OpenRaiser/Intern-Atlas huggingface dataset:
Character-level Intra Attention Network for Natural Language Inference
Venue: arXiv cs.CL 2017
Year: 2017

Natural language inference (NLI) is a central problem in language understanding. End-to-end artificial neural networks have reached state-of-the-art performance in NLI field recently. In this paper, we propose Character-level Intra Attention Network (CIAN) for the NLI task. In our model, we use the character-level convolutional network to replace the standard word embedding layer, and we use the intra attention to capture the intra-sentence semantics. The proposed CIAN model provides improved results based on a newly published MNLI corpus.
Likelihood Estimation for Generative Adversarial Networks
Venue: arXiv cs.LG 2017
Year: 2017

We present a simple method for assessing the quality of generated images in Generative Adversarial Networks (GANs). The method can be applied in any kind of GAN without interfering with the learning procedure or affecting the learning objective. The central idea is to define a likelihood function that correlates with the quality of the generated images. In particular, we derive a Gaussian likelihood function from the distribution of the embeddings (hidden activations) of the real images in the discriminator, and based on this, define two simple measures of how likely it is that the embeddings of generated images are from the distribution of the embeddings of the real images. This yields a simple measure of fitness for generated images, for all varieties of GANs. Empirical results on CIFAR-10 demonstrate a strong correlation between the proposed measures and the perceived quality of the generated images.
Deep Architectures for Neural Machine Translation
Venue: arXiv cs.CL 2017
Year: 2017

It has been shown that increasing model depth improves the quality of neural machine translation. However, different architectural variants to increase model depth have been proposed, and so far, there has been no thorough comparative study. In this work, we describe and evaluate several existing approaches to introduce depth in neural machine translation. Additionally, we explore novel architectural variants, including deep transition RNNs, and we vary how attention is used in the deep decoder. We introduce a novel "BiDeep" RNN architecture that combines deep transition RNNs and stacked RNNs. Our evaluation is carried out on the English to German WMT news translation dataset, using a single-GPU machine for both training and inference. We find that several of our proposed architectures improve upon existing approaches in terms of speed and translation quality. We obtain best improvements with a BiDeep RNN of combined depth 8, obtaining an average improvement of 1.5 BLEU over a strong shallow baseline. We release our code for ease of adoption.
Spectrum Sharing for LTE-A Network in TV White Space
Venue: arXiv cs.NI 2017
Year: 2017

Rural areas in the developing countries are predominantly devoid of Internet access as it is not viable for operators to provide broadband service in these areas. To solve this problem, we propose a middle mile Long erm Evolution Advanced (LTE-A) network operating in TV white space to connect villages to an optical Point of Presence (PoP) located in the vicinity of a rural area. We study the problem of spectrum sharing for the middle mile networks deployed by multiple operators. A graph theory based Fairness Constrained Channel Allocation (FCCA) algorithm is proposed, employing Carrier Aggregation (CA) and Listen Before Talk (LBT) features of LTE-A. We perform extensive system level simulations to demonstrate that FCCA not only increases spectral efficiency but also improves system fairness.
Extracting Core Claims from Scientific Articles
Venue: arXiv cs.IR 2017
Year: 2017

The number of scientific articles has grown rapidly over the years and there are no signs that this growth will slow down in the near future. Because of this, it becomes increasingly difficult to keep up with the latest developments in a scientific field. To address this problem, we present here an approach to help researchers learn about the latest developments and findings by extracting in a normalized form core claims from scientific articles. This normalized representation is a controlled natural language of English sentences called AIDA, which has been proposed in previous work as a method to formally structure and organize scientific findings and discourse. We show how such AIDA sentences can be automatically extracted by detecting the core claim of an article, checking for AIDA compliance, and - if necessary - transforming it into a compliant sentence. While our algorithm is still far from perfect, our results indicate that the different steps are feasible and they support the claim that AIDA sentences might be a promising approach to improve scientific communication in the future.
Multiple-Kernel Local-Patch Descriptor
Venue: arXiv cs.CV 2017
Year: 2017

We propose a multiple-kernel local-patch descriptor based on efficient match kernels of patch gradients. It combines two parametrizations of gradient position and direction, each parametrization provides robustness to a different type of patch miss-registration: polar parametrization for noise in the patch dominant orientation detection, Cartesian for imprecise location of the feature point. Even though handcrafted, the proposed method consistently outperforms the state-of-the-art methods on two local patch benchmarks.
Applications of Economic and Pricing Models for Wireless Network Security: A Survey
Venue: arXiv cs.GT 2017
Year: 2017

This paper provides a comprehensive literature review on applications of economic and pricing theory to security issues in wireless networks. Unlike wireline networks, the broadcast nature and the highly dynamic change of network environments pose a number of nontrivial challenges to security design in wireless networks. While the security issues have not been completely solved by traditional or system-based solutions, economic and pricing models recently were employed as one efficient solution to discourage attackers and prevent attacks to be performed. In this paper, we review economic and pricing approaches proposed to address major security issues in wireless networks including eavesdropping attack, Denial-of-Service (DoS) attack such as jamming and Distributed DoS (DDoS), and illegitimate behaviors of malicious users. Additionally, we discuss integrating economic and pricing models with cryptography methods to reduce information privacy leakage as well as to guarantee the confidentiality and integrity of information in wireless networks. Finally, we highlight important challenges, open issues and future research directions of applying economic and pricing models to wireless security issues.
Performance evaluation of energy detector over generalized non-linear and shadowed composite fading channels using a Mixture Gamma Distribution
Venue: arXiv cs.IT 2017
Year: 2017

The performance of energy detection (ED) for independent and identically distribution (i.i.d.) signal models is analyzed over generalized composite non-linear line-of-sight (LOS) and non-line-of-sight (NLOS) shadowed fading scenarios. The novel expressions for α-\k{appa}-μ/Gamma and α-η-μ/Gamma fading channels have been derived to approximate by using the mixture gamma (MG) distribution under low instantaneous signal-to-noise (SNR) condition. On the basis of the deduced fading distributions, novel, exact and close-form detective models are derived to evaluate the sensing performance with different key fading parameters over generalized non-linear and shadowed composite fading channels.
Not a Technology Person: Motivating Older Adults Toward the Use of Mobile Technology
Venue: arXiv cs.HC 2017
Year: 2017

Older users population is rapidly increasing all over the World. Presently, we observe efforts in the human-computer interaction domain aiming to improve life quality of age 65 and over through the use of mobile apps. Nonetheless, these efforts focus primary on interface and interaction de- sign. Little work has focused on the study of motivation to use and adherence to, of elderly to technology. Developing specific design guidelines for this population is relevant, however it should be parallel to the study of desire of elderly to embrace specific technology in their life. Designers should not be limited to technology design but consider as well how to fully convey the value that technology can bring to the lives of the users and motivate adoption. This position paper discusses techniques that might nudge elderly towards the use of new technology.
Mutual Alignment Transfer Learning
Venue: arXiv cs.AI 2017
Year: 2017

Training robots for operation in the real world is a complex, time consuming and potentially expensive task. Despite significant success of reinforcement learning in games and simulations, research in real robot applications has not been able to match similar progress. While sample complexity can be reduced by training policies in simulation, such policies can perform sub-optimally on the real platform given imperfect calibration of model dynamics. We present an approach -- supplemental to fine tuning on the real robot -- to further benefit from parallel access to a simulator during training and reduce sample requirements on the real robot. The developed approach harnesses auxiliary rewards to guide the exploration for the real world agent based on the proficiency of the agent in simulation and vice versa. In this context, we demonstrate empirically that the reciprocal alignment for both agents provides further benefit as the agent in simulation can adjust to optimize its behaviour for states commonly visited by the real-world agent.
Machine Translation at Booking.com: Journey and Lessons Learned
Venue: arXiv cs.CL 2017
Year: 2017

We describe our recently developed neural machine translation (NMT) system and benchmark it against our own statistical machine translation (SMT) system as well as two other general purpose online engines (statistical and neural). We present automatic and human evaluation results of the translation output provided by each system. We also analyze the effect of sentence length on the quality of output for SMT and NMT systems.
Spatio-temporal profiling of public transport delays based on large scale vehicle positioning data from GPS in Wrocław
Venue: arXiv cs.SI 2017
Year: 2017

In recent years many studies of urban mobility based on large data sets have been published: most of them based on crowdsourced GPS data or smart-card data. We present, what is to our knowledge the first, exploration of public transport delay data harvested from a large-scale, official public transport positioning system, provided by the Wrocław Municipality. We evaluate the characteristics of delays between stops in relation to direction, time and delay variance of 1648 stop pairs from 15 mln delay reports. We construct a normalized feature matrix of likelihood of a given delay change happening at a given hour on the edge between two stops. We then calculate distances between such matrices using earth mover's distance and cluster them using hierarchical agglomerative clustering with Ward's linkage method. We obtain four profiles of delay changes in Wrocław: edges without impact on delay, edges likely to cause delay, edges likely to decrease delay and edges likely to strongly decrease delay (ex. when a public transport vehicle is speeding). We analyze the spatial and mode of transport properties of each cluster and provide insights into reasons of delay change patterns in each of the detected profiles.
Un modèle pour la représentation des connaissances temporelles dans les documents historiques
Venue: arXiv cs.AI 2017
Year: 2017

Processing and publishing the data of the historical sciences in the semantic web is an interesting challenge in which the representation of temporal aspects plays a key role. We propose in this paper a model of temporal knowledge representation adapted to work on historical documents. This model is based on the notion of fluent that is represented in RDF graphs. We show how this model allows to represent the knowledge necessary to the historians and how it can be used to reason on this knowledge using the SWRL and SPARQL languages. This model is being used in a project to digitize, study and publish the manuscripts of linguist Ferdinand de Saussure.
Dynamic Policies for Cooperative Networked Systems
Venue: arXiv cs.GT 2017
Year: 2017

A set of economic entities embedded in a network graph collaborate by opportunistically exchanging their resources to satisfy their dynamically generated needs. Under what conditions their collaboration leads to a sustainable economy? Which online policy can ensure a feasible resource exchange point will be attained, and what information is needed to implement it? Furthermore, assuming there are different resources and the entities have diverse production capabilities, which production policy each entity should employ in order to maximize the economy's sustainability? Importantly, can we design such policies that are also incentive compatible even when there is no a priori information about the entities' needs? We introduce a dynamic production scheduling and resource exchange model to capture this fundamental problem and provide answers to the above questions. Applications range from infrastructure sharing, trade and organisation management, to social networks and sharing economy services.
On the Exponential Rate of Convergence of Fictitious Play in Potential Games
Venue: arXiv cs.GT 2017
Year: 2017

The paper studies fictitious play (FP) learning dynamics in continuous time. It is shown that in almost every potential game, and for almost every initial condition, the rate of convergence of FP is exponential. In particular, the paper focuses on studying the behavior of FP in potential games in which all equilibria of the game are regular, as introduced by Harsanyi. Such games are referred to as regular potential games. Recently it has been shown that almost all potential games (in the sense of the Lebesgue measure) are regular. In this paper it is shown that in any regular potential game (and hence, in almost every potential game), FP converges to the set of Nash equilibria at an exponential rate from almost every initial condition.
Optimal Sensing and Data Estimation in a Large Sensor Network
Venue: arXiv cs.IT 2017
Year: 2017

An energy efficient use of large scale sensor networks necessitates activating a subset of possible sensors for estimation at a fusion center. The problem is inherently combinatorial; to this end, a set of iterative, randomized algorithms are developed for sensor subset selection by exploiting the underlying statistics. Gibbs sampling-based methods are designed to optimize the estimation error and the mean number of activated sensors. The optimality of the proposed strategy is proven, along with guarantees on their convergence speeds. Also, another new algorithm exploiting stochastic approximation in conjunction with Gibbs sampling is derived for a constrained version of the sensor selection problem. The methodology is extended to the scenario where the fusion center has access to only a parametric form of the joint statistics, but not the true underlying distribution. Therein, expectation-maximization is effectively employed to learn the distribution. Strategies for iid time-varying data are also outlined. Numerical results show that the proposed methods converge very fast to the respective optimal solutions, and therefore can be employed for optimal sensor subset selection in practical sensor networks.
Physical problem solving: Joint planning with symbolic, geometric, and dynamic constraints
Venue: arXiv cs.AI 2017
Year: 2017

In this paper, we present a new task that investigates how people interact with and make judgments about towers of blocks. In Experiment~1, participants in the lab solved a series of problems in which they had to re-configure three blocks from an initial to a final configuration. We recorded whether they used one hand or two hands to do so. In Experiment~2, we asked participants online to judge whether they think the person in the lab used one or two hands. The results revealed a close correspondence between participants' actions in the lab, and the mental simulations of participants online. To explain participants' actions and mental simulations, we develop a model that plans over a symbolic representation of the situation, executes the plan using a geometric solver, and checks the plan's feasibility by taking into account the physical constraints of the scene. Our model explains participants' actions and judgments to a high degree of quantitative accuracy.
A Gossip Algorithm based Clock Synchronization Scheme for Smart Grid Applications
Venue: arXiv cs.SY 2017
Year: 2017

The uprising interest in multi-agent based networked system, and the numerous number of applications in the distributed control of the smart grid leads us to address the problem of time synchronization in the smart grid. Utility companies look for new packet based time synchronization solutions with Global Positioning System (GPS) level accuracies beyond traditional packet methods such as Network Time Proto- col (NTP). However GPS based solutions have poor reception in indoor environments and dense urban canyons as well as GPS antenna installation might be costly. Some smart grid nodes such as Phasor Measurement Units (PMUs), fault detection, Wide Area Measurement Systems (WAMS) etc., requires synchronous accuracy as low as 1 ms. On the other hand, 1 sec accuracy is acceptable in management information domain. Acknowledging this, in this study, we introduce gossip algorithm based clock synchronization method among network entities from the decision control and communication point of view. Our method synchronizes clock within dense network with a bandwidth limited environment. Our technique has been tested in different kinds of network topologies- complete, star and random geometric network and demonstrated satisfactory performance.
Kinetic Simulation of Collisional Magnetized Plasmas with Semi-Implicit Time Integration
Venue: arXiv cs.CE 2017
Year: 2017

Plasmas with varying collisionalities occur in many applications, such as tokamak edge regions, where the flows are characterized by significant variations in density and temperature. While a kinetic model is necessary for weakly-collisional high-temperature plasmas, high collisionality in colder regions render the equations numerically stiff due to disparate time scales. In this paper, we propose an implicit-explicit algorithm for such cases, where the collisional term is integrated implicitly in time, while the advective term is integrated explicitly in time, thus allowing time step sizes that are comparable to the advective time scales. This partitioning results in a more efficient algorithm than those using explicit time integrators, where the time step sizes are constrained by the stiff collisional time scales. We implement semi-implicit additive Runge-Kutta methods in COGENT, a finite-volume gyrokinetic code for mapped, multiblock grids and test the accuracy, convergence, and computational cost of these semi-implicit methods for test cases with highly-collisional plasmas.
Fast Deep Matting for Portrait Animation on Mobile Phone
Venue: arXiv cs.CV 2017
Year: 2017

Image matting plays an important role in image and video editing. However, the formulation of image matting is inherently ill-posed. Traditional methods usually employ interaction to deal with the image matting problem with trimaps and strokes, and cannot run on the mobile phone in real-time. In this paper, we propose a real-time automatic deep matting approach for mobile devices. By leveraging the densely connected blocks and the dilated convolution, a light full convolutional network is designed to predict a coarse binary mask for portrait images. And a feathering block, which is edge-preserving and matting adaptive, is further developed to learn the guided filter and transform the binary mask into alpha matte. Finally, an automatic portrait animation system based on fast deep matting is built on mobile devices, which does not need any interaction and can realize real-time matting with 15 fps. The experiments show that the proposed approach achieves comparable results with the state-of-the-art matting solvers.
Product recognition in store shelves as a sub-graph isomorphism problem
Venue: arXiv cs.CV 2017
Year: 2017

The arrangement of products in store shelves is carefully planned to maximize sales and keep customers happy. However, verifying compliance of real shelves to the ideal layout is a costly task routinely performed by the store personnel. In this paper, we propose a computer vision pipeline to recognize products on shelves and verify compliance to the planned layout. We deploy local invariant features together with a novel formulation of the product recognition problem as a sub-graph isomorphism between the items appearing in the given image and the ideal layout. This allows for auto-localizing the given image within the aisle or store and improving recognition dramatically.
Reduction of Overfitting in Diabetes Prediction Using Deep Learning Neural Network
Venue: arXiv cs.CV 2017
Year: 2017

Augmented accuracy in prediction of diabetes will open up new frontiers in health prognostics. Data overfitting is a performance-degrading issue in diabetes prognosis. In this study, a prediction system for the disease of diabetes is pre-sented where the issue of overfitting is minimized by using the dropout method. Deep learning neural network is used where both fully connected layers are fol-lowed by dropout layers. The output performance of the proposed neural network is shown to have outperformed other state-of-art methods and it is recorded as by far the best performance for the Pima Indians Diabetes Data Set.
All that is English may be Hindi: Enhancing language identification through automatic ranking of likeliness of word borrowing in social media
Venue: arXiv cs.CL 2017
Year: 2017

In this paper, we present a set of computational methods to identify the likeliness of a word being borrowed, based on the signals from social media. In terms of Spearman correlation coefficient values, our methods perform more than two times better (nearly 0.62) in predicting the borrowing likeliness compared to the best performing baseline (nearly 0.26) reported in literature. Based on this likeliness estimate we asked annotators to re-annotate the language tags of foreign words in predominantly native contexts. In 88 percent of cases the annotators felt that the foreign language tag should be replaced by native language tag, thus indicating a huge scope for improvement of automatic language identification systems.
A New Framework for Synthetic Aperture Sonar Micronavigation
Venue: arXiv cs.SY 2017
Year: 2017

Synthetic aperture imaging systems achieve constant azimuth resolution by coherently summating the observations acquired along the aperture path. At this aim, their locations have to be known with subwavelength accuracy. In underwater Synthetic Aperture Sonar (SAS), the nature of propagation and navigation in water makes the retrieval of this information challenging. Inertial sensors have to be employed in combination with signal processing techniques, which are usually referred to as micronavigation. In this paper we propose a novel micronavigation approach based on the minimization of an error function between two contiguous pings having some mutual information. This error is obtained by comparing the vector space intersections between the pings orthogonal projectors. The effectiveness and generality of the proposed approach is demonstrated by means of simulations and by means of an experiment performed in a controlled environment.