pwc-archive/papers-with-abstracts huggingface dataset:
A Comprehensive Pipeline for Hotel Recommendation System
Authors: J. Chen, Z. Gao
Date: 2020-08-13 00:00:00

This paper addresses a comprehensive pipeline to build a hotel recommendation system with the raw data collected by Apps in users' smartphones. The pipeline mainly consists of pre-processing of the raw data and training prediction models. We use two methods, Support Vector Machine (SVM) and Recurrent Neural Network (RNN). The results show that two methods achieved a reasonable accuracy with the pre-processing of the raw data. Therefore, we conclude that this paper provides a comprehensive pipeline, in which a hotel recommendation system was successfully built from the raw data to specific applications.
Flow Field Reconstructions with GANs based on Radial Basis Functions
Authors: Liwei Hu, Wenyong Wang, Yu Xiang, Jun Zhang
Date: 2020-08-11 00:00:00
Tasks: regression

Nonlinear sparse data regression and generation have been a long-term challenge, to cite the flow field reconstruction as a typical example. The huge computational cost of computational fluid dynamics (CFD) makes it much expensive for large scale CFD data producing, which is the reason why we need some cheaper ways to do this, of which the traditional reduced order models (ROMs) were promising but they couldn't generate a large number of full domain flow field data (FFD) to realize high-precision flow field reconstructions. Motivated by the problems of existing approaches and inspired by the success of the generative adversarial networks (GANs) in the field of computer vision, we prove an optimal discriminator theorem that the optimal discriminator of a GAN is a radial basis function neural network (RBFNN) while dealing with nonlinear sparse FFD regression and generation. Based on this theorem, two radial basis function-based GANs (RBF-GAN and RBFC-GAN), for regression and generation purposes, are proposed. Three different datasets are applied to verify the feasibility of our models. The results show that the performance of the RBF-GAN and the RBFC-GAN are better than that of GANs/cGANs by means of both the mean square error (MSE) and the mean square percentage error (MSPE). Besides, compared with GANs/cGANs, the stability of the RBF-GAN and the RBFC-GAN improve by 34.62% and 72.31%, respectively. Consequently, our proposed models can be used to generate full domain FFD from limited and sparse datasets, to meet the requirement of high-precision flow field reconstructions.
Deep Reinforcement Learning Based Power Allocation for D2D Network
Authors: Zhengran Bi ; Wenan Zhou
Date: 2020-05-25 00:00:00
Proceeding: Conference 2020 5
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

deep reinforement learning
q learning
dqn
On gaze deployment to audio-visual cues of social interactions
Authors: Giuseppe Boccignone, Vittorio Cuculo, Alessandro D’Amelio, Giuliano Grossi, Raffaella Lanzarotti
Date: 2020-09-02 00:00:00
Proceeding: IEEE Access 2020 9
Tasks: Scanpath prediction

Attention supports our urge to forage on social cues. Under certain circumstances, we spend the majority of time scrutinising people, markedly their eyes and faces, and spotting persons that are talking. To account for such behaviour, this paper develops a computational model for the deployment of gaze within a multimodal landscape, namely a conversational scene. Gaze dynamics is derived in a principled way by reformulating attention deployment as a stochastic foraging problem. Model simulation experiments on a publicly available dataset of eye-tracked subjects are presented. Results show that the simulated scan paths exhibit similar trends of eye movements of human observers watching and listening to conversational clips in a free-viewing condition.
Measuring Massive Multitask Language Understanding
Authors: Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, Jacob Steinhardt
Date: 2020-09-07 00:00:00
Tasks: Elementary Mathematics, Multi-task Language Understanding, Multi-Task Learning, World Knowledge

We propose a new test to measure a text model's multitask accuracy. The test covers 57 tasks including elementary mathematics, US history, computer science, law, and more. To attain high accuracy on this test, models must possess extensive world knowledge and problem solving ability. We find that while most recent models have near random-chance accuracy, the very largest GPT-3 model improves over random chance by almost 20 percentage points on average. However, on every one of the 57 tasks, the best models still need substantial improvements before they can reach expert-level accuracy. Models also have lopsided performance and frequently do not know when they are wrong. Worse, they still have near-random accuracy on some socially important subjects such as morality and law. By comprehensively evaluating the breadth and depth of a model's academic and professional understanding, our test can be used to analyze models across many tasks and to identify important shortcomings.
Improved Modeling of 3D Shapes with Multi-view Depth Maps
Authors: Kamal Gupta, Susmija Jabbireddy, Ketul Shah, Abhinav Shrivastava, Matthias Zwicker
Date: 2020-09-07 00:00:00
Tasks: Decoder, Image Generation

We present a simple yet effective general-purpose framework for modeling 3D shapes by leveraging recent advances in 2D image generation using CNNs. Using just a single depth image of the object, we can output a dense multi-view depth map representation of 3D objects. Our simple encoder-decoder framework, comprised of a novel identity encoder and class-conditional viewpoint generator, generates 3D consistent depth maps. Our experimental results demonstrate the two-fold advantage of our approach. First, we can directly borrow architectures that work well in the 2D image domain to 3D. Second, we can effectively generate high-resolution 3D shapes with low computational memory. Our quantitative evaluations show that our method is superior to existing depth map methods for reconstructing and synthesizing 3D objects and is competitive with other representations, such as point clouds, voxel grids, and implicit functions.
GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training
Authors: Tianle Cai, Shengjie Luo, Keyulu Xu, Di He, Tie-Yan Liu, Li-Wei Wang
Date: 2020-09-07 00:00:00
Tasks: Graph Classification, Graph Neural Network, Graph Representation Learning, Representation Learning

Normalization is known to help the optimization of deep neural networks. Curiously, different architectures require specialized normalization methods. In this paper, we study what normalization is effective for Graph Neural Networks (GNNs). First, we adapt and evaluate the existing methods from other domains to GNNs. Faster convergence is achieved with InstanceNorm compared to BatchNorm and LayerNorm. We provide an explanation by showing that InstanceNorm serves as a preconditioner for GNNs, but such preconditioning effect is weaker with BatchNorm due to the heavy batch noise in graph datasets. Second, we show that the shift operation in InstanceNorm results in an expressiveness degradation of GNNs for highly regular graphs. We address this issue by proposing GraphNorm with a learnable shift. Empirically, GNNs with GraphNorm converge faster compared to GNNs using other normalization. GraphNorm also improves the generalization of GNNs, achieving better performance on graph classification benchmarks.
Data-Driven Transferred Energy Management Strategy for Hybrid Electric Vehicles via Deep Reinforcement Learning
Authors: Hao Chen, Gang Guo, Bangbei Tang, Guo Hu, Xiaolin Tang, Teng Liu
Date: 2020-09-07 00:00:00
Tasks: Deep Reinforcement Learning, energy management, Management, Transfer Learning

Real-time applications of energy management strategies (EMSs) in hybrid electric vehicles (HEVs) are the harshest requirements for researchers and engineers. Inspired by the excellent problem-solving capabilities of deep reinforcement learning (DRL), this paper proposes a real-time EMS via incorporating the DRL method and transfer learning (TL). The related EMSs are derived from and evaluated on the real-world collected driving cycle dataset from Transportation Secure Data Center (TSDC). The concrete DRL algorithm is proximal policy optimization (PPO) belonging to the policy gradient (PG) techniques. For specification, many source driving cycles are utilized for training the parameters of deep network based on PPO. The learned parameters are transformed into the target driving cycles under the TL framework. The EMSs related to the target driving cycles are estimated and compared in different training conditions. Simulation results indicate that the presented transfer DRL-based EMS could effectively reduce time consumption and guarantee control performance.
System Identification Through Lipschitz Regularized Deep Neural Networks
Authors: Elisa Negrini, Giovanna Citti, Luca Capogna
Date: 2020-09-07 00:00:00

In this paper we use neural networks to learn governing equations from data. Specifically we reconstruct the right-hand side of a system of ODEs $\dot{x}(t) = f(t, x(t))$ directly from observed uniformly time-sampled data using a neural network. In contrast with other neural network based approaches to this problem, we add a Lipschitz regularization term to our loss function. In the synthetic examples we observed empirically that this regularization results in a smoother approximating function and better generalization properties when compared with non-regularized models, both on trajectory and non-trajectory data, especially in presence of noise. In contrast with sparse regression approaches, since neural networks are universal approximators, we don't need any prior knowledge on the ODE system. Since the model is applied component wise, it can handle systems of any dimension, making it usable for real-world data.
A novel action recognition system for smart monitoring of elderly people using Action Pattern Image and Series CNN with transfer learning
Authors: L. Aneesh Euprazia, K. K. Thyagharajan
Date: 2020-09-07 00:00:00
Tasks: Action Classification, Action Recognition, Image Augmentation, Transfer Learning

Falling of elderly people who are staying alone at home leads to health risks. If they are not attended immediately even it may lead to fatal danger to their life. In this paper a novel computer vision-based system for smart monitoring of elderly people using Series Convolutional Neural Network (SCNN) with transfer learning is proposed. When CNN is trained by the frames of the videos directly, it learns from all pixels including the background pixels. Generally, the background in a video does not contribute anything in identifying the action and actually it will mislead the action classification. So, we propose a novel action recognition system and our contributions are 1) to generate more general action patterns which are not affected by illumination and background variations of the video sequences and eliminate the obligation of image augmentation in CNN training 2) to design SCNN architecture and enhance the feature extraction process to learn large amount of data, 3) to present the patterns learnt by the neurons in the layers and analyze how these neurons capture the action when the input pattern is passing through these neurons, and 4) to extend the capability of the trained SCNN for recognizing fall actions using transfer learning.
User-assisted Video Reflection Removal
Authors: Amgad Ahmed, Suhong Kim, Mohamed Elgharib, Mohamed Hefeeda
Date: 2020-09-07 00:00:00
Tasks: Reflection Removal, valid

Reflections in videos are obstructions that often occur when videos are taken behind reflective surfaces like glass. These reflections reduce the quality of such videos, lead to information loss and degrade the accuracy of many computer vision algorithms. A video containing reflections is a combination of background and reflection layers. Thus, reflection removal is equivalent to decomposing the video into two layers. This, however, is a challenging and ill-posed problem as there is an infinite number of valid decompositions. To address this problem, we propose a user-assisted method for video reflection removal. We rely on both spatial and temporal information and utilize sparse user hints to help improve separation. The key idea of the proposed method is to use motion cues to separate the background layer from the reflection layer with minimal user assistance. We show that user-assistance significantly improves the layer separation results. We implement and evaluate the proposed method through quantitative and qualitative results on real and synthetic videos. Our experiments show that the proposed method successfully removes reflection from video sequences, does not introduce visual distortions, and significantly outperforms the state-of-the-art reflection removal methods in the literature.
Driving Tasks Transfer in Deep Reinforcement Learning for Decision-making of Autonomous Vehicles
Authors: Hong Shu, Teng Liu, Xingyu Mu, Dongpu Cao
Date: 2020-09-07 00:00:00
Tasks: Autonomous Vehicles, Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning (RL), Transfer Learning

Knowledge transfer is a promising concept to achieve real-time decision-making for autonomous vehicles. This paper constructs a transfer deep reinforcement learning framework to transform the driving tasks in inter-section environments. The driving missions at the un-signalized intersection are cast into a left turn, right turn, and running straight for automated vehicles. The goal of the autonomous ego vehicle (AEV) is to drive through the intersection situation efficiently and safely. This objective promotes the studied vehicle to increase its speed and avoid crashing other vehicles. The decision-making pol-icy learned from one driving task is transferred and evaluated in another driving mission. Simulation results reveal that the decision-making strategies related to similar tasks are transferable. It indicates that the presented control framework could reduce the time consumption and realize online implementation.
Implicit Multidimensional Projection of Local Subspaces
Authors: Rongzheng Bian, Yumeng Xue, Liang Zhou, Jian Zhang, Baoquan Chen, Daniel Weiskopf, Yunhai Wang
Date: 2020-09-07 00:00:00

We propose a visualization method to understand the effect of multidimensional projection on local subspaces, using implicit function differentiation. Here, we understand the local subspace as the multidimensional local neighborhood of data points. Existing methods focus on the projection of multidimensional data points, and the neighborhood information is ignored. Our method is able to analyze the shape and directional information of the local subspace to gain more insights into the global structure of the data through the perception of local structures. Local subspaces are fitted by multidimensional ellipses that are spanned by basis vectors. An accurate and efficient vector transformation method is proposed based on analytical differentiation of multidimensional projections formulated as implicit functions. The results are visualized as glyphs and analyzed using a full set of specifically-designed interactions supported in our efficient web-based visualization tool. The usefulness of our method is demonstrated using various multi- and high-dimensional benchmark datasets. Our implicit differentiation vector transformation is evaluated through numerical comparisons; the overall method is evaluated through exploration examples and use cases.
Personalized Review Ranking for Improving Shopper's Decision Making: A Term Frequency based Approach
Authors: Akhil Sai Peddireddy
Date: 2020-09-07 00:00:00
Tasks: Decision Making

User-generated reviews serve as crucial references in shopper's decision-making process. Moreover, they improve product sales and validate the reputation of the website as a whole. Thus, it becomes important to design reviews ranking methods that help shoppers make informed decisions quickly. However, reviews ranking has its unique challenges. First, there is no relevance labels for reviews. A relevant review for shopper A might not be relevant to shopper B. Second, since shoppers cannot click on reviews, we have no ways of getting relevance feedback. Eventually, reviews ranking suffers from the lack of ground truth due to the variability in the standard of relevance for different users. In this paper, we aim to address the challenges of helping users to find information they might be interested in from the sea of customer reviews. Using the Amazon Customer Reviews Dataset collected and organized by UCSD, we first constructed user profiles based on user's personal web trails, recent shopping history and previous reviews, incorporated user profiles into our ranking algorithm, and assigned higher ranks to reviews that address individual shopper's concerns to the largest extent. Also, we leveraged user profiles to recommend products based on reviews texts. We evaluated our model based on both empirical evaluations and numerical evaluations of review scores. The results from both evaluation methods reveal a significant increase in the quality of top reviews as well as user satisfaction for over 1000 products. Our reviews based recommendation system also suggests that there's a large chance of user viewing and liking the product we recommend. Our work shows the basic steps of developing a ranking method that learns from a particular end-user's preferences.
Improving Problem Identification via Automated Log Clustering using Dimensionality Reduction
Authors: Carl Martin Rosenberg, Leon Moonen
Date: 2020-09-07 00:00:00
Tasks: Clustering, Dimensionality Reduction

Goal: We consider the problem of automatically grouping logs of runs that failed for the same underlying reasons, so that they can be treated more effectively, and investigate the following questions: (1) Does an approach developed to identify problems in system logs generalize to identifying problems in continuous deployment logs? (2) How does dimensionality reduction affect the quality of automated log clustering? (3) How does the criterion used for merging clusters in the clustering algorithm affect clustering quality? Method: We replicate and extend earlier work on clustering system log files to assess its generalization to continuous deployment logs. We consider the optional inclusion of one of these dimensionality reduction techniques: Principal Component Analysis (PCA), Latent Semantic Indexing (LSI), and Non-negative Matrix Factorization (NMF). Moreover, we consider three alternative cluster merge criteria (Single Linkage, Average Linkage, and Weighted Linkage), in addition to the Complete Linkage criterion used in earlier work. We empirically evaluate the 16 resulting configurations on continuous deployment logs provided by our industrial collaborator. Results: Our study shows that (1) identifying problems in continuous deployment logs via clustering is feasible, (2) including NMF significantly improves overall accuracy and robustness, and (3) Complete Linkage performs best of all merge criteria analyzed. Conclusions: We conclude that problem identification via automated log clustering is improved by including dimensionality reduction, as it decreases the pipeline's sensitivity to parameter choice, thereby increasing its robustness for handling different inputs.
Integrating Egocentric Localization for More Realistic Point-Goal Navigation Agents
Authors: Samyak Datta, Oleksandr Maksymets, Judy Hoffman, Stefan Lee, Dhruv Batra, Devi Parikh
Date: 2020-09-07 00:00:00
Tasks: Navigate, Robot Navigation, Visual Odometry

Recent work has presented embodied agents that can navigate to point-goal targets in novel indoor environments with near-perfect accuracy. However, these agents are equipped with idealized sensors for localization and take deterministic actions. This setting is practically sterile by comparison to the dirty reality of noisy sensors and actuations in the real world -- wheels can slip, motion sensors have error, actuations can rebound. In this work, we take a step towards this noisy reality, developing point-goal navigation agents that rely on visual estimates of egomotion under noisy action dynamics. We find these agents outperform naive adaptions of current point-goal agents to this setting as well as those incorporating classic localization baselines. Further, our model conceptually divides learning agent dynamics or odometry (where am I?) from task-specific navigation policy (where do I want to go?). This enables a seamless adaption to changing dynamics (a different robot or floor type) by simply re-calibrating the visual odometry model -- circumventing the expense of re-training of the navigation policy. Our agent was the runner-up in the PointNav track of CVPR 2020 Habitat Challenge.
Information Theoretic Meta Learning with Gaussian Processes
Authors: Michalis K. Titsias, Francisco J. R. Ruiz, Sotirios Nikoloutsopoulos, Alexandre Galashov
Date: 2020-09-07 00:00:00
Tasks: Gaussian Processes, Meta-Learning

We formulate meta learning using information theoretic concepts; namely, mutual information and the information bottleneck. The idea is to learn a stochastic representation or encoding of the task description, given by a training set, that is highly informative about predicting the validation set. By making use of variational approximations to the mutual information, we derive a general and tractable framework for meta learning. This framework unifies existing gradient-based algorithms and also allows us to derive new algorithms. In particular, we develop a memory-based algorithm that uses Gaussian processes to obtain non-parametric encoding representations. We demonstrate our method on a few-shot regression problem and on four few-shot classification problems, obtaining competitive accuracy when compared to existing baselines.
A Review on Near Duplicate Detection of Images using Computer Vision Techniques
Authors: K. K. Thyagharajan, G. Kalaiarasi
Date: 2020-09-07 00:00:00
Tasks: object-detection, Object Detection, Object Recognition

Nowadays, digital content is widespread and simply redistributable, either lawfully or unlawfully. For example, after images are posted on the internet, other web users can modify them and then repost their versions, thereby generating near-duplicate images. The presence of near-duplicates affects the performance of the search engines critically. Computer vision is concerned with the automatic extraction, analysis and understanding of useful information from digital images. The main application of computer vision is image understanding. There are several tasks in image understanding such as feature extraction, object detection, object recognition, image cleaning, image transformation, etc. There is no proper survey in literature related to near duplicate detection of images. In this paper, we review the state-of-the-art computer vision-based approaches and feature extraction methods for the detection of near duplicate images. We also discuss the main challenges in this field and how other researchers addressed those challenges. This review provides research directions to the fellow researchers who are interested to work in this field.