OpenRaiser/Intern-Atlas huggingface dataset:
Detecting Moving Regions in CrowdCam Images
Venue: arXiv cs.CV 2016
Year: 2016

We address the novel problem of detecting dynamic regions in CrowdCam images, a set of still images captured by a group of people. These regions capture the most interesting parts of the scene, and detecting them plays an important role in the analysis of visual data. Our method is based on the observation that matching static points must satisfy the epipolar geometry constraints, but computing exact matches is challenging. Instead, we compute the probability that a pixel has a match, not necessarily the correct one, along the corresponding epipolar line. The complement of this probability is not necessarily the probability of a dynamic point because of occlusions, noise, and matching errors. Therefore, information from all pairs of images is aggregated to obtain a high quality dynamic probability map, per image. Experiments on challenging datasets demonstrate the effectiveness of the algorithm on a broad range of settings; no prior knowledge about the scene, the camera characteristics or the camera locations is required.
X-ray Scattering Image Classification Using Deep Learning
Venue: arXiv cs.CV 2016
Year: 2016

Visual inspection of x-ray scattering images is a powerful technique for probing the physical structure of materials at the molecular scale. In this paper, we explore the use of deep learning to develop methods for automatically analyzing x-ray scattering images. In particular, we apply Convolutional Neural Networks and Convolutional Autoencoders for x-ray scattering image classification. To acquire enough training data for deep learning, we use simulation software to generate synthetic x-ray scattering images. Experiments show that deep learning methods outperform previously published methods by 10\% on synthetic and real datasets.
Variables effecting photomosaic reconstruction and ortho-rectification from aerial survey datasets
Venue: arXiv cs.CV 2016
Year: 2016

Unmanned aerial vehicles now make it possible to obtain high quality aerial imagery at a low cost, but processing those images into a single, useful entity is neither simple nor seamless. Specifically, there are factors that must be addressed when merging multiple images into a single coherent one. While ortho-rectification can be done, it tends to be expensive and time consuming. Image stitching offers a more economical, low-tech approach. However direct application tends to fail for low-elevation imagery due to one or more factors including insufficient keypoints, parallax issues, and homogeneity of the surveyed area. This paper discusses these problems and possible solutions when using techniques such as image stitching and structure from motion for generating ortho-rectified imagery. These are presented in terms of actual Irish projects including the Boland's Mills building in Dublin's city centre, the Kilmoon Cross Farm, and the Richview buildings on the University College Dublin campus. Implications for various Irish industries are explained in terms of both urban and rural projects.
Optimization of Generalized Unary Coding
Venue: arXiv cs.DM 2016
Year: 2016

This paper proposes an optimum version of the recently advanced scheme for generalized unary coding. In this method, the block of 1s that identifies the number is allowed to be broken up, which extends the count. The result is established by a theorem. The number count is now n(n-k-1)+1 rather than the previously described (n-k)(n-k)-1.
Learning an Astronomical Catalog of the Visible Universe through Scalable Bayesian Inference
Venue: arXiv cs.DC 2016
Year: 2016

Celeste is a procedure for inferring astronomical catalogs that attains state-of-the-art scientific results. To date, Celeste has been scaled to at most hundreds of megabytes of astronomical images: Bayesian posterior inference is notoriously demanding computationally. In this paper, we report on a scalable, parallel version of Celeste, suitable for learning catalogs from modern large-scale astronomical datasets. Our algorithmic innovations include a fast numerical optimization routine for Bayesian posterior inference and a statistically efficient scheme for decomposing astronomical optimization problems into subproblems. Our scalable implementation is written entirely in Julia, a new high-level dynamic programming language designed for scientific and numerical computing. We use Julia's high-level constructs for shared and distributed memory parallelism, and demonstrate effective load balancing and efficient scaling on up to 8192 Xeon cores on the NERSC Cori supercomputer.
Efficient Implementation of a Higher-Order Language with Built-In AD
Venue: arXiv cs.PL 2016
Year: 2016

We show that Automatic Differentiation (AD) operators can be provided in a dynamic language without sacrificing numeric performance. To achieve this, general forward and reverse AD functions are added to a simple high-level dynamic language, and support for them is included in an aggressive optimizing compiler. Novel technical mechanisms are discussed, which have the ability to migrate the AD transformations from run-time to compile-time. The resulting system, although only a research prototype, exhibits startlingly good performance. In fact, despite the potential inefficiencies entailed by support of a functional-programming language and a first-class AD operator, performance is competitive with the fastest available preprocessor-based Fortran AD systems. On benchmarks involving nested use of the AD operators, it can even dramatically exceed their performance.
Why is it Difficult to Detect Sudden and Unexpected Epidemic Outbreaks in Twitter?
Venue: arXiv cs.CY 2016
Year: 2016

Social media services such as Twitter are a valuable source of information for decision support systems. Many studies have shown that this also holds for the medical domain, where Twitter is considered a viable tool for public health officials to sift through relevant information for the early detection, management, and control of epidemic outbreaks. This is possible due to the inherent capability of social media services to transmit information faster than traditional channels. However, the majority of current studies have limited their scope to the detection of common and seasonal health recurring events (e.g., Influenza-like Illness), partially due to the noisy nature of Twitter data, which makes outbreak detection and management very challenging. Within the European project M-Eco, we developed a Twitter-based Epidemic Intelligence (EI) system, which is designed to also handle a more general class of unexpected and aperiodic outbreaks. In particular, we faced three main research challenges in this endeavor: 1) dynamic classification to manage terminology evolution of Twitter messages, 2) alert generation to produce reliable outbreak alerts analyzing the (noisy) tweet time series, and 3) ranking and recommendation to support domain experts for better assessment of the generated alerts. In this paper, we empirically evaluate our proposed approach to these challenges using real-world outbreak datasets and a large collection of tweets. We validate our solution with domain experts, describe our experiences, and give a more realistic view on the benefits and issues of analyzing social media for public health.
Moving Participants Turtle Consensus
Venue: arXiv cs.DC 2016
Year: 2016

We present Moving Participants Turtle Consensus (MPTC), an asynchronous consensus protocol for crash and Byzantine-tolerant distributed systems. MPTC uses various moving target defense strategies to tolerate certain Denial-of-Service (DoS) attacks issued by an adversary capable of compromising a bounded portion of the system. MPTC supports on the fly reconfiguration of the consensus strategy as well as of the processes executing this strategy when solving the problem of agreement. It uses existing cryptographic techniques to ensure that reconfiguration takes place in an unpredictable fashion thus eliminating the adversary's advantage on predicting protocol and execution-specific information that can be used against the protocol. We implement MPTC as well as a State Machine Replication protocol and evaluate our design under different attack scenarios. Our evaluation shows that MPTC approximates best case scenario performance even under a well-coordinated DoS attack.
Collision-based Testers are Optimal for Uniformity and Closeness
Venue: arXiv cs.DS 2016
Year: 2016

We study the fundamental problems of (i) uniformity testing of a discrete distribution, and (ii) closeness testing between two discrete distributions with bounded $\ell_2$-norm. These problems have been extensively studied in distribution testing and sample-optimal estimators are known for them~\cite{Paninski:08, CDVV14, VV14, DKN:15}. In this work, we show that the original collision-based testers proposed for these problems ~\cite{GRdist:00, BFR+:00} are sample-optimal, up to constant factors. Previous analyses showed sample complexity upper bounds for these testers that are optimal as a function of the domain size $n$, but suboptimal by polynomial factors in the error parameter $ε$. Our main contribution is a new tight analysis establishing that these collision-based testers are information-theoretically optimal, up to constant factors, both in the dependence on $n$ and in the dependence on $ε$.
Learning Multi-Scale Deep Features for High-Resolution Satellite Image Classification
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we propose a multi-scale deep feature learning method for high-resolution satellite image classification. Specifically, we firstly warp the original satellite image into multiple different scales. The images in each scale are employed to train a deep convolutional neural network (DCNN). However, simultaneously training multiple DCNNs is time-consuming. To address this issue, we explore DCNN with spatial pyramid pooling (SPP-net). Since different SPP-nets have the same number of parameters, which share the identical initial values, and only fine-tuning the parameters in fully-connected layers ensures the effectiveness of each network, thereby greatly accelerating the training process. Then, the multi-scale satellite images are fed into their corresponding SPP-nets respectively to extract multi-scale deep features. Finally, a multiple kernel learning method is developed to automatically learn the optimal combination of such features. Experiments on two difficult datasets show that the proposed method achieves favorable performance compared to other state-of-the-art methods.
Show me the material evidence: Initial experiments on evaluating hypotheses from user-generated multimedia data
Venue: arXiv cs.AI 2016
Year: 2016

Subjective questions such as `does neymar dive', or `is clinton lying', or `is trump a fascist', are popular queries to web search engines, as can be seen by autocompletion suggestions on Google, Yahoo and Bing. In the era of cognitive computing, beyond search, they could be handled as hypotheses issued for evaluation. Our vision is to leverage on unstructured data and metadata of the rich user-generated multimedia that is often shared as material evidence in favor or against hypotheses in social media platforms. In this paper we present two preliminary experiments along those lines and discuss challenges for a cognitive computing system that collects material evidence from user-generated multimedia towards aggregating it into some form of collective decision on the hypothesis.
Deep Convolutional Neural Network for Inverse Problems in Imaging
Venue: arXiv cs.CV 2016
Year: 2016

In this paper, we propose a novel deep convolutional neural network (CNN)-based algorithm for solving ill-posed inverse problems. Regularized iterative algorithms have emerged as the standard approach to ill-posed inverse problems in the past few decades. These methods produce excellent results, but can be challenging to deploy in practice due to factors including the high computational cost of the forward and adjoint operators and the difficulty of hyper parameter selection. The starting point of our work is the observation that unrolled iterative methods have the form of a CNN (filtering followed by point-wise non-linearity) when the normal operator (H*H, the adjoint of H times H) of the forward model is a convolution. Based on this observation, we propose using direct inversion followed by a CNN to solve normal-convolutional inverse problems. The direct inversion encapsulates the physical model of the system, but leads to artifacts when the problem is ill-posed; the CNN combines multiresolution decomposition and residual learning in order to learn to remove these artifacts while preserving image structure. We demonstrate the performance of the proposed network in sparse-view reconstruction (down to 50 views) on parallel beam X-ray computed tomography in synthetic phantoms as well as in real experimental sinograms. The proposed network outperforms total variation-regularized iterative reconstruction for the more realistic phantoms and requires less than a second to reconstruct a 512 x 512 image on GPU.
Practical Interpolation for Spectrum Cartography through Local Path Loss Modeling
Venue: arXiv cs.CE 2016
Year: 2016

A fundamental building block for supporting better utilization of radio spectrum involves predicting the impact that an emitter will have at different geographic locations. To this end, fixed sensors can be deployed to spatially sample the RF environment over an area of interest, with interpolation methods used to infer received power at locations between sensors. This paper describes a radio map interpolation method that exploits the known properties of most path loss models, with the aim of minimizing the RMS errors in predicted dB-power. We show that the results come very close to those for ideal Simple Kriging. Moreover, the method is simpler in terms of real-time computation by the network and it requires no knowledge of the spatial correlation of shadow fading. Our analysis of the method is general, but we exemplify it for a specific network geometry, comprising a grid-like pattern of sensors. We also provide comparisons to other widely used interpolation methods.
Applying Chatbots to the Internet of Things: Opportunities and Architectural Elements
Venue: arXiv cs.AI 2016
Year: 2016

Internet of Things (IoT) is emerging as a significant technology in shaping the future by connecting physical devices or things with internet. It also presents various opportunities for intersection of other technological trends which can allow it to become even more intelligent and efficient. In this paper we focus our attention on the integration of Intelligent Conversational Software Agents or Chatbots with IoT. Literature surveys have looked into various applications, features, underlying technologies and known challenges of IoT. On the other hand, Chatbots are being adopted in greater numbers due to major strides in development of platforms and frameworks. The novelty of this paper lies in the specific integration of Chatbots in the IoT scenario. We analyzed the shortcomings of existing IoT systems and put forward ways to tackle them by incorporating chatbots. A general architecture is proposed for implementing such a system, as well as platforms and frameworks, both commercial and open source, which allow for implementation of such systems. Identification of the newer challenges and possible future directions with this new integration, have also been addressed.
HoneyFaces: Increasing the Security and Privacy of Authentication Using Synthetic Facial Images
Venue: arXiv cs.CR 2016
Year: 2016

One of the main challenges faced by Biometric-based authentication systems is the need to offer secure authentication while maintaining the privacy of the biometric data. Previous solutions, such as Secure Sketch and Fuzzy Extractors, rely on assumptions that cannot be guaranteed in practice, and often affect the authentication accuracy. In this paper, we introduce HoneyFaces: the concept of adding a large set of synthetic faces (indistinguishable from real) into the biometric "password file". This password inflation protects the privacy of users and increases the security of the system without affecting the accuracy of the authentication. In particular, privacy for the real users is provided by "hiding" them among a large number of fake users (as the distributions of synthetic and real faces are equal). In addition to maintaining the authentication accuracy, and thus not affecting the security of the authentication process, HoneyFaces offer several security improvements: increased exfiltration hardness, improved leakage detection, and the ability to use a Two-server setting like in HoneyWords. Finally, HoneyFaces can be combined with other security and privacy mechanisms for biometric data. We implemented the HoneyFaces system and tested it with a password file composed of 270 real users. The "password file" was then inflated to accommodate up to $2^{36.5}$ users (resulting in a 56.6 TB "password file"). At the same time, the inclusion of additional faces does not affect the true acceptance rate or false acceptance rate which were 93.33\% and 0.01\%, respectively.
traIXroute: Detecting IXPs in traceroute paths
Venue: arXiv cs.NI 2016
Year: 2016

Internet eXchange Points (IXP) are critical components of the Internet infrastructure that affect its performance, evolution, security and economics. In this work, we introduce techniques to augment the well-known traceroute tool with the capability of identifying if and where exactly IXPs are crossed in endto- end paths. Knowing this information can help end-users have more transparency over how their traffic flows in the Internet. Our tool, called traIXroute, exploits data from the PeeringDB (PDB) and the Packet Clearing House (PCH) about IXP IP addresses of BGP routers, IXP members, and IXP prefixes. We show that the used data are both rich, i.e., we find 12,716 IP addresses of BGP routers in 460 IXPs, and mostly accurate, i.e., our validation shows 92-93% accuracy. In addition, 78.2% of the detected IXPs in our data are based on multiple diverse evidence and therefore help have higher confidence on the detected IXPs than when relying solely on IXP prefixes. To demonstrate the utility of our tool, we use it to show that one out of five paths in our data cross an IXP and that paths do not normally cross more than a single IXP, as it is expected based on the valley-free model about Internet policies. Furthermore, although the top IXPs both in terms of paths and members are located in Europe, US IXPs attract many more paths than their number of members indicates.
An Elementary Proof of Convex Phase Retrieval in the Natural Parameter Space via the Linear Program PhaseMax
Venue: arXiv cs.IT 2016
Year: 2016

The phase retrieval problem has garnered significant attention since the development of the PhaseLift algorithm, which is a convex program that operates in a lifted space of matrices. Because of the substantial computational cost due to lifting, many approaches to phase retrieval have been developed, including non-convex optimization algorithms which operate in the natural parameter space, such as Wirtinger Flow. Very recently, a convex formulation called PhaseMax has been discovered, and it has been proven to achieve phase retrieval via linear programming in the natural parameter space under optimal sample complexity. The current proofs of PhaseMax rely on statistical learning theory or geometric probability theory. Here, we present a short and elementary proof that PhaseMax exactly recovers real-valued vectors from random measurements under optimal sample complexity. Our proof only relies on standard probabilistic concentration and covering arguments, yielding a simpler and more direct proof than those that require statistical learning theory, geometric probability or the highly technical arguments for Wirtinger Flow-like approaches.
Signal Processing with Pulse Trains: An Algebraic Approach- Part II
Venue: arXiv cs.ET 2016
Year: 2016

The integrate and fire converter (IFC) enables an alternative to digital signal processing. IFC converts analog signal voltages into time between pulses and it is possible to reconstruct the analog signal from the IFC pulses with an error as small as required. In this paper, we present the definition of multiplication in pulse trains created by the IFC based on time domain operations and prove that it constitutes an Abelian group in the space of IFC pulse trains. We also show that pulse domain multiplication corresponds to pointwise multiplication of analog signals. It is further proved that pulse domain multiplication is distributive over pulse domain addition and hence it forms a field in the space of IFC pulse trains, which is an important property for linear signal processing.
Dual Teaching: A Practical Semi-supervised Wrapper Method
Venue: arXiv cs.LG 2016
Year: 2016

Semi-supervised wrapper methods are concerned with building effective supervised classifiers from partially labeled data. Though previous works have succeeded in some fields, it is still difficult to apply semi-supervised wrapper methods to practice because the assumptions those methods rely on tend to be unrealistic in practice. For practical use, this paper proposes a novel semi-supervised wrapper method, Dual Teaching, whose assumptions are easy to set up. Dual Teaching adopts two external classifiers to estimate the false positives and false negatives of the base learner. Only if the recall of every external classifier is greater than zero and the sum of the precision is greater than one, Dual Teaching will train a base learner from partially labeled data as effectively as the fully-labeled-data-trained classifier. The effectiveness of Dual Teaching is proved in both theory and practice.
Timestamps for Partial Replication
Venue: arXiv cs.DC 2016
Year: 2016

Maintaining causal consistency in distributed shared memory systems using vector timestamps has received a lot of attention from both theoretical and practical prospective. However, most of the previous literature focuses on full replication where each data is stored in all replicas, which may not be scalable due to the increasing amount of data. In this report, we investigate how to achieve causal consistency in partial replicated systems, where each replica may store different set of data. We propose an algorithm that tracks causal dependencies via vector timestamp in client-server model for partial replication. The cost of our algorithm in terms of timestamps size varies as a function of the manner in which the replicas share data, and the set of replicas accessed by each client. We also establish a connection between our algorithm with the previous work on full replication.