OpenRaiser/Intern-Atlas huggingface dataset:
Smart Parking: IoT and Blockchain
Venue: arXiv cs.DC 2019
Year: 2019

Distributed ledger technology and IoT has revolutionized the world by finding its application in all the domains. It promises to transform the digital infrastructure which powers extensive evolutions and impacts a lot of areas. Vehicle parking is a major problem in major cities around the world in both developed and developing countries. The common problems are unavailability or shortage of parking spaces, no information about tariff and no mean of searching availability of parking space online. The struggle doesn't end even if an individual finds a spot, he is required to pay in cash. This traditional and manual process takes a lot of time and causes a lot of hassle. In this paper, we provide a novel solution to the parking problem using IoT and distributed ledger technology. This system is based on pervasive computing and provides auto check-in and check-out. The user can control the system and their profile using the app on their smartphone. The major advantage of the system is an easy and online payment method. Users can pay for their parking tickets using their credit cards from their smartphone app. This decreases their hassle of carrying cash and coins for purchasing parking tickets. Smart Parking will optimize the parking mechanism, save time, reduce traffic and pollution, and provide an enhanced user experience. It is robust, secure, scalable and automated using the combination of cutting-edge technologies.
Mining Top-k Trajectory-Patterns from Anonymized Data
Venue: arXiv cs.DS 2019
Year: 2019

The ubiquity of GPS enabled devices result into the generation of an enormous amount of user movement data consisting of a sequence of locations together with activities performed at those locations. Such data, commonly known as {\it activity-trajectory data}, can be analysed to find common user movements and preferred activities, which will have tremendous business value. However, various countries have now introduced data privacy regulations that make it mandatory to anonymize any data before releasing it. This makes it difficult to look for patterns as the existing mining techniques may not be directly applicable over anonymized data. User locations in an activity-trajectory dataset are anonymized to regions of different shapes and sizes making them uncomparable; therefore, it is unclear how to define suitable patterns over those regions. In this paper, we propose a top-k pattern mining technique called TopKMintra that employs a pre-processing step to transform anonymized activity-trajectory into an intermediate representation that address the above problem. Unlike usual sequential data, activity-trajectory data is 2-dimensional that can lead to generation of duplicate patterns. To handle this, TopKMintra restricts arbitrary extensions in the two dimensions by imposing an order over the search space; this also helps in addressing the common problem of updating the threshold in top-k pattern mining algorithms during various stages. We perform extensive experiments on real datasets to demonstrate the efficiency and the effectiveness of TopKMintra. Our results show that even after anonymization, certain patterns may remain in a dataset and those could be mined efficiently.
Veni Vidi Dixi: Reliable Wireless Communication with Depth Images
Venue: arXiv cs.IT 2019
Year: 2019

The upcoming industrial revolution requires deployment of critical wireless sensor networks for automation and monitoring purposes. However, the reliability of the wireless communication is rendered unpredictable by mobile elements in the communication environment such as humans or mobile robots which lead to dynamically changing radio environments. Changes in the wireless channel can be monitored with frequent pilot transmission. However, that would stress the battery life of sensors. In this work a new wireless channel estimation technique, Veni Vidi Dixi, VVD, is proposed. VVD leverages the redundant information in depth images obtained from the surveillance cameras in the communication environment and utilizes Convolutional Neural Networks CNNs to map the depth images of the communication environment to complex wireless channel estimations. VVD increases the wireless communication reliability without the need for frequent pilot transmission and with no additional complexity on the receiver. The proposed method is tested by conducting measurements in an indoor environment with a single mobile human. Up to authors best knowledge our work is the first to obtain complex wireless channel estimation from only depth images without any pilot transmission. The collected wireless trace, depth images and codes are publicly available.
A Method of Fluorescent Fibers Detection on Identity Documents under Ultraviolet Light
Venue: arXiv cs.CV 2019
Year: 2019

In this work we consider the problem of the fluorescent security fibers detection on the images of identity documents captured under ultraviolet light. As an example we use images of the second and third pages of the Russian passport and show features that render known methods and approaches based on image binarization non applicable. We propose a solution based on ridge detection in the gray-scale image of the document with preliminary normalized background. The algorithm was tested on a private dataset consisting of both authentic and model passports. Abandonment of binarization allowed to provide reliable and stable functioning of the proposed detector on a target dataset.
Active Learning of SVDD Hyperparameter Values
Venue: arXiv cs.LG 2019
Year: 2019

Support Vector Data Description is a popular method for outlier detection. However, its usefulness largely depends on selecting good hyperparameter values -- a difficult problem that has received significant attention in literature. Existing methods to estimate hyperparameter values are purely heuristic, and the conditions under which they work well are unclear. In this article, we propose LAMA (Local Active Min-Max Alignment), the first principled approach to estimate SVDD hyperparameter values by active learning. The core idea bases on kernel alignment, which we adapt to active learning with small sample sizes. In contrast to many existing approaches, LAMA provides estimates for both SVDD hyperparameters. These estimates are evidence-based, i.e., rely on actual class labels, and come with a quality score. This eliminates the need for manual validation, an issue with current heuristics. LAMA outperforms state-of-the-art competitors in extensive experiments on real-world data. In several cases, LAMA even yields results close to the empirical upper bound.
Trajectory-Based Recognition of Dynamic Persian Sign Language Using Hidden Markov Model
Venue: arXiv cs.CV 2019
Year: 2019

Sign Language Recognition (SLR) is an important step in facilitating the communication among deaf people and the rest of society. Existing Persian sign language recognition systems are mainly restricted to static signs which are not very useful in everyday communications. In this study, a dynamic Persian sign language recognition system is presented. A collection of 1200 videos were captured from 12 individuals performing 20 dynamic signs with a simple white glove. The trajectory of the hands, along with hand shape information were extracted from each video using a simple region-growing technique. These time-varying trajectories were then modeled using Hidden Markov Model (HMM) with Gaussian probability density functions as observations. The performance of the system was evaluated in different experimental strategies. Signer-independent and signer-dependent experiments were performed on the proposed system and the average accuracy of 97.48% was obtained. The experimental results demonstrated that the performance of the system is independent of the subject and it can also perform excellently even with a limited number of training data.
A probabilistic assessment of the Indo-Aryan Inner-Outer Hypothesis
Venue: arXiv cs.CL 2019
Year: 2019

This paper uses a novel data-driven probabilistic approach to address the century-old Inner-Outer hypothesis of Indo-Aryan. I develop a Bayesian hierarchical mixed-membership model to assess the validity of this hypothesis using a large data set of automatically extracted sound changes operating between Old Indo-Aryan and Modern Indo-Aryan speech varieties. I employ different prior distributions in order to model sound change, one of which, the logistic normal distribution, has not received much attention in linguistics outside of Natural Language Processing, despite its many attractive features. I find evidence for cohesive dialect groups that have made their imprint on contemporary Indo-Aryan languages, and find that when a logistic normal prior is used, the distribution of dialect components across languages is largely compatible with a core-periphery pattern similar to that proposed under the Inner-Outer hypothesis.
Research on dynamic target detection and tracking system of hexapod robot
Venue: arXiv cs.RO 2019
Year: 2019

Dynamic target detection and target tracking are hot issues in the field of image. In order to explore its application value in the field of mobile robot, a dynamic target detection and tracking system is designed based on hexapod robot. Firstly, the dynamic target detection method is introduced with region merging and adaptive external point filtering based on motion compensation method. This method achieves the accurate compensation of the moving background through symmetric matching and adaptive external point filtering, and achieves complete detection of non-rigid objects by region merging. Secondly, the application of target tracking algorithm based on KCF in hexapod robot platform is studied, and the Angle tracking of moving target is realized by adaptive adjustment of tracking speed. The last, the architecture of robot monitoring system is designed, which consists of operator, processor, hexapod robot and vision sensor, and the moving object detection and tracking algorithm proposed in this paper is applied to the system. The experimental results show that the improved algorithm can effectively detect and track the moving target when applied to the system of the mobile hexapod robot.
Fire Resistance Deformable Soft Gripper Based on Wire Jamming Mechanism
Venue: arXiv cs.RO 2019
Year: 2019

Able to grasp objects of any shape and size, universal grippers using variable stiffness phenomenon such as granular jamming have been developed for disaster robotics application. However, as their contact interface is mainly composed of unrigid and burnable silicone rubber, conventional soft grippers are not applicable to objects with sharp sections such as broken valves and glass fragments, especially on fire. In this research, the authors proposed a new method of variable stiffness mechanism using a string of beads that can be composed of cut-resistant and incombustible metals, arrange the mechanism to form a torus gripper, and conducted experiments to show its effectiveness.
Using Sequence-to-Sequence Learning for Repairing C Vulnerabilities
Venue: arXiv cs.SE 2019
Year: 2019

Software vulnerabilities affect all businesses and research is being done to avoid, detect or repair them. In this article, we contribute a new technique for automatic vulnerability fixing. We present a system that uses the rich software development history that can be found on GitHub to train an AI system that generates patches. We apply sequence-to-sequence learning on a big dataset of code changes and we evaluate the trained system on real world vulnerabilities from the CVE database. The result shows the feasibility of using sequence-to-sequence learning for fixing software vulnerabilities.
Evaluating the Data Quality of Eye Tracking Signals from a Virtual Reality System: Case Study using SMI's Eye-Tracking HTC Vive
Venue: arXiv cs.HC 2019
Year: 2019

We evaluated the data quality of SMI's tethered eye-tracking head-mounted display based on the HTC Vive (ET-HMD) during a random saccade task. We measured spatial accuracy, spatial precision, temporal precision, linearity, and crosstalk. We proposed the use of a non-parametric spatial precision measure based on the median absolute deviation (MAD). Our linearity analysis considered both the slope and adjusted R-squared of a best-fitting line. We were the first to test for a quadratic component to crosstalk. We prepended a calibration task to the random saccade task and evaluated 2 methods to employ this user-supplied calibration. For this, we used a unique binning approach to choose samples to be included in the recalibration analyses. We compared our quality measures between the ET-HMD and our EyeLink 1000 (SR-Research, Ottawa, Ontario, CA). We found that the ET-HMD had significantly better spatial accuracy and linearity fit than our EyeLink, but both devices had similar spatial precision and linearity slope. We also found that, while the EyeLink had no significant crosstalk, the ET-HMD generally exhibited quadratic crosstalk. Fourier analysis revealed that the binocular signal was a low-pass filtered version of the monocular signal. Such filtering resulted in the binocular signal being useless for the study of high-frequency components such as saccade dynamics.
Walking on the Edge: Fast, Low-Distortion Adversarial Examples
Venue: arXiv cs.CV 2019
Year: 2019

Adversarial examples of deep neural networks are receiving ever increasing attention because they help in understanding and reducing the sensitivity to their input. This is natural given the increasing applications of deep neural networks in our everyday lives. When white-box attacks are almost always successful, it is typically only the distortion of the perturbations that matters in their evaluation. In this work, we argue that speed is important as well, especially when considering that fast attacks are required by adversarial training. Given more time, iterative methods can always find better solutions. We investigate this speed-distortion trade-off in some depth and introduce a new attack called boundary projection (BP) that improves upon existing methods by a large margin. Our key idea is that the classification boundary is a manifold in the image space: we therefore quickly reach the boundary and then optimize distortion on this manifold.
MORPHOLO C++ Library for glasses-free multi-view stereo vision and streaming of live 3D video
Venue: arXiv cs.CV 2019
Year: 2019

The MORPHOLO C++ extended Library allows to convert a specific stereoscopic snapshot into a Native multi-view image through morphing algorithms taking into account display calibration data for specific slanted lenticular 3D monitors. MORPHOLO can also be implemented for glasses-free live applicatons of 3D video streaming, and for diverse innovative scientific, engineering and 3D video game applications -see http://www.morpholo.it
Learning from Interventions using Hierarchical Policies for Safe Learning
Venue: arXiv cs.RO 2019
Year: 2019

Learning from Demonstrations (LfD) via Behavior Cloning (BC) works well on multiple complex tasks. However, a limitation of the typical LfD approach is that it requires expert demonstrations for all scenarios, including those in which the algorithm is already well-trained. The recently proposed Learning from Interventions (LfI) overcomes this limitation by using an expert overseer. The expert overseer only intervenes when it suspects that an unsafe action is about to be taken. Although LfI significantly improves over LfD, the state-of-the-art LfI fails to account for delay caused by the expert's reaction time and only learns short-term behavior. We address these limitations by 1) interpolating the expert's interventions back in time, and 2) by splitting the policy into two hierarchical levels, one that generates sub-goals for the future and another that generates actions to reach those desired sub-goals. This sub-goal prediction forces the algorithm to learn long-term behavior while also being robust to the expert's reaction time. Our experiments show that LfI using sub-goals in a hierarchical policy framework trains faster and achieves better asymptotic performance than typical LfD.
Multiple Anchor Learning for Visual Object Detection
Venue: arXiv cs.CV 2019
Year: 2019

Classification and localization are two pillars of visual object detectors. However, in CNN-based detectors, these two modules are usually optimized under a fixed set of candidate (or anchor) bounding boxes. This configuration significantly limits the possibility to jointly optimize classification and localization. In this paper, we propose a Multiple Instance Learning (MIL) approach that selects anchors and jointly optimizes the two modules of a CNN-based object detector. Our approach, referred to as Multiple Anchor Learning (MAL), constructs anchor bags and selects the most representative anchors from each bag. Such an iterative selection process is potentially NP-hard to optimize. To address this issue, we solve MAL by repetitively depressing the confidence of selected anchors by perturbing their corresponding features. In an adversarial selection-depression manner, MAL not only pursues optimal solutions but also fully leverages multiple anchors/features to learn a detection model. Experiments show that MAL improves the baseline RetinaNet with significant margins on the commonly used MS-COCO object detection benchmark and achieves new state-of-the-art detection performance compared with recent methods.
The effect of task and training on intermediate representations in convolutional neural networks revealed with modified RV similarity analysis
Venue: arXiv cs.LG 2019
Year: 2019

Centered Kernel Alignment (CKA) was recently proposed as a similarity metric for comparing activation patterns in deep networks. Here we experiment with the modified RV-coefficient (RV2), which has very similar properties as CKA while being less sensitive to dataset size. We compare the representations of networks that received varying amounts of training on different layers: a standard trained network (all parameters updated at every step), a freeze trained network (layers gradually frozen during training), random networks (only some layers trained), and a completely untrained network. We found that RV2 was able to recover expected similarity patterns and provide interpretable similarity matrices that suggested hypotheses about how representations are affected by different training recipes. We propose that the superior performance achieved by freeze training can be attributed to representational differences in the penultimate layer. Our comparisons of random networks suggest that the inputs and targets serve as anchors on the representations in the lowest and highest layers.
Information Retrieval and Its Sister Disciplines
Venue: arXiv cs.IR 2019
Year: 2019

This article presents a summary graph to show the relationships between Information Retrieval (IR) and other related disciplines. The figure tells the key differences between them and the conditions under which one would transition into another.
SemEval-2015 Task 10: Sentiment Analysis in Twitter
Venue: arXiv cs.CL 2019
Year: 2019

In this paper, we describe the 2015 iteration of the SemEval shared task on Sentiment Analysis in Twitter. This was the most popular sentiment analysis shared task to date with more than 40 teams participating in each of the last three years. This year's shared task competition consisted of five sentiment prediction subtasks. Two were reruns from previous years: (A) sentiment expressed by a phrase in the context of a tweet, and (B) overall sentiment of a tweet. We further included three new subtasks asking to predict (C) the sentiment towards a topic in a single tweet, (D) the overall sentiment towards a topic in a set of tweets, and (E) the degree of prior polarity of a phrase.
OASIS: One-pass aligned Atlas Set for Image Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

Medical image segmentation is a fundamental task in medical image analysis. Despite that deep convolutional neural networks have gained stellar performance in this challenging task, they typically rely on large labeled datasets, which have limited their extension to customized applications. By revisiting the superiority of atlas based segmentation methods, we present a new framework of One-pass aligned Atlas Set for Images Segmentation (OASIS). To address the problem of time-consuming iterative image registration used for atlas warping, the proposed method takes advantage of the power of deep learning to achieve one-pass image registration. In addition, by applying label constraint, OASIS also makes the registration process to be focused on the regions to be segmented for improving the performance of segmentation. Furthermore, instead of using image based similarity for label fusion, which can be distracted by the large background areas, we propose a novel strategy to compute the label similarity based weights for label fusion. Our experimental results on the challenging task of prostate MR image segmentation demonstrate that OASIS is able to significantly increase the segmentation performance compared to other state-of-the-art methods.
Analysis of the Optimization Landscapes for Overcomplete Representation Learning
Venue: arXiv cs.LG 2019
Year: 2019

We study nonconvex optimization landscapes for learning overcomplete representations, including learning (i) sparsely used overcomplete dictionaries and (ii) convolutional dictionaries, where these unsupervised learning problems find many applications in high-dimensional data analysis. Despite the empirical success of simple nonconvex algorithms, theoretical justifications of why these methods work so well are far from satisfactory. In this work, we show these problems can be formulated as $\ell^4$-norm optimization problems with spherical constraint, and study the geometric properties of their nonconvex optimization landscapes. For both problems, we show the nonconvex objectives have benign (global) geometric structures, in the sense that every local minimizer is close to one of the target solutions and every saddle point exhibits negative curvature. This discovery enables the development of guaranteed global optimization methods using simple initializations. For both problems, we show the nonconvex objectives have benign geometric structures -- every local minimizer is close to one of the target solutions and every saddle point exhibits negative curvature -- either in the entire space or within a sufficiently large region. This discovery ensures local search algorithms (such as Riemannian gradient descent) with simple initializations approximately find the target solutions. Finally, numerical experiments justify our theoretical discoveries.