pwc-archive/papers-with-abstracts huggingface dataset:
Disengagement Cause-and-Effect Relationships Extraction Using an NLP Pipeline
Authors: Yangtao Zhang, X. Jessie Yang, Feng Zhou
Date: 2021-11-05 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Transfer Learning

The advancement in machine learning and artificial intelligence is promoting the testing and deployment of autonomous vehicles (AVs) on public roads. The California Department of Motor Vehicles (CA DMV) has launched the Autonomous Vehicle Tester Program, which collects and releases reports related to Autonomous Vehicle Disengagement (AVD) from autonomous driving. Understanding the causes of AVD is critical to improving the safety and stability of the AV system and provide guidance for AV testing and deployment. In this work, a scalable end-to-end pipeline is constructed to collect, process, model, and analyze the disengagement reports released from 2014 to 2020 using natural language processing deep transfer learning. The analysis of disengagement data using taxonomy, visualization and statistical tests revealed the trends of AV testing, categorized cause frequency, and significant relationships between causes and effects of AVD. We found that (1) manufacturers tested AVs intensively during the Spring and/or Winter, (2) test drivers initiated more than 80% of the disengagement while more than 75% of the disengagement were led by errors in perception, localization & mapping, planning and control of the AV system itself, and (3) there was a significant relationship between the initiator of AVD and the cause category. This study serves as a successful practice of deep transfer learning using pre-trained models and generates a consolidated disengagement database allowing further investigation for other researchers.
Learning Model Predictive Controllers for Real-Time Ride-Hailing Vehicle Relocation and Pricing Decisions
Authors: Enpeng Yuan, Pascal Van Hentenryck
Date: 2021-11-05 00:00:00
Tasks: Model Predictive Control

Large-scale ride-hailing systems often combine real-time routing at the individual request level with a macroscopic Model Predictive Control (MPC) optimization for dynamic pricing and vehicle relocation. The MPC relies on a demand forecast and optimizes over a longer time horizon to compensate for the myopic nature of the routing optimization. However, the longer horizon increases computational complexity and forces the MPC to operate at coarser spatial-temporal granularity, degrading the quality of its decisions. This paper addresses these computational challenges by learning the MPC optimization. The resulting machine-learning model then serves as the optimization proxy and predicts its optimal solutions. This makes it possible to use the MPC at higher spatial-temporal fidelity, since the optimizations can be solved and learned offline. Experimental results show that the proposed approach improves quality of service on challenging instances from the New York City dataset.
Regular Decision Processes for Grid Worlds
Authors: Nicky Lenaers, Martijn van Otterlo
Date: 2021-11-05 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty, Incremental Learning, Sequential Decision Making

Markov decision processes are typically used for sequential decision making under uncertainty. For many aspects however, ranging from constrained or safe specifications to various kinds of temporal (non-Markovian) dependencies in task and reward structures, extensions are needed. To that end, in recent years interest has grown into combinations of reinforcement learning and temporal logic, that is, combinations of flexible behavior learning methods with robust verification and guarantees. In this paper we describe an experimental investigation of the recently introduced regular decision processes that support both non-Markovian reward functions as well as transition functions. In particular, we provide a tool chain for regular decision processes, algorithmic extensions relating to online, incremental learning, an empirical evaluation of model-free and model-based solution algorithms, and applications in regular, but non-Markovian, grid worlds.
End-to-end deep meta modelling to calibrate and optimize energy consumption and comfort
Authors: Max Cohen, Sylvain Le Corff, Maurice Charbit, Marius Preda, Gilles Nozière
Date: 2021-02-01 00:00:00
Tasks: Multiobjective Optimization

In this paper, we propose a new end-to-end methodology to optimize the energy performance as well as comfort and air quality in large buildings without any renovation work. We introduce a metamodel based on recurrent neural networks and trained to predict the behavior of a general class of buildings using a database sampled from a simulation program. This metamodel is then deployed in different frameworks and its parameters are calibrated using the specific data of two real buildings. Parameters are estimated by comparing the predictions of the metamodel with real data obtained from sensors using the CMA-ES algorithm, a derivative free optimization procedure. Then, energy consumptions are optimized while maintaining a target thermal comfort and air quality, using the NSGA-II multi-objective optimization procedure. The numerical experiments illustrate how this metamodel ensures a significant gain in energy efficiency, up to almost 10%, while being computationally much more appealing than numerical models and flexible enough to be adapted to several types of buildings.
Dynamic Human-Robot Role Allocation based on Human Ergonomics Risk Prediction and Robot Actions Adaptation
Authors: Elena Merlo, Edoardo Lamon, Fabio Fusaro, Marta Lorenzini, Alessandro Carfì, Fulvio Mastrogiovanni, Arash Ajoudani, .
Date: 2021-11-05 00:00:00

Despite cobots have high potential in bringing several benefits in the manufacturing and logistic processes, but their rapid (re-)deployment in changing environments is still limited. To enable fast adaptation to new product demands and to boost the fitness of the human workers to the allocated tasks, we propose a novel method that optimizes assembly strategies and distributes the effort among the workers in human-robot cooperative tasks. The cooperation model exploits AND/OR Graphs that we adapted to solve also the role allocation problem. The allocation algorithm considers quantitative measurements that are computed online to describe human operator's ergonomic status and task properties. We conducted preliminary experiments to demonstrate that the proposed approach succeeds in controlling the task allocation process to ensure safe and ergonomic conditions for the human worker.
Gradient-based learning applied to document recognition
Authors: Y. LeCun, L. Bottou, Y. Bengio, P. Haffner
Date: 1998-11-01 00:00:00
Proceeding: Proceedings of the IEEE 1998 11
Tasks: Handwriting Recognition, Handwritten Digit Recognition, Language Modeling, Language Modelling

Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life document recognition systems are composed of multiple modules including field extraction, segmentation recognition, and language modeling. A new learning paradigm, called graph transformer networks (GTN), allows such multimodule systems to be trained globally using gradient-based methods so as to minimize an overall performance measure. Two systems for online handwriting recognition are described. Experiments demonstrate the advantage of global training, and the flexibility of graph transformer networks. A graph transformer network for reading a bank cheque is also described. It uses convolutional neural network character recognizers combined with global training techniques to provide record accuracy on business and personal cheques. It is deployed commercially and reads several million cheques per day.
Axis Learning for Orientated Objects Detection in Aerial Images
Authors: Zhifeng Xiao, Linjun Qian, Weiping Shao, Xiaowei Tan, Kai Wang
Date: 2020-03-20 00:00:00
Proceeding: Remote Sensing 2020 3
Tasks: Object, object-detection, Object Detection, Object Detection In Aerial Images, One-stage Anchor-free Oriented Object Detection, Oriented Object Detection

Orientated object detection in aerial images is still a challenging task due to the bird’s eye view and the various scales and arbitrary angles of objects in aerial images. Most current methods for orientated object detection are anchor-based, which require considerable pre-defined anchors and are time consuming. In this article, we propose a new one-stage anchor-free method to detect orientated objects in per-pixel prediction fashion with less computational complexity. Arbitrary orientated objects are detected by predicting the axis of the object, which is the line connecting the head and tail of the object, and the width of the object is vertical to the axis. By predicting objects at the pixel level of feature maps directly, the method avoids setting a number of hyperparameters related to anchor and is computationally efficient. Besides, a new aspect-ratio-aware orientation centerness method is proposed to better weigh positive pixel points, in order to guide the network to learn discriminative features from a complex background, which brings improvements for large aspect ratio object detection. The method is tested on two common aerial image datasets, achieving better performance compared with most one-stage orientated methods and many two-stage anchor-based methods with a simpler procedure and lower computational complexity.
AF-EMS Detector: Improve the Multi-Scale Detection Per- formance of the Anchor-Free Detector
Authors: Jiangqiao Yan, Liangjin Zhao, Wenhui Diao, Hongqi Wang, Xian Sun
Date: 2021-01-06 00:00:00
Proceeding: Remote Sensing 2021 1
Tasks: Object, object-detection, Object Detection, One-stage Anchor-free Oriented Object Detection, Oriented Object Detection

As a precursor step for computer vision algorithms, object detection plays an important role in various practical application scenarios. With the objects to be detected becoming more complex, the problem of multi-scale object detection has attracted more and more attention, especially in the field of remote sensing detection. Early convolutional neural network detection algorithms are mostly based on artificially preset anchor-boxes to divide different regions in the image, and then obtain the prior position of the target. However, the anchor box is difficult to set reasonably and will cause a large amount of computational redundancy, which affects the generality of the detection model obtained under fixed parameters. In the past two years, anchor-free detection algorithm has achieved remarkable development in the field of detection on natural image. However, there is no sufficient research on how to deal with multi-scale detection more effectively in anchor-free framework and use these detectors on remote sensing images. In this paper, we propose a specific-attention Feature Pyramid Network (FPN) module, which is able to generate a feature pyramid, basing on the characteristics of objects with various sizes. In addition, this pyramid suits multi-scale object detection better. Besides, a scale-aware detection head is proposed which contains a multi-receptive feature fusion module and a size-based feature compensation module. The new anchor-free detector can obtain a more effective multi-scale feature expression. Experiments on challenging datasets show that our approach performs favorably against other methods in terms of the multi-scale object detection performance.
Arbitrary-Oriented Object Detection in Remote Sensing Images Based on Polar Coordinates
Authors: Lin Zhou, Haoran Wei, Hao Li, Wenzhe Zhao, Yi Zhang, Yue Zhang
Date: 2020-11-27 00:00:00
Proceeding: IEEE Access 2020 11
Tasks: Object, object-detection, Object Detection, One-stage Anchor-free Oriented Object Detection, Oriented Object Detection, regression

Arbitrary-oriented object detection is an important task in the field of remote sensing object detection. Existing studies have shown that the polar coordinate system has obvious advantages in dealing with the problem of rotating object modeling, that is, using fewer parameters to achieve more accurate rotating object detection. However, present state-of-the-art detectors based on deep learning are all modeled in Cartesian coordinates. In this article, we introduce the polar coordinate system to the deep learning detector for the first time, and propose an anchor free Polar Remote Sensing Object Detector (P-RSDet), which can achieve competitive detection accuracy via using simpler object representation model and less regression parameters. In P-RSDet method, arbitrary-oriented object detection can be achieved by predicting the center point and regressing one polar radius and two polar angles. Besides, in order to express the geometric constraint relationship between the polar radius and the polar angle, a Polar Ring Area Loss function is proposed to improve the prediction accuracy of the corner position. Experiments on DOTA, UCAS-AOD and NWPU VHR-10 datasets show that our P-RSDet achieves state-of-the-art performances with simpler model and less regression parameters.
Indoor Visual Positioning Aided by CNN-Based Image Retrieval: Training-Free, 3D Modeling-Free
Authors: Yujin Chen, Ruizhi Chen, Mengyun Liu, Aoran Xiao, Dewen Wu  and Shuheng Zhao
Date: 2018-06-15 00:00:00
Proceeding: journal 2018 6
Tasks: geo-localization, Image Retrieval, Indoor Localization, Marketing, Pose Estimation, Retrieval, Visual Localization

Indoor localization is one of the fundamentals of location-based services (LBS) such as
seamless indoor and outdoor navigation, location-based precision marketing, spatial cognition of
robotics, etc. Visual features take up a dominant part of the information that helps human and
robotics understand the environment, and many visual localization systems have been proposed.
However, the problem of indoor visual localization has not been well settled due to the tough
trade-off of accuracy and cost. To better address this problem, a localization method based on image
retrieval is proposed in this paper, which mainly consists of two parts. The first one is CNN-based
image retrieval phase, CNN features extracted by pre-trained deep convolutional neural networks
(DCNNs) from images are utilized to compare the similarity, and the output of this part are the
matched images of the target image. The second one is pose estimation phase that computes accurate
localization result. Owing to the robust CNN feature extractor, our scheme is applicable to complex
indoor environments and easily transplanted to outdoor environments. The pose estimation scheme
was inspired by monocular visual odometer, therefore, only RGB images and poses of reference
images are needed for accurate image geo-localization. Furthermore, our method attempts to use
lightweight datum to present the scene. To evaluate the performance, experiments are conducted,
and the result demonstrates that our scheme can efficiently result in high location accuracy as well as
orientation estimation. Currently the positioning accuracy and usability enhanced compared with
similar solutions. Furthermore, our idea has a good application foreground, because the algorithms
of data acquisition and pose estimation are compatible with the current state of data expansion
A Visual Positioning System for Indoor Blind Navigation
Authors: He Zhang and Cang Ye
Date: 2020-08-31 00:00:00
Proceeding: Conference 2020 8
Tasks: Pose Estimation

This paper presents a visual positioning system
(VPS) for real-time pose estimation of a robotic navigation aid
(RNA) for assistive navigation. The core of the VPS is a new
method called depth-enhanced visual-inertial odometry (DVIO)
that uses an RGB-D camera and an inertial measurement unit
(IMU) to estimate the RNA’s pose. The DVIO method extracts
the geometric feature (the floor plane) from the camera’s depth
data and integrates its measurement residuals with that of the
visual features and the inertial data in a graph optimization
framework for pose estimation. A new measure based on the
Sampson error is introduced to describe the measurement
residuals of the near-range visual features with a known depth
and that of the far-range visual features whose depths are
unknown. The measure allows for the incorporation of both
types of visual features into graph optimization. The use of
the geometric feature and the Sampson error improves pose
estimation accuracy and precision. The DVIO method is paired
with a particle filter localization (PFL) method to locate the
RNA in a 2D floor plan and the information is used to guide a
visually impaired person. The PFL reduces the RNA’s position
and heading error by aligning the camera’s depth data with
the floor plan map. Together, the DVIO and the PFL allow
for accurate pose estimation for wayfinding and 3D mapping
for obstacle avoidance. Experimental results demonstrate the
usefulness of the RNA in assistive navigation in indoor spaces
Learning Word Vectors for Sentiment Analysis
Authors: Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng
Date: 2011-06-01 00:00:00
Proceeding: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies 2011 6
Tasks: Sentiment Analysis, Sentiment Classification

Unsupervised vector-based approaches to semantics can model rich lexical meanings, but they largely fail to capture sentiment information that is central to many word meanings and important for a wide range of NLP tasks. We present a model that uses a mix of unsupervised and supervised techniques to learn word vectors capturing semantic term–document information as well as rich sentiment content. The proposed model can leverage both continuous and multi-dimensional sentiment information as well as non-sentiment annotations. We instantiate the model to utilize the document-level sentiment polarity annotations present in many online documents (e.g. star ratings). We evaluate the model using small, widely used sentiment and subjectivity corpora and find it out-performs several previously introduced methods for sentiment classification. We also introduce a large dataset of movie reviews to serve as a more robust benchmark for work in this area.
SPECTRe: Substructure Processing, Enumeration, and Comparison Tool Resource: An efficient tool to encode all substructures of molecules represented in SMILES
Authors: Yasemin Yesiltepe, Ryan S. Renslow, Thomas O. Metz
Date: 2021-11-05 00:00:00
Tasks: All, Drug Discovery, Property Prediction

Functional groups and moieties are chemical descriptors of biomolecules that can be used to interpret their properties and functions, leading to the understanding of chemical or biological mechanisms. These chemical building blocks, or sub-structures, enable the identification of common molecular subgroups, assessing the structural similarities and critical interactions among a set of biological molecules with known activities, and designing novel compounds with similar chemical properties. Here, we introduce a Python-based tool, SPECTRe (Substructure Processing, Enumeration, and Comparison Tool Resource), designed to provide all substructures in a given molecular structure, regardless of the molecule size, employing efficient enumeration and generation of substructures represented in a human-readable SMILES format through the use of classical graph traversal (breadth-first and depth-first search) algorithms. We demonstrate the application of SPECTRe for a set of 10,375 molecules in the molecular weight range 27 to 350 Da (<=26 non-hydrogen atoms), spanning a wide array of structure-based chemical functionalities and chemical classes. We found that the substructure count as a measure of molecular complexity depends strongly on the number of unique atom and bond types present, degree of branching, and presence of rings. The substructure counts are found to be similar for a set of molecules belonging to particular chemical classes and classified based on the characteristic features of certain topologies. We demonstrate that SPECTRe shows promise to be useful in many applications of cheminformatics such as virtual screening for drug discovery, property prediction, fingerprint-based molecular similarity searching, and data mining for identifying frequent substructures.
Cooperative Transportation of UAVs Without Inter-UAV Communication
Authors: Pin-Xian Wu, Cheng-Cheng Yang, Teng-Hu Cheng
Date: 2021-11-05 00:00:00

A leader-follower system is developed for cooperative transportation. To the best of our knowledge, this is the first work that inter-UAV communication is not required and the reference trajectory of the payload can be modified in real time, so that it can be applied to a dynamically changing environment. To track the modified reference trajectory in real time under the communication-free condition, the leader-follower system is considered as a nonholonomic system in which a controller is developed for the leader to achieve asymptotic tracking of the payload. To eliminate the need to install force sensors, UKFs (unscented Kalman filters) are developed to estimate the forces applied by the leader and follower. Stability analysis is conducted to prove the tracking error of the closed-loop system. Simulation results demonstrate the good performance of the tracking controller. The experiments show the controllers of the leader and the follower can work in the real world, but the tracking errors were affected by the disturbance of airflow in a restricted space.
Target Speech Extraction: Independent Vector Extraction Guided by Supervised Speaker Identification
Authors: Jiri Malek, Jakub Jansky, Zbynek Koldovsky, Tomas Kounovsky, Jaroslav Cmejla, Jindrich Zdansky
Date: 2021-11-05 00:00:00
Tasks: Speaker Identification, Speech Extraction

This manuscript proposes a novel robust procedure for the extraction of a speaker of interest (SOI) from a mixture of audio sources. The estimation of the SOI is performed via independent vector extraction (IVE). Since the blind IVE cannot distinguish the target source by itself, it is guided towards the SOI via frame-wise speaker identification based on deep learning. Still, an incorrect speaker can be extracted due to guidance failings, especially when processing challenging data. To identify such cases, we propose a criterion for non-intrusively assessing the estimated speaker. It utilizes the same model as the speaker identification, so no additional training is required. When incorrect extraction is detected, we propose a ``deflation'' step in which the incorrect source is subtracted from the mixture and, subsequently, another attempt to extract the SOI is performed. The process is repeated until successful extraction is achieved. The proposed procedure is experimentally tested on artificial and real-world datasets containing challenging phenomena: source movements, reverberation, transient noise, or microphone failures. The method is compared with state-of-the-art blind algorithms as well as with current fully supervised deep learning-based methods.