pwc-archive/papers-with-abstracts huggingface dataset:
A Topological Approach to Compare Document Semantics Based on a New Variant of Syntactic N-grams
Authors: Fanchao Meng
Date: 2021-03-08 00:00:00
Tasks: Clustering

This paper delivers a new perspective of thinking and utilizing syntactic n-grams (sn-grams). Sn-grams are a type of non-linear n-grams which have been playing a critical role in many NLP tasks. Introducing sn-grams to comparing document semantics thus is an appealing application, and few studies have reported progress at this. However, when proceeding on this application, we found three major issues of sn-grams: lack of significance, being sensitive to word orders and failing on capture indirect syntactic relations. To address these issues, we propose a new variant of sn-grams named generalized phrases (GPs). Then based on GPs we propose a topological approach, named DSCoH, to compute document semantic similarities. DSCoH has been extensively tested on the document semantics comparison and the document clustering tasks. The experimental results show that DSCoH can outperform state-of-the-art embedding-based methods.
Contrastive Semi-supervised Learning for ASR
Authors: Alex Xiao, Christian Fuegen, Abdelrahman Mohamed
Date: 2021-03-09 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Representation Learning, speech-recognition, Speech Recognition

Pseudo-labeling is the most adopted method for pre-training automatic speech recognition (ASR) models. However, its performance suffers from the supervised teacher model's degrading quality in low-resource setups and under domain transfer. Inspired by the successes of contrastive representation learning for computer vision and speech applications, and more recently for supervised learning of visual objects, we propose Contrastive Semi-supervised Learning (CSL). CSL eschews directly predicting teacher-generated pseudo-labels in favor of utilizing them to select positive and negative examples. In the challenging task of transcribing public social media videos, using CSL reduces the WER by 8% compared to the standard Cross-Entropy pseudo-labeling (CE-PL) when 10hr of supervised data is used to annotate 75,000hr of videos. The WER reduction jumps to 19% under the ultra low-resource condition of using 1hr labels for teacher supervision. CSL generalizes much better in out-of-domain conditions, showing up to 17% WER reduction compared to the best CE-PL pre-trained model.
Improving Document-Level Sentiment Classification Using Importance of Sentences
Authors: Gihyeon Choi, Shinhyeok Oh, Harksoo Kim
Date: 2021-03-09 00:00:00
Tasks: Classification, Document Classification, General Classification, Sentence, Sentence Classification, Sentiment Analysis, Sentiment Classification, Text Classification

Previous researchers have considered sentiment analysis as a document classification task, in which input documents are classified into predefined sentiment classes. Although there are sentences in a document that support important evidences for sentiment analysis and sentences that do not, they have treated the document as a bag of sentences. In other words, they have not considered the importance of each sentence in the document. To effectively determine polarity of a document, each sentence in the document should be dealt with different degrees of importance. To address this problem, we propose a document-level sentence classification model based on deep neural networks, in which the importance degrees of sentences in documents are automatically determined through gate mechanisms. To verify our new sentiment analysis model, we conducted experiments using the sentiment datasets in the four different domains such as movie reviews, hotel reviews, restaurant reviews, and music reviews. In the experiments, the proposed model outperformed previous state-of-the-art models that do not consider importance differences of sentences in a document. The experimental results show that the importance of sentences should be considered in a document-level sentiment classification task.
Detecting Inappropriate Messages on Sensitive Topics that Could Harm a Company's Reputation
Authors: Nikolay Babakov, Varvara Logacheva, Olga Kozlova, Nikita Semenov, Alexander Panchenko
Date: 2021-03-09 00:00:00

Not all topics are equally "flammable" in terms of toxicity: a calm discussion of turtles or fishing less often fuels inappropriate toxic dialogues than a discussion of politics or sexual minorities. We define a set of sensitive topics that can yield inappropriate and toxic messages and describe the methodology of collecting and labeling a dataset for appropriateness. While toxicity in user-generated data is well-studied, we aim at defining a more fine-grained notion of inappropriateness. The core of inappropriateness is that it can harm the reputation of a speaker. This is different from toxicity in two respects: (i) inappropriateness is topic-related, and (ii) inappropriate message is not toxic but still unacceptable. We collect and release two datasets for Russian: a topic-labeled dataset and an appropriateness-labeled dataset. We also release pre-trained classification models trained on this data.
A Parallelizable Lattice Rescoring Strategy with Neural Language Models
Authors: Ke Li, Daniel Povey, Sanjeev Khudanpur
Date: 2021-03-08 00:00:00
Tasks: ARC, Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

This paper proposes a parallel computation strategy and a posterior-based lattice expansion algorithm for efficient lattice rescoring with neural language models (LMs) for automatic speech recognition. First, lattices from first-pass decoding are expanded by the proposed posterior-based lattice expansion algorithm. Second, each expanded lattice is converted into a minimal list of hypotheses that covers every arc. Each hypothesis is constrained to be the best path for at least one arc it includes. For each lattice, the neural LM scores of the minimal list are computed in parallel and are then integrated back to the lattice in the rescoring stage. Experiments on the Switchboard dataset show that the proposed rescoring strategy obtains comparable recognition performance and generates more compact lattices than a competitive baseline method. Furthermore, the parallel rescoring method offers more flexibility by simplifying the integration of PyTorch-trained neural LMs for lattice rescoring with Kaldi.
Benchmarking Off-The-Shelf Solutions to Robotic Assembly Tasks
Authors: Wenzhao Lian, Tim Kelch, Dirk Holz, Adam Norton, Stefan Schaal
Date: 2021-03-08 00:00:00
Tasks: Benchmarking

In recent years, many learning based approaches have been studied to realize robotic manipulation and assembly tasks, often including vision and force/tactile feedback. However, it remains frequently unclear what is the baseline state-of-the-art performance and what are the bottleneck problems. In this work, we evaluate some off-the-shelf (OTS) industrial solutions on a recently introduced benchmark, the National Institute of Standards and Technology (NIST) Assembly Task Boards. A set of assembly tasks are introduced and baseline methods are provided to understand their intrinsic difficulty. Multiple sensor-based robotic solutions are then evaluated, including hybrid force/motion control and 2D/3D pattern matching algorithms. An end-to-end integrated solution that accomplishes the tasks is also provided. The results and findings throughout the study reveal a few noticeable factors that impede the adoptions of the OTS solutions: expertise dependent, limited applicability, lack of interoperability, no scene awareness or error recovery mechanisms, and high cost. This paper also provides a first attempt of an objective benchmark performance on the NIST Assembly Task Boards as a reference comparison for future works on this problem.
Explanations in Autonomous Driving: A Survey
Authors: Daniel Omeiza, Helena Webb, Marina Jirotka, Lars Kunze
Date: 2021-03-09 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Management, Survey

The automotive industry has witnessed an increasing level of development in the past decades; from manufacturing manually operated vehicles to manufacturing vehicles with a high level of automation. With the recent developments in Artificial Intelligence (AI), automotive companies now employ blackbox AI models to enable vehicles to perceive their environments and make driving decisions with little or no input from a human. With the hope to deploy autonomous vehicles (AV) on a commercial scale, the acceptance of AV by society becomes paramount and may largely depend on their degree of transparency, trustworthiness, and compliance with regulations. The assessment of the compliance of AVs to these acceptance requirements can be facilitated through the provision of explanations for AVs' behaviour. Explainability is therefore seen as an important requirement for AVs. AVs should be able to explain what they have 'seen', done, and might do in environments in which they operate. In this paper, we provide a comprehensive survey of the existing body of work around explainable autonomous driving. First, we open with a motivation for explanations by highlighting and emphasising the importance of transparency, accountability, and trust in AVs; and examining existing regulations and standards related to AVs. Second, we identify and categorise the different stakeholders involved in the development, use, and regulation of AVs and elicit their explanation requirements for AV. Third, we provide a rigorous review of previous work on explanations for the different AV operations (i.e., perception, localisation, planning, control, and system management). Finally, we identify pertinent challenges and provide recommendations, such as a conceptual framework for AV explainability. This survey aims to provide the fundamental knowledge required of researchers who are interested in explainability in AVs.
A Scavenger Hunt for Service Robots
Authors: Harel Yedidsion, Jennifer Suriadinata, Zifan Xu, Stefan Debruyn, Peter Stone
Date: 2021-03-09 00:00:00
Tasks: Reinforcement Learning (RL)

Creating robots that can perform general-purpose service tasks in a human-populated environment has been a longstanding grand challenge for AI and Robotics research. One particularly valuable skill that is relevant to a wide variety of tasks is the ability to locate and retrieve objects upon request. This paper models this skill as a Scavenger Hunt (SH) game, which we formulate as a variation of the NP-hard stochastic traveling purchaser problem. In this problem, the goal is to find a set of objects as quickly as possible, given probability distributions of where they may be found. We investigate the performance of several solution algorithms for the SH problem, both in simulation and on a real mobile robot. We use Reinforcement Learning (RL) to train an agent to plan a minimal cost path, and show that the RL agent can outperform a range of heuristic algorithms, achieving near optimal performance. In order to stimulate research on this problem, we introduce a publicly available software stack and associated website that enable users to upload scavenger hunts which robots can download, perform, and learn from to continually improve their performance on future hunts.
Decentralized Circle Formation Control for Fish-like Robots in the Real-world via Reinforcement Learning
Authors: Tianhao Zhang, Yueheng Li, Shuai Li, Qiwei Ye, Chen Wang, Guangming Xie
Date: 2021-03-09 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

In this paper, the circle formation control problem is addressed for a group of cooperative underactuated fish-like robots involving unknown nonlinear dynamics and disturbances. Based on the reinforcement learning and cognitive consistency theory, we propose a decentralized controller without the knowledge of the dynamics of the fish-like robots. The proposed controller can be transferred from simulation to reality. It is only trained in our established simulation environment, and the trained controller can be deployed to real robots without any manual tuning. Simulation results confirm that the proposed model-free robust formation control method is scalable with respect to the group size of the robots and outperforms other representative RL algorithms. Several experiments in the real world verify the effectiveness of our RL-based approach for circle formation control.
A model-based framework for learning transparent swarm behaviors
Authors: Mario Coppola, Jian Guo, Eberhard Gill, Guido C. H. E. de Croon
Date: 2021-03-09 00:00:00

This paper proposes a model-based framework to automatically and efficiently design understandable and verifiable behaviors for swarms of robots. The framework is based on the automatic extraction of two distinct models: 1) a neural network model trained to estimate the relationship between the robots' sensor readings and the global performance of the swarm, and 2) a probabilistic state transition model that explicitly models the local state transitions (i.e., transitions in observations from the perspective of a single robot in the swarm) given a policy. The models can be trained from a data set of simulated runs featuring random policies. The first model is used to automatically extract a set of local states that are expected to maximize the global performance. These local states are referred to as desired local states. The second model is used to optimize a stochastic policy so as to increase the probability that the robots in the swarm observe one of the desired local states. Following these steps, the framework proposed in this paper can efficiently lead to effective controllers. This is tested on four case studies, featuring aggregation and foraging tasks. Importantly, thanks to the models, the framework allows us to understand and inspect a swarm's behavior. To this end, we propose verification checks to identify some potential issues that may prevent the swarm from achieving the desired global objective. In addition, we explore how the framework can be used in combination with a "standard" evolutionary robotics strategy (i.e., where performance is measured via simulation), or with online learning.
I am Robot: Neuromuscular Reinforcement Learning to Actuate Human Limbs through Functional Electrical Stimulation
Authors: Nat Wannawas, Ali Shafti, A. Aldo Faisal
Date: 2021-03-09 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning (RL)

Human movement disorders or paralysis lead to the loss of control of muscle activation and thus motor control. Functional Electrical Stimulation (FES) is an established and safe technique for contracting muscles by stimulating the skin above a muscle to induce its contraction. However, an open challenge remains on how to restore motor abilities to human limbs through FES, as the problem of controlling the stimulation is unclear. We are taking a robotics perspective on this problem, by developing robot learning algorithms that control the ultimate humanoid robot, the human body, through electrical muscle stimulation. Human muscles are not trivial to control as actuators due to their force production being non-stationary as a result of fatigue and other internal state changes, in contrast to robot actuators which are well-understood and stationary over broad operation ranges. We present our Deep Reinforcement Learning approach to the control of human muscles with FES, using a recurrent neural network for dynamic state representation, to overcome the unobserved elements of the behaviour of human muscles under external stimulation. We demonstrate our technique both in neuromuscular simulations but also experimentally on a human. Our results show that our controller can learn to manipulate human muscles, applying appropriate levels of stimulation to achieve the given tasks while compensating for advancing muscle fatigue which arises throughout the tasks. Additionally, our technique can learn quickly enough to be implemented in real-world human-in-the-loop settings.
Did Chatbots Miss Their 'Apollo Moment'? A Survey of the Potential, Gaps and Lessons from Using Collaboration Assistants During COVID-19
Authors: Biplav Srivastava
Date: 2021-02-27 00:00:00

Artificial Intelligence (AI) technologies have long been positioned as a tool to provide crucial data-driven decision support to people. In this survey paper, we look at how AI in general, and collaboration assistants (CAs or chatbots for short) in particular, have been used during a true global exigency - the COVID-19 pandemic. The key observation is that chatbots missed their "Apollo moment" when they could have really provided contextual, personalized, reliable decision support at scale that the state-of-the-art makes possible. We review the existing capabilities that are feasible and methods, identify the potential that chatbots could have met, the use-cases they were deployed on, the challenges they faced and gaps that persisted, and draw lessons that, if implemented, would make them more relevant in future health emergencies.
CRPN-SFNet: A High-Performance Object Detector on Large-Scale Remote Sensing Images
Authors: QiFeng Lin, Jianhui Zhao, Gang Fu, and Zhiyong Yuan, Member, IEEE
Date: 2020-10-28 00:00:00
Tasks: GPU, Object, object-detection, Object Detection, Object Detection In Aerial Images, Region Proposal

Limited by the GPU memory, the current
mainstream detectors fail to directly apply to large-scale remote
sensing images for object detection. Moreover, the scale range
of objects in remote sensing images is much wider than that
of general images, which also greatly hinders the existing
methods to effectively detect geospatial objects of various scales.
For achieving high-performance object detection on large-scale
remote sensing images, this article proposes a much faster
and more accurate detecting framework, called cropping region
proposal network-based scale folding network (CRPN-SFNet).
In our framework, the CRPN includes a weak semantic RPN for
quickly locating interesting regions and a strategy of generating
cropping regions to effectively filter out meaningless regions,
which can greatly reduce the computation and storage burden.
Meanwhile, the proposed SFNet leverages the scale folding-based
training and testing methods to extend the valid detection range
of existing detectors, which is beneficial for detecting remote
sensing objects of various scales, including very small and very
large geospatial objects. Extensive experiments on the public
Dataset for Object deTection in Aerial images data set indicate
that our CRPN can help our detector deal the larger image
faster with the limited GPU memory; meanwhile, the SFNet is
beneficial to achieve more accurate detection of geospatial objects
with wide-scale range. For large-scale remote sensing images, the
proposed detection framework outperforms the state-of-the-art
object detection methods in terms of accuracy and speed
Learning Affinity via Spatial Propagation Network
Authors: Sifei Liu, Shalini De Mello, Jinwei Gu, Guangyu Zhong, Ming-Hsuan Yang, Jan Kautz
Date: 2017-10-03 00:00:00
Tasks: Colorization, Face Parsing, Image Matting, Image Segmentation, Segmentation, Semantic Segmentation

In this paper, we propose spatial propagation networks for learning the affinity matrix for vision tasks. We show that by constructing a row/column linear propagation
model, the spatially varying transformation matrix exactly constitutes an affinity
matrix that models dense, global pairwise relationships of an image. Specifically,
we develop a three-way connection for the linear propagation model, which (a)
formulates a sparse transformation matrix, where all elements can be the output
from a deep CNN, but (b) results in a dense affinity matrix that effectively models
any task-specific pairwise similarity matrix. Instead of designing the similarity
kernels according to image features of two points, we can directly output all the
similarities in a purely data-driven manner. The spatial propagation network is a
generic framework that can be applied to many affinity-related tasks, including
but not limited to image matting, segmentation and colorization, to name a few.
Essentially, the model can learn semantically-aware affinity values for high-level
vision tasks due to the powerful learning capability of the deep neural network classifier. We validate the framework on the task of refinement for image segmentation
boundaries. Experiments on the HELEN face parsing and PASCAL VOC-2012
semantic segmentation tasks show that the spatial propagation network provides a
general, effective and efficient solution for generating high-quality segmentation
results.
Deep Convolutional Autoencoders for reconstructing magnetic resonance images of the healthy brain
Authors: Adrián Arnaiz-Rodríguez, Baris Kanber
Date: 2021-01-19 00:00:00
Tasks: Data Augmentation, Dimensionality Reduction, Image Enhancement, Image Reconstruction, MRI Reconstruction, Representation Learning

The analysis of brain magnetic resonance imaging (MRI) is critical for a proper diagnosis and treatment of neurological diseases. Improvements in this eld can lead to better health quality. Numerous branches can be still enhanced due to the nature of MRI recompilation: disease detection and segmentation, data augmentation, improvement in data collection, or image enhancement are some of them. For several years, many approaches have been taken to address this. Machine Learning and Deep Learning emerge as very popular approaches to solve problems. Several kinds of data mining solutions (supervised, unsupervised, dimension reduction, generative models, etc) and algorithms can be applied to the problem-solving of MRI. Besides, new emerging deep learning architectures for other kinds of image processing tasks can be helpful. New types of convolution, autoencoders or generative adversarial networks are some of them. Therefore, the purpose of this work is to apply one of these new techniques to T1 weighted brain MRI (T1WMRI). We will develop a Deep Convolutional Autoencoder, which can be used to help with some problems in neuroimaging. The input of the Autoencoder will be control T1WMRI and will aim to return the same image, with the problem that, inside its architecture, the image travels through a lower-dimensional space, so the reconstruction of the original image becomes more difficult. Thus, the Autoencoder represents a normative model. This normative model will define a distribution (or normal range) for the neuroanatomical variability for the illness absence. Once trained with these control images, we will discuss the potential application of the autoencoder like noise reducer or disease detector.