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Relative Comparison Of Modern Computing To Computer Technology Of Ages, Iwasan D. Kejawa Dr., Hailly Rubio Ms.

School of Computing: Faculty Publications

Abstract

Abstract

Are there differences and similarities between the computer technology of today and yesterdays. Research had shown that there had been tremendous improvements from the computers of ages (traditional Computers) as we enter the 21st century. Both the physicality and the functionalities of computers have changed but some remain the same. The memory capacity and functions have changed, but all are still based on the old concepts of yesteryears.

3dgaunet: 3d Generative Adversarial Networks With A 3d U-Net Based Generator To Achieve The Accurate And Effective Synthesis Of Clinical Tumor Image Data For Pancreatic Cancer, Yu Shi, Hannah Tang, Michael J. Baine, Michael A. Hollingsworth, Huijing Du, Dandan Zheng, Chi Zhang, Hongfeng Yu

School of Computing: Faculty Publications

Pancreatic ductal adenocarcinoma (PDAC) presents a critical global health challenge, and early detection is crucial for improving the 5-year survival rate. Recent medical imaging and computational algorithm advances offer potential solutions for early diagnosis. Deep learning, particularly in the form of convolutional neural networks (CNNs), has demonstrated success in medical image analysis tasks, including classification and segmentation. However, the limited availability of clinical data for training purposes continues to represent a significant obstacle. Data augmentation, generative adversarial networks (GANs), and cross-validation are potential techniques to address this limitation and improve model performance, but effective solutions are still rare for 3D …

Osc-Co2: Coattention And Cosegmentation Framework For Plant State Change With Multiple Features, Rubi Quiñones, Ashok Samal, Sruti Das Choudhury, Francisco Muñoz-Arriola

School of Computing: Faculty Publications

Cosegmentation and coattention are extensions of traditional segmentation methods aimed at detecting a common object (or objects) in a group of images. Current cosegmentation and coattention methods are ineffective for objects, such as plants, that change their morphological state while being captured in different modalities and views. The Object State Change using Coattention-Cosegmentation (OSC-CO²) is an end-to-end unsupervised deep-learning framework that enhances traditional segmentation techniques, processing, analyzing, selecting, and combining suitable segmentation results that may contain most of our target object’s pixels, and then displaying a final segmented image. The framework leverages coattention-based convolutional neural networks (CNNs) and …

Rapid: Region-Based Pointer Disambiguation, Khushboo Chitre, Piyus Kedia, Rahul Purandare

School of Computing: Faculty Publications

Interprocedural alias analyses often sacrifice precision for scalability. Thus, modern compilers such as GCC and LLVM implement more scalable but less precise intraprocedural alias analyses. This compromise makes the compilers miss out on potential optimization opportunities, affecting the performance of the application. Modern compilers implement loop-versioning with dynamic checks for pointer disambiguation to enable the missed optimizations. Polyhedral access range analysis and symbolic range analysis enable O(1) range checks for non-overlapping of memory accesses inside loops. However, these approaches work only for the loops in which the loop bounds are loop invariants. To address this limitation, researchers proposed a …

Perceptual Cue-Guided Adaptive Image Downscaling For Enhanced Semantic Segmentation On Large Document Images, Chulwoo Pack, Leen-Kiat Soh, Elizabeth Lorang

School of Computing: Faculty Publications

Image downscaling is an essential operation to reduce spatial complexity for various applications and is becoming increasingly important due to the growing number of solutions that rely on memory-intensive approaches, such as applying deep convolutional neural networks to semantic segmentation tasks on large images. Although conventional content-independent image downscaling can efficiently reduce complexity, it is vulnerable to losing perceptual details, which are important to preserve. Alternatively, existing content-aware downscaling severely distorts spatial structure and is not effectively applicable for segmentation tasks involving document images. In this paper, we propose a novel image downscaling approach that combines the strengths of both …

Revealing Gene Regulation-Based Neural Network Computing In Bacteria, Samitha S. Somathilaka, Sasitharan Balasubramaniam, Daniel P. Martins, Xu Li

School of Computing: Faculty Publications

Bacteria are known to interpret a range of external molecular signals that are crucial for sensing environmental conditions and adapting their behaviors accordingly. These external signals are processed through a multitude of signaling transduction networks that include the gene regulatory network (GRN). From close observation, the GRN resembles and exhibits structural and functional properties that are similar to artificial neural networks. An in-depth analysis of gene expression dynamics further provides a new viewpoint of characterizing the inherited computing properties underlying the GRN of bacteria despite being non-neuronal organisms. In this study, we introduce a model to quantify the gene-to-gene interaction …

Mfa-Dvr: Direct Volume Rendering Of Mfa Models, Jianxin Sun, David Lenz, Hongfeng Yu, Tom Peterka

School of Computing: Faculty Publications

3D volume rendering is widely used to reveal insightful intrinsic patterns of volumetric datasets across many domains. However, the complex structures and varying scales of volumetric data can make efficiently generating high-quality volume rendering results a challenging task. Multivariate functional approximation (MFA) is a new data model that addresses some of the critical challenges: high-order evaluation of both value and derivative anywhere in the spatial domain, compact representation for largescale volumetric data, and uniform representation of both structured and unstructured data. In this paper, we present MFA-DVR, the first direct volume rendering pipeline utilizing the MFA model, for both structured …

A Roadmap For The Human Gut Cell Atlas, Matthias Zilbauer, Kylie R. James, Mandeep Kaur, Sebastian Pott, Zhixin Li, Albert Burger, Jay R. Thiagarajah, Joseph Burclaff, Frode L. Jahnsen, Francesca Perrone, Alexander D. Ross, Gianluca Matteoli, Nathalie Stakenborg, Tomohisa Sujino, Andreas Moor, Raquel Bartolome-Casado, Espen S. Bækkevold, Ran Zhou, Bingqing Xie, Ken S. Lau, Shahida Din, Scott T. Magness, Qiuming Yao, Semir Beyaz, Mark Arends, Alexandre Denadai-Souza, Lori A. Coburn, Jellert T. Gaublomme, Richard Baldock, Irene Papatheodorou, Jose Ordovas-Montanes, Guy Boeckxstaens, Anna Hupalowska, Sarah A. Teichmann

School of Computing: Faculty Publications

The number of studies investigating the human gastrointestinal tract using various single-cell profiling methods has increased substantially in the past few years. Although this increase provides a unique opportunity for the generation of the first comprehensive Human Gut Cell Atlas (HGCA), there remains a range of major challenges ahead. Above all, the ultimate success will largely depend on a structured and coordinated approach that aligns global efforts undertaken by a large number of research groups. In this Roadmap, we discuss a comprehensive forward-thinking direction for the generation of the HGCA on behalf of the Gut Biological Network of the Human …

Agris: Wind-Adaptive Wideband Reconfigurable Intelligent Surfaces For Resilient Wireless Agricultural Networks At Millimeter-Wave Spectrum, Shuai Nie, M. C. Vuran

School of Computing: Faculty Publications

Wireless networks in agricultural environments are unique in many ways. Recent measurements reveal that the dynamics of crop growth impact wireless propagation channels with a long-term seasonal pattern. Additionally, short-term environmental factors, such as strong wind, result in variations in channel statistics. Next-generation agricultural fields, populated by autonomous tractors, drones, and high-throughput sensing systems, require high-throughput connectivity infrastructure, resulting in the future deployment of high-frequency networks, where they have not been deployed before. More specifically, when millimeter-wave (mmWave) communication systems, a viable candidate for 5G and 6G high-throughput solutions, are deployed for higher throughput, these issues become more prominent due …

Next-Generation Sequencing Data-Based Association Testing Of A Group Of Genetic Markers For Complex Responses Using A Generalized Linear Model Framework, Zheng Xu, Song Yan, Cong Wu, Qing Duan, Sixia Chen, Yun Li

School of Computing: Faculty Publications

To study the relationship between genetic variants and phenotypes, association testing is adopted; however, most association studies are conducted by genotype-based testing. Testing methods based on next-generation sequencing (NGS) data without genotype calling demonstrate an advantage over testing methods based on genotypes in the scenarios when genotype estimation is not accurate. Our objective was to develop NGS data-based methods for association studies to fill the gap in the literature. Single-variant testing methods based on NGS data have been proposed, including our previously proposed single-variant NGS data-based testing method, i.e., UNC combo method. The NGS data-based group testing method has been …

Dynamic Resource Optimization For Energy-Efficient 6g-Iot Ecosystems, James Adu Ansere, Mohsin Kamal, Izaz Ahmad Khan, Muhammad Naveed Aman

School of Computing: Faculty Publications

The problem of energy optimization for Internet of Things (IoT) devices is crucial for two reasons. Firstly, IoT devices powered by renewable energy sources have limited energy resources. Secondly, the aggregate energy requirement for these small and low-powered devices is translated into significant energy consumption. Existing works show that a significant portion of an IoT device’s energy is consumed by the radio sub-system. With the emerging sixth generation (6G), energy efficiency is a major design criterion for significantly increasing the IoT network’s performance. To solve this issue, this paper focuses on maximizing the energy efficiency of the radio sub-system. In …

A Generalization Of The Chomsky-Halle Phonetic Representation Using Real Numbers For Robust Speech Recognition In Noisy Environments, Peter Z. Revesz

School of Computing: Faculty Publications

Speech recognition is difficult when the speech signal is weak or occurs in a noisy environment. This paper presents an efficient and robust method that can reconstruct the standard pronunciation of English phonemes and words given a weak or noisy signal. The reconstruction is based on a novel representation of the reconstruction task as a problem of data retrieval from a database in two different cases: (1) when the phonemes are represented in the database as binary tuples and the input is also a binary tuple from which deletion errors occur, and (2) when the phonemes are represented in the …

Studying Developer Eye Movements To Measure Cognitive Workload And Visual Effort For Expertise Assessment, Salwa D. Aljehane, Bonita Sharif, Jonathan I. Maletic

School of Computing: Faculty Publications

Eye movement data provides valuable insights that help test hypotheses about a software developer’s comprehension process. The pupillary response is successfully used to assess mental processing effort and attentional focus. Relatively little is known about the impact of expertise level in cognitive effort during programming tasks. This paper presents a quantitative analysis that compares the eye movements of 207 experts and novices collected while solving program comprehension tasks. The goal is to examine changes of developers’ eye movement metrics in accordance with their expertise. The results indicate significant increase in pupil size with the novice group compared to the experts, …

Convolutional Neural Networks Analysis Reveals Three Possible Sources Of Bronze Age Writings Between Greece And India, Shruti Daggumati, Peter Z. Revesz

School of Computing: Faculty Publications

This paper analyzes the relationships among eight ancient scripts from between Greece and India. We used convolutional neural networks combined with support vector machines to give a numerical rating of the similarity between pairs of signs (one sign from each of two different scripts). Two scripts that had a one-to-one matching of their signs were determined to be related. The result of the analysis is the finding of the following three groups, which are listed in chronological order: (1) Sumerian pictograms, the Indus Valley script, and the proto-Elamite script; (2) Cretan hieroglyphs and Linear B; and (3) the Phoenician, Greek, …

Conversion Of Fat To Cellular Fuel—Fatty Acids 𝛽-Oxidation Model, Sylwester M. Kloska, Krzysztof Pałczyński, Tomasz Marciniak, Tomasz Talaśka, Marissa Miller, Beata J. Wysocki, Paul Davis, Tadeusz A. Wysocki

School of Computing: Faculty Publications

𝛽-oxidation of fatty acids plays a significant role in the energy metabolism of the cell. This paper presents a 𝛽-oxidation model of fatty acids based on queueing theory. It uses Michaelis–Menten enzyme kinetics, and literature data on metabolites’ concentration and enzymatic constants. A genetic algorithm was used to optimize the parameters for the pathway reactions. The model enables real-time tracking of changes in the concentrations of metabolites with different carbon chain lengths. Another application of the presented model is to predict the changes caused by system disturbance, such as altered enzyme activity or abnormal fatty acid concentration. The model has …

From Laboratory To Field: Unsupervised Domain Adaptation For Plant Disease Recognition In The Wild, Xinlu Wu, Xijian Fan, Peng Luo, Sruti Das Choudhury, Tardi Tjahjadi, Chunhua Hu

School of Computing: Faculty Publications

Plant disease recognition is of vital importance to monitor plant development and predicting crop production. However, due to data degradation caused by different conditions of image acquisition, e.g., laboratory vs. field environment, machine learning-based recognition models generated within a specific dataset (source domain) tend to lose their validity when generalized to a novel dataset (target domain). To this end, domain adaptation methods can be leveraged for the recognition by learning invariant representations across domains. In this paper, we aim at addressing the issues of domain shift existing in plant disease recognition and propose a novel unsupervised domain adaptation method via …

Efficient Two-Stage Analysis For Complex Trait Association With Arbitrary Depth Sequencing Data, Zheng Xu, Song Yan, Shuai Yuan, Cong Wu, Sixia Chen, Zifang Guo

School of Computing: Faculty Publications

Sequencing-based genetic association analysis is typically performed by first generating genotype calls from sequence data and then performing association tests on the called genotypes. Standard approaches require accurate genotype calling (GC), which can be achieved either with high sequencing depth (typically available in a small number of individuals) or via computationally intensive multi-sample linkage disequilibrium (LD)-aware methods. We propose a computationally efficient two-stage combination approach for association analysis, in which single-nucleotide polymorphisms (SNPs) are screened in the first stage via a rapid maximum likelihood (ML)-based method on sequence data directly (without first calling genotypes), and then the selected SNPs are …

Leaf-Counting In Monocot Plants Using Deep Regression Models, Xinyan Xie, Yufeng Ge, Harkamal Walia, Jinliang Yang, Hongfeng Yu

School of Computing: Faculty Publications

Leaf numbers are vital in estimating the yield of crops. Traditional manual leaf-counting is tedious, costly, and an enormous job. Recent convolutional neural network-based approaches achieve promising results for rosette plants. However, there is a lack of effective solutions to tackle leaf counting for monocot plants, such as sorghum and maize. The existing approaches often require substantial training datasets and annotations, thus incurring significant overheads for labeling. Moreover, these approaches can easily fail when leaf structures are occluded in images. To address these issues, we present a new deep neural network-based method that does not require any effort to label …

Emergenet: A Novel Deep-Learning Based Ensemble Segmentation Model For Emergence Timing Detection Of Coleoptile, Aankit Das, Sruti Das Choudhury, Amit Kumar Das, Ashok Samal, Tala Awada

School of Computing: Faculty Publications

The emergence timing of a plant, i.e., the time at which the plant is first visible from the surface of the soil, is an important phenotypic event and is an indicator of the successful establishment and growth of a plant. The paper introduces a novel deep-learning based model called EmergeNet with a customized loss function that adapts to plant growth for coleoptile (a rigid plant tissue that encloses the first leaves of a seedling) emergence timing detection. It can also track its growth from a time-lapse sequence of images with cluttered backgrounds and extreme variations in illumination. EmergeNet is a …

Metamobility: Connecting Future Mobility With Metaverse, Haoxin Wang, Ziran Wang, Dawei Chen, Qiang Liu, Hongyu Ke, Kyungtae Han

School of Computing: Faculty Publications

A Metaverse is a perpetual, immersive, and shared digital universe that is linked to but beyond the physical reality, and this emerging technology is attracting enormous attention from different industries. In this article, we define the first holistic realization of the metaverse in the mobility domain, coined as “metamobility”. We present our vision of what metamobility will be and describe its basic architecture. We also propose two use cases, tactile live maps and meta-empowered advanced driver-assistance systems (ADAS), to demonstrate how the metamobility will benefit and reshape future mobility systems. Each use case is discussed from the perspective of the …

Extending The Breadth Of Saliva Metabolome Fngerprinting By Smart Template Strategies And Efective Pattern Realignment On Comprehensive Two‑Dimensional Gas Chromatographic Data, Simone Squara, Friederike Manig, Thomas Henle, Michael Hellwig, Andrea Caratti, Carlo Bicchi, Stephen E. Reichenbach, Qingping Tao, Massimo Collino, Chiara Cordero

School of Computing: Faculty Publications

Comprehensive two-dimensional gas chromatography with time-of-fight mass spectrometry (GC×GC-TOFMS) is one the most powerful analytical platforms for chemical investigations of complex biological samples. It produces large datasets that are rich in information, but highly complex, and its consistency may be affected by random systemic fluctuations and/ or changes in the experimental parameters. This study details the optimization of a data processing strategy that compensates for severe 2D pattern misalignments and detector response fluctuations for saliva samples analyzed across 2 years. The strategy was trained on two batches: one with samples from healthy subjects who had undergone dietary intervention with high/low-Maillard …

Network Slicing Via Transfer Learning Aided Distributed Deep Reinforcement Learning, Tianlun Hu, Qi Liao, Qiang Liu, Georg Carle

School of Computing: Faculty Publications

Deep reinforcement learning (DRL) has been increasingly employed to handle the dynamic and complex resource management in network slicing. The deployment of DRL policies in real networks, however, is complicated by heterogeneous cell conditions. In this paper, we propose a novel transfer learning (TL) aided multi-agent deep reinforcement learning (MADRL) approach with inter-agent similarity analysis for inter-cell inter-slice resource partitioning. First, we design a coordinated MADRL method with information sharing to intelligently partition resource to slices and manage inter-cell interference. Second, we propose an integrated TL method to transfer the learned DRL policies among different local agents for accelerating the …

Dynamic Field Programmable Logic-Driven Soft Exosuit, Frances Cleary, Witawas Srisa-An, David C. Henshall, Sasitharan Balasubramaniam

School of Computing: Faculty Publications

The next generation of etextiles foresees an era of smart wearable garments where embedded seamless intelligence provides the ability to sense, process and perform. Core to this vision is embedded textile functionality enabling dynamic configuration. In this paper we detail a methodology, design and implementation of a dynamic field programmable logic-driven fabric soft exosuit. Dynamic field programmability allows the soft exosuit to alter its functionality and adapt to specific exercise programs depending on the wearers need. The dynamic field programmability is enabled through motion based control arm movements of the soft exosuit triggering momentary sensors embedded in the fabric exosuit …

A Light-Weight Technique To Detect Gps Spoofing Using Attenuated Signal Envelopes, Xiao Wei, Muhammad Naveed Aman, Biplab Sikdar

School of Computing: Faculty Publications

Global Positioning System (GPS) spoofing attacks have attracted more attention as one of the most effective GPS attacks. Since the signals from an authentic satellite and the spoofer undergo different attenuation, the captured envelope of fake GPS signals exhibits distinctive transmission characteristics due to short transmission paths. This can be utilized for GPS spoofing detection. The existing technique for GPS spoofing are either computationally too expensive, require specialize hardware/ software updates, or are not accurate enough. To solve these issues, we propose a light-weight GPS spoofing detection method based on a dynamic threshold and captured signal envelope. We validate the …

A Markovian Error Model For False Negatives In Dnn-Based Perception-Driven Control Systems, Kruttidipta Samal, Thomas Walton, Tran Hoang-Dung, Marilyn Wolf

School of Computing: Faculty Publications

vehicles and other perception-driven control systems. Many modern autonomous systems rely on DNN-driven perception-based control/ planning methodologies such as autonomous navigation, where the perception errors significantly affect the control/planning performance and the systems’ safety. The traditional independent, identically-distributed (IID) perception error model is inadequate for perception-based control/planning applications because image sequences supplied to a DNN-based perception module are not independent in the real world. Based on this observation, we develop a novel Markov model to describe the error behavior of a DNN perception model—an error in one frame is likely to signal errors in successive frames, effectively reducing sample rate …

Ethical Design Of Computers: From Semiconductors To Iot And Artificial Intelligence, Sudeep Pasricha, Marilyn Wolf

School of Computing: Faculty Publications

Computing systems are tightly integrated today into our professional, social, and private lives. An important consequence of this growing ubiquity of computing is that it can have significant ethical implications of which computing professionals should take account. In most real-world scenarios, it is not immediately obvious how particular technical choices during the design and use of computing systems could be viewed from an ethical perspective. This article provides a perspective on the ethical challenges within semiconductor chip design, IoT applications, and the increasing use of artificial intelligence in the design processes, tools, and hardware-software stacks of these systems.

On Approximating Total Variation Distance, Arnab Bhattacharyya, Sutanu Gayen, Kuldeep S. Meel, Dimitrios Myrisiotis, A. Pavan, N. V. Vinodchandran

School of Computing: Faculty Publications

Total variation distance (TV distance) is a fundamental notion of distance between probability distributions. In this work, we introduce and study the problem of computing the TV distance of two product distributions over the domain {0, 1}ⁿ. In particular, we establish the following results.

1. The problem of exactly computing the TV distance of two product distributions is #P-complete. This is in stark contrast with other distance measures such as KL, Chisquare, and Hellinger which tensorize over the marginals leading to efficient algorithms.
2. There is a fully polynomial-time deterministic approximation scheme (FPTAS) for computing the TV distance of two …

Towards Modeling Human Attention From Eye Movements For Neural Source Code Summarization, Aakash Bansal, Bonita Sharif, Collin Mcmillan

School of Computing: Faculty Publications

Neural source code summarization is the task of generating natural language descriptions of source code behavior using neural networks. A fundamental component of most neural models is an attention mechanism. The attention mechanism learns to connect features in source code to specific words to use when generating natural language descriptions. Humans also pay attention to some features in code more than others. This human attention reflects experience and high-level cognition well beyond the capability of any current neural model. In this paper, we use data from published eye-tracking experiments to create a model of this human attention. The model predicts …

Co-Existence With Ieee 802.11 Networks In The Ism Band Without Channel Estimation, Muhammad Naveed Aman, Muhammad Ishfaq, Biplab Sikdar

School of Computing: Faculty Publications

Any new deployment of networks in the industrial, scientific, and medical (ISM) band, even though it is license-free, has to co-exist with IEEE 802.11 networks. IoT devices are typically deployed in the ISM band, creating a spectrum bottleneck for competing networks. This paper investigates the issue of co-existence of wireless networks with WiFi networks. In our scenario, we consider WiFi as the “primary” or higher priority network co-existing with multiple “secondary” networks that may be used for low priority devices, with both networks operating in the ISM band. Towards this end, we first develop an analytical model for a metric …

Realizing Molecular Machine Learning Through Communications For Biological Ai: Future Directions And Challenges, Sasitharan Balasubramaniam, Samitha Somathilaka, Sehee Sun, Adrian Ratwatte, Massimiliano Pierobon

School of Computing: Faculty Publications

Artificial Intelligence (AI) and Machine Learning (ML) are weaving their way into the fabric of society, where they are playing a crucial role in numerous facets of our lives. As we witness the increased deployment of AI and ML in various types of devices, we benefit from their use into energy-efficient algorithms for low powered devices. In this paper, we investigate a scale and medium that is far smaller than conventional devices as we move towards molecular systems that can be utilized to perform machine learning functions, i.e., Molecular Machine Learning (MML). Fundamental to the operation of MML is the …