pwc-archive/papers-with-abstracts huggingface dataset:
Photosequencing of Motion Blur using Short and Long Exposures
Authors: Vijay Rengarajan, Shuo Zhao, Ruiwen Zhen, John Glotzbach, Hamid Sheikh, Aswin C. Sankaranarayanan
Date: 2019-12-11 00:00:00

Photosequencing aims to transform a motion blurred image to a sequence of sharp images. This problem is challenging due to the inherent ambiguities in temporal ordering as well as the recovery of lost spatial textures due to blur. Adopting a computational photography approach, we propose to capture two short exposure images, along with the original blurred long exposure image to aid in the aforementioned challenges. Post-capture, we recover the sharp photosequence using a novel blur decomposition strategy that recursively splits the long exposure image into smaller exposure intervals. We validate the approach by capturing a variety of scenes with interesting motions using machine vision cameras programmed to capture short and long exposure sequences. Our experimental results show that the proposed method resolves both fast and fine motions better than prior works.
MAGSAC++, a fast, reliable and accurate robust estimator
Authors: Daniel Barath, Jana Noskova, Maksym Ivashechkin, Jiri Matas
Date: 2019-12-11 00:00:00
Proceeding: CVPR 2020 6

A new method for robust estimation, MAGSAC++, is proposed. It introduces a new model quality (scoring) function that does not require the inlier-outlier decision, and a novel marginalization procedure formulated as an iteratively re-weighted least-squares approach. We also propose a new sampler, Progressive NAPSAC, for RANSAC-like robust estimators. Exploiting the fact that nearby points often originate from the same model in real-world data, it finds local structures earlier than global samplers. The progressive transition from local to global sampling does not suffer from the weaknesses of purely localized samplers. On six publicly available real-world datasets for homography and fundamental matrix fitting, MAGSAC++ produces results superior to state-of-the-art robust methods. It is faster, more geometrically accurate and fails less often.
Performance Analysis of Deep Autoencoder and NCA Dimensionality Reduction Techniques with KNN, ENN and SVM Classifiers
Authors: Md. Abu Bakr Siddique, Shadman Sakib, Md. Abdur Rahman
Date: 2019-12-11 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification

The central aim of this paper is to implement Deep Autoencoder and Neighborhood Components Analysis (NCA) dimensionality reduction methods in Matlab and to observe the application of these algorithms on nine unlike datasets from UCI machine learning repository. These datasets are CNAE9, Movement Libras, Pima Indians diabetes, Parkinsons, Knowledge, Segmentation, Seeds, Mammographic Masses, and Ionosphere. First of all, the dimension of these datasets has been reduced to fifty percent of their original dimension by selecting and extracting the most relevant and appropriate features or attributes using Deep Autoencoder and NCA dimensionality reduction techniques. Afterward, each dataset is classified applying K-Nearest Neighbors (KNN), Extended Nearest Neighbors (ENN) and Support Vector Machine (SVM) classification algorithms. All classification algorithms are developed in the Matlab environment. In each classification, the training test data ratio is always set to ninety percent: ten percent. Upon classification, variation between accuracies is observed and analyzed to find the degree of compatibility of each dimensionality reduction technique with each classifier and to evaluate each classifier performance on each dataset.
Leveraging End-to-End Speech Recognition with Neural Architecture Search
Authors: Ahmed Baruwa, Mojeed Abisiga, Ibrahim Gbadegesin, Afeez Fakunle
Date: 2019-12-11 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Neural Architecture Search, speech-recognition, Speech Recognition

Deep neural networks (DNNs) have been demonstrated to outperform many traditional machine learning algorithms in Automatic Speech Recognition (ASR). In this paper, we show that a large improvement in the accuracy of deep speech models can be achieved with effective Neural Architecture Optimization at a very low computational cost. Phone recognition tests with the popular LibriSpeech and TIMIT benchmarks proved this fact by displaying the ability to discover and train novel candidate models within a few hours (less than a day) many times faster than the attention-based seq2seq models. Our method achieves test error of 7% Word Error Rate (WER) on the LibriSpeech corpus and 13% Phone Error Rate (PER) on the TIMIT corpus, on par with state-of-the-art results.
Non-linearity identification for construction workers' personality-safety behaviour predictive relationship using neural network and linear regression modelling
Authors: Yifan Gao, Vicente A. Gonzalez, Tak Wing Yiu, Guillermo Cabrera-Guerrerod
Date: 2019-12-11 00:00:00
Tasks: Decision Making, Diversity, Management

The prediction of workers' safety behaviour can help identify vulnerable workers who intend to undertake unsafe behaviours and be useful in the design of management practices to minimise the occurrence of accidents. The latest literature has evidenced that there is within-population diversity that leads people's intended safety behaviours in the workplace, which are found to vary among individuals as a function of their personality traits. In this study, an innovative forecasting model, which employs neural network algorithms, is developed to numerically simulate the predictive relationship between construction workers' personality traits and their intended safety behaviour. The data-driven nature of neural network enabled a reliable estimate of the relationship, which allowed this research to find that a nonlinear effect exists in the relationship. This research has practical implications. The neural network developed is shown to have highly satisfactory prediction accuracy and is thereby potentially useful for assisting project decision-makers to assess how prone workers are to carry out unsafe behaviours in the workplace.
Towards a Robust Classifier: An MDL-Based Method for Generating Adversarial Examples
Authors: Behzad Asadi, Vijay Varadharajan
Date: 2019-12-11 00:00:00
Tasks: Malware Detection

We address the problem of adversarial examples in machine learning where an adversary tries to misguide a classifier by making functionality-preserving modifications to original samples. We assume a black-box scenario where the adversary has access to only the feature set, and the final hard-decision output of the classifier. We propose a method to generate adversarial examples using the minimum description length (MDL) principle. Our final aim is to improve the robustness of the classifier by considering generated examples in rebuilding the classifier. We evaluate our method for the application of static malware detection in portable executable (PE) files. We consider API calls of PE files as their distinguishing features where the feature vector is a binary vector representing the presence-absence of API calls. In our method, we first create a dataset of benign samples by querying the target classifier. We next construct a code table of frequent patterns for the compression of this dataset using the MDL principle. We finally generate an adversarial example corresponding to a malware sample by selecting and adding a pattern from the benign code table to the malware sample. The selected pattern is the one that minimizes the length of the compressed adversarial example given the code table. This modification preserves the functionalities of the original malware sample as all original API calls are kept, and only some new API calls are added. Considering a neural network, we show that the evasion rate is 78.24 percent for adversarial examples compared to 8.16 percent for original malware samples. This shows the effectiveness of our method in generating examples that need to be considered in rebuilding the classifier.
Deep One-bit Compressive Autoencoding
Authors: Shahin Khobahi, Arindam Bose, Mojtaba Soltanalian
Date: 2019-12-10 00:00:00

Parameterized mathematical models play a central role in understanding and design of complex information systems. However, they often cannot take into account the intricate interactions innate to such systems. On the contrary, purely data-driven approaches do not need explicit mathematical models for data generation and have a wider applicability at the cost of interpretability. In this paper, we consider the design of a one-bit compressive autoencoder, and propose a novel hybrid model-based and data-driven methodology that allows us to not only design the sensing matrix for one-bit data acquisition, but also allows for learning the latent-parameters of an iterative optimization algorithm specifically designed for the problem of one-bit sparse signal recovery. Our results demonstrate a significant improvement compared to state-of-the-art model-based algorithms.
Occlusion-Robust Online Multi-Object Visual Tracking using a GM-PHD Filter with CNN-Based Re-Identification
Authors: Nathanael L. Baisa
Date: 2019-12-10 00:00:00
Tasks: Large-Scale Person Re-Identification, Multiple Object Tracking, Object Tracking, Person Re-Identification, Visual Tracking

We propose a novel online multi-object visual tracker using a Gaussian mixture Probability Hypothesis Density (GM-PHD) filter and deep appearance learning. The GM-PHD filter has a linear complexity with the number of objects and observations while estimating the states and cardinality of time-varying number of objects, however, it is susceptible to miss-detections and does not include the identity of objects. We use visual-spatio-temporal information obtained from object bounding boxes and deeply learned appearance representations to perform estimates-to-tracks data association for target labeling as well as formulate an augmented likelihood and then integrate into the update step of the GM-PHD filter. We also employ additional unassigned tracks prediction after the data association step to overcome the susceptibility of the GM-PHD filter towards miss-detections caused by occlusion. Extensive evaluations on MOT16, MOT17 and HiEve benchmark datasets show that our tracker significantly outperforms several state-of-the-art trackers in terms of tracking accuracy and identification.
Encoding Musical Style with Transformer Autoencoders
Authors: Kristy Choi, Curtis Hawthorne, Ian Simon, Monica Dinculescu, Jesse Engel
Date: 2019-12-10 00:00:00
Proceeding: ICML 2020 1
Tasks: Music Generation

We consider the problem of learning high-level controls over the global structure of generated sequences, particularly in the context of symbolic music generation with complex language models. In this work, we present the Transformer autoencoder, which aggregates encodings of the input data across time to obtain a global representation of style from a given performance. We show it is possible to combine this global representation with other temporally distributed embeddings, enabling improved control over the separate aspects of performance style and melody. Empirically, we demonstrate the effectiveness of our method on various music generation tasks on the MAESTRO dataset and a YouTube dataset with 10,000+ hours of piano performances, where we achieve improvements in terms of log-likelihood and mean listening scores as compared to baselines.
Measuring Mother-Infant Emotions By Audio Sensing
Authors: Xuewen Yao, Dong He, Tiancheng Jing, Kaya de Barbaro
Date: 2019-12-10 00:00:00
Tasks: Active Learning

It has been suggested in developmental psychology literature that the communication of affect between mothers and their infants correlates with the socioemotional and cognitive development of infants. In this study, we obtained day-long audio recordings of 10 mother-infant pairs in order to study their affect communication in speech with a focus on mother's speech. In order to build a model for speech emotion detection, we used the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) and trained a Convolutional Neural Nets model which is able to classify 6 different emotions at 70% accuracy. We applied our model to mother's speech and found the dominant emotions were angry and sad, which were not true. Based on our own observations, we concluded that emotional speech databases made with the help of actors cannot generalize well to real-life settings, suggesting an active learning or unsupervised approach in the future.
Measuring the Reliability of Reinforcement Learning Algorithms
Authors: Stephanie C. Y. Chan, Samuel Fishman, John Canny, Anoop Korattikara, Sergio Guadarrama
Date: 2019-12-10 00:00:00
Proceeding: ICLR 2020 1
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Lack of reliability is a well-known issue for reinforcement learning (RL) algorithms. This problem has gained increasing attention in recent years, and efforts to improve it have grown substantially. To aid RL researchers and production users with the evaluation and improvement of reliability, we propose a set of metrics that quantitatively measure different aspects of reliability. In this work, we focus on variability and risk, both during training and after learning (on a fixed policy). We designed these metrics to be general-purpose, and we also designed complementary statistical tests to enable rigorous comparisons on these metrics. In this paper, we first describe the desired properties of the metrics and their design, the aspects of reliability that they measure, and their applicability to different scenarios. We then describe the statistical tests and make additional practical recommendations for reporting results. The metrics and accompanying statistical tools have been made available as an open-source library at https://github.com/google-research/rl-reliability-metrics. We apply our metrics to a set of common RL algorithms and environments, compare them, and analyze the results.
Managing Machine Learning Workflow Components
Authors: Marcio Moreno, Vítor Lourenço, Sandro Rama Fiorini, Polyana Costa, Rafael Brandão, Daniel Civitarese, Renato Cerqueira
Date: 2019-12-10 00:00:00
Tasks: BIG-bench Machine Learning, Management, Retrieval

Machine Learning Workflows (MLWfs) have become essential and a disruptive approach in problem-solving over several industries. However, the development process of MLWfs may be complicated, hard to achieve, time-consuming, and error-prone. To handle this problem, in this paper, we introduce machine learning workflow management (MLWfM) as a technique to aid the development and reuse of MLWfs and their components through three aspects: representation, execution, and creation. More precisely, we discuss our approach to structure the MLWfs' components and their metadata to aid retrieval and reuse of components in new MLWfs. Also, we consider the execution of these components within a tool. The hybrid knowledge representation, called Hyperknowledge, frames our methodology, supporting the three MLWfM's aspects. To validate our approach, we show a practical use case in the Oil & Gas industry.
Self-regularizing restricted Boltzmann machines
Authors: Orestis Loukas
Date: 2019-12-09 00:00:00

Focusing on the grand-canonical extension of the ordinary restricted Boltzmann machine, we suggest an energy-based model for feature extraction that uses a layer of hidden units with varying size. By an appropriate choice of the chemical potential and given a sufficiently large number of hidden resources the generative model is able to efficiently deduce the optimal number of hidden units required to learn the target data with exceedingly small generalization error. The formal simplicity of the grand-canonical ensemble combined with a rapidly converging ansatz in mean-field theory enable us to recycle well-established numerical algothhtims during training, like contrastive divergence, with only minor changes. As a proof of principle and to demonstrate the novel features of grand-canonical Boltzmann machines, we train our generative models on data from the Ising theory and MNIST.
Exploratory Not Explanatory: Counterfactual Analysis of Saliency Maps for Deep Reinforcement Learning
Authors: Akanksha Atrey, Kaleigh Clary, David Jensen
Date: 2019-12-09 00:00:00
Proceeding: ICLR 2020 1
Tasks: Atari Games, counterfactual, Counterfactual Reasoning, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Saliency maps are frequently used to support explanations of the behavior of deep reinforcement learning (RL) agents. However, a review of how saliency maps are used in practice indicates that the derived explanations are often unfalsifiable and can be highly subjective. We introduce an empirical approach grounded in counterfactual reasoning to test the hypotheses generated from saliency maps and assess the degree to which they correspond to the semantics of RL environments. We use Atari games, a common benchmark for deep RL, to evaluate three types of saliency maps. Our results show the extent to which existing claims about Atari games can be evaluated and suggest that saliency maps are best viewed as an exploratory tool rather than an explanatory tool.
A case study of Consistent Vehicle Routing Problem with Time Windows
Authors: Hernán Lespay, Karol Suchan
Date: 2019-12-06 00:00:00

We develop a heuristic for the Consistent Vehicle Routing Problem with Time Windows (ConVRPTW), which is motivated by a real-world application at a food company's distribution center. Besides standard VRPTW restrictions, ConVRPTW assigns each customer just one driver to fulfill their orders during the whole multi-period planning horizon. For each driver and period, a route is sought to serve all their customers with positive demand. For each customer, the number of periods between consecutive orders and the ordered quantities are highly irregular. This causes difficulties in the daily routing, negatively impacting the service level of the company. Similar problems have been studied as ConVRP, where the number of drivers is fixed a priori, and only the total travel time is minimized. Moreover, the clients present no time window constraints, but the visits should be scheduled with a small arrival time variation. In our model, the objective is to minimize the number of drivers. We impose hard time windows but do not consider time consistency in more detail. We compare solutions given by the heuristic with solutions of a MILP model on a set of small artificial instances and solutions used by the food company on real-world instances. The results show the effectiveness of the heuristic. For the company, we obtain significant improvements in the routing plans, with a lower number of vehicles and a higher rate of orders delivered within the prescribed time window.
Learning Human Objectives by Evaluating Hypothetical Behavior
Authors: Siddharth Reddy, Anca D. Dragan, Sergey Levine, Shane Legg, Jan Leike
Date: 2019-12-05 00:00:00
Proceeding: ICML 2020 1
Tasks: Car Racing, Reinforcement Learning

We seek to align agent behavior with a user's objectives in a reinforcement learning setting with unknown dynamics, an unknown reward function, and unknown unsafe states. The user knows the rewards and unsafe states, but querying the user is expensive. To address this challenge, we propose an algorithm that safely and interactively learns a model of the user's reward function. We start with a generative model of initial states and a forward dynamics model trained on off-policy data. Our method uses these models to synthesize hypothetical behaviors, asks the user to label the behaviors with rewards, and trains a neural network to predict the rewards. The key idea is to actively synthesize the hypothetical behaviors from scratch by maximizing tractable proxies for the value of information, without interacting with the environment. We call this method reward query synthesis via trajectory optimization (ReQueST). We evaluate ReQueST with simulated users on a state-based 2D navigation task and the image-based Car Racing video game. The results show that ReQueST significantly outperforms prior methods in learning reward models that transfer to new environments with different initial state distributions. Moreover, ReQueST safely trains the reward model to detect unsafe states, and corrects reward hacking before deploying the agent.
Linear algorithm for solution n-Queens Completion problem
Authors: E. Grigoryan
Date: 2019-12-05 00:00:00

A linear algorithm is described for solving the n-Queens Completion problem for an arbitrary composition of k queens, consistently distributed on a chessboard of size n x n. Two important rules are used in the algorithm: a) the rule of sequential risk elimination for the entire system as a whole; b) the rule of formation of minimal damage in the given selection conditions. For any composition of k queens (1<= k<n), a solution is provided, or a decision is made that this composition can't be completed. The probability of an error in making such a decision does not exceed 0.0001, and its value decreases, with increasing n. It is established that the average time, required for the queen to be placed on one row, decreases with increasing value of n. A description is given of two random selection models and the results of their comparative analysis. A model for organizing the Back Tracking procedure is proposed based on the separation of the solution matrix into two basic levels. Regression formulas are given for the dependence of basic levels on the value of n. It was found that for n=(7-100000) the number of solutions in which the Back Tracking procedure has never been used exceeds 35%. Moreover, for n=(320-22500), the number of such cases exceeds 50 %. A quick algorithm for verifying the correctness of n-Queens problem solution or arbitrary composition of k queens is given.
Lion Algorithm- Optimized Long Short-Term Memory Network for Groundwater Level Forecasting in Udupi District, India
Authors: Supreetha B. S, Narayan Shenoy, Prabhakar Nayak
Date: 2019-12-05 00:00:00
Tasks: Management

Groundwater is a precious natural resource. Groundwater level (GWL) forecasting is crucial in the field of water resource management. Measurement of GWL from observation-wells is the principle source of information about the aquifer and is critical to its evaluation. Most part of the Udupi district of Karnataka State in India consists of geological formations: lateritic terrain and gneissic complex. Due to the topographical ruggedness and inconsistency in rainfall, the GWL in Udupi region is declining continually and most of the open wells are drying-up during the summer. Hence, the current research aimed at developing a groundwater level forecasting model by using hybrid Long Short-term Memory-Lion Algorithm (LSTM-LA). The historical GWL and rainfall data from an observation well from Udupi district, located in Karnataka state, India, were used to develop the model. The prediction accuracy of the hybrid LSTM-LA model was better than that of the Feedforward Neural network (FFNN) and the isolated LSTM models. The hybrid LSTM-LA based forecasting model is promising for a larger dataset.
Prioritized Unit Propagation with Periodic Resetting is (Almost) All You Need for Random SAT Solving
Authors: Xujie Si, Yujia Li, Vinod Nair, Felix Gimeno
Date: 2019-12-04 00:00:00
Tasks: All

We propose prioritized unit propagation with periodic resetting, which is a simple but surprisingly effective algorithm for solving random SAT instances that are meant to be hard. In particular, an evaluation on the Random Track of the 2017 and 2018 SAT competitions shows that a basic prototype of this simple idea already ranks at second place in both years. We share this observation in the hope that it helps the SAT community better understand the hardness of random instances used in competitions and inspire other interesting ideas on SAT solving.