pwc-archive/papers-with-abstracts huggingface dataset:
EEG-connectivity: A fundamental guide and checklist for optimal study design and evaluation
Authors: Aleksandra Miljevic, Neil W. Bailey, Fidel Vila-Rodriguez, Sally E. Herring, Paul B. Fitzgerald
Date: 2021-08-31 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Brain connectivity can be estimated through a wide number of analyses applied to electroencephalographic (EEG) data. However, substantial heterogeneity in the implementation of connectivity methods exist. Heterogeneity in conceptualization of connectivity measures, data collection, or data pre-processing may be associated with variability in robustness of measurement. While it is difficult to compare the results of studies using different EEG connectivity measures, standardization of processing and reporting may facilitate the task. We discuss how factors such as referencing, epoch length and number, controls for volume conduction, artefact removal, and statistical control of multiple comparisons influence the EEG connectivity estimate for connectivity measures, and what can be done to control for potential confounds associated with these factors. Based on the results reported in previous literature, this article presents recommendations and a novel checklist developed for quality assessment of EEG connectivity studies. This checklist and its recommendations are made in an effort to draw attention to factors that may influence connectivity estimates and factors that need to be improved in future research. Standardization of procedures and reporting in EEG connectivity may lead to EEG connectivity studies to be made more synthesisable and comparable despite variations in the methodology underlying connectivity estimates.
Applying and testing a novel method to estimate animal density from motion-triggered cameras
Authors: Marcus Becker, David J. Huggard, Melanie Dickie, Camille Warbington, Jim Schieck, Emily Herdman, Robert Serrouya, Stan Boutin
Date: 2021-08-31 00:00:00

Estimating animal abundance and density are fundamental goals of many wildlife monitoring programs. Camera trapping has become an increasingly popular tool to achieve these monitoring goals due to recent advances in modeling approaches and the capacity to simultaneously collect data on multiple species. However, estimating the density of unmarked populations continues to be problematic due to the difficulty in implementing complex modeling approaches, low precision of estimates, and absence of rigor in testing of model assumptions and their influence on results. Here, we describe a novel approach that uses still image camera traps to estimate animal density without the need for individual identification, based on the Time spent In Front of the Camera (TIFC). Using results from a large-scale multi-species monitoring program with nearly 3,000 cameras deployed over six years in Alberta, Canada, we provide a reproducible methodology to estimate parameters and we test key assumptions of the TIFC model. We compare moose (Alces alces) density estimates from aerial surveys and TIFC, including incorporating correction factors for known TIFC assumption violations. The resulting corrected TIFC density estimates are comparable to aerial density estimates. We discuss the limitations of the TIFC method and areas needing further investigation, including the need for long-term monitoring of assumption violations and the number of cameras necessary to provide precise estimates. Despite the challenges of assumption violations and high measurement error, cameras and the TIFC method can provide useful alternative or complementary animal density estimates for multi-species monitoring when compared to traditional monitoring methods.
Automated Tracking of Primate Behavior
Authors: Benjamin Hayden, Hyun Soo Park, Jan Zimmermann
Date: 2021-08-30 00:00:00
Tasks: Pose Tracking

Understanding primate behavior is a mission-critical goal of both biology and biomedicine. Despite the importance of behavior, our ability to rigorously quantify it has heretofore been limited to low-information measures like preference, looking time, and reaction time, or to non-scaleable measures like ethograms. However, recent technological advances have led to a major revolution in behavioral measurement. Specifically, digital video cameras and automated pose tracking software can provide detailed measures of full body position (i.e., pose) of multiple primates over time (i.e., behavior) with high spatial and temporal resolution. Pose-tracking technology in turn can be used to detect behavioral states, such as eating, sleeping, and mating. The availability of such data has in turn spurred developments in data analysis techniques. Together, these changes are poised to lead to major advances in scientific fields that rely on behavioral as a dependent variable. In this review, we situate the tracking revolution in the history of the study of behavior, argue for investment in and development of analytical and research techniques that can profit from the advent of the era of big behavior, and propose that zoos will have a central role to play in this era.
On the link between monetary and star-shaped risk measures
Authors: Marlon Moresco, Marcelo Brutti Righi
Date: 2021-08-30 00:00:00
Tasks: Translation

Recently, Castagnoli et al. (2021) introduce the class of star-shaped risk measures as a generalization of convex and coherent ones, proving that there is a representation as the pointwise minimum of some family composed by convex risk measures. Concomitantly, Jia et al. (2020) prove a similar representation result for monetary risk measures, which are more general than star-shaped ones. Then, there is a question on how both classes are connected. In this letter, we provide an answer by casting light on the importance of the acceptability of 0, which is linked to the property of normalization. We then show that under mild conditions, a monetary risk measure is only a translation away from star-shapedness.
Deep Color Transfer using Histogram Analogy
Authors: Junyong Lee, Hyeongseok Son, GunHee Lee, Jonghyeop Lee, Sunghyun Cho, Seungyong Lee
Date: 2020-10-01 00:00:00
Proceeding: The Visual Computer 2020 10
Tasks: Diversity

We propose a novel approach to transferring the color of a reference image to a given source image. Although there can be diverse pairs of source and reference images in terms of content and composition similarity, previous methods are not capable of covering the whole diversity. To resolve this limitation, we propose a deep neural network that leverages color histogram analogy for color transfer. A histogram contains essential color information of an image, and our network utilizes the analogy between the source and reference histograms to modulate the color of the source image with abstract color features of the reference image. In our approach, histogram analogy is exploited basically among the whole images, but it can also be applied to semantically corresponding regions in the case that the source and reference images have similar contents with different compositions. Experimental results show that our approach effectively transfers the reference colors to the source images in a variety of settings. We also demonstrate a few applications of our approach, such as palette-based recolorization, color enhancement, and color editing.
Dataset of Standard Tests of Nafion 112 Membrane and Membrane Electrode Assembly (MEA) Activation Tests of Proton Exchange Membrane (PEM) Fuel Cell
Authors: Sarmin Hamidi, Sepand Haghighi, Kasra Askari
Date: 2020-02-27 00:00:00
Proceeding: ChemRxiv 2020 2

Reported data in this paper are about Nafion 112 membrane standard tests and MEA activation tests of PEM fuel cell in various operation condition. Dataset include two general electrochemical analysis method, Polarization and Impedance curves. In this dataset, effect of different pressure of H2/O2 gas, different voltages and various humidity conditions in several steps are considered. Details of experimental methods has been explained in this paper. Behavior of PEM fuel cell during distinct operation condition tests, activation procedure and different operation condition before and after activation analysis can be concluded from data. In Polarization curves, voltage and power density change as a function of flows of H2/O2 and relative humidity. Resistance of the used equivalent circuit of fuel cell can be calculated from Impedance data. Thus, experimental response of the cell is obvious in the presented data, which is useful in depth analysis, simulation and material performance investigation in PEM fuel cell researches.
PTRAIL - A python package for parallel trajectory data preprocessing
Authors: Salman Haidri, Yaksh J. Haranwala, Vania Bogorny, Chiara Renso, Vinicius Prado da Fonseca, Amilcar Soares
Date: 2021-08-26 00:00:00
Proceeding: SoftwareX 2021 8
Tasks: Position

Trajectory data represent a trace of an object that changes its position
in space over time. This kind of data is complex to handle and analyze, since it is generally produced in huge quantities, often prone to errors generated by the geolocation device, human mishandling, or area coverage limitation. Therefore, there is a need for software specifically tailored to preprocess trajectory data. In this work we propose PTRAIL, a python package oring several trajectory preprocessing steps, including ltering, feature extraction, and interpolation. PTRAIL uses parallel computation and vectorization, being suitable for large datasets and fast compared to other python libraries.
The Role of Graphlets in Viral Processes on Networks
Authors: Samira Khorshidi, Mohammad Al Hasan, George Mohler & Martin B. Short
Date: 2020-10-01 00:00:00
Proceeding: Journal of Nonlinear Science 2020 10

Predicting the evolution of viral processes on networks is an important problem with applications arising in biology, the social sciences, and the study of the Internet. In existing works, mean-field analysis based upon degree distribution is used for the prediction of viral spreading across networks of different types. However, it has been shown that degree distribution alone fails to predict the behavior of viruses on some real-world networks and recent attempts have been made to use assortativity to address this shortcoming. In this paper, we show that adding assortativity does not fully explain the variance in the spread of viruses for a number of real-world networks. We propose using the graphlet frequency distribution in combination with assortativity to explain variations in the evolution of viral processes across networks with identical degree distribution. Using a data-driven approach by coupling predictive modeling with viral process simulation on real-world networks, we show that simple regression models based on graphlet frequency distribution can explain over 95% of the variance in virality on networks with the same degree distribution but different network topologies. Our results not only highlight the importance of graphlets but also identify a small collection of graphlets which may have the highest influence over the viral processes on a network.
Assessing GAN-based approaches for generative modeling of crime text reports
Authors: Samira Khorshidi, George Mohler, Jeremy G. Carter
Date: 2020-11-09 00:00:00
Proceeding: 2020 IEEE International Conference on Intelligence and Security Informatics (ISI) 2020 11
Tasks: Clustering, Generative Adversarial Network

Analysis and modeling of crime text report data has important applications, including refinement of crime classifications, clustering of documents, and feature extraction for spatio-temporal forecasts. Having better neural network representations of crime text data may facilitate all of these tasks. This paper evaluates the ability of generative adversarial network models to represent crime text data and generate realistic crime reports. We compare four state of the art GAN algorithms in terms of quantitative metrics such as coherence, embedding similarity, negative log-likelihood, and qualitatively based on inspection of generated text. We discuss current challenges with crime text representation and directions for future research.
Repurposing recidivism models for forecasting police officer use of force
Authors: Samira Khorshidi, George Mohler, Jeremy G. Carter
Date: 2020-12-10 00:00:00
Proceeding: IEEE International Conference on Big Data (Big Data) 2020 12
Tasks: BIG-bench Machine Learning, Fairness, Feature Engineering

We review several concepts and modeling techniques from statistical and machine learning that have been developed to forecast recidivism. We show how these methods might be repurposed for forecasting police officer use of force. Using open Chicago police department use-of-force complaint data for illustration, we discuss feature engineering, construction of black-box models, interpretable forecasts, and fairness.
Explaining crime diversity with Google street view
Authors: Samira Khorshidi, Jeremy Carter, George Mohler, George Tita
Date: 2021-06-01 00:00:00
Proceeding: Springer US 2021 6
Tasks: Diversity

Crime diversity is a measure of the variety of criminal offenses in a local environment, similar to ecological diversity. While crime diversity distributions have been explained via neutral models, to date the environmental and social mechanisms behind crime diversity have not been investigated. Building on recent work demonstrating that crime rates can be inferred from street level imagery with neural network computer vision models, in this paper we consider the task of inferring crime diversity through street level imagery.
You Only Hypothesize Once: Point Cloud Registration with Rotation-equivariant Descriptors
Authors: Haiping Wang, YuAn Liu, Zhen Dong, Wenping Wang
Date: 2021-09-01 00:00:00
Tasks: Point Cloud Registration

In this paper, we propose a novel local descriptor-based framework, called You Only Hypothesize Once (YOHO), for the registration of two unaligned point clouds. In contrast to most existing local descriptors which rely on a fragile local reference frame to gain rotation invariance, the proposed descriptor achieves the rotation invariance by recent technologies of group equivariant feature learning, which brings more robustness to point density and noise. Meanwhile, the descriptor in YOHO also has a rotation equivariant part, which enables us to estimate the registration from just one correspondence hypothesis. Such property reduces the searching space for feasible transformations, thus greatly improves both the accuracy and the efficiency of YOHO. Extensive experiments show that YOHO achieves superior performances with much fewer needed RANSAC iterations on four widely-used datasets, the 3DMatch/3DLoMatch datasets, the ETH dataset and the WHU-TLS dataset. More details are shown in our project page: https://hpwang-whu.github.io/YOHO/.
Memory Augmented Multi-Instance Contrastive Predictive Coding for Sequential Recommendation
Authors: Ruihong Qiu, Zi Huang, Hongzhi Yin
Date: 2021-09-01 00:00:00
Tasks: Contrastive Learning, Sequential Recommendation

The sequential recommendation aims to recommend items, such as products, songs and places, to users based on the sequential patterns of their historical records. Most existing sequential recommender models consider the next item prediction task as the training signal. Unfortunately, there are two essential challenges for these methods: (1) the long-term preference is difficult to capture, and (2) the supervision signal is too sparse to effectively train a model. In this paper, we propose a novel sequential recommendation framework to overcome these challenges based on a memory augmented multi-instance contrastive predictive coding scheme, denoted as MMInfoRec. The basic contrastive predictive coding (CPC) serves as encoders of sequences and items. The memory module is designed to augment the auto-regressive prediction in CPC to enable a flexible and general representation of the encoded preference, which can improve the ability to capture the long-term preference. For effective training of the MMInfoRec model, a novel multi-instance noise contrastive estimation (MINCE) loss is proposed, using multiple positive samples, which offers effective exploitation of samples inside a mini-batch. The proposed MMInfoRec framework falls into the contrastive learning style, within which, however, a further finetuning step is not required given that its contrastive training task is well aligned with the target recommendation task. With extensive experiments on four benchmark datasets, MMInfoRec can outperform the state-of-the-art baselines.
Impossibility Results in AI: A Survey
Authors: Mario Brcic, Roman V. Yampolskiy
Date: 2021-09-01 00:00:00
Tasks: Decision Making, Ethics, Survey

An impossibility theorem demonstrates that a particular problem or set of problems cannot be solved as described in the claim. Such theorems put limits on what is possible to do concerning artificial intelligence, especially the super-intelligent one. As such, these results serve as guidelines, reminders, and warnings to AI safety, AI policy, and governance researchers. These might enable solutions to some long-standing questions in the form of formalizing theories in the framework of constraint satisfaction without committing to one option. We strongly believe this to be the most prudent approach to long-term AI safety initiatives. In this paper, we have categorized impossibility theorems applicable to AI into five mechanism-based categories: deduction, indistinguishability, induction, tradeoffs, and intractability. We found that certain theorems are too specific or have implicit assumptions that limit application. Also, we added new results (theorems) such as the unfairness of explainability, the first explainability-related result in the induction category. The remaining results deal with misalignment between the clones and put a limit to the self-awareness of agents. We concluded that deductive impossibilities deny 100%-guarantees for security. In the end, we give some ideas that hold potential in explainability, controllability, value alignment, ethics, and group decision-making. They can be deepened by further investigation.
From Movement Kinematics to Object Properties: Online Recognition of Human Carefulness
Authors: Linda Lastrico, Alessandro Carfì, Francesco Rea, Alessandra Sciutti, Fulvio Mastrogiovanni
Date: 2021-09-01 00:00:00
Tasks: Object

When manipulating objects, humans finely adapt their motions to the characteristics of what they are handling. Thus, an attentive observer can foresee hidden properties of the manipulated object, such as its weight, temperature, and even whether it requires special care in manipulation. This study is a step towards endowing a humanoid robot with this last capability. Specifically, we study how a robot can infer online, from vision alone, whether or not the human partner is careful when moving an object. We demonstrated that a humanoid robot could perform this inference with high accuracy (up to 81.3%) even with a low-resolution camera. Only for short movements without obstacles, carefulness recognition was insufficient. The prompt recognition of movement carefulness from observing the partner's action will allow robots to adapt their actions on the object to show the same degree of care as their human partners.
Planning from video game descriptions
Authors: Ignacio Vellido, Carlos Núñez-Molina, Vladislav Nikolov, Juan Fdez-Olivares
Date: 2021-09-01 00:00:00

This project proposes a methodology for the automatic generation of action models from video game dynamics descriptions, as well as its integration with a planning agent for the execution and monitoring of the plans. Planners use these action models to get the deliberative behaviour for an agent in many different video games and, combined with a reactive module, solve deterministic and no-deterministic levels. Experimental results validate the methodology and prove that the effort put by a knowledge engineer can be greatly reduced in the definition of such complex domains. Furthermore, benchmarks of the domains has been produced that can be of interest to the international planning community to evaluate planners in international planning competitions.
Proceedings of KDD 2020 Workshop on Data-driven Humanitarian Mapping: Harnessing Human-Machine Intelligence for High-Stake Public Policy and Resilience Planning
Authors: Snehalkumar, S. Gaikwad, Shankar Iyer, Dalton Lunga, Yu-Ru Lin
Date: 2021-09-01 00:00:00
Tasks: Ethics, Fairness, Humanitarian

Humanitarian challenges, including natural disasters, food insecurity, climate change, racial and gender violence, environmental crises, the COVID-19 coronavirus pandemic, human rights violations, and forced displacements, disproportionately impact vulnerable communities worldwide. According to UN OCHA, 235 million people will require humanitarian assistance in 2021 . Despite these growing perils, there remains a notable paucity of data science research to scientifically inform equitable public policy decisions for improving the livelihood of at-risk populations. Scattered data science efforts exist to address these challenges, but they remain isolated from practice and prone to algorithmic harms concerning lack of privacy, fairness, interpretability, accountability, transparency, and ethics. Biases in data-driven methods carry the risk of amplifying inequalities in high-stakes policy decisions that impact the livelihood of millions of people. Consequently, proclaimed benefits of data-driven innovations remain inaccessible to policymakers, practitioners, and marginalized communities at the core of humanitarian actions and global development. To help fill this gap, we propose the Data-driven Humanitarian Mapping Research Program, which focuses on developing novel data science methodologies that harness human-machine intelligence for high-stakes public policy and resilience planning. The proceedings of the 1st Data-driven Humanitarian Mapping workshop at the 26th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, August 24th, 2020.
ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues
Authors: Guojun Yan, Jiahuan Pei, Pengjie Ren, Zhaochun Ren, Xin Xin, Huasheng Liang, Maarten de Rijke, Zhumin Chen
Date: 2021-09-01 00:00:00
Tasks: Benchmarking, Contrastive Learning, Dialogue Generation, Language Modelling

Medical dialogue systems (MDSs) aim to assist doctors and patients with a range of professional medical services, i.e., diagnosis, treatment and consultation. The development of MDSs is hindered because of a lack of resources. In particular. (1) there is no dataset with large-scale medical dialogues that covers multiple medical services and contains fine-grained medical labels (i.e., intents, actions, slots, values), and (2) there is no set of established benchmarks for MDSs for multi-domain, multi-service medical dialogues. In this paper, we present ReMeDi, a set of resource for medical dialogues. ReMeDi consists of two parts, the ReMeDi dataset and the ReMeDi benchmarks. The ReMeDi dataset contains 96,965 conversations between doctors and patients, including 1,557 conversations with fine-gained labels. It covers 843 types of diseases, 5,228 medical entities, and 3 specialties of medical services across 40 domains. To the best of our knowledge, the ReMeDi dataset is the only medical dialogue dataset that covers multiple domains and services, and has fine-grained medical labels. The second part of the ReMeDi resources consists of a set of state-of-the-art models for (medical) dialogue generation. The ReMeDi benchmark has the following methods: (1) pretrained models (i.e., BERT-WWM, BERT-MED, GPT2, and MT5) trained, validated, and tested on the ReMeDi dataset, and (2) a self-supervised contrastive learning(SCL) method to expand the ReMeDi dataset and enhance the training of the state-of-the-art pretrained models. We describe the creation of the ReMeDi dataset, the ReMeDi benchmarking methods, and establish experimental results using the ReMeDi benchmarking methods on the ReMeDi dataset for future research to compare against. With this paper, we share the dataset, implementations of the benchmarks, and evaluation scripts.