pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning for Vision-Based Fall Detection System: Enhanced Optical Dynamic Flow
Authors: Sagar Chhetri, Abeer Alsadoon, Thair Al Dala in, P. W. C. Prasad, Tarik A. Rashid, Angelika Maag
Date: 2021-03-18 00:00:00
Tasks: Action Recognition, General Classification, Optical Flow Estimation

Accurate fall detection for the assistance of older people is crucial to reduce incidents of deaths or injuries due to falls. Meanwhile, a vision-based fall detection system has shown some significant results to detect falls. Still, numerous challenges need to be resolved. The impact of deep learning has changed the landscape of the vision-based system, such as action recognition. The deep learning technique has not been successfully implemented in vision-based fall detection systems due to the requirement of a large amount of computation power and the requirement of a large amount of sample training data. This research aims to propose a vision-based fall detection system that improves the accuracy of fall detection in some complex environments such as the change of light condition in the room. Also, this research aims to increase the performance of the pre-processing of video images. The proposed system consists of the Enhanced Dynamic Optical Flow technique that encodes the temporal data of optical flow videos by the method of rank pooling, which thereby improves the processing time of fall detection and improves the classification accuracy in dynamic lighting conditions. The experimental results showed that the classification accuracy of the fall detection improved by around 3% and the processing time by 40 to 50ms. The proposed system concentrates on decreasing the processing time of fall detection and improving classification accuracy. Meanwhile, it provides a mechanism for summarizing a video into a single image by using a dynamic optical flow technique, which helps to increase the performance of image pre-processing steps.
Leveraging Label Information in a Knowledge-Driven Approach for Rolling-Element Bearings Remaining Useful Life Prediction
Authors: Tarek Berghout, Mohamed Benbouzid, Leïla-Hayet Mouss
Date: 2021-04-14 00:00:00
Tasks: Denoising, Transfer Learning

Since bearing deterioration patterns are difficult to collect from real, long lifetime scenarios,
data-driven research has been directed towards recovering them by imposing accelerated life tests.
Consequently, insufficiently recovered features due to rapid damage propagation seem more likely
to lead to poorly generalized learning machines. Knowledge-driven learning comes as a solution by
providing prior assumptions from transfer learning. Likewise, the absence of true labels was able to
create inconsistency related problems between samples, and teacher-given label behaviors led to more
ill-posed predictors. Therefore, in an attempt to overcome the incomplete, unlabeled data drawbacks,
a new autoencoder has been designed as an additional source that could correlate inputs and labels
by exploiting label information in a completely unsupervised learning scheme. Additionally, its
stacked denoising version seems to more robustly be able to recover them for new unseen data. Due
to the non-stationary and sequentially driven nature of samples, recovered representations have
been fed into a transfer learning, convolutional, long–short-term memory neural network for further
meaningful learning representations. The assessment procedures were benchmarked against recent
methods under different training datasets. The obtained results led to more efficiency confirming the
strength of the new learning path.
Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?
Authors: Martin Schrimpf, Jonas Kubilius, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Franziska Geiger, Kailyn Schmidt, Daniel L. K. Yamins, James J. DiCarlo
Date: 2020-01-02 00:00:00
Tasks: Object Recognition

The internal representations of early deep artificial neural networks (ANNs) were found to be remarkably similar to the internal neural representations measured experimentally in the primate brain. Here we ask, as deep ANNs have continued to evolve, are they becoming more or less brain-like? ANNs that are most functionally similar to the brain will contain mechanisms that are most like those used by the brain. We therefore developed Brain-Score – a composite of multiple neural and behavioral benchmarks that score any ANN on how similar it is to the brain’s mechanisms for core object recognition – and we deployed it to evaluate a wide range of state-of-the-art deep ANNs. Using this scoring system, we here report that: (1) DenseNet-169, CORnet-S and ResNet-101 are the most brain-like ANNs. (2) There remains considerable variability in neural and behavioral responses that is not predicted by any ANN, suggesting that no ANN model has yet captured all the relevant mechanisms. (3) Extending prior work, we found that gains in ANN ImageNet performance led to gains on Brain-Score. However, correlation weakened at ≥ 70% top-1 ImageNet performance, suggesting that additional guidance from neuroscience is needed to make further advances in capturing brain mechanisms. (4) We uncovered smaller (i.e. less complex) ANNs that are more brain-like than many of the best-performing ImageNet models, which suggests the opportunity to simplify ANNs to better understand the ventral stream. The scoring system used here is far from complete. However, we propose that evaluating and tracking model-benchmark correspondences through a Brain-Score that is regularly updated with new brain data is an exciting opportunity: experimental benchmarks can be used to guide machine network evolution, and machine networks are mechanistic hypotheses of the brain’s network and thus drive next experiments. To facilitate both of these, we release Brain-Score.org: a platform that hosts the neural and behavioral benchmarks, where ANNs for visual processing can be submitted to receive a Brain-Score and their rank relative to other models, and where new experimental data can be naturally incorporated.
Least-Squares Policy Iteration
Authors: Michail G. Lagoudakis, Ronald Parr
Date: 2003-12-04 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning (RL)

We propose a new approach to reinforcement learning for control problems which combines value-function approximation with linear architectures and approximate policy iteration. This new approach is motivated by the least-squares temporal-difference learning algorithm (LSTD) for prediction problems, which is known for its efficient use of sample experiences compared to pure temporal-difference algorithms. Heretofore, LSTD has not had a straightforward application to control problems mainly because LSTD learns the state value function of a fixed policy which cannot be used for action selection and control without a model of the underlying process. Our new algorithm, least-squares policy iteration (LSPI), learns the state-action value function which allows for action selection without a model and for incremental policy improvement within a policy-iteration framework. LSPI is a model-free, off-policy method which can use efficiently (and reuse in each iteration) sample experiences collected in any manner. By separating the sample collection method, the choice of the linear approximation architecture, and the solution method, LSPI allows for focused attention on the distinct elements that contribute to practical reinforcement learning. LSPI is tested on the simple task of balancing an inverted pendulum and the harder task of balancing and riding a bicycle to a target location. In both cases, LSPI learns to control the pendulum or the bicycle by merely observing a relatively small number of trials where actions are selected randomly. LSPI is also compared against Q-learning (both with and without experience replay) using the same value function architecture. While LSPI achieves good performance fairly consistently on the difficult bicycle task, Q-learning variants were rarely able to balance for more than a small fraction of the time needed to reach the target location.
Evaluation of Unsupervised Entity and Event Salience Estimation
Authors: Jiaying Lu, Jinho D. Choi
Date: 2021-04-14 00:00:00
Tasks: Entity Linking

Salience Estimation aims to predict term importance in documents. Due to few existing human-annotated datasets and the subjective notion of salience, previous studies typically generate pseudo-ground truth for evaluation. However, our investigation reveals that the evaluation protocol proposed by prior work is difficult to replicate, thus leading to few follow-up studies existing. Moreover, the evaluation process is problematic: the entity linking tool used for entity matching is very noisy, while the ignorance of event argument for event evaluation leads to boosted performance. In this work, we propose a light yet practical entity and event salience estimation evaluation protocol, which incorporates the more reliable syntactic dependency parser. Furthermore, we conduct a comprehensive analysis among popular entity and event definition standards, and present our own definition for the Salience Estimation task to reduce noise during the pseudo-ground truth generation process. Furthermore, we construct dependency-based heterogeneous graphs to capture the interactions of entities and events. The empirical results show that both baseline methods and the novel GNN method utilizing the heterogeneous graph consistently outperform the previous SOTA model in all proposed metrics.
Simultaneous Face Hallucination and Translation for Thermal to Visible Face Verification using Axial-GAN
Authors: Rakhil Immidisetti, Shuowen Hu, Vishal M. Patel
Date: 2021-04-13 00:00:00
Tasks: Face Hallucination, Face Verification, Generative Adversarial Network, Hallucination, Translation

Existing thermal-to-visible face verification approaches expect the thermal and visible face images to be of similar resolution. This is unlikely in real-world long-range surveillance systems, since humans are distant from the cameras. To address this issue, we introduce the task of thermal-to-visible face verification from low-resolution thermal images. Furthermore, we propose Axial-Generative Adversarial Network (Axial-GAN) to synthesize high-resolution visible images for matching. In the proposed approach we augment the GAN framework with axial-attention layers which leverage the recent advances in transformers for modelling long-range dependencies. We demonstrate the effectiveness of the proposed method by evaluating on two different thermal-visible face datasets. When compared to related state-of-the-art works, our results show significant improvements in both image quality and face verification performance, and are also much more efficient.
An Introduction of mini-AlphaStar
Authors: Ruo-Ze Liu, Wenhai Wang, Yanjie Shen, Zhiqi Li, Yang Yu, Tong Lu
Date: 2021-04-14 00:00:00
Tasks: Starcraft, Starcraft II

StarCraft II (SC2) is a real-time strategy game in which players produce and control multiple units to fight against opponent's units. Due to its difficulties, such as huge state space, various action space, a long time horizon, and imperfect information, SC2 has been a research hotspot in reinforcement learning. Recently, an agent called AlphaStar (AS) has been proposed, which shows good performance, obtaining a high win rate of 99.8% against human players. We implemented a mini-scaled version of it called mini-AlphaStar (mAS) based on AS's paper and pseudocode. The difference between AS and mAS is that we substituted the hyper-parameters of AS with smaller ones for mini-scale training. Codes of mAS are all open-sourced (https://github.com/liuruoze/mini-AlphaStar) for future research.
To Trust or Not to Trust a Regressor: Estimating and Explaining Trustworthiness of Regression Predictions
Authors: Kim de Bie, Ana Lucic, Hinda Haned
Date: 2021-04-14 00:00:00
Tasks: Prediction, regression

In hybrid human-AI systems, users need to decide whether or not to trust an algorithmic prediction while the true error in the prediction is unknown. To accommodate such settings, we introduce RETRO-VIZ, a method for (i) estimating and (ii) explaining trustworthiness of regression predictions. It consists of RETRO, a quantitative estimate of the trustworthiness of a prediction, and VIZ, a visual explanation that helps users identify the reasons for the (lack of) trustworthiness of a prediction. We find that RETRO-scores negatively correlate with prediction error across 117 experimental settings, indicating that RETRO provides a useful measure to distinguish trustworthy predictions from untrustworthy ones. In a user study with 41 participants, we find that VIZ-explanations help users identify whether a prediction is trustworthy or not: on average, 95.1% of participants correctly select the more trustworthy prediction, given a pair of predictions. In addition, an average of 75.6% of participants can accurately describe why a prediction seems to be (not) trustworthy. Finally, we find that the vast majority of users subjectively experience RETRO-VIZ as a useful tool to assess the trustworthiness of algorithmic predictions.
[RE] Double-Hard Debias: Tailoring Word Embeddings for Gender Bias Mitigation
Authors: Haswanth Aekula, Sugam Garg, Animesh Gupta
Date: 2021-04-14 00:00:00
Tasks: Word Embeddings

Despite widespread use in natural language processing (NLP) tasks, word embeddings have been criticized for inheriting unintended gender bias from training corpora. programmer is more closely associated with man and homemaker is more closely associated with woman. Such gender bias has also been shown to propagate in downstream tasks.
ComBiNet: Compact Convolutional Bayesian Neural Network for Image Segmentation
Authors: Martin Ferianc, Divyansh Manocha, Hongxiang Fan, Miguel Rodrigues
Date: 2021-04-14 00:00:00
Tasks: Bayesian Inference, Decision Making, Image Segmentation, Semantic Segmentation, Uncertainty Quantification

Fully convolutional U-shaped neural networks have largely been the dominant approach for pixel-wise image segmentation. In this work, we tackle two defects that hinder their deployment in real-world applications: 1) Predictions lack uncertainty quantification that may be crucial to many decision-making systems; 2) Large memory storage and computational consumption demanding extensive hardware resources. To address these issues and improve their practicality we demonstrate a few-parameter compact Bayesian convolutional architecture, that achieves a marginal improvement in accuracy in comparison to related work using significantly fewer parameters and compute operations. The architecture combines parameter-efficient operations such as separable convolutions, bilinear interpolation, multi-scale feature propagation and Bayesian inference for per-pixel uncertainty quantification through Monte Carlo Dropout. The best performing configurations required fewer than 2.5 million parameters on diverse challenging datasets with few observations.
Aligning Latent and Image Spaces to Connect the Unconnectable
Authors: Ivan Skorokhodov, Grigorii Sotnikov, Mohamed Elhoseiny
Date: 2021-04-14 00:00:00
Proceeding: ICCV 2021 10
Tasks: Diversity, Image Generation, Infinite Image Generation

In this work, we develop a method to generate infinite high-resolution images with diverse and complex content. It is based on a perfectly equivariant generator with synchronous interpolations in the image and latent spaces. Latent codes, when sampled, are positioned on the coordinate grid, and each pixel is computed from an interpolation of the nearby style codes. We modify the AdaIN mechanism to work in such a setup and train the generator in an adversarial setting to produce images positioned between any two latent vectors. At test time, this allows for generating complex and diverse infinite images and connecting any two unrelated scenes into a single arbitrarily large panorama. Apart from that, we introduce LHQ: a new dataset of \lhqsize high-resolution nature landscapes. We test the approach on LHQ, LSUN Tower and LSUN Bridge and outperform the baselines by at least 4 times in terms of quality and diversity of the produced infinite images. The project page is located at https://universome.github.io/alis.
Is Disentanglement all you need? Comparing Concept-based & Disentanglement Approaches
Authors: Dmitry Kazhdan, Botty Dimanov, Helena Andres Terre, Mateja Jamnik, Pietro Liò, Adrian Weller
Date: 2021-04-14 00:00:00
Tasks: All, Disentanglement

Concept-based explanations have emerged as a popular way of extracting human-interpretable representations from deep discriminative models. At the same time, the disentanglement learning literature has focused on extracting similar representations in an unsupervised or weakly-supervised way, using deep generative models. Despite the overlapping goals and potential synergies, to our knowledge, there has not yet been a systematic comparison of the limitations and trade-offs between concept-based explanations and disentanglement approaches. In this paper, we give an overview of these fields, comparing and contrasting their properties and behaviours on a diverse set of tasks, and highlighting their potential strengths and limitations. In particular, we demonstrate that state-of-the-art approaches from both classes can be data inefficient, sensitive to the specific nature of the classification/regression task, or sensitive to the employed concept representation.
Towards a framework for evaluating the safety, acceptability and efficacy of AI systems for health: an initial synthesis
Authors: Jessica Morley, Caroline Morton, Kassandra Karpathakis, Mariarosaria Taddeo, Luciano Floridi
Date: 2021-04-14 00:00:00

The potential presented by Artificial Intelligence (AI) for healthcare has long been recognised by the technical community. More recently, this potential has been recognised by policymakers, resulting in considerable public and private investment in the development of AI for healthcare across the globe. Despite this, excepting limited success stories, real-world implementation of AI systems into front-line healthcare has been limited. There are numerous reasons for this, but a main contributory factor is the lack of internationally accepted, or formalised, regulatory standards to assess AI safety and impact and effectiveness. This is a well-recognised problem with numerous ongoing research and policy projects to overcome it. Our intention here is to contribute to this problem-solving effort by seeking to set out a minimally viable framework for evaluating the safety, acceptability and efficacy of AI systems for healthcare. We do this by conducting a systematic search across Scopus, PubMed and Google Scholar to identify all the relevant literature published between January 1970 and November 2020 related to the evaluation of: output performance; efficacy; and real-world use of AI systems, and synthesising the key themes according to the stages of evaluation: pre-clinical (theoretical phase); exploratory phase; definitive phase; and post-market surveillance phase (monitoring). The result is a framework to guide AI system developers, policymakers, and regulators through a sufficient evaluation of an AI system designed for use in healthcare.
Enhancing Interpretable Clauses Semantically using Pretrained Word Representation
Authors: Rohan Kumar Yadav, Lei Jiao, Ole-Christoffer Granmo, Morten Goodwin
Date: 2021-04-14 00:00:00
Proceeding: EMNLP (BlackboxNLP) 2021 11
Tasks: Sentiment Analysis, Text Classification, Word Sense Disambiguation

Tsetlin Machine (TM) is an interpretable pattern recognition algorithm based on propositional logic, which has demonstrated competitive performance in many Natural Language Processing (NLP) tasks, including sentiment analysis, text classification, and Word Sense Disambiguation. To obtain human-level interpretability, legacy TM employs Boolean input features such as bag-of-words (BOW). However, the BOW representation makes it difficult to use any pre-trained information, for instance, word2vec and GloVe word representations. This restriction has constrained the performance of TM compared to deep neural networks (DNNs) in NLP. To reduce the performance gap, in this paper, we propose a novel way of using pre-trained word representations for TM. The approach significantly enhances the performance and interpretability of TM. We achieve this by extracting semantically related words from pre-trained word representations as input features to the TM. Our experiments show that the accuracy of the proposed approach is significantly higher than the previous BOW-based TM, reaching the level of DNN-based models.
I Wish I Would Have Loved This One, But I Didn't -- A Multilingual Dataset for Counterfactual Detection in Product Reviews
Authors: James O'Neill, Polina Rozenshtein, Ryuichi Kiryo, Motoko Kubota, Danushka Bollegala
Date: 2021-04-14 00:00:00
Tasks: counterfactual, Counterfactual Detection, Machine Translation, Sentence, Specificity, Translation

Counterfactual statements describe events that did not or cannot take place. We consider the problem of counterfactual detection (CFD) in product reviews. For this purpose, we annotate a multilingual CFD dataset from Amazon product reviews covering counterfactual statements written in English, German, and Japanese languages. The dataset is unique as it contains counterfactuals in multiple languages, covers a new application area of e-commerce reviews, and provides high quality professional annotations. We train CFD models using different text representation methods and classifiers. We find that these models are robust against the selectional biases introduced due to cue phrase-based sentence selection. Moreover, our CFD dataset is compatible with prior datasets and can be merged to learn accurate CFD models. Applying machine translation on English counterfactual examples to create multilingual data performs poorly, demonstrating the language-specificity of this problem, which has been ignored so far.
CelebHair: A New Large-Scale Dataset for Hairstyle Recommendation based on CelebA
Authors: Yutao Chen, Yuxuan Zhang, Zhongrui Huang, Zhenyao Luo, Jinpeng Chen
Date: 2021-04-14 00:00:00
Tasks: Facial Landmark Detection

In this paper, we present a new large-scale dataset for hairstyle recommendation, CelebHair, based on the celebrity facial attributes dataset, CelebA. Our dataset inherited the majority of facial images along with some beauty-related facial attributes from CelebA. Additionally, we employed facial landmark detection techniques to extract extra features such as nose length and pupillary distance, and deep convolutional neural networks for face shape and hairstyle classification. Empirical comparison has demonstrated the superiority of our dataset to other existing hairstyle-related datasets regarding variety, veracity, and volume. Analysis and experiments have been conducted on the dataset in order to evaluate its robustness and usability.