pwc-archive/papers-with-abstracts huggingface dataset:
Learn to Focus: Hierarchical Dynamic Copy Network for Dialogue State Tracking
Authors: Linhao Zhang, Houfeng Wang
Date: 2021-07-25 00:00:00
Tasks: Decoder, Dialogue State Tracking, Task-Oriented Dialogue Systems

Recently, researchers have explored using the encoder-decoder framework to tackle dialogue state tracking (DST), which is a key component of task-oriented dialogue systems. However, they regard a multi-turn dialogue as a flat sequence, failing to focus on useful information when the sequence is long. In this paper, we propose a Hierarchical Dynamic Copy Network (HDCN) to facilitate focusing on the most informative turn, making it easier to extract slot values from the dialogue context. Based on the encoder-decoder framework, we adopt a hierarchical copy approach that calculates two levels of attention at the word- and turn-level, which are then renormalized to obtain the final copy distribution. A focus loss term is employed to encourage the model to assign the highest turn-level attention weight to the most informative turn. Experimental results show that our model achieves 46.76% joint accuracy on the MultiWOZ 2.1 dataset.
A Joint and Domain-Adaptive Approach to Spoken Language Understanding
Authors: Linhao Zhang, Yu Shi, Linjun Shou, Ming Gong, Houfeng Wang, Michael Zeng
Date: 2021-07-25 00:00:00
Tasks: Domain Adaptation, Intent Detection, slot-filling, Slot Filling, Spoken Language Understanding

Spoken Language Understanding (SLU) is composed of two subtasks: intent detection (ID) and slot filling (SF). There are two lines of research on SLU. One jointly tackles these two subtasks to improve their prediction accuracy, and the other focuses on the domain-adaptation ability of one of the subtasks. In this paper, we attempt to bridge these two lines of research and propose a joint and domain adaptive approach to SLU. We formulate SLU as a constrained generation task and utilize a dynamic vocabulary based on domain-specific ontology. We conduct experiments on the ASMixed and MTOD datasets and achieve competitive performance with previous state-of-the-art joint models. Besides, results show that our joint model can be effectively adapted to a new domain.
DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learning
Authors: Haoyi Niu, Jianming Hu, Zheyu Cui, Yi Zhang
Date: 2021-07-25 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL)

How to explore corner cases as efficiently and thoroughly as possible has long been one of the top concerns in the context of deep reinforcement learning (DeepRL) autonomous driving. Training with simulated data is less costly and dangerous than utilizing real-world data, but the inconsistency of parameter distribution and the incorrect system modeling in simulators always lead to an inevitable Sim2real gap, which probably accounts for the underperformance in novel, anomalous and risky cases that simulators can hardly generate. Domain Randomization(DR) is a methodology that can bridge this gap with little or no real-world data. Consequently, in this research, an adversarial model is put forward to robustify DeepRL-based autonomous vehicles trained in simulation to gradually surfacing harder events, so that the models could readily transfer to the real world.
Learning Risk-aware Costmaps for Traversability in Challenging Environments
Authors: David D. Fan, Sharmita Dey, Ali-akbar Agha-mohammadi, Evangelos A. Theodorou
Date: 2021-07-25 00:00:00

One of the main challenges in autonomous robotic exploration and navigation in unknown and unstructured environments is determining where the robot can or cannot safely move. A significant source of difficulty in this determination arises from stochasticity and uncertainty, coming from localization error, sensor sparsity and noise, difficult-to-model robot-ground interactions, and disturbances to the motion of the vehicle. Classical approaches to this problem rely on geometric analysis of the surrounding terrain, which can be prone to modeling errors and can be computationally expensive. Moreover, modeling the distribution of uncertain traversability costs is a difficult task, compounded by the various error sources mentioned above. In this work, we take a principled learning approach to this problem. We introduce a neural network architecture for robustly learning the distribution of traversability costs. Because we are motivated by preserving the life of the robot, we tackle this learning problem from the perspective of learning tail-risks, i.e. the Conditional Value-at-Risk (CVaR). We show that this approach reliably learns the expected tail risk given a desired probability risk threshold between 0 and 1, producing a traversability costmap which is more robust to outliers, more accurately captures tail risks, and is more computationally efficient, when compared against baselines. We validate our method on data collected a legged robot navigating challenging, unstructured environments including an abandoned subway, limestone caves, and lava tube caves.
Efficient QUBO transformation for Higher Degree Pseudo Boolean Functions
Authors: Amit Verma, Mark Lewis, Gary Kochenberger
Date: 2021-07-24 00:00:00

Quadratic Unconstrained Binary Optimization (QUBO) is recognized as a unifying framework for modeling a wide range of problems. Problems can be solved with commercial solvers customized for solving QUBO and since QUBO have degree two, it is useful to have a method for transforming higher degree pseudo-Boolean problems to QUBO format. The standard transformation approach requires additional auxiliary variables supported by penalty terms for each higher degree term. This paper improves on the existing cubic-to-quadratic transformation approach by minimizing the number of additional variables as well as penalty coefficient. Extensive experimental testing on Max 3-SAT modeled as QUBO shows a near 100% reduction in the subproblem size used for minimization of the number of auxiliary variables.
Graph Convolutional Network with Generalized Factorized Bilinear Aggregation
Authors: Hao Zhu, Piotr Koniusz
Date: 2021-07-24 00:00:00
Tasks: text-classification, Text Classification

Although Graph Convolutional Networks (GCNs) have demonstrated their power in various applications, the graph convolutional layers, as the most important component of GCN, are still using linear transformations and a simple pooling step. In this paper, we propose a novel generalization of Factorized Bilinear (FB) layer to model the feature interactions in GCNs. FB performs two matrix-vector multiplications, that is, the weight matrix is multiplied with the outer product of the vector of hidden features from both sides. However, the FB layer suffers from the quadratic number of coefficients, overfitting and the spurious correlations due to correlations between channels of hidden representations that violate the i.i.d. assumption. Thus, we propose a compact FB layer by defining a family of summarizing operators applied over the quadratic term. We analyze proposed pooling operators and motivate their use. Our experimental results on multiple datasets demonstrate that the GFB-GCN is competitive with other methods for text classification.
Stress Test Evaluation of Biomedical Word Embeddings
Authors: Vladimir Araujo, Andrés Carvallo, Carlos Aspillaga, Camilo Thorne, Denis Parra
Date: 2021-07-24 00:00:00
Proceeding: NAACL (BioNLP) 2021 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Embeddings

The success of pretrained word embeddings has motivated their use in the biomedical domain, with contextualized embeddings yielding remarkable results in several biomedical NLP tasks. However, there is a lack of research on quantifying their behavior under severe "stress" scenarios. In this work, we systematically evaluate three language models with adversarial examples -- automatically constructed tests that allow us to examine how robust the models are. We propose two types of stress scenarios focused on the biomedical named entity recognition (NER) task, one inspired by spelling errors and another based on the use of synonyms for medical terms. Our experiments with three benchmarks show that the performance of the original models decreases considerably, in addition to revealing their weaknesses and strengths. Finally, we show that adversarial training causes the models to improve their robustness and even to exceed the original performance in some cases.
Clustering by Maximizing Mutual Information Across Views
Authors: Kien Do, Truyen Tran, Svetha Venkatesh
Date: 2021-07-24 00:00:00
Proceeding: ICCV 2021 10
Tasks: Clustering, Image Clustering, Representation Learning

We propose a novel framework for image clustering that incorporates joint representation learning and clustering. Our method consists of two heads that share the same backbone network - a "representation learning" head and a "clustering" head. The "representation learning" head captures fine-grained patterns of objects at the instance level which serve as clues for the "clustering" head to extract coarse-grain information that separates objects into clusters. The whole model is trained in an end-to-end manner by minimizing the weighted sum of two sample-oriented contrastive losses applied to the outputs of the two heads. To ensure that the contrastive loss corresponding to the "clustering" head is optimal, we introduce a novel critic function called "log-of-dot-product". Extensive experimental results demonstrate that our method significantly outperforms state-of-the-art single-stage clustering methods across a variety of image datasets, improving over the best baseline by about 5-7% in accuracy on CIFAR10/20, STL10, and ImageNet-Dogs. Further, the "two-stage" variant of our method also achieves better results than baselines on three challenging ImageNet subsets.
FedLab: A Flexible Federated Learning Framework
Authors: Dun Zeng, Siqi Liang, Xiangjing Hu, Hui Wang, Zenglin Xu
Date: 2021-07-24 00:00:00
Tasks: Federated Learning

Federated learning (FL) is a machine learning field in which researchers try to facilitate model learning process among multiparty without violating privacy protection regulations. Considerable effort has been invested in FL optimization and communication related researches. In this work, we introduce \texttt{FedLab}, a lightweight open-source framework for FL simulation. The design of \texttt{FedLab} focuses on FL algorithm effectiveness and communication efficiency. Also, \texttt{FedLab} is scalable in different deployment scenario. We hope \texttt{FedLab} could provide flexible API as well as reliable baseline implementations, and relieve the burden of implementing novel approaches for researchers in FL community.
Automatic tempered posterior distributions for Bayesian inversion problems
Authors: L. Martino, F. Llorente, E. Curbelo, J. Lopez-Santiago, J. Miguez
Date: 2021-07-24 00:00:00

We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the inference of the variables of interest and the power of the data noise is split. More specifically, we consider a Bayesian analysis for the variables of interest (i.e., the parameters of the model to invert), whereas we employ a maximum likelihood approach for the estimation of the noise power. The whole technique is implemented by means of an iterative procedure, alternating sampling and optimization steps. Moreover, the noise power is also used as a tempered parameter for the posterior distribution of the the variables of interest. Therefore, a sequence of tempered posterior densities is generated, where the tempered parameter is automatically selected according to the actual estimation of the noise power. A complete Bayesian study over the model parameters and the scale parameter can be also performed. Numerical experiments show the benefits of the proposed approach.
A Model-Agnostic Algorithm for Bayes Error Determination in Binary Classification
Authors: Umberto Michelucci, Michela Sperti, Dario Piga, Francesca Venturini, Marco A. Deriu
Date: 2021-07-24 00:00:00
Tasks: Binary Classification

This paper presents the intrinsic limit determination algorithm (ILD Algorithm), a novel technique to determine the best possible performance, measured in terms of the AUC (area under the ROC curve) and accuracy, that can be obtained from a specific dataset in a binary classification problem with categorical features {\sl regardless} of the model used. This limit, namely the Bayes error, is completely independent of any model used and describes an intrinsic property of the dataset. The ILD algorithm thus provides important information regarding the prediction limits of any binary classification algorithm when applied to the considered dataset. In this paper the algorithm is described in detail, its entire mathematical framework is presented and the pseudocode is given to facilitate its implementation. Finally, an example with a real dataset is given.
The USYD-JD Speech Translation System for IWSLT 2021
Authors: Liang Ding, Di wu, DaCheng Tao
Date: 2021-07-24 00:00:00
Tasks: Knowledge Distillation, NMT, Reranking, Translation

This paper describes the University of Sydney& JD's joint submission of the IWSLT 2021 low resource speech translation task. We participated in the Swahili-English direction and got the best scareBLEU (25.3) score among all the participants. Our constrained system is based on a pipeline framework, i.e. ASR and NMT. We trained our models with the officially provided ASR and MT datasets. The ASR system is based on the open-sourced tool Kaldi and this work mainly explores how to make the most of the NMT models. To reduce the punctuation errors generated by the ASR model, we employ our previous work SlotRefine to train a punctuation correction model. To achieve better translation performance, we explored the most recent effective strategies, including back translation, knowledge distillation, multi-feature reranking and transductive finetuning. For model structure, we tried auto-regressive and non-autoregressive models, respectively. In addition, we proposed two novel pre-train approaches, i.e. \textit{de-noising training} and \textit{bidirectional training} to fully exploit the data. Extensive experiments show that adding the above techniques consistently improves the BLEU scores, and the final submission system outperforms the baseline (Transformer ensemble model trained with the original parallel data) by approximately 10.8 BLEU score, achieving the SOTA performance.
Semantic-guided Pixel Sampling for Cloth-Changing Person Re-identification
Authors: Xiujun Shu, Ge Li, Xiao Wang, Weijian Ruan, Qi Tian
Date: 2021-07-24 00:00:00
Tasks: Cloth-Changing Person Re-Identification, Person Re-Identification

Cloth-changing person re-identification (re-ID) is a new rising research topic that aims at retrieving pedestrians whose clothes are changed. This task is quite challenging and has not been fully studied to date. Current works mainly focus on body shape or contour sketch, but they are not robust enough due to view and posture variations. The key to this task is to exploit cloth-irrelevant cues. This paper proposes a semantic-guided pixel sampling approach for the cloth-changing person re-ID task. We do not explicitly define which feature to extract but force the model to automatically learn cloth-irrelevant cues. Specifically, we first recognize the pedestrian's upper clothes and pants, then randomly change them by sampling pixels from other pedestrians. The changed samples retain the identity labels but exchange the pixels of clothes or pants among different pedestrians. Besides, we adopt a loss function to constrain the learned features to keep consistent before and after changes. In this way, the model is forced to learn cues that are irrelevant to upper clothes and pants. We conduct extensive experiments on the latest released PRCC dataset. Our method achieved 65.8% on Rank1 accuracy, which outperforms previous methods with a large margin. The code is available at https://github.com/shuxjweb/pixel_sampling.git.
Caveats for the use of Web of Science Core Collection in old literature retrieval and historical bibliometric analysis
Authors: Weishu Liu
Date: 2021-07-24 00:00:00
Tasks: Retrieval

By using publications from Web of Science Core Collection (WoSCC), Fosso Wamba and his colleagues published an interesting and comprehensive paper in Technological Forecasting and Social Change to explore the structure and dynamics of artificial intelligence (AI) scholarship. Data demonstrated in Fosso Wamba's study implied that the year 1991 seemed to be a "watershed" of AI research. This research note tried to uncover the 1991 phenomenon from the perspective of database limitation by probing the limitations of search in abstract/author keywords/keywords plus fields of WoSCC empirically. The low availability rates of abstract/author keywords/keywords plus information in WoSCC found in this study can explain the "watershed" phenomenon of AI scholarship in 1991 to a large extent. Some other caveats for the use of WoSCC in old literature retrieval and historical bibliometric analysis were also mentioned in the discussion section. This research note complements Fosso Wamba and his colleagues' study and also helps avoid improper interpretation in the use of WoSCC in old literature retrieval and historical bibliometric analysis.
Crosslink-Net: Double-branch Encoder Segmentation Network via Fusing Vertical and Horizontal Convolutions
Authors: Qian Yu, Lei Qi, Luping Zhou, Lei Wang, Yilong Yin, Yinghuan Shi, Wuzhang Wang, Yang Gao
Date: 2021-07-24 00:00:00
Tasks: Decoder, Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Segmentation, Semantic Segmentation

Accurate image segmentation plays a crucial role in medical image analysis, yet it faces great challenges of various shapes, diverse sizes, and blurry boundaries. To address these difficulties, square kernel-based encoder-decoder architecture has been proposed and widely used, but its performance remains still unsatisfactory. To further cope with these challenges, we present a novel double-branch encoder architecture. Our architecture is inspired by two observations: 1) Since the discrimination of features learned via square convolutional kernels needs to be further improved, we propose to utilize non-square vertical and horizontal convolutional kernels in the double-branch encoder, so features learned by the two branches can be expected to complement each other. 2) Considering that spatial attention can help models to better focus on the target region in a large-sized image, we develop an attention loss to further emphasize the segmentation on small-sized targets. Together, the above two schemes give rise to a novel double-branch encoder segmentation framework for medical image segmentation, namely Crosslink-Net. The experiments validate the effectiveness of our model on four datasets. The code is released at https://github.com/Qianyu1226/Crosslink-Net.
Multi-Perspective Content Delivery Networks Security Framework Using Optimized Unsupervised Anomaly Detection
Authors: Li Yang, Abdallah Moubayed, Abdallah Shami, Parisa Heidari, Amine Boukhtouta, Adel Larabi, Richard Brunner, Stere Preda, Daniel Migault
Date: 2021-07-24 00:00:00
Tasks: Anomaly Detection, Feature Engineering, Unsupervised Anomaly Detection

Content delivery networks (CDNs) provide efficient content distribution over the Internet. CDNs improve the connectivity and efficiency of global communications, but their caching mechanisms may be breached by cyber-attackers. Among the security mechanisms, effective anomaly detection forms an important part of CDN security enhancement. In this work, we propose a multi-perspective unsupervised learning framework for anomaly detection in CDNs. In the proposed framework, a multi-perspective feature engineering approach, an optimized unsupervised anomaly detection model that utilizes an isolation forest and a Gaussian mixture model, and a multi-perspective validation method, are developed to detect abnormal behaviors in CDNs mainly from the client Internet Protocol (IP) and node perspectives, therefore to identify the denial of service (DoS) and cache pollution attack (CPA) patterns. Experimental results are presented based on the analytics of eight days of real-world CDN log data provided by a major CDN operator. Through experiments, the abnormal contents, compromised nodes, malicious IPs, as well as their corresponding attack types, are identified effectively by the proposed framework and validated by multiple cybersecurity experts. This shows the effectiveness of the proposed method when applied to real-world CDN data.
Cycled Compositional Learning between Images and Text
Authors: Jongseok Kim, Youngjae Yu, Seunghwan Lee, GunheeKim
Date: 2021-07-24 00:00:00
Tasks: Relation

We present an approach named the Cycled Composition Network that can measure the semantic distance of the composition of image-text embedding. First, the Composition Network transit a reference image to target image in an embedding space using relative caption. Second, the Correction Network calculates a difference between reference and retrieved target images in the embedding space and match it with a relative caption. Our goal is to learn a Composition mapping with the Composition Network. Since this one-way mapping is highly under-constrained, we couple it with an inverse relation learning with the Correction Network and introduce a cycled relation for given Image We participate in Fashion IQ 2020 challenge and have won the first place with the ensemble of our model.
$μ$DARTS: Model Uncertainty-Aware Differentiable Architecture Search
Authors: Biswadeep Chakraborty, Saibal Mukhopadhyay
Date: 2021-07-24 00:00:00
Tasks: model, Neural Architecture Search

We present a Model Uncertainty-aware Differentiable ARchiTecture Search ($\mu$DARTS) that optimizes neural networks to simultaneously achieve high accuracy and low uncertainty. We introduce concrete dropout within DARTS cells and include a Monte-Carlo regularizer within the training loss to optimize the concrete dropout probabilities. A predictive variance term is introduced in the validation loss to enable searching for architecture with minimal model uncertainty. The experiments on CIFAR10, CIFAR100, SVHN, and ImageNet verify the effectiveness of $\mu$DARTS in improving accuracy and reducing uncertainty compared to existing DARTS methods. Moreover, the final architecture obtained from $\mu$DARTS shows higher robustness to noise at the input image and model parameters compared to the architecture obtained from existing DARTS methods.
Similarity Based Label Smoothing For Dialogue Generation
Authors: Sougata Saha, Souvik Das, Rohini Srihari
Date: 2021-07-23 00:00:00
Tasks: Dialogue Generation, Word Similarity

Generative neural conversational systems are generally trained with the objective of minimizing the entropy loss between the training "hard" targets and the predicted logits. Often, performance gains and improved generalization can be achieved by using regularization techniques like label smoothing, which converts the training "hard" targets to "soft" targets. However, label smoothing enforces a data independent uniform distribution on the incorrect training targets, which leads to an incorrect assumption of equi-probable incorrect targets for each correct target. In this paper we propose and experiment with incorporating data dependent word similarity based weighing methods to transforms the uniform distribution of the incorrect target probabilities in label smoothing, to a more natural distribution based on semantics. We introduce hyperparameters to control the incorrect target distribution, and report significant performance gains over networks trained using standard label smoothing based loss, on two standard open domain dialogue corpora.