Computer Sciences Commons^™

Pervasive Healthcare Internet Of Things: A Survey, Kim Anh Phung, Cemil Kirbas, Leyla Dereci, Tam Van Nguyen

Computer Science Faculty Publications

Thanks to the proliferation of the Internet of Things (IoT), pervasive healthcare is gaining popularity day by day as it offers health support to patients irrespective of their location. In emergency medical situations, medical aid can be sent quickly. Though not yet standardized, this research direction, healthcare Internet of Things (H-IoT), attracts the attention of the research community, both academia and industry. In this article, we conduct a comprehensive survey of pervasive computing H-IoT. We would like to visit the wide range of applications. We provide a broad vision of key components, their roles, and connections in the big picture. …

Reducing Leakage Current And Enhancing Polarization In Multiferroic 3d Supernanocomposites By Microstructure Engineering, Erik Enriquez, Ping Lu, Leigang Li, Bruce Zhang, Haiyan Wang, Quanxi Jia, Aiping Chen

Computer Science Faculty Publications

Multiferroic materials have generated great interest due to their potential as functional device materials. Nanocomposites have been increasingly used to design and generate new functionalities by pairing dissimilar ferroic materials, though the combination often introduces new complexity and challenges unforeseeable in single-phase counterparts. The recently developed approaches to fabricate 3D super-nanocomposites (3D‐sNC) open new avenues to control and enhance functional properties. In this work, we develop a new 3D‐sNC with CoFe2O4 (CFO) short nanopillar arrays embedded in BaTiO3 (BTO) film matrix via microstructure engineering by alternatively depositing BTO:CFO vertically-aligned nanocomposite layers and single-phase BTO layers. This microstructure engineering method allows …

Dynamic Robot Chain Networks For Swarm Foraging, Dohee Lee, Qi Lu, Tsz-Chiu Au

Computer Science Faculty Publications

The objective of foraging robot swarms is to search for and collect resources in an unknown arena as quickly as possible. To avoid the congestion near the central collection zone, we previously proposed an extension to the multiple-place foraging in which robot chains are deployed dynamically so that foraging robots can deliver to the robot chains instead of the central collection zone. However, a robot chain can only reach one location at a time, and congestion can occur at the end of the robot chain. This paper presents an extension to dynamic robot chains called dynamic robot chain networks, which …

Penguin: A Tool For Predicting Pseudouridine Sites In Direct Rna Nanopore Sequencing Data, Doaa Hassan, Daniel Acevedo, Swapna Vidhur Daulatabad, Quoseena Mir, Sarath Chandra Janga

Computer Science Faculty Publications

Pseudouridine is one of the most abundant RNA modifications, occurring when uridines are catalyzed by Pseudouridine synthase proteins. It plays an important role in many biological processes and also has an importance in drug development. Recently, the single-molecule sequencing techniques such as the direct RNA sequencing platform offered by Oxford Nanopore technologies enable direct detection of RNA modifications on the molecule that is being sequenced, but to our knowledge this technology has not been used to identify RNA Pseudouridine sites. To this end, in this paper, we address this limitation by introducing a tool called Penguin that integrates several developed …

Abstract Argumentation And Answer Set Programming: Two Faces Of Nelson’S Logic, Jorge Fandinno, Luis Fariñas Del Cerro

Computer Science Faculty Publications

In this work, we show that both logic programming and abstract argumentation frameworks can be interpreted in terms of Nelson’s constructive logic N4. We do so by formalising, in this logic, two principles that we call noncontradictory inference and strengthened closed world assumption: the first states that no belief can be held based on contradictory evidence while the latter forces both unknown and contradictory evidence to be regarded as false. Using these principles, both logic programming and abstract argumentation frameworks are translated into constructive logic in a modular way and using the object language. Logic programming implication and abstract argumentation …

Visualcommunity: A Platform For Archiving And Studying Communities, Suphanut Jamonnak, Deepshikha Bhati, Md Amiruzzaman, Ye Zhao, Xinyue Ye, Andrew Curtis

Computer Science Faculty Publications

VisualCommunity is a platform designed to support community or neighborhood scale research. The platform integrates mobile, AI, visualization techniques, along with tools to help domain researchers, practitioners, and students collecting and working with spatialized video and geo-narratives. These data, which provide granular spatialized imagery and associated context gained through expert commentary have previously provided value in understanding various community-scale challenges. This paper further enhances this work AI-based image processing and speech transcription tools available in VisualCommunity, allowing for the easy exploration of the acquired semantic and visual information about the area under investigation. In this paper we describe the specific …

How Hard Is Bribery In Elections With Randomly Selected Voters, Liangde Tao, Lin Chen, Lei Xu, Weidong Shi, Ahmed Sunny, Md Mahabub Uz Zaman

Computer Science Faculty Publications

Many research works in computational social choice assume a fixed set of voters in an election and study the resistance of different voting rules against electoral manipulation. In recent years, however, a new technique known as random sample voting has been adopted in many multi-agent systems. One of the most prominent examples is blockchain. Many proof-of-stake based blockchain systems like Algorand will randomly select a subset of participants of the system to form a committee, and only the committee members will be involved in the decision of some important system parameters. This can be viewed as running an election where …

Few-Shot Object Detection Via Baby Learning, Anh-Khoa Nguyen Vu, Nhat-Duy Nguyen, Khanh-Duy Nguyen, Vinh-Tiep Nguyen, Thanh Duc Ngo, Thanh-Toan Do, Tam Nguyen

Computer Science Faculty Publications

Few-shot learning is proposed to overcome the problem of scarce training data in novel classes. Recently, few-shot learning has been well adopted in various computer vision tasks such as object recognition and object detection. However, the state-of-the-art (SOTA) methods have less attention to effectively reuse the information from previous stages. In this paper, we propose a new framework of few-shot learning for object detection. In particular, we adopt Baby Learning mechanism along with the multiple receptive fields to effectively utilize the former knowledge in novel domain. The propoed framework imitates the learning process of a baby through visual cues. The …

Efficient Message Dissemination On Curve Road In Vehicular Networks, Shidong Huang, Chuanhe Huang, Dongfang Wu, Yabo Yin, M. Wasim Abbas Ashraf, Bin Fu

Computer Science Faculty Publications

Effective emergency message dissemination is a great importance on a specific road in vehicular networks (VN). The existing methods are not most efficient solutions for message dissemination on the curve road, which primarily focus on highway and urban road. In order to improve the efficiency of message dissemination on the curved road, the paper proposed a message dissemination method based on bidirectional relay nodes. The message can be disseminated in two directions simultaneously. The paper designed a relay node selection method based on the neighbor nodes’ coverage length of the road. Different waiting delays are assigned to the neighbor nodes …

Automation And Coupling Of Models For Coastal Flood Forecasting In South Texas, Cesar Davila Hernandez, Sara E. Davila, Martin Flores, Jungseok Ho, Dong-Chul Kim

Computer Science Faculty Publications

Forecasting natural disasters such as inundations can be of great help for emergency bodies and first responders. In coastal communities, this risk is often associated with storm surge. To produce flood forecasts for coastal communities, a system must incorporate models capable of simulating such events based on forecasted weather conditions. In this work, a system for forecasting inundations based predominantly on storm surge is explored. An automation and a coupling strategy were implemented to produce forecasted flood maps automatically. The system leverages an ocean circulation model and a channel water flow model to estimate flood events in South Texas specially …

Mmzda: Enabling Social Welfare Maximization In Cross-Silo Federated Learning, Jianan Chen, Qin Hu, Honglu Jiang

Computer Science Faculty Publications

—As one of the typical settings of Federated Learning (FL), cross-silo FL allows organizations to jointly train an optimal Machine Learning (ML) model. In this case, some organizations may try to obtain the global model without contributing their local training, lowering the social welfare. In this paper, we model the interactions among organizations in cross-silo FL as a public goods game for the first time and theoretically prove that there exists a social dilemma where the maximum social welfare is not achieved in Nash equilibrium. To overcome this social dilemma, we employ the Multi-player Multi-action ZeroDeterminant (MMZD) strategy to maximize …

Robots Need The Ability To Navigate Abusive Interactions, Hideki Garcia Goo, Katie Winkle, Tom Williams, Megan K. Strait

Computer Science Faculty Publications

Researchers are seeing more and more cases of abusive disinhibition towards robots in public realms. Because robots embody gendered identities, poor navigation of antisocial dynamics may reinforce or exacerbate gender-based violence. It is essential that robots deployed in social settings be able to recognize and respond to abuse in a way that minimises ethical risk. Enabling this capability requires designers to first understand the risk posed by abuse of robots, and hence how humans perceive robot-directed abuse. To that end, we experimentally investigated reactions to a physically abusive interaction between a human perpetrator and a victimized agent. Given extensions of …

Deep Learning Model With Adaptive Regularization For Eeg-Based Emotion Recognition Using Temporal And Frequency Features, Alireza Samavat, Ebrahim Khalili, Bentolhoda Ayati, Marzieh Ayati

Computer Science Faculty Publications

Since EEG signal acquisition is non-invasive and portable, it is convenient to be used for different applications. Recognizing emotions based on Brain-Computer Interface (BCI) is an important active BCI paradigm for recognizing the inner state of persons. There are extensive studies about emotion recognition, most of which heavily rely on staged complex handcrafted EEG feature extraction and classifier design. In this paper, we propose a hybrid multi-input deep model with convolution neural networks (CNNs) and bidirectional Long Short-term Memory (Bi-LSTM). CNNs extract time-invariant features from raw EEG data, and Bi-LSTM allows long-range lateral interactions between features. First, we propose a …

Differential Privacy In Privacy-Preserving Big Data And Learning: Challenge And Opportunity, Honglu Jiang, Yifeng Gao, S. M. Sarwar, Luis Garza Perez, Mahmudul Robin

Computer Science Faculty Publications

Differential privacy (DP) has become the de facto standard of privacy preservation due to its strong protection and sound mathematical foundation, which is widely adopted in different applications such as big data analysis, graph data process, machine learning, deep learning, and federated learning. Although DP has become an active and influential area, it is not the best remedy for all privacy problems in different scenarios. Moreover, there are also some misunderstanding, misuse, and great challenges of DP in specific applications. In this paper, we point out a series of limits and open challenges of corresponding research areas. Besides, we offer …

Automatic Segmentation Of Sinkholes Using A Convolutional Neural Network, Muhammad Usman Rafique, Junfeng Zhu, Nathan Jacobs

Computer Science Faculty Publications

Sinkholes are the most abundant surface features in karst areas worldwide. Understanding sinkhole occurrences and characteristics is critical for studying karst aquifers and mitigating sinkhole-related hazards. Most sinkholes appear on the land surface as depressions or cover collapses and are commonly mapped from elevation data, such as digital elevation models (DEMs). Existing methods for identifying sinkholes from DEMs often require two steps: locating surface depressions and separating sinkholes from non-sinkhole depressions. In this study, we explored deep learning to directly identify sinkholes from DEM data and aerial imagery. A key contribution of our study is an evaluation of various ways …

On The Security Of Bluetooth Low Energy In Two Consumer Wearable Heart Rate Monitors/Sensing Devices, Yesem Kurt Peker, Gabriel Bello, Alfredo J. Perez

Computer Science Faculty Publications

Since its inception in 2013, Bluetooth Low Energy (BLE) has become the standard for short-distance wireless communication in many consumer devices, as well as special-purpose devices. In this study, we analyze the security features available in Bluetooth LE standards and evaluate the features implemented in two BLE wearable devices (a Fitbit heart rate wristband and a Polar heart rate chest wearable) and a BLE keyboard to explore which security features in the BLE standards are implemented in the devices. In this study, we used the ComProbe Bluetooth Protocol Analyzer, along with the ComProbe software to capture the BLE traffic of …

Factors Affecting Student Educational Choices Regarding Oer Material In Computer Science, Anastasia Angelopoulou, Rania Hodhod, Alfredo J. Perez

Computer Science Faculty Publications

The use of Open Educational Resources (OER) in course settings provides a solution to reduce the textbook barrier. Several published studies have concluded that high textbook costs may influence students' educational choices. However, there are other student characteristics that may be relevant to OER. In this work, we study various factors that may influence students' educational choices regarding OER and their impact on a student’s perspectives on OER use and quality. More specifically, we investigate whether there are significant differences in the frequency of use and perceived quality of the OER textbook based on gender, prior academic achievements, income, seniority, …

Can Lethal Autonomous Weapons Be Just?, Noreen L. Herzfeld, Robert H. Latiff

Computer Science Faculty Publications

In 2018 the United States Department of Defense (DoD) created a new Joint Artificial Intelligence Center to study the adoption of AI by the military. Their strategy, outlined in a document entitled, “Harnessing AI to Advance Our Security and Prosperity,” proposes to accelerate the adoption of AI in the military by fostering a culture of experimentation and calculated risk taking, noting that AI will change the character of the future battlefield and, even more, the pace of battle. Is there any way to ensure that this future battlefield will be just? Can the age-old precepts of just warfare help guide …

Human Leukocyte Antigen Distributions Do Not Share A Copula Across Sub-Populations, Daniel Schellhas, Robert C. Green Ii

Computer Science Faculty Publications

The distribution of human leukocyte antigens in the population assists in matching solid organ donors and recipients when the typing methods used do not provide sufficiently precise information. This is made possible by linkage disequilibrium (LD), where alleles co-occur more often than random chance would suggest. There is a trade-off between the high bias and low variance of a broad sample from the population and the low bias but high variance of a focused sample. Some of this trade-off could be alleviated if subpopulations shared LD despite having different allele frequencies. These experiments show that Bayesian estimation can balance bias …

Studying Spread Patterns Of Covid-19 Based On Spatiotemporal Data, Beiyu Lin, Xiaowei Jia, Zhiqian Chen

Computer Science Faculty Publications

The current COVID-19 epidemic have transformed every aspect of our lives, especially our behavior and routines. These changes have been drastically impacting the economy in each region, such as local restaurants and transportation systems. With massive amounts of ambient data being collected everywhere, we now can develop innovative algorithms to have a much greater understanding of epidemic spread patterns of COVID-19 based on spatiotemporal data. The findings will open up the possibility to design adaptive planning or scheduling systems that will help preventing the spread of COVID-19 and other infectious diseases.

In this tutorial, we will review the trending state-of-theart …

Vietnamese Document Analysis: Dataset, Method And Benchmark Suite, Khang Nguyen, An Nguyen, Nguyen D. Vo, Tam Nguyen

Computer Science Faculty Publications

Document image understanding is increasingly useful since the number of digital documents is increasing day-by-day and the need for automation is increasing. Object detection plays a significant role in detecting vital objects and layouts in document images and contributes to providing a clearer understanding of the documents. Nonetheless, previous research mainly focuses on English document images, and studies on Vietnamese document images are limited. In this study, we extensively benchmark state-of-the-art object detectors and analyze the performance of each method on Vietnamese document images. Moreover, we also investigate the effectiveness of four different loss functions on the experimental object detection …

Multiple-Place Swarm Foraging With Dynamic Robot Chains, Dohee Lee, Qi Lu, Tsz-Chiu Au

Computer Science Faculty Publications

The goal of foraging robot swarms is to search and deliver resources to a specific central collection zone quickly. In the previously proposed multiple-place foraging algorithm with dynamic depots, foraging performance decreases as search areas and swarm sizes increase: depots need to travel long distances to deliver resources to the center, and more robots produce more congestion on their journeys. We propose a novel extension to the multiple-place foraging in which multiple robot chains are deployed dynamically. Each robot chain connects a foraging location to the central collection zone. Instead of delivering resources by a single robot, resources are passed …

Online Deep Learning From Doubly-Streaming Data, Heng Lian, John S. Atwood, Bo-Jian Hou, Jian Wu, Yi He

Computer Science Faculty Publications

This paper investigates a new online learning problem with doubly-streaming data, where the data streams are described by feature spaces that constantly evolve, with new features emerging and old features fading away. A plausible idea to deal with such data streams is to establish a relationship between the old and new feature spaces, so that an online learner can leverage the knowledge learned from the old features to better the learning performance on the new features. Unfortunately, this idea does not scale up to high-dimensional multimedia data with complex feature interplay, which suffers a tradeoff between onlineness, which biases shallow …

Eye Movement And Pupil Measures: A Review, Bhanuka Mahanama, Yasith Jayawardana, Sundararaman Rengarajan, Gavindya Jayawardena, Leanne Chukoskie, Joseph Snider, Sampath Jayarathna

Computer Science Faculty Publications

Our subjective visual experiences involve complex interaction between our eyes, our brain, and the surrounding world. It gives us the sense of sight, color, stereopsis, distance, pattern recognition, motor coordination, and more. The increasing ubiquity of gaze-aware technology brings with it the ability to track gaze and pupil measures with varying degrees of fidelity. With this in mind, a review that considers the various gaze measures becomes increasingly relevant, especially considering our ability to make sense of these signals given different spatio-temporal sampling capacities. In this paper, we selectively review prior work on eye movements and pupil measures. We first …

Streaminghub: Interactive Stream Analysis Workflows, Yasith Jayawardana, Vikas G. Ashok, Sampath Jayarathna

Computer Science Faculty Publications

Reusable data/code and reproducible analyses are foundational to quality research. This aspect, however, is often overlooked when designing interactive stream analysis workflows for time-series data (e.g., eye-tracking data). A mechanism to transmit informative metadata alongside data may allow such workflows to intelligently consume data, propagate metadata to downstream tasks, and thereby auto-generate reusable, reproducible analytic outputs with zero supervision. Moreover, a visual programming interface to design, develop, and execute such workflows may allow rapid prototyping for interdisciplinary research. Capitalizing on these ideas, we propose StreamingHub, a framework to build metadata propagating, interactive stream analysis workflows using visual programming. We conduct …

M-Cubes: An Efficient And Portable Implementation Of Multi-Dimensional Integration For Gpus, Ioannis Sakiotis, Kamesh Arumugam, Marc Paterno, Desh Ranjan, Balŝa Terzić, Mohammad Zubair

Computer Science Faculty Publications

The task of multi-dimensional numerical integration is frequently encountered in physics and other scientific fields, e.g., in modeling the effects of systematic uncertainties in physical systems and in Bayesian parameter estimation. Multi-dimensional integration is often time-prohibitive on CPUs. Efficient implementation on many-core architectures is challenging as the workload across the integration space cannot be predicted a priori. We propose m-Cubes, a novel implementation of the well-known Vegas algorithm for execution on GPUs. Vegas transforms integration variables followed by calculation of a Monte Carlo integral estimate using adaptive partitioning of the resulting space. mCubes improves performance on GPUs by maintaining relatively …

Introducing A Real-Time Advanced Eye Movements Analysis Pipeline, Gavindya Jayawardana

Computer Science Faculty Publications

Real-Time Advanced Eye Movements Analysis Pipeline (RAEMAP) is an advanced pipeline to analyze traditional positional gaze measurements as well as advanced eye gaze measurements. The proposed implementation of RAEMAP includes real-time analysis of fixations, saccades, gaze transition entropy, and low/high index of pupillary activity. RAEMAP will also provide visualizations of fixations, fixations on AOIs, heatmaps, and dynamic AOI generation in real-time. This paper outlines the proposed architecture of RAEMAP.

Camouflaged Poisoning Attack On Graph Neural Networks, Chao Jiang, Yi He, Richard Chapman, Hongyi Wu

Computer Science Faculty Publications

Graph neural networks (GNNs) have enabled the automation of many web applications that entail node classification on graphs, such as scam detection in social media and event prediction in service networks. Nevertheless, recent studies revealed that the GNNs are vulnerable to adversarial attacks, where feeding GNNs with poisoned data at training time can lead them to yield catastrophically devastative test accuracy. This finding heats up the frontier of attacks and defenses against GNNs. However, the prior studies mainly posit that the adversaries can enjoy free access to manipulate the original graph, while obtaining such access could be too costly in …

Completing Single-Cell Dna Methylome Profiles Via Transfer Learning Together With Kl-Divergence, Sanjeeva Dodlapati, Zongliang Jiang, Jiangwen Sun

Computer Science Faculty Publications

The high level of sparsity in methylome profiles obtained using whole-genome bisulfite sequencing in the case of low biological material amount limits its value in the study of systems in which large samples are difficult to assemble, such as mammalian preimplantation embryonic development. The recently developed computational methods for addressing the sparsity by imputing missing have their limits when the required minimum data coverage or profiles of the same tissue in other modalities are not available. In this study, we explored the use of transfer learning together with Kullback-Leibler (KL) divergence to train predictive models for completing methylome profiles with …

Visual Descriptor Extraction From Patent Figure Captions: A Case Study Of Data Efficiency Between Bilstm And Transformer, Xin Wei, Jian Wu, Kehinde Ajayi, Diane Oyen

Computer Science Faculty Publications

Technical drawings used for illustrating designs are ubiquitous in patent documents, especially design patents. Different from natural images, these drawings are usually made using black strokes with little color information, making it challenging for models trained on natural images to recognize objects. To facilitate indexing and searching, we propose an effective and efficient visual descriptor model that extracts object names and aspects from patent captions to annotate benchmark patent figure datasets. We compared two state-of-the-art named entity recognition (NER) models and found that with a limited number of annotated samples, the BiLSTM-CRF model outperforms the Transformer model by a significant …