Data Science Commons^™

Inter-Frame Video Compression Based On Adaptive Fuzzy Inference System Compression Of Multiple Frame Characteristics, Arief Bramanto Wicaksono Putra, Rheo Malani, Bedi Suprapty, Achmad Fanany Onnilita Gaffar, Roman Voliansky

Knowledge Engineering and Data Science

Video compression is used for storage or bandwidth efficiency in clip video information. Video compression involves encoders and decoders. Video compression uses intra-frame, inter-frame, and block-based methods. Video compression compresses nearby frame pairs into one compressed frame using inter-frame compression. This study defines odd and even neighboring frame pairings. Motion estimation, compensation, and frame difference underpin video compression methods. In this study, adaptive FIS (Fuzzy Inference System) compresses and decompresses each odd-even frame pair. First, adaptive FIS trained on all feature pairings of each odd-even frame pair. Video compression-decompression uses the taught adaptive FIS as a codec. The features utilized …

Ant Colony Optimization For Resistor Color Code Detection, Slamet Wibawanto, Kartika Candra Kirana, Hani Ramadhan

Knowledge Engineering and Data Science

In the early stages of learning resistors, introducing color-based values is needed. Moreover, some combinations require a resistor trip analysis to identify. Unfortunately, a resistor body color is considered a local solution, which often confuses resistor coloration. Ant Colony Optimization (ACO) is a heuristic algorithm that can recognize problems with traveling a group of ants. ACO is proposed to select commercial matrix values to be computed without preventing local solutions. In this study, each explores the matrix based on pheromones and heuristic information to generate local solutions. Global solutions are selected based on their high degree of similarity with other …

Maximum Marginal Relevance And Vector Space Model For Summarizing Students' Final Project Abstracts, Gunawan Gunawan, Fitria Fitria, Esther Irawati Setiawan, Kimiya Fujisawa

Knowledge Engineering and Data Science

Automatic summarization is reducing a text document with a computer program to create a summary that retains the essential parts of the original document. Automatic summarization is necessary to deal with information overload, and the amount of data is increasing. A summary is needed to get the contents of the article briefly. A summary is an effective way to present extended information in a concise form of the main contents of an article, and the aim is to tell the reader the essence of a central idea. The simple concept of a summary is to take an essential part of …

K-Means Clustering And Multilayer Perceptron For Categorizing Student Business Groups, Miftahul Walid, Norfiah Lailatin Nispi Sahbaniya, Hozairi Hozairi, Fajar Baskoro, Arya Yudhi Wijaya

Knowledge Engineering and Data Science

The research conducted in this study was driven by the East Java provincial government's requirement to assess the transaction levels of the Student Business Group (KUS) in the SMA Double Track program. These transaction levels are a basis for allocating supplementary financial aid to each business group. The system's primary objective is to assist the provincial government of East Java in making well-informed choices pertaining to the distribution of supplementary capital to the KUS. The classification technique employed in this study is the multilayer perceptron. However, the K-Means Clustering method is utilised to generate target data due to the limited …

Round-Robin Algorithm In Load Balancing For National Data Centers, I Kadek Wahyu Sudiatmika, Gede Indrawan, Sariyasa Sariyasa

Knowledge Engineering and Data Science

The Provincial Government of Bali assumes a crucial role in administering various public service applications to meet the requirements of its community, traditional villages, and regional apparatus. Nevertheless, the escalating magnitude of traffic and uneven distribution of requests have resulted in substantial server burdens, which may jeopardize the operation of applications and heighten the likelihood of downtime. Ensuring efficient load distribution is of utmost importance in tackling these difficulties, and the Round Robin algorithm is often utilized for this purpose. However, the current body of research has not extensively examined the distinct circumstances surrounding on-premise servers in the Bali Provincial …

Long-Term Traffic Prediction Based On Stacked Gcn Model, Atkia Akila Karim, Naushin Nower

Knowledge Engineering and Data Science

With the recent surge in road traffic within major cities, the need for both short and long-term traffic flow forecasting has become paramount for city authorities. Previous research efforts have predominantly focused on short-term traffic flow estimations for specific road segments and paths. However, applications of paramount importance, such as traffic management and schedule routing planning, demand a deep understanding of long-term traffic flow predictions. However, due to the intricate interplay of underlying factors, there exists a scarcity of studies dedicated to long-term traffic prediction. Previous research has also highlighted the challenge of lower accuracy in long-term predictions owing to …

Optimizing Random Forest Algorithm To Classify Player's Memorisation Via In-Game Data, Akmal Vrisna Alzuhdi, Harits Ar Rosyid, Mohammad Yasser Chuttur, Shah Nazir

Knowledge Engineering and Data Science

Assessment of a player's knowledge in game education has been around for some time. Traditional evaluation in and around a gaming session may disrupt the players' immersion. This research uses an optimized Random Forest to construct a non-invasive prediction of a game education player's Memorization via in-game data. Firstly, we obtained the dataset from a 3-month survey to record in-game data of 50 players who play 4-15 game stages of the Chem Fight (a test case game). Next, we generated three variants of datasets via the preprocessing stages: resampling method (SMOTE), normalization (min-max), and a combination of resampling and normalization. …

Minimal Positional Substring Cover Is A Haplotype Threading Alternative To Li And Stephens Model

Faculty, Staff and Student Publications

The Li and Stephens (LS) hidden Markov model (HMM) models the process of reconstructing a haplotype as a mosaic copy of haplotypes in a reference panel. For small panels, the probabilistic parameterization of LS enables modeling the uncertainties of such mosaics. However, LS becomes inefficient when sample size is large, because of its linear time complexity. Recently the PBWT, an efficient data structure capturing the local haplotype matching among haplotypes, was proposed to offer a fast method for giving some optimal solution (Viterbi) to the LS HMM. Previously, we introduced the minimal positional substring cover (MPSC) problem as an alternative …

Privacy Preserving Identification Of Population Stratification For Collaborative Genomic Research, Jaideep Vaidya, Erman Ayday

Faculty, Staff and Student Publications

The rapid improvements in genomic sequencing technology have led to the proliferation of locally collected genomic datasets. Given the sensitivity of genomic data, it is crucial to conduct collaborative studies while preserving the privacy of the individuals. However, before starting any collaborative research effort, the quality of the data needs to be assessed. One of the essential steps of the quality control process is population stratification: identifying the presence of genetic difference in individuals due to subpopulations. One of the common methods used to group genomes of individuals based on ancestry is principal component analysis (PCA). In this article, we …

Unraveling The Neural Basis Of Emotions: Advancing Understanding With Ecologically Valid Paradigms And High-Resolution Intracranial Eeg, Tiankang Xie

Dartmouth College Ph.D Dissertations

Background

Emotion arises from integrating information about the external world with memories of past experiences, current homeostatic states, and future goals. They play a vital role in regulating our thoughts, feelings and behaviors, significantly impacting our mental health. Thus, it is important to understand the neurobiological mechanisms that give rise to emotions. While there has been considerable work investigating the neural basis of emotions, progress has been hampered by several methodological limitations. For example, prior work has relied on relatively simple and isolated stimuli, which often fail to effectively capture the dynamic and multifaceted nature of emotional experiences in real-life …

Acquisition Of A Lexicon For Family History Information: Bidirectional Encoder Representations From Transformers-Assisted Sublanguage Analysis, Sunyang Fu, Kevin J Peterson, Xuguang Ai, Sijia Liu, Ramakanth Kavuluru, Hongfang Liu

Faculty, Staff and Student Publications

BACKGROUND: A patient's family history (FH) information significantly influences downstream clinical care. Despite this importance, there is no standardized method to capture FH information in electronic health records and a substantial portion of FH information is frequently embedded in clinical notes. This renders FH information difficult to use in downstream data analytics or clinical decision support applications. To address this issue, a natural language processing system capable of extracting and normalizing FH information can be used.

OBJECTIVE: In this study, we aimed to construct an FH lexical resource for information extraction and normalization.

METHODS: We exploited a transformer-based method to …

Instagram Travel Influencers Coping With Covid-19 Travel Disruption, Andrei Kirilenko, Katarzyna Emin, Karen Tavares

ITSA 2022 Gran Canaria - 9th Biennial Conference: Corporate Entrepreneurship and Global Tourism Strategies After Covid 19

A significant portion of today’s marketing is done through social media influencers, that is, through bloggers with established online credibility in a certain area who are recognized and followed by a sizable online audience. In the travel and hospitality industry, the influencer marketing is primarily done through Instagram due to its emphasis on visual images rather than texts. Covid-19 related travel restrictions and shrinking social media advertisement in travel industry have heavily impacted travel influencers, reducing their income and forcing many out of business. We present the outcomes of a study of the top 150 online travel influencers. The analysis …

Using Ai-Generated Suggestions From Chatgpt To Optimize Clinical Decision Support, Robert W Turer, Scott D Nelson, Allison B Mccoy, Dean F Sittig, Adam Wright

Faculty, Staff and Student Publications

OBJECTIVE: To determine if ChatGPT can generate useful suggestions for improving clinical decision support (CDS) logic and to assess noninferiority compared to human-generated suggestions.

METHODS: We supplied summaries of CDS logic to ChatGPT, an artificial intelligence (AI) tool for question answering that uses a large language model, and asked it to generate suggestions. We asked human clinician reviewers to review the AI-generated suggestions as well as human-generated suggestions for improving the same CDS alerts, and rate the suggestions for their usefulness, acceptance, relevance, understanding, workflow, bias, inversion, and redundancy.

RESULTS: Five clinicians analyzed 36 AI-generated suggestions and 29 human-generated suggestions …

Say That Again: The Role Of Multimodal Redundancy In Communication And Context, Brandon Javier Dormes

Cognitive Science Senior Theses

With several modes of expression, such as facial expressions, body language, and speech working together to convey meaning, social communication is rich in redundancy. While typically relegated to signal preservation, this study investigates the role of cross-modal redundancies in establishing performance context, focusing on unaided, solo performances. Drawing on information theory, I operationalize redundancy as predictability and use an array of machine learning models to featurize speakers' facial expressions, body poses, movement speeds, acoustic features, and spoken language from 24 TEDTalks and 16 episodes of Comedy Central Stand-Up Presents. This analysis demonstrates that it is possible to distinguish between these …

Information Diffusion In Online Social Networks: A Simulation Experiment, Maxwell Jacob Blum

Quantitative Social Science Undergraduate Senior Theses

The advent of online social networks has completely transformed the way we communicate, with news, opinions, and ideas now spreading faster than ever before (Guille et al., 2013; Lee et al., 2022). That online social networks have a profound impact on the spread of information suggests further investigation of the relationship between network structure and information diffusion (Light & Moody, 2020). This honors thesis investigates degree assortativity – a measure of large-scale network structure that has often only been a footnote in relevant literature on infor- mation diffusion in online social networks – and its effect on the speed of …

Utilizing Few-Shot Meta Learning Algorithms For Medical Image Segmentation, Nick Littlefield

Thinking Matters Student Research Symposium

Deep learning models can be difficult to train because they require large amounts of data, which we usually do not have or are too expensive to get or annotate. To overcome this problem, we can use few-shot meta-learning, which allows us to train deep learning models with little data. Using a few examples, meta-learning, or learning-to-learn, aims to use the experience learned during training to generalize to unknown tasks. Medical imaging is an industry where it is particularly useful, as there is limited publicly available data due to patient privacy concerns and annotating costs.

This project examines how meta-learning performs …

A Nurse-Led Telehealth Program For Diabetes Foot Care: Feasibility And Usability Study, Jeffery Alford, Constance Johnson

Faculty, Staff and Student Publications

BACKGROUND: Diabetes mellitus can lead to severe and debilitating foot complications, such as infections, ulcerations, and amputations. Despite substantial progress in diabetes care, foot disease remains a major challenge in managing this chronic condition that causes serious health complications worldwide.

OBJECTIVE: The primary aim of this study was to examine the feasibility and usability of a telehealth program focused on preventive diabetes foot care. A secondary aim was to descriptively measure self-reported changes in diabetes knowledge, self-care, and foot care behaviors before and after participating in the program.

METHODS: The study used a single-arm, pre-post design in 2 large family …

Towards An Experimental Bibliography Of Hemispheric Reconstruction Newspapers, Joshua Ortiz Baco, Benjamin Charles Germain Lee, Jim Casey, Sarah H. Salter

Criticism

Digital collections of newspapers have drawn broader attention to the fragmented and scattered print histories of minoritized communities. Attempts to survey these histories through bibliography, however, quickly meet with a fundamental problem: the practice of bibliographic description calls for creating a static record of social affiliations. Given the overwhelming scholarly consensus that categories such as race, ethnicity, and language are socially constructed, this article introduces an experimental bibliographic method for mapping the vast landscape of historical newspapers. This method extends the machine learning affordances of a recent project called Newspaper Navigator to enumerate the newspapers in Chronicling America according to …

Variant Spectrum Of Von Hippel-Lindau Disease And Its Genomic Heterogeneity In Japan, Kazuhiro Maejima, Shingo Ashida, Takashi Karashima, Shohei Kojima, Nickolas F Parrish, Shunichi Kosugi, Chikashi Terao, Shota Sasagawa, Masashi Fujita, Todd A Johnson, Yukihide Momozawa, Keiji Inoue, Taro Shuin, Hidewaki Nakagawa

Faculty, Staff and Student Publications

Von Hippel-Lindau (VHL) disease is an autosomal dominant, inherited syndrome with variants in the VHL gene, causing predisposition to multi-organ neoplasms with vessel abnormality. Germline variants in VHL can be detected in 80-90% of patients clinically diagnosed with VHL disease. Here, we summarize the results of genetic tests for 206 Japanese VHL families, and elucidate the molecular mechanisms of VHL disease, especially in variant-negative unsolved cases. Of the 206 families, genetic diagnosis was positive in 175 families (85%), including 134 families (65%) diagnosed by exon sequencing (15 novel variants) and 41 (20%) diagnosed by multiplex ligation-dependent probe amplification (MLPA) (one …

Stereotypes And Language Models: Understanding How Language Models Encode Stereotypes, Debiasing Language Models, And Examining How Stereotypes Affect Conversations, Brian C. Wang

Computer Science Senior Theses

This thesis describes a variety of approaches in examining how language models encode stereotypes (understanding stereotypes from a model point-of-view), debiasing language models, and using language models to understand how stereotypes affect conversations (understanding stereotypes from a conversational point-of-view). We present a novel approach for textual clues analysis that makes language models more interpretable, combining the understanding of what stereotypes the internal structures of language models have encoded during their initial training (via attention-based analysis) and understanding what textual clues are most relevant to identifying stereotypes for models trained to detect stereotypes (via SHAP-based analysis). We find that different pre-trained …