pwc-archive/papers-with-abstracts huggingface dataset:
Solving the scalarization issues of Advantage-based Reinforcement Learning Algorithms
Authors: Federico A. Galatolo, Mario G. C. A. Cimino, Gigliola Vaglini
Date: 2020-04-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this research, some of the issues that arise from the scalarization of the multi-objective optimization problem in the Advantage Actor Critic (A2C) reinforcement learning algorithm are investigated. The paper shows how a naive scalarization can lead to gradients overlapping. Furthermore, the possibility that the entropy regularization term can be a source of uncontrolled noise is discussed. With respect to the above issues, a technique to avoid gradient overlapping is proposed, while keeping the same loss formulation. Moreover, a method to avoid the uncontrolled noise, by sampling the actions from distributions with a desired minimum entropy, is investigated. Pilot experiments have been carried out to show how the proposed method speeds up the training. The proposed approach can be applied to any Advantage-based Reinforcement Learning algorithm.
DeepStreamCE: A Streaming Approach to Concept Evolution Detection in Deep Neural Networks
Authors: Lorraine Chambers, Mohamed Medhat Gaber, Zahraa S. Abdallah
Date: 2020-04-08 00:00:00
Tasks: Clustering, Decision Making, General Classification

Deep neural networks have experimentally demonstrated superior performance over other machine learning approaches in decision-making predictions. However, one major concern is the closed set nature of the classification decision on the trained classes, which can have serious consequences in safety critical systems. When the deep neural network is in a streaming environment, fast interpretation of this classification is required to determine if the classification result is trusted. Un-trusted classifications can occur when the input data to the deep neural network changes over time. One type of change that can occur is concept evolution, where a new class is introduced that the deep neural network was not trained on. In the majority of deep neural network architectures, the only option is to assign this instance to one of the classes it was trained on, which would be incorrect. The aim of this research is to detect the arrival of a new class in the stream. Existing work on interpreting deep neural networks often focuses on neuron activations to provide visual interpretation and feature extraction. Our novel approach, coined DeepStreamCE, uses streaming approaches for real-time concept evolution detection in deep neural networks. DeepStreamCE applies neuron activation reduction using an autoencoder and MCOD stream-based clustering in the offline phase. Both outputs are used in the online phase to analyse the neuron activations in the evolving stream in order to detect concept evolution occurrence in real time. We evaluate DeepStreamCE by training VGG16 convolutional neural networks on combinations of data from the CIFAR-10 dataset, holding out some classes to be used as concept evolution. For comparison, we apply the data and VGG16 networks to an open-set deep network solution - OpenMax. DeepStreamCE outperforms OpenMax when identifying concept evolution for our datasets.
Reservoir Computing using High Order Synchronization of Coupled Oscillators
Authors: A. A. Velichko, D. V. Ryabokon, S. D. Khanin, A. V. Sidorenko, A. G. Rikkiev
Date: 2020-04-08 00:00:00
Tasks: Form, Vocal Bursts Intensity Prediction

We propose a concept for reservoir computing on oscillators using the high-order synchronization effect. The reservoir output is presented in the form of oscillator synchronization metrics: fractional high-order synchronization value and synchronization efficiency, expressed as a percentage. Using two coupled relaxation oscillators built on VO2 switches, we created an oscillator reservoir that allows simulating the XOR operation. The reservoir can operate as with static input data (power currents, coupling forces), as with dynamic data in the form of spike sequences. Having a small number of oscillators and significant non-linearity, the reservoir expresses a wide range of dynamic states. The proposed computing concept can be implemented on oscillators of diverse nature.
Frequency, Acceptability, and Selection: A case study of clause-embedding
Authors: Aaron Steven White, Kyle Rawlins
Date: 2020-04-08 00:00:00

We investigate the relationship between the frequency with which verbs are found in particular subcategorization frames and the acceptability of those verbs in those frames, focusing in particular on subordinate clause-taking verbs, such as "think", "want", and "tell". We show that verbs' subcategorization frame frequency distributions are poor predictors of their acceptability in those frames---explaining, at best, less than 1/3 of the total information about acceptability across the lexicon---and, further, that common matrix factorization techniques used to model the acquisition of verbs' acceptability in subcategorization frames fare only marginally better. All data and code are available at http://megaattitude.io.
Resource Management for Blockchain-enabled Federated Learning: A Deep Reinforcement Learning Approach
Authors: Nguyen Quang Hieu, Tran The Anh, Nguyen Cong Luong, Dusit Niyato, Dong In Kim, Erik Elmroth
Date: 2020-04-08 00:00:00
Tasks: CPU, Deep Reinforcement Learning, Federated Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Blockchain-enabled Federated Learning (BFL) enables mobile devices to collaboratively train neural network models required by a Machine Learning Model Owner (MLMO) while keeping data on the mobile devices. Then, the model updates are stored in the blockchain in a decentralized and reliable manner. However, the issue of BFL is that the mobile devices have energy and CPU constraints that may reduce the system lifetime and training efficiency. The other issue is that the training latency may increase due to the blockchain mining process. To address these issues, the MLMO needs to (i) decide how much data and energy that the mobile devices use for the training and (ii) determine the block generation rate to minimize the system latency, energy consumption, and incentive cost while achieving the target accuracy for the model. Under the uncertainty of the BFL environment, it is challenging for the MLMO to determine the optimal decisions. We propose to use the Deep Reinforcement Learning (DRL) to derive the optimal decisions for the MLMO.
Cross-lingual Emotion Intensity Prediction
Authors: Irean Navas Alejo, Toni Badia, Jeremy Barnes
Date: 2020-04-08 00:00:00
Proceeding: COLING (PEOPLES) 2020 12
Tasks: Cross-Lingual Transfer, Emotion Classification, Machine Translation, Prediction, Translation

Emotion intensity prediction determines the degree or intensity of an emotion that the author expresses in a text, extending previous categorical approaches to emotion detection. While most previous work on this topic has concentrated on English texts, other languages would also benefit from fine-grained emotion classification, preferably without having to recreate the amount of annotated data available in English in each new language. Consequently, we explore cross-lingual transfer approaches for fine-grained emotion detection in Spanish and Catalan tweets. To this end we annotate a test set of Spanish and Catalan tweets using Best-Worst scaling. We compare six cross-lingual approaches, e.g., machine translation and cross-lingual embeddings, which have varying requirements for parallel data -- from millions of parallel sentences to completely unsupervised. The results show that on this data, methods with low parallel-data requirements perform surprisingly better than methods that use more parallel data, which we explain through an in-depth error analysis. We make the dataset and the code available at \url{https://github.com/jerbarnes/fine-grained_cross-lingual_emotion}
KdConv: A Chinese Multi-domain Dialogue Dataset Towards Multi-turn Knowledge-driven Conversation
Authors: Hao Zhou, Chujie Zheng, Kaili Huang, Minlie Huang, Xiaoyan Zhu
Date: 2020-04-08 00:00:00
Proceeding: ACL 2020 6
Tasks: Domain Adaptation, Knowledge Graphs, Transfer Learning

The research of knowledge-driven conversational systems is largely limited due to the lack of dialog data which consist of multi-turn conversations on multiple topics and with knowledge annotations. In this paper, we propose a Chinese multi-domain knowledge-driven conversation dataset, KdConv, which grounds the topics in multi-turn conversations to knowledge graphs. Our corpus contains 4.5K conversations from three domains (film, music, and travel), and 86K utterances with an average turn number of 19.0. These conversations contain in-depth discussions on related topics and natural transition between multiple topics. To facilitate the following research on this corpus, we provide several benchmark models. Comparative results show that the models can be enhanced by introducing background knowledge, yet there is still a large space for leveraging knowledge to model multi-turn conversations for further research. Results also show that there are obvious performance differences between different domains, indicating that it is worth to further explore transfer learning and domain adaptation. The corpus and benchmark models are publicly available.
Time accelerated image super-resolution using shallow residual feature representative network
Authors: Meenu Ajith, Aswathy Rajendra Kurup, Manel Martínez-Ramón
Date: 2020-04-08 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

The recent advances in deep learning indicate significant progress in the field of single image super-resolution. With the advent of these techniques, high-resolution image with high peak signal to noise ratio (PSNR) and excellent perceptual quality can be reconstructed. The major challenges associated with existing deep convolutional neural networks are their computational complexity and time; the increasing depth of the networks, often result in high space complexity. To alleviate these issues, we developed an innovative shallow residual feature representative network (SRFRN) that uses a bicubic interpolated low-resolution image as input and residual representative units (RFR) which include serially stacked residual non-linear convolutions. Furthermore, the reconstruction of the high-resolution image is done by combining the output of the RFR units and the residual output from the bicubic interpolated LR image. Finally, multiple experiments have been performed on the benchmark datasets and the proposed model illustrates superior performance for higher scales. Besides, this model also exhibits faster execution time compared to all the existing approaches.
Weakly Supervised Semantic Point Cloud Segmentation:Towards 10X Fewer Labels
Authors: Xun Xu, Gim Hee Lee
Date: 2020-04-08 00:00:00
Tasks: Point Cloud Segmentation, Segmentation

Point cloud analysis has received much attention recently; and segmentation is one of the most important tasks. The success of existing approaches is attributed to deep network design and large amount of labelled training data, where the latter is assumed to be always available. However, obtaining 3d point cloud segmentation labels is often very costly in practice. In this work, we propose a weakly supervised point cloud segmentation approach which requires only a tiny fraction of points to be labelled in the training stage. This is made possible by learning gradient approximation and exploitation of additional spatial and color smoothness constraints. Experiments are done on three public datasets with different degrees of weak supervision. In particular, our proposed method can produce results that are close to and sometimes even better than its fully supervised counterpart with 10$\times$ fewer labels.
Beyond Photometric Consistency: Gradient-based Dissimilarity for Improving Visual Odometry and Stereo Matching
Authors: Jan Quenzel, Radu Alexandru Rosu, Thomas Läbe, Cyrill Stachniss, Sven Behnke
Date: 2020-04-08 00:00:00
Tasks: Camera Pose Estimation, Image Registration, Pose Estimation, Stereo Matching, Visual Odometry

Pose estimation and map building are central ingredients of autonomous robots and typically rely on the registration of sensor data. In this paper, we investigate a new metric for registering images that builds upon on the idea of the photometric error. Our approach combines a gradient orientation-based metric with a magnitude-dependent scaling term. We integrate both into stereo estimation as well as visual odometry systems and show clear benefits for typical disparity and direct image registration tasks when using our proposed metric. Our experimental evaluation indicats that our metric leads to more robust and more accurate estimates of the scene depth as well as camera trajectory. Thus, the metric improves camera pose estimation and in turn the mapping capabilities of mobile robots. We believe that a series of existing visual odometry and visual SLAM systems can benefit from the findings reported in this paper.
Satellite-based Prediction of Forage Conditions for Livestock in Northern Kenya
Authors: Andrew Hobbs, Stacey Svetlichnaya
Date: 2020-04-08 00:00:00
Tasks: BIG-bench Machine Learning

This paper introduces the first dataset of satellite images labeled with forage quality by on-the-ground experts and provides proof of concept for applying computer vision methods to index-based drought insurance. We also present the results of a collaborative benchmark tool used to crowdsource an accurate machine learning model on the dataset. Our methods significantly outperform the existing technology for an insurance program in Northern Kenya, suggesting that a computer vision-based approach could substantially benefit pastoralists, whose exposure to droughts is severe and worsening with climate change.
Continual Learning with Gated Incremental Memories for sequential data processing
Authors: Andrea Cossu, Antonio Carta, Davide Bacciu
Date: 2020-04-08 00:00:00
Tasks: Continual Learning, Reinforcement Learning, Reinforcement Learning (RL)

The ability to learn in dynamic, nonstationary environments without forgetting previous knowledge, also known as Continual Learning (CL), is a key enabler for scalable and trustworthy deployments of adaptive solutions. While the importance of continual learning is largely acknowledged in machine vision and reinforcement learning problems, this is mostly under-documented for sequence processing tasks. This work proposes a Recurrent Neural Network (RNN) model for CL that is able to deal with concept drift in input distribution without forgetting previously acquired knowledge. We also implement and test a popular CL approach, Elastic Weight Consolidation (EWC), on top of two different types of RNNs. Finally, we compare the performances of our enhanced architecture against EWC and RNNs on a set of standard CL benchmarks, adapted to the sequential data processing scenario. Results show the superior performance of our architecture and highlight the need for special solutions designed to address CL in RNNs.
Are All Good Word Vector Spaces Isomorphic?
Authors: Ivan Vulić, Sebastian Ruder, Anders Søgaard
Date: 2020-04-08 00:00:00
Proceeding: EMNLP 2020 11
Tasks: All

Existing algorithms for aligning cross-lingual word vector spaces assume that vector spaces are approximately isomorphic. As a result, they perform poorly or fail completely on non-isomorphic spaces. Such non-isomorphism has been hypothesised to result from typological differences between languages. In this work, we ask whether non-isomorphism is also crucially a sign of degenerate word vector spaces. We present a series of experiments across diverse languages which show that variance in performance across language pairs is not only due to typological differences, but can mostly be attributed to the size of the monolingual resources available, and to the properties and duration of monolingual training (e.g. "under-training").
Convolutional neural net face recognition works in non-human-like ways
Authors: P. J. B. Hancock, R. S. Somai, V. R. Mileva
Date: 2020-04-08 00:00:00
Tasks: Face Recognition

Convolutional neural networks (CNNs) give state of the art performance in many pattern recognition problems but can be fooled by carefully crafted patterns of noise. We report that CNN face recognition systems also make surprising "errors". We tested six commercial face recognition CNNs and found that they outperform typical human participants on standard face matching tasks. However, they also declare matches that humans would not, where one image from the pair has been transformed to look a different sex or race. This is not due to poor performance; the best CNNs perform almost perfectly on the human face matching tasks, but also declare the most matches for faces of a different apparent race or sex. Although differing on the salience of sex and race, humans and computer systems are not working in completely different ways. They tend to find the same pairs of images difficult, suggesting some agreement about the underlying similarity space.
Self-Attention Gazetteer Embeddings for Named-Entity Recognition
Authors: Stanislav Peshterliev, Christophe Dupuy, Imre Kiss
Date: 2020-04-08 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Recent attempts to ingest external knowledge into neural models for named-entity recognition (NER) have exhibited mixed results. In this work, we present GazSelfAttn, a novel gazetteer embedding approach that uses self-attention and match span encoding to build enhanced gazetteer embeddings. In addition, we demonstrate how to build gazetteer resources from the open source Wikidata knowledge base. Evaluations on CoNLL-03 and Ontonotes 5 datasets, show F1 improvements over baseline model from 92.34 to 92.86 and 89.11 to 89.32 respectively, achieving performance comparable to large state-of-the-art models.
DynaBERT: Dynamic BERT with Adaptive Width and Depth
Authors: Lu Hou, Zhiqi Huang, Lifeng Shang, Xin Jiang, Xiao Chen, Qun Liu
Date: 2020-04-08 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Language Modeling, Language Modelling

The pre-trained language models like BERT, though powerful in many natural language processing tasks, are both computation and memory expensive. To alleviate this problem, one approach is to compress them for specific tasks before deployment. However, recent works on BERT compression usually compress the large BERT model to a fixed smaller size. They can not fully satisfy the requirements of different edge devices with various hardware performances. In this paper, we propose a novel dynamic BERT model (abbreviated as DynaBERT), which can flexibly adjust the size and latency by selecting adaptive width and depth. The training process of DynaBERT includes first training a width-adaptive BERT and then allowing both adaptive width and depth, by distilling knowledge from the full-sized model to small sub-networks. Network rewiring is also used to keep the more important attention heads and neurons shared by more sub-networks. Comprehensive experiments under various efficiency constraints demonstrate that our proposed dynamic BERT (or RoBERTa) at its largest size has comparable performance as BERT-base (or RoBERTa-base), while at smaller widths and depths consistently outperforms existing BERT compression methods. Code is available at https://github.com/huawei-noah/Pretrained-Language-Model/tree/master/DynaBERT.
Neural Networks Model for Travel Time Prediction Based on ODTravel Time Matrix
Authors: Ayobami E. Adewale, Amnir Hadachi
Date: 2020-04-08 00:00:00

Public transportation system commuters are often interested in getting accurate travel time information to plan their daily activities. However, this information is often difficult to predict accurately due to the irregularities of road traffic, caused by factors such as weather conditions, road accidents, and traffic jams. In this study, two neural network models namely multi-layer(MLP) perceptron and long short-term model(LSTM) are developed for predicting link travel time of a busy route with input generated using Origin-Destination travel time matrix derived from a historical GPS dataset. The experiment result showed that both models can make near-accurate predictions however, LSTM is more susceptible to noise as time step increases.
Physics-Informed Neural Networks for Non-linear System Identification for Power System Dynamics
Authors: Jochen Stiasny, George S. Misyris, Spyros Chatzivasileiadis
Date: 2020-04-08 00:00:00
Tasks: parameter estimation

Varying power-infeed from converter-based generation units introduces great uncertainty on system parameters such as inertia and damping. As a consequence, system operators face increasing challenges in performing dynamic security assessment and taking real-time control actions. Exploiting the widespread deployment of phasor measurement units (PMUs) and aiming at developing a fast dynamic state and parameter estimation tool, this paper investigates the performance of Physics-Informed Neural Networks (PINN) for discovering the frequency dynamics of future power systems. PINNs have the potential to address challenges such as the stronger non-linearities of low-inertia systems, increased measurement noise, and limited availability of data. The estimator is demonstrated in several test cases using a 4-bus system, and compared with state of the art algorithms, such as the Unscented Kalman Filter (UKF), to assess its performance.
A Deep Learning Approach for Determining Effects of Tuta Absoluta in Tomato Plants
Authors: Denis P. Rubanga, Loyani K. Loyani, Mgaya Richard, Sawahiko Shimada
Date: 2020-04-08 00:00:00
Tasks: Management

Early quantification of Tuta absoluta pest's effects in tomato plants is a very important factor in controlling and preventing serious damages of the pest. The invasion of Tuta absoluta is considered a major threat to tomato production causing heavy loss ranging from 80 to 100 percent when not properly managed. Therefore, real-time and early quantification of tomato leaf miner Tuta absoluta, can play an important role in addressing the issue of pest management and enhance farmers' decisions. In this study, we propose a Convolutional Neural Network (CNN) approach in determining the effects of Tuta absoluta in tomato plants. Four CNN pre-trained architectures (VGG16, VGG19, ResNet and Inception-V3) were used in training classifiers on a dataset containing health and infested tomato leaves collected from real field experiments. Among the pre-trained architectures, experimental results showed that Inception-V3 yielded the best results with an average accuracy of 87.2 percent in estimating the severity status of Tuta absoluta in tomato plants. The pre-trained models could also easily identify High Tuta severity status compared to other severity status (Low tuta and No tuta)