pwc-archive/papers-with-abstracts huggingface dataset:
AI Research Considerations for Human Existential Safety (ARCHES)
Authors: Andrew Critch, David Krueger
Date: 2020-05-30 00:00:00

Framed in positive terms, this report examines how technical AI research might be steered in a manner that is more attentive to humanity's long-term prospects for survival as a species. In negative terms, we ask what existential risks humanity might face from AI development in the next century, and by what principles contemporary technical research might be directed to address those risks. A key property of hypothetical AI technologies is introduced, called \emph{prepotence}, which is useful for delineating a variety of potential existential risks from artificial intelligence, even as AI paradigms might shift. A set of \auxref{dirtot} contemporary research \directions are then examined for their potential benefit to existential safety. Each research direction is explained with a scenario-driven motivation, and examples of existing work from which to build. The research directions present their own risks and benefits to society that could occur at various scales of impact, and in particular are not guaranteed to benefit existential safety if major developments in them are deployed without adequate forethought and oversight. As such, each direction is accompanied by a consideration of potentially negative side effects.
IMUTube: Automatic Extraction of Virtual on-body Accelerometry from Video for Human Activity Recognition
Authors: Hyeokhyen Kwon, Catherine Tong, Harish Haresamudram, Yan Gao, Gregory D. Abowd, Nicholas D. Lane, Thomas Ploetz
Date: 2020-05-29 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

The lack of large-scale, labeled data sets impedes progress in developing robust and generalized predictive models for on-body sensor-based human activity recognition (HAR). Labeled data in human activity recognition is scarce and hard to come by, as sensor data collection is expensive, and the annotation is time-consuming and error-prone. To address this problem, we introduce IMUTube, an automated processing pipeline that integrates existing computer vision and signal processing techniques to convert videos of human activity into virtual streams of IMU data. These virtual IMU streams represent accelerometry at a wide variety of locations on the human body. We show how the virtually-generated IMU data improves the performance of a variety of models on known HAR datasets. Our initial results are very promising, but the greater promise of this work lies in a collective approach by the computer vision, signal processing, and activity recognition communities to extend this work in ways that we outline. This should lead to on-body, sensor-based HAR becoming yet another success story in large-dataset breakthroughs in recognition.
Machine Learning Systems for Intelligent Services in the IoT: A Survey
Authors: Wiebke Toussaint, Aaron Yi Ding
Date: 2020-05-29 00:00:00
Tasks: BIG-bench Machine Learning, Survey

Machine learning (ML) technologies are emerging in the Internet of Things (IoT) to provision intelligent services. This survey moves beyond existing ML algorithms and cloud-driven design to investigate the less-explored systems, scaling and socio-technical aspects for consolidating ML and IoT. It covers the latest developments (up to 2020) on scaling and distributing ML across cloud, edge, and IoT devices. With a multi-layered framework to classify and illuminate system design choices, this survey exposes fundamental concerns of developing and deploying ML systems in the rising cloud-edge-device continuum in terms of functionality, stakeholder alignment and trustworthiness.
Inverse Estimation of Elastic Modulus Using Physics-Informed Generative Adversarial Networks
Authors: James E. Warner, Julian Cuevas, Geoffrey F. Bomarito, Patrick E. Leser, William P. Leser
Date: 2020-05-20 00:00:00

While standard generative adversarial networks (GANs) rely solely on training data to learn unknown probability distributions, physics-informed GANs (PI-GANs) encode physical laws in the form of stochastic partial differential equations (PDEs) using auto differentiation. By relating observed data to unobserved quantities of interest through PDEs, PI-GANs allow for the estimation of underlying probability distributions without their direct measurement (i.e. inverse problems). The scalable nature of GANs allows high-dimensional, spatially-dependent probability distributions (i.e., random fields) to be inferred, while incorporating prior information through PDEs allows the training datasets to be relatively small. In this work, PI-GANs are demonstrated for the application of elastic modulus estimation in mechanical testing. Given measured deformation data, the underlying probability distribution of spatially-varying elastic modulus (stiffness) is learned. Two feed-forward deep neural network generators are used to model the deformation and material stiffness across a two dimensional domain. Wasserstein GANs with gradient penalty are employed for enhanced stability. In the absence of explicit training data, it is demonstrated that the PI-GAN learns to generate realistic, physically-admissible realizations of material stiffness by incorporating the PDE that relates it to the measured deformation. It is shown that the statistics (mean, standard deviation, point-wise distributions, correlation length) of these generated stiffness samples have good agreement with the true distribution.
Adaptive Reinforcement Learning through Evolving Self-Modifying Neural Networks
Authors: Samuel Schmidgall
Date: 2020-05-22 00:00:00
Tasks: Meta-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The adaptive learning capabilities seen in biological neural networks are largely a product of the self-modifying behavior emerging from online plastic changes in synaptic connectivity. Current methods in Reinforcement Learning (RL) only adjust to new interactions after reflection over a specified time interval, preventing the emergence of online adaptivity. Recent work addressing this by endowing artificial neural networks with neuromodulated plasticity have been shown to improve performance on simple RL tasks trained using backpropagation, but have yet to scale up to larger problems. Here we study the problem of meta-learning in a challenging quadruped domain, where each leg of the quadruped has a chance of becoming unusable, requiring the agent to adapt by continuing locomotion with the remaining limbs. Results demonstrate that agents evolved using self-modifying plastic networks are more capable of adapting to complex meta-learning learning tasks, even outperforming the same network updated using gradient-based algorithms while taking less time to train.
3D geometric moment invariants from the point of view of the classical invariant theory
Authors: Leonid Bedratyuk
Date: 2020-05-23 00:00:00

The aim of this paper is to clear up the problem of the connection between the 3D geometric moments invariants and the invariant theory, considering a problem of describing of the 3D geometric moments invariants as a problem of the classical invariant theory. Using the remarkable fact that the groups $SO(3)$ and $SL(2)$ are locally isomorphic, we reduced the problem of deriving 3D geometric moments invariants to the well-known problem of the classical invariant theory. We give a precise statement of the 3D geometric invariant moments computation, introducing the notions of the algebras of simultaneous 3D geometric moment invariants, and prove that they are isomorphic to the algebras of joint $SL(2)$-invariants of several binary forms. To simplify the calculating of the invariants we proceed from an action of Lie group $SO(3)$ to an action of its Lie algebra $\mathfrak{sl}_2$. The author hopes that the results will be useful to the researchers in the fields of image analysis and pattern recognition.
Experiments on route choice set generation using a large GPS trajectory set
Authors: Rui Yao, Shlomo Bekhor
Date: 2020-05-22 00:00:00

Several route choice models developed in the literature were based on a relatively small number of observations. With the extensive use of tracking devices in recent surveys, there is a possibility to obtain insights with respect to the traveler's choice behavior. In this paper, different path generation algorithms are evaluated using a large GPS trajectory dataset. The dataset contains 6,000 observations from Tel-Aviv metropolitan area. An initial analysis is performed by generating a single route based on the shortest path. Almost 60% percent of the 6,000 observations can be covered (assuming a threshold of 80% overlap) using a single path. This result significantly contrasts previous literature findings. Link penalty, link elimination, simulation and via-node methods are applied to generate route sets, and the consistency of the algorithms are compared. A modified link penalty method, which accounts for preference of using higher hierarchical roads, provides a route set with 97% coverage (80% overlap threshold). The via-node method produces route set with satisfying coverage, and generates routes that are more heterogeneous (in terms number of links and routes ratio).
A fully recurrent feature extraction for single channel speech enhancement
Authors: Muhammed PV Shifas, Santelli Claudio, Vassilis Tsiaras, Yannis Stylianou
Date: 2020-06-09 00:00:00
Proceeding: arXiv 2020 6
Tasks: Speech Enhancement

Convolutional neural network (CNN) modules are widely being used to build high-end speech enhancement neural models. However, the feature extraction power of vanilla CNN modules has been limited by the dimensionality constraint of the convolution kernels that are integrated - thereby, they have limitations to adequately model the noise context information at the feature extraction stage. To this end, adding recurrency factor into the feature extracting CNN layers, we introduce a robust context-aware feature extraction strategy for single-channel speech enhancement. As shown, adding recurrency results in capturing the local statistics of noise attributes at the extracted features level and thus, the suggested model is effective in differentiating speech cues even at very noisy conditions. When evaluated against enhancement models using vanilla CNN modules, in unseen noise conditions, the suggested model with recurrency in the feature extraction layers has produced a segmental SNR (SSNR) gain of up to 1.5 dB, an improvement of 0.4 in subjective quality in the Mean Opinion Score scale, while the parameters to be optimized are reduced by 25%.
Single Image Haze Removal Using Dark Channel Prior
Authors: Kaiming He, Jian Sun, Xiaoou Tang
Date: 2010-09-09 00:00:00
Proceeding: IEEE Transactions on Pattern Analysis and Machine Intelligence 2010 9
Tasks: Single Image Haze Removal

In this paper, we propose a simple but effective image prior-dark channel prior to remove haze from a single input image. The dark channel prior is a kind of statistics of outdoor haze-free images. It is based on a key observation-most local patches in outdoor haze-free images contain some pixels whose intensity is very low in at least one color channel. Using this prior with the haze imaging model, we can directly estimate the thickness of the haze and recover a high-quality haze-free image. Results on a variety of hazy images demonstrate the power of the proposed prior. Moreover, a high-quality depth map can also be obtained as a byproduct of haze removal.
Instance Ranking and Numerosity Reduction Using Matrix Decomposition and Subspace Learning
Authors: Benyamin Ghojogh, Mark Crowley
Date: 2019-04-24 00:00:00
Proceeding: Canadian Conference on Artificial Intelligence 2019 4
Tasks: Clustering, Dimensionality Reduction, Matrix Factorization / Decomposition, Prototype Selection, regression

One way to deal with the ever increasing amount of available data for processing is to rank data instances by usefulness and reduce the dataset size. In this work, we introduce a framework to achieve this using matrix decomposition and subspace learning. Our central contribution is a novel similarity measure for data instances that uses the basis obtained from matrix decomposition of the dataset. Using this similarity measure, we propose several related algorithms for ranking data instances and performing numerosity reduction. We then validate the effectiveness of these algorithms for data reduction on several datasets for classification, regression, and clustering tasks.
Evaluating Semantic Feature Representations to Efficiently Detect Hate Intent on Social Media
Authors: Yasas Senarath, Hemant Purohit
Date: 2020-02-01 00:00:00
Proceeding: ICSC 2020 2
Tasks: General Classification, Hate Speech Detection

Detecting malicious intent behavior such as sharing hate speech has become an important challenge for social networking platforms. The method of automated hate speech detection for social media posts is often challenged by the complexity of capturing the context of the user expression with potential hate intent. We hypothesize that semantic features can help enrich the context representation of word senses in a social media post for machine learning algorithms. This paper presents a novel empirical study of diverse semantic features for hate speech classification task on social media posts. Specifically, we present an extensive empirical analysis, where we test the features of vector space model representation for corpus-based semantics, neural word embedding representation for distributional semantics, and declarative knowledge patterns from external knowledge base for domain semantics. Our experimental results show that ensembling the diverse feature representations improves the efficiency of hateful behavior classification in contrast to the case of a single type of feature representation. Results on two popular Twitter datasets for the hate speech detection task showed a consistent performance gain for the classification models that were based on the hybrid feature representation (absolute gain in F1 score up to 3.0%). The application of the proposed method of combining diverse feature representations can help in improving social media analytics systems for monitoring human behavior.
Recognizing Art Style Automatically in painting with deep learning
Authors: Adrian Lecoutre, Benjamin Negrevergne, Florian Yger
Date: 2017-12-21 00:00:00
Proceeding: Proceedings of the Ninth Asian Conference on Machine Learning 2017 12
Tasks: Artistic style classification, Deep Learning

The artistic style (or artistic movement) of a painting is a rich descriptor that captures both
visual and historical information about the painting. Correctly identifying the artistic style
of a paintings is crucial for indexing large artistic databases. In this paper, we investigate
the use of deep residual neural to solve the problem of detecting the artistic style of a
painting and outperform existing approaches by almost 10% on the Wikipaintings dataset
(for 25 dierent style). To achieve this result, the network is rst pre-trained on ImageNet,
and deeply retrained for artistic style. We empirically evaluate that to achieve the best
performance, one need to retrain about 20 layers. This suggests that the two tasks are as
similar as expected, and explain the previous success of hand crafted features. We also
demonstrate that the style detected on the Wikipaintings dataset are consistent with styles
detected on an independent dataset and describe a number of experiments we conducted
to validate this approach both qualitatively and quantitatively.
Speech intelligibility enhancement based on a non-causal Wavenet-like model
Authors: Muhammed Shifas PV, Vassilis Tsiaras, Yannis Stylianou
Date: 2018-09-02 00:00:00
Proceeding: Interspeech 2018 9

Low speech intelligibility in noisy listening conditions makes more difficult our communication with others. Various strate- gies have been suggested to modify a speech signal before it is presented in a noisy listening environment with the goal to increase its intelligibility. A state-of-the art approach, referred to as Spectral Shaping and Dynamic Range Compression (SS- DRC), relies on modifying spectral and temporal structure of the clean speech and has been shown to considerably improve the intelligibility of speech in noisy listening conditions. In this paper, we present a non-causal Wavenet-like model for mapping clean speech samples to samples generated by SSDRC. A suc- cessful non-linear mapping function has the potential to be used a) in improving the intelligibility of noisy speech and b) in the Wavenet-based speech synthesizers as a model based intelligi- bility improvement layer. Objective and subjective results show that the Wavenet-based mapping function is able to reproduce the intelligibility gains of SSDRC, while by far it improves the quality of the modified signal compared to the quality obtained by SSDRC.
Fine-tuning Convolutional Neural Networks for fine art classification
Authors: Eva Cetinic, Tomislav Lipic, Sonja Grgic
Date: 2018-12-30 00:00:00
Proceeding: Expert Systems with Applications 2018 12
Tasks: Artist classification, Artistic style classification, Classification, General Classification, Genre classification, image-classification, Image Classification, Object Recognition, Scene Recognition

The increasing availability of large digitized fine art collections opens new research perspectives in the intersection of artificial intelligence and art history. Motivated by the successful performance of Convolutional Neural Networks (CNN) for a wide variety of computer vision tasks, in this paper we explore their applicability for art-related image classification tasks. We perform extensive CNN fine-tuning experiments and consolidate in one place the results for five different art-related classification tasks on three large fine art datasets. Along with addressing the previously explored tasks of artist, genre, style and time period classification, we introduce a novel task of classifying artworks based on their association with a specific national artistic context. We present state-of-the-art classification results of the addressed tasks, signifying the impact of our method on computational analysis of art, as well as other image classification related research areas. Furthermore, in order to question transferability of deep representations across various source and target domains, we systematically compare the effects of domain-specific weight initialization by evaluating networks pre-trained for different tasks, varying from object and scene recognition to sentiment and memorability labelling. We show that fine-tuning networks pre-trained for scene recognition and sentiment prediction yields better results than fine-tuning networks pre-trained for object recognition. This novel outcome of our work suggests that the semantic correlation between different domains could be inherent in the CNN weights. Additionally, we address the practical applicability of our results by analysing different aspects of image similarity. We show that features derived from fine-tuned networks can be employed to retrieve images similar in either style or content, which can be used to enhance capabilities of search systems in different online art collections.
Understanding Human Hands in Contact at Internet Scale
Authors: Dandan Shan, Jiaqi Geng, Michelle Shu, David F. Fouhey
Date: 2020-06-11 00:00:00
Proceeding: CVPR 2020 6

Hands are the central means by which humans manipulate their world and being able to reliably extract hand state information from Internet videos of humans engaged in their hands has the potential to pave the way to systems that can learn from petabytes of video data. This paper proposes steps towards this by inferring a rich representation of hands engaged in interaction method that includes: hand location, side, contact state, and a box around the object in contact. To support this effort, we gather a large-scale dataset of hands in contact with objects consisting of 131 days of footage as well as a 100K annotated hand-contact video frame dataset. The learned model on this dataset can serve as a foundation for hand-contact understanding in videos. We quantitatively evaluate it both on its own and in service of predicting and learning from 3D meshes of human hands.
Disentangled Non-Local Neural Networks
Authors: Minghao Yin, Zhuliang Yao, Yue Cao, Xiu Li, Zheng Zhang, Stephen Lin, Han Hu
Date: 2020-06-11 00:00:00
Proceeding: ECCV 2020 8
Tasks: Action Recognition, object-detection, Object Detection, Semantic Segmentation

The non-local block is a popular module for strengthening the context modeling ability of a regular convolutional neural network. This paper first studies the non-local block in depth, where we find that its attention computation can be split into two terms, a whitened pairwise term accounting for the relationship between two pixels and a unary term representing the saliency of every pixel. We also observe that the two terms trained alone tend to model different visual clues, e.g. the whitened pairwise term learns within-region relationships while the unary term learns salient boundaries. However, the two terms are tightly coupled in the non-local block, which hinders the learning of each. Based on these findings, we present the disentangled non-local block, where the two terms are decoupled to facilitate learning for both terms. We demonstrate the effectiveness of the decoupled design on various tasks, such as semantic segmentation on Cityscapes, ADE20K and PASCAL Context, object detection on COCO, and action recognition on Kinetics.
VirTex: Learning Visual Representations from Textual Annotations
Authors: Karan Desai, Justin Johnson
Date: 2020-06-11 00:00:00
Proceeding: CVPR 2021 1
Tasks: General Classification, Image Captioning, image-classification, Image Classification, Instance Segmentation, object-detection, Object Detection, Semantic Segmentation

The de-facto approach to many vision tasks is to start from pretrained visual representations, typically learned via supervised training on ImageNet. Recent methods have explored unsupervised pretraining to scale to vast quantities of unlabeled images. In contrast, we aim to learn high-quality visual representations from fewer images. To this end, we revisit supervised pretraining, and seek data-efficient alternatives to classification-based pretraining. We propose VirTex -- a pretraining approach using semantically dense captions to learn visual representations. We train convolutional networks from scratch on COCO Captions, and transfer them to downstream recognition tasks including image classification, object detection, and instance segmentation. On all tasks, VirTex yields features that match or exceed those learned on ImageNet -- supervised or unsupervised -- despite using up to ten times fewer images.