OpenRaiser/Intern-Atlas huggingface dataset:
Auto-Documenation for Software Development
Venue: arXiv cs.SE 2017
Year: 2017

Software documentation is an essential but labor intensive task that often requires a dedicated team of developers to ensure coverage and accuracy. Good documentation will help shorten the development cycle and improve the overall team efficiency as well as maintainability. In today's crowd-driven development environment, good documentation can go a long way in building a developer community from scratch. To that end, we took the first steps in building a tool called Autodoc that can assist software developers in writing better documentation faster. Autodoc goes beyond traditional boilerplate template generation. Our integrated tool uses Deep Learning methods to construct a semantic understanding of the code. Just like machine translation in natural languages, Autodoc can translate snippets of code to comments, and insert them as short summaries inside the docstring. We also demonstrate the integration of Autodoc as an IDE plugin as well as a web hook from within software hosting platforms when submitting auto-documented code to user's Git repository.
Low Dimensional Atomic Norm Representations in Line Spectral Estimation
Venue: arXiv cs.IT 2017
Year: 2017

The line spectral estimation problem consists in recovering the frequencies of a complex valued time signal that is assumed to be sparse in the spectral domain from its discrete observations. Unlike the gridding required by the classical compressed sensing framework, line spectral estimation reconstructs signals whose spectral supports lie continuously in the Fourier domain. If recent advances have shown that atomic norm relaxation produces highly robust estimates in this context, the computational cost of this approach remains, however, the major flaw for its application to practical systems. In this work, we aim to bridge the complexity issue by studying the atomic norm minimization problem from low dimensional projection of the signal samples. We derive conditions on the sub-sampling matrix under which the partial atomic norm can be expressed by a low-dimensional semidefinite program. Moreover, we illustrate the tightness of this relaxation by showing that it is possible to recover the original signal in poly-logarithmic time for two specific sub-sampling patterns.
Cross-layer optimized routing with low duty cycle TDMA across multiple wireless body area networks
Venue: arXiv cs.NI 2017
Year: 2017

In this paper, we study the performance of two cross-layer optimized dynamic routing techniques for radio interference mitigation across multiple coexisting wireless body area networks (BANs), based on real-life measurements. At the network layer, the best route is selected according to channel state information from the physical layer, associated with low duty cycle TDMA at the MAC layer. The routing techniques (i.e., shortest path routing (SPR), and novel cooperative multi-path routing (CMR) incorporating 3-branch selection combining) perform real-time and reliable data transfer across BANs operating near the 2.4 GHz ISM band. An open-access experimental data set of 'everyday' mixed-activities is used for analyzing the proposed cross-layer optimization. We show that CMR gains up to 14 dB improvement with 8.3% TDMA duty cycle, and even 10 dB improvement with 0.2% TDMA duty cycle over SPR, at 10% outage probability at a realistic signal-to-interference-plus-noise ratio (SINR). Acceptable packet delivery ratios (PDR) and spectral efficiencies are obtained from SPR and CMR with reasonably sensitive receivers across a range of TDMA low duty cycles, with up to 9 dB improvement of CMR over SPR at 90% PDR. The distribution fits for received SINR through routing are also derived and validated with theoretical analysis.
The perceived assortativity of social networks: Methodological problems and solutions
Venue: arXiv cs.SI 2017
Year: 2017

Networks describe a range of social, biological and technical phenomena. An important property of a network is its degree correlation or assortativity, describing how nodes in the network associate based on their number of connections. Social networks are typically thought to be distinct from other networks in being assortative (possessing positive degree correlations); well-connected individuals associate with other well-connected individuals, and poorly-connected individuals associate with each other. We review the evidence for this in the literature and find that, while social networks are more assortative than non-social networks, only when they are built using group-based methods do they tend to be positively assortative. Non-social networks tend to be disassortative. We go on to show that connecting individuals due to shared membership of a group, a commonly used method, biases towards assortativity unless a large enough number of censuses of the network are taken. We present a number of solutions to overcoming this bias by drawing on advances in sociological and biological fields. Adoption of these methods across all fields can greatly enhance our understanding of social networks and networks in general.
Fog-Assisted wIoT: A Smart Fog Gateway for End-to-End Analytics in Wearable Internet of Things
Venue: arXiv cs.DC 2017
Year: 2017

Today, wearable internet-of-things (wIoT) devices continuously flood the cloud data centers at an enormous rate. This increases a demand to deploy an edge infrastructure for computing, intelligence, and storage close to the users. The emerging paradigm of fog computing could play an important role to make wIoT more efficient and affordable. Fog computing is known as the cloud on the ground. This paper presents an end-to-end architecture that performs data conditioning and intelligent filtering for generating smart analytics from wearable data. In wIoT, wearable sensor devices serve on one end while the cloud backend offers services on the other end. We developed a prototype of smart fog gateway (a middle layer) using Intel Edison and Raspberry Pi. We discussed the role of the smart fog gateway in orchestrating the process of data conditioning, intelligent filtering, smart analytics, and selective transfer to the cloud for long-term storage and temporal variability monitoring. We benchmarked the performance of developed prototypes on real-world data from smart e-textile gloves. Results demonstrated the usability and potential of proposed architecture for converting the real-world data into useful analytics while making use of knowledge-based models. In this way, the smart fog gateway enhances the end-to-end interaction between wearables (sensor devices) and the cloud.
Diversification Methods for Zero-One Optimization
Venue: arXiv cs.AI 2017
Year: 2017

We introduce new diversification methods for zero-one optimization that significantly extend strategies previously introduced in the setting of metaheuristic search. Our methods incorporate easily implemented strategies for partitioning assignments of values to variables, accompanied by processes called augmentation and shifting which create greater flexibility and generality. We then show how the resulting collection of diversified solutions can be further diversified by means of permutation mappings, which equally can be used to generate diversified collections of permutations for applications such as scheduling and routing. These methods can be applied to non-binary vectors by the use of binarization procedures and by Diversification-Based Learning (DBL) procedures which also provide connections to applications in clustering and machine learning. Detailed pseudocode and numerical illustrations are provided to show the operation of our methods and the collections of solutions they create.
Variable-Length Resolvability for General Sources and Channels
Venue: arXiv cs.IT 2017
Year: 2017

We introduce the problem of variable-length source resolvability, where a given target probability distribution is approximated by encoding a variable-length uniform random number, and the asymptotically minimum average length rate of the uniform random numbers, called the (variable-length) resolvability, is investigated. We first analyze the variable-length resolvability with the variational distance as an approximation measure. Next, we investigate the case under the divergence as an approximation measure. When the asymptotically exact approximation is required, it is shown that the resolvability under the two kinds of approximation measures coincides. We then extend the analysis to the case of channel resolvability, where the target distribution is the output distribution via a general channel due to the fixed general source as an input. The obtained characterization of the channel resolvability is fully general in the sense that when the channel is just the identity mapping, the characterization reduces to the general formula for the source resolvability. We also analyze the second-order variable-length resolvability.
Does Weather Matter? Causal Analysis of TV Logs
Venue: arXiv cs.CY 2017
Year: 2017

Weather affects our mood and behaviors, and many aspects of our life. When it is sunny, most people become happier; but when it rains, some people get depressed. Despite this evidence and the abundance of data, weather has mostly been overlooked in the machine learning and data science research. This work presents a causal analysis of how weather affects TV watching patterns. We show that some weather attributes, such as pressure and precipitation, cause major changes in TV watching patterns. To the best of our knowledge, this is the first large-scale causal study of the impact of weather on TV watching patterns.
On the Lattice of Cyclic Linear Codes Over Finite Chain Rings
Venue: arXiv cs.IT 2017
Year: 2017

Let $\texttt{R}$ be a commutative finite chain ring of invariants $(q,s).$ In this paper, the trace representation of any free cyclic $\texttt{R}$-linear code of length $\ell,$ is presented, via the $q$-cyclotomic cosets modulo $\ell,$ when $\texttt{gcd}(\ell, q) = 1.$ The lattice $\left(\texttt{Cy}(\texttt{R},\ell), +, \cap\right)$ of cyclic $\texttt{R}$-linear codes of length $\ell,$ is investigated. A lower bound on the Hamming distance of cyclic $\texttt{R}$-linear codes of length $\ell,$ is established. When $q$ is even, a family of MDS and self-orthogonal $\texttt{R}$-linear cyclic codes, is constructed.
C3A: A Cognitive Collaborative Control Architecture For an Intelligent Wheelchair
Venue: arXiv cs.RO 2017
Year: 2017

Retention of residual skills for persons who partially lose their cognitive or physical ability is of utmost importance. Research is focused on developing systems that provide need-based assistance for retention of such residual skills. This paper describes a novel cognitive collaborative control architecture C3A, designed to address the challenges of developing need- based assistance for wheelchair navigation. Organization of C3A is detailed and results from simulation of the proposed architecture is presented. For simulation of our proposed architecture, we have used ROS (Robot Operating System) as a control framework and a 3D robotic simulator called USARSim (Unified System for Automation and Robot Simulation).
Mutual Inclusivity of the Critical Path and its Partial Schedule on Heterogeneous Systems
Venue: arXiv cs.DC 2017
Year: 2017

The critical path of a group of tasks is an important measure that is commonly used to guide task allocation and scheduling on parallel computers. The critical path is the longest chain of dependencies in an acyclic task dependence graph. A problem arises on heterogeneous parallel machines where computation and communication costs can vary between different types of processor. Existing solutions for heterogeneous machines attempt to estimate the critical path using average values of computation and communication costs. However, this ignores opportunities to match specific tasks to specific classes of processor and communication links, and can result in quite misleading paths being identified as critical. We argue that an accurate critical path must consider the mapping of tasks to classes of processor and communication links. We formulate a polynomial time algorithm to find such a critical path. Our Critical Earliest Finish Time (CEFT) algorithm finds both the length of the critical path and an allocation of tasks to processors on that path. We compared CEFT experimentally to existing approaches such as averaging execution times across processors. The latter approach fails to accurately model the execution cost of tasks, and as a result fails to identify a correct critical path in 83.99% of cases in our experiments. We also adapted a critical path-oriented scheduling algorithm (CPOP) to use our critical path algorithm and found that the resulting schedules are faster.
Language Independent Single Document Image Super-Resolution using CNN for improved recognition
Venue: arXiv cs.CV 2017
Year: 2017

Recognition of document images have important applications in restoring old and classical texts. The problem involves quality improvement before passing it to a properly trained OCR to get accurate recognition of the text. The image enhancement and quality improvement constitute important steps as subsequent recognition depends upon the quality of the input image. There are scenarios when high resolution images are not available and our experiments show that the OCR accuracy reduces significantly with decrease in the spatial resolution of document images. Thus the only option is to improve the resolution of such document images. The goal is to construct a high resolution image, given a single low resolution binary image, which constitutes the problem of single image super-resolution. Most of the previous work in super-resolution deal with natural images which have more information-content than the document images. Here, we use Convolution Neural Network to learn the mapping between low and the corresponding high resolution images. We experiment with different number of layers, parameter settings and non-linear functions to build a fast end-to-end framework for document image super-resolution. Our proposed model shows a very good PSNR improvement of about 4 dB on 75 dpi Tamil images, resulting in a 3 % improvement of word level accuracy by the OCR. It takes less time than the recent sparse based natural image super-resolution technique, making it useful for real-time document recognition applications.
Deep Reinforcement Learning for Robotic Manipulation-The state of the art
Venue: arXiv cs.RO 2017
Year: 2017

The focus of this work is to enumerate the various approaches and algorithms that center around application of reinforcement learning in robotic ma- ]]nipulation tasks. Earlier methods utilized specialized policy representations and human demonstrations to constrict the policy. Such methods worked well with continuous state and policy space of robots but failed to come up with generalized policies. Subsequently, high dimensional non-linear function approximators like neural networks have been used to learn policies from scratch. Several novel and recent approaches have also embedded control policy with efficient perceptual representation using deep learning. This has led to the emergence of a new branch of dynamic robot control system called deep r inforcement learning(DRL). This work embodies a survey of the most recent algorithms, architectures and their implementations in simulations and real world robotic platforms. The gamut of DRL architectures are partitioned into two different branches namely, discrete action space algorithms(DAS) and continuous action space algorithms(CAS). Further, the CAS algorithms are divided into stochastic continuous action space(SCAS) and deterministic continuous action space(DCAS) algorithms. Along with elucidating an organ- isation of the DRL algorithms this work also manifests some of the state of the art applications of these approaches in robotic manipulation tasks.
Stable and Controllable Neural Texture Synthesis and Style Transfer Using Histogram Losses
Venue: arXiv cs.GR 2017
Year: 2017

Recently, methods have been proposed that perform texture synthesis and style transfer by using convolutional neural networks (e.g. Gatys et al. [2015,2016]). These methods are exciting because they can in some cases create results with state-of-the-art quality. However, in this paper, we show these methods also have limitations in texture quality, stability, requisite parameter tuning, and lack of user controls. This paper presents a multiscale synthesis pipeline based on convolutional neural networks that ameliorates these issues. We first give a mathematical explanation of the source of instabilities in many previous approaches. We then improve these instabilities by using histogram losses to synthesize textures that better statistically match the exemplar. We also show how to integrate localized style losses in our multiscale framework. These losses can improve the quality of large features, improve the separation of content and style, and offer artistic controls such as paint by numbers. We demonstrate that our approach offers improved quality, convergence in fewer iterations, and more stability over the optimization.
On-off Analog Beamforming for Massive MIMO
Venue: arXiv cs.IT 2017
Year: 2017

This paper investigates a new analog beamforming architecture for massive multiple-input multiple-output (MIMO) systems, where each of the multiple transmit antennas is switched to be on or off to form a beam according to the channel state information at transmitters. This on-off analog beamforming (OABF) scheme has the advantages in terms of both hardware complexities and algorithmic complexities. OABF can completely remove the high-cost, power-consuming and bulky analog phase-shifters that are extensively employed by traditional analog beamforming schemes; it only requires the deployment of low-cost analog switches that are easy to implement. Moreover, we show that the beams formed by such simple antenna on-off switch operations can achieve rather good performances with low-complexity beamforming algorithms. Specifically, we first propose two optimal signal-to-noise ratio maximization algorithms to determine the on-off state of each switch under the per-antenna power constraint and the total power constraint, respectively. After that, we theoretically prove that OABF can achieve the full diversity gain and the full array gain with complexities up to a polynomial order. Numerical results are consistent with our theoretical analysis. We believe that the simple structure of OABF makes massive MIMO much easier to implement in real systems.
One-step Estimation of Networked Population Size with Anonymity Using Respondent-Driven Capture-Recapture and Hashing
Venue: arXiv cs.SI 2017
Year: 2017

Estimates of population size for hidden and hard-to-reach individuals are of particular interest to health officials when health problems are concentrated in such populations. Efforts to derive these estimates are often frustrated by a range of factors including social stigma or an association with illegal activities that ordinarily preclude conventional survey strategies. This paper builds on and extends prior work that proposed a method to meet these challenges. Here we describe a rigorous formalization of a one-step, network-based population estimation procedure that can be employed under conditions of anonymity. The estimation procedure is designed to be implemented alongside currently accepted strategies for research with hidden populations. Simulation experiments are described that test the efficacy of the method across a range of implementation conditions and hidden population sizes. The results of these experiments show that reliable population estimates can be derived for hidden, networked population as large as 12,500 and perhaps larger for one family of random graphs. As such, the method shows potential for cost-effective implementation health and disease surveillance officials concerned with hidden populations. Limitations and future work are discussed in the concluding section.
Deep Reinforcement Learning for Visual Object Tracking in Videos
Venue: arXiv cs.CV 2017
Year: 2017

In this paper we introduce a fully end-to-end approach for visual tracking in videos that learns to predict the bounding box locations of a target object at every frame. An important insight is that the tracking problem can be considered as a sequential decision-making process and historical semantics encode highly relevant information for future decisions. Based on this intuition, we formulate our model as a recurrent convolutional neural network agent that interacts with a video overtime, and our model can be trained with reinforcement learning (RL) algorithms to learn good tracking policies that pay attention to continuous, inter-frame correlation and maximize tracking performance in the long run. The proposed tracking algorithm achieves state-of-the-art performance in an existing tracking benchmark and operates at frame-rates faster than real-time. To the best of our knowledge, our tracker is the first neural-network tracker that combines convolutional and recurrent networks with RL algorithms.
Supervised Learning in Automatic Channel Selection for Epileptic Seizure Detection
Venue: arXiv cs.CV 2017
Year: 2017

Detecting seizure using brain neuroactivations recorded by intracranial electroencephalogram (iEEG) has been widely used for monitoring, diagnosing, and closed-loop therapy of epileptic patients, however, computational efficiency gains are needed if state-of-the-art methods are to be implemented in implanted devices. We present a novel method for automatic seizure detection based on iEEG data that outperforms current state-of-the-art seizure detection methods in terms of computational efficiency while maintaining the accuracy. The proposed algorithm incorporates an automatic channel selection (ACS) engine as a pre-processing stage to the seizure detection procedure. The ACS engine consists of supervised classifiers which aim to find iEEGchannelswhich contribute the most to a seizure. Seizure detection stage involves feature extraction and classification. Feature extraction is performed in both frequency and time domains where spectral power and correlation between channel pairs are calculated. Random Forest is used in classification of interictal, ictal and early ictal periods of iEEG signals. Seizure detection in this paper is retrospective and patient-specific. iEEG data is accessed via Kaggle, provided by International Epilepsy Electro-physiology Portal. The dataset includes a training set of 6.5 hours of interictal data and 41 minin ictal data and a test set of 9.14 hours. Compared to the state-of-the-art on the same dataset, we achieve 49.4% increase in computational efficiency and 400 mins better in average for detection delay. The proposed model is able to detect a seizure onset at 91.95% sensitivity and 94.05% specificity with a mean detection delay of 2.77 s. The area under the curve (AUC) is 96.44%, that is comparable to the current state-of-the-art with AUC of 96.29%.
Towards Adversarial Retinal Image Synthesis
Venue: arXiv cs.CV 2017
Year: 2017

Synthesizing images of the eye fundus is a challenging task that has been previously approached by formulating complex models of the anatomy of the eye. New images can then be generated by sampling a suitable parameter space. In this work, we propose a method that learns to synthesize eye fundus images directly from data. For that, we pair true eye fundus images with their respective vessel trees, by means of a vessel segmentation technique. These pairs are then used to learn a mapping from a binary vessel tree to a new retinal image. For this purpose, we use a recent image-to-image translation technique, based on the idea of adversarial learning. Experimental results show that the original and the generated images are visually different in terms of their global appearance, in spite of sharing the same vessel tree. Additionally, a quantitative quality analysis of the synthetic retinal images confirms that the produced images retain a high proportion of the true image set quality.