pwc-archive/papers-with-abstracts huggingface dataset:
A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments
Authors: Sindhu Padakandla
Date: 2020-05-19 00:00:00
Tasks: Cloud Computing, Management, Recommendation Systems, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Survey

Reinforcement learning (RL) algorithms find applications in inventory control, recommender systems, vehicular traffic management, cloud computing and robotics. The real-world complications of many tasks arising in these domains makes them difficult to solve with the basic assumptions underlying classical RL algorithms. RL agents in these applications often need to react and adapt to changing operating conditions. A significant part of research on single-agent RL techniques focuses on developing algorithms when the underlying assumption of stationary environment model is relaxed. This paper provides a survey of RL methods developed for handling dynamically varying environment models. The goal of methods not limited by the stationarity assumption is to help autonomous agents adapt to varying operating conditions. This is possible either by minimizing the rewards lost during learning by RL agent or by finding a suitable policy for the RL agent which leads to efficient operation of the underlying system. A representative collection of these algorithms is discussed in detail in this work along with their categorization and their relative merits and demerits. Additionally we also review works which are tailored to application domains. Finally, we discuss future enhancements for this field.
Triple-GAIL: A Multi-Modal Imitation Learning Framework with Generative Adversarial Nets
Authors: Cong Fei, Bin Wang, Yuzheng Zhuang, Zongzhang Zhang, Jianye Hao, Hongbo Zhang, Xuewu Ji, Wulong Liu
Date: 2020-05-19 00:00:00
Tasks: Autonomous Vehicles, Data Augmentation, Imitation Learning

Generative adversarial imitation learning (GAIL) has shown promising results by taking advantage of generative adversarial nets, especially in the field of robot learning. However, the requirement of isolated single modal demonstrations limits the scalability of the approach to real world scenarios such as autonomous vehicles' demand for a proper understanding of human drivers' behavior. In this paper, we propose a novel multi-modal GAIL framework, named Triple-GAIL, that is able to learn skill selection and imitation jointly from both expert demonstrations and continuously generated experiences with data augmentation purpose by introducing an auxiliary skill selector. We provide theoretical guarantees on the convergence to optima for both of the generator and the selector respectively. Experiments on real driver trajectories and real-time strategy game datasets demonstrate that Triple-GAIL can better fit multi-modal behaviors close to the demonstrators and outperforms state-of-the-art methods.
Dynamic Sparsity Neural Networks for Automatic Speech Recognition
Authors: Zhaofeng Wu, Ding Zhao, Qiao Liang, Jiahui Yu, Anmol Gulati, Ruoming Pang
Date: 2020-05-16 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition

In automatic speech recognition (ASR), model pruning is a widely adopted technique that reduces model size and latency to deploy neural network models on edge devices with resource constraints. However, multiple models with different sparsity levels usually need to be separately trained and deployed to heterogeneous target hardware with different resource specifications and for applications that have various latency requirements. In this paper, we present Dynamic Sparsity Neural Networks (DSNN) that, once trained, can instantly switch to any predefined sparsity configuration at run-time. We demonstrate the effectiveness and flexibility of DSNN using experiments on internal production datasets with Google Voice Search data, and show that the performance of a DSNN model is on par with that of individually trained single sparsity networks. Our trained DSNN model, therefore, can greatly ease the training process and simplify deployment in diverse scenarios with resource constraints.
Near Instance-Optimality in Differential Privacy
Authors: Hilal Asi, John C. Duchi
Date: 2020-05-16 00:00:00
Tasks: regression, Sensitivity

We develop two notions of instance optimality in differential privacy, inspired by classical statistical theory: one by defining a local minimax risk and the other by considering unbiased mechanisms and analogizing the Cramer-Rao bound, and we show that the local modulus of continuity of the estimand of interest completely determines these quantities. We also develop a complementary collection mechanisms, which we term the inverse sensitivity mechanisms, which are instance optimal (or nearly instance optimal) for a large class of estimands. Moreover, these mechanisms uniformly outperform the smooth sensitivity framework on each instance for several function classes of interest, including real-valued continuous functions. We carefully present two instantiations of the mechanisms for median and robust regression estimation with corresponding experiments.
Extreme Theory of Functional Connections: A Physics-Informed Neural Network Method for Solving Parametric Differential Equations
Authors: Enrico Schiassi, Carl Leake, Mario De Florio, Hunter Johnston, Roberto Furfaro, Daniele Mortari
Date: 2020-05-15 00:00:00

In this work we present a novel, accurate, and robust physics-informed method for solving problems involving parametric differential equations (DEs) called the Extreme Theory of Functional Connections, or X-TFC. The proposed method is a synergy of two recently developed frameworks for solving problems involving parametric DEs, 1) the Theory of Functional Connections, TFC, and the Physics-Informed Neural Networks, PINN. Although this paper focuses on the solution of exact problems involving parametric DEs (i.e. problems where the modeling error is negligible) with known parameters, X-TFC can also be used for data-driven solutions and data-driven discovery of parametric DEs. In the proposed method, the latent solution of the parametric DEs is approximated by a TFC constrained expression that uses a Neural Network (NN) as the free-function. This approximate solution form always analytically satisfies the constraints of the DE, while maintaining a NN with unconstrained parameters, like the Deep-TFC method. X-TFC differs from PINN and Deep-TFC; whereas PINN and Deep-TFC use a deep-NN, X-TFC uses a single-layer NN, or more precisely, an Extreme Learning Machine, ELM. This choice is based on the properties of the ELM algorithm. In order to numerically validate the method, it was tested over a range of problems including the approximation of solutions to linear and non-linear ordinary DEs (ODEs), systems of ODEs (SODEs), and partial DEs (PDEs). Furthermore, a few of these problems are of interest in physics and engineering such as the Classic Emden-Fowler equation, the Radiative Transfer (RT) equation, and the Heat-Transfer (HT) equation. The results show that X-TFC achieves high accuracy with low computational time and thus it is comparable with the other state-of-the-art methods.
How to Learn from Others: Transfer Machine Learning with Additive Regression Models to Improve Sales Forecasting
Authors: Robin Hirt, Niklas Kühl, Yusuf Peker, Gerhard Satzger
Date: 2020-05-15 00:00:00
Tasks: BIG-bench Machine Learning

In a variety of business situations, the introduction or improvement of machine learning approaches is impaired as these cannot draw on existing analytical models. However, in many cases similar problems may have already been solved elsewhere-but the accumulated analytical knowledge cannot be tapped to solve a new problem, e.g., because of privacy barriers. For the particular purpose of sales forecasting for similar entities, we propose a transfer machine learning approach based on additive regression models that lets new entities benefit from models of existing entities. We evaluate the approach on a rich, multi-year dataset of multiple restaurant branches. We differentiate the options to simply transfer models from one branch to another ("zero shot") or to transfer and adapt them. We analyze feasibility and performance against several forecasting benchmarks. The results show the potential of the approach to exploit the collectively available analytical knowledge. Thus, we contribute an approach that is generalizable beyond sales forecasting and the specific use case in particular. In addition, we demonstrate its feasibility for a typical use case as well as the potential for improving forecasting quality. These results should inform academia, as they help to leverage knowledge across various entities, and have immediate practical application in industry.
Recent Developments Combining Ensemble Smoother and Deep Generative Networks for Facies History Matching
Authors: Smith W. A. Canchumun, Jose D. B. Castro, Júlia Potratz, Alexandre A. Emerick, Marco Aurelio C. Pacheco
Date: 2020-05-08 00:00:00
Tasks: Generative Adversarial Network, Video Generation

Ensemble smoothers are among the most successful and efficient techniques currently available for history matching. However, because these methods rely on Gaussian assumptions, their performance is severely degraded when the prior geology is described in terms of complex facies distributions. Inspired by the impressive results obtained by deep generative networks in areas such as image and video generation, we started an investigation focused on the use of autoencoders networks to construct a continuous parameterization for facies models. In our previous publication, we combined a convolutional variational autoencoder (VAE) with the ensemble smoother with multiple data assimilation (ES-MDA) for history matching production data in models generated with multiple-point geostatistics. Despite the good results reported in our previous publication, a major limitation of the designed parameterization is the fact that it does not allow applying distance-based localization during the ensemble smoother update, which limits its application in large-scale problems. The present work is a continuation of this research project focusing in two aspects: firstly, we benchmark seven different formulations, including VAE, generative adversarial network (GAN), Wasserstein GAN, variational auto-encoding GAN, principal component analysis (PCA) with cycle GAN, PCA with transfer style network and VAE with style loss. These formulations are tested in a synthetic history matching problem with channelized facies. Secondly, we propose two strategies to allow the use of distance-based localization with the deep learning parameterizations.
DETECT: A Hierarchical Clustering Algorithm for Behavioural Trends in Temporal Educational Data
Authors: Jessica McBroom, Kalina Yacef, Irena Koprinska
Date: 2020-05-04 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

Techniques for clustering student behaviour offer many opportunities to improve educational outcomes by providing insight into student learning. However, one important aspect of student behaviour, namely its evolution over time, can often be challenging to identify using existing methods. This is because the objective functions used by these methods do not explicitly aim to find cluster trends in time, so these trends may not be clearly represented in the results. This paper presents `DETECT' (Detection of Educational Trends Elicited by Clustering Time-series data), a novel divisive hierarchical clustering algorithm that incorporates temporal information into its objective function to prioritise the detection of behavioural trends. The resulting clusters are similar in structure to a decision tree, with a hierarchy of clusters defined by decision rules on features. DETECT is easy to apply, highly customisable, applicable to a wide range of educational datasets and yields easily interpretable results. Through a case study of two online programming courses (N>600), this paper demonstrates two example applications of DETECT: 1) to identify how cohort behaviour develops over time and 2) to identify student behaviours that characterise exercises where many students give up.
Nonlinear Regression Analysis Using Multi-Verse Optimizer
Authors: Jayri Bagchi, Tapas Si
Date: 2020-04-28 00:00:00
Tasks: BIG-bench Machine Learning, regression

Regression analysis is an important machine learning task used for predictive analytic in business, sports analysis, etc. In regression analysis, optimization algorithms play a significant role in search the coefficients in the regression model. In this paper, nonlinear regression analysis using a recently developed meta-heuristic Multi-Verse Optimizer (MVO) is proposed. The proposed method is applied to 10 well-known benchmark nonlinear regression problems. A comparative study has been conducted with Particle Swarm Optimizer (PSO). The experimental results demonstrate that the proposed method statistically outperforms PSO algorithm.
Development of a hand pose recognition system on an embedded computer using Artificial Intelligence
Authors: Dennis Núñez-Fernández
Date: 2019-10-03 00:00:00
Proceeding: IEEE International Conference on Electronics, Electrical Engineering and Computing (INTERCON) 2019 10
Tasks: Autonomous Driving, Gesture Recognition, Hand Detection

The recognition of hand gestures is a very interesting research topic due to the growing demand in recent years in robotics, virtual reality, autonomous driving systems, human-machine interfaces and in other new technologies. Despite several approaches for a robust recognition system, gesture recognition based on visual perception has many advantages over devices such as sensors, or electronic gloves. This paper describes the implementation of a visual-based recognition system on a embedded computer for 10 hand poses recognition. Hand detection is achieved using a tracking algorithm and classification by a light convolutional neural network. Results show an accuracy of 94.50%, a low power consumption and a near real-time response. Thereby, the proposed system could be applied in a large range of applications, from robotics to entertainment.
Development of a hand gesture based control interface using Deep Learning
Authors: Dennis Núñez-Fernández
Date: 2020-04-23 00:00:00
Proceeding: International Conference on Information Management and Big Data (SIMBig) 2020 4
Tasks: Autonomous Driving, Hand Detection

This paper describes the implementation of a control system based on ten different hand gestures, providing a useful approach for the implementation of better user-friendly human-machine interfaces. Hand detection is achieved using fast detection and tracking algorithms, and classification by a light convolutional neural network. The experimental results show a real-time response with an accuracy of 95.09%, and making use of low power consumption. These results demonstrate that the proposed system could be applied in a large range of applications such as virtual reality, robotics, autonomous driving systems, human-machine interfaces, augmented reality among others.
On time series representations for multi-label NILM
Authors: Christoforos Nalmpantis, Dimitris Vrakas
Date: 2020-05-02 00:00:00
Proceeding: Neural Computing and Applications 2020 5
Tasks: Dimensionality Reduction, Non-Intrusive Load Monitoring, Time Series, Time Series Analysis

Given only the main power consumption of a household, a non-intrusive load monitoring (NILM) system identifies which appliances are operating. With the rise of Internet of things, running energy disaggregation models on the edge is more and more essential for privacy concerns and economic reasons. However, current NILM solutions use data-hungry deep learning models that can recognize only one device and are impossible to run on a device with limited resources. This research investigates in-depth multi-label NILM systems and suggests a novel framework which enables a cost-effective solution. It can be deployed on an embedded device, and thus, privacy can be preserved. The proposed system leverages dimensionality reduction using Signal2Vec, is evaluated on two popular public datasets and outperforms another state-of-the-art multi-label NILM system.
Implementation of an Indoor Location System for Mobile-Based Museum Guidance
Authors: Dennis Núñez-Fernández
Date: 2020-04-23 00:00:00
Proceeding: International Conference on Information Management and Big Data (SIMBig) 2020 4

In this work we proposed an indoor location system that makes use of a mobile phone and WiFi signal levels to determine the location of a person in the museum “Eduardo de Habich” at the Universidad Nacional de Ingeniería, Peru. Therefore, by determining the location, additional information such as recommendations, multimedia, an more could be shown to each user in order to have a better user experience. The main advantage with similar indoor location systems such as Beacons or RFID technology is that the proposed system does not require additional hardware as it only uses pre-installed WiFi hotspots. The experimental tests show promising results, achieving a location accuracy of 93.61%, which is useful for similar navigation tasks.
Implementation of a WiFi-based indoor location system on a mobile device for a university area
Authors: Dennis Núñez-Fernández
Date: 2019-10-03 00:00:00
Proceeding: IEEE International Conference on Electronics, Electrical Engineering and Computing (INTERCON) 2019 10
Tasks: Position

This paper describes the implementation of an indoor location system on a mobile phone for the Faculty of Electrical, Electronic and Telecomunications at the Universidad Nacional de Ingeniería, Peru. The proposed system makes use of WiFi signals and the Bayes filter in order to predict the user location. The principal advantage of the proposed method in comparison with similar methodologies is that our system does not need additional hardware or special equipments. As the experimental results show, the system achieves 92.31% for position accuracy and works in real-time. In addition, the proposed methodology could be used for similar location tasks.
A Compressed Sensing Approach to Pooled RT-PCR Testing for COVID-19 Detection
Authors: Sabyasachi Ghosh, Rishi Agarwal, Mohammad Ali Rehan, Shreya Pathak, Pratyush Agrawal, Yash Gupta, Sarthak Consul, Nimay Gupta, Ritika, Ritesh Goenka, Ajit Rajwade, Manoj Gopalkrishnan
Date: 2020-05-16 00:00:00
Tasks: compressed sensing

We propose `Tapestry', a novel approach to pooled testing with application to COVID-19 testing with quantitative Reverse Transcription Polymerase Chain Reaction (RT-PCR) that can result in shorter testing time and conservation of reagents and testing kits. Tapestry combines ideas from compressed sensing and combinatorial group testing with a novel noise model for RT-PCR used for generation of synthetic data. Unlike Boolean group testing algorithms, the input is a quantitative readout from each test and the output is a list of viral loads for each sample relative to the pool with the highest viral load. While other pooling techniques require a second confirmatory assay, Tapestry obtains individual sample-level results in a single round of testing, at clinically acceptable false positive or false negative rates. We also propose designs for pooling matrices that facilitate good prediction of the infected samples while remaining practically viable. When testing $n$ samples out of which $k \ll n$ are infected, our method needs only $O(k \log n)$ tests when using random binary pooling matrices, with high probability. However, we also use deterministic binary pooling matrices based on combinatorial design ideas of Kirkman Triple Systems to balance between good reconstruction properties and matrix sparsity for ease of pooling. In practice, we have observed the need for fewer tests with such matrices than with random pooling matrices. This makes Tapestry capable of very large savings at low prevalence rates, while simultaneously remaining viable even at prevalence rates as high as 9.5\%. Empirically we find that single-round Tapestry pooling improves over two-round Dorfman pooling by almost a factor of 2 in the number of tests required. We validate Tapestry in simulations and wet lab experiments with oligomers in quantitative RT-PCR assays. Lastly, we describe use-case scenarios for deployment.
Attention-Based Multi-Context Guiding for Few-Shot Semantic Segmentation
Authors: Tao Hu, Pengwan Yang, Chiliang Zhang, Gang Yu, Yadong Mu, Cees G. M. Snoek
Date: 2019-07-01 00:00:00
Proceeding: Proceedings of the AAAI Conference on Artificial Intelligence 2019 7
Tasks: Few-Shot Learning, Few-Shot Semantic Segmentation, One-Shot Learning, Semantic Segmentation

Few-shot learning is a nascent research topic, motivated by the fact that traditional deep learning methods require tremen- dous amounts of data. The scarcity of annotated data becomes even more challenging in semantic segmentation since pixel- level annotation in segmentation task is more labor-intensive to acquire. To tackle this issue, we propose an Attention- based Multi-Context Guiding (A-MCG) network, which con- sists of three branches: the support branch, the query branch, the feature fusion branch. A key differentiator of A-MCG is the integration of multi-scale context features between sup- port and query branches, enforcing a better guidance from the support set. In addition, we also adopt a spatial atten- tion along the fusion branch to highlight context information from several scales, enhancing self-supervision in one-shot learning. To address the fusion problem in multi-shot learn- ing, Conv-LSTM is adopted to collaboratively integrate the sequential support features to elevate the final accuracy. Our architecture obtains state-of-the-art on unseen classes in a variant of PASCAL VOC12 dataset and performs favorably against previous work with large gains of 1.1%, 1.4% mea- sured in mIoU in the 1-shot and 5-shot setting.
Detection of Human Falls on Furniture Using Scene Analysis Based on Deep Learning and Activity Characteristics
Authors: WEIDONG MIN, (Member, IEEE), HAO CUI, HONG RAO, ZHIXUN LI, AND LEIYUE YAO
Date: 2018-01-18 00:00:00
Proceeding: IEEE 2018 1

Automatic human fall detection is one important research topic in caring for vulnerable people,
such as elders at home and patients in medical places. Over the past decade, numerous methods aiming
at solving the problem were proposed. However, the existing methods only focus on detecting human
themselves and cannot work effectively in complicated environments, especially for the falls on furniture.
To alleviate this problem, a new method for human fall detection on furniture using scene analysis based on
deep learning and activity characteristics is presented in this paper. The proposed method first performs scene
analysis using a deep learning method faster R-CNN to detect human and furniture. Meanwhile, the space
relation between human and furniture is detected. The activity characteristics of the detected people, such as
human shape aspect ratio, centroid, motion speed are detected and tracked. Through measuring the changes of
these characteristics and judging the relations between the people and furniture nearby, the falls on furniture
can be effectively detected. Experiment results demonstrated that our approach not only accurately and
effectively detected falls on furniture, such as sofa and chairs but also distinguished them from other fall-like
activities, such as sitting or lying down, while the existing methods have difficulties to handle these. In our
experiments, our algorithm achieved 94.44% precision, 94.95% recall, and 95.50% accuracy. The proposed
method can be potentially used and integrated as a medical assistance in health care and medical places and
appliances.