Digital Commons Network^™

Adastreamlite: Environment-Adaptive Streaming Speech Recognition On Mobile Devices, Yuheng Wei, Jie Xiong, Hui Liu, Yingtao Yu, Jiangtao Pan, Junzhao Du

Computer Science Department Faculty Publication Series

Streaming speech recognition aims to transcribe speech to text in a streaming manner, providing real-time speech interaction for smartphone users. However, it is not trivial to develop a high-performance streaming speech recognition system purely running on mobile platforms, due to the complex real-world acoustic environments and the limited computational resources of smartphones. Most existing solutions lack the generalization to unseen environments and have difficulty to work with streaming speech. In this paper, we design AdaStreamLite, an environment-adaptive streaming speech recognition tool for smartphones. AdaStreamLite interacts with its surroundings to capture the characteristics of the current acoustic environment to improve the …

Contactless Monitoring System Versus Gold Standard For Respiratory Rate Monitoring In Emergency Department Patients: Pilot Comparison Study, Md Farhan Tasnim Oshim, Brittany P. Chapman, Deepak Ganesan, Stephanie P. Carreiro, Et. Al.

Computer Science Department Faculty Publication Series

Background: Respiratory rate is a crucial indicator of disease severity yet is the most neglected vital sign. Subtle changes in respiratory rate may be the first sign of clinical deterioration in a variety of disease states. Current methods of respiratory rate monitoring are labor-intensive and sensitive to motion artifacts, which often leads to inaccurate readings or underreporting; therefore, new methods of respiratory monitoring are needed. The PulsON 440 (P440; TSDR Ultra Wideband Radios and Radars) radar module is a contactless sensor that uses an ultrawideband impulse radar to detect respiratory rate. It has previously demonstrated accuracy in a laboratory setting …

Wall Matters: Rethinking The Effect Of Wall For Wireless Sensing, Binbin Xie, Minhao Cui, Deepak Ganesan, Jie Xiong

Computer Science Department Faculty Publication Series

Wireless sensing has demonstrated its potential of utilizing radio frequency (RF) signals to sense individuals and objects. Among different wireless signals, LoRa signal is particularly promising for through-wall sensing owing to its strong penetration capability. However, existing works view walls as a "bad" thing as they attenuate signal power and decrease the sensing coverage. In this paper, we show a counter-intuitive observation, i.e., walls can be used to increase the sensing coverage if the RF devices are placed properly with respect to walls. To fully understand the underlying principle behind this observation, we develop a through-wall sensing model to mathematically …

Cross-Shape Attention For Part Segmentation Of 3d Point Clouds, Marios Loizou, Siddhant Garg, Dmitry Petrov, Melinos Averkiou, Evangelos Kalogerakis

Computer Science Department Faculty Publication Series

We present a deep learning method that propagates point-wise feature representations across shapes within a collection for the purpose of 3D shape segmentation. We propose a cross-shape attention mechanism to enable interactions between a shape's point-wise features and those of other shapes. The mechanism assesses both the degree of interaction between points and also mediates feature propagation across shapes, improving the accuracy and consistency of the resulting point-wise feature representations for shape segmentation. Our method also proposes a shape retrieval measure to select suitable shapes for cross-shape attention operations for each test shape. Our experiments demonstrate that our approach yields …

From Community Governance To Customer Service And Back Again: Re-Examining Pre-Web Models Of Online Governance To Address Platforms’ Crisis Of Legitimacy, Ethan Zuckerman, Chand Rajendra-Nicolucci

Computer Science Department Faculty Publication Series

As online platforms grow, they find themselves increasingly trying to balance two competing priorities: individual rights and public health. This has coincided with the professionalization of platforms’ trust and safety operations—what we call the “customer service” model of online governance. As professional trust and safety teams attempt to balance individual rights and public health, platforms face a crisis of legitimacy, with decisions in the name of individual rights or public health scrutinized and criticized as corrupt, arbitrary, and irresponsible by stakeholders of all stripes. We review early accounts of online governance to consider whether the customer service model has obscured …

Greedy Spanners In Euclidean Spaces Admit Sublinear Separators, Hung Le, Cuong Than

Computer Science Department Faculty Publication Series

The greedy spanner in low dimensional Euclidean space is a fundamental geometric construction that has been extensively studied over three decades as it possesses the two most basic properties of a good spanner: constant maximum degree and constant lightness.

The Right Spin: Learning Object Motion From Rotation-Compensated Flow Fields, Pia Bideau, Erik Learned-Miller, Cordelia Schmid, Karteek Alahari

Computer Science Department Faculty Publication Series

A good understanding of geometrical concepts as well as a broad familiarity with objects lead to excellent human perception of moving objects. The human ability to detect and segment moving objects works in the presence of multiple objects, complex background geometry, motion of the observer and even camouflage. How we perceive moving objects so reliably is a longstanding research question in computer vision and borrows findings from related areas such as psychology, cognitive science and physics. One approach to the problem is to teach a deep network to model all of these effects. This is in contrast with the strategy …

Machine Learning-Based Nicotine Addiction Prediction Models For Youth E-Cigarette And Waterpipe (Hookah) Users, Jeeyae Choi, Hee-Tae Jung, Anastasiya Ferrell, Seoyoon Woo, Linda Haddad

Computer Science Department Faculty Publication Series

Despite the harmful effect on health, e-cigarette and hookah smoking in youth in the U.S. has increased. Developing tailored e-cigarette and hookah cessation programs for youth is imperative. The aim of this study was to identify predictor variables such as social, mental, and environmental determinants that cause nicotine addiction in youth e-cigarette or hookah users and build nicotine addiction prediction models using machine learning algorithms. A total of 6511 participants were identified as ever having used e-cigarettes or hookah from the National Youth Tobacco Survey (2019) datasets. Prediction models were built by Random Forest with ReliefF and Least Absolute Shrinkage …

Unique Scales Preserve Self-Similar Integrate-And-Fire Functionality Of Neuronal Clusters, Anar Amgalan, Patrick Taylor, Lilianne R. Mujica-Parodi, Hava T. Siegelmann

Computer Science Department Faculty Publication Series

Brains demonstrate varying spatial scales of nested hierarchical clustering. Identifying the brain's neuronal cluster size to be presented as nodes in a network computation is critical to both neuroscience and artificial intelligence, as these define the cognitive blocks capable of building intelligent computation. Experiments support various forms and sizes of neural clustering, from handfuls of dendrites to thousands of neurons, and hint at their behavior. Here, we use computational simulations with a brain-derived fMRI network to show that not only do brain networks remain structurally self-similar across scales but also neuron-like signal integration functionality (integrate and fire) is preserved at …

Evolutionary Dynamics Of Bertrand Duopoly, Julian Killingback, Timothy Killingback

Computer Science Department Faculty Publication Series

Duopolies are one of the simplest economic situations where interactions between firms determine market behavior. The standard model of a price-setting duopoly is the Bertrand model, which has the unique solution that both firms set their prices equal to their costs-a paradoxical result where both firms obtain zero profit, which is generally not observed in real market duopolies. Here we propose a new game theory model for a price-setting duopoly, which we show resolves the paradoxical behavior of the Bertrand model and provides a consistent general model for duopolies.

Correlation Clustering In Data Streams, Kook Jin Ahn, Graham Cormode, Sudipto Guha, Andrew Mcgregor, Anthony Wirth

Computer Science Department Faculty Publication Series

Clustering is a fundamental tool for analyzing large data sets. A rich body of work has been devoted to designing data-stream algorithms for the relevant optimization problems such as k-center, k-median, and k-means. Such algorithms need to be both time and and space efcient. In this paper, we address the problem of correlation clustering in the dynamic data stream model. The stream consists of updates to the edge weights of a graph on n nodes and the goal is to find a node-partition such that the end-points of negative-weight edges are typically in diferent clusters whereas the …

Near-Term Ecological Forecasting For Dynamic Aeroconservation Of Migratory Birds, Kyle G. Horton, Benjamin M. Van Doren, Heidi J. Albers, Andrew Farnsworth, Daniel Sheldon

Computer Science Department Faculty Publication Series

Near-term ecological forecasting has the potential to mitigate negative impacts of human modifications on wildlife by directing efficient action through relevant and timely predictions. We used the U.S. avian migration system to highlight ecological forecasting applications for aeroconservation. We used millions of observations from 143 weather surveillance radars to construct and evaluate a migration forecasting system for nocturnal bird migration over the contiguous United States. We identified the number of nights of mitigation required to reduce the risk of aerial hazards to 50% of avian migrants passing a given area in spring and autumn based on dynamic forecasts of migration …

Decomposition Of Reaching Movements Enables Detection And Measurement Of Ataxia, Brandon Oubre, Jean-Francois Daneault, Kallie Whritenour, Nergis C. Khan, Christopher D. Stephen, Jeremy D. Schmahmann, Sunghoon Ivan Lee, Anoopum S. Gupta

Computer Science Department Faculty Publication Series

Technologies that enable frequent, objective, and precise measurement of ataxia severity would benefit clinical trials by lowering participation barriers and improving the ability to measure disease state and change. We hypothesized that analyzing characteristics of sub-second movement profiles obtained during a reaching task would be useful for objectively quantifying motor characteristics of ataxia. Participants with ataxia (N=88), participants with parkinsonism (N=44), and healthy controls (N=34) performed a computer tablet version of the finger-to-nose test while wearing inertial sensors on their wrists. Data features designed to capture signs of ataxia were extracted from participants’ decomposed …

Relation Classification For Bleeding Events From Electronic Health Records Using Deep Learning Systems: An Empirical Study, Avijit Mitra, Bhanu Pratap Singh Rawat, David D. Mcmanus, Hong Yu

Computer Science Department Faculty Publication Series

Background: Accurate detection of bleeding events from electronic health records (EHRs) is crucial for identifying and characterizing different common and serious medical problems. To extract such information from EHRs, it is essential to identify the relations between bleeding events and related clinical entities (eg, bleeding anatomic sites and lab tests). With the advent of natural language processing (NLP) and deep learning (DL)-based techniques, many studies have focused on their applicability for various clinical applications. However, no prior work has utilized DL to extract relations between bleeding events and relevant entities. Objective: In this study, we aimed to evaluate multiple DL …

Scalable And High-Fidelity Quantum Random Access Memory In Spin-Photon Networks, Kevin C. Chen, Wenhan Dai, Carlos Errando-Herranz, Seth Lloyd, Dirk Englund

Computer Science Department Faculty Publication Series

A quantum random access memory (qRAM) is considered an essential computing unit to enable polynomial speedups in quantum information processing. Proposed implementations include the use of neutral atoms and superconducting circuits to construct a binary tree but these systems still require demonstrations of the elementary components. Here, we propose a photonic-integrated-circuit (PIC) architecture integrated with solid-state memories as a viable platform for constructing a qRAM. We also present an alternative scheme based on quantum teleportation and extend it to the context of quantum networks. Both implementations realize the two key qRAM operations, (1) quantum state transfer and (2) quantum routing, …

Evaluating The Effectiveness Of Noteaid In A Community Hospital Setting: Randomized Trial Of Electronic Health Record Note Comprehension Interventions With Patients, John P. Lalor, Wen Hu, Matthew Tran, Hao Wu, Kathleen M. Mazor, Hong Yu

Computer Science Department Faculty Publication Series

Background: Interventions to define medical jargon have been shown to improve electronic health record (EHR) note comprehension among crowdsourced participants on Amazon Mechanical Turk (AMT). However, AMT participants may not be representative of the general population or patients who are most at-risk for low health literacy. Objective: In this work, we assessed the efficacy of an intervention (NoteAid) for EHR note comprehension among participants in a community hospital setting. Methods: Participants were recruited from Lowell General Hospital (LGH), a community hospital in Massachusetts, to take the ComprehENotes test, a web-based test of EHR note comprehension. Participants were randomly assigned to …

Quantifying The Impact Of Non-Stationarity In Reinforcement Learning-Based Traffic Signal Control, Lucas N. Alegre, Ana L.C. Bazzan, Bruno C. Da Silva

Computer Science Department Faculty Publication Series

In reinforcement learning (RL), dealing with non-stationarity is a challenging issue. However, some domains such as traffic optimization are inherently non-stationary. Causes for and effects of this are manifold. In particular, when dealing with traffic signal controls, addressing non-stationarity is key since traffic conditions change over time and as a function of traffic control decisions taken in other parts of a network. In this paper we analyze the effects that different sources of non-stationarity have in a network of traffic signals, in which each signal is modeled as a learning agent. More precisely, we study both the effects of changing …

Theory Of Charged Gels: Swelling, Elasticity, And Dynamics, Di Jia, Murugappan Muthukumar

Computer Science Department Faculty Publication Series

The fundamental attributes of charged hydrogels containing predominantly water and controllable amounts of low molar mass electrolytes are of tremendous significance in biological context and applications in healthcare. However, a rigorous theoretical formulation of gel behavior continues to be a challenge due to the presence of multiple length and time scales in the system which operate simultaneously. Furthermore, chain connectivity, the electrostatic interaction, and the hydrodynamic interaction all lead to long-range interactions. In spite of these complications, considerable progress has been achieved over the past several decades in generating theories of variable complexity. The present review presents an analytically tractable …

Effect Of Sleep And Biobehavioral Patterns On Multidimensional Cognitive Performance: Longitudinal, In-The-Wild Study, Manasa Kalanadhabhatta, Tauhidur Rahman, Deepak Ganesan

Computer Science Department Faculty Publication Series

Background: With nearly 20% of the US adult population using fitness trackers, there is an increasing focus on how physiological data from these devices can provide actionable insights about workplace performance. However, in-the-wild studies that understand how these metrics correlate with cognitive performance measures across a diverse population are lacking, and claims made by device manufacturers are vague. While there has been extensive research leading to a variety of theories on how physiological measures affect cognitive performance, virtually all such studies have been conducted in highly controlled settings and their validity in the real world is poorly understood. Objective: We …

Effectiveness Of A Serious Game For Cognitive Training In Chronic Stroke Survivors With Mild-To-Moderate Cognitive Impairment: A Pilot Randomized Controlled Trial, Hee-Tae Jung, Jean-Francois Daneault, Tenzin Nanglo, Hyunsuk Lee, Byeongil Kim, Yangsoo Kim, Sunghoon Ivan Lee

Computer Science Department Faculty Publication Series

Previous cognitive training games for stroke survivors required the close supervision of therapists. We aim to demonstrate the preliminary therapeutic effectiveness of Neuro-World, serious mobile games for cognitive training, in chronic stroke survivors with mild-to-moderate cognitive impairment without therapist supervision. For that, we conducted a randomized, controlled clinical trial at a single long-term care rehabilitation center with 50 stroke survivors in the chronic stage with mild-to-moderate cognitive impairment. Participants were randomized to standard medical care (n = 25) or standard medical care plus administration of Neuro-World (n = 25) over 12 weeks. A two-way mixed model ANOVA and Tukey’s post …

Risk Response For Municipal Solid Waste Crisis Using Ontology-Based Reasoning, Qing Yang, Chen Zuo, Xingxing Liu, Zhichao Yang, Hui Zhou

Computer Science Department Faculty Publication Series

Many cities in the world are besieged by municipal solid waste (MSW). MSW not only pollutes the ecological environment but can even induce a series of public safety crises. Risk response for MSW needs novel changes. This paper innovatively adopts the ideas and methods of semantic web ontology to build an ontology-based reasoning system for MSW risk response. Through the integration of crisis information and case resources in the field of MSW, combined with the reasoning ability of Semantic Web Rule Language (SWRL), a system of rule reasoning for risk transformation is constructed. Knowledge extraction and integration of MSW risk …

The P72r Polymorphism In R248q/W P53 Mutants Modifies The Mutant Effect On Epithelial To Mesenchymal Transition Phenotype And Cell Invasion Via Cxcl1 Expression, Cristabelle De Souza, Jill A. Madden, Dennis Minn, Vigneshwari Easwar Kumar, Dennia J. Montoya, Roshni Nambiar, Zheng Zhu, Wen-Wu Xiao, Neeki Tahmassebi, Harikumara Kathi, Nina Nelson, Anthony N. Karnezis, Jeremy Chien

Computer Science Department Faculty Publication Series

High-grade serous carcinoma (HGSC), the most lethal subtype of epithelial ovarian cancer (EOC), is characterized by widespread TP53 mutations (>90%), most of which are missense mutations (>70%). The objective of this study was to investigate differential transcriptional targets affected by a common germline P72R SNP (rs1042522) in two p53 hotspot mutants, R248Q and R248W, and identify the mechanism through which the P72R SNP affects the neomorphic properties of these mutants. Using isogenic cell line models, transcriptomic analysis, xenografts, and patient data, we found that the P72R SNP modifies the effect of p53 hotspot mutants on cellular morphology and …

Using The Gibbs Function As A Measure Of Human Brain Development Trends From Fetal Stage To Advanced Age, Edward A. Rietman, Sophie Taylor, Hava T. Siegelmann, Marco A. Deriu, Marco Cavaglia, Jack A. Tuszynski

Computer Science Department Faculty Publication Series

We propose to use a Gibbs free energy function as a measure of the human brain development. We adopt this approach to the development of the human brain over the human lifespan: from a prenatal stage to advanced age. We used proteomic expression data with the Gibbs free energy to quantify human brain’s protein–protein interaction networks. The data, obtained from BioGRID, comprised tissue samples from the 16 main brain areas, at different ages, of 57 post-mortem human brains. We found a consistent functional dependence of the Gibbs free energies on age for most of the areas and both sexes. A …

Online Row Sampling, Michael B. Cohen, Cameron Musco, Jakub Pachocki

Computer Science Department Faculty Publication Series

Finding a small spectral approximation for a tall n X d matrix A is a fundamental numerical primitive. For a number of reasons, one often seeks an approximation whose rows are sampled from those of A. Row sampling improves interpretability, saves space when A is sparse, and preserves structure, which is important, e.g., when A represents a graph.

However, correct sampling rows from A can be costly when the matrix is large and cannot be stored and processed in memory. Hence, a number of recent publications focus on row sampling in the streaming setting, using little more space than …

On Implementing Autonomic Systems With A Serverless Computing Approach: The Case Of Self-Partitioning Cloud Caches, Edwin F. Boza, Xavier Andrade, Jorge Cedeno, Jorge Murillo, Harold Aragon, Cristina L. Abad, Andres G. Abad

Computer Science Department Faculty Publication Series

The research community has made significant advances towards realizing self-tuning cloud caches; notwithstanding, existing products still require manual expert tuning to maximize performance. Cloud (software) caches are built to swiftly serve requests; thus, avoiding costly functionality additions not directly related to the request-serving control path is critical. We show that serverless computing cloud services can be leveraged to solve the complex optimization problems that arise during self-tuning loops and can be used to optimize cloud caches for free. To illustrate that our approach is feasible and useful, we implement SPREDS (Self-Partitioning REDiS), a modified version of Redis that optimizes memory …

Evidence To Support Common Application Switching Behaviour On Smartphones, Liam D. Turner, Roger M. Whitaker, Stuart M. Allen, David E. J. Linden, Kun Tu, Don Towsley

Computer Science Department Faculty Publication Series

We find evidence to support common behaviour in smartphone usage based on analysis of application (app) switching. This is an overlooked aspect of smartphone usage that gives additional insight beyond screen time and the particular apps that are accessed. Using a dataset of usage behaviour from 53 participants over a six-week period, we find strong similarity in the structure of networks built from app switching, despite diversity in the apps used, and the volume of app switching. App switch networks exhibit small-world, broad-scale network features, with a rapid popularity decay, suggesting that preferential attachment may drive next-app decision-making.

Quantum Walk Neural Networks With Feature Dependent Coins, Stefan Dernbach, Arman Mohseni-Kabir, Siddarth Pal, Miles Gepner, Don Towsley

Computer Science Department Faculty Publication Series

Recent neural networks designed to operate on graph-structured data have proven effective in many domains. These graph neural networks often diffuse information using the spatial structure of the graph. We propose a quantum walk neural network that learns a diffusion operation that is not only dependent on the geometry of the graph but also on the features of the nodes and the learning task. A quantum walk neural network is based on learning the coin operators that determine the behavior of quantum random walks, the quantum parallel to classical random walks. We demonstrate the effectiveness of our method on multiple …

The Role Of Caching In Future Communication Systems And Networks, Georgios S. Paschos, George Iosifidis, Meixia Tao, Don Towsley, Giuseppe Caire

Computer Science Department Faculty Publication Series

This paper has the following ambitious goal: to convince the reader that content caching is an exciting research topic for the future communication systems and networks. Caching has been studied for more than 40 years, and has recently received increased attention from industry and academia. Novel caching techniques promise to push the network performance to unprecedented limits, but also pose significant technical challenges. This tutorial provides a brief overview of existing caching solutions, discusses seminal papers that open new directions in caching, and presents the contributions of this special issue. We analyze the challenges that caching needs to address today, …

Special Issue On Wearable Computing And Machine Learning For Applications In Sports, Health, And Medical Engineering, Sunghoon I. Lee, Bjoern M. Eskofier

Computer Science Department Faculty Publication Series

Note: In lieu of an abstract, this is an excerpt from the first page.

Recent advancement in digital technologies is driving a remarkable transformation in sports, health, and medical engineering, aiming to achieve the accurate quantification of performance, well-being, and disease condition, and the optimization of sports, clinical, and therapeutic training and treatment programs. Traditionally, understanding and monitoring of functional performance and capacity has been performed in gait laboratories based on optoelectronic motion capture systems. However, gait laboratories in practical settings are often not readily available because the systems are costly and require trained experts to operate. Most importantly, when …

An Overview Of Smart Shoes In The Internet Of Health Things: Gait And Mobility Assessment In Health Promotion And Disease Monitoring, Bjoern M. Eskofier, Sunghoon Ivan Lee, Manuela Baron, André Simon, Christine F. Martindale, Heiko Gaßner, Jochen Klucken

Computer Science Department Faculty Publication Series

New smart technologies and the internet of things increasingly play a key role in healthcare and wellness, contributing to the development of novel healthcare concepts. These technologies enable a comprehensive view of an individual’s movement and mobility, potentially supporting healthy living as well as complementing medical diagnostics and the monitoring of therapeutic outcomes. This overview article specifically addresses smart shoes, which are becoming one such smart technology within the future internet of health things, since the ability to walk defines large aspects of quality of life in a wide range of health and disease conditions. Smart shoes offer the possibility …