pwc-archive/papers-with-abstracts huggingface dataset:
365 Dots in 2019: Quantifying Attention of News Sources
Authors: Alexander C. Nwala, Michele C. Weigle, Michael L. Nelson
Date: 2020-03-22 00:00:00
Tasks: Articles

We investigate the overlap of topics of online news articles from a variety of sources. To do this, we provide a platform for studying the news by measuring this overlap and scoring news stories according to the degree of attention in near-real time. This can enable multiple studies, including identifying topics that receive the most attention from news organizations and identifying slow news days versus major news days. Our application, StoryGraph, periodically (10-minute intervals) extracts the first five news articles from the RSS feeds of 17 US news media organizations across the partisanship spectrum (left, center, and right). From these articles, StoryGraph extracts named entities (PEOPLE, LOCATIONS, ORGANIZATIONS, etc.) and then represents each news article with its set of extracted named entities. Finally, StoryGraph generates a news similarity graph where the nodes represent news articles, and an edge between a pair of nodes represents a high degree of similarity between the nodes (similar news stories). Each news story within the news similarity graph is assigned an attention score which quantifies the amount of attention the topics in the news story receive collectively from the news media organizations. The StoryGraph service has been running since August 2017, and using this method, we determined that the top news story of 2018 was the "Kavanaugh hearings" with attention score of 25.85 on September 27, 2018. Similarly, the top news story for 2019 so far (2019-12-12) is "AG William Barr's release of his principal conclusions of the Mueller Report," with an attention score of 22.93 on March 24, 2019.
Toward Tag-free Aspect Based Sentiment Analysis: A Multiple Attention Network Approach
Authors: Yao Qiang, Xin Li, Dongxiao Zhu
Date: 2020-03-22 00:00:00
Tasks: Aspect-Based Sentiment Analysis, Aspect-Based Sentiment Analysis (ABSA), Sentiment Analysis, TAG

Existing aspect based sentiment analysis (ABSA) approaches leverage various neural network models to extract the aspect sentiments via learning aspect-specific feature representations. However, these approaches heavily rely on manual tagging of user reviews according to the predefined aspects as the input, a laborious and time-consuming process. Moreover, the underlying methods do not explain how and why the opposing aspect level polarities in a user review lead to the overall polarity. In this paper, we tackle these two problems by designing and implementing a new Multiple-Attention Network (MAN) approach for more powerful ABSA without the need for aspect tags using two new tag-free data sets crawled directly from TripAdvisor ({https://www.tripadvisor.com}). With the Self- and Position-Aware attention mechanism, MAN is capable of extracting both aspect level and overall sentiments from the text reviews using the aspect level and overall customer ratings, and it can also detect the vital aspect(s) leading to the overall sentiment polarity among different aspects via a new aspect ranking scheme. We carry out extensive experiments to demonstrate the strong performance of MAN compared to other state-of-the-art ABSA approaches and the explainability of our approach by visualizing and interpreting attention weights in case studies.
A Better Variant of Self-Critical Sequence Training
Authors: Ruotian Luo
Date: 2020-03-22 00:00:00
Tasks: Image Captioning

In this work, we present a simple yet better variant of Self-Critical Sequence Training. We make a simple change in the choice of baseline function in REINFORCE algorithm. The new baseline can bring better performance with no extra cost, compared to the greedy decoding baseline.
The Instantaneous Accuracy: a Novel Metric for the Problem of Online Human Behaviour Recognition in Untrimmed Videos
Authors: Marcos Baptista Rios, Roberto J. López-Sastre, Fabian Caba Heilbron, Jan van Gemert, Francisco Javier Acevedo-Rodríguez, Saturnino Maldonado-Bascón
Date: 2020-03-22 00:00:00
Tasks: Action Detection, Online Action Detection

The problem of Online Human Behaviour Recognition in untrimmed videos, aka Online Action Detection (OAD), needs to be revisited. Unlike traditional offline action detection approaches, where the evaluation metrics are clear and well established, in the OAD setting we find few works and no consensus on the evaluation protocols to be used. In this paper we introduce a novel online metric, the Instantaneous Accuracy ($IA$), that exhibits an \emph{online} nature, solving most of the limitations of the previous (offline) metrics. We conduct a thorough experimental evaluation on TVSeries dataset, comparing the performance of various baseline methods to the state of the art. Our results confirm the problems of previous evaluation protocols, and suggest that an IA-based protocol is more adequate to the online scenario for human behaviour understanding. Code of the metric available https://github.com/gramuah/ia
Spectral Clustering Revisited: Information Hidden in the Fiedler Vector
Authors: Adela DePavia, Stefan Steinerberger
Date: 2020-03-22 00:00:00
Tasks: Clustering, Stochastic Block Model

We are interested in the clustering problem on graphs: it is known that if there are two underlying clusters, then the signs of the eigenvector corresponding to the second largest eigenvalue of the adjacency matrix can reliably reconstruct the two clusters. We argue that the vertices for which the eigenvector has the largest and the smallest entries, respectively, are unusually strongly connected to their own cluster and more reliably classified than the rest. This can be regarded as a discrete version of the Hot Spots conjecture and should be useful in applications. We give a rigorous proof for the stochastic block model and several examples.
Design Multimedia Expert Diagnosing Diseases System Using Fuzzy Logic (MEDDSFL)
Authors: Mohammed Salah Ibrahim, Doaa Waleed Al-Dulaimee
Date: 2020-03-22 00:00:00

In this paper we designed an efficient expert system to diagnose diseases for human beings. The system depended on several clinical features for different diseases which will be used as knowledge base for this system. We used fuzzy logic system which is one of the most expert systems techniques that used in building knowledge base of expert systems. Fuzzy logic will be used to inference the results of disease diagnosing. We also provided the system with multimedia such as videos, pictures and information for most of disease that have been achieved in our system. The system implemented using Matlab ToolBox and fifteen diseases were studied. Five cases for normal, affected and unaffected people's different diseases have been tested on this system. The results show that system was able to predict the status whether a human has a disease or not accurately. All system results are reported in tables and discussed in detail.
Efficient Clustering for Stretched Mixtures: Landscape and Optimality
Authors: Kaizheng Wang, Yuling Yan, Mateo Díaz
Date: 2020-03-22 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Clustering

This paper considers a canonical clustering problem where one receives unlabeled samples drawn from a balanced mixture of two elliptical distributions and aims for a classifier to estimate the labels. Many popular methods including PCA and k-means require individual components of the mixture to be somewhat spherical, and perform poorly when they are stretched. To overcome this issue, we propose a non-convex program seeking for an affine transform to turn the data into a one-dimensional point cloud concentrating around $-1$ and $1$, after which clustering becomes easy. Our theoretical contributions are two-fold: (1) we show that the non-convex loss function exhibits desirable geometric properties when the sample size exceeds some constant multiple of the dimension, and (2) we leverage this to prove that an efficient first-order algorithm achieves near-optimal statistical precision without good initialization. We also propose a general methodology for clustering with flexible choices of feature transforms and loss objectives.
Software System for Road Condition Forecast Correction
Authors: Dmitrii Smolyakov, Evgeny Burnaev
Date: 2020-03-22 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we present a monitoring system that allows increasing road safety by predicting ice formation. The system consists of a network of road weather stations and intelligence data processing program module. The results were achieved by combining physical models for forecasting road conditions based on measurements from stations and machine learning models for detecting incorrect data and forecast correction.
On Calibration of Mixup Training for Deep Neural Networks
Authors: Juan Maroñas, Daniel Ramos, Roberto Paredes
Date: 2020-03-22 00:00:00
Tasks: Data Augmentation, Uncertainty Quantification

Deep Neural Networks (DNN) represent the state of the art in many tasks. However, due to their overparameterization, their generalization capabilities are in doubt and still a field under study. Consequently, DNN can overfit and assign overconfident predictions -- effects that have been shown to affect the calibration of the confidences assigned to unseen data. Data Augmentation (DA) strategies have been proposed to regularize these models, being Mixup one of the most popular due to its ability to improve the accuracy, the uncertainty quantification and the calibration of DNN. In this work however we argue and provide empirical evidence that, due to its fundamentals, Mixup does not necessarily improve calibration. Based on our observations we propose a new loss function that improves the calibration, and also sometimes the accuracy, of DNN trained with this DA technique. Our loss is inspired by Bayes decision theory and introduces a new training framework for designing losses for probabilistic modelling. We provide state-of-the-art accuracy with consistent improvements in calibration performance. Appendix and code are provided here: https://github.com/jmaronas/calibration_MixupDNN_ARCLoss.pytorch.git
Deep Multi-attributed Graph Translation with Node-Edge Co-evolution
Authors: Xiaojie Guo, Liang Zhao, Cameron Nowzari, Setareh Rafatirad, Houman Homayoun, Sai Manoj Pudukotai Dinakarrao
Date: 2020-03-22 00:00:00
Tasks: Translation

Generalized from image and language translation, graph translation aims to generate a graph in the target domain by conditioning an input graph in the source domain. This promising topic has attracted fast-increasing attention recently. Existing works are limited to either merely predicting the node attributes of graphs with fixed topology or predicting only the graph topology without considering node attributes, but cannot simultaneously predict both of them, due to substantial challenges: 1) difficulty in characterizing the interactive, iterative, and asynchronous translation process of both nodes and edges and 2) difficulty in discovering and maintaining the inherent consistency between the node and edge in predicted graphs. These challenges prevent a generic, end-to-end framework for joint node and edge attributes prediction, which is a need for real-world applications such as malware confinement in IoT networks and structural-to-functional network translation. These real-world applications highly depend on hand-crafting and ad-hoc heuristic models, but cannot sufficiently utilize massive historical data. In this paper, we termed this generic problem "multi-attributed graph translation" and developed a novel framework integrating both node and edge translations seamlessly. The novel edge translation path is generic, which is proven to be a generalization of the existing topology translation models. Then, a spectral graph regularization based on our non-parametric graph Laplacian is proposed in order to learn and maintain the consistency of the predicted nodes and edges. Finally, extensive experiments on both synthetic and real-world application data demonstrated the effectiveness of the proposed method.
Curved Buildings Reconstruction from Airborne LiDAR Data by Matching and Deforming Geometric Primitives
Authors: Jingwei Song, Shaobo Xia, Jun Wang, Dong Chen
Date: 2020-03-22 00:00:00

Airborne LiDAR (Light Detection and Ranging) data is widely applied in building reconstruction, with studies reporting success in typical buildings. However, the reconstruction of curved buildings remains an open research problem. To this end, we propose a new framework for curved building reconstruction via assembling and deforming geometric primitives. The input LiDAR point cloud are first converted into contours where individual buildings are identified. After recognizing geometric units (primitives) from building contours, we get initial models by matching basic geometric primitives to these primitives. To polish assembly models, we employ a warping field for model refinements. Specifically, an embedded deformation (ED) graph is constructed via downsampling the initial model. Then, the point-to-model displacements are minimized by adjusting node parameters in the ED graph based on our objective function. The presented framework is validated on several highly curved buildings collected by various LiDAR in different cities. The experimental results, as well as accuracy comparison, demonstrate the advantage and effectiveness of our method. {The new insight attributes to an efficient reconstruction manner.} Moreover, we prove that the primitive-based framework significantly reduces the data storage to 10-20 percent of classical mesh models.
Effects of Discretization of Decision and Objective Spaces on the Performance of Evolutionary Multiobjective Optimization Algorithms
Authors: Wei-Yu Chen, Hisao Ishibuchi, Ke Shang
Date: 2020-03-22 00:00:00
Tasks: Multiobjective Optimization

Recently, the discretization of decision and objective spaces has been discussed in the literature. In some studies, it is shown that the decision space discretization improves the performance of evolutionary multi-objective optimization (EMO) algorithms on continuous multi-objective test problems. In other studies, it is shown that the objective space discretization improves the performance on combinatorial multi-objective problems. However, the effect of the simultaneous discretization of both spaces has not been examined in the literature. In this paper, we examine the effects of the decision space discretization, objective space discretization and simultaneous discretization on the performance of NSGA-II through computational experiments on the DTLZ and WFG problems. Using various settings about the number of decision variables and the number of objectives, our experiments are performed on four types of problems: standard problems, large-scale problems, many-objective problems, and large-scale many-objective problems. We show that the decision space discretization has a positive effect for large-scale problems and the objective space discretization has a positive effect for many-objective problems. We also show the discretization of both spaces is useful for large-scale many-objective problems.
Overcoming Catastrophic Forgetting in Graph Neural Networks with Experience Replay
Authors: Fan Zhou, Chengtai Cao
Date: 2020-03-22 00:00:00
Tasks: Graph Classification, Graph Learning, Link Prediction

Graph Neural Networks (GNNs) have recently received significant research attention due to their superior performance on a variety of graph-related learning tasks. Most of the current works focus on either static or dynamic graph settings, addressing a single particular task, e.g., node/graph classification, link prediction. In this work, we investigate the question: can GNNs be applied to continuously learning a sequence of tasks? Towards that, we explore the Continual Graph Learning (CGL) paradigm and present the Experience Replay based framework ER-GNN for CGL to alleviate the catastrophic forgetting problem in existing GNNs. ER-GNN stores knowledge from previous tasks as experiences and replays them when learning new tasks to mitigate the catastrophic forgetting issue. We propose three experience node selection strategies: mean of feature, coverage maximization, and influence maximization, to guide the process of selecting experience nodes. Extensive experiments on three benchmark datasets demonstrate the effectiveness of our ER-GNN and shed light on the incremental graph (non-Euclidean) structure learning.
K-Core based Temporal Graph Convolutional Network for Dynamic Graphs
Authors: Jingxin Liu, Chang Xu, Chang Yin, Weiqiang Wu, You Song
Date: 2020-03-22 00:00:00
Tasks: Dynamic graph embedding, Graph Embedding, Graph Representation Learning, Link Prediction, Representation Learning

Graph representation learning is a fundamental task in various applications that strives to learn low-dimensional embeddings for nodes that can preserve graph topology information. However, many existing methods focus on static graphs while ignoring evolving graph patterns. Inspired by the success of graph convolutional networks(GCNs) in static graph embedding, we propose a novel k-core based temporal graph convolutional network, the CTGCN, to learn node representations for dynamic graphs. In contrast to previous dynamic graph embedding methods, CTGCN can preserve both local connective proximity and global structural similarity while simultaneously capturing graph dynamics. In the proposed framework, the traditional graph convolution is generalized into two phases, feature transformation and feature aggregation, which gives the CTGCN more flexibility and enables the CTGCN to learn connective and structural information under the same framework. Experimental results on 7 real-world graphs demonstrate that the CTGCN outperforms existing state-of-the-art graph embedding methods in several tasks, including link prediction and structural role classification. The source code of this work can be obtained from \url{https://github.com/jhljx/CTGCN}.
Multi-target regression via output space quantization
Authors: Eleftherios Spyromitros-Xioufis, Konstantinos Sechidis, Ioannis Vlahavas
Date: 2020-03-22 00:00:00
Tasks: Computational Efficiency, Multi-target regression, Quantization, regression

Multi-target regression is concerned with the prediction of multiple continuous target variables using a shared set of predictors. Two key challenges in multi-target regression are: (a) modelling target dependencies and (b) scalability to large output spaces. In this paper, a new multi-target regression method is proposed that tries to jointly address these challenges via a novel problem transformation approach. The proposed method, called MRQ, is based on the idea of quantizing the output space in order to transform the multiple continuous targets into one or more discrete ones. Learning on the transformed output space naturally enables modeling of target dependencies while the quantization strategy can be flexibly parameterized to control the trade-off between prediction accuracy and computational efficiency. Experiments on a large collection of benchmark datasets show that MRQ is both highly scalable and also competitive with the state-of-the-art in terms of accuracy. In particular, an ensemble version of MRQ obtains the best overall accuracy, while being an order of magnitude faster than the runner up method.
Ensembles of Deep Neural Networks for Action Recognition in Still Images
Authors: Sina Mohammadi, Sina Ghofrani Majelan, Shahriar B. Shokouhi
Date: 2020-03-22 00:00:00
Tasks: Action Classification, Action Recognition, Action Recognition In Still Images, Ensemble Learning, General Classification, Temporal Action Localization, Transfer Learning

Despite the fact that notable improvements have been made recently in the field of feature extraction and classification, human action recognition is still challenging, especially in images, in which, unlike videos, there is no motion. Thus, the methods proposed for recognizing human actions in videos cannot be applied to still images. A big challenge in action recognition in still images is the lack of large enough datasets, which is problematic for training deep Convolutional Neural Networks (CNNs) due to the overfitting issue. In this paper, by taking advantage of pre-trained CNNs, we employ the transfer learning technique to tackle the lack of massive labeled action recognition datasets. Furthermore, since the last layer of the CNN has class-specific information, we apply an attention mechanism on the output feature maps of the CNN to extract more discriminative and powerful features for classification of human actions. Moreover, we use eight different pre-trained CNNs in our framework and investigate their performance on Stanford 40 dataset. Finally, we propose using the Ensemble Learning technique to enhance the overall accuracy of action classification by combining the predictions of multiple models. The best setting of our method is able to achieve 93.17$\%$ accuracy on the Stanford 40 dataset.
Low Latency ASR for Simultaneous Speech Translation
Authors: Thai Son Nguyen, Jan Niehues, Eunah Cho, Thanh-Le Ha, Kevin Kilgour, Markus Muller, Matthias Sperber, Sebastian Stueker, Alex Waibel
Date: 2020-03-22 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, Translation

User studies have shown that reducing the latency of our simultaneous lecture translation system should be the most important goal. We therefore have worked on several techniques for reducing the latency for both components, the automatic speech recognition and the speech translation module. Since the commonly used commitment latency is not appropriate in our case of continuous stream decoding, we focused on word latency. We used it to analyze the performance of our current system and to identify opportunities for improvements. In order to minimize the latency we combined run-on decoding with a technique for identifying stable partial hypotheses when stream decoding and a protocol for dynamic output update that allows to revise the most recent parts of the transcription. This combination reduces the latency at word level, where the words are final and will never be updated again in the future, from 18.1s to 1.1s without sacrificing performance in terms of word error rate.
Audio Impairment Recognition Using a Correlation-Based Feature Representation
Authors: Alessandro Ragano, Emmanouil Benetos, Andrew Hines
Date: 2020-03-22 00:00:00
Tasks: Deep Learning

Audio impairment recognition is based on finding noise in audio files and categorising the impairment type. Recently, significant performance improvement has been obtained thanks to the usage of advanced deep learning models. However, feature robustness is still an unresolved issue and it is one of the main reasons why we need powerful deep learning architectures. In the presence of a variety of musical styles, hand-crafted features are less efficient in capturing audio degradation characteristics and they are prone to failure when recognising audio impairments and could mistakenly learn musical concepts rather than impairment types. In this paper, we propose a new representation of hand-crafted features that is based on the correlation of feature pairs. We experimentally compare the proposed correlation-based feature representation with a typical raw feature representation used in machine learning and we show superior performance in terms of compact feature dimensionality and improved computational speed in the test stage whilst achieving comparable accuracy.