pwc-archive/papers-with-abstracts huggingface dataset:
From Video Game to Real Robot: The Transfer between Action Spaces
Authors: Janne Karttunen, Anssi Kanervisto, Ville Kyrki, Ville Hautamäki
Date: 2019-05-02 00:00:00
Tasks: Deep Reinforcement Learning, Reinforcement Learning, Transfer Learning

Deep reinforcement learning has proven to be successful for learning tasks in simulated environments, but applying same techniques for robots in real-world domain is more challenging, as they require hours of training. To address this, transfer learning can be used to train the policy first in a simulated environment and then transfer it to physical agent. As the simulation never matches reality perfectly, the physics, visuals and action spaces by necessity differ between these environments to some degree. In this work, we study how general video games can be directly used instead of fine-tuned simulations for the sim-to-real transfer. Especially, we study how the agent can learn the new action space autonomously, when the game actions do not match the robot actions. Our results show that the different action space can be learned by re-training only part of neural network and we obtain above 90% mean success rate in simulation and robot experiments.
The 2019 DAVIS Challenge on VOS: Unsupervised Multi-Object Segmentation
Authors: Sergi Caelles, Jordi Pont-Tuset, Federico Perazzi, Alberto Montes, Kevis-Kokitsi Maninis, Luc van Gool
Date: 2019-05-02 00:00:00
Tasks: Object, Segmentation, Semantic Segmentation, Video Object Segmentation, Video Semantic Segmentation

We present the 2019 DAVIS Challenge on Video Object Segmentation, the third edition of the DAVIS Challenge series, a public competition designed for the task of Video Object Segmentation (VOS). In addition to the original semi-supervised track and the interactive track introduced in the previous edition, a new unsupervised multi-object track will be featured this year. In the newly introduced track, participants are asked to provide non-overlapping object proposals on each image, along with an identifier linking them between frames (i.e. video object proposals), without any test-time human supervision (no scribbles or masks provided on the test video). In order to do so, we have re-annotated the train and val sets of DAVIS 2017 in a concise way that facilitates the unsupervised track, and created new test-dev and test-challenge sets for the competition. Definitions, rules, and evaluation metrics for the unsupervised track are described in detail in this paper.
KnowBias: A Novel AI Method to Detect Polarity in Online Content
Authors: Aditya Saligrama
Date: 2019-05-02 00:00:00
Tasks: Articles, Domain Adaptation, General Classification, Sentence, Sentence Embeddings, Text Classification

We propose a novel training and inference method for detecting political bias in long text content such as newspaper opinion articles. Obtaining long text data and annotations at sufficient scale for training is difficult, but it is relatively easy to extract political polarity from tweets through their authorship; as such, we train on tweets and perform inference on articles. Universal sentence encoders and other existing methods that aim to address this domain-adaptation scenario deliver inaccurate and inconsistent predictions on articles, which we show is due to a difference in opinion concentration between tweets and articles. We propose a two-step classification scheme that utilizes a neutral detector trained on tweets to remove neutral sentences from articles in order to align opinion concentration and therefore improve accuracy on that domain. We evaluate our two-step approach using a variety of test suites, including a set of tweets and long-form articles where annotations were crowd-sourced to decrease label noise, measuring accuracy and Spearman-rho rank correlation. In practice, KnowBias achieves a high accuracy of 86 (rho = 0.65) on these tweets and 75 (rho = 0.69) on long-form articles. While we validate our method on political bias, our scheme is general and can be readily applied to other settings, where there exist such domain mismatches between source and target domains. Our implementation is available for public use at https://knowbias.ml.
Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size!
Authors: Niels Bruun Ipsen, Lars Kai Hansen
Date: 2019-05-02 00:00:00

How does missing data affect our ability to learn signal structures? It has been shown that learning signal structure in terms of principal components is dependent on the ratio of sample size and dimensionality and that a critical number of observations is needed before learning starts (Biehl and Mietzner, 1993). Here we generalize this analysis to include missing data. Probabilistic principal component analysis is regularly used for estimating signal structures in datasets with missing data. Our analytic result suggests that the effect of missing data is to effectively reduce signal-to-noise ratio rather than - as generally believed - to reduce sample size. The theory predicts a phase transition in the learning curves and this is indeed found both in simulation data and in real datasets.
Face Identification using Local Ternary Tree Pattern based Spatial Structural Components
Authors: Rinku Datta Rakshit, Dakshina Ranjan Kisku, Massimo Tistarelli, Phalguni Gupta
Date: 2019-05-02 00:00:00
Tasks: Face Identification

This paper reports a face identification system which makes use of a novel local descriptor called Local Ternary Tree Pattern (LTTP). Exploiting and extracting distinctive local descriptor from a face image plays a crucial role in face identification task in the presence of a variety of face images including constrained, unconstrained and plastic surgery images. LTTP has been used to extract robust and useful spatial features which use to describe the various structural components on a face. To extract the features, a ternary tree is formed for each pixel with its eight neighbors in each block. LTTP pattern can be generated in four forms such as LTTP Left Depth (LTTP LD), LTTP Left Breadth (LTTP LB), LTTP Right Depth (LTTP RD) and LTTP Right Breadth (LTTP RB). The encoding schemes of these patterns are very simple and efficient in terms of computational as well as time complexity. The proposed face identification system is tested on six face databases, namely, the UMIST, the JAFFE, the extended Yale face B, the Plastic Surgery, the LFW and the UFI. The experimental evaluation demonstrates the most promising results considering a variety of faces captured under different environments. The proposed LTTP based system is also compared with some local descriptors under identical conditions.
Approximate LSTMs for Time-Constrained Inference: Enabling Fast Reaction in Self-Driving Cars
Authors: Alexandros Kouris, Stylianos I. Venieris, Michail Rizakis, Christos-Savvas Bouganis
Date: 2019-05-02 00:00:00
Tasks: Autonomous Navigation, Self-Driving Cars

The need to recognise long-term dependencies in sequential data such as video streams has made Long Short-Term Memory (LSTM) networks a prominent Artificial Intelligence model for many emerging applications. However, the high computational and memory demands of LSTMs introduce challenges in their deployment on latency-critical systems such as self-driving cars which are equipped with limited computational resources on-board. In this paper, we introduce a progressive inference computing scheme that combines model pruning and computation restructuring leading to the best possible approximation of the result given the available latency budget of the target application. The proposed methodology enables mission-critical systems to make informed decisions even in early stages of the computation, based on approximate LSTM inference, meeting their specifications on safety and robustness. Our experiments on a state-of-the-art driving model for autonomous vehicle navigation demonstrate that the proposed approach can yield outputs with similar quality of result compared to a faithful LSTM baseline, up to 415x faster (198x on average, 76x geo. mean).
DS-VIO: Robust and Efficient Stereo Visual Inertial Odometry based on Dual Stage EKF
Authors: Xiaogang Xiong, Wenqing Chen, Zhichao Liu, Qiang Shen
Date: 2019-05-02 00:00:00
Tasks: Computational Efficiency

This paper presents a dual stage EKF (Extended Kalman Filter)-based algorithm
for the real-time and robust stereo VIO (visual inertial odometry). The first
stage of this EKF-based algorithm performs the fusion of accelerometer and
gyroscope while the second performs the fusion of stereo camera and IMU. Due to
the sufficient complementary characteristics between accelerometer and
gyroscope as well as stereo camera and IMU, the dual stage EKF-based algorithm
can achieve a high precision of odometry estimations. At the same time, because
of the low dimension of state vector in this algorithm, its computational
efficiency is comparable to previous filter-based approaches. We call our
approach DS-VIO (dual stage EKFbased stereo visual inertial odometry) and
evaluate our DSVIO algorithm by comparing it with the state-of-art approaches
including OKVIS, ROVIO, VINS-MONO and S-MSCKF on the EuRoC dataset. Results
show that our algorithm can achieve comparable or even better performances in
terms of the RMS error
Temporal Ordered Clustering in Dynamic Networks: Unsupervised and Semi-supervised Learning Algorithms
Authors: Krzysztof Turowski, Jithin K. Sreedharan, Wojciech Szpankowski
Date: 2019-05-02 00:00:00
Tasks: Clustering

In temporal ordered clustering, given a single snapshot of a dynamic network in which nodes arrive at distinct time instants, we aim at partitioning its nodes into $K$ ordered clusters $\mathcal{C}_1 \prec \cdots \prec \mathcal{C}_K$ such that for $i<j$, nodes in cluster $\mathcal{C}_i$ arrived before nodes in cluster $\mathcal{C}_j$, with $K$ being a data-driven parameter and not known upfront. Such a problem is of considerable significance in many applications ranging from tracking the expansion of fake news to mapping the spread of information. We first formulate our problem for a general dynamic graph, and propose an integer programming framework that finds the optimal clustering, represented as a strict partial order set, achieving the best precision (i.e., fraction of successfully ordered node pairs) for a fixed density (i.e., fraction of comparable node pairs). We then develop a sequential importance procedure and design unsupervised and semi-supervised algorithms to find temporal ordered clusters that efficiently approximate the optimal solution. To illustrate the techniques, we apply our methods to the vertex copying (duplication-divergence) model which exhibits some edge-case challenges in inferring the clusters as compared to other network models. Finally, we validate the performance of the proposed algorithms on synthetic and real-world networks.
Impact of Argument Type and Concerns in Argumentation with a Chatbot
Authors: Lisa A. Chalaguine, Anthony Hunter, Fiona L. Hamilton, Henry W. W. Potts
Date: 2019-05-02 00:00:00
Tasks: Chatbot, Vocal Bursts Type Prediction

Conversational agents, also known as chatbots, are versatile tools that have
the potential of being used in dialogical argumentation. They could possibly be
deployed in tasks such as persuasion for behaviour change (e.g. persuading
people to eat more fruit, to take regular exercise, etc.) However, to achieve
this, there is a need to develop methods for acquiring appropriate arguments
and counterargument that reflect both sides of the discussion. For instance, to
persuade someone to do regular exercise, the chatbot needs to know
counterarguments that the user might have for not doing exercise. To address
this need, we present methods for acquiring arguments and counterarguments, and
importantly, meta-level information that can be useful for deciding when
arguments can be used during an argumentation dialogue. We evaluate these
methods in studies with participants and show how harnessing these methods in a
chatbot can make it more persuasive.
Quality Evaluation of GANs Using Cross Local Intrinsic Dimensionality
Authors: Sukarna Barua, Xingjun Ma, Sarah Monazam Erfani, Michael E. Houle, James Bailey
Date: 2019-05-02 00:00:00

Generative Adversarial Networks (GANs) are an elegant mechanism for data
generation. However, a key challenge when using GANs is how to best measure
their ability to generate realistic data. In this paper, we demonstrate that an
intrinsic dimensional characterization of the data space learned by a GAN model
leads to an effective evaluation metric for GAN quality. In particular, we
propose a new evaluation measure, CrossLID, that assesses the local intrinsic
dimensionality (LID) of real-world data with respect to neighborhoods found in
GAN-generated samples. Intuitively, CrossLID measures the degree to which
manifolds of two data distributions coincide with each other. In experiments on
4 benchmark image datasets, we compare our proposed measure to several
state-of-the-art evaluation metrics. Our experiments show that CrossLID is
strongly correlated with the progress of GAN training, is sensitive to mode
collapse, is robust to small-scale noise and image transformations, and robust
to sample size. Furthermore, we show how CrossLID can be used within the GAN
training process to improve generation quality.
LivDet in Action - Fingerprint Liveness Detection Competition 2019
Authors: Giulia Orrù, Roberto Casula, Pierluigi Tuveri, Carlotta Bazzoni, Giovanna Dessalvi, Marco Micheletto, Luca Ghiani, Gian Luca Marcialis
Date: 2019-05-02 00:00:00

The International Fingerprint liveness Detection Competition (LivDet) is an
open and well-acknowledged meeting point of academies and private companies
that deal with the problem of distinguishing images coming from reproductions
of fingerprints made of artificial materials and images relative to real
fingerprints. In this edition of LivDet we invited the competitors to propose
integrated algorithms with matching systems. The goal was to investigate at
which extent this integration impact on the whole performance. Twelve
algorithms were submitted to the competition, eight of which worked on
integrated systems.
Inverse Halftoning Through Structure-Aware Deep Convolutional Neural Networks
Authors: Chang-Hwan Son
Date: 2019-05-02 00:00:00
Tasks: Image Reconstruction

The primary issue in inverse halftoning is removing noisy dots on flat areas and restoring image structures (e.g., lines, patterns) on textured areas. Hence, a new structure-aware deep convolutional neural network that incorporates two subnetworks is proposed in this paper. One subnetwork is for image structure prediction while the other is for continuous-tone image reconstruction. First, to predict image structures, patch pairs comprising continuous-tone patches and the corresponding halftoned patches generated through digital halftoning are trained. Subsequently, gradient patches are generated by convolving gradient filters with the continuous-tone patches. The subnetwork for the image structure prediction is trained using the mini-batch gradient descent algorithm given the halftoned patches and gradient patches, which are fed into the input and loss layers of the subnetwork, respectively. Next, the predicted map including the image structures is stacked on the top of the input halftoned image through a fusion layer and fed into the image reconstruction subnetwork such that the entire network is trained adaptively to the image structures. The experimental results confirm that the proposed structure-aware network can remove noisy dot-patterns well on flat areas and restore details clearly on textured areas. Furthermore, it is demonstrated that the proposed method surpasses the conventional state-of-the-art methods based on deep convolutional neural networks and locally learned dictionaries.
Frustratingly Easy Truth Discovery
Authors: Reshef Meir, Ofra Amir, Omer Ben-Porat, Tsviel Ben-Shabat, Gal Cohensius, Lirong Xia
Date: 2019-05-02 00:00:00

Truth discovery is a general name for a broad range of statistical methods aimed to extract the correct answers to questions, based on multiple answers coming from noisy sources. For example, workers in a crowdsourcing platform. In this paper, we consider an extremely simple heuristic for estimating workers' competence using average proximity to other workers. We prove that this estimates well the actual competence level and enables separating high and low quality workers in a wide spectrum of domains and statistical models. Under Gaussian noise, this simple estimate is the unique solution to the MLE with a constant regularization factor. Finally, weighing workers according to their average proximity in a crowdsourcing setting, results in substantial improvement over unweighted aggregation and other truth discovery algorithms in practice.
On Linear Learning with Manycore Processors
Authors: Eliza Wszola, Celestine Mendler-Dünner, Martin Jaggi, Markus Püschel
Date: 2019-05-02 00:00:00

A new generation of manycore processors is on the rise that offers dozens and more cores on a chip and, in a sense, fuses host processor and accelerator. In this paper we target the efficient training of generalized linear models on these machines. We propose a novel approach for achieving parallelism which we call Heterogeneous Tasks on Homogeneous Cores (HTHC). It divides the problem into multiple fundamentally different tasks, which themselves are parallelized. For evaluation, we design a detailed, architecture-cognizant implementation of our scheme on a recent 72-core Knights Landing processor that is adaptive to the cache, memory, and core structure. Our library efficiently supports dense and sparse datasets as well as 4-bit quantized data for further possible gains in performance. We show benchmarks for Lasso and SVM with different data sets against straightforward parallel implementations and prior software. In particular, for Lasso on dense data, we improve the state-of-the-art by an order of magnitude.
Variational Autoencoders for Sparse and Overdispersed Discrete Data
Authors: He Zhao, Piyush Rai, Lan Du, Wray Buntine, Mingyuan Zhou
Date: 2019-05-02 00:00:00
Tasks: Collaborative Filtering, Multi-Label Learning, Recommendation Systems

Many applications, such as text modelling, high-throughput sequencing, and recommender systems, require analysing sparse, high-dimensional, and overdispersed discrete (count-valued or binary) data. Although probabilistic matrix factorisation and linear/nonlinear latent factor models have enjoyed great success in modelling such data, many existing models may have inferior modelling performance due to the insufficient capability of modelling overdispersion in count-valued data and model misspecification in general. In this paper, we comprehensively study these issues and propose a variational autoencoder based framework that generates discrete data via negative-binomial distribution. We also examine the model's ability to capture properties, such as self- and cross-excitations in discrete data, which is critical for modelling overdispersion. We conduct extensive experiments on three important problems from discrete data analysis: text analysis, collaborative filtering, and multi-label learning. Compared with several state-of-the-art baselines, the proposed models achieve significantly better performance on the above problems.
Alternative Techniques for Mapping Paths to HLAI
Authors: Ross Gruetzemacher, David Paradice
Date: 2019-05-02 00:00:00
Tasks: Articles

The only systematic mapping of the HLAI technical landscape was conducted at
a workshop in 2009 [Adams et al., 2012]. However, the results from it were not
what organizers had hoped for [Goertzel 2014, 2016], merely just a series of
milestones, up to 50% of which could be argued to have been completed already.
We consider two more recent articles outlining paths to human-like intelligence
[Mikolov et al., 2016; Lake et al., 2017]. These offer technical and more
refined assessments of the requirements for HLAI rather than just milestones.
While useful, they also have limitations. To address these limitations we
propose the use of alternative techniques for an updated systematic mapping of
the paths to HLAI. The newly proposed alternative techniques can model complex
paths of future technologies using intricate directed graphs. Specifically,
there are two classes of alternative techniques that we consider: scenario
mapping methods and techniques for eliciting expert opinion through digital
platforms and crowdsourcing. We assess the viability and utility of both the
previous and alternative techniques, finding that the proposed alternative
techniques could be very beneficial in advancing the existing body of knowledge
on the plausible frameworks for creating HLAI. In conclusion, we encourage
discussion and debate to initiate efforts to use these proposed techniques for
mapping paths to HLAI.
Synthetic Oversampling of Multi-Label Data based on Local Label Distribution
Authors: Bin Liu, Grigorios Tsoumakas
Date: 2019-05-02 00:00:00
Tasks: Multi-Label Learning

Class-imbalance is an inherent characteristic of multi-label data which affects the prediction accuracy of most multi-label learning methods. One efficient strategy to deal with this problem is to employ resampling techniques before training the classifier. Existing multilabel sampling methods alleviate the (global) imbalance of multi-label datasets. However, performance degradation is mainly due to rare subconcepts and overlapping of classes that could be analysed by looking at the local characteristics of the minority examples, rather than the imbalance of the whole dataset. We propose a new method for synthetic oversampling of multi-label data that focuses on local label distribution to generate more diverse and better labeled instances. Experimental results on 13 multi-label datasets demonstrate the effectiveness of the proposed approach in a variety of evaluation measures, particularly in the case of an ensemble of classifiers trained on repeated samples of the original data.
A knowledge-based intelligent system for control of dirt recognition process in the smart washing machines
Authors: Mohsen Annabestani, Alireza Rowhanimanesh, Akram Rezaei, Ladan Avazpour, Fatemeh Sheikhhasani
Date: 2019-05-02 00:00:00
Tasks: Decision Making

In this paper, we propose an intelligence approach based on fuzzy logic to modeling human intelligence in washing clothes. At first, an intelligent feedback loop is designed for perception-based sensing of dirt inspired by human color understanding. Then, when color stains leak out of some colored clothes the human probabilistic decision making is computationally modeled to detect this stain leakage and thus the problem of recognizing dirt from stain can be considered in the washing process. Finally, we discuss the fuzzy control of washing clothes and design and simulate a smart controller based on the fuzzy intelligence feedback loop.
Human Activity Recognition Using LSTM-RNN Deep Neural Network Architecture
Authors: Schalk Wilhelm Pienaar, Reza Malekian
Date: 2019-05-02 00:00:00
Tasks: Activity Recognition, Human Activity Recognition

Using raw sensor data to model and train networks for Human Activity
Recognition can be used in many different applications, from fitness tracking
to safety monitoring applications. These models can be easily extended to be
trained with different data sources for increased accuracies or an extension of
classifications for different prediction classes. This paper goes into the
discussion on the available dataset provided by WISDM and the unique features
of each class for the different axes. Furthermore, the design of a Long Short
Term Memory (LSTM) architecture model is outlined for the application of human
activity recognition. An accuracy of above 94% and a loss of less than 30% has
been reached in the first 500 epochs of training.