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A Reduced State-Space Generation Method for Concurrent Systems Based on CPN-PR Model

IEEE Transactions on computer-Aided Design of Integrated Circuits and Systems 2025年第6期44卷 2328-2342页

作者： Zhong, Wenjie Sun, Tao Zhou, Jian-Tao Wang, Zhuowei Song, Xiaoyu Inner Mongolia University College of Computer Science the Engineering Research Center of Ecological Big Data Ministry of Education the Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software the Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot010000 China Guangdong University of Technology School of Computer Science and Technology Guangzhou510006 China Portland State University Department of Electrical and Computer Engineering PortlandOR97207 United States

Colored Petri nets (CPNs) provide descriptions of the concurrent behaviors for software and hardware. Model checking based on CPNs is an effective method to simulate and verify the concurrent behavior in system design. However, the model-checking method traverses the full state space, which suffers from the state-space explosion problem. A reduced state-space generation method related to the property of concurrent systems is proposed. Specifically, we extend CPNs to define a property-related model (CPN-PR) and give a property-related analysis method whose results can be used to generate the CPN-PR model. A reduced state-space generation method is developed based on enabled binding element filtering rules. The stutter trace equivalence between the state spaces of CPN and CPN-PR has been proven by showing that the reduced state space may not change the model-checking result. A comparison experiment is conducted to demonstrate the effectiveness of our method. © 1982-2012 IEEE.

关键词： Model checking

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A Comparative Analysis of the Imputing of Missing data on Air Pollution 2

A Comparative Analysis of the Imputing of Missing Data on Ai...

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

作者： Gayathri, M. Kavitha, V. SRM Institute of Science and Technology Department of Computer Science and Engineering Kattankulathur Chennai India SRM Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India

ISBN: (纸本)9798350365269

Air pollution is a significant environmental hazard in modern society because of its serious impact on human health and the environment. In point of fact, there has been a substantial rise in the levels of pollution in the environment over the course of the past few years. data analyst is commonly encountering the issue of data missing. It is crucial to handle them efficiently to get more accurate and dependable data analysis findings. This study aims to improve understanding of data missing mechanisms, data imputation techniques, and examine the efficacy of frequently utilized data imputation techniques on quantitative datasets. Analysts and data scientists frequently face the challenge of missing data in their investigations. Therefore, it is essential to handle them in an efficient manner so as to gain improved and more trustworthy results through the analysis of the data. The objective of this research is to enhance comprehension of data missing processes and data imputed methods, while also evaluating the effectiveness of data imputed approaches often employed in quantitative datasets. A complete comparison of 5 different data imputation methods is presented in this article. These approaches include mean imputation, median imputation, mode imputation, kNN imputation and Linear Regression. For the purpose of analysing and contrasting the efficacy of the various data imputation approaches, we have utilized five distinct numeric datasets that were gathered from several air pollution repositories. Standardized Root Mean Square Error (RMSE) is the technique for assessing the efficacy of data imputation techniques. According on the findings of the analysis, the kNN imputation approach offers superior performance than the other methods. There is no correlation between the length of the dataset or the number of values that are missing in the dataset and the accuracy of the data imputed approach. © 2024 IEEE.

关键词： Air pollution

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Exploring LLM and Neural Networks Towards Malicious Prompt Detection 5

Exploring LLM and Neural Networks Towards Malicious Prompt D...

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5th International Conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Deshpande, Himani Chaudhari, Dhawal Sarode, Tanmay Kamath, Ayush Thadomal Shahani Engineering College Department of Artificial Intelligence & Data Science Mumbai India Thadomal Shahani Engineering College Department of Computer Science Mumbai India Science Thadomal Shahani Engineering College Department of Computer Mumbai India

ISBN: (纸本)9798350379945

Atthe forefront of the Artificial Intelligence Revolution is the Generative AI domain which is making splashes in generation of new content from existing Large Language Models. Large Language Models (LLMs) are flexible, effective tools with many uses. On the other hand, they can be tricked by devious provocation. This work explores the detection of fraudulent prompts intended to produce false or damaging outputs using Long Short-Term Memory (LSTM) networks. During this study we analysed the LTSM model against a neural network model. We found a 91.69% accuracy for the LTSM model and a 92.52% accuracy for the simple neural network. However, the simple neural network had a higher F1 score of 0.73 compared to the LTSM score of 0.69. We found that the standard neural model is more efficient at identifying harmful user prompts. Our research highlights the need for taking into account various architectures of models to reduce the risk of malicious prompting in language learning models. © 2024 IEEE.

关键词： Neural network models

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Improving Cybersecurity Measures Through the Revelation of Malignant URLs using Machine Learning Techniques 5

Improving Cybersecurity Measures Through the Revelation of M...

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5th International Conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Saravanan, T.M. Muthusamy, A. Thamil Selvi, C.P. Senthil Kumar, M. Maheshwari, D. Kongu Engineering College Department of Computer Technology Perundurai Erode India Department of Artificial Intelligence and Data Science Rathinam Technical Campus Coimbatore India Erode Sengunthar Engineering College Department of Artificial Intelligence and Data Science Thudupathi Erode India Kpr College of Arts Science and Research Department of Computer Technology Coimbatore India

ISBN: (纸本)9798350389609

In today's world the internet has ingrained itself deeply into our lives, and web searching has become essential for people of all ages, locations, and occupations. However, due to the rise in internet usage, there has been a rise in spoofing attacks through malicious websites. Online purchases, reservations, recharges, and various other transactions are now commonly conducted online. Internet users are increasing;it has become crucial to develop automatic URL detection systems to protect users. This proposed research aims to compare different categorization models like Decision Tree, Random Forest and Logistic Regression to determine which one achieves finest accuracy in distinguishing among genuine websites and False websites. Objective is to identify attacking sites effectively and determine the artificial intelligence algorithm that provides the best accuracy for this use. © 2024 IEEE.

关键词： Adversarial machine learning

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Octopus: Experiences with a Hybrid Event-Driven Architecture for Distributed Scientific Computing

Octopus: Experiences with a Hybrid Event-Driven Architecture...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： Pan, Haochen Chard, Ryan Zhou, Sicheng Kamatar, Alok Vescovi, Rafael Hayot-Sasson, Valerie Bauer, Andre Gonthier, Maxime Chard, Kyle Foster, Ian Southern University of Science and Technology Department of Computer Science China Illinois Institute of Technology Department of Computer Science ChicagoIL United States University of Chicago Department of Computer Science ChicagoIL United States Argonne National Laboratory Data Science and Learning Division LemontIL United States

ISBN: (纸本)9798350355543

Scientific research increasingly relies on distributed computational resources, storage systems, networks, and instruments, ranging from HPC and cloud systems to edge devices. Event-driven architecture (EDA) benefits applications targeting distributed research infrastructures by enabling the organization, communication, processing, reliability, and security of events generated from many sources. To support the development of scientific EDA, we introduce Octopus, a hybrid, cloud-to-edge event fabric designed to link many local event producers and consumers with cloud-hosted brokers, and to provide a fabric for developing resilient applications. Octopus can be scaled to meet demand, permits the deployment of highly available Triggers for automatic event processing, and enforces fine-grained access control. We identify requirements in self-driving laboratories, scientific data automation, online task scheduling, epidemic modeling, and dynamic workflow management use cases, and present results demonstrating Octopus' ability to meet those requirements. Octopus supports producing and consuming events at a rate of over 4.2 M and 9.6 M events per second, respectively, from distributed clients. © 2024 IEEE.

关键词： Event-driven architecture research automation scientific computing

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Integrating Multimodal AI Models for Enhanced Oral Cancer Detection and Diagnosis 2

Integrating Multimodal AI Models for Enhanced Oral Cancer De...

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2nd IEEE International Conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Shantha Kumari, K. Udhya shree, S. Calarany, C. Department of Data Science and Business Systems SRM Institue of Science and Technology Kattankulathur India Department of Computer Science and Engineering Rajiv Gandhi College of Engineering and Technology Pondicherry India Department of Computer Science and Engineering Tagore Government Arts and Science College Pondicherry India

ISBN: (纸本)9798331504960

Oral cancer is a major global health issue marked by high morbidity and mortality rates, primarily due to late-stage diagnosis. This work proposes the Advanced AI models that use multimodal data for early detection and diagnosis of oral cancer. Currently, traditional machine learning algorithms coupled with deep learning algorithms such as Convolutional neural networks (CNNs), focus on image-based analysis for detecting cancer. These works are delimited with minimal robustness and input data efficacy. Through the proposed multimodal AI model, histopathological images and clinical notes of the oral cancer patients are combinedly analyzed by which diagnostic accuracy is enhanced. This framework integrates CNNs for image analysis and Transformer-based models for text analysis, with a sophisticated fusion layer for data integration. Enhanced preprocessing techniques and performance optimization through Vision Transformers (ViTs) and BERT models significantly improve sensitivity, specificity, and F1-score. The multimodal AI model achieved a sensitivity of 0.90, specificity of 0.85, and an F1-score of 0.89, outperforming previous models like SVMs, RF, and CNNs. This indicates a high accuracy in identifying both positive and negative cases, ensuring reliable diagnoses. The results demonstrate a substantial improvement over traditional methods, that lays foundations for early diagnosis. © 2024 IEEE.

关键词： Lung cancer

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The Impact of Stuttering Event Representation on Detection Performance 2

The Impact of Stuttering Event Representation on Detection P...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Al-Banna, Abedal-Kareem Fang, Hui Shubita, Ahmad F. Arafah, Mohammad Eqteeshat, Amani Abuaisheh, Saif B. University Of Petra Department Of Ai And Data Science Jordan Loughborough University Department Of Computer Science United Kingdom University Of Petra Department Of Software Engineering Jordan University Of Petra Department Of Computer Science Jordan Malaya University Department Of Information System Malaysia

ISBN: (纸本)9798350394962

Stuttering or stammering is a common neurogenic, psychogenic fluency disorder wherein people have uncontrolled blocks on the natural flow of speech. Automatic Stuttering Event Detection (SED) is a widely recognised challenge due to the heterogenic and overlapped nature of stuttering speech. Therefore, this comparative study investigates the impact of stuttering event representation on detection performance. The paper evaluates the performance of SED and shows the impact of employing ASR pre-trained feature extractions on each stuttering event. Moreover, the paper provides different groups of experiments that evaluate the impact of three acoustic features, the Zero Crossing Rate (ZCR), the Spectral Flux Onset Strength Envelope (SFO), and Fundamental Frequency (FF) features, on SED performance. The paper demonstrates that the fusion of spectral and ASR features improves SED performance across all stuttering events by 8%. The F1 score of the Block class increased by approximately 10%, from 15% to 25%, while the word repetition and interjection increased by 8%. In addition, our experiments proved that the fusion of temporal features increased the average F1 score of SED by 2% across all stuttering events. The F1 score of the prolongation and sound repetition classes increased by 6% and 3%, respectively. © 2024 IEEE.

关键词： Deep learning

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Indonesian Dynamic Sign Language Recognition for Individuals with Sensory Disabilities using LSTM 4

Indonesian Dynamic Sign Language Recognition for Individuals...

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4th International Conference of science and Information Technology in Smart Administration, ICSINTESA 2024

作者： Yunita, Reni Nababan, Erna Budhiarti Lydia, Maya Silvi Universitas Sumatera Utara Department of Data Science and Artificial Intelligence Medan Indonesia Universitas Sumatera Utara Computer Science Medan Indonesia

ISBN: (纸本)9798350376111

People with disabilities often face difficulties communicating with the general public, especially regarding sign language. Sign Language Recognition (SLR) is a technology focused on recognizing, interpreting, and translating sign language into text or speech. SLRs can be divided into two types: static and dynamic. This research focuses on dynamic sign language recognition to develop recognition for Indonesian Sign Language, using the Python programming language with libraries such as Mediapipe, cvzone, sklearn, numpy, and the Long Short-Term Memory (LSTM) technique. These findings show the potential of SLR technology in assisting communication for individuals with disabilities in Indonesia. This research significantly contributes to the development of Indonesian Sign Language SLR. © 2024 IEEE.

关键词： Disabled persons

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Derandomizing Multi-Distribution Learning 38

Derandomizing Multi-Distribution Learning

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Larsen, Kasper Green Montasser, Omar Zhivotovskiy, Nikita Department of Computer Science Aarhus University Denmark Department of Statistics and Data Science Yale University United States Department of Statistics University of California Berkeley United States

Multi-distribution or collaborative learning involves learning a single predictor that works well across multiple data distributions, using samples from each during training. Recent research on multi-distribution learning, focusing on binary loss and finite VC dimension classes, has shown near-optimal sample complexity that is achieved with oracle efficient algorithms. That is, these algorithms are computationally efficient given an efficient ERM for the class. Unlike in classical PAC learning, where the optimal sample complexity is achieved with deterministic predictors, current multi-distribution learning algorithms output randomized predictors. This raises the question: can these algorithms be derandomized to produce a deterministic predictor for multiple distributions? Through a reduction to discrepancy minimization, we show that derandomizing multi-distribution learning is computationally hard, even when ERM is computationally efficient. On the positive side, we identify a structural condition enabling an efficient black-box reduction, converting existing randomized multi-distribution predictors into deterministic ones. © 2024 Neural information processing systems foundation. All rights reserved.

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Gloss-Free Sign Language Translation using Sign2gpt-Next Technique

Gloss-Free Sign Language Translation using Sign2gpt-Next Tec...

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2024 International Conference on Computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Babisha, A. Umarani Srikanth, G. Angel Kiruba, D.R. Sundar, R. Department of Artificial Intelligence and Data Science Panimalar Engineering College Chennai India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering DMI College of Engineering Chennai India Department of Computer Science and Engineering School of Computing Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331510299

This work proposes a new approach to convert the data from sign language to spoken language without exposing the data to a gloss layer. Whenever gloss annotations are used which frequently are incomplete and act as an intermediate representation, it is usually found to be a bottleneck in translating sign language videos. In order to avoid this, the authors use a combination of modern machine learning algorithms to arrive at the conclusion. They use Dino-V2 Vision Transformer to implement the function of extracting the spatial features of the input sign language video frames. This model is particularly suitable for learning and visual patterns in general and particularly for capturing fine details of these patterns. Further, a spatio-temporal transformer layer is added to automate the temporal processing of the Sign-Language which helps in the arrangement of the body gestures and limb movements needed at a specific temporal frame. It is then translated into the desired spoken language using a multilingual Generative Pre-trained Transformer (GPT) model. The approach can be said to be a big leap forward in sign language transliteration especially for such situations where few Gloss annotations are available, and making way for better and improved tools for the Deaf and the hard-of-hearing. © 2024 IEEE.

关键词： Audition

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