OpenRaiser/Intern-Atlas huggingface dataset:
Simulating Structure-from-Motion
Venue: arXiv cs.CV 2017
Year: 2017

The implementation of a Structure-from-Motion (SfM) pipeline from a synthetically generated scene as well as the investigation of the faithfulness of diverse reconstructions is the subject of this project. A series of different SfM reconstructions are implemented and their camera pose estimations are being contrasted with their respective ground truth locations. Finally, injection of ground truth location data into the rendered images in order to reduce the estimation error of the camera poses is studied as well.
Resolution limits on visual speech recognition
Venue: arXiv cs.CV 2017
Year: 2017

Visual-only speech recognition is dependent upon a number of factors that can be difficult to control, such as: lighting; identity; motion; emotion and expression. But some factors, such as video resolution are controllable, so it is surprising that there is not yet a systematic study of the effect of resolution on lip-reading. Here we use a new data set, the Rosetta Raven data, to train and test recognizers so we can measure the affect of video resolution on recognition accuracy. We conclude that, contrary to common practice, resolution need not be that great for automatic lip-reading. However it is highly unlikely that automatic lip-reading can work reliably when the distance between the bottom of the lower lip and the top of the upper lip is less than four pixels at rest.
Some observations on computer lip-reading: moving from the dream to the reality
Venue: arXiv cs.CV 2017
Year: 2017

In the quest for greater computer lip-reading performance there are a number of tacit assumptions which are either present in the datasets (high resolution for example) or in the methods (recognition of spoken visual units called visemes for example). Here we review these and other assumptions and show the surprising result that computer lip-reading is not heavily constrained by video resolution, pose, lighting and other practical factors. However, the working assumption that visemes, which are the visual equivalent of phonemes, are the best unit for recognition does need further examination. We conclude that visemes, which were defined over a century ago, are unlikely to be optimal for a modern computer lip-reading system.
Isotropic and Steerable Wavelets in N Dimensions. A multiresolution analysis framework for ITK
Venue: arXiv cs.CV 2017
Year: 2017

This document describes the implementation of the external module ITKIsotropicWavelets, a multiresolution (MRA) analysis framework using isotropic and steerable wavelets in the frequency domain. This framework provides the backbone for state of the art filters for denoising, feature detection or phase analysis in N-dimensions. It focus on reusability, and highly decoupled modules for easy extension and implementation of new filters, and it contains a filter for multiresolution phase analysis, The backbone of the multi-scale analysis is provided by an isotropic band-limited wavelet pyramid, and the detection of directional features is provided by coupling the pyramid with a generalized Riesz transform. The generalized Riesz transform of order N behaves like a smoothed version of the Nth order derivatives of the signal. Also, it is steerable: its components impulse responses can be rotated to any spatial orientation, reducing computation time when detecting directional features.
HPC optimal parallel communication algorithm for the simulation of fractional-order systems
Venue: arXiv cs.MS 2017
Year: 2017

A parallel numerical simulation algorithm is presented for fractional-order systems involving Caputo-type derivatives, based on the Adams-Bashforth-Moulton (ABM) predictor-corrector scheme. The parallel algorithm is implemented using several different approaches: a pure MPI version, a combination of MPI with OpenMP optimization and a memory saving speedup approach. All tests run on a BlueGene/P cluster, and comparative improvement results for the running time are provided. As an applied experiment, the solutions of a fractional-order version of a system describing a forced series LCR circuit are numerically computed, depicting cascades of period-doubling bifurcations which lead to the onset of chaotic behavior.
A MAP-Based Layered Detection Algorithm and Outage Analysis over MIMO Channels
Venue: arXiv cs.IT 2017
Year: 2017

Efficient symbol detection algorithms carry critical importance for achieving the spatial multiplexing gains promised by multi-input multi-output (MIMO) systems. In this paper, we consider a maximum a posteriori probability (MAP) based symbol detection algorithm, called M-BLAST, over uncoded quasi-static MIMO channels. Relying on the successive interference cancellation (SIC) receiver, M-BLAST algorithm offers a superior error performance over its predecessor V-BLAST with a signal-to-noise ratio (SNR) gain of as large as 2 dB under various settings of recent interest. Performance analysis of the M-BLAST algorithm is very complicated since the proposed detection order depends on the decision errors dynamically, which makes an already complex analysis of the conventional ordered SIC receivers even more difficult. To this end, a rigorous analytical framework is proposed to analyze the outage behavior of the M-BLAST algorithm over binary complex alphabets and two transmitting antennas, which has a potential to be generalized to multiple transmitting antennas and multidimensional constellation sets. The numerical results show a very good match between the analytical and simulation data under various SNR values and modulation alphabets.
Secure Private Information Retrieval from Colluding Databases with Eavesdroppers
Venue: arXiv cs.IT 2017
Year: 2017

The problem of private information retrieval (PIR) is to retrieve one message out of $K$ messages replicated at $N$ databases, without revealing the identity of the desired message to the databases. We consider the problem of PIR with colluding servers and eavesdroppers, named T-EPIR. Specifically, any $T$ out of $N$ databases may collude, i.e. they may communicate their interactions with the user to guess the identity of the requested message. An eavesdropper is curious to know the database and can tap in on the incoming and outgoing transmissions of any $E$ databases. The databases share some common randomness unknown to the eavesdropper and the user, and use the common randomness to generate the answers, such that the eavesdropper can learn no information about the $K$ messages. Define $R^*$ as the optimal ratio of the number of the desired message information bits to the number of total downloaded bits, and $ρ^*$ to be the optimal ratio of the information bits of the shared common randomness to the information bits of the desired file. In our previous work, we found that when $E \geq T$, the optimal ratio that can be achieved equals $1-\frac{E}{N}$. In this work, we focus on the case when $E \leq T$. We derive an outer bound $R^* \leq (1-\frac{T}{N}) \frac{1-\frac{E}{N} \cdot (\frac{T}{N})^{K-1}}{1-(\frac{T}{N})^K}$. We also obtain a lower bound of $ρ^* \geq \frac{\frac{E}{N}(1-(\frac{T}{N})^K)}{(1-\frac{T}{N})(1-\frac{E}{N} \cdot (\frac{T}{N})^{K-1})}$. For the achievability, we propose a scheme which achieves the rate (inner bound) $R=\frac{1-\frac{T}{N}}{1-(\frac{T}{N})^K}-\frac{E}{KN}$. The amount of shared common randomness used in the achievable scheme is $\frac{\frac{E}{N}(1-(\frac{T}{N})^K)}{1-\frac{T}{N}-\frac{E}{KN}(1-(\frac{T}{N})^K)}$ times the file size. The gap between the derived inner and outer bounds vanishes as the number of messages $K$ tends to infinity.
Person Re-Identification with Vision and Language
Venue: arXiv cs.CV 2017
Year: 2017

In this paper we propose a new approach to person re-identification using images and natural language descriptions. We propose a joint vision and language model based on CCA and CNN architectures to match across the two modalities as well as to enrich visual examples for which there are no language descriptions. We also introduce new annotations in the form of natural language descriptions for two standard Re-ID benchmarks, namely CUHK03 and VIPeR. We perform experiments on these two datasets with techniques based on CNN, hand-crafted features as well as LSTM for analysing visual and natural description data. We investigate and demonstrate the advantages of using natural language descriptions compared to attributes as well as CNN compared to LSTM in the context of Re-ID. We show that the joint use of language and vision can significantly improve the state-of-the-art performance on standard Re-ID benchmarks.
The Cafe Wall Illusion: Local and Global Perception from multiple scale to multiscale
Venue: arXiv cs.CV 2017
Year: 2017

Geometrical illusions are a subclass of optical illusions in which the geometrical characteristics of patterns such as orientations and angles are distorted and misperceived as the result of low- to high-level retinal/cortical processing. Modelling the detection of tilt in these illusions and their strengths as they are perceived is a challenging task computationally and leads to development of techniques that match with human performance. In this study, we present a predictive and quantitative approach for modeling foveal and peripheral vision in the induced tilt in Café Wall illusion in which parallel mortar lines between shifted rows of black and white tiles appear to converge and diverge. A bioderived filtering model for the responses of retinal/cortical simple cells to the stimulus using Difference of Gaussians is utilized with an analytic processing pipeline introduced in our previous studies to quantify the angle of tilt in the model. Here we have considered visual characteristics of foveal and peripheral vision in the perceived tilt in the pattern to predict different degrees of tilt in different areas of the fovea and periphery as the eye saccades to different parts of the image. The tilt analysis results from several sampling sizes and aspect ratios, modelling variant foveal views are used from our previous investigations on the local tilt, and we specifically investigate in this work, different configurations of the whole pattern modelling variant Gestalt views across multiple scales in order to provide confidence intervals around the predicted tilts. The foveal sample sets are verified and quantified using two different sampling methods. We present here a precise and quantified comparison contrasting local tilt detection in the foveal sets with a global average across all of the Café Wall configurations tested in this work.
A Comparative Study of CNN, BoVW and LBP for Classification of Histopathological Images
Venue: arXiv cs.CV 2017
Year: 2017

Despite the progress made in the field of medical imaging, it remains a large area of open research, especially due to the variety of imaging modalities and disease-specific characteristics. This paper is a comparative study describing the potential of using local binary patterns (LBP), deep features and the bag-of-visual words (BoVW) scheme for the classification of histopathological images. We introduce a new dataset, \emph{KIMIA Path960}, that contains 960 histopathology images belonging to 20 different classes (different tissue types). We make this dataset publicly available. The small size of the dataset and its inter- and intra-class variability makes it ideal for initial investigations when comparing image descriptors for search and classification in complex medical imaging cases like histopathology. We investigate deep features, LBP histograms and BoVW to classify the images via leave-one-out validation. The accuracy of image classification obtained using LBP was 90.62\% while the highest accuracy using deep features reached 94.72\%. The dictionary approach (BoVW) achieved 96.50\%. Deep solutions may be able to deliver higher accuracies but they need extensive training with a large number of (balanced) image datasets.
Coverage and Rate Analysis for Co-Existing RF/VLC Downlink Cellular Networks
Venue: arXiv cs.IT 2017
Year: 2017

This paper provides a stochastic geometry framework to perform the coverage and rate analysis of a typical user in co-existing VLC and RF networks covering a large indoor area. The developed framework can be customized to capture the performance of a typical user in various network configurations such as (i) RF-only, in which only small base-stations (SBSs) are available to provide the coverage to a user, (ii) VLC-only, in which only optical BSs (OBSs) are available to provide the coverage to a user, (iii) opportunistic RF/VLC, where a user selects the network with maximum received signal power, and (iv) hybrid RF/VLC, where a user can simultaneously utilize the available resources from both RF and VLC networks. The developed model for VLC network precisely captures the impact of the field-of-view (FOV) of the photo-detector (PD) receiver on the number of interferers, distribution of the aggregate interference, association probability, and the coverage of a typical user. Closed-form approximations are presented for special cases of practical interest and for asymptotic scenarios such as when the intensity of SBSs becomes very low. The derived expressions enable us to obtain closed-form solutions for various network design parameters (such as intensity of OBSs and SBSs, transmit power, and/or FOV) such that the number of active users can be distributed optimally among RF and VLC networks. Also, we optimize the network parameters in order to prioritize the association of users to VLC network. Finally, simulations are carried out to verify the derived analytical solutions. Important trade-offs between height and intensity of OBSs are highlighted to optimize the performance of a user in VLC networks.
Indexing the Event Calculus with Kd-trees to Monitor Diabetes
Venue: arXiv cs.AI 2017
Year: 2017

Personal Health Systems (PHS) are mobile solutions tailored to monitoring patients affected by chronic non communicable diseases. A patient affected by a chronic disease can generate large amounts of events. Type 1 Diabetic patients generate several glucose events per day, ranging from at least 6 events per day (under normal monitoring) to 288 per day when wearing a continuous glucose monitor (CGM) that samples the blood every 5 minutes for several days. This is a large number of events to monitor for medical doctors, in particular when considering that they may have to take decisions concerning adjusting the treatment, which may impact the life of the patients for a long time. Given the need to analyse such a large stream of data, doctors need a simple approach towards physiological time series that allows them to promptly transfer their knowledge into queries to identify interesting patterns in the data. Achieving this with current technology is not an easy task, as on one hand it cannot be expected that medical doctors have the technical knowledge to query databases and on the other hand these time series include thousands of events, which requires to re-think the way data is indexed. In order to tackle the knowledge representation and efficiency problem, this contribution presents the kd-tree cached event calculus (\ceckd) an event calculus extension for knowledge engineering of temporal rules capable to handle many thousands events produced by a diabetic patient. \ceckd\ is built as a support to a graphical interface to represent monitoring rules for diabetes type 1. In addition, the paper evaluates the \ceckd\ with respect to the cached event calculus (CEC) to show how indexing events using kd-trees improves scalability with respect to the current state of the art.
Multiple domination models for placement of electric vehicle charging stations in road networks
Venue: arXiv cs.DM 2017
Year: 2017

Electric and hybrid vehicles play an increasing role in the road transport networks. Despite their advantages, they have a relatively limited cruising range in comparison to traditional diesel/petrol vehicles, and require significant battery charging time. We propose to model the facility location problem of the placement of charging stations in road networks as a multiple domination problem on reachability graphs. This model takes into consideration natural assumptions such as a threshold for remaining battery load, and provides some minimal choice for a travel direction to recharge the battery. Experimental evaluation and simulations for the proposed facility location model are presented in the case of real road networks corresponding to the cities of Boston and Dublin.
Visual gesture variability between talkers in continuous visual speech
Venue: arXiv cs.CV 2017
Year: 2017

Recent adoption of deep learning methods to the field of machine lipreading research gives us two options to pursue to improve system performance. Either, we develop end-to-end systems holistically or, we experiment to further our understanding of the visual speech signal. The latter option is more difficult but this knowledge would enable researchers to both improve systems and apply the new knowledge to other domains such as speech therapy. One challenge in lipreading systems is the correct labeling of the classifiers. These labels map an estimated function between visemes on the lips and the phonemes uttered. Here we ask if such maps are speaker-dependent? Prior work investigated isolated word recognition from speaker-dependent (SD) visemes, we extend this to continuous speech. Benchmarked against SD results, and the isolated words performance, we test with RMAV dataset speakers and observe that with continuous speech, the trajectory between visemes has a greater negative effect on the speaker differentiation.
A Fully Convolutional Network for Semantic Labeling of 3D Point Clouds
Venue: arXiv cs.CV 2017
Year: 2017

When classifying point clouds, a large amount of time is devoted to the process of engineering a reliable set of features which are then passed to a classifier of choice. Generally, such features - usually derived from the 3D-covariance matrix - are computed using the surrounding neighborhood of points. While these features capture local information, the process is usually time-consuming, and requires the application at multiple scales combined with contextual methods in order to adequately describe the diversity of objects within a scene. In this paper we present a 1D-fully convolutional network that consumes terrain-normalized points directly with the corresponding spectral data,if available, to generate point-wise labeling while implicitly learning contextual features in an end-to-end fashion. Our method uses only the 3D-coordinates and three corresponding spectral features for each point. Spectral features may either be extracted from 2D-georeferenced images, as shown here for Light Detection and Ranging (LiDAR) point clouds, or extracted directly for passive-derived point clouds,i.e. from muliple-view imagery. We train our network by splitting the data into square regions, and use a pooling layer that respects the permutation-invariance of the input points. Evaluated using the ISPRS 3D Semantic Labeling Contest, our method scored second place with an overall accuracy of 81.6%. We ranked third place with a mean F1-score of 63.32%, surpassing the F1-score of the method with highest accuracy by 1.69%. In addition to labeling 3D-point clouds, we also show that our method can be easily extended to 2D-semantic segmentation tasks, with promising initial results.
SpaceTEE: Secure and Tamper-Proof Computing in Space using CubeSats
Venue: arXiv cs.CR 2017
Year: 2017

Sensitive computation often has to be performed in a trusted execution environment (TEE), which, in turn, requires tamper-proof hardware. If the computational fabric can be tampered with, we may no longer be able to trust the correctness of the computation. We study the idea of using computational platforms in space as a means to protect data from adversarial physical access. In this paper, we propose SpaceTEE - a practical implementation of this approach using low-cost nano-satellites called CubeSats. We study the constraints of such a platform, the cost of deployment, and discuss possible applications under those constraints. As a case study, we design a hardware security module solution (called SpaceHSM) and describe how it can be used to implement a root-of-trust for a certi cate authority (CA).
Improving Compression Based Dissimilarity Measure for Music Score Analysis
Venue: arXiv cs.SD 2017
Year: 2017

In this paper, we propose a way to improve the compression based dissimilarity measure, CDM. We propose to use a modified value of the file size, where the original CDM uses an unmodified file size. Our application is a music score analysis. We have chosen piano pieces from five different composers. We have selected 75 famous pieces (15 pieces for each composer). We computed the distances among all pieces by using the modified CDM. We use the K-nearest neighbor method when we estimate the composer of each piece of music. The modified CDM shows improved accuracy. The difference is statistically significant.
Sum of Square Proof for Brascamp-Lieb Type Inequality
Venue: arXiv cs.CC 2017
Year: 2017

Brascamp-Lieb inequality is an important mathematical tool in analysis, geometry and information theory. There are various ways to prove Brascamp-Lieb inequality such as heat flow method, Brownian motion and subadditivity of the entropy. While Brascamp-Lieb inequality is originally stated in Euclidean Space, discussed Brascamp-Lieb inequality for discrete Abelian group and discussed Brascamp-Lieb inequality for Markov semigroups. Many mathematical inequalities can be formulated as algebraic inequalities which asserts some given polynomial is nonnegative. In 1927, Artin proved that any non- negative polynomial can be represented as a sum of squares of rational functions, which can be further formulated as a polynomial certificate of the nonnegativity of the polynomial. This is a Sum of Square proof of the inequality. Take the degree of the polynomial certificate as the degree of Sum of Square proof. The degree of an Sum of Square proof determines the complexity of generating such proof by Sum of Square algorithm which is a powerful tool for optimization and computer aided proof. In this paper, we give a Sum of Square proof for some special settings of Brascamp- Lieb inequality following and and discuss some applications of Brascamp-Lieb inequality on Abelian group and Euclidean Sphere. If the original description of the inequality has constant degree and d is constant, the degree of the proof is also constant. Therefore, low degree sum of square algorithm can fully capture the power of low degree finite Brascamp-Lieb inequality.
Discourse Structure in Machine Translation Evaluation
Venue: arXiv cs.CL 2017
Year: 2017

In this article, we explore the potential of using sentence-level discourse structure for machine translation evaluation. We first design discourse-aware similarity measures, which use all-subtree kernels to compare discourse parse trees in accordance with the Rhetorical Structure Theory (RST). Then, we show that a simple linear combination with these measures can help improve various existing machine translation evaluation metrics regarding correlation with human judgments both at the segment- and at the system-level. This suggests that discourse information is complementary to the information used by many of the existing evaluation metrics, and thus it could be taken into account when developing richer evaluation metrics, such as the WMT-14 winning combined metric DiscoTKparty. We also provide a detailed analysis of the relevance of various discourse elements and relations from the RST parse trees for machine translation evaluation. In particular we show that: (i) all aspects of the RST tree are relevant, (ii) nuclearity is more useful than relation type, and (iii) the similarity of the translation RST tree to the reference tree is positively correlated with translation quality.