OpenRaiser/Intern-Atlas huggingface dataset:
Deep Transform: Cocktail Party Source Separation via Complex Convolution in a Deep Neural Network
Venue: arXiv cs.SD 2015
Year: 2015

Convolutional deep neural networks (DNN) are state of the art in many engineering problems but have not yet addressed the issue of how to deal with complex spectrograms. Here, we use circular statistics to provide a convenient probabilistic estimate of spectrogram phase in a complex convolutional DNN. In a typical cocktail party source separation scenario, we trained a convolutional DNN to re-synthesize the complex spectrograms of two source speech signals given a complex spectrogram of the monaural mixture - a discriminative deep transform (DT). We then used this complex convolutional DT to obtain probabilistic estimates of the magnitude and phase components of the source spectrograms. Our separation results are on a par with equivalent binary-mask based non-complex separation approaches.
A High Reliability Asymptotic Approach for Packet Inter-Delivery Time Optimization in Cyber-Physical Systems
Venue: arXiv cs.NI 2015
Year: 2015

In cyber-physical systems such as automobiles, measurement data from sensor nodes should be delivered to other consumer nodes such as actuators in a regular fashion. But, in practical systems over unreliable media such as wireless, it is a significant challenge to guarantee small enough inter-delivery times for different clients with heterogeneous channel conditions and inter-delivery requirements. In this paper, we design scheduling policies aiming at satisfying the inter-delivery requirements of such clients. We formulate the problem as a risk-sensitive Markov Decision Process (MDP). Although the resulting problem involves an infinite state space, we first prove that there is an equivalent MDP involving only a finite number of states. Then we prove the existence of a stationary optimal policy and establish an algorithm to compute it in a finite number of steps. However, the bane of this and many similar problems is the resulting complexity, and, in an attempt to make fundamental progress, we further propose a new high reliability asymptotic approach. In essence, this approach considers the scenario when the channel failure probabilities for different clients are of the same order, and asymptotically approach zero. We thus proceed to determine the asymptotically optimal policy: in a two-client scenario, we show that the asymptotically optimal policy is a "modified least time-to-go" policy, which is intuitively appealing and easily implementable; in the general multi-client scenario, we are led to an SN policy, and we develop an algorithm of low computational complexity to obtain it. Simulation results show that the resulting policies perform well even in the pre-asymptotic regime with moderate failure probabilities.
Sampling promotes community structure in social and information networks
Venue: arXiv cs.SI 2015
Year: 2015

Any network studied in the literature is inevitably just a sampled representative of its real-world analogue. Additionally, network sampling is lately often applied to large networks to allow for their faster and more efficient analysis. Nevertheless, the changes in network structure introduced by sampling are still far from understood. In this paper, we study the presence of characteristic groups of nodes in sampled social and information networks. We consider different network sampling techniques including random node and link selection, network exploration and expansion. We first observe that the structure of social networks reveals densely linked groups like communities, while the structure of information networks is better described by modules of structurally equivalent nodes. However, despite these notable differences, the structure of sampled networks exhibits stronger characterization by community-like groups than the original networks, irrespective of their type and consistently across various sampling techniques. Hence, rich community structure commonly observed in social and information networks is to some extent merely an artifact of sampling.
DVB-S2x Enabled Precoding for High Throughput Satellite Systems
Venue: arXiv cs.IT 2015
Year: 2015

Multi-user Multiple-Input Multiple-Output (MU-MIMO) has allowed recent releases of terrestrial LTE standards to achieve significant improvements in terms of offered system capacity. The publications of the DVB-S2x standard and particularly of its novel superframe structure is a key enabler for applying similar interference management techniques -such as precoding- to multibeam High Throughput Satellite (HTS) systems. This paper presents results resulting from European Space Agency (ESA) funded R&D activities concerning the practical issues that arise when precoding is applied over an aggressive frequency re-use HTS network. In addressing these issues, the paper also proposes pragmatic solutions that have been developed in order to overcome these limitations. Through the application of a comprehensive system simulator, it is demonstrated that important capacity gains (beyond 40%) are to be expected from applying precoding even after introducing a number of significant practical impairments.
Secure Transmission in Amplify-and-Forward Diamond Networks with a Single Eavesdropper
Venue: arXiv cs.IT 2015
Year: 2015

Unicast communication over a network of $M$-parallel relays in the presence of an eavesdropper is considered. The relay nodes, operating under individual power constraints, amplify and forward the signals received at their inputs. The problem of the maximum secrecy rate achievable with AF relaying is addressed. Previous work on this problem provides iterative algorithms based on semidefinite relaxation. However, those algorithms result in suboptimal performance without any performance and convergence guarantees. We address this problem for three specific network models, with real-valued channel gains. We propose a novel transformation that leads to convex optimization problems. Our analysis leads to (i)a polynomial-time algorithm to compute the optimal secure AF rate for two of the models and (ii) a closed-form expression for the optimal secure rate for the other.
Upper Bounds on the Error of Sparse Vector and Low-Rank Matrix Recovery
Venue: arXiv cs.IT 2015
Year: 2015

Suppose that a solution $\widetilde{\mathbf{x}}$ to an underdetermined linear system $\mathbf{b} = \mathbf{A} \mathbf{x}$ is given. $\widetilde{\mathbf{x}}$ is approximately sparse meaning that it has a few large components compared to other small entries. However, the total number of nonzero components of $\widetilde{\mathbf{x}}$ is large enough to violate any condition for the uniqueness of the sparsest solution. On the other hand, if only the dominant components are considered, then it will satisfy the uniqueness conditions. One intuitively expects that $\widetilde{\mathbf{x}}$ should not be far from the true sparse solution $\mathbf{x}_0$. We show that this intuition is the case by providing an upper bound on $\| \widetilde{\mathbf{x}} - \mathbf{x}_0\|$ which is a function of the magnitudes of small components of $\widetilde{\mathbf{x}}$ but independent from $\mathbf{x}_0$. This result is extended to the case that $\mathbf{b}$ is perturbed by noise. Additionally, we generalize the upper bounds to the low-rank matrix recovery problem.
Flexible Allocation of Heterogeneous Resources to Services on an IoT Device
Venue: arXiv cs.NI 2015
Year: 2015

In the Internet of Things (IoT), devices and gateways may be equipped with multiple, heterogeneous network interfaces which should be utilized by a large number of services. In this work, we model the problem of assigning services' resource demands to a device's heterogeneous interfaces and give a Mixed Integer Linear Program (MILP) formulation for it. For meaningful instance sizes the MILP model gives optimal solutions to the presented computationally-hard problem. We provide insightful results discussing the properties of the derived solutions with respect to the splitting of services to different interfaces.
The (Non)-Existence of Stable Mechanisms in Incomplete Information Environments
Venue: arXiv cs.GT 2015
Year: 2015

We consider two-sided matching markets, and study the incentives of agents to circumvent a centralized clearing house by signing binding contracts with one another. It is well-known that if the clearing house implements a stable match and preferences are known, then no group of agents can profitably deviate in this manner. We ask whether this property holds even when agents have incomplete information about their own preferences or the preferences of others. We find that it does not. In particular, when agents are uncertain about the preferences of others, every mechanism is susceptible to deviations by groups of agents. When, in addition, agents are uncertain about their own preferences, every mechanism is susceptible to deviations in which a single pair of agents agrees in advance to match to each other.
Outage Probability for Multi-Hop Full-Duplex Decode and Forward MIMO Relay
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, a multi-hop (MH) decode-and-forward (DF) multiple-input multiple-output (MIMO) relay network has been studied. To consider a more realistic scenario, Full-Duplex (FD) operation with Relay Self-Interference (RSI) is employed. Assuming that the MIMO channels are subject to Rayleigh fading, a simple and compact closed-form outage probability expression has been derived. The key assumption to derive this result is that the mutual information of each channel could be well approximated by a Gaussian random variable. In order to obtain the resultant outage probability, a new excellent accurate approximation has been obtained for the sum of Wishart distributed complex random matrices. Numerical Monte Carlo simulations have been performed to validate our result. These simulations have shown that, for low and medium interference regime, FD mode performs better than Half-Duplex (HD) mode. On the other hand, when RSI increases, HD mode can outperforms FD mode.
An average-case depth hierarchy theorem for Boolean circuits
Venue: arXiv cs.CC 2015
Year: 2015

We prove an average-case depth hierarchy theorem for Boolean circuits over the standard basis of $\mathsf{AND}$, $\mathsf{OR}$, and $\mathsf{NOT}$ gates. Our hierarchy theorem says that for every $d \geq 2$, there is an explicit $n$-variable Boolean function $f$, computed by a linear-size depth-$d$ formula, which is such that any depth-$(d-1)$ circuit that agrees with $f$ on $(1/2 + o_n(1))$ fraction of all inputs must have size $\exp({n^{Ω(1/d)}}).$ This answers an open question posed by Håstad in his Ph.D. thesis. Our average-case depth hierarchy theorem implies that the polynomial hierarchy is infinite relative to a random oracle with probability 1, confirming a conjecture of Håstad, Cai, and Babai. We also use our result to show that there is no "approximate converse" to the results of Linial, Mansour, Nisan and Boppana on the total influence of small-depth circuits, thus answering a question posed by O'Donnell, Kalai, and Hatami. A key ingredient in our proof is a notion of \emph{random projections} which generalize random restrictions.
The Private Key Capacity of a Cooperative Pairwise-Independent Network
Venue: arXiv cs.IT 2015
Year: 2015

This paper studies the private key generation of a cooperative pairwise-independent network (PIN) with M+2 terminals (Alice, Bob and M relays), M >= 2. In this PIN, the correlated sources observed by every pair of terminals are independent of those sources observed by any other pair of terminal. All the terminals can communicate with each other over a public channel which is also observed by Eve noiselessly. The objective is to generate a private key between Alice and Bob under the help of the M relays; such a private key needs to be protected not only from Eve but also from individual relays simultaneously. The private key capacity of this PIN model is established, whose lower bound is obtained by proposing a novel random binning (RB) based key generation algorithm, and the upper bound is obtained based on the construction of M enhanced source models. The two bounds are shown to be exactly the same. Then, we consider a cooperative wireless network and use the estimates of fading channels to generate private keys. It has been shown that the proposed RB-based algorithm can achieve a multiplexing gain M-1, an improvement in comparison with the existing XOR- based algorithm whose achievable multiplexing gain is about [M]/2.
Harnessing Natural Fluctuations: Analogue Computer for Efficient Socially Maximal Decision Making
Venue: arXiv cs.AI 2015
Year: 2015

Each individual handles many tasks of finding the most profitable option from a set of options that stochastically provide rewards. Our society comprises a collection of such individuals, and the society is expected to maximise the total rewards, while the individuals compete for common rewards. Such collective decision making is formulated as the `competitive multi-armed bandit problem (CBP)', requiring a huge computational cost. Herein, we demonstrate a prototype of an analog computer that efficiently solves CBPs by exploiting the physical dynamics of numerous fluids in coupled cylinders. This device enables the maximisation of the total rewards for the society without paying the conventionally required computational cost; this is because the fluids estimate the reward probabilities of the options for the exploitation of past knowledge and generate random fluctuations for the exploration of new knowledge. Our results suggest that to optimise the social rewards, the utilisation of fluid-derived natural fluctuations is more advantageous than applying artificial external fluctuations. Our analog computing scheme is expected to trigger further studies for harnessing the huge computational power of natural phenomena for resolving a wide variety of complex problems in modern information society.
Analysis of Software Binaries for Reengineering-Driven Product Line ArchitectureâAn Industrial Case Study
Venue: arXiv cs.SE 2015
Year: 2015

This paper describes a method for the recovering of software architectures from a set of similar (but unrelated) software products in binary form. One intention is to drive refactoring into software product lines and combine architecture recovery with run time binary analysis and existing clustering methods. Using our runtime binary analysis, we create graphs that capture the dependencies between different software parts. These are clustered into smaller component graphs, that group software parts with high interactions into larger entities. The component graphs serve as a basis for further software product line work. In this paper, we concentrate on the analysis part of the method and the graph clustering. We apply the graph clustering method to a real application in the context of automation / robot configuration software tools.
Detection of Information leakage in cloud
Venue: arXiv cs.DC 2015
Year: 2015

Recent research shows that colluded malware in different VMs sharing a single physical host may use a resource as a channel to leak critical information. Covert channels employ time or storage characteristics to transmit confidential information to attackers leaving no trail.These channels were not meant for communication and hence control mechanisms do not exist. This means these remain undetected by traditional security measures employed in firewalls etc in a network. The comprehensive survey to address the issue highlights that accurate methods for fast detection in cloud are very expensive in terms of storage and processing. The proposed framework builds signature by extracting features which accurately classify the regular from covert traffic in cloud and estimates difference in distribution of data under analysis by means of scores. It then adds context to the signature and finally using machine learning (Support Vector Machines),a model is built and trained for deploying in cloud. The results show that the framework proposed is high in accuracy while being low cost and robust as it is tested after adding noise which is likely to exist in public cloud environments.
SDP-based State Estimation of Multi-phase Active Distribution Networks using micro-PMUs
Venue: arXiv cs.SY 2015
Year: 2015

Distribution system state estimation (DSSE) is an essential tool for operation of distribution networks, the results of which enables the operator to have a thorough observation of the system. Thus, most distribution management systems (DMS) include a single-phase state estimator. Due to non-convexity of the SE problem, heuristic and Newton methods do not guarantee the global solution. In contrast, SDP based SE is more promising to guarantee the globally optimal solution since it represents and solves the problem in a convex format. However, the observability of the power system is highly vulnerable to the set of measurements while employing the SDP-based SE, which is addressed in this report. An algorithm is proposed to generate additional measurements using the measurement data already gathered. The SDP-based SE is very sensitive to the level of noise in large power networks. Also, bad data detection algorithms proposed for Newton methods do not work for the SDP-based SE method due to larger number of state variables in SDP representation of power network. In this report, an algorithm is proposed to generate additional measurements using the measurement data already gathered in order to solve the observability issue. A network separation algorithm is developed to solve the entire problem for smaller sub-networks which include micro-PMUs to mitigate the adverse effects of noise for huge networks. An algorithm based on redundancy test is also developed for bad data detection. The algorithms are tested on single phase and multiphase test systems. The algorithms are applied EPRI Circuit 5 (2998-bus) test feeder to demonstrate the flexibility of the algorithms developed.
A Coalitional Game for Distributed Inference in Sensor Networks with Dependent Observations
Venue: arXiv cs.IT 2015
Year: 2015

We consider the problem of collaborative inference in a sensor network with heterogeneous and statistically dependent sensor observations. Each sensor aims to maximize its inference performance by forming a coalition with other sensors and sharing information within the coalition. It is proved that the inference performance is a nondecreasing function of the coalition size. However, in an energy constrained network, the energy consumption of inter-sensor communication also increases with increasing coalition size, which discourages the formation of the grand coalition (the set of all sensors). In this paper, the formation of non-overlapping coalitions with statistically dependent sensors is investigated under a specific communication constraint. We apply a game theoretical approach to fully explore and utilize the information contained in the spatial dependence among sensors to maximize individual sensor performance. Before formulating the distributed inference problem as a coalition formation game, we first quantify the gain and loss in forming a coalition by introducing the concepts of diversity gain and redundancy loss for both estimation and detection problems. These definitions, enabled by the statistical theory of copulas, allow us to characterize the influence of statistical dependence among sensor observations on inference performance. An iterative algorithm based on merge-and-split operations is proposed for the solution and the stability of the proposed algorithm is analyzed. Numerical results are provided to demonstrate the superiority of our proposed game theoretical approach.
Probabilistic Clustering of Time-Evolving Distance Data
Venue: arXiv cs.LG 2015
Year: 2015

We present a novel probabilistic clustering model for objects that are represented via pairwise distances and observed at different time points. The proposed method utilizes the information given by adjacent time points to find the underlying cluster structure and obtain a smooth cluster evolution. This approach allows the number of objects and clusters to differ at every time point, and no identification on the identities of the objects is needed. Further, the model does not require the number of clusters being specified in advance -- they are instead determined automatically using a Dirichlet process prior. We validate our model on synthetic data showing that the proposed method is more accurate than state-of-the-art clustering methods. Finally, we use our dynamic clustering model to analyze and illustrate the evolution of brain cancer patients over time.
On the Requirements of New Software Development
Venue: arXiv cs.SE 2015
Year: 2015

Changes, they use to say, are the only constant in life. Everything changes rapidly around us, and more and more key to survival is the ability to rapidly adapt to changes. This consideration applies to many aspects of our lives. Strangely enough, this nearly self-evident truth is not always considered by software engineers with the seriousness that it calls for: The assumptions we draw for our systems often do not take into due account that e.g., the run-time environments, the operational conditions, or the available resources will vary. Software is especially vulnerable to this threat, and with today's software-dominated systems controlling crucial services in nuclear plants, airborne equipments, health care systems and so forth, it becomes clear how this situation may potentially lead to catastrophes. This paper discusses this problem and defines some of the requirements towards its effective solution, which we call "New Software Development" as a software equivalent of the well-known concept of New Product Development. The paper also introduces and discusses a practical example of a software tool designed taking those requirements into account --- an adaptive data integrity provision in which the degree of redundancy is not fixed once and for all at design time, but rather it changes dynamically with respect to the disturbances experienced during the run time.
Near-Optimal Hybrid Processing for Massive MIMO Systems via Matrix Decomposition
Venue: arXiv cs.IT 2015
Year: 2015

For the practical implementation of massive multiple-input multiple-output (MIMO) systems, the hybrid processing (precoding/combining) structure is promising to reduce the high cost rendered by large number of RF chains of the traditional processing structure. The hybrid processing is performed through low-dimensional digital baseband processing combined with analog RF processing enabled by phase shifters. We propose to design hybrid RF and baseband precoders/combiners for multi-stream transmission in point-to-point massive MIMO systems, by directly decomposing the pre-designed unconstrained digital precoder/combiner of a large dimension. The constant amplitude constraint of analog RF processing results in the matrix decomposition problem non-convex. Based on an alternate optimization technique, the non-convex matrix decomposition problem can be decoupled into a series of convex sub-problems and effectively solved by restricting the phase increment of each entry in the RF precoder/combiner within a small vicinity of its preceding iterate. A singular value decomposition based technique is proposed to secure an initial point sufficiently close to the global solution of the original non-convex problem. Through simulation, the convergence of the alternate optimization for such a matrix decomposition based hybrid processing (MD-HP) scheme is examined, and the performance of the MD-HP scheme is demonstrated to be near-optimal.
End-to-end verifiability
Venue: arXiv cs.CR 2015
Year: 2015

This pamphlet describes end-to-end election verifiability (E2E-V) for a nontechnical audience: election officials, public policymakers, and anyone else interested in secure, transparent, evidence-based electronic elections. This work is part of the Overseas Vote Foundation's End-to-End Verifiable Internet Voting: Specification and Feasibility Assessment Study (E2E VIV Project), funded by the Democracy Fund.