OpenRaiser/Intern-Atlas huggingface dataset:
A Simple Way to Initialize Recurrent Networks of Rectified Linear Units
Venue: arXiv cs.NE 2015
Year: 2015

Learning long term dependencies in recurrent networks is difficult due to vanishing and exploding gradients. To overcome this difficulty, researchers have developed sophisticated optimization techniques and network architectures. In this paper, we propose a simpler solution that use recurrent neural networks composed of rectified linear units. Key to our solution is the use of the identity matrix or its scaled version to initialize the recurrent weight matrix. We find that our solution is comparable to LSTM on our four benchmarks: two toy problems involving long-range temporal structures, a large language modeling problem and a benchmark speech recognition problem.
Penetration Testing in Agile Software Development Projects
Venue: arXiv cs.SE 2015
Year: 2015

Agile development methods are commonly used to iteratively develop the information systems and they can easily handle ever-changing business requirements. Scrum is one of the most popular agile software development frameworks. The popularity is caused by the simplified process framework and its focus on teamwork. The objective of Scrum is to deliver working software and demonstrate it to the customer faster and more frequent during the software development project. However the security requirements for the developing information systems have often a low priority. This requirements prioritization issue results in the situations where the solution meets all the business requirements but it is vulnerable to potential security threats. The major benefit of the Scrum framework is the iterative development approach and the opportunity to automate penetration tests. Therefore the security vulnerabilities can be discovered and solved more often which will positively contribute to the overall information system protection against potential hackers. In this research paper the authors propose how the agile software development framework Scrum can be enriched by considering the penetration tests and related security requirements during the software development lifecycle. Authors apply in this paper the knowledge and expertise from their previous work focused on development of the new information system penetration tests methodology PETA with focus on using COBIT 4.1 as the framework for management of these tests, and on previous work focused on tailoring the project management framework PRINCE2 with Scrum.
Outage Analysis of Full-Duplex Architectures in Cellular Networks
Venue: arXiv cs.IT 2015
Year: 2015

The implementation of full-duplex (FD) radio in wireless communications is a potential approach for achieving higher spectral efficiency. A possible application is its employment in the next generation of cellular networks. However, the performance of large-scale FD multiuser networks is an area mostly unexplored. Most of the related work focuses on the performance analysis of small-scale networks or on loop interference cancellation schemes. In this paper, we derive the outage probability performance of large-scale FD cellular networks in the context of two architectures: two-node and three-node. We show how the performance is affected with respect to the model's parameters and provide a comparison between the two architectures.
A parameter free similarity index based on clustering ability for link prediction in complex networks
Venue: arXiv cs.SI 2015
Year: 2015

Link prediction in complex network based on solely topological information is a challenging problem. In this paper, we propose a novel similarity index, which is efficient and parameter free, based on clustering ability. Here clustering ability is defined as average clustering coefficient of nodes with the same degree. The motivation of our idea is that common-neighbors are able to contribute to the likelihood of forming a link because they own some ability of clustering their neighbors together, and then clustering ability defined here is a measure for this capacity. Experimental numerical simulations on both real-world networks and modeled networks demonstrated the high accuracy and high efficiency of the new similarity index compared with three well-known common-neighbor based similarity indices: CN, AA and RA.
Profit Maximizing Prior-free Multi-unit Procurement Auctions with Capacitated Sellers
Venue: arXiv cs.GT 2015
Year: 2015

In this paper, we derive bounds for profit maximizing prior-free procurement auctions where a buyer wishes to procure multiple units of a homogeneous item from n sellers who are strategic about their per unit valuation. The buyer earns the profit by reselling these units in an external consumer market. The paper looks at three scenarios of increasing complexity. First, we look at unit capacity sellers where per unit valuation is private information of each seller and the revenue curve is concave. For this setting, we define two benchmarks. We show that no randomized prior free auction can be constant competitive against any of these two benchmarks. However, for a lightly constrained benchmark we design a prior-free auction PEPA (Profit Extracting Procurement Auction) which is 4-competitive and we show this bound is tight. Second, we study a setting where the sellers have non-unit capacities that are common knowledge and derive similar results. In particular, we propose a prior free auction PEPAC (Profit Extracting Procurement Auction with Capacity) which is truthful for any concave revenue curve. Third, we obtain results in the inherently harder bi-dimensional case where per unit valuation as well as capacities are private information of the sellers. We show that PEPAC is truthful and constant competitive for the specific case of linear revenue curves. We believe that this paper represents the first set of results on single dimensional and bi-dimensional profit maximizing prior-free multi-unit procurement auctions.
Finite element numerical integration for first order approximations on multi-core architectures
Venue: arXiv cs.MS 2015
Year: 2015

The paper presents investigations on the implementation and performance of the finite element numerical integration algorithm for first order approximations and three processor architectures, popular in scientific computing, classical CPU, Intel Xeon Phi and NVIDIA Kepler GPU. A unifying programming model and portable OpenCL implementation is considered for all architectures. Variations of the algorithm due to different problems solved and different element types are investigated and several optimizations aimed at proper optimization and mapping of the algorithm to computer architectures are demonstrated. Performance models of execution are developed for different processors and tested in practical experiments. The results show the varying levels of performance for different architectures, but indicate that the algorithm can be effectively ported to all of them. The general conclusion is that the finite element numerical integration can achieve sufficient performance on different multi- and many-core architectures and should not become a performance bottleneck for finite element simulation codes. Specific observations lead to practical advises on how to optimize the kernels and what performance can be expected for the tested architectures.
An $\tilde{O}(\frac{1}{\sqrt{T}})$-error online algorithm for retrieving heavily perturbated statistical databases in the low-dimensional querying mode
Venue: arXiv cs.DB 2015
Year: 2015

We give the first $\tilde{O}(\frac{1}{\sqrt{T}})$-error online algorithm for reconstructing noisy statistical databases, where $T$ is the number of (online) sample queries received. The algorithm, which requires only $O(\log T)$ memory, aims to learn a hidden database-vector $w^{*} \in \mathbb{R}^{D}$ in order to accurately answer a stream of queries regarding the hidden database, which arrive in an online fashion from some unknown distribution $\mathcal{D}$. We assume the distribution $\mathcal{D}$ is defined on the neighborhood of a low-dimensional manifold. The presented algorithm runs in $O(dD)$-time per query, where $d$ is the dimensionality of the query-space. Contrary to the classical setting, there is no separate training set that is used by the algorithm to learn the database --- the stream on which the algorithm will be evaluated must also be used to learn the database-vector. The algorithm only has access to a binary oracle $\mathcal{O}$ that answers whether a particular linear function of the database-vector plus random noise is larger than a threshold, which is specified by the algorithm. We note that we allow for a significant $O(D)$ amount of noise to be added while other works focused on the low noise $o(\sqrt{D})$-setting. For a stream of $T$ queries our algorithm achieves an average error $\tilde{O}(\frac{1}{\sqrt{T}})$ by filtering out random noise, adapting threshold values given to the oracle based on its previous answers and, as a consequence, recovering with high precision a projection of a database-vector $w^{*}$ onto the manifold defining the query-space.
Learning how to rank from heavily perturbed statistics - digraph clustering approach
Venue: arXiv cs.DS 2015
Year: 2015

Ranking is one of the most fundamental problems in machine learning with applications in many branches of computer science such as: information retrieval systems, recommendation systems, machine translation and computational biology. Ranking objects based on possibly conflicting preferences is a central problem in voting research and social choice theory. In this paper we present a new simple combinatorial ranking algorithm adapted to the preference-based setting. We apply this new algorithm to the well-known scenario where the edges of the preference tournament are determined by the majority-voting model. It outperforms existing methods when it cannot be assumed that there exists global ranking of good enough quality and applies combinatorial techniques that havent been used in the ranking context before. Performed experiments show the superiority of the new algorithm over existing methods, also over these that were designed to handle heavily perturbed statistics. By combining our techniques with those presented in \cite{mohri}, we obtain a purely combinatorial algorithm that answers correctly most of the queries in the heterogeneous scenario, where the preference tournament is only locally of good quality but is not necessarily pseudotransitive. As a byproduct of our methods, we obtain the algorithm solving clustering problem for the directed planted partition model. To the best of our knowledge, it is the first purely combinatorial algorithm tackling this problem.
Dualization in Lattices Given by Ordered Sets of Irreducibles
Venue: arXiv cs.LO 2015
Year: 2015

Dualization of a monotone Boolean function on a finite lattice can be represented by transforming the set of its minimal 1 to the set of its maximal 0 values. In this paper we consider finite lattices given by ordered sets of their meet and join irreducibles (i.e., as a concept lattice of a formal context). We show that in this case dualization is equivalent to the enumeration of so-called minimal hypotheses. In contrast to usual dualization setting, where a lattice is given by the ordered set of its elements, dualization in this case is shown to be impossible in output polynomial time unless P = NP. However, if the lattice is distributive, dualization is shown to be possible in subexponential time.
Byzantine Attack and Defense in Cognitive Radio Networks: A Survey
Venue: arXiv cs.NI 2015
Year: 2015

The Byzantine attack in cooperative spectrum sensing (CSS), also known as the spectrum sensing data falsification (SSDF) attack in the literature, is one of the key adversaries to the success of cognitive radio networks (CRNs). In the past couple of years, the research on the Byzantine attack and defense strategies has gained worldwide increasing attention. In this paper, we provide a comprehensive survey and tutorial on the recent advances in the Byzantine attack and defense for CSS in CRNs. Specifically, we first briefly present the preliminaries of CSS for general readers, including signal detection techniques, hypothesis testing, and data fusion. Second, we analyze the spear and shield relation between Byzantine attack and defense from three aspects: the vulnerability of CSS to attack, the obstacles in CSS to defense, and the games between attack and defense. Then, we propose a taxonomy of the existing Byzantine attack behaviors and elaborate on the corresponding attack parameters, which determine where, who, how, and when to launch attacks. Next, from the perspectives of homogeneous or heterogeneous scenarios, we classify the existing defense algorithms, and provide an in-depth tutorial on the state-of-the-art Byzantine defense schemes, commonly known as robust or secure CSS in the literature. Furthermore, we highlight the unsolved research challenges and depict the future research directions.
Preprint Traffic Management and Forecasting System Based on 3D GIS
Venue: arXiv cs.OH 2015
Year: 2015

This is the preprint version of our paper on 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). This paper takes Shenzhen Futian comprehensive transportation junction as the case, and makes use of continuous multiple real-time dynamic traffic information to carry out monitoring and analysis on spatial and temporal distribution of passenger flow under different means of transportation and service capacity of junction from multi-dimensional space-time perspectives such as different period and special period. Virtual reality geographic information system is employed to present the forecasting result.
Shannon's entropy revisited
Venue: arXiv cs.IT 2015
Year: 2015

I consider the effect of a finite sample size on the entropy of a sample of independent events. I propose formula for entropy which satisfies Shannon's axioms, and which reduces to Shannon's entropy when sample size is infinite. I discuss the physical meaning of the difference between two formulas, including some practical implications, such as maximum achievable channel utilization, and minimum achievable communication protocol overhead, for a given message size.
Deep Recurrent Neural Networks for Acoustic Modelling
Venue: arXiv cs.LG 2015
Year: 2015

We present a novel deep Recurrent Neural Network (RNN) model for acoustic modelling in Automatic Speech Recognition (ASR). We term our contribution as a TC-DNN-BLSTM-DNN model, the model combines a Deep Neural Network (DNN) with Time Convolution (TC), followed by a Bidirectional Long Short-Term Memory (BLSTM), and a final DNN. The first DNN acts as a feature processor to our model, the BLSTM then generates a context from the sequence acoustic signal, and the final DNN takes the context and models the posterior probabilities of the acoustic states. We achieve a 3.47 WER on the Wall Street Journal (WSJ) eval92 task or more than 8% relative improvement over the baseline DNN models.
ReHub. Extending Hub Labels for Reverse k-Nearest Neighbor Queries on Large-Scale networks
Venue: arXiv cs.DS 2015
Year: 2015

Quite recently, the algorithmic community has focused on solving multiple shortest-path query problems beyond simple vertex-to-vertex queries, especially in the context of road networks. Unfortunately, this research cannot be generalized for large-scale graphs, e.g., social or collaboration networks, or to efficiently answer Reverse k-Nearest Neighbor (RkNN) queries, which are of practical relevance to a wide range of applications. To remedy this, we propose ReHub, a novel main-memory algorithm that extends the Hub Labeling technique to efficiently answer RkNN queries on large-scale networks. Our experimentation will show that ReHub is the best overall solution for this type of queries, requiring only minimal preprocessing and providing very fast query times.
Service Discovery and Trust in Mobile Social Network in Proximity
Venue: arXiv cs.SI 2015
Year: 2015

Service-oriented Mobile Social Network in Proximity (MSNP) lets participants establish new social interactions with strangers in public proximity using heterogeneous platforms and devices. Such characteristic faces challenges in discovery latency and trustworthiness. In a public service-oriented MSNP environment, which consists of a large number of participants, a content requester who searches for a particular service provided by other MSNP participants will need to retrieve and process a large number of Service Description Metadata (SDM) files, associated semantic metadata files and identifying the trustworthiness of the content providers. Performing such tasks on a resource constraint mobile device can be time consuming, and the overall discovery performance will be affected and will result in high latency. This paper analyses the service discovery models of MSNP and presents corresponding solutions to improve the service discovery performance of MSNP. We firstly present and analyse the basic service discovery models of service-oriented MSNP. To follow up, we apply a context-aware user preference prediction scheme to enhance the speed of the semantic service discovery process. Later, we address the trustworthiness issue in MSNP and propose a scheme to reduce the latency of the trustworthy service discovery for MSNP. The proposed scheme has been tested and evaluated on MSNP application prototype operating on real mobile devices and MSNP simulation environments.
Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing
Venue: arXiv cs.CV 2015
Year: 2015

In a plethora of applications dealing with inverse problems, e.g. in image processing, social networks, compressive sensing, biological data processing etc., the signal of interest is known to be structured in several ways at the same time. This premise has recently guided the research to the innovative and meaningful idea of imposing multiple constraints on the parameters involved in the problem under study. For instance, when dealing with problems whose parameters form sparse and low-rank matrices, the adoption of suitably combined constraints imposing sparsity and low-rankness, is expected to yield substantially enhanced estimation results. In this paper, we address the spectral unmixing problem in hyperspectral images. Specifically, two novel unmixing algorithms are introduced, in an attempt to exploit both spatial correlation and sparse representation of pixels lying in homogeneous regions of hyperspectral images. To this end, a novel convex mixed penalty term is first defined consisting of the sum of the weighted $\ell_1$ and the weighted nuclear norm of the abundance matrix corresponding to a small area of the image determined by a sliding square window. This penalty term is then used to regularize a conventional quadratic cost function and impose simultaneously sparsity and row-rankness on the abundance matrix. The resulting regularized cost function is minimized by a) an incremental proximal sparse and low-rank unmixing algorithm and b) an algorithm based on the alternating minimization method of multipliers (ADMM). The effectiveness of the proposed algorithms is illustrated in experiments conducted both on simulated and real data.
Energy saving market for mobile operators
Venue: arXiv cs.NI 2015
Year: 2015

Ensuring seamless coverage accounts for the lion's share of the energy consumed in a mobile network. Overlapping coverage of three to five mobile network operators (MNOs) results in enormous amount of energy waste which is avoidable. The traffic demands of the mobile networks vary significantly throughout the day. As the offered load for all networks are not same at a given time and the differences in energy consumption at different loads are significant, multi-MNO capacity/coverage sharing can dramatically reduce energy consumption of mobile networks and provide the MNOs a cost effective means to cope with the exponential growth of traffic. In this paper, we propose an energy saving market for a multi-MNO network scenario. As the competing MNOs are not comfortable with information sharing, we propose a double auction clearinghouse market mechanism where MNOs sell and buy capacity in order to minimize energy consumption. In our setting, each MNO proposes its bids and asks simultaneously for buying and selling multi-unit capacities respectively to an independent auctioneer, i.e., clearinghouse and ends up either as a buyer or as a seller in each round. We show that the mechanism allows the MNOs to save significant percentage of energy cost throughout a wide range of network load. Different than other energy saving features such as cell sleep or antenna muting which can not be enabled at heavy traffic load, dynamic capacity sharing allows MNOs to handle traffic bursts with energy saving opportunity.
Byzantine Gathering in Networks
Venue: arXiv cs.DC 2015
Year: 2015

This paper investigates an open problem introduced in [14]. Two or more mobile agents start from different nodes of a network and have to accomplish the task of gathering which consists in getting all together at the same node at the same time. An adversary chooses the initial nodes of the agents and assigns a different positive integer (called label) to each of them. Initially, each agent knows its label but does not know the labels of the other agents or their positions relative to its own. Agents move in synchronous rounds and can communicate with each other only when located at the same node. Up to f of the agents are Byzantine. A Byzantine agent can choose an arbitrary port when it moves, can convey arbitrary information to other agents and can change its label in every round, in particular by forging the label of another agent or by creating a completely new one. What is the minimum number M of good agents that guarantees deterministic gathering of all of them, with termination? We provide exact answers to this open problem by considering the case when the agents initially know the size of the network and the case when they do not. In the former case, we prove M=f+1 while in the latter, we prove M=f+2. More precisely, for networks of known size, we design a deterministic algorithm gathering all good agents in any network provided that the number of good agents is at least f+1. For networks of unknown size, we also design a deterministic algorithm ensuring the gathering of all good agents in any network but provided that the number of good agents is at least f+2. Both of our algorithms are optimal in terms of required number of good agents, as each of them perfectly matches the respective lower bound on M shown in [14], which is of f+1 when the size of the network is known and of f+2 when it is unknown.
Quickest Eigenvalue-Based Spectrum Sensing using Random Matrix Theory
Venue: arXiv cs.IT 2015
Year: 2015

We investigate the potential of quickest detection based on the eigenvalues of the sample covariance matrix for spectrum sensing applications. A simple phase shift keying (PSK) model with additive white Gaussian noise (AWGN), with $1$ primary user (PU) and $K$ secondary users (SUs) is considered. Under both detection hypotheses $\mathcal{H}_0$ (noise only) and $\mathcal{H}_1$ (signal + noise) the eigenvalues of the sample covariance matrix follow Wishart distributions. For the case of $K = 2$ SUs, we derive an analytical formulation of the probability density function (PDF) of the maximum-minimum eigenvalue (MME) detector under $\mathcal{H}_1$. Utilizing results from the literature under $\mathcal{H}_0$, we investigate two detection schemes. First, we calculate the receiver operator characteristic (ROC) for MME block detector based on analytical results. Second, we introduce two eigenvalue-based quickest detection algorithms: a cumulative sum (CUSUM) algorithm, when the signal-to-noise ratio (SNR) of the PU signal is known and an algorithm using the generalized likelihood ratio, in case the SNR is unknown. Bounds on the mean time to false-alarm $τ_\text{fa}$ and the mean time to detection $τ_\text{d}$ are given for the CUSUM algorithm. Numerical simulations illustrate the potential advantages of the quickest detection approach over the block detection scheme.