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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Kim, Seunghyun Lee, Kunyoung Lee, Eui Chul Sangmyung University Graduate School Department of AI & Informatics Korea Republic of Sangmyung University Graduate School Department of Computer Science Korea Republic of Sangmyung University Department of Human-Centered Artificial Intelligence Korea Republic of

ISBN: (纸本)9798350302493

This paper proposes a novel model for predicting body mass index and various body part sizes using front, side, and back body images. The model is trained on a large dataset of labeled images. The results show that the model can accurately predict body mass index and various body part sizes such as chest, waist, hip, thigh, forearm, and shoulder width. One significant advantage of the proposed model is that it can use multiple views of the body to achieve more accurate predictions, overcoming the limitations of models that only used a single image. The model also does not require complex pre-processing or feature extraction, making it straightforward to apply in practice. We also explore the impact of different environmental factors, such as clothing and posture, on the model's performance. The findings show that the model is relatively insensitive to posture but is more sensitive to clothing, emphasizing the importance of controlling for clothing when using this model. Overall, the proposed model represents a step forward in predicting body mass index and various body part sizes from images. The model's accuracy, convenience, and ability to use multiple views of the body make it a promising tool for a wide range of applications. The proposed method is expected to be utilized as a parameter for accurate sensing of various vision-based non-contact biomarkers, in addition to body mass index inference. © 2023 IEEE.

关键词： Large dataset

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Machine Learning Approach for Evaluating Industry-Based Employer Ranking and Financial Stability 5

Machine Learning Approach for Evaluating Industry-Based Empl...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Swain, Prakash Chandra Balan, Seema Lakshmi, S.R. Choudhary, Ankur Patjoshi, Pramod Kumar Raja, J. School of Social Financial & Human Sciences Odisha Bhubaneswar India Rajalakshmi Engineering College Department of Management Studies Chennai India Christ University Department of Business & Management Karnataka Bangalore560073 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Centurion University of Technology and Management BBSR Campus School of Management India St. Martins Engineering College Department of Computer Science and Engineering Telangana Secunderabad India

ISBN: (纸本)9798350351378

Using the computational prowess of machine learning, this study presents a fresh method for assessing the relative standing and fiscal health of employers across different sectors. The research makes use of a wide variety of data, including financial reports, statistics on the labor market, employee evaluations, and indicators unique to the business, to arrive at in-depth judgements. The financial stability assessment applies a linear regression model, whereas employer ranking is predicted using a logistic regression model. Financial data, employment market dynamics, and sentiment research are used as foundational characteristics for these models. Company A is more financially stable than Company B, yet it is anticipated to be ranked lower as an employer. This highlights the difficulty of judging businesses. The implications of these results for job-seekers, investors, and businesses are varied. The study also highlights the significance of ethics, openness, and addressing biases in assessment. This study paves the way for future advancements in this crucial subject and provides a basis for data-driven, well-informed decision-making in the ever-changing landscapes of contemporary industrial evaluations. © 2024 IEEE.

关键词： Commerce

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Combined Correlational Network for Identifying Behavioral Health Cases from First Responder Report

Combined Correlational Network for Identifying Behavioral He...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Pederson, Mason Azmee, Abm Adnan Nweke, Francis Al Hafiz Khan, Md Abdullah Pei, Yong Thomas, Dominic Nandan, Monica Kennesaw State University Department of Computer Science MariettaGA United States Kennesaw State University Department of Information Systems and Security KennesawGA United States Kennesaw State University Department of Social Work and Human Services KennesawGA United States

ISBN: (纸本)9798350362480

Behavioral health is a broad term which encompasses our overall mental and physical well-being. Behavioral health issues do not just impact an individual's life, they also impact wider society. First responders are often the first point of contact for individuals facing emergency. Reports from first responders are a great resource to identify potential behavioral health cases. Manually identifying these cases are time consuming and prone to error. Automation in identifying these reports would be a huge time saver for facilities and allow for them to spend more time on addressing these problems or on more pressing matters. Natural language processing techniques are widely used by researchers for analyzing texts. However, due to the linguistic complexity of the first responder reports it is challenging for traditional natural language processing and deep learning technique to identify these reports. To address this issues in this work we proposed a novel correlational network which utilizes domain experts knowledge along with their correlational relationships to appropriately identify behavioral health cases. Our proposed framework uses the correlation between features and combines them which allows for granular and broad scope insights into a report. Moreover, we performed multi-label classification which reflects the real world scenarios where reports can contain multiple classes. We performed extensive evaluation to determine the efficiency and efficacy of our model. Furthermore, our proposed model outperformed the state-of-the-art model with an accuracy of 72%. © 2024 IEEE.

关键词： Deep learning

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Instruction-Driven Game Engine: A Poker Case Study

Instruction-Driven Game Engine: A Poker Case Study

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Wu, Hongqiu Liu, Xingyuan Wang, Yan Zhao, Hai Department of Computer Science and Engineering Shanghai Jiao Tong University China Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Shanghai Jiao Tong University China Shanghai Key Laboratory of Trusted Data Circulation and Governance in Web3 China Tencent China

ISBN: (纸本)9798891761674

The Instruction-Driven Game Engine (IDGE) project aims to democratize game development by enabling a large language model (LLM) to follow free-form game descriptions and generate game-play processes. The IDGE allows users to create games simply by natural language instructions, which significantly lowers the barrier for game development. We approach the learning process for IDGEs as a Next State Prediction task, wherein the model autoregressively predicts the game states given player actions. The computation of game states must be precise;otherwise, slight errors could corrupt the game-play experience. This is challenging because of the gap between stability and diversity. To address this, we train the IDGE in a curriculum manner that progressively increases its exposure to complex scenarios. Our initial progress lies in developing an IDGE for Poker, which not only supports a wide range of poker variants but also allows for highly individualized new poker games through natural language inputs. This work lays the groundwork for future advancements in transforming how games are created and played. © 2024 Association for Computational Linguistics.

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Domain Knowledge-Driven Multi-Label Behavioral Health Identification from Police Report

Domain Knowledge-Driven Multi-Label Behavioral Health Identi...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Azmee, Abm Adnan Nweke, Francis Khan, Md Abdullah Al Hafiz Pei, Yong Thomas, Dominic Nandan, Monica Kennesaw State University Department of Computer Science MariettaGA United States Kennesaw State University Department of Information Systems and Security KennesawGA United States Kennesaw State University Department of Social Work and Human Services KennesawGA United States

ISBN: (纸本)9798350362480

Behavioral health plays a pivotal role in individuals' overall quality of life. Timely identification and intervention of behavioral health concerns are essential for building a supportive community. In case of emergencies, first responders (police, fire, EMT) provide support to the community. The report of the first responders contains valuable cues about individuals struggling with behavioral health concerns. Currently, first responders manually screen these reports to determine individuals with behavioral health concerns. Manual screening of these reports is time-consuming and prone to error. Automated analysis of these reports is essential for providing timely intervention. Natural language processing techniques demonstrated potential for automated analysis of textual data. However, due to the unstructured and complex nature of the data, it's challenging for traditional deep learning and natural language processing techniques to identify these cases. Moreover, support from subject matter experts is essential for the effective identification of these cases. In this research, we have developed a multi-label behavioral health detection framework that utilizes domain knowledge from subject matter experts for the effective identification of behavioral health cases. In addition, we have expanded the domain knowledge base by recognizing new behavioral health-related terms. We validated the efficacy and effectiveness of our proposed model through a comprehensive evaluation of real-world data. Furthermore, our proposed model demonstrated 6-24% performance improvement over the state-of-the-art models. © 2024 IEEE.

关键词： Deep learning

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Explainable Multi-Label Classification Framework for Behavioral Health Based on Domain Concepts

Explainable Multi-Label Classification Framework for Behavio...

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Nweke, Francis Azmee, Abm Adnan Al Hafiz Khan, Md Abdullah Pei, Yong Thomas, Dominic Nandan, Monica Kennesaw State University Department of Computer Science MariettaGA United States Kennesaw State University Department of Information Systems and Security KennesawGA United States Kennesaw State University Department of Social Work and Human Services KennesawGA United States

ISBN: (纸本)9798350362480

Behavioral health, which covers mental health, lifestyle choices, addictions, and crises, poses serious issues in the community. Thus, appropriately analyzing and classifying behavioral health data is crucial for making informed health-care decisions. Traditional deep learning and natural language processing approaches struggle to effectively identify behavioral health issues because the data is unstructured, complex, and lacks sufficient context. Furthermore, subject matter experts must be consulted to ensure effective identification. In this work, we proposed a deep learning-based framework consisting of several modules: A) domain concept encoder converts the keywords and their evidence types to vectors, which were predefined by a subject matter expert;B) the semantic representation encoder (SRE) is trained on the vectors to learn the relationship between them;C) transformed-based feature learner is an advanced learner that extracts feature embeddings from documents and generates attention weights since it has more context given the incorporated relationship weights;D) The behavioral health multilabel classifier utilizes feature embeddings to classify a document into one or more behavioral health classes;and E) The LLM-enabled explainer provides explanations based on attention weights and classifications. Our proposed framework outperformed state-of-the-art models in multilabel behavioral health case classification while also providing explanations for each classification. Which is crucial in behavioral health analysis. © 2024 IEEE.

关键词： Electronic health record

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Discrepancies in online postoperative care protocols for Mohs surgery

JAAD Reviews

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JAAD Reviews 2025年 3卷 128-130页

作者： Afua Ofori-Darko Ashley M. Perez Devin Barzallo Jill Lehman Bryan T. Carroll Case Western Reserve University School of Medicine Cleveland Ohio Carnegie Mellon University Human-Computer Interaction Institute Pittsburgh Pennsylvania Department of Dermatology University Hospitals Cleveland Medical Center Cleveland Ohio

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Collaborative Synergy: Enhancing Face-to-Face Computing Courses 54

Collaborative Synergy: Enhancing Face-to-Face Computing Cour...

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54th IEEE Frontiers in Education Conference, FIE 2024

作者： Ashby, Iryna Tagare, Deepti Thomas, Cassandra Exter, Marisa College of Education Purdue University West Lafayette United States College of Education and Human Development University of Texas at San Antonio San Antonio United States Tuskegee University Department of Computer Science Tuskegee United States

ISBN: (纸本)9798350351507

This innovative practice paper explores the varied perspectives of a computing faculty member and a group of instructional designers, who partnered to revise courses to increase active learning practices and integrate cross-disciplinary skills and dispositions into two face-to-face computing courses (CS 1 and CS 2), as part of a larger, grant-funded systemic change effort. The instructional design team members varied in terms of level of formal educational experience in engineering, computing, and instructional design © 2024 IEEE.

关键词： Collaborative learning

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A Passive Variable Impedance Control Strategy with Viscoelastic Parameters Estimation of Soft Tissues for Safe Ultrasonography

A Passive Variable Impedance Control Strategy with Viscoelas...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Luca Beber Edoardo Lamon Davide Nardi Daniele Fontanelli Matteo Saveriano Luigi Palopoli Department of Industrial Engineering Università di Trento Trento Italy Department of Information Engineering and Computer Science Università di Trento Trento Italy Human-Robot Interfaces and Interaction Istituto Italiano di Tecnologia Genoa Italy

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In the context of telehealth, robotic approaches have proven a valuable solution to in-person visits in remote areas, with decreased costs for patients and infection risks. In particular, in ultrasonography, robots have the potential to reproduce the skills required to acquire high-quality images while reducing the sonographer’s physical efforts. In this paper, we address the control of the interaction of the probe with the patient’s body, a critical aspect of ensuring safe and effective ultrasonography. We introduce a novel approach based on variable impedance control, allowing the real-time optimisation of compliant controller parameters during ultrasound procedures. This optimisation is formulated as a quadratic programming problem and incorporates physical constraints derived from viscoelastic parameter estimations. Safety and passivity constraints, including an energy tank, are also integrated to minimise potential risks during human-robot interaction. The proposed method’s efficacy is demonstrated through experiments on a patient’s dummy torso, highlighting its potential for achieving safe behaviour and accurate force control during ultrasound procedures, even in cases of contact loss.

关键词： Torso Parameter estimation Tracking Telemedicine Ultrasonography Safety Impedance

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Revisiting Checkpoint Averaging for Neural Machine Translation 2

Revisiting Checkpoint Averaging for Neural Machine Translati...

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2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing, AACL-IJCNLP 2022

作者： Gao, Yingbo Herold, Christian Yang, Zijian Ney, Hermann Human Language Technology and Pattern Recognition Group Computer Science Department Rwth Aachen University AachenD-52056 Germany

ISBN: (纸本)9781959429043

Checkpoint averaging is a simple and effectivemethod to boost the performance of convergedneural machine translation models. The calculation is cheap to perform and the fact thatthe translation improvement almost comes forfree, makes it widely adopted in neural machine translation research. Despite the popularity, the method itself simply takes the mean ofthe model parameters from several checkpoints,the selection of which is mostly based on empirical recipes without many justifications. In thiswork, we revisit the concept of checkpoint averaging and consider several extensions. Specifically, we experiment with ideas such as usingdifferent checkpoint selection strategies, calculating weighted average instead of simplemean, making use of gradient information andfine-tuning the interpolation weights on development data. Our results confirm the necessityof applying checkpoint averaging for optimalperformance, but also suggest that the landscape between the converged checkpoints israther flat and not much further improvementcompared to simple averaging is to be obtained. © AACL-IJCNLP *** rights reserved

关键词： Neural machine translation

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