OpenRaiser/Intern-Atlas huggingface dataset:
WoT Store: a Thing and Application Management Ecosystem for the W3C Web of Things
Venue: arXiv cs.NI 2019
Year: 2019

In the next few years, the W3C Web of Things (WoT) platform will represent a reference solution toward the deployment of fully interoperable systems, hence unlocking the potential of the IoT paradigm on several use-cases characterized by the current fragmentation of devices and technologies. At the same time, the worlwide adoption of the W3C WoT architecture depends on many factors, including also the availability of support tools that might facilitate the deployment of novel WoT applications or the integration with traditional IoT systems. To this purpose, the paper presents the WoT Store, a complete software platform enabling the discovery and management of W3C Things, the monitoring of its properties and events, and the invoking of actions, all within the same dashboard. In addition, the platform leverages on the semantic description of each Thing with the goal of easing and automatizing the installation and execution of WoT applications, e.g. defining the behaviour of a Thing or implementing mash-up operations from multiple Things. We sketch the main features, the architecture and the prototypal implementation of the WoT Store. Moreover, we discuss the WoT Store capabilities on three IoT use-cases, i.e. industry 4.0, smart agriculture and home automation.
IDEAIS: Smart Voice Assistants to Improve Interaction with SDIs
Venue: arXiv cs.HC 2019
Year: 2019

A critical goal, is that organizations and citizens can easily access the geographic information required for good governance. However, despite the costly efforts of governments to create and implement Spatial Data Infrastructures (SDIs), this goal is far from being achieved. This is partly due to the lack of usability of the geoportals through which the geographic information is accessed. In this position paper, we present IDEAIS, a research network composed of multiple Ibero-American partners to address this usability issue through the use of Intelligent Systems, in particular Smart Voice Assistants, to efficiently recover and access geographic information.
Brown Ring Experiment in Virtual Reality
Venue: arXiv cs.HC 2019
Year: 2019

Brown Ring Experiment is a very popular test to detect the presence of Nitrate in salts commonly performed in chemical laboratories with supplies of required chemicals. Our work clears out the need for a chemical laboratory and chemicals in order to understand the experiment practically. We have used the technology of Virtual Reality to fulfill this requirement. Our research work can be extensively utilized to create virtual environments for conducting other chemical processes in a virtual environment hence, eliminating the need for a chemical laboratory. This can help students in remote areas with minimal resources to fill in the void of practical experiments they have in their learning process due to space constraints.
Using Neural Networks for Programming by Demonstration
Venue: arXiv cs.LG 2019
Year: 2019

Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as opposed to agent) behavior is easier from a demonstration perspective. Without the involvement of manual behavior specification via code or reliance on a defined taxonomy of possible behaviors, the demonstrator specifies the desired emergent behavior of the system over time, and retrieves agent-level parameters required to execute that motion. A low time-complexity and data requirement favoring framework for reproducing emergent behavior, given an abstract demonstration, is discussed in [1], [2]. The existing framework does, however, observe an inherent limitation in scalability because of an exponentially growing search space (with the number of agent-level parameters). Our work addresses this limitation by pursuing a more scalable architecture with the use of neural networks. While the (proof-of-concept) architecture is not suitable for many evaluated domains because of its lack of representational capacity for that domain, it is more suitable than existing work for larger datasets for the Civil Violence agent-based model.
DBCSR: A Library for Dense Matrix Multiplications on Distributed GPU-Accelerated Systems
Venue: arXiv cs.DC 2019
Year: 2019

Most, if not all the modern scientific simulation packages utilize matrix algebra operations. Among the operation of the linear algebra, one of the most important kernels is the multiplication of matrices, dense and sparse. Examples of application of such a kernel are in electronic structure calculations, machine learning, data mining, graph processing, and digital signal processing. Several optimized libraries exist that can achieve high-performance on distributed systems. Only a few of them target distributed GPU-accelerated systems. In most of the cases, these libraries are provided and optimized by system vendors for their specific computer systems. In this paper, we present the DBCSR library (Distributed Block Compressed Sparse Row) for the distributed dense matrix-matrix multiplications. Although the library is specifically designed for block-sparse matrix-matrix multiplications, we optimized it for the dense case on GPU-accelerated systems. We show that the DBCSR outperforms the multiplication of matrices of different sizes and shapes provided by a vendor optimized GPU version of the ScaLAPACK library up to 2.5x (1.4x on average).
Autonomous Driving using Safe Reinforcement Learning by Incorporating a Regret-based Human Lane-Changing Decision Model
Venue: arXiv cs.RO 2019
Year: 2019

It is expected that many human drivers will still prefer to drive themselves even if the self-driving technologies are ready. Therefore, human-driven vehicles and autonomous vehicles (AVs) will coexist in a mixed traffic for a long time. To enable AVs to safely and efficiently maneuver in this mixed traffic, it is critical that the AVs can understand how humans cope with risks and make driving-related decisions. On the other hand, the driving environment is highly dynamic and ever-changing, and it is thus difficult to enumerate all the scenarios and hard-code the controllers. To face up these challenges, in this work, we incorporate a human decision-making model in reinforcement learning to control AVs for safe and efficient operations. Specifically, we adapt regret theory to describe a human driver's lane-changing behavior, and fit the personalized models to individual drivers for predicting their lane-changing decisions. The predicted decisions are incorporated in the safety constraints for reinforcement learning in training and in implementation. We then use an extended version of double deep Q-network (DDQN) to train our AV controller within the safety set. By doing so, the amount of collisions in training is reduced to zero, while the training accuracy is not impinged.
Generative One-Shot Face Recognition
Venue: arXiv cs.CV 2019
Year: 2019

One-shot face recognition measures the ability to identify persons with only seeing them at one glance, and is a hallmark of human visual intelligence. It is challenging for conventional machine learning approaches to mimic this way, since limited data are hard to effectively represent the data variance. The goal of one-shot face recognition is to learn a large-scale face recognizer, which is capable to fight off the data imbalance challenge. In this paper, we propose a novel generative adversarial one-shot face recognizer, attempting to synthesize meaningful data for one-shot classes by adapting the data variances from other normal classes. Specifically, we target at building a more effective general face classifier for both normal persons and one-shot persons. Technically, we design a new loss function by formulating knowledge transfer generator and a general classifier into a unified framework. Such a two-player minimax optimization can guide the generation of more effective data, which effectively promote the underrepresented classes in the learned model and lead to a remarkable improvement in face recognition performance. We evaluate our proposed model on the MS-Celeb-1M one-shot learning benchmark task, where we could recognize 94.98% of the test images at the precision of 99% for the one-shot classes, keeping an overall Top1 accuracy at $99.80\%$ for the normal classes. To the best of our knowledge, this is the best performance among all the published methods using this benchmark task with the same setup, including all the participants in the recent MS-Celeb-1M challenge at ICCV 2017\footnote{http://www.msceleb.org/challenge2/2017}.
Merging Virtual and Real Environments for Visualizing Seismic Hazards and Risk
Venue: arXiv cs.HC 2019
Year: 2019

Earthquake research in the last few decades has led to considerable advances in seismic hazard and risk modeling across academia, industry, and government. Technological advances such as high performance computing and visualization can further facilitate earthquake hazard and risk research. This work utilizes the CAVE of the Marquette Visualization Laboratory to visualize seismic hazards and risk by integrating hazard characterization, structural modeling, and emergency response. Building upon the framework of performance-based earthquake engineering, site-specific ground motions, which link seismic hazards to structural responses, serve as loading inputs to structural models. The resulting structural responses can then be translated into damage states of building elements in the immediate room environment based on fragility functions. To illustrate, we display a map of the Los Angeles region with ground motions for the Mw7.8 ShakeOut scenario, create a virtual room in a residential building subjected to such earthquake shaking, and simulate emergency response in this immersive environment. The illustrative visualization can be extended to various scenarios and help communicate site- and structure-specific hazards and risk to the general public.
Visual Natural Language Query Auto-Completion for Estimating Instance Probabilities
Venue: arXiv cs.CV 2019
Year: 2019

We present a new task of query auto-completion for estimating instance probabilities. We complete a user query prefix conditioned upon an image. Given the complete query, we fine tune a BERT embedding for estimating probabilities of a broad set of instances. The resulting instance probabilities are used for selection while being agnostic to the segmentation or attention mechanism. Our results demonstrate that auto-completion using both language and vision performs better than using only language, and that fine tuning a BERT embedding allows to efficiently rank instances in the image. In the spirit of reproducible research we make our data, models, and code available.
Hindsight Analysis of the Chicago Food Inspection Forecasting Model
Venue: arXiv cs.CY 2019
Year: 2019

The Chicago Department of Public Health (CDPH) conducts routine food inspections of over 15,000 food establishments to ensure the health and safety of their patrons. In 2015, CDPH deployed a machine learning model to schedule inspections of establishments based on their likelihood to commit critical food code violations. The City of Chicago released the training data and source code for the model, allowing anyone to examine the model. We provide the first independent analysis of the model, the data, the predictor variables, the performance metrics, and the underlying assumptions. We present a summary of our findings, share lessons learned, and make recommendations to address some of the issues our analysis unearthed.
DiabDeep: Pervasive Diabetes Diagnosis based on Wearable Medical Sensors and Efficient Neural Networks
Venue: arXiv cs.CV 2019
Year: 2019

Diabetes impacts the quality of life of millions of people. However, diabetes diagnosis is still an arduous process, given that the disease develops and gets treated outside the clinic. The emergence of wearable medical sensors (WMSs) and machine learning points to a way forward to address this challenge. WMSs enable a continuous mechanism to collect and analyze physiological signals. However, disease diagnosis based on WMS data and its effective deployment on resource-constrained edge devices remain challenging due to inefficient feature extraction and vast computation cost. In this work, we propose a framework called DiabDeep that combines efficient neural networks (called DiabNNs) with WMSs for pervasive diabetes diagnosis. DiabDeep bypasses the feature extraction stage and acts directly on WMS data. It enables both an (i) accurate inference on the server, e.g., a desktop, and (ii) efficient inference on an edge device, e.g., a smartphone, based on varying design goals and resource budgets. On the server, we stack sparsely connected layers to deliver high accuracy. On the edge, we use a hidden-layer long short-term memory based recurrent layer to cut down on computation and storage. At the core of DiabDeep lies a grow-and-prune training flow: it leverages gradient-based growth and magnitude-based pruning algorithms to learn both weights and connections for DiabNNs. We demonstrate the effectiveness of DiabDeep through analyzing data from 52 participants. For server (edge) side inference, we achieve a 96.3% (95.3%) accuracy in classifying diabetics against healthy individuals, and a 95.7% (94.6%) accuracy in distinguishing among type-1/type-2 diabetic, and healthy individuals. Against conventional baselines, DiabNNs achieve higher accuracy, while reducing the model size (FLOPs) by up to 454.5x (8.9x). Therefore, the system can be viewed as pervasive and efficient, yet very accurate.
GREASE: A Generative Model for Relevance Search over Knowledge Graphs
Venue: arXiv cs.IR 2019
Year: 2019

Relevance search is to find top-ranked entities in a knowledge graph (KG) that are relevant to a query entity. Relevance is ambiguous, particularly over a schema-rich KG like DBpedia which supports a wide range of different semantics of relevance based on numerous types of relations and attributes. As users may lack the expertise to formalize the desired semantics, supervised methods have emerged to learn the hidden user-defined relevance from user-provided examples. Along this line, in this paper we propose a novel generative model over KGs for relevance search, named GREASE. The model applies to meta-path based relevance where a meta-path characterizes a particular type of semantics of relating the query entity to answer entities. It is also extended to support properties that constrain answer entities. Extensive experiments on two large-scale KGs demonstrate that GREASE has advanced the state of the art in effectiveness, expressiveness, and efficiency.
Trajectory Planning for Autonomous Parking in Complex Environments: A Tunnel-based Optimal Control Approach
Venue: arXiv cs.RO 2019
Year: 2019

This paper proposes a fast and accurate trajectory planning algorithm for autonomous parking. Nominally, an optimal control problem should be formulated to describe this scheme, but the dimensionality of the optimal control problem is usually large, because the vehicle needs to avoid collision with every obstacle at every moment during the entire dynamic process. Although an initial guess obtained by a sample-and-search based planner facilitates the numerical optimization process, it is still far from being as fast as real-time. To address this issue, we replace all of the collision-avoidance constraints by series of within-tunnel conditions. Concretely, we develop a tunnel-based strategy such that the vehicle is restricted to move within the tunnels which naturally separate the vehicle from the obstacles. Unification, efficiency, and robustness of the proposed trajectory planning method have been verified by simulations.
Remote UAV Online Path Planning via Neural Network Based Opportunistic Control
Venue: arXiv cs.NI 2019
Year: 2019

This letter proposes a neural network (NN) aided remote unmanned aerial vehicle (UAV) online control algorithm, coined oHJB. By downloading a UAV's state, a base station (BS) trains an HJB NN that solves the Hamilton-Jacobi-Bellman equation (HJB) in real time, yielding the optimal control action. Initially, the BS uploads this control action to the UAV. If the HJB NN is sufficiently trained and the UAV is far away, the BS uploads the HJB NN model, enabling to locally carry out control decisions even when the connection is lost. Simulations corroborate the effectiveness of oHJB in reducing the UAV's travel time and energy by utilizing the trade-off between uploading delays and control robustness in poor channel conditions.
The Expressivity and Training of Deep Neural Networks: toward the Edge of Chaos?
Venue: arXiv cs.LG 2019
Year: 2019

Expressivity is one of the most significant issues in assessing neural networks. In this paper, we provide a quantitative analysis of the expressivity for the deep neural network (DNN) from its dynamic model, where the Hilbert space is employed to analyze the convergence and criticality. We study the feature mapping of several widely used activation functions obtained by Hermite polynomials, and find sharp declines or even saddle points in the feature space, which stagnate the information transfer in DNNs. We then present a new activation function design based on the Hermite polynomials for better utilization of spatial representation. Moreover, we analyze the information transfer of DNNs, emphasizing the convergence problem caused by the mismatch between input and topological structure. We also study the effects of input perturbations and regularization operators on critical expressivity. Our theoretical analysis reveals that DNNs use spatial domains for information representation and evolve to the edge of chaos as depth increases. In actual training, whether a particular network can ultimately arrive the edge of chaos depends on its ability to overcome convergence and pass information to the required network depth. Finally, we demonstrate the empirical performance of the proposed hypothesis via multivariate time series prediction and image classification examples.
Autonomous Shuttles for Last-Mile Connectivity
Venue: arXiv cs.RO 2019
Year: 2019

This paper describes an autonomous shuttle which targets providing last-mile transportation. Often, this involves operation in crowded areas with high levels of pedestrian traffic, and little to no lane markings or traffic control. We aim to create a functional shuttle to be improved upon in the future as new robust solutions are developed to replace the current components. An initial implementation of such a shuttle presented, detailing the overall architecture, controller structure, waypoint following, obstacle detection and avoidance, LiDAR based sign detection, and pedestrian communication. The performance of each component is evaluated, and future improvements are discussed.
A Quality Metric for Symmetric Graph Drawings
Venue: arXiv cs.DS 2019
Year: 2019

Symmetry is an important aesthetic criteria in graph drawing and network visualisation. Symmetric graph drawings aim to faithfully represent automorphisms of graphs as geometric symmetries in a drawing. In this paper, we design and implement a framework for quality metrics that measure symmetry, that is, how faithfully a drawing of a graph displays automorphisms as geometric symmetries. The quality metrics are based on geometry (i.e. Euclidean distance) as well as mathematical group theory (i.e. orbits of automorphisms). More specifically, we define two varieties of symmetry quality metrics: (1) for displaying a single automorphism as a symmetry (axial or rotational) and (2) for displaying a group of automorphisms (cyclic or dihedral). We also present algorithms to compute the symmetric quality metrics in O(n log n) time for rotational symmetry and axial symmetry. We validate our symmetry quality metrics using deformation experiments. We then use the metrics to evaluate a number of established graph drawing layouts to compare how faithfully they display automorphisms of a graph as geometric symmetries.
Road Damage Detection Based on Unsupervised Disparity Map Segmentation
Venue: arXiv cs.CV 2019
Year: 2019

This paper presents a novel road damage detection algorithm based on unsupervised disparity map segmentation. Firstly, a disparity map is transformed by minimizing an energy function with respect to stereo rig roll angle and road disparity projection model. Instead of solving this energy minimization problem using non-linear optimization techniques, we directly find its numerical solution. The transformed disparity map is then segmented using Otus's thresholding method, and the damaged road areas can be extracted. The proposed algorithm requires no parameters when detecting road damage. The experimental results illustrate that our proposed algorithm performs both accurately and efficiently. The pixel-level road damage detection accuracy is approximately 97.56%.
Bayesian Optimization Meets Riemannian Manifolds in Robot Learning
Venue: arXiv cs.RO 2019
Year: 2019

Bayesian optimization (BO) recently became popular in robotics to optimize control parameters and parametric policies in direct reinforcement learning due to its data efficiency and gradient-free approach. However, its performance may be seriously compromised when the parameter space is high-dimensional. A way to tackle this problem is to introduce domain knowledge into the BO framework. We propose to exploit the geometry of non-Euclidean parameter spaces, which often arise in robotics (e.g. orientation, stiffness matrix). Our approach, built on Riemannian manifold theory, allows BO to properly measure similarities in the parameter space through geometry-aware kernel functions and to optimize the acquisition function on the manifold as an unconstrained problem. We test our approach in several benchmark artificial landscapes and using a 7-DOF simulated robot to learn orientation and impedance parameters for manipulation skills.
CHD:Consecutive Horizontal Dropout for Human Gait Feature Extraction
Venue: arXiv cs.CV 2019
Year: 2019

Despite gait recognition and person re-identification researches have made a lot of progress, the accuracy of identification is not high enough in some specific situations, for example, people carrying bags or changing coats. In order to alleviate above situations, we propose a simple but effective Consecutive Horizontal Dropout (CHD) method apply on human feature extraction in deep learning network to avoid overfitting. Within the CHD, we intensify the robust of deep learning network for cross-view gait recognition and person re-identification. The experiments illustrate that the rank-1 accuracy on cross-view gait recognition task has been increased about 10% from 68.0% to 78.201% and 8% from 83.545% to 91.364% in person re-identification task in wearing coat or jacket condition. In addition, 100% accuracy of NM condition was first obtained with CHD. On the benchmarks of CASIA-B, above accuracies are state-of-the-arts.
Rosetta: Large scale system for text detection and recognition in images
Venue: arXiv cs.CV 2019
Year: 2019

In this paper we present a deployed, scalable optical character recognition (OCR) system, which we call Rosetta, designed to process images uploaded daily at Facebook scale. Sharing of image content has become one of the primary ways to communicate information among internet users within social networks such as Facebook and Instagram, and the understanding of such media, including its textual information, is of paramount importance to facilitate search and recommendation applications. We present modeling techniques for efficient detection and recognition of text in images and describe Rosetta's system architecture. We perform extensive evaluation of presented technologies, explain useful practical approaches to build an OCR system at scale, and provide insightful intuitions as to why and how certain components work based on the lessons learnt during the development and deployment of the system.