pwc-archive/papers-with-abstracts huggingface dataset:
Joint Characterization of the Cryospheric Spectral Feature Space
Authors: Christopher Small, Daniel Sousa
Date: 2021-12-02 00:00:00
Tasks: Clustering, Dimensionality Reduction

Hyperspectral feature spaces are useful for many remote sensing applications ranging from spectral mixture modeling to discrete thematic classification. In such cases, characterization of the feature space dimensionality, geometry and topology can provide guidance for effective model design. The objective of this study is to compare and contrast two approaches for identifying feature space basis vectors via dimensionality reduction. These approaches can be combined to render a joint characterization that reveals spectral properties not apparent using either approach alone. We use a diverse collection of AVIRIS-NG reflectance spectra of the snow-firn-ice continuum to illustrate the utility of joint characterization and identify physical properties inferred from the spectra. Spectral feature spaces combining principal components (PCs) and t-distributed Stochastic Neighbor Embeddings (t-SNEs) provide physically interpretable dimensions representing the global (PC) structure of cryospheric reflectance properties and local (t-SNE) manifold structures revealing clustering not resolved in the global continuum. Joint characterization reveals distinct continua for snow-firn gradients on different parts of the Greenland Ice Sheet and multiple clusters of ice reflectance properties common to both glacier and sea ice in different locations. Clustering revealed in t-SNE feature spaces, and extended to the joint characterization, distinguishes differences in spectral curvature specific to location within the snow accumulation zone, and BRDF effects related to view geometry. The ability of PC+t-SNE joint characterization to produce a physically interpretable spectral feature spaces revealing global topology while preserving local manifold structures suggests that this characterization might be extended to the much higher dimensional hyperspectral feature space of all terrestrial land cover.
Asymptotic properties of one-layer artificial neural networks with sparse connectivity
Authors: Christian Hirsch, Matthias Neumann, Volker Schmidt
Date: 2021-12-01 00:00:00

A law of large numbers for the empirical distribution of parameters of a one-layer artificial neural networks with sparse connectivity is derived for a simultaneously increasing number of both, neurons and training iterations of the stochastic gradient descent.
Where the Earth is flat and 9/11 is an inside job: A comparative algorithm audit of conspiratorial information in web search results
Authors: Aleksandra Urman, Mykola Makhortykh, Roberto Ulloa, Juhi Kulshrestha
Date: 2021-12-02 00:00:00

Web search engines are important online information intermediaries that are frequently used and highly trusted by the public despite multiple evidence of their outputs being subjected to inaccuracies and biases. One form of such inaccuracy, which so far received little scholarly attention, is the presence of conspiratorial information, namely pages promoting conspiracy theories. We address this gap by conducting a comparative algorithm audit to examine the distribution of conspiratorial information in search results across five search engines: Google, Bing, DuckDuckGo, Yahoo and Yandex. Using a virtual agent-based infrastructure, we systematically collect search outputs for six conspiracy theory-related queries (flat earth, new world order, qanon, 9/11, illuminati, george soros) across three locations (two in the US and one in the UK) and two observation periods (March and May 2021). We find that all search engines except Google consistently displayed conspiracy-promoting results and returned links to conspiracy-dedicated websites in their top results, although the share of such content varied across queries. Most conspiracy-promoting results came from social media and conspiracy-dedicated websites while conspiracy-debunking information was shared by scientific websites and, to a lesser extent, legacy media. The fact that these observations are consistent across different locations and time periods highlight the possibility of some search engines systematically prioritizing conspiracy-promoting content and, thus, amplifying their distribution in the online environments.
LDA2Net: Digging under the surface of COVID-19 topics in scientific literature
Authors: Giorgia Minello, Carlo R. M. A. Santagiustina, Massimo Warglien
Date: 2021-12-02 00:00:00
Tasks: Topic Models

During the COVID-19 pandemic, the scientific literature related to SARS-COV-2 has been growing dramatically, both in terms of the number of publications and of its impact on people's life. This literature encompasses a varied set of sensible topics, ranging from vaccination, to protective equipment efficacy, to lockdown policy evaluation. Up to now, hundreds of thousands of papers have been uploaded on online repositories and published in scientific journals. As a result, the development of digital methods that allow an in-depth exploration of this growing literature has become a relevant issue, both to identify the topical trends of COVID-related research and to zoom-in its sub-themes. This work proposes a novel methodology, called LDA2Net, which combines topic modelling and network analysis to investigate topics under their surface. Specifically, LDA2Net exploits the frequencies of pairs of consecutive words to reconstruct the network structure of topics discussed in the Cord-19 corpus. The results suggest that the effectiveness of topic models can be magnified by enriching them with word network representations, and by using the latter to display, analyse, and explore COVID-related topics at different levels of granularity.
Contrastive Cross-domain Recommendation in Matching
Authors: Ruobing Xie, Qi Liu, Liangdong Wang, Shukai Liu, Bo Zhang, Leyu Lin
Date: 2021-12-02 00:00:00
Tasks: Contrastive Learning, Representation Learning, Transfer Learning

Cross-domain recommendation (CDR) aims to provide better recommendation results in the target domain with the help of the source domain, which is widely used and explored in real-world systems. However, CDR in the matching (i.e., candidate generation) module struggles with the data sparsity and popularity bias issues in both representation learning and knowledge transfer. In this work, we propose a novel Contrastive Cross-Domain Recommendation (CCDR) framework for CDR in matching. Specifically, we build a huge diversified preference network to capture multiple information reflecting user diverse interests, and design an intra-domain contrastive learning (intra-CL) and three inter-domain contrastive learning (inter-CL) tasks for better representation learning and knowledge transfer. The intra-CL enables more effective and balanced training inside the target domain via a graph augmentation, while the inter-CL builds different types of cross-domain interactions from user, taxonomy, and neighbor aspects. In experiments, CCDR achieves significant improvements on both offline and online evaluations in a real-world system. Currently, we have deployed our CCDR on WeChat Top Stories, affecting plenty of users. The source code is in https://github.com/lqfarmer/CCDR.
Tiny-NewsRec: Effective and Efficient PLM-based News Recommendation
Authors: Yang Yu, Fangzhao Wu, Chuhan Wu, Jingwei Yi, Qi Liu
Date: 2021-12-02 00:00:00
Tasks: Knowledge Distillation, Natural Language Understanding, News Recommendation

News recommendation is a widely adopted technique to provide personalized news feeds for the user. Recently, pre-trained language models (PLMs) have demonstrated the great capability of natural language understanding and benefited news recommendation via improving news modeling. However, most existing works simply finetune the PLM with the news recommendation task, which may suffer from the known domain shift problem between the pre-training corpus and downstream news texts. Moreover, PLMs usually contain a large volume of parameters and have high computational overhead, which imposes a great burden on low-latency online services. In this paper, we propose Tiny-NewsRec, which can improve both the effectiveness and the efficiency of PLM-based news recommendation. We first design a self-supervised domain-specific post-training method to better adapt the general PLM to the news domain with a contrastive matching task between news titles and news bodies. We further propose a two-stage knowledge distillation method to improve the efficiency of the large PLM-based news recommendation model while maintaining its performance. Multiple teacher models originated from different time steps of our post-training procedure are used to transfer comprehensive knowledge to the student in both its post-training and finetuning stage. Extensive experiments on two real-world datasets validate the effectiveness and efficiency of our method.
Secure and Safety Mobile Network System for Visually Impaired People
Authors: Shyama Kumari Arunachalam, Roopa V, Meena H B, Vijayalakshmi, T Malavika
Date: 2021-12-01 00:00:00

The proposed system aims to be a techno-friend of visually impaired people to assist them in orientation and mobility both indoor and outdoor. Moving through an unknown environment becomes a real challenge for most of them, although they rely on their other senses. An age old mechanism used for assistance for the blind people is a white cane commonly known as walking cane a simple and purely mechanical device to detect the ground, uneven surfaces, holes and steps using simple Tactile-force feedback.
Are Investors Biased Against Women? Analyzing How Gender Affects Startup Funding in Europe
Authors: Michael Färber, Alexander Klein
Date: 2021-12-01 00:00:00

One of the main challenges of startups is to raise capital from investors. For startup founders, it is therefore crucial to know whether investors have a bias against women as startup founders and in which way startups face disadvantages due to gender bias. Existing works on gender studies have mainly analyzed the US market. In this paper, we aim to give a more comprehensive picture of gender bias in early-stage startup funding. We examine European startups listed on Crunchbase using Semantic Web technologies and analyze how the share of female founders in a founding team affects the funding amount. We find that the relative amount of female founders has a negative impact on the funding raised. Furthermore, we observe that founder characteristics have an effect on the funding raised based on the founders' gender. Moreover, we find that gender bias in early-stage funding is less prevalent for serial founders with entrepreneurial experience as female founders benefit three times more than male founders from already having founded a startup. Overall, our study suggests that gender bias exists and is worth to be considered in the context of startup funding.
Lists of Top Artists to Watch computed algorithmically
Authors: Tomasz Imielinski
Date: 2021-11-30 00:00:00

Lists of top artists to watch are periodically published by various art world media publications. These lists are selected editorially and reflect the subjective opinions of their creators. We show an application of ranking by momentum method to algorithmically produce lists of artists to watch derived from our platform Articker. We use our algorithms every month to produce a new list of momentum leaders on www.articker.org. The lists of momentum leaders computed this way has the following properties: a.It is small (because of the small frontier property). b.It is unbiased -- with no bias towards famous artists nor emerging, and yet unknown, artists c It is fair -- artists who are not included in the list must be Pareto dominated by at least one member of the momentum leaders list (Pareto frontier) d.It is objective -- it is computed automatically, not editorially selected.
Spatio-Temporal-Categorical Graph Neural Networks for Fine-Grained Multi-Incident Co-Prediction
Authors: Zhaonan Wang, Renhe Jiang, Zekun Cai, Zipei Fan, Xin Liu, Kyoung-Sook Kim, Xuan Song, Ryosuke Shibasaki
Date: 2021-10-01 00:00:00
Proceeding: CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management 2021 10
Tasks: Decision Making, Management, Prediction

Forecasting incident occurrences (e.g. crime, EMS, traffic accident) is a crucial task for emergency service providers and transportation agencies in performing response time optimization and dynamic fleet management. However, such events are by nature rare and sparse, which causes the label imbalance problem and inferior performance of models relying on data sufficiency. The existing studies circumvent, instead of truly solving, this issue by defining the incident prediction problem in a coarse-grained temporal (e.g. daily) setting, which leaves the proposed models unrobust to fine-grained dynamics and trivial for the real-world decision making. In this paper, we tackle the temporally fine-grained incident prediction problem in a sparse setting by explicitly exploiting the behind-thescene chainlike triggering mechanism. Moreover, this chain effect roots in multiple domains (i.e. spatial, categorical), which further entangles with the temporal dimension and happens to be timevariant. To be specific, we propose a novel deep learning framework, namely Spatio-Temporal-Categorical Graph Neural Networks (STCGNN), to handle the multidimensional and dynamic chain effect for performing fine-grained multi-incident co-prediction. Extensive experiments on three real-world city-level incident datasets verify the insightfulness of our perspective and effectiveness of the proposed model.
Predicting Student's Performance Through Data Mining
Authors: Aaditya Bhusal
Date: 2021-11-20 00:00:00
Tasks: Management

Predicting the performance of students early and as accurately as possible is one of the biggest challenges of educational institutions. Analyzing the performance of students early can help in finding the strengths and weakness of students and help the perform better in examinations. Using machine learning the student's performance can be predicted with the help of students' data collected from Learning Management Systems (LMS). The data collected from LMSs can provide insights about student's behavior that will result in good or bad performance in examinations which then can be studied and used in helping students performing poorly in examinations to perform better.
An AI-based Learning Companion Promoting Lifelong Learning Opportunities for All
Authors: Maria Perez-Ortiz, Erik Novak, Sahan Bulathwela, John Shawe-Taylor
Date: 2021-11-16 00:00:00
Tasks: All, Lifelong learning

Artifical Intelligence (AI) in Education has great potential for building more personalised curricula, as well as democratising education worldwide and creating a Renaissance of new ways of teaching and learning. We believe this is a crucial moment for setting the foundations of AI in education in the beginning of this Fourth Industrial Revolution. This report aims to synthesize how AI might change (and is already changing) how we learn, as well as what technological features are crucial for these AI systems in education, with the end goal of starting this pressing dialogue of how the future of AI in education should unfold, engaging policy makers, engineers, researchers and obviously, teachers and learners. This report also presents the advances within the X5GON project, a European H2020 project aimed at building and deploying a cross-modal, cross-lingual, cross-cultural, cross-domain and cross-site personalised learning platform for Open Educational Resources (OER).
Course Difficulty Estimation Based on Mapping of Bloom's Taxonomy and ABET Criteria
Authors: Premalatha M, Suganya G, Viswanathan V, G Jignesh Chowdary
Date: 2021-11-16 00:00:00

Current Educational system uses grades or marks to assess the performance of the student. The marks or grades a students scores depends on different parameters, the main parameter being the difficulty level of a course. Computation of this difficulty level may serve as a support for both the students and teachers to fix the level of training needed for successful completion of course. In this paper, we proposed a methodology that estimates the difficulty level of a course by mapping the Bloom's Taxonomy action words along with Accreditation Board for Engineering and Technology (ABET) criteria and learning outcomes. The estimated difficulty level is validated based on the history of grades secured by the students.
The MAIEI Learning Community Report
Authors: Brittany Wills, Christina Isaicu, Heather von Stackelberg, Lujain Ibrahim, Matthew Hutson, Mitchel Fleming, Nanditha Narayanamoorthy, Samuel Curtis, Shreyasha Paudel, Sofia Trejo, Tiziana Zevallos, Victoria Martín del Campo, Wilson Lee
Date: 2021-11-10 00:00:00
Tasks: Ethics

This is a labor of the Learning Community cohort that was convened by MAIEI in Winter 2021 to work through and discuss important research issues in the field of AI ethics from a multidisciplinary lens. The community came together supported by facilitators from the MAIEI staff to vigorously debate and explore the nuances of issues like bias, privacy, disinformation, accountability, and more especially examining them from the perspective of industry, civil society, academia, and government. The outcome of these discussions is reflected in the report that you are reading now - an exploration of a variety of issues with deep-dive, critical commentary on what has been done, what worked and what didn't, and what remains to be done so that we can meaningfully move forward in addressing the societal challenges posed by the deployment of AI systems. The chapters titled "Design and Techno-isolationism", "Facebook and the Digital Divide: Perspectives from Myanmar, Mexico, and India", "Future of Work", and "Media & Communications & Ethical Foresight" will hopefully provide with you novel lenses to explore this domain beyond the usual tropes that are covered in the domain of AI ethics.
Local Justice and the Algorithmic Allocation of Societal Resources
Authors: Sanmay Das
Date: 2021-11-10 00:00:00
Tasks: Decision Making, Fairness, Philosophy

AI is increasingly used to aid decision-making about the allocation of scarce societal resources, for example housing for homeless people, organs for transplantation, and food donations. Recently, there have been several proposals for how to design objectives for these systems that attempt to achieve some combination of fairness, efficiency, incentive compatibility, and satisfactory aggregation of stakeholder preferences. This paper lays out possible roles and opportunities for AI in this domain, arguing for a closer engagement with the political philosophy literature on local justice, which provides a framework for thinking about how societies have over time framed objectives for such allocation problems. It also discusses how we may be able to integrate into this framework the opportunities and risks opened up by the ubiquity of data and the availability of algorithms that can use them to make accurate predictions about the future.
Using word embedding for environmental violation analysis: Evidence from Pennsylvania unconventional oil and gas compliance reports
Authors: Dan Bi, Ju-e Guo, Erlong Zhao, Shaolong Sun, Shouyang Wang
Date: 2021-11-10 00:00:00

With the booming of the unconventional oil and gas industry, its inevitable damage to the environment and human health has attracted public attention. We applied text mining on a total 6057 the type of Environmental Health and Safety compliance reports from 2008 to 2018 lunched by the Department of Environmental Protection in Pennsylvania, USA, to discover the intern mechanism of environmental violations.
Internationalizing AI: Evolution and Impact of Distance Factors
Authors: Xuli Tang, Xin Li, Feicheng Ma
Date: 2021-11-10 00:00:00
Tasks: Descriptive

International collaboration has become imperative in the field of AI. However, few studies exist concerning how distance factors have affected the international collaboration in AI research. In this study, we investigate this problem by using 1,294,644 AI related collaborative papers harvested from the Microsoft Academic Graph (MAG) dataset. A framework including 13 indicators to quantify the distance factors between countries from 5 perspectives (i.e., geographic distance, economic distance, cultural distance, academic distance, and industrial distance) is proposed. The relationships were conducted by the methods of descriptive analysis and regression analysis. The results show that international collaboration in the field of AI today is not prevalent (only 15.7%). All the separations in international collaborations have increased over years, except for the cultural distance in masculinity/felinity dimension and the industrial distance. The geographic distance, economic distance and academic distances have shown significantly negative relationships with the degree of international collaborations in the field of AI. The industrial distance has a significant positive relationship with the degree of international collaboration in the field of AI. Also, the results demonstrate that the participation of the United States and China have promoted the international collaboration in the field of AI. This study provides a comprehensive understanding of internationalizing AI research in geographic, economic, cultural, academic, and industrial aspects.
Early Prediction of Mortality in Critical Care Setting in Sepsis Patients Using Structured Features and Unstructured Clinical Notes
Authors: Jiyoung Shin, Yikuan Li, Yuan Luo
Date: 2021-11-09 00:00:00

Sepsis is an important cause of mortality, especially in intensive care unit (ICU) patients. Developing novel methods to identify early mortality is critical for improving survival outcomes in sepsis patients. Using the MIMIC-III database, we integrated demographic data, physiological measurements and clinical notes. We built and applied several machine learning models to predict the risk of hospital mortality and 30-day mortality in sepsis patients. From the clinical notes, we generated clinically meaningful word representations and embeddings. Supervised learning classifiers and a deep learning architecture were used to construct prediction models. The configurations that utilized both structured and unstructured clinical features yielded competitive F-measure of 0.512. Our results showed that the approaches integrating both structured and unstructured clinical features can be effectively applied to assist clinicians in identifying the risk of mortality in sepsis patients upon admission to the ICU.