pwc-archive/papers-with-abstracts huggingface dataset:
An Empirical Study of UMLS Concept Extraction from Clinical Notes using Boolean Combination Ensembles
Authors: Greg M. Silverman, Raymond L. Finzel, Michael V. Heinz, Jake Vasilakes, Jacob C. Solinsky, Reed McEwan, Benjamin C. Knoll, Christopher J. Tignanelli, Hongfang Liu, Hua Xu, Xiaoqian Jiang, Genevieve B. Melton, Serguei VS Pakhomov
Date: 2021-08-04 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Our objective in this study is to investigate the behavior of Boolean operators on combining annotation output from multiple Natural Language Processing (NLP) systems across multiple corpora and to assess how filtering by aggregation of Unified Medical Language System (UMLS) Metathesaurus concepts affects system performance for Named Entity Recognition (NER) of UMLS concepts. We used three corpora annotated for UMLS concepts: 2010 i2b2 VA challenge set (31,161 annotations), Multi-source Integrated Platform for Answering Clinical Questions (MiPACQ) corpus (17,457 annotations including UMLS concept unique identifiers), and Fairview Health Services corpus (44,530 annotations). Our results showed that for UMLS concept matching, Boolean ensembling of the MiPACQ corpus trended towards higher performance over individual systems. Use of an approximate grid-search can help optimize the precision-recall tradeoff and can provide a set of heuristics for choosing an optimal set of ensembles.
Automatic Detection of COVID-19 Vaccine Misinformation with Graph Link Prediction
Authors: Maxwell A. Weinzierl, Sanda M. Harabagiu
Date: 2021-08-04 00:00:00
Tasks: Link Prediction, Misinformation

Enormous hope in the efficacy of vaccines became recently a successful reality in the fight against the COVID-19 pandemic. However, vaccine hesitancy, fueled by exposure to social media misinformation about COVID-19 vaccines became a major hurdle. Therefore, it is essential to automatically detect where misinformation about COVID-19 vaccines on social media is spread and what kind of misinformation is discussed, such that inoculation interventions can be delivered at the right time and in the right place, in addition to interventions designed to address vaccine hesitancy. This paper is addressing the first step in tackling hesitancy against COVID-19 vaccines, namely the automatic detection of known misinformation about the vaccines on Twitter, the social media platform that has the highest volume of conversations about COVID-19 and its vaccines. We present CoVaxLies, a new dataset of tweets judged relevant to several misinformation targets about COVID-19 vaccines on which a novel method of detecting misinformation was developed. Our method organizes CoVaxLies in a Misinformation Knowledge Graph as it casts misinformation detection as a graph link prediction problem. The misinformation detection method detailed in this paper takes advantage of the link scoring functions provided by several knowledge embedding methods. The experimental results demonstrate the superiority of this method when compared with classification-based methods, widely used currently.
Robust Transfer Learning with Pretrained Language Models through Adapters
Authors: Wenjuan Han, Bo Pang, YingNian Wu
Date: 2021-08-05 00:00:00
Proceeding: ACL 2021 5
Tasks: Adversarial Attack, Adversarial Robustness, Transfer Learning

Transfer learning with large pretrained transformer-based language models like BERT has become a dominating approach for most NLP tasks. Simply fine-tuning those large language models on downstream tasks or combining it with task-specific pretraining is often not robust. In particular, the performance considerably varies as the random seed changes or the number of pretraining and/or fine-tuning iterations varies, and the fine-tuned model is vulnerable to adversarial attack. We propose a simple yet effective adapter-based approach to mitigate these issues. Specifically, we insert small bottleneck layers (i.e., adapter) within each layer of a pretrained model, then fix the pretrained layers and train the adapter layers on the downstream task data, with (1) task-specific unsupervised pretraining and then (2) task-specific supervised training (e.g., classification, sequence labeling). Our experiments demonstrate that such a training scheme leads to improved stability and adversarial robustness in transfer learning to various downstream tasks.
Knowledge Distillation from BERT Transformer to Speech Transformer for Intent Classification
Authors: Yidi Jiang, Bidisha Sharma, Maulik Madhavi, Haizhou Li
Date: 2021-08-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Classification, intent-classification, Intent Classification, Knowledge Distillation, Language Modeling, Language Modelling, speech-recognition, Speech Recognition, Spoken Language Understanding

End-to-end intent classification using speech has numerous advantages compared to the conventional pipeline approach using automatic speech recognition (ASR), followed by natural language processing modules. It attempts to predict intent from speech without using an intermediate ASR module. However, such end-to-end framework suffers from the unavailability of large speech resources with higher acoustic variation in spoken language understanding. In this work, we exploit the scope of the transformer distillation method that is specifically designed for knowledge distillation from a transformer based language model to a transformer based speech model. In this regard, we leverage the reliable and widely used bidirectional encoder representations from transformers (BERT) model as a language model and transfer the knowledge to build an acoustic model for intent classification using the speech. In particular, a multilevel transformer based teacher-student model is designed, and knowledge distillation is performed across attention and hidden sub-layers of different transformer layers of the student and teacher models. We achieve an intent classification accuracy of 99.10% and 88.79% for Fluent speech corpus and ATIS database, respectively. Further, the proposed method demonstrates better performance and robustness in acoustically degraded condition compared to the baseline method.
Exploring Out-of-Distribution Generalization in Text Classifiers Trained on Tobacco-3482 and RVL-CDIP
Authors: Stefan Larson, Navtej Singh, Saarthak Maheshwari, Shanti Stewart, Uma Krishnaswamy
Date: 2021-08-05 00:00:00
Tasks: Document Classification, Out-of-Distribution Generalization, text-classification, Text Classification

To be robust enough for widespread adoption, document analysis systems involving machine learning models must be able to respond correctly to inputs that fall outside of the data distribution that was used to generate the data on which the models were trained. This paper explores the ability of text classifiers trained on standard document classification datasets to generalize to out-of-distribution documents at inference time. We take the Tobacco-3482 and RVL-CDIP datasets as a starting point and generate new out-of-distribution evaluation datasets in order to analyze the generalization performance of models trained on these standard datasets. We find that models trained on the smaller Tobacco-3482 dataset perform poorly on our new out-of-distribution data, while text classification models trained on the larger RVL-CDIP exhibit smaller performance drops.
Decoupled Transformer for Scalable Inference in Open-domain Question Answering
Authors: Haytham ElFadeel, Stan Peshterliev
Date: 2021-08-05 00:00:00
Tasks: Knowledge Distillation, Machine Reading Comprehension, Open-Domain Question Answering, Question Answering, Reading Comprehension

Large transformer models, such as BERT, achieve state-of-the-art results in machine reading comprehension (MRC) for open-domain question answering (QA). However, transformers have a high computational cost for inference which makes them hard to apply to online QA systems for applications like voice assistants. To reduce computational cost and latency, we propose decoupling the transformer MRC model into input-component and cross-component. The decoupling allows for part of the representation computation to be performed offline and cached for online use. To retain the decoupled transformer accuracy, we devised a knowledge distillation objective from a standard transformer model. Moreover, we introduce learned representation compression layers which help reduce by four times the storage requirement for the cache. In experiments on the SQUAD 2.0 dataset, a decoupled transformer reduces the computational cost and latency of open-domain MRC by 30-40% with only 1.2 points worse F1-score compared to a standard transformer.
MSTRE-Net: Multistreaming Acoustic Modeling for Automatic Lyrics Transcription
Authors: Emir Demirel, Sven Ahlbäck, Simon Dixon
Date: 2021-08-05 00:00:00
Tasks: Automatic Lyrics Transcription, TAG

This paper makes several contributions to automatic lyrics transcription (ALT) research. Our main contribution is a novel variant of the Multistreaming Time-Delay Neural Network (MTDNN) architecture, called MSTRE-Net, which processes the temporal information using multiple streams in parallel with varying resolutions keeping the network more compact, and thus with a faster inference and an improved recognition rate than having identical TDNN streams. In addition, two novel preprocessing steps prior to training the acoustic model are proposed. First, we suggest using recordings from both monophonic and polyphonic domains during training the acoustic model. Second, we tag monophonic and polyphonic recordings with distinct labels for discriminating non-vocal silence and music instances during alignment. Moreover, we present a new test set with a considerably larger size and a higher musical variability compared to the existing datasets used in ALT literature, while maintaining the gender balance of the singers. Our best performing model sets the state-of-the-art in lyrics transcription by a large margin. For reproducibility, we publicly share the identifiers to retrieve the data used in this paper.
Itinerary-aware Personalized Deep Matching at Fliggy
Authors: Jia Xu, Ziyi Wang, Zulong Chen, Detao Lv, Yao Yu, Chuanfei Xu
Date: 2021-08-05 00:00:00
Tasks: Diversity, Recommendation Systems

Matching items for a user from a travel item pool of large cardinality have been the most important technology for increasing the business at Fliggy, one of the most popular online travel platforms (OTPs) in China. There are three major challenges facing OTPs: sparsity, diversity, and implicitness. In this paper, we present a novel Fliggy ITinerary-aware deep matching NETwork (FitNET) to address these three challenges. FitNET is designed based on the popular deep matching network, which has been successfully employed in many industrial recommendation systems, due to its effectiveness. The concept itinerary is firstly proposed under the context of recommendation systems for OTPs, which is defined as the list of unconsumed orders of a user. All orders in a user itinerary are learned as a whole, based on which the implicit travel intention of each user can be more accurately inferred. To alleviate the sparsity problem, users' profiles are incorporated into FitNET. Meanwhile, a series of itinerary-aware attention mechanisms that capture the vital interactions between user's itinerary and other input categories are carefully designed. These mechanisms are very helpful in inferring a user's travel intention or preference, and handling the diversity in a user's need. Further, two training objectives, i.e., prediction accuracy of user's travel intention and prediction accuracy of user's click behavior, are utilized by FitNET, so that these two objectives can be optimized simultaneously. An offline experiment on Fliggy production dataset with over 0.27 million users and 1.55 million travel items, and an online A/B test both show that FitNET effectively learns users' travel intentions, preferences, and diverse needs, based on their itineraries and gains superior performance compared with state-of-the-art methods. FitNET now has been successfully deployed at Fliggy, serving major online traffic.
LHRM: A LBS based Heterogeneous Relations Model for User Cold Start Recommendation in Online Travel Platform
Authors: Ziyi Wang, Wendong Xiao, Yu Li, Zulong Chen, Zhi Jiang
Date: 2021-08-05 00:00:00
Tasks: Recommendation Systems

Most current recommender systems used the historical behaviour data of user to predict user' preference. However, it is difficult to recommend items to new users accurately. To alleviate this problem, existing user cold start methods either apply deep learning to build a cross-domain recommender system or map user attributes into the space of user behaviour. These methods are more challenging when applied to online travel platform (e.g., Fliggy), because it is hard to find a cross-domain that user has similar behaviour with travel scenarios and the Location Based Services (LBS) information of users have not been paid sufficient attention. In this work, we propose a LBS-based Heterogeneous Relations Model (LHRM) for user cold start recommendation, which utilizes user's LBS information and behaviour information in related domains and user's behaviour information in travel platforms (e.g., Fliggy) to construct the heterogeneous relations between users and items. Moreover, an attention-based multi-layer perceptron is applied to extract latent factors of users and items. Through this way, LHRM has better generalization performance than existing methods. Experimental results on real data from Fliggy's offline log illustrate the effectiveness of LHRM.
Time-aware Path Reasoning on Knowledge Graph for Recommendation
Authors: Yuyue Zhao, Xiang Wang, Jiawei Chen, Yashen Wang, Wei Tang, Xiangnan He, Haiyong Xie
Date: 2021-08-05 00:00:00
Tasks: Explainable Recommendation, Relation Extraction

Reasoning on knowledge graph (KG) has been studied for explainable recommendation due to it's ability of providing explicit explanations. However, current KG-based explainable recommendation methods unfortunately ignore the temporal information (such as purchase time, recommend time, etc.), which may result in unsuitable explanations. In this work, we propose a novel Time-aware Path reasoning for Recommendation (TPRec for short) method, which leverages the potential of temporal information to offer better recommendation with plausible explanations. First, we present an efficient time-aware interaction relation extraction component to construct collaborative knowledge graph with time-aware interactions (TCKG for short), and then introduce a novel time-aware path reasoning method for recommendation. We conduct extensive experiments on three real-world datasets. The results demonstrate that the proposed TPRec could successfully employ TCKG to achieve substantial gains and improve the quality of explainable recommendation.
Anomalous Event Recognition in Videos Based on Joint Learningof Motion and Appearance with Multiple Ranking Measures
Authors: Shikha Dubey, Abhijeet Boragule, Jeonghwan Gwak, Moongu Jeon
Date: 2021-02-02 00:00:00
Proceeding: journal 2021 2
Tasks: Action Detection, Activity Detection, Anomaly Detection In Surveillance Videos, Multiple Instance Learning

Given the scarcity of annotated datasets, learning the context-dependency of anomalous events as well as mitigating false alarms represent challenges in the task of anomalous activity detection.  We propose a framework, Deep-network with Multiple Ranking Measures(DMRMs), which addresses context-dependency using a joint learning technique for motion and appearance features.  In DMRMs, the spatial-time-dependent features are extracted from a video using a 3Dresidual  network(ResNet),  and deep motion features are extracted by integrating the  Motionflow maps’ information with the 3D ResNet. Afterward, the extracted features are fused for joint learning. This data fusion is then passed through a deep neural network for deep multiple instance learning(DMIL)to learn the context-dependency in a weakly-supervised manner using the proposed multiple ranking measures(MRMs). These MRMs consider multiple measures of false alarms, and the network is trained with both normal and anomalous events, thus lowering the false alarm rate. Meanwhile, in the inference phase, the network predicts each frame’s abnormality score along with the localization of moving objects using motion flow maps. A higher abnormality score indicates the presence of an anomalous event. Experimental results on two recent and challenging datasets demonstrate that our proposed framework improves the area under the curve(AUC)score by6.5%compared to the state-of-the-art method on the UCF-Crime dataset and shows AUC of68.5%on theShanghaiTech dataset.
Speech Enhancement using Adaptive Mean Median Deviation and EMD Technique
Authors: Shikha Dubey, Ashish Kumar Singh, Manoj Kumar Singh
Date: 2019-08-26 00:00:00
Proceeding: Conference 2019 8
Tasks: Speech Enhancement

During the acquisition of the speech signal by the non-contact Speech Sensor (SS), the signal is degraded by severe colored noises which are non-linear and non-uniform in nature. Therefore, in this study, a new approach for advancing the speech enhancement technique is proposed to suppress these noises from the acquired speech signal. This technique is based on adaptive thresholding, which uses Mean Median Deviation(MMD) method to determine adaptive threshold points and the Empirical Mode Decomposition (EMD) method. This algorithm has been validated by simulation data, and the results of the proposed algorithm have been compared with other existing enhancement algorithms. Spectrograms and Signal to Noise Ratio (SNR)comparison are used to analyze the quality of the enhanced speech signal. From the results, we demonstrate that the proposed algorithm offers better speech enhancement than previously existing speech enhancement techniques.
The 3D Linear Inverted Pendulum Mode : A simple modeling for a biped walking pattern generation
Authors: Shuuji Kajita, Fumio Kanehiro, Kenji Kaneko, Kazuhito Yokoiand, Hirohisa Hirukawa
Date: 2001-11-03 00:00:00
Proceeding: International Conference on Intelligent Robots and Systems 2001 11

For 3D walking control of a biped robot we analyse the dynamics of a three dimensional inverted pendulum in which motion is constrained to move along an arbitrarily defined plane. This analysis leads us a simple linear dynamics the Three Dimensional Linear Inverted Pendulum Mode (3D-LIPM) Geometric nature of trajectories under the 3D-LIPM and a method for walking pattern generation are discussed. A simulation result of a walking control using a 12 d.o.f. biped robot model is also shown.
A 2-$μ$J, 12-class, 91% Accuracy Spiking Neural Network Approach For Radar Gesture Recognition
Authors: Ali Safa, André Bourdoux, Ilja Ocket, Francky Catthoor, Georges G. E. Gielen
Date: 2021-08-05 00:00:00
Tasks: Gesture Recognition

Radar processing via spiking neural networks (SNNs) has recently emerged as a solution in the field of ultra-low-power wireless human-computer interaction. Compared to traditional energy- and area-hungry deep learning methods, SNNs are significantly more energy efficient and can be deployed in the growing number of compact SNN accelerator chips, making them a better solution for ubiquitous IoT applications. We propose a novel SNN strategy for radar gesture recognition, achieving more than 91% of accuracy on two different radar datasets. Our work significantly differs from previous approaches as 1) we use a novel radar-SNN training strategy, 2) we use quantized weights, enabling power-efficient implementation in real-world SNN hardware, and 3) we report the SNN energy consumption per classification, clearly demonstrating the real-world feasibility and power savings induced by SNN-based radar processing. We release evaluation code to help future research.
Joint Geometry and Color Projection-based Point Cloud Quality Metric
Authors: Alireza Javaheri, Catarina Brites, Fernando Pereira, João Ascenso
Date: 2021-08-05 00:00:00
Tasks: Point Cloud Quality Assessment

Point cloud coding solutions have been recently standardized to address the needs of multiple application scenarios. The design and assessment of point cloud coding methods require reliable objective quality metrics to evaluate the level of degradation introduced by compression or any other type of processing. Several point cloud objective quality metrics has been recently proposed to reliable estimate human perceived quality, including the so-called projection-based metrics. In this context, this paper proposes a joint geometry and color projection-based point cloud objective quality metric which solves the critical weakness of this type of quality metrics, i.e., the misalignment between the reference and degraded projected images. Moreover, the proposed point cloud quality metric exploits the best performing 2D quality metrics in the literature to assess the quality of the projected images. The experimental results show that the proposed projection-based quality metric offers the best subjective-objective correlation performance in comparison with other metrics in the literature. The Pearson correlation gains regarding D1-PSNR and D2-PSNR metrics are 17% and 14.2 when data with all coding degradations is considered.
Safe Motion Planning against Multimodal Distributions based on a Scenario Approach
Authors: Heejin Ahn, Colin Chen, Ian M. Mitchell, Maryam Kamgarpour
Date: 2021-08-05 00:00:00
Tasks: Motion Planning

We present the design of a motion planning algorithm that ensures safety for an autonomous vehicle. In particular, we consider a multimodal distribution over uncertainties; for example, the uncertain predictions of future trajectories of surrounding vehicles reflect discrete decisions, such as turning or going straight at intersections. We develop a computationally efficient, scenario-based approach that solves the motion planning problem with high confidence given a quantifiable number of samples from the multimodal distribution. Our approach is based on two preprocessing steps, which 1) separate the samples into distinct clusters and 2) compute a bounding polytope for each cluster. Then, we rewrite the motion planning problem approximately as a mixed-integer problem using the polytopes. We demonstrate via simulation on the nuScenes dataset that our approach ensures safety with high probability in the presence of multimodal uncertainties, and is computationally more efficient and less conservative than a conventional scenario approach.
An Encoder-Decoder Based Audio Captioning System With Transfer and Reinforcement Learning
Authors: Xinhao Mei, Qiushi Huang, Xubo Liu, Gengyun Chen, Jingqian Wu, Yusong Wu, Jinzheng Zhao, Shengchen Li, Tom Ko, H Lilian Tang, Xi Shao, Mark D. Plumbley, Wenwu Wang
Date: 2021-08-05 00:00:00
Tasks: Audio captioning, Decoder, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Transfer Learning

Automated audio captioning aims to use natural language to describe the content of audio data. This paper presents an audio captioning system with an encoder-decoder architecture, where the decoder predicts words based on audio features extracted by the encoder. To improve the proposed system, transfer learning from either an upstream audio-related task or a large in-domain dataset is introduced to mitigate the problem induced by data scarcity. Besides, evaluation metrics are incorporated into the optimization of the model with reinforcement learning, which helps address the problem of ``exposure bias'' induced by ``teacher forcing'' training strategy and the mismatch between the evaluation metrics and the loss function. The resulting system was ranked 3rd in DCASE 2021 Task 6. Ablation studies are carried out to investigate how much each element in the proposed system can contribute to final performance. The results show that the proposed techniques significantly improve the scores of the evaluation metrics, however, reinforcement learning may impact adversely on the quality of the generated captions.