pwc-archive/papers-with-abstracts huggingface dataset:
Learning from Drivers to Tackle the Amazon Last Mile Routing Research Challenge
Authors: Chen Wu, Yin Song, Verdi March, Eden Duthie
Date: 2022-05-09 00:00:00

The goal of the Amazon Last Mile Routing Research Challenge is to integrate the real-life experience of Amazon drivers into the solution of optimal route planning and optimization. This paper presents our method that tackles this challenge by hierarchically combining machine learning and conventional Traveling Salesperson Problem (TSP) solvers. Our method reaps the benefits from both worlds. On the one hand, our method encodes driver know-how by learning a sequential probability model from historical routes at the zone level, where each zone contains a few parcel stops. It then uses a single step policy iteration method, known as the Rollout algorithm, to generate plausible zone sequences sampled from the learned probability model. On the other hand, our method utilizes proven methods developed in the rich TSP literature to sequence stops within each zone efficiently. The outcome of such a combination appeared to be promising. Our method obtained an evaluation score of $0.0374$, which is comparable to what the top three teams have achieved on the official Challenge leaderboard. Moreover, our learning-based method is applicable to driving routes that may exhibit distinct sequential patterns beyond the scope of this Challenge. The source code of our method is publicly available at https://github.com/aws-samples/amazon-sagemaker-amazon-routing-challenge-sol
Building Machine Translation Systems for the Next Thousand Languages
Authors: Ankur Bapna, Isaac Caswell, Julia Kreutzer, Orhan Firat, Daan van Esch, Aditya Siddhant, Mengmeng Niu, Pallavi Baljekar, Xavier Garcia, Wolfgang Macherey, Theresa Breiner, Vera Axelrod, Jason Riesa, Yuan Cao, Mia Xu Chen, Klaus Macherey, Maxim Krikun, Pidong Wang, Alexander Gutkin, Apurva Shah, Yanping Huang, Zhifeng Chen, Yonghui Wu, Macduff Hughes
Date: 2022-05-09 00:00:00
Tasks: Language Identification, Machine Translation, Translation

In this paper we share findings from our effort to build practical machine translation (MT) systems capable of translating across over one thousand languages. We describe results in three research domains: (i) Building clean, web-mined datasets for 1500+ languages by leveraging semi-supervised pre-training for language identification and developing data-driven filtering techniques; (ii) Developing practical MT models for under-served languages by leveraging massively multilingual models trained with supervised parallel data for over 100 high-resource languages and monolingual datasets for an additional 1000+ languages; and (iii) Studying the limitations of evaluation metrics for these languages and conducting qualitative analysis of the outputs from our MT models, highlighting several frequent error modes of these types of models. We hope that our work provides useful insights to practitioners working towards building MT systems for currently understudied languages, and highlights research directions that can complement the weaknesses of massively multilingual models in data-sparse settings.
ACM -- Attribute Conditioning for Abstractive Multi Document Summarization
Authors: Aiswarya Sankar, Ankit Chadha
Date: 2022-05-09 00:00:00
Tasks: Attribute, Document Summarization, Informativeness, Multi-Document Summarization

Abstractive multi document summarization has evolved as a task through the basic sequence to sequence approaches to transformer and graph based techniques. Each of these approaches has primarily focused on the issues of multi document information synthesis and attention based approaches to extract salient information. A challenge that arises with multi document summarization which is not prevalent in single document summarization is the need to effectively summarize multiple documents that might have conflicting polarity, sentiment or subjective information about a given topic. In this paper we propose ACM, attribute conditioned multi document summarization,a model that incorporates attribute conditioning modules in order to decouple conflicting information by conditioning for a certain attribute in the output summary. This approach shows strong gains in ROUGE score over baseline multi document summarization approaches and shows gains in fluency, informativeness and reduction in repetitiveness as shown through a human annotation analysis study.
Robust (Controlled) Table-to-Text Generation with Structure-Aware Equivariance Learning
Authors: Fei Wang, Zhewei Xu, Pedro Szekely, Muhao Chen
Date: 2022-05-08 00:00:00
Proceeding: NAACL 2022 7
Tasks: Data Augmentation, Data-to-Text Generation, Graph Attention, Position, Table-to-Text Generation, Text Generation

Controlled table-to-text generation seeks to generate natural language descriptions for highlighted subparts of a table. Previous SOTA systems still employ a sequence-to-sequence generation method, which merely captures the table as a linear structure and is brittle when table layouts change. We seek to go beyond this paradigm by (1) effectively expressing the relations of content pieces in the table, and (2) making our model robust to content-invariant structural transformations. Accordingly, we propose an equivariance learning framework, which encodes tables with a structure-aware self-attention mechanism. This prunes the full self-attention structure into an order-invariant graph attention that captures the connected graph structure of cells belonging to the same row or column, and it differentiates between relevant cells and irrelevant cells from the structural perspective. Our framework also modifies the positional encoding mechanism to preserve the relative position of tokens in the same cell but enforce position invariance among different cells. Our technology is free to be plugged into existing table-to-text generation models, and has improved T5-based models to offer better performance on ToTTo and HiTab. Moreover, on a harder version of ToTTo, we preserve promising performance, while previous SOTA systems, even with transformation-based data augmentation, have seen significant performance drops. Our code is available at https://github.com/luka-group/Lattice.
Rebellion and Disobedience as Useful Tools in Human-Robot Interaction Research -- The Handheld Robotics Case
Authors: Walterio W. Mayol-Cuevas
Date: 2022-05-08 00:00:00
Tasks: Position

This position paper argues on the utility of rebellion and disobedience (RaD) in human-robot interaction (HRI). In general, we see two main opportunities in the use of controlled and well designed rebellion and disobedience: i) illuminate insight into the effectiveness of the collaboration (or lack of) and ii) prevent mistakes and correct user actions when in the user's own interest. Through the use of a close interaction modality, that of handheld robots, we discuss use cases for utility of rebellion and disobedience that can be applicable to other instances of HRI.
Write It Like You See It: Detectable Differences in Clinical Notes By Race Lead To Differential Model Recommendations
Authors: Hammaad Adam, Ming Ying Yang, Kenrick Cato, Ioana Baldini, Charles Senteio, Leo Anthony Celi, Jiaming Zeng, Moninder Singh, Marzyeh Ghassemi
Date: 2022-05-08 00:00:00

Clinical notes are becoming an increasingly important data source for machine learning (ML) applications in healthcare. Prior research has shown that deploying ML models can perpetuate existing biases against racial minorities, as bias can be implicitly embedded in data. In this study, we investigate the level of implicit race information available to ML models and human experts and the implications of model-detectable differences in clinical notes. Our work makes three key contributions. First, we find that models can identify patient self-reported race from clinical notes even when the notes are stripped of explicit indicators of race. Second, we determine that human experts are not able to accurately predict patient race from the same redacted clinical notes. Finally, we demonstrate the potential harm of this implicit information in a simulation study, and show that models trained on these race-redacted clinical notes can still perpetuate existing biases in clinical treatment decisions.
Unsupervised Discovery and Composition of Object Light Fields
Authors: Cameron Smith, Hong-Xing Yu, Sergey Zakharov, Fredo Durand, Joshua B. Tenenbaum, Jiajun Wu, Vincent Sitzmann
Date: 2022-05-08 00:00:00
Tasks: Novel View Synthesis, Object, Scene Understanding

Neural scene representations, both continuous and discrete, have recently emerged as a powerful new paradigm for 3D scene understanding. Recent efforts have tackled unsupervised discovery of object-centric neural scene representations. However, the high cost of ray-marching, exacerbated by the fact that each object representation has to be ray-marched separately, leads to insufficiently sampled radiance fields and thus, noisy renderings, poor framerates, and high memory and time complexity during training and rendering. Here, we propose to represent objects in an object-centric, compositional scene representation as light fields. We propose a novel light field compositor module that enables reconstructing the global light field from a set of object-centric light fields. Dubbed Compositional Object Light Fields (COLF), our method enables unsupervised learning of object-centric neural scene representations, state-of-the-art reconstruction and novel view synthesis performance on standard datasets, and rendering and training speeds at orders of magnitude faster than existing 3D approaches.
Network Traffic Anomaly Detection Method Based on Multi scale Residual Feature
Authors: Xueyuan Duan, Yu Fu, Kun Wang
Date: 2022-05-08 00:00:00
Tasks: Anomaly Detection, Diversity, Traffic Classification

To address the problem that traditional network traffic anomaly detection algorithms do not suffi-ciently mine potential features in long time domain, an anomaly detection method based on mul-ti-scale residual features of network traffic is proposed. The original traffic is divided into subse-quences of different time spans using sliding windows, and each subsequence is decomposed and reconstructed into data sequences of different levels using wavelet transform technique; the stacked autoencoder (SAE) constructs similar feature space using normal network traffic, and gen-erates reconstructed error vector using the difference between reconstructed samples and input samples in the similar feature space; the multi-path residual group is used to learn reconstructed error The traffic classification is completed by a lightweight classifier. The experimental results show that the detection performance of the proposed method for anomalous network traffic is sig-nificantly improved compared with traditional methods; it confirms that the longer time span and more S transformation scales have positive effects on discovering potential diversity information in the original network traffic.
SoftPool++: An Encoder-Decoder Network for Point Cloud Completion
Authors: Yida Wang, David Joseph Tan, Nassir Navab, Federico Tombari
Date: 2022-05-08 00:00:00
Tasks: Decoder, Point Cloud Completion

We propose a novel convolutional operator for the task of point cloud completion. One striking characteristic of our approach is that, conversely to related work it does not require any max-pooling or voxelization operation. Instead, the proposed operator used to learn the point cloud embedding in the encoder extracts permutation-invariant features from the point cloud via a soft-pooling of feature activations, which are able to preserve fine-grained geometric details. These features are then passed on to a decoder architecture. Due to the compression in the encoder, a typical limitation of this type of architectures is that they tend to lose parts of the input shape structure. We propose to overcome this limitation by using skip connections specifically devised for point clouds, where links between corresponding layers in the encoder and the decoder are established. As part of these connections, we introduce a transformation matrix that projects the features from the encoder to the decoder and vice-versa. The quantitative and qualitative results on the task of object completion from partial scans on the ShapeNet dataset show that incorporating our approach achieves state-of-the-art performance in shape completion both at low and high resolutions.
VPN: Verification of Poisoning in Neural Networks
Authors: Youcheng Sun, Muhammad Usman, Divya Gopinath, Corina S. Păsăreanu
Date: 2022-05-08 00:00:00
Tasks: Data Poisoning, image-classification, Image Classification, Neural Network Security

Neural networks are successfully used in a variety of applications, many of them having safety and security concerns. As a result researchers have proposed formal verification techniques for verifying neural network properties. While previous efforts have mainly focused on checking local robustness in neural networks, we instead study another neural network security issue, namely data poisoning. In this case an attacker inserts a trigger into a subset of the training data, in such a way that at test time, this trigger in an input causes the trained model to misclassify to some target class. We show how to formulate the check for data poisoning as a property that can be checked with off-the-shelf verification tools, such as Marabou and nneum, where counterexamples of failed checks constitute the triggers. We further show that the discovered triggers are `transferable' from a small model to a larger, better-trained model, allowing us to analyze state-of-the art performant models trained for image classification tasks.
Demo: Real-Time Semantic Communications with a Vision Transformer
Authors: Hanju Yoo, Taehun Jung, Linglong Dai, Songkuk Kim, Chan-Byoung Chae
Date: 2022-05-08 00:00:00

Semantic communications are expected to enable the more effective delivery of meaning rather than a precise transfer of symbols. In this paper, we propose an end-to-end deep neural network-based architecture for image transmission and demonstrate its feasibility in a real-time wireless channel by implementing a prototype based on a field-programmable gate array (FPGA). We demonstrate that this system outperforms the traditional 256-quadrature amplitude modulation system in the low signal-to-noise ratio regime with the popular CIFAR-10 dataset. To the best of our knowledge, this is the first work that implements and investigates real-time semantic communications with a vision transformer.
Multimodal Semi-Supervised Learning for Text Recognition
Authors: Aviad Aberdam, Roy Ganz, Shai Mazor, Ron Litman
Date: 2022-05-08 00:00:00
Tasks: Language Modelling, Representation Learning, Scene Text Recognition, Self-Supervised Learning

Until recently, the number of public real-world text images was insufficient for training scene text recognizers. Therefore, most modern training methods rely on synthetic data and operate in a fully supervised manner. Nevertheless, the amount of public real-world text images has increased significantly lately, including a great deal of unlabeled data. Leveraging these resources requires semi-supervised approaches; however, the few existing methods do not account for vision-language multimodality structure and therefore suboptimal for state-of-the-art multimodal architectures. To bridge this gap, we present semi-supervised learning for multimodal text recognizers (SemiMTR) that leverages unlabeled data at each modality training phase. Notably, our method refrains from extra training stages and maintains the current three-stage multimodal training procedure. Our algorithm starts by pretraining the vision model through a single-stage training that unifies self-supervised learning with supervised training. More specifically, we extend an existing visual representation learning algorithm and propose the first contrastive-based method for scene text recognition. After pretraining the language model on a text corpus, we fine-tune the entire network via a sequential, character-level, consistency regularization between weakly and strongly augmented views of text images. In a novel setup, consistency is enforced on each modality separately. Extensive experiments validate that our method outperforms the current training schemes and achieves state-of-the-art results on multiple scene text recognition benchmarks.
CCMB: A Large-scale Chinese Cross-modal Benchmark
Authors: Chunyu Xie, Heng Cai, Jincheng Li, Fanjing Kong, Xiaoyu Wu, Jianfei Song, Henrique Morimitsu, Lin Yao, Dexin Wang, Xiangzheng Zhang, Dawei Leng, Baochang Zhang, Xiangyang Ji, Yafeng Deng
Date: 2022-05-08 00:00:00
Tasks: image-classification, Image Classification, Image Generation, Image Retrieval, Image-text matching, Image-text Retrieval, Retrieval, Text Matching, Text Retrieval, Text to Image Generation, Text-to-Image Generation, Zero-Shot Image Classification, Zero-shot Image Retrieval

Vision-language pre-training (VLP) on large-scale datasets has shown premier performance on various downstream tasks. In contrast to plenty of available benchmarks with English corpus, large-scale pre-training datasets and downstream datasets with Chinese corpus remain largely unexplored. In this work, we build a large-scale high-quality Chinese Cross-Modal Benchmark named CCMB for the research community, which contains the currently largest public pre-training dataset Zero and five human-annotated fine-tuning datasets for downstream tasks. Zero contains 250 million images paired with 750 million text descriptions, plus two of the five fine-tuning datasets are also currently the largest ones for Chinese cross-modal downstream tasks. Along with the CCMB, we also develop a VLP framework named R2D2, applying a pre-Ranking + Ranking strategy to learn powerful vision-language representations and a two-way distillation method (i.e., target-guided Distillation and feature-guided Distillation) to further enhance the learning capability. With the Zero and the R2D2 VLP framework, we achieve state-of-the-art performance on twelve downstream datasets from five broad categories of tasks including image-text retrieval, image-text matching, image caption, text-to-image generation, and zero-shot image classification. The datasets, models, and codes are available at https://github.com/yuxie11/R2D2
Introduction to Soar
Authors: John E. Laird
Date: 2022-05-08 00:00:00
Tasks: Chunking, Decision Making, Procedure Learning, Reinforcement Learning (RL), Visual Reasoning

This paper is the recommended initial reading for a functional overview of Soar, version 9.6. It includes an abstract overview of the architectural structure of Soar including its processing, memories, learning modules, their interfaces, and the representations of knowledge used by those modules. From there it describes the processing supported by those modules, including decision making, impasses and substates, procedure learning via chunking, reinforcement learning, semantic memory, episodic memory, and spatial-visual reasoning. It then reviews the levels of decision making and variety of learning in Soar, and analysis of Soar as an architecture supporting general human-level AI. Following the references is an appendix that contains short descriptions of recent Soar agents and a glossary of the terminology we use in describing Soar.
Assigning Species Information to Corresponding Genes by a Sequence Labeling Framework
Authors: Ling Luo, Chih-Hsuan Wei, Po-Ting Lai, Qingyu Chen, Rezarta Islamaj Doğan, Zhiyong Lu
Date: 2022-05-08 00:00:00
Tasks: Benchmarking, Binary Classification

The automatic assignment of species information to the corresponding genes in a research article is a critically important step in the gene normalization task, whereby a gene mention is normalized and linked to a database record or identifier by a text-mining algorithm. Existing methods typically rely on heuristic rules based on gene and species co-occurrence in the article, but their accuracy is suboptimal. We therefore developed a high-performance method, using a novel deep learning-based framework, to classify whether there is a relation between a gene and a species. Instead of the traditional binary classification framework in which all possible pairs of genes and species in the same article are evaluated, we treat the problem as a sequence-labeling task such that only a fraction of the pairs needs to be considered. Our benchmarking results show that our approach obtains significantly higher performance compared to that of the rule-based baseline method for the species assignment task (from 65.8% to 81.3% in accuracy). The source code and data for species assignment are freely available at https://github.com/ncbi/SpeciesAssignment.
Fully Automated Binary Pattern Extraction For Finger Vein Identification using Double Optimization Stages-Based Unsupervised Learning Approach
Authors: Ali Salah Hameed, Adil Al-Azzawi
Date: 2022-05-08 00:00:00
Tasks: Deep Learning, Image Clustering

Today, finger vein identification is gaining popularity as a potential biometric identification framework solution. Machine learning-based unsupervised, supervised, and deep learning algorithms have had a significant influence on finger vein detection and recognition at the moment. Deep learning, on the other hand, necessitates a large number of training datasets that must be manually produced and labeled. In this research, we offer a completely automated unsupervised learning strategy for training dataset creation. Our method is intended to extract and build a decent binary mask training dataset completely automated. In this technique, two optimization steps are devised and employed. The initial stage of optimization is to create a completely automated unsupervised image clustering based on finger vein image localization. Worldwide finger vein pattern orientation estimation is employed in the second optimization to optimize the retrieved finger vein lines. Finally, the proposed system achieves 99.6 - percent pattern extraction accuracy, which is significantly higher than other common unsupervised learning methods like k-means and Fuzzy C-Means (FCM).
On the Use of BERT for Automated Essay Scoring: Joint Learning of Multi-Scale Essay Representation
Authors: Yongjie Wang, Chuan Wang, Ruobing Li, Hui Lin
Date: 2022-05-08 00:00:00
Proceeding: NAACL 2022 7
Tasks: Automated Essay Scoring, Transfer Learning

In recent years, pre-trained models have become dominant in most natural language processing (NLP) tasks. However, in the area of Automated Essay Scoring (AES), pre-trained models such as BERT have not been properly used to outperform other deep learning models such as LSTM. In this paper, we introduce a novel multi-scale essay representation for BERT that can be jointly learned. We also employ multiple losses and transfer learning from out-of-domain essays to further improve the performance. Experiment results show that our approach derives much benefit from joint learning of multi-scale essay representation and obtains almost the state-of-the-art result among all deep learning models in the ASAP task. Our multi-scale essay representation also generalizes well to CommonLit Readability Prize data set, which suggests that the novel text representation proposed in this paper may be a new and effective choice for long-text tasks.