Open Access. Powered by Scholars. Published by Universities.®
Databases and Information Systems Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Institution
- Keyword
-
- AWS (1)
- Analytics (1)
- Azure (1)
- Baseball (1)
- CSES and DEMETER satellites (1)
-
- Cloud providers (1)
- Comparison (1)
- Computer Science Algorithm Web Application Fitness (1)
- Configurable social network (1)
- Cybersecurity (1)
- DartPost (1)
- Deep learning (1)
- Earthquake (1)
- Experiment platform (1)
- Face recognition (1)
- Facebook (1)
- Framework (1)
- Google cloud (1)
- IBM Cloud (1)
- Impression data (1)
- Instagram (1)
- Ionospheric plasma (1)
- MLB (1)
- Morph attack detection (1)
- National Culture (1)
- OSI (1)
- Online social influence (1)
- Organizational Culture (1)
- Personality Traits (1)
- Physical mechanisms (1)
- Publication
- Publication Type
Articles 1 - 13 of 13
Full-Text Articles in Databases and Information Systems
Comparison Of Major Cloud Providers, Justin Berman
Comparison Of Major Cloud Providers, Justin Berman
Other Student Works
This paper will compare the following major cloud providers: Microsoft Azure, Amazon AWS, Google Cloud, and IBM Cloud. An introduction to the companies and their history, fundamentals and services, strengths and weaknesses, costs, and their security will be discussed throughout this writing.
Pre-Earthquake Ionospheric Perturbation Identification Using Cses Data Via Transfer Learning, Pan Xiong, Cheng Long, Huiyu Zhou, Roberto Battiston, Angelo De Santis, Dimitar Ouzounov, Xuemin Zhang, Xuhui Shen
Pre-Earthquake Ionospheric Perturbation Identification Using Cses Data Via Transfer Learning, Pan Xiong, Cheng Long, Huiyu Zhou, Roberto Battiston, Angelo De Santis, Dimitar Ouzounov, Xuemin Zhang, Xuhui Shen
Mathematics, Physics, and Computer Science Faculty Articles and Research
During the lithospheric buildup to an earthquake, complex physical changes occur within the earthquake hypocenter. Data pertaining to the changes in the ionosphere may be obtained by satellites, and the analysis of data anomalies can help identify earthquake precursors. In this paper, we present a deep-learning model, SeqNetQuake, that uses data from the first China Seismo-Electromagnetic Satellite (CSES) to identify ionospheric perturbations prior to earthquakes. SeqNetQuake achieves the best performance [F-measure (F1) = 0.6792 and Matthews correlation coefficient (MCC) = 0.427] when directly trained on the CSES dataset with a spatial window centered on the earthquake epicenter with the Dobrovolsky …
Transfer-Learned Pruned Deep Convolutional Neural Networks For Efficient Plant Classification In Resource-Constrained Environments, Martinson Ofori
Transfer-Learned Pruned Deep Convolutional Neural Networks For Efficient Plant Classification In Resource-Constrained Environments, Martinson Ofori
Masters Theses & Doctoral Dissertations
Traditional means of on-farm weed control mostly rely on manual labor. This process is time-consuming, costly, and contributes to major yield losses. Further, the conventional application of chemical weed control can be economically and environmentally inefficient. Site-specific weed management (SSWM) counteracts this by reducing the amount of chemical application with localized spraying of weed species. To solve this using computer vision, precision agriculture researchers have used remote sensing weed maps, but this has been largely ineffective for early season weed control due to problems such as solar reflectance and cloud cover in satellite imagery. With the current advances in artificial …
A Configurable Social Network For Running Irb-Approved Experiments, Mihovil Mandic
A Configurable Social Network For Running Irb-Approved Experiments, Mihovil Mandic
Dartmouth College Undergraduate Theses
Our world has never been more connected, and the size of the social media landscape draws a great deal of attention from academia. However, social networks are also a growing challenge for the Institutional Review Boards concerned with the subjects’ privacy. These networks contain a monumental variety of personal information of almost 4 billion people, allow for precise social profiling, and serve as a primary news source for many users. They are perfect environments for influence operations that are becoming difficult to defend against. Motivated to study online social influence via IRB-approved experiments, we designed and implemented a flexible, scalable, …
Pitcher Effectiveness: A Step Forward For In Game Analytics And Pitcher Evaluation, Christopher Watkins, Vincent Berardi, Cyril Rakovski
Pitcher Effectiveness: A Step Forward For In Game Analytics And Pitcher Evaluation, Christopher Watkins, Vincent Berardi, Cyril Rakovski
Mathematics, Physics, and Computer Science Faculty Articles and Research
With the introduction of Statcast in 2015, baseball analytics have become more precise. Statcast allows every play to be accurately tracked and the data it generates is easily accessible through Baseball Savant, which opens the opportunity for improved performance statistics to be developed. In this paper we propose a new tool, Pitcher Effectiveness, that uses Statcast data to evaluate starting pitchers dynamically, based on the results of in-game outcomes after each pitch. Pitcher Effectiveness successfully predicts instances where starting pitchers give up several runs, which we believe make it a new and important tool for the in-game and post-game evaluation …
Analysis Of Theoretical And Applied Machine Learning Models For Network Intrusion Detection, Jonah Baron
Analysis Of Theoretical And Applied Machine Learning Models For Network Intrusion Detection, Jonah Baron
Masters Theses & Doctoral Dissertations
Network Intrusion Detection System (IDS) devices play a crucial role in the realm of network security. These systems generate alerts for security analysts by performing signature-based and anomaly-based detection on malicious network traffic. However, there are several challenges when configuring and fine-tuning these IDS devices for high accuracy and precision. Machine learning utilizes a variety of algorithms and unique dataset input to generate models for effective classification. These machine learning techniques can be applied to IDS devices to classify and filter anomalous network traffic. This combination of machine learning and network security provides improved automated network defense by developing highly-optimized …
A Framework To Detect The Susceptibility Of Employees To Social Engineering Attacks, Hashim H. Alneami
A Framework To Detect The Susceptibility Of Employees To Social Engineering Attacks, Hashim H. Alneami
Doctoral Dissertations and Master's Theses
Social engineering attacks (SE-attacks) in enterprises are hastily growing and are becoming increasingly sophisticated. Generally, SE-attacks involve the psychological manipulation of employees into revealing confidential and valuable company data to cybercriminals. The ramifications could bring devastating financial and irreparable reputation loss to the companies. Because SE-attacks involve a human element, preventing these attacks can be tricky and challenging and has become a topic of interest for many researchers and security experts. While methods exist for detecting SE-attacks, our literature review of existing methods identified many crucial factors such as the national cultural, organizational, and personality traits of employees that enable …
A Deep Analysis And Algorithmic Approach To Solving Complex Fitness Issues In Collegiate Student Athletes, Holly N. Puckett
A Deep Analysis And Algorithmic Approach To Solving Complex Fitness Issues In Collegiate Student Athletes, Holly N. Puckett
Honors College Theses
Sports are not simply an entertainment source. For many, it creates a sense of community, support, and trust among both fans and athletes alike. In order to continue the sense of community sports provides, athletes must be properly cared for in order to perform at the highest level possible. Thus, their fitness and health must be monitored continuously. In a professional sense, one can expect individualized attention to athletes daily due to an abundance of funding and resources. However, when looking at college communities and student athletes within them, the number of athletes per athletic trainer increases due to both …
Analysis Of System Performance Metrics Towards The Detection Of Cryptojacking In Iot Devices, Richard Matthews
Analysis Of System Performance Metrics Towards The Detection Of Cryptojacking In Iot Devices, Richard Matthews
Masters Theses & Doctoral Dissertations
This single-case mechanism study examined the effects of cryptojacking on Internet of Things (IoT) device performance metrics. Cryptojacking is a cyber-threat that involves stealing the computational resources of devices belonging to others to generate cryptocurrencies. The resources primarily include the processing cycles of devices and the additional electricity needed to power this additional load. The literature surveyed showed that cryptojacking has been gaining in popularity and is now one of the top cyberthreats. Cryptocurrencies offer anyone more freedom and anonymity than dealing with traditional financial institutions which make them especially attractive to cybercriminals. Other reasons for the increasing popularity of …
A Consent Framework For The Internet Of Things In The Gdpr Era, Gerald Chikukwa
A Consent Framework For The Internet Of Things In The Gdpr Era, Gerald Chikukwa
Masters Theses & Doctoral Dissertations
The Internet of Things (IoT) is an environment of connected physical devices and objects that communicate amongst themselves over the internet. The IoT is based on the notion of always-connected customers, which allows businesses to collect large volumes of customer data to give them a competitive edge. Most of the data collected by these IoT devices include personal information, preferences, and behaviors. However, constant connectivity and sharing of data create security and privacy concerns. Laws and regulations like the General Data Protection Regulation (GDPR) of 2016 ensure that customers are protected by providing privacy and security guidelines to businesses. Data …
Block The Root Takeover: Validating Devices Using Blockchain Protocol, Sharmila Paul
Block The Root Takeover: Validating Devices Using Blockchain Protocol, Sharmila Paul
Masters Theses & Doctoral Dissertations
This study addresses a vulnerability in the trust-based STP protocol that allows malicious users to target an Ethernet LAN with an STP Root-Takeover Attack. This subject is relevant because an STP Root-Takeover attack is a gateway to unauthorized control over the entire network stack of a personal or enterprise network. This study aims to address this problem with a potentially trustless research solution called the STP DApp. The STP DApp is the combination of a kernel /net modification called stpverify and a Hyperledger Fabric blockchain framework in a NodeJS runtime environment in userland. The STP DApp works as an Intrusion …
Jrevealpeg: A Semi-Blind Jpeg Steganalysis Tool Targeting Current Open-Source Embedding Programs, Charles A. Badami
Jrevealpeg: A Semi-Blind Jpeg Steganalysis Tool Targeting Current Open-Source Embedding Programs, Charles A. Badami
Masters Theses & Doctoral Dissertations
Steganography in computer science refers to the hiding of messages or data within other messages or data; the detection of these hidden messages is called steganalysis. Digital steganography can be used to hide any type of file or data, including text, images, audio, and video inside other text, image, audio, or video data. While steganography can be used to legitimately hide data for non-malicious purposes, it is also frequently used in a malicious manner. This paper proposes JRevealPEG, a software tool written in Python that will aid in the detection of steganography in JPEG images with respect to identifying a …
Single And Differential Morph Attack Detection, Baaria Chaudhary
Single And Differential Morph Attack Detection, Baaria Chaudhary
Graduate Theses, Dissertations, and Problem Reports
Face recognition systems operate on the assumption that a person's face serves as the unique link to their identity. In this thesis, we explore the problem of morph attacks, which have become a viable threat to face verification scenarios precisely because of their inherent ability to break this unique link. A morph attack occurs when two people who share similar facial features morph their faces together such that the resulting face image is recognized as either of two contributing individuals. Morphs inherit enough visual features from both individuals that both humans and automatic algorithms confuse them. The contributions of this …