OpenRaiser/Intern-Atlas huggingface dataset:
Feed Forward and Backward Run in Deep Convolution Neural Network
Venue: arXiv cs.CV 2017
Year: 2017

Convolution Neural Networks (CNN), known as ConvNets are widely used in many visual imagery application, object classification, speech recognition. After the implementation and demonstration of the deep convolution neural network in Imagenet classification in 2012 by krizhevsky, the architecture of deep Convolution Neural Network is attracted many researchers. This has led to the major development in Deep learning frameworks such as Tensorflow, caffe, keras, theno. Though the implementation of deep learning is quite possible by employing deep learning frameworks, mathematical theory and concepts are harder to understand for new learners and practitioners. This article is intended to provide an overview of ConvNets architecture and to explain the mathematical theory behind it including activation function, loss function, feedforward and backward propagation. In this article, grey scale image is taken as input information image, ReLU and Sigmoid activation function are considered for developing the architecture and cross-entropy loss function are used for computing the difference between predicted value and actual value. The architecture is developed in such a way that it can contain one convolution layer, one pooling layer, and multiple dense layers
What Is Decidable about String Constraints with the ReplaceAll Function
Venue: arXiv cs.LO 2017
Year: 2017

Recently, it was shown that any theory of strings containing the string-replace function (even the most restricted version where pattern/replacement strings are both constant strings) becomes undecidable if we do not impose some kind of straight-line (aka acyclicity) restriction on the formulas. Despite this, the straight-line restriction is still practically sensible since this condition is typically met by string constraints that are generated by symbolic execution. In this paper, we provide the first systematic study of straight-line string constraints with the string-replace function and the regular constraints as the basic operations. We show that a large class of such constraints (i.e. when only a constant string or a regular expression is permitted in the pattern) is decidable. We note that the string-replace function, even under this restriction, is sufficiently powerful for expressing the concatenation operator and much more (e.g. extensions of regular expressions with string variables). This gives us the most expressive decidable logic containing concatenation, replace, and regular constraints under the same umbrella. Our decision procedure for the straight-line fragment follows an automata-theoretic approach, and is modular in the sense that the string-replace terms are removed one by one to generate more and more regular constraints, which can then be discharged by the state-of-the-art string constraint solvers. We also show that this fragment is, in a way, a maximal decidable subclass of the straight-line fragment with string-replace and regular constraints. To this end, we show undecidability results for the following two extensions: (1) variables are permitted in the pattern parameter of the replace function, (2) length constraints are permitted.
Machine Learning Based Fast Power Integrity Classifier
Venue: arXiv cs.OH 2017
Year: 2017

In this paper, we proposed a new machine learning based fast power integrity classifier that quickly flags the EM/IR hotspots. We discussed the features to extract to describe the power grid, cell power density, routing impact and controlled collapse chip connection (C4) bumps, etc. The continuous and discontinuous cases are identified and treated using different machine learning models. Nearest neighbors, random forest and neural network models are compared to select the best performance candidates. Experiments are run on open source benchmark, and result is showing promising prediction accuracy.
Using Phone Sensors and an Artificial Neural Network to Detect Gait Changes During Drinking Episodes in the Natural Environment
Venue: arXiv cs.CY 2017
Year: 2017

Phone sensors could be useful in assessing changes in gait that occur with alcohol consumption. This study determined (1) feasibility of collecting gait-related data during drinking occasions in the natural environment, and (2) how gait-related features measured by phone sensors relate to estimated blood alcohol concentration (eBAC). Ten young adult heavy drinkers were prompted to complete a 5-step gait task every hour from 8pm to 12am over four consecutive weekends. We collected 3-xis accelerometer, gyroscope, and magnetometer data from phone sensors, and computed 24 gait-related features using a sliding window technique. eBAC levels were calculated at each time point based on Ecological Momentary Assessment (EMA) of alcohol use. We used an artificial neural network model to analyze associations between sensor features and eBACs in training (70% of the data) and validation and test (30% of the data) datasets. We analyzed 128 data points where both eBAC and gait-related sensor data was captured, either when not drinking (n=60), while eBAC was ascending (n=55) or eBAC was descending (n=13). 21 data points were captured at times when the eBAC was greater than the legal limit (0.08 mg/dl). Using a Bayesian regularized neural network, gait-related phone sensor features showed a high correlation with eBAC (Pearson's r > 0.9), and >95% of estimated eBAC would fall between -0.012 and +0.012 of actual eBAC. It is feasible to collect gait-related data from smartphone sensors during drinking occasions in the natural environment. Sensor-based features can be used to infer gait changes associated with elevated blood alcohol content.
IP Video Conferencing: A Tutorial
Venue: arXiv cs.NI 2017
Year: 2017

Video conferencing is a well-established area of communications, which have been studied for decades. Recently this area has received a new impulse due to significantly increased bandwidth of Local and Wide area networks, appearance of low-priced video equipment and development of web based media technologies. This paper presents the main techniques behind the modern IP-based videoconferencing services, with a particular focus on codecs, network protocols, architectures and standardization efforts. Questions of security and topologies are also tackled. A description of a typical video conference scenario is provided, demonstrating how the technologies, responsible for different conference aspects, are working together. Traditional industrial disposition as well as modern innovative approaches are both addressed. Current industry trends are highlighted in respect to the topics, described in the tutorial. Legacy analog/digital technologies, together with the gateways between the traditional and the IP videoconferencing systems, are not considered.
Repairing Ontologies via Axiom Weakening
Venue: arXiv cs.AI 2017
Year: 2017

Ontology engineering is a hard and error-prone task, in which small changes may lead to errors, or even produce an inconsistent ontology. As ontologies grow in size, the need for automated methods for repairing inconsistencies while preserving as much of the original knowledge as possible increases. Most previous approaches to this task are based on removing a few axioms from the ontology to regain consistency. We propose a new method based on weakening these axioms to make them less restrictive, employing the use of refinement operators. We introduce the theoretical framework for weakening DL ontologies, propose algorithms to repair ontologies based on the framework, and provide an analysis of the computational complexity. Through an empirical analysis made over real-life ontologies, we show that our approach preserves significantly more of the original knowledge of the ontology than removing axioms.
Discovery of potential collaboration networks from open knowledge sources
Venue: arXiv cs.DL 2017
Year: 2017

Scientific publishing conveys the outputs of an academic or research activity, in this sense; it also reflects the efforts and issues in which people engage. To identify potential collaborative networks one of the simplest approaches is to leverage the co-authorship relations. In this approach, semantic and hierarchic relationships defined by a Knowledge Organization System are used in order to improve the system's ability to recommend potential networks beyond the lexical or syntactic analysis of the topics or concepts that are of interest to academics.
A Change-Detection based Framework for Piecewise-stationary Multi-Armed Bandit Problem
Venue: arXiv cs.LG 2017
Year: 2017

The multi-armed bandit problem has been extensively studied under the stationary assumption. However in reality, this assumption often does not hold because the distributions of rewards themselves may change over time. In this paper, we propose a change-detection (CD) based framework for multi-armed bandit problems under the piecewise-stationary setting, and study a class of change-detection based UCB (Upper Confidence Bound) policies, CD-UCB, that actively detects change points and restarts the UCB indices. We then develop CUSUM-UCB and PHT-UCB, that belong to the CD-UCB class and use cumulative sum (CUSUM) and Page-Hinkley Test (PHT) to detect changes. We show that CUSUM-UCB obtains the best known regret upper bound under mild assumptions. We also demonstrate the regret reduction of the CD-UCB policies over arbitrary Bernoulli rewards and Yahoo! datasets of webpage click-through rates.
Scripted GUI Testing of Android Apps: A Study on Diffusion, Evolution and Fragility
Venue: arXiv cs.SE 2017
Year: 2017

Background. Evidence suggests that mobile applications are not thoroughly tested as their desktop counterparts. In particular GUI testing is generally limited. Like web-based applications, mobile apps suffer from GUI test fragility, i.e. GUI test classes failing due to minor modifications in the GUI, without the application functionalities being altered. Aims. The objective of our study is to examine the diffusion of GUI testing on Android, and the amount of changes required to keep test classes up to date, and in particular the changes due to GUI test fragility. We define metrics to characterize the modifications and evolution of test classes and test methods, and proxies to estimate fragility-induced changes. Method. To perform our experiments, we selected six widely used open-source tools for scripted GUI testing of mobile applications previously described in the literature. We have mined the repositories on GitHub that used those tools, and computed our set of metrics. Results. We found that none of the considered GUI testing frameworks achieved a major diffusion among the open-source Android projects available on GitHub. For projects with GUI tests, we found that test suites have to be modified often, specifically 5\%-10\% of developers' modified LOCs belong to tests, and that a relevant portion (60\% on average) of such modifications are induced by fragility. Conclusions. Fragility of GUI test classes constitute a relevant concern, possibly being an obstacle for developers to adopt automated scripted GUI tests. This first evaluation and measure of fragility of Android scripted GUI testing can constitute a benchmark for developers, and the basis for the definition of a taxonomy of fragility causes, and actionable guidelines to mitigate the issue.
Fast matrix-free evaluation of discontinuous Galerkin finite element operators
Venue: arXiv cs.MS 2017
Year: 2017

We present an algorithmic framework for matrix-free evaluation of discontinuous Galerkin finite element operators based on sum factorization on quadrilateral and hexahedral meshes. We identify a set of kernels for fast quadrature on cells and faces targeting a wide class of weak forms originating from linear and nonlinear partial differential equations. Different algorithms and data structures for the implementation of operator evaluation are compared in an in-depth performance analysis. The sum factorization kernels are optimized by vectorization over several cells and faces and an even-odd decomposition of the one-dimensional compute kernels. In isolation our implementation then reaches up to 60\% of arithmetic peak on Intel Haswell and Broadwell processors and up to 50\% of arithmetic peak on Intel Knights Landing. The full operator evaluation reaches only about half that throughput due to memory bandwidth limitations from loading the input and output vectors, MPI ghost exchange, as well as handling variable coefficients and the geometry. Our performance analysis shows that the results are often within 10\% of the available memory bandwidth for the proposed implementation, with the exception of the Cartesian mesh case where the cost of gather operations and MPI communication are more substantial.
Stability and Transparency Analysis of a Bilateral Teleoperation in Presence of Data Loss
Venue: arXiv cs.SY 2017
Year: 2017

This paper presents a novel approach for stability and transparency analysis for bilateral teleoperation in the presence of data loss in communication media. A new model for data loss is proposed based on a set of periodic continuous pulses and its finite series representation. The passivity of the overall system is shown using wave variable approach including the newly defined model for data loss. Simulation results are presented to show the effectiveness of the proposed approach.
TARCO: Two-Stage Auction for D2D Relay Aided Computation Resource Allocation in Hetnet
Venue: arXiv cs.NI 2017
Year: 2017

In heterogeneous cellular network, task scheduling for computation offloading is one of the biggest challenges. Most works focus on alleviating heavy burden of macro base stations by moving the computation tasks on macro-cell user equipment (MUE) to remote cloud or small-cell base stations. But the selfishness of network users is seldom considered. Motivated by the cloud edge computing, this paper provides incentive for task transfer from macro cell users to small cell base stations. The proposed incentive scheme utilizes small cell user equipment to provide relay service. The problem of computation offloading is modelled as a two-stage auction, in which the remote MUEs with common social character can form a group and then buy the computation resource of small-cell base stations with the relay of small cell user equipment. A two-stage auction scheme named TARCO is contributed to maximize utilities for both sellers and buyers in the network. The truthful, individual rationality and budget balance of the TARCO are also proved in this paper. In addition, two algorithms are proposed to further refine TARCO on the social welfare of the network. Extensive simulation results demonstrate that, TARCO is better than random algorithm by about 104.90% in terms of average utility of MUEs, while the performance of TARCO is further improved up to 28.75% and 17.06% by the proposed two algorithms, respectively.
Online Deep Learning: Learning Deep Neural Networks on the Fly
Venue: arXiv cs.LG 2017
Year: 2017

Deep Neural Networks (DNNs) are typically trained by backpropagation in a batch learning setting, which requires the entire training data to be made available prior to the learning task. This is not scalable for many real-world scenarios where new data arrives sequentially in a stream form. We aim to address an open challenge of "Online Deep Learning" (ODL) for learning DNNs on the fly in an online setting. Unlike traditional online learning that often optimizes some convex objective function with respect to a shallow model (e.g., a linear/kernel-based hypothesis), ODL is significantly more challenging since the optimization of the DNN objective function is non-convex, and regular backpropagation does not work well in practice, especially for online learning settings. In this paper, we present a new online deep learning framework that attempts to tackle the challenges by learning DNN models of adaptive depth from a sequence of training data in an online learning setting. In particular, we propose a novel Hedge Backpropagation (HBP) method for online updating the parameters of DNN effectively, and validate the efficacy of our method on large-scale data sets, including both stationary and concept drifting scenarios.
On Time-of-Arrival Estimation in NB-IoT Systems
Venue: arXiv cs.IT 2017
Year: 2017

We consider time-of-arrival (ToA) estimation of a first arrival-path for a device working in narrowband Internet-of-Things (NB-IoT) systems. Due to a limited 180 KHz bandwidth used in NB-IoT, the time-domain auto-correlation function (ACF) of transmitted NB positioning reference signal (NPRS) has a wide main lobe. Without considering that, the performance of ToA estimation can be degraded for two reasons. Firstly, under multiple-path channel environments, the NPRS corresponding to different received paths are superimposed on each other, and so are the cross-correlations corresponding to them. Secondly, the measured peak-to-average-power-ratio (PAPR) used for detecting the presence of NPRS is inaccurate. Therefore, in this paper we propose a space-alternating generalized expectation-maximization (SAGE) based method to jointly estimate the number of channel taps, the channel coefficients and the corresponding delays in NB-IoT systems, with considering the imperfect ACF of NPRS. Such a proposed method only uses the time-domain cross-correlations between the received signal and the transmitted NPRS, and has a low complexity. We show through simulations that, the ToA estimation of the proposed method performs close to the maximum likelihood (ML) estimation for a single-path channel, and significantly outperforms a traditional ToA estimator that uses signal-to-noise (SNR) or power thresholds based estimation.
Arrhythmia Classification from the Abductive Interpretation of Short Single-Lead ECG Records
Venue: arXiv cs.AI 2017
Year: 2017

In this work we propose a new method for the rhythm classification of short single-lead ECG records, using a set of high-level and clinically meaningful features provided by the abductive interpretation of the records. These features include morphological and rhythm-related features that are used to build two classifiers: one that evaluates the record globally, using aggregated values for each feature; and another one that evaluates the record as a sequence, using a Recurrent Neural Network fed with the individual features for each detected heartbeat. The two classifiers are finally combined using the stacking technique, providing an answer by means of four target classes: Normal sinus rhythm, Atrial fibrillation, Other anomaly, and Noisy. The approach has been validated against the 2017 Physionet/CinC Challenge dataset, obtaining a final score of 0.83 and ranking first in the competition.
Accelerated Method for Stochastic Composition Optimization with Nonsmooth Regularization
Venue: arXiv cs.LG 2017
Year: 2017

Stochastic composition optimization draws much attention recently and has been successful in many emerging applications of machine learning, statistical analysis, and reinforcement learning. In this paper, we focus on the composition problem with nonsmooth regularization penalty. Previous works either have slow convergence rate or do not provide complete convergence analysis for the general problem. In this paper, we tackle these two issues by proposing a new stochastic composition optimization method for composition problem with nonsmooth regularization penalty. In our method, we apply variance reduction technique to accelerate the speed of convergence. To the best of our knowledge, our method admits the fastest convergence rate for stochastic composition optimization: for strongly convex composition problem, our algorithm is proved to admit linear convergence; for general composition problem, our algorithm significantly improves the state-of-the-art convergence rate from $O(T^{-1/2})$ to $O((n_1+n_2)^{{2}/{3}}T^{-1})$. Finally, we apply our proposed algorithm to portfolio management and policy evaluation in reinforcement learning. Experimental results verify our theoretical analysis.
A Characterization of Mobility Management in User-centric Networks
Venue: arXiv cs.NI 2017
Year: 2017

Mobility management is a key aspect to consider in future Internet architectures, as these architectures include a highly nomadic end-user which often relies on services provided by multi-access networks. In contrast, today's mobility management solutions were designed having in mind simpler scenarios and requirements from the network and where roaming could often be taken care of with previously established agreements. With a more dynamic behavior in the network, and also with a more prominent role from the end-user, mobility management has to deal with additional requirements derived from new Internet paradigms. To assist in understanding such requirements and also how to deal with them, this paper proposes a starting point to dismantle current mobility management notions. Our contribution is an initial proposal on defining mobility management in concrete functional blocks, their interaction, as well as a potential grouping which later can assist in deriving novel and more flexible mobility management architectures.
Automated Migration of Hierarchical Data to Relational Tables using Programming-by-Example
Venue: arXiv cs.PL 2017
Year: 2017

While many applications export data in hierarchical formats like XML and JSON, it is often necessary to convert such hierarchical documents to a relational representation. This paper presents a novel programming-by-example approach, and its implementation in a tool called Mitra, for automatically migrating tree-structured documents to relational tables. We have evaluated the proposed technique using two sets of experiments. In the first experiment, we used Mitra to automate 98 data transformation tasks collected from StackOverflow. Our method can generate the desired program for 94% of these benchmarks with an average synthesis time of 3.8 seconds. In the second experiment, we used Mitra to generate programs that can convert real-world XML and JSON datasets to full-fledged relational databases. Our evaluation shows that Mitra can automate the desired transformation for all datasets.
Cloud Computing and Content Management Systems: A Case Study in Macedonian Education
Venue: arXiv cs.CY 2017
Year: 2017

Technologies have become inseparable of our lives, economy, and the society as a whole. For example, clouds provide numerous computing resources that can facilitate our lives, whereas the Content Management Systems (CMSs) can provide the right content for the right user. Thus, education must embrace these emerging technologies in order to prepare citizens for the 21st century. The research explored 'if' and 'how' Cloud Computing influences the application of CMSs, and 'if' and 'how' it fosters the usage of mobile technologies to access cloud resources. The analyses revealed that some of the respondents have sound experience in using clouds and in using CMSs. Nevertheless, it was evident that significant number of respondents have limited or no experience in cloud computing concepts, cloud security and CMSs, as well. Institutions of the system should update educational policies in order to enable education innovation, provide means and support, and continuously update/upgrade educational infrastructure.