OpenRaiser/Intern-Atlas huggingface dataset:
Efficient Hardware Realization of Convolutional Neural Networks using Intra-Kernel Regular Pruning
Venue: arXiv cs.CV 2018
Year: 2018

The recent trend toward increasingly deep convolutional neural networks (CNNs) leads to a higher demand of computational power and memory storage. Consequently, the deployment of CNNs in hardware has become more challenging. In this paper, we propose an Intra-Kernel Regular (IKR) pruning scheme to reduce the size and computational complexity of the CNNs by removing redundant weights at a fine-grained level. Unlike other pruning methods such as Fine-Grained pruning, IKR pruning maintains regular kernel structures that are exploitable in a hardware accelerator. Experimental results demonstrate up to 10x parameter reduction and 7x computational reduction at a cost of less than 1% degradation in accuracy versus the un-pruned case.
Adaptive Tube-based Nonlinear MPC for Ecological Autonomous Cruise Control of Plug-in Hybrid Electric Vehicles
Venue: arXiv cs.SY 2018
Year: 2018

This paper proposes an adaptive tube-based nonlinear model predictive control (AT-NMPC) approach to the design of autonomous cruise control (ACC) systems. The proposed method utilizes two separate models to define the constrained receding horizon optimal control problem. A fixed nominal model is used to handle the problem constraints based on a robust tube-based approach. A separate adaptive model is used to define the objective function, which utilizes least square online parameter estimators for adaption. By having two separate models, this method takes into account uncertainties, modeling errors and delayed data in the design of the controller and guaranties robust constraint handling, while adapting to them to improve control performance. Furthermore, to be able implement the designed AT-NMPC in real-time, a Newton/GMRES fast solver is employed to solve the optimization problem. Simulations performed on a high-fidelity model of the baseline vehicle, the Toyota plug-in Prius, which is a plug-in hybrid electric vehicle (PHEV), show that the proposed controller is able to handle the defined constraints in the presence of uncertainty, while improving the energy cost of the trip. Moreover, the result of the hardware-in-loop experiment demonstrates the performance of the proposed controller in real time application.
Enhanced navigation systems in GPS denied environments for visually impaired people: A Survey
Venue: arXiv cs.CY 2018
Year: 2018

Although outdoor navigation systems are mostly dependent on GPS, indoor systems have to rely upon different techniques for localizing the user, due to unavailability of GPS signals in indoor environments. Over the past decade various indoor navigation systems have been developed. In this paper an overview of some existing indoor navigation systems for visually impaired people are presented and they are compared from different perspectives. The evaluated techniques are ultrasonic systems, RFID-based solutions, computer vision aided navigation systems, ans smartphone-based applications.
On the Constituent Attributes of Software and Organisational Resilience
Venue: arXiv cs.CY 2018
Year: 2018

Our societies are increasingly dependent on services supplied by computers & their software. New technology only exacerbates this dependence by increasing the number, performance, and degree of autonomy and inter-connectivity of software-empowered computers and cyber-physical "things", which translates into unprecedented scenarios of interdependence. As a consequence, guaranteeing the persistence-of-identity of individual & collective software systems and software-backed organisations becomes an important prerequisite toward sustaining the safety, security, & quality of the computer services supporting human societies. Resilience is the term used to refer to the ability of a system to retain its functional and non-functional identity. In this article we conjecture that a better understanding of resilience may be reached by decomposing it into ancillary constituent properties, the same way as a better insight in system dependability was obtained by breaking it down into sub-properties. 3 of the main sub-properties of resilience proposed here refer respectively to the ability to perceive environmental changes; understand the implications introduced by those changes; and plan & enact adjustments intended to improve the system-environment fit. A fourth property characterises the way the above abilities manifest themselves in computer systems. The 4 properties are then analyzed in 3 families of case studies, each consisting of 3 software systems that embed different resilience methods. Our major conclusion is that reasoning in terms of resilience sub-properties may help revealing the characteristics and limitations of classic methods and tools meant to achieve system and organisational resilience. We conclude by suggesting that our method may prelude to meta-resilient systems -- systems, that is, able to adjust optimally their own resilience with respect to changing environmental conditions.
Towards Advanced Phenotypic Mutations in Cartesian Genetic Programming
Venue: arXiv cs.NE 2018
Year: 2018

Cartesian Genetic Programming is often used with a point mutation as the sole genetic operator. In this paper, we propose two phenotypic mutation techniques and take a step towards advanced phenotypic mutations in Cartesian Genetic Programming. The functionality of the proposed mutations is inspired by biological evolution which mutates DNA sequences by inserting and deleting nucleotides. Experiments with symbolic regression and boolean functions problems show a better search performance when the proposed mutations are in use. The results of our experiments indicate that the use of phenotypic mutations could be beneficial for the use of Cartesian Genetic Programming.
A closer look at Intrusion Detection System for web applications
Venue: arXiv cs.CR 2018
Year: 2018

Intrusion Detection System (IDS) is one of the security measures being used as an additional defence mechanism to prevent the security breaches on web. It has been well known methodology for detecting network-based attacks but still immature in the domain of securing web application. The objective of the paper is to thoroughly understand the design methodology of the detection system in respect to web applications. In this paper, we discuss several specific aspects of a web application in detail that makes challenging for a developer to build an efficient web IDS. The paper also provides a comprehensive overview of the existing detection systems exclusively designed to observe web traffic. Furthermore, we identify various dimensions for comparing the IDS from different perspectives based on their design and functionalities. We also provide a conceptual framework of an IDS with prevention mechanism to offer a systematic guidance for the implementation of the system specific to the web applications. We compare its features with five existing detection systems, namely AppSensor, PHPIDS, ModSecurity, Shadow Daemon and AQTRONIX WebKnight. The paper will highly facilitate the interest groups with the cutting edge information to understand the stronger and weaker sections of the web IDS and provide a firm foundation for developing an intelligent and efficient system.
Unambiguous languages exhaust the index hierarchy
Venue: arXiv cs.FL 2018
Year: 2018

This work is a study of the expressive power of unambiguity in the case of automata over infinite trees. An automaton is called unambiguous if it has at most one accepting run on every input, the language of such an automaton is called an unambiguous language. It is known that not every regular language of infinite trees is unambiguous. Except that, very little is known about which regular tree languages are unambiguous. This paper answers the question whether unambiguous languages are of bounded complexity among all regular tree languages. The notion of complexity is the canonical one, called the (parity or Rabin-Mostowski) index hierarchy. The answer is negative, as exhibited by a family of examples of unambiguous languages that cannot be recognised by any alternating parity tree automata of bounded range of priorities. Hardness of the given examples is based on the theory of signatures in parity games, previously studied by Walukiewicz. This theory is further developed here to construct canonical signatures. The technical core of the article is a parity game that compares signatures of a given pair of parity games (without an increase in the index).
Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks
Venue: arXiv cs.CV 2018
Year: 2018

Early and accurate diagnosis of interstitial lung diseases (ILDs) is crucial for making treatment decisions, but can be challenging even for experienced radiologists. The diagnostic procedure is based on the detection and recognition of the different ILD pathologies in thoracic CT scans, yet their manifestation often appears similar. In this study, we propose the use of a deep purely convolutional neural network for the semantic segmentation of ILD patterns, as the basic component of a computer aided diagnosis (CAD) system for ILDs. The proposed CNN, which consists of convolutional layers with dilated filters, takes as input a lung CT image of arbitrary size and outputs the corresponding label map. We trained and tested the network on a dataset of 172 sparsely annotated CT scans, within a cross-validation scheme. The training was performed in an end-to-end and semi-supervised fashion, utilizing both labeled and non-labeled image regions. The experimental results show significant performance improvement with respect to the state of the art.
Energy Sustainable Mobile Networks via Energy Routing, Learning and Foresighted Optimization
Venue: arXiv cs.NI 2018
Year: 2018

The design of self-sustainable base station (BS) deployments is addressed in this paper: BSs have energy harvesting and storage capabilities, they can use ambient energy to serve the local traffic or store it for later use. A dedicated power packet grid allows energy transfer across BSs, compensating for imbalance in the harvested energy or in the traffic load. Some BSs are offgrid, i.e., they can only use the locally harvested energy and that transferred from other BSs, whereas others are ongrid, i.e., they can also purchase energy from the power grid. Within this setup, an optimization problem is formulated where: energy harvested and traffic processes are estimated at the BSs through Gaussian Processes (GPs), and a Model Predictive Control (MPC) framework is devised for the computation of energy allocation and transfer schedules. Numerical results, obtained using real energy harvesting and traffic profiles, show substantial improvements in terms of energy self-sustainability of the system, outage probability (zero in most cases), and in the amount of energy purchased from the power grid, which is of more than halved with respect to the case where the optimization does not consider GP forecasting and MPC.
Two-Layered Falsification of Hybrid Systems guided by Monte Carlo Tree Search
Venue: arXiv cs.SY 2018
Year: 2018

Few real-world hybrid systems are amenable to formal verification, due to their complexity and black box components. Optimization-based falsification---a methodology of search-based testing that employs stochastic optimization---is attracting attention as an alternative quality assurance method. Inspired by the recent works that advocate coverage and exploration in falsification, we introduce a two-layered optimization framework that uses Monte Carlo tree search (MCTS), a popular machine learning technique with solid mathematical and empirical foundations. MCTS is used in the upper layer of our framework; it guides the lower layer of local hill-climbing optimization, thus balancing exploration and exploitation in a disciplined manner.
Corpus Statistics in Text Classification of Online Data
Venue: arXiv cs.CL 2018
Year: 2018

Transformation of Machine Learning (ML) from a boutique science to a generally accepted technology has increased importance of reproduction and transportability of ML studies. In the current work, we investigate how corpus characteristics of textual data sets correspond to text classification results. We work with two data sets gathered from sub-forums of an online health-related forum. Our empirical results are obtained for a multi-class sentiment analysis application.
Experiments with Neural Networks for Small and Large Scale Authorship Verification
Venue: arXiv cs.CL 2018
Year: 2018

We propose two models for a special case of authorship verification problem. The task is to investigate whether the two documents of a given pair are written by the same author. We consider the authorship verification problem for both small and large scale datasets. The underlying small-scale problem has two main challenges: First, the authors of the documents are unknown to us because no previous writing samples are available. Second, the two documents are short (a few hundred to a few thousand words) and may differ considerably in the genre and/or topic. To solve it we propose transformation encoder to transform one document of the pair into the other. This document transformation generates a loss which is used as a recognizable feature to verify if the authors of the pair are identical. For the large scale problem where various authors are engaged and more examples are available with larger length, a parallel recurrent neural network is proposed. It compares the language models of the two documents. We evaluate our methods on various types of datasets including Authorship Identification datasets of PAN competition, Amazon reviews, and machine learning articles. Experiments show that both methods achieve stable and competitive performance compared to the baselines.
Queuing Theory Guided Intelligent Traffic Scheduling through Video Analysis using Dirichlet Process Mixture Model
Venue: arXiv cs.CV 2018
Year: 2018

Accurate prediction of traffic signal duration for roadway junction is a challenging problem due to the dynamic nature of traffic flows. Though supervised learning can be used, parameters may vary across roadway junctions. In this paper, we present a computer vision guided expert system that can learn the departure rate of a given traffic junction modeled using traditional queuing theory. First, we temporally group the optical flow of the moving vehicles using Dirichlet Process Mixture Model (DPMM). These groups are referred to as tracklets or temporal clusters. Tracklet features are then used to learn the dynamic behavior of a traffic junction, especially during on/off cycles of a signal. The proposed queuing theory based approach can predict the signal open duration for the next cycle with higher accuracy when compared with other popular features used for tracking. The hypothesis has been verified on two publicly available video datasets. The results reveal that the DPMM based features are better than existing tracking frameworks to estimate $μ$. Thus, signal duration prediction is more accurate when tested on these datasets.The method can be used for designing intelligent operator-independent traffic control systems for roadway junctions at cities and highways.
Evolving Deep Convolutional Neural Networks by Variable-length Particle Swarm Optimization for Image Classification
Venue: arXiv cs.NE 2018
Year: 2018

Convolutional neural networks (CNNs) are one of the most effective deep learning methods to solve image classification problems, but the best architecture of a CNN to solve a specific problem can be extremely complicated and hard to design. This paper focuses on utilising Particle Swarm Optimisation (PSO) to automatically search for the optimal architecture of CNNs without any manual work involved. In order to achieve the goal, three improvements are made based on traditional PSO. First, a novel encoding strategy inspired by computer networks which empowers particle vectors to easily encode CNN layers is proposed; Second, in order to allow the proposed method to learn variable-length CNN architectures, a Disabled layer is designed to hide some dimensions of the particle vector to achieve variable-length particles; Third, since the learning process on large data is slow, partial datasets are randomly picked for the evaluation to dramatically speed it up. The proposed algorithm is examined and compared with 12 existing algorithms including the state-of-art methods on three widely used image classification benchmark datasets. The experimental results show that the proposed algorithm is a strong competitor to the state-of-art algorithms in terms of classification error. This is the first work using PSO for automatically evolving the architectures of CNNs.
Convolutional Point-set Representation: A Convolutional Bridge Between a Densely Annotated Image and 3D Face Alignment
Venue: arXiv cs.CV 2018
Year: 2018

We present a robust method for estimating the facial pose and shape information from a densely annotated facial image. The method relies on Convolutional Point-set Representation (CPR), a carefully designed matrix representation to summarize different layers of information encoded in the set of detected points in the annotated image. The CPR disentangles the dependencies of shape and different pose parameters and enables updating different parameters in a sequential manner via convolutional neural networks and recurrent layers. When updating the pose parameters, we sample reprojection errors along with a predicted direction and update the parameters based on the pattern of reprojection errors. This technique boosts the model's capability in searching a local minimum under challenging scenarios. We also demonstrate that annotation from different sources can be merged under the framework of CPR and contributes to outperforming the current state-of-the-art solutions for 3D face alignment. Experiments indicate the proposed CPRFA (CPR-based Face Alignment) significantly improves 3D alignment accuracy when the densely annotated image contains noise and missing values, which is common under "in-the-wild" acquisition scenarios.
Structural query-by-committee
Venue: arXiv cs.LG 2018
Year: 2018

In this work, we describe a framework that unifies many different interactive learning tasks. We present a generalization of the {\it query-by-committee} active learning algorithm for this setting, and we study its consistency and rate of convergence, both theoretically and empirically, with and without noise.
Feature Selection of Post-Graduation Income of College Students in the United States
Venue: arXiv cs.CY 2018
Year: 2018

This study investigated the most important attributes of the 6-year post-graduation income of college graduates who used financial aid during their time at college in the United States. The latest data released by the United States Department of Education was used. Specifically, 1,429 cohorts of graduates from three years (2001, 2003, and 2005) were included in the data analysis. Three attribute selection methods, including filter methods, forward selection, and Genetic Algorithm, were applied to the attribute selection from 30 relevant attributes. Five groups of machine learning algorithms were applied to the dataset for classification using the best selected attribute subsets. Based on our findings, we discuss the role of neighborhood professional degree attainment, parental income, SAT scores, and family college education in post-graduation incomes and the implications for social stratification.
A View-based Programmable Architecture for Controlling and Integrating Decentralized Data
Venue: arXiv cs.DB 2018
Year: 2018

The view and the view update are known mechanism for controlling access of data and for integrating data of different schemas. Despite intensive and long research on them in both the database community and the programming language community, we are facing difficulties to use them in practice. The main reason is that we are lacking of control over the view update strategy to deal with inherited ambiguity of view update for a given view. This vision paper aims to provide a new language-based approach to controlling and integrating decentralized data based on the view, and establish a software foundation for systematic construction of such data management systems. Our key observation is that a view should be defined through a view update strategy rather than a query. In other words, the view definition should be extracted from the view update strategy, which is in sharp contrast to the traditional approaches where the view update strategy is derived from the view definition. In this paper, we present the first programmable architecture with a declarative language for specifying update strategies over views, whose unique view definition can be automatically derived, and show how it can be effectively used to control data access, integrate data generally allowing coexistence of GAV (global as view) and LAV (local as view), and perform both analysis and updates on the integrated data. We demonstrate its usefulness through development of a privacy-preserving ride-sharing alliance system, discuss its application scope, and highlight future challenges.
Fast Neural Architecture Construction using EnvelopeNets
Venue: arXiv cs.NE 2018
Year: 2018

Fast Neural Architecture Construction (NAC) is a method to construct deep network architectures by pruning and expansion of a base network. In recent years, several automated search methods for neural network architectures have been proposed using methods such as evolutionary algorithms and reinforcement learning. These methods use a single scalar objective function (usually accuracy) that is evaluated after a full training and evaluation cycle. In contrast NAC directly compares the utility of different filters using statistics derived from filter featuremaps reach a state where the utility of different filters within a network can be compared and hence can be used to construct networks. The training epochs needed for filters within a network to reach this state is much less than the training epochs needed for the accuracy of a network to stabilize. NAC exploits this finding to construct convolutional neural nets (CNNs) with close to state of the art accuracy, in < 1 GPU day, faster than most of the current neural architecture search methods. The constructed networks show close to state of the art performance on the image classification problem on well known datasets (CIFAR-10, ImageNet) and consistently show better performance than hand constructed and randomly generated networks of the same depth, operators and approximately the same number of parameters.
Sentiment Analysis of Code-Mixed Indian Languages: An Overview of SAIL_Code-Mixed Shared Task @ICON-2017
Venue: arXiv cs.CL 2018
Year: 2018

Sentiment analysis is essential in many real-world applications such as stance detection, review analysis, recommendation system, and so on. Sentiment analysis becomes more difficult when the data is noisy and collected from social media. India is a multilingual country; people use more than one languages to communicate within themselves. The switching in between the languages is called code-switching or code-mixing, depending upon the type of mixing. This paper presents overview of the shared task on sentiment analysis of code-mixed data pairs of Hindi-English and Bengali-English collected from the different social media platform. The paper describes the task, dataset, evaluation, baseline and participant's systems.