OpenRaiser/Intern-Atlas huggingface dataset:
Deep Speech Enhancement for Reverberated and Noisy Signals using Wide Residual Networks
Venue: arXiv cs.SD 2019
Year: 2019

This paper proposes a deep speech enhancement method which exploits the high potential of residual connections in a wide neural network architecture, a topology known as Wide Residual Network. This is supported on single dimensional convolutions computed alongside the time domain, which is a powerful approach to process contextually correlated representations through the temporal domain, such as speech feature sequences. We find the residual mechanism extremely useful for the enhancement task since the signal always has a linear shortcut and the non-linear path enhances it in several steps by adding or subtracting corrections. The enhancement capacity of the proposal is assessed by objective quality metrics and the performance of a speech recognition system. This was evaluated in the framework of the REVERB Challenge dataset, including simulated and real samples of reverberated and noisy speech signals. Results showed that enhanced speech from the proposed method succeeded for both, the enhancement task with intelligibility purposes and the speech recognition system. The DNN model, trained with artificial synthesized reverberation data, was able to deal with far-field reverberated speech from real scenarios. Furthermore, the method was able to take advantage of the residual connection achieving to enhance signals with low noise level, which is usually a strong handicap of traditional enhancement methods.
Real-time Structural Health Monitoring System Using Internet of Things and Cloud Computing
Venue: arXiv cs.OH 2019
Year: 2019

Real-time monitoring of various structural behaviors, particularly displacement and acceleration, serves important and valuable information for people; for example, they can be used for active control or damage warning. With recent advancement of the Internet of Things and client-side web technologies, wireless integrated sensor devices nowadays can process real-time raw sensor signal data into target measurements, such as displacement, and then send the results through a standard protocol to the servers on the Internet. The monitoring results are further processed for visualization purpose in the servers and the computed results are pushed to connected clients like browsers or mobile applications in real-time. We build a real-time cloud-based system that can receive heterogeneous IoT data, allow users to create a three-dimensional model online according to the real world structure, and the monitoring results can be visualized in that model. In this paper, we illustrate the software architecture of the proposed system and focus on the technologies that are used, like client-side scripting, NoSql database, and socket communication. We also present the challenges of displaying the overall movement and shape transformation of the 3D structural model. Thus, each internal-connected element's rotations and translations are obtained by converting the monitoring results of each sensor device measured in the global coordinate system. To overcome this, we create an inverted movement calculation method. A simple 3D two-level structural model and simulated sensor displacements are used to demonstrate system function and validate the inverted movement calculation method.
Une nouvelle approche de complétion des valeurs manquantes dans les bases de données
Venue: arXiv cs.DB 2019
Year: 2019

When tackling real-life datasets, it is common to face the existence of scrambled missing values within data. Considered as 'dirty data', usually it is removed during a pre-processing step. Starting from the fact that 'making up this missing data is better than throwing out it away', we present a new approach trying to complete missing data. The main singularity of the introduced approach is that it sheds light on a fruitful synergy between generic basis of association rules and the topic of missing values handling. In fact, beyond interesting compactness rate, such generic association rules make it possible to get a considerable reduction of conflicts during the completion step. A new metric called 'Robustness' is also introduced, and aims to select the robust association rule for the completion of a missing value whenever a conflict appears. Carried out experiments on benchmark datasets confirm the soundness of our approach. Thus, it reduces conflict during the completion step while offering a high percentage of correct completion accuracy.
Virtual Web Based Personalized PageRank Updating
Venue: arXiv cs.SI 2019
Year: 2019

Growing popularity of social networks demands a highly efficient Personalized PageRank (PPR) updating due to the fast-evolving web graphs of enormous size. While current researches are focusing on PPR updating under link structure modification, efficiently updating PPR when node insertion/ deletion involved remains a challenge. In the previous work called Virtual Web (VW), a few VW architectures are designed, which results in some highly effective initializations to significantly accelerate PageRank updating under both link modification and page insertion/deletion. In the paper, under the general scenario of link modification and node insertion/deletion we tackle the fast PPR updating problem. Specifically, we combine VW with the TrackingPPR method to generate initials, which are then used by the Gauss-Southwell method for fast PPR updating. The algorithm is named VWPPR method. In extensive experiments, three real-world datasets are used that contain 1~5.6M nodes and 6.7M~129M links, while a node perturbation of 40k and link perturbation of 1% are applied. Comparing to the more recent LazyForwardUpdate method, which handles the general PPR updating problem, the VWPPR method is 3~6 times faster in terms of running time, or 4.4~10 times faster in terms of iteration numbers.
A Hierarchical Grocery Store Image Dataset with Visual and Semantic Labels
Venue: arXiv cs.CV 2019
Year: 2019

Image classification models built into visual support systems and other assistive devices need to provide accurate predictions about their environment. We focus on an application of assistive technology for people with visual impairments, for daily activities such as shopping or cooking. In this paper, we provide a new benchmark dataset for a challenging task in this application - classification of fruits, vegetables, and refrigerated products, e.g. milk packages and juice cartons, in grocery stores. To enable the learning process to utilize multiple sources of structured information, this dataset not only contains a large volume of natural images but also includes the corresponding information of the product from an online shopping website. Such information encompasses the hierarchical structure of the object classes, as well as an iconic image of each type of object. This dataset can be used to train and evaluate image classification models for helping visually impaired people in natural environments. Additionally, we provide benchmark results evaluated on pretrained convolutional neural networks often used for image understanding purposes, and also a multi-view variational autoencoder, which is capable of utilizing the rich product information in the dataset.
Dataset search: a survey
Venue: arXiv cs.DB 2019
Year: 2019

Generating value from data requires the ability to find, access and make sense of datasets. There are many efforts underway to encourage data sharing and reuse, from scientific publishers asking authors to submit data alongside manuscripts to data marketplaces, open data portals and data communities. Google recently beta released a search service for datasets, which allows users to discover data stored in various online repositories via keyword queries. These developments foreshadow an emerging research field around dataset search or retrieval that broadly encompasses frameworks, methods and tools that help match a user data need against a collection of datasets. Here, we survey the state of the art of research and commercial systems in dataset retrieval. We identify what makes dataset search a research field in its own right, with unique challenges and methods and highlight open problems. We look at approaches and implementations from related areas dataset search is drawing upon, including information retrieval, databases, entity-centric and tabular search in order to identify possible paths to resolve these open problems as well as immediate next steps that will take the field forward.
Classification of Functioning, Disability, and Health for Children and Youth: ICF-CY Self Care (SCADI Dataset) Using Predictive Analytics
Venue: arXiv cs.CY 2019
Year: 2019

The International Classification of Functioning, Disability, and Health for Children and Youth (ICF-CY) is a scaffold for designating and systematizing data on functioning and disability. It offers a standard semantic and a theoretical foundation for the demarcation and extent of wellbeing and infirmity. The multidimensional layout of ICF-CY comprehends a plethora of information with about 1400 categories making it difficult to analyze. Our research proposes a predictive model that classify self-care problems on Self-Care Activities Dataset based on the ICF- CY. The data used in this study resides 206 attributes of 70 children with motor and physical disability. Our study implements, compare and analyze Random Forest, Support vector machine, Naive Bayes, Hoeffding tree, and Lazy locally weighted learning using two-tailed T-test at 95% confidence interval. Boruta algorithm involved in the study minimizes the data dimensionality to advocate the minimal-optimal set of predictors. Random forest gave the best classification accuracy of 84.75%; root mean squared error of 0.18 and receiver operating characteristic of 0.99. Predictive analytics can simplify the usage of ICF-CY by automating the classification process of disability, functioning, and health.
Robotic Tankette for Intelligent BioEnergy Agriculture: Design, Development and Field Tests
Venue: arXiv cs.RO 2019
Year: 2019

In recent years, the use of robots in agriculture has been increasing mainly due to the high demand of productivity, precision and efficiency, which follow the climate change effects and world population growth. Unlike conventional agriculture, sugarcane farms are usually regions with dense vegetation, gigantic areas, and subjected to extreme weather conditions, such as intense heat, moisture and rain. TIBA - Tankette for Intelligent BioEnergy Agriculture - is the first result of an R&D project which strives to develop an autonomous mobile robotic system for carrying out a number of agricultural tasks in sugarcane fields. The proposed concept consists of a semi-autonomous, low-cost, dust and waterproof tankette-type vehicle, capable of infiltrating dense vegetation in plantation tunnels and carry several sensing systems, in order to perform mapping of hard-to-access areas and collecting samples. This paper presents an overview of the robot mechanical design, the embedded electronics and software architecture, and the construction of a first prototype. Preliminary results obtained in field tests validate the proposed conceptual design and bring about several challenges and potential applications for robot autonomous navigation, as well as to build a new prototype with additional functionality.
Towards Global Remote Discharge Estimation: Using the Few to Estimate The Many
Venue: arXiv cs.LG 2019
Year: 2019

Learning hydrologic models for accurate riverine flood prediction at scale is a challenge of great importance. One of the key difficulties is the need to rely on in-situ river discharge measurements, which can be quite scarce and unreliable, particularly in regions where floods cause the most damage every year. Accordingly, in this work we tackle the problem of river discharge estimation at different river locations. A core characteristic of the data at hand (e.g. satellite measurements) is that we have few measurements for many locations, all sharing the same physics that underlie the water discharge. We capture this scenario in a simple but powerful common mechanism regression (CMR) model with a local component as well as a shared one which captures the global discharge mechanism. The resulting learning objective is non-convex, but we show that we can find its global optimum by leveraging the power of joining local measurements across sites. In particular, using a spectral initialization with provable near-optimal accuracy, we can find the optimum using standard descent methods. We demonstrate the efficacy of our approach for the problem of discharge estimation using simulations.
Massive MIMO Unsourced Random Access
Venue: arXiv cs.IT 2019
Year: 2019

We consider an extension of the massive unsourced random access originally proposed by Polyanskiy to the case where the receiver has a very large number of antennas (a massive MIMO base station) and no channel state information is given to the receiver (fully non-coherent detection). Our coding approach borrows the concatenated coding idea from Amalladinne et. al., combined with a novel non-Bayesian `activity detection' algorithm for massive MIMO random access channels, that outperforms currently proposed Bayesian vector AMP (VAMP) schemes currently proposed for activity detection, and does not suffer from the numerical instabilities and requirement for accurate a priori statistics as VAMP. We show that the required transmit $E_b/N_0$ for reliable communication can be made arbitrarily small as the number of receiver antennas M grows sufficiently large.
An Interactive Robotic Framework to Facilitate Sensory Experiences for Children with ASD
Venue: arXiv cs.RO 2019
Year: 2019

The diagnosis of Autism Spectrum Disorder (ASD) in children is commonly accompanied by a diagnosis of sensory processing disorders as well. Abnormalities are usually reported in multiple sensory processing domains, showing a higher prevalence of unusual responses, particularly to tactile, auditory and visual stimuli. This paper discusses a novel robot-based framework designed to target sensory difficulties faced by children with ASD in a controlled setting. The setup consists of a number of sensory stations, together with robotic agents that navigate the stations and interact with the stimuli as they are presented. These stimuli are designed to resemble real world scenarios that form a common part of one's everyday experiences. Given the strong interest of children with ASD in technology in general and robots in particular, we attempt to utilize our robotic platform to demonstrate socially acceptable responses to the stimuli in an interactive, pedagogical setting that encourages the child's social, motor and vocal skills, while providing a diverse sensory experience. A user study was conducted to evaluate the efficacy of the proposed framework, with a total of 18 participants (5 with ASD and 13 typically developing) between the ages of 4 and 12 years. We describe our methods of data collection, coding of video data and the analysis of the results obtained from the study. We also discuss the limitations of the current work and detail our plans for the future work to improve the validity of the obtained results.
A probabilistic framework for tracking uncertainties in robotic manipulation
Venue: arXiv cs.RO 2019
Year: 2019

Precisely tracking uncertainties is crucial for robots to successfully and safely operate in unstructured and dynamic environments. We present a probabilistic framework to precisely keep track of uncertainties throughout the entire manipulation process. In agreement with common manipulation pipelines, we decompose the process into two subsequent stages, namely perception and physical interaction. Each stage is associated with different sources and types of uncertainties, requiring different techniques. We discuss which representation of uncertainties is the most appropriate for each stage (e.g. as probability distributions in SE(3) during perception, as weighted particles during physical interactions), how to convert from one representation to another, and how to initialize or update the uncertainties at each step of the process (camera calibration, image processing, pushing, grasping, etc.). Finally, we demonstrate the benefit of this fine-grained knowledge of uncertainties in an actual assembly task.
A variational approach to high-order r-adaptation
Venue: arXiv cs.CG 2019
Year: 2019

A variational framework, initially developed for high-order mesh optimisation, is being extended for r-adaptation. The method is based on the minimisation of a functional of the mesh deformation. To achieve adaptation, elements of the initial mesh are manipulated using metric tensors to obtain target elements. The nonlinear optimisation in turns adapts the final high-order mesh to best fit the description of the target elements by minimising the element distortion. Encouraging preliminary results prove that the method behaves well and can be used in the future for more extensive work which shall include the use of error indicators from CFD simulations.
Concentration bounds for CVaR estimation: The cases of light-tailed and heavy-tailed distributions
Venue: arXiv cs.LG 2019
Year: 2019

Conditional Value-at-Risk (CVaR) is a widely used risk metric in applications such as finance. We derive concentration bounds for CVaR estimates, considering separately the cases of light-tailed and heavy-tailed distributions. In the light-tailed case, we use a classical CVaR estimator based on the empirical distribution constructed from the samples. For heavy-tailed random variables, we assume a mild `bounded moment' condition, and derive a concentration bound for a truncation-based estimator. Notably, our concentration bounds enjoy an exponential decay in the sample size, for heavy-tailed as well as light-tailed distributions. To demonstrate the applicability of our concentration results, we consider a CVaR optimization problem in a multi-armed bandit setting. Specifically, we address the best CVaR-arm identification problem under a fixed budget. We modify the well-known successive rejects algorithm to incorporate a CVaR-based criterion. Using the CVaR concentration result, we derive an upper-bound on the probability of incorrect identification by the proposed algorithm.
A Joint Model for Multimodal Document Quality Assessment
Venue: arXiv cs.CL 2019
Year: 2019

The quality of a document is affected by various factors, including grammaticality, readability, stylistics, and expertise depth, making the task of document quality assessment a complex one. In this paper, we explore this task in the context of assessing the quality of Wikipedia articles and academic papers. Observing that the visual rendering of a document can capture implicit quality indicators that are not present in the document text --- such as images, font choices, and visual layout --- we propose a joint model that combines the text content with a visual rendering of the document for document quality assessment. Experimental results over two datasets reveal that textual and visual features are complementary, achieving state-of-the-art results.
Towards a Live Anonymous Question Queue To Address Student Apprehension
Venue: arXiv cs.CY 2019
Year: 2019

In today's university climate many first and second year classes have over a hundred students. Large classrooms make some students apprehensive about asking questions. An anonymous method of submitting questions to an instructor would allow students to ask their questions without feeling apprehensive. In this paper we propose a Live Anonymous Question Queue (LAQQ), a system that facilitates anonymous question submissions in real time to mitigate student apprehension, increase student participation, and provide real-time feedback to the instructor. To study the necessary features of an LAQQ, we conducted a study of a system, namely Google Moderator, which best approached our concept of an LAQQ. We deployed Google moderator in large lectures and studied its support of a number of features that we envisioned for an LAQQ. Through our class observations, interviews with instructors, and surveys with the students, our results suggest that an LAQQ system must provide support for: notification of question submission to provide awareness for the instructor, and context for questions to allow an instructor to easily answer a question. Additionally our results suggest that an LAQQ system must be accessible and usable on multiple platforms. Finally our results suggest that in order to be successful in the classroom an LAQQ system must be fully adopted by the instructor and the classroom organizational structure must change to accommodate the use of the LAQQ.
Fully Convolutional Deep Network Architectures for Automatic Short Glass Fiber Semantic Segmentation from CT scans
Venue: arXiv cs.CV 2019
Year: 2019

We present the first attempt to perform short glass fiber semantic segmentation from X-ray computed tomography volumetric datasets at medium (3.9 μm isotropic) and low (8.3 μm isotropic) resolution using deep learning architectures. We performed experiments on both synthetic and real CT scans and evaluated deep fully convolutional architectures with both 2D and 3D kernels. Our artificial neural networks outperform existing methods at both medium and low resolution scans.
Generic Primitive Detection in Point Clouds Using Novel Minimal Quadric Fits
Venue: arXiv cs.CV 2019
Year: 2019

We present a novel and effective method for detecting 3D primitives in cluttered, unorganized point clouds, without axillary segmentation or type specification. We consider the quadric surfaces for encapsulating the basic building blocks of our environments - planes, spheres, ellipsoids, cones or cylinders, in a unified fashion. Moreover, quadrics allow us to model higher degree of freedom shapes, such as hyperboloids or paraboloids that could be used in non-rigid settings. We begin by contributing two novel quadric fits targeting 3D point sets that are endowed with tangent space information. Based upon the idea of aligning the quadric gradients with the surface normals, our first formulation is exact and requires as low as four oriented points. The second fit approximates the first, and reduces the computational effort. We theoretically analyze these fits with rigor, and give algebraic and geometric arguments. Next, by re-parameterizing the solution, we devise a new local Hough voting scheme on the null-space coefficients that is combined with RANSAC, reducing the complexity from $O(N^4)$ to $O(N^3)$ (three points). To the best of our knowledge, this is the first method capable of performing a generic cross-type multi-object primitive detection in difficult scenes without segmentation. Our extensive qualitative and quantitative results show that our method is efficient and flexible, as well as being accurate.