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Artificial Intelligence x Humanities, Education, and Art (AIxHEART)

作者： Siyeol Jung Yubin Choi E. Cho Smith Mia Y. Wang Artificial Intelligence Graduate School UNIST Ulsan Korea Computer Science and Engineering Soongsil University Seoul Korea Computer and Information Technology Purdue University West Lafayette USA Department of Computer Science College of Charleston Charleston USA

ISBN: (数字)9798350391015

ISBN: (纸本)9798350391022

The Music Emotion Recognition (MER) task has garnered significant attention from both academic and industrial fields due to its versatility in various fields, such as music recommendation systems and psychotherapy. Due to advancements in language model performance, there has been a rise in approaches that address the MER tasks using only lyric data. Despite the empirical evidence suggesting that valence is a more reliable indicator of emotion disambiguation than arousal, previous studies have often overlooked this relationship. To address this research gap in the literature and enhance performance, we propose considering valence and arousal within a hierarchical framework. To preserve this hierarchical structure in the embedding space, we employed Hierarchical Contrastive Learning to enhance the MER performance while preserving the hierarchical organization. The experimental results show that this approach improves the classification performance and leads to a more balanced model. Furthermore, it is noteworthy that the model’s performance improvement rate is faster when compared to the application of cross-entropy loss.

关键词： Measurement Emotion recognition Standards organizations Contrastive learning Organizations Data models Reliability Proposals Recommender systems Convergence

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Answer Comments As Reviews: Predicting Acceptance By Measuring Valence On Stack Exchange

Answer Comments As Reviews: Predicting Acceptance By Measuri...

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IEEE International Conference on Big Data

作者： William Ledbetter John Springer Department of Computer and Information Technology Purdue University West Lafayette Indiana USA

ISBN: (纸本)9781665480468

Online communication has increased the need to interpret complex emotions rapidly; due to the volatility of the data involved, machine learning tasks that process text aim to address the related challenges. Exploring text in comments that supports ideas through computational methods is a logical next step considering similar research for these question-and-answer sites. A lack of current algorithms that can accurately predict accepted answers with equal votes suggests a gap in this knowledge. Measuring collaborative signals in comments finds common keywords to move a problem toward a *** a dataset from questions posted to Stack Exchange on the subject area of machine learning, the researchers constructed a model using the comments of posts made on accepted answers. Intending to discover whether predictions of marked solutions are accurate by treating the comments as reviews, the researchers find a reduction in error by incorporating reviews of answers as a feature in the predictive algorithm.

关键词： Knowledge engineering Machine learning algorithms Natural languages Collaboration Machine learning Manuals Big Data

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A Corpus of Encoded Malware Byte information as Images for Efficient Classification

A Corpus of Encoded Malware Byte Information as Images for E...

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International IEEE Conference on Signal-Image Technologies and Internet-Based System

作者： Ryan Frederick Joseph Shapiro Ricardo A. Calix Computer Information Technology Purdue University Northwest Hammond United States of America

Malware has become an increasing problem in today’s world. In an attempt to combat this ever-changing problem, this research presents a machine-learning approach for analyzing malware using image classification. This paper discusses the idea of using image-based machine learning (ML) in malware analysis. Ways of applying this technique into the real-world are also presented and discussed. The highest accuracy achieved with the dataset used for training was 98.21%. Other datasets were also tested and achieved up to 93.22%. This shows that not only is this an effective method, but also that it can be used on other datasets and that a pre-trained model is more effective. The pre-trained model, dataset, and application are made available here to the wider research community.

关键词： Training Machine learning Predictive models Malware Anti-virus software Task analysis computer crime

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A face recognition algorithm based on feature fusion

A face recognition algorithm based on feature fusion

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作者： Zhang, Jiwei Yan, Xiaodan Cheng, Zelei Shen, Xueqi School of Software Engineering Beijing University of Posts and Telecommunications Beijing China School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Department of Computer and Information Technology Purdue University West LafayetteIN United States

In the process of building a smart city, face recognition can be applied to the transformation of enterprises, communities, and parks. The combination of building security system and face recognition technology can improve the security experience of enterprises and citizens through the solution of hardware and software integration. Face recognition is still facing the challenges of illumination, occlusion, and attitude change in the actual application process. In addition, the end-to-end convolutional neural networks (CNN) seldom make use of the hierarchical feature of the network. So, we propose a hierarchy feature fusion method for face recognition, which uses supervisory information to learn shallow and deep facial features. The features are fused to enhance the recognition accuracy of face recognition against illumination and occlusion. The method is applied to the transformation of the visual geometry group network and Lightened CNN. The face recognition experiments are carried out using the hierarchy network. Our method has achieved good recognition results in the labeled faces in the wild (LFW) and AR face databases. © 2020 John Wiley & Sons, Ltd.

关键词： Face recognition

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Assessing perceived stress, sleep disturbance, and fatigue among pilot and non-pilot trainees

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Smart Health 2024年 32卷

作者： Gomez, Samuel Andres Vhaduri, Sudip Wilson, Mark D. Keller, Julius C. Department of Computer and Information Technology Purdue University West Lafayette 47907 IN United States School of Health Sciences Purdue University West Lafayette 47907 IN United States School of Aviation and Transportation Technology Purdue University West Lafayette 47907 IN United States

Pilots operating in a distinctive professional realm face substantial stress and fatigue from the crucial responsibility of navigating airplanes at high altitudes with inherent risks. Similarly, college-level students encounter heightened stress and fatigue while pursuing academic goals and engaging in different activities. Stress, a non-specific response to various demands, and fatigue, characterized by extreme tiredness, are prevalent health conditions experienced across a spectrum of intensity in daily life. In this study, we conduct an extensive analysis to address a fundamental question: how do stress, sleep disturbance, and fatigue experiences differ between pilots and non-pilot college students? Delving into stress and fatigue levels within these populations contributes to understanding these phenomena and their potential implications for overall well-being and performance. Building on a comprehensive analysis of the Perceived Stress Scale (PSS), Jenkins Sleep Scale (JSS), and Multidimensional Fatigue Inventory (MFI) scores, we explore variations in stress, sleep disturbance, and fatigue across multiple dimensions. Our findings indicate intriguing disparities among pilot and non-pilot cohorts. Through graphical representations and statistical tests, we reveal that non-pilot college students exhibit higher perceived stress and sleep disturbance levels. In contrast, pilots demonstrate expected higher perceived fatigue levels. Our detailed analysis of subcategories, including General Fatigue, Physical Fatigue, Reduced Activity, Reduced Motivation, and Mental Fatigue, sheds light on the complexity of these differences. Notably, pilot students experience heightened fatigue, potentially linked to the demanding nature of their tasks. In conclusion, our extended analysis contributes valuable insights into the intricate dynamics of stress, sleep disturbance, and fatigue among pilot and non-pilot college students. These findings hold implications for future research an

关键词： Fatigue Pilot Sleep disturbance Stress Well-being

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Extraction of Medication-Effect Relations in Twitter Data with Neural Embedding and Recurrent Neural Network 18

Extraction of Medication-Effect Relations in Twitter Data wi...

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18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021

作者： Jiang, Keyuan Zhang, Dingkai Bernard, Gordon R. Department of Computer Information Technology & Graphics Purdue University Northwest 2200 169th Street HammondIN46323 United States School of Information and Intelligent Engineering Ningbo City College of Vocational Technology Zhejiang Ningbo China Department of Medicine Vanderbilt University NashvilleTN United States

ISBN: (纸本)9781643682648

Recently, an active area of research in pharmacovigilance is to use social media such as Twitter as an alternative data source to gather patient-generated information pertaining to medication use. Most of thr published work focuses on identifying mentions of adverse effects in social media data but rarely investigating the relationship between a mentioned medication and any mentioned effect expressions. In this study, we treated this relation extraction task as a classification problem, and represented the Twitter text with neural embedding which was fed to a recurrent neural network classifier. The classification performance of our method was investigated in comparison with 4 baseline word embedding methods on a corpus of 9516 annotated tweets. © 2022 International Medical Informatics Association (IMIA) and IOS Press.

关键词： Recurrent neural networks

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The Ability of Pretrained Large Language Models in Understanding Health Concepts in Social Media Posts

The Ability of Pretrained Large Language Models in Understan...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Keyuan Jiang Gordon R. Bernard Department of Computer Information Technology & Graphics Purdue University Northwest Hammond Indiana U.S.A. Department of Medicine Vanderbilt University Nashville Tennessee U.S.A.

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Lay people’s terms of health concepts are widely found in social medial data. Understanding their terms can help learn what they have experienced. The existing consumer health vocabularies do not include many health concept phrases found in social media. Recent advancement of pretrained large language models has demonstrated their state-of-the-art performance in a number of natural language processing tasks. In this work, we investigated 3 commonly used pretrained large language models on their ability of understanding consumer health concepts. The results on a corpus of 156 phrases related to 3 common COVID-19 symptoms showed that these pretrained general domain language models generated encouraging outputs, indicating that they may be a promising alternative to the current consumer health vocabularies.

关键词： COVID-19 Vocabulary Accuracy Social networking (online) Large language models Biological system modeling Blogs Media Natural language processing Bioinformatics

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spaCI: deciphering spatial cellular communications through adaptive graph model

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Briefings in Bioinformatics 2023年第1期24卷 bbac563-bbac563页

作者： Tang, Ziyang Zhang, Tonglin Yang, Baijian Su, Jing Song, Qianqian Department of Computer and Information Technology Purdue University IN United States Department of Statistics Purdue University IN United States Department of Biostatistics and Health Data Science Indiana University School of Medicine IN United States Center for Cancer Genomics and Precision Oncology Wake Forest Baptist Comprehensive Cancer Center Atrium Health Wake Forest Baptist Winston Salem NC United States Department of Cancer Biology Wake Forest School of Medicine Winston Salem NC United States

Cell-cell communications are vital for biological signalling and play important roles in complex diseases. Recent advances in single-cell spatial transcriptomics (SCST) technologies allow examining the spatial cell communication landscapes and hold the promise for disentangling the complex ligand-receptor (L-R) interactions across cells. However, due to frequent dropout events and noisy signals in SCST data, it is challenging and lack of effective and tailored methods to accurately infer cellular communications. Herein, to decipher the cell-to-cell communications from SCST profiles, we propose a novel adaptive graph model with attention mechanisms named spaCI. spaCI incorporates both spatial locations and gene expression profiles of cells to identify the active L-R signalling axis across neighbouring cells. Through benchmarking with currently available methods, spaCI shows superior performance on both simulation data and real SCST datasets. Furthermore, spaCI is able to identify the upstream transcriptional factors mediating the active L-R interactions. For biological insights, we have applied spaCI to the seqFISH+ data of mouse cortex and the NanoString CosMx Spatial Molecular Imager (SMI) data of non-small cell lung cancer samples. spaCI reveals the hidden L-R interactions from the sparse seqFISH+ data, meanwhile identifies the inconspicuous L-R interactions including THBS1-ITGB1 between fibroblast and tumours in NanoString CosMx SMI data. spaCI further reveals that SMAD3 plays an important role in regulating the crosstalk between fibroblasts and tumours, which contributes to the prognosis of lung cancer patients. Collectively, spaCI addresses the challenges in interrogating SCST data for gaining insights into the underlying cellular communications, thus facilitates the discoveries of disease mechanisms, effective biomarkers and therapeutic targets. © 2022 The Author(s). Published by Oxford university Press.

关键词： adaptive graph model single-cell spatial transcriptomics spatial cell graph triplet loss

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Hybrid LoRa Network Architecture: Automatic Switching between LoRaWAN and LoRa Mesh Network in Environments with Dynamic Obstacle Variations

Hybrid LoRa Network Architecture: Automatic Switching betwee...

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International Conference on computer Communications and Networks (ICCCN)

作者： Hyungsub Kim Hayoung Kim Somi Baek Ryan Melenchuk Jaden Soroka Anthony Smith Software Engineering Sangmyung University Cheonan Republic of Korea School of Software Soongsil University Seoul Republic of Korea Computer Engineering Kyonggi University Suwon Republic of Korea Computer and Information Technology Purdue University West Lafayette IN USA

ISBN: (数字)9798350384611

ISBN: (纸本)9798350348439

The LoRaWAN network is widely employed in agricultural Internet of Things (IoT) applications requiring long-range wireless communication and low-energy consumption. However, challenges arise in dynamic environments like woodlands, where obstacles such as tree foliage disrupt the Fresnel zone and absorb signals. A previous study proposes a solution utilizing a LoRa Mesh Network (LoRa Meshnet) capable of establishing connections under tree canopies. Still, LoRa Meshnet is less battery-efficient than LoRaWAN. Thus, relying solely on LoRa Meshnet is inefficient in situations where LoRaWAN communication is affected by dynamic obstacles like seasonally varying foliage. To address this, we introduce a hybrid LoRa network architecture that utilizes LoRaWAN as the primary network and LoRa Meshnet as the backup, automatically switching between them. This approach ensures energy efficiency in sparse foliage situations by using LoRaWAN while guaranteeing stable data transmission in dense foliage scenarios with the use of LoRa Meshnet. Additionally, the system offers easy network deployment and cost-effectiveness.

关键词： Wireless communication Mesh networks Urban areas LoRaWAN Switches Network architecture Energy efficiency

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Rumi: An Intelligent Agent Enhancing Learning Management Systems Using Machine Learning Techniques

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Journal of Software Engineering and Applications 2022年第9期15卷 325-343页

作者： Behafarid Mohammad Jafari Mengyuan Zhao Ali Jafari Department of Electrical and Computer Engineering Indiana University-Purdue University Indianapolis Indianapolis USA Department of Instructional System Technology School of Education Indiana University Bloomington USA Department of Computer and Information Technology Indiana University-Purdue University Indianapolis Indianapolis USA

The dramatic improvement of information and communication technology (ICT) has made an evolution in learning management systems (LMS). The rapid growth in LMSs has caused users to demand more advanced, automated, and intelligent services. This paper discusses how Artificial Intelligence and Machine Learning techniques are adopted to fulfill users’ needs in a social learning management system named “CourseNetworking”. The paper explains how machine learning contributed to developing an intelligent agent called “Rumi” as a personal assistant in CourseNetworking platform to add personalization, gamification, and more dynamics to the system. This paper aims to introduce machine learning to traditional learning platforms and guide the developers working in LMS field to benefit from advanced technologies in learning platforms by offering customized services.

关键词： Learning Management Systems Intelligent Agents Artificial Intelligence Machine Learning

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