Other Computer Sciences Commons^™

Nerf This: Copyright Highly Creative Video Game Streams As Sports Broadcasts, Madeleine A. Ball

William & Mary Law Review

Since the 1980s, video games have grown exponentially as an entertainment medium. Once relegated to the niche subcultures of nerds, video games are now decidedly mainstream, drawing over 200 million American consumers yearly. As a result, the industry has stepped up its game. No longer simply a diversion to be enjoyed individually, Americans are increasingly watching others play video games like they might watch television. This practice, where enthusiastic gamers broadcast their video game session online to crowds of viewers, is called “live streaming.”

While streaming has become lucrative and popular, American copyright law currently nerfs this nascent industry. Streams …

The Internet Of Bodies, Andrea M. Matwyshyn

William & Mary Law Review

This Article introduces the ongoing progression of the Internet of Things (IoT) into the Internet of Bodies (IoB)—a network of human bodies whose integrity and functionality rely at least in part on the Internet and related technologies, such as artificial intelligence. IoB devices will evidence the same categories of legacy security flaws that have plagued IoT devices. However, unlike most IoT, IoB technologies will directly, physically harm human bodies—a set of harms courts, legislators, and regulators will deem worthy of legal redress. As such, IoB will herald the arrival of (some forms of) corporate software liability and a new legal …

Overview Of The System Integration Lab And Its Role In The Space Launch System, Matthew Pickard, David Rose

Von Braun Symposium Student Posters

No abstract provided.

Image Labeler: Label Earth Science Images For Machine Learning, Ashish Acharya, Iksha Gurung, Brian Freitag, Manil Maskey, Rahul Ramachandran

Von Braun Symposium Student Posters

No abstract provided.

Radio Frequency Based Multi-Source Positioning In Indoor Environments, Vishal Perekadan, Tathagata Mukherjee

Von Braun Symposium Student Posters

No abstract provided.

Identifying Irrigated Agriculture Land Using Remote Sensing And Machine Learning, Ryann Firestine, Cameron Handyside, Tamseel Syed, Leiqiu Hu

Von Braun Symposium Student Posters

No abstract provided.

Technical Report 2019-01: Pupil Labs Eye Tracking User Guide, Joan D. Gannon, Augustine Ubah, Chris Dancy

Other Faculty Research and Publications

No abstract provided.

Editorial: Machine Learning In Biomolecular Simulations, Gennady M. Verkhivker, Vojtech Spiwok, Francesco Luigi Gervasio

Mathematics, Physics, and Computer Science Faculty Articles and Research

"Interest in machine learning is growing in all fields of science, industry, and business. This interest was not primarily initiated by new theoretical findings. Interestingly, the theoretical basis of the majority of machine learning techniques, such as artificial neural networks, decision trees, or kernel methods, have been known for a relatively long time. Instead, there are other effects that triggered the recent boom of machine learning."

Pristine Sentence Translation: A New Approach To A Timeless Problem, Meenu Ahluwalia, Brian Coari, Ben Brock

SMU Data Science Review

Abstract.

Pristine Sentence Translation (PST) is a new approach to language translation based upon sentence-level granularity. Traditional translation approaches, including those utilizing advanced machine learning or neural network-based approaches, translate on a word-by-word or phrase-by-phrase basis; thereby, potentially missing the context or meaning of the complete sentence. Instead of these piecewise translations, PST utilizes deep learning and predictive modeling techniques to translate complete sentences from their source language into their target language. With these approaches we were able to translate sentences that closely conveyed the meaning of the original sentences. Our results demonstrated that PST’s method of translating an entire …

Bootbandit: A Macos Bootloader Attack, Armen Boursalian, Mark Stamp

Faculty Publications, Computer Science

Historically, the boot phase on personal computers left systems in a relatively vulnerable state. Because traditional antivirus software runs within the operating system, the boot environment is difficult to protect from malware. Examples of attacks against bootloaders include so‐called “evil maid” attacks, in which an intruder physically obtains a boot disk to install malicious software for obtaining the password used to encrypt a disk. The password then must be stored and retrieved again through physical access. In this paper, we discuss an attack that borrows concepts from the evil maid. We assume exploitation can be used to infect a bootloader …

Prediction Of Hierarchical Classification Of Transposable Elements Using Machine Learning Techniques, Manisha Panta

LSU New Orleans Theses and Dissertations

Transposable Elements (TEs) or jumping genes are the DNA sequences that have an intrinsic capability to move within a host genome from one genomic location to another. Studies show that the presence of a TE within or adjacent to a functional gene may alter its expression. TEs can also cause an increase in the rate of mutation and can even promote gross genetic arrangements. Thus, the proper classification of the identified jumping genes is important to understand their genetic and evolutionary effects. While computational methods have been developed that perform either binary classification or multi-label classification of TEs, few studies …

Effective Statistical Energy Function Based Protein Un/Structure Prediction, Avdesh Mishra

LSU New Orleans Theses and Dissertations

Proteins are an important component of living organisms, composed of one or more polypeptide chains, each containing hundreds or even thousands of amino acids of 20 standard types. The structure of a protein from the sequence determines crucial functions of proteins such as initiating metabolic reactions, DNA replication, cell signaling, and transporting molecules. In the past, proteins were considered to always have a well-defined stable shape (structured proteins), however, it has recently been shown that there exist intrinsically disordered proteins (IDPs), which lack a fixed or ordered 3D structure, have dynamic characteristics and therefore, exist in multiple states. Based on …

A Multimodal Approach To Sarcasm Detection On Social Media, Dipto Das

Graduate Theses/Dissertations

In recent times, a major share of human communication takes place online. The main reason being the ease of communication on social networking sites (SNSs). Due to the variety and large number of users, SNSs have drawn the attention of the computer science (CS) community, particularly the affective computing (also known as emotional AI), information retrieval, natural language processing, and data mining groups. Researchers are trying to make computers understand the nuances of human communication including sentiment and sarcasm. Emotion or sentiment detection requires more insights about the communication than it does for factual information retrieval. Sarcasm detection is particularly …

Publication And Evaluation Challenges In Games & Interactive Media, Elizabeth L. Lawley

Presentations and other scholarship

Faculty in the fields of games and interactive media face significant challenges in publishing and documenting their scholarly work for evaluation in the tenure and promotion process. These challenges include selecting appropriate publication venues and assigning authorship for works spanning multiple disciplines; archiving and accurately citing collaborative digital projects; and redefining “peer review,” impact, and dissemination in the context of creative digital works. In this paper I describe many of these challenges, and suggest several potential solutions.

Predicting Switch-Like Behavior In Proteins Using Logistic Regression On Sequence-Based Descriptors, Benjamin Strauss

Master's Projects

Ligands can bind at specific protein locations, inducing conformational changes such as those involving secondary structure. Identifying these possible switches from sequence, including homology, is an important ongoing area of research. We attempt to predict possible secondary structure switches from sequence in proteins using machine learning, specifically a logistic regression approach with 48 N-acetyltransferases as our learning set and 5 sirtuins as our test set. Validated residue binary assignments of 0 (no change in secondary structure) and 1 (change in secondary structure) were determined (DSSP) from 3D X-ray structures for sets of virtually identical chains crystallized under different conditions. Our …

Designing Of The Electronic Components Used In The Device For Biomedical Signals Measurement, H.N Zaynidinov, Sarvar Mahmudjonov

Bulletin of TUIT: Management and Communication Technologies

Wireless health care monitoring technologies have the energetic to replace our lifestyle with various application uses in the field of such as healthcare management, retailer, travels, company, dependents care and urgent management, in addition to many area for improving. Electrocardiography (ECG) is a common technique for recording the electrical activity of human heart. Accurate computer analysis of ECG signal is challenging, as it is exceedingly prone to high frequency noise and various other artifacts due to its low amplitude. The accuracy of these algorithms relies on the low-pass and high-pass filtration of the input ECG signal. In this paper, it …

Identifying Depression In The National Health And Nutrition Examination Survey Data Using A Deep Learning Algorithm, Jihoon Oh, Kyongsik Yun, Uri Maoz, Tae-Suk Kim, Jeong-Ho Chae

Psychology Faculty Articles and Research

Background

As depression is the leading cause of disability worldwide, large-scale surveys have been conducted to establish the occurrence and risk factors of depression. However, accurately estimating epidemiological factors leading up to depression has remained challenging. Deep-learning algorithms can be applied to assess the factors leading up to prevalence and clinical manifestations of depression.

Methods

Customized deep-neural-network and machine-learning classifiers were assessed using survey data from 19,725 participants from the NHANES database (from 1999 through 2014) and 4949 from the South Korea NHANES (K-NHANES) database in 2014.

Results

A deep-learning algorithm showed area under the receiver operating characteristic curve (AUCs) …

An Introduction To Declarative Programming In Clips And Prolog, Jack L. Watkin, Adam C. Volk, Saverio Perugini

Computer Science Faculty Publications

We provide a brief introduction to CLIPS—a declarative/logic programming language for implementing expert systems—and PROLOG—a declarative/logic programming language based on first-order, predicate calculus. Unlike imperative languages in which the programmer specifies how to compute a solution to a problem, in a declarative language, the programmer specifies what they what to find, and the system uses a search strategy built into the language. We also briefly discuss applications of CLIPS and PROLOG.

Mathematical Model And Algorithm For Calculating Complex Words In The Karakalpak Language, Shaxnoza Abidova

Bulletin of TUIT: Management and Communication Technologies

The article examines the morphology of the Karakalpak language, which belongs to the Kipchak group of the Turkic language family. The forms of word formation in the Karakalpak language, their sequences and the affixes added to the core are analyzed. On the basis of the analyzed affixes and suffixes, a complex mathematical model of word formation in the Karakalpak language was developed. On the basis of the developed mathematical model, an algorithm for creating a complex word in the Karakalpak language was developed. Using the developed mathematical model, a four-stage scheme was created for creating complex words of the Karakalpak …

Integration Of Random Forest Classifiers And Deep Convolutional Neural Networks For Classification And Biomolecular Modeling Of Cancer Driver Mutations, Steve Agajanian, Odeyemi Oluyemi, Gennady M. Verkhivker

Mathematics, Physics, and Computer Science Faculty Articles and Research

Development of machine learning solutions for prediction of functional and clinical significance of cancer driver genes and mutations are paramount in modern biomedical research and have gained a significant momentum in a recent decade. In this work, we integrate different machine learning approaches, including tree based methods, random forest and gradient boosted tree (GBT) classifiers along with deep convolutional neural networks (CNN) for prediction of cancer driver mutations in the genomic datasets. The feasibility of CNN in using raw nucleotide sequences for classification of cancer driver mutations was initially explored by employing label encoding, one hot encoding, and embedding to …