OpenRaiser/Intern-Atlas huggingface dataset:
Physics Informed Convex Artificial Neural Networks (PICANNs) for Optimal Transport based Density Estimation
Venue: arXiv cs.LG 2021
Year: 2021

Optimal Mass Transport (OMT) is a well studied problem with a variety of applications in a diverse set of fields ranging from Physics to Computer Vision and in particular Statistics and Data Science. Since the original formulation of Monge in 1781 significant theoretical progress been made on the existence, uniqueness and properties of the optimal transport maps. The actual numerical computation of the transport maps, particularly in high dimensions, remains a challenging problem. By Brenier's theorem, the continuous OMT problem can be reduced to that of solving a non-linear PDE of Monge-Ampere type whose solution is a convex function. In this paper, building on recent developments of input convex neural networks and physics informed neural networks for solving PDE's, we propose a Deep Learning approach to solve the continuous OMT problem. To demonstrate the versatility of our framework we focus on the ubiquitous density estimation and generative modeling tasks in statistics and machine learning. Finally as an example we show how our framework can be incorporated with an autoencoder to estimate an effective probabilistic generative model.
Autonomous Driving Data Chain & Interfaces
Venue: arXiv cs.RO 2021
Year: 2021

Recent developments in autonomous driving technology have proven that map data may be used, not only for general routing purposes, but also for to enhance and complement common sensor data. This document reviews the most commonly used interfaces and formats at each step of a selfhealing map data chain.
A Semantic Segmentation Network for Urban-Scale Building Footprint Extraction Using RGB Satellite Imagery
Venue: arXiv cs.CV 2021
Year: 2021

Urban areas consume over two-thirds of the world's energy and account for more than 70 percent of global CO2 emissions. As stated in IPCC's Global Warming of 1.5C report, achieving carbon neutrality by 2050 requires a clear understanding of urban geometry. High-quality building footprint generation from satellite images can accelerate this predictive process and empower municipal decision-making at scale. However, previous Deep Learning-based approaches face consequential issues such as scale invariance and defective footprints, partly due to ever-present class-wise imbalance. Additionally, most approaches require supplemental data such as point cloud data, building height information, and multi-band imagery - which has limited availability and are tedious to produce. In this paper, we propose a modified DeeplabV3+ module with a Dilated Res-Net backbone to generate masks of building footprints from three-channel RGB satellite imagery only. Furthermore, we introduce an F-Beta measure in our objective function to help the model account for skewed class distributions and prevent false-positive footprints. In addition to F-Beta, we incorporate an exponentially weighted boundary loss and use a cross-dataset training strategy to further increase the quality of predictions. As a result, we achieve state-of-the-art performances across three public benchmarks and demonstrate that our RGB-only method produces higher quality visual results and is agnostic to the scale, resolution, and urban density of satellite imagery.
Designing for human-AI complementarity in K-12 education
Venue: arXiv cs.HC 2021
Year: 2021

Recent work has explored how complementary strengths of humans and artificial intelligence (AI) systems might be productively combined. However, successful forms of human-AI partnership have rarely been demonstrated in real-world settings. We present the iterative design and evaluation of Lumilo, smart glasses that help teachers help their students in AI-supported classrooms by presenting real-time analytics about students' learning, metacognition, and behavior. Results from a field study conducted in K-12 classrooms indicate that students learn more when teachers and AI tutors work together during class. We discuss implications of this research for the design of human-AI partnerships. We argue for more participatory approaches to research and design in this area, in which practitioners and other stakeholders are deeply, meaningfully involved throughout the process. Furthermore, we advocate for theory-building and for principled approaches to the study of human-AI decision-making in real-world contexts.
Feature Evolution and Reuse -- An Exploratory Study of Eclipse
Venue: arXiv cs.SE 2021
Year: 2021

One of the purported ways to increase productivity and reduce development time is to reuse existing features and modules. If reuse is adopted, logically then, it will have a direct impact on a system's evolution. However, the evidence in the literature is not clear on the extent to which reuse is practiced in real-world projects, nor how it is practiced. In this paper we report the results of an investigation of reuse and evolution of software features in one of the largest open-source ecosystems - Eclipse. Eclipse provides a leading example of how a system can grow dramatically in size and number of features while maintaining its quality. Our results demonstrate the extent of feature reuse and evolution and also patterns of reuse across ten different Eclipse releases (from Europa to Neon).
Measuring Linguistic Diversity During COVID-19
Venue: arXiv cs.CL 2021
Year: 2021

Computational measures of linguistic diversity help us understand the linguistic landscape using digital language data. The contribution of this paper is to calibrate measures of linguistic diversity using restrictions on international travel resulting from the COVID-19 pandemic. Previous work has mapped the distribution of languages using geo-referenced social media and web data. The goal, however, has been to describe these corpora themselves rather than to make inferences about underlying populations. This paper shows that a difference-in-differences method based on the Herfindahl-Hirschman Index can identify the bias in digital corpora that is introduced by non-local populations. These methods tell us where significant changes have taken place and whether this leads to increased or decreased diversity. This is an important step in aligning digital corpora like social media with the real-world populations that have produced them.
Representations of Language Varieties Are Reliable Given Corpus Similarity Measures
Venue: arXiv cs.CL 2021
Year: 2021

This paper measures similarity both within and between 84 language varieties across nine languages. These corpora are drawn from digital sources (the web and tweets), allowing us to evaluate whether such geo-referenced corpora are reliable for modelling linguistic variation. The basic idea is that, if each source adequately represents a single underlying language variety, then the similarity between these sources should be stable across all languages and countries. The paper shows that there is a consistent agreement between these sources using frequency-based corpus similarity measures. This provides further evidence that digital geo-referenced corpora consistently represent local language varieties.
Multi-Unit Directional Measures of Association: Moving Beyond Pairs of Words
Venue: arXiv cs.CL 2021
Year: 2021

This paper formulates and evaluates a series of multi-unit measures of directional association, building on the pairwise ΔP measure, that are able to quantify association in sequences of varying length and type of representation. Multi-unit measures face an additional segmentation problem: once the implicit length constraint of pairwise measures is abandoned, association measures must also identify the borders of meaningful sequences. This paper takes a vector-based approach to the segmentation problem by using 18 unique measures to describe different aspects of multi-unit association. An examination of these measures across eight languages shows that they are stable across languages and that each provides a unique rank of associated sequences. Taken together, these measures expand corpus-based approaches to association by generalizing across varying lengths and types of representation.
Generation of Gradient-Preserving Images allowing HOG Feature Extraction
Venue: arXiv cs.CV 2021
Year: 2021

In this paper, we propose a method for generating visually protected images, referred to as gradient-preserving images. The protected images allow us to directly extract Histogram-of-Oriented-Gradients (HOG) features for privacy-preserving machine learning. In an experiment, HOG features extracted from gradient-preserving images are applied to a face recognition algorithm to demonstrate the effectiveness of the proposed method.
Random Features for the Neural Tangent Kernel
Venue: arXiv cs.LG 2021
Year: 2021

The Neural Tangent Kernel (NTK) has discovered connections between deep neural networks and kernel methods with insights of optimization and generalization. Motivated by this, recent works report that NTK can achieve better performances compared to training neural networks on small-scale datasets. However, results under large-scale settings are hardly studied due to the computational limitation of kernel methods. In this work, we propose an efficient feature map construction of the NTK of fully-connected ReLU network which enables us to apply it to large-scale datasets. We combine random features of the arc-cosine kernels with a sketching-based algorithm which can run in linear with respect to both the number of data points and input dimension. We show that dimension of the resulting features is much smaller than other baseline feature map constructions to achieve comparable error bounds both in theory and practice. We additionally utilize the leverage score based sampling for improved bounds of arc-cosine random features and prove a spectral approximation guarantee of the proposed feature map to the NTK matrix of two-layer neural network. We benchmark a variety of machine learning tasks to demonstrate the superiority of the proposed scheme. In particular, our algorithm can run tens of magnitude faster than the exact kernel methods for large-scale settings without performance loss.
Convex Aggregation for Opinion Summarization
Venue: arXiv cs.CL 2021
Year: 2021

Recent advances in text autoencoders have significantly improved the quality of the latent space, which enables models to generate grammatical and consistent text from aggregated latent vectors. As a successful application of this property, unsupervised opinion summarization models generate a summary by decoding the aggregated latent vectors of inputs. More specifically, they perform the aggregation via simple average. However, little is known about how the vector aggregation step affects the generation quality. In this study, we revisit the commonly used simple average approach by examining the latent space and generated summaries. We found that text autoencoders tend to generate overly generic summaries from simply averaged latent vectors due to an unexpected $L_2$-norm shrinkage in the aggregated latent vectors, which we refer to as summary vector degeneration. To overcome this issue, we develop a framework Coop, which searches input combinations for the latent vector aggregation using input-output word overlap. Experimental results show that Coop successfully alleviates the summary vector degeneration issue and establishes new state-of-the-art performance on two opinion summarization benchmarks. Code is available at \url{https://github.com/megagonlabs/coop}.
Human-Centric Issues in eHealth App Development and Usage: A Preliminary Assessment
Venue: arXiv cs.SE 2021
Year: 2021

Health-related mobile applications are known as eHealth apps. These apps make people more aware of their health, help during critical situations, provide home-based disease management, and monitor/support personalized care through sensing/interaction. eHealth app usage is rapidly increasing with a large number of new apps being developed. Unfortunately, many eHealth apps do not successfully adopt Human-Centric Issues (HCI) in the app development process and its deployment stages, leading them to become ineffective and not inclusive of diverse end-users. This paper provides an initial assessment of key human factors related to eHealth apps by literature review, existing guidelines analysis, and user studies. Preliminary results suggest that Usability, Accessibility, Reliability, Versatility, and User Experience are essential HCIs for eHealth apps, and need further attention from researchers and practitioners. Therefore, outcomes of this research will look to amend support for users, developers, and stakeholders of eHealth apps in the form of improved actionable guidelines, best practice examples, and evaluation techniques. The research also aims to trial the proposed solutions on real-world projects.
Semi matrix-free twogrid shifted Laplacian preconditioner for the Helmholtz equation with near optimal shifts
Venue: arXiv cs.CE 2021
Year: 2021

Due to its significance in terms of wave phenomena a considerable effort has been put into the design of preconditioners for the Helmholtz equation. One option to derive a preconditioner is to apply a multigrid method on a shifted operator. In such an approach, the wavenumber is shifted by some imaginary value. This step is motivated by the observation that the shifted problem can be more efficiently handled by iterative solvers when compared to the standard Helmholtz equation. However, up to now, it is not obvious what the best strategy for the choice of the shift parameter is. It is well known that a good shift parameter depends sensitively on the wavenumber and the discretization parameters such as the order and the mesh size. Therefore, we study the choice of a near optimal complex shift such that an FGMRES solver converges with fewer iterations. Our goal is to provide a map which returns the near optimal shift for the preconditioner depending on the wavenumber and the mesh size. In order to compute this map, a data driven approach is considered: We first generate many samples, and in a second step, we perform a nonlinear regression on this data. With this representative map, the near optimal shift can be obtained by a simple evaluation. Our preconditioner is based on a twogrid V-cycle applied to the shifted problem, allowing us to implement a semi matrix-free method. The performance of our preconditioned FGMRES solver is illustrated by several benchmark problems with heterogeneous wavenumbers in two and three space dimensions.
Uplink Coverage in Heterogeneous mmWave Cellular Networks with Clustered Users
Venue: arXiv cs.IT 2021
Year: 2021

A K-tier heterogeneous mmWave uplink cellular network with clustered user equipments (UEs) is considered in this paper. In particular, UEs are assumed to be clustered around small-cell base stations (BSs) according to a Gaussian distribution, leading to the Thomas cluster process based modeling. Specific and practical line-of-sight (LOS) and non-line-of-sight (NLOS) models are adopted with different parameters for different tiers. The probability density functions (PDFs) and complementary cumulative distribution functions (CCDFs) of different distances from UEs to BSs are characterized. Coupled association strategy and largest long-term averaged biased received power criterion are considered, and general expressions for association probabilities are provided. Following the identification of the association probabilities, the Laplace transforms of the inter-cell interference and the intra-cluster interference are characterized. Using tools from stochastic geometry, general expressions of the SINR coverage probability are provided. As extensions, fractional power control is incorporated into the analysis, tractable closed-form expressions are provided for special cases, and average ergodic spectral efficiency is analyzed. Via numerical and simulation results, analytical characterizations are confirmed and the impact of key system and network parameters on the performance is identified.
ASPER: Attention-based Approach to Extract Syntactic Patterns denoting Semantic Relations in Sentential Context
Venue: arXiv cs.CL 2021
Year: 2021

Semantic relationships, such as hyponym-hypernym, cause-effect, meronym-holonym etc. between a pair of entities in a sentence are usually reflected through syntactic patterns. Automatic extraction of such patterns benefits several downstream tasks, including, entity extraction, ontology building, and question answering. Unfortunately, automatic extraction of such patterns has not yet received much attention from NLP and information retrieval researchers. In this work, we propose an attention-based supervised deep learning model, ASPER, which extracts syntactic patterns between entities exhibiting a given semantic relation in the sentential context. We validate the performance of ASPER on three distinct semantic relations -- hyponym-hypernym, cause-effect, and meronym-holonym on six datasets. Experimental results show that for all these semantic relations, ASPER can automatically identify a collection of syntactic patterns reflecting the existence of such a relation between a pair of entities in a sentence. In comparison to the existing methodologies of syntactic pattern extraction, ASPER's performance is substantially superior.
Hierarchical Image Peeling: A Flexible Scale-space Filtering Framework
Venue: arXiv cs.CV 2021
Year: 2021

The importance of hierarchical image organization has been witnessed by a wide spectrum of applications in computer vision and graphics. Different from image segmentation with the spatial whole-part consideration, this work designs a modern framework for disassembling an image into a family of derived signals from a scale-space perspective. Specifically, we first offer a formal definition of image disassembly. Then, by concerning desired properties, such as peeling hierarchy and structure preservation, we convert the original complex problem into a series of two-component separation sub-problems, significantly reducing the complexity. The proposed framework is flexible to both supervised and unsupervised settings. A compact recurrent network, namely hierarchical image peeling net, is customized to efficiently and effectively fulfill the task, which is about 3.5Mb in size, and can handle 1080p images in more than 60 fps per recurrence on a GTX 2080Ti GPU, making it attractive for practical use. Both theoretical findings and experimental results are provided to demonstrate the efficacy of the proposed framework, reveal its superiority over other state-of-the-art alternatives, and show its potential to various applicable scenarios. Our code is available at \url{https://github.com/ForawardStar/HIPe}.
Conversational Question Answering over Knowledge Graphs with Transformer and Graph Attention Networks
Venue: arXiv cs.CL 2021
Year: 2021

This paper addresses the task of (complex) conversational question answering over a knowledge graph. For this task, we propose LASAGNE (muLti-task semAntic parSing with trAnsformer and Graph atteNtion nEtworks). It is the first approach, which employs a transformer architecture extended with Graph Attention Networks for multi-task neural semantic parsing. LASAGNE uses a transformer model for generating the base logical forms, while the Graph Attention model is used to exploit correlations between (entity) types and predicates to produce node representations. LASAGNE also includes a novel entity recognition module which detects, links, and ranks all relevant entities in the question context. We evaluate LASAGNE on a standard dataset for complex sequential question answering, on which it outperforms existing baseline averages on all question types. Specifically, we show that LASAGNE improves the F1-score on eight out of ten question types; in some cases, the increase in F1-score is more than 20% compared to the state of the art.
STOPPAGE: Spatio-temporal Data Driven Cloud-Fog-Edge Computing Framework for Pandemic Monitoring and Management
Venue: arXiv cs.LG 2021
Year: 2021

Several researches and evidence show the increasing likelihood of pandemics (large-scale outbreaks of infectious disease) which has far reaching sequels in all aspects of human lives ranging from rapid mortality rates to economic and social disruption across the world. In the recent time, COVID-19 (Coronavirus Disease 2019) pandemic disrupted normal human lives, and motivated by the urgent need of combating COVID-19, researchers have put significant efforts in modelling and analysing the disease spread patterns for effective preventive measures (in addition to developing pharmaceutical solutions, like vaccine). In this regards, it is absolutely necessary to develop an analytics framework by extracting and incorporating the knowledge of heterogeneous datasources to deliver insights in improving administrative policy and enhance the preparedness to combat the pandemic. Specifically, human mobility, travel history and other transport statistics have significant impacts on the spread of any infectious disease. In this direction, this paper proposes a spatio-temporal knowledge mining framework, named STOPPAGE to model the impact of human mobility and other contextual information over large geographic area in different temporal scales. The framework has two major modules: (i) Spatio-temporal data and computing infrastructure using fog/edge based architecture; and (ii) Spatio-temporal data analytics module to efficiently extract knowledge from heterogeneous data sources. Typically, we develop a Pandemic-knowledge graph to discover correlations among mobility information and disease spread, a deep learning architecture to predict the next hot-spot zones; and provide necessary support in home-health monitoring utilizing Femtolet and fog/edge based solutions. The experimental evaluations on real-life datasets related to COVID-19 in India illustrate the efficacy of the proposed methods.
Marked for Disruption: Tracing the Evolution of Malware Delivery Operations Targeted for Takedown
Venue: arXiv cs.CR 2021
Year: 2021

The malware and botnet phenomenon is among the most significant threats to cybersecurity today. Consequently, law enforcement agencies, security companies, and researchers are constantly seeking to disrupt these malicious operations through so-called takedown counter-operations. Unfortunately, the success of these takedowns is mixed. Furthermore, very little is understood as to how botnets and malware delivery operations respond to takedown attempts. We present a comprehensive study of three malware delivery operations that were targeted for takedown in 2015-16 using global download metadata provided by a major security company. In summary, we found that: (1) Distributed delivery architectures were commonly used, indicating the need for better security hygiene and coordination by the (ab)used service providers. (2) A minority of malware binaries were responsible for the majority of download activity, suggesting that detecting these "super binaries" would yield the most benefit to the security community. (3) The malware operations exhibited displacing and defiant behaviours following their respective takedown attempts. We argue that these "predictable" behaviours could be factored into future takedown strategies. (4) The malware operations also exhibited previously undocumented behaviours, such as Dridex dropping competing brands of malware, or Dorkbot and Upatre heavily relying on upstream dropper malware. These "unpredictable" behaviours indicate the need for researchers to use better threat-monitoring techniques.