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IEEE MIT Undergraduate Research Technology Conference (URTC)

作者： Alexander Pietrick Alyssa Romportl Shailen Smith Oluseun Olulana Kathleen Cachel Elke Rundensteiner Departments of Computer Science and Data Science Worcester Polytechnic Institute Worcester MA United States Departments of Mathematics and Psychology St. Olaf College Northfield MN United States Department of Mathematics Stony Brook University Stony Brook NY United States Department of Data Science Worcester Polytechnic Institute Worcester MA United States

Fair Learning To Rank (LTR) frameworks require demographic information; however, that information is often unavailable. Inference algorithms may infer the missing demographic information to supply to the fair LTR model. In this study, we analyze the effect of using a trained fair LTR model with uncertain demographic inferences. We show that inferred data results in varying levels of fairness and utility depending on inference accuracy. Specifically, less accurate inferred data adversely affects the rankings’ fairness, while more accurate inferred data creates fairer rankings. Therefore, we recommend that a careful evaluation of demographic inference algorithms before use is critical.

关键词： Analytical models Inference algorithms

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Recurrent Variational Open-Set Recognition

Recurrent Variational Open-Set Recognition

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2021 IEEE MIT Undergraduate Research Technology Conference, URTC 2021

作者： Diamond, N'Yoma Jordan, Sean Norman, Amelia Buquicchio, Luke Rundensteiner, Elke Worcester Polytechnic Institute Department of Data Science WorcesterMA United States University of Maryland Baltimore County Department of Mathematics and Statistics BaltimoreMD United States University of Virginia Department of Computer Science CharlottesvilleVA United States

ISBN: (纸本)9781665405959

The Variational Open-Set Recognition (VOSR) model was developed to mitigate shortcomings of previous open-set classification algorithms. However, one of the problems with VOSR is that it was not developed to operate recurrently, meaning it potentially underperforms when operating on time-series data. In this paper we explore alterations to VOSR toward developing a recurrent rVOSR model which is more adept at the task of open-set recognition on time-series data. Based on our experimentation, we found that the implementation of rVOSR over VOSR results in a slight performance improvement on open-set recognition tasks when operating over time sequence data. © 2021 IEEE.

关键词： Recurrent neural networks

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An Augmented Reality Measurement Tool for the da Vinci Research Kit

An Augmented Reality Measurement Tool for the da Vinci Resea...

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International Symposium on Medical Robotics (ISMR)

作者： Shiyue Wang Jing-Fen Wang Yubin Koh Haoying Zhou Anton Deguet Peter Kazanzides Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Department of Computer Science Purdue University West Lafayette IN USA Department of Robotics Engineering Worcester Polytechnic Institute Worcester MA USA Department of Computer Science Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798331599003

ISBN: (纸本)9798331599010

We present an augmented reality surgical robotics console for the da Vinci Research Kit (dVRK) that integrates a real-time distance measurement tool to enhance spatial awareness during robotic-assisted surgical procedures through an interactive system. The system integrates an RViz-based interface with ROS topics, enabling virtual cursor visualization and interactive controls for precise measurements on tissue or phantom models. It supports two distinct measurement approaches: (1) a virtual measurement capability, where the operator uses the two Master Tool Manipulators (MTMs) to drive virtual 3D measurement cursors in the stereo display (a “masters as mice” mode), and (2) a physical measurement tool, where the user teleoperates one or more Patient Side Manipulators (PSMs) to touch two features and report the intervening distance. We conduct a user study on virtual (MTM) and teleoperation (PSM) modes to assess performance and accuracy and gather user feedback. The results show a Root Mean Square Error (RMSE) of 2.19 mm to 7.25 mm across different modes and tasks. This work underscores the importance of such systems for enhancing accuracy and usability in endoscopic surgery, where spatial awareness and intraoperative assessment are critical. Code is available at https://***/capricieuxV/RVinci_***.

关键词： Visualization Three-dimensional displays Medical robotics Accuracy Surgery Manipulators Real-time systems Usability Root mean square Augmented reality

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Optimization for Secure and Humane Border Operations

arXiv

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arXiv 2022年

作者： Farajzadeh, Fatemeh Trapp, Andrew C. Data Science Program Worcester Polytechnic Institute WorcesterMA01609 United States WPI Business School Worcester Polytechnic Institute WorcesterMA01609 United States

In 2020 tens of millions of migrants worldwide sought safety due to various push-pull factors such as economic, geographic, demographic, and societal challenges [1, 2]. Large movements of human flows toward national borders constitute a complex humanitarian crisis that requires appropriate preparations of border resources. Motivated by prevailing national agenda issues for improved border support, we identify common operational challenges along the border and discuss how they can be addressed using advanced analytics. Multiple stakeholders, corresponding goals and complex decisions call for advanced optimization techniques to holistically treat such challenges. This chapter highlights how optimization can be employed to improve both the security and humanitarian aspects of border operations in the context of migration flows. © 2022, CC0.

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Microservice Architecture of Modern eLearning Application

Microservice Architecture of Modern eLearning Application

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Communications, Information, Electronic and Energy Systems (CIEES), International Conference on

作者： Alexander Schukin Nikolai Scerbakov Eugenia Rezedinova Institute of Computer Science and Cybersecurity Peter the Great St. Petersburg Polytechnic University St. Petersburg Russia Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

Learning Management Systems (LMS) have been integral to educational institutions for over 20 years. Technically, an LMS consists of functional components designed to deliver, discuss, explain educational materials, and assess user knowledge through various paradigms such as uploading assignments and online questionnaires. The prolonged and intensive use of such systems emphasizes the importance of their maintainability, allowing the accommodation of new components and the replacement of outdated ones. Over the years, the technological foundation of LMS components has transitioned from early versions of PHP to advanced Java, Python, and JavaScript packages. data repositories have evolved from local relational databases to shared NoSQL databases. Communication technologies have shifted from NNTP news servers to multiple social applications like WhatsApp and *** this paper, we present architectural solutions implemented in a modern LMS called WBT-Master. This architecture specifically addresses the maintainability of modern LMS, ensuring they evolve alongside emerging Internet technologies. The practical significance of our work lies in its ability to support very heterogeneous components within a logically unified LMS framework. By discussing the architecture of WBT-Master, we offer a method for incorporating both old and new technological platforms into a single system. Additionally, we use this architecture to interface with cloud services, foster social interactions, and optimize user experience. This contributes to the evolution of E-Learning platforms, offering enhanced and more interactive educational experiences for both learners and educators.

关键词： Training Java Electronic learning computer architecture Relational databases User interfaces User experience Servers Usability Python

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Discrete Event Simulation to Evaluate Shelter Capacity Expansion Options for LGBTQ+ Homeless Youth 22

Discrete Event Simulation to Evaluate Shelter Capacity Expan...

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Proceedings of the Winter Simulation Conference

作者： Yaren Bilge Kaya Sophia Mantell Kayse Lee Maass Renata Konrad Andrew C. Trapp Geri L. Dimas Meredith Dank Mechanical and Industrial Engineering Northeastern University Boston MA USA School of Business Worcester Polytechnic Institute Worcester MA USA Data Science Program Worcester Polytechnic Institute Worcester MA USA Marron Institute of Urban Management New York University New York NY USA

The New York City (NYC) youth shelter system provides housing, counseling, and other support services to runaway and homeless youth and young adults (RHY). These resources reduce RHY's vulnerability to human trafficking, yet most shelters are unable to meet demand. This paper presents a Discrete Event Simulation (DES) model of a crisis-emergency and drop-in center for LGBTQ+ youth in NYC, which aims to analyze the current operations and test potential capacity expansion interventions. The model uses data from publicly available resources and interviews with service providers and key stakeholders. The simulated shelter has 66 crisis-emergency beds, offers five different support services, and serves on average 1,399 LGBTQ+ RHY per year. The capacity expansion interventions examined in this paper are adding crisis-emergency beds and psychiatric therapists. This application of DES serves as a tool to communicate with policymakers, funders, and service providers---potentially having a strong humanitarian impact.

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Dual-neighbourhood information aggregation and feature fusion for prediction of miRNA–disease association

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computers in Biology and Medicine 2024年 181卷 109068-109068页

作者： Liu, Wei Lan, Zixin Li, Zejun Sun, Xingen Lu, Xu School of Computer Science Xiangtan University Xiangtan411105 China School of Computer Science and Engineering Hunan Institute of Technology Hengyang421002 China School of Mathematics and Computational Science Xiangtan University Xiangtan411105 China School of Computer Science Guangdong Polytechnic Normal University Guangdong Provincial Key Laboratory of Intellectual Property Big Data Guangzhou510665 China

Studying the intricate relationship between miRNAs and diseases is crucial to prevent and treat miRNA-related disorders. Existing computational methods often overlook the importance of features of different nodes and the propagation of features among heterogeneous nodes. Many prediction models focus only on the feature coding of miRNA and diseases and ignore the importance of feature aggregation. We propose a prediction method via dual-neighbourhood feature aggregation and feature fusion, which uses multiple sources of information, aggregates information on homogeneous and heterogeneous nodes and fuses learned features to predict multiple representations of disease nodes. We constructed similarity networks of multiple homogeneous nodes based on different similarity computation methods respectively, and fused the attention mechanism by using graph convolutional networks to obtain information of different levels of importance. To alleviate the problem of sparse connectivity in the dataset, we built a two-neighbourhood heterogeneous graph neural network model to integrate the homogeneous similarity network into a miRNA-disease heterogeneous network by using known miRNA–disease association information. We used the neighbourhood information associated with the nodes in the network to perform feature aggregation. In addition, we used a feature fusion module to learn the importance of different types of nodes to predict miRNA–disease associations. Our experimental results on the Human microRNA Disease database (HMDD v3.2) show that the model demonstrates superior performance. This work demonstrates the capability of our model to identify potential miRNAs associated with diseases through a case study of two common cancers. © 2024

关键词： Prediction models

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Dynamic Provision of Workload Allocation on Cloud Resources Using Machine Learning Techniques

Dynamic Provision of Workload Allocation on Cloud Resources ...

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Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： C. Saravanakumar M. Prakash V. Govindaraj Department of Computer Engineering Government Polytechnic College Chennai School of Computer Science and Engineering Vellore Institute of Technology Vellore SME- Data Science MPHASIS Ltd Chennai

Cloud tasks are scheduled to the respective VM for execution. Scheduling process selects the resources and allocates the requesting task to them. The resources are selected in advance and then the request is forwarded for processing of tasks. It suffers the overestimation problem which means that the requested task size is less than the allocated resource level. Various scheduling problems are addressed but still the problem arises during the resource allocation. Auto scaling process is implemented in the cloud which allocates the resource based on the demand. The proposed model uses the dynamic method of cloud allocation and provisioning based on the workload level and resource level. Priority queue is implemented prior to the provisioning process for ordering the resource in specific order. The main aim of the proposed method is to allocate the resources using dynamic priority manner. The efficiency is achieved at maximum level by selecting and allocating the workload.

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Integrating computational thinking practices into early childhood education in culturally responsive ways: Insights from research–practice partnership

Future in Educational Research

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Future in Educational Research 2024年第4期2卷 359-381页

作者： Jeanne Hubelbank Mia Dubosarsky Shakhnoza Kayumova Tiffany Davis Nea Sann Shawn Fortin Gillian Smith Independent Evaluator Sudbury Massachusetts USA The STEM Education Center Worcester Polytechnic Institute Worcester Massachusetts USA Department of STEM Education and Teacher Development University of Massachusetts Dartmouth Massachusetts USA Ashburnham Westminster Regional School District Ashburnham Massachusetts USA Worcester Child Development Head Start Worcester Massachusetts USA Teaching & Learning Belchertown Public Schools Belchertown Massachusetts USA Interactive Media & Game Development Department of Computer Science Worcester Polytechnic Institute Worcester Massachusetts USA

The study examines the research–practice partnership (RPP) model aimed at co-constructing and integrating computational thinking (CT) in culturally responsive (CR) ways within early childhood and elementary (PreK-5) classrooms, focusing on teachers' RPP experiences. Recent research underlines the importance of integrating CT in early childhood and elementary education, demonstrating that culturally, linguistically, and developmentally responsive computational activities in PreK-5 settings are key to such efforts. However, there is a gap in research regarding effective professional development models that would prepare early childhood teachers in terms of both disciplinary ideas and practices of CT, as well as attending to cultural, linguistic, and developmental differences in young children in asset-based ways. Our research contributes to this body of knowledge by examining how RPP can be a model for teacher learning in which teachers, administrators, and researchers can co-develop knowledge and confidence in integrating CT into the PreK-5 teaching curriculum and practices in CR ways. The study outlines the collaborative development of processes and frameworks co-constructed by the RPP educators, as well as teacher-developed curricular materials and lesson plans integrating CT in CR ways. Findings include teachers' experience of these lessons and teacher insights about the support needed to incorporate CT and CR practices into their existing curricula. The study concludes by highlighting the potential challenges and opportunities inherent in such endeavors, thereby contributing to the broader discourse on supporting CT integration into early childhood classrooms in CR ways.

关键词： computational thinking cultural responsive practices early childhood research practice partnership teacher professional development

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An Enhanced BERTopic Framework and Algorithm for Improving Topic Coherence and Diversity

An Enhanced BERTopic Framework and Algorithm for Improving T...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Sahil Sawant Jinhong Yu Kirtikumar Pandya Chun-Kit Ngan Rolf Bardeli Data Science Program Worcester Polytechnic Institute Worcester MA USA Big Data & Consumer Analytics Vodafone GmbH Duesseldorf NRW Germany

In this paper, we enhance and customize the existing BERTopic framework to develop and implement an automated pipeline that delivers a more coherent and diverse set of topics with an even moderate dataset. More specifically, the contributions of this work are threefold: (1) integrate a dynamic and advanced optimizer into the existing BERTopic framework to learn the optimal number of dimensions of different document embeddings, (2) develop a k-means-based algorithm in the optimizer to support the dimension-embedding learning, and (3) conduct an extensive experimental study on three distinct types of datasets, including DBPedia, AG News, and Reuters, to evaluate the performance of our approach in terms of the topic quality (TQ) score computed by the topic coherence and the topic diversity. From the results, we can conclude that our enhanced, automated BERTopic framework with its dimension-embedding learning algorithm on documents outperforms the TQ score of the existing framework by 4.49% (before removing the stop words) and 16.52% (after removing the stop words) among all the four representable document-embedding approaches, including the BERTopic's Default Sentence Transformer, Google's Universal Sentence Encoder, OpenAI GPT-2, and our investigators' developed Context-aware Embedding Model, on all the three datasets.

关键词： Heuristic algorithms Pipelines Coherence Transformers Internet Context modeling

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