pwc-archive/papers-with-abstracts huggingface dataset:
Exploring the Assessment List for Trustworthy AI in the Context of Advanced Driver-Assistance Systems
Authors: Markus Borg, Joshua Bronson, Linus Christensson, Fredrik Olsson, Olof Lennartsson, Elias Sonnsjö, Hamid Ebabi, Martin Karsberg
Date: 2021-03-04 00:00:00

Artificial Intelligence (AI) is increasingly used in critical applications. Thus, the need for dependable AI systems is rapidly growing. In 2018, the European Commission appointed experts to a High-Level Expert Group on AI (AI-HLEG). AI-HLEG defined Trustworthy AI as 1) lawful, 2) ethical, and 3) robust and specified seven corresponding key requirements. To help development organizations, AI-HLEG recently published the Assessment List for Trustworthy AI (ALTAI). We present an illustrative case study from applying ALTAI to an ongoing development project of an Advanced Driver-Assistance System (ADAS) that relies on Machine Learning (ML). Our experience shows that ALTAI is largely applicable to ADAS development, but specific parts related to human agency and transparency can be disregarded. Moreover, bigger questions related to societal and environmental impact cannot be tackled by an ADAS supplier in isolation. We present how we plan to develop the ADAS to ensure ALTAI-compliance. Finally, we provide three recommendations for the next revision of ALTAI, i.e., life-cycle variants, domain-specific adaptations, and removed redundancy.
Towards New Multiwavelets: Associated Filters and Algorithms. Part I: Theoretical Framework and Investigation of Biomedical Signals, ECG and Coronavirus Cases
Authors: Malika Jallouli, Makerem Zemni, Anouar Ben Mabrouk, Mohamed Ali Mahjoub
Date: 2021-03-09 00:00:00

Biosignals are nowadays important subjects for scientific researches from both theory and applications especially with the appearance of new pandemics threatening humanity such as the new Coronavirus. One aim in the present work is to prove that Wavelets may be successful machinery to understand such phenomena by applying a step forward extension of wavelets to multiwavelets. We proposed in a first step to improve the multiwavelet notion by constructing more general families using independent components for multi-scaling and multiwavelet mother functions. A special multiwavelet is then introduced, continuous and discrete multiwavelet transforms are associated, as well as new filters and algorithms of decomposition and reconstruction. The constructed multiwavelet framework is applied for some experimentations showing fast algorithms, ECG signal, and a strain of Coronavirus processing.
Rapid parameter estimation of discrete decaying signals using autoencoder networks
Authors: Jim C. Visschers, Dmitry Budker, Lykourgos Bougas
Date: 2021-03-10 00:00:00
Tasks: parameter estimation

In this work we demonstrate the use of neural networks for rapid extraction of signal parameters of discretely sampled signals. In particular, we use dense autoencoder networks to extract the parameters of interest from exponentially decaying signals and decaying oscillations. By using a three-stage training method and careful choice of the neural network size, we are able to retrieve the relevant signal parameters directly from the latent space of the autoencoder network at significantly improved rates compared to traditional algorithmic signal-analysis approaches. We show that the achievable precision and accuracy of this method of analysis is similar to conventional algorithm-based signal analysis methods, by demonstrating that the extracted signal parameters are approaching their fundamental parameter estimation limit as provided by the Cram\'er-Rao bound. Furthermore, we demonstrate that autoencoder networks are able to achieve signal analysis, and, hence, parameter extraction, at rates of 75 kHz, orders-of-magnitude faster than conventional techniques with similar precision. Finally, we explore the limitations of our approach, demonstrating that analysis rates of $>$200 kHz are feasible with further optimization of the transfer rate between the data-acquisition system and data-analysis system.
Selective Intervention Planning using Restless Multi-Armed Bandits to Improve Maternal and Child Health Outcomes
Authors: Siddharth Nishtala, Lovish Madaan, Aditya Mate, Harshavardhan Kamarthi, Anirudh Grama, Divy Thakkar, Dhyanesh Narayanan, Suresh Chaudhary, Neha Madhiwalla, Ramesh Padmanabhan, Aparna Hegde, Pradeep Varakantham, Balaraman Ravindran, Milind Tambe
Date: 2021-03-07 00:00:00
Tasks: Multi-Armed Bandits

India has a maternal mortality ratio of 113 and child mortality ratio of 2830 per 100,000 live births. Lack of access to preventive care information is a major contributing factor for these deaths, especially in low resource households. We partner with ARMMAN, a non-profit based in India employing a call-based information program to disseminate health-related information to pregnant women and women with recent child deliveries. We analyze call records of over 300,000 women registered in the program created by ARMMAN and try to identify women who might not engage with these call programs that are proven to result in positive health outcomes. We built machine learning based models to predict the long term engagement pattern from call logs and beneficiaries' demographic information, and discuss the applicability of this method in the real world through a pilot validation. Through a pilot service quality improvement study, we show that using our model's predictions to make interventions boosts engagement metrics by 61.37%. We then formulate the intervention planning problem as restless multi-armed bandits (RMABs), and present preliminary results using this approach.
Model-Agnostic Meta-Learning for EEG Motor Imagery Decoding in Brain-Computer-Interfacing
Authors: Denghao Li, Pablo Ortega, Xiaoxi Wei, Aldo Faisal
Date: 2021-03-10 00:00:00
Tasks: Decoder, EEG, Electroencephalogram (EEG), Meta-Learning, Motor Imagery, Transfer Learning

We introduce here the idea of Meta-Learning for training EEG BCI decoders. Meta-Learning is a way of training machine learning systems so they learn to learn. We apply here meta-learning to a simple Deep Learning BCI architecture and compare it to transfer learning on the same architecture. Our Meta-learning strategy operates by finding optimal parameters for the BCI decoder so that it can quickly generalise between different users and recording sessions -- thereby also generalising to new users or new sessions quickly. We tested our algorithm on the Physionet EEG motor imagery dataset. Our approach increased motor imagery classification accuracy between 60% to 80%, outperforming other algorithms under the little-data condition. We believe that establishing the meta-learning or learning-to-learn approach will help neural engineering and human interfacing with the challenges of quickly setting up decoders of neural signals to make them more suitable for daily-life.
Hate, Obscenity, and Insults: Measuring the Exposure of Children to Inappropriate Comments in YouTube
Authors: Sultan Alshamrani, Ahmed Abusnaina, Mohammed Abuhamad, DaeHun Nyang, David Mohaisen
Date: 2021-03-03 00:00:00

Social media has become an essential part of the daily routines of children and adolescents. Moreover, enormous efforts have been made to ensure the psychological and emotional well-being of young users as well as their safety when interacting with various social media platforms. In this paper, we investigate the exposure of those users to inappropriate comments posted on YouTube videos targeting this demographic. We collected a large-scale dataset of approximately four million records and studied the presence of five age-inappropriate categories and the amount of exposure to each category. Using natural language processing and machine learning techniques, we constructed ensemble classifiers that achieved high accuracy in detecting inappropriate comments. Our results show a large percentage of worrisome comments with inappropriate content: we found 11% of the comments on children's videos to be toxic, highlighting the importance of monitoring comments, particularly on children's platforms.
Sentiment Analysis for Troll Detection on Weibo
Authors: Zidong Jiang, Fabio Di Troia, Mark Stamp
Date: 2021-03-07 00:00:00
Tasks: Sentence, Sentence segmentation, Sentiment Analysis

The impact of social media on the modern world is difficult to overstate. Virtually all companies and public figures have social media accounts on popular platforms such as Twitter and Facebook. In China, the micro-blogging service provider, Sina Weibo, is the most popular such service. To influence public opinion, Weibo trolls -- the so called Water Army -- can be hired to post deceptive comments. In this paper, we focus on troll detection via sentiment analysis and other user activity data on the Sina Weibo platform. We implement techniques for Chinese sentence segmentation, word embedding, and sentiment score calculation. In recent years, troll detection and sentiment analysis have been studied, but we are not aware of previous research that considers troll detection based on sentiment analysis. We employ the resulting techniques to develop and test a sentiment analysis approach for troll detection, based on a variety of machine learning strategies. Experimental results are generated and analyzed. A Chrome extension is presented that implements our proposed technique, which enables real-time troll detection when a user browses Sina Weibo.
A Bayesian Multiscale Deep Learning Framework for Flows in Random Media
Authors: Govinda Anantha Padmanabha, Nicholas Zabaras
Date: 2021-03-08 00:00:00
Tasks: Uncertainty Quantification

Fine-scale simulation of complex systems governed by multiscale partial differential equations (PDEs) is computationally expensive and various multiscale methods have been developed for addressing such problems. In addition, it is challenging to develop accurate surrogate and uncertainty quantification models for high-dimensional problems governed by stochastic multiscale PDEs using limited training data. In this work to address these challenges, we introduce a novel hybrid deep-learning and multiscale approach for stochastic multiscale PDEs with limited training data. For demonstration purposes, we focus on a porous media flow problem. We use an image-to-image supervised deep learning model to learn the mapping between the input permeability field and the multiscale basis functions. We introduce a Bayesian approach to this hybrid framework to allow us to perform uncertainty quantification and propagation tasks. The performance of this hybrid approach is evaluated with varying intrinsic dimensionality of the permeability field. Numerical results indicate that the hybrid network can efficiently predict well for high-dimensional inputs.
Application of Gradient Boosting Algorithms for Anti-money Laundering in Cryptocurrencies
Authors: Dylan Vassallo, Vincent Vella, Joshua Ellul
Date: 2021-03-17 00:00:00

The recent emergence of cryptocurrencies has added another layer of complexity in the fight towards financial crime. Cryptocurrencies require no central authority and offer pseudo-anonymity to its users, allowing criminals to disguise themselves among legitimate users. On the other hand, the openness of data fuels the investigator’s toolkit to conduct forensic examinations. This study focuses on the detection of illicit activities (e.g., scams, financing terrorism, and Ponzi schemes) on cryptocurrency infrastructures, both at an account and transaction level. Previous work has identified that class imbalance and the dynamic environment created by the evolving techniques deployed by criminals to avoid detection are widespread in this domain. In our study, we propose Adaptive Stacked eXtreme Gradient Boosting (ASXGB), an adaptation of eXtreme Gradient Boosting (XGBoost), to better handle dynamic environments and present a comparative analysis of various offline decision tree-based ensembles and heuristic-based data-sampling techniques. Our results show that: (i) offline decision tree-based gradient boosting algorithms outperform state-of-the-art Random Forest (RF) results at both an account and transaction level, (ii) the data-sampling approach NCL-SMOTE further improves recall at a transaction level, and (iii) our proposed ASXGB successfully reduced the impact of concept drift while further improving recall at a transaction level.
CAP-Context-Aware-Pruning-for-Semantic-Segmentation
Authors: wei he, Meiqing Wu, Mingfu Liang, Siew-Kei Lam
Date: 2021-01-06 00:00:00
Tasks: Network Pruning, Segmentation, Semantic Segmentation

Network pruning for deep convolutional neural networks (CNNs) has recently achieved notable research progress on image-level classification. However, most existing pruning methods are not catered to or evaluated on semantic segmentation networks. In this paper, we advocate the importance of contextual information during channel pruning for semantic segmentation networks by presenting a novel Context-aware Pruning framework. Concretely, we formulate the embedded contextual information by leveraging the layer-wise channels interdependency via the Context-aware Guiding Module (CAGM) and introduce the Context-aware Guided Sparsification (CAGS) to adaptively identify the informative channels on the cumbersome model by inducing channel-wise sparsity on the scaling factors in batch normalization (BN) layers. The resulting pruned models require significantly lesser operations for inference while maintaining comparable performance to (at times outperforming) the original models. We evaluated our framework on widely-used benchmarks and showed its effectiveness on both large and lightweight models. On Cityscapes dataset, our framework reduces the number of parameters by 32%, 47%, 54%, and 63%, on PSPNet101, PSPNet50, ICNet, and SegNet, respectively, while preserving the performance.
CAP: Context-Aware Pruning for Semantic-Segmentation
Authors: wei he, Meiqing Wu, Mingfu Liang, Siew-Kei Lam
Date: 2021-01-06 00:00:00
Tasks: Network Pruning, Segmentation, Semantic Segmentation

Network pruning for deep convolutional neural networks (CNNs) has recently achieved notable research progress on image-level classification. However, most existing pruning methods are not catered to or evaluated on semantic segmentation networks. In this paper, we advocate the importance of contextual information during channel pruning for semantic segmentation networks by presenting a novel Context-aware Pruning framework. Concretely, we formulate the embedded contextual information by leveraging the layer-wise channels interdependency via the Context-aware Guiding Module (CAGM) and introduce the Context-aware Guided Sparsification (CAGS) to adaptively identify the informative channels on the cumbersome model by inducing channel-wise sparsity on the scaling factors in batch normalization (BN) layers. The resulting pruned models require significantly lesser operations for inference while maintaining comparable performance to (at times outperforming) the original models. We evaluated our framework on widely-used benchmarks and showed its effectiveness on both large and lightweight models. On Cityscapes dataset, our framework reduces the number of parameters by 32%, 47%, 54%, and 63%, on PSPNet101, PSPNet50, ICNet, and SegNet, respectively, while preserving the performance.
A new dataset of dog breed images and a benchmark for fine-grained classification
Authors: Ding-Nan Zo, Song-Hai Zhang, Tai-Jiang M, Min Zhang
Date: 2020-10-01 00:00:00
Tasks: Benchmarking, Classification, Diversity, Fine-Grained Image Classification, General Classification, image-classification, Image Classification, Metric Learning

In this paper, we introduce an image dataset for fine-grained classification of dog breeds: the Tsinghua Dogs  Dataset. It is currently the largest dataset for fine-grained classification of dogs, including130 dog breeds and 70,428 real-world images. It has only one dog in each image and provides annotated bounding boxes for the whole body and head. In comparison to previous similar datasets, it contains more breeds and more carefully chosen images for each breed. The diversity within each breed is greater, with between 200 and 7000+ images for each breed. Annotation of the whole body and head makes the dataset not only suitable for the improvement of fine-grained image classification models based on overall features, but also for those locating local informative parts. We show that dataset provides a tough challenge by benchmarking several state-of-the-art deep neural models. The dataset is available for academic purposes at https://cg.cs.tsinghua.edu.cn/ThuDogs/.
Optic Disc, Cup and Fovea Detection from Retinal Images Using U-Net++ with EfficientNet Encoder
Authors: Ravi Kamble, Pranab Samanta, Nitin Singhal
Date: 2020-11-20 00:00:00
Tasks: Fovea Detection, Optic Cup Detection, Optic Cup Segmentation, Optic Disc Detection, Optic Disc Segmentation, Segmentation

The accurate detection of retinal structures like an optic disc (OD), cup, and fovea is crucial for the analysis of Age-related Macular Degeneration (AMD), Glaucoma, and other retinal conditions. Most segmentation methods rely on separate detection of these retinal structures due to which a combined analysis for computer-aided ophthalmic diagnosis and screening is challenging. To address this issue, the paper introduces an approach incorporating OD, cup, and fovea analysis together. The paper presents a novel method for the detection of OD with a cup and fovea using modified U-Net++ architecture with the EfficientNet-B4 model as a backbone. The extracted features from the EfficientNet are utilized using skip connections in U-Net++ for precise segmentation. Datasets from ADAM and REFUGE challenges are used for evaluating the performance. The proposed method achieved a success rate of 94.74% and 95.73% dice value for OD segmentation on ADAM and REFUGE data, respectively. For fovea detection, the average Euclidean distance of 26.17 pixels is achieved for the ADAM dataset. The proposed method stood first for OD detection and segmentation tasks in ISBI ADAM 2020 challenge.
Drishti-GS: Retinal image dataset for optic nerve head(ONH) segmentation
Authors: Jayanthi Sivaswamy, S. R. Krishnadas, Gopal Datt Joshi, Madhulika Jain, A. Ujjwaft Syed Tabish
Date: 2014-05-02 00:00:00
Tasks: Segmentation

Optic nerve head (ONH) segmentation problem has been of interest for automated glaucoma assessment. Although various segmentation methods have been proposed in the recent past, it is difficult to evaluate and compare the performance of individual methods due to a lack of a benchmark dataset. The problem of segmentation involves segmentation of optic disk and cup region within ONH region. Available datasets do not incorporate challenges present in this problem. In this data paper, we present a comprehensive dataset of retinal images which include both normal and glaucomatous eyes and manual segmentations from multiple human experts. Both area and boundary-based evaluation measures are presented to evaluate a method on various aspects relevant to the problem of glaucoma assessment.
Interpretable Natural Language Segmentation Based on Link Grammar
Authors: Vignav Ramesh, Anton Kolonin
Date: 2020-11-14 00:00:00
Tasks: Question Answering, Retrieval, Segmentation, Sentence, Text Segmentation, valid

Natural language segmentation (NLS), or text segmentation, refers to the process of dividing written text into meaningful units. Sentence segmentation, a subfield of text segmentation, is the problem of dividing a string of natural language text into its component sentences. Current methods of sentence segmentation are often either hardcoded-they require manual implementation of fixed grammar and segmentation rules-or require extensive training on labeled corpora and are not explainable-they are "black box" algorithms that cannot be understood by humans. In this paper, we present a novel explainable sentence segmentation method capable of separating bodies of text into grammatically valid sentences solely based on the grammatical relationships between individual words or tokens. The proposed NLS architecture can both automate the input query parsing and semantic query execution components of voice-activated question answering and information retrieval systems as well as enable automatic summarization, entity extraction, sentiment identification, and a variety of other natural language processing (NLP) algorithms that operate at the sentential level.
ClefNet: Recurrent Autoencoders with Dynamic Time Warping for Near-Lossless Music Compression and Minimal-Latency Transmission
Authors: Vignav Ramesh, Mason Wang
Date: 2021-03-15 00:00:00
Tasks: Audio Compression, Dynamic Time Warping, Music Compression

The onset of coronavirus disease 2019 (COVID-19), an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has sparked unprecedented change. Due to the public health guidelines imposed during the COVID-19 pandemic, there is no longer sufficient street traffic for remaining buskers to generate sufficient revenue, leading a majority of street musicians to pursue remote music production. However, real-time music production is notoriously difficult due to the excessively high latencies that current video call platforms such as Zoom and Google Meet harbor. In this paper, we propose an architecture for a platform with end-to-end, near-lossless audio transmission tailored specifically to online joint music production, called Latent Space. We discuss the usage of a recurrent autoencoder with sequence-aware encoding (RAES) and a 1D convolutional layer for audio compression, which we dub ClefNet, as well as propose a new evaluation metric for naive autoencoders (AEs), MSE-DTW loss, which combines the traditional mean square error (MSE) loss function with dynamic time warping (DTW) to prevent an increase in loss when the target sequence predicted by the AE is strictly a temporal variation of the source sequence. Moreover, we detail the logistics of a live system implementation which uses the Web Audio API to extract raw audio samples in real-time to feed into our client-side model before relaying the traffic using peer-to-peer WebRTC technology. The Latent Space platform can be accessed at https://latent-space.tech, and the code and data can be found under the MIT License at https://github.com/rvignav/ClefNet.
On the Role of Images for Analyzing Claims in Social Media
Authors: Gullal S. Cheema, Sherzod Hakimov, Eric Müller-Budack, Ralph Ewerth
Date: 2021-03-17 00:00:00
Tasks: Fake News Detection

Fake news is a severe problem in social media. In this paper, we present an empirical study on visual, textual, and multimodal models for the tasks of claim, claim check-worthiness, and conspiracy detection, all of which are related to fake news detection. Recent work suggests that images are more influential than text and often appear alongside fake text. To this end, several multimodal models have been proposed in recent years that use images along with text to detect fake news on social media sites like Twitter. However, the role of images is not well understood for claim detection, specifically using transformer-based textual and multimodal models. We investigate state-of-the-art models for images, text (Transformer-based), and multimodal information for four different datasets across two languages to understand the role of images in the task of claim and conspiracy detection.