution via superset selection and pruning
Venue: arXiv cs.IT 2013
Year: 2013

We present a pursuit-like algorithm that we call the "superset method" for recovery of sparse vectors from consecutive Fourier measurements in the super-resolution regime. The algorithm has a subspace identification step that hinges on the translation invariance of the Fourier transform, followed by a removal step to estimate the solution's support. The superset method is always successful in the noiseless regime (unlike L1-minimization) and generalizes to higher dimensions (unlike the matrix pencil method). Relative robustness to noise is demonstrated numerically.
Network traffic prediction based on ARFIMA model
Venue: arXiv cs.NI 2013
Year: 2013

ARFIMA is a time series forecasting model, which is an improved ARMA model, the ARFIMA model proposed in this article is demonstrated and deduced in detail. combined with network traffic of CERNET backbone and the ARFIMA model,the result shows that,compare to the ARMA model, the prediction efficiency and accuracy has increased significantly, and not susceptible to sampling.
Bigraphical Nets
Venue: arXiv cs.LO 2013
Year: 2013

Interaction nets are a graphical model of computation, which has been used to define efficient evaluators for functional calculi, and specifically lambda calculi with patterns. However, the flat structure of interaction nets forces pattern matching and functional behaviour to be encoded at the same level, losing some potential parallelism. In this paper, we introduce bigraphical nets, or binets for short, as a generalisation of interaction nets using ideas from bigraphs and port graphs, and we present a formal notation and operational semantics for binets. We illustrate their expressive power by examples of applications.
A Domain-Specific Language for Rich Motor Skill Architectures
Venue: arXiv cs.RO 2013
Year: 2013

Model-driven software development is a promising way to cope with the complexity of system integration in advanced robotics, as it already demonstrated its benefits in domains with comparably challenging system integration requirements. This paper reports on work in progress in this area which aims to improve the research and experimentation process in a collaborative research project developing motor skill architectures for compliant robots. Our goal is to establish a model-driven development process throughout the project around a domain-specific language (DSL) facilitating the compact description of adaptive modular architectures for rich motor skills. Incorporating further languages for other aspects (e.g. mapping to a technical component architecture) the approach allows not only the formal description of motor skill architectures but also automated code-generation for experimentation on technical robot platforms. This paper reports on a first case study exemplifying how the developed AMARSi DSL helps to conceptualize different architectural approaches and to identify their similarities and differences.
Energy and Sampling Constrained Asynchronous Communication
Venue: arXiv cs.IT 2013
Year: 2013

The minimum energy, and, more generally, the minimum cost, to transmit one bit of information has been recently derived for bursty communication when information is available infrequently at random times at the transmitter. This result assumes that the receiver is always in the listening mode and samples all channel outputs until it makes a decision. If the receiver is constrained to sample only a fraction f>0 of the channel outputs, what is the cost penalty due to sparse output sampling? Remarkably, there is no penalty: regardless of f>0 the asynchronous capacity per unit cost is the same as under full sampling, ie, when f=1. There is not even a penalty in terms of decoding delay---the elapsed time between when information is available until when it is decoded. This latter result relies on the possibility to sample adaptively; the next sample can be chosen as a function of past samples. Under non-adaptive sampling, it is possible to achieve the full sampling asynchronous capacity per unit cost, but the decoding delay gets multiplied by 1/f. Therefore adaptive sampling strategies are of particular interest in the very sparse sampling regime.