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COVID-19 Vaccine and Booster Shots Compatibility Analyzer using CMOS-based Monochrome Encoding 14

COVID-19 Vaccine and Booster Shots Compatibility Analyzer us...

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14th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2022

作者： Camacho, Ken Sammuel I. Chua-Unsu, Megan Victoria Hillary Y. Vicerra, Ryan Rhay P. Concepcion, Ronnie Naguib, Raouf N.G. De la Salle University Department of Manufacturing Engineering and Management Manila Philippines Liverpool Hope University School of Mathematics Computer Science and Engineering Liverpool United Kingdom

ISBN: (纸本)9781665464932

The continuous increase in COVID-19 positive cases in the Philippines might further weaken the local healthcare system. As such, an efficient system must be implemented to further improve the immunization efforts of the country. In this paper, a contactless digital electronic device is proposed to assess the vaccine and booster brand compatibility. Using Logisim 2.7.1, the logic diagrams were designed and simulated with the help of truth tables and Boolean functions. Moreover, the finalized logic circuit design was converted into its equivalent complementary metal-oxide semiconductor (CMOS) and stick diagrams to help contextualize how the integrated circuits will be designed. Results have shown that the proposed device was able to accept three inputs of the top three COVID-19 vaccine brands (Sinovac, AstraZeneca, and Pfizer) and assess the compatibility of heterologous vaccinations. With the successful results of the circuit, it can be concluded that this low-power device can be used to manufacture a physical prototype for use in booster vaccination sites. © 2022 IEEE.

关键词： COVID-19

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Generalizing Functional Error Correction for language and Vision-language Models

Generalizing Functional Error Correction for Language and Vi...

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International Conference on Machine Learning and Applications (ICMla)

作者： Wenyu Peng Simeng Zheng Michael Baluja Tao Xie Anxiao Jiang Paul H. Siegel Electrical and Computer Engineering Department University of California San Diego La Jolla CA USA Computer Science San Diego State University San Diego CA USA Department of Computer Science and Engineering Texas A&M University College Station TX USA

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

The goal of functional error correction is to preserve neural network performance when stored network weights are corrupted by noise. To achieve this goal, a selective protection (SP) scheme was proposed to optimally protect the functionally important bits in binary weight representations in a layer-dependent manner. Although it showed its effectiveness in image classification tasks on some relatively simple networks such as ResNet-18 and VGG-16, it becomes inadequate for emerging complex machine learning tasks generated from natural language processing and vision-language association domains. To solve this problem, we extend the SP scheme in three directions: task complexity, model complexity, and storage complexity. Extensions to complex natural language and vision-language tasks include text categorization and “zero-shot” textual classification of images. Extensions to more complex models with deeper block structures and attention mechanisms consist of Very Deep Convolutional Neural Network (VDCNN) and Contrastive language-Image Pre-Training (CLIP) networks. Extensions to more complex storage configurations focus on distributed storage architectures to support model parallelism. Experimental results show that the optimized SP scheme preserves network performance in all of these settings. The results also provide insights into redundancy-performance tradeoffs, generalizability of SP across datasets and tasks, and robustness of partitioned network architectures.

关键词： Adaptation models Text categorization Neural networks Noise Parallel processing Network architecture Robustness Error correction Complexity theory Protection

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Initial Exploration of Zero-Shot Privacy Utility Tradeoffs in Tabular Data Using GPT-4

Initial Exploration of Zero-Shot Privacy Utility Tradeoffs i...

引用

International Joint Conference on Neural Networks (IJCNN)

作者： Bishwas Mandal George Amariucai Shuangqing Wei Department of Computer Science Kansas State University Manhattan KS USA Division of Electrical & Computer Engineering Louisiana State University Baton Rouge LA USA

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

We investigate the application of large language models (LLMs), specifically GPT-4, to scenarios involving the tradeoff between privacy and utility in tabular data. Our approach entails prompting GPT-4 by transforming tabular data points into textual format, followed by the inclusion of precise sanitization instructions in a zero-shot manner. The primary objective is to sanitize the tabular data in such a way that it hinders existing machine learning models from accurately inferring private features while allowing models to accurately infer utility-related attributes. We explore various sanitization instructions. Notably, we discover that this relatively simple approach yields performance comparable to more complex adversarial optimization methods used for managing privacy-utility tradeoffs. Furthermore, while the prompts successfully obscure private features from the detection capabilities of existing machine learning models, we observe that this obscuration alone does not necessarily meet a range of fairness metrics. Nevertheless, our research indicates the potential effectiveness of LLMs in adhering to these fairness metrics, with some of our experimental results aligning with those achieved by well-established adversarial optimization techniques.

关键词： Measurement Training Privacy Data privacy large language models Neural networks Optimization methods

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Towards Efficient Motion Planning for UAVs: lazy A* Search with Motion Primitives

Towards Efficient Motion Planning for UAVs: Lazy A* Search w...

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IEEE International Conference on Mechatronics, Robotics and Automation (ICMRA)

作者： Wentao Wang Yi Shen Kaiyang Chen Kaifan Lu Department of Computer Science University of Southern California LA USA Robotics Department University of Michigan MI USA Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Champaign USA

ISBN: (数字)9798350352474

ISBN: (纸本)9798350352481

Search-based motion planning algorithms have been widely utilized for unmanned aerial vehicles (UAVs). However, deploying these algorithms on real UAVs faces challenges due to limited onboard computational resources. The algorithms struggle to find solutions in high-dimensional search spaces and require considerable time to ensure that the trajectories are dynamically feasible. This paper incorporates the lazy search concept into search-based planning algorithms to address the critical issue of real-time planning for collision-free and dynamically feasible trajectories on UAVs. We demonstrate that the lazy search motion planning algorithm can efficiently find optimal trajectories and significantly improve computational efficiency.

关键词： Mechatronics Trajectory planning Heuristic algorithms Autonomous aerial vehicles Real-time systems Planning Trajectory Computational efficiency Robotics and automation Faces

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CovidXNet: An Ensemble of Deep Neural Networks for Non-invasive Binary Identification of COVID-19 through Chest Radiographs 14

CovidXNet: An Ensemble of Deep Neural Networks for Non-invas...

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14th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2022

作者： Ong, Jonathan Daniel S. Vidad, Aron Jake P. Valenzuela, Ira C. Concepcion, Ronnie Bugtai, Nilo T. Naguib, Raouf N.G. De la Salle University Department of Manufacturing Engineering and Management Manila Philippines Liverpool Hope University School of Mathematics Computer Science and Engineering Liverpool United Kingdom

ISBN: (纸本)9781665464932

This study compared five different image classification algorithms, namely VGG16, VGG19, AlexNet, DenseNet, and ConVNext, based on their ability to detect and classify COVID-19-related cases given chest X-ray images. Using performance metrics like accuracy, F1 score, precision, recall, and MCC compared these intelligent classification algorithms. Upon testing these algorithms, the accuracy for each model was quite unsatisfactory, ranging from 80.00% to 92.50%, provided it is for medical application. As such, an ensemble learning-based image classification model, made up of AlexNet and VGG19 called CovidXNet, was proposed to detect COVID-19 through chest X-ray images discriminating between health and pneumonic lung images. CovidXNet achieved an accuracy of 97.00%, which was significantly better considering past results. Further studies may be conducted to increase the accuracy, particularly for identifying and classifying chest radiographs for COVID-19-related cases, since the current model may still provide false negatives, which may be detrimental to the prevention of the spread of the virus. © 2022 IEEE.

关键词： COVID-19

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Fast Blind Recovery of Linear Block Codes over Noisy Channels

Fast Blind Recovery of Linear Block Codes over Noisy Channel...

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IEEE International Symposium on Information Theory

作者： Peng Wang Yong Liang Guan Lipo Wang Peng Cheng School of Electrical and Information Engineering The University of Sydney School of Electrical and Electronic Engineering Nanyang Technological University Singapore Department of Computer Science and Information Technology La Trobe University Australia

This paper addresses the blind recovery of the parity check matrix of an (n, k) linear block code over noisy channels by proposing a fast recovery scheme consisting of 3 parts. Firstly, this scheme performs initial error position detection among the received codewords and selects the desirable codewords. Then, this scheme conducts Gaussian elimination (GE) on a k-by-k full-rank matrix and uses a threshold and the reliability associated to verify the recovered dual words, aiming to improve the reliability of recovery. Finally, it performs decoding on the received codewords with partially recovered dual words. These three parts can be combined into different schemes for different noise level scenarios. The GEV that combines Gaussian elimination and verification has a significantly lower recovery failure probability and a much lower computational complexity than an existing Canteaut-Chabaud-based algorithm, which relies on GE on n-by-n full-rank matrices. The decoding-aided recovery (DAR) and error-detection-&-codeword-selection-&-decoding-aided recovery (EDCSDAR) schemes can improve the code recovery performance over GEV for high noise level scenarios, and their computational complexities remain much lower than the Canteaut-Chabaud-based algorithm.

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Sparse Dynamic Network Reconstruction Through L1-regularization of a Lyapunov Equation 63

Sparse Dynamic Network Reconstruction Through L1-regularizat...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Belaustegui, Ian Xul Arango, Marcela Ordorica Rossi-Pool, Roman Leonard, Naomi Ehrich Franci, Alessio Princeton University Department of Mechanical and Aerospace Engineering United States Universidad Nacional Autónoma de México Instituto de Fisiología Celular - Neurociencias and Centro de Ciencias de la Complejidad Mexico University of Liege Wel Research Institute Department of Electrical Engineering and Computer Science Wavre Belgium

ISBN: (纸本)9798350316339

An important problem in many areas of science is that of recovering interaction networks from high-dimensional time-series of many interacting dynamical processes. A common approach is to use the elements of the correlation matrix or its inverse as proxies of the interaction strengths, but the reconstructed networks are necessarily undirected. Transfer entropy methods have been proposed to reconstruct directed networks, but the reconstructed network lacks information about interaction strengths. We propose a network reconstruction method that inherits the best of the two approaches by reconstructing a directed weighted network from noisy data under the assumption that the network is sparse and the dynamics are governed by a linear (or weakly-nonlinear) stochastic dynamical system. The two steps of our method are i) constructing an (infinite) family of candidate networks by solving the covariance matrix Lyapunov equation for the state matrix and ii) using L1-regularization to select a sparse solution. We further show how to use prior information on the (non)existence of a few directed edges to dramatically improve the quality of the reconstruction. © 2024 IEEE.

关键词： Stochastic systems

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Machine Learning on Stroke Risk Prediction Systems as Complementary Technology for Neurologists: A Critical Review 14

Machine Learning on Stroke Risk Prediction Systems as Comple...

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14th IEEE International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment, and Management, HNICEM 2022

作者： Lucas, Chrischell Padrique, Kathrina Clarisse Christa lansangan, Mariah Isabel Gusi, Maria Jena Lubag, Marian Concepcion, Ronnie lauguico, Sandy Vicerra, Ryan Rhay Naguib, Raouf N.G. De la Salle University Department of Manufacturing Engineering and Management Manila Philippines Liverpool Hope University School of Mathematics Computer Science and Engineering Liverpool United Kingdom

ISBN: (纸本)9781665464932

Stroke is considered the second-leading cause of death all over the world. Due to the risks entangled with stroke, the need to identity its early signs and symptoms is vital in preventing it. By recognizing the modifiable and non-modifiable risk factors related to stroke, numerous systems are developed through a machine learning (ML) approach. Ergo, utilizing stroke-risk prediction systems could prevent its occurrence and minimize fatality. This literature review focuses on assessing the current models and systems available. Numerous studies stick to utilizing traditional machine learning algorithms although the machine learning pipelines of the reviewed studies are mostly in question due to the lack of performing data pre-processing techniques. On the other hand, there are several studies which develop new machine learning algorithms based on traditional algorithms to perform more specific predictions. There has also been a perceived increase in studies wherein graphical user interface (GUI) is integrated in the process of presenting the results of a prediction system and this trend helps deliver the established systems to be used in the healthcare industry. Although that seems to be the end goal, implementation of machine learning algorithms still can be improved by setting guidelines and reporting standards to increase the acceptability and face validity of the algorithms' predictions. Factor assessment is also key in addressing issues surrounding the transparency of machine learning by showing how risk factors have contributed to the prediction. Overall, further study and continued research on its reliability and dependability may aid in the forward trajectory of modern diagnosis. © 2022 IEEE.

关键词： Graphical user interfaces

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Comparative Study of Machine Learning Algorithms for IoT Cyber Threat Detection in Healthcare Information Systems 13th

Comparative Study of Machine Learning Algorithms for IoT C...

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13th International Conference on Health Information science, HIS 2024

作者： Ngo, Tien Yin, Jiao Ge, Yong-Feng Zhou, Changjun Cao, Jinli Institute for Sustainable Industries and Liveable Cities Victoria University MelbourneVIC3011 Australia Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China

ISBN: (纸本)9789819655960

The modeling of health information systems is crucial for enhancing healthcare decision-making, often involving the integration of multiple Internet of Things (IoT) devices. Consequently, robust cybersecurity measures are essential to safeguard information and ensure network security. Even though machine learning-based algorithms have demonstrated their effectiveness and efficiency in various applications, there is a lack of existing works that comprehensively explore the performance of different machine learning algorithms on different classification problems under the same setting. Therefore, this paper explores and compares the performance of four machine learning algorithms, i.e., Random Forest (RF), Naive Bayes (NB), Decision Tree (DT), and XGBoost (XGB), in IoT cyber threat detection for both binary and multiclass classification problems. Experiments conducted on two open-source datasets, NF-ToN IoT and NF-BoT IoT from the Machine Learning-Based NIDS Datasets, demonstrate that the XGBoost algorithm performs best in all scenarios. Further tuning of the XGBoost hyper-parameters significantly enhances its F1-Score performance, achieving up to 98.64% for binary classification and 83.76% for multiclass classification. Among all the tested algorithms, Naive Bayes is the fastest. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cyber attacks

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Classification of Philippine Herbal Medicine Plant Using EfficientNet on Mobile Platform

Classification of Philippine Herbal Medicine Plant Using Eff...

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2022 IEEE Region 10 International Conference, TENCON 2022

作者： Mirandilla, Jayde Paolo C. Bating, Carlos B. Cabatuan, Melvin K. Jose, John Anthony C. Gokongwei College of Engineering De la Salle University Department of Electronics and Computer Engineering Manila Philippines Engineering Research and Development for Technology of the Republic of the Philippines Department of Science and Technology Philippines

ISBN: (数字)9781665450959

ISBN: (纸本)9781665450959

So far, there is a lack of study on deep learning that is compatible with a mobile platform for Philippine herbal plant identification. This paper applied EfficientNet-B0 to classify Philippine herbal plants with 97.4% accuracy. Using a mobile phone camera, the model achieved top-1 inference accuracy of >92.2% versus top-2 with © 2022 IEEE.

关键词： Image classification

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