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检索条件"机构=Purdue University: Computer and Information Technology"

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Distributed Agent-Based Collaborative Learning in Cross-Individual Wearable Sensor-Based Human Activity Recognition

Distributed Agent-Based Collaborative Learning in Cross-Indi...

引用

IEEE International Conference on Robotic Computing (IRC)

作者： Ahmad Esmaeili Zahra Ghorrati Eric T. Matson Computer and Information Technology Purdue University West Lafayette IN USA

The rapid growth of wearable sensor technologies holds substantial promise for the field of personalized and context-aware Human Activity Recognition. Given the inherently decentralized nature of data sources within this domain, the utilization of multi-agent systems with their inherent decentralization capabilities presents an opportunity to facilitate the development of scalable, adaptable, and privacy-conscious methodologies. This paper introduces a collaborative distributed learning approach rooted in multi-agent principles, wherein individual users of sensor-equipped devices function as agents within a distributed network, collectively contributing to the comprehensive process of learning and classifying human ac-tivities. In this proposed methodology, not only is the privacy of activity monitoring data upheld for each individual, eliminating the need for an external server to oversee the learning process, but the system also exhibits the potential to surmount the limitations of conventional centralized models and adapt to the unique attributes of each user. The proposed approach has been empirically tested on two publicly accessible human activity recognition datasets, specifically PAMAP2 and HARTH, across varying settings. The provided empirical results conclusively highlight the efficacy of inter-individual collaborative learning when contrasted with centralized configurations, both in terms of local and gdobal generalization.

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Stealthy Adversarial Attacks Against Automated Modulation Classification in Cognitive Radio

Stealthy Adversarial Attacks Against Automated Modulation Cl...

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IEEE Cognitive Communications for Aerospace Applications Workshop (CCAAW)

作者： Praveen Fernando Jin Wei-Kocsis Department of Computer and Information Technology Purdue University West Lafayette IN

In cognitive radio systems, wireless spectrum sensing plays a crucial role in identifying the state of the wireless environment, which leads to the effective utilization of scarce spectral resources for various application fields. As a critical part of wireless spectrum sensing, automatic modulation classification (AMC) is used to identify the modulation types of the received signals automatically. With the advances in sensing and computing technologies, artificial intelligence (AI), especially deep learning (DL), has been widely applied to enhance the effectiveness and timely response of AMC. However, in recent years, increasing evidence shows that carefully-crafted adversarial noise can mislead learning models, which raises concerns about the trustworthiness of DL solutions in AMC. Therefore, it is crucial to sufficiently mitigate the adversarial perturbations in DL-powered AMC. To realize a successful mitigation strategy, it can be beneficial to first adopt an adversarial mindset and formulate threat models of practical perturbations in DL-powered AMC. However, there is still limited work focusing on generating practical adversarial attacks in DL-powered AMC that has complex domain knowledge. In this paper, we propose an innovative and stealthy adversarial attack method that can practically compromise the DL-based decision-making of AMCs. In the performance evaluation section, different scenarios will be considered to illustrate the effectiveness of our proposed adversarial attack method in achieving a high success rate and sufficient stealthiness in AMC.

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Analog Dopplegangers: Twinning with Deep Continuous-Time Recurrent Neural Networks

Analog Dopplegangers: Twinning with Deep Continuous-Time Rec...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： John C. Gallagher Eric T. Matson Electrical and Computer Enginnering University of Cincinnati Cincinnati OH USA Computer and Information Technology Purdue University West Lafayette IN

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

Digital computers are built of analog circuit components that "twin" conceptual digital entities such as switches or logic gates. Employing such "analog doppelgangers" to twin conceptual discrete components modulo thresholding and clocking constraints was a necessary step in implementing modern digital computer. These digital computers have gone on to be so successful that in many circles the word "digital" has almost become synonymous with "computational". We may see some of this effect even the term "digital twin". This paper speculatively addresses the idea that the twin need not be digital in the strictest sense so long as it actually models, indistinguishably, the behavior of some other system. We will draw from previous work using fully-recurrent continuous time and continuous valued neural networks to twin optimal controllers for a simplified legged-locomotion agent. We will then argue that the use of "analog doppelgangers" to twin systems directly has benefits and utility that strictly digital twins may not possess in equal measure. Finally, we will examine and discuss what types of practical problems might be positively impacted by this alternative view.

关键词： Digital computers Recurrent neural networks Computational modeling Switches Logic gates Analog circuits Digital twins

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Benchmarking the Robustness of UAV Tracking Against Common Corruptions

Benchmarking the Robustness of UAV Tracking Against Common C...

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IEEE Conference on Multimedia information Processing and Retrieval (MIPR)

作者： Xiaoqiong Liu Yunhe Feng Shu Hu Xiaohui Yuan Heng Fan Department of Computer Science and Engineering University of North Texas USA Department of Computer and Information Technology Purdue University USA

ISBN: (数字)9798350351422

ISBN: (纸本)9798350351439

The robustness of unmanned aerial vehicle (UAV) tracking is crucial in many tasks like surveillance and robotics. Despite its importance, little attention is paid to the performance of UAV trackers under common corruptions due to lack of a dedicated platform. Addressing this, we propose UAV-C, a large-scale benchmark for assessing robustness of UAV trackers under common corruptions. Specifically, UAV-C is built upon two popular UAV datasets by introducing 17 common corruptions from 4 representative categories including adversarial, sensor, blur, and composite corruptions in different levels. Finally, UAV-C contains more than 10K sequences. To understand the robustness of existing UAV trackers against corruptions, we extensively evaluate 12 representative algorithms on UAV-C. Our study reveals several key findings: 1) Current trackers are vulnerable to corruptions, indicating more attention needed in enhancing the robustness of UAV trackers; 2) When accompanying together, composite corruptions result in more severe degradation to trackers; and 3) While each tracker has its unique performance profile, some trackers may be more sensitive to specific corruptions. By releasing UAV-C, we hope it, along with comprehensive analysis, serves as a valuable resource for advancing the robustness of UAV tracking against corruption. Our UAV-C will be available at https://***/Xiaoqiong-Liu/UAV-C.

关键词： Resistance Visualization Surveillance Benchmark testing Autonomous aerial vehicles Transformers Robot sensing systems Market research Robustness Standards

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Transformative Pedagogy as a Reflective Approach for Promoting Intercultural Self-Awareness in the Context of Teamwork

Transformative Pedagogy as a Reflective Approach for Promoti...

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Frontiers in Education (FIE) Conference

作者： Irene Hensista Shreya Guddeti Devang A. Patel Sakhi Aggrawal Gaurav Nanda Alejandra J. Magana Computer and Information Technology Purdue University West Lafayette USA School of Engineering Technology Purdue University West Lafayette USA Computer and Information Technology School of Engineering Education Purdue University West Lafayette USA

This study implemented transformative pedagogy as a reflective approach to promote intercultural self-awareness and its potential consequences in the context of teamwork. The context was a second-year systems analysis and design course with 118 students in the fall 2021 semester and 155 students in the spring 2022 semester. The research question was: What are students' beliefs regarding their own cultural values and the potential implications of those values on their teamwork interactions? Findings from the study indicate that students realized that team dynamics and values are crucial to team experience. We found that students believed that commitment to the team and communication of values contributed to the experience of teamwork and teamwork success. Students also believed that coming together and making decisions together in a collectivistic manner would help the progress of the team.

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Friend or Foe? AI and the Evolving Landscape of Ransomware-as-a-Service (RaaS)

Friend or Foe? AI and the Evolving Landscape of Ransomware-a...

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Cyber Awareness and Research Symposium (CARS)

作者： Frank Offei Gyimah Ernest Ofori-Mensah Henrietta Boowuo Sakhi Aggrawal Interdisciplinary Information Security Purdue University West Lafayette U.S.A. Computer and Information Technology Purdue University West Lafayette U.S.A.

ISBN: (数字)9798350386417

ISBN: (纸本)9798350386424

This paper examines Ransomware-as-a-Service (RaaS) and its impact on cybercrime. RaaS has made sophisticated attacks accessible to a wider range of criminals, increasing the number of ransomware attacks. The paper explores how Artificial Intelligence (AI) is being used by both attackers and defenders in this evolving landscape. AI empowers RaaS attackers by improving target selection, vulnerability identification, and social engineering tactics. It also automates attack processes, making them more efficient. For defenders, AI offers potential in threat detection, vulnerability assessment, and incident response through real-time data analysis. However, challenges like model development complexity, false positives, and the need for explainable AI models exist for both sides. The paper concludes that AI use by both attackers and defenders creates an "AI arms race" in cybersecurity. It further aim(s) to illuminate future cybersecurity strategies and equip defenders with proactive measures against evolving cyber threats.

关键词： Data analysis Explainable AI Weapons Fasteners Threat assessment Real-time systems Ransomware Artificial intelligence computer crime Research and development

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Empowering Shared Mobility Vehicle Riders, Stopping Scams: A Cyber Kill Chain and Awareness Approach to QRishing on College Campuses

Empowering Shared Mobility Vehicle Riders, Stopping Scams: A...

引用

Cyber Awareness and Research Symposium (CARS)

ISBN: (数字)9798350386417

ISBN: (纸本)9798350386424

QRishing, a phishing technique utilizing QR codes, presents a burgeoning threat in the digital age. This research explores the susceptibility of shared micromobility vehicles on US college campuses to QRishing attacks. The study uses a large Midwestern university as a case study to analyze how e-bikes/e-scooters can be exploited by malicious QR code placement. The study investigates common QRishing tactics and their potential impact on college students, staff and communities. It discusses the technical aspects of a simulated QRishing attack, highlighting the involvement of Artificial Intelligence (AI). Furthermore, the paper proposes countermeasures for e-bike rental programs and best practices for students to avoid falling victim. Emphasizing the importance of collaboration between universities, rental companies, and law enforcement, the paper aims to mitigate QRishing risks and safeguard the college community. The findings underscore the need for comprehensive cybersecurity awareness programs, secure QR code generation, and enhanced app security to protect users from evolving cyber threats.

关键词： Law enforcement Phishing Collaboration QR codes Companies information age Automobiles Artificial intelligence Best practices

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Dog Bowel Pad Training IoT by Automatic Snack Feeder and Owner's Voice Compliment Speaker for Human Convenience 14

Dog Bowel Pad Training IoT by Automatic Snack Feeder and Own...

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14th International Conference on information and Communication technology Convergence, ICTC 2023

作者： Kim, Subin Jeong, Yujin Smith, Anthony Kim, Eunsoo Song, Ahyeon Lee, Choeun Soroka, Jaden Chungnam National University The Division of Computer Convergence Yuseong-gu Daejeon Korea Republic of Purdue University Computer and Information Technology West LafayetteIN United States Chungnam National University School of Business Yuseong-gu Daejeon Korea Republic of

ISBN: (纸本)9798350313277

Dog bowel pad training is a common activity for indoor dog owners. Dog owners have been training bowel pads through reward learning to dogs directly. Dog owners are impossible to observe dogs 24 hours a day and difficult to conduct identical bowel pad training each time. This paper aims to create an IoT (Internet of Things) system that helps dog bowel pad training proceed equally each time without time constraints. It includes a system for detecting dog bowel movements on a pad, an automatic snack feeder system, an owner's compliment voice playback speaker system, and a product execution data collection website. The use of sensors to verify the accuracy of checking a dog bowel movement is explained by experiments on the dog's defecation temperature information and weight information. For future works, actual dog testing is expected to provide better accuracy. © 2023 IEEE.

关键词： Bowel Pad Training IoT Load Cell Reward Learning Thermal Camera

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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

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.

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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UPPLIED: UAV Path Planning for Inspection Through Demonstration

UPPLIED: UAV Path Planning for Inspection Through Demonstrat...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Shyam Sundar Kannan Vishnunandan L. N. Venkatesh Revanth Krishna Senthilkumaran Byung-Cheol Min Department of Computer and Information Technology SMART Lab Purdue University West Lafayette IN USA Department of Computer and Information Technology SMART Lab Purdue University School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

In this paper, a new demonstration-based path-planning framework for the visual inspection of large structures using UAVs is proposed. We introduce UPPLIED: UAV Path PLanning for InspEction through Demonstration, which utilizes a demonstrated trajectory to generate a new trajectory to inspect other structures of the same kind. The demonstrated trajectory can inspect specific regions of the structure and the new trajectory generated by UPPLIED inspects similar regions in the other structure. The proposed method generates inspection points from the demonstrated trajectory and uses standardization to translate those inspection points to inspect the new structure. Finally, the position of these inspection points is optimized to refine their view. Numerous experiments were conducted with various structures and the proposed framework was able to generate inspection trajectories of various kinds for different structures based on the demonstration. The trajectories generated match with the demonstrated trajectory in geometry and at the same time inspect the regions inspected by the demonstration trajectory with minimum deviation. The experimental video of the work can be found at https://***/YqPx-cLkv04.

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