Engineering Commons^™

A Novel Energy-Efficient Sensor Cloud Model Using Data Prediction And Forecasting Techniques, Kalyan Das, Satyabrata Das, Aurobindo Mohapatra

Karbala International Journal of Modern Science

An energy-efficient sensor cloud model is proposed based on the combination of prediction and forecasting methods. The prediction using Artificial Neural Network (ANN) with single activation function and forecasting using Autoregressive Integrated Moving Average (ARIMA) models use to reduce the communication of data. The requests of the users generate in every second. These requests must be transferred to the wireless sensor network (WSN) through the cloud system in the traditional model, which consumes extra energy. In our approach, instead of one second, the sensors generally communicate with the cloud every 24 hours, and most of the requests reply using the …

Supply Chain Analysis To Determine E-Commerce Distribution Center Locations, Fatima Chebchoub

Masters Theses & Specialist Projects

Supply chain management is the key success for each business. Having a robust supply chain will help the business to improve service, quality, reduce the costs, improve the speed, and be more flexible. Organizations need to look at data and deploy plans to move the product from operation to logistics to manage a global chain. In this paper we will use E-Comm shipment data to identify the best locations to build a new distribution center (DC).

A Mathematical Framework For Estimating Risk Of Airborne Transmission Of Covid-19 With Application To Face Mask Use And Social Distancing, Rajat Mittal, Charles Meneveau, Wen Wu

Faculty and Student Publications

© 2020 Author(s). A mathematical model for estimating the risk of airborne transmission of a respiratory infection such as COVID-19 is presented. The model employs basic concepts from fluid dynamics and incorporates the known scope of factors involved in the airborne transmission of such diseases. Simplicity in the mathematical form of the model is by design so that it can serve not only as a common basis for scientific inquiry across disciplinary boundaries but it can also be understandable by a broad audience outside science and academia. The caveats and limitations of the model are discussed in detail. The model …

Analysis Of Cloud Bursting On Openstack Infrastructure To Aws, Bao Pham, Ronald C. Jones, Majid Shaalan

Other Student Works

Cloud computing is the development of distributed and parallel computing that seeks to provide a new model of business computing by automating services and efficiently storing proprietary data. Cloud bursting is one of the cloud computing techniques that adopts the hybrid cloud model which seeks to expand the resources of a private cloud through the integration with a public cloud infrastructure. In this paper, the viability of cloud bursting is experimented and an attempt to integrate AWS EC2 onto an Openstack cloud environment using the Openstack OMNI driver is conducted.

Poland’S Challenge To Eu Directive 2019/790: Standing Up To The Destruction Of European Freedom Of Expression, Michaela Cloutier

Dickinson Law Review (2017-Present)

In 2019, the European Parliament and Council passed Directive 2019/790. The Directive’s passage marked the end of a fouryear- long legislative attempt to impose more liability for copyright violations on Online Service Providers, an effort which was controversial from the start. Online Service Providers fear that the 2019 Directive, especially its Article 17, will completely change the structure of liability on the Internet, forcing providers to adopt expensive content filtering systems. Free speech advocates fear that ineffective filtering technology will infringe upon Internet users’ rights to express themselves, and legal scholars have pointed out the Directive’s inconsistency with prior European …

Predicting General Aviation Accidents Using Machine Learning Algorithms, Bradley S. Baugh

Doctoral Dissertations and Master's Theses

No abstract provided.

A Novel Framework Using Neutrosophy For Integrated Speech And Text Sentiment Analysis, Florentin Smarandache, Kritika Mishra, Ilanthenral Kandasamy, Vasantha Kandasamy W.B.

Branch Mathematics and Statistics Faculty and Staff Publications

With increasing data on the Internet, it is becoming difficult to analyze every bit and make sure it can be used efficiently for all the businesses. One useful technique using Natural Language Processing (NLP) is sentiment analysis. Various algorithms can be used to classify textual data based on various scales ranging from just positive-negative, positive-neutral-negative to a wide spectrum of emotions. While a lot of work has been done on text, only a lesser amount of research has been done on audio datasets. An audio file contains more features that can be extracted from its amplitude and frequency than a …

Enginews, Fall 2020, School Of Computer Science & Engineering

News, Magazines and Reports

In this issue:

COVID Adjustments • Summer Internship + COVID study with Gabriel Bitencourt • IDEA Lab News with Chelsea Coelho • Recent Publications and Press Releases • Student Quotes.

Camera Placement Meeting Restrictions Of Computer Vision, Sara Aghajanzadeh, Roopasree Naidu, Shuo-Han Chen, Caleb Tung, Abhinav Goel, Yung-Hsiang Lu, George K. Thiruvathukal

Computer Science: Faculty Publications and Other Works

In the blooming era of smart edge devices, surveillance cam- eras have been deployed in many locations. Surveillance cam- eras are most useful when they are spaced out to maximize coverage of an area. However, deciding where to place cam- eras is an NP-hard problem and researchers have proposed heuristic solutions. Existing work does not consider a signifi- cant restriction of computer vision: in order to track a moving object, the object must occupy enough pixels. The number of pixels depends on many factors (how far away is the object? What is the camera resolution? What is the focal length?). …

From Inductive To Deductive Learning, Mikhail Mayers, Brian Henson

Maseeh Summer Undergraduate Research Experience

Using Machine Vision as a way to give information to Prolog. Using Prolog to solve deductive problems and analogical problems without having to manually enter all facts and information.

Peer-Inspired Student Performance Prediction In Interactive Online Question Pools With Graph Neural Network, Haotian Li, Huan Wei, Yong Wang, Yangqiu Song, Huamin. Qu

Research Collection School Of Computing and Information Systems

Student performance prediction is critical to online education. It can benefit many downstream tasks on online learning platforms, such as estimating dropout rates, facilitating strategic intervention, and enabling adaptive online learning. Interactive online question pools provide students with interesting interactive questions to practice their knowledge in online education. However, little research has been done on student performance prediction in interactive online question pools. Existing work on student performance prediction targets at online learning platforms with predefined course curriculum and accurate knowledge labels like MOOC platforms, but they are not able to fully model knowledge evolution of students in interactive online …

The Influence Of Marking Methods On Mobility, Survivorship, And Field Recovery Of Halyomorpha Halys (Hemiptera: Pentatomidae) Adults And Nymphs, Danielle M. Kirkpatrick, Kevin B. Rice, Aya Ibrahim, Shelby J. Fleischer, John F. Tooker, Amy Tabb, Henry Medeiros, William R. Morrison Iii, Tracy C. Leskey

Electrical and Computer Engineering Faculty Research and Publications

Halyomorpha halys (Stål), the brown marmorated stink bug, is an invasive and highly polyphagous insect that has caused serious economic injury to specialty and row crops in the United States and Europe. Here, we evaluated the effects of marking adult and nymphal H. halys with four different colors of fluorescent powder (Blaze Orange, Corona Pink, Horizon Blue, and Signal Green) on mobility and survivorship in laboratory bioassays. Adults and nymphs were marked using liquified fluorescent powder solutions and allowed to dry prior to bioassay. The presence of the marking solution had no significant effects on adult or nymphal mobility, adult …

Applying The Principle Of Least Privilege To System Management Interrupt Handlers With The Intel Smi Transfer Monitor, Brian Delgado, Tejaswini Vibhute, Karen L. Karavanic

Computer Science Faculty Publications and Presentations

Recent years have seen a growing concern over System Management Mode (SMM) and its broad access to platform resources. The SMI Transfer Monitor (STM) is Intel’s most powerful executing CPU context. The STM is a firmware-based hypervisor that applies the principle of least privilege to powerful System Management Interrupt (SMI) handlers that control runtime firmware. These handlers have traditionally had full access to memory as well as the register state of applications and kernel code even when their functionality did not require it. The STM has been been enabled for UEFI and, most recently, coreboot firmware, adding protection against runtime …

Memory Foreshadow: Memory Forensics Of Hardware Cryptocurrency Wallets – A Tool And Visualization Framework, Tyler Thomas, Mathew Piscitelli, Ilya Shavrov, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

We present Memory FORESHADOW: Memory FOREnSics of HArDware cryptOcurrency Wallets. To the best of our knowledge, this is the primary account of cryptocurrency hardware wallet client memory forensics. Our exploratory analysis revealed forensically relevant data in memory including transaction history, extended public keys, passphrases, and unique device identifiers. Data extracted with FORESHADOW can be used to associate a hardware wallet with a computer and allow an observer to deanonymize all past and future transactions due to hierarchical deterministic wallet address derivation. Additionally, our novel visualization framework enabled us to measure both the persistence and integrity of artifacts produced by the …

Forecasting Vegetation Health In The Mena Region By Predicting Vegetation Indicators With Machine Learning Models, Sachi Perera, Wenzhao Li, Erik Linstead, Hesham El-Askary

Mathematics, Physics, and Computer Science Faculty Articles and Research

Machine learning (ML) techniques can be applied to predict and monitor drought conditions due to climate change. Predicting future vegetation health indicators (such as EVI, NDVI, and LAI) is one approach to forecast drought events for hotspots (e.g. Middle East and North Africa (MENA) regions). Recently, ML models were implemented to predict EVI values using parameters such as land types, time series, historical vegetation indices, land surface temperature, soil moisture, evapotranspiration etc. In this work, we collected the MODIS atmospherically corrected surface spectral reflectance imagery with multiple vegetation related indices for modeling and evaluation of drought conditions in the MENA …

Exploring The Learning Efficacy Of Digital Forensics Concepts And Bagging & Tagging Of Digital Devices In Immersive Virtual Reality, Courtney Hassenfeldt, Jillian Jacques, Ibrahim Baggili

Electrical & Computer Engineering and Computer Science Faculty Publications

This work presents the first account of evaluating learning inside a VR experience created to teach Digital Forensics (DF) concepts, and a hands-on laboratory exercise in Bagging & Tagging a crime scene with digital devices. First, we designed and developed an immersive VR experience which included a lecture and a lab. Next, we tested it with (n = 57) participants in a controlled experiment where they were randomly assigned to a VR group or a physical group. Both groups were subjected to the same lecture and lab, but one was in VR and the other was in the real world. …

Fixing Social Media: Toward A Democratic Digital Commons, Michael Kwet

Markets, Globalization & Development Review

In the past few years, big Social Media networks like Facebook, Twitter, and YouTube have received intense scrutiny from the intellectual classes. This article critiques the dominant strain of criticism, the neo-Brandeisian School of antitrust, for its narrow focus on “regulated competition” as an appropriate means to “fix social media”. This essay calls for a socialist alternative: a democratic social media commons based on free and open source technology, decentralization, and democratic socialist legal solutions. It reviews how existing solutions like the Fediverse and LibreSocial work, and how they may provide answers for a better way forward.

The Role Of Network Components In Improving The Reliability And Survivability Of Mobile Communication Networks, Dilmurod Davronbekov, Utkir Karimovich Matyokubov

Acta of Turin Polytechnic University in Tashkent

This article compares the different architectures of mobile communication networks (MCN) and examines the survivability of the network. Typical survival strategies for improving MCN survivability, failure mitigation strategies for network elements, wireless network survival rates, failure scenarios at MCN levels, and survival indicators are presented. The importance of fiber-optic communication in the construction of communication lines between MCN components has been studied. The issue of designing MCNs in a cost-effective and highly viable way is considered, and the necessary expressions of the design process are given.

A Comparative Study For Methodologies And Algorithms Used In Colon Cancer Diagnoses And Detection, Mona Mohamed Nasr, Laila Mohamed Abdelhamid, Naglaa Shehata

Future Computing and Informatics Journal

Colon cancer is also referred to as colorectal cancer; it is a kind of cancer that starts with colon damage to the large intestine in the last section of the digestive tract. Elderly people typically suffer from colon cancer, but this may occur at any age. It normally starts as a little, noncancerous (benign) mass of cells named polyps that structure within the colon. After a period of time these polyps can turn into advanced malignant tumors that attack the human body and some of these polyps can become colon cancers. So far, no concrete causes have been identified and …

Machine Learning Techniques For Credit Card Fraud Detection, Hossam Eldin Mohammed Abd El-Hamid Ahmed Abdou, Wael Khalifa, Mohamed Ismail Roushdy, Abdel-Badeeh M. Salem

Future Computing and Informatics Journal

The term “fraud”, it always concerned about credit card fraud in our minds. And after the significant increase in the transactions of credit card, the fraud of credit card increased extremely in last years. So the fraud detection should include surveillance of the spending attitude for the person/customer to the determination, avoidance, and detection of unwanted behavior. Because the credit card is the most payment predominant way for the online and regular purchasing, the credit card fraud raises highly. The Fraud detection is not only concerned with capturing of the fraudulent practices, but also, discover it as fast as they …

Proposed Framework For Smart Healthcare Services, Dalia K. A. A. Rizk, Hoda Hosny Prof. Dr., El-Sayed M. El-Horbaty Prof. Dr., Abdel-Badeeh M. Salem Prof. Dr.

Future Computing and Informatics Journal

Smart healthcare is of great interest to researchers and governments due to the increasing development of new smart cities. However, there is no current standard practice to format the cloud computing infrastructure and to assist the healthcare system architect in designing a comprehensive solution for the basic services that are required by the healthcare users while taking into consideration a balanced approach towards their specific functional and non-functional needs such as openness, scalability, concurrency, interoperability and security factors. The integration of smart healthcare services with cloud computing needs a concrete framework. The main objective of this paper is to analyze …

Intelligent Technique For Automating The Conversion Between Major And Minor Melodies, Nermin N. J. Siphocly, El-Sayed M. El-Horbaty, Abd El-Badea Mohamed Salem Prof

Future Computing and Informatics Journal

Nowadays, computers are extremely beneficial to music composers. Computer music generation tools are developed for aiding composers in producing satisfying musical pieces. The automation of music composition tasks is a challenging research point, specially to the field of Artificial Intelligence. Converting melodies that are played on a major scale to minor (or vice versa) is interesting to both composers and music listeners. Newly converted melodies of famous songs, either from major to minor or the opposite, are becoming blockbusters on the social media. In this paper we propose an intelligent method for automating the conversion between major and minor melodies …

Ontological Engineering For Source Code Generation, Anas Hamid Aloklah, Walaa Gad, Mostafa Mohamed Aref Prof, Abd El-Badea Mohamed Salem Prof

Future Computing and Informatics Journal

Source Code Generation (SCG) is the sub-domain of the Automatic Programming (AP) that helps programmers to program using high-level abstraction. Recently, many researchers investigated many techniques to access SCG. The problem is to use the appropriate technique to generate the source code due to its purposes and the inputs. This paper introduces a review and an analysis related SCG techniques. Moreover, comparisons are presented for: techniques mapping, Natural Language Processing (NLP), knowledge base, ontology, Specification Configuration Template (SCT) model and deep learning

Detecting Symptoms Of Chronic Obstructive Pulmonary Disease And Congestive Heart Failure Via Cough And Wheezing Sounds Using Smart-Phones And Machine Learning, Anthony Windmon

USF Tampa Graduate Theses and Dissertations

Chronic Obstructive Pulmonary Disease (COPD) and Congestive Heart Failure (CHF) are progressive disorders, and major health concerns among today’s aging population. COPD causes a large mucus buildup in the lungs, leading to chronic cough and difficulty to breathe. CHF causes fluid buildup in the lower lungs due to the failing heart, causing cough and difficulty to breath. People who are clinically diagnosed with COPD or CHF are expected to regularly monitor their symptoms and follow complex medical recommendations in an effort to prevent exacerbation. In this dissertation, we elaborate upon three different machine learning based techniques that we developed for …

Chess As A Testing Grounds For The Oracle Approach To Ai Safety, James D. Miller, Roman Yampolskiy, Olle Häggström, Stuart Armstrong

Faculty and Staff Scholarship

To reduce the danger of powerful super-intelligent AIs, we might make the first such AIs oracles that can only send and receive messages. This paper proposes a possibly practical means of using machine learning to create two classes of narrow AI oracles that would provide chess advice: those aligned with the player's interest, and those that want the player to lose and give deceptively bad advice. The player would be uncertain which type of oracle it was interacting with. As the oracles would be vastly more intelligent than the player in the domain of chess, experience with these oracles might …

3d-Printed Leg Design And Modification For Improved Support On A Quadruped Robot, Jasmin S. Collins

Maseeh Summer Undergraduate Research Experience

The Agile and Adaptive Robotics Lab aims to uncover the biological and physiological complexities in animal agility and adaptive control, which can be replicated through robotics and provide further applications in biology and medicine. One project within the lab focuses on understanding structure, actuation, and control through the modeling of a canine quadruped robot.

The AARL has developed a full-body quadruped robot with artificial muscles that control limb movement and a body that is built from 3D-printed parts. This specific project involved modification of these existing parts to (a) minimize deflections in the front legs, causing unwanted lateral and abduction/adduction …

Autonomous Pev Charging Scheduling Using Dyna-Q Reinforcement Learning, Fan Wang, Jie Gao, Mushu Li, Lian Zhao

Electrical and Computer Engineering Faculty Research and Publications

This paper proposes a demand response method to reduce the long-term charging cost of single plug-in electric vehicles (PEV) while overcoming obstacles such as the stochastic nature of the user's driving behaviour, traffic condition, energy usage, and energy price. The problem is formulated as a Markov Decision Process (MDP) with an unknown transition probability matrix and solved using deep reinforcement learning (RL) techniques. The proposed method does not require any initial data on the PEV driver's behaviour and shows improvement on learning speed when compared to a pure model-free reinforcement learning method. A combination of model-based and model-free learning methods …

Eeg Classification Based On Multi-Domain Features And Random Subspace Ensemble, Deng Xin, Can Long, Jianxun Mi, Boxian Zhang, Kaiwei Sun, Wang Jin

Journal of System Simulation

Abstract: Aiming at the preprocessing feature extraction and classification recognition in BCI system, a method for EEG classification of motion imagery based on random subspaces ensemble learning of multi-domain features is proposed. Based on the analysis on the ERD/ERS characteristics of motion imagery (MI) signals, the multi-domain features of best effective time and frequency bands are extracted as the feature vectors, and the scale of the random subspace ensemble with cross-validation is adaptively chosen, and the EEG classification is realized by using linear discriminant analysis (LDA) classifiers ensemble. The test results show that the accuracy of the multi-domain features and …

The Expansion From System Simulation To Domain Simulation, Xiaogang Qiu, Yazhou Chen, Zhang Peng

Journal of System Simulation

Abstract: With the generalization of simulation application and complex system simulation becoming the focus, domain simulation is becoming more and more important. In 1995 United States Department of Defense (DoD) presented the master plan of modeling and simulation, which gives birth to the basic idea of domain simulation. The advances in information technology such as network technology and cloud computing help domain simulation to access to use. Taking the change of simulation research object as the basis, the development of simulation research from the aspects of model researches, simulation tools and simulation applications are summarized. The goal of domain simulation …

Review On Agv Scheduling Optimization, Jianlin Fu, Hengzhi Zhang, Zhang Jian, Liangkui Jiang

Journal of System Simulation

Abstract: AGV scheduling plays an important role in improving the efficiency and reducing manufacturing cost, but it is also a very complex combinatorial optimization procedure. AGV scheduling optimization is divided into three types, AGV static scheduling, AGV dynamic scheduling and AGV simultaneous scheduling with other resources scheduling. Various methods are summarized and listed, including traditional analysis method, modeling and simulation method, intelligent optimization algorithm and hybrid optimization method, and the advantages and disadvantages of each method are also analyzed. The deficiencies of AGV scheduling research are pointed out and the research directions for future are presented.