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arXiv 2021年

作者： Park, Gilchan Taylor, Julia M. Department of Computer and Information Technology Purdue University West LafayetteIN47907 United States

This paper reports on an experiment into text-based phishing detection using readily available resources and without the use of semantics. The developed algorithm is a modified version of previously published work that works with the same tools. The results obtained in recognizing phishing emails are considerably better than the previously reported work;but the rate of text falsely identified as phishing is slightly worse. It is expected that adding semantic component will reduce the false positive rate while preserving the detection accuracy. © 2021, CC BY.

关键词： computer crime

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A Community Detection and Graph Neural Network Based Link Prediction Approach for Scientific Literature

arXiv

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arXiv 2024年

作者： Liu, Chunjiang Han, Yikun Xu, Haiyun Yang, Shihan Wang, Kaidi Su, Yongye Chengdu Library and Information Center Chinese Academy of Sciences Chengdu610299 China Department of Statistics University of Michigan Michigan48109 United States School of Business Shandong University of Technology Zibo255000 China Faculty of Management and Economics Kunming University of Science and Technology Kunming650031 China School of Business Macau University of Science and Technology 999078 China Department of Computer Science Purdue University IN47907 United States

This study introduces an innovative approach that integrates community detection algorithms with Graph Neural Network (GNN) models to enhance link prediction in scientific literature networks. We specifically focus on the utilization of the Louvain community detection algorithm to uncover latent community structures within these networks, which are then incorporated into GNN architectures to predict potential links. Our methodology demonstrates the importance of understanding community dynamics in complex networks and leverages the strengths of both community detection and GNNs to improve predictive accuracy. Through extensive experiments on bipartite graphs representing scientific collaborations and citations, our approach not only highlights the synergy between community detection and GNNs but also addresses some of the prevalent challenges in link prediction, such as scalability and resolution limits. The results suggest that incorporating community-level information can significantly enhance the performance of GNNs in link prediction tasks. This work contributes to the evolving field of network science by offering a novel perspective on integrating advanced machine learning techniques with traditional network analysis methods to better understand and predict the intricate patterns of scientific collaborations. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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HCoop: A Cooperative and Hybrid Resource Scheduling for Heterogeneous Jobs in Clouds

HCoop: A Cooperative and Hybrid Resource Scheduling for Hete...

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IEEE International Conference on Cloud Computing technology and Science (CloudCom)

作者： Jinwei Liu Rui Gong Wei Dai Wei Zheng Ying Mao Wei Zhou Feng Deng Department of Computer and Information Sciences Florida A&M University Tallahassee FL USA Department of Informatics and Mathematics Mercer University Macon GA USA Department of Computer Science Purdue University Northwest Hammond IN USA Department of Civil and Environmental Engineering Jackson State University Jackson MS USA Department of Computer and Information Science Fordham University Bronx NY USA Center For Water Resources Florida A&M University Tallahassee FL USA School of Automation Beijing Information Science & Technology University Beijing China

Heterogeneous workloads composed of long batch jobs and short term jobs are increasingly common in modern datacenters, which poses a challenge to resource scheduling for improving throughput and resource utilization. Achieving high resource utilization and throughput is important to the cloud provider for achieving high revenue. To address this challenge, we propose HCoop, a cooperative and hybrid resource scheduling for high resource utilization and throughput in clouds. HCoop first uses the machine learning algorithm to classify jobs into two categories (long jobs and short jobs) based on the extracted features. Then, it allocates the regular virtual machines (VMs) to tasks of long jobs with higher SLO (Service Level Objective) availability guarantee, and selectively allocates the spot instances to tasks of short jobs for improving the overall resource utilization while ensuring the SLO availability for short jobs. Also, HCoop leverages the complementary of tasks’ requirements on different resource types and the heterogeneity of jobs/tasks in job/task size, and it packs complementary tasks whose demands on multiple resource types are complementary to each other and allocates them to a VM to further increase the resource utilization. Extensive experimental results based on a real cluster and Amazon EC2 cloud service show that HCoop achieves high resource utilization and throughput compared to existing strategies.

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Characterizing students' design strategies during simulation-based engineering of sustainable buildings

Characterizing students' design strategies during simulation...

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2020 ASEE Virtual Annual Conference, ASEE 2020

作者： Karabiyik, Tugba Magana, Alejandra J. Parsons, Paul Seah, Ying Ying Purdue Polytechnic Institute Purdue University Main Campus College of Engineering West Lafayette United States Department of Computer and Information Technology and an Affiliated Faculty School of Engineering Education Purdue University Main Campus College of Engineering West Lafayette United States Polytechnic Institute Purdue University United States Department of Computer Information Technology Purdue University United States

This is a research paper. Investigating the use of design strategies is one of the most important approaches in understanding the design thinking processes of students in engineering design. Understanding students' use of design strategies-specifically, generating ideas, conducting experiments, revising and iterating, and troubleshooting-allows educators to better help students improve in their design thinking processes. In this study, we aimed to investigate and characterize students' use of four design strategies while they were designing with an educational CAD tool. Using students' captured videos and think-aloud transcripts, we have identified and characterized patterns of students' four design strategies. In this study, we have classified students into five categories based on their design patterns and descriptively identified the least and most prominent patterns students implemented. © American Society for Engineering Education 2020.

关键词： Students

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Scheduling and Pricing Non-Preemptive Electric Loads: A Convex Approach

Scheduling and Pricing Non-Preemptive Electric Loads: A Conv...

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IEEE Conference on Decision and Control

作者： Mingyu Chen Junjie Qin School of Electronics Information and Communications Huazhong University of Science and Technology Wuhan China School of Electrical and Computer Engineering Purdue University West Lafayette Indiana U.S

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper studies the problem of scheduling and pricing a large collection of non-preemptive electric loads. For the scheduling problem, we formulate the problem as a mixed integer program and propose an algorithm for solving it based on its convex relaxation. We establish that the admissible schedule produced by our algorithm is near optimal for any finite number of loads, and asymptotically optimal in a per-load cost sense when the number of loads grows to infinite. For the pricing problem, we analyze a natural marginal pricing policy defined for the convex relaxation. We show that the pricing policy is approximately budget adequate, and self-scheduling. Furthermore, we demonstrate that the pricing policy provides proper incentives for each flexible load to report its true flexibility window when the maximum duration of all loads is one. When there are some loads spanning multiple time slots, we show that this flexibility revealing property fails to hold. Our results suggest that while it is possible to achieve most of the aforementioned desirable properties by scheduling and pricing non-preemptive loads via convex relaxation, the marginal pricing rule needs to be adjusted to incentivize flexibility revealing for general non-preemptive loads.

关键词： Schedules Costs Pricing Approximation algorithms Scheduling

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An improved Personalized Customer Banking Model using Machine Learning

An improved Personalized Customer Banking Model using Machin...

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International Conference on Computing and Networking technology (ICCNT)

作者： Piyush Rohella Sheetal Temara Sagar Varma Samanthapudi T. Kiruthiga Payments & Commerce GoDaddy Edmonds Washington USA Dept of Computer & Information Sciences University of the Cumberlands Williamsburg KY USA Applied Data Science The Polis Center at Indiana University Purdue University Indianapolis Indianapolis Indiana USA Department of ECE Vetri Vinayaha College of Engineering and Technology Trichy Tamil Nadu INDIA

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

System getting to know has come to be a chief tool in many areas of modern-day lifestyles. One such place is patron banking. Via its predictive energy, gadget mastering has the potential to improve the manner banks interact with consumers substantially. In particular, gadget learning can supply effective and personalized carriers to clients, allowing banks to provide greater tailored solutions to personal customers. At the same time, device studying can help banks automate and streamline many strategies. By automating the execution and interpretation of patron records, banks can serve customers quicker and greater appropriately. Moreover, the system getting to know can be used to locate anomalous activities and frauds, supporting banks in defending their customers from dangers and threats. Furthermore, system-gaining knowledge can empower banks to improve their consumer segmentation, marketing, and pricing. With the aid of leveraging the information they’ve available, banks can craft new techniques and techniques to better birthday celebrations for their clients. They are able to offer greater centered rewards to customers with healthy positive profiles, as well as extra tailored offerings and pricing. In precis, device studying can revolutionize customer banking. Through their predictive and analytical skills, banks can dramatically enhance the way they interact with customers.

关键词： Industries Accuracy Banking Pricing Machine learning Fraud Financial services

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Complete Frequency-Bin Bell Basis Synthesizer

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Physical Review Letters 2022年第23期129卷 230505-230505页

作者： Suparna Seshadri Hsuan-Hao Lu Daniel E. Leaird Andrew M. Weiner Joseph M. Lukens Elmore Family School of Electrical and Computer Engineering and Purdue Quantum Science and Engineering Institute Purdue University West Lafayette Indiana 47907 USA Quantum Information Science Section Oak Ridge National Laboratory Oak Ridge Tennessee 37831 USA Research Technology Office Arizona State University Tempe Arizona 85287 USA

We report the experimental generation of all four frequency-bin Bell states in a single versatile setup via successive pumping of spontaneous parametric down-conversion with single and dual spectral lines. Our scheme utilizes intensity modulation to control the pump configuration and offers turn-key generation of any desired Bell state using only off-the-shelf telecommunication equipment. We employ Bayesian inference to reconstruct the density matrices of the generated Bell states, finding fidelities ≥97% for all cases. Additionally, we demonstrate the sensitivity of the frequency-bin Bell states to common-mode and differential-mode temporal delays traversed by the photons comprising the state—presenting the potential for either enhanced resolution or nonlocal sensing enabled by our complete Bell basis synthesizer.

关键词： Entanglement production Quantum correlations in quantum information Quantum entanglement Quantum optics Quantum state engineering

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Quantum nonlocal modulation cancellation with distributed clocks

arXiv

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arXiv 2024年

作者： Chapman, Stephen D. Seshadri, Suparna Lukens, Joseph M. Peters, Nicholas A. McKinney, Jason D. Weiner, Andrew M. Lu, Hsuan-Hao Elmore Family School of Electrical and Computer Engineering Purdue Quantum Science and Engineering Institute Purdue University West LafayetteIN47907 United States Research Technology Office and Quantum Collaborative Arizona State University TempeAZ85287 United States Quantum Information Science Section Computational Sciences and Engineering Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

We demonstrate nonlocal modulation of entangled photons with truly distributed RF clocks. Leveraging a custom radio-over-fiber (RFoF) system characterized via classical spectral interference, we validate its effectiveness for quantum networking by multiplexing the RFoF clock with one photon from a frequency-bin-entangled pair and distributing the coexisting quantum-classical signals over fiber. Phase modulation of the two photons reveals nonlocal correlations in excellent agreement with theory: in-phase modulation produces additional sidebands in the joint spectral intensity, while out-of-phase modulation is nonlocally canceled. Our simple, feedback-free design attains sub-picosecond synchronization—namely, drift less than ∼0.5 ps in a 5.5 km fiber over 30 min (fractionally only ∼2×10−8 of the total fiber delay)—and should facilitate frequency-encoded quantum networking protocols such as high-dimensional quantum key distribution and entanglement swapping, unlocking frequency-bin qubits for practical quantum communications in deployed metropolitan-scale networks. Copyright © 2024, The Authors. All rights reserved.

关键词： Radio-over-fiber

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Autonomous Drone-Chasing System: A Comprehensive Solution for Reducing the Risk of Mitigation Failure

Autonomous Drone-Chasing System: A Comprehensive Solution fo...

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IEEE International Conference on Robotic Computing (IRC)

作者： Seungchol Cho Uk Jang Youngseo Chang William Choi Nicole Hornbrook Justin Allange Mia Y. Wang Eric T. Matson Human Centered Artificial Intelligence Sangmyung University Seoul Republic of Korea Department of Computer Engineering Jeju National University Jeju-si Republic of Korea Computer Science and Engineering Kyonggi University Suwon-si Republic of Korea Department of Business Administration Kyonggi University Suwon-si Republic of Korea Computer and Information Technology Purdue University West Lafayette IN USA Department of Computer Science College of Charleston Charleston SC USA

With the rapid advancement of unmanned aerial vehicles (UAVs) in various fields, the development of anti-drone systems has become increasingly important. Even though current research on anti-drone systems suggests means of mitigating malicious UAVs, some mitigation techniques overlook its risks when they fail to mitigate them. Therefore, we propose a system that lowers the risks of mitigation failure, which is implemented with a self-driving defensive drone capable of predicting and chasing the future position of detected malicious drones.

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Uncovering the Benefits of Machine Learning for Automating Financial Regulatory Tasks

Uncovering the Benefits of Machine Learning for Automating F...

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E-mobility, Power Control and Smart Systems (ICEMPS), International Conference on

作者： Sagar Varma Samanthapudi Piyush Rohella Sheetal Temara Ketan Gupta S. Dhanasekaran Applied Data Science The Polis Center at Indiana University Purdue University Indianapolis Indianapolis Indiana USA Principal Product Manager Payments & Commerce GoDaddy Edmonds Washington USA Dept of Computer & Information Sciences University of the Cumberlands Williamsburg KY USA Dept. of Information Technology University of The Cumberlands Williamsburg KY USA Department of ECE Sri Eshwar College of Engineering Coimbatore Tamil Nadu INDIA

ISBN: (数字)9798350394399

ISBN: (纸本)9798350394405

Machine Learning (ML) gives the ability to automate economic regulatory responsibilities in terms of price savings and more green process control. Current advances in ML have enabled the improvement of models that may interpret complicated sets of facts and automate the technique of extracting the preferred facts from these records. The development of such models gives a price-effective and efficient opportunity to greater luxurious and time-consuming guide labor. Moreover, these fashions are capable of perceiving and correctly categorizing data to ensure information accuracy and compliance with regulatory policies. It allows the company to adhere to policies without human oversight, lowering each value and time constraints. It will be further prolonged to other factors of the enterprise by making use of unsupervised fashions and fashions advanced on switch learning, together with deep learning. Those models allow the automation of responsibilities inclusive of assessing the threat associated with investments or transactions, facilitating the development of quicker and more knowledgeable techniques. Ordinary, the usage of ML fashions for financial law automatization processes is precious for decreasing price and time constraints and making sure of compliance. Through leveraging the ability of ML, financial institutions can take advantage of extra internal manner efficiency and statistics accuracy with fewer risks.

关键词： Accuracy Regulators Biological system modeling Machine learning Switches Time factors Stakeholders

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