pwc-archive/papers-with-abstracts huggingface dataset:
A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for Question Answering Over Dynamic Contexts
Authors: Wanjun Zhong, Duyu Tang, Nan Duan, Ming Zhou, Jiahai Wang, Jian Yin
Date: 2020-04-25 00:00:00
Tasks: Decision Making, Graph Neural Network, Question Answering

We study question answering over a dynamic textual environment. Although neural network models achieve impressive accuracy via learning from input-output examples, they rarely leverage various types of knowledge and are generally not interpretable. In this work, we propose a graph-based approach, where a heterogeneous graph is automatically built with factual knowledge of the context, temporal knowledge of the past states, and logical knowledge that combines human-curated knowledge bases and rule bases. We develop a graph neural network over the constructed graph, and train the model in an end-to-end manner. Experimental results on a benchmark dataset show that the injection of various types of knowledge improves a strong neural network baseline. An additional benefit of our approach is that the graph itself naturally serves as a rational behind the decision making.
GPO: Global Plane Optimization for Fast and Accurate Monocular SLAM Initialization
Authors: Sicong Du, Hengkai Guo, Yao Chen, Yilun Lin, Xiangbing Meng, Linfu Wen, Fei-Yue Wang
Date: 2020-04-25 00:00:00
Tasks: Homography Estimation, Simultaneous Localization and Mapping

Initialization is essential to monocular Simultaneous Localization and Mapping (SLAM) problems. This paper focuses on a novel initialization method for monocular SLAM based on planar features. The algorithm starts by homography estimation in a sliding window. It then proceeds to a global plane optimization (GPO) to obtain camera poses and the plane normal. 3D points can be recovered using planar constraints without triangulation. The proposed method fully exploits the plane information from multiple frames and avoids the ambiguities in homography decomposition. We validate our algorithm on the collected chessboard dataset against baseline implementations and present extensive analysis. Experimental results show that our method outperforms the fine-tuned baselines in both accuracy and real-time.
The Dynamic Travelling Thief Problem: Benchmarks and Performance of Evolutionary Algorithms
Authors: Ragav Sachdeva, Frank Neumann, Markus Wagner
Date: 2020-04-25 00:00:00
Tasks: Evolutionary Algorithms, valid

Many real-world optimisation problems involve dynamic and stochastic components. While problems with multiple interacting components are omnipresent in inherently dynamic domains like supply-chain optimisation and logistics, most research on dynamic problems focuses on single-component problems. With this article, we define a number of scenarios based on the Travelling Thief Problem to enable research on the effect of dynamic changes to sub-components. Our investigations of 72 scenarios and seven algorithms show that -- depending on the instance, the magnitude of the change, and the algorithms in the portfolio -- it is preferable to either restart the optimisation from scratch or to continue with the previously valid solutions.
When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People?
Authors: Kenneth Joseph, Jonathan H. Morgan
Date: 2020-04-25 00:00:00
Proceeding: ACL 2020 6
Tasks: Survey, Word Embeddings

Social biases are encoded in word embeddings. This presents a unique opportunity to study society historically and at scale, and a unique danger when embeddings are used in downstream applications. Here, we investigate the extent to which publicly-available word embeddings accurately reflect beliefs about certain kinds of people as measured via traditional survey methods. We find that biases found in word embeddings do, on average, closely mirror survey data across seventeen dimensions of social meaning. However, we also find that biases in embeddings are much more reflective of survey data for some dimensions of meaning (e.g. gender) than others (e.g. race), and that we can be highly confident that embedding-based measures reflect survey data only for the most salient biases.
Memory-efficient training with streaming dimensionality reduction
Authors: Siyuan Huang, Brian D. Hoskins, Matthew W. Daniels, Mark D. Stiles, Gina C. Adam
Date: 2020-04-25 00:00:00
Tasks: BIG-bench Machine Learning, Dimensionality Reduction

The movement of large quantities of data during the training of a Deep Neural Network presents immense challenges for machine learning workloads. To minimize this overhead, especially on the movement and calculation of gradient information, we introduce streaming batch principal component analysis as an update algorithm. Streaming batch principal component analysis uses stochastic power iterations to generate a stochastic k-rank approximation of the network gradient. We demonstrate that the low rank updates produced by streaming batch principal component analysis can effectively train convolutional neural networks on a variety of common datasets, with performance comparable to standard mini batch gradient descent. These results can lead to both improvements in the design of application specific integrated circuits for deep learning and in the speed of synchronization of machine learning models trained with data parallelism.
Deep convolutional neural networks for face and iris presentation attack detection: Survey and case study
Authors: Yomna Safaa El-Din, Mohamed N. Moustafa, Hani Mahdi
Date: 2020-04-25 00:00:00
Tasks: Iris Recognition

Biometric presentation attack detection is gaining increasing attention. Users of mobile devices find it more convenient to unlock their smart applications with finger, face or iris recognition instead of passwords. In this paper, we survey the approaches presented in the recent literature to detect face and iris presentation attacks. Specifically, we investigate the effectiveness of fine tuning very deep convolutional neural networks to the task of face and iris antispoofing. We compare two different fine tuning approaches on six publicly available benchmark datasets. Results show the effectiveness of these deep models in learning discriminative features that can tell apart real from fake biometric images with very low error rate. Cross-dataset evaluation on face PAD showed better generalization than state of the art. We also performed cross-dataset testing on iris PAD datasets in terms of equal error rate which was not reported in literature before. Additionally, we propose the use of a single deep network trained to detect both face and iris attacks. We have not noticed accuracy degradation compared to networks trained for only one biometric separately. Finally, we analyzed the learned features by the network, in correlation with the image frequency components, to justify its prediction decision.
Fuzzy Logic Based Integration of Web Contextual Linguistic Structures for Enriching Conceptual Visual Representations
Authors: M. Belkhatir
Date: 2020-04-25 00:00:00

Due to the difficulty of automatically mapping visual features with semantic descriptors, state-of-the-art frameworks have exhibited poor performance in terms of coverage and effectiveness for indexing the visual content. This prompted us to investigate the use of both the Web as a large information source from where to extract relevant contextual linguistic information and bimodal visual-textual indexing as a technique to enrich the vocabulary of index concepts. Our proposal is based on the Signal/Semantic approach for multimedia indexing which generates multi-facetted conceptual representations of the visual content. We propose to enrich these image representations with concepts automatically extracted from the visual contextual information. We specifically target the integration of semantic concepts which are more specific than the initial index concepts since they represent the visual content with greater accuracy and precision. Also, we aim to correct the faulty indexes resulting from the automatic semantic tagging. Experimentally, the details of the prototyping are given and the presented technique is tested in a Web-scale evaluation on 30 queries representing elaborate image scenes.
Analysis & Shortcomings of E-Recruitment Systems: Towards a Semantics-based Approach Addressing Knowledge Incompleteness and Limited Domain Coverage
Authors: M. Maree, A. Kmail, M. Belkhatir
Date: 2020-04-25 00:00:00
Tasks: Management

The rapid development of the Internet has led to introducing new methods for e-recruitment and human resources management. These methods aim to systematically address the limitations of conventional recruitment procedures through incorporating natural language processing tools and semantics-based methods. In this context, for a given job post, applicant resumes (usually uploaded as free-text unstructured documents in different formats such as .pdf, .doc, or .rtf) are matched/screened out using the conventional keyword-based model enriched by additional resources such as occupational categories and semantics-based techniques. Employing these techniques has proved to be effective in reducing the cost, time, and efforts required in traditional recruitment and candidate selection methods. However, the skill gap, i.e. the propensity to precisely detect and extract relevant skills in applicant resumes and job posts, and the hidden semantic dimensions encoded in applicant resumes still form a major obstacle for e-recruitment systems. This is due to the fact that resources exploited by current e-recruitment systems are obtained from generic domain-independent sources, therefore resulting in knowledge incompleteness and the lack of domain coverage. In this paper, we review state-of-the-art e-recruitment approaches and highlight recent advancements in this domain. An e-recruitment framework addressing current shortcomings through the use of multiple cooperative semantic resources, feature extraction techniques and skill relatedness measures is detailed. An instantiation of the proposed framework is proposed and an experimental validation using a real-world recruitment dataset from two employment portals demonstrates the effectiveness of the proposed approach.
StRDAN: Synthetic-to-Real Domain Adaptation Network for Vehicle Re-Identification
Authors: Sangrok Lee, Eunsoo Park, Hongsuk Yi, Sang Hun Lee
Date: 2020-04-25 00:00:00
Tasks: Domain Adaptation, Vehicle Re-Identification

Vehicle re-identification aims to obtain the same vehicles from vehicle images. This is challenging but essential for analyzing and predicting traffic flow in the city. Although deep learning methods have achieved enormous progress for this task, their large data requirement is a critical shortcoming. Therefore, we propose a synthetic-to-real domain adaptation network (StRDAN) framework, which can be trained with inexpensive large-scale synthetic and real data to improve performance. The StRDAN training method combines domain adaptation and semi-supervised learning methods and their associated losses. StRDAN offers significant improvement over the baseline model, which can only be trained using real data, for VeRi and CityFlow-ReID datasets, achieving 3.1% and 12.9% improved mean average precision, respectively.
On the Role of Visual Cues in Audiovisual Speech Enhancement
Authors: Zakaria Aldeneh, Anushree Prasanna Kumar, Barry-John Theobald, Erik Marchi, Sachin Kajarekar, Devang Naik, Ahmed Hussen Abdelaziz
Date: 2020-04-25 00:00:00
Tasks: Self-Supervised Learning, Speech Enhancement

We present an introspection of an audiovisual speech enhancement model. In particular, we focus on interpreting how a neural audiovisual speech enhancement model uses visual cues to improve the quality of the target speech signal. We show that visual cues provide not only high-level information about speech activity, i.e., speech/silence, but also fine-grained visual information about the place of articulation. One byproduct of this finding is that the learned visual embeddings can be used as features for other visual speech applications. We demonstrate the effectiveness of the learned visual embeddings for classifying visemes (the visual analogy to phonemes). Our results provide insight into important aspects of audiovisual speech enhancement and demonstrate how such models can be used for self-supervision tasks for visual speech applications.
Two-Stage Penalized Regression Screening to Detect Biomarker-Treatment Interactions in Randomized Clinical Trials
Authors: Jixiong Wang, Ashish Patel, James M. S. Wason, Paul J. Newcombe
Date: 2020-04-25 00:00:00
Tasks: regression

High-dimensional biomarkers such as genomics are increasingly being measured in randomized clinical trials. Consequently, there is a growing interest in developing methods that improve the power to detect biomarker-treatment interactions. We adapt recently proposed two-stage interaction detecting procedures in the setting of randomized clinical trials. We also propose a new stage 1 multivariate screening strategy using ridge regression to account for correlations among biomarkers. For this multivariate screening, we prove the asymptotic between-stage independence, required for family-wise error rate control, under biomarker-treatment independence. Simulation results show that in various scenarios, the ridge regression screening procedure can provide substantially greater power than the traditional one-biomarker-at-a-time screening procedure in highly correlated data. We also exemplify our approach in two real clinical trial data applications.
Deepfakes Detection with Automatic Face Weighting
Authors: Daniel Mas Montserrat, Hanxiang Hao, S. K. Yarlagadda, Sriram Baireddy, Ruiting Shao, János Horváth, Emily Bartusiak, Justin Yang, David Güera, Fengqing Zhu, Edward J. Delp
Date: 2020-04-25 00:00:00
Tasks: DeepFake Detection, Face Swapping

Altered and manipulated multimedia is increasingly present and widely distributed via social media platforms. Advanced video manipulation tools enable the generation of highly realistic-looking altered multimedia. While many methods have been presented to detect manipulations, most of them fail when evaluated with data outside of the datasets used in research environments. In order to address this problem, the Deepfake Detection Challenge (DFDC) provides a large dataset of videos containing realistic manipulations and an evaluation system that ensures that methods work quickly and accurately, even when faced with challenging data. In this paper, we introduce a method based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) that extracts visual and temporal features from faces present in videos to accurately detect manipulations. The method is evaluated with the DFDC dataset, providing competitive results compared to other techniques.
A Relation Spectrum Inheriting Taylor Series: Muscle Synergy and Coupling for Hand
Authors: Gang Liu, Jing Wang
Date: 2020-04-25 00:00:00
Tasks: Math, Relation

There are two famous function decomposition methods in math: Taylor Series and Fourier Series. Fourier series developed into Fourier spectrum, which was applied to signal decomposition\analysis. However, because the Taylor series whose function without a definite functional expression cannot be solved, Taylor Series has rarely been used in engineering. Here, we developed Taylor series by our Dendrite Net, constructed a relation spectrum, and applied it to model or system decomposition\analysis. Specific engineering: the knowledge of the intuitive link between muscle activity and the finger movement is vital for the design of commercial prosthetic hands that do not need user pre-training. However, this link has yet to be understood due to the complexity of human hand. In this study, the relation spectrum was applied to analyze the muscle-finger system. One single muscle actuates multiple fingers, or multiple muscles actuate one single finger simultaneously. Thus, the research was in muscle synergy and muscle coupling for hand. This paper has two main contributions. (1) The findings of hand contribute to designing prosthetic hands. (2) The relation spectrum makes the online model human-readable, which unifies online performance and offline results. Code (novel tool for most fields) is available at https://github.com/liugang1234567/Gang-neuron.
Finite-sample Analysis of Interpolating Linear Classifiers in the Overparameterized Regime
Authors: Niladri S. Chatterji, Philip M. Long
Date: 2020-04-25 00:00:00

We prove bounds on the population risk of the maximum margin algorithm for two-class linear classification. For linearly separable training data, the maximum margin algorithm has been shown in previous work to be equivalent to a limit of training with logistic loss using gradient descent, as the training error is driven to zero. We analyze this algorithm applied to random data including misclassification noise. Our assumptions on the clean data include the case in which the class-conditional distributions are standard normal distributions. The misclassification noise may be chosen by an adversary, subject to a limit on the fraction of corrupted labels. Our bounds show that, with sufficient over-parameterization, the maximum margin algorithm trained on noisy data can achieve nearly optimal population risk.
Contextualized Representations Using Textual Encyclopedic Knowledge
Authors: Mandar Joshi, Kenton Lee, Yi Luan, Kristina Toutanova
Date: 2020-04-24 00:00:00
Tasks: Language Modeling, Language Modelling, Reading Comprehension, TriviaQA

We present a method to represent input texts by contextualizing them jointly with dynamically retrieved textual encyclopedic background knowledge from multiple documents. We apply our method to reading comprehension tasks by encoding questions and passages together with background sentences about the entities they mention. We show that integrating background knowledge from text is effective for tasks focusing on factual reasoning and allows direct reuse of powerful pretrained BERT-style encoders. Moreover, knowledge integration can be further improved with suitable pretraining via a self-supervised masked language model objective over words in background-augmented input text. On TriviaQA, our approach obtains improvements of 1.6 to 3.1 F1 over comparable RoBERTa models which do not integrate background knowledge dynamically. On MRQA, a large collection of diverse QA datasets, we see consistent gains in-domain along with large improvements out-of-domain on BioASQ (2.1 to 4.2 F1), TextbookQA (1.6 to 2.0 F1), and DuoRC (1.1 to 2.0 F1).
Neural Head Reenactment with Latent Pose Descriptors
Authors: Egor Burkov, Igor Pasechnik, Artur Grigorev, Victor Lempitsky
Date: 2020-04-24 00:00:00
Proceeding: CVPR 2020 6
Tasks: Foreground Segmentation, Image Reconstruction, Retrieval

We propose a neural head reenactment system, which is driven by a latent pose representation and is capable of predicting the foreground segmentation alongside the RGB image. The latent pose representation is learned as a part of the entire reenactment system, and the learning process is based solely on image reconstruction losses. We show that despite its simplicity, with a large and diverse enough training dataset, such learning successfully decomposes pose from identity. The resulting system can then reproduce mimics of the driving person and, furthermore, can perform cross-person reenactment. Additionally, we show that the learned descriptors are useful for other pose-related tasks, such as keypoint prediction and pose-based retrieval.
Syntactic Data Augmentation Increases Robustness to Inference Heuristics
Authors: Junghyun Min, R. Thomas McCoy, Dipanjan Das, Emily Pitler, Tal Linzen
Date: 2020-04-24 00:00:00
Proceeding: ACL 2020 6
Tasks: Data Augmentation, Natural Language Inference, Sensitivity

Pretrained neural models such as BERT, when fine-tuned to perform natural language inference (NLI), often show high accuracy on standard datasets, but display a surprising lack of sensitivity to word order on controlled challenge sets. We hypothesize that this issue is not primarily caused by the pretrained model's limitations, but rather by the paucity of crowdsourced NLI examples that might convey the importance of syntactic structure at the fine-tuning stage. We explore several methods to augment standard training sets with syntactically informative examples, generated by applying syntactic transformations to sentences from the MNLI corpus. The best-performing augmentation method, subject/object inversion, improved BERT's accuracy on controlled examples that diagnose sensitivity to word order from 0.28 to 0.73, without affecting performance on the MNLI test set. This improvement generalized beyond the particular construction used for data augmentation, suggesting that augmentation causes BERT to recruit abstract syntactic representations.
New Protocols and Negative Results for Textual Entailment Data Collection
Authors: Samuel R. Bowman, Jennimaria Palomaki, Livio Baldini Soares, Emily Pitler
Date: 2020-04-24 00:00:00
Proceeding: EMNLP 2020 11
Tasks: Benchmarking, Diversity, Natural Language Inference, Transfer Learning

Natural language inference (NLI) data has proven useful in benchmarking and, especially, as pretraining data for tasks requiring language understanding. However, the crowdsourcing protocol that was used to collect this data has known issues and was not explicitly optimized for either of these purposes, so it is likely far from ideal. We propose four alternative protocols, each aimed at improving either the ease with which annotators can produce sound training examples or the quality and diversity of those examples. Using these alternatives and a fifth baseline protocol, we collect and compare five new 8.5k-example training sets. In evaluations focused on transfer learning applications, our results are solidly negative, with models trained on our baseline dataset yielding good transfer performance to downstream tasks, but none of our four new methods (nor the recent ANLI) showing any improvements over that baseline. In a small silver lining, we observe that all four new protocols, especially those where annotators edit pre-filled text boxes, reduce previously observed issues with annotation artifacts.
Explicit Domain Adaptation with Loosely Coupled Samples
Authors: Oliver Scheel, Loren Schwarz, Nassir Navab, Federico Tombari
Date: 2020-04-24 00:00:00
Tasks: Autonomous Driving, Domain Adaptation, image-classification, Image Classification, Time Series, Time Series Analysis, Transfer Learning

Transfer learning is an important field of machine learning in general, and particularly in the context of fully autonomous driving, which needs to be solved simultaneously for many different domains, such as changing weather conditions and country-specific driving behaviors. Traditional transfer learning methods often focus on image data and are black-box models. In this work we propose a transfer learning framework, core of which is learning an explicit mapping between domains. Due to its interpretability, this is beneficial for safety-critical applications, like autonomous driving. We show its general applicability by considering image classification problems and then move on to time-series data, particularly predicting lane changes. In our evaluation we adapt a pre-trained model to a dataset exhibiting different driving and sensory characteristics.