OpenRaiser/Intern-Atlas huggingface dataset:
LSB Matching Steganalysis Based on Patterns of Pixel Differences and Random Embedding
Venue: arXiv cs.MM 2017
Year: 2017

This paper presents a novel method for detection of LSB matching steganogra- phy in grayscale images. This method is based on the analysis of the differences between neighboring pixels before and after random data embedding. In natu- ral images, there is a strong correlation between adjacent pixels. This correla- tion is disturbed by LSB matching generating new types of correlations. The pre- sented method generates patterns from these correlations and analyzes their varia- tion when random data are hidden. The experiments performed for two different image databases show that the method yields better classification accuracy com- pared to prior art for both LSB matching and HUGO steganography. In addition, although the method is designed for the spatial domain, some experiments show its applicability also for detecting JPEG steganography.
Scaling up the software development process, a case study highlighting the complexities of large team software development
Venue: arXiv cs.SE 2017
Year: 2017

Diamond Light Source is the UK's National Synchrotron Facility and as such provides access to world class experimental services for UK and international researchers. As a user facility, that is one that focuses on providing a good user experience to our varied visitors, Diamond invests heavily in software infrastructure and staff. Over 100 members of the 600 strong workforce consider software development as a significant tool to help them achieve their primary role. These staff work on a diverse number of different software packages, providing support for installation and configuration, maintenance and bug fixing, as well as additional research and development of software when required. This talk focuses on one of the software projects undertaken to unify and improve the user experience of several experiments. The "mapping project" is a large 2 year, multi group project targeting the collection and processing experiments which involve scanning an X-ray beam over a sample and building up an image of that sample, similar to the way that google maps bring together small pieces of information to produce a full map of the world. The project itself is divided into several work packages, ranging from teams of one to 5 or 6 in size, with varying levels of time commitment to the project. This paper aims to explore one of these work packages as a case study, highlighting the experiences of the project team, the methodologies employed, their outcomes, and the lessons learnt from the experience.
Belief Propagation in Conditional RBMs for Structured Prediction
Venue: arXiv cs.LG 2017
Year: 2017

Restricted Boltzmann machines~(RBMs) and conditional RBMs~(CRBMs) are popular models for a wide range of applications. In previous work, learning on such models has been dominated by contrastive divergence~(CD) and its variants. Belief propagation~(BP) algorithms are believed to be slow for structured prediction on conditional RBMs~(e.g., Mnih et al. [2011]), and not as good as CD when applied in learning~(e.g., Larochelle et al. [2012]). In this work, we present a matrix-based implementation of belief propagation algorithms on CRBMs, which is easily scalable to tens of thousands of visible and hidden units. We demonstrate that, in both maximum likelihood and max-margin learning, training conditional RBMs with BP as the inference routine can provide significantly better results than current state-of-the-art CD methods on structured prediction problems. We also include practical guidelines on training CRBMs with BP, and some insights on the interaction of learning and inference algorithms for CRBMs.
Online Popularity under Promotion: Viral Potential, Forecasting, and the Economics of Time
Venue: arXiv cs.SI 2017
Year: 2017

Modeling the popularity dynamics of an online item is an important open problem in computational social science. This paper presents an in-depth study of popularity dynamics under external promotions, especially in predicting popularity jumps of online videos, and determining effective and efficient schedules to promote online content. The recently proposed Hawkes Intensity Process (HIP) models popularity as a non-linear interplay between exogenous stimuli and the endogenous reactions. Here, we propose two novel metrics based on HIP: to describe popularity gain per unit of promotion, and to quantify the time it takes for such effects to unfold. We make increasingly accurate forecasts of future popularity by including information about the intrinsic properties of the video, promotions it receives, and the non-linear effects of popularity ranking. We illustrate by simulation the interplay between the unfolding of popularity over time, and the time-sensitive value of resources. Lastly, our model lends a novel explanation of the commonly adopted periodic and constant promotion strategy in advertising, as increasing the perceived viral potential. This study provides quantitative guidelines about setting promotion schedules considering content virality, timing, and economics.
A Novel Multi-task Deep Learning Model for Skin Lesion Segmentation and Classification
Venue: arXiv cs.CV 2017
Year: 2017

In this study, a multi-task deep neural network is proposed for skin lesion analysis. The proposed multi-task learning model solves different tasks (e.g., lesion segmentation and two independent binary lesion classifications) at the same time by exploiting commonalities and differences across tasks. This results in improved learning efficiency and potential prediction accuracy for the task-specific models, when compared to training the individual models separately. The proposed multi-task deep learning model is trained and evaluated on the dermoscopic image sets from the International Skin Imaging Collaboration (ISIC) 2017 Challenge - Skin Lesion Analysis towards Melanoma Detection, which consists of 2000 training samples and 150 evaluation samples. The experimental results show that the proposed multi-task deep learning model achieves promising performances on skin lesion segmentation and classification. The average value of Jaccard index for lesion segmentation is 0.724, while the average values of area under the receiver operating characteristic curve (AUC) on two individual lesion classifications are 0.880 and 0.972, respectively.
Ultra-Dense Edge Caching under Spatio-Temporal Demand and Network Dynamics
Venue: arXiv cs.NI 2017
Year: 2017

This paper investigates a cellular edge caching design under an extremely large number of small base stations (SBSs) and users. In this ultra-dense edge caching network (UDCN), SBS-user distances shrink, and each user can request a cached content from multiple SBSs. Unfortunately, the complexity of existing caching controls' mechanisms increases with the number of SBSs, making them inapplicable for solving the fundamental caching problem: How to maximize local caching gain while minimizing the replicated content caching? Furthermore, spatial dynamics of interference is no longer negligible in UDCNs due to the surge in interference. In addition, the caching control should consider temporal dynamics of user demands. To overcome such difficulties, we propose a novel caching algorithm weaving together notions of mean-field game theory and stochastic geometry. These enable our caching algorithm to become independent of the number of SBSs and users, while incorporating spatial interference dynamics as well as temporal dynamics of content popularity and storage constraints. Numerical evaluation validates the fact that the proposed algorithm reduces not only the long run average cost by at least 24% but also the number of replicated content by 56% compared to a popularity-based algorithm.
Optimal Non-blocking Decentralized Supervisory Control Using G-Control Consistency
Venue: arXiv cs.SY 2017
Year: 2017

Supervisory control synthesis encounters with computational complexity. This can be reduced by decentralized supervisory control approach. In this paper, we define intrinsic control consistency for a pair of states of the plant. G-control consistency (GCC) is another concept which is defined for a natural projection w.r.t. the plant. We prove that, if a natural projection is output control consistent for the closed language of the plant, and is a natural observer for the marked language of the plant, then it is G-control consistent. Namely, we relax the conditions for synthesis the optimal non-blocking decentralized supervisory control by substituting GCC property for L-OCC and Lm-observer properties of a natural projection. We propose a method to synthesize the optimal non-blocking decentralized supervisory control based on GCC property for a natural projection. In fact, we change the approach from language-based properties of a natural projection to DES-based property by defining GCC property.
Good cyclic codes and the uncertainty principle
Venue: arXiv cs.IT 2017
Year: 2017

A long standing problem in the area of error correcting codes asks whether there exist good cyclic codes. Most of the known results point in the direction of a negative answer. The uncertainty principle is a classical result of harmonic analysis asserting that given a non-zero function $f$ on some abelian group, either $f$ or its Fourier transform $\hat{f}$ has large support. In this note, we observe a connection between these two subjects. We point out that even a weak version of the uncertainty principle for fields of positive characteristic would imply that good cyclic codes do exist. We also provide some heuristic arguments supporting that this is indeed the case.
A compact Verilog-A ReRAM switching model
Venue: arXiv cs.ET 2017
Year: 2017

The translation of emerging application concepts that exploit Resistive Random Access Memory (ReRAM) into large-scale practical systems requires realistic, yet computationally efficient, empirical models that can capture all observed physical devices. Here, we present a Verilog-A ReRAM model built upon experimental routines performed on TiOx-based prototypes. This model was based on custom biasing protocols, specifically designed to reveal device switching rate dependencies on a) bias voltage and b) initial resistive state. Our model is based on the assumption that a stationary switching rate surface m(R,v) exists for sufficiently low voltage stimulation. The proposed model comes in compact form as it is expressed by a simple voltage dependent exponential function multiplied with a voltage and initial resistive state dependent second order polynomial expression, which makes it suitable for fast and/or large-scale simulations.
Secure and Reconfigurable Network Design for Critical Information Dissemination in the Internet of Battlefield Things (IoBT)
Venue: arXiv cs.NI 2017
Year: 2017

The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas such as smart homes, smart cities, health care, transportation, etc. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to the battlefield specific challenges such as the absence of communication infrastructure, and the susceptibility of devices to cyber and physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and the information dissemination in the presence of adversaries. This work aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to study the communication of mission-critical data among different types of network devices and consequently design the network in a cost effective manner.
Defining Domain-Independent Discovery Informatics
Venue: arXiv cs.DB 2017
Year: 2017

This paper presents a personal account of the early legacy of discovery informatics, especially surrounding the first published definition of domain-independent DI. The state of DI is traced across various reference sources and the literature on the fourth paradigm of the scientific method. Observations are offered on DI, concluding that it will retain its appeal as a highly apt descriptor for research and practice activities that are inherent in our human nature.
On The Automated Planning And Design Of SMATV Systems
Venue: arXiv cs.NI 2017
Year: 2017

The paper presents some theoretical and practical considerations regarding the TV information distribution in local (small and medium) networks, using different technologies and architectures. The SMATV concept is chosen to be presented extensively. The most important design formulae are presented with a software package supporting the network planner to design and optimize the network. A case study is realized, using standard components in SMATV, for a 5 floor building. The study proved that it is possible to design and optimize the entire network, without realizing first a costly experimental setup. It is also possible to run different architectures, optimizing also the costs of the final solution of network.
Approximate Convex Hulls: sketching the convex hull using curvature
Venue: arXiv cs.CG 2017
Year: 2017

Convex hulls are fundamental objects in computational geometry. In moderate dimensions or for large numbers of vertices, computing the convex hull can be impractical due to the computational complexity of convex hull algorithms. In this article we approximate the convex hull in using a scalable algorithm which finds high curvature vertices with high probability. The algorithm is particularly effective for approximating convex hulls which have a relatively small number of extreme points.
Internalization of Externalities in Interdependent Security: Large Network Cases
Venue: arXiv cs.SI 2017
Year: 2017

With increasing connectivity among comprising agents or (sub-)systems in large, complex systems, there is a growing interest in understanding interdependent security and dealing with inefficiency in security investments. Making use of a population game model and the well-known Chung-Lu random graph model, we study how one could encourage selfish agents to invest more in security by internalizing the externalities produced by their security investments. To this end, we first establish an interesting relation between the local minimizers of social cost and the Nash equilibria of a population game with slightly altered costs. Secondly, under a mild technical assumption, we demonstrate that there exists a unique minimizer of social cost and it coincides with the unique Nash equilibrium of the population game. This finding tells us how to modify the private cost functions of selfish agents in order to enhance the overall security and reduce social cost. In addition, it reveals how the sensitivity of overall security to security investments of agents influences their externalities and, consequently, penalties or taxes that should be imposed for internalization of externalities. Finally, we illustrate how the degree distribution of agents influences their security investments and overall security at both the NEs of population games and social optima.
Multi-Scale Wavelet Domain Residual Learning for Limited-Angle CT Reconstruction
Venue: arXiv cs.CV 2017
Year: 2017

Limited-angle computed tomography (CT) is often used in clinical applications such as C-arm CT for interventional imaging. However, CT images from limited angles suffers from heavy artifacts due to incomplete projection data. Existing iterative methods require extensive calculations but can not deliver satisfactory results. Based on the observation that the artifacts from limited angles have some directional property and are globally distributed, we propose a novel multi-scale wavelet domain residual learning architecture, which compensates for the artifacts. Experiments have shown that the proposed method effectively eliminates artifacts, thereby preserving edge and global structures of the image.
Looking at Outfit to Parse Clothing
Venue: arXiv cs.CV 2017
Year: 2017

This paper extends fully-convolutional neural networks (FCN) for the clothing parsing problem. Clothing parsing requires higher-level knowledge on clothing semantics and contextual cues to disambiguate fine-grained categories. We extend FCN architecture with a side-branch network which we refer outfit encoder to predict a consistent set of clothing labels to encourage combinatorial preference, and with conditional random field (CRF) to explicitly consider coherent label assignment to the given image. The empirical results using Fashionista and CFPD datasets show that our model achieves state-of-the-art performance in clothing parsing, without additional supervision during training. We also study the qualitative influence of annotation on the current clothing parsing benchmarks, with our Web-based tool for multi-scale pixel-wise annotation and manual refinement effort to the Fashionista dataset. Finally, we show that the image representation of the outfit encoder is useful for dress-up image retrieval application.
Convex recovery of continuous domain piecewise constant images from non-uniform Fourier samples
Venue: arXiv cs.IT 2017
Year: 2017

We consider the recovery of a continuous domain piecewise constant image from its non-uniform Fourier samples using a convex matrix completion algorithm. We assume the discontinuities/edges of the image are localized to the zero levelset of a bandlimited function. This assumption induces linear dependencies between the Fourier coefficients of the image, which results in a two-fold block Toeplitz matrix constructed from the Fourier coefficients being low-rank. The proposed algorithm reformulates the recovery of the unknown Fourier coefficients as a structured low-rank matrix completion problem, where the nuclear norm of the matrix is minimized subject to structure and data constraints. We show that exact recovery is possible with high probability when the edge set of the image satisfies an incoherency property. We also show that the incoherency property is dependent on the geometry of the edge set curve, implying higher sampling burden for smaller curves. This paper generalizes recent work on the super-resolution recovery of isolated Diracs or signals with finite rate of innovation to the recovery of piecewise constant images.
Automated Top View Registration of Broadcast Football Videos
Venue: arXiv cs.CV 2017
Year: 2017

In this paper, we propose a novel method to register football broadcast video frames on the static top view model of the playing surface. The proposed method is fully automatic in contrast to the current state of the art which requires manual initialization of point correspondences between the image and the static model. Automatic registration using existing approaches has been difficult due to the lack of sufficient point correspondences. We investigate an alternate approach exploiting the edge information from the line markings on the field. We formulate the registration problem as a nearest neighbour search over a synthetically generated dictionary of edge map and homography pairs. The synthetic dictionary generation allows us to exhaustively cover a wide variety of camera angles and positions and reduce this problem to a minimal per-frame edge map matching procedure. We show that the per-frame results can be improved in videos using an optimization framework for temporal camera stabilization. We demonstrate the efficacy of our approach by presenting extensive results on a dataset collected from matches of football World Cup 2014.
Algebraic geometry codes with complementary duals exceed the asymptotic Gilbert-Varshamov bound
Venue: arXiv cs.IT 2017
Year: 2017

It was shown by Massey that linear complementary dual (LCD for short) codes are asymptotically good. In 2004, Sendrier proved that LCD codes meet the asymptotic Gilbert-Varshamov (GV for short) bound. Until now, the GV bound still remains to be the best asymptotical lower bound for LCD codes. In this paper, we show that an algebraic geometry code over a finite field of even characteristic is equivalent to an LCD code and consequently there exists a family of LCD codes that are equivalent to algebraic geometry codes and exceed the asymptotical GV bound.
Minimizing Latency for Secure Distributed Computing
Venue: arXiv cs.IT 2017
Year: 2017

We consider the setting of a master server who possesses confidential data (genomic, medical data, etc.) and wants to run intensive computations on it, as part of a machine learning algorithm for example. The master wants to distribute these computations to untrusted workers who have volunteered or are incentivized to help with this task. However, the data must be kept private (in an information theoretic sense) and not revealed to the individual workers. The workers may be busy, or even unresponsive, and will take a random time to finish the task assigned to them. We are interested in reducing the aggregate delay experienced by the master. We focus on linear computations as an essential operation in many iterative algorithms. A known solution is to use a linear secret sharing scheme to divide the data into secret shares on which the workers can compute. We propose to use instead new secure codes, called Staircase codes, introduced previously by two of the authors. We study the delay induced by Staircase codes which is always less than that of secret sharing. The reason is that secret sharing schemes need to wait for the responses of a fixed fraction of the workers, whereas Staircase codes offer more flexibility in this respect. For instance, for codes with rate $R=1/2$ Staircase codes can lead to up to $40\%$ reduction in delay compared to secret sharing.
Machine Learning Friendly Set Version of Johnson-Lindenstrauss Lemma
Venue: arXiv cs.DS 2017
Year: 2017

In this paper we make a novel use of the Johnson-Lindenstrauss Lemma. The Lemma has an existential form saying that there exists a JL transformation $f$ of the data points into lower dimensional space such that all of them fall into predefined error range $δ$. We formulate in this paper a theorem stating that we can choose the target dimensionality in a random projection type JL linear transformation in such a way that with probability $1-ε$ all of them fall into predefined error range $δ$ for any user-predefined failure probability $ε$. This result is important for applications such a data clustering where we want to have a priori dimensionality reducing transformation instead of trying out a (large) number of them, as with traditional Johnson-Lindenstrauss Lemma. In particular, we take a closer look at the $k$-means algorithm and prove that a good solution in the projected space is also a good solution in the original space. Furthermore, under proper assumptions local optima in the original space are also ones in the projected space. We define also conditions for which clusterability property of the original space is transmitted to the projected space, so that special case algorithms for the original space are also applicable in the projected space.