OpenRaiser/Intern-Atlas huggingface dataset:
A survey on haptic technologies for mobile augmented reality
Venue: arXiv cs.HC 2017
Year: 2017

Augmented Reality (AR) and Mobile Augmented Reality (MAR) applications have gained much research and industry attention these days. The mobile nature of MAR applications limits users' interaction capabilities such as inputs, and haptic feedbacks. This survey reviews current research issues in the area of human computer interaction for MAR and haptic devices. The survey first presents human sensing capabilities and their applicability in AR applications. We classify haptic devices into two groups according to the triggered sense: cutaneous/tactile: touch, active surfaces, and mid-air, kinesthetic: manipulandum, grasp, and exoskeleton. Due to the mobile capabilities of MAR applications, we mainly focus our study on wearable haptic devices for each category and their AR possibilities. To conclude, we discuss the future paths that haptic feedbacks should follow for MAR applications and their challenges.
Sushi Dish - Object detection and classification from real images
Venue: arXiv cs.CV 2017
Year: 2017

In conveyor belt sushi restaurants, billing is a burdened job because one has to manually count the number of dishes and identify the color of them to calculate the price. In a busy situation, there can be a mistake that customers are overcharged or under-charged. To deal with this problem, we developed a method that automatically identifies the color of dishes and calculate the total price using real images. Our method consists of ellipse fitting and convol-utional neural network. It achieves ellipse detection precision 85% and recall 96% and classification accuracy 92%.
TARN: A SDN-based Traffic Analysis Resistant Network Architecture
Venue: arXiv cs.NI 2017
Year: 2017

Destination IP prefix-based routing protocols are core to Internet routing today. Internet autonomous systems (AS) possess fixed IP prefixes, while packets carry the intended destination AS's prefix in their headers, in clear text. As a result, network communications can be easily identified using IP addresses and become targets of a wide variety of attacks, such as DNS/IP filtering, distributed Denial-of-Service (DDoS) attacks, man-in-the-middle (MITM) attacks, etc. In this work, we explore an alternative network architecture that fundamentally removes such vulnerabilities by disassociating the relationship between IP prefixes and destination networks, and by allowing any end-to-end communication session to have dynamic, short-lived, and pseudo-random IP addresses drawn from a range of IP prefixes rather than one. The concept is seemingly impossible to realize in todays Internet. We demonstrate how this is doable today with three different strategies using software defined networking (SDN), and how this can be done at scale to transform the Internet addressing and routing paradigms with the novel concept of a distributed software defined Internet exchange (SDX). The solution works with both IPv4 and IPv6, whereas the latter provides higher degrees of IP addressing freedom. Prototypes based on OpenvSwitches (OVS) have been implemented for experimentation across the PEERING BGP testbed. The SDX solution not only provides a technically sustainable pathway towards large-scale traffic analysis resistant network (TARN) support, it also unveils a new business model for customer driven, customizable and trustable end-to-end network services.
Machine learning methods for histopathological image analysis
Venue: arXiv cs.CV 2017
Year: 2017

Abundant accumulation of digital histopathological images has led to the increased demand for their analysis, such as computer-aided diagnosis using machine learning techniques. However, digital pathological images and related tasks have some issues to be considered. In this mini-review, we introduce the application of digital pathological image analysis using machine learning algorithms, address some problems specific to such analysis, and propose possible solutions.
Non-rigid image registration using fully convolutional networks with deep self-supervision
Venue: arXiv cs.CV 2017
Year: 2017

We propose a novel non-rigid image registration algorithm that is built upon fully convolutional networks (FCNs) to optimize and learn spatial transformations between pairs of images to be registered. Different from most existing deep learning based image registration methods that learn spatial transformations from training data with known corresponding spatial transformations, our method directly estimates spatial transformations between pairs of images by maximizing an image-wise similarity metric between fixed and deformed moving images, similar to conventional image registration algorithms. At the same time, our method also learns FCNs for encoding the spatial transformations at the same spatial resolution of images to be registered, rather than learning coarse-grained spatial transformation information. The image registration is implemented in a multi-resolution image registration framework to jointly optimize and learn spatial transformations and FCNs at different resolutions with deep self-supervision through typical feedforward and backpropagation computation. Since our method simultaneously optimizes and learns spatial transformations for the image registration, our method can be directly used to register a pair of images, and the registration of a set of images is also a training procedure for FCNs so that the trained FCNs can be directly adopted to register new images by feedforward computation of the learned FCNs without any optimization. The proposed method has been evaluated for registering 3D structural brain magnetic resonance (MR) images and obtained better performance than state-of-the-art image registration algorithms.
Optimizing Networks for Internet Access Using Tethering
Venue: arXiv cs.NI 2017
Year: 2017

We investigate scenarios where Internet access to a user device (node) is available only via the cellular network. However, not every node may connect directly to it. Instead, some may use tethering to connect over WiFi to a node sharing its Internet connection. In effect, nodes split into hotspots and clients. Hotspots are nodes that connect directly to the cellular network and can provide Internet connectivity to other nodes to whom they are connected over WiFi. Clients connect to the cellular network only via hotspots. In this work, we consider the problem of determining the split of hotspots and clients, and the association between them, which maximizes the sum of the rates of all nodes, subject to the constraint that any node gets at least the rate it gets when all nodes are directly connected to the cellular network. Via tractable networks, we provide insights into the interplay between WiFi connectivity amongst nodes and rates of their links to the cellular tower, the splits that maximize sum rate, with provably optimal splits for a few cases. We propose a novel heuristic approach to split any network and provide a detailed exposition of gains available from tethering, via simulations.
Neural Networks for Safety-Critical Applications - Challenges, Experiments and Perspectives
Venue: arXiv cs.SE 2017
Year: 2017

We propose a methodology for designing dependable Artificial Neural Networks (ANN) by extending the concepts of understandability, correctness, and validity that are crucial ingredients in existing certification standards. We apply the concept in a concrete case study in designing a high-way ANN-based motion predictor to guarantee safety properties such as impossibility for the ego vehicle to suggest moving to the right lane if there exists another vehicle on its right.
ARIGAN: Synthetic Arabidopsis Plants using Generative Adversarial Network
Venue: arXiv cs.CV 2017
Year: 2017

In recent years, there has been an increasing interest in image-based plant phenotyping, applying state-of-the-art machine learning approaches to tackle challenging problems, such as leaf segmentation (a multi-instance problem) and counting. Most of these algorithms need labelled data to learn a model for the task at hand. Despite the recent release of a few plant phenotyping datasets, large annotated plant image datasets for the purpose of training deep learning algorithms are lacking. One common approach to alleviate the lack of training data is dataset augmentation. Herein, we propose an alternative solution to dataset augmentation for plant phenotyping, creating artificial images of plants using generative neural networks. We propose the Arabidopsis Rosette Image Generator (through) Adversarial Network: a deep convolutional network that is able to generate synthetic rosette-shaped plants, inspired by DCGAN (a recent adversarial network model using convolutional layers). Specifically, we trained the network using A1, A2, and A4 of the CVPPP 2017 LCC dataset, containing Arabidopsis Thaliana plants. We show that our model is able to generate realistic 128x128 colour images of plants. We train our network conditioning on leaf count, such that it is possible to generate plants with a given number of leaves suitable, among others, for training regression based models. We propose a new Ax dataset of artificial plants images, obtained by our ARIGAN. We evaluate this new dataset using a state-of-the-art leaf counting algorithm, showing that the testing error is reduced when Ax is used as part of the training data.
Distributed Computation of Linear Inverse Problems with Application to Computed Tomography
Venue: arXiv cs.DC 2017
Year: 2017

The inversion of linear systems is a fundamental step in many inverse problems. Computational challenges exist when trying to invert large linear systems, where limited computing resources mean that only part of the system can be kept in computer memory at any one time. We are here motivated by tomographic inversion problems that often lead to linear inverse problems. In state of the art x-ray systems, even a standard scan can produce 4 million individual measurements and the reconstruction of x-ray attenuation profiles typically requires the estimation of a million attenuation coefficients. To deal with the large data sets encountered in real applications and to utilise modern graphics processing unit (GPU) based computing architectures, combinations of iterative reconstruction algorithms and parallel computing schemes are increasingly applied. Although both row and column action methods have been proposed to utilise parallel computing architectures, individual computations in current methods need to know either the entire set of observations or the entire set of estimated x-ray absorptions, which can be prohibitive in many realistic big data applications. We present a fully parallelizable computed tomography (CT) image reconstruction algorithm that works with arbitrary partial subsets of the data and the reconstructed volume. We further develop a non-homogeneously randomised selection criteria which guarantees that sub-matrices of the system matrix are selected more frequently if they are dense, thus maximising information flow through the algorithm. A grouped version of the algorithm is also proposed to further improve convergence speed and performance. Algorithm performance is verified experimentally.
Distributed Triangle Counting in the Graphulo Matrix Math Library
Venue: arXiv cs.DC 2017
Year: 2017

Triangle counting is a key algorithm for large graph analysis. The Graphulo library provides a framework for implementing graph algorithms on the Apache Accumulo distributed database. In this work we adapt two algorithms for counting triangles, one that uses the adjacency matrix and another that also uses the incidence matrix, to the Graphulo library for server-side processing inside Accumulo. Cloud-based experiments show a similar performance profile for these different approaches on the family of power law Graph500 graphs, for which data skew increasingly bottlenecks. These results motivate the design of skew-aware hybrid algorithms that we propose for future work.
Computing Storyline Visualizations with Few Block Crossings
Venue: arXiv cs.CG 2017
Year: 2017

Storyline visualizations show the structure of a story, by depicting the interactions of the characters over time. Each character is represented by an x-monotone curve from left to right, and a meeting is represented by having the curves of the participating characters run close together for some time. There have been various approaches to drawing storyline visualizations in an automated way. In order to keep the visual complexity low, rather than minimizing pairwise crossings of curves, we count block crossings, that is, pairs of intersecting bundles of lines. Partly inspired by the ILP-based approach of Gronemann et al. [GD 2016] for minimizing the number of pairwise crossings, we model the problem as a satisfiability problem (since the straightforward ILP formulation becomes more complicated and harder to solve). Having restricted ourselves to a decision problem, we can apply powerful SAT solvers to find optimal drawings in reasonable time. We compare this SAT-based approach with two exact algorithms for block crossing minimization, using both the benchmark instances of Gronemann et al. and random instances. We show that the SAT approach is suitable for real-world instances and identify cases where the other algorithms are preferable.
MIP Formulations for the Steiner Forest Problem
Venue: arXiv cs.DM 2017
Year: 2017

The Steiner Forest problem is among the fundamental network design problems. Finding tight linear programming bounds for the problem is the key for both fast Branch-and-Bound algorithms and good primal-dual approximations. On the theoretical side, the best known bound can be obtained from an integer program [KLSv08]. It guarantees a value that is a (2-eps)-approximation of the integer optimum. On the practical side, bounds from a mixed integer program by Magnanti and Raghavan [MR05] are very close to the integer optimum in computational experiments, but the size of the model limits its practical usefulness. We compare a number of known integer programming formulations for the problem and propose three new formulations. We can show that the bounds from our two new cut-based formulations for the problem are within a factor of 2 of the integer optimum. In our experiments, the formulations prove to be both tractable and provide better bounds than all other tractable formulations. In particular, the factor to the integer optimum is much better than 2 in the experiments.
Multi-View Spectral Clustering via Structured Low-Rank Matrix Factorization
Venue: arXiv cs.CV 2017
Year: 2017

Multi-view data clustering attracts more attention than their single view counterparts due to the fact that leveraging multiple independent and complementary information from multi-view feature spaces outperforms the single one. Multi-view Spectral Clustering aims at yielding the data partition agreement over their local manifold structures by seeking eigenvalue-eigenvector decompositions. However, as we observed, such classical paradigm still suffers from (1) overlooking the flexible local manifold structure, caused by (2) enforcing the low-rank data correlation agreement among all views; worse still, (3) LRR is not intuitively flexible to capture the latent data clustering structures. In this paper, we present the structured LRR by factorizing into the latent low-dimensional data-cluster representations, which characterize the data clustering structure for each view. Upon such representation, (b) the laplacian regularizer is imposed to be capable of preserving the flexible local manifold structure for each view. (c) We present an iterative multi-view agreement strategy by minimizing the divergence objective among all factorized latent data-cluster representations during each iteration of optimization process, where such latent representation from each view serves to regulate those from other views, such intuitive process iteratively coordinates all views to be agreeable. (d) We remark that such data-cluster representation can flexibly encode the data clustering structure from any view with adaptive input cluster number. To this end, (e) a novel non-convex objective function is proposed via the efficient alternating minimization strategy. The complexity analysis are also presented. The extensive experiments conducted against the real-world multi-view datasets demonstrate the superiority over state-of-the-arts.
Truly Concurrent Process Algebra with Timing
Venue: arXiv cs.LO 2017
Year: 2017

We extend truly concurrent process algebra APTC with timing related properties. Just like ACP with timing, APTC with timing also has four parts: discrete relative timing, discrete absolute timing, continuous relative timing and continuous absolute timing.
Probabilistic Surfel Fusion for Dense LiDAR Mapping
Venue: arXiv cs.RO 2017
Year: 2017

With the recent development of high-end LiDARs, more and more systems are able to continuously map the environment while moving and producing spatially redundant information. However, none of the previous approaches were able to effectively exploit this redundancy in a dense LiDAR mapping problem. In this paper, we present a new approach for dense LiDAR mapping using probabilistic surfel fusion. The proposed system is capable of reconstructing a high-quality dense surface element (surfel) map from spatially redundant multiple views. This is achieved by a proposed probabilistic surfel fusion along with a geometry considered data association. The proposed surfel data association method considers surface resolution as well as high measurement uncertainty along its beam direction which enables the mapping system to be able to control surface resolution without introducing spatial digitization. The proposed fusion method successfully suppresses the map noise level by considering measurement noise caused by laser beam incident angle and depth distance in a Bayesian filtering framework. Experimental results with simulated and real data for the dense surfel mapping prove the ability of the proposed method to accurately find the canonical form of the environment without further post-processing.
Implicit Cooperative Positioning in Vehicular Networks
Venue: arXiv cs.NI 2017
Year: 2017

Absolute positioning of vehicles is based on Global Navigation Satellite Systems (GNSS) combined with on-board sensors and high-resolution maps. In Cooperative Intelligent Transportation Systems (C-ITS), the positioning performance can be augmented by means of vehicular networks that enable vehicles to share location-related information. This paper presents an Implicit Cooperative Positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle (V2V) connectivity in an innovative manner, avoiding the use of explicit V2V measurements such as ranging. In the ICP approach, vehicles jointly localize non-cooperative physical features (such as people, traffic lights or inactive cars) in the surrounding areas, and use them as common noisy reference points to refine their location estimates. Information on sensed features are fused through V2V links by a consensus procedure, nested within a message passing algorithm, to enhance the vehicle localization accuracy. As positioning does not rely on explicit ranging information between vehicles, the proposed ICP method is amenable to implementation with off-the-shelf vehicular communication hardware. The localization algorithm is validated in different traffic scenarios, including a crossroad area with heterogeneous conditions in terms of feature density and V2V connectivity, as well as a real urban area by using Simulation of Urban MObility (SUMO) for traffic data generation. Performance results show that the proposed ICP method can significantly improve the vehicle location accuracy compared to the stand-alone GNSS, especially in harsh environments, such as in urban canyons, where the GNSS signal is highly degraded or denied.
Boosting the kernelized shapelets: Theory and algorithms for local features
Venue: arXiv cs.LG 2017
Year: 2017

We consider binary classification problems using local features of objects. One of motivating applications is time-series classification, where features reflecting some local closeness measure between a time series and a pattern sequence called shapelet are useful. Despite the empirical success of such approaches using local features, the generalization ability of resulting hypotheses is not fully understood and previous work relies on a bunch of heuristics. In this paper, we formulate a class of hypotheses using local features, where the richness of features is controlled by kernels. We derive generalization bounds of sparse ensembles over the class which is exponentially better than a standard analysis in terms of the number of possible local features. The resulting optimization problem is well suited to the boosting approach and the weak learning problem is formulated as a DC program, for which practical algorithms exist. In preliminary experiments on time-series data sets, our method achieves competitive accuracy with the state-of-the-art algorithms with small parameter-tuning cost.
Simple Necessary Conditions for the Existence of a Hamiltonian Path with Applications to Cactus Graphs
Venue: arXiv cs.DM 2017
Year: 2017

We describe some necessary conditions for the existence of a Hamiltonian path in any graph (in other words, for a graph to be traceable). These conditions result in a linear time algorithm to decide the Hamiltonian path problem for cactus graphs. We apply this algorithm to several molecular databases to report the numbers of graphs that are traceable cactus graphs.
KUKA Sunrise Toolbox: Interfacing Collaborative Robots with MATLAB
Venue: arXiv cs.RO 2017
Year: 2017

Collaborative robots are increasingly present in our lives. The KUKA LBR iiwa equipped with the KUKA Sunrise.OS controller is a good example of a collaborative/sensitive robot. This paper presents a MATLAB Toolbox, the KUKA Sunrise Toolbox (KST), to interface KUKA Sunrise.OS using MATLAB. The KST contains functionalities for networking, real-time control, point-to-point motion, setters and getters of parameters and physical interaction. KST includes more than 50 functions and runs on a remote computer connected with the KUKA Sunrise controller via transmission control Protocol/Internet Protocol (TCP/IP). The KST potentialities are demonstrated in three use cases.
Dense Face Alignment
Venue: arXiv cs.CV 2017
Year: 2017

Face alignment is a classic problem in the computer vision field. Previous works mostly focus on sparse alignment with a limited number of facial landmark points, i.e., facial landmark detection. In this paper, for the first time, we aim at providing a very dense 3D alignment for large-pose face images. To achieve this, we train a CNN to estimate the 3D face shape, which not only aligns limited facial landmarks but also fits face contours and SIFT feature points. Moreover, we also address the bottleneck of training CNN with multiple datasets, due to different landmark markups on different datasets, such as 5, 34, 68. Experimental results show our method not only provides high-quality, dense 3D face fitting but also outperforms the state-of-the-art facial landmark detection methods on the challenging datasets. Our model can run at real time during testing.
Linking Generative Adversarial Learning and Binary Classification
Venue: arXiv cs.LG 2017
Year: 2017

In this note, we point out a basic link between generative adversarial (GA) training and binary classification -- any powerful discriminator essentially computes an (f-)divergence between real and generated samples. The result, repeatedly re-derived in decision theory, has implications for GA Networks (GANs), providing an alternative perspective on training f-GANs by designing the discriminator loss function.