pwc-archive/papers-with-abstracts huggingface dataset:
TensorNetwork: A Library for Physics and Machine Learning
Authors: Chase Roberts, Ashley Milsted, Martin Ganahl, Adam Zalcman, Bruce Fontaine, Yijian Zou, Jack Hidary, Guifre Vidal, Stefan Leichenauer
Date: 2019-05-03 00:00:00
Tasks: BIG-bench Machine Learning, Tensor Networks

TensorNetwork is an open source library for implementing tensor network algorithms. Tensor networks are sparse data structures originally designed for simulating quantum many-body physics, but are currently also applied in a number of other research areas, including machine learning. We demonstrate the use of the API with applications both physics and machine learning, with details appearing in companion papers.
Meta-learners' learning dynamics are unlike learners'
Authors: Neil C. Rabinowitz
Date: 2019-05-03 00:00:00
Tasks: Meta-Learning, Multi-Armed Bandits, regression, Reinforcement Learning, Reinforcement Learning (RL)

Meta-learning is a tool that allows us to build sample-efficient learning systems. Here we show that, once meta-trained, LSTM Meta-Learners aren't just faster learners than their sample-inefficient deep learning (DL) and reinforcement learning (RL) brethren, but that they actually pursue fundamentally different learning trajectories. We study their learning dynamics on three sets of structured tasks for which the corresponding learning dynamics of DL and RL systems have been previously described: linear regression (Saxe et al., 2013), nonlinear regression (Rahaman et al., 2018; Xu et al., 2018), and contextual bandits (Schaul et al., 2019). In each case, while sample-inefficient DL and RL Learners uncover the task structure in a staggered manner, meta-trained LSTM Meta-Learners uncover almost all task structure concurrently, congruent with the patterns expected from Bayes-optimal inference algorithms. This has implications for research areas wherever the learning behaviour itself is of interest, such as safety, curriculum design, and human-in-the-loop machine learning.
TriDepth: Triangular Patch-based Deep Depth Prediction
Authors: Masaya Kaneko, Ken Sakurada, Kiyoharu Aizawa
Date: 2019-05-03 00:00:00
Tasks: 3D Scene Reconstruction, Depth Estimation, Depth Prediction, Prediction

We propose a novel and efficient representation for single-view depth estimation using Convolutional Neural Networks (CNNs). Point-cloud is generally used for CNN-based 3D scene reconstruction; however it has some drawbacks: (1) it is redundant as a representation for planar surfaces, and (2) no spatial relationships between points are available (e.g, texture and surface). As a more efficient representation, we introduce a triangular-patch-cloud, which represents the surface of the 3D structure using a set of triangular patches, and propose a CNN framework for its 3D structure estimation. In our framework, we create it by separating all the faces in a 2D mesh, which are determined adaptively from the input image, and estimate depths and normals of all the faces. Using a common RGBD-dataset, we show that our representation has a better or comparable performance than the existing point-cloud-based methods, although it has much less parameters.
On Transfer Learning For Chatter Detection in Turning Using Wavelet Packet Transform and Empirical Mode Decomposition
Authors: Melih C. Yesilli, Firas A. Khasawneh, Andreas Otto
Date: 2019-05-03 00:00:00
Tasks: General Classification, Time Series Analysis, Transfer Learning

The increasing availability of sensor data at machine tools makes automatic chatter detection algorithms a trending topic in metal cutting. Two prominent and advanced methods for feature extraction via signal decomposition are Wavelet Packet Transform (WPT) and Ensemble Empirical Mode Decomposition (EEMD). We apply these two methods to time series acquired from an acceleration sensor at the tool holder of a lathe. Different turning experiments with varying dynamic behavior of the machine tool structure were performed. We compare the performance of these two methods with Support Vector Machine (SVM), Logistic Regression, Random Forest Classification and Gradient Boosting combined with Recursive Feature Elimination (RFE). We also show that the common WPT-based approach of choosing wavelet packets with the highest energy ratios as representative features for chatter does not always result in packets that enclose the chatter frequency, thus reducing the classification accuracy. Further, we test the transfer learning capability of each of these methods by training the classifier on one of the cutting configurations and then testing it on the other cases. It is found that when training and testing on data from the same cutting configuration both methods yield high accuracies reaching in one of the cases as high as 94% and 95%, respectively, for WPT and EEMD. However, our experimental results show that EEMD can outperform WPT in transfer learning applications with accuracy of up to 95%.
Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models
Authors: Neha Soni, Enakshi Khular Sharma, Narotam Singh, Amita Kapoor
Date: 2019-05-03 00:00:00

The fast pace of artificial intelligence (AI) and automation is propelling strategists to reshape their business models. This is fostering the integration of AI in the business processes but the consequences of this adoption are underexplored and need attention. This paper focuses on the overall impact of AI on businesses - from research, innovation, market deployment to future shifts in business models. To access this overall impact, we design a three-dimensional research model, based upon the Neo-Schumpeterian economics and its three forces viz. innovation, knowledge, and entrepreneurship. The first dimension deals with research and innovation in AI. In the second dimension, we explore the influence of AI on the global market and the strategic objectives of the businesses and finally, the third dimension examines how AI is shaping business contexts. Additionally, the paper explores AI implications on actors and its dark sides.
DisplaceNet: Recognising Displaced People from Images by Exploiting Dominance Level
Authors: Grigorios Kalliatakis, Shoaib Ehsan, Maria Fasli, Klaus McDonald-Maier
Date: 2019-05-03 00:00:00
Tasks: Displaced People Recognition, Humanitarian, image-classification, Image Classification

Every year millions of men, women and children are forced to leave their homes and seek refuge from wars, human rights violations, persecution, and natural disasters. The number of forcibly displaced people came at a record rate of 44,400 every day throughout 2017, raising the cumulative total to 68.5 million at the years end, overtaken the total population of the United Kingdom. Up to 85% of the forcibly displaced find refuge in low- and middle-income countries, calling for increased humanitarian assistance worldwide. To reduce the amount of manual labour required for human-rights-related image analysis, we introduce DisplaceNet, a novel model which infers potential displaced people from images by integrating the control level of the situation and conventional convolutional neural network (CNN) classifier into one framework for image classification. Experimental results show that DisplaceNet achieves up to 4% coverage-the proportion of a data set for which a classifier is able to produce a prediction-gain over the sole use of a CNN classifier. Our dataset, codes and trained models will be available online at https://github.com/GKalliatakis/DisplaceNet.
Efficient Discrete Supervised Hashing for Large-scale Cross-modal Retrieval
Authors: Tao Yao, Xiangwei Kong, Lianshan Yan, Wenjing Tang, Qi Tian
Date: 2019-05-03 00:00:00
Tasks: Cross-Modal Retrieval, Quantization, Retrieval

Supervised cross-modal hashing has gained increasing research interest on large-scale retrieval task owning to its satisfactory performance and efficiency. However, it still has some challenging issues to be further studied: 1) most of them fail to well preserve the semantic correlations in hash codes because of the large heterogenous gap; 2) most of them relax the discrete constraint on hash codes, leading to large quantization error and consequent low performance; 3) most of them suffer from relatively high memory cost and computational complexity during training procedure, which makes them unscalable. In this paper, to address above issues, we propose a supervised cross-modal hashing method based on matrix factorization dubbed Efficient Discrete Supervised Hashing (EDSH). Specifically, collective matrix factorization on heterogenous features and semantic embedding with class labels are seamlessly integrated to learn hash codes. Therefore, the feature based similarities and semantic correlations can be both preserved in hash codes, which makes the learned hash codes more discriminative. Then an efficient discrete optimal algorithm is proposed to handle the scalable issue. Instead of learning hash codes bit-by-bit, hash codes matrix can be obtained directly which is more efficient. Extensive experimental results on three public real-world datasets demonstrate that EDSH produces a superior performance in both accuracy and scalability over some existing cross-modal hashing methods.
Question Relatedness on Stack Overflow: The Task, Dataset, and Corpus-inspired Models
Authors: Amirreza Shirani, Bowen Xu, David Lo, Thamar Solorio, Amin Alipour
Date: 2019-05-03 00:00:00
Tasks: Community Question Answering, Multi-class Classification, Question Answering

Domain-specific community question answering is becoming an integral part of professions. Finding related questions and answers in these communities can significantly improve the effectiveness and efficiency of information seeking. Stack Overflow is one of the most popular communities that is being used by millions of programmers. In this paper, we analyze the problem of predicting knowledge unit (question thread) relatedness in Stack Overflow. In particular, we formulate the question relatedness task as a multi-class classification problem with four degrees of relatedness. We present a large-scale dataset with more than 300K pairs. To the best of our knowledge, this dataset is the largest domain-specific dataset for Question-Question relatedness. We present the steps that we took to collect, clean, process, and assure the quality of the dataset. The proposed dataset Stack Overflow is a useful resource to develop novel solutions, specifically data-hungry neural network models, for the prediction of relatedness in technical community question-answering forums. We adopt a neural network architecture and a traditional model for this task that effectively utilize information from different parts of knowledge units to compute the relatedness between them. These models can be used to benchmark novel models, as they perform well in our task and in a closely similar task.
Autonomous Air Traffic Controller: A Deep Multi-Agent Reinforcement Learning Approach
Authors: Marc Brittain, Peng Wei
Date: 2019-05-02 00:00:00
Tasks: Decision Making, Multi-agent Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Air traffic control is a real-time safety-critical decision making process in highly dynamic and stochastic environments. In today's aviation practice, a human air traffic controller monitors and directs many aircraft flying through its designated airspace sector. With the fast growing air traffic complexity in traditional (commercial airliners) and low-altitude (drones and eVTOL aircraft) airspace, an autonomous air traffic control system is needed to accommodate high density air traffic and ensure safe separation between aircraft. We propose a deep multi-agent reinforcement learning framework that is able to identify and resolve conflicts between aircraft in a high-density, stochastic, and dynamic en-route sector with multiple intersections and merging points. The proposed framework utilizes an actor-critic model, A2C that incorporates the loss function from Proximal Policy Optimization (PPO) to help stabilize the learning process. In addition we use a centralized learning, decentralized execution scheme where one neural network is learned and shared by all agents in the environment. We show that our framework is both scalable and efficient for large number of incoming aircraft to achieve extremely high traffic throughput with safety guarantee. We evaluate our model via extensive simulations in the BlueSky environment. Results show that our framework is able to resolve 99.97% and 100% of all conflicts both at intersections and merging points, respectively, in extreme high-density air traffic scenarios.
Visually-aware Recommendation with Aesthetic Features
Authors: Wenhui Yu, Xiangnan He, Jian Pei, Xu Chen, Li Xiong, Jinfei Liu, Zheng Qin
Date: 2019-05-02 00:00:00
Tasks: Decision Making, Recommendation Systems, Tensor Decomposition

Visual information plays a critical role in human decision-making process. While recent developments on visually-aware recommender systems have taken the product image into account, none of them has considered the aesthetic aspect. We argue that the aesthetic factor is very important in modeling and predicting users' preferences, especially for some fashion-related domains like clothing and jewelry. This work addresses the need of modeling aesthetic information in visually-aware recommender systems. Technically speaking, we make three key contributions in leveraging deep aesthetic features: (1) To describe the aesthetics of products, we introduce the aesthetic features extracted from product images by a deep aesthetic network. We incorporate these features into recommender system to model users' preferences in the aesthetic aspect. (2) Since in clothing recommendation, time is very important for users to make decision, we design a new tensor decomposition model for implicit feedback data. The aesthetic features are then injected to the basic tensor model to capture the temporal dynamics of aesthetic preferences (e.g., seasonal patterns). (3) We also use the aesthetic features to optimize the learning strategy on implicit feedback data. We enrich the pairwise training samples by considering the similarity among items in the visual space and graph space; the key idea is that a user may likely have similar perception on similar items. We perform extensive experiments on several real-world datasets and demonstrate the usefulness of aesthetic features and the effectiveness of our proposed methods.
A multi-agent system approach in evaluating human spatio-temporal vulnerability to seismic risk using social attachment
Authors: Julius Bañgate, Julie Dugdale, Elise Beck, Carole Adam
Date: 2019-05-02 00:00:00
Tasks: Management

Social attachment theory states that individuals seek the proximity of attachment figures (e.g. family members, friends, colleagues, familiar places or objects) when faced with threat. During disasters, this means that family members may seek each other before evacuating, gather personal property before heading to familiar exits and places, or follow groups/crowds, etc. This hard-wired human tendency should be considered in the assessment of risk and the creation of disaster management plans. Doing so may result in more realistic evacuation procedures and may minimise the number of casualties and injuries. In this context, a dynamic spatio-temporal analysis of seismic risk is presented using SOLACE, a multi-agent model of pedestrian behaviour based on social attachment theory implemented using the Belief-Desire-Intention approach. The model focuses on the influence of human, social, physical and temporal factors on successful evacuation. Human factors considered include perception and mobility defined by age. Social factors are defined by attachment bonds, social groups, population distribution, and cultural norms. Physical factors refer to the location of the epicentre of the earthquake, spatial distribution/layout and attributes of environmental objects such as buildings, roads, barriers (cars), placement of safe areas, evacuation routes, and the resulting debris/damage from the earthquake. Experiments tested the influence of time of the day, presence of disabled persons and earthquake intensity. Initial results show that factors that influence arrivals in safe areas include (a) human factors (age, disability, speed), (b) pre-evacuation behaviours, (c) perception distance (social attachment, time of day), (d) social interaction during evacuation, and (e) physical and spatial aspects, such as limitations imposed by debris (damage), and the distance to safe areas. To validate the results, scenarios will be designed with stakeholders, who will also take part in the definition of a serious game. The recommendation of this research is that both social and physical aspects should be considered when defining vulnerability in the analysis of risk.
Conditioning LSTM Decoder and Bi-directional Attention Based Question Answering System
Authors: Heguang Liu
Date: 2019-05-02 00:00:00
Tasks: Decoder, Question Answering

Applying neural-networks on Question Answering has gained increasing
popularity in recent years. In this paper, I implemented a model with
Bi-directional attention flow layer, connected with a Multi-layer LSTM encoder,
connected with one start-index decoder and one conditioning end-index decoder.
I introduce a new end-index decoder layer, conditioning on start-index output.
The Experiment shows this has increased model performance by 15.16%. For
prediction, I proposed a new smart-span equation, rewarding both short answer
length and high probability in start-index and end-index, which further
improved the prediction accuracy. The best single model achieves an F1 score of
73.97% and EM score of 64.95% on test set.
AI-Powered Text Generation for Harmonious Human-Machine Interaction: Current State and Future Directions
Authors: Qiuyun Zhang, Bin Guo, Hao Wang, Yunji Liang, Shaoyang Hao, Zhiwen Yu
Date: 2019-05-01 00:00:00
Tasks: Survey, Text Generation

In the last two decades, the landscape of text generation has undergone
tremendous changes and is being reshaped by the success of deep learning. New
technologies for text generation ranging from template-based methods to neural
network-based methods emerged. Meanwhile, the research objectives have also
changed from generating smooth and coherent sentences to infusing personalized
traits to enrich the diversification of newly generated content. With the rapid
development of text generation solutions, one comprehensive survey is urgent to
summarize the achievements and track the state of the arts. In this survey
paper, we present the general systematical framework, illustrate the widely
utilized models and summarize the classic applications of text generation.
Beyond Personalization: Research Directions in Multistakeholder Recommendation
Authors: Himan Abdollahpouri, Gediminas Adomavicius, Robin Burke, Ido Guy, Dietmar Jannach, Toshihiro Kamishima, Jan Krasnodebski, Luiz Pizzato
Date: 2019-05-01 00:00:00
Tasks: Fairness, Recommendation Systems

Recommender systems are personalized information access applications; they are ubiquitous in today's online environment, and effective at finding items that meet user needs and tastes. As the reach of recommender systems has extended, it has become apparent that the single-minded focus on the user common to academic research has obscured other important aspects of recommendation outcomes. Properties such as fairness, balance, profitability, and reciprocity are not captured by typical metrics for recommender system evaluation. The concept of multistakeholder recommendation has emerged as a unifying framework for describing and understanding recommendation settings where the end user is not the sole focus. This article describes the origins of multistakeholder recommendation, and the landscape of system designs. It provides illustrative examples of current research, as well as outlining open questions and research directions for the field.
Clustering-Based Collaborative Filtering Using an Incentivized/Penalized User Model
Authors: Cong Tran, Jang-Young Kim, Won-Yong Shin, Sang-Wook Kim
Date: 2019-05-01 00:00:00
Tasks: Clustering, Collaborative Filtering, Recommendation Systems

Giving or recommending appropriate content based on the quality of experience
is the most important and challenging issue in recommender systems. As
collaborative filtering (CF) is one of the most prominent and popular
techniques used for recommender systems, we propose a new clustering-based CF
(CBCF) method using an incentivized/penalized user (IPU) model only with
ratings given by users, which is thus easy to implement. We aim to design such
a simple clustering-based approach with no further prior information while
improving the recommendation accuracy. To be precise, the purpose of CBCF with
the IPU model is to improve recommendation performance such as precision,
recall, and $F_1$ score by carefully exploiting different preferences among
users. Specifically, we formulate a constrained optimization problem, in which
we aim to maximize the recall (or equivalently $F_1$ score) for a given
precision. To this end, users are divided into several clusters based on the
actual rating data and Pearson correlation coefficient. Afterwards, we give
each item an incentive/penalty according to the preference tendency by users
within the same cluster. Our experimental results show a significant
performance improvement over the baseline CF scheme without clustering in terms
of recall or $F_1$ score for a given precision.
Disease Identification From Unstructured User Input
Authors: Fahim Faisal, Shafkat Ahmed Bhuiyan, Dr. Abu Raihan Mostofa Kamal
Date: 2019-05-01 00:00:00
Tasks: General Classification, Question Answering, text-classification, Text Classification

A method to identify probable diseases from the unstructured textual input (eg, health forum posts) by incorporating a lexicographic and semantic feature based two-phase text classification module and a symptom-disease correlation-based similarity measurement module. One notable aspect of my approach was to develop a competent algorithm to extract all inherent features from the data source to make a better decision.
Semi-Unsupervised Lifelong Learning for Sentiment Classification: Less Manual Data Annotation and More Self-Studying
Authors: Xianbin Hong, Gautam Pal, Sheng-Uei Guan, Prudence Wong, Dawei Liu, Ka Lok Man, Xin Huang
Date: 2019-04-30 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, Lifelong learning, Sentiment Analysis, Sentiment Classification

Lifelong machine learning is a novel machine learning paradigm which can continually accumulate knowledge during learning. The knowledge extracting and reusing abilities enable the lifelong machine learning to solve the related problems. The traditional approaches like Na\"ive Bayes and some neural network based approaches only aim to achieve the best performance upon a single task. Unlike them, the lifelong machine learning in this paper focuses on how to accumulate knowledge during learning and leverage them for further tasks. Meanwhile, the demand for labelled data for training also is significantly decreased with the knowledge reusing. This paper suggests that the aim of the lifelong learning is to use less labelled data and computational cost to achieve the performance as well as or even better than the supervised learning.