pwc-archive/papers-with-abstracts huggingface dataset:
Attention on Global-Local Representation Spaces in Recommender Systems
Authors: Munlika Rattaphun, Wen-Chieh Fang, Chih-Yi Chiu
Date: 2021-04-25 00:00:00
Tasks: Clustering, Collaborative Filtering, Recommendation Systems

In this study, we present a novel clustering-based collaborative filtering (CF) method for recommender systems. Clustering-based CF methods can effectively deal with data sparsity and scalability problems. However, most of them are applied to a single representation space, which might not characterize complex user-item interactions well. We argue that the user-item interactions should be observed from multiple views and characterized in an adaptive way. To address this issue, we leveraged the global and local properties to construct multiple representation spaces by learning various training datasets and loss functions. An attention network was built to generate a blended representation according to the relative importance of the representation spaces for each user-item pair, providing a flexible way to characterize diverse user-item interactions. Substantial experiments were evaluated on four popular benchmark datasets. The results show that the proposed method is superior to several CF methods where only one representation space is considered.
Learning Passage Impacts for Inverted Indexes
Authors: Antonio Mallia, Omar Khattab, Nicola Tonellotto, Torsten Suel
Date: 2021-04-24 00:00:00
Tasks: Information Retrieval, Language Modeling, Language Modelling, Re-Ranking, Retrieval

Neural information retrieval systems typically use a cascading pipeline, in which a first-stage model retrieves a candidate set of documents and one or more subsequent stages re-rank this set using contextualized language models such as BERT. In this paper, we propose DeepImpact, a new document term-weighting scheme suitable for efficient retrieval using a standard inverted index. Compared to existing methods, DeepImpact improves impact-score modeling and tackles the vocabulary-mismatch problem. In particular, DeepImpact leverages DocT5Query to enrich the document collection and, using a contextualized language model, directly estimates the semantic importance of tokens in a document, producing a single-value representation for each token in each document. Our experiments show that DeepImpact significantly outperforms prior first-stage retrieval approaches by up to 17% on effectiveness metrics w.r.t. DocT5Query, and, when deployed in a re-ranking scenario, can reach the same effectiveness of state-of-the-art approaches with up to 5.1x speedup in efficiency.
Automatic Description Construction for Math Expression via Topic Relation Graph
Authors: Ke Yuan, Zuoyu Yan, Yibo Li, Liangcai Gao, Zhi Tang
Date: 2021-04-24 00:00:00
Tasks: Math, Relation

Math expressions are important parts of scientific and educational documents, but some of them may be challenging for junior scholars or students to understand. Nevertheless, constructing textual descriptions for math expressions is nontrivial. In this paper, we explore the feasibility to automatically construct descriptions for math expressions. But there are two challenges that need to be addressed: 1) finding relevant documents since a math equation understanding usually requires several topics, but these topics are often explained in different documents. 2) the sparsity of the collected relevant documents making it difficult to extract reasonable descriptions. Different documents mainly focus on different topics which makes model hard to extract salient information and organize them to form a description of math expressions. To address these issues, we propose a hybrid model (MathDes) which contains two important modules: Selector and Summarizer. In the Selector, a Topic Relation Graph (TRG) is proposed to obtain the relevant documents which contain the comprehensive information of math expressions. TRG is a graph built according to the citations between expressions. In the Summarizer, a summarization model under the Integer Linear Programming (ILP) framework is proposed. This module constructs the final description with the help of a timeline that is extracted from TRG. The experimental results demonstrate that our methods are promising for this task and outperform the baselines in all aspects.
Form 10-Q Itemization
Authors: Yanci Zhang, Tianming Du, Yujie Sun, Lawrence Donohue, Rui Dai
Date: 2021-04-23 00:00:00
Tasks: Form, Retrieval

The quarterly financial statement, or Form 10-Q, is one of the most frequently required filings for US public companies to disclose financial and other important business information. Due to the massive volume of 10-Q filings and the enormous variations in the reporting format, it has been a long-standing challenge to retrieve item-specific information from 10-Q filings that lack machine-readable hierarchy. This paper presents a solution for itemizing 10-Q files by complementing a rule-based algorithm with a Convolutional Neural Network (CNN) image classifier. This solution demonstrates a pipeline that can be generalized to a rapid data retrieval solution among a large volume of textual data using only typographic items. The extracted textual data can be used as unlabeled content-specific data to train transformer models (e.g., BERT) or fit into various field-focus natural language processing (NLP) applications.
EduPal leaves no professor behind: Supporting faculty via a peer-powered recommender system
Authors: Nourhan Sakr, Aya Salama, Nadeen Tameesh, Gihan Osman
Date: 2021-04-20 00:00:00
Tasks: Chatbot, Knowledge Distillation, Recommendation Systems

The swift transitions in higher education after the COVID-19 outbreak identified a gap in the pedagogical support available to faculty. We propose a smart, knowledge-based chatbot that addresses issues of knowledge distillation and provides faculty with personalized recommendations. Our collaborative system crowdsources useful pedagogical practices and continuously filters recommendations based on theory and user feedback, thus enhancing the experiences of subsequent peers. We build a prototype for our local STEM faculty as a proof concept and receive favorable feedback that encourages us to extend our development and outreach, especially to underresourced faculty.
Avoiding bias when inferring race using name-based approaches
Authors: Diego Kozlowski, Dakota S. Murray, Alexis Bell, Will Hulsey, Vincent Larivière, Thema Monroe-White, Cassidy R. Sugimoto
Date: 2021-04-14 00:00:00
Tasks: Imputation

Racial disparity in academia is a widely acknowledged problem. The quantitative understanding of racial based systemic inequalities is an important step towards a more equitable research system. However, because of the lack of robust information on authors' race, few large scale analyses have been performed on this topic. Algorithmic approaches offer one solution, using known information about authors, such as their names, to infer their perceived race. As with any other algorithm, the process of racial inference can generate biases if it is not carefully considered. The goal of this article is to assess the extent to which algorithmic bias is introduced using different approaches for name based racial inference. We use information from the U.S. Census and mortgage applications to infer the race of U.S. affiliated authors in the Web of Science. We estimate the effects of using given and family names, thresholds or continuous distributions, and imputation. Our results demonstrate that the validity of name based inference varies by race/ethnicity and that threshold approaches underestimate Black authors and overestimate White authors. We conclude with recommendations to avoid potential biases. This article lays the foundation for more systematic and less biased investigations into racial disparities in science.
Attentional Autoencoder for Course Recommendation in MOOC with Course Relevance
Authors: Jindan Tan, Liang Chang, Tieyuan Liu*, Xuemei Zhao
Date: 2020-10-29 00:00:00
Tasks: Recommendation Systems

With the rapid development of online education, a large number of students are learning in the Massive Open Online Course (MOOC) platform. To attract students’ interest, the recommendation system is applied by MOOC providers to recommend courses to students. State-of-the-art course recommendation systems still face several challenging problems: (1) It is difficult to model the nonlinear implicit feedback interaction between students and courses; (2) Text information is not contained in the network, such as the course description. To solve these problems, we propose an Attentional Manhattan Siamese Long Short Term Memory (AMSLSTM) network and based autoencoder to construct course relevance from the course description and self-attention adaptively distinguish the student’s preference level in multiple aspects. The proposed AMSLSTM learns the preference of student’s interest by considering the course relevance, and can significantly improve the recommendation accuracy. We compare the existing recommendation system baselines to evaluate our model. The experimental results show that the proposed model is effective in the real data set.
Image Modeling with Deep Convolutional Gaussian Mixture Models
Authors: Alexander Gepperth, Benedikt Pfülb
Date: 2021-04-19 00:00:00
Tasks: Clustering, Outlier Detection

In this conceptual work, we present Deep Convolutional Gaussian Mixture Models (DCGMMs): a new formulation of deep hierarchical Gaussian Mixture Models (GMMs) that is particularly suitable for describing and generating images. Vanilla (i.e., flat) GMMs require a very large number of components to describe images well, leading to long training times and memory issues. DCGMMs avoid this by a stacked architecture of multiple GMM layers, linked by convolution and pooling operations. This allows to exploit the compositionality of images in a similar way as deep CNNs do. DCGMMs can be trained end-to-end by Stochastic Gradient Descent. This sets them apart from vanilla GMMs which are trained by Expectation-Maximization, requiring a prior k-means initialization which is infeasible in a layered structure. For generating sharp images with DCGMMs, we introduce a new gradient-based technique for sampling through non-invertible operations like convolution and pooling. Based on the MNIST and FashionMNIST datasets, we validate the DCGMMs model by demonstrating its superiority over flat GMMs for clustering, sampling and outlier detection.
A Novel Interaction-based Methodology Towards Explainable AI with Better Understanding of Pneumonia Chest X-ray Images
Authors: Shaw-Hwa Lo, Yiqiao Yin
Date: 2021-04-19 00:00:00
Tasks: Explainable Artificial Intelligence (XAI)

In the field of eXplainable AI (XAI), robust "blackbox" algorithms such as Convolutional Neural Networks (CNNs) are known for making high prediction performance. However, the ability to explain and interpret these algorithms still require innovation in the understanding of influential and, more importantly, explainable features that directly or indirectly impact the performance of predictivity. A number of methods existing in literature focus on visualization techniques but the concepts of explainability and interpretability still require rigorous definition. In view of the above needs, this paper proposes an interaction-based methodology -- Influence Score (I-score) -- to screen out the noisy and non-informative variables in the images hence it nourishes an environment with explainable and interpretable features that are directly associated to feature predictivity. We apply the proposed method on a real world application in Pneumonia Chest X-ray Image data set and produced state-of-the-art results. We demonstrate how to apply the proposed approach for more general big data problems by improving the explainability and interpretability without sacrificing the prediction performance. The contribution of this paper opens a novel angle that moves the community closer to the future pipelines of XAI problems.
The Effects of Air Quality on the Spread of the COVID-19 Pandemic in Italy: An Artificial Intelligence Approach
Authors: Andrea Loreggia, Anna Passarelli, Maria Silvia Pini
Date: 2021-04-09 00:00:00

The COVID-19 pandemic considerably affects public health systems around the world. The lack of knowledge about the virus, the extension of this phenomenon, and the speed of the evolution of the infection are all factors that highlight the necessity of employing new approaches to study these events. Artificial intelligence techniques may be useful in analyzing data related to areas affected by the virus. The aim of this work is to investigate any possible relationships between air quality and confirmed cases of COVID-19 in Italian districts. Specifically, we report an analysis of the correlation between daily COVID-19 cases and environmental factors, such as temperature, relative humidity, and atmospheric pollutants. Our analysis confirms a significant association of some environmental parameters with the spread of the virus. This suggests that machine learning models trained on the environmental parameters to predict the number of future infected cases may be accurate. Predictive models may be useful for helping institutions in making decisions for protecting the population and contrasting the pandemic.
COVID-19 Modeling: A Review
Authors: Longbing Cao, Qing Liu
Date: 2021-04-16 00:00:00
Tasks: Misinformation

The SARS-CoV-2 virus and COVID-19 disease have posed unprecedented and overwhelming demand, challenges and opportunities to domain, model and data driven modeling. This paper provides a comprehensive review of the challenges, tasks, methods, progress, gaps and opportunities in relation to modeling COVID-19 problems, data and objectives. It constructs a research landscape of COVID-19 modeling tasks and methods, and further categorizes, summarizes, compares and discusses the related methods and progress of modeling COVID-19 epidemic transmission processes and dynamics, case identification and tracing, infection diagnosis and medical treatments, non-pharmaceutical interventions and their effects, drug and vaccine development, psychological, economic and social influence and impact, and misinformation, etc. The modeling methods involve mathematical and statistical models, domain-driven modeling by epidemiological compartmental models, medical and biomedical analysis, AI and data science in particular shallow and deep machine learning, simulation modeling, social science methods, and hybrid modeling.
A Framework for Ethical AI at the United Nations
Authors: Lambert Hogenhout
Date: 2021-04-09 00:00:00

This paper aims to provide an overview of the ethical concerns in artificial intelligence (AI) and the framework that is needed to mitigate those risks, and to suggest a practical path to ensure the development and use of AI at the United Nations (UN) aligns with our ethical values. The overview discusses how AI is an increasingly powerful tool with potential for good, albeit one with a high risk of negative side-effects that go against fundamental human rights and UN values. It explains the need for ethical principles for AI aligned with principles for data governance, as data and AI are tightly interwoven. It explores different ethical frameworks that exist and tools such as assessment lists. It recommends that the UN develop a framework consisting of ethical principles, architectural standards, assessment methods, tools and methodologies, and a policy to govern the implementation and adherence to this framework, accompanied by an education program for staff.
Accuracy-aware Deep Knowledge Tracing with Knowledge State Vector Loss
Authors: Qiushi Pan, Taro Tezuka
Date: 2020-09-01 00:00:00
Tasks: Knowledge Tracing

In major e-learning platforms such as intelligent tutoring systems (ITSs) and massive open online courses (MOOCs), the students are often recommended what course materials to take based on their past interactions. Knowledge Tracing (KT) is the task of modeling students' academic abilities. Given a sequence of student's learning history, it predicts how well they will perform in the next interaction. Deep Knowledge Tracing (DKT) uses a recurrent neural network (RNN) to capture the underlying structure of the student's understanding. In this paper, we point out the ​accuracy rate problem that the model won't reproduce the accuracy ratio. This is a limitation of the existing loss function in DKT that it only learns the probability of correctly answering a problem in the next interaction. We introduced the ​Knowledge State Vector loss​, which captures the accuracy rate of all knowledge concepts, to measure and train the model.
Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
Authors: Fan, Shaohua and Zhu, Junxiong and Han, Xiaotian and Shi, Chuan and Hu, Linmei and Ma, Biyu and Li, Yongliang
Date: 2019-08-04 00:00:00
Tasks: Attribute, Feature Engineering, Graph Neural Network

With the prevalence of mobile e-commerce nowadays, a new type of recommendation services, called intent recommendation, is widely used in many mobile e-commerce Apps, such as Taobao and Amazon. Different from traditional query recommendation and item recommendation, intent recommendation is to automatically recommend user intent according to user historical behaviors without any input when users open the App. Intent recommendation becomes very popular in the past two years, because of revealing user latent intents and avoiding tedious input in mobile phones. Existing methods used in industry usually need laboring feature engineering. Moreover, they only utilize attribute and statistic information of users and queries, and fail to take full advantage of rich interaction information in intent recommendation, which may result in limited performances. In this paper, we propose to model the complex objects and rich interactions in intent recommendation as a Heterogeneous Information Network. Furthermore, we present a novel M etapath-guided E mbedding method for I ntent Rec ommendation~(called MEIRec). In order to fully utilize rich structural information, we design a metapath-guided heterogeneous Graph Neural Network to learn the embeddings of objects in intent recommendation. In addition, in order to alleviate huge learning parameters in embeddings, we propose a uniform term embedding mechanism, in which embeddings of objects are made up with the same term embedding space. Offline experiments on real large-scale data show the superior performance of the proposed MEIRec, compared to representative methods.Moreover, the results of online experiments on Taobao e-commerce platform show that MEIRec not only gains a performance improvement of 1.54% on CTR metric, but also attracts up to 2.66% of new users to search queries.
ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research
Authors: Zachary J. Lee, Sunash Sharma, Daniel Johansson, Steven H. Low
Date: 2020-12-04 00:00:00
Tasks: OpenAI Gym, Reinforcement Learning (RL)

ACN-Sim is a data-driven, open-source simulation environment designed to accelerate research in the field of smart electric vehicle (EV) charging. It fills the need in this community for a widely available, realistic simulation environment in which researchers can evaluate algorithms and test assumptions. ACN-Sim provides a modular, extensible architecture, which models the complexity of real charging systems, including battery charging behavior and unbalanced three-phase infrastructure. It also integrates with a broader ecosystem of research tools. These include ACN-Data, an open dataset of EV charging sessions, which provides realistic simulation scenarios and ACN-Live, a framework for field-testing charging algorithms. It also integrates with grid simulators like MATPOWER, PandaPower and OpenDSS, and OpenAI Gym for training reinforcement learning agents.
Using Radio Archives for Low-Resource Speech Recognition: Towards an Intelligent Virtual Assistant for Illiterate Users
Authors: Moussa Doumbouya, Lisa Einstein, Chris Piech
Date: 2021-04-27 00:00:00
Tasks: Language Identification, Representation Learning, speech-recognition, Speech Recognition, Speech Representation Learning

For many of the 700 million illiterate people around the world, speech recognition technology could provide a bridge to valuable information and services. Yet, those most in need of this technology are often the most underserved by it. In many countries, illiterate people tend to speak only low-resource languages, for which the datasets necessary for speech technology development are scarce. In this paper, we investigate the effectiveness of unsupervised speech representation learning on noisy radio broadcasting archives, which are abundant even in low-resource languages. We make three core contributions. First, we release two datasets to the research community. The first, West African Radio Corpus, contains 142 hours of audio in more than 10 languages with a labeled validation subset. The second, West African Virtual Assistant Speech Recognition Corpus, consists of 10K labeled audio clips in four languages. Next, we share West African wav2vec, a speech encoder trained on the noisy radio corpus, and compare it with the baseline Facebook speech encoder trained on six times more data of higher quality. We show that West African wav2vec performs similarly to the baseline on a multilingual speech recognition task, and significantly outperforms the baseline on a West African language identification task. Finally, we share the first-ever speech recognition models for Maninka, Pular and Susu, languages spoken by a combined 10 million people in over seven countries, including six where the majority of the adult population is illiterate. Our contributions offer a path forward for ethical AI research to serve the needs of those most disadvantaged by the digital divide.
Proceedings - AI/ML for Cybersecurity: Challenges, Solutions, and Novel Ideas at SIAM Data Mining 2021
Authors: John Emanuello, Kimberly Ferguson-Walter, Erik Hemberg, Una-May O Reilly, Ahmad Ridley, Dennis Ross, Diane Staheli, William Streilein
Date: 2021-04-27 00:00:00

Malicious cyber activity is ubiquitous and its harmful effects have dramatic and often irreversible impacts on society. Given the shortage of cybersecurity professionals, the ever-evolving adversary, the massive amounts of data which could contain evidence of an attack, and the speed at which defensive actions must be taken, innovations which enable autonomy in cybersecurity must continue to expand, in order to move away from a reactive defense posture and towards a more proactive one. The challenges in this space are quite different from those associated with applying AI in other domains such as computer vision. The environment suffers from an incredibly high degree of uncertainty, stemming from the intractability of ingesting all the available data, as well as the possibility that malicious actors are manipulating the data. Another unique challenge in this space is the dynamism of the adversary causes the indicators of compromise to change frequently and without warning. In spite of these challenges, machine learning has been applied to this domain and has achieved some success in the realm of detection. While this aspect of the problem is far from solved, a growing part of the commercial sector is providing ML-enhanced capabilities as a service. Many of these entities also provide platforms which facilitate the deployment of these automated solutions. Academic research in this space is growing and continues to influence current solutions, as well as strengthen foundational knowledge which will make autonomous agents in this space a possibility.