pwc-archive/papers-with-abstracts huggingface dataset:
A Variational Auto-Encoder for Reservoir Monitoring
Authors: Kristian Gundersen, Seyyed A. Hosseini, Anna Oleynik, Guttorm Alendal
Date: 2020-09-23 00:00:00
Tasks: Deep Learning

Carbon dioxide Capture and Storage (CCS) is an important strategy in mitigating anthropogenic CO$_2$ emissions. In order for CCS to be successful, large quantities of CO$_2$ must be stored and the storage site conformance must be monitored. Here we present a deep learning method to reconstruct pressure fields and classify the flux out of the storage formation based on the pressure data from Above Zone Monitoring Interval (AZMI) wells. The deep learning method is a version of a semi conditional variational auto-encoder tailored to solve two tasks: reconstruction of an incremental pressure field and leakage rate classification. The method, predictions and associated uncertainty estimates are illustrated on the synthetic data from a high-fidelity heterogeneous 2D numerical reservoir model, which was used to simulate subsurface CO$_2$ movement and pressure changes in the AZMI due to a CO$_2$ leakage.
Eye Movement Feature Classification for Soccer Goalkeeper Expertise Identification in Virtual Reality
Authors: Benedikt Hosp, Florian Schultz, Oliver Höner, Enkelejda Kasneci
Date: 2020-09-23 00:00:00
Tasks: Decision Making, General Classification

The latest research in expertise assessment of soccer players has affirmed the importance of perceptual skills (especially for decision making) by focusing either on high experimental control or on a realistic presentation. To assess the perceptual skills of athletes in an optimized manner, we captured omnidirectional in-field scenes and showed these to 12 expert, 10 intermediate and 13 novice soccer goalkeepers on virtual reality glasses. All scenes were shown from the same natural goalkeeper perspective and ended after the return pass to the goalkeeper. Based on their gaze behavior we classified their expertise with common machine learning techniques. This pilot study shows promising results for objective classification of goalkeepers expertise based on their gaze behaviour and provided valuable insight to inform the design of training systems to enhance perceptual skills of athletes.
Cloud2Edge Elastic AI Framework for Prototyping and Deployment of AI Inference Engines in Autonomous Vehicles
Authors: Sorin Grigorescu, Tiberiu Cocias, Bogdan Trasnea, Andrea Margheri, Federico Lombardi, Leonardo Aniello
Date: 2020-09-23 00:00:00
Tasks: Autonomous Driving, Autonomous Vehicles, Edge-computing, Self-Driving Cars

Self-driving cars and autonomous vehicles are revolutionizing the automotive sector, shaping the future of mobility altogether. Although the integration of novel technologies such as Artificial Intelligence (AI) and Cloud/Edge computing provides golden opportunities to improve autonomous driving applications, there is the need to modernize accordingly the whole prototyping and deployment cycle of AI components. This paper proposes a novel framework for developing so-called AI Inference Engines for autonomous driving applications based on deep learning modules, where training tasks are deployed elastically over both Cloud and Edge resources, with the purpose of reducing the required network bandwidth, as well as mitigating privacy issues. Based on our proposed data driven V-Model, we introduce a simple yet elegant solution for the AI components development cycle, where prototyping takes place in the cloud according to the Software-in-the-Loop (SiL) paradigm, while deployment and evaluation on the target ECUs (Electronic Control Units) is performed as Hardware-in-the-Loop (HiL) testing. The effectiveness of the proposed framework is demonstrated using two real-world use-cases of AI inference engines for autonomous vehicles, that is environment perception and most probable path prediction.
The Next Era of American Law Amid the Advent of Autonomous AI Legal Reasoning
Authors: Lance Eliot
Date: 2020-09-21 00:00:00
Tasks: Legal Reasoning, Open-Ended Question Answering

Legal scholars have postulated that there have been three eras of American law to-date, consisting in chronological order of the initial Age of Discovery, the Age of Faith, and then the Age of Anxiety. An open question that has received erudite attention in legal studies is what the next era, the fourth era, might consist of, and for which various proposals exist including examples such as the Age of Consent, the Age of Information, etc. There is no consensus in the literature as yet on what the fourth era is, and nor whether the fourth era has already begun or will instead emerge in the future. This paper examines the potential era-elucidating impacts amid the advent of autonomous Artificial Intelligence Legal Reasoning (AILR), entailing whether such AILR will be an element of a fourth era or a driver of a fourth, fifth, or perhaps the sixth era of American law. Also, a set of meta-characteristics about the means of identifying a legal era changeover are introduced, along with an innovative discussion of the role entailing legal formalism versus legal realism in the emergence of the American law eras.
Heuristics based Mosaic of Social-Sensor Services for Scene Reconstruction
Authors: Tooba Aamir, Hai Dong, Athman Bouguettaya
Date: 2020-09-21 00:00:00
Tasks: Service Composition

We propose a heuristics-based social-sensor cloud service selection and composition model to reconstruct mosaic scenes. The proposed approach leverages crowdsourced social media images to create an image mosaic to reconstruct a scene at a designated location and an interval of time. The novel approach relies on the set of features defined on the bases of the image metadata to determine the relevance and composability of services. Novel heuristics are developed to filter out non-relevant services. Multiple machine learning strategies are employed to produce smooth service composition resulting in a mosaic of relevant images indexed by geolocation and time. The preliminary analytical results prove the feasibility of the proposed composition model.
Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models
Authors: Sidra Mehtab, Jaydip Sen, Abhishek Dutta
Date: 2020-09-20 00:00:00
Tasks: BIG-bench Machine Learning, regression, Stock Price Prediction, Time Series Analysis

Prediction of stock prices has been an important area of research for a long time. While supporters of the efficient market hypothesis believe that it is impossible to predict stock prices accurately, there are formal propositions demonstrating that accurate modeling and designing of appropriate variables may lead to models using which stock prices and stock price movement patterns can be very accurately predicted. In this work, we propose an approach of hybrid modeling for stock price prediction building different machine learning and deep learning-based models. For the purpose of our study, we have used NIFTY 50 index values of the National Stock Exchange (NSE) of India, during the period December 29, 2014 till July 31, 2020. We have built eight regression models using the training data that consisted of NIFTY 50 index records during December 29, 2014 till December 28, 2018. Using these regression models, we predicted the open values of NIFTY 50 for the period December 31, 2018 till July 31, 2020. We, then, augment the predictive power of our forecasting framework by building four deep learning-based regression models using long-and short-term memory (LSTM) networks with a novel approach of walk-forward validation. We exploit the power of LSTM regression models in forecasting the future NIFTY 50 open values using four different models that differ in their architecture and in the structure of their input data. Extensive results are presented on various metrics for the all the regression models. The results clearly indicate that the LSTM-based univariate model that uses one-week prior data as input for predicting the next week open value of the NIFTY 50 time series is the most accurate model.
Gated Res2Net for Multivariate Time Series Analysis
Authors: Chao Yang, Mingxing Jiang, Zhongwen Guo, Yu-An Liu
Date: 2020-09-19 00:00:00
Tasks: Time Series, Time Series Analysis

Multivariate time series analysis is an important problem in data mining because of its widespread applications. With the increase of time series data available for training, implementing deep neural networks in the field of time series analysis is becoming common. Res2Net, a recently proposed backbone, can further improve the state-of-the-art networks as it improves the multi-scale representation ability through connecting different groups of filters. However, Res2Net ignores the correlations of the feature maps and lacks the control on the information interaction process. To address that problem, in this paper, we propose a backbone convolutional neural network based on the thought of gated mechanism and Res2Net, namely Gated Res2Net (GRes2Net), for multivariate time series analysis. The hierarchical residual-like connections are influenced by gates whose values are calculated based on the original feature maps, the previous output feature maps and the next input feature maps thus considering the correlations between the feature maps more effectively. Through the utilization of gated mechanism, the network can control the process of information sending hence can better capture and utilize the both the temporal information and the correlations between the feature maps. We evaluate the GRes2Net on four multivariate time series datasets including two classification datasets and two forecasting datasets. The results demonstrate that GRes2Net have better performances over the state-of-the-art methods thus indicating the superiority
Compressed imitation learning
Authors: Nathan Zhao, Beicheng Lou
Date: 2020-09-18 00:00:00
Tasks: compressed sensing, Imitation Learning

In analogy to compressed sensing, which allows sample-efficient signal reconstruction given prior knowledge of its sparsity in frequency domain, we propose to utilize policy simplicity (Occam's Razor) as a prior to enable sample-efficient imitation learning. We first demonstrated the feasibility of this scheme on linear case where state-value function can be sampled directly. We also extended the scheme to scenarios where only actions are visible and scenarios where the policy is obtained from nonlinear network. The method is benchmarked against behavior cloning and results in significantly higher scores with limited expert demonstrations.
Principles and Practice of Explainable Machine Learning
Authors: Vaishak Belle, Ioannis Papantonis
Date: 2020-09-18 00:00:00
Tasks: BIG-bench Machine Learning

Artificial intelligence (AI) provides many opportunities to improve private and public life. Discovering patterns and structures in large troves of data in an automated manner is a core component of data science, and currently drives applications in diverse areas such as computational biology, law and finance. However, such a highly positive impact is coupled with significant challenges: how do we understand the decisions suggested by these systems in order that we can trust them? In this report, we focus specifically on data-driven methods -- machine learning (ML) and pattern recognition models in particular -- so as to survey and distill the results and observations from the literature. The purpose of this report can be especially appreciated by noting that ML models are increasingly deployed in a wide range of businesses. However, with the increasing prevalence and complexity of methods, business stakeholders in the very least have a growing number of concerns about the drawbacks of models, data-specific biases, and so on. Analogously, data science practitioners are often not aware about approaches emerging from the academic literature, or may struggle to appreciate the differences between different methods, so end up using industry standards such as SHAP. Here, we have undertaken a survey to help industry practitioners (but also data scientists more broadly) understand the field of explainable machine learning better and apply the right tools. Our latter sections build a narrative around a putative data scientist, and discuss how she might go about explaining her models by asking the right questions.
CNNPruner: Pruning Convolutional Neural Networks with Visual Analytics
Authors: Guan Li, Junpeng Wang, Han-Wei Shen, Kaixin Chen, Guihua Shan, Zhonghua Lu
Date: 2020-09-08 00:00:00

Convolutional neural networks (CNNs) have demonstrated extraordinarily good performance in many computer vision tasks. The increasing size of CNN models, however, prevents them from being widely deployed to devices with limited computational resources, e.g., mobile/embedded devices. The emerging topic of model pruning strives to address this problem by removing less important neurons and fine-tuning the pruned networks to minimize the accuracy loss. Nevertheless, existing automated pruning solutions often rely on a numerical threshold of the pruning criteria, lacking the flexibility to optimally balance the trade-off between model size and accuracy. Moreover, the complicated interplay between the stages of neuron pruning and model fine-tuning makes this process opaque, and therefore becomes difficult to optimize. In this paper, we address these challenges through a visual analytics approach, named CNNPruner. It considers the importance of convolutional filters through both instability and sensitivity, and allows users to interactively create pruning plans according to a desired goal on model size or accuracy. Also, CNNPruner integrates state-of-the-art filter visualization techniques to help users understand the roles that different filters played and refine their pruning plans. Through comprehensive case studies on CNNs with real-world sizes, we validate the effectiveness of CNNPruner.
A Deep Neural Network Tool for Automatic Segmentation of Human Body Parts in Natural Scenes
Authors: Patrick McClure, Gabrielle Reimann, Michal Ramot, Francisco Pereira
Date: 2020-09-08 00:00:00

This short article describes a deep neural network trained to perform automatic segmentation of human body parts in natural scenes. More specifically, we trained a Bayesian SegNet with concrete dropout on the Pascal-Parts dataset to predict whether each pixel in a given frame was part of a person's hair, head, ear, eyebrows, legs, arms, mouth, neck, nose, or torso.
3D Pictorial Structures Revisited: Multiple Human Pose Estimation
Authors: Vasileios Belagiannis, Sikandar Amin, Mykhaylo Andriluka, Bernt Schiele, Nassir Navab, Slobodan Ilic
Date: 2016-10-01 00:00:00
Tasks: 3D Multi-Person Pose Estimation, 3D Pose Estimation, Pose Estimation

We address the problem of 3D pose estimation of multiple humans from multiple views. The transition from single to multiple human pose estimation and from the 2D to 3D space is challenging due to a much larger state space, occlusions and across-view ambiguities when not knowing the identity of the humans in advance. To address these problems, we first create a reduced state space by triangulation of corresponding pairs of body parts obtained by part detectors for each camera view. In order to resolve ambiguities of wrong and mixed parts of multiple humans after triangulation and also those coming from false positive detections, we introduce a 3D pictorial structures (3DPS) model. Our model builds on multi-view unary potentials, while a prior model is integrated into pairwise and ternary potential functions. To balance the potentials' influence, the model parameters are learnt using a Structured SVM (SSVM). The model is generic and applicable to both single and multiple human pose estimation. To evaluate our model on single and multiple human pose estimation, we rely on four different datasets. We first analyse the contribution of the potentials and then compare our results with related work where we demonstrate superior performance.
Multiple human 3d pose estimation from multiview images
Authors: Sara Ershadi-Nasab, Erfan Noury, Shohreh Kasaei, Esmaeil Sanaei
Date: 2017-09-04 00:00:00
Tasks: 3D Multi-Person Pose Estimation, 3D Pose Estimation, Pose Estimation

Multiple human 3D pose estimation is a challenging task. It is mainly because of large variations in the scale and pose of humans, fast motions, multiple persons in the scene, and arbitrary number of visible body parts due to occlusion or truncation. Some of these ambiguities can be resolved by using multiview images. This is due to the fact that more evidences of body parts would be available in multiple views. In this work, a novel method for multiple human 3D pose estimation using evidences in multiview images is proposed. The proposed method utilizes a fully connected pairwise conditional random field that contains two types of pairwise terms. The first pairwise term encodes the spatial dependencies among human body joints based on an articulated human body configuration. The second pairwise term is based on the output of a 2D deep part detector. An approximate inference is then performed using the loopy belief propagation algorithm. The proposed method is evaluated on the Campus, Shelf, Utrecht Multi-Person Motion benchmark, Human3.6M, KTH Football II, and MPII Cooking datasets. Experimental results indicate that the proposed method achieves substantial improvements over the existing state-of-the-art methods in terms of the probability of correct pose and the mean per joint position error performance measures.
Quasi-Succinct Indices
Authors: Sebastiano Vigna
Date: 2012-06-19 00:00:00

Compressed inverted indices in use today are based on the idea of gap compression: documents pointers are stored in increasing order, and the gaps between successive document pointers are stored using suitable codes which represent smaller gaps using less bits. Additional data such as counts and positions is stored using similar techniques. A large body of research has been built in the last 30 years around gap compression, including theoretical modeling of the gap distribution, specialized instantaneous codes suitable for gap encoding, and ad hoc document reorderings which increase the efficiency of instantaneous codes. This paper proposes to represent an index using a different architecture based on quasi-succinct representation of monotone sequences. We show that, besides being theoretically elegant and simple, the new index provides expected constant-time operations and, in practice, significant performance improvements on conjunctive, phrasal and proximity queries.
Identifying Highly Correlated Stocks Using the Last Few Principal Components
Authors: Libin Yang, William Rea, and Alethea Rea
Date: 2015-12-11 00:00:00

We show that the last few components in principal component analysis of the correlation matrix of a group of stocks may contain useful financial information by identifying highly correlated pairs or larger groups of stocks. The results of this type of analysis can easily be included in the information an investor uses to manage their portfolio.
Network Together: Node Classification via Cross-Network Deep Network Embedding
Authors: Xiao Shen, Quanyu Dai, Sitong Mao, Fu-Lai Chung, Kup-Sze Choi
Date: 2020-06-04 00:00:00
Tasks: Domain Adaptation, General Classification, Network Embedding, Node Classification

Network embedding is a highly effective method to learn low-dimensional node vector representations with original network structures being well preserved. However, existing network embedding algorithms are mostly developed for a single network, which fail to learn generalized feature representations across different networks. In this paper, we study a cross-network node classification problem, which aims at leveraging the abundant labeled information from a source network to help classify the unlabeled nodes in a target network. To succeed in such a task, transferable features should be learned for nodes across different networks. To this end, a novel cross-network deep network embedding (CDNE) model is proposed to incorporate domain adaptation into deep network embedding so as to learn label-discriminative and network-invariant node vector representations. On one hand, CDNE leverages network structures to capture the proximities between nodes within a network, by mapping more strongly connected nodes to have more similar latent vector representations. On the other hand, node attributes and labels are leveraged to capture the proximities between nodes across different networks by making the same labeled nodes across networks have aligned latent vector representations. Extensive experiments have been conducted, demonstrating that the proposed CDNE model significantly outperforms the state-of-the-art network embedding algorithms in cross-network node classification.
A Hybrid Classification Approach using Topic Modeling and Graph Convolution Networks
Authors: Thoudam Doren Singh, Divyansha, Apoorva Vikram Singh, Abdullah Faiz Ur Rahman Khilji
Date: 2020-09-19 00:00:00
Tasks: Classification, General Classification, Multi Class Text Classification, Text Classification

Text classification has become a key operation in various natural language processing tasks. The efficiency of most classification algorithms predominantly confide in the quality of input features. In this work, we propose a novel multi-class text classification technique that harvests features from two distinct feature extraction methods. Firstly, a structured heterogeneous text graph built based on document-word relations and word co-occurrences is leveraged using a Graph Convolution Network (GCN). Secondly, the documents are topic modeled to use the document-topic score as features into the classification model. The concerned graph is constructed using Point-Wise Mutual Information (PMI) between pair of word co-occurrences and Term Frequency-Inverse Document Frequency (TF-IDF) score for words in the documents for word co-occurrences. Experimentation reveals that our text classification model outperforms the existing techniques for five benchmark text classification data sets.
COVID-19 Literature Topic-Based Search via Hierarchical NMF
Authors: Rachel Grotheer, Yihuan Huang, Pengyu Li, Elizaveta Rebrova, Deanna Needell, Longxiu Huang, Alona Kryshchenko, Xia Li, Kyung Ha, Oleksandr Kryshchenko
Date: 2020-09-07 00:00:00
Proceeding: EMNLP (NLP-COVID19) 2020 12
Tasks: Articles, Virology

A dataset of COVID-19-related scientific literature is compiled, combining the articles from several online libraries and selecting those with open access and full text available. Then, hierarchical nonnegative matrix factorization is used to organize literature related to the novel coronavirus into a tree structure that allows researchers to search for relevant literature based on detected topics. We discover eight major latent topics and 52 granular subtopics in the body of literature, related to vaccines, genetic structure and modeling of the disease and patient studies, as well as related diseases and virology. In order that our tool may help current researchers, an interactive website is created that organizes available literature using this hierarchical structure.