OpenRaiser/Intern-Atlas huggingface dataset:
Generalized Interference Alignment --- Part I: Theoretical Framework
Venue: arXiv cs.IT 2015
Year: 2015

Interference alignment (IA) has attracted enormous research interest as it achieves optimal capacity scaling with respect to signal to noise ratio on interference networks. IA has also recently emerged as an effective tool in engineering interference for secrecy protection on wireless wiretap networks. However, despite the numerous works dedicated to IA, two of its fundamental issues, i.e., feasibility conditions and transceiver design, are not completely addressed in the literature. In this two part paper, a generalised interference alignment (GIA) technique is proposed to enhance the IA's capability in secrecy protection. A theoretical framework is established to analyze the two fundamental issues of GIA in Part I and then the performance of GIA in large-scale stochastic networks is characterized to illustrate how GIA benefits secrecy protection in Part II. The theoretical framework for GIA adopts methodologies from algebraic geometry, determines the necessary and sufficient feasibility conditions of GIA, and generates a set of algorithms that can solve the GIA problem. This framework sets up a foundation for the development and implementation of GIA.
Generalized Interference Alignment --- Part II: Application to Wireless Secrecy
Venue: arXiv cs.IT 2015
Year: 2015

In contrast to its wired counterpart, wireless communication is highly susceptible to eavesdropping due to the broadcast nature of the wireless propagation medium. Recent works have proposed the use of interference to reduce eavesdropping capabilities in wireless wiretap networks. However, the concurrent effect of interference on both eavesdropping receivers (ERs) and legitimate receivers (LRs) has not been thoroughly investigated, and carefully engineering the network interference is required to harness the full potential of interference for wireless secrecy. This two part paper addresses this issue by proposing a generalized interference alignment (GIA) technique, which jointly designs the transceivers at the legitimate partners to impede the ERs without interfering with LRs. In Part I, we have established a theoretical framework for the GIA technique. In Part II, we will first propose an efficient GIA algorithm that is applicable to large-scale networks and then evaluate the performance of this algorithm in stochastic wireless wiretap network via both analysis and simulation. These results reveal insights into when and how GIA contributes to wireless secrecy.
On the tradeoff of average delay, average service cost, and average utility for single server queues with monotone policies
Venue: arXiv cs.PF 2015
Year: 2015

In this thesis, we study the optimal tradeoff of average delay, average service cost, and average utility for single server queueing models, with and without admission control. The continuous time and discrete time queueing models that we consider are motivated by cross-layer models for noisy point-to-point links, with random packet arrivals. We study the above tradeoff problem for a class of admissible policies, which are monotone and stationary and obtain an asymptotic characterization of the minimum average delay as a function of the average service cost and average utility constraints.
Unsupervised model compression for multilayer bootstrap networks
Venue: arXiv cs.LG 2015
Year: 2015

Recently, multilayer bootstrap network (MBN) has demonstrated promising performance in unsupervised dimensionality reduction. It can learn compact representations in standard data sets, i.e. MNIST and RCV1. However, as a bootstrap method, the prediction complexity of MBN is high. In this paper, we propose an unsupervised model compression framework for this general problem of unsupervised bootstrap methods. The framework compresses a large unsupervised bootstrap model into a small model by taking the bootstrap model and its application together as a black box and learning a mapping function from the input of the bootstrap model to the output of the application by a supervised learner. To specialize the framework, we propose a new technique, named compressive MBN. It takes MBN as the unsupervised bootstrap model and deep neural network (DNN) as the supervised learner. Our initial result on MNIST showed that compressive MBN not only maintains the high prediction accuracy of MBN but also is over thousands of times faster than MBN at the prediction stage. Our result suggests that the new technique integrates the effectiveness of MBN on unsupervised learning and the effectiveness and efficiency of DNN on supervised learning together for the effectiveness and efficiency of compressive MBN on unsupervised learning.
Model predictive controllers for reduction of mechanical fatigue in wind farms
Venue: arXiv cs.SY 2015
Year: 2015

We consider the problem of dispatching WindFarm (WF) power demand to individual Wind Turbines (WT) with the goal of minimizing mechanical stresses. We assume wind is strong enough to let each WTs to produce the required power and propose different closed-loop Model Predictive Control (MPC) dispatching algorithms. Similarly to existing approaches based on MPC, our methods do not require changes in WT hardware but only software changes in the SCADA system of the WF. However, differently from previous MPC schemes, we augment the model of a WT with an ARMA predictor of the wind turbulence, which reduces uncertainty in wind predictions over the MPC control horizon. This allows us to develop both stochastic and deterministic MPC algorithms. In order to compare different MPC schemes and demonstrate improvements with respect to classic open-loop schedulers, we performed simulations using the SimWindFarm toolbox for MatLab. We demonstrate that MPC controllers allow to achieve reduction of stresses even in the case of large installations such as the 100-WTs Thanet offshore WF.
Privacy and the City: User Identification and Location Semantics in Location-Based Social Networks
Venue: arXiv cs.CR 2015
Year: 2015

With the advent of GPS enabled smartphones, an increasing number of users is actively sharing their location through a variety of applications and services. Along with the continuing growth of Location-Based Social Networks (LBSNs), security experts have increasingly warned the public of the dangers of exposing sensitive information such as personal location data. Most importantly, in addition to the geographical coordinates of the user's location, LBSNs allow easy access to an additional set of characteristics of that location, such as the venue type or popularity. In this paper, we investigate the role of location semantics in the identification of LBSN users. We simulate a scenario in which the attacker's goal is to reveal the identity of a set of LBSN users by observing their check-in activity. We then propose to answer the following question: what are the types of venues that a malicious user has to monitor to maximize the probability of success? Conversely, when should a user decide whether to make his/her check-in to a location public or not? We perform our study on more than 1 million check-ins distributed over 17 urban regions of the United States. Our analysis shows that different types of venues display different discriminative power in terms of user identity, with most of the venues in the "Residence" category providing the highest re-identification success across the urban regions. Interestingly, we also find that users with a high entropy of their check-ins distribution are not necessarily the hardest to identify, suggesting that it is the collective behaviour of the users' population that determines the complexity of the identification task, rather than the individual behaviour.
A Class of Two-Weight and Three-Weight Codes and Their Applications in Secret Sharing
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, a class of two-weight and three-weight linear codes over $\gf(p)$ is constructed, and their application in secret sharing is investigated. Some of the linear codes obtained are optimal in the sense that they meet certain bounds on linear codes. These codes have applications also in authentication codes, association schemes, and strongly regular graphs, in addition to their applications in consumer electronics, communication and data storage systems.
GAIL---Guaranteed Automatic Integration Library in MATLAB: Documentation for Version 2.1
Venue: arXiv cs.MS 2015
Year: 2015

Automatic and adaptive approximation, optimization, or integration of functions in a cone with guarantee of accuracy is a relatively new paradigm. Our purpose is to create an open-source MATLAB package, Guaranteed Automatic Integration Library (GAIL), following the philosophy of reproducible research and sustainable practices of robust scientific software development. For our conviction that true scholarship in computational sciences are characterized by reliable reproducibility, we employ the best practices in mathematical research and software engineering known to us and available in MATLAB. This document describes the key features of functions in GAIL, which includes one-dimensional function approximation and minimization using linear splines, one-dimensional numerical integration using trapezoidal rule, and last but not least, mean estimation and multidimensional integration by Monte Carlo methods or Quasi Monte Carlo methods.
An Item-Based Collaborative Filtering using Dimensionality Reduction Techniques on Mahout Framework
Venue: arXiv cs.IR 2015
Year: 2015

Collaborative Filtering is the most widely used prediction technique in Recommendation System. Most of the current CF recommender systems maintains single criteria user rating in user item matrix. However, recent studies indicate that recommender system depending on multi criteria can improve prediction and accuracy levels of recommendation by considering the user preferences in multi aspects of items. This gives birth to Multi Criteria Collaborative Filtering. In MC CF users provide the rating on multiple aspects of an item in new dimensions,thereby increasing the size of rating matrix, sparsity and scalability problem. Appropriate dimensionality reduction techniques are thus needed to take care of these challenges to reduce the dimension of user item rating matrix to improve the prediction accuracy and efficiency of CF recommender system. The process of dimensionality reduction maps the high dimensional input space into lower dimensional space. Thus, the objective of this paper is to propose an efficient MC CF algorithm using dimensionality reduction technique to improve the recommendation quality and prediction accuracy. Dimensionality reduction techniques such as Singular Value Decomposition and Principal Component Analysis are used to solve the scalability and alleviate the sparsity problems in overall rating. The proposed MC CF approach will be implemented using Apache Mahout, which allows processing of massive dataset stored in distributed/non-distributed file system.
A Machine Learning Approach to Predicting the Smoothed Complexity of Sorting Algorithms
Venue: arXiv cs.LG 2015
Year: 2015

Smoothed analysis is a framework for analyzing the complexity of an algorithm, acting as a bridge between average and worst-case behaviour. For example, Quicksort and the Simplex algorithm are widely used in practical applications, despite their heavy worst-case complexity. Smoothed complexity aims to better characterize such algorithms. Existing theoretical bounds for the smoothed complexity of sorting algorithms are still quite weak. Furthermore, empirically computing the smoothed complexity via its original definition is computationally infeasible, even for modest input sizes. In this paper, we focus on accurately predicting the smoothed complexity of sorting algorithms, using machine learning techniques. We propose two regression models that take into account various properties of sorting algorithms and some of the known theoretical results in smoothed analysis to improve prediction quality. We show experimental results for predicting the smoothed complexity of Quicksort, Mergesort, and optimized Bubblesort for large input sizes, therefore filling the gap between known theoretical and empirical results.
Dynamic Power Splitting Policies for AF Relay Networks with Wireless Energy Harvesting
Venue: arXiv cs.IT 2015
Year: 2015

Wireless energy harvesting (WEH) provides an exciting way to supply energy for relay nodes to forward information for the source-destination pairs. In this paper, we investigate the problem on how the relay node dynamically adjusts the power splitting ratio of information transmission (IT) and energy harvesting (EH) in order to achieve the optimal outage performance. According to the knowledge of channel state information (CSI) at the relay, optimal dynamic power splitting policy with full CSI and partial CSI are both provided. Finally, through simulations, the proposed power splitting policies can improve the outage performances and the policy with full CSI achieves the best performance. It is also shown that the policy with partial CSI can approach the policy with full CSI closely and incurs far less system overhead.
Proficiency Comparison of LADTree and REPTree Classifiers for Credit Risk Forecast
Venue: arXiv cs.LG 2015
Year: 2015

Predicting the Credit Defaulter is a perilous task of Financial Industries like Banks. Ascertaining non-payer before giving loan is a significant and conflict-ridden task of the Banker. Classification techniques are the better choice for predictive analysis like finding the claimant, whether he/she is an unpretentious customer or a cheat. Defining the outstanding classifier is a risky assignment for any industrialist like a banker. This allow computer science researchers to drill down efficient research works through evaluating different classifiers and finding out the best classifier for such predictive problems. This research work investigates the productivity of LADTree Classifier and REPTree Classifier for the credit risk prediction and compares their fitness through various measures. German credit dataset has been taken and used to predict the credit risk with a help of open source machine learning tool.
Vehicle Local Position Estimation System
Venue: arXiv cs.CV 2015
Year: 2015

In this paper, a robust vehicle local position estimation with the help of single camera sensor and GPS is presented. A modified Inverse Perspective Mapping, illuminant Invariant techniques and object detection based approach is used to localize the vehicle in the road. Vehicles current lane, its position from road boundary and other cars are used to define its local position. For this purpose Lane markings are detected using a Laplacian edge feature, robust to shadowing. Effect of shadowing and extra sun light are removed using Lab color space and illuminant invariant techniques. Lanes are assumed to be as parabolic model and fitted using robust RANSAC. This method can reliably detect all lanes of the road, estimate lane departure angle and local position of vehicle relative to lanes, road boundary and other cars. Different type of obstacle like pedestrians, vehicles are detected using HOG feature based deformable part model.
Improving M-SBL for Joint Sparse Recovery using a Subspace Penalty
Venue: arXiv cs.IT 2015
Year: 2015

The multiple measurement vector problem (MMV) is a generalization of the compressed sensing problem that addresses the recovery of a set of jointly sparse signal vectors. One of the important contributions of this paper is to reveal that the seemingly least related state-of-art MMV joint sparse recovery algorithms - M-SBL (multiple sparse Bayesian learning) and subspace-based hybrid greedy algorithms - have a very important link. More specifically, we show that replacing the $\log\det(\cdot)$ term in M-SBL by a rank proxy that exploits the spark reduction property discovered in subspace-based joint sparse recovery algorithms, provides significant improvements. In particular, if we use the Schatten-$p$ quasi-norm as the corresponding rank proxy, the global minimiser of the proposed algorithm becomes identical to the true solution as $p \rightarrow 0$. Furthermore, under the same regularity conditions, we show that the convergence to a local minimiser is guaranteed using an alternating minimization algorithm that has closed form expressions for each of the minimization steps, which are convex. Numerical simulations under a variety of scenarios in terms of SNR, and condition number of the signal amplitude matrix demonstrate that the proposed algorithm consistently outperforms M-SBL and other state-of-the art algorithms.
Broadbeam for Massive MIMO Systems
Venue: arXiv cs.IT 2015
Year: 2015

Massive MIMO has been identified as one of the promising disruptive air interface techniques to address the huge capacity requirement demanded by 5G wireless communications. For practical deployment of such systems, the control message need to be broadcast to all users reliably in the cell using broadbeam. A broadbeam is expected to have the same radiated power in all directions to cover users in any place in a cell. In this paper, we will show that there is no perfect broadbeam. Therefore, we develop a method for generating broadbeam that can allow tiny fluctuations in radiated power. Overall, this can serve as an ingredient for practical deployment of the massive MIMO systems.
Comparing published multi-label classifier performance measures to the ones obtained by a simple multi-label baseline classifier
Venue: arXiv cs.LG 2015
Year: 2015

In supervised learning, simple baseline classifiers can be constructed by only looking at the class, i.e., ignoring any other information from the dataset. The single-label learning community frequently uses as a reference the one which always predicts the majority class. Although a classifier might perform worse than this simple baseline classifier, this behaviour requires a special explanation. Aiming to motivate the community to compare experimental results with the ones provided by a multi-label baseline classifier, calling the attention about the need of special explanations related to classifiers which perform worse than the baseline, in this work we propose the use of General_B, a multi-label baseline classifier. General_B was evaluated in contrast to results published in the literature which were carefully selected using a systematic review process. It was found that a considerable number of published results on 10 frequently used datasets are worse than or equal to the ones obtained by General_B, and for one dataset it reaches up to 43% of the dataset published results. Moreover, although a simple baseline classifier was not considered in these publications, it was observed that even for very poor results no special explanations were provided in most of them. We hope that the findings of this work would encourage the multi-label community to consider the idea of using a simple baseline classifier, such that further explanations are provided when a classifiers performs worse than a baseline.
Fast keypoint detection in video sequences
Venue: arXiv cs.CV 2015
Year: 2015

A number of computer vision tasks exploit a succinct representation of the visual content in the form of sets of local features. Given an input image, feature extraction algorithms identify a set of keypoints and assign to each of them a description vector, based on the characteristics of the visual content surrounding the interest point. Several tasks might require local features to be extracted from a video sequence, on a frame-by-frame basis. Although temporal downsampling has been proven to be an effective solution for mobile augmented reality and visual search, high temporal resolution is a key requirement for time-critical applications such as object tracking, event recognition, pedestrian detection, surveillance. In recent years, more and more computationally efficient visual feature detectors and decriptors have been proposed. Nonetheless, such approaches are tailored to still images. In this paper we propose a fast keypoint detection algorithm for video sequences, that exploits the temporal coherence of the sequence of keypoints. According to the proposed method, each frame is preprocessed so as to identify the parts of the input frame for which keypoint detection and description need to be performed. Our experiments show that it is possible to achieve a reduction in computational time of up to 40%, without significantly affecting the task accuracy.
Coordinated Multibeam Satellite Co-location: The Dual Satellite Paradigm
Venue: arXiv cs.IT 2015
Year: 2015

In the present article, a new system architecture for the next generation of satellite communication (SatComs) is presented. The key concept lies in the collaboration between multibeam satellites that share one orbital position. Multi-satellite constellations in unique orbital slots offer gradual deployment to cover unpredictable traffic patterns and redundancy to hardware failure advantages. They are also of high relevance during the satellite replacement phases or necessitated by constraints in the maximum communications payload that a single satellite can bear. In this context, the potential gains of advanced architectures, that is architectures enabled by the general class of cooperative and cognitive techniques, are exhibited via a simple paradigm. More specifically, the scenario presented herein, involves two co-existing multibeam satellites which illuminate overlapping coverage areas. Based on this scenario, specific types of cooperative and cognitive techniques are herein considered as candidate technologies that can boost the performance of multibeam satellite constellations. These techniques are compared to conventional frequency splitting configurations in terms of three different criteria, namely the spectral efficiency, the power efficiency and the fairness. Consequently, insightful guidelines for the design of future high throughput constellations of multibeam satellites are given.
Big Data and the Internet of Things
Venue: arXiv cs.CY 2015
Year: 2015

Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things - IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.
On Reverse Pinsker Inequalities
Venue: arXiv cs.IT 2015
Year: 2015

New upper bounds on the relative entropy are derived as a function of the total variation distance. One bound refines an inequality by Verdú for general probability measures. A second bound improves the tightness of an inequality by Csiszár and Talata for arbitrary probability measures that are defined on a common finite set. The latter result is further extended, for probability measures on a finite set, leading to an upper bound on the Rényi divergence of an arbitrary non-negative order (including $\infty$) as a function of the total variation distance. Another lower bound by Verdú on the total variation distance, expressed in terms of the distribution of the relative information, is tightened and it is attained under some conditions. The effect of these improvements is exemplified.