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Frontiers in Education (FIE) Conference

作者： Sakhi Aggrawal Henrietta Boowuo Department of Computer and Information Technology Purdue University West Lafayette Indiana

This paper presents a systematic literature review that examines the enhancement of teamwork through games. The importance of teamwork in various domains is highlighted, emphasizing its role in achieving common goals and fostering effective group efforts. However, teaching teamwork poses challenges, as educators need to address dynamic interactions and develop students' collaborative skills and abilities. Furthermore, the history of games in education is explored, tracing their roots to John Locke's educational theories and their application in facilitating engaging and effective learning experiences. The methodology section outlines the inclusion and exclusion criteria for selecting relevant studies. Peer-reviewed articles published after 2010 were included, focusing on primary research that specifically addresses teamwork. Two electronic databases, Taylor & Francis, PubMed, and IEEE Xplore, were utilized to gather resources, ensuring reliable and abundant data. The data analysis involved a summary and evaluation of published research on enhanced teamwork approaches. A systematic literature review table was constructed to synthesize and summarize the selected studies. The PRISMA model was followed for identifying, assessing eligibility, and including relevant articles in the review. The results section presents key findings from the selected studies, showcasing their study designs, research questions, target populations, domains, and learning outcomes. The studies highlight the potential of teamwork to enhance team performance in games and the effectiveness of game-based training for fostering teamwork. Additionally, computer-based learning games were found to encourage collaborative learning and help students understand the significance of teamwork in achieving desired outcomes. This systematic literature review contributes to the understanding of how games can be used as effective tools for enhancing teamwork. It provides insights into the existing research lands

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Evaluating LoRaWAN performance in intentional and unintentional DoS attacks by legacy 900MHz network devices 6

Evaluating LoRaWAN performance in intentional and unintentio...

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6th International Conference on Advanced Communication Technologies and Networking, CommNet 2023

作者： Bang, Seokhyeon Jang, Junyoung Ro, Minju Choi, Yuseon Kwon, Doyong Lee, Kangyeon Smith, Anthony Chung-Ang University School of Computer Science and Engineering Seoul Korea Republic of Chonnam National University Department of Software Engineering Gwangju Korea Republic of Kyungpook National University School of Computer Science and Engineering Daegu Korea Republic of Purdue University Department of Computer and Information Technology IN United States

ISBN: (纸本)9798350329391

The deployment of LoRaWAN on the Internet of Things (IoT) has increased since its advent and LoRaWAN now predominates the IoT market over other Low Powered Wide Area Networks (LPWAN). However, since LoRaWAN uses Chirp Spread Spectrum (CSS), it is susceptible to wideband jamming attacks. In this paper, we demonstrate with experiments and concrete numerical results that jamming US915 LoRaWAN frequency is possible by the usual data transmission and reception process of 900MHz Canopy, one of the legacy 900MHz network device. Intentional attack is possible in the same manner. The experiments emulate the real-world environment operated in medical and agriculture industries, in outdoor and indoor conditions, respectively. In addition, this paper introduces and utilizes the novel metric, Jamming Effect (JE), that indicates the network performance of wireless networks that spread the data on air. © 2023 IEEE.

关键词： Risk assessment

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How are Smart Phones Used by Students in Higher Education? Exploring the Intersection of Digital Learning Activities and Personal technology

How are Smart Phones Used by Students in Higher Education? E...

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Conference on Software Engineering Education & Training (CSEE&T)

作者： Rob Elliott Computer and Information Technology Indiana University Purdue University Indianapolis Indianapolis IN USA

Students in higher education face a de facto expectation that they will supply the personal technology required to participate in learning activities. Although traditional computing devices are most commonly used for academic work, students report significant engagement in their studies via their mobile devices. Understanding the intersection of student behavior and the technology students choose to use could allow instructors and instructional designers to develop digital learning materials that meet students’ expectations. This study examines computer-based learning activities through the lens of several theoretical frameworks to analyze and quantify the types of academic work students are most likely to – and would like to be able to – perform on their smart phones. The results show that most students perform at least some academic tasks on their smart phones but are highly selective about which tasks they choose to carry out. However, it is demonstrated that younger students and students with higher levels of financial need are more dependent on their smart phones and use them to perform a much wider range of learning activities.

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LoRaWAN Implementation for Acoustic Localization of Coyotes 23

LoRaWAN Implementation for Acoustic Localization of Coyotes

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23rd International Conference on Control, Automation and Systems, ICCAS 2023

作者： Boo, Jaehui Lim, Hyemin Kim, Hyeongjun Kim, Nayoun Anderson, Justin Qiao Chin, WeiChieh Wang, Mia Y. Smith, Anthony H. Dankook University Department of Computer Engineering Yongin-si16890 Korea Republic of Chung-Ang University Department of Computer Science and Engineering Seoul06974 Korea Republic of Daegu Catholic University Department of Computer Engineering Gyeongsan-si38430 Korea Republic of Woosong University Department of Information Technology Convergence Daejeon34606 Korea Republic of Purdue University Department of Computer and Information Technology West LafayetteIN47907 United States College of Charleston Department of Computer Science CharlestonSC29424 United States

ISBN: (纸本)9788993215274

Wild coyotes in the United States cause serious damage to pets, livestock, and even humans. Currently, there are solutions to this problem like wildlife image detection [1]. However, a benefit to detecting coyotes remotely with sound is prevention: users can detect coyotes before they attack. This project aims to detect, classify, and locate wild coyotes with acoustic sensors within a certain area, to avoid damage from coyotes in a cost-effective manner. The acoustic sensors were deployed on a Long Range Wide Area Network (LoRaWAN) to cover a large area. Acoustic localization was utilized to locate detected coyotes. After classification and localization, the results are visualized on a map by the Unity engine, which is intuitive and user-friendly. This solution utilizes a machine learning model to differentiate coyote sounds from other sounds, which has been illustrated in other publications. The goal of this research is to create a simple LoRaWAN network architecture with low-cost sensors that help people protect their land from coyotes. The solution is cost-effective, effective over a large area, and energy efficient. © 2023 ICROS.

关键词： Engines

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AHP-CM: Attentional Homogeneous-Padded Composite Model for Respiratory Anomalies Prediction

AHP-CM: Attentional Homogeneous-Padded Composite Model for R...

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IEEE International Conference on Healthcare Informatics (ICHI)

作者： Md Motiur Rahman Miad Faezipour Smriti Bhatt Sudip Vhaduri School of Engineering Technology Electrical & Computer Engineering Technology Purdue University West Lafayette IN USA Department of Computer and Information Technology Purdue University West Lafayette IN USA

Early diagnosis, treatment and regular monitoring can limit the spread and adverse effects of respiratory diseases. Shortage of trained physicians is one of the main obstacles to ensure early diagnosis and treatment which can be overcome by making lung auscultations automated. To automate lung auscultations and to identify anomalies like crackles, wheezes and/or both, in this work, we propose a hybrid deep learning model combining a Convolutional Neural Network (CNN) model, ResNet34 as a feature extractor, and a Long Short-Term Memory (LSTM) as a predictor, along with a novel augmentation technique called homogeneous padding over the ICBHI-2017 dataset. We have also added an attention layer in the feature extractor to allow the model learn the important region of the feature vector. The proposed model has outperformed the recent state-of-the-art models in this regard. We have also found that the inclusion of the attention layer, and the LSTM as a predictor has improved the performance of our model in 2-class and 4-class anomaly predictions.

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Abnormality Detection in Lung Sounds Using Feature Augmentation

Abnormality Detection in Lung Sounds Using Feature Augmentat...

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computer Science, computer Engineering, & Applied Computing (CSCE), Congress in

作者： Shiva Shokouhmand Md Motiur Rahman Miad Faezipour Smriti Bhatt School of Engineering Technology Electrical & Computer Engineering Technology Purdue University West Lafayette IN USA Department of Computer and Information Technology Purdue University West Lafayette IN USA

Assessing the lung sounds reveals important information about the lungs and the existence or severity of possible underlying respiratory related conditions. This paper presents a research-work-in-progress exploring a hybrid signal processing and machine learning-based approach for effectively analyzing the lung sounds. The objective of this study is to achieve high accuracy in detecting adventitious sounds for pulmonary diseases non-invasively. To this end, we are augmenting the feature space and introduce a feature-based model. For the analysis, we are utilizing the ICBHI dataset, which comprises lung sounds collected from 126 patients. The dataset includes various abnormalities, such as wheezing and crackling sounds, providing us with valuable data to train and evaluate the proposed model.

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Benchmarking the Robustness of UAV Tracking Against Common Corruptions

arXiv

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arXiv 2024年

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

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 18 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. Copyright © 2024, The Authors. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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FileDES: A Secure, Scalable and Succinct Decentralized Encrypted Storage Network

FileDES: A Secure, Scalable and Succinct Decentralized Encry...

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IEEE Annual Joint Conference: INFOCOM, IEEE computer and Communications Societies

作者： Minghui Xu Jiahao Zhang Hechuan Guo Xiuzhen Cheng Dongxiao Yu Qin Hu Yijun Li Yipu Wu School of Computer Science and Technology Shandong University ETH Zurich Department of Computer and Information Science Indiana University-Purdue University Indianapolis BaishanCloud

ISBN: (数字)9798350383508

ISBN: (纸本)9798350383515

Decentralized Storage Network (DSN) is an emerging technology that challenges traditional cloud-based storage systems by consolidating storage capacities from independent providers and coordinating to provide decentralized storage and retrieval services. However, current DSNs face several challenges associated with data privacy and efficiency of the proof systems. To address these issues, we propose FileDES ( Decentralized Encrypted Storage), which incorporates three essential elements: privacy preservation, scalable storage proof, and batch verification. FileDES provides encrypted data storage while maintaining data availability, with a scalable Proof of Encrypted Storage (PoES) algorithm that is resilient to Sybil and Generation attacks. Additionally, we introduce a rollup-based batch verification approach to simultaneously verify multiple files using publicly verifiable succinct proofs. We conducted a comparative evaluation on FileDES, Filecoin, Storj and Sia under various conditions, including a WAN composed of up to 120 geographically dispersed nodes. Our protocol outperforms the others in terms of proof generation/verification efficiency, storage costs, and scalability.

关键词： Wide area networks Privacy Data privacy Protocols Costs Scalability Memory

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Efficient Image Super-Resolution via Symmetric Visual Attention Network

Efficient Image Super-Resolution via Symmetric Visual Attent...

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International Joint Conference on Neural Networks (IJCNN)

作者： Qinrui Fan Chengxu Wu Shu Hu Xi Wu Xin Wang Jing Hu School of Computer Chengdu University of Information Technology Chengdu China Department of Computer Information and Graphics Technology Indiana University–Purdue University Indianapolis IN USA School of Public Health University at Albany SUNY USA

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In recent years, efficient super-resolution research has focused on reducing model complexity and improving efficiency by leveraging deep small-kernel convolution, but it has the problem of a small receptive field, which leads to a limited ability of the network to reconstruct details. Large kernel convolution can provide a large receptive field and lead to a substantial enhancement in the quality of image reconstruction, but its computational cost is too high. To minimize the model’s parameter count and achieve efficient super-resolution reconstruction, this study introduces a symmetric visual attention network. The network decomposes the large kernel convolution into three different lightweight and efficient convolutions. It then forms a bottleneck structure by leveraging the varied receptive field sizes of these convolutions in combination. The attention mechanism is integrated to create a bottleneck attention module, enhancing the network’s feature awareness. Furthermore, the bottleneck attention modules are symmetrically arranged to construct a symmetric large kernel attention block, thereby further enhancing the network’s capability to extract deep features. The experimental results demonstrate that the proposed model achieves competitive quantitative metrics when compared to other lightweight super-resolution methods, and the details of the reconstructed images are enhanced. With only 183K parameters, the model achieves a lightweight yet high-quality super-resolution model, offering a novel solution approach for efficient super-resolution.

关键词： Training Measurement Visualization Convolution Computational modeling Superresolution Neural networks

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Learning from Demonstration Framework for Multi-Robot Systems Using Interaction Keypoints and Soft Actor-Critic Methods

arXiv

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arXiv 2024年

作者： Venkatesh, Vishnunandan L.N. Min, Byung-Cheol SMART Lab Department of Computer and Information Technology Purdue University West LafayetteIN47907 United States

Learning from Demonstration (LfD) is a promising approach to enable Multi-Robot Systems (MRS) to acquire complex skills and behaviors. However, the intricate interactions and coordination challenges in MRS pose significant hurdles for effective LfD. In this paper, we present a novel LfD framework specifically designed for MRS, which leverages visual demonstrations to capture and learn from robot-robot and robot-object interactions. Our framework introduces the concept of Interaction Keypoints (IKs) to transform the visual demonstrations into a representation that facilitates the inference of various skills necessary for the task. The robots then execute the task using sensorimotor actions and reinforcement learning (RL) policies when required. A key feature of our approach is the ability to handle unseen contact-based skills that emerge during the demonstration. In such cases, RL is employed to learn the skill using a classifier-based reward function, eliminating the need for manual reward engineering and ensuring adaptability to environmental changes. We evaluate our framework across a range of mobile robot tasks, covering both behavior-based and contact-based domains. The results demonstrate the effectiveness of our approach in enabling robots to learn complex multi-robot tasks and behaviors from visual demonstrations. © 2024, CC BY.

关键词： Multipurpose robots

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