pwc-archive/papers-with-abstracts huggingface dataset:
Face recognition with small and large size databases
Authors: Josep Roure-Alcobé, Marcos Faundez-Zanuy
Date: 2022-04-16 00:00:00
Tasks: Face Recognition

This paper presents experimental results using the ORL (40 people) and FERET (994 people) databases. The ORL database can be useful for securing applications where few users attempting to access are expected. This is the case, for instance, of a PDA or PC where the password is the face of the user. On the other hand, the FERET database is useful for studying those situations where the number of authorized users is around a thousand people.
Machine Learning-Based Automated Thermal Comfort Prediction: Integration of Low-Cost Thermal and Visual Cameras for Higher Accuracy
Authors: Roshanak Ashrafi, Mona Azarbayjani, Hamed Tabkhi
Date: 2022-04-14 00:00:00

Recent research is trying to leverage occupants' demand in the building's control loop to consider individuals' well-being and the buildings' energy savings. To that end, a real-time feedback system is needed to provide data about occupants' comfort conditions that can be used to control the building's heating, cooling, and air conditioning (HVAC) system. The emergence of thermal imaging techniques provides an excellent opportunity for contactless data gathering with no interruption in occupant conditions and activities. There is increasing attention to infrared thermal camera usage in public buildings because of their non-invasive quality in reading the human skin temperature. However, the state-of-the-art methods need additional modifications to become more reliable. To capitalize potentials and address some existing limitations, new solutions are required to bring a more holistic view toward non-intrusive thermal scanning by leveraging the benefit of machine learning and image processing. This research implements an automated approach to collect and register simultaneous thermal and visual images and read the facial temperature in different regions. This paper also presents two additional investigations. First, through utilizing IButton wearable thermal sensors on the forehead area, we investigate the reliability of an in-expensive thermal camera (FLIR Lepton) in reading the skin temperature. Second, by studying the false-color version of thermal images, we look into the possibility of non-radiometric thermal images for predicting personalized thermal comfort. The results shows the strong performance of Random Forest and K-Nearest Neighbor prediction algorithms in predicting personalized thermal comfort. In addition, we have found that non-radiometric images can also indicate thermal comfort when the algorithm is trained with larger amounts of data.
CapillaryX: A Software Design Pattern for Analyzing Medical Images in Real-time using Deep Learning
Authors: Maged Abdalla Helmy Abdou, Paulo Ferreira, Eric Jul, Tuyen Trung Truong
Date: 2022-04-13 00:00:00

Recent advances in digital imaging, e.g., increased number of pixels captured, have meant that the volume of data to be processed and analyzed from these images has also increased. Deep learning algorithms are state-of-the-art for analyzing such images, given their high accuracy when trained with a large data volume of data. Nevertheless, such analysis requires considerable computational power, making such algorithms time- and resource-demanding. Such high demands can be met by using third-party cloud service providers. However, analyzing medical images using such services raises several legal and privacy challenges and does not necessarily provide real-time results. This paper provides a computing architecture that locally and in parallel can analyze medical images in real-time using deep learning thus avoiding the legal and privacy challenges stemming from uploading data to a third-party cloud provider. To make local image processing efficient on modern multi-core processors, we utilize parallel execution to offset the resource-intensive demands of deep neural networks. We focus on a specific medical-industrial case study, namely the quantifying of blood vessels in microcirculation images for which we have developed a working system. It is currently used in an industrial, clinical research setting as part of an e-health application. Our results show that our system is approximately 78% faster than its serial system counterpart and 12% faster than a master-slave parallel system architecture.
Investigating Temporal Convolutional Neural Networks for Satellite Image Time Series Classification: A survey
Authors: James Brock, Zahraa S. Abdallah
Date: 2022-04-13 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Classification

Satellite Image Time Series (SITS) of the Earth's surface provide detailed land cover maps, with their quality in the spatial and temporal dimensions consistently improving. These image time series are integral for developing systems that aim to produce accurate, up-to-date land cover maps of the Earth's surface. Applications are wide-ranging, with notable examples including ecosystem mapping, vegetation process monitoring and anthropogenic land-use change tracking. Recently proposed methods for SITS classification have demonstrated respectable merit, but these methods tend to lack native mechanisms that exploit the temporal dimension of the data; commonly resulting in extensive data pre-processing contributing to prohibitively long training times. To overcome these shortcomings, Temporal CNNs have recently been employed for SITS classification tasks with encouraging results. This paper seeks to survey this method against a plethora of other contemporary methods for SITS classification to validate the existing findings in recent literature. Comprehensive experiments are carried out on two benchmark SITS datasets with the results demonstrating that Temporal CNNs display a superior performance to the comparative benchmark algorithms across both studied datasets, achieving accuracies of 95.0\% and 87.3\% respectively. Investigations into the Temporal CNN architecture also highlighted the non-trivial task of optimising the model for a new dataset.
3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition
Authors: Pierre-Etienne Martin, J Benois-Pineau, R Péteri, A Zemmari, J Morlier
Date: 2022-04-13 00:00:00
Tasks: Action Recognition, Classification, Gesture Recognition, Segmentation, Video Segmentation, Video Semantic Segmentation

3D convolutional networks is a good means to perform tasks such as video segmentation into coherent spatio-temporal chunks and classification of them with regard to a target taxonomy. In the chapter we are interested in the classification of continuous video takes with repeatable actions, such as strokes of table tennis. Filmed in a free marker less ecological environment, these videos represent a challenge from both segmentation and classification point of view. The 3D convnets are an efficient tool for solving these problems with window-based approaches.
Accelerating Inhibitor Discovery With A Deep Generative Foundation Model: Validation for SARS-CoV-2 Drug Targets
Authors: Vijil Chenthamarakshan, Samuel C. Hoffman, C. David Owen, Petra Lukacik, Claire Strain-Damerell, Daren Fearon, Tika R. Malla, Anthony Tumber, Christopher J. Schofield, Helen M. E. Duyvesteyn, Wanwisa Dejnirattisai, Loic Carrique, Thomas S. Walter, Gavin R. Screaton, Tetiana Matviiuk, Aleksandra Mojsilovic, Jason Crain, Martin A. Walsh, David I. Stuart, Payel Das
Date: 2022-04-19 00:00:00

The discovery of novel inhibitor molecules for emerging drug-target proteins is widely acknowledged as a challenging inverse design problem: Exhaustive exploration of the vast chemical search space is impractical, especially when the target structure or active molecules are unknown. Here we validate experimentally the broad utility of a deep generative framework trained at-scale on protein sequences, small molecules, and their mutual interactions -- that is unbiased toward any specific target. As demonstrators, we consider two dissimilar and relevant SARS-CoV-2 targets: the main protease and the spike protein (receptor binding domain, RBD). To perform target-aware design of novel inhibitor molecules, a protein sequence-conditioned sampling on the generative foundation model is performed. Despite using only the target sequence information, and without performing any target-specific adaptation of the generative model, micromolar-level inhibition was observed in in vitro experiments for two candidates out of only four synthesized for each target. The most potent spike RBD inhibitor also exhibited activity against several variants in live virus neutralization assays. These results therefore establish that a single, broadly deployable generative foundation model for accelerated hit discovery is effective and efficient, even in the most general case where neither target structure nor binder information is available.
A stochastic Stein Variational Newton method
Authors: Alex Leviyev, Joshua Chen, Yifei Wang, Omar Ghattas, Aaron Zimmerman
Date: 2022-04-19 00:00:00
Tasks: Bayesian Inference

Stein variational gradient descent (SVGD) is a general-purpose optimization-based sampling algorithm that has recently exploded in popularity, but is limited by two issues: it is known to produce biased samples, and it can be slow to converge on complicated distributions. A recently proposed stochastic variant of SVGD (sSVGD) addresses the first issue, producing unbiased samples by incorporating a special noise into the SVGD dynamics such that asymptotic convergence is guaranteed. Meanwhile, Stein variational Newton (SVN), a Newton-like extension of SVGD, dramatically accelerates the convergence of SVGD by incorporating Hessian information into the dynamics, but also produces biased samples. In this paper we derive, and provide a practical implementation of, a stochastic variant of SVN (sSVN) which is both asymptotically correct and converges rapidly. We demonstrate the effectiveness of our algorithm on a difficult class of test problems -- the Hybrid Rosenbrock density -- and show that sSVN converges using three orders of magnitude fewer gradient evaluations of the log likelihood than its stochastic SVGD counterpart. Our results show that sSVN is a promising approach to accelerating high-precision Bayesian inference tasks with modest-dimension, $d\sim\mathcal{O}(10)$.
CPU- and GPU-based Distributed Sampling in Dirichlet Process Mixtures for Large-scale Analysis
Authors: Or Dinari, Raz Zamir, John W. Fisher III, Oren Freifeld
Date: 2022-04-19 00:00:00
Tasks: CPU, GPU

In the realm of unsupervised learning, Bayesian nonparametric mixture models, exemplified by the Dirichlet Process Mixture Model (DPMM), provide a principled approach for adapting the complexity of the model to the data. Such models are particularly useful in clustering tasks where the number of clusters is unknown. Despite their potential and mathematical elegance, however, DPMMs have yet to become a mainstream tool widely adopted by practitioners. This is arguably due to a misconception that these models scale poorly as well as the lack of high-performance (and user-friendly) software tools that can handle large datasets efficiently. In this paper we bridge this practical gap by proposing a new, easy-to-use, statistical software package for scalable DPMM inference. More concretely, we provide efficient and easily-modifiable implementations for high-performance distributed sampling-based inference in DPMMs where the user is free to choose between either a multiple-machine, multiple-core, CPU implementation (written in Julia) and a multiple-stream GPU implementation (written in CUDA/C++). Both the CPU and GPU implementations come with a common (and optional) python wrapper, providing the user with a single point of entry with the same interface. On the algorithmic side, our implementations leverage a leading DPMM sampler from (Chang and Fisher III, 2013). While Chang and Fisher III's implementation (written in MATLAB/C++) used only CPU and was designed for a single multi-core machine, the packages we proposed here distribute the computations efficiently across either multiple multi-core machines or across mutiple GPU streams. This leads to speedups, alleviates memory and storage limitations, and lets us fit DPMMs to significantly larger datasets and of higher dimensionality than was possible previously by either (Chang and Fisher III, 2013) or other DPMM methods.
CodexDB: Generating Code for Processing SQL Queries using GPT-3 Codex
Authors: Immanuel Trummer
Date: 2022-04-19 00:00:00

CodexDB is an SQL processing engine whose internals can be customized via natural language instructions. CodexDB is based on OpenAI's GPT-3 Codex model which translates text into code. It is a framework on top of GPT-3 Codex that decomposes complex SQL queries into a series of simple processing steps, described in natural language. Processing steps are enriched with user-provided instructions and descriptions of database properties. Codex translates the resulting text into query processing code. An early prototype of CodexDB is able to generate correct code for a majority of queries of the WikiSQL benchmark and can be customized in various ways.
GestureLens: Visual Analysis of Gestures in Presentation Videos
Authors: Haipeng Zeng, Xingbo Wang, Yong Wang, Aoyu Wu, Ting Chuen Pong, Huamin Qu
Date: 2022-04-19 00:00:00

Appropriate gestures can enhance message delivery and audience engagement in both daily communication and public presentations. In this paper, we contribute a visual analytic approach that assists professional public speaking coaches in improving their practice of gesture training through analyzing presentation videos. Manually checking and exploring gesture usage in the presentation videos is often tedious and time-consuming. There lacks an efficient method to help users conduct gesture exploration, which is challenging due to the intrinsically temporal evolution of gestures and their complex correlation to speech content. In this paper, we propose GestureLens, a visual analytics system to facilitate gesture-based and content-based exploration of gesture usage in presentation videos. Specifically, the exploration view enables users to obtain a quick overview of the spatial and temporal distributions of gestures. The dynamic hand movements are firstly aggregated through a heatmap in the gesture space for uncovering spatial patterns, and then decomposed into two mutually perpendicular timelines for revealing temporal patterns. The relation view allows users to explicitly explore the correlation between speech content and gestures by enabling linked analysis and intuitive glyph designs. The video view and dynamic view show the context and overall dynamic movement of the selected gestures, respectively. Two usage scenarios and expert interviews with professional presentation coaches demonstrate the effectiveness and usefulness of GestureLens in facilitating gesture exploration and analysis of presentation videos.
Antipatterns in Software Classification Taxonomies
Authors: Cezar Sas, Andrea Capiluppi
Date: 2022-04-19 00:00:00
Tasks: Classification

Empirical results in software engineering have long started to show that findings are unlikely to be applicable to all software systems, or any domain: results need to be evaluated in specified contexts, and limited to the type of systems that they were extracted from. This is a known issue, and requires the establishment of a classification of software types. This paper makes two contributions: the first is to evaluate the quality of the current software classifications landscape. The second is to perform a case study showing how to create a classification of software types using a curated set of software systems. Our contributions show that existing, and very likely even new, classification attempts are deemed to fail for one or more issues, that we named as the `antipatterns' of software classification tasks. We collected 7 of these antipatterns that emerge from both our case study, and the existing classifications. These antipatterns represent recurring issues in a classification, so we discuss practical ways to help researchers avoid these pitfalls. It becomes clear that classification attempts must also face the daunting task of formulating a taxonomy of software types, with the objective of establishing a hierarchy of categories in a classification.
Compressed Empirical Measures (in finite dimensions)
Authors: Steffen Grünewälder
Date: 2022-04-19 00:00:00

We study approaches for compressing the empirical measure in the context of finite dimensional reproducing kernel Hilbert spaces (RKHSs). In this context, the empirical measure is contained within a natural convex set and can be approximated using convex optimization methods. Such an approximation gives rise to a coreset of data points. A key quantity that controls how large such a coreset has to be is the size of the largest ball around the empirical measure that is contained within the empirical convex set. The bulk of our work is concerned with deriving high probability lower bounds on the size of such a ball under various conditions and in various settings: we show how conditions on the density of the data and the kernel function can be used to infer such lower bounds; we further develop an approach that uses a lower bound on the smallest eigenvalue of a covariance operator to provide lower bounds on the size of such a ball; we extend the approach to approximate covariance operators and we show how it can be used in the context of kernel ridge regression. We also derive compression guarantees when standard algorithms like the conditional gradient method are used and we discuss variations of such algorithms to improve the runtime of these standard algorithms. We conclude with a construction of an infinite dimensional RKHS for which the compression is poor, highlighting some of the difficulties one faces when trying to move to infinite dimensional RKHSs.
Rumor Detection with Self-supervised Learning on Texts and Social Graph
Authors: Yuan Gao, Xiang Wang, Xiangnan He, Huamin Feng, Yongdong Zhang
Date: 2022-04-19 00:00:00
Tasks: Self-Supervised Learning

Rumor detection has become an emerging and active research field in recent years. At the core is to model the rumor characteristics inherent in rich information, such as propagation patterns in social network and semantic patterns in post content, and differentiate them from the truth. However, existing works on rumor detection fall short in modeling heterogeneous information, either using one single information source only (e.g. social network, or post content) or ignoring the relations among multiple sources (e.g. fusing social and content features via simple concatenation). Therefore, they possibly have drawbacks in comprehensively understanding the rumors, and detecting them accurately. In this work, we explore contrastive self-supervised learning on heterogeneous information sources, so as to reveal their relations and characterize rumors better. Technically, we supplement the main supervised task of detection with an auxiliary self-supervised task, which enriches post representations via post self-discrimination. Specifically, given two heterogeneous views of a post (i.e. representations encoding social patterns and semantic patterns), the discrimination is done by maximizing the mutual information between different views of the same post compared to that of other posts. We devise cluster-wise and instance-wise approaches to generate the views and conduct the discrimination, considering different relations of information sources. We term this framework as Self-supervised Rumor Detection (SRD). Extensive experiments on three real-world datasets validate the effectiveness of SRD for automatic rumor detection on social media.
System Analysis for Responsible Design of Modern AI/ML Systems
Authors: Virginia H. Goodwin, Rajmonda S. Caceres
Date: 2022-04-19 00:00:00

The irresponsible use of ML algorithms in practical settings has received a lot of deserved attention in the recent years. We posit that the traditional system analysis perspective is needed when designing and implementing ML algorithms and systems. Such perspective can provide a formal way for evaluating and enabling responsible ML practices. In this paper, we review components of the System Analysis methodology and highlight how they connect and enable responsible practices of ML design.
Radio Galaxy Zoo: Using semi-supervised learning to leverage large unlabelled data-sets for radio galaxy classification under data-set shift
Authors: Inigo V. Slijepcevic, Anna M. M. Scaife, Mike Walmsley, Micah Bowles, Ivy Wong, Stanislav S. Shabala, Hongming Tang
Date: 2022-04-19 00:00:00
Tasks: Benchmarking, Classification

In this work we examine the classification accuracy and robustness of a state-of-the-art semi-supervised learning (SSL) algorithm applied to the morphological classification of radio galaxies. We test if SSL with fewer labels can achieve test accuracies comparable to the supervised state-of-the-art and whether this holds when incorporating previously unseen data. We find that for the radio galaxy classification problem considered, SSL provides additional regularisation and outperforms the baseline test accuracy. However, in contrast to model performance metrics reported on computer science benchmarking data-sets, we find that improvement is limited to a narrow range of label volumes, with performance falling off rapidly at low label volumes. Additionally, we show that SSL does not improve model calibration, regardless of whether classification is improved. Moreover, we find that when different underlying catalogues drawn from the same radio survey are used to provide the labelled and unlabelled data-sets required for SSL, a significant drop in classification performance is observered, highlighting the difficulty of applying SSL techniques under dataset shift. We show that a class-imbalanced unlabelled data pool negatively affects performance through prior probability shift, which we suggest may explain this performance drop, and that using the Frechet Distance between labelled and unlabelled data-sets as a measure of data-set shift can provide a prediction of model performance, but that for typical radio galaxy data-sets with labelled sample volumes of O(1000), the sample variance associated with this technique is high and the technique is in general not sufficiently robust to replace a train-test cycle.
Making Progress Based on False Discoveries
Authors: Roi Livni
Date: 2022-04-19 00:00:00

The study of adaptive data analysis examines how many statistical queries can be answered accurately using a fixed dataset while avoiding false discoveries (statistically inaccurate answers). In this paper, we tackle a question that precedes the field of study: Is data only valuable when it provides accurate answers to statistical queries? To answer this question, we use Stochastic Convex Optimization as a case study. In this model, algorithms are considered as analysts who query an estimate of the gradient of a noisy function at each iteration and move towards its minimizer. It is known that $O(1/\epsilon^2)$ examples can be used to minimize the objective function, but none of the existing methods depend on the accuracy of the estimated gradients along the trajectory. Therefore, we ask: How many samples are needed to minimize a noisy convex function if we require $\epsilon$-accurate estimates of $O(1/\epsilon^2)$ gradients? Or, might it be that inaccurate gradient estimates are \emph{necessary} for finding the minimum of a stochastic convex function at an optimal statistical rate? We provide two partial answers to this question. First, we show that a general analyst (queries that may be maliciously chosen) requires $\Omega(1/\epsilon^3)$ samples, ruling out the possibility of a foolproof mechanism. Second, we show that, under certain assumptions on the oracle, $\tilde \Omega(1/\epsilon^{2.5})$ samples are necessary for gradient descent to interact with the oracle. Our results are in contrast to classical bounds that show that $O(1/\epsilon^2)$ samples can optimize the population risk to an accuracy of $O(\epsilon)$, but with spurious gradients.