pwc-archive/papers-with-abstracts huggingface dataset:
Medi-Care AI: Predicting Medications From Billing Codes via Robust Recurrent Neural Networks
Authors: Deyin Liu, Lin Wu, Xue Li
Date: 2019-11-14 00:00:00
Tasks: Diagnostic, Missing Values

In this paper, we present an effective deep prediction framework based on robust recurrent neural networks (RNNs) to predict the likely therapeutic classes of medications a patient is taking, given a sequence of diagnostic billing codes in their record. Accurately capturing the list of medications currently taken by a given patient is extremely challenging due to undefined errors and omissions. We present a general robust framework that explicitly models the possible contamination through overtime decay mechanism on the input billing codes and noise injection into the recurrent hidden states, respectively. By doing this, billing codes are reformulated into its temporal patterns with decay rates on each medical variable, and the hidden states of RNNs are regularised by random noises which serve as dropout to improved RNNs robustness towards data variability in terms of missing values and multiple errors. The proposed method is extensively evaluated on real health care data to demonstrate its effectiveness in suggesting medication orders from contaminated values.
Short Text Classification via Term Graph
Authors: Wei Pang
Date: 2020-01-20 00:00:00
Tasks: Classification, General Classification, Sentence, text-classification, Text Classification

Short text classi cation is a method for classifying short sentence with prede ned labels. However, short text is limited in shortness in text length that leads to a challenging problem of sparse features. Most of existing methods treat each short sentences as independently and identically distributed (IID), local context only in the sentence itself is focused and the relational information between sentences are lost. To overcome these limitations, we propose a PathWalk model that combine the strength of graph networks and short sentences to solve the sparseness of short text. Experimental results on four different available datasets show that our PathWalk method achieves the state-of-the-art results, demonstrating the efficiency and robustness of graph networks for short text classification.
A Novel Approach Towards Identification of Alcohol and Drug Induced People
Authors: Joyjit Chatterjee, Anita Thakur, Vajja Mukesh
Date: 2020-01-14 00:00:00
Tasks: General Classification

The paper proposes a novel approach towards identification of alcohol and drug induced people, through the use of a wearable bracelet.As alcohol and drug induced human people are in an unconscious state of mind, they need external help from the surroundings.With proposed Bracelet system we can identify the alcohol and drug indused people and warning trigger message is sent to their care takers. There is a definite relationship between an individual's Blood Alcohol Content (BAC) and Pulse Rate to identify the alcohol or drug consumed person .This relationship of pulse rate with BAC is sensed by piezoelectric sensor and warning system is developed as a Bracelet device . The viability of the Bracelet is verified by Simulating a Database of 199 People's BAC and Pulse Rate Features and classification is done among the Alcohol Induced and Normal People. For classification,Ensemble Boosted Tree Algorithm is used which is having 81.9% accuracy in decision.
The Enron Corpus: Where the Email Bodies are Buried?
Authors: David Noever
Date: 2020-01-24 00:00:00

To probe the largest public-domain email database for indicators of fraud, we apply machine learning and accomplish four investigative tasks. First, we identify persons of interest (POI), using financial records and email, and report a peak accuracy of 95.7%. Secondly, we find any publicly exposed personally identifiable information (PII) and discover 50,000 previously unreported instances. Thirdly, we automatically flag legally responsive emails as scored by human experts in the California electricity blackout lawsuit, and find a peak 99% accuracy. Finally, we track three years of primary topics and sentiment across over 10,000 unique people before, during and after the onset of the corporate crisis. Where possible, we compare accuracy against execution times for 51 algorithms and report human-interpretable business rules that can scale to vast datasets.
MetaSelector: Meta-Learning for Recommendation with User-Level Adaptive Model Selection
Authors: Mi Luo, Fei Chen, Pengxiang Cheng, Zhenhua Dong, Xiuqiang He, Jiashi Feng, Zhenguo Li
Date: 2020-01-22 00:00:00
Tasks: Meta-Learning, Model Selection, Recommendation Systems

Recommender systems often face heterogeneous datasets containing highly personalized historical data of users, where no single model could give the best recommendation for every user. We observe this ubiquitous phenomenon on both public and private datasets and address the model selection problem in pursuit of optimizing the quality of recommendation for each user. We propose a meta-learning framework to facilitate user-level adaptive model selection in recommender systems. In this framework, a collection of recommenders is trained with data from all users, on top of which a model selector is trained via meta-learning to select the best single model for each user with the user-specific historical data. We conduct extensive experiments on two public datasets and a real-world production dataset, demonstrating that our proposed framework achieves improvements over single model baselines and sample-level model selector in terms of AUC and LogLoss. In particular, the improvements may lead to huge profit gain when deployed in online recommender systems.
SANST: A Self-Attentive Network for Next Point-of-Interest Recommendation
Authors: Qianyu Guo, Jianzhong Qi
Date: 2020-01-22 00:00:00
Tasks: Sequential Recommendation

Next point-of-interest (POI) recommendation aims to offer suggestions on which POI to visit next, given a user's POI visit history. This problem has a wide application in the tourism industry, and it is gaining an increasing interest as more POI check-in data become available. The problem is often modeled as a sequential recommendation problem to take advantage of the sequential patterns of user check-ins, e.g., people tend to visit Central Park after The Metropolitan Museum of Art in New York City. Recently, self-attentive networks have been shown to be both effective and efficient in general sequential recommendation problems, e.g., to recommend products, video games, or movies. Directly adopting self-attentive networks for next POI recommendation, however, may produce sub-optimal recommendations. This is because vanilla self-attentive networks do not consider the spatial and temporal patterns of user check-ins, which are two critical features in next POI recommendation. To address this limitation, in this paper, we propose a model named SANST that incorporates spatio-temporal patterns of user check-ins into self-attentive networks. To incorporate the spatial patterns, we encode the relative positions of POIs into their embeddings before feeding the embeddings into the self-attentive network. To incorporate the temporal patterns, we discretize the time of POI check-ins and model the temporal relationship between POI check-ins by a relation-aware self-attention module. We evaluate the performance of our SANST model with three real-world datasets. The results show that SANST consistently outperforms the state-of-theart models, and the advantage in nDCG@10 is up to 13.65%.
Bibliometric-enhanced Information Retrieval 10th Anniversary Workshop Edition
Authors: Guillaume Cabanac, Ingo Frommholz, Philipp Mayr
Date: 2020-01-20 00:00:00
Tasks: Information Retrieval, Retrieval

The Bibliometric-enhanced Information Retrieval workshop series (BIR) was launched at ECIR in 2014 \cite{MayrEtAl2014} and it was held at ECIR each year since then. This year we organize the 10th iteration of BIR. The workshop series at ECIR and JCDL/SIGIR tackles issues related to academic search, at the crossroads between Information Retrieval, Natural Language Processing and Bibliometrics. In this overview paper, we summarize the past workshops, present the workshop topics for 2020 and reflect on some future steps for this workshop series.
Predictive analysis of Bitcoin price considering social sentiments
Authors: Pratikkumar Prajapati
Date: 2020-01-16 00:00:00
Tasks: Articles, Sentiment Analysis

We report on the use of sentiment analysis on news and social media to analyze and predict the price of Bitcoin. Bitcoin is the leading cryptocurrency and has the highest market capitalization among digital currencies. Predicting Bitcoin values may help understand and predict potential market movement and future growth of the technology. Unlike (mostly) repeating phenomena like weather, cryptocurrency values do not follow a repeating pattern and mere past value of Bitcoin does not reveal any secret of future Bitcoin value. Humans follow general sentiments and technical analysis to invest in the market. Hence considering people's sentiment can give a good degree of prediction. We focus on using social sentiment as a feature to predict future Bitcoin value, and in particular, consider Google News and Reddit posts. We find that social sentiment gives a good estimate of how future Bitcoin values may move. We achieve the lowest test RMSE of 434.87 using an LSTM that takes as inputs the historical price of various cryptocurrencies, the sentiment of news articles and the sentiment of Reddit posts.
Wirtinger's Calculus in general Hilbert Spaces
Authors: P. Bouboulis
Date: 2010-05-25 00:00:00

The present report, has been inspired by the need of the author and its colleagues to understand the underlying theory of Wirtinger's Calculus and to further extend it to include the kernel case. The aim of the present manuscript is twofold: a) it endeavors to provide a more rigorous presentation of the related material, focusing on aspects that the author finds more insightful and b) it extends the notions of Wirtinger's calculus on general Hilbert spaces (such as Reproducing Hilbert Kernel Spaces).
Batch Normalization: Accelerating Deep Network Training byReducing Internal Covariate Shift
Authors: Sergey Ioffe, Christian Szegedy
Date: 2015-07-07 00:00:00
Proceeding: ICML 2015 2015 7
Tasks: General Classification, image-classification, Image Classification

Training Deep Neural Networks is complicated by the factthat the distribution of each layer’s inputs changes duringtraining, as the parameters of the previous layers change.This slows down the training by requiring lower learningrates and careful parameter initialization, and makes it no-toriously hard to train models with saturating nonlineari-ties.  We refer to this phenomenon asinternal covariateshift,  and  address the  problem by  normalizing layer in-puts. Our method draws its strength from making normal-ization a part of the model architecture and performing thenormalizationfor each training mini-batch.  Batch Nor-malization allows us to use much higher learning rates andbe less careful about initialization.  It also acts as a regu-larizer, in some cases eliminating the need for Dropout.Applied to a state-of-the-art image classification model,Batch Normalization achieves the same accuracy with 14times fewer training steps,  and beats the original modelby  a  significant  margin.   Using  an  ensemble  of  batch-normalized networks, we improve upon the best publishedresult  on  ImageNet  classification:   reaching  4.9%  top-5validation error (and 4.8% test error), exceeding the ac-curacy of human raters.
Infodemiological Study Using Google Trends on Coronavirus Epidemic in Wuhan, China
Authors: Artur Strzelecki, Mariia Rizun
Date: 2020-01-29 00:00:00

The recent emergence of a new coronavirus (COVID-19) has gained a high cover in public media and worldwide news. The virus has caused a viral pneumonia in tens of thousands of people in Wuhan, a central city of China. This short paper gives a brief introduction on how the demand for information on this new epidemic is reported through Google Trends. The reported period is 31 December 2020 to 20 March 2020. The authors draw conclusions on current infodemiological data on COVID-19 using three main search keywords: coronavirus, SARS and MERS. Two approaches are set. First is the worldwide perspective, second - the Chinese one, which reveals that in China this disease in the first days was more often referred to SARS then to general coronaviruses, whereas worldwide, since the beginning, it is more often referred to coronaviruses.
Improving Language Identification for Multilingual Speakers
Authors: Andrew Titus, Jan Silovsky, Nanxin Chen, Roger Hsiao, Mary Young, Arnab Ghoshal
Date: 2020-01-29 00:00:00
Tasks: Language Identification, Spoken language identification

Spoken language identification (LID) technologies have improved in recent years from discriminating largely distinct languages to discriminating highly similar languages or even dialects of the same language. One aspect that has been mostly neglected, however, is discrimination of languages for multilingual speakers, despite being a primary target audience of many systems that utilize LID technologies. As we show in this work, LID systems can have a high average accuracy for most combinations of languages while greatly underperforming for others when accented speech is present. We address this by using coarser-grained targets for the acoustic LID model and integrating its outputs with interaction context signals in a context-aware model to tailor the system to each user. This combined system achieves an average 97% accuracy across all language combinations while improving worst-case accuracy by over 60% relative to our baseline.
On the Convergence of Artificial Intelligence and Distributed Ledger Technology: A Scoping Review and Future Research Agenda
Authors: Konstantin D. Pandl, Scott Thiebes, Manuel Schmidt-Kraepelin, Ali Sunyaev
Date: 2020-01-29 00:00:00
Tasks: Systematic Literature Review

Developments in Artificial Intelligence (AI) and Distributed Ledger Technology (DLT) currently lead to lively debates in academia and practice. AI processes data to perform tasks that were previously thought possible only for humans. DLT has the potential to create consensus over data among a group of participants in uncertain environments. In recent research, both technologies are used in similar and even the same systems. Examples include the design of secure distributed ledgers or the creation of allied learning systems distributed across multiple nodes. This can lead to technological convergence, which in the past, has paved the way for major innovations in information technology. Previous work highlights several potential benefits of the convergence of AI and DLT but only provides a limited theoretical framework to describe upcoming real-world integration cases of both technologies. We aim to contribute by conducting a systematic literature review on previous work and providing rigorously derived future research opportunities. This work helps researchers active in AI or DLT to overcome current limitations in their field, and practitioners to develop systems along with the convergence of both technologies.
Modeling Global and Local Node Contexts for Text Generation from Knowledge Graphs
Authors: Leonardo F. R. Ribeiro, Yue Zhang, Claire Gardent, Iryna Gurevych
Date: 2020-01-29 00:00:00
Tasks: Data-to-Text Generation, Graph-to-Sequence, KG-to-Text Generation, Knowledge Graphs, Text Generation

Recent graph-to-text models generate text from graph-based data using either global or local aggregation to learn node representations. Global node encoding allows explicit communication between two distant nodes, thereby neglecting graph topology as all nodes are directly connected. In contrast, local node encoding considers the relations between neighbor nodes capturing the graph structure, but it can fail to capture long-range relations. In this work, we gather both encoding strategies, proposing novel neural models which encode an input graph combining both global and local node contexts, in order to learn better contextualized node embeddings. In our experiments, we demonstrate that our approaches lead to significant improvements on two graph-to-text datasets achieving BLEU scores of 18.01 on AGENDA dataset, and 63.69 on the WebNLG dataset for seen categories, outperforming state-of-the-art models by 3.7 and 3.1 points, respectively.
A4 : Evading Learning-based Adblockers
Authors: Shitong Zhu, Zhongjie Wang, Xun Chen, Shasha Li, Umar Iqbal, Zhiyun Qian, Kevin S. Chan, Srikanth V. Krishnamurthy, Zubair Shafiq
Date: 2020-01-29 00:00:00
Tasks: Blocking

Efforts by online ad publishers to circumvent traditional ad blockers towards regaining fiduciary benefits, have been demonstrably successful. As a result, there have recently emerged a set of adblockers that apply machine learning instead of manually curated rules and have been shown to be more robust in blocking ads on websites including social media sites such as Facebook. Among these, AdGraph is arguably the state-of-the-art learning-based adblocker. In this paper, we develop A4, a tool that intelligently crafts adversarial samples of ads to evade AdGraph. Unlike the popular research on adversarial samples against images or videos that are considered less- to un-restricted, the samples that A4 generates preserve application semantics of the web page, or are actionable. Through several experiments we show that A4 can bypass AdGraph about 60% of the time, which surpasses the state-of-the-art attack by a significant margin of 84.3%; in addition, changes to the visual layout of the web page due to these perturbations are imperceptible. We envision the algorithmic framework proposed in A4 is also promising in improving adversarial attacks against other learning-based web applications with similar requirements.
Functional Sequential Treatment Allocation with Covariates
Authors: Anders Bredahl Kock, David Preinerstorfer, Bezirgen Veliyev
Date: 2020-01-29 00:00:00

We consider a multi-armed bandit problem with covariates. Given a realization of the covariate vector, instead of targeting the treatment with highest conditional expectation, the decision maker targets the treatment which maximizes a general functional of the conditional potential outcome distribution, e.g., a conditional quantile, trimmed mean, or a socio-economic functional such as an inequality, welfare or poverty measure. We develop expected regret lower bounds for this problem, and construct a near minimax optimal assignment policy.
The Case for Bayesian Deep Learning
Authors: Andrew Gordon Wilson
Date: 2020-01-29 00:00:00
Tasks: Bayesian Inference, Deep Learning, Diversity

The key distinguishing property of a Bayesian approach is marginalization instead of optimization, not the prior, or Bayes rule. Bayesian inference is especially compelling for deep neural networks. (1) Neural networks are typically underspecified by the data, and can represent many different but high performing models corresponding to different settings of parameters, which is exactly when marginalization will make the biggest difference for both calibration and accuracy. (2) Deep ensembles have been mistaken as competing approaches to Bayesian methods, but can be seen as approximate Bayesian marginalization. (3) The structure of neural networks gives rise to a structured prior in function space, which reflects the inductive biases of neural networks that help them generalize. (4) The observed correlation between parameters in flat regions of the loss and a diversity of solutions that provide good generalization is further conducive to Bayesian marginalization, as flat regions occupy a large volume in a high dimensional space, and each different solution will make a good contribution to a Bayesian model average. (5) Recent practical advances for Bayesian deep learning provide improvements in accuracy and calibration compared to standard training, while retaining scalability.
An Upper Bound of the Bias of Nadaraya-Watson Kernel Regression under Lipschitz Assumptions
Authors: Samuele Tosatto, Riad Akrour, Jan Peters
Date: 2020-01-29 00:00:00
Tasks: regression, valid

The Nadaraya-Watson kernel estimator is among the most popular nonparameteric regression technique thanks to its simplicity. Its asymptotic bias has been studied by Rosenblatt in 1969 and has been reported in a number of related literature. However, Rosenblatt's analysis is only valid for infinitesimal bandwidth. In contrast, we propose in this paper an upper bound of the bias which holds for finite bandwidths. Moreover, contrarily to the classic analysis we allow for discontinuous first order derivative of the regression function, we extend our bounds for multidimensional domains and we include the knowledge of the bound of the regression function when it exists and if it is known, to obtain a tighter bound. We believe that this work has potential applications in those fields where some hard guarantees on the error are needed
Developing a gender classification approach in human face images using modified local binary patterns and tani-moto based nearest neighbor algorithm
Authors: Shervan Fekri-Ershad
Date: 2020-01-29 00:00:00
Tasks: CPU, Gender Classification, General Classification, Sensitivity

Human identification is a much attention problem in computer vision. Gender classification plays an important role in human identification as preprocess step. So far, various methods have been proposed to solve this problem. Absolutely, classification accuracy is the main challenge for researchers in gender classification. But, some challenges such as rotation, gray scale variations, pose, illumination changes may be occurred in smart phone image capturing. In this respect, a multi step approach is proposed in this paper to classify genders in human face images based on improved local binary patters (MLBP). LBP is a texture descriptor, which extract local contrast and local spatial structure information. Some issues such as noise sensitivity, rotation sensitivity and low discriminative features can be considered as disadvantages of the basic LBP. MLBP handle disadvantages using a new theory to categorize extracted binary patterns of basic LBP. The proposed approach includes two stages. First of all, a feature vector is extracted for human face images based on MLBP. Next, non linear classifiers can be used to classify gender. In this paper nearest neighborhood classifier is evaluated based on Tani-Moto metric as distance measure. In the result part, two databases, self-collected and ICPR are used as human face database. Results are compared by some state-ofthe-art algorithms in this literature that shows the high quality of the proposed approach in terms of accuracy rate. Some of other main advantages of the proposed approach are rotation invariant, low noise sensitivity, size invariant and low computational complexity. The proposed approach decreases the computational complexity of smartphone applications because of reducing the number of database comparisons. It can also improve performance of the synchronous applications in the smarphones because of memory and CPU usage reduction.