Computer Sciences Commons^™

A Review Of Iot Security And Privacy Using Decentralized Blockchain Techniques, Vinay Gugueoth, Sunitha Safavat, Sachin Shetty, Danda Rawat

Electrical & Computer Engineering Faculty Publications

IoT security is one of the prominent issues that has gained significant attention among the researchers in recent times. The recent advancements in IoT introduces various critical security issues and increases the risk of privacy leakage of IoT data. Implementation of Blockchain can be a potential solution for the security issues in IoT. This review deeply investigates the security threats and issues in IoT which deteriorates the effectiveness of IoT systems. This paper presents a perceptible description of the security threats, Blockchain based solutions, security characteristics and challenges introduced during the integration of Blockchain with IoT. An analysis of different …

Comparison Of Machine Learning Methods For Classification Of Alexithymia In Individuals With And Without Autism From Eye-Tracking Data, Furkan Iigin, Megan A. Witherow, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

Alexithymia describes a psychological state where individuals struggle with feeling and expressing their emotions. Individuals with alexithymia may also have a more difficult time understanding the emotions of others and may express atypical attention to the eyes when recognizing emotions. This is known to affect individuals with Autism Spectrum Disorder (ASD) differently than neurotypical (NT) individuals. Using a public data set of eye-tracking data from seventy individuals with and without autism who have been assessed for alexithymia, we train multiple traditional machine learning models for alexithymia classification including support vector machines, logistic regression, decision trees, random forest, and multilayer perceptron. …

An Explainable Artificial Intelligence Framework For The Predictive Analysis Of Hypo And Hyper Thyroidism Using Machine Learning Algorithms, Md. Bipul Hossain, Anika Shama, Apurba Adhikary, Avi Deb Raha, K. M. Aslam Uddin, Mohammad Amzad Hossain, Imtia Islam, Saydul Akbar Murad, Md. Shirajum Munir, Anupam Kumur Bairagi

Electrical & Computer Engineering Faculty Publications

The thyroid gland is the crucial organ in the human body, secreting two hormones that help to regulate the human body's metabolism. Thyroid disease is a severe medical complaint that could be developed by high Thyroid Stimulating Hormone (TSH) levels or an infection in the thyroid tissues. Hypothyroidism and hyperthyroidism are two critical conditions caused by insufficient thyroid hormone production and excessive thyroid hormone production, respectively. Machine learning models can be used to precisely process the data generated from different medical sectors and to build a model to predict several diseases. In this paper, we use different machine-learning algorithms to …

Social-Engineering, Bio-Economies, And Nation-State Ontological Security: A Commentary, Brandon Griffin, Keitavius Alexander, Xavier-Lewis Palmer, Lucas Potter

Electrical & Computer Engineering Faculty Publications

Biocybersecurity is an evolving discipline that aims to identify the gaps and risks associated with the convergence of Biology (the science of life and living organisms) and cybersecurity (the science, study, and theory of cyberspace and cybernetics) to protect the bioeconomy. The biological industries’ increased reliance on digitization, automation, and computing power has resulted in benefits for the scientific community, it has simultaneously multiplied the risk factors associated with industrial espionage and the protection of data both commercial and proprietary. The sensitive and potentially destructive power of this data and its access inherently poses a risk to the national and …

Implications Of Cyberbiosecurity In Advanced Agriculture, Simone Stephen, Keitavius Alexander, Lucas Potter, Xavier-Lewis Palmer

Electrical & Computer Engineering Faculty Publications

The world is currently undergoing a rapid digital transformation sometimes referred to as the fourth industrial revolution. During this transformation, it is increasingly clear that many scientific fields are not prepared for this change. One specific area is agriculture. As the sector which creates global food supply, this critical infrastructure requires detailed assessment and research via newly developed technologies (Millett et al, 2019; Peccoud et al, 2018) . Despite its fundamental significance to modern civilization, many aspects of industrial agriculture have not yet adapted to the digital world. This is evident in the many vulnerabilities currently present within agricultural systems, …

A Reflection Of Typology And Verification Flaws In Consideration Of Biocybersecurity/Cyberbiosecurity: Just Another Gap In The Wall, Luke Potter, Kim Mossberg, Xavier Palmer

Electrical & Computer Engineering Faculty Publications

Verification is central to any process in a functional and enduring cyber-secure organization. This verification is how the validity or accuracy of a state of being is assessed (Schlick, 1936; Balci, 1998). Conversely, breakdown in verification procedures is core to the interruption of normal operations for an organization. A key problem for organizations that utilize biology as an interlock within their systems is that personnel lack sufficient ability to verify all practically relevant biological information for procedures such as a nurse logging a blood draw, or a molecular biology technician preparing agar to culture microbes for study. This has several …

Mechanical Devices For Census And Detection Of Off-Host Larval Ticks (Acari: Ixodidae), Donald B. Thomas, Holly D. Gaff, Brenda Leal-Galvan

Biological Sciences Faculty Publications

Standard off-host tick census techniques are generally inefficient for larval stages. Also, such techniques expose the investigator to environmental hazards including tick bites. Mechanical devices are an option which were tested under field conditions for proof of concept and in side-by-side comparison to the standard techniques. The tick-vac collected more ticks than the legging technique, but with lower frequency of positive detections. It may have utility for sampling small areas, such as weedy corrals suspected of harboring ticks. Different configurations of tick-bots, robotic vehicles with flags, depended on intensive operator control to avoid obstacles, limiting operation to line of sight. …

Identifying The Serious Clinical Outcomes Of Adverse Reactions To Drugs By A Multi-Task Deep Learning Framework, Haochen Zhao, Peng Ni, Qichang Zhao, Xiao Liang, Di Ai, Shannon Erhardt, Jun Wang, Yaohang Li, Jiianxin Wang

Computer Science Faculty Publications

Adverse Drug Reactions (ADRs) have a direct impact on human health. As continuous pharmacovigilance and drug monitoring prove to be costly and time-consuming, computational methods have emerged as promising alternatives. However, most existing computational methods primarily focus on predicting whether or not the drug is associated with an adverse reaction and do not consider the core issue of drug benefit-risk assessment-whether the treatment outcome is serious when adverse drug reactions occur. To this end, we categorize serious clinical outcomes caused by adverse reactions to drugs into seven distinct classes and present a deep learning framework, so-called GCAP, for predicting the …

Efficient Gpu Implementation Of Automatic Differentiation For Computational Fluid Dynamics, Mohammad Zubair, Desh Ranjan, Aaron Walden, Gabriel Nastac, Eric Nielsen, Boris Diskin, Marc Paterno, Samuel Jung, Joshua Hoke Davis

Computer Science Faculty Publications

Many scientific and engineering applications require repeated calculations of derivatives of output functions with respect to input parameters. Automatic Differentiation (AD) is a method that automates derivative calculations and can significantly speed up code development. In Computational Fluid Dynamics (CFD), derivatives of flux functions with respect to state variables (Jacobian) are needed for efficient solutions of the nonlinear governing equations. AD of flux functions on graphics processing units (GPUs) is challenging as flux computations involve many intermediate variables that create high register pressure and require significant memory traffic because of the need to store the derivatives. This paper presents a …

Cellbrf: A Feature Selection Method For Single-Cell Clustering Using Cell Balance And Random Forest, Yunpei Xu, Hong-Dong Li, Cui-Xiang Lin, Ruiqing Zheng, Yaohang Li, Jinhui Xu, Jianxin Wang

Computer Science Faculty Publications

Motivation

Single-cell RNA sequencing (scRNA-seq) offers a powerful tool to dissect the complexity of biological tissues through cell sub-population identification in combination with clustering approaches. Feature selection is a critical step for improving the accuracy and interpretability of single-cell clustering. Existing feature selection methods underutilize the discriminatory potential of genes across distinct cell types. We hypothesize that incorporating such information could further boost the performance of single cell clustering. Results

We develop CellBRF, a feature selection method that considers genes’ relevance to cell types for single-cell clustering. The key idea is to identify genes that are most important for discriminating …

Enabling Customization Of Discussion Forums For Blind Users, Mohan Sunkara, Yash Prakash, Hae-Na Lee, Sampath Jayarathna, Vikas Ashok

Computer Science Faculty Publications

Online discussion forums have become an integral component of news, entertainment, information, and video-streaming websites, where people all over the world actively engage in discussions on a wide range of topics including politics, sports, music, business, health, and world affairs. Yet, little is known about their usability for blind users, who aurally interact with the forum conversations using screen reader assistive technology. In an interview study, blind users stated that they often had an arduous and frustrating interaction experience while consuming conversation threads, mainly due to the highly redundant content and the absence of customization options to selectively view portions …

Mitigating Anomalous Electricity Consumption In Smart Cities Using An Ai-Based Stacked-Generalization Technique, Arshid Ali, Laiq Khan, Nadeem Javaid, Safdar Hussain Bouk, Abdulaziz Aldegheishem, Nabil Alrahjeh

Computer Science Faculty Publications

Energy management and efficient asset utilization play an important role in the economic development of a country. The electricity produced at the power station faces two types of losses from the generation point to the end user. These losses are technical losses (TL) and non-technical losses (NTL). TLs occurs due to the use of inefficient equipment. While NTLs occur due to the anomalous consumption of electricity by the customers, which happens in many ways; energy theft being one of them. Energy theft majorly happens to cut down on the electricity bills. These losses in the smart grid (SG) are the …

Msdrp: A Deep Learning Model Based On Multisource Data For Predicting Drug Response, Haochen Zhao, Xiaoyu Zhang, Qichang Zhao, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Cancer heterogeneity drastically affects cancer therapeutic outcomes. Predicting drug response in vitro is expected to help formulate personalized therapy regimens. In recent years, several computational models based on machine learning and deep learning have been proposed to predict drug response in vitro. However, most of these methods capture drug features based on a single drug description (e.g. drug structure), without considering the relationships between drugs and biological entities (e.g. target, diseases, and side effects). Moreover, most of these methods collect features separately for drugs and cell lines but fail to consider the pairwise interactions between drugs and cell …

Claimdistiller: Scientific Claim Extraction With Supervised Contrastive Learning, Xin Wei, Md Reshad Ul Hoque, Jian Wu, Jiang Li

Computer Science Faculty Publications

The growth of scientific papers in the past decades calls for effective claim extraction tools to automatically and accurately locate key claims from unstructured text. Such claims will benefit content-wise aggregated exploration of scientific knowledge beyond the metadata level. One challenge of building such a model is how to effectively use limited labeled training data. In this paper, we compared transfer learning and contrastive learning frameworks in terms of performance, time and training data size. We found contrastive learning has better performance at a lower cost of data across all models. Our contrastive-learning-based model ClaimDistiller has the highest performance, boosting …

An Approach To Developing Benchmark Datasets For Protein Secondary Structure Segmentation From Cryo-Em Density Maps, Thu Nguyen, Yongcheng Mu, Jiangwen Sun, Jing He

Computer Science Faculty Publications

More and more deep learning approaches have been proposed to segment secondary structures from cryo-electron density maps at medium resolution range (5--10Å). Although the deep learning approaches show great potential, only a few small experimental data sets have been used to test the approaches. There is limited understanding about potential factors, in data, that affect the performance of segmentation. We propose an approach to generate data sets with desired specifications in three potential factors - the protein sequence identity, structural contents, and data quality. The approach was implemented and has generated a test set and various training sets to study …

Deeppatent2: A Large-Scale Benchmarking Corpus For Technical Drawing Understanding, Kehinde Ajayi, Xin Wei, Martin Gryder, Winston Shields, Jian Wu, Shawn M. Jones, Michal Kucer, Diane Oyen

Computer Science Faculty Publications

Recent advances in computer vision (CV) and natural language processing have been driven by exploiting big data on practical applications. However, these research fields are still limited by the sheer volume, versatility, and diversity of the available datasets. CV tasks, such as image captioning, which has primarily been carried out on natural images, still struggle to produce accurate and meaningful captions on sketched images often included in scientific and technical documents. The advancement of other tasks such as 3D reconstruction from 2D images requires larger datasets with multiple viewpoints. We introduce DeepPatent2, a large-scale dataset, providing more than 2.7 million …

Progenitor Cell Isolation From Mouse Epididymal Adipose Tissue And Sequencing Library Construction, Qianglin Liu, Chaoyang Li, Yuxia Li, Leshan Wang, Xujia Zhang, Buhao Deng, Peidong Gao, Mohammad Shiri, Fozi Alkaifi, Junxing Zhao, Jacqueline M. Stephens, Constantine A. Simintiras, Joseph Francis, Jiangwen Sun, Xing Fu

Computer Science Faculty Publications

Here, we present a protocol to isolate progenitor cells from mouse epididymal visceral adipose tissue and construct bulk RNA and assay for transposase-accessible chromatin with sequencing (ATAC-seq) libraries. We describe steps for adipose tissue collection, cell isolation, and cell staining and sorting. We then detail procedures for both ATAC-seq and RNA sequencing library construction. This protocol can also be applied to other tissues and cell types directly or with minor modifications.

For complete details on the use and execution of this protocol, please refer to Liu et al. (2023).¹

*¹ Liu, Q., Li, C., Deng, B., Gao, P., …

Optimization Of Ported Cfd Kernels On Intel Data Center Gpu Max 1550 Using Oneapi Esimd, Mohammad Zubair, Aaron Walden, Gabriel Nastac, Eric Nielsen, Christoph Bauinger, Xiao Zhu

Computer Science Faculty Publications

We describe our experience porting FUN3D’s CUDA-optimized kernels to Intel oneAPI SYCL.We faced several challenges, including foremost the suboptimal performance of the oneAPI code on Intel’s new data center GPU. Suboptimal performance of the oneAPI code was due primarily to high register spills, memory latency, and poor vectorization. We addressed these issues by implementing the kernels using Intel oneAPI’s Explicit SIMD SYCL extension (ESIMD) API. The ESIMD API enables the writing of explicitly vectorized kernel code, gives more precise control over register usage and prefetching, and better handles thread divergence compared to SYCL. The ESIMD code outperforms the optimized SYCL …

Comparison Of Physics-Based Deformable Registration Methods For Image-Guided Neurosurgery, Nikos Chrisochoides, Yixun Liu, Fotis Drakopoulos, Andriy Kot, Panos Foteinos, Christos Tsolakis, Emmanuel Billias, Olivier Clatz, Nicholas Ayache, Andrey Fedorov, Alex Golby, Peter Black, Ron Kikinis

Computer Science Faculty Publications

This paper compares three finite element-based methods used in a physics-based non-rigid registration approach and reports on the progress made over the last 15 years. Large brain shifts caused by brain tumor removal affect registration accuracy by creating point and element outliers. A combination of approximation- and geometry-based point and element outlier rejection improves the rigid registration error by 2.5 mm and meets the real-time constraints (4 min). In addition, the paper raises several questions and presents two open problems for the robust estimation and improvement of registration error in the presence of outliers due to sparse, noisy, and incomplete …

Dial "N" For Nxdomain: The Scale, Origin, And Security Implications Of Dns Queries To Non-Existent Domains, Gunnan Liu, Lin Jin, Shuai Hao, Yubao Zhang, Daiping Liu, Angelos Stavrou, Haining Wang

Computer Science Faculty Publications

Non-Existent Domain (NXDomain) is one type of the Domain Name System (DNS) error responses, indicating that the queried domain name does not exist and cannot be resolved. Unfortunately, little research has focused on understanding why and how NXDomain responses are generated, utilized, and exploited. In this paper, we conduct the first comprehensive and systematic study on NXDomain by investigating its scale, origin, and security implications. Utilizing a large-scale passive DNS database, we identify 146,363,745,785 NXDomains queried by DNS users between 2014 and 2022. Within these 146 billion NXDomains, 91 million of them hold historic WHOIS records, of which 5.3 million …

Toward A Generative Modeling Analysis Of Clas Exclusive 2𝜋 Photoproduction, T. Alghamdi, Y. Alanazi, M. Battaglieri, Ł. Bibrzycki, A. V. Golda, A. N. Hiller Blin, E. L. Isupov, Y. Li, L. Marsicano, W. Melnitchouk, V. I. Mokeev, G. Montaña, A. Pilloni, N. Sato, A. P. Szczepaniak, T. Vittorini

Computer Science Faculty Publications

AI-supported algorithms, particularly generative models, have been successfully used in a variety of different contexts. This work employs a generative modeling approach to unfold detector effects specifically tailored for exclusive reactions that involve multiparticle final states. Our study demonstrates the preservation of correlations between kinematic variables in a multidimensional phase space. We perform a full closure test on two-pion photoproduction pseudodata generated with a realistic model in the kinematics of the Jefferson Lab CLAS g11 experiment. The overlap of different reaction mechanisms leading to the same final state associated with the CLAS detector’s nontrivial effects represents an ideal test case …

Detecting Deceptive Dark-Pattern Web Advertisements For Blind Screen-Reader Users, Satwick Ram Kodandaram, Mohan Sunkara, Sampath Jayarathna, Vikas Ashok

Computer Science Faculty Publications

Advertisements have become commonplace on modern websites. While ads are typically designed for visual consumption, it is unclear how they affect blind users who interact with the ads using a screen reader. Existing research studies on non-visual web interaction predominantly focus on general web browsing; the specific impact of extraneous ad content on blind users' experience remains largely unexplored. To fill this gap, we conducted an interview study with 18 blind participants; we found that blind users are often deceived by ads that contextually blend in with the surrounding web page content. While ad blockers can address this problem via …

A Hybrid Deep Learning Approach For Crude Oil Price Prediction, Hind Aldabagh, Xianrong Zheng, Ravi Mukkamala

Computer Science Faculty Publications

Crude oil is one of the world’s most important commodities. Its price can affect the global economy, as well as the economies of importing and exporting countries. As a result, forecasting the price of crude oil is essential for investors. However, crude oil price tends to fluctuate considerably during significant world events, such as the COVID-19 pandemic and geopolitical conflicts. In this paper, we propose a deep learning model for forecasting the crude oil price of one-step and multi-step ahead. The model extracts important features that impact crude oil prices and uses them to predict future prices. The prediction model …

Charged Track Reconstruction With Artificial Intelligence For Clas12, Gagik Gavalian, Polykarpos Thomadakis, Angelos Angelopoulos, Nikos Chrisochoides

Computer Science Faculty Publications

In this paper, we present the results of charged particle track reconstruction in CLAS12 using artificial intelligence. In our approach, we use neural networks working together to identify tracks based on the raw signals in the Drift Chambers. A Convolutional Auto-Encoder is used to de-noise raw data by removing the hits that do not satisfy the patterns for tracks, and second Multi-Layer Perceptron is used to identify tracks from combinations of clusters in the drift chambers. Our method increases the tracking efficiency by 50% for multi-particle final states already conducted experiments. The de-noising results indicate that future experiments can run …

A Structure-Aware Generative Adversarial Network For Bilingual Lexicon Induction, Bocheng Han, Qian Tao, Lusi Li, Zhihao Xiong

Computer Science Faculty Publications

Bilingual lexicon induction (BLI) is the task of inducing word translations with a learned mapping function that aligns monolingual word embedding spaces in two different languages. However, most previous methods treat word embeddings as isolated entities and fail to jointly consider both the intra-space and inter-space topological relations between words. This limitation makes it challenging to align words from embedding spaces with distinct topological structures, especially when the assumption of isomorphism may not hold. To this end, we propose a novel approach called the Structure-Aware Generative Adversarial Network (SA-GAN) model to explicitly capture multiple topological structure information to achieve accurate …

Unttangling Irregular Actin Cytoskeleton Architectures In Tomograms Of The Cell With Struwwel Tracer, Salim Sazzed, Peter Scheible, Jing He, Willy Wriggers

Computer Science Faculty Publications

In this work, we established, validated, and optimized a novel computational framework for tracing arbitrarily oriented actin filaments in cryo-electron tomography maps. Our approach was designed for highly complex intracellular architectures in which a long-range cytoskeleton network extends throughout the cell bodies and protrusions. The irregular organization of the actin network, as well as cryo-electron-tomography-specific noise, missing wedge artifacts, and map dimensions call for a specialized implementation that is both robust and efficient. Our proposed solution, Struwwel Tracer, accumulates densities along paths of a specific length in various directions, starting from locally determined seed points. The highest-density paths originating …

Federated Learning And Applications In Cybersecurity, Ani Sreekumar

Cybersecurity Undergraduate Research Showcase

Machine learning is a subfield of artificial intelligence that focuses on making predictions about some outcome based on information from a dataset. In cybersecurity, machine learning is often used to improve intrusion detection systems and identify trends in data that could indicate an oncoming cyber attack. Data privacy is an extremely important aspect of cybersecurity, and there are many industries that have more demanding laws to ensure the security of user data. Due to these regulations, machine learning algorithms can not be widely utilized in these industries to improve outcomes and accuracy of predictions. However, federated learning is a recent …

Is Cybersecurity Training Practical Or Not?, Bhawnish Sharma

Cybersecurity Undergraduate Research Showcase

With technology growing, there has been an increase in cybercrime. Because of this, private and public sectors face global problems, i.e., phishing, security breaches, and identity theft. With cybersecurity software available on the internet, anyone can access it. As technology advances, cybersecurity experts must answer the tough question of, “is cybersecurity training practical or not”?

A Call For Research: Ethical Dilemmas Of Autonomous Vehicle Manufacturers, Remy Harwood

Cybersecurity Undergraduate Research Showcase

While autonomous vehicles accounted for about 31.4 million vehicles on the road in 2019 (Placek). They have continued to flood the market and have a projected growth to 58 million in just 8 years from now (Placek) As well as a market cap in the billions of dollars. Even the comparatively new AV company Tesla has over 3 times the market cap value of the leading non AV brand Toyota (Market Cap) who are also working toward AVs as well, like their level 2 teammate driver assistance. Following Moore’s Law, as technology continues to improve, their social impact and ethical …

Investigating Privacy Policies Using Policylint Tool, Tricia Camaya

Cybersecurity Undergraduate Research Showcase

Organizations essentially inform clients about data collection and sharing practices through privacy policies. Recent research has proposed tools to help users better comprehend these lengthy and intricate legal documents that summarize collection and sharing. However, these instruments have a significant flaw. They overlook the possibility of contradictions within a particular policy. This paper introduces PolicyLint, a tool for analyzing privacy policies that simultaneously considers negation and varying semantic levels of data objects and entities. PolicyLint accomplishes this by using sentence-level natural language processing to automatically create ontologies from a large corpus of privacy policies and capturing both positive and negative …