OpenRaiser/Intern-Atlas huggingface dataset:
Map++: A Crowd-sensing System for Automatic Map Semantics Identification
Venue: arXiv cs.CY 2015
Year: 2015

Digital maps have become a part of our daily life with a number of commercial and free map services. These services have still a huge potential for enhancement with rich semantic information to support a large class of mapping applications. In this paper, we present Map++, a system that leverages standard cell-phone sensors in a crowdsensing approach to automatically enrich digital maps with different road semantics like tunnels, bumps, bridges, footbridges, crosswalks, road capacity, among others. Our analysis shows that cell-phones sensors with humans in vehicles or walking get affected by the different road features, which can be mined to extend the features of both free and commercial mapping services. We present the design and implementation of Map++ and evaluate it in a large city. Our evaluation shows that we can detect the different semantics accurately with at most 3% false positive rate and 6% false negative rate for both vehicle and pedestrian-based features. Moreover, we show that Map++ has a small energy footprint on the cell-phones, highlighting its promise as a ubiquitous digital maps enriching service.
Media Usage Survey: Overall Comparison of Faculty and Students
Venue: arXiv cs.CY 2015
Year: 2015

Recent developments in the use of technologies in education have provided unique opportunities for teaching and learning. This paper describes the results of a survey conducted at Western University (Canada) in 2013, regarding the use of media by students and instructors. The results of this study support the assumption that the media usage of students and instructors include a mixture of traditional and new media. The main traditional media continue to be important, and some new media have emerged as seemingly on equal footing or even more important than the traditional forms of media. Some new media that have recently been in the public spotlight do not seem to be as important as expected. These new media may still be emerging but it is not possible to know their ultimate importance at this point. There was some variation in media usage across different Faculties but perhaps not as much variation as might have been expected.
Cultural Anthropology through the Lens of Wikipedia: Historical Leader Networks, Gender Bias, and News-based Sentiment
Venue: arXiv cs.CY 2015
Year: 2015

In this paper we study the differences in historical World View between Western and Eastern cultures, represented through the English, the Chinese, Japanese, and German Wikipedia. In particular, we analyze the historical networks of the World's leaders since the beginning of written history, comparing them in the different Wikipedias and assessing cultural chauvinism. We also identify the most influential female leaders of all times in the English, German, Spanish, and Portuguese Wikipedia. As an additional lens into the soul of a culture we compare top terms, sentiment, emotionality, and complexity of the English, Portuguese, Spanish, and German Wikinews.
The Name-Passing Calculus
Venue: arXiv cs.LO 2015
Year: 2015

Name-passing calculi are foundational models for mobile computing. Research into these models has produced a wealth of results ranging from relative expressiveness to programming pragmatics. The diversity of these results call for clarification and reorganization. This paper applies a model independent approach to the study of the name-passing calculi, leading to a uniform treatment and simplification. The technical tools and the results presented in the paper form the foundation for a theory of name-passing calculus.
Volume of Metric Balls in High-Dimensional Complex Grassmann Manifolds
Venue: arXiv cs.IT 2015
Year: 2015

Volume of metric balls relates to rate-distortion theory and packing bounds on codes. In this paper, the volume of balls in complex Grassmann manifolds is evaluated for an arbitrary radius. The ball is defined as a set of hyperplanes of a fixed dimension with reference to a center of possibly different dimension, and a generalized chordal distance for unequal dimensional subspaces is used. First, the volume is reduced to one-dimensional integral representation. The overall problem boils down to evaluating a determinant of a matrix of the same size as the subspace dimensionality. Interpreting this determinant as a characteristic function of the Jacobi ensemble, an asymptotic analysis is carried out. The obtained asymptotic volume is moreover refined using moment-matching techniques to provide a tighter approximation in finite-size regimes. Lastly, the pertinence of the derived results is shown by rate-distortion analysis of source coding on Grassmann manifolds.
SOARAN: A Service-oriented Architecture for Radio Access Network Sharing in Evolving Mobile Networks
Venue: arXiv cs.NI 2015
Year: 2015

Mobile networks are undergoing fast evolution to software-defined networking (SDN) infrastructure in order to accommodate the ever-growing mobile traffic and overcome the network management nightmares caused by unremitting acceleration in technology innovations and evolution of the service market.Enabled by virtualized network functionalities, evolving carrier wireless networks tend to share radio access network (RAN) among multiple (virtual) network operators so as to increase network capacity and reduce expenses.However, existing RAN sharing models are operator-oriented, which expose extensive resource details, e.g. infrastructure and spectrum,to participating network operators for resource-sharing purposes. These old-fashioned models violate the design principles of SDN abstraction and are infeasible to manage the thriving traffic of on-demand customized services. This paper presents SOARAN, a service-oriented framework for RAN sharing in mobile networks evolving from LTE/LTE advanced to software-defined carrier wireless networks(SD-CWNs), which decouples network operators from radio resource by providing application-level differentiated services. SOARAN defines a serial of abstract applications with distinct Quality of Experience (QoE) requirements. The central controller periodically computes application-level resource allocation for each radio element with respect to runtime traffic demands and channel conditions, and disseminate these allocation decisions as service-oriented policies to respect element. The radio elements then independently determine flow-level resource allocation within each application to accomplish these policies. We formulate the application-level resource allocation as an optimization problem and develop a fast algorithm to solve it with a provably approximate guarantee.
Using Behavior Objects to Manage Complexity in Virtual Worlds
Venue: arXiv cs.AI 2015
Year: 2015

The quality of high-level AI of non-player characters (NPCs) in commercial open-world games (OWGs) has been increasing during the past years. However, due to constraints specific to the game industry, this increase has been slow and it has been driven by larger budgets rather than adoption of new complex AI techniques. Most of the contemporary AI is still expressed as hard-coded scripts. The complexity and manageability of the script codebase is one of the key limiting factors for further AI improvements. In this paper we address this issue. We present behavior objects - a general approach to development of NPC behaviors for large OWGs. Behavior objects are inspired by object-oriented programming and extend the concept of smart objects. Our approach promotes encapsulation of data and code for multiple related behaviors in one place, hiding internal details and embedding intelligence in the environment. Behavior objects are a natural abstraction of five different techniques that we have implemented to manage AI complexity in an upcoming AAA OWG. We report the details of the implementations in the context of behavior trees and the lessons learned during development. Our work should serve as inspiration for AI architecture designers from both the academia and the industry.
Kernelized Multiview Projection
Venue: arXiv cs.CV 2015
Year: 2015

Conventional vision algorithms adopt a single type of feature or a simple concatenation of multiple features, which is always represented in a high-dimensional space. In this paper, we propose a novel unsupervised spectral embedding algorithm called Kernelized Multiview Projection (KMP) to better fuse and embed different feature representations. Computing the kernel matrices from different features/views, KMP can encode them with the corresponding weights to achieve a low-dimensional and semantically meaningful subspace where the distribution of each view is sufficiently smooth and discriminative. More crucially, KMP is linear for the reproducing kernel Hilbert space (RKHS) and solves the out-of-sample problem, which allows it to be competent for various practical applications. Extensive experiments on three popular image datasets demonstrate the effectiveness of our multiview embedding algorithm.
Partial Decode-Forward Relaying for the Gaussian Two-Hop Relay Network
Venue: arXiv cs.IT 2015
Year: 2015

The multicast capacity of the Gaussian two-hop relay network with one source, $N$ relays, and $L$ destinations is studied. It is shown that a careful modification of the partial decode--forward coding scheme, whereby the relays cooperate through degraded sets of message parts, achieves the cutset upper bound within $(1/2)\log N$ bits regardless of the channel gains and power constraints. This scheme improves upon a previous scheme by Chern and Ozgur, which is also based on partial decode--forward yet has an unbounded gap from the cutset bound for $L \ge 2$ destinations. When specialized to independent codes among relays, the proposed scheme achieves within $\log N$ bits from the cutset bound. The computation of this relaxation involves evaluating mutual information across $L(N+1)$ cuts out of the total $L 2^N$ possible cuts, providing a very simple linear-complexity algorithm to approximate the single-source multicast capacity of the Gaussian two-hop relay network.
On the Base Station Association Problem in HetSNets
Venue: arXiv cs.GT 2015
Year: 2015

The dense deployment of small-cell base stations in HetSNets requires efficient resource allocation techniques. More precisely, the problem of associating users to SBSs must be revised and carefully studied. This problem is NP-hard and requires solving an integer optimization problem. In order to efficiently solve this problem, we model it using non-cooperative game theory. First, we design two non-cooperative games to solve the problem and show the existence of pure Nash equilibria (PNE) in both games. These equilibria are shown to be far from the social optimum. Hence, we propose a better game design in order to approach this optimum. This new game is proved to have no PNE in general. However, simulations show, for Rayleigh fading channels, that a PNE always exists for all instances of the game. In addition, we show that its prices of anarchy and stability are close to one. We propose a best response dynamics (BRD) algorithm that converges to a PNE when it exists. Because of the high information exchange of BRD, a completely distributed algorithm, based on the theory of learning, is proposed. Simulations show that this algorithm has tight-to-optimal performance and further it converges to a PNE (when existing) with high probability.
Evaluating software-based fingerprint liveness detection using Convolutional Networks and Local Binary Patterns
Venue: arXiv cs.CV 2015
Year: 2015

With the growing use of biometric authentication systems in the past years, spoof fingerprint detection has become increasingly important. In this work, we implement and evaluate two different feature extraction techniques for software-based fingerprint liveness detection: Convolutional Networks with random weights and Local Binary Patterns. Both techniques were used in conjunction with a Support Vector Machine (SVM) classifier. Dataset Augmentation was used to increase classifier's performance and a variety of preprocessing operations were tested, such as frequency filtering, contrast equalization, and region of interest filtering. The experiments were made on the datasets used in The Liveness Detection Competition of years 2009, 2011 and 2013, which comprise almost 50,000 real and fake fingerprints' images. Our best method achieves an overall rate of 95.2% of correctly classified samples - an improvement of 35% in test error when compared with the best previously published results.
Temporal Pattern of Online Communication Spike Trains in Spreading a Scientific Rumor: How Often, Who Interacts with Whom?
Venue: arXiv cs.SI 2015
Year: 2015

We study complex time series (spike trains) of online user communication while spreading messages about the discovery of the Higgs boson in Twitter. We focus on online social interactions among users such as retweet, mention, and reply, and construct different types of active (performing an action) and passive (receiving an action) spike trains for each user. The spike trains are analyzed by means of local variation, to quantify the temporal behavior of active and passive users, as a function of their activity and popularity. We show that the active spike trains are bursty, independently of their activation frequency. For passive spike trains, in contrast, the local variation of popular users presents uncorrelated (Poisson random) dynamics. We further characterize the correlations of the local variation in different interactions. We obtain high values of correlation, and thus consistent temporal behavior, between retweets and mentions, but only for popular users, indicating that creating online attention suggests an alignment in the dynamics of the two interactions.
How Scale Affects Structure in Java Programs
Venue: arXiv cs.SE 2015
Year: 2015

Many internal software metrics and external quality attributes of Java programs correlate strongly with program size. This knowledge has been used pervasively in quantitative studies of software through practices such as normalization on size metrics. This paper reports size-related super- and sublinear effects that have not been known before. Findings obtained on a very large collection of Java programs -- 30,911 projects hosted at Google Code as of Summer 2011 -- unveils how certain characteristics of programs vary disproportionately with program size, sometimes even non-monotonically. Many of the specific parameters of nonlinear relations are reported. This result gives further insights for the differences of "programming in the small" vs. "programming in the large." The reported findings carry important consequences for OO software metrics, and software research in general: metrics that have been known to correlate with size can now be properly normalized so that all the information that is left in them is size-independent.
Wiretapped Signal Leakage Minimization for Secure Multiuser MIMO Systems via Interference Alignment
Venue: arXiv cs.IT 2015
Year: 2015

In this paper, interference alignment (IA) is designed for secure multiuser multiple-input multiple-output systems in the presence of an eavesdropper. The proposed IA technique designs the transmit precoding and receiving subspace matrices to minimize both the total inter-main-link interference and the wiretapped signals. With perfect channel state information is known at the transmitters (Txs), the cost function of the optimization problem is alternatively minimized over the precoding matrices at the Txs and the receiving subspace matrices at the receivers (Rxs) and the eavesdropper. The feasible condition, the proof of convergence of the proposed IA approach are provided. Numerical results reveal a significant improvement in terms of average secrecy sum rate of our IA algorithm as compared to the conventional IA design.
Simplified Multiuser Detection for SCMA with Sum-Product Algorithm
Venue: arXiv cs.IT 2015
Year: 2015

Sparse code multiple access (SCMA) is a novel non-orthogonal multiple access technique, which fully exploits the shaping gain of multi-dimensional codewords. However, the lack of simplified multiuser detection algorithm prevents further implementation due to the inherently high computation complexity. In this paper, general SCMA detector algorithms based on Sum-product algorithm are elaborated. Then two improved algorithms are proposed, which simplify the detection structure and curtail exponent operations quantitatively in logarithm domain. Furthermore, to analyze these detection algorithms fairly, we derive theoretical expression of the average mutual information (AMI) of SCMA (SCMA-AMI), and employ a statistical method to calculate SCMA-AMI based specific detection algorithm. Simulation results show that the performance is almost as well as the based message passing algorithm in terms of both BER and AMI while the complexity is significantly decreased, compared to the traditional Max-Log approximation method.
Are You Really Hidden? Predicting Current City from Profile and Social Relationship
Venue: arXiv cs.SI 2015
Year: 2015

Privacy has become a major concern in Online Social Networks (OSNs) due to threats such as advertising spam, online stalking and identity theft. Although many users hide or do not fill out their private attributes in OSNs, prior studies point out that the hidden attributes may be inferred from some other public information. Thus, users' private information could still be at stake to be exposed. Hitherto, little work helps users to assess the exposure probability/risk that the hidden attributes can be correctly predicted, let alone provides them with pointed countermeasures. In this article, we focus our study on the exposure risk assessment by a particular privacy-sensitive attribute - current city - in Facebook. Specifically, we first design a novel current city prediction approach that discloses users' hidden `current city' from their self-exposed information. Based on 371,913 Facebook users' data, we verify that our proposed prediction approach can predict users' current city more accurately than state-of-the-art approaches. Furthermore, we inspect the prediction results and model the current city exposure probability via some measurable characteristics of the self-exposed information. Finally, we construct an exposure estimator to assess the current city exposure risk for individual users, given their self-exposed information. Several case studies are presented to illustrate how to use our proposed estimator for privacy protection.
Fast Consensus under Eventually Stabilizing Message Adversaries
Venue: arXiv cs.DC 2015
Year: 2015

This paper is devoted to deterministic consensus in synchronous dynamic networks with unidirectional links, which are under the control of an omniscient message adversary. Motivated by unpredictable node/system initialization times and long-lasting periods of massive transient faults, we consider message adversaries that guarantee periods of less erratic message loss only eventually: We present a tight bound of $2D+1$ for the termination time of consensus under a message adversary that eventually guarantees a single vertex-stable root component with dynamic network diameter $D$, as well as a simple algorithm that matches this bound. It effectively halves the termination time $4D+1$ achieved by an existing consensus algorithm, which also works under our message adversary. We also introduce a generalized, considerably stronger variant of our message adversary, and show that our new algorithm, unlike the existing one, still works correctly under it.
Equality of Participation Online Versus Face to Face: Condensed Analysis of the Community Forum Deliberative Methods Demonstration
Venue: arXiv cs.CY 2015
Year: 2015

Online deliberation may provide a more cost-effective and/or less inhibiting environment for public participation than face to face (F2F). But do online methods bias participation toward certain individuals or groups? We compare F2F versus online participation in an experiment affording within-participants and cross-modal comparisons. For English speakers required to have Internet access as a condition of participation, we find no negative effects of online modes on equality of participation (EoP) related to gender, age, or educational level. Asynchronous online discussion appears to improve EoP for gender relative to F2F. Data suggest a dampening effect of online environments on black participants, as well as amplification for whites. Synchronous online voice communication EoP is on par with F2F across individuals. But individual-level EoP is much lower in the online forum, and greater online forum participation predicts greater F2F participation for individuals. Measured rates of participation are compared to self-reported experiences, and other findings are discussed.
3D Automatic Segmentation Method for Retinal Optical Coherence Tomography Volume Data Using Boundary Surface Enhancement
Venue: arXiv cs.CV 2015
Year: 2015

With the introduction of spectral-domain optical coherence tomography (SDOCT), much larger image datasets are routinely acquired compared to what was possible using the previous generation of time-domain OCT. Thus, there is a critical need for the development of 3D segmentation methods for processing these data. We present here a novel 3D automatic segmentation method for retinal OCT volume data. Briefly, to segment a boundary surface, two OCT volume datasets are obtained by using a 3D smoothing filter and a 3D differential filter. Their linear combination is then calculated to generate new volume data with an enhanced boundary surface, where pixel intensity, boundary position information, and intensity changes on both sides of the boundary surface are used simultaneously. Next, preliminary discrete boundary points are detected from the A-Scans of the volume data. Finally, surface smoothness constraints and a dynamic threshold are applied to obtain a smoothed boundary surface by correcting a small number of error points. Our method can extract retinal layer boundary surfaces sequentially with a decreasing search region of volume data. We performed automatic segmentation on eight human OCT volume datasets acquired from a commercial Spectralis OCT system, where each volume of data consisted of 97 OCT images with a resolution of 496 512; experimental results show that this method can accurately segment seven layer boundary surfaces in normal as well as some abnormal eyes.
Progressive EM for Latent Tree Models and Hierarchical Topic Detection
Venue: arXiv cs.LG 2015
Year: 2015

Hierarchical latent tree analysis (HLTA) is recently proposed as a new method for topic detection. It differs fundamentally from the LDA-based methods in terms of topic definition, topic-document relationship, and learning method. It has been shown to discover significantly more coherent topics and better topic hierarchies. However, HLTA relies on the Expectation-Maximization (EM) algorithm for parameter estimation and hence is not efficient enough to deal with large datasets. In this paper, we propose a method to drastically speed up HLTA using a technique inspired by recent advances in the moments method. Empirical experiments show that our method greatly improves the efficiency of HLTA. It is as efficient as the state-of-the-art LDA-based method for hierarchical topic detection and finds substantially better topics and topic hierarchies.
Quantum Codes from Generalized Reed-Solomon Codes and Matrix-Product Codes
Venue: arXiv cs.IT 2015
Year: 2015

One of the central tasks in quantum error-correction is to construct quantum codes that have good parameters. In this paper, we construct three new classes of quantum MDS codes from classical Hermitian self-orthogonal generalized Reed-Solomon codes. We also present some classes of quantum codes from matrix-product codes. It turns out that many of our quantum codes are new in the sense that the parameters of quantum codes cannot be obtained from all previous constructions.