pwc-archive/papers-with-abstracts huggingface dataset:
Subjective and Objective Quality Assessment of High Frame Rate Videos
Authors: Pavan C. Madhusudana, Xiangxu Yu, Neil Birkbeck, Yilin Wang, Balu Adsumilli, Alan C. Bovik
Date: 2020-07-22 00:00:00
Tasks: Vocal Bursts Intensity Prediction

High frame rate (HFR) videos are becoming increasingly common with the tremendous popularity of live, high-action streaming content such as sports. Although HFR contents are generally of very high quality, high bandwidth requirements make them challenging to deliver efficiently, while simultaneously maintaining their quality. To optimize trade-offs between bandwidth requirements and video quality, in terms of frame rate adaptation, it is imperative to understand the intricate relationship between frame rate and perceptual video quality. Towards advancing progression in this direction we designed a new subjective resource, called the LIVE-YouTube-HFR (LIVE-YT-HFR) dataset, which is comprised of 480 videos having 6 different frame rates, obtained from 16 diverse contents. In order to understand the combined effects of compression and frame rate adjustment, we also processed videos at 5 compression levels at each frame rate. To obtain subjective labels on the videos, we conducted a human study yielding 19,000 human quality ratings obtained from a pool of 85 human subjects. We also conducted a holistic evaluation of existing state-of-the-art Full and No-Reference video quality algorithms, and statistically benchmarked their performance on the new database. The LIVE-YT-HFR database has been made available online for public use and evaluation purposes, with hopes that it will help advance research in this exciting video technology direction. It may be obtained at \url{https://live.ece.utexas.edu/research/LIVE_YT_HFR/LIVE_YT_HFR/index.html}
Learning User-Preferred Mappings for Intuitive Robot Control
Authors: Mengxi Li, Dylan P. Losey, Jeannette Bohg, Dorsa Sadigh
Date: 2020-07-22 00:00:00
Tasks: Robot Manipulation

When humans control drones, cars, and robots, we often have some preconceived notion of how our inputs should make the system behave. Existing approaches to teleoperation typically assume a one-size-fits-all approach, where the designers pre-define a mapping between human inputs and robot actions, and every user must adapt to this mapping over repeated interactions. Instead, we propose a personalized method for learning the human's preferred or preconceived mapping from a few robot queries. Given a robot controller, we identify an alignment model that transforms the human's inputs so that the controller's output matches their expectations. We make this approach data-efficient by recognizing that human mappings have strong priors: we expect the input space to be proportional, reversable, and consistent. Incorporating these priors ensures that the robot learns an intuitive mapping from few examples. We test our learning approach in robot manipulation tasks inspired by assistive settings, where each user has different personal preferences and physical capabilities for teleoperating the robot arm. Our simulated and experimental results suggest that learning the mapping between inputs and robot actions improves objective and subjective performance when compared to manually defined alignments or learned alignments without intuitive priors. The supplementary video showing these user studies can be found at: https://youtu.be/rKHka0_48-Q.
TinyTL: Reduce Activations, Not Trainable Parameters for Efficient On-Device Learning
Authors: Han Cai, Chuang Gan, Ligeng Zhu, Song Han
Date: 2020-07-22 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Transfer Learning

On-device learning enables edge devices to continually adapt the AI models to new data, which requires a small memory footprint to fit the tight memory constraint of edge devices. Existing work solves this problem by reducing the number of trainable parameters. However, this doesn't directly translate to memory saving since the major bottleneck is the activations, not parameters. In this work, we present Tiny-Transfer-Learning (TinyTL) for memory-efficient on-device learning. TinyTL freezes the weights while only learns the bias modules, thus no need to store the intermediate activations. To maintain the adaptation capacity, we introduce a new memory-efficient bias module, the lite residual module, to refine the feature extractor by learning small residual feature maps adding only 3.8% memory overhead. Extensive experiments show that TinyTL significantly saves the memory (up to 6.5x) with little accuracy loss compared to fine-tuning the full network. Compared to fine-tuning the last layer, TinyTL provides significant accuracy improvements (up to 34.1%) with little memory overhead. Furthermore, combined with feature extractor adaptation, TinyTL provides 7.3-12.9x memory saving without sacrificing accuracy compared to fine-tuning the full Inception-V3.
Privacy-preserving Artificial Intelligence Techniques in Biomedicine
Authors: Reihaneh Torkzadehmahani, Reza Nasirigerdeh, David B. Blumenthal, Tim Kacprowski, Markus List, Julian Matschinske, Julian Späth, Nina Kerstin Wenke, Béla Bihari, Tobias Frisch, Anne Hartebrodt, Anne-Christin Hausschild, Dominik Heider, Andreas Holzinger, Walter Hötzendorfer, Markus Kastelitz, Rudolf Mayer, Cristian Nogales, Anastasia Pustozerova, Richard Röttger, Harald H. H. W. Schmidt, Ameli Schwalber, Christof Tschohl, Andrea Wohner, Jan Baumbach
Date: 2020-07-22 00:00:00
Tasks: Privacy Preserving

Artificial intelligence (AI) has been successfully applied in numerous scientific domains. In biomedicine, AI has already shown tremendous potential, e.g. in the interpretation of next-generation sequencing data and in the design of clinical decision support systems. However, training an AI model on sensitive data raises concerns about the privacy of individual participants. For example, summary statistics of a genome-wide association study can be used to determine the presence or absence of an individual in a given dataset. This considerable privacy risk has led to restrictions in accessing genomic and other biomedical data, which is detrimental for collaborative research and impedes scientific progress. Hence, there has been a substantial effort to develop AI methods that can learn from sensitive data while protecting individuals' privacy. This paper provides a structured overview of recent advances in privacy-preserving AI techniques in biomedicine. It places the most important state-of-the-art approaches within a unified taxonomy and discusses their strengths, limitations, and open problems. As the most promising direction, we suggest combining federated machine learning as a more scalable approach with other additional privacy preserving techniques. This would allow to merge the advantages to provide privacy guarantees in a distributed way for biomedical applications. Nonetheless, more research is necessary as hybrid approaches pose new challenges such as additional network or computation overhead.
Convergence of Langevin Monte Carlo in Chi-Squared and Renyi Divergence
Authors: Murat A. Erdogdu, Rasa Hosseinzadeh, Matthew S. Zhang
Date: 2020-07-22 00:00:00

We study sampling from a target distribution $\nu_* = e^{-f}$ using the unadjusted Langevin Monte Carlo (LMC) algorithm when the potential $f$ satisfies a strong dissipativity condition and it is first-order smooth with a Lipschitz gradient. We prove that, initialized with a Gaussian random vector that has sufficiently small variance, iterating the LMC algorithm for $\widetilde{\mathcal{O}}(\lambda^2 d\epsilon^{-1})$ steps is sufficient to reach $\epsilon$-neighborhood of the target in both Chi-squared and Renyi divergence, where $\lambda$ is the logarithmic Sobolev constant of $\nu_*$. Our results do not require warm-start to deal with the exponential dimension dependency in Chi-squared divergence at initialization. In particular, for strongly convex and first-order smooth potentials, we show that the LMC algorithm achieves the rate estimate $\widetilde{\mathcal{O}}(d\epsilon^{-1})$ which improves the previously known rates in both of these metrics, under the same assumptions. Translating this rate to other metrics, our results also recover the state-of-the-art rate estimates in KL divergence, total variation and $2$-Wasserstein distance in the same setup. Finally, as we rely on the logarithmic Sobolev inequality, our framework covers a range of non-convex potentials that are first-order smooth and exhibit strong convexity outside of a compact region.
SIZER: A Dataset and Model for Parsing 3D Clothing and Learning Size Sensitive 3D Clothing
Authors: Garvita Tiwari, Bharat Lal Bhatnagar, Tony Tung, Gerard Pons-Moll
Date: 2020-07-22 00:00:00
Proceeding: ECCV 2020 8
Tasks: 3D Human Pose Estimation

While models of 3D clothing learned from real data exist, no method can predict clothing deformation as a function of garment size. In this paper, we introduce SizerNet to predict 3D clothing conditioned on human body shape and garment size parameters, and ParserNet to infer garment meshes and shape under clothing with personal details in a single pass from an input mesh. SizerNet allows to estimate and visualize the dressing effect of a garment in various sizes, and ParserNet allows to edit clothing of an input mesh directly, removing the need for scan segmentation, which is a challenging problem in itself. To learn these models, we introduce the SIZER dataset of clothing size variation which includes $100$ different subjects wearing casual clothing items in various sizes, totaling to approximately 2000 scans. This dataset includes the scans, registrations to the SMPL model, scans segmented in clothing parts, garment category and size labels. Our experiments show better parsing accuracy and size prediction than baseline methods trained on SIZER. The code, model and dataset will be released for research purposes.
Evolving Multi-label Classification Rules by Exploiting High-order Label Correlation
Authors: Shabnam Nazmi, Xuyang Yan, Abdollah Homaifar, Emily Doucette
Date: 2020-07-22 00:00:00
Tasks: General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION, Vocal Bursts Intensity Prediction

In multi-label classification tasks, each problem instance is associated with multiple classes simultaneously. In such settings, the correlation between labels contains valuable information that can be used to obtain more accurate classification models. The correlation between labels can be exploited at different levels such as capturing the pair-wise correlation or exploiting the higher-order correlations. Even though the high-order approach is more capable of modeling the correlation, it is computationally more demanding and has scalability issues. This paper aims at exploiting the high-order label correlation within subsets of labels using a supervised learning classifier system (UCS). For this purpose, the label powerset (LP) strategy is employed and a prediction aggregation within the set of the relevant labels to an unseen instance is utilized to increase the prediction capability of the LP method in the presence of unseen labelsets. Exact match ratio and Hamming loss measures are considered to evaluate the rule performance and the expected fitness value of a classifier is investigated for both metrics. Also, a computational complexity analysis is provided for the proposed algorithm. The experimental results of the proposed method are compared with other well-known LP-based methods on multiple benchmark datasets and confirm the competitive performance of this method.
Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing
Authors: Ashkan Ebadi, Pengcheng Xi, Stéphane Tremblay, Bruce Spencer, Raman Pall, Alexander Wong
Date: 2020-07-22 00:00:00
Tasks: BIG-bench Machine Learning

The outbreak of the novel coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been continuously affecting human lives and communities around the world in many ways, from cities under lockdown to new social experiences. Although in most cases COVID-19 results in mild illness, it has drawn global attention due to the extremely contagious nature of SARS-CoV-2. Governments and healthcare professionals, along with people and society as a whole, have taken any measures to break the chain of transition and flatten the epidemic curve. In this study, we used multiple data sources, i.e., PubMed and ArXiv, and built several machine learning models to characterize the landscape of current COVID-19 research by identifying the latent topics and analyzing the temporal evolution of the extracted research themes, publications similarity, and sentiments, within the time-frame of January- May 2020. Our findings confirm the types of research available in PubMed and ArXiv differ significantly, with the former exhibiting greater diversity in terms of COVID-19 related issues and the latter focusing more on intelligent systems/tools to predict/diagnose COVID-19. The special attention of the research community to the high-risk groups and people with complications was also confirmed.
Shape-CD: Change-Point Detection in Time-Series Data with Shapes and Neurons
Authors: Varsha Suresh, Wei Tsang Ooi
Date: 2020-07-22 00:00:00
Tasks: Change Point Detection, Time Series, Time Series Analysis

Change-point detection in a time series aims to discover the time points at which some unknown underlying physical process that generates the time-series data has changed. We found that existing approaches become less accurate when the underlying process is complex and generates large varieties of patterns in the time series. To address this shortcoming, we propose Shape-CD, a simple, fast, and accurate change point detection method. Shape-CD uses shape-based features to model the patterns and a conditional neural field to model the temporal correlations among the time regions. We evaluated the performance of Shape-CD using four highly dynamic time-series datasets, including the ExtraSensory dataset with up to 2000 classes. Shape-CD demonstrated improved accuracy (7-60% higher in AUC) and faster computational speed compared to existing approaches. Furthermore, the Shape-CD model consists of only hundreds of parameters and require less data to train than other deep supervised learning models.
CNN+RNN Depth and Skeleton based Dynamic Hand Gesture Recognition
Authors: Kenneth Lai, Svetlana N. Yanushkevich
Date: 2020-07-22 00:00:00
Tasks: Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition

Human activity and gesture recognition is an important component of rapidly growing domain of ambient intelligence, in particular in assisting living and smart homes. In this paper, we propose to combine the power of two deep learning techniques, the convolutional neural networks (CNN) and the recurrent neural networks (RNN), for automated hand gesture recognition using both depth and skeleton data. Each of these types of data can be used separately to train neural networks to recognize hand gestures. While RNN were reported previously to perform well in recognition of sequences of movement for each skeleton joint given the skeleton information only, this study aims at utilizing depth data and apply CNN to extract important spatial information from the depth images. Together, the tandem CNN+RNN is capable of recognizing a sequence of gestures more accurately. As well, various types of fusion are studied to combine both the skeleton and depth information in order to extract temporal-spatial information. An overall accuracy of 85.46% is achieved on the dynamic hand gesture-14/28 dataset.
Dog Identification using Soft Biometrics and Neural Networks
Authors: Kenneth Lai, Xinyuan Tu, Svetlana Yanushkevich
Date: 2020-07-22 00:00:00
Tasks: Transfer Learning

This paper addresses the problem of biometric identification of animals, specifically dogs. We apply advanced machine learning models such as deep neural network on the photographs of pets in order to determine the pet identity. In this paper, we explore the possibility of using different types of "soft" biometrics, such as breed, height, or gender, in fusion with "hard" biometrics such as photographs of the pet's face. We apply the principle of transfer learning on different Convolutional Neural Networks, in order to create a network designed specifically for breed classification. The proposed network is able to achieve an accuracy of 90.80% and 91.29% when differentiating between the two dog breeds, for two different datasets. Without the use of "soft" biometrics, the identification rate of dogs is 78.09% but by using a decision network to incorporate "soft" biometrics, the identification rate can achieve an accuracy of 84.94%.
Multi-Metric Evaluation of Thermal-to-Visual Face Recognition
Authors: Kenneth Lai, Svetlana N. Yanushkevich
Date: 2020-07-22 00:00:00
Tasks: BIG-bench Machine Learning, Face Identification, Face Recognition, Face Verification, Image Generation

In this paper, we aim to address the problem of heterogeneous or cross-spectral face recognition using machine learning to synthesize visual spectrum face from infrared images. The synthesis of visual-band face images allows for more optimal extraction of facial features to be used for face identification and/or verification. We explore the ability to use Generative Adversarial Networks (GANs) for face image synthesis, and examine the performance of these images using pre-trained Convolutional Neural Networks (CNNs). The features extracted using CNNs are applied in face identification and verification. We explore the performance in terms of acceptance rate when using various similarity measures for face verification.
Deep Learning Based Segmentation of Various Brain Lesions for Radiosurgery
Authors: Siang-Ruei Wu, Hao-Yun Chang, Florence T Su, Heng-Chun Liao, Wanju Tseng, Chun-Chih Liao, Feipei Lai, Feng-Ming Hsu, Furen Xiao
Date: 2020-07-22 00:00:00
Tasks: Deep Learning, Segmentation, Semantic Segmentation

Semantic segmentation of medical images with deep learning models is rapidly developed. In this study, we benchmarked state-of-the-art deep learning segmentation algorithms on our clinical stereotactic radiosurgery dataset, demonstrating the strengths and weaknesses of these algorithms in a fairly practical scenario. In particular, we compared the model performances with respect to their sampling method, model architecture, and the choice of loss functions, identifying the suitable settings for their applications and shedding light on the possible improvements.
Unsupervised Deep Representation Learning for Real-Time Tracking
Authors: Ning Wang, Wengang Zhou, Yibing Song, Chao Ma, Wei Liu, Houqiang Li
Date: 2020-07-22 00:00:00
Tasks: Representation Learning, Visual Tracking

The advancement of visual tracking has continuously been brought by deep learning models. Typically, supervised learning is employed to train these models with expensive labeled data. In order to reduce the workload of manual annotations and learn to track arbitrary objects, we propose an unsupervised learning method for visual tracking. The motivation of our unsupervised learning is that a robust tracker should be effective in bidirectional tracking. Specifically, the tracker is able to forward localize a target object in successive frames and backtrace to its initial position in the first frame. Based on such a motivation, in the training process, we measure the consistency between forward and backward trajectories to learn a robust tracker from scratch merely using unlabeled videos. We build our framework on a Siamese correlation filter network, and propose a multi-frame validation scheme and a cost-sensitive loss to facilitate unsupervised learning. Without bells and whistles, the proposed unsupervised tracker achieves the baseline accuracy as classic fully supervised trackers while achieving a real-time speed. Furthermore, our unsupervised framework exhibits a potential in leveraging more unlabeled or weakly labeled data to further improve the tracking accuracy.
A Computation-Efficient CNN System for High-Quality Brain Tumor Segmentation
Authors: Yanming Sun, Chunyan Wang
Date: 2020-07-22 00:00:00
Tasks: Brain Tumor Segmentation, Semantic Segmentation, Tumor Segmentation, Vocal Bursts Intensity Prediction

The work presented in this paper is to propose a reliable high-quality system of Convolutional Neural Network (CNN) for brain tumor segmentation with a low computation requirement. The system consists of a CNN for the main processing for the segmentation, a pre-CNN block for data reduction and post-CNN refinement block. The unique CNN consists of 7 convolution layers involving only 108 kernels and 20308 trainable parameters. It is custom-designed, following the proposed paradigm of ASCNN (application specific CNN), to perform mono-modality and cross-modality feature extraction, tumor localization and pixel classification. Each layer fits the task assigned to it, by means of (i) appropriate normalization applied to its input data, (ii) correct convolution modes for the assigned task, and (iii) suitable nonlinear transformation to optimize the convolution results. In this specific design context, the number of kernels in each of the 7 layers is made to be just-sufficient for its task, instead of exponentially growing over the layers, to increase information density and to reduce randomness in the processing. The proposed activation function Full-ReLU helps to halve the number of kernels in convolution layers of high-pass filtering without degrading processing quality. A large number of experiments with BRATS2018 dataset have been conducted to measure the processing quality and reproducibility of the proposed system. The results demonstrate that the system reproduces reliably almost the same output to the same input after retraining. The mean dice scores for enhancing tumor, whole tumor and tumor core are 77.2%, 89.2% and 76.3%, respectively. The simple structure and reliable high processing quality of the proposed system will facilitate its implementation and medical applications.
PhishZip: A New Compression-based Algorithm for Detecting Phishing Websites
Authors: Rizka Purwanto, Arindam Pal, Alan Blair, Sanjay Jha
Date: 2020-07-22 00:00:00
Tasks: BIG-bench Machine Learning

Phishing has grown significantly in the past few years and is predicted to further increase in the future. The dynamics of phishing introduce challenges in implementing a robust phishing detection system and selecting features which can represent phishing despite the change of attack. In this paper, we propose PhishZip which is a novel phishing detection approach using a compression algorithm to perform website classification and demonstrate a systematic way to construct the word dictionaries for the compression models using word occurrence likelihood analysis. PhishZip outperforms the use of best-performing HTML-based features in past studies, with a true positive rate of 80.04%. We also propose the use of compression ratio as a novel machine learning feature which significantly improves machine learning based phishing detection over previous studies. Using compression ratios as additional features, the true positive rate significantly improves by 30.3% (from 51.47% to 81.77%), while the accuracy increases by 11.84% (from 71.20% to 83.04%).