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Understanding the Quantum Teleportation Protocol

Understanding the Quantum Teleportation Protocol

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

作者： Kurt T. Spranger Michael R. Grimaila Douglas D. Hodson Dept. of Systems Engineering and Mgmt. Air Force Institute of Technology Wright-Patterson AFB OH Dept. of Electrical & Computer Engineering Air Force Institute of Technology Wright-Patterson AFB OH

Decomposing a complex system into smaller abstract functional blocks and developing mathematical models to represent their behavior is an important activity towards developing comprehensive system understanding. In this paper, we decompose the ideal Quantum Teleportation protocol into a collection of simple quantum circuit blocks, examine the behavior of each block, and show how collections of blocks operate to create more complex circuits. We believe this approach greatly simplifies the understanding of how the Quantum Teleportation protocol works. This paper is introductory in nature and is intended to help those who are new to modeling, simulating, and analyzing ideal quantum circuits.

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AI Regulation and Capitalist Growth: Balancing Innovation, Ethics, and Global Governance

AI Regulation and Capitalist Growth: Balancing Innovation, E...

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IEEE International Conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart Computing (HPSC) and Intelligent Data and Security (IDS)

作者： Vikram Kulothungan Priya Ranjani Mohan Deepti Gupta Capitol Technology University Laurel Maryland USA Rutgers University NJ USA Dept. of Computer Information Systems Texas A&M University - Central Texas Texas USA

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

Artificial Intelligence (AI) is increasingly central to economic growth, promising new efficiencies and markets. This economic significance has sparked debate over AI regulation: do rules and oversight bolster long term growth by building trust and safeguarding the public, or do they constrain innovation and free enterprise? This paper examines the balance between AI regulation and capitalist ideals, focusing on how different approaches to AI data privacy can impact innovation in AI-driven applications. The central question is whether AI regulation enhances or inhibits growth in a capitalist economy. Our analysis synthesizes historical precedents, the current U.S. regulatory landscape, economic projections, legal challenges, and case studies of recent AI policies. We discuss that carefully calibrated AI data privacy regulations—balancing innovation incentives with the public interest—can foster sustainable growth by building trust and ensuring responsible data use, while excessive regulation may risk stifling innovation and entrenching incumbents.

关键词： Economics Technological innovation Data privacy Ethics Law Buildings Focusing Regulation Security Artificial intelligence

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Development and Comparison of Windpower Prediction Models: A Case Study in Sri Lanka

Development and Comparison of Windpower Prediction Models: A...

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IEEE Southeastcon

作者： Praharshin M. Senadeera Rashmi Semasinghe Jeevani Jayasinghe Dept. of Computer Systems Technology North Carolina A&T State University Greensboro NC USA Department of Electronics Wayamba University of Sri Lanka Kuliyapitiya Sri Lanka

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

Wind power is becoming a cornerstone of renewable energy, providing a sustainable solution for electricity generation worldwide. Accurate prediction models are essential for maximizing energy output, ensuring grid stability, and seamlessly integrating renewable energy into power systems. This research focus on the Musalpetti Wind Power (Pvt) Ltd wind farm in Kalpitiya, Sri Lanka, for developing the wind power generation models. The total generated power is the dependent variable, while the average wind speed (m/s) and average ambient temperature (°C) are the independent variables. By employing advanced predictive modelling techniques tailored to local climate conditions and turbine specifications, the study aims to perform accurate wind power prediction with R 2 = 0.940. The findings demonstrate the potential of these refined models to enhance operational efficiency and support Sri Lanka's renewable energy ambitions. Ultimately, this study highlights the importance of localized approaches in optimizing wind energy production and driving the global shift towards sustainable energy solutions.

关键词： Renewable energy sources Accuracy Wind energy Wind speed Wind power generation Predictive models Power system stability Wind farms Wind forecasting Turbines

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Development of a Modified Dummy Gas Lift Valve for Subsurface dept.-Resolved Microbiological Sampling and 3D Mapping

Development of a Modified Dummy Gas Lift Valve for Subsurfac...

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2024 SPE Water Lifecycle Management Conference and Exhibition, WLMC 2024

作者： McElhinney, J.M.W.R. Gomes, J.S. Hasan, A. Dias, J. BME Department Khalifa University Abu Dhabi United Arab Emirates Centre for Membranes and Advanced Water Technology Arzanah Building Khalifa University SAN Campus Abu Dhabi United Arab Emirates Computer and Electrical Engineering Dept. Khalifa University Abu Dhabi United Arab Emirates KU Center for Autonomous Robotic Systems

ISBN: (纸本)9781959025320

Objectives/Scope: This study outlines the development and use of a modified dummy gas lift valve designed to obtain subsurface microbiological samples from a defined dept. series across a production well. An overview of the initial field pilot is presented wherein microbiological data from the modified valve will be contrasted with topside samples to evaluate the information yielded from sampling downhole biofilm communities in place of planktonic populations from the well head. Methods, Procedures, Process: In this approach, the modified dummy gas lift valve, preloaded topside with carbonate plugs and coupons, is deployed at various dept.s in the well's completion for downhole incubation. This modification allows production fluids to wet the payloads, enabling in situ biofilm colonization by reservoir communities across dept. and temperature gradients. Post retrieval using a slickline tool, sample DNA is extracted for metagenomic sequencing, assessing microbial composition and metabolic capabilities (including souring and MIC-related activities). Concurrent production fluid samples from the wellhead will be analyzed for comparison to evaluate any information gained from biofilm over planktonic population sampling. Results, Observations, Conclusions: Samples from the modified valve are expected to provide sufficient yields of DNA, uncontaminated by non-resident microbes from outside of the producer. Microbial data from downhole biofilms is anticipated to provide a more diverse community makeup than production fluid communities sampled at the wellhead, with a corresponding increase in problematic species associated with MIC and souring. Corrosion coupon analyses will enable conclusions regarding proactive decision-making enablement and extrapolations of 3D projections of microbiological communities between modified valves across the completion column will provide the relationship between in situ conditions, microbial colonization in the context of pore space and risk

关键词： Decision making

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Robust Heart Disease Prediction: A Hybrid Approach to Feature Selection and Model Building 4

Robust Heart Disease Prediction: A Hybrid Approach to Featur...

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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Kodete, Chandra Shikhi Pasupuleti, Vikram Thuraka, Bharadwaj Sangaraju, Varun Varma Tirumanadham, N. S. Koti Mani Kumar Shariff, Vahiduddin School of Technology Eastern Illinois University CharlestonIL61920 United States School of Computer Science and Information Systems Northwest Missouri State University MaryvilleMO64468 United States University of the Cumberlands WilliamsburgKY4076 United States Sir C R Reddy College of Engineering Dept. of Cse Eluru India

ISBN: (纸本)9798331529635

This research work integrates advanced data preparation methods and advanced modelling approaches to offer a complete approach for creating a predictive model for heart disease. Data cleansing starts the procedure in order to fix disparities and manage missing values, thereby guaranteeing the dependability and accuracy of the dataset. By producing synthetic samples for the minority class, the Synthetic Minority Over-sampling Technique (SMOTE) solves class imbalances and improves the accuracy of the model to forecast minority class occurrences. The z-score approach helps to control outliers thereby preventing distortion in the study. Using a hybrid Filter-Recursive Feature Elimination (FRFE) approach-which combines filter and wrapper techniques to find the most pertinent features, therefore lowering multicollinearity and boosting model performance-feature selection is accomplished. Important characteristics found are age, kind of chest discomfort, maximal heart rate attained, ST depression brought on by exercise, and fluoroscopy-induced coloration of several major arteries. Using AdaBoost, an ensemble learning technique combining several weak classifiers to create a strong one, the improved dataset is utilised to develop a predictive model. The resultant model shows great accuracy (93%), precision (93%), recall (94%), and an amazing ROC AUC score (0.98), therefore highlighting its potential use in healthcare environments and heart disease prediction ability. © 2024 IEEE.

关键词： Feature Selection

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Federated Learning Model for Early Detection of Dementia Using Blood Biosamples

Federated Learning Model for Early Detection of Dementia Usi...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Elsersy, Mohamed Sherif, Ahmed Imam, Ahmad Abdel-Aliem Khan Mamun, Mohammad M. R. Khalil, Kasem Haitham, Mohamed Higher Colleges of Technology Computer and Information Systems Dept. Abu Dhabi United Arab Emirates University of Southern Mississippi School of Computing Sciences and Computer Engineering MS United States William Carey University College of Osteopathic Medicine Mississippi MS United States Tennessee Technological University Electrical and Computer Engineering Department TN United States University of Mississippi Electrical and Computer Engineering Department MS United States Cairo Egypt

ISBN: (纸本)9798350322347

Alzheimer's disease (AD) is a serious, long-term health problem that causes much pain and loss for the person with it and their family. Its early and accurate detection might result in a substantial reduction of the disease outcomes and consequences. Blood biosamples are a simple and inexpensive technique in medical testing. This paper proposes diagnostic models for blood biosamples based on federated learning (FL) and its modifications to detect AD early. Our experiments used blood biosample data sets from the ADNI website to evaluate our models. Our performance analysis indicates that our algorithms are more accurate and achieve an accuracy of 87% for early detection. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Virtual-Device-Based Policy Enforcement in Multi-Admin Smart Environments

Virtual-Device-Based Policy Enforcement in Multi-Admin Smart...

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IEEE International Conference on Cloud Networking (CLOUDNET)

作者： Yunping Fang Chenglong Fu Xiaojiang Du Dept. of Electrical and Computer Engineering Stevens Institute of Technology Hoboken NJ USA Dept. of Software and Information Systems Univ. of North Carolina at Charlotte Charlotte NC USA

The Matter standard, formerly known as Connected Home over IP, has emerged as the preferred choice for most smart home IoT vendors and service providers for the next-generation smart home IoT systems. It enhances interoperability across different smart home ecosystems and introduces the multi-admin feature, allowing a device to be commissioned and managed by multiple platforms simultaneously. While this standard offers considerable convenience, it also presents challenges for security policy enforcement. Policy enforcement has been highlighted in various studies as a crucial countermeasure against smart home IoT system vulnerabilities. Existing smart home policy enforcement methods, designed for individual IoT admin platforms, operate under the assumption of having a global view and control over all IoT devices in a smart home. This assumption, however, is untenable in a multi-admin environment, where an admin may only have partial device access or a partial view. In this work, we thoroughly analyze these challenges in a multi-admin setting and propose the first cross-admin policy enforcement solution. Our solution can parse complex policies into deployable sub-policies for each admin, and create virtual device instances and virtual automation rules to interconnect various admins. We test our solution on a real-world testbed involving 12 IoT devices and three popular Matter-enabled IoT platforms. Our results show that our solution can enforce cross-admin policies with a 100% success rate and a very small delay.

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Tactile Sensing with Contextually Guided CNNs: A Semisupervised Approach for Texture Classification

Tactile Sensing with Contextually Guided CNNs: A Semisupervi...

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IEEE International Conference on Robotic Computing (IRC)

作者： Olcay Kursun Beiimbet Sarsekeyev Mahdi Hasanzadeh Ahmad Patooghy Oleg V. Favorov Dept. of Computer Science Auburn University at Montgomery Montgomery Alabama Dept. of Computer Science University of Central Arkansas Conway Arkansas Dept. of Computer Systems Technology North Carolina A&T State University Greensboro North Carolina Dept. of Biomedical Engineering University of North Carolina at Chapel Hill Chapel Hill North Carolina

Texture classification plays a crucial role in applications ranging from object recognition and product design to surface exploration. Utilizing deep learning methods with sensors, such as accelerometers, offers a way to identify key surface features without the need to precisely replicate human touch. A Contextually Guided Convolutional Neural Network (CG-CNN) employs contextual guidance by developing auxiliary tasks during its training. These tasks offer implicit, yet rigorous, internal supervision signals. When trained with these subtasks, CG-CNN learns to represent the innate structure and patterns within the data, resulting in robust, transferrable, and local/contextual-neighborhood-preserving domain representations. This paper extends the CG-CNN framework for texture classification by integrating semisupervised learning. Empirical evaluations on the VibTac-12 texture dataset reveal that CG-CNN effectively generalizes to novel and unfamiliar textures, even when trained with scarce labeled examples. By harnessing vast amounts of unlabeled, contextually relevant data alongside the labeled samples, CG-CNN ensures robust and precise texture classification. Such advancements hold promise for applications in robotics, prosthetics, and haptic interfaces.

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IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW

作者： Felix Dobslaw Robert Feldt Dept. of Communication Quality Mngmt. and Inf. Systems Mid Sweden University Östersund Sweden Dept. of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden

This study examines to what extent the testing of traditional software components and machine learning (ML) models fundamentally differs or not. While some researchers argue that ML software requires new concepts and perspectives for testing, our analysis highlights that, at a fundamental level, the specification and testing of a software component are not dependent on the development process used or on implementation details. Although the software engineering/computer science (SE/CS) and Data Science/ML (DS/ML) communities have developed different expectations, unique perspectives, and varying testing methods, they share clear commonalities that can be leveraged. We argue that both areas can learn from each other, and a non-dual perspective could provide novel insights not only for testing ML but also for testing traditional software. Therefore, we call upon researchers from both communities to collaborate more closely and develop testing methods and tools that can address both traditional and ML software components. While acknowledging their differences has merits, we believe there is great potential in working on unified methods and tools that can address both types of software.

关键词： Software testing Conferences Machine learning Software Testing

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CDP: Selective Influential Nodes Based on Cliques in the Diffusion Process in Social Networks

CDP: Selective Influential Nodes Based on Cliques in the Dif...

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International Conference on Web Research (ICWR)

作者： Fatemeh Yazdanifar Hamid Ahmadi Beni Nikoo Salimi Asgarali Bouyer Dept. of Mathematics and Computer Science Amirkabir University of Technology Tehran Iran Dept. Software Engineering Azarbijan Shahid Madani University Tabriz Iran Dept. of the Electrical Electronics Electronics and Telecommunication Engineering and Naval Architecture University of Genoa Genoa Italy Dept. of Computer Engineering Azarbaijan Shahid Madani University Tabriz Iran

Influence maximization techniques emphasize selecting a set of influential nodes in order to maximize influence. Because the algorithms presented in this field ignore the topology of cliques for diffusion, there are two major challenges in influence maximization algorithms: optimal diffusion and computational overhead reduction. As a result, the CDP algorithm is presented in this article to address these issues. This algorithm first selects suitable cliques for diffusion based on their position and strategy in social networks. Furthermore, for each clique, a score is calculated based on the topological criteria of the clique, and suitable cliques are selected for diffusion by applying a threshold limit. The seed nodes are chosen from the cliques in the second step. To avoid the rich club phenomenon, only a few nodes from each clique are chosen as seed candidate nodes. Finally, the seed nodes are chosen based on the node’s topology and the strength of the node’s level one neighbor. In the experiment section, the CDP algorithm significantly outperforms the best algorithms presented in recent years in terms of influence spread rate and execution time.

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