pwc-archive/papers-with-abstracts huggingface dataset:
Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-Ray Images
Authors: Anas Tahir, Yazan Qiblawey, Amith Khandakar, Tawsifur Rahman, Uzair Khurshid, Farayi Musharavati, M. T. Islam, Serkan Kiranyaz, Muhammad E. H. Chowdhury
Date: 2020-05-23 00:00:00

Novel Coronavirus disease (COVID-19) is an extremely contagious and quickly spreading Coronavirus infestation. Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS), which outbreak in 2002 and 2011, and the current COVID-19 pandemic are all from the same family of coronavirus. This work aims to classify COVID-19, SARS, and MERS chest X-ray (CXR) images using deep Convolutional Neural Networks (CNNs). A unique database was created, so-called QU-COVID-family, consisting of 423 COVID-19, 144 MERS, and 134 SARS CXR images. Besides, a robust COVID-19 recognition system was proposed to identify lung regions using a CNN segmentation model (U-Net), and then classify the segmented lung images as COVID-19, MERS, or SARS using a pre-trained CNN classifier. Furthermore, the Score-CAM visualization method was utilized to visualize classification output and understand the reasoning behind the decision of deep CNNs. Several Deep Learning classifiers were trained and tested; four outperforming algorithms were reported. Original and preprocessed images were used individually and all together as the input(s) to the networks. Two recognition schemes were considered: plain CXR classification and segmented CXR classification. For plain CXRs, it was observed that InceptionV3 outperforms other networks with a 3-channel scheme and achieves sensitivities of 99.5%, 93.1%, and 97% for classifying COVID-19, MERS, and SARS images, respectively. In contrast, for segmented CXRs, InceptionV3 outperformed using the original CXR dataset and achieved sensitivities of 96.94%, 79.68%, and 90.26% for classifying COVID-19, MERS, and SARS images, respectively. All networks showed high COVID-19 detection sensitivity (>96%) with the segmented lung images. This indicates the unique radiographic signature of COVID-19 cases in the eyes of AI, which is often a challenging task for medical doctors.
A First Step Towards Content Protecting Plagiarism Detection
Authors: Cornelius Ihle, Moritz Schubotz, Norman Meuschke, Bela Gipp
Date: 2020-05-23 00:00:00

Plagiarism detection systems are essential tools for safeguarding academic and educational integrity. However, today's systems require disclosing the full content of the input documents and the document collection to which the input documents are compared. Moreover, the systems are centralized and under the control of individual, typically commercial providers. This situation raises procedural and legal concerns regarding the confidentiality of sensitive data, which can limit or prohibit the use of plagiarism detection services. To eliminate these weaknesses of current systems, we seek to devise a plagiarism detection approach that does not require a centralized provider nor exposing any content as cleartext. This paper presents the initial results of our research. Specifically, we employ Private Set Intersection to devise a content-protecting variant of the citation-based similarity measure Bibliographic Coupling implemented in our plagiarism detection system HyPlag. Our evaluation shows that the content-protecting method achieves the same detection effectiveness as the original method while making common attacks to disclose the protected content practically infeasible. Our future work will extend this successful proof-of-concept by devising plagiarism detection methods that can analyze the entire content of documents without disclosing it as cleartext.
Pythia: Grammar-Based Fuzzing of REST APIs with Coverage-guided Feedback and Learning-based Mutations
Authors: Vaggelis Atlidakis, Roxana Geambasu, Patrice Godefroid, Marina Polishchuk, Baishakhi Ray
Date: 2020-05-23 00:00:00
Tasks: valid

This paper introduces Pythia, the first fuzzer that augments grammar-based fuzzing with coverage-guided feedback and a learning-based mutation strategy for stateful REST API fuzzing. Pythia uses a statistical model to learn common usage patterns of a target REST API from structurally valid seed inputs. It then generates learning-based mutations by injecting a small amount of noise deviating from common usage patterns while still maintaining syntactic validity. Pythia's mutation strategy helps generate grammatically valid test cases and coverage-guided feedback helps prioritize the test cases that are more likely to find bugs. We present experimental evaluation on three production-scale, open-source cloud services showing that Pythia outperforms prior approaches both in code coverage and new bugs found. Using Pythia, we found 29 new bugs which we are in the process of reporting to the respective service owners.
From Witch's Shot to Music Making Bones -- Resources for Medical Laymen to Technical Language and Vice Versa
Authors: Laura Seiffe, Oliver Marten, Michael Mikhailov, Sven Schmeier, Sebastian Möller, Roland Roller
Date: 2020-05-23 00:00:00
Tasks: Descriptive

Many people share information in social media or forums, like food they eat, sports activities they do or events which have been visited. This also applies to information about a person's health status. Information we share online unveils directly or indirectly information about our lifestyle and health situation and thus provides a valuable data resource. If we can make advantage of that data, applications can be created that enable e.g. the detection of possible risk factors of diseases or adverse drug reactions of medications. However, as most people are not medical experts, language used might be more descriptive rather than the precise medical expression as medics do. To detect and use those relevant information, laymen language has to be translated and/or linked to the corresponding medical concept. This work presents baseline data sources in order to address this challenge for German. We introduce a new data set which annotates medical laymen and technical expressions in a patient forum, along with a set of medical synonyms and definitions, and present first baseline results on the data.
Summarizing and Exploring Tabular Data in Conversational Search
Authors: Shuo Zhang, Zhuyun Dai, Krisztian Balog, Jamie Callan
Date: 2020-05-23 00:00:00
Tasks: Conversational Search

Tabular data provide answers to a significant portion of search queries. However, reciting an entire result table is impractical in conversational search systems. We propose to generate natural language summaries as answers to describe the complex information contained in a table. Through crowdsourcing experiments, we build a new conversation-oriented, open-domain table summarization dataset. It includes annotated table summaries, which not only answer questions but also help people explore other information in the table. We utilize this dataset to develop automatic table summarization systems as SOTA baselines. Based on the experimental results, we identify challenges and point out future research directions that this resource will support.
AnimGAN: A Spatiotemporally-Conditioned Generative Adversarial Network for Character Animation
Authors: Maryam Sadat Mirzaei, Kourosh Meshgi, Etienne Frigo, Toyoaki Nishida
Date: 2020-05-23 00:00:00
Tasks: Generative Adversarial Network

Producing realistic character animations is one of the essential tasks in human-AI interactions. Considered as a sequence of poses of a humanoid, the task can be considered as a sequence generation problem with spatiotemporal smoothness and realism constraints. Additionally, we wish to control the behavior of AI agents by giving them what to do and, more specifically, how to do it. We proposed a spatiotemporally-conditioned GAN that generates a sequence that is similar to a given sequence in terms of semantics and spatiotemporal dynamics. Using LSTM-based generator and graph ConvNet discriminator, this system is trained end-to-end on a large gathered dataset of gestures, expressions, and actions. Experiments showed that compared to traditional conditional GAN, our method creates plausible, realistic, and semantically relevant humanoid animation sequences that match user expectations.
Self-Training for Domain Adaptive Scene Text Detection
Authors: Yudi Chen, Wei Wang, Yu Zhou, Fei Yang, Dongbao Yang, Weiping Wang
Date: 2020-05-23 00:00:00
Tasks: Image to Video Generation, Scene Text Detection, Text Detection, Video Generation

Though deep learning based scene text detection has achieved great progress, well-trained detectors suffer from severe performance degradation for different domains. In general, a tremendous amount of data is indispensable to train the detector in the target domain. However, data collection and annotation are expensive and time-consuming. To address this problem, we propose a self-training framework to automatically mine hard examples with pseudo-labels from unannotated videos or images. To reduce the noise of hard examples, a novel text mining module is implemented based on the fusion of detection and tracking results. Then, an image-to-video generation method is designed for the tasks that videos are unavailable and only images can be used. Experimental results on standard benchmarks, including ICDAR2015, MSRA-TD500, ICDAR2017 MLT, demonstrate the effectiveness of our self-training method. The simple Mask R-CNN adapted with self-training and fine-tuned on real data can achieve comparable or even superior results with the state-of-the-art methods.
TIPRDC: Task-Independent Privacy-Respecting Data Crowdsourcing Framework for Deep Learning with Anonymized Intermediate Representations
Authors: Ang Li, Yixiao Duan, Huanrui Yang, Yiran Chen, Jianlei Yang
Date: 2020-05-23 00:00:00

The success of deep learning partially benefits from the availability of various large-scale datasets. These datasets are often crowdsourced from individual users and contain private information like gender, age, etc. The emerging privacy concerns from users on data sharing hinder the generation or use of crowdsourcing datasets and lead to hunger of training data for new deep learning applications. One na\"{\i}ve solution is to pre-process the raw data to extract features at the user-side, and then only the extracted features will be sent to the data collector. Unfortunately, attackers can still exploit these extracted features to train an adversary classifier to infer private attributes. Some prior arts leveraged game theory to protect private attributes. However, these defenses are designed for known primary learning tasks, the extracted features work poorly for unknown learning tasks. To tackle the case where the learning task may be unknown or changing, we present TIPRDC, a task-independent privacy-respecting data crowdsourcing framework with anonymized intermediate representation. The goal of this framework is to learn a feature extractor that can hide the privacy information from the intermediate representations; while maximally retaining the original information embedded in the raw data for the data collector to accomplish unknown learning tasks. We design a hybrid training method to learn the anonymized intermediate representation: (1) an adversarial training process for hiding private information from features; (2) maximally retain original information using a neural-network-based mutual information estimator.
Short-term Load Forecasting Based on Hybrid Strategy Using Warm-start Gradient Tree Boosting
Authors: Yuexin Zhang, Jiahong Wang
Date: 2020-05-23 00:00:00
Tasks: Load Forecasting

A deep-learning-based hybrid strategy for short-term load forecasting is presented. The strategy proposes a novel tree-based ensemble method Warm-start Gradient Tree Boosting (WGTB). Current strategies either ensemble submodels of a single type, which fail to take advantage of the statistical strengths of different inference models. Or they simply sum the outputs from completely different inference models, which doesn't maximize the potential of ensemble. Inspired by the bias-variance trade-off, WGTB is proposed and tailored to the great disparity among different inference models on accuracy, volatility and linearity. The complete strategy integrates four different inference models of different capacities. WGTB then ensembles their outputs by a warm-start and a hybrid of bagging and boosting, which lowers bias and variance concurrently. It is validated on two real datasets from State Grid Corporation of China of hourly resolution. The result demonstrates the effectiveness of the proposed strategy that hybridizes the statistical strengths of both low-bias and low-variance inference models.
Attention-guided Context Feature Pyramid Network for Object Detection
Authors: Junxu Cao, Qi Chen, Jun Guo, Ruichao Shi
Date: 2020-05-23 00:00:00
Tasks: Instance Segmentation, Object, object-detection, Object Detection, Open-Ended Question Answering, Semantic Segmentation

For object detection, how to address the contradictory requirement between feature map resolution and receptive field on high-resolution inputs still remains an open question. In this paper, to tackle this issue, we build a novel architecture, called Attention-guided Context Feature Pyramid Network (AC-FPN), that exploits discriminative information from various large receptive fields via integrating attention-guided multi-path features. The model contains two modules. The first one is Context Extraction Module (CEM) that explores large contextual information from multiple receptive fields. As redundant contextual relations may mislead localization and recognition, we also design the second module named Attention-guided Module (AM), which can adaptively capture the salient dependencies over objects by using the attention mechanism. AM consists of two sub-modules, i.e., Context Attention Module (CxAM) and Content Attention Module (CnAM), which focus on capturing discriminative semantics and locating precise positions, respectively. Most importantly, our AC-FPN can be readily plugged into existing FPN-based models. Extensive experiments on object detection and instance segmentation show that existing models with our proposed CEM and AM significantly surpass their counterparts without them, and our model successfully obtains state-of-the-art results. We have released the source code at https://github.com/Caojunxu/AC-FPN.
Delving into the Imbalance of Positive Proposals in Two-stage Object Detection
Authors: Zheng Ge, Zequn Jie, Xin Huang, Chengzheng Li, Osamu Yoshie
Date: 2020-05-23 00:00:00
Tasks: object-detection, Object Detection

Imbalance issue is a major yet unsolved bottleneck for the current object detection models. In this work, we observe two crucial yet never discussed imbalance issues. The first imbalance lies in the large number of low-quality RPN proposals, which makes the R-CNN module (i.e., post-classification layers) become highly biased towards the negative proposals in the early training stage. The second imbalance stems from the unbalanced ground-truth numbers across different testing images, resulting in the imbalance of the number of potentially existing positive proposals in testing phase. To tackle these two imbalance issues, we incorporates two innovations into Faster R-CNN: 1) an R-CNN Gradient Annealing (RGA) strategy to enhance the impact of positive proposals in the early training stage. 2) a set of Parallel R-CNN Modules (PRM) with different positive/negative sampling ratios during training on one same backbone. Our RGA and PRM can totally bring 2.0% improvements on AP on COCO minival. Experiments on CrowdHuman further validates the effectiveness of our innovations across various kinds of object detection tasks.
Learning from Naturalistic Driving Data for Human-like Autonomous Highway Driving
Authors: Donghao Xu, Zhezhang Ding, Xu He, Huijing Zhao, Mathieu Moze, François Aioun, Franck Guillemard
Date: 2020-05-23 00:00:00
Tasks: Motion Planning

Driving in a human-like manner is important for an autonomous vehicle to be a smart and predictable traffic participant. To achieve this goal, parameters of the motion planning module should be carefully tuned, which needs great effort and expert knowledge. In this study, a method of learning cost parameters of a motion planner from naturalistic driving data is proposed. The learning is achieved by encouraging the selected trajectory to approximate the human driving trajectory under the same traffic situation. The employed motion planner follows a widely accepted methodology that first samples candidate trajectories in the trajectory space, then select the one with minimal cost as the planned trajectory. Moreover, in addition to traditional factors such as comfort, efficiency and safety, the cost function is proposed to incorporate incentive of behavior decision like a human driver, so that both lane change decision and motion planning are coupled into one framework. Two types of lane incentive cost -- heuristic and learning based -- are proposed and implemented. To verify the validity of the proposed method, a data set is developed by using the naturalistic trajectory data of human drivers collected on the motorways in Beijing, containing samples of lane changes to the left and right lanes, and car followings. Experiments are conducted with respect to both lane change decision and motion planning, and promising results are achieved.
Joint Training Capsule Network for Cold Start Recommendation
Authors: Ting-Ting Liang, Congying Xia, Yuyu Yin, Philip S. Yu
Date: 2020-05-23 00:00:00

This paper proposes a novel neural network, joint training capsule network (JTCN), for the cold start recommendation task. We propose to mimic the high-level user preference other than the raw interaction history based on the side information for the fresh users. Specifically, an attentive capsule layer is proposed to aggregate high-level user preference from the low-level interaction history via a dynamic routing-by-agreement mechanism. Moreover, JTCN jointly trains the loss for mimicking the user preference and the softmax loss for the recommendation together in an end-to-end manner. Experiments on two publicly available datasets demonstrate the effectiveness of the proposed model. JTCN improves other state-of-the-art methods at least 7.07% for CiteULike and 16.85% for Amazon in terms of Recall@100 in cold start recommendation.
Misalignment Resilient Diffractive Optical Networks
Authors: Deniz Mengu, Yifan Zhao, Nezih T. Yardimci, Yair Rivenson, Mona Jarrahi, Aydogan Ozcan
Date: 2020-05-23 00:00:00
Tasks: Object Recognition

As an optical machine learning framework, Diffractive Deep Neural Networks (D2NN) take advantage of data-driven training methods used in deep learning to devise light-matter interaction in 3D for performing a desired statistical inference task. Multi-layer optical object recognition platforms designed with this diffractive framework have been shown to generalize to unseen image data achieving e.g., >98% blind inference accuracy for hand-written digit classification. The multi-layer structure of diffractive networks offers significant advantages in terms of their diffraction efficiency, inference capability and optical signal contrast. However, the use of multiple diffractive layers also brings practical challenges for the fabrication and alignment of these diffractive systems for accurate optical inference. Here, we introduce and experimentally demonstrate a new training scheme that significantly increases the robustness of diffractive networks against 3D misalignments and fabrication tolerances in the physical implementation of a trained diffractive network. By modeling the undesired layer-to-layer misalignments in 3D as continuous random variables in the optical forward model, diffractive networks are trained to maintain their inference accuracy over a large range of misalignments; we term this diffractive network design as vaccinated D2NN (v-D2NN). We further extend this vaccination strategy to the training of diffractive networks that use differential detectors at the output plane as well as to jointly-trained hybrid (optical-electronic) networks to reveal that all of these diffractive designs improve their resilience to misalignments by taking into account possible 3D fabrication variations and displacements during their training phase.
COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining
Authors: Yixian Zhang, Jieren Chen, Boyi Liu, Yifan Yang, Haocheng Li, Xinyi Zheng, Xi Chen, Tenglong Ren, Naixue Xiong
Date: 2020-05-23 00:00:00
Tasks: Chinese Sentiment Analysis, Data Visualization, Sentiment Analysis, Time Series, Time Series Analysis

With the spread and development of new epidemics, it is of great reference value to identify the changing trends of epidemics in public emotions. We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining. A new word structure discovery scheme based on the timing explosion of network topics and a Chinese sentiment analysis method for the COVID-19 public opinion environment is proposed. Establish a "Scrapy-Redis-Bloomfilter" distributed crawler framework to collect data. The system can judge the positive and negative emotions of the reviewer based on the comments, and can also reflect the depth of the seven emotions such as Hopeful, Happy, and Depressed. Finally, we improved the sentiment discriminant model of this system and compared the sentiment discriminant error of COVID-19 related comments with the Jiagu deep learning model. The results show that our model has better generalization ability and smaller discriminant error. We designed a large data visualization screen, which can clearly show the trend of public emotions, the proportion of various emotion categories, keywords, hot topics, etc., and fully and intuitively reflect the development of public opinion.
Fine-Grain Few-Shot Vision via Domain Knowledge as Hyperspherical Priors
Authors: Bijan Haney, Alexander Lavin
Date: 2020-05-23 00:00:00
Tasks: Classification, Few-Shot Learning, General Classification

Prototypical networks have been shown to perform well at few-shot learning tasks in computer vision. Yet these networks struggle when classes are very similar to each other (fine-grain classification) and currently have no way of taking into account prior knowledge (through the use of tabular data). Using a spherical latent space to encode prototypes, we can achieve few-shot fine-grain classification by maximally separating the classes while incorporating domain knowledge as informative priors. We describe how to construct a hypersphere of prototypes that embed a-priori domain information, and demonstrate the effectiveness of the approach on challenging benchmark datasets for fine-grain classification, with top results for one-shot classification and 5x speedups in training time.