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5th International Conference on Computer and Applications, ICCA 2023

作者： Haque, Sardar Anisul Jaoua, Ali Al Ja'am, Jihad Mohamed Ur Rehman, Hafeez Oryx Universal College in partnership with LJMU School of Computing and Data Science Doha Qatar

ISBN: (纸本)9798350303254

This paper describes a novel algorithm that enumerates a set of Boolean variables from a 3-SAT instance such that for any truth assignments, it will be reduced to a 2-SAT instance or an empty formula or a formula with one or more empty clauses. We showed that the set of Boolean variables of interest can be found by formulating the given 3-SAT instance into an instance of set covering problem. © 2023 IEEE.

关键词： NP-Hard SAT set cover problem

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On eigenvalues of a high-dimensional Kendall's rank correlation matrix with dependence

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science China Mathematics 2023年第11期66卷 2615-2640页

作者： Zeng Li Cheng Wang Qinwen Wang Department of Statistics and Data Science Southern University of Science and TechnologyShenzhen 518055China School of Mathematical Sciences Shanghai Jiao Tong UniversityShanghai 200240China Key Laboratory of Scientific and Engineering Computing(Shanghai Jiao Tong University) Shanghai 200240China School of Data Science Fudan UniversityShanghai 200433China

In this paper,we investigate the limiting spectral distribution of a high-dimensional Kendall’s rank correlation *** underlying population is allowed to have a general dependence *** result no longer follows the generalized Marcenko-Pastur law,which is brand *** is the first result on rank correlation matrices with *** applications,we study Kendall’s rank correlation matrix for multivariate normal distributions with a general covariance *** these results,we further gain insights into Kendall’s rank correlation matrix and its connections with the sample covariance/correlation matrix.

关键词： Hoeffding decomposition Kendall's rank correlation matrix limiting spectral distribution random matrix theory

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Vanessa : Visual Connotation and Aesthetic Attributes Understanding Network for Multimodal Aspect-based Sentiment Analysis

Vanessa : Visual Connotation and Aesthetic Attributes Unders...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Xiao, Luwei Mao, Rui Zhang, Xulang He, Liang Cambria, Erik School of Computer Science and Technology East China Normal University Shanghai China College of Computing and Data Science Nanyang Technological University Singapore

ISBN: (纸本)9798891761681

Prevailing research concentrates on superficial features or descriptions of images, revealing a significant gap in the systematic exploration of their connotative and aesthetic attributes. Furthermore, the use of cross-modal relation detection modules to eliminate noise from comprehensive image representations leads to the omission of subtle contextual information. We present Vanessa, a visual connotation and aesthetic Attributes understanding network for multimodal aspect-based sentiment analysis. It incorporates a multi-aesthetic attributes aggregation (MA3) module that models intra- and inter-dependencies among bi-modal representations as well as emotion-laden aesthetic attributes. Moreover, we devise a self-supervised contrastive learning framework to explore the pairwise relevance between images and text via the Gaussian distribution of their CLIP scores. By dynamically clustering and merging multimodal tokens, Vanessa effectively captures both implicit and explicit sentimental cues. Extensive experiments on two widely adopted benchmarks verify Vanessa's effectiveness. © 2024 Association for Computational Linguistics.

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Creating a Blueprint for Success in First-Year computing

Creating a Blueprint for Success in First-Year Computing

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2023 ASEE Annual Conference and Exposition - The Harbor of Engineering: Education for 130 Years, ASEE 2023

作者： Kreimendahl, Frank Schuster, Micah Suresh-Menon, Durga School of Computing and Data Science Wentworth Institute of Technology BostonMA United States

First-year computing is the most important pathway to undergraduate degrees and student success in computing, data science, engineering, and related fields. While there is an abundance of research in the area, we still lack retention of students in first-year computing. This is due to many factors including, but not limited to, 1) student math preparation and readiness from high school, 2) adjunct faculty teaching classes where no common coursework is employed, 3) faculty teaching the same courses in different ways, creating inconsistency in material covered, and 4) lack of tutoring for students who are struggling, behind, or do not understand the material. This paper presents task force results from a collaboration between faculty and academic support specialists. The paper focuses on two high-impact areas of improvement: standardizing curricula and building support scaffolding outside the classroom. The results, a comprehensive course blueprint, include core resources for a first-semester computing course and recommendations for identification and support of struggling students. © American Society for Engineering Education, 2023.

关键词： Students

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Feasibility Study on Restoring Chinese Documents by Convolutional Autoencoder

Feasibility Study on Restoring Chinese Documents by Convolut...

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2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023

作者： Lo, Shi Wei Chou, Hsiu-Mei National Center for High-Performance Computing Data Science and Technology Division Hsinchu Taiwan

ISBN: (纸本)9798350324174

Digitalizing documents has become a crucial aspect of modern information management. However, various factors, such as manual handwriting, stains, and degradation, can significantly impact the quality of digitalized documents. This paper proposes a autoencoder-based network cleaning technology to completely erase manual writing, stains, colors, and background noise from paper documents to enhance their digitalized quality. Our proposed approach utilizes machine learning algorithms to identify and isolate unwanted elements from the document and then replace them with clean and transparent backgrounds. The experimental results demonstrate that our proposed approach significantly improves the digitalized quality of paper documents, making them more suitable for long-term storage and retrieval. Our proposed technology has the potential to revolutionize the field of paper document digitalization and improve the efficiency of information management. © 2023 IEEE.

关键词： Information management

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Skin Cancer Classification using CNN and Transfer Learning (TL) 7

Skin Cancer Classification using CNN and Transfer Learning (...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Patel, Shashank Pandey, Mudita Rajeswari, D. Srm Institute of Science And Technology School of Computing College of Engineering And Technology Department of Data Science And Business Systems Chennai Kattankulathur603203 India

ISBN: (纸本)9798350359299

Skin cancer is a predominant and possibly lethal condition that distresses people across the world. Primary detection and precise finding are crucial for leveraging efficacious treatment and enhanced patient consequences. This research study explores the application off Artificial Intelligence (AI) resolutions for the primary finding and analysis of skin cancer. The study leverages a dataset of dermatological images and employs various ML methods, including Convolutional Neural Networks (CNNs), Support Vector Machines (SVMs), and decision trees, to analyse the existence of skin cancer with a higher degree of accuracy. By utilizing a combination of image processing and feature extraction, the AI model demonstrates higher performance in classifying skin lesions into malignant or benign categories. The proposed model has achieved an accuracy rate of 98.52%, making the AI-based system a promising tool for dermatologists and healthcare professionals. © 2024 IEEE.

关键词： Convolutional neural networks

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Fraud Detection in Financial Transactions: A Machine Learning Approach 9

Fraud Detection in Financial Transactions: A Machine Learnin...

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9th International Conference on science, Technology, Engineering and Mathematics, ICONSTEM 2024

作者： Patel, Shashank Pandey, Mudita Rajeswari, D. School of Computing College of Engineering And Technology Srm Institute of Science And Technology Department of Data Science And Business Systems Chennai Kattankulathur603203 India

ISBN: (纸本)9798350365092

Financial fraud presents substantial risks to individuals and financial institutions globally, necessitating efficient detection mechanisms to mitigate probable fatalities. In this study, the development and evaluation of machine learning (ML) models for detecting fraudulent activities in mobile money transactions are investigated. Using a synthetic dataset, realworld transaction scenarios involving a variety of transaction kinds and features are simulated. The effectiveness of many machine learning (ML) methods, such as LGBM, random forests, XGBoost, and logistic regression, in spotting fraudulent transactions is investigated through data preparation, feature engineering, and model-building procedures. Employing techniques such as SMOTE-Tomek resampling and hyperparameter tuning enhances the performance and robustness of our models. Our results reveal that the XGBoost classifier emerges as the top-performing model, exhibiting a remarkable accuracy of 99.95%. The findings underscore the potential of MLbased approaches to bolster security and trust in mobile financial services, contributing to the ongoing advancement of fraud detection methodologies in the financial sector. © 2024 IEEE.

关键词： Logistic regression

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Signs to Speech 2

Signs to Speech

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2nd IEEE International Conference on Networking and Communications, ICNWC 2024

作者： Vardhan, Harsh Harsavardhan, S. Lakshmi, M. SRM Institute of Science and Technology School of Computing College of Engineering and Technology Department of Data Science and Business Systems Kattankulathur Chennai603203 India

ISBN: (纸本)9798350365269

This research introduces an innovative Sign Language to Speech Conversion Model using Convolutional Neural Networks (CNNs) to address communication barriers for the people who are deaf and unable to hear properly. The model employs deep learning for the automatic extraction of spatial patterns from sign language images. A diverse sign language dataset is carefully curated, undergoing Pre-processing for improved quality. The CNN architecture, designed for adaptability, captures local and global features, enabling accurate *** the training phase, the model learns to map sign language gestures to spoken language representations. Performance evaluation methods which include accuracy, recall, precision and F1 score, demonstrate the effectiveness of the model. Results underscore the significance for the deaf community. The discussion explores broader implications in education, healthcare, and social interactions, acknowledging limitations and proposing future research *** conclusion, the Sign Language to Speech Conversion Model contributes to sign language recognition, offering an advanced solution for communication barriers and promoting inclusivity in diverse social settings. © 2024 IEEE.

关键词： Convolution

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Anatomization of Neural Networks based models for Semantic Analysis of Tabular dataset 9

Anatomization of Neural Networks based models for Semantic A...

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9th IEEE International Conference for Convergence in Technology, I2CT 2024

作者： Bharath, B. Sai Bollineni, Jahnavi Mandala, Sandeep Preetham Ganguly, Tania Amudha, J. Shukla, Nikhil Joseph, Raj Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science And Engineering Bengaluru India Intellectyx Data Science Pvt Ltd India

ISBN: (纸本)9798350394474

The Paper focuses on analysing neural network models that are used for semantically classifying tabular customer datasets. Additionally, we propose a custom neural network architecture to analyze tabular datasets and enable the model to extract and comprehend the underlying semantics within the data. The research focuses on three distinct neural network models: CharCNN, Bi-LSTM, and CNN+Bi-LSTM. Through comprehensive evaluation based on accuracy, and confidence scores of the models' performance, we determined that Bi-LSTM proved to be the best fit for this approach and dataset. The findings suggest that the custom CHAR-CNN model can be effective in classifying tabular data and can potentially be applied on various datasets considering both computational time and accuracy. This research contributes to the advancement in the field of semantic analysis for tabular dataset, opening avenues for further research on how to handle different kinds of Tabular datasets and enhancement of semantic models in NLP. © 2024 IEEE.

关键词： Neural network models

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Image Encryption Design Based on Henon Mapping 5

Image Encryption Design Based on Henon Mapping

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5th Asia-Pacific Conference on Image Processing, Electronics and Computers, IPEC 2024

作者： Su, Wenyi Zhou, Mingjie Bao, Jiaqing Yang, Lanjing Zhu, Liping Li, Guodong Guilin University of Electronic Technology School of Mathematics and Computing Science Guilin China Changji University School of Mathematics and Data Science Changji China

ISBN: (纸本)9798350374407

Image data, compared to textual information, possesses visual characteristics that require novel encryption methods to ensure its security. Therefore, it is necessary to explore new encryption techniques tailored specifically for image data. By applying the Henon chaotic map, two random sequences are generated. These sequences, along with a given key, are used to produce four equally-sized random matrices through specific formulas. Two of these matrices are employed in the forward and backward diffusion modules, while the remaining two are utilized in the permutation module. Through two rounds of diffusion operations and one permutation operation, the four random matrices are utilized to encrypt the image, resulting in a secure, encrypted image. The security performance of this encryption algorithm is analyzed theoretically, considering encryption and decryption speeds, histogram statistical characteristics, correlations between adjacent pixels, and information entropy. Additionally, simulation experiments demonstrate the effectiveness of this digital image encryption scheme, showcasing excellent encryption results and security performance. © 2024 IEEE.

关键词： Chaos theory

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