Computer Engineering Commons^™

Named Entity Recognition In Spanish Biomedical Literature: Short Review And Bert Model, Liliya Akhtyamova

Conference Papers

Named Entity Recognition (NER) is the rst step for knowledge acquisition when we deal with an unknown corpus of texts. Having received these entities, we have an opportunity to form parameters space and to solve problems of text mining as concept normalization, speech recognition, etc. The recent advances in NER are related to the technology of word embeddings, which transforms text to the form being effective for Deep Learning. In the paper, we show how NER detects pharmacological substances, compounds, and proteins in the dataset obtained from the Spanish Clinical Case Corpus (SPACCC). To achieve this goal, we use contextualized …

Annual Report 2019-2020, Depaul University College Of Computing And Digital Media

CDM Annual Reports

LETTER FROM THE DEAN

As I write this letter wrapping up the 2019-20 academic year, we remain in a global pandemic that has profoundly altered our lives. While many things have changed, some stayed the same: our CDM community worked hard, showed up for one another, and continued to advance their respective fields. A year that began like many others changed swiftly on March 11th when the University announced that spring classes would run remotely. By March 28th, the first day of spring quarter, we had moved 500 CDM courses online thanks to the diligent work of our faculty, staff, …

Timcc: On Data Freshness In Privacy-Preserving Incentive Mechanism Design For Continuous Crowdsensing Using Reverse Auction, Xiaoqiang Ma, Weiwei Deng, Feng Wang, Menglan Hu, Fei Chen, Mohammad Mehedi Hassan

Faculty and Student Publications

© 2013 IEEE. As an emerging paradigm that leverages the wisdom and efforts of the crowd, mobile crowdsensing has shown its great potential to collect distributed data. The crowd may incur such costs and risks as energy consumption, memory consumption, and privacy leakage when performing various tasks, so they may not be willing to participate in crowdsensing tasks unless they are well-paid. Hence, a proper privacy-preserving incentive mechanism is of great significance to motivate users to join, which has attracted a lot of research efforts. Most of the existing works regard tasks as one-shot tasks, which may not work very …

Promoting A Growth Mindset In Cs1: Does One Size Fit All? A Pilot Study, Keith Quille, Susan Bergin

Conference Papers

This paper describes a pilot intervention conducted in CS1, in the
academic year of 2016-2017. The intervention was based on the
work of Dweck, promoting a growth Mindset in an effort to in-
crease performance in introductory programming. The study also
examined data from a previous year (as a control group) to compare
and contrast the results. Multiple factors related to programming
performance were recorded with the control and treatment group,
which were measured at multiple intervals throughout the course,
to monitor changes as the pilot intervention was implemented.
This study found a significant increase in programming perfor-
mance when …

Edge-Cloud Computing For Iot Data Analytics: Embedding Intelligence In The Edge With Deep Learning, Ananda Mohon M. Ghosh, Katarina Grolinger

Electrical and Computer Engineering Publications

Rapid growth in numbers of connected devices including sensors, mobile, wearable, and other Internet of Things (IoT) devices, is creating an explosion of data that are moving across the network. To carry out machine learning (ML), IoT data are typically transferred to the cloud or another centralized system for storage and processing; however, this causes latencies and increases network traffic. Edge computing has the potential to remedy those issues by moving computation closer to the network edge and data sources. On the other hand, edge computing is limited in terms of computational power and thus is not well suited for …

Deep Learning For Load Forecasting With Smart Meter Data: Online Adaptive Recurrent Neural Network, Mohammad Navid Fekri, Harsh Patel, Katarina Grolinger, Vinay Sharma

Electrical and Computer Engineering Publications

No abstract provided.

Water Conservation Potential Of Self-Funded Foam-Based Flexible Surface-Mounted Floatovoltaics, Koami Soulemane Hayibo, Pierce Mayville, Ravneet Kaur Kailey, Joshua M. Pearce

Electrical and Computer Engineering Publications

A potential solution to the coupled water–energy–food challenges in land use is the concept of floating photovoltaics or floatovoltaics (FPV). In this study, a new approach to FPV is investigated using a flexible crystalline silicon-based photovoltaic (PV) module backed with foam, which is less expensive than conventional pontoon-based FPV. This novel form of FPV is tested experimentally for operating temperature and performance and is analyzed for water-savings using an evaporation calculation adapted from the Penman–Monteith model. The results show that the foam-backed FPV had a lower operating temperature than conventional pontoon-based FPV, and thus a 3.5% higher energy output per …

Comparative Analysis Of Single-Cell Transcriptomics In Human And Zebrafish Oocytes, Handan Can, Sree K. Chanumolu, Elena Gonzalez-Muñoz, Sukumal Prukudom, Hasan H. Otu, Jose Cibelli

Department of Electrical and Computer Engineering: Faculty Publications

Background: Zebrafish is a popular model organism, which is widely used in developmental biology research. Despite its general use, the direct comparison of the zebrafish and human oocyte transcriptomes has not been well studied. It is significant to see if the similarity observed between the two organisms at the gene sequence level is also observed at the expression level in key cell types such as the oocyte.

Results: We performed single-cell RNA-seq of the zebrafish oocyte and compared it with two studies that have performed single-cell RNA-seq of the human oocyte. We carried out a comparative analysis of genes expressed …

Cdc42-Dependent Transfer Of Mir301 From Breast Cancer-Derived Extracellular Vesicles Regulates The Matrix Modulating Ability Of Astrocytes At The Blood–Brain Barrier, Golnaz Morad, Cassandra C. Daisy, Hasan H. Otu, Towia A. Libermann, Simon T. Dillon, Marsha A. Moses

Department of Electrical and Computer Engineering: Faculty Publications

Breast cancer brain metastasis is a major clinical challenge and is associated with a dismal prognosis. Understanding the mechanisms underlying the early stages of brain metastasis can provide opportunities to develop efficient diagnostics and therapeutics for this significant clinical challenge. We have previously reported that breast cancer-derived extracellular vesicles (EVs) breach the blood–brain barrier (BBB) via transcytosis and can promote brain metastasis. Here, we elucidate the functional consequences of EV transport across the BBB. We demonstrate that brain metastasis-promoting EVs can be internalized by astrocytes and modulate the behavior of these cells to promote extracellular matrix remodeling in vivo. We …

Ieee Access Special Section Editorial: Green Signal Processing For Wireless Communicationsand Networking, Wen-Long Chin, David Shiung, Yi Qian, Woongsup Lee, Andres Kwasinki, Yansha Deng

Department of Electrical and Computer Engineering: Faculty Publications

No abstract provided.

Topical Review Of Vulnerability Management For Local Hampton Roads Industry, Gregory W. Hubbard Jr., Matthew Eunice

OUR Journal: ODU Undergraduate Research Journal

The progress towards an interconnected digital world offers an exciting level of advancement for humanity. Unfortunately, this “online” connection is not safe from the threats and dangers typically associated with physical operations. With the foundation of Cyber Command of DoD cyberspace, the United States Government is taking a prominent stance in cyberspace operations. Like the federal government, both industries and individuals are not immune and are oftentimes unknowingly at risk to cyberattack. This report hopes to bring awareness to common vulnerabilities in multi-user networks by describing a historical background on cyber security as well as outlining current methods of vulnerability …

Assessment Of Dobhoff Tube Malposition On Radiographs Using Deep Learning, Kevin George, Paras Lakhani, Md

Phase 1

Introduction: Dobhoff tubes (DHT) are narrow-bore flexible devices that deliver enteral nutrition for critically ill patients. Tracheobronchial insertion of DHTs presents a significant risk for pulmonary complications. Thus, DHT insertion requires radiologist confirmation of correct placement with chest x-ray (CXR), increasing clinical delays. To address this, we demonstrate the novel application of Deep Convolutional Neural Networks (DCNNs) to automatically and accurately identify DHTs in CXRs in real time.

Methods: 141 de-identified HIPAA compliant frontal view chest radiographs containing DHTs in various positions were obtained. The DHTs were first manually segmented and verified by a board certified radiologist. Images were split …

3d Convolutional Neural Networks For The Diagnosis Of 6 Unique Pathologies On Head Ct, Travis Clarke, Paras Lakhani, Md

Phase 1

Introduction: Head CT scans are a standard first-line tool used by physicians in the diagnosis of neurological pathologies. Recently, the development of deep learning models such as convolutional neural networks (CNNs) has allowed the rapid identification of bleeds and other pathologies on CT scans. This study aims to show that by training 3D CNNs with a larger, curated dataset, a more comprehensive list of potential diagnoses can be included in the detailed model.

Methods: A retrospective study was performed using a dataset of 66,000 head CT studies from the Thomas Jefferson University health system. Studies were acquired using a natural …

Comparison Of Object Detection And Patch-Based Classification Deep Learning Models On Mid- To Late-Season Weed Detection In Uav Imagery, Arun Narenthiran Veeranampalayam Sivakumar, Jiating Li, Stephen Scott, Eric T. Psota, Amit J. Jhala, Joe D. Luck, Yeyin Shi

Department of Agricultural and Biological Systems Engineering: Faculty Publications

Mid- to late-season weeds that escape from the routine early-season weed management threaten agricultural production by creating a large number of seeds for several future growing seasons. Rapid and accurate detection of weed patches in field is the first step of site-specific weed management. In this study, object detection-based convolutional neural network models were trained and evaluated over low-altitude unmanned aerial vehicle (UAV) imagery for mid- to late-season weed detection in soybean fields. The performance of two object detection models, Faster RCNN and the Single Shot Detector (SSD), were evaluated and compared in terms of weed detection performance using mean …

Control Strategies For Multi-Controller Multi-Objective Systems, Raaed Al-Azzawi

Electronic Theses and Dissertations, 2020-2023

This dissertation's focus is control systems controlled by multiple controllers, each having its own objective function. The control of such systems is important in many practical applications such as economic systems, the smart grid, military systems, robotic systems, and others. To reap the benefits of feedback, we consider and discuss the advantages of implementing both the Nash and the Leader-Follower Stackelberg controls in a closed-loop form. However, closed-loop controls require continuous measurements of the system's state vector, which may be expensive or even impossible in many cases. As an alternative, we consider a sampled closed-loop implementation. Such an implementation requires …

Target Acquisition Performance Improvement With Boost And Restoration Filtering Using Deep-Electron-Well Infrared Detectors, Robert Short

Electronic Theses and Dissertations, 2020-2023

Recent advances in infrared focal plane fabrication have allowed for the production of sensors with small detector size (small pitch) and long integration time (deep electron wells) in large-format arrays. Individually, these are all welcome developments, but we raise the question of whether it is possible to utilize all of these technologies in concert to optimize performance. If so, a key part of such a system will be digital boost filtering, to recover the performance loss due to diffraction blur. We describe a system design concept called PWP (Pitch-Well-Processing) that uses each of these features along with Wiener filtering to …

On The Automorphisms And Isomorphisms Of Mds Matrices And Their Efficient Implementations, Muharrem Tolga Sakalli, Sedat Akleylek, Kemal Akkanat, Vincent Rijmen

Turkish Journal of Electrical Engineering and Computer Sciences

In this paper, we explicitly define the automorphisms of MDS matrices over the same binary extension field. By extending this idea, we present the isomorphisms between MDS matrices over $\mathbb{F}_{2^{m}}$ and MDS matrices over $\mathbb{F}_{2^{mt}}$, where $t \ge 1$ and $m>1$, which preserves the software implementation properties in view of XOR operations and table lookups of any given MDS matrix over $\mathbb{F}_{2^{m}}$. Then we propose a novel method to obtain distinct functions related to these automorphisms and isomorphisms to be used in generating isomorphic MDS matrices (new MDS matrices in view of implementation properties) using the existing ones. The …

Wideband Patch Array Antenna Using Superstrate Configuration For Future 5gapplications, Sidra Farhat, Farzana Arshad, Yasar Amin, Jonathan Loo

Turkish Journal of Electrical Engineering and Computer Sciences

In this work, four distinct antenna configurations for future-centric 5G applications are proposed. Initially, a single rectangular patch is designed to operate at the frequency of 28 GHz while maintaining a wide operational band. Performance of the antenna is improved by incorporating an array of identical rectangular elements resulting in a higher gain and wider bandwidth. The proposed arrangement consists of three rectangular elements realized using 0.508-mm thick Rogers RT/Duroid 5880 laminate. The bandwidth is further enhanced by increasing the number of radiating elements in the array from three to five. Evolution of the proposed design is concluded by stacking …

Revised Polyhedral Conic Functions Algorithm For Supervised Classification, Gürhan Ceylan, Gürkan Öztürk

Turkish Journal of Electrical Engineering and Computer Sciences

In supervised classification, obtaining nonlinear separating functions from an algorithm is crucial for prediction accuracy. This paper analyzes the polyhedral conic functions (PCF) algorithm that generates nonlinear separating functions by only solving simple subproblems. Then, a revised version of the algorithm is developed that achieves better generalization and fast training while maintaining the simplicity and high prediction accuracy of the original PCF algorithm. This is accomplished by making the following modifications to the subproblem: extension of the objective function with a regularization term, relaxation of a hard constraint set and introduction of a new error term. Experimental results show that …

Cdc42-Dependent Transfer Of Mir301 From Breast Cancer-Derived Extracellular Vesicles Regulates The Matrix Modulating Ability Of Astrocytes At The Blood–Brain Barrier, Golnaz Morad, Cassandra C. Daisy, Hasan H. Otu, Towia A. Libermann, Simon T. Dillon, Marsha A. Moses

Department of Electrical and Computer Engineering: Faculty Publications

Breast cancer brain metastasis is a major clinical challenge and is associated with a dismal prognosis. Understanding the mechanisms underlying the early stages of brain metastasis can provide opportunities to develop efficient diagnostics and therapeutics for this significant clinical challenge. We have previously reported that breast cancer-derived extracellular vesicles (EVs) breach the blood–brain barrier (BBB) via transcytosis and can promote brain metastasis. Here, we elucidate the functional consequences of EV transport across the BBB. We demonstrate that brain metastasis-promoting EVs can be internalized by astrocytes and modulate the behavior of these cells to promote extracellular matrix remodeling in vivo. We …

Power-Over-Tether Uas Leveraged For Nearly Indefinite Meteorological Data Acquisition In The Platte River Basin, Daniel Rico, Carrick Detweiler, Francisco Munoz-Arriola

School of Computing: Conference and Workshop Papers

The integration of unmanned aerial systems (UASs) has increased in the field of agriculture. These systems can provide data that was previously difficult to obtain to help increase efficiency and production. Typical commercial off the shelf (COTS) UASs have significant limitations in the form of small payloads, and short flight times which inhibit their ability to provide significant quantities of useful data. We present the development of a novel power-over-tether UAS that leverages the physical presence of the tether to integrate sensors at multiple altitudes along the tether. The UAS can acquire data nearly indefinitely to sense atmospheric conditions and …

Automated Assessment Of Cardiothoracic Ratios On Chest Radiographs Using Deep Learning, Varun Danda, Paras Lakhani, Md

Phase 1

Introduction: The cardiothoracic ratio (CTR) is a quantitative measure of cardiac size that can measured from chest radiography (CXR). Although radiologists using digital workstations possess the ability to calculate CTR, clinical demands prevent calculation for every case. In this study, the efficacy of a deep convolutional neural network (dCNN) to assess CTR was evaluated.

Methods: 611 HIPAA-compliant de-identified CXRs were obtained from [institution blinded] and public databases. Using ImageJ, a board-certified radiologist (reader #1) and a medical student (reader #2), measured the CTR by marking four pixels on all CXRs: the right- and left-most chest wall, the right- and left-most …

The Influence Of Blockchain Technology On Fraud And Fake Protection, Youngju Yun

OUR Journal: ODU Undergraduate Research Journal

No abstract provided.

Hierarchical Anomaly Detection For Time Series Data, Ryan E. Sperl

Browse all Theses and Dissertations

With the rise of Big Data and the Internet of Things, there is an increasing availability of large volumes of real-time streaming data. Unusual occurrences in the underlying system will be reflected in these streams, but any human analysis will quickly become out of date. There is a need for automatic analysis of streaming data capable of identifying these anomalous behaviors as they occur, to give ample time to react. In order to handle many high-velocity data streams, detectors must minimize the processing requirements per value. In this thesis, we have developed a novel anomaly detection method which makes use …

Corrections To ‘‘Glaciernet: A Deep-Learning Approach For Debris-Covered Glacier Mapping’’, Zhiyuan Xie, Umesh K. Haritashya, Vijayan K. Asari, Brennan W. Young, Michael P. Bishop, Jeffrey S. Kargel

Electrical and Computer Engineering Faculty Publications

In the above article [1], Figure 2 was incorrect. Unfortunately, we mixed the color label of "CONV $\to $ BN $\to $ ReLu" and "Unpooling" in the CNN structure section of Figure 2. The color label of "CONV $\to $ BN $\to $ ReLu" should be orange while the color label of "Unpooling" should be green. Also, the word "Decoder" is misspelled. That same figure with the same error is also used for the graphic abstract. The corrected figure is given here. None of the sections in the figure is modified. The only change is in the color label of …

Development Of The Payload System And Obc Microcontroller Coding For A Cubic Satellite Performing An Additive Self-Repair Experiment In Space, Eduardo Macias-Zugasti

Open Access Theses & Dissertations

Additive manufacturing, which is also known as three-dimensional printing, in space is one of the most promising technologies advancing current capabilities for in-orbit space manufacturing and assembly. Additive manufacturing contributes to the reduction of cost per kilogram and number of launches, thus facilitating extraterrestrial colonization and deep-space exploration. The state of the art includes advancing efforts inside the International Space Station (ISS). However, the ISS is a controlled environment and, to the best of our knowledge, no spacecraft or satellite has performed additive manufacturing tasks in the extreme environment of outer space. In this work a 1U CubeSat named Orbital …

การสกัดตารางและรายการบนเว็บเป็นอาร์ดีเอฟ, จุลเทพ นันทขว้าง

Chulalongkorn University Theses and Dissertations (Chula ETD)

ทุกวันนี้ ลิงก์เดต้าได้เติบโตเพิ่มขึ้นอย่างรวดเร็วตามการเติบโตของเว็บ นอกเหนือจากข้อมูลใหม่ที่สร้างขึ้นในรูปแบบซีแมนติกโดยเฉพาะ ส่วนหนึ่งมาจากการแปลงข้อมูลโครงสร้างที่มีอยู่ให้อยู่ในรูปแบบของข้อมูลเปิดระดับห้าดาว อย่างไรก็ตามยังคงมีข้อมูลจำนวนมากในรูปแบบโครงสร้างและกึ่งโครงสร้าง ตัวอย่างเช่นตารางและรายการซึ่งเป็นรูปแบบหลักที่มนุษย์ใช้อ่าน ยังรอการแปลงอยู่ งานวิจัยนี้กล่าวถึงงานวิจัยต่าง ๆ ที่เกี่ยวกับการแปลงตารางและรายการมาเป็นข้อมูลในรูปแบบต่าง ๆ เพื่อให้เครื่องสามารถอ่านได้ นอกจากนี้ยังเสนอวิธีการในการแปลงตารางและรายการเป็นรูปแบบ Resource Description Framework และยังคงเก็บโครงสร้างต้นฉบับที่จำเป็นไว้อย่างละเอียด ซึ่งทำให้สามารถที่จะสร้างข้อมูลโครงสร้างเดิมกลับมาได้ ระบบ TULIP ถูกสร้างขึ้นเพื่อเป็นเครื่องมือสำหรับการพัฒนาซีแมนติกเว็บ วิธีการที่เสนอมีความยืดหยุ่นมากกว่าเมื่อเทียบกับงานอื่น ๆ เดต้าโมเดลของ TULIP สามารถรองรับการเก็บข้อมูลต้นฉบับอย่างครบถ้วน และสามารถนำมาแสดงใหม่ในมุมมองที่แตกต่างไปจากเดิม เครื่องมือนี้สามารถใช้สร้างข้อมูลจำนวนมหาศาลสำหรับเครื่องคอมพิวเตอร์เพื่อให้ใช้งานได้กว้างมากขึ้นกว่าเดิม

การสรุปใจความสำคัญของข้อความแบบสกัดสำหรับข่าวท่องเที่ยวภาษาไทย, ศรัญญา นาทองห่อ

Chulalongkorn University Theses and Dissertations (Chula ETD)

ปัจจุบันเทคโนโลยีทางด้านคอมพิวเตอร์มีความสำคัญต่อการดำเนินชีวิตประจำวันของมนุษย์เป็นอย่างมากและยังถือว่าเป็นเครื่องมือที่ใช้ในการอำนวยความสะดวกให้แก่มนุษย์มากมายโดยเฉพาะทางด้านการสื่อสารผ่านสังคมออนไลน์ เพื่อลดเวลาในการอ่านข่าวหรืออ่านบทความและข่าวออนไลน์ต่างๆ จากการวิจัยที่ผ่านมามีการศึกษาและพัฒนาการสรุปใจความสำคัญของภาษาไทยเป็นจำนวนมาก ในงานวิจัยนี้ได้นำเสนอวิธีการสรุปใจความสำคัญจากข่าวการท่องเที่ยวภาษาไทย 2 วิธีคือการเลือกประโยคจากการจัดกลุ่มประโยคด้วยเคมีนและการเลือกประโยคด้วยวิธีหาคำสำคัญประโยคจากหัวข้อข่าว โดยมีการพัฒนาและสร้างคลังข้อมูลรายการคำประสมเพื่อช่วยเพิ่มประสิทธิภาพในการตัดคำ โดยการทดลองนี้ใช้ข้อมูลข่าวการท่องเที่ยวไทย ทั้งหมด 400 ข่าวสำหรับใช้ทดลองในการสรุปใจความสำคัญ และ 5,000 ข่าวสำหรับการสร้างคลังข้อมูลรายการคำประสม การวัดประสิทธิภาพของวิธีการที่นำเสนอ มีการวัดประสิทธิภาพการสรุปใจความสำคัญโดยการเปรียบเทียบผลจากการสรุปที่ได้จากผู้เชี่ยวชาญด้านภาษาไทยเทียบกับผลสรุปที่ได้จากวิธีการที่นำเสนอ จากงานวิจัยนี้ในขั้นตอนการสร้างคำประสมได้คำประสมทั้งหมด จำนวน 2,340 คำ ผลการทดลองพบว่าวิธีตัดคำด้วยคัตคำร่วมกับตัดคำประสมได้ผลดีกว่าการตัดคำจากคัตคำเพียงอย่างเดียว และการสรุปใจความสำคัญโดยใช้การคำนวณค่าน้ำหนักของคำสำคัญโดยหาค่าความถี่ของคำจากหัวข้อข่าวเพียงอย่างเดียวและเลือกประโยคเรียงลำดับจากผลรวมความถี่ของคำสำคัญจากหัวข้อข่าวมีประสิทธิภาพและความแม่นยำสูงสุดโดยมีค่าความแม่นยำ ค่าความระลึกและค่าวัดประสิทธิภาพอยู่ที่ 0.8097 0.8367 และ 0.8216 ตามลำดับและเมื่อใช้คัตคำร่วมกับการตัดคำแบบเอ็นแกรมโดยวิธีการสรุปใจความสำคัญแบบเดียวกันได้ค่าความแม่นยำ ค่าความระลึกและค่าวัดประสิทธิภาพอยู่ที่ 0.8119 0.8398 และ 0.8242 ตามลำดับที่อัตราการบีบอัดร้อยละ 20

Semantic Segmentation Using Modified U-Net Architecture For Crack Detection, Michael Sun

Electronic Theses and Dissertations

The visual inspection of a concrete crack is essential to maintaining its good condition during the service life of the bridge. The visual inspection has been done manually by inspectors, but unfortunately, the results are subjective. On the other hand, automated visual inspection approaches are faster and less subjective. Concrete crack is an important deficiency type that is assessed by inspectors. Recently, various Convolutional Neural Networks (CNNs) have become a prominent strategy to spot concrete cracks mechanically. The CNNs outperforms the traditional image processing approaches in accuracy for the high-level recognition task. Of them, U-Net, a CNN based semantic segmentation …

Kernel-Controlled Dqn Based Cnn Pruning For Model Compression And Acceleration, Romancha Khatri

Electronic Theses and Dissertations

Apart from the accuracy, the size of convolutional neural networks (CNN) models is another principal factor for facilitating the deployment of models on memory, power and budget constrained devices. However, conventional model compression techniques require human experts to setup parameters to explore the design space which is suboptimal and time consuming. Various pruning techniques are implemented to gain compression, trading off speed and accuracy. Given a CNN model [11], we propose an automated deep reinforcement learning [9] based model compression technique that can effectively turned off kernels on each layer by observing its significance on decision making. By observing accuracy, …