OpenRaiser/Intern-Atlas huggingface dataset:
The Tradeoff Analysis in RF-Powered Backscatter Cognitive Radio Networks
Venue: arXiv cs.NI 2016
Year: 2016

In this paper, we introduce a new model for RF-powered cognitive radio networks with the aim to improve the performance for secondary systems. In our proposed model, when the primary channel is busy, the secondary transmitter is able either to backscatter the primary signals to transmit data to the secondary receiver or to harvest RF energy from the channel. The harvested energy then will be used to transmit data to the receiver when the channel becomes idle. We first analyze the tradeoff between backscatter communication and harvest-then-transmit protocol in the network. To maximize the overall transmission rate of the secondary network, we formulate an optimization problem to find time ratio between taking backscatter and harvest-then-transmit modes. Through numerical results, we show that under the proposed model can achieve the overall transmission rate higher than using either the backscatter communication or the harvest-then-transmit protocol.
Cooperative Wi-Fi Deployment: A One-to-Many Bargaining Framework
Venue: arXiv cs.GT 2016
Year: 2016

We study the cooperation of the mobile network operator (MNO) and the venue owners (VOs) on the public Wi-Fi deployment. We consider a one-to-many bargaining framework, where the MNO bargains with VOs sequentially to determine where to deploy Wi-Fi and how much to pay. Taking into account the negative externalities among different steps of bargaining, we analyze the following two cases: for the exogenous bargaining sequence case, we compute the optimal bargaining solution on the cooperation decisions and payments under a predetermined bargaining sequence; for the endogenous bargaining sequence case, the MNO decides the bargaining sequence to maximize its payoff. Through exploring the structural property of the optimal bargaining sequence, we design a low-complexity Optimal VO Bargaining Sequencing (OVBS) algorithm to search the optimal sequence. More specifically, we categorize the VOs into three types based on the impact of the Wi-Fi deployment at their venues, and show that it is optimal for the MNO to bargain with these three types of VOs sequentially. Numerical results show that compared with the random and worst bargaining sequences, the optimal bargaining sequence improves the MNO's payoff by up to 14.8% and 45.3%, respectively.
Wireless-Powered Two-Way Relaying with Power Splitting-based Energy Accumulation
Venue: arXiv cs.IT 2016
Year: 2016

This paper investigates a wireless-powered two-way relay network (WP-TWRN), in which two sources exchange information with the aid of one amplify-and-forward (AF) relay. Contrary to the conventional two-way relay networks, we consider the scenario that the AF relay has no embedded energy supply, and it is equipped with an energy harvesting unit and rechargeable battery. As such, it can accumulate the energy harvested from both sources' signals before helping forwarding their information. In this paper, we develop a power splitting-based energy accumulation (PS-EA) scheme for the considered WP-TWRN. To determine whether the relay has accumulated sufficient energy, we set a predefined energy threshold for the relay. When the accumulated energy reaches the threshold, relay splits the received signal power into two parts, one for energy harvesting and the other for information forwarding. If the stored energy at the relay is below the threshold, all the received signal power will be accumulated at the relay's battery. By modeling the finite-capacity battery of relay as a finite-state Markov Chain (MC), we derive a closed-form expression for the system throughput of the proposed PS-EA scheme over Nakagami-m fading channels. Numerical results validate our theoretical analysis and show that the proposed PS-EA scheme outperforms the conventional time switching-based energy accumulation (TS-EA) scheme and the existing power splitting schemes without energy accumulation.
Fusing Deep Convolutional Networks for Large Scale Visual Concept Classification
Venue: arXiv cs.CV 2016
Year: 2016

Deep learning architectures are showing great promise in various computer vision domains including image classification, object detection, event detection and action recognition. In this study, we investigate various aspects of convolutional neural networks (CNNs) from the big data perspective. We analyze recent studies and different network architectures both in terms of running time and accuracy. We present extensive empirical information along with best practices for big data practitioners. Using these best practices we propose efficient fusion mechanisms both for single and multiple network models. We present state-of-the art results on benchmark datasets while keeping computational costs at a lower level. Another contribution of our paper is that these state-of-the-art results can be reached without using extensive data augmentation techniques.
Perfectly Secure Message Transmission in Two Rounds
Venue: arXiv cs.CR 2016
Year: 2016

In the model that has become known as "Perfectly Secure Message Transmission"(PSMT), a sender Alice is connected to a receiver Bob through n parallel two-way channels. A computationally unbounded adversary Eve controls t of these channels, meaning she can acquire and alter any data that is transmitted over these channels. The sender Alice wishes to communicate a secret message to Bob privately and reliably, i.e. in such a way that Eve will not get any information about the message while Bob will be able to recover it completely. In this paper, we focus on protocols that work in two transmission rounds for n= 2t+1. We break from previous work by following a conceptually simpler blueprint for achieving a PSMT protocol. We reduce the previously best-known communication complexity, i.e. the number of transmitted bits necessary to communicate a 1-bit secret, from O(n^3 log n) to O(n^2 log n). Our protocol also answers a question raised by Kurosawa and Suzuki and hitherto left open: their protocol reaches optimal transmission rate for a secret of size O(n^2 log n) bits, and the authors raised the problem of lowering this threshold. The present solution does this for a secret of O(n log n) bits. Additionally, we show how our protocol can be adapted to a Network Coding context.
AngularJS in the Wild: A Survey with 460 Developers
Venue: arXiv cs.SE 2016
Year: 2016

To implement modern web applications, a new family of JavaScript frameworks has emerged, using the MVC pattern. Among these frameworks, the most popular one is AngularJS, which is supported by Google. In spite of its popularity, there is not a clear knowledge on how AngularJS design and features affect the development experience of Web applications. Therefore, this paper reports the results of a survey about AngularJS, including answers from 460 developers. Our contributions include the identification of the most appreciated features of AngularJS (e.g., custom interface components, dependency injection, and two-way data binding) and the most problematic aspects of the framework (e.g., performance and implementation of directives).
Unique coverage in Boolean models
Venue: arXiv cs.IT 2016
Year: 2016

Consider a wireless cellular network consisting of small, densely scattered base stations. A user $u$ is {\em uniquely covered} by a base station $b$ if $u$ is the only user within distance $r$ of $b$. This makes it possible to assign the user $u$ to the base station $b$ without interference from any other user $u'$. We investigate the maximum possible proportion of users who are uniquely covered. We solve this problem completely in one dimension and provide bounds, approximations and simulation results for the two-dimensional case.
Analysis of time series and signals using the Square Wave Method
Venue: arXiv cs.NA 2016
Year: 2016

The Square Wave Method (SWM), previously introduced for the analysis of signals and images, is presented here as a mathematical tool suitable for the analysis of time series and signals. To show the potential that the SWM has to analyze many different types of time series, the results of the analysis of a time series composed of a sequence of 10,000 numerical values are presented here. These values were generated by using the Mathematical Random Number Generator (MRNG).
Automating Political Bias Prediction
Venue: arXiv cs.SI 2016
Year: 2016

Every day media generate large amounts of text. An unbiased view on media reports requires an understanding of the political bias of media content. Assistive technology for estimating the political bias of texts can be helpful in this context. This study proposes a simple statistical learning approach to predict political bias from text. Standard text features extracted from speeches and manifestos of political parties are used to predict political bias in terms of political party affiliation and in terms of political views. Results indicate that political bias can be predicted with above chance accuracy. Mistakes of the model can be interpreted with respect to changes of policies of political actors. Two approaches are presented to make the results more interpretable: a) discriminative text features are related to the political orientation of a party and b) sentiment features of texts are correlated with a measure of political power. Political power appears to be strongly correlated with positive sentiment of a text. To highlight some potential use cases a web application shows how the model can be used for texts for which the political bias is not clear such as news articles.
An intelligent classification model for phishing email detection
Venue: arXiv cs.CR 2016
Year: 2016

Phishing attacks are one of the trending cyber attacks that apply socially engineered messages that are communicated to people from professional hackers aiming at fooling users to reveal their sensitive information, the most popular communication channel to those messages is through users emails. This paper presents an intelligent classification model for detecting phishing emails using knowledge discovery, data mining and text processing techniques. This paper introduces the concept of phishing terms weighting which evaluates the weight of phishing terms in each email. The pre processing phase is enhanced by applying text stemming and WordNet ontology to enrich the model with word synonyms. The model applied the knowledge discovery procedures using five popular classification algorithms and achieved a notable enhancement in classification accuracy, 0.991 accuracy was achieved using the Random Forest algorithm and 0.984 using J48, which is to our knowledge the highest accuracy rate for an accredited data set. This paper also presents a comparative study with similar proposed classification techniques.
Online Adaptation of Deep Architectures with Reinforcement Learning
Venue: arXiv cs.LG 2016
Year: 2016

Online learning has become crucial to many problems in machine learning. As more data is collected sequentially, quickly adapting to changes in the data distribution can offer several competitive advantages such as avoiding loss of prior knowledge and more efficient learning. However, adaptation to changes in the data distribution (also known as covariate shift) needs to be performed without compromising past knowledge already built in into the model to cope with voluminous and dynamic data. In this paper, we propose an online stacked Denoising Autoencoder whose structure is adapted through reinforcement learning. Our algorithm forces the network to exploit and explore favourable architectures employing an estimated utility function that maximises the accuracy of an unseen validation sequence. Different actions, such as Pool, Increment and Merge are available to modify the structure of the network. As we observe through a series of experiments, our approach is more responsive, robust, and principled than its counterparts for non-stationary as well as stationary data distributions. Experimental results indicate that our algorithm performs better at preserving gained prior knowledge and responding to changes in the data distribution.
Uncovering Voice Misuse Using Symbolic Mismatch
Venue: arXiv cs.LG 2016
Year: 2016

Voice disorders affect an estimated 14 million working-aged Americans, and many more worldwide. We present the first large scale study of vocal misuse based on long-term ambulatory data collected by an accelerometer placed on the neck. We investigate an unsupervised data mining approach to uncovering latent information about voice misuse. We segment signals from over 253 days of data from 22 subjects into over a hundred million single glottal pulses (closures of the vocal folds), cluster segments into symbols, and use symbolic mismatch to uncover differences between patients and matched controls, and between patients pre- and post-treatment. Our results show significant behavioral differences between patients and controls, as well as between some pre- and post-treatment patients. Our proposed approach provides an objective basis for helping diagnose behavioral voice disorders, and is a first step towards a more data-driven understanding of the impact of voice therapy.
Interdependency of Transmission and Distribution Pricing
Venue: arXiv cs.SY 2016
Year: 2016

Distribution markets are among the prospect being considered for the future of power systems. They would facilitate integration of distributed energy resources (DERs) and microgrids via a market mechanism and enable them to monetize services they can provide. This paper follows the ongoing work in implementing the distribution market operator (DMO) concept, and its clearing and settlement procedures, and focuses on investigating the pricing conducted by the DMO. The distribution locational marginal prices (D-LMPs) and their relationship with the transmission system locational marginal prices (T-LMPs) are subject of this paper. Numerical simulations on a test distribution system exhibit the benefits and drawbacks of the proposed DMO pricing processes.
Towards Representation Learning with Tractable Probabilistic Models
Venue: arXiv cs.LG 2016
Year: 2016

Probabilistic models learned as density estimators can be exploited in representation learning beside being toolboxes used to answer inference queries only. However, how to extract useful representations highly depends on the particular model involved. We argue that tractable inference, i.e. inference that can be computed in polynomial time, can enable general schemes to extract features from black box models. We plan to investigate how Tractable Probabilistic Models (TPMs) can be exploited to generate embeddings by random query evaluations. We devise two experimental designs to assess and compare different TPMs as feature extractors in an unsupervised representation learning framework. We show some experimental results on standard image datasets by applying such a method to Sum-Product Networks and Mixture of Trees as tractable models generating embeddings.
A Fault-Tolerant Sequentially Consistent DSM With a Compositional Correctness Proof
Venue: arXiv cs.DC 2016
Year: 2016

We present the SC-ABD algorithm that implements sequentially consistent distributed shared memory (DSM). The algorithm tolerates that less than half of the processes are faulty (crash-stop). Compared to the multi-writer ABD algorithm, SC-ABD requires one instead of two round-trips of communication to perform a write operation, and an equal number of round-trips (two) to perform a read operation. Although sequential consistency is not a compositional consistency condition, the provided correctness proof is compositional.
Interpolated Discretized Embedding of Single Vectors and Vector Pairs for Classification, Metric Learning and Distance Approximation
Venue: arXiv cs.LG 2016
Year: 2016

We propose a new embedding method for a single vector and for a pair of vectors. This embedding method enables: a) efficient classification and regression of functions of single vectors; b) efficient approximation of distance functions; and c) non-Euclidean, semimetric learning. To the best of our knowledge, this is the first work that enables learning any general, non-Euclidean, semimetrics. That is, our method is a universal semimetric learning and approximation method that can approximate any distance function with as high accuracy as needed with or without semimetric constraints. The project homepage including code is at: http://www.ariel.ac.il/sites/ofirpele/ID
Identifying Stereotypes in the Online Perception of Physical Attractiveness
Venue: arXiv cs.CY 2016
Year: 2016

Stereotyping can be viewed as oversimplified ideas about social groups. They can be positive, neutral or negative. The main goal of this paper is to identify stereotypes for female physical attractiveness in images available in the Web. We look at the search engines as possible sources of stereotypes. We conducted experiments on Google and Bing by querying the search engines for beautiful and ugly women. We then collect images and extract information of faces. We propose a methodology and apply it to analyze photos gathered from search engines to understand how race and age manifest in the observed stereotypes and how they vary according to countries and regions. Our findings demonstrate the existence of stereotypes for female physical attractiveness, in particular negative stereotypes about black women and positive stereotypes about white women in terms of beauty. We also found negative stereotypes associated with older women in terms of physical attractiveness. Finally, we have identified patterns of stereotypes that are common to groups of countries.
Toward Early and Order-of-Magnitude Cascade Prediction in Social Networks
Venue: arXiv cs.SI 2016
Year: 2016

When a piece of information (microblog, photograph, video, link, etc.) starts to spread in a social network, an important question arises: will it spread to viral proportions - where viral can be defined as an order-of-magnitude increase. However, several previous studies have established that cascade size and frequency are related through a power-law - which leads to a severe imbalance in this classification problem. In this paper, we devise a suite of measurements based on structural diversity - the variety of social contexts (communities) in which individuals partaking in a given cascade engage. We demonstrate these measures are able to distinguish viral from non-viral cascades, despite the severe imbalance of the data for this problem. Further, we leverage these measurements as features in a classification approach, successfully predicting microblogs that grow from 50 to 500 reposts with precision of 0.69 and recall of 0.52 for the viral class - despite this class comprising under 2% of samples. This significantly outperforms our baseline approach as well as the current state-of-the-art. We also show this approach also performs well for identifying if cascades observed for 60 minutes will grow to 500 reposts as well as demonstrate how we can tradeoff between precision and recall.
Node Overlap Removal by Growing a Tree
Venue: arXiv cs.CG 2016
Year: 2016

Node overlap removal is a necessary step in many scenarios including laying out a graph, or visualizing a tag cloud. Our contribution is a new overlap removal algorithm that iteratively builds a Minimum Spanning Tree on a Delaunay triangulation of the node centers and removes the node overlaps by "growing" the tree. The algorithm is simple to implement yet produces high quality layouts. According to our experiments it runs several times faster than the current state-of-the-art methods.
A Factorization Approach to Inertial Affine Structure from Motion
Venue: arXiv cs.CV 2016
Year: 2016

We consider the problem of reconstructing a 3-D scene from a moving camera with high frame rate using the affine projection model. This problem is traditionally known as Affine Structure from Motion (Affine SfM), and can be solved using an elegant low-rank factorization formulation. In this paper, we assume that an accelerometer and gyro are rigidly mounted with the camera, so that synchronized linear acceleration and angular velocity measurements are available together with the image measurements. We extend the standard Affine SfM algorithm to integrate these measurements through the use of image derivatives.
Constructions of Maximum Distance Separable Symbol-Pair Codes Using Cyclic and Constacyclic Codes
Venue: arXiv cs.IT 2016
Year: 2016

Symbol-pair code is a new coding framework which is proposed to correct errors in the symbol-pair read channel. In particular, maximum distance separable (MDS) symbol-pair codes are a kind of symbol-pair codes with the best possible error-correction capability. Employing cyclic and constacyclic codes, we construct three new classes of MDS symbol-pair codes with minimum pair-distance five or six. Moreover, we find a necessary and sufficient condition which ensures a class of cyclic codes to be MDS symbol-pair codes. This condition is related to certain property of a special kind of linear fractional transformations. A detailed analysis on these linear fractional transformations leads to an algorithm, which produces many MDS symbol-pair codes with minimum pair-distance seven.
Deeply Semantic Inductive Spatio-Temporal Learning
Venue: arXiv cs.AI 2016
Year: 2016

We present an inductive spatio-temporal learning framework rooted in inductive logic programming. With an emphasis on visuo-spatial language, logic, and cognition, the framework supports learning with relational spatio-temporal features identifiable in a range of domains involving the processing and interpretation of dynamic visuo-spatial imagery. We present a prototypical system, and an example application in the domain of computing for visual arts and computational cognitive science.
A Rule-Based Change Impact Analysis Approach in Software Architecture for Requirements Changes
Venue: arXiv cs.SE 2016
Year: 2016

Software systems usually operate in a dynamic context where their requirements change continuously and new requirements emerge frequently. A single requirement hardly exists in isolation: it is related to other requirements and to the software development artifacts that implement it. When a requirements change is introduced, the requirements engineer may have to manually analyze all requirements and architectural elements for a single change. This may result in neglecting the actual impact of a change. We aim at improving change impact analysis in software architecture for requirements changes by using formal semantics of requirements relations, requirements changes and traces between Requirements & Architecture. In our previous work we presented a technique for change impact analysis in requirements. The technique uses the formal semantics of requirements relations and changes. Its output is a set of candidate requirements for the impact with proposed changes and a propagation path in the requirements model. In this paper we present a complementary technique which propagates requirements changes to software architecture and finds out which architectural elements are impacted by these changes. The formalization of requirements relations, changes and traces between R&A is used to determine candidate architectural elements for the impact of requirements changes in the architecture. The tool support is an extension of our Tool for Requirements Inferencing and Consistency Checking (TRIC). Our approach helps in the elimination of some false positive impacts in change propagation. We illustrate our approach in an industrial example which shows that the formal semantics of requirements relations, changes and traces enables the identification of candidate architectural elements with the reduction of some false positive impacts.