OpenRaiser/Intern-Atlas huggingface dataset:
On damping created by heterogeneous yielding in the numerical analysis of nonlinear reinforced concrete frame elements
Venue: arXiv cs.CE 2015
Year: 2015

In the dynamic analysis of structural engineering systems, it is common practice to introduce damping models to reproduce experimentally observed features. These models, for instance Rayleigh damping, account for the damping sources in the system altogether and often lack physical basis. We report on an alternative path for reproducing damping coming from material nonlinear response through the consideration of the heterogeneous character of material mechanical properties. The parameterization of that heterogeneity is performed through a stochastic model. It is shown that such a variability creates the patterns in the concrete cyclic response that are classically regarded as source of damping.
Robust Connectivity Analysis for Multi-Agent Systems
Venue: arXiv cs.SY 2015
Year: 2015

In this report we provide a decentralized robust control approach, which guarantees that connectivity of a multi-agent network is maintained when certain bounded input terms are added to the control strategy. Our main motivation for this framework is to determine abstractions for multi-agent systems under coupled constraints which are further exploited for high level plan generation.
Acoustic event detection for multiple overlapping similar sources
Venue: arXiv cs.SD 2015
Year: 2015

Many current paradigms for acoustic event detection (AED) are not adapted to the organic variability of natural sounds, and/or they assume a limit on the number of simultaneous sources: often only one source, or one source of each type, may be active. These aspects are highly undesirable for applications such as bird population monitoring. We introduce a simple method modelling the onsets, durations and offsets of acoustic events to avoid intrinsic limits on polyphony or on inter-event temporal patterns. We evaluate the method in a case study with over 3000 zebra finch calls. In comparison against a HMM-based method we find it more accurate at recovering acoustic events, and more robust for estimating calling rates.
Data-Driven Power Control for State Estimation: A Bayesian Inference Approach
Venue: arXiv cs.SY 2015
Year: 2015

We consider sensor transmission power control for state estimation, using a Bayesian inference approach. A sensor node sends its local state estimate to a remote estimator over an unreliable wireless communication channel with random data packet drops. As related to packet dropout rate, transmission power is chosen by the sensor based on the relative importance of the local state estimate. The proposed power controller is proved to preserve Gaussianity of local estimate innovation, which enables us to obtain a closed-form solution of the expected state estimation error covariance. Comparisons with alternative non data-driven controllers demonstrate performance improvement using our approach.
Shortest-Path Queries in Planar Graphs on GPU-Accelerated Architectures
Venue: arXiv cs.DS 2015
Year: 2015

We develop an efficient parallel algorithm for answering shortest-path queries in planar graphs and implement it on a multi-node CPU/GPU clusters. The algorithm uses a divide-and-conquer approach for decomposing the input graph into small and roughly equal subgraphs and constructs a distributed data structure containing shortest distances within each of those subgraphs and between their boundary vertices. For a planar graph with $n$ vertices, that data structure needs $O(n)$ storage per processor and allows queries to be answered in $O(n^{1/4})$ time.
Dual Polynomials for Collision and Element Distinctness
Venue: arXiv cs.CC 2015
Year: 2015

The approximate degree of a Boolean function $f: \{-1, 1\}^n \to \{-1, 1\}$ is the minimum degree of a real polynomial that approximates $f$ to within error $1/3$ in the $\ell_\infty$ norm. In an influential result, Aaronson and Shi (J. ACM 2004) proved tight $\tildeΩ(n^{1/3})$ and $\tildeΩ(n^{2/3})$ lower bounds on the approximate degree of the Collision and Element Distinctness functions, respectively. Their proof was non-constructive, using a sophisticated symmetrization argument and tools from approximation theory. More recently, several open problems in the study of approximate degree have been resolved via the construction of dual polynomials. These are explicit dual solutions to an appropriate linear program that captures the approximate degree of any function. We reprove Aaronson and Shi's results by constructing explicit dual polynomials for the Collision and Element Distinctness functions.
Towards Controlling Refinements of Statecharts
Venue: arXiv cs.SE 2015
Year: 2015

In incremental development strategies, modelers frequently refine Statecharts models to satisfy requirements and changes. Although several solutions exist to the problem of Statecharts refinement, they provide such levels of freedom that a statechart cannot make assumptions or guarantees about its future structure. In this paper, we propose a set of bounding rules to limit the allowable Statecharts refinement operations such that certain assumptions will hold.
Multi-Bernoulli Sensor-Control via Minimization of Expected Estimation Errors
Venue: arXiv cs.SY 2015
Year: 2015

This paper presents a sensor-control method for choosing the best next state of the sensor(s), that provide(s) accurate estimation results in a multi-target tracking application. The proposed solution is formulated for a multi-Bernoulli filter and works via minimization of a new estimation error-based cost function. Simulation results demonstrate that the proposed method can outperform the state-of-the-art methods in terms of computation time and robustness to clutter while delivering similar accuracy.
An Experiment on Using Bayesian Networks for Process Mining
Venue: arXiv cs.AI 2015
Year: 2015

Process mining is a technique that performs an automatic analysis of business processes from a log of events with the promise of understanding how processes are executed in an organisation. Several models have been proposed to address this problem, however, here we propose a different approach to deal with uncertainty. By uncertainty, we mean estimating the probability of some sequence of tasks occurring in a business process, given that only a subset of tasks may be observable. In this sense, this work proposes a new approach to perform process mining using Bayesian Networks. These structures can take into account the probability of a task being present or absent in the business process. Moreover, Bayesian Networks are able to automatically learn these probabilities through mechanisms such as the maximum likelihood estimate and EM clustering. Experiments made over a Loan Application Case study suggest that Bayesian Networks are adequate structures for process mining and enable a deep analysis of the business process model that can be used to answer queries about that process.
A Survey on Backup of Data on Remote Server
Venue: arXiv cs.DC 2015
Year: 2015

Large amount of electronic data is generated in Cloud computing every day. Efficient maintenance of this data requires proper services. Hence a method to collect data securely, by protecting and developing backups is mentioned. The Objective is to provide Auto Response Server, better solutions for data backup and restoring using Cloud. Data can be collected and sent to a centralized repository in a platform independent format without any network consideration. This data can then be used according to the requirement. The purpose of this particular Remote Backup Server is to collect information from any remote location even if network connectivity is not available at that point of time and provide proper services as well as to recover data in case of loss.
Role of Matrix Factorization Model in Collaborative Filtering Algorithm: A Survey
Venue: arXiv cs.IR 2015
Year: 2015

Recommendation Systems apply Information Retrieval techniques to select the online information relevant to a given user. Collaborative Filtering is currently most widely used approach to build Recommendation System. CF techniques uses the user behavior in form of user item ratings as their information source for prediction. There are major challenges like sparsity of rating matrix and growing nature of data which is faced by CF algorithms. These challenges are been well taken care by Matrix Factorization. In this paper we attempt to present an overview on the role of different MF model to address the challenges of CF algorithms, which can be served as a roadmap for research in this area.
Energy-Efficient Adaptive Power Allocation for Incremental MIMO Systems
Venue: arXiv cs.IT 2015
Year: 2015

We consider energy-efficient adaptive power allocation for three incremental multiple-input multiple-output (IMIMO) systems employing ARQ, hybrid ARQ (HARQ) with Chase combining (CC), and HARQ with incremental redundancy (IR), to minimize their rate-outage probability (or equivalently packet drop rate) under a constraint on average energy consumption per data packet. We first provide the rate-outage probability expressions for the three IMIMO systems, and then propose methods to convert them into a tractable form and formulate the corresponding non-convex optimization problems that can be solved by an interior-point algorithm for finding a local optimum. To further reduce the solution complexity, using an asymptotically equivalent approximation of the rate-outage probability expressions, we approximate the non-convex optimization problems as a unified geometric programming problem (GPP), for which we derive the closed-form solution. Illustrative results indicate that the proposed power allocation (PPA) offers significant gains in energy savings as compared to the equal-power allocation (EPA), and the simple closed-form GPP solution can provide closer performance to the exact method at lower values of rate-outage probability, for the three IMIMO systems.
A Low-throughput Wavelet-based Steganography Audio Scheme
Venue: arXiv cs.MM 2015
Year: 2015

This paper presents the preliminary of a novel scheme of steganography, and introduces the idea of combining two secret keys in the operation. The first secret key encrypts the text using a standard cryptographic scheme (e.g. IDEA, SAFER+, etc.) prior to the wavelet audio decomposition. The way in which the cipher text is embedded in the file requires another key, namely a stego-key, which is associated with features of the audio wavelet analysis.
Primal robustness and semidefinite cones
Venue: arXiv cs.SY 2015
Year: 2015

This paper reformulates and streamlines the core tools of robust stability and performance for LTI systems using now-standard methods in convex optimization. In particular, robustness analysis can be formulated directly as a primal convex (semidefinite program or SDP) optimization problem using sets of gramians whose closure is a semidefinite cone. This allows various constraints such as structured uncertainty to be included directly, and worst-case disturbances and perturbations constructed directly from the primal variables. Well known results such as the KYP lemma and various scaled small gain tests can also be obtained directly through standard SDP duality. To readers familiar with robustness and SDPs, the framework should appear obvious, if only in retrospect. But this is also part of its appeal and should enhance pedagogy, and we hope suggest new research. There is a key lemma proving closure of a grammian that is also obvious but our current proof appears unnecessarily cumbersome, and a final aim of this paper is to enlist the help of experts in robust control and convex optimization in finding simpler alternatives.
Simultaneous Bidirectional Link Selection in Full Duplex MIMO Systems
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, we consider a point to point full duplex (FD) MIMO communication system. We assume that each node is equipped with an arbitrary number of antennas which can be used for transmission or reception. With FD radios, bidirectional information exchange between two nodes can be achieved at the same time. In this paper we design bidirectional link selection schemes by selecting a pair of transmit and receive antenna at both ends for communications in each direction to maximize the weighted sum rate or minimize the weighted sum symbol error rate (SER). The optimal selection schemes require exhaustive search, so they are highly complex. To tackle this problem, we propose a Serial-Max selection algorithm, which approaches the exhaustive search methods with much lower complexity. In the Serial-Max method, the antenna pairs with maximum "obtainable SINR" at both ends are selected in a two-step serial way. The performance of the proposed Serial-Max method is analyzed, and the closed-form expressions of the average weighted sum rate and the weighted sum SER are derived. The analysis is validated by simulations. Both analytical and simulation results show that as the number of antennas increases, the Serial-Max method approaches the performance of the exhaustive-search schemes in terms of sum rate and sum SER.
Upper Bound on the Capacity of a Cascade of Nonlinear and Noisy Channels
Venue: arXiv cs.IT 2015
Year: 2015

An upper bound on the capacity of a cascade of nonlinear and noisy channels is presented. The cascade mimics the split-step Fourier method for computing waveform propagation governed by the stochastic generalized nonlinear Schroedinger equation. It is shown that the spectral efficiency of the cascade is at most log(1+SNR), where SNR is the receiver signal-to-noise ratio. The results may be applied to optical fiber channels. However, the definition of bandwidth is subtle and leaves open interpretations of the bound. Some of these interpretations are discussed.
Sensitivity versus Certificate Complexity of Boolean Functions
Venue: arXiv cs.CC 2015
Year: 2015

Sensitivity, block sensitivity and certificate complexity are basic complexity measures of Boolean functions. The famous sensitivity conjecture claims that sensitivity is polynomially related to block sensitivity. However, it has been notoriously hard to obtain even exponential bounds. Since block sensitivity is known to be polynomially related to certificate complexity, an equivalent of proving this conjecture would be showing that certificate complexity is polynomially related to sensitivity. Previously, it has been shown that $bs(f) \leq C(f) \leq 2^{s(f)-1} s(f) - (s(f)-1)$. In this work, we give a better upper bound of $bs(f) \leq C(f) \leq \max\left(2^{s(f)-1}\left(s(f)-\frac 1 3\right), s(f)\right)$ using a recent theorem limiting the structure of function graphs. We also examine relations between these measures for functions with small 1-sensitivity $s_1(f)$ and arbitrary 0-sensitivity $s_0(f)$.
Transductive Multi-label Zero-shot Learning
Venue: arXiv cs.LG 2015
Year: 2015

Zero-shot learning has received increasing interest as a means to alleviate the often prohibitive expense of annotating training data for large scale recognition problems. These methods have achieved great success via learning intermediate semantic representations in the form of attributes and more recently, semantic word vectors. However, they have thus far been constrained to the single-label case, in contrast to the growing popularity and importance of more realistic multi-label data. In this paper, for the first time, we investigate and formalise a general framework for multi-label zero-shot learning, addressing the unique challenge therein: how to exploit multi-label correlation at test time with no training data for those classes? In particular, we propose (1) a multi-output deep regression model to project an image into a semantic word space, which explicitly exploits the correlations in the intermediate semantic layer of word vectors; (2) a novel zero-shot learning algorithm for multi-label data that exploits the unique compositionality property of semantic word vector representations; and (3) a transductive learning strategy to enable the regression model learned from seen classes to generalise well to unseen classes. Our zero-shot learning experiments on a number of standard multi-label datasets demonstrate that our method outperforms a variety of baselines.
Numerical solution of moving plate problem with uncertain parameters
Venue: arXiv cs.CE 2015
Year: 2015

This paper deals with uncertain parabolic fluid flow problem where the uncertainty occurs due to the initial conditions and parameters involved in the system. Uncertain values are considered as fuzzy and these are handled through a recently developed method. Here the concepts of fuzzy numbers are combined with Finite Difference Method (FDM) and then Fuzzy Finite Difference Method (FFDM) has been proposed. The proposed FFDM has been used to solve the fluid flow problem bounded by two parallel plates. Finally sensitivity of the fuzzy parameters has also been analysed.
Transductive Multi-class and Multi-label Zero-shot Learning
Venue: arXiv cs.LG 2015
Year: 2015

Recently, zero-shot learning (ZSL) has received increasing interest. The key idea underpinning existing ZSL approaches is to exploit knowledge transfer via an intermediate-level semantic representation which is assumed to be shared between the auxiliary and target datasets, and is used to bridge between these domains for knowledge transfer. The semantic representation used in existing approaches varies from visual attributes to semantic word vectors and semantic relatedness. However, the overall pipeline is similar: a projection mapping low-level features to the semantic representation is learned from the auxiliary dataset by either classification or regression models and applied directly to map each instance into the same semantic representation space where a zero-shot classifier is used to recognise the unseen target class instances with a single known 'prototype' of each target class. In this paper we discuss two related lines of work improving the conventional approach: exploiting transductive learning ZSL, and generalising ZSL to the multi-label case.
Design and Implementation of an Inertial Navigation System for Pedestrians Based on a Low-Cost MEMS IMU
Venue: arXiv cs.RO 2015
Year: 2015

Inertial navigation systems for pedestrians are infrastructure-less and can achieve sub-meter accuracy in the short/medium period. However, when low-cost inertial measurement units (IMU) are employed for their implementation, they suffer from a slowly growing drift between the true pedestrian position and the corresponding estimated position. In this paper we illustrate a novel solution to mitigate such a drift by: a) using only accelerometer and gyroscope measurements (no magnetometers required); b) including the sensor error model parameters in the state vector of an extended Kalman filter; c) adopting a novel soft heuristic for foot stance detection and for zero-velocity updates. Experimental results evidence that our inertial-only navigation system can achieve similar or better performance with respect to pedestrian dead-reckoning systems presented in related studies, although the adopted IMU is less accurate than more expensive counterparts.
Codes from Jacobian surfaces
Venue: arXiv cs.IT 2015
Year: 2015

This paper is concerned with some Algebraic Geometry codes on Jacobians of genus 2 curves. We derive a lower bound for the minimum distance of these codes from an upper "Weil type" bound for the number of rational points on irreducible (possibly singular or non-absolutely irreducible) curves lying on an abelian surface over a finite field.
SecureFind: Secure and Privacy-Preserving Object Finding via Mobile Crowdsourcing
Venue: arXiv cs.CR 2015
Year: 2015

The plummeting cost of Bluetooth tags and the ubiquity of mobile devices are revolutionizing the traditional lost-and-found service. This paper presents SecureFind, a secure and privacy-preserving object-finding system via mobile crowdsourcing. In SecureFind, a unique Bluetooth tag is attached to every valuable object, and the owner of a lost object submits an object-finding request to many mobile users via the SecureFind service provider. Each mobile user involved searches his vicinity for the lost object on behalf of the object owner who can infer the location of his lost object based on the responses from mobile users. SecureFind is designed to ensure strong object security such that only the object owner can discover the location of his lost object as well as offering strong location privacy to mobile users involved. The high efficacy and efficiency of SecureFind are confirmed by extensive simulations.
Adaptive Spectrum Sharing of LTE Co-existing with WLAN in Unlicensed Frequency Bands
Venue: arXiv cs.NI 2015
Year: 2015

With the increase of wireless communication demands, licensed spectrum for long term evolution (LTE) is no longer enough. The research effort has focused on implementing LTE to unlicensed frequency bands in recent years, which unavoidably brings the problem of LTE co-existence with other existing systems on the same band. This paper proposes an adaptive co-existence mechanism for LTE and wireless local area networks (WLAN) to enable a significant system performance of WLAN while LTE does not lose much as well. LTE realizes the co-existence by allocating time resources dynamically according to the traffic load of WLAN system.
Discriminative Bayesian Dictionary Learning for Classification
Venue: arXiv cs.CV 2015
Year: 2015

We propose a Bayesian approach to learn discriminative dictionaries for sparse representation of data. The proposed approach infers probability distributions over the atoms of a discriminative dictionary using a Beta Process. It also computes sets of Bernoulli distributions that associate class labels to the learned dictionary atoms. This association signifies the selection probabilities of the dictionary atoms in the expansion of class-specific data. Furthermore, the non-parametric character of the proposed approach allows it to infer the correct size of the dictionary. We exploit the aforementioned Bernoulli distributions in separately learning a linear classifier. The classifier uses the same hierarchical Bayesian model as the dictionary, which we present along the analytical inference solution for Gibbs sampling. For classification, a test instance is first sparsely encoded over the learned dictionary and the codes are fed to the classifier. We performed experiments for face and action recognition; and object and scene-category classification using five public datasets and compared the results with state-of-the-art discriminative sparse representation approaches. Experiments show that the proposed Bayesian approach consistently outperforms the existing approaches.
Evolutionary Poisson Games for Controlling Large Population Behaviors
Venue: arXiv cs.GT 2015
Year: 2015

Emerging applications in engineering such as crowd-sourcing and (mis)information propagation involve a large population of heterogeneous users or agents in a complex network who strategically make dynamic decisions. In this work, we establish an evolutionary Poisson game framework to capture the random, dynamic and heterogeneous interactions of agents in a holistic fashion, and design mechanisms to control their behaviors to achieve a system-wide objective. We use the antivirus protection challenge in cyber security to motivate the framework, where each user in the network can choose whether or not to adopt the software. We introduce the notion of evolutionary Poisson stable equilibrium for the game, and show its existence and uniqueness. Online algorithms are developed using the techniques of stochastic approximation coupled with the population dynamics, and they are shown to converge to the optimal solution of the controller problem. Numerical examples are used to illustrate and corroborate our results.