Luddy School of Informatics, Computing, and Engineering

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https://hdl.handle.net/1805/193
The Indiana University Luddy School of Informatics, Computing, and Engineering is a core school with programs on the Bloomington and Indianapolis campuses. Works found here were created by Indianapolis faculty, staff, and students.

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Browsing Luddy School of Informatics, Computing, and Engineering by Title

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    10x10=100: Best Practices and Lessons Learned from a Decade of Teaching Online Courses
    (2015-11-21) Hook, Sara Anne
    Drawn from the literature and the Quality Matters rubric as well as the presenter’s own experiences of 10 years of teaching online and in developing 10 courses on a wide variety of subjects, this presentation will offer a generous number of practical approaches and strategies that can be taken to enhance instructor-to-student and student-to-student interaction, encourage active learning and accountability, incorporate peer review and self-reflection, assess student learning outcomes and utilize technology most effectively.
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    3D Printed Cast and Interim Obturator for Maxillectomy with Pedicled Buccal Fat Pad Flap
    (2022) Bellicchi, T.; Jacobs, C.B.T.; Wood, Z.M.; Ghoneima, A.; Levon, J.; Morton, D.
    This poster presents a hybrid workflow using intra-oral digital scanning, 30 printing, Biocryl vacuform matrix, and soft denture reline material to obturate a partiallyhealed pedicled buccal fat pad flap maxillectomy. The goal of this poster is to demonstrate an effective workflow for interim obturation with recent post-surgical reconstruction patients unable to tolerate traditional intraoral impression techniques and materials.
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    3D Printing Law
    (2016) Hook, Sara Anne
    Whoever you represent in relation to 3D printing, you need to ensure that you're in-the-know regarding the latest rules and regulations. In this fast paced legal program, you'll maximize insight and gain cutting-edge pointers for advising your clients on this new area of law. Dig deep into the science, technology, rules and requirements regulating 3D printing today - AND identify key business, legal, and technical issues that will oversee this evolving landscape now, and in the near future. Review 3D printing laws and get the latest legislative updates, rules and regulations. Identify top liability traps, legal landmines and mistakes. Review intellectual property rights and issues. Analyze 3D printing taxation considerations.
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    A framework for graph-base neural network using numerical simulation of metal powder bed fusion for correlating process parameters and defect generation
    (Elsevier, 2022) Akter Jahan, Suchana; Al Hasan, Mohammad; El-Mounayri, Hazim; Computer Science, Luddy School of Informatics, Computing, and Engineering
    Powder bed fusion (PBF) is the most common technique used for metal additive manufacturing. This process involves consolidation of metal powder using a heat source such as laser or electron beam. During the formation of three-dimensional(3D) objects by sintering metal powders layer by layer, many different thermal phenomena occur that can create defects or anomalies on the final printed part. Similar to other additive manufacturing techniques, PBF has been in practice for decades, yet it is still going through research and development endeavors which is required to understand the physics behind this process. Defects and deformations highly impact the product quality and reliability of the overall manufacturing process; hence, it is essential that we understand the reason and mechanism of defect generation in PBF process and take appropriate measures to rectify them. In this paper, we have attempted to study the effect of processing parameters (scanning speed, laser power) on the generation of defects in PBF process using a graph-based artificial neural network that uses numerical simulation results as input or training data. Use of graph-based machine learning is novel in the area of manufacturing let alone additive manufacturing or powder bed fusion. The outcome of this study provides an opportunity to design a feedback controlled in-situ online monitoring system in powder bed fusion to reduce printing defects and optimize the manufacturing process.
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    A Large Open Access Dataset of Brain Metastasis 3D Segmentations with Clinical and Imaging Feature Information
    (Springer Nature, 2024-02-29) Ramakrishnan, Divya; Jekel, Leon; Chadha, Saahil; Janas, Anastasia; Moy, Harrison; Maleki, Nazanin; Sala, Matthew; Kaur, Manpreet; Cassinelli Petersen, Gabriel; Merkaj, Sara; von Reppert, Marc; Baid, Ujjwal; Bakas, Spyridon; Kirsch, Claudia; Davis, Melissa; Bousabarah, Khaled; Holler, Wolfgang; Lin, MingDe; Westerhoff, Malte; Aneja, Sanjay; Memon, Fatima; Aboian, Mariam S.; Pathology and Laboratory Medicine, School of Medicine
    Resection and whole brain radiotherapy (WBRT) are standard treatments for brain metastases (BM) but are associated with cognitive side effects. Stereotactic radiosurgery (SRS) uses a targeted approach with less side effects than WBRT. SRS requires precise identification and delineation of BM. While artificial intelligence (AI) algorithms have been developed for this, their clinical adoption is limited due to poor model performance in the clinical setting. The limitations of algorithms are often due to the quality of datasets used for training the AI network. The purpose of this study was to create a large, heterogenous, annotated BM dataset for training and validation of AI models. We present a BM dataset of 200 patients with pretreatment T1, T1 post-contrast, T2, and FLAIR MR images. The dataset includes contrast-enhancing and necrotic 3D segmentations on T1 post-contrast and peritumoral edema 3D segmentations on FLAIR. Our dataset contains 975 contrast-enhancing lesions, many of which are sub centimeter, along with clinical and imaging information. We used a streamlined approach to database-building through a PACS-integrated segmentation workflow.
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    A measurement of faculty views on the meaning and value of student privacy
    (Springer, 2022-06-04) Jones, Kyle M. L.; VanScoy, Amy; Bright, Kawanna; Harding, Alison; Vedak, Sanika; Library and Information Science, School of Computing and Informatics
    Learning analytics tools are becoming commonplace in educational technologies, but extant student privacy issues remain largely unresolved. It is unknown whether faculty care about student privacy and see privacy as valuable for learning. The research herein addresses findings from a survey of over 500 full-time higher education instructors. In the findings, we detail faculty perspectives of their privacy, students’ privacy, and the high degree to which they value both. Data indicate that faculty believe privacy is important to intellectual behaviors and learning, but the discussion argues that faculty make choices that put students at risk. While there seems to be a “privacy paradox,” our discussion argues that faculty are making assumptions about existing privacy protections and making instructional choices that could harm students because their “risk calculus” is underinformed. We conclude the article with recommendations to improve a faculty member’s privacy decision-making strategies and improve institutional conditions for student privacy.
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    A note on the multiplicative fairness score in the NIJ recidivism forecasting challenge
    (Springer Nature, 2021) Mohler, George; Porter, Michael D.; Computer Science, Luddy School of Informatics, Computing, and Engineering
    Background: The 2021 NIJ recidivism forecasting challenge asks participants to construct predictive models of recidivism while balancing false positive rates across groups of Black and white individuals through a multiplicative fairness score. We investigate the performance of several models for forecasting 1-year recidivism and optimizing the NIJ multiplicative fairness metric. Methods: We consider standard linear and logistic regression, a penalized regression that optimizes a convex surrogate loss (that we show has an analytical solution), two post-processing techniques, linear regression with re-balanced data, a black-box general purpose optimizer applied directly to the NIJ metric and a gradient boosting machine learning approach. Results: For the set of models investigated, we find that a simple heuristic of truncating scores at the decision threshold (thus predicting no recidivism across the data) yields as good or better NIJ fairness scores on held out data compared to other, more sophisticated approaches. We also find that when the cutoff is further away from the base rate of recidivism, as is the case in the competition where the base rate is 0.29 and the cutoff is 0.5, then simply optimizing the mean square error gives nearly optimal NIJ fairness metric solutions. Conclusions: The multiplicative metric in the 2021 NIJ recidivism forecasting competition encourages solutions that simply optimize MSE and/or use truncation, therefore yielding trivial solutions that forecast no one will recidivate.
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    A Putative long-range RNA-RNA interaction between ORF8 and Spike of SARS-CoV-2
    (Public Library of Science, 2022-09-01) Omoru, Okiemute Beatrice; Pereira, Filipe; Janga, Sarath Chandra; Manzourolajdad, Amirhossein; BioHealth Informatics, School of Informatics and Computing
    SARS-CoV-2 has affected people worldwide as the causative agent of COVID-19. The virus is related to the highly lethal SARS-CoV-1 responsible for the 2002-2003 SARS outbreak in Asia. Research is ongoing to understand why both viruses have different spreading capacities and mortality rates. Like other beta coronaviruses, RNA-RNA interactions occur between different parts of the viral genomic RNA, resulting in discontinuous transcription and production of various sub-genomic RNAs. These sub-genomic RNAs are then translated into other viral proteins. In this work, we performed a comparative analysis for novel long-range RNA-RNA interactions that may involve the Spike region. Comparing in-silico fragment-based predictions between reference sequences of SARS-CoV-1 and SARS-CoV-2 revealed several predictions amongst which a thermodynamically stable long-range RNA-RNA interaction between (23660-23703 Spike) and (28025-28060 ORF8) unique to SARS-CoV-2 was observed. The patterns of sequence variation using data gathered worldwide further supported the predicted stability of the sub-interacting region (23679-23690 Spike) and (28031-28042 ORF8). Such RNA-RNA interactions can potentially impact viral life cycle including sub-genomic RNA production rates.
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    A systematic review of library makerspaces research
    (Elsevier, 2022-10) Kim, Soo Hyeon; Jung, Yong Ju; Choi, Gi Woong; Library and Information Science, School of Computing and Informatics
    Despite the abundance of research on library makerspaces, systematic reviews of library makerspace research are lacking. As research on library makerspaces advances, the field needs reliable empirical findings to examine the impact of library makerspaces and identify research areas that are valuable for future research. Guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) statement, 43 out of 838 records were selected for the systematic review. The overall trend of research methodologies and theories, settings, participants, research purposes, as well as tools, technologies and programming in library makerspace research were identified. The findings reveal that qualitative studies that were descriptive in nature were the predominant approaches. While appropriate literatures were explored, theoretical frameworks were less used. This systematic review contributes new areas and directions for future research, including the need for expansion of research methodologies and theoretical frameworks and investigation of diverse users and types of making.
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    A trustless architecture of blockchain-enabled metaverse
    (Elsevier, 2023-03) Xu, Minghui; Guo, Yihao; Hu, Qin; Xiong, Zehui; Yu, Dongxiao; Cheng, Xuizhen; Computer and Information Science, School of Science
    Metaverse has rekindled human beings’ desire to further break space-time barriers by fusing the virtual and real worlds. However, security and privacy threats hinder us from building a utopia. A metaverse embraces various techniques, while at the same time inheriting their pitfalls and thus exposing large attack surfaces. Blockchain, proposed in 2008, was regarded as a key building block of metaverses. it enables transparent and trusted computing environments using tamper-resistant decentralized ledgers. Currently, blockchain supports Decentralized Finance (DeFi) and Non-fungible Tokens (NFT) for metaverses. However, the power of a blockchain has not been sufficiently exploited. In this article, we propose a novel trustless architecture of blockchain-enabled metaverse, aiming to provide efficient resource integration and allocation by consolidating hardware and software components. To realize our design objectives, we provide an On-Demand Trusted Computing Environment (OTCE) technique based on local trust evaluation. Specifically, the architecture adopts a hypergraph to represent a metaverse, in which each hyperedge links a group of users with certain relationship. Then the trust level of each user group can be evaluated based on graph analytics techniques. Based on the trust value, each group can determine its security plan on demand, free from interference by irrelevant nodes. Besides, OTCEs enable large-scale and flexible application environments (sandboxes) while preserving a strong security guarantee.