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Events and findings of the purdue university Calumet computer information technology & Graphics (CIT & G) department teaching partnership with the College of Software Engineering of Southeast university in Nanjing China 09

Events and findings of the Purdue University Calumet Compute...

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10th ACM Special Interest Group for information technology Education, SIGITE 2009

作者： Winer, Charles Wu, Hanqian Department of Computer Information Technology and Graphics Purdue University Calumet Hammond IN United States College of Software Engineering Southeast University Nanjing China

ISBN: (纸本)9781605587653

In this poster session we are reporting on the results of two, three-week summer graduate teaching experiences that took place in Nanjing, China over a two-year period. A faculty exchange program was entered into between Southeast university of Nanjing China and purdue university Calumet of Hammond, Indiana, USA. One of the goals of the exchange program was to expose Chinese students to the instructional methods employed by United States Universities. By understanding the cultural differences and utilizing various teaching methodologies employed by American teachers, the faculty and students involved in these three-week classroom intensive training courses were able to adapt and successfully complete the graduate level material that was presented.

关键词： Teaching

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DEXO: A Secure and Fair Exchange Mechanism for Decentralized IoT Data Markets

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IEEE Internet of Things Journal 2025年第11期12卷 16095-16111页

作者： Li, Yue Alom, Ifteher Sun, Wenhai Xiao, Yang University of Kentucky Department of Computer Science LexingtonKY United States Purdue University Department of Computer and Information Technology West LafayetteIN United States

Opening up data produced by the Internet of Things (IoT) and mobile devices for public utilization can maximize their economic value. Challenges remain in the trustworthiness of the data sources and the security of the trading process, particularly when there is no trust between the data providers and consumers. In this paper, we propose DEXO, a decentralized data exchange mechanism that facilitates secure and fair data exchange between data consumers and distributed IoT/mobile data providers at scale, allowing the consumer to verify the data generation process and the providers to be compensated for providing authentic data, with correctness guarantees from the exchange platform. To realize this, DEXO extends the decentralized oracle network model that has been successful in the blockchain applications domain to incorporate novel hardware-cryptographic co-design that harmonizes trusted execution environment, secret sharing, and smart contract-assisted fair exchange. For the first time, DEXO ensures end-to-end data confidentiality, source verifiability, and fairness of the exchange process with strong resilience against participant collusion. We implemented a prototype of the DEXO system to demonstrate feasibility. The evaluation shows a moderate deployment cost and significantly improved blockchain operation efficiency compared to a popular data exchange mechanism. © 2025 IEEE.

关键词： Internet of Things Blockchains Smart contracts Soft sensors Security Servers Resilience Decentralized applications Data models Oceans

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Hire Me or Not? Examining Language Model's Behavior with Occupation Attributes 31

Hire Me or Not? Examining Language Model's Behavior with Occ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Zhang, Damin Zhang, Yi Bihani, Geetanjali Rayz, Julia Department of Computer and Information Technology Purdue University West Lafayette United States

ISBN: (纸本)9798891761964

With the impressive performance in various downstream tasks, large language models (LLMs) have been widely integrated into production pipelines, such as recruitment and recommendation systems. A known issue of models trained on natural language data is the presence of human biases, which can impact the fairness of the system. This paper investigates LLMs' behavior with respect to gender stereotypes in the context of occupation decision making. Our framework is designed to investigate and quantify the presence of gender stereotypes in LLMs' behavior via multi-round question answering. Inspired by prior work, we constructed a dataset using a standard occupation classification knowledge base released by authoritative agencies. We tested it on three families of LMs (RoBERTa, GPT, and Llama) and found that all models exhibit gender stereotypes analogous to human biases, but with different preferences. The distinct preferences of GPT-3.5-turbo and Llama2-70b-chat, along with additional analysis indicating GPT-4o-mini favors female subjects, may imply that the current alignment methods are insufficient for debiasing and could introduce new biases contradicting the traditional gender stereotypes. Our contribution includes a 73,500 prompts dataset constructed with a taxonomy of real-world occupations and a multi-step verification framework to evaluate model's behavior regarding gender stereotype. © 2025 Association for Computational Linguistics.

关键词： Computational linguistics

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Assessing Personalized AI Mentoring with Large Language Models in the Computing Field

Assessing Personalized AI Mentoring with Large Language Mode...

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2025 IEEE Symposium on Computational Intelligence in Natural Language Processing and Social Media, CI-NLPSoMe Companion 2025

作者： Luo, Xiao O'Connell, Sean Mithun, Shamima Oklahoma State University Department of Management Science and Information Systems United States Purdue University Department of Computer Information Technology Indianapolis United States

ISBN: (纸本)9798331519742

This paper provides an in-depth evaluation of three state-of-the-art Large Language Models (LLMs) for personalized career mentoring in the computing field, using three distinct student profiles that consider gender, race, and professional levels. We evaluated the performance of GPT-4, LLaMA 3, and Palm 2 using a zero-shot learning approach without human intervention. A quantitative evaluation was conducted through a custom natural language processing analytics pipeline to highlight the uniqueness of the responses and to identify words reflecting each student's profile, including race, gender, or professional level. The analysis of frequently used words in the responses indicates that GPT-4 offers more personalized mentoring compared to the other two LLMs. Additionally, a qualitative evaluation was performed to see if human experts reached similar conclusions. The analysis of survey responses shows that GPT-4 outperformed the other two LLMs in delivering more accurate and useful mentoring while addressing specific challenges with encouragement languages. Our work establishes a foundation for developing personalized mentoring tools based on LLMs, incorporating human mentors in the process to deliver a more impactful and tailored mentoring experience. © 2025 IEEE.

关键词： Natural language processing systems

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Audio-Based Classification of Insect Species Using Machine Learning Models: Cicada, Beetle, Termite, and Cricket

arXiv

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arXiv 2025年

作者： Shetty, Manas V. Kumar, Yoga Disha Sendhil Computer and Information Technology Department Purdue University IN47907 United States

This project addresses the challenge of classifying insect species—Cicada, Beetle, Termite, and Cricket—using sound recordings. Accurate species identification is crucial for ecological monitoring and pest management. We employ machine learning models such as XGBoost, Random Forest, and K-Nearest Neighbors (KNN) to analyze audio features, including Mel Frequency Cepstral Coefficients (MFCC). The potential novelty of this work lies in the combination of diverse audio features and machine learning models to tackle insect classification, specifically focusing on capturing subtle acoustic variations between species that have not been fully leveraged in previous research. The dataset is compiled from various open sources, and we anticipate achieving high classification accuracy, contributing to improved automated insect detection systems. © 2025, CC BY.

关键词： Adversarial machine learning

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FocalSR: Revisiting image super-resolution transformers with fourier-transform cross attention layers for remote sensing image enhancement

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Geomatica 2025年第1期77卷

作者： Ou, Botong Shao, Gang Yang, Baijian Fei, Songlin Computer and Information Technology Purdue University West LafayetteIN47907 United States Libraries and School of Information Studies Purdue University West LafayetteIN47907 United States Forestry and Natural Resources Purdue University West LafayetteIN47907 United States

Transformer architecture has attained noteworthy performance achievements in recent image super-resolution research. However, current transformer-based methods still expose limitations in fully harnessing domain-specific information within images, particularly when applied to broader-scale remote sensing images that contain diverse landscape objects on one scene. Remote sensing images have relatively lower resolution compared to the common super-resolution training dataset and each landscape object covers a small area on the image. These natures of remote sensing images significantly reduced the attention pixels for image restoration in existing transformer-based methods. To address this challenge and enhance domain-specific multi-object image reconstruction, we introduce FocalSR, a Transformer model featuring FOurier-transform Cross Attention Layers for Super-Resolution. Drawing inspiration from state-of-the-art Transformer models like Hybrid Attention Transformer (HAT), FocalSR incorporates channel-focused and window-centric self-attention mechanisms. By integrating Fast Fourier Convolution into the cross-attention layer, FocalSR extends its capacity to capture image-wide information and intricate details in low-resolution images. Through unified task pretraining during model development, we validate the efficacy of these enhancements through extensive testing, resulting in substantial performance improvements. Notably, our experiments showcase FocalSR's superior performance in remote sensing datasets, demonstrating a notable 1 dB enhancement in the PSNR metric compared to other state-of-the-art methods. Additionally, significant improvements are observed in challenging scenarios such as pattern restoration and vegetation detail preservation, underscoring the transformative potential of FocalSR in advancing image processing and domain-specific vision tasks. © 2024 The Authors

关键词： Restoration

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Multi-Agent LLM Actor-Critic Framework for Social Robot Navigation

arXiv

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arXiv 2025年

作者： Wang, Weizheng Obi, Ike Min, Byung-Cheol SMART Laboratory Department of Computer and Information Technology Purdue University West LafayetteIN United States

Recent advances in robotics and large language models (LLMs) have sparked growing interest in human-robot collaboration and embodied intelligence. To enable the broader deployment of robots in human-populated environments, socially-aware robot navigation (SAN) has become a key research area. While deep reinforcement learning approaches that integrate human-robot interaction (HRI) with path planning have demonstrated strong benchmark performance, they often struggle to adapt to new scenarios and environments. LLMs offer a promising avenue for zero-shot navigation through commonsense inference. However, most existing LLM-based frameworks rely on centralized decision-making, lack robust verification mechanisms, and face inconsistencies in translating macro-actions into precise low-level control signals. To address these challenges, we propose SAMALM, a decentralized multi-agent LLM actor-critic framework for multirobot social navigation. In this framework, a set of parallel LLM actors, each reflecting distinct robot personalities or configurations, directly generate control signals. These actions undergo a two-tier verification process via a global critic that evaluates group-level behaviors and individual critics that assess each robot’s context. An entropy-based score fusion mechanism further enhances self-verification and re-query, improving both robustness and coordination. Experimental results confirm that SAMALM effectively balances local autonomy with global oversight, yielding socially compliant behaviors and strong adaptability across diverse multi-robot scenarios. More details and videos about this work are available at: https://sites. ***/view/SAMALM. Copyright © 2025, The Authors. All rights reserved.

关键词： Decision making

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Assessing Personalized AI Mentoring with Large Language Models in the Computing Field

Assessing Personalized AI Mentoring with Large Language Mode...

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Computational Intelligence in Natural Language Processing and Social Media (CI-NLPSoMe Companion), IEEE Symposium on

作者： Xiao Luo Sean O'Connell Shamima Mithun Department of Management Science and Information Systems Oklahoma State University USA Department of Computer Information Technology Purdue University Indianapolis USA

ISBN: (数字)9798331519742

ISBN: (纸本)9798331519759

关键词： Surveys Accuracy Social networking (online) Engineering profession Large language models Zero shot learning Pipelines Natural language processing Mentoring Computational intelligence

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A Systematic Review of Generative AI on Game Character Creation: Applications, Challenges, and Future Trends

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IEEE Transactions on Games 2025年

作者： Wu, Zhenhua Chen, Zhuohao Zhu, Di Mousas, Christos Kao, Dominic Purdue University Department of Computer Information Technology West LafayetteIN United States Purdue University Department of Computer Graphics Technology West LafayetteIN United States

In this paper, we review the impact of generative artificial intelligence (AI) on game character creation. We critically examine the application of AI-driven computer graphics (AICG) technology across the character creation workflow, including concept generation, clothing design, modeling, props, cultural embedding, personality traits, and behaviors. Our research identifies potential applications, challenges, and future trends of these technologies in game development. Furthermore, it explores how AI and large language models (LLMs) can streamline workflows, automate asset generation, and reduce technical barriers in character creation. In this systematic review, we provide valuable insights for AI developers, game designers, and researchers. © 2018 IEEE.

关键词： Game design

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LuxKnit: Fabricating Interactive Display Textiles Integrated with Sensing by Machine Knitting 25

LuxKnit: Fabricating Interactive Display Textiles Integrated...

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Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems

作者： Tongyan Wang Mohan Chi Yue Yu Kedi Yan Mo Li Yiyue Luo Rua Mae Williams Purdue University West Lafayette Indiana USA Computer and Information Technology Purdue University West Lafayette Indiana USA University of Washington Seattle Washington USA ECE University of Washington Seattle Washington USA Computer Graphics and Technology Purdue University West Lafayette Indiana USA

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