OpenRaiser/Intern-Atlas huggingface dataset:
Remote health monitoring and diagnosis in the time of COVID-19
Venue: arXiv cs.CY 2020
Year: 2020

Coronavirus disease (COVID-19) is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that is rapidly spreading across the globe. The clinical spectrum of SARS-CoV-2 pneumonia ranges from mild to critically ill cases and requires early detection and monitoring, within a clinical environment for critical cases and remotely for mild cases. The fear of contamination in clinical environments has led to a dramatic reduction in on-site referrals for routine care. There has also been a perceived need to continuously monitor non-severe COVID- 19 patients, either from their quarantine site at home, or dedicated quarantine locations (e.g., hotels). Thus, the pandemic has driven incentives to innovate and enhance or create new routes for providing healthcare services at distance. In particular, this has created a dramatic impetus to find innovative ways to remotely and effectively monitor patient health status. In this paper we present a short review of remote health monitoring initiatives taken in 19 states during the time of the pandemic. We emphasize in the discussion particular aspects that are common ground for the reviewed states, in particular the future impact of the pandemic on remote health monitoring and consideration on data privacy.
Acoustic Integrity Codes: Secure Device Pairing Using Short-Range Acoustic Communication
Venue: arXiv cs.CR 2020
Year: 2020

Secure Device Pairing (SDP) relies on an out-of-band channel to authenticate devices. This requires a common hardware interface, which limits the use of existing SDP systems. We propose to use short-range acoustic communication for the initial pairing. Audio hardware is commonly available on existing off-the-shelf devices and can be accessed from user space without requiring firmware or hardware modifications. We improve upon previous approaches by designing Acoustic Integrity Codes (AICs): a modulation scheme that provides message authentication on the acoustic physical layer. We analyze their security and demonstrate that we can defend against signal cancellation attacks by designing signals with low autocorrelation. Our system can detect overshadowing attacks using a ternary decision function with a threshold. In our evaluation of this SDP scheme's security and robustness, we achieve a bit error ratio below 0.1% for a net bit rate of 100 bps with a signal-to-noise ratio (SNR) of 14 dB. Using our open-source proof-of-concept implementation on Android smartphones, we demonstrate pairing between different smartphone models.
An Overview on Audio, Signal, Speech, & Language Processing for COVID-19
Venue: arXiv cs.CY 2020
Year: 2020

Recently, there has been an increased attention towards innovating, enhancing, building, and deploying applications of speech signal processing for providing assistance and relief to human mankind from the Coronavirus (COVID-19) pandemic. Many AI with speech initiatives are taken to combat with the present situation and also to create a safe and secure environment for the future. This paper summarises all these efforts taken by the re-search community towards helping the individuals and the society in the fight against COVID-19 over the past 3-4 months using speech signal processing. We also summarise the deep techniques used in this direction to come up with capable solutions in a short span of time. This paper further gives an overview of the contributions from non-speech modalities that may complement or serve as inspiration for audio and speech analysis. In addition, we discuss our observations with respect to solution usability, challenges, and the significant technology achievements.
Product Insights: Analyzing Product Intents in Web Search
Venue: arXiv cs.IR 2020
Year: 2020

Web search engines are frequently used to access information about products. This has increased in recent times with the rising popularity of e-commerce. However, there is limited understanding of what users search for and their intents when it comes to product search on the web. In this work, we study search logs from Bing web search engine to characterize user intents and study user behavior for product search. We propose a taxonomy of product intents by analyzing product search queries. This is a challenging task given that only 15%-17% of web search queries are about products. We train machine learning classifiers with query log features to classify queries based on intent with an overall F1-score of 78%. We further analyze various characteristics of product search queries in terms of search metrics like dwell time, success, popularity and session-specific information.
Learn Class Hierarchy using Convolutional Neural Networks
Venue: arXiv cs.CV 2020
Year: 2020

A large amount of research on Convolutional Neural Networks has focused on flat Classification in the multi-class domain. In the real world, many problems are naturally expressed as problems of hierarchical classification, in which the classes to be predicted are organized in a hierarchy of classes. In this paper, we propose a new architecture for hierarchical classification of images, introducing a stack of deep linear layers with cross-entropy loss functions and center loss combined. The proposed architecture can extend any neural network model and simultaneously optimizes loss functions to discover local hierarchical class relationships and a loss function to discover global information from the whole class hierarchy while penalizing class hierarchy violations. We experimentally show that our hierarchical classifier presents advantages to the traditional classification approaches finding application in computer vision tasks.
JointsGait:A model-based Gait Recognition Method based on Gait Graph Convolutional Networks and Joints Relationship Pyramid Mapping
Venue: arXiv cs.CV 2020
Year: 2020

Gait, as one of unique biometric features, has the advantage of being recognized from a long distance away, can be widely used in public security. Considering 3D pose estimation is more challenging than 2D pose estimation in practice , we research on using 2D joints to recognize gait in this paper, and a new model-based gait recognition method JointsGait is put forward to extract gait information from 2D human body joints. Appearance-based gait recognition algorithms are prevalent before. However, appearance features suffer from external factors which can cause drastic appearance variations, e.g. clothing. Unlike previous approaches, JointsGait firstly extracted spatio-temporal features from 2D joints using gait graph convolutional networks, which are less interfered by external factors. Secondly, Joints Relationship Pyramid Mapping (JRPM) are proposed to map spatio-temporal gait features into a discriminative feature space with biological advantages according to the relationship of human joints when people are walking at various scales. Finally, we design a fusion loss strategy to help the joints features to be insensitive to cross-view. Our method is evaluated on two large datasets, Kinect Gait Biometry Dataset and CASIA-B. On Kinect Gait Biometry Dataset database, JointsGait only uses corresponding 2D coordinates of joints, but achieves satisfactory recognition accuracy compared with those model-based algorithms using 3D joints. On CASIA-B database, the proposed method greatly outperforms advanced model-based methods in all walking conditions, even performs superior to state-of-art appearance-based methods when clothing seriously affect people's appearance. The experimental results demonstrate that JointsGait achieves the state-of-art performance despite the low dimensional feature (2D body joints) and is less affected by the view variations and clothing variation.
Evolutionary-Based Sparse Regression for the Experimental Identification of Duffing Oscillator
Venue: arXiv cs.CE 2020
Year: 2020

In this paper, an evolutionary-based sparse regression algorithm is proposed and applied onto experimental data collected from a Duffing oscillator setup and numerical simulation data. Our purpose is to identify the Coulomb friction terms as part of the ordinary differential equation of the system. Correct identification of this nonlinear system using sparse identification is hugely dependent on selecting the correct form of nonlinearity included in the function library. Consequently, in this work, the evolutionary-based sparse identification is replacing the need for user knowledge when constructing the library in sparse identification. Constructing the library based on the data-driven evolutionary approach is an effective way to extend the space of nonlinear functions, allowing for the sparse regression to be applied on an extensive space of functions. ,e results show that the method provides an effective algorithm for the purpose of unveiling the physical nature of the Duffing oscillator. In addition, the robustness of the identification algorithm is investigated for various levels of noise in simulation. ,e proposed method has possible applications to other nonlinear dynamic systems in mechatronics, robotics, and electronics.
Atom Search Optimization with Simulated Annealing -- a Hybrid Metaheuristic Approach for Feature Selection
Venue: arXiv cs.CV 2020
Year: 2020

'Hybrid meta-heuristics' is one of the most interesting recent trends in the field of optimization and feature selection (FS). In this paper, we have proposed a binary variant of Atom Search Optimization (ASO) and its hybrid with Simulated Annealing called ASO-SA techniques for FS. In order to map the real values used by ASO to the binary domain of FS, we have used two different transfer functions: S-shaped and V-shaped. We have hybridized this technique with a local search technique called, SA We have applied the proposed feature selection methods on 25 datasets from 4 different categories: UCI, Handwritten digit recognition, Text, non-text separation, and Facial emotion recognition. We have used 3 different classifiers (K-Nearest Neighbor, Multi-Layer Perceptron and Random Forest) for evaluating the strength of the selected featured by the binary ASO, ASO-SA and compared the results with some recent wrapper-based algorithms. The experimental results confirm the superiority of the proposed method both in terms of classification accuracy and number of selected features.
Deep Learning for Post-Processing Ensemble Weather Forecasts
Venue: arXiv cs.LG 2020
Year: 2020

Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which consist of many perturbed numerical weather simulations, or trajectories, run in parallel. These systems are associated with a high computational cost and often involve statistical post-processing steps to inexpensively improve their raw prediction qualities. We propose a mixed model that uses only a subset of the original weather trajectories combined with a post-processing step using deep neural networks. These enable the model to account for non-linear relationships that are not captured by current numerical models or post-processing methods. Applied to global data, our mixed models achieve a relative improvement in ensemble forecast skill (CRPS) of over 14%. Furthermore, we demonstrate that the improvement is larger for extreme weather events on select case studies. We also show that our post-processing can use fewer trajectories to achieve comparable results to the full ensemble. By using fewer trajectories, the computational costs of an ensemble prediction system can be reduced, allowing it to run at higher resolution and produce more accurate forecasts.
Classification of Spam Emails through Hierarchical Clustering and Supervised Learning
Venue: arXiv cs.CL 2020
Year: 2020

Spammers take advantage of email popularity to send indiscriminately unsolicited emails. Although researchers and organizations continuously develop anti-spam filters based on binary classification, spammers bypass them through new strategies, like word obfuscation or image-based spam. For the first time in literature, we propose to classify spam email in categories to improve the handle of already detected spam emails, instead of just using a binary model. First, we applied a hierarchical clustering algorithm to create SPEMC-$11$K (SPam EMail Classification), the first multi-class dataset, which contains three types of spam emails: Health and Technology, Personal Scams, and Sexual Content. Then, we used SPEMC-$11$K to evaluate the combination of TF-IDF and BOW encodings with Naïve Bayes, Decision Trees and SVM classifiers. Finally, we recommend for the task of multi-class spam classification the use of (i) TF-IDF combined with SVM for the best micro F1 score performance, $95.39\%$, and (ii) TD-IDF along with NB for the fastest spam classification, analyzing an email in $2.13$ms.
Causal Feature Learning for Utility-Maximizing Agents
Venue: arXiv cs.AI 2020
Year: 2020

Discovering high-level causal relations from low-level data is an important and challenging problem that comes up frequently in the natural and social sciences. In a series of papers, Chalupka et al. (2015, 2016a, 2016b, 2017) develop a procedure for causal feature learning (CFL) in an effort to automate this task. We argue that CFL does not recommend coarsening in cases where pragmatic considerations rule in favor of it, and recommends coarsening in cases where pragmatic considerations rule against it. We propose a new technique, pragmatic causal feature learning (PCFL), which extends the original CFL algorithm in useful and intuitive ways. We show that PCFL has the same attractive measure-theoretic properties as the original CFL algorithm. We compare the performance of both methods through theoretical analysis and experiments.
Simple and robust element-free Galerkin method with interpolating shape functions for finite deformation elasticity
Venue: arXiv cs.CE 2020
Year: 2020

In this paper, we present a meshless method belonging to the family of element-free Galerkin (EFG) methods. The distinguishing feature of the presented meshless method is that it allows accurate enforcement of essential boundary conditions. The method uses total Lagrangian formulation with explicit time integration to facilitate code simplicity and robust computations in applications that involve large deformations and non-linear materials. We use a regularized weight function, which closely approximates the Kronecker delta, to generate interpolating shape functions. The imposition of the prescribed displacements on the boundary becomes as straightforward as in the finite element (FE) method. The effectiveness and accuracy of the proposed method is demonstrated using 3D numerical examples that include cylinder indentation by 70% of its initial height, and indentation of the brain.
Power Allocation and Link Selection for Multicell Cooperative NOMA Hybrid VLC/RF Systems
Venue: arXiv cs.IT 2020
Year: 2020

This paper proposes and optimizes a cooperative non-orthogonal multiple-access (Co-NOMA) scheme in the context of multicell visible light communications (VLC) networks, as a means to mitigate inter-cell interference in Co-NOMA-enabled systems. Consider a network with multiple VLC access points (APs), where each AP serves two users using light intensity. In each cell, the weak user (the cell edge user) can be served either directly by the VLC AP, or through the strong user that can decode the weak user's message and forward it through the radio-frequency (RF) link. The paper then considers the problem of maximizing the network throughput under quality-of-service (QoS) constraints by allocating the powers of the users' messages and APs' transmit powers, and determining the serving links of each weak user (i.e., VLC or hybrid VLC/RF). The paper solves such a non-convex problem by first finding closed form solutions of the joint users' powers and link selection for a fixed AP power allocation. The APs' transmit powers are then iteratively solved in an outer loop using the golden section method. Simulation results show how the proposed solution and scheme improve the system sum-rate and fairness as compared to conventional non-orthogonal multiple-access (NOMA) schemes.
Assertion Detection in Multi-Label Clinical Text using Scope Localization
Venue: arXiv cs.LG 2020
Year: 2020

Multi-label sentences (text) in the clinical domain result from the rich description of scenarios during patient care. The state-of-theart methods for assertion detection mostly address this task in the setting of a single assertion label per sentence (text). In addition, few rules based and deep learning methods perform negation/assertion scope detection on single-label text. It is a significant challenge extending these methods to address multi-label sentences without diminishing performance. Therefore, we developed a convolutional neural network (CNN) architecture to localize multiple labels and their scopes in a single stage end-to-end fashion, and demonstrate that our model performs atleast 12% better than the state-of-the-art on multi-label clinical text.
Selling Data at an Auction under Privacy Constraints
Venue: arXiv cs.GT 2020
Year: 2020

Private data query combines mechanism design with privacy protection to produce aggregated statistics from privately-owned data records. The problem arises in a data marketplace where data owners have personalised privacy requirements and private data valuations. We focus on the case when the data owners are single-minded, i.e., they are willing to release their data only if the data broker guarantees to meet their announced privacy requirements. For a data broker who wants to purchase data from such data owners, we propose the SingleMindedQuery (SMQ) mechanism, which uses a reverse auction to select data owners and determine compensations. SMQ satisfies interim incentive compatibility, individual rationality, and budget feasibility. Moreover, it uses purchased privacy expectation maximisation as a principle to produce accurate outputs for commonly-used queries such as counting, median and linear predictor. The effectiveness of our method is empirically validated by a series of experiments.
Multiple Source Replacement Path Problem
Venue: arXiv cs.DS 2020
Year: 2020

One of the classical line of work in graph algorithms has been the Replacement Path Problem: given a graph $G$, $s$ and $t$, find shortest paths from $s$ to $t$ avoiding each edge $e$ on the shortest path from $s$ to $t$. These paths are called replacement paths in literature. For an undirected and unweighted graph, (Malik, Mittal, and Gupta, Operation Research Letters, 1989) and (Hershberger and Suri, FOCS 2001) designed an algorithm that solves the replacement path problem in $\tilde O(m+n)$ time. It is natural to ask whether we can generalize the replacement path problem: {\em can we find all replacement paths from a source $s$ to all vertices in $G$?} This problem is called the Single Source Replacement Path Problem. Recently (Chechik and Cohen, SODA 2019) designed a randomized combinatorial algorithm that solves the Single Source Replacement Path Problem in $\tilde O(m\sqrt n\ + n^2)$ time. One of the questions left unanswered by their work is the case when there are many sources, not one. When there are $n$ sources, the combinatorial algorithm of (Bernstein and Karger, STOC 2009) can be used to find all pair replacement path in $\tilde O(mn + n^3)$ time. However, there is no result known for any general $σ$. Thus, the problem we study is defined as follows: given a set of $σ$ sources, we want to find the replacement path from these sources to all vertices in $G$. We give a randomized combinatorial algorithm for this problem that takes $\tilde O(m\sqrt{n σ} +\ σn^2)$ time. This result generalizes both results known for this problem. Our algorithm is much different and arguably simpler than (Chechik and Cohen, SODA 2019). Like them, we show a matching conditional lower bound using the Boolean Matrix Multiplication conjecture.
Towards a Perceived Audiovisual Quality Model for Immersive Content
Venue: arXiv cs.MM 2020
Year: 2020

This paper studies the quality of multimedia content focusing on 360 video and ambisonic spatial audio reproduced using a head-mounted display and a multichannel loudspeaker setup. Encoding parameters following basic video quality test conditions for 360 videos were selected and a low-bitrate codec was used for the audio encoder. Three subjective experiments were performed for the audio, video, and audiovisual respectively. Peak signal-to-noise ratio (PSNR) and its variants for 360 videos were computed to obtain objective quality metrics and subsequently correlated with the subjective video scores. This study shows that a Cross-Format SPSNR-NN has a slightly higher linear and monotonic correlation over all video sequences. Based on the audiovisual model, a power model shows a highest correlation between test data and predicted scores. We concluded that to enable the development of superior predictive model, a high quality, critical, synchronized audiovisual database is required. Furthermore, comprehensive assessor training may be beneficial prior to the testing to improve the assessors' discrimination ability particularly with respect to multichannel audio reproduction. In order to further improve the performance of audiovisual quality models for immersive content, in addition to developing broader and critical audiovisual databases, the subjective testing methodology needs to be evolved to provide greater resolution and robustness.
Self-Learning Detector for the Cell-Free Massive MIMO Uplink: The Line-of-Sight Case
Venue: arXiv cs.IT 2020
Year: 2020

The precoding in cell-free massive multiple-input multiple-output (MIMO) technology relies on accurate knowledge of channel responses between users (UEs) and access points (APs). Obtaining high-quality channel estimates in turn requires the path losses between pairs of UEs and APs to be known. These path losses may change rapidly especially in line-of-sight environments with moving blocking objects. A difficulty in the estimation of path losses is pilot contamination, that is, simultaneously transmitted pilots from different UEs that may add up destructively or constructively by chance, seriously affecting the estimation quality (and hence the eventual performance). A method for estimation of path losses, along with an accompanying pilot transmission scheme, is proposed that works for both Rayleigh fading and line-of-sight channels and that significantly improves performance over baseline state-of-the-art. The salient feature of the pilot transmission scheme is that pilots are structurally phase-rotated over different coherence blocks (according to a pre-determined function known to all parties), in order to create an effective statistical distribution of the received pilot signal that can be efficiently exploited by the proposed estimation algorithm.
On Restricting Real-Valued Genotypes in Evolutionary Algorithms
Venue: arXiv cs.NE 2020
Year: 2020

Real-valued genotypes together with the variation operators, mutation and crossover, constitute some of the fundamental building blocks of Evolutionary Algorithms. Real-valued genotypes are utilized in a broad range of contexts, from weights in Artificial Neural Networks to parameters in robot control systems. Shared between most uses of real-valued genomes is the need for limiting the range of individual parameters to allowable bounds. In this paper we will illustrate the challenge of limiting the parameters of real-valued genomes and analyse the most promising method to properly limit these values. We utilize both empirical as well as benchmark examples to demonstrate the utility of the proposed method and through a literature review show how the insight of this paper could impact other research within the field. The proposed method requires minimal intervention from Evolutionary Algorithm practitioners and behaves well under repeated application of variation operators, leading to better theoretical properties as well as significant differences in well-known benchmarks.