pwc-archive/papers-with-abstracts huggingface dataset:
Efficient Neuromorphic Signal Processing with Loihi 2
Authors: Garrick Orchard, E. Paxon Frady, Daniel Ben Dayan Rubin, Sophia Sanborn, Sumit Bam Shrestha, Friedrich T. Sommer, Mike Davies
Date: 2021-11-05 00:00:00
Tasks: Audio Classification, Optical Flow Estimation

The biologically inspired spiking neurons used in neuromorphic computing are nonlinear filters with dynamic state variables -- very different from the stateless neuron models used in deep learning. The next version of Intel's neuromorphic research processor, Loihi 2, supports a wide range of stateful spiking neuron models with fully programmable dynamics. Here we showcase advanced spiking neuron models that can be used to efficiently process streaming data in simulation experiments on emulated Loihi 2 hardware. In one example, Resonate-and-Fire (RF) neurons are used to compute the Short Time Fourier Transform (STFT) with similar computational complexity but 47x less output bandwidth than the conventional STFT. In another example, we describe an algorithm for optical flow estimation using spatiotemporal RF neurons that requires over 90x fewer operations than a conventional DNN-based solution. We also demonstrate promising preliminary results using backpropagation to train RF neurons for audio classification tasks. Finally, we show that a cascade of Hopf resonators - a variant of the RF neuron - replicates novel properties of the cochlea and motivates an efficient spike-based spectrogram encoder.
Artistic Autonomy in AI Art
Authors: Alayt Issak
Date: 2021-10-22 00:00:00

The concept of art has transposed meaning and medium across time, with its context being a deciding factor for its evolution. However, human beings' innermost functionality remains the same, and art, to this day, serves as an expression of the subconscious. Accelerated by the conception of GANs in 2014, automation has become a central medium in Artificial Intelligence (AI) Art. However, this raises concern over AI's influence on artistic autonomy within the process of creativity. This paper introduces the ethical responsibility of AI towards maintaining the artist's volition in exercising autonomy and utilizes principles of self-determination theory alongside fundamental limits of creativity to do so.
Learning an Adaptive Meta Model-Generator for Incrementally Updating Recommender Systems
Authors: Danni Peng, Sinno Jialin Pan, Jie Zhang, AnXiang Zeng
Date: 2021-11-08 00:00:00
Tasks: Computational Efficiency, Recommendation Systems

Recommender Systems (RSs) in real-world applications often deal with billions of user interactions daily. To capture the most recent trends effectively, it is common to update the model incrementally using only the newly arrived data. However, this may impede the model's ability to retain long-term information due to the potential overfitting and forgetting issues. To address this problem, we propose a novel Adaptive Sequential Model Generation (ASMG) framework, which generates a better serving model from a sequence of historical models via a meta generator. For the design of the meta generator, we propose to employ Gated Recurrent Units (GRUs) to leverage its ability to capture the long-term dependencies. We further introduce some novel strategies to apply together with the GRU meta generator, which not only improve its computational efficiency but also enable more accurate sequential modeling. By instantiating the model-agnostic framework on a general deep learning-based RS model, we demonstrate that our method achieves state-of-the-art performance on three public datasets and one industrial dataset.
Em-K Indexing for Approximate Query Matching in Large-scale ER
Authors: Samudra Herath, Matthew Roughan, Gary Glonek
Date: 2021-11-07 00:00:00
Tasks: Blocking, Entity Resolution

Accurate and efficient entity resolution (ER) is a significant challenge in many data mining and analysis projects requiring integrating and processing massive data collections. It is becoming increasingly important in real-world applications to develop ER solutions that produce prompt responses for entity queries on large-scale databases. Some of these applications demand entity query matching against large-scale reference databases within a short time. We define this as the query matching problem in ER in this work. Indexing or blocking techniques reduce the search space and execution time in the ER process. However, approximate indexing techniques that scale to very large-scale datasets remain open to research. In this paper, we investigate the query matching problem in ER to propose an indexing method suitable for approximate and efficient query matching. We first use spatial mappings to embed records in a multidimensional Euclidean space that preserves the domain-specific similarity. Among the various mapping techniques, we choose multidimensional scaling. Then using a Kd-tree and the nearest neighbour search, the method returns a block of records that includes potential matches for a query. Our method can process queries against a large-scale dataset using only a fraction of the data $L$ (given the dataset size is $N$), with a $O(L^2)$ complexity where $L \ll N$. The experiments conducted on several datasets showed the effectiveness of the proposed method.
FAIR Metadata: A Community-driven Vocabulary Application
Authors: Christopher B. Rauch, Mat Kelly, John A. Kunze, Jane Greenberg
Date: 2021-11-06 00:00:00

FAIR metadata is critical to supporting FAIR data overall. Transparency, community engagement, and flexibility are key aspects of FAIR that apply to metadata. This paper presents YAMZ (Yet Another Metadata Zoo), a community-driven vocabulary application that supports FAIR. The history ofYAMZ and its original features are reviewed, followed by a presentation of recent innovations and a discussion of how YAMZ supports FAIR principles. The conclusion identifies next steps and key outputs.
Twitter Big Data as a Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions
Authors: Nirmalya Thakur
Date: 2021-11-04 00:00:00
Tasks: Information Retrieval, Retrieval

The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and diverse use-cases in assisted living, military, healthcare, firefighting, and industry 4.0. The exoskeleton market is projected to increase by multiple times of its current value within the next two years. Therefore, it is crucial to study the degree and trends of user interest, views, opinions, perspectives, attitudes, acceptance, feedback, engagement, buying behavior, and satisfaction, towards exoskeletons, for which the availability of Big Data of conversations about exoskeletons is necessary. The Internet of Everything style of today's living, characterized by people spending more time on the internet than ever before, with a specific focus on social media platforms, holds the potential for the development of such a dataset by the mining of relevant social media conversations. Twitter, one such social media platform, is highly popular amongst all age groups, where the topics found in the conversation paradigms include emerging technologies such as exoskeletons. To address this research challenge, this work makes two scientific contributions to this field. First, it presents an open-access dataset of about 140,000 tweets about exoskeletons that were posted in a 5-year period from May 21, 2017, to May 21, 2022. Second, based on a comprehensive review of the recent works in the fields of Big Data, Natural Language Processing, Information Retrieval, Data Mining, Pattern Recognition, and Artificial Intelligence that may be applied to relevant Twitter data for advancing research, innovation, and discovery in the field of exoskeleton research, a total of 100 Research Questions are presented for researchers to study, analyze, evaluate, ideate, and investigate based on this dataset.
Treatise on Hearing: The Temporal Auditory Imaging Theory Inspired by Optics and Communication
Authors: Adam Weisser
Date: 2021-11-08 00:00:00

A new theory of mammalian hearing is presented, which accounts for the auditory image in the midbrain (inferior colliculus) of objects in the acoustical environment of the listener. It is shown that the ear is a temporal imaging system that comprises three transformations of the envelope functions: cochlear group-delay dispersion, cochlear time lensing, and neural group-delay dispersion. These elements are analogous to the optical transformations in vision of diffraction between the object and the eye, spatial lensing by the lens, and second diffraction between the lens and the retina. Unlike the eye, it is established that the human auditory system is naturally defocused, so that coherent stimuli do not react to the defocus, whereas completely incoherent stimuli are impacted by it and may be blurred by design. It is argued that the auditory system can use this differential focusing to enhance or degrade the images of real-world acoustical objects that are partially coherent. The theory is founded on coherence and temporal imaging theories that were adopted from optics. In addition to the imaging transformations, the corresponding inverse-domain modulation transfer functions are derived and interpreted with consideration to the nonuniform neural sampling operation of the auditory nerve. These ideas are used to rigorously initiate the concepts of sharpness and blur in auditory imaging, auditory aberrations, and auditory depth of field. In parallel, ideas from communication theory are used to show that the organ of Corti functions as a multichannel phase-locked loop (PLL) that constitutes the point of entry for auditory phase locking and hence conserves the signal coherence. It provides an anchor for a dual coherent and noncoherent auditory detection in the auditory brain that culminates in auditory accommodation. Implications on hearing impairments are discussed as well.
Revisiting the Properties of Money
Authors: Isaiah Hull, Or Sattath
Date: 2021-11-08 00:00:00

The properties of money commonly referenced in the economics literature were originally identified by Jevons (1876) and Menger (1892) in the late 1800s and were intended to describe physical currencies, such as commodity money, metallic coins, and paper bills. In the digital era, many non-physical currencies have either entered circulation or are under development, including demand deposits, cryptocurrencies, stablecoins, central bank digital currencies (CBDCs), in-game currencies, and quantum money. These forms of money have novel properties that have not been studied extensively within the economics literature, but may be important determinants of the monetary equilibrium that emerges in the forthcoming era of heightened currency competition. This paper makes the first exhaustive attempt to identify and define the properties of all physical and digital forms of money. It reviews both the economics and computer science literatures and categorizes properties within an expanded version of the original functions-and-properties framework of money that includes societal and regulatory objectives.
RawBoost: A Raw Data Boosting and Augmentation Method applied to Automatic Speaker Verification Anti-Spoofing
Authors: Hemlata Tak, Madhu Kamble, Jose Patino, Massimiliano Todisco, Nicholas Evans
Date: 2021-11-08 00:00:00
Tasks: Speaker Verification, Voice Anti-spoofing

This paper introduces RawBoost, a data boosting and augmentation method for the design of more reliable spoofing detection solutions which operate directly upon raw waveform inputs. While RawBoost requires no additional data sources, e.g. noise recordings or impulse responses and is data, application and model agnostic, it is designed for telephony scenarios. Based upon the combination of linear and non-linear convolutive noise, impulsive signal-dependent additive noise and stationary signal-independent additive noise, RawBoost models nuisance variability stemming from, e.g., encoding, transmission, microphones and amplifiers, and both linear and non-linear distortion. Experiments performed using the ASVspoof 2021 logical access database show that RawBoost improves the performance of a state-of-the-art raw end-to-end baseline system by 27% relative and is only outperformed by solutions that either depend on external data or that require additional intervention at the model level.
Creating A Coefficient of Change in the Built Environment After a Natural Disaster
Authors: Karla Saldana Ochoa
Date: 2021-10-31 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

This study proposes a novel method to assess damages in the built environment using a deep learning workflow to quantify it. Thanks to an automated crawler, aerial images from before and after a natural disaster of 50 epicenters worldwide were obtained from Google Earth, generating a 10,000 aerial image database with a spatial resolution of 2 m per pixel. The study utilizes the algorithm Seg-Net to perform semantic segmentation of the built environment from the satellite images in both instances (prior and post-natural disasters). For image segmentation, Seg-Net is one of the most popular and general CNN architectures. The Seg-Net algorithm used reached an accuracy of 92% in the segmentation. After the segmentation, we compared the disparity between both cases represented as a percentage of change. Such coefficient of change represents the damage numerically an urban environment had to quantify the overall damage in the built environment. Such an index can give the government an estimate of the number of affected households and perhaps the extent of housing damage.
Systematic Review for AI-based Language Learning Tools
Authors: Jin Ha Woo, Heeyoul Choi
Date: 2021-10-29 00:00:00
Tasks: Language Acquisition

The Second Language Acquisition field has been significantly impacted by a greater emphasis on individualized learning and rapid developments in artificial intelligence (AI). Although increasingly adaptive language learning tools are being developed with the application of AI to the Computer Assisted Language Learning field, there have been concerns regarding insufficient information and teacher preparation. To effectively utilize these tools, teachers need an in-depth overview on recently developed AI-based language learning tools. Therefore, this review synthesized information on AI tools that were developed between 2017 and 2020. A majority of these tools utilized machine learning and natural language processing, and were used to identify errors, provide feedback, and assess language abilities. After using these tools, learners demonstrated gains in their language abilities and knowledge. This review concludes by presenting pedagogical implications and emerging themes in the future research of AI-based language learning tools.
The Invisible COVID-19 Crisis: Post-Traumatic Stress Disorder Risk Among Frontline Physicians Treating COVID-19 Patients
Authors: Sayanti Mukherjee, Lance Rintamaki, Janet L. Shucard, Zhiyuan Wei, Lindsey E. Carlasare, Christine A. Sinsky
Date: 2021-10-25 00:00:00

This study evaluated post traumatic stress disorder (PTSD) among frontline US physicians (treating COVID-19 patients) in comparison with second-line physicians (not treating COVID-19 patients), and identified the significance and patterns of factors associated with higher PTSD risk. A cross-sectional, web-based survey was deployed during August and September, 2020, to practicing physicians in the 18 states with the largest COVID-19 cases. Among 1,478 responding physicians, 1,017 completed the PTSD Checklist (PCL-5). First, the PCL-5 was used to compare symptom endorsement between the two physician groups. A greater percentage of frontline than second-line physicians had clinically significant endorsement of PCL-5 symptoms and higher PCL-5 scores. Second, logistic regression and seven nonlinear machine learning (ML) algorithms were leveraged to identify potential predictors of PTSD risk by analyzing variable importance and partial dependence plots. Predictors of PTSD risk included cognitive/psychological measures, occupational characteristics, work experiences, social support, demographics, and workplace characteristics. Importantly, the final ML model random forest, identified patterns of both damaging and protective predictors of PTSD risk among frontline physicians. Key damaging factors included depression, burnout, negative coping, fears of contracting/transmitting COVID-19, perceived stigma, and insufficient resources to treat COVID-19 patients. Protective factors included resilience and support from employers/friends/family/significant others. This study underscores the value of ML algorithms to uncover nonlinear relationships among protective/damaging risk factors for PTSD in frontline physicians, which may better inform interventions to prepare healthcare systems for future epidemics/pandemics.
Addressing Privacy Threats from Machine Learning
Authors: Mary Anne Smart
Date: 2021-10-25 00:00:00
Tasks: BIG-bench Machine Learning, Disaster Response

Every year at NeurIPS, machine learning researchers gather and discuss exciting applications of machine learning in areas such as public health, disaster response, climate change, education, and more. However, many of these same researchers are expressing growing concern about applications of machine learning for surveillance (Nanayakkara et al., 2021). This paper presents a brief overview of strategies for resisting these surveillance technologies and calls for greater collaboration between machine learning and human-computer interaction researchers to address the threats that these technologies pose.
A Framework for Deprecating Datasets: Standardizing Documentation, Identification, and Communication
Authors: Alexandra Sasha Luccioni, Frances Corry, Hamsini Sridharan, Mike Ananny, Jason Schultz, Kate Crawford
Date: 2021-10-18 00:00:00

Datasets are central to training machine learning (ML) models. The ML community has recently made significant improvements to data stewardship and documentation practices across the model development life cycle. However, the act of deprecating, or deleting, datasets has been largely overlooked, and there are currently no standardized approaches for structuring this stage of the dataset life cycle. In this paper, we study the practice of dataset deprecation in ML, identify several cases of datasets that continued to circulate despite having been deprecated, and describe the different technical, legal, ethical, and organizational issues raised by such continuations. We then propose a Dataset Deprecation Framework that includes considerations of risk, mitigation of impact, appeal mechanisms, timeline, post-deprecation protocols, and publication checks that can be adapted and implemented by the ML community. Finally, we propose creating a centralized, sustainable repository system for archiving datasets, tracking dataset modifications or deprecations, and facilitating practices of care and stewardship that can be integrated into research and publication processes.
Topic Modeling, Clade-assisted Sentiment Analysis, and Vaccine Brand Reputation Analysis of COVID-19 Vaccine-related Facebook Comments in the Philippines
Authors: Jasper Kyle Catapang, Jerome V. Cleofas
Date: 2021-10-11 00:00:00
Tasks: Clustering, Misinformation, Sentiment Analysis

Vaccine hesitancy and other COVID-19-related concerns and complaints in the Philippines are evident on social media. It is important to identify these different topics and sentiments in order to gauge public opinion, use the insights to develop policies, and make necessary adjustments or actions to improve public image and reputation of the administering agency and the COVID-19 vaccines themselves. This paper proposes a semi-supervised machine learning pipeline to perform topic modeling, sentiment analysis, and an analysis of vaccine brand reputation to obtain an in-depth understanding of national public opinion of Filipinos on Facebook. The methodology makes use of a multilingual version of Bidirectional Encoder Representations from Transformers or BERT for topic modeling, hierarchical clustering, five different classifiers for sentiment analysis, and cosine similarity of BERT topic embeddings for vaccine brand reputation analysis. Results suggest that any type of COVID-19 misinformation is an emergent property of COVID-19 public opinion, and that the detection of COVID-19 misinformation can be an unsupervised task. Sentiment analysis aided by hierarchical clustering reveal that 21 of the 25 topics extrapolated by topic modeling are negative topics. Such negative comments spike in count whenever the Department of Health in the Philippines posts about the COVID-19 situation in other countries. Additionally, the high numbers of laugh reactions on the Facebook posts by the same agency -- without any humorous content -- suggest that the reactors of these posts tend to react the way they do, not because of what the posts are about but because of who posted them.
Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions
Authors: Hui Yin, XiangYu Song, Shuiqiao Yang, JianXin Li
Date: 2021-10-08 00:00:00
Tasks: Sentiment Analysis

The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has lasted for nearly two years and caused unprecedented impacts on people's daily life around the world. Even worse, the emergence of the COVID-19 Delta variant once again puts the world in danger. Fortunately, many countries and companies have started to develop coronavirus vaccines since the beginning of this disaster. Till now, more than 20 vaccines have been approved by the World Health Organization (WHO), bringing light to people besieged by the pandemic. The promotion of COVID-19 vaccination around the world also brings a lot of discussions on social media about different aspects of vaccines, such as efficacy and security. However, there does not exist much research work to systematically analyze public opinion towards COVID-19 vaccines. In this study, we conduct an in-depth analysis of tweets related to the coronavirus vaccine on Twitter to understand the trending topics and their corresponding sentimental polarities regarding the country and vaccine levels. The results show that a majority of people are confident in the effectiveness of vaccines and are willing to get vaccinated. In contrast, the negative tweets are often associated with the complaints of vaccine shortages, side effects after injections and possible death after being vaccinated. Overall, this study exploits popular NLP and topic modeling methods to mine people's opinions on the COVID-19 vaccines on social media and to analyse and visualise them objectively. Our findings can improve the readability of the noisy information on social media and provide effective data support for the government and policy makers.