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  • Stock Price Prediction Using Sentiment Analysis and LSTM

    Carter, Caymen (2022-05)
    This work presents multiple Long Short-Term Memory neural networks used in con- junction with sentiment analysis to predict stock prices over time. Multiple datasets and input features are used on a LSTM model to decipher which features produce the best output predictions and if there is correlation to the sentiment of posts and the rising of a stock. This project uses embedding based sentiment analysis on a dataset collected from Kaggle which includes over one million posts made on the subreddit r/wallstreetbets. This subreddit recently came under fire by the media with the shorting of Gamestop in the stock market. It was theorized that this subreddit was working as a collective to drive up the price of multiple stock, therefore hurting large corporations such as hedge funds that had large short positions on multiple stocks.
  • Application of Resistive Random Access Memory (RRAM) For Non-Von Neumann Computing

    Rafiq, Sarah (2022-05)
    The movement of data between physically separated memory and processing units in conventional computing systems (the so-called von Neumann architecture) incurs significant costs in energy and latency. This is known as the von Neumann bottleneck. With the advent of the Internet of Things (IoT) and edge computing, computing systems are also becoming significantly power limited. In this work, hafnium oxide resistive random access memory (ReRAM) integrated with 65nm CMOS technology on a 300 mm wafer platform was assessed to carry out two novel non-von Neumann computing applications that processes data within memory and avoid excessive data movement. These computing applications are based on regulating the flow of sneak path currents in memory arrays to perform computation, called flow-based computing, and detecting degree of association (correlation) between binary processes in an unsupervised manner using the ReRAM non-volatile accumulative behavior, termed as temporal correlation detection. Electrical characterization of hafnium oxide ReRAM arrays was conducted for multi-level resistance states for flow-based computing, which was then investigated for two functions, approximate edge detection and XOR Boolean logic, through both experiments and simulation. The effect of device non-idealities was also evaluated. A trade-off between the flow-based output resistance ratio and the variability of flow-based outputs was found for different patterned binary resistance Roff/Ron ratios. For the second non-von Neumann application, the feasibility of ReRAM as a non-volatile candidate device was investigated with an empirical ReRAM model through simulation. Experimental ReRAM analog incremental switching data, from both SET and RESET regimes, was also evaluated on the modified temporal correlation detection algorithm, where the RESET regime resulted in better performance. The ReRAM based implementation yielded 36,000-53,000 vi times lower energy consumption than similar implementation with phase change memory for 25 binary processes, and a speed-up of computation time by 1,600-2,100 times than that of a CPU-based implementation using 1xPOWER8 CPU. 1xPOWER8 CPU is a CPU available on the IBM* Power* System S822LC system, the POWER8 system series, where the CPU was run for 1 thread. In summary, hafnium oxide ReRAM based on 65nm CMOS technology has been evaluated for two non-von Neumann computing applications, and the effect of device non-idealities has also been assessed. These ReRAM in-memory computing applications show the promising potential of ReRAM in overcoming the von-Neumann bottleneck.
  • Generating Facial Character: A Systematic Method Accumulating Expressive Histories

    Jofre, Ana (MIT Press, 2022-04)
    The author presents a method to simulate facial character development by accumulating an expressive history onto a face. The model analytically combines facial features from Paul Ekman’s seven universal facial expressions using a simple Markov chain algorithm. The output is a series of 3D digital faces created in Blender with Python. The results show that systematically imprinting features from emotional expressions onto a neutral face transforms it into one with distinct character. This method could be applied to creative works that depend on character creation, ranging from figurative sculpture to game design, and allows the creator to incorporate chance into the creative process. The author demonstrates the method’s application to sculpture with ceramic casts of generated faces.
  • Crowdsourcing Image Extraction and Annotation: Software Development and Case Study

    Jofre, Ana; Berardi, Vincent; Brennan, Kathleen P.J.; Cornejo, Aisha; Bennett, Carl; Harlan, John (Digital Humanities Quarterly, 2020-03)
    We describe the development of web-based software that facilitates large-scale, crowdsourced image extraction and annotation within image-heavy corpora that are of interest to the digital humanities. An application of this software is then detailed and evaluated through a case study where it was deployed within Amazon Mechanical Turk to extract and annotate faces from the archives of Time magazine. Annotation labels included categories such as age, gender, and race that were subsequently used to train machine learning models. The systemization of our crowdsourced data collection and worker quality verification procedures are detailed within this case study. We outline a data verification methodology that used validation images and required only two annotations per image to produce high-fidelity data that has comparable results to methods using five annotations per image. Finally, we provide instructions for customizing our software to meet the needs for other studies, with the goal of offering this resource to researchers undertaking the analysis of objects within other image-heavy archives.
  • Faces extracted from Time Magazine 1923-2014

    Jofre, Ana; Berardi, Vincent; Bennett, Carl; Reale, Michael; Cole, Josh (Journal of Cultural Analytics, 2020-03-16)
    We present metadata of labeled faces extracted from a Time magazine archive that contains 3,389 issues ranging from 1923 to 2012. The data we are publishing consists of three subsets: Dataset 1) the gender labels and image characteristics for each of the 327,322 faces that were automatically-extracted from the entire Time archive, Dataset 2) a subset of 8,789 faces from a sample of 100 issues that were labeled by Amazon Mechanical Turk (AMT) workers according to ten dimensions (including gender) and used as training data to produce Dataset 1, and Dataset 3) the raw data collected from the AMT workers before being processed to produce Dataset 2.
  • What’s in a Face? Gender Representation of Faces in Time, 1940s-1990s

    Jofre, Ana; Cole, Josh; Berardi, Vincent; Bennett, Carl; Reale, Michael (Journal of Cultural Analytics, 2020-03-16)
    We extracted 327,322 faces from an archive of Time magazine containing 3,389 issues dating from 1923 to 2014, classified the gender of each extracted face, and discovered that the proportion of female faces contained within this archive varied in interesting ways over time. The proportion of female faces first peaked in the mid-to-late 1940s. This was followed by a dip lasting from the mid-1950s to the early 1960s. The 1970s saw another peak followed by a dip over the course of the 1980s. Finally, we see a slow and steady rise in the proportion of female faces from the early 1990s onwards. In this paper, we seek to make sense of these variations through an interdisciplinary framework drawing on psychology, visual studies (in particular, photography theory), and history. Through a close reading of our Time archive from the 1940s through the 1990s, we conclude that the visual representation of women in Time magazine correlates with attitudes toward women in both the historical context of the era and the textual content of the magazine.
  • Face Recognition and Emotion Identification

    Andriamanalimanana, Bruno R.; Thesis Advisor; Spetka, Scott; Thesis Committee; Chiang, Chen-Fu; Thesis Committee; Lagwankar, Akshara Avadhut (2021-05)
    While face recognition has been around in one form or another since the 1960s, recent technological developments have led to a wide proliferation of this technology. This technology is no longer seen as something out of science fiction movies like Minority Report. With the release of the iPhone X, millions of people now literally have face recognition technology in the palms of their hands, protecting their data and personal information. While mobile phone access control might be the most recognizable way face recognition is being used, it is being employed for a wide range of use cases including preventing crime, protecting events and making air travel more convenient. This project focuses on various advanced Python libraries to improve the face recognition accuracy such as OpenCV, Sklearn, face_recognition. The project understands the data and model, train it for further usage. The real time videos are considered for evaluating the results. Further the project glances the emotion recognition algorithms using CV2, Seaborn. The areas of the human faces are highlighted according to different emotions. The large data sets (fer2013, Olivetti faces) are used for training and testing the data sets. PCA, leave one out cross validation, grid search CV, machine learning pipelines, CNN models are used to estimate and increase the accuracy. The project is executed in Anaconda environment Jupyter Notebook. As the data sets are huge Google Collaboratory is used for execution.
  • sxRNA Switches: Hypothesis Through Automated Design Via a Genetic Algorithm Approach

    Tenenbaum, Scott; Chair; Melendez, J.A.; Cady, Nathaniel; Fasullo, Michael; Begley, Thomas; Doyle, Francis J., II (2021-12)
    The following document is meant to represent an overview of my work on structurally interacting RNA (sxRNA), which has already resulted in three publications with another two in preparation. Where appropriate, some text and data from these publications have been reproduced here. Ribonucleic Acid (RNA) is one of the fundamental macromolecules present in living systems. It can be found in all cells as varying length polymer chains composed of four primary bases (adenine, cytosine, guanine, uracil) capable of numerous modifications. Though generally characterized as an information carrier, RNA is a versatile molecule that exhibits both intra and inter-strand base pairing to form complex structures. Similar to protein, the particular shape of an RNA structure in combination with some degree of sequence specificity, can dictate its function (RNA binding protein recognition sites, ribozyme activity, aptameric affinity, etc.). Structurally interacting RNA (sxRNA) is a molecular switch technology that exploits predictable intermolecular RNA base pairing to form an otherwise absent functional structure in one RNA strand when it interacts with a specific, targeted second strand. Originally proposed as a potential regulatory mechanism in natural systems, we used characteristics of predicted pairings in that context to engineer purely synthetic sxRNA switches that have been successfully tested. There are many non-coding RNAs associated with pathological conditions, the ability to use these as triggers for sxRNA opens the door to potential applications ranging from diagnostics to therapeutics. Furthermore, other prospective triggers (including those synthetically designed) may allow use of the technology as a molecular tool for a variety of purposes including as an alternative to antibiotic selection in cell line development. The typical trigger sequences targeted by sxRNA switches are at least 20 bases in length. Combinatorial options with regard to structure positioning and base composition produce an enormous number of potential sxRNA sequences for any given target. Exhaustively examining these for feasible candidates (i.e., analyzing predicted interactions with unintended targets) is computationally impossible with current systems. Evolutionary computing is a subfield of artificial intelligence (AI) that has been inspired by biology. Genetic algorithms are a type of evolutionary algorithm and apply operators (such as recombination and mutation) to find candidate solutions to an optimization problem. The presented dissertation will describe the original sxRNA research as well as the development and testing of a genetic algorithm that automates the production of new sxRNA switch candidates. This algorithm takes into consideration factors that were previously impossible to account for in manual designs.
  • Multicyclic Loss for Multidomain Image-to-Image Translation

    Reale, Michael, Chair; Confer, Amos; Andriamanalimanana, Bruno; Schneider, Ethan H.
    GANs developed to Translate an Image’s style between different domains often only care about the initial translation, and not the ability to further translate upon an image This can cause issues where, if one would want to generate upon an image and then further on, change that image even more that person may come into issues. This creates a ”gap” between the base images and the generated images, and in this paper a Multicyclic Loss is presented, where the Neural Network also trains on further translations to images that were already translated. iv
  • Development of High-Performance Hafnium Oxide based Non-Volatile Memory Devices on 300mm Wafer Platform for Data Storage and Neuromorphic Applications

    Diebold, Alaine (Committee member); Ventrice, Carl A. Jr. (Committee member); Lloyd, James (Committee member); Kurinec, Santosh (External committee member); Cady, Nathaniel (Dissertation Committee Chair); Hazra, Jubin (2021-08)
    Fundamental limitations associated with scaling and modern von Neumann computing architectures illustrates the need for emerging memory solutions in the semiconductor industry. One such promising non-volatile memory (NVM) solution is resistive random access memory (RRAM), which is seen as a potential candidate that can meet the performance needs of DRAM and the density of NAND Flash in terms of scalability, reliability and switching performance. However, reliable operation of RRAM devices requires further development to remedy device- to-device and cycle-to-cycle uniformity variation, increase the conductance window, and to improve retention, yield and endurance properties. This research work primarily focuses on improving RRAM performance metrics through optimization of processing conditions and programming algorithms for CMOS-integrated nanoscale HfO2 RRAM devices on a full scale 300mm wafer platform. It was observed that tuning of ALD parameters during RRAM switching layer HfO2 deposition had a significant impact on device switching performance. An excellent memory window of >30 with switching yield ~90%, along with low cycle-to-cycle (σ <0.5) and cell-to-cell variability (σ <0.4) were achieved for tested 1 Transistor 1 RRAM (1T1R) cells across full 300mm wafers. The devices demonstrated excellent endurance (>1010 switching cycles) and data retention performance at elevated temperature (105 s at 373K). The fabricated RRAM cells were also optimized for multi-level-cell switching behavior and ~10 distinct resistance levels were obtained through a combined current- and voltage-control based programming approach. An incremental pulse write technique combined with read verification algorithm enabled accurate resistance states programming within a large resistance window along with linear and symmetric potentiation-depression characteristics yielding superior analog synaptic functionality of fabricated RRAM devices. In addition to RRAM devices, hafnium zirconium oxide (HZO) based nanoscale ferroelectric tunnel junction (FTJ) devices were successfully implemented on a 300 mm wafer platform. Current measurement, as a function of voltage for both up and down polarization states, yielded a tunneling electroresistance (TER) ratio of ~5 and switching endurance up to 106 cycles in TiN/ Al2O3/ Hf0.5Zr0.5O2/ TiN FTJ devices distributed across full 300 mm wafer. Investigation of current transport mechanisms showed that the conduction in these FTJ devices is dominated by direct tunneling (DT) at low electric field and by Fowler-Nordheim (F-N) tunneling at high electric field. The realization of CMOS-compatible nanoscale RRAM and FTJ devices on 300mm wafers demonstrates the promising potential of these devices in large scale high-yield NVM manufacturing for high performance embedded memory and mass data storage applications.
  • Text Detection from an Image

    Andriamanalimanana, Bruno R.; Thesis Advisor; Novillo, Jorge; Thesis Committee; Spetka, Scott; Thesis Committee; Goda, Piyush Jain (2020-12)
    Recently, a variety of real-world applications have triggered a huge demand for techniques that can extract textual information from images and videos. Therefore, image text detection and recognition have become active research topics in computer vision. The current trend in object detection and localization is to learn predictions with high capacity deep neural networks trained on a very large amount of annotated data and using a high amount of processing power. In this project, I have built an approach for text detection using the object detection technique. Our approach is to deal with the text as objects. We use an object detection method, YOLO (You Only Look Once), to detect the text in the images. We frame object detection as a regression problem to spatially separated bounding boxes and associated class probabilities. YOLO, a single neural network, that predicts bounding boxes and class probabilities directly from full images in one evaluation. Since the whole detection pipeline is a single network, it can be optimized end-to-end directly on detection performance. The MobileNet pre-trained deep learning model architecture was used and modified in different ways to find the best performing model. The goal is to achieve high accuracy in text spotting. Experiments on standard datasets ICDAR 2015 demonstrate that the proposed algorithm significantly outperforms methods in terms of both accuracy and efficiency.
  • ULTRATHIN HIGH-K OXIDES FOR AREA-SELECTIVE DEPOSITION AND CHARACTERIZATION BY BALLISTIC ELECTRON EMISSION MICROSCOPY AND X-RAY PHOTOEMISSION SPECTROSCOPY

    Rogers, Jack (2021-05)
    Insulators play an important role in the architecture and resulting performance of semiconductor devices manufactured today. Materials such as HfO2 and Al2O3 are utilized as gate oxides and spacers to control leakage current and enable bottom-up self-aligned patterning of device features. Understanding the electrostatic barrier that forms at the metal-oxide-semiconductor (MOS) interface is crucial in the development of field effect transistors and other devices, especially as the scaling of device features continues to shrink into the nanoscale. Characterization of the barrier height using current-voltage (IV) and capacitance-voltage (CV) techniques provides only a spatially averaged view of the interface, and is incapable of accounting for local nonuniformity which arises at nanoscale dimensions. Additionally, common lithographic strategies for patterning small feature oxides are limited by printing misalignments such as edge placement error (EPE), and in order to achieve smaller pitch sizes lithography steps must be repeated multiple times which adds time and cost to the process. The feasibility of uniform, cost-effective insulator films at the 5 nm technology node and beyond relies on the development of new deposition strategies. In this thesis, hafnium oxide grown using atomic layer deposition (ALD) is examined with ballistic electron emission microscopy (BEEM). Localized nonuniformities in the barrier height are found to exist for two identically prepared samples which reveal three distinct electrostatic barriers at the buried Au/HfO2/SiO2/Si-p interface, including a novel barrier found at 0.45 eV due to ultrathin HfO2. The results uncover changes in electrostatic behavior of the film which are otherwise impossible to detect using spatially averaged techniques. These variations in barrier height are visualized in a novel way that produces spatial maps showing transitions between high energy and lower energy barriers across a few nanometers. The resolution of this mapping technique is determined by comparing the measured barrier heights of Au/Si(001) and Au/Si(111) interfaces. Momentum conservation and electron scattering result in slightly different barrier heights for both interfaces that depends on metal thickness. The Rayleigh criterion is applied to the barrier height distributions as a function of metal thickness, resulting in a 10 meV resolution. Both aluminum oxide and hafnium oxide are also selectively grown on patterned metal / low-k silicon wafers using ALD. Self-assembled monolayer (SAM) materials such as octodecanethiol (ODT) and dodecanethiol (DDT) -which are functionalized to metal -are first deposited on the copper lines in order to block high-k film deposi¬tion on metal. Both HfO2 and Al2O3 are shown to selectively cover the low-k lines for linespace pitches greater than 100 nm and 5 mM concentration of SAM, and better selectivity is achieved for smaller pitches using lower SAM concentrations. Selectivity is measured qualitatively and quantitatively using x-ray photoemission spectroscopy and confirmed with transmission electron microscopy.
  • Podcast Creation for In-Home Use: An Overview of Podcast Creation Methodology Using Bloom’s Taxonomy

    Stam, Kathryn; Thesis Advisor; Lizardi, Ryan; Second Reader; Holmes, William (2021-05)
    Making an effective podcast is more complex than just using your laptop mic to record your thoughts to post on your favorite social media, there are methods or approaches with which to achieve the best results. My project was to overview the methods for making effective audio podcasts and making them available professionally and accessibly to an audience using a subject matter revolving around 3D printing. The resulting example podcasts and the framework used to create them was examined using Bloom’s Taxonomy to assess the formation and delivery of the content for maximum information retention and engagement of the listener. The final goal of this research was to examine and identify the most important characteristics in meaningful audio development for creating podcasts that will properly deliver subject matter and engage the listener. I did an audio-only podcast using a standard Windows computer to create a short series of 15–20-minute podcasts to exemplify the points I discuss in this research. The hope for this research was to outline and display this discipline in a meaningful overview, I particularly planned to use Bloom’s Taxonomy to review this subject matter in how it can be used as a framework for making effective podcasts.
  • Building a University Information Technology Knowledge Base Using TiddlyWiki

    Stam, Kathryn; Thesis Advisor; Lizardi, Ryan; Second Reader; Fischer, Sam (2021-05)
    Keeping a knowledge base up to date can be a difficult and endless task for an Information Technology Services (ITS) department at a university. As technology emerges and is embraced by students and faculty, ITS can become responsible for supporting end users with all sorts of questions and problems. A knowledge base is a website or application used to store helpful information in the form of articles that can be easily shared with end users, giving them a way to support their technology problems without contacting a live person. When managing a large-scale knowledge base, information can be inadvertently replicated in many different areas and contradict other articles, especially when more than one manager can add or edit information. Information modules, or chunks of verified information that can be referenced and inserted into an article, can help eliminate contradictory information and ensure consistency with information that has to be in multiple places through the use of hypertextuality and transclusion. Hypertextuality is a way of organizing information and documents by creating individual bits of information that are assembled together to create a larger document, and transclusion is a way of assembling hypertext documents by simply referencing one into the other. TiddlyWiki is an application that is built around the idea of hypertextuality and transclusion. This project aims to explore the potential for a knowledge base to be built with the TiddlyWiki application.
  • Infographics - Migrating from paper methods to electronic using an enterprise system - Jenzabar EX

    Stam, Kathryn; Thesis Advisor; Lizardi, Ryan; Second Reader; Psarudakis, Robert J. (2021-05)
    SUNY Sullivan has been facing challenges of declining enrollment since the start of the recession in 2008. For the most part the college seems to go through a roller-coaster ride of increasing but steadily declining enrollment spirals ever since. One of our key survival strategies is adaptation to change. Adaptations with programs, delivery methods in the way we offer programs and courses. We tend to be on the cutting edge of technology as it evolves and we adapt to new challenges as long as our funds from SUNY are available. In this paper I will identify some of the gaps in processes that are simply not working. I will look at the theories behind the changes and challenges that are associated with modern adaptations in addition to the reasons why we must change. In this respect I will be looking at our old processes such as paper forms and face to face contact. We are encountering a new world in response to the COVID-19 pandemic. COVID-19 causes all of our in-person activities to be remote. Currently, we are going through our Middle States self-study evaluation. "The Middle States Commission on Higher Education is a voluntary, non-governmental, institutional membership association currently serving higher education institutions in Delaware, the District of Columbia, Maryland, New Jersey, New York, Pennsylvania, Puerto Rico, the Virgin Islands, and any other geographic areas in which the Commission elects to conduct accrediting activities within the United States." (MSCHE, website). Looking at what we do as an institution, we found that we have many "gaps" that need to be filled in. Nothing has proven this more than COVID-19.
  • To Upgrade or Not To Upgrade Application

    Stam, Kathryn; Thesis Advisor; Lizardi, Ryan; Second Reader; Francisco, Neil (2021-05)
    New Technology consists of new hardware devices, computational workflows, digital advances, and information systems. As technology continues to evolve over the years, this never-ending cycle of new devices and experiences will always be present amongst consumers. Traditionally, new hardware devices are intriguing because they are designed to improve our access to information, media, and a connection to the digital world, but does this mean our previous-gen devices are no longer valuable? This project involves creating a prototype application designed for both computer and mobile interfaces to help improve the accessibility to information and the overall user experience with an older device. The “To Upgrade or Not To Upgrade” app will inform end-users of their older technological device specifications and suggest hardware/software methods to unlock their full potential. The goal of this paper is to shed some light on consumers that upgrading to the following gen devices is not always necessary to receive the best human-to-computer interactions. It is likely the computer or mobile device that one owns now, with some slight modifications, is all that is needed to provide a pleasant experience.
  • Research Paper And Video Project About Music Technology Evolution And Its Effects On Artist Revenue and Consumer Listening

    Stam, Kathryn; Thesis Advisor; Lizardi, Ryan; Second Reader; Lazar, Neer (2021-06)
    In this paper I will try to show how the technology that changed over time affected both artists and music consumers. The theory from some of the literature I found is that disruptions in music technology also had many economic impacts to both artists and their audience. In the literature it is suggested that streaming rectified the revenue hit that was derived from the ill effects of digital piracy. In this paper and in the accompanying video that can be found here - https://drive.google.com/file/d/15IIBwwFLZqpnfqPl_pqmR YsAHgfjMuiN/view?usp=sharing I will describe the evolution of the technological changes and show that these advances also had the same exponential curve in change very similar to the changes that occurred in print although the ability to record sound arrived thousands of years after the ability to record letters and words. However, at some point the technological changes created the same kind of disruptions - meaning the ability to record sound and transport it and listen to it in various ways. This created markets and economic advancements that were not there before - again similar in many ways to technological changes in other fields. Later in this paper I will focus on the last few years of these changes with an emphasis on streaming technologies. This in my opinion a change that is still ongoing and was transformational in the way revenue distribution came back after a very tough time that included a breakdown in the established industry and the piracy that affected it during this crisis. Another theory is that the instant gratification of on demand technology that gives consumers exactly what they want when they want it was also solved with streaming music services. The literature that I chose to cite and that I based my research on was primarily from trade magazines and industry news. These articles paint a picture of a changing landscape in the music industry and also talk about some of my ideas that led me to research and write this paper.
  • Assisting Seniors with Technology Challenges: Video Tutorials for Password Development and Management

    Stam, Kathryn; Thesis Advisor; Lizardi, Ryan; Second Reader; Hanna, Margie (2021-05)
    Seniors often have difficulties using evolving technology while keeping their valuable personal data safe. This research effort began with assessing how and why seniors use technology and determining the best educational techniques to help them connect with instructional information. Additional research determined how design principles including layering and progressive disclosure could be applied to enhance these techniques. In an effort to maintain a manageable scope, password development and management was selected as an example technology challenge. The current state of training resources related to these topics on Facebook and YouTube was surveyed and analysis performed to determine if the design principles and instructional techniques discovered through the aforementioned research were evident. Good tips and tricks for strong password development were incorporated into the instructional design/development plan. A Facebook group and training videos were developed to facilitate assisting seniors with password creation and management.
  • HAIR LOVE: SHOWCASING THE DIVERSITY OF NATURAL HAIR THROUGH PHOTOGRAPHY AND PERSONAL NARRATIVES

    Stam, Kathryn; Thesis Advisor; Lizardi, Ryan; Second Reader; Niles, JoAnna (2021-05)
    Throughout the years, Natural Hair has been used as a form of expression, self-acceptance, and controversy in the United States of America. The invention of Web 2.0, the rise of social media, blogs, and other web-based platforms has given members of the Natural Hair community a platform to share their personal stories and tips related to natural hair care with others. This project looked at the effects of Web 2.0 during the second wave of the Natural Hair Movement since the year 2008, showcasing various hairstyles through digital photography and personal narratives of five individuals. Previous studies have shown that men and women with naturally curly hair have faced some form of discrimination based on their hairstyle, causing them to assimilate to societal norms of wearing their hair straight. The goal for this project was to showcase the versatility of natural hair through digital photography and personal narratives as a form of storytelling, and sharing how social media and other outlets across the web have impacted the individual’s decision to wear their hair in its natural state or how it has helped them in their natural hair journey. Photos of five different people of various cultural backgrounds, hair textures, and lifestyles were taken and questions based on their Natural Hair journey were asked. With the collection of photographs and narratives, a Capstone portfolio section of my media website was created for public viewing. This platform can be updated as photos of more individuals with natural hair at local events and expos in the future are taken.
  • Design and Implement a Small Business with Digital Marketing Be Happy, Se Feliz: Find Your Happiness and Learn to Put It First

    Stam, Kathryn; Thesis Advisor; Lizardi, Ryan; Second Reader; Flores, Jacqueline (2021-05)
    The purpose of this study was to identify and organize a plan to market an Airbnb vacation rental property in the Caribbean by a single female parent in her 30’s. The study includes research on Universal Design Uses for Marketing in Tourism or Travel, Digital Marketing/Social Media Marketing (including for travel sites) and Small business and branding development. The design of the study includes a proposal, methodology for the design and the production of digital artifacts including branding development with personal website and social media sites. This capstone project was developed and implemented using graphic design concepts, digital photography’s composition techniques, user experience considerations, evaluation of select technologies and select Universal Principles of Design. Throughout the research and design process, similar social media accounts and websites were reviewed to identify likeability, professionalism and conclusions were drawn for why some sites were liked or followed more than others. While viewing sites, session duration, or length of time spent, was also taken into consideration. The successful concepts on these sites were also taken into consideration or applied in the production of web-based marketing materials and platforms to support marketing the rental property in the Caribbean for this capstone. Branding was a major component of successful marketing for all sites. In summary, leveraging many SUNY Polytechnic Information Design and Technology course concepts from Digital Photography, User Experience, Graphic Design and Evaluating Information Technology a new brand was formed: Be Happy, Se Feliz. Branding was designed utilizing elements from graphic design and digital photography, from development of vision and mission statements, audience focus, logo design, typography selection, color palette selection and site development. Several concepts from “Universal Principles of Design” (Lidwell et al., 2010) were reviewed and utilized in the design for projecting the users into an idealistic vacation environment. This was the primary focus of the marketing techniques utilized. Final selection of the tools and platforms used were conducted by utilizing techniques from Evaluating Information Technology.

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