OpenRaiser/Intern-Atlas huggingface dataset:
Toward Human-Centered Simulation Modeling for Critical Infrastructure Disaster Recovery Planning
Venue: arXiv cs.HC 2018
Year: 2018

Critical infrastructure is vulnerable to a broad range of hazards. Timely and effective recovery of critical infrastructure after extreme events is crucial. However, critical infrastructure disaster recovery planning is complicated and involves both domain- and user-centered characteristics and complexities. Recovery planning currently uses few quantitative computer-based tools and instead largely relies on expert judgment. Simulation modeling can simplify domain-centered complexities but not the human factors. Conversely, human-centered design places end-users at the center of design. We discuss the benefits of combining simulation modeling with human-centered design and refer it as human-centered simulation modeling. Human-centered simulation modeling has the capability to make recovery planning simpler and more understandable for critical infrastructure and emergency management experts and other recovery planning decision-makers.
Domain Agnostic Real-Valued Specificity Prediction
Venue: arXiv cs.CL 2018
Year: 2018

Sentence specificity quantifies the level of detail in a sentence, characterizing the organization of information in discourse. While this information is useful for many downstream applications, specificity prediction systems predict very coarse labels (binary or ternary) and are trained on and tailored toward specific domains (e.g., news). The goal of this work is to generalize specificity prediction to domains where no labeled data is available and output more nuanced real-valued specificity ratings. We present an unsupervised domain adaptation system for sentence specificity prediction, specifically designed to output real-valued estimates from binary training labels. To calibrate the values of these predictions appropriately, we regularize the posterior distribution of the labels towards a reference distribution. We show that our framework generalizes well to three different domains with 50%~68% mean absolute error reduction than the current state-of-the-art system trained for news sentence specificity. We also demonstrate the potential of our work in improving the quality and informativeness of dialogue generation systems.
A Local Regret in Nonconvex Online Learning
Venue: arXiv cs.LG 2018
Year: 2018

We consider an online learning process to forecast a sequence of outcomes for nonconvex models. A typical measure to evaluate online learning algorithms is regret but such standard definition of regret is intractable for nonconvex models even in offline settings. Hence, gradient based definition of regrets are common for both offline and online nonconvex problems. Recently, a notion of local gradient based regret was introduced. Inspired by the concept of calibration and a local gradient based regret, we introduce another definition of regret and we discuss why our definition is more interpretable for forecasting problems. We also provide bound analysis for our regret under certain assumptions.
Multi-unit Bilateral Trade
Venue: arXiv cs.GT 2018
Year: 2018

We characterise the set of dominant strategy incentive compatible (DSIC), strongly budget balanced (SBB), and ex-post individually rational (IR) mechanisms for the multi-unit bilateral trade setting. In such a setting there is a single buyer and a single seller who holds a finite number k of identical items. The mechanism has to decide how many units of the item are transferred from the seller to the buyer and how much money is transferred from the buyer to the seller. We consider two classes of valuation functions for the buyer and seller: Valuations that are increasing in the number of units in possession, and the more specific class of valuations that are increasing and submodular. Furthermore, we present some approximation results about the performance of certain such mechanisms, in terms of social welfare: For increasing submodular valuation functions, we show the existence of a deterministic 2-approximation mechanism and a randomised e/(1-e) approximation mechanism, matching the best known bounds for the single-item setting.
Vehicle Re-identification Using Quadruple Directional Deep Learning Features
Venue: arXiv cs.CV 2018
Year: 2018

In order to resist the adverse effect of viewpoint variations for improving vehicle re-identification performance, we design quadruple directional deep learning networks to extract quadruple directional deep learning features (QD-DLF) of vehicle images. The quadruple directional deep learning networks are with similar overall architecture, including the same basic deep learning architecture but different directional feature pooling layers. Specifically, the same basic deep learning architecture is a shortly and densely connected convolutional neural network to extract basic feature maps of an input square vehicle image in the first stage. Then, the quadruple directional deep learning networks utilize different directional pooling layers, i.e., horizontal average pooling (HAP) layer, vertical average pooling (VAP) layer, diagonal average pooling (DAP) layer and anti-diagonal average pooling (AAP) layer, to compress the basic feature maps into horizontal, vertical, diagonal and anti-diagonal directional feature maps, respectively. Finally, these directional feature maps are spatially normalized and concatenated together as a quadruple directional deep learning feature for vehicle re-identification. Extensive experiments on both VeRi and VehicleID databases show that the proposed QD-DLF approach outperforms multiple state-of-the-art vehicle re-identification methods.
FusionStitching: Deep Fusion and Code Generation for Tensorflow Computations on GPUs
Venue: arXiv cs.DC 2018
Year: 2018

In recent years, there is a surge on machine learning applications in industry. Many of them are based on popular AI frameworks like Tensorflow, Torch, Caffe, or MxNet, etc, and are enpowered by accelerator platforms such as GPUs. One important challenge of running Tensorflow computations on GPUs is the fine granularity problem, namely, FLOPS of individual ops are far from enough to fully exploit the computing power of underlying accelerators. The XLA framework provides a solid foundation to explore this problem further. In this paper, we propose FusionStitching, a novel, comprehensive Op fusion and code generation system to stitch computations into large GPU kernels. Experimental results on four public models and two of our large inhouse applications show another 55% (geometric mean) reduction of GPU kernel launches, compared to the XLA fusion baseline. This increases the E2E performance of both of our latency critical inhouse applications up to 20%.
Deep Neural Network Concepts for Background Subtraction: A Systematic Review and Comparative Evaluation
Venue: arXiv cs.CV 2018
Year: 2018

Conventional neural networks show a powerful framework for background subtraction in video acquired by static cameras. Indeed, the well-known SOBS method and its variants based on neural networks were the leader methods on the largescale CDnet 2012 dataset during a long time. Recently, convolutional neural networks which belong to deep learning methods were employed with success for background initialization, foreground detection and deep learned features. Currently, the top current background subtraction methods in CDnet 2014 are based on deep neural networks with a large gap of performance in comparison on the conventional unsupervised approaches based on multi-features or multi-cues strategies. Furthermore, a huge amount of papers was published since 2016 when Braham and Van Droogenbroeck published their first work on CNN applied to background subtraction providing a regular gain of performance. In this context, we provide the first review of deep neural network concepts in background subtraction for novices and experts in order to analyze this success and to provide further directions. For this, we first surveyed the methods used background initialization, background subtraction and deep learned features. Then, we discuss the adequacy of deep neural networks for background subtraction. Finally, experimental results are presented on the CDnet 2014 dataset.
How Secure are Deep Learning Algorithms from Side-Channel based Reverse Engineering?
Venue: arXiv cs.LG 2018
Year: 2018

Deep Learning algorithms have recently become the de-facto paradigm for various prediction problems, which include many privacy-preserving applications like online medical image analysis. Presumably, the privacy of data in a deep learning system is a serious concern. There have been several efforts to analyze and exploit the information leakages from deep learning architectures to compromise data privacy. In this paper, however, we attempt to provide an evaluation strategy for such information leakages through deep neural network architectures by considering a case study on Convolutional Neural Network (CNN) based image classifier. The approach takes the aid of low-level hardware information, provided by Hardware Performance Counters (HPCs), during the execution of a CNN classifier and a simple hypothesis testing in order to produce an alarm if there exists any information leakage on the actual input.
Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters
Venue: arXiv cs.AI 2018
Year: 2018

Translating natural language to SQL queries for table-based question answering is a challenging problem and has received significant attention from the research community. In this work, we extend a pointer-generator and investigate the order-matters problem in semantic parsing for SQL. Even though our model is a straightforward extension of a general-purpose pointer-generator, it outperforms early works for WikiSQL and remains competitive to concurrently introduced, more complex models. Moreover, we provide a deeper investigation of the potential order-matters problem that could arise due to having multiple correct decoding paths, and investigate the use of REINFORCE as well as a dynamic oracle in this context.
On the extreme power of nonstandard programming languages
Venue: arXiv cs.PL 2018
Year: 2018

Suenaga and Hasuo introduced a nonstandard programming language ${\bf While}^{\bf dt}$ which models hybrid systems. We demonstrate why ${\bf While}^{\bf dt}$ is not suitable for modeling actual computations.
On Distributed Routing in Underwater Optical Wireless Sensor Networks
Venue: arXiv cs.NI 2018
Year: 2018

Underwater optical wireless communication (UOWC) is becoming an attractive technology for underwater wireless sensor networks (UWSNs) since it offers high-speed communication links. Although UOWC overcomes the drawbacks of acoustic and radio frequency communication channels such as high latency and low data rate, yet, it has its own limitations. One of the major limitations of UOWC is its limited transmission range which demands to develop a multi-hop network with efficient routing protocols. Currently, the routing protocols for UOWSNs are centralized having high complexity and large end-to-end delay. In this article, first, we present the existing routing protocols for UOWSNs. Based on the existing protocols, we then propose distributed routing protocols to address the problems of high complexity and large end-to-end delay. Numerical results have been provided to show that the proposed routing protocol is superior to the existing protocols in terms of complexity and end-to-end delay. Finally, we have presented open research directions in UOWSNs.
Detect or Track: Towards Cost-Effective Video Object Detection/Tracking
Venue: arXiv cs.CV 2018
Year: 2018

State-of-the-art object detectors and trackers are developing fast. Trackers are in general more efficient than detectors but bear the risk of drifting. A question is hence raised -- how to improve the accuracy of video object detection/tracking by utilizing the existing detectors and trackers within a given time budget? A baseline is frame skipping -- detecting every N-th frames and tracking for the frames in between. This baseline, however, is suboptimal since the detection frequency should depend on the tracking quality. To this end, we propose a scheduler network, which determines to detect or track at a certain frame, as a generalization of Siamese trackers. Although being light-weight and simple in structure, the scheduler network is more effective than the frame skipping baselines and flow-based approaches, as validated on ImageNet VID dataset in video object detection/tracking.
Comparison of Feature Extraction Methods and Predictors for Income Inference
Venue: arXiv cs.CY 2018
Year: 2018

Patterns of mobile phone communications, coupled with the information of the social network graph and financial behavior, allow us to make inferences of users' socio-economic attributes such as their income level. We present here several methods to extract features from mobile phone usage (calls and messages), and compare different combinations of supervised machine learning techniques and sets of features used as input for the inference of users' income. Our experimental results show that the Bayesian method based on the communication graph outperforms standard machine learning algorithms using node-based features.
Kriging-Based Robotic Exploration for Soil Moisture Mapping Using a Cosmic-Ray Sensor
Venue: arXiv cs.RO 2018
Year: 2018

Soil moisture monitoring is a fundamental process to enhance agricultural outcomes and to protect the environment. The traditional methods for measuring moisture content in soil are laborious and expensive, and therefore there is a growing interest in developing sensors and technologies which can reduce the effort and costs. In this work, we propose to use an autonomous mobile robot equipped with a state-of-the-art non-contact soil moisture sensor that builds moisture maps on the fly and automatically selects the most optimal sampling locations. The robot is guided by an autonomous exploration strategy driven by the quality of the soil moisture model which indicates areas of the field where the information is less precise. The sensor model follows the Poisson distribution and we demonstrate how to integrate such measurements into the kriging framework. We also investigate a range of different exploration strategies and assess their usefulness through a set of evaluation experiments based on real soil moisture data collected from two different fields. We demonstrate the benefits of using the adaptive measurement interval and adaptive sampling strategies for building better quality soil moisture models. The presented method is general and can be applied to other scenarios where the measured phenomena directly affects the acquisition time and needs to be spatially mapped.
ABox Abduction via Forgetting in ALC (Long Version)
Venue: arXiv cs.AI 2018
Year: 2018

Abductive reasoning generates explanatory hypotheses for new observations using prior knowledge. This paper investigates the use of forgetting, also known as uniform interpolation, to perform ABox abduction in description logic (ALC) ontologies. Non-abducibles are specified by a forgetting signature which can contain concept, but not role, symbols. The resulting hypotheses are semantically minimal and each consist of a set of disjuncts. These disjuncts are each independent explanations, and are not redundant with respect to the background ontology or the other disjuncts, representing a form of hypothesis space. The observations and hypotheses handled by the method can contain both atomic or complex ALC concepts, excluding role assertions, and are not restricted to Horn clauses. Two approaches to redundancy elimination are explored for practical use: full and approximate. Using a prototype implementation, experiments were performed over a corpus of real world ontologies to investigate the practicality of both approaches across several settings.
Towards Neural Machine Translation for African Languages
Venue: arXiv cs.CL 2018
Year: 2018

Given that South African education is in crisis, strategies for improvement and sustainability of high-quality, up-to-date education must be explored. In the migration of education online, inclusion of machine translation for low-resourced local languages becomes necessary. This paper aims to spur the use of current neural machine translation (NMT) techniques for low-resourced local languages. The paper demonstrates state-of-the-art performance on English-to-Setswana translation using the Autshumato dataset. The use of the Transformer architecture beat previous techniques by 5.33 BLEU points. This demonstrates the promise of using current NMT techniques for African languages.
Semi-dual Regularized Optimal Transport
Venue: arXiv cs.LG 2018
Year: 2018

Variational problems that involve Wasserstein distances and more generally optimal transport (OT) theory are playing an increasingly important role in data sciences. Such problems can be used to form an examplar measure out of various probability measures, as in the Wasserstein barycenter problem, or to carry out parametric inference and density fitting, where the loss is measured in terms of an optimal transport cost to the measure of observations. Despite being conceptually simple, such problems are computationally challenging because they involve minimizing over quantities (Wasserstein distances) that are themselves hard to compute. Entropic regularization has recently emerged as an efficient tool to approximate the solution of such variational Wasserstein problems. In this paper, we give a thorough duality tour of these regularization techniques. In particular, we show how important concepts from classical OT such as c-transforms and semi-discrete approaches translate into similar ideas in a regularized setting. These dual formulations lead to smooth variational problems, which can be solved using smooth, differentiable and convex optimization problems that are simpler to implement and numerically more stable that their un-regularized counterparts. We illustrate the versatility of this approach by applying it to the computation of Wasserstein barycenters and gradient flows of spatial regularization functionals.
Native Language Identification using i-vector
Venue: arXiv cs.CL 2018
Year: 2018

The task of determining a speaker's native language based only on his speeches in a second language is known as Native Language Identification or NLI. Due to its increasing applications in various domains of speech signal processing, this has emerged as an important research area in recent times. In this paper we have proposed an i-vector based approach to develop an automatic NLI system using MFCC and GFCC features. For evaluation of our approach, we have tested our framework on the 2016 ComParE Native language sub-challenge dataset which has English language speakers from 11 different native language backgrounds. Our proposed method outperforms the baseline system with an improvement in accuracy by 21.95% for the MFCC feature based i-vector framework and 22.81% for the GFCC feature based i-vector framework.
An Analysis of the Semantic Annotation Task on the Linked Data Cloud
Venue: arXiv cs.CL 2018
Year: 2018

Semantic annotation, the process of identifying key-phrases in texts and linking them to concepts in a knowledge base, is an important basis for semantic information retrieval and the Semantic Web uptake. Despite the emergence of semantic annotation systems, very few comparative studies have been published on their performance. In this paper, we provide an evaluation of the performance of existing systems over three tasks: full semantic annotation, named entity recognition, and keyword detection. More specifically, the spotting capability (recognition of relevant surface forms in text) is evaluated for all three tasks, whereas the disambiguation (correctly associating an entity from Wikipedia or DBpedia to the spotted surface forms) is evaluated only for the first two tasks. Our evaluation is twofold: First, we compute standard precision and recall on the output of semantic annotators on diverse datasets, each best suited for one of the identified tasks. Second, we build a statistical model using logistic regression to identify significant performance differences. Our results show that systems that provide full annotation perform better than named entities annotators and keyword extractors, for all three tasks. However, there is still much room for improvement for the identification of the most relevant entities described in a text.
Improving Distantly Supervised Relation Extraction with Neural Noise Converter and Conditional Optimal Selector
Venue: arXiv cs.CL 2018
Year: 2018

Distant supervised relation extraction has been successfully applied to large corpus with thousands of relations. However, the inevitable wrong labeling problem by distant supervision will hurt the performance of relation extraction. In this paper, we propose a method with neural noise converter to alleviate the impact of noisy data, and a conditional optimal selector to make proper prediction. Our noise converter learns the structured transition matrix on logit level and captures the property of distant supervised relation extraction dataset. The conditional optimal selector on the other hand helps to make proper prediction decision of an entity pair even if the group of sentences is overwhelmed by no-relation sentences. We conduct experiments on a widely used dataset and the results show significant improvement over competitive baseline methods.
Style and Content Disentanglement in Generative Adversarial Networks
Venue: arXiv cs.CV 2018
Year: 2018

Disentangling factors of variation within data has become a very challenging problem for image generation tasks. Current frameworks for training a Generative Adversarial Network (GAN), learn to disentangle the representations of the data in an unsupervised fashion and capture the most significant factors of the data variations. However, these approaches ignore the principle of content and style disentanglement in image generation, which means their learned latent code may alter the content and style of the generated images at the same time. This paper describes the Style and Content Disentangled GAN (SC-GAN), a new unsupervised algorithm for training GANs that learns disentangled style and content representations of the data. We assume that the representation of an image can be decomposed into a content code that represents the geometrical information of the data, and a style code that captures textural properties. Consequently, by fixing the style portion of the latent representation, we can generate diverse images in a particular style. Reversely, we can set the content code and generate a specific scene in a variety of styles. The proposed SC-GAN has two components: a content code which is the input to the generator, and a style code which modifies the scene style through modification of the Adaptive Instance Normalization (AdaIN) layers' parameters. We evaluate the proposed SC-GAN framework on a set of baseline datasets.