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Children In 2077: Designing Children’S Technologies In The Age Of Transhumanism, Oğuz Oz Buruk, Oğuzhan Özcan, Gökçe Elif Baykal, Tilbe Göksun, Selçuk Acar, Güler Akduman, Mehmet Aydın Baytaş, Ceylan Beşevli, Joe Best, Aykut Coşkun, Hüseyin Uğur Genç, Baki Kocaballi, Samuli Laato, Cássia Mota, Konstantinos Papangelis, Marigo Raftopoulos, Richard Ramchurn, Juan Sádaba, Mattia Thibault, Annika Wolff, Mert Yıldız 2020 Tampere University of Technology

Children In 2077: Designing Children’S Technologies In The Age Of Transhumanism, Oğuz Oz Buruk, Oğuzhan Özcan, Gökçe Elif Baykal, Tilbe Göksun, Selçuk Acar, Güler Akduman, Mehmet Aydın Baytaş, Ceylan Beşevli, Joe Best, Aykut Coşkun, Hüseyin Uğur Genç, Baki Kocaballi, Samuli Laato, Cássia Mota, Konstantinos Papangelis, Marigo Raftopoulos, Richard Ramchurn, Juan Sádaba, Mattia Thibault, Annika Wolff, Mert Yıldız

Presentations and other scholarship

What for and how will we design children’s technologies in the transhumanism age, and what stance will we take as designers? This paper aims to answer this question with 13 fictional abstracts from sixteen authors of different countries, institutions and disciplines. Transhumanist thinking envisions enhancing human body and mind by blending human biology with technological augmentations. Fundamentally, it seeks to improve the human species, yet the impacts of such movement are unknown and the implications on children’s lives and technologies were not explored deeply. In an age, where technologies can clearly be defined as transhumanist, such as under-skin ...


Evidence-Based Detection Of Pancreatic Canc, Rajeshwari Deepak Chandratre 2020 San Jose State University

Evidence-Based Detection Of Pancreatic Canc, Rajeshwari Deepak Chandratre

Master's Projects

This study is an effort to develop a tool for early detection of pancreatic cancer using evidential reasoning. An evidential reasoning model predicts the likelihood of an individual developing pancreatic cancer by processing the outputs of a Support Vector Classifier, and other input factors such as smoking history, drinking history, sequencing reads, biopsy location, family and personal health history. Certain features of the genomic data along with the mutated gene sequence of pancreatic cancer patients was obtained from the National Cancer Institute (NIH) Genomic Data Commons (GDC). This data was used to train the SVC. A prediction accuracy of ~85 ...


Predicting Students’ Performance By Learning Analytics, Sandeep Subhash Madnaik 2020 San Jose State University

Predicting Students’ Performance By Learning Analytics, Sandeep Subhash Madnaik

Master's Projects

The field of Learning Analytics (LA) has many applications in today’s technology and online driven education. Learning Analytics is a multidisciplinary topic for learn- ing purposes that uses machine learning, statistic, and visualization techniques [1]. We can harness academic performance data of various components in a course, along with the data background of each student (learner), and other features that might affect his/her academic performance. This collected data then can be fed to a sys- tem with the task to predict the final academic performance of the student, e.g., the final grade. Moreover, it allows students to ...


Probabilistic And Machine Learning Enhancement To Conn Toolbox, Gayathri Hanuma Ravali Kuppachi 2020 San Jose State University

Probabilistic And Machine Learning Enhancement To Conn Toolbox, Gayathri Hanuma Ravali Kuppachi

Master's Projects

Clinical depression is a state of mind where the person suffers from persevering and overpowering sorrow. Existing examinations have exhibited that the course of action of arrangement in the brain of patients with clinical depression has a weird framework topology structure. In the earlier decade, resting-state images of the brain have been under the radar a. Specifically, the topological relationship of the brain aligned with graph hypothesis has discovered a strong connection in patients experiencing clinical depression. However, the systems to break down brain networks still have a couple of issues to be unwound. This paper attempts to give a ...


Detection Of Mild Cognitive Impairment Using Diffusion Compartment Imaging, Matthew Jones 2020 San Jose State University

Detection Of Mild Cognitive Impairment Using Diffusion Compartment Imaging, Matthew Jones

Master's Projects

The result of applying the Neurite Orientation Density and Dispersion Index (NODDI) algorithm to improve the prediction accuracy for patients diagnosed with MCI is reported. Calculations were carried out using a collection of 68 patients (34 control and 34 with MCI) gathered from the Alzheimer’s Disease Neuroimaging Initiative database (ADNI). Patient data includes the use of high-resolution Magnetic Resonance Images as with as Diffusion Tensor Imaging. A Linear Regression accuracy of 83% was observed using the added NODDI summary statistic: Orientation Dispersion Index (ODI). A statistically significant difference in groups was found between control patients and patients with MCI ...


Pattern Analysis And Prediction Of Mild Cognitive Impairment Using The Conn Toolbox, Meenakshi Anbukkarasu 2020 San Jose State University

Pattern Analysis And Prediction Of Mild Cognitive Impairment Using The Conn Toolbox, Meenakshi Anbukkarasu

Master's Projects

Alzheimer's is an irreversible neurodegenerative disorder described by dynamic psychological and memory defalcation. It has been accounted for that the pervasiveness of Alzheimer's is to increase by 4 times in a few years, where one in every 75 people will have this disorder. Hence, there is a critical requirement for the analysis of Alzheimer's at its beginning stage to diminish the difficulty of the overall medical complications. The initial state of Alzheimer’s is called Mild cognitive impairment (MCI), and hence it is a decent target for premature diagnosis and treatment of Alzheimer's. This project focuses ...


Housing Market Crash Prediction Using Machine Learning And Historical Data, Parnika De 2020 San Jose State University

Housing Market Crash Prediction Using Machine Learning And Historical Data, Parnika De

Master's Projects

The 2008 housing crisis was caused by faulty banking policies and the use of credit derivatives of mortgages for investment purposes. In this project, we look into datasets that are the markers to a typical housing crisis. Using those data sets we build three machine learning techniques which are, Linear regression, Hidden Markov Model, and Long Short-Term Memory. After building the model we did a comparative study to show the prediction done by each model. The linear regression model did not predict a housing crisis, instead, it showed that house prices would be rising steadily and the R-squared score of ...


Using Color Thresholding And Contouring To Understand Coral Reef Biodiversity, Scott Vuong Tran 2020 San Jose State University

Using Color Thresholding And Contouring To Understand Coral Reef Biodiversity, Scott Vuong Tran

Master's Projects

This paper presents research outcomes of understanding coral reef biodiversity through the usage of various computer vision applications and techniques. It aims to help further analyze and understand the coral reef biodiversity through the usage of color thresholding and contouring onto images of the ARMS plates to extract groups of microorganisms based on color. The results are comparable to the manual markup tool developed to do the same tasks and shows that the manual process can be sped up using computer vision. The paper presents an automated way to extract groups of microorganisms based on color without the use of ...


Implementing Tontinecoin, Prashant Pardeshi 2020 San Jose State University

Implementing Tontinecoin, Prashant Pardeshi

Master's Projects

One of the alternatives to proof-of-work (PoW) consensus protocols is proof-of- stake (PoS) protocols, which address its energy and cost related issues. But they suffer from the nothing-at-stake problem; validators (PoS miners) are bound to lose nothing if they support multiple blockchain forks. Tendermint, a PoS protocol, handles this problem by forcing validators to bond their stake and then seizing a cheater’s stake when caught signing multiple competing blocks. The seized stake is then evenly distributed amongst the rest of validators. However, as the number of validators increases, the benefit in finding a cheater compared to the cost of ...


Exploring Attacks And Defenses In Additive Manufacturing Processes: Implications In Cyber-Physical Security, Nicholas Deily 2020 Washington University in St. Louis

Exploring Attacks And Defenses In Additive Manufacturing Processes: Implications In Cyber-Physical Security, Nicholas Deily

Engineering and Applied Science Theses & Dissertations

Many industries are rapidly adopting additive manufacturing (AM) because of the added versatility this technology offers over traditional manufacturing techniques. But with AM, there comes a unique set of security challenges that must be addressed. In particular, the issue of part verification is critically important given the growing reliance of safety-critical systems on 3D printed parts.

In this thesis, the current state of part verification technologies will be examined in the con- text of AM-specific geometric-modification attacks, and an automated tool for 3D printed part verification will be presented. This work will cover: 1) the impacts of malicious attacks on ...


Understanding Impact Of Twitter Feed On Bitcoin Price And Trading Patterns, Ashrit Deebadi 2020 San Jose State University

Understanding Impact Of Twitter Feed On Bitcoin Price And Trading Patterns, Ashrit Deebadi

Master's Projects

‘‘Cryptocurrency trading was one of the most exciting jobs of 2017’’. ‘‘Bit- coin’’,‘‘Blockchain’’, ‘‘Bitcoin Trading’’ were the most searched words in Google during 2017. High return on investment has attracted many people towards this crypto market. Existing research has shown that the trading price is completely based on speculation, and its trading volume is highly impacted by news media. This paper discusses the existing work to evaluate the sentiment and price of the cryptocurrency, the issues with the current trading models. It builds possible solutions to understand better the semantic orientation of text by comparing different machine learning techniques ...


Land Registry On Blockchain, Mugdha Patil 2020 San Jose State University

Land Registry On Blockchain, Mugdha Patil

Master's Projects

The commercial real estate market is a significant part of the global economy, currently dominated by a small set of firms and organizations that lack transparency. The process of property transfers also requires third party intervention which is expensive. In many countries, the process of title transfers is problematic. We are still in the initial steps of digitization, due to the improvement required in terms of use of technology to represent assets in digital forms. Increase in liquidity of investments and purchases, proper management, documentation as well as ease of access is the future of real estate. Blockchain technologies have ...


Javafx Application, Pengfei Huang 2020 Minnesota State University Moorhead

Javafx Application, Pengfei Huang

Student Academic Conference

Developing java GUI application by using JavaFX.


The Use Of Digital Millenium Copyright Act To Stifle Speech Through Non-Copyright Related Takedowns, Miller Freeman 2020 Seattle University School of Law

The Use Of Digital Millenium Copyright Act To Stifle Speech Through Non-Copyright Related Takedowns, Miller Freeman

Seattle Journal of Technology, Environmental & Innovation Law

In 1998, Congress passed the Digital Millennium Copyright Act. This law provided new methods of protecting copyright in online media. These protections shift the normal judicial process that would stop the publication of infringing materials to private actors: the online platforms. As a result, online platforms receive notices of infringement and issue takedowns of allegedly copyrighted works without the judicial process which normally considers the purpose of the original notice of infringement. In at least one case, discussed in detail below, this has resulted in a notice and takedown against an individual for reasons not related to the purpose of ...


Integrated Machine Learning And Bioinformatics Approaches For Prediction Of Cancer-Driving Gene Mutations, Oluyemi Odeyemi 2020 Chapman University

Integrated Machine Learning And Bioinformatics Approaches For Prediction Of Cancer-Driving Gene Mutations, Oluyemi Odeyemi

Computational and Data Sciences (PhD) Dissertations

Cancer arises from the accumulation of somatic mutations and genetic alterations in cell division checkpoints and apoptosis, this often leads to abnormal tumor proliferation. Proper classification of cancer-linked driver mutations will considerably help our understanding of the molecular dynamics of cancer. In this study, we compared several cancer-specific predictive models for prediction of driver mutations in cancer-linked genes that were validated on canonical data sets of functionally validated mutations and applied to a raw cancer genomics data. By analyzing pathogenicity prediction and conservation scores, we have shown that evolutionary conservation scores play a pivotal role in the classification of cancer ...


Knot Flow Classification And Its Applications In Vehicular Ad-Hoc Networks (Vanet), David Schmidt 2020 East Tennessee State University

Knot Flow Classification And Its Applications In Vehicular Ad-Hoc Networks (Vanet), David Schmidt

Electronic Theses and Dissertations

Intrusion detection systems (IDSs) play a crucial role in the identification and mitigation for attacks on host systems. Of these systems, vehicular ad hoc networks (VANETs) are difficult to protect due to the dynamic nature of their clients and their necessity for constant interaction with their respective cyber-physical systems. Currently, there is a need for a VANET-specific IDS that meets this criterion. To this end, a spline-based intrusion detection system has been pioneered as a solution. By combining clustering with spline-based general linear model classification, this knot flow classification method (KFC) allows for robust intrusion detection to occur. Due its ...


An Exploration Of Methods For Classifying Air-Written Letters From The Spanish Alphabet, Manuel Serna-Aguilera 2020 University of Arkansas, Fayetteville

An Exploration Of Methods For Classifying Air-Written Letters From The Spanish Alphabet, Manuel Serna-Aguilera

Computer Science and Computer Engineering Undergraduate Honors Theses

The ability to recognize human activity, especially air-writing, is an interesting challenge as one could identify any letter from many languages. I intend to investigate this problem of air-writing, but with the added twist of including the following letters from the Spanish alphabet: Á, É, Í, Ó, Ú, Ü, and Ñ. With this new alphabet, I set out to see what kinds of classifiers work best and on what kinds of data, since letters can be represented in multiple ways.

My tracking system will consist of a regular camera and a subject who will draw with a brightly colored marker ...


Learning & Planning For Self-Driving Ride-Hailing Fleets, Jack Morris 2020 William & Mary

Learning & Planning For Self-Driving Ride-Hailing Fleets, Jack Morris

Undergraduate Honors Theses

Through simulation, we demonstrate that incorporation of self-driving vehicles into ride-hailing fleets can greatly improve urban mobility. After modeling existing driver-rider matching algorithms including Uber’s Batched Matching and Didi Chuxing’s Learning and Planning approach, we develop a novel algorithm adapting the latter to a fleet of Autos – self-driving ride-hailing vehicles – and Garages – specialized hubs for storage and refueling. By compiling driver-rider matching, idling, storage, refueling, and redistribution decisions in one unifying framework, we enable a system-wide optimization approach for self-driving ride-hailing previously unseen in the literature. In contrast with existing literature that labeled driverless taxis as economically infeasible ...


Gait Characterization Using Computer Vision Video Analysis, Martha T. Gizaw 2020 College of William and Mary

Gait Characterization Using Computer Vision Video Analysis, Martha T. Gizaw

Undergraduate Honors Theses

The World Health Organization reports that falls are the second-leading cause of accidental death among senior adults around the world. Currently, a research team at William & Mary’s Department of Kinesiology & Health Sciences attempts to recognize and correct aging-related factors that can result in falling. To meet this goal, the members of that team videotape walking tests to examine individual gait parameters of older subjects. However, they undergo a slow, laborious process of analyzing video frame by video frame to obtain such parameters. This project uses computer vision software to reconstruct walking models from residents of an independent living retirement ...


A Model For The Spread Of Infectious Diseases In A Region, Elizabeth Hunter, Brian Mac Namee, John D. Kelleher 2020 Technological University Dublin

A Model For The Spread Of Infectious Diseases In A Region, Elizabeth Hunter, Brian Mac Namee, John D. Kelleher

Articles

In understanding the dynamics of the spread of an infectious disease, it is important to understand how a town’s place in a network of towns within a region will impact how the disease spreads to that town and from that town. In this article, we take a model for the spread of an infectious disease in a single town and scale it up to simulate a region containing multiple towns. The model is validated by looking at how adding additional towns and commuters influences the outbreak in a single town. We then look at how the centrality of a ...


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