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Automation and Computation (AUTOCOM), International Conference on

作者： Sanjay Kumar Sonkar Balkar Singh Dept. of Computer Science and Engineering SunRise University Alwar Rajasthan India

In the landscape of rapidly expanding data streams, deriving meaningful insights, particularly frequent itemsets, within massive datasets at a swift pace poses a significant challenge. Addressing this challenge, various methodologies for Frequent Itemset Mining have been proposed, yet they struggle with low support counts and efficiency concerns in handling large datasets. In response, our research introduces Jagged Itemset Counting (JIC) methodologies, aiming to effectively mine Frequent Itemsets from extensive data. The core objective revolves around devising a robust algorithm capable of identifying all Frequent Itemsets, irrespective of database size or the nature of the itemset. Central to this approach is the introduction of a straightforward label representation, GPLN (Geometric Progression Label Number), assigned to each frequent item. Utilizing CGPLN (Cumulative Geometric Progression Label Number), derived from the arithmetic sum of GPLNs within transaction subsets, forms the CGPLN-Label representation for each transaction subset (itemset). Comparative analysis reveals superior performance of JIC over Apriori and Eclat algorithms for small and medium-sized datasets, exhibiting efficiency even at minimal support thresholds. In the realm of Big Data, where FP-Growth and Eclat falter, the proposed technique shines with faster execution times, optimized main memory utilization, and efficient disc memory usage.

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Beta-Sigma VAE: Separating beta and decoder variance in Gaussian variational autoencoder

arXiv

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arXiv 2024年

作者： Kim, Seunghwan Lee, Seungkyu Dept. of Computer Science and Engineering Kyung Hee University Korea Republic of

Variational autoencoder (VAE) is an established generative model but is notorious for its blurriness. In this work, we investigate the blurry output problem of VAE and resolve it, exploiting the variance of Gaussian decoder and β of beta-VAE [14]. Specifically, we reveal that the indistinguishability of decoder variance and β hinders appropriate analysis of the model by random likelihood value, and limits performance improvement by omitting the gain from β. To address the problem, we propose Beta-Sigma VAE (BS-VAE) that explicitly separates β and decoder variance σx2 in the model. Our method demonstrates not only superior performance in natural image synthesis but also controllable parameters and predictable analysis compared to conventional VAE. In our experimental evaluation, we employ the analysis of rate-distortion curve and proxy metrics on computer vision datasets. The code is available on https://***/overnap/BS-VAE. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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Optimizing Resume Parsing Processes by Leveraging Large Language Models

Optimizing Resume Parsing Processes by Leveraging Large Lang...

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IEEE Region 10 Symposium

作者： Vazzula Manish Yugandhar Manchala Y. Vijayalata Sudeep Banerjee Chopra Kothi Yashwanth Reddy Dept. of Information Technology Vardhaman College of Engineering Hyderabad India Dept. of Computer Science & Engineering Vardhaman College of Engineering Hyderabad India Dept. of Computer Science (AI & ML) Vardhaman College of Engineering Hyderabad India

ISBN: (数字)9798350364866

ISBN: (纸本)9798350364873

In the present era of internet revolution, organizations have to go through numerous resumes in order to identify the most suitable candidates for their Job Description (JD), while ensuring that no acquisition of talent is overlooked through human mistakes. Thus, tools like Applicant Tracking Systems (ATS) have taken over the human process of resume screening, enabling assessment of thousands of resumes in a matter of seconds. Although these technologies are incredibly effective, they are not flawless. Consequently, highly qualified individuals may miss out on said opportunities if their resumes are not formatted correctly. Therefore, individuals must ensure that their resume is appropriately structured prior to submitting it to any organization. The study centers on the present literature and suggests an enhanced approach for parsing resumes by leveraging Large Language Models (LLMs).

关键词： Large language models Resumes Organizations Cleaning Data mining IEEE Regions

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Accelerating supersingular isogeny Diffie-Hellman (SIDH) cryptosystem for the security of resource-constrained IoT devices with FPGA 3

Accelerating supersingular isogeny Diffie-Hellman (SIDH) cry...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Khadra, Shaimaa Abo Ismail, Nabil A. Attiya, Gamal M. Abdulrahman, Salah Eldin S. E. High Institute of Engineering and Technology Dept. Mechatronics Engineerng Al-Mahalla-El-Kobra Egypt Menoufia University Faculty of Electronic En. Dept. Computer Science & Engineering Menouf Egypt

ISBN: (纸本)9798350323511

It is imperative to note that post-quantum cryptography, such as supersingular isogeny Diffie-Hellman (SIDH), is essential for ensuring that Internet of Things (IoT) devices have a restricted amount of resources. The primary challenges in assuring the security of IoT devices are addressed by this work's analysis of SIDH implementations designed for field programmable gate array (FPGA) architecture. Extensive efforts towards implementing the architecture for a rapid and constant-time FPGA implementation of SIDH. This quantum-resistant cryptographic primitive is a crucial component for adhering to NIST's PQC standardization. Our goal with this paper is to demonstrate how FPGA architectures can enhance the parallelism of SIDH, making it a more practical option for securing resource-limited IoT devices. Our focus is on providing a reliable speed record for SIDH, which is crucial for ensuring the security of IoT devices. The design for isogeny computation was built over p434 in Xilinx Virtex 6 and produced 45ms for public key generation in addition to 35ms for secret key generation. © 2023 IEEE.

关键词： Internet of things

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Implementation of human-following robot system applying virtual delay to posture of a leader

Implementation of human-following robot system applying virt...

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Image Processing and Robotics (ICIP), International Conference on

作者： Fumiaki Takemori Dept. of Electrical Engineering and Computer Science Faculty of Engineering Tottori University Tottori JAPAN

ISBN: (数字)9798350374766

ISBN: (纸本)9798350374773

This paper develops an implementation of a measurement and control system in which a vehicle follows its predecessor while maintaining a certain distance. First, we construct a model that virtually delays the reference coordinates of the leading target backwards, and propose a control law that follows the steering angle of the following vehicle to that virtual delay point. Next, a range sensor is used to detect the position and orientation of the leader, and the position and orientation is estimated using a template matching method based on the person’s body contour point group measured by the sensor. Finally, the effectiveness (including robustness) of the proposed system is demonstrated in some simulations and human tracking experiments.

关键词： Target tracking Robot kinematics Image processing Position measurement Robot sensing systems Control systems Turning

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In-memory Machine Learning using Adaptive Multivariate Decision Trees and Memristors

In-memory Machine Learning using Adaptive Multivariate Decis...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Akash Chavan Pranav Sinha Sunny Raj Dept. of Computer Science and Engineering Oakland University Rochester MI USA

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

We introduce a framework to design in-memory decision tree machine-learning (ML) circuits using memristor crossbars. Decision trees (DTs) offer many advantages over neural networks, such as enhanced energy efficiency, interpretability, safety, privacy, and speed, along with reduced dependence on extensive training data. We propose an adaptive multivariate decision tree (AMDT) training algorithm, which constructs decision trees that incorporate both univariate and multivariate features, facilitating the creation of higher accuracy and energy-efficient crossbar designs compared to the state-of-the-art (SOTA). Our circuits are realized using pure memristor crossbars, requiring just one memristor per cell and no transistors while employing sneak-paths for flow-based in-memory computations. In comparison to the SOTA, our approach produces designs that are, on average, 4% more accurate and require 12.6% lower energy.

关键词： Training Accuracy Circuits Memristors Training data Machine learning Energy efficiency

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Fraud Detection in Credit Card through the Machine Learning Algorithms 1

Fraud Detection in Credit Card through the Machine Learning ...

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1st IEEE International Conference on Advances in Computing, Communication and Networking, ICAC2N 2024

作者： Kummarikuta, Sandhya Muppagowni, Ganesh Karthik Patam, Keerthi Pennera, Mounika Patimeedi, Upendra Harshith Reddy, Poreddy Chandra Mohan Babu University School of Computing Dept. AIandML Tirupati India GITAM University-Bengaluru Campus GITAM School of Technology Department of CSE Bengaluru India Sree Vidyanikethan Engineering College Dept. Computer Science and Engineering Tirupati India

ISBN: (纸本)9798350356816

The threat of credit card fraud is high and so there should be efficient ways to combat it. The research is being employed to focus on how to enhance credit card fraud detection, machine learning methodologies can be employed. The major benefit of this structured design model is that it observes many transaction variables and find out any potential threat transaction since its trained to do so. To start the project, a large dataset of both authentic and fraudulent credit card transactions will be collected and preprocessed. For the machine to learn how we want it to extract correct location, time, date and users' behavior and an important amount of transaction has been made. This improves our dataset by adding relevant information. This research is based on identifying fake activities it will discuss how to XGBoost, CatBoost and Logistic Regression used for this purpose. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Pneumonia Detection: CNN Interpretability with LIME and SHAP

Enhancing Pneumonia Detection: CNN Interpretability with LIM...

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International Conference on Electrical engineering and Information & Communication Technology (ICEEICT)

作者： Md. Hamid Hosen Arnob Saha Altaf Uddin Kahakashan Ashraf Sadia Nawar Dept. of Computer Science & Engineering East Delta University Chittagong Bangladesh

ISBN: (数字)9798350385779

ISBN: (纸本)9798350385786

A bacterial or viral infection of the lungs can cause pneumonia, one of the dangerous and potentially fatal illnesses that can have dire repercussions in a short amount of time. Therefore, a key component of a successful treatment plan is an early diagnosis. Therefore, a sophisticated and automated system that can diagnose chest X-rays and make the process of diagnosing pneumonia easier for both specialists and novices is required. This research aims to create a CNN model that will help with the accurate classification of pneumonia. In this work, we have presented our Deep Learning method for the classification challenge, which is taught using modified images through several pre-processing stages. With an overall accuracy of 93.30%, we were able to classify X-ray images of pneumonia using a custom CNN model. Our proposed model is able to precisely detect pneumonia from X-ray images with amazing accuracy and loss. Furthermore, we employed the LIME and SHAP tools of the XAI technique to generate a noteworthy conclusion to persuade medical practitioners.

关键词： Training Deep learning Pneumonia Computational modeling Predictive models Loss measurement Robustness

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Quantum Machine Learning for Anomaly Detection in Consumer Electronics

Quantum Machine Learning for Anomaly Detection in Consumer E...

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IEEE computer Society Annual Symposium on VLSI

作者： Sounak Bhowmik Himanshu Thaplival Dept. of Electrical Engineering and Computer Science University of Tennessee Knoxville Tennessee

ISBN: (数字)9798350354119

ISBN: (纸本)9798350354126

Anomaly detection is a crucial task in cyber security. Technological advancement brings new cyber-physical threats like network intrusion, financial fraud, identity theft, and property invasion. In the rapidly changing world, with frequently emerging new types of anomalies, classical machine learning models are insufficient to prevent all the threats. Quantum Machine Learning (QML) is emerging as a powerful computational tool that can detect anomalies more efficiently. In this work, we have introduced QML and its applications for anomaly detection in consumer electronics. We have shown a generic framework for applying QML algorithms in anomaly detection tasks. We have also briefly discussed popular supervised, unsupervised, and reinforcement learning-based QML algorithms and included five case studies of recent works to show their applications in anomaly detection in the consumer electronics field.

关键词： Quantum computing Machine learning algorithms Identity theft Finance Machine learning Very large scale integration Network intrusion

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PointOfView: A Multi-modal Network for Few-shot 3D Point Cloud Classification Fusing Point and Multi-view Image Features

PointOfView: A Multi-modal Network for Few-shot 3D Point Clo...

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IEEE computer Society Conference on computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Huantao Ren Jiyang Wang Minmin Yang Senem Velipasalar Electrical Engineering and Computer Science Dept Syracuse University Syracuse NY USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Most existing 3D point cloud analysis approaches employ traditional supervised methods, which require large amounts of labeled data, and data annotation is labor-intensive, and costly. On the other hand, although many existing works use either raw 3D point clouds or multiple 2D depth images, their joint use is relatively under-explored. To address these issues, we propose PointOfView, a novel, multi-modal few-shot 3D point cloud classification model, to classify never-before-seen classes with only a few annotated samples. A 2D multi-view learning branch is proposed for processing multiple projection images, and it contains two sub-branches to extract information at individual image level as well as among all six depth images. In addition, we propose a multi-scale 2D pooling layer, which employs various 2D max-pooling and 2D average pooling operations, with different pooling sizes. This allows fusing features at different scales. The second main branch processes raw 3D point clouds by first sorting them, and then using DGCNN to extract features. We perform within-dataset and cross-domain experiments on ModelNel40, ModelNet40-C and ScanobjectNN datasets, and compare with six state-of-the-art baselines. The results show that our approach outperforms all baselines in all experimental settings and achieve the state-of-the-art performance.

关键词： Point cloud compression Solid modeling computer vision Three-dimensional displays Shape Computational modeling Conferences

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