OpenRaiser/Intern-Atlas huggingface dataset:
Controlling False Discoveries During Interactive Data Exploration
Venue: arXiv cs.DB 2016
Year: 2016

Recent tools for interactive data exploration significantly increase the chance that users make false discoveries. The crux is that these tools implicitly allow the user to test a large body of different hypotheses with just a few clicks thus incurring in the issue commonly known in statistics as the multiple hypothesis testing error. In this paper, we propose solutions to integrate multiple hypothesis testing control into interactive data exploration tools. A key insight is that existing methods for controlling the false discovery rate (such as FDR) are not directly applicable for interactive data exploration. We therefore discuss a set of new control procedures that are better suited and integrated them in our system called Aware. By means of extensive experiments using both real-world and synthetic data sets we demonstrate how Aware can help experts and novice users alike to efficiently control false discoveries.
Usability Investigation on the Localization of Text CAPTCHAs: Take Chinese Characters as a Case Study
Venue: arXiv cs.HC 2016
Year: 2016

Text CAPTCHA has been an effective means to protect online systems from spams and abuses caused by automatic scripts which pretend to be human beings. However, nearly all the Text CAPTCHA designs in nowadays are based on English characters, which may not be the most user-friendly option for non-English speakers. Therefore, under the background of globalization, there is an increasing interest in designing local-language CAPTCHA, which is expected to be more usable for native speakers. However, systematic studies on the usability of localized CAPTCHAs are rare, and a general procedure for the design of usable localized CAPTCHA is still unavailable. Here, we comprehensively explored the design of CAPTCHAs based on Chinese characters from a usability perspective: cognitive processes of solving alphanumeric and Chinese CAPTCHAs are analyzed, followed by a usability comparison of those two types of CAPTCHAs and the evaluation of intrinsic design factors of Chinese CAPTCHAs. It was found that Chinese CAPTCHAs could be equally usable comparing with alphanumeric ones. Meanwhile, guidelines for the design of usable Chinese CAPTCHAs were also presented. Moreover, those design practices were also summarized as a general procedure which is expected to be applicable for the design of CAPTCHAs based on other languages.
Neural Symbolic Machines: Learning Semantic Parsers on Freebase with Weak Supervision (Short Version)
Venue: arXiv cs.CL 2016
Year: 2016

Extending the success of deep neural networks to natural language understanding and symbolic reasoning requires complex operations and external memory. Recent neural program induction approaches have attempted to address this problem, but are typically limited to differentiable memory, and consequently cannot scale beyond small synthetic tasks. In this work, we propose the Manager-Programmer-Computer framework, which integrates neural networks with non-differentiable memory to support abstract, scalable and precise operations through a friendly neural computer interface. Specifically, we introduce a Neural Symbolic Machine, which contains a sequence-to-sequence neural "programmer", and a non-differentiable "computer" that is a Lisp interpreter with code assist. To successfully apply REINFORCE for training, we augment it with approximate gold programs found by an iterative maximum likelihood training process. NSM is able to learn a semantic parser from weak supervision over a large knowledge base. It achieves new state-of-the-art performance on WebQuestionsSP, a challenging semantic parsing dataset, with weak supervision. Compared to previous approaches, NSM is end-to-end, therefore does not rely on feature engineering or domain specific knowledge.
Deep Image Category Discovery using a Transferred Similarity Function
Venue: arXiv cs.CV 2016
Year: 2016

Automatically discovering image categories in unlabeled natural images is one of the important goals of unsupervised learning. However, the task is challenging and even human beings define visual categories based on a large amount of prior knowledge. In this paper, we similarly utilize prior knowledge to facilitate the discovery of image categories. We present a novel end-to-end network to map unlabeled images to categories as a clustering network. We propose that this network can be learned with contrastive loss which is only based on weak binary pair-wise constraints. Such binary constraints can be learned from datasets in other domains as transferred similarity functions, which mimic a simple knowledge transfer. We first evaluate our experiments on the MNIST dataset as a proof of concept, based on predicted similarities trained on Omniglot, showing a 99\% accuracy which significantly outperforms clustering based approaches. Then we evaluate the discovery performance on Cifar-10, STL-10, and ImageNet, which achieves both state-of-the-art accuracy and shows it can be scalable to various large natural images.
Stereo image de-fencing using smartphones
Venue: arXiv cs.CV 2016
Year: 2016

Conventional approaches to image de-fencing have limited themselves to using only image data in adjacent frames of the captured video of an approximately static scene. In this work, we present a method to harness disparity using a stereo pair of fenced images in order to detect fence pixels. Tourists and amateur photographers commonly carry smartphones/phablets which can be used to capture a short video sequence of the fenced scene. We model the formation of the occluded frames in the captured video. Furthermore, we propose an optimization framework to estimate the de-fenced image using the total variation prior to regularize the ill-posed problem.
Geodesic equations and their numerical solutions in geodetic and Cartesian coordinates on an oblate spheroid
Venue: arXiv cs.CE 2016
Year: 2016

The direct geodesic problem on an oblate spheroid is described as an initial value problem and is solved numerically in geodetic and Cartesian coordinates. The geodesic equations are formulated by means of the theory of differential geometry. The initial value problem under consideration is reduced to a system of first-order ordinary differential equations, which is solved using a numerical method. The solution provides the coordinates and the azimuths at any point along the geodesic. The Clairaut constant is not assumed known but it is computed, allowing to check the precision of the method. An extended data set of geodesics is used, in order to evaluate the performance of the method in each coordinate system. The results for the direct geodesic problem are validated by comparison to Karney's method. We conclude that a complete, stable, precise, accurate and fast solution of the problem in Cartesian coordinates is accomplished.
Proportional Rankings
Venue: arXiv cs.GT 2016
Year: 2016

In this paper we extend the principle of proportional representation to rankings. We consider the setting where alternatives need to be ranked based on approval preferences. In this setting, proportional representation requires that cohesive groups of voters are represented proportionally in each initial segment of the ranking. Proportional rankings are desirable in situations where initial segments of different lengths may be relevant, e.g., hiring decisions (if it is unclear how many positions are to be filled), the presentation of competing proposals on a liquid democracy platform (if it is unclear how many proposals participants are taking into consideration), or recommender systems (if a ranking has to accommodate different user types). We study the proportional representation provided by several ranking methods and prove theoretical guarantees. Furthermore, we experimentally evaluate these methods and present preliminary evidence as to which methods are most suitable for producing proportional rankings.
N-gram Opcode Analysis for Android Malware Detection
Venue: arXiv cs.CR 2016
Year: 2016

Android malware has been on the rise in recent years due to the increasing popularity of Android and the proliferation of third party application markets. Emerging Android malware families are increasingly adopting sophisticated detection avoidance techniques and this calls for more effective approaches for Android malware detection. Hence, in this paper we present and evaluate an n-gram opcode features based approach that utilizes machine learning to identify and categorize Android malware. This approach enables automated feature discovery without relying on prior expert or domain knowledge for pre-determined features. Furthermore, by using a data segmentation technique for feature selection, our analysis is able to scale up to 10-gram opcodes. Our experiments on a dataset of 2520 samples showed an f-measure of 98% using the n-gram opcode based approach. We also provide empirical findings that illustrate factors that have probable impact on the overall n-gram opcodes performance trends.
Modeling and Control of an Autonomous Three Wheeled Mobile Robot with Front Steer
Venue: arXiv cs.SY 2016
Year: 2016

Modeling and control strategies for a design of an autonomous three wheeled mobile robot with front wheel steer is presented. Although, the three-wheel vehicle design with front wheel steer is common in automotive vehicles used often in public transport, but its advantages in navigation and localization of autonomous vehicles is seldom utilized. We present the system model for such a robotic vehicle. A PID controller for speed control is designed for the model obtained and has been implemented in a digital control framework. The trajectory control framework, which is a challenging task for such a three-wheeled robot has also been presented in the paper. The derived system model has been verified using experimental results obtained for the robot vehicle design. Controller performance and robustness issues have also been discussed briefly.
Review of "Continuous Finite-Time Stabilization of Translational and Rotational Double Integrators"
Venue: arXiv cs.SY 2016
Year: 2016

We review Bhat et al's paper where a class of bounded, continuous time-invariant finite time stabilizing feedback laws are derived for the double integrator and Lyapunov theory is employed in establishing finite-time convergence.
A Graph Grammar for Modelling RNA Folding
Venue: arXiv cs.FL 2016
Year: 2016

We propose a new approach for modelling the process of RNA folding as a graph transformation guided by the global value of free energy. Since the folding process evolves towards a configuration in which the free energy is minimal, the global behaviour resembles the one of a self-adaptive system. Each RNA configuration is a graph and the evolution of configurations is constrained by precise rules that can be described by a graph grammar.
Optimal Approximate Polytope Membership
Venue: arXiv cs.CG 2016
Year: 2016

In the polytope membership problem, a convex polytope $K$ in $R^d$ is given, and the objective is to preprocess $K$ into a data structure so that, given a query point $q \in R^d$, it is possible to determine efficiently whether $q \in K$. We consider this problem in an approximate setting and assume that $d$ is a constant. Given an approximation parameter $\varepsilon > 0$, the query can be answered either way if the distance from $q$ to $K$'s boundary is at most $\varepsilon$ times $K$'s diameter. Previous solutions to the problem were on the form of a space-time trade-off, where logarithmic query time demands $O(1/\varepsilon^{d-1})$ storage, whereas storage $O(1/\varepsilon^{(d-1)/2})$ admits roughly $O(1/\varepsilon^{(d-1)/8})$ query time. In this paper, we present a data structure that achieves logarithmic query time with storage of only $O(1/\varepsilon^{(d-1)/2})$, which matches the worst-case lower bound on the complexity of any $\varepsilon$-approximating polytope. Our data structure is based on a new technique, a hierarchy of ellipsoids defined as approximations to Macbeath regions. As an application, we obtain major improvements to approximate Euclidean nearest neighbor searching. Notably, the storage needed to answer $\varepsilon$-approximate nearest neighbor queries for a set of $n$ points in $O(\log \frac{n}{\varepsilon})$ time is reduced to $O(n/\varepsilon^{d/2})$. This halves the exponent in the $\varepsilon$-dependency of the existing space bound of roughly $O(n/\varepsilon^d)$, which has stood for 15 years (Har-Peled, 2001).
Analytical approach to the multiband filter synthesis and comparison to other approaches
Venue: arXiv cs.SY 2016
Year: 2016

A novel analytical approach to the synthesis of electrical (e.g. analogue, digital or microwave) filters is proposed. This approach allows to obtain lowest possible degree filters with given involved specification including e.g. many pass and stop bands, narrow transition bands, high attenuation at the stop bands and low magnitude oscillations at the pass bands. Comparison to other existing approaches is given.
Control Matching via Discharge Code Sequences
Venue: arXiv cs.LG 2016
Year: 2016

In this paper, we consider the patient similarity matching problem over a cancer cohort of more than 220,000 patients. Our approach first leverages on Word2Vec framework to embed ICD codes into vector-valued representation. We then propose a sequential algorithm for case-control matching on this representation space of diagnosis codes. The novel practice of applying the sequential matching on the vector representation lifted the matching accuracy measured through multiple clinical outcomes. We reported the results on a large-scale dataset to demonstrate the effectiveness of our method. For such a large dataset where most clinical information has been codified, the new method is particularly relevant.
On a Well-behaved Relational Generalisation of Rough Set Approximations
Venue: arXiv cs.AI 2016
Year: 2016

We examine non-dual relational extensions of rough set approximations and find an extension which satisfies surprisingly many of the usual rough set properties. We then use this definition to give an explanation for an observation made by Samanta and Chakraborty in their recent paper [P. Samanta and M.K. Chakraborty. Interface of rough set systems and modal logics: A survey. Transactions on Rough Sets XIX, pages 114-137, 2015].
Invariant Representations for Noisy Speech Recognition
Venue: arXiv cs.CL 2016
Year: 2016

Modern automatic speech recognition (ASR) systems need to be robust under acoustic variability arising from environmental, speaker, channel, and recording conditions. Ensuring such robustness to variability is a challenge in modern day neural network-based ASR systems, especially when all types of variability are not seen during training. We attempt to address this problem by encouraging the neural network acoustic model to learn invariant feature representations. We use ideas from recent research on image generation using Generative Adversarial Networks and domain adaptation ideas extending adversarial gradient-based training. A recent work from Ganin et al. proposes to use adversarial training for image domain adaptation by using an intermediate representation from the main target classification network to deteriorate the domain classifier performance through a separate neural network. Our work focuses on investigating neural architectures which produce representations invariant to noise conditions for ASR. We evaluate the proposed architecture on the Aurora-4 task, a popular benchmark for noise robust ASR. We show that our method generalizes better than the standard multi-condition training especially when only a few noise categories are seen during training.
Voltage Uncertainties in the Presence of Photovoltaic Systems
Venue: arXiv cs.SY 2016
Year: 2016

With the rising demand for solar energy installation, there is a pressing need for utilities to regulate the voltages at the distribution level. In grids with high penetration of photovoltaic (PV) systems, voltage fluctuations can occur at the distribution systems, resulting in inverter tripping and insufficient power to meet the load. We present a linear model for voltage rise versus PV output power. This model can be used to study the effect of increasing PV system capacities on distribution system voltages. It is observed that voltage fluctuations have greater correlation with the location of the PV systems on the grid than with the PV system capacities, i.e., more randomness and disorder in the behavior of voltage occurs with PV systems with larger line impedance.
Collaborative Visual Area Coverage
Venue: arXiv cs.SY 2016
Year: 2016

This article examines the problem of visual area coverage by a network of Mobile Aerial Agents (MAAs). Each MAA is assumed to be equipped with a downwards facing camera with a conical field of view which covers all points within a circle on the ground. The diameter of that circle is proportional to the altitude of the MAA, whereas the quality of the covered area decreases with the altitude. A distributed control law that maximizes a joint coverage-quality criterion by adjusting the MAAs' spatial coordinates is developed. The effectiveness of the proposed control scheme is evaluated through simulation studies.
Discrete and Continuous ambush games: optimal policies and approximate solutions
Venue: arXiv cs.RO 2016
Year: 2016

We consider an autonomous navigation problem, whereby a traveler aims at traversing an environment in which an adversary tries to set an ambush. A two players zero sum game is introduced. Players' strategies are computed as random path distributions, a realization of which is the path chosen by the traveler. A parallel is drawn between the discrete problem, where the traveler moves on a network, and the continuous problem, where the traveler moves in the plane. Analytical optimal policies are derived. Using assumptions from the Minimal Cut - Maximal Flow literature, the optimal value of the game is shown to be related to the maximum flow on the environment in both the discrete and the continuous cases, when the reward function for the ambusher is uniform. A linear program is introduced that allows for the computation of optimal policies, compatible with non-uniform reward functions. In order to relax the assumptions for the computation of the players' optimal strategies of the continuous game, a network is created, inspired by recently introduced sampling based motion planning techniques, and the linear program is adapted for continuous constraints.
EMC Regulations and Spectral Constraints for Multicarrier Modulation in PLC
Venue: arXiv cs.IT 2016
Year: 2016

This paper considers Electromagnetic Compatibility (EMC) aspects in the context of Power Line Communication (PLC) systems. It offers a complete overview of both narrow band PLC and broad band PLC EMC norms. How to interpret and translate such norms and measurement procedures into typical constraints used by designers of communication systems, is discussed. In particular, the constraints to the modulated signal spectrum are considered and the ability of pulse shaped OFDM (PS-OFDM), used in most of the PLC standards as IEEE P1901 and P1901.2, to fulfill them is analyzed. In addition, aiming to improve the spectrum management ability, a novel scheme named Pulse Shaped Cyclic Block Filtered Multitone modulation (PS-CB-FMT) is introduced and compared to PS-OFDM. It is shown that, PS-CB-FMT offers better ability to fulfill the norms which translates in higher system capacity.
Efficient use of paired spectrum bands through TDD small cell deployments
Venue: arXiv cs.IT 2016
Year: 2016

Traditionally, wireless cellular systems have been designed to operate in Frequency Division Duplexing (FDD) paired bands that allocates the same amount of spectrum for both downlink (DL) and uplink (UL) communication. Such design is very convenient under symmetric DL/UL traffic conditions, as it used to be the case when the voice transmission was the predominant service. However, with the overwhelming advent of data services, bringing along large asymmetries between DL and UL, the conventional FDD solution becomes inefficient. In this regard, flexible duplexing concepts aim to derive procedures for improving the spectrum utilization, by adjusting resources to the actual traffic demand. In this work we review these concepts and propose the use of unpaired Time Division Duplexing (TDD) spectrum on the unused resources for small eNBs (SeNB), so that user equipment (UEs) associated to those SeNB could be served either in DL or UL. This proposal alleviates the saturated DL in FDD-based system through user offloading towards the TDD-based system. The flexible duplexing concept is analyzed from three points of view: a) regulation, b) Long Term Evolution (LTE)standardization, and c) technical solutions.
Exploring the potential of combining time of flight and thermal infrared cameras for person detection
Venue: arXiv cs.CV 2016
Year: 2016

Combining new, low-cost thermal infrared and time-of-flight range sensors provides new opportunities. In this position paper we explore the possibilities of combining these sensors and using their fused data for person detection. The proposed calibration approach for this sensor combination differs from the traditional stereo camera calibration in two fundamental ways. A first distinction is that the spectral sensitivity of the two sensors differs significantly. In fact, there is no sensitivity range overlap at all. A second distinction is that their resolution is typically very low, which requires special attention. We assume a situation in which the sensors' relative position is known, but their orientation is unknown. In addition, some of the typical measurement errors are discussed, and methods to compensate for them are proposed. We discuss how the fused data could allow increased accuracy and robustness without the need for complex algorithms requiring large amounts of computational power and training data.
Secure and reliable connectivity in heterogeneous wireless sensor networks
Venue: arXiv cs.NI 2016
Year: 2016

We consider wireless sensor networks secured by the heterogeneous random key predistribution scheme under an on/off channel model. The heterogeneous random key predistribution scheme considers the case when the network includes sensor nodes with varying levels of resources, features, or connectivity requirements; e.g., regular nodes vs. cluster heads, but does not incorporate the fact that wireless channel are unreliable. To capture the unreliability of the wireless medium, we use an on/off channel model; wherein, each wireless channel is either on (with probability $α$) or off (with probability $1-α$) independently. We present conditions (in the form of zero-one laws) on how to scale the parameters of the network model so that with high probability the network is $k$-connected, i.e., the network remains connected even if any $k-1$ nodes fail or leave the network. We also present numerical results to support these conditions in the finite-node regime.
High Dimensional Consistent Digital Segments
Venue: arXiv cs.CG 2016
Year: 2016

We consider the problem of digitalizing Euclidean line segments from $\mathbb{R}^d$ to $\mathbb{Z}^d$. Christ {\em et al.} (DCG, 2012) showed how to construct a set of {\em consistent digital segment} (CDS) for $d=2$: a collection of segments connecting any two points in $\mathbb{Z}^2$ that satisfies the natural extension of the Euclidean axioms to $\mathbb{Z}^d$. In this paper we study the construction of CDSs in higher dimensions. We show that any total order can be used to create a set of {\em consistent digital rays} CDR in $\mathbb{Z}^d$ (a set of rays emanating from a fixed point $p$ that satisfies the extension of the Euclidean axioms). We fully characterize for which total orders the construction holds and study their Hausdorff distance, which in particular positively answers the question posed by Christ {\em et al.}.