pwc-archive/papers-with-abstracts huggingface dataset:
DefSLAM: Tracking and Mapping of Deforming Scenes from Monocular Sequences
Authors: Jose Lamarca, Shaifali Parashar, Adrien Bartoli, J. M. M. Montiel
Date: 2019-08-20 00:00:00

Monocular SLAM algorithms perform robustly when observing rigid scenes, however, they fail when the observed scene deforms, for example, in medical endoscopy applications. We present DefSLAM, the first monocular SLAM capable of operating in deforming scenes in real-time. Our approach intertwines Shape-from-Template (SfT) and Non-Rigid Structure-from-Motion (NRSfM) techniques to deal with the exploratory sequences typical of SLAM. A deformation tracking thread recovers the pose of the camera and the deformation of the observed map, at frame rate, by means of SfT processing a template that models the scene shape-at-rest. A deformation mapping thread runs in parallel with the tracking to update the template, at keyframe rate, by means of an isometric NRSfM processing a batch of full perspective keyframes. In our experiments, DefSLAM processes close-up sequences of deforming scenes, both in a laboratory controlled experiment and in medical endoscopy sequences, producing accurate 3D models of the scene with respect to the moving camera.
Human activity recognition from skeleton poses
Authors: Frederico Belmonte Klein, Angelo Cangelosi
Date: 2019-08-20 00:00:00
Tasks: Action Recognition, Activity Recognition, Human Activity Recognition, Temporal Action Localization

Human Action Recognition is an important task of Human Robot Interaction as cooperation between robots and humans requires that artificial agents recognise complex cues from the environment. A promising approach is using trained classifiers to recognise human actions through sequences of skeleton poses extracted from images or RGB-D data from a sensor. However, with many different data-sets focused on slightly different sets of actions and different algorithms it is not clear which strategy produces highest accuracy for indoor activities performed in a home environment. This work discussed, tested and compared classic algorithms, namely, support vector machines and k-nearest neighbours, to 2 similar hierarchical neural gas approaches, the growing when required neural gas and the growing neural gas.
A Lost Croatian Cybernetic Machine Translation Program
Authors: Sandro Skansi, Leo Mršić, Ines Skelac
Date: 2019-08-20 00:00:00
Tasks: Machine Translation, Translation

We are exploring the historical significance of research in the field of machine translation conducted by Bulcsu Laszlo, Croatian linguist, who was a pioneer in machine translation in Yugoslavia during the 1950s. We are focused on two important seminal papers written by members of his research group from 1959 and 1962, as well as their legacy in establishing a Croatian machine translation program based around the Faculty of Humanities and Social Sciences of the University of Zagreb in the late 1950s and early 1960s. We are exploring their work in connection with the beginnings of machine translation in the USA and USSR, motivated by the Cold War and the intelligence needs of the period. We also present the approach to machine translation advocated by the Croatian group in Yugoslavia, which is different from the usual logical approaches of the period, and his advocacy of cybernetic methods, which would be adopted as a canon by the mainstream AI community only decades later.
Cross-Enhancement Transform Two-Stream 3D ConvNets for Action Recognition
Authors: Dong Cao, Lisha Xu, Dong-dong Zhang
Date: 2019-08-19 00:00:00
Tasks: Action Recognition, Autonomous Driving, Autonomous Vehicles, Optical Flow Estimation, Transfer Learning, Vocal Bursts Valence Prediction

Action recognition is an important research topic in computer vision. It is the basic work for visual understanding and has been applied in many fields. Since human actions can vary in different environments, it is difficult to infer actions in completely different states with a same structural model. For this case, we propose a Cross-Enhancement Transform Two-Stream 3D ConvNets algorithm, which considers the action distribution characteristics on the specific dataset. As a teaching model, stream with better performance in both streams is expected to assist in training another stream. In this way, the enhanced-trained stream and teacher stream are combined to infer actions. We implement experiments on the video datasets UCF-101, HMDB-51, and Kinetics-400, and the results confirm the effectiveness of our algorithm.
Estimation of Energy Prices in Turkey in the Nash-Cournot Framework
Authors: Prof. Dr. Rengin Ak; Assist. Prof. Dr. Armağan Türk; Assoc. Prof. Dr. Hasan İslatince
Date: 2019-07-30 00:00:00
Proceeding: International Journal of Business and Applied Social Science 2019 7

Increase of mass production with the industrial revolution has increased the country's dependence on energy. Even today, energy constitutes the main weight of industrialization. Especially for countries dependent on energy from abroad, energy prices are very important, and fluctuations in energy prices are decisive for the economies of these countries. Fluctuations in energy prices make the future course of energy prices important for both energy demanding countries and energy-supplying countries. There are many methods in the related literature. Our estimate of energy prices for Turkey will be based on the Nash-Cournot framework.

In the Nash-Cournot framework, energy is considered as a homogeneous commodity and market equilibrium is determined by the capacity-setting decisions of the suppliers. The model indicates that competitors aim to produce more by reacting to higher prices. A fundamental offer-based stochastic model is being put forward in order to predict the energy prices and the mean price in a given period. Two ambiguous sources are addressed, these are energy producers and demand. Studies have shown that the expected number of prices increases significantly with the decrease in the number of firms in the market. Within this framework, factors determining the energy prices in Turkey will be determined and forecasts for the future will be made.
Entrepreneurial Growth: Challenges to Young Omani Entrepreneurs
Authors: Dr. Subrahmanian Muthuraman;Dr. Mohammed Al Haziazi
Date: 2019-07-30 00:00:00
Proceeding: International Journal of Business and Applied Social Science 2019 7
Tasks: Cultural Vocal Bursts Intensity Prediction, Descriptive

Entrepreneurship plays an important role in economic prosperity and social stability in many developed countries. Entrepreneurship has been adopted as a strategy to promote economic activities among young people. There is a growing interest in understanding the various challenges of youth entrepreneurship. The main purpose of this study was to assess the challenges in entrepreneurial growth of young entrepreneurs in the Sultanate of Oman. This research was set out to investigate the obstacles that young people encounter when setting up their businesses; the current obstacles that prevent the expansion of their entrepreneurial ventures; as well as the prospects for youth entrepreneurship development in this community. The study employed a descriptive survey research type and used convenience sampling technique to collect data. A standardized questionnaire was used as an instrument to collect data to establish the perceptions of 52 young Omani entrepreneurs. This paper is significant in that it brings insights on challenges for entrepreneurship in Oman. The importance of stimulating the entrepreneurial spirit, values, and attitudes of young people and encouraging innovative business start-ups while fostering a more entrepreneur-friendly culture must be translated into actual and effective policy actions in Oman. We consider that supporting youth entrepreneurship must be an Oman’s priority.
The Private Income Tax Shock Premium
Authors: Zornitsa Todorova
Date: 2019-07-30 00:00:00
Proceeding: International Journal of Business and Applied Social Science 2019 7

This paper investigates the asset pricing implications of tax policy changes. News about tax cuts decreases future tax revenues and increases future consumer demand and output. Using cross-sectional variation in industry exposure to structurally identified tax news, I develop a factor mimicking private income tax shocks. I construct an investment strategy, which generates annualized risk-adjusted returns of 5.16 % over the Fama-French 3-factor model. I rationalize the finding by arguing that firms with more elastic demands bear higher consumption risk, which works through a wealth effect.
The Relationship between Foreign Portfolio Investment, Foreign Direct Investment and Economic Performance of Nigerian Economy: (1980-2017): An Empirical Analysis
Authors: Ekine, D.I, Ewubare, Dennis Brown, Ajie, Charity
Date: 2019-07-30 00:00:00
Proceeding: International Journal of Business and Applied Social Science 2019 7

The study examined the impact of foreign portfolio investment and Foreign Direct Investment on the performance of the Nigerian Economy over a period of 1980-2017. The data used were purely secondary sourced from the central Bank of Nigeria statistical Bulletin and World Bank Development indicator. The ordinary least square (OLS) regression analysis was used. The findings revealed that the performance of the Nigerian Economy is directly related to inflow of foreign portfolio investment and foreign direct investment and it is also statistically significant at 5% level. This means that a good performance of the economy depends on the inflow of these variables, or that the variables serve as an engine of economic growth. The study therefore recommends that policy makers should work on improvement of economic incentives capable of mobilizing external resources to the country to engender macroeconomic stability. A stable economy will attract foreign investment and this result to increased inflow of foreign capital.
Compensation Practice and Job Satisfaction in Selected Consumer Goods in Lagos state Nigeria
Authors: Egbuta, Olive U. Ph.D.
Date: 2019-07-30 00:00:00
Proceeding: International Journal of Business and Applied Social Science 2019 7

Job satisfaction in work organization influences the general efficiency and effectiveness of the whole employees. This is usually the results of the compensation practices that predominate in a firm. This paper examined the Compensation practices and how it affects the job satisfaction of employees of selected consumer goods firms in Lagos State, Nigeria. The paper adopted the survey research design through quantitative research approach. Primary data was used through administration of 300 copies of questionnaires to employees in the selected consumer goods firms. Findings reveal that Compensation practice has a positive and significant effect on Job satisfaction (R = 0.296, Adj. R2 =0.071, p < 0.05, F =11.497). It was found that compensation practice significantly affects job satisfaction of employees of selected consumer goods firms in Nigeria. The paper suggests that managers should always think about what compensation packages really motivate and excite their employees.
The Relation Between Infrastructure Quality and Government Effectiveness in Egypt.
Authors: Mustafa Elnemr
Date: 2019-07-30 00:00:00
Proceeding: International Journal of Business and Applied Social Science 2019 7
Tasks: Abstractive Text Summarization, Relation

The article examines the relationship between infrastructure quality and government effectiveness in Egypt. The hypothesis is that public private partnership can help Egypt increase its infrastructure efficiency and lower burden in government budget.

The paper conclude that Egypt have a considerable opportunity to finance its infrastructure investment gap through private investment. Public-Private partnership could be adopted as it provides the fastest gains in efficiency.
6D-VNet: End-to-end 6DoF Vehicle Pose Estimation from Monocular RGB Images
Authors: Di Wu, Zhaoyong Zhuang, Canqun Xiang, Wenbin Zou and Xia Li
Date: 2019-06-15 00:00:00
Proceeding: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2019 2019 6
Tasks: Autonomous Driving, Pose Estimation, Translation, Vehicle Pose Estimation

We present a conceptually simple framework for 6DoF object pose estimation, especially for autonomous driving scenario. Our approach efficiently detects traffic partic- ipants in a monocular RGB image while simultaneously regressing their 3D translation and rotation vectors. The method, called 6D-VNet, extends Mask R-CNN by adding customised heads for predicting vehicle s finer class, ro- tation and translation. The proposed 6D-VNet is trained end-to-end compared to previous methods. Furthermore, we show that the inclusion of translational regression in the joint losses is crucial for the 6DoF pose estimation task, where object translation distance along longitudinal axis varies significantly, e.g., in autonomous driving sce- narios. Additionally, we incorporate the mutual informa- tion between traffic participants via a modified non-local block. As opposed to the original non-local block imple- mentation, the proposed weighting modification takes the spatial neighbouring information into consideration whilst counteracting the effect of extreme gradient values. Our 6D-VNet reaches the 1 st place in ApolloScape challenge 3D Car Instance task1 [21]. Code has been made available at: https://github.com/stevenwudi/6DVNET.
Incorporating Interpretability into Latent Factor Models via Fast Influence Analysis
Authors: Weiyu Cheng, Yanyan Shen, Linpeng Huang, Yanmin Zhu
Date: 2019-08-04 00:00:00
Proceeding: KDD 2019 2019 8
Tasks: Collaborative Filtering

Latent factor models (LFMs) such as matrix factorization have achieved the state-of-the-art performance among various collaborative filtering approaches for recommendation. Despite the high recommendation accuracy of LFMs, a critical issue to be resolved is their lack of interpretability. Extensive efforts have been devoted to interpreting the prediction results of LFMs. However, they either rely on auxiliary information which may not be available in practice, or sacrifice recommendation accuracy for interpretability. Influence functions, stemming from robust statistics, have been developed to understand the effect of training points on the predictions of black-box models. Inspired by this, we propose a novel explanation method named FIA (Fast Influence Analysis) to understand the prediction of trained LFMs by tracing back to the training data with influence functions. We present how to employ influence functions to measure the impact of historical user-item interactions on the prediction results of LFMs and provide intuitive neighbor-style explanations based on the most influential interactions. Our proposed FIA exploits the characteristics of two important LFMs, matrix factorization and neural collaborative filtering, and is capable of accelerating the overall influence analysis process. We provide a detailed complexity analysis for FIA over LFMs and conduct extensive experiments to evaluate its performance using real-world datasets. The results demonstrate the effectiveness and efficiency of FIA, and the usefulness of the generated explanations for the recommendation results.
Knowledgeable Storyteller: A Commonsense-Driven Generative Model for Visual Storytelling
Authors: Pengcheng Yang, Fuli Luo, Peng Chen, Lei LI, Zhiyi Yin, Xiaodong He, Xu sun
Date: 2019-05-04 00:00:00
Proceeding: IJCAI 2019 2019 5
Tasks: AI Agent, Knowledge Graphs, Semantic Similarity, Semantic Textual Similarity, Text Generation, Visual Storytelling

The visual storytelling (VST) task aims at generating a reasonable and coherent paragraph-level story with the image stream as input. Different from caption that is a direct and literal description of image content, the story in the VST task tends to contain plenty of imaginary concepts that do not appear in the image. This requires the AI agent to reason and associate with the imaginary concepts based on implicit commonsense knowledge to generate a reasonable story describing the image stream. Therefore, in this work, we present a commonsensedriven generative model, which aims to introduce crucial commonsense from the external knowledge base for visual storytelling. Our approach first extracts a set of candidate knowledge graphs from the knowledge base. Then, an elaborately designed vision-aware directional encoding schema is adopted to effectively integrate the most informative commonsense. Besides, we strive to maximize the semantic similarity within the output during decoding to enhance the coherence of the generated text. Results show that our approach can outperform the state-of-the-art systems by a large margin, which achieves a 29% relative improvement of CIDEr score. With additional commonsense and semantic-relevance based objective, the generated stories are more diverse and coherent.
Jointly Learning Convolutional Representations to Compress Radiological Images and Classify Thoracic Diseases in the Compressed Domain
Authors: Ekagra Ranjan, Soumava Paul, Siddharth Kapoor, Aupendu Kar, Ramanathan Sethuraman, Debdoot Sheet
Date: 2018-12-18 00:00:00
Proceeding: ICVGIP 2018 2018 12
Tasks: Classification, General Classification, Pneumonia Detection, Thoracic Disease Classification

Deep learning models trained in natural images are commonly used for different classification tasks in the medical domain. Generally, very high dimensional medical images are down-sampled by us- ing interpolation techniques before feeding them to deep learning models that are ImageNet compliant and accept only low-resolution images of size 224 × 224 px. This popular technique may lead to the loss of key information thus hampering the classification. Signifi- cant pathological features in medical images typically being small sized and highly affected. To combat this problem, we introduce a convolutional neural network (CNN) based classification approach which learns to reduce the resolution of the image using an autoen- coder and at the same time classify it using another network, while both the tasks are trained jointly. This algorithm guides the model to learn essential representations from high-resolution images for classification along with reconstruction. We have used the publicly available dataset of chest x-rays to evaluate this approach and have outperformed state-of-the-art on test data. Besides, we have experi- mented with the effects of different augmentation approaches in this dataset and report baselines using some well known ImageNet class of CNNs.
Mining Electronic Health Records: A Survey
Authors: Pranjul Yadav, Michael Steinbach, Vipin Kumar, Gyorgy Simon
Date: 2017-02-09 00:00:00
Tasks: Management, Prognosis, Survey

The continuously increasing cost of the US healthcare system has received significant attention. Central to the ideas aimed at curbing this trend is the use of technology, in the form of the mandate to implement electronic health records (EHRs). EHRs consist of patient information such as demographics, medications, laboratory test results, diagnosis codes and procedures. Mining EHRs could lead to improvement in patient health management as EHRs contain detailed information related to disease prognosis for large patient populations. In this manuscript, we provide a structured and comprehensive overview of data mining techniques for modeling EHR data. We first provide a detailed understanding of the major application areas to which EHR mining has been applied and then discuss the nature of EHR data and its accompanying challenges. Next, we describe major approaches used for EHR mining, the metrics associated with EHRs, and the various study designs. With this foundation, we then provide a systematic and methodological organization of existing data mining techniques used to model EHRs and discuss ideas for future research. We conclude this survey with a comprehensive summary of clinical data mining applications of EHR data, as illustrated in the online supplement.
Scalable Modeling of Spatiotemporal Data using the Variational Autoencoder: an Application in Glaucoma
Authors: Samuel I. Berchuck, Felipe A. Medeiros, Sayan Mukherjee
Date: 2019-08-24 00:00:00
Tasks: Bayesian Inference

As big spatial data becomes increasingly prevalent, classical spatiotemporal (ST) methods often do not scale well. While methods have been developed to account for high-dimensional spatial objects, the setting where there are exceedingly large samples of spatial observations has had less attention. The variational autoencoder (VAE), an unsupervised generative model based on deep learning and approximate Bayesian inference, fills this void using a latent variable specification that is inferred jointly across the large number of samples. In this manuscript, we compare the performance of the VAE with a more classical ST method when analyzing longitudinal visual fields from a large cohort of patients in a prospective glaucoma study. Through simulation and a case study, we demonstrate that the VAE is a scalable method for analyzing ST data, when the goal is to obtain accurate predictions. R code to implement the VAE can be found on GitHub: https://github.com/berchuck/vaeST.
BERT for Coreference Resolution: Baselines and Analysis
Authors: Mandar Joshi, Omer Levy, Daniel S. Weld, Luke Zettlemoyer
Date: 2019-08-24 00:00:00
Proceeding: IJCNLP 2019 11
Tasks: Coreference Resolution

We apply BERT to coreference resolution, achieving strong improvements on the OntoNotes (+3.9 F1) and GAP (+11.5 F1) benchmarks. A qualitative analysis of model predictions indicates that, compared to ELMo and BERT-base, BERT-large is particularly better at distinguishing between related but distinct entities (e.g., President and CEO). However, there is still room for improvement in modeling document-level context, conversations, and mention paraphrasing. Our code and models are publicly available.
Class-balanced Grouping and Sampling for Point Cloud 3D Object Detection
Authors: Benjin Zhu, Zhengkai Jiang, Xiangxin Zhou, Zeming Li, Gang Yu
Date: 2019-08-26 00:00:00
Tasks: 3D Object Detection, Autonomous Driving, object-detection, Object Detection

This report presents our method which wins the nuScenes3D Detection Challenge [17] held in Workshop on Autonomous Driving(WAD, CVPR 2019). Generally, we utilize sparse 3D convolution to extract rich semantic features, which are then fed into a class-balanced multi-head network to perform 3D object detection. To handle the severe class imbalance problem inherent in the autonomous driving scenarios, we design a class-balanced sampling and augmentation strategy to generate a more balanced data distribution. Furthermore, we propose a balanced group-ing head to boost the performance for the categories withsimilar shapes. Based on the Challenge results, our methodoutperforms the PointPillars [14] baseline by a large mar-gin across all metrics, achieving state-of-the-art detection performance on the nuScenes dataset. Code will be released at CBGS.