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2024 IEEE International Conference on Communications, Computing, Cybersecurity and Informatics, CCCI 2024

作者： Gautam, Deepika Thakur, Garima Obaidat, Mohammad S. Hsiao, Kuei-Fang Kumar, Pankaj Central University of Himachal Pradesh Srinivasa Ramanujan Department of Mathematics HP Dharamshala176215 India School of Computer and Communication Engineering University of Science and Technology Beijing China School of Computing SRM University Department of Computational Intelligence TN Kattankulathur India School of Engineering The Amity University UP 201301 India

ISBN: (纸本)9798350349832

The Internet of Medical Things (IoMT) is a network of medical devices, hardware infrastructure, and software that accumulates real-Time data through remote monitoring and minimizes doctor-patient interaction, enhancing the quality of remedies. However, the facile availability and accessibility of data over the web have given rise to serious information security and credibility issues in the IoMT. Recently, Chen et al. [1] proposed a provably secure authenticated key agreement protocol for remote patient monitoring in IoMT. However, after revisiting their scheme, we found security flaws such as user impersonation, ephemeral secret leakage attacks, denial-of-service attacks, and session key disclosure attacks. Therefore, this paper presents cryptanalysis as well as an improvement of Chen et al. 's scheme that strives to furnish protocols with robust security in opposition to the mentioned attacks. The security and effectiveness are displayed through informal analysis and evaluation with other existing state-of-The-Art metrics like computational and commu-nication cost. At last, the findings project the superiority of the proposed improved scheme. © 2024 IEEE.

关键词： Authentication Protocol

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Blending Spline Surfaces Over Polygon Mesh and Their Application to Isogeometric Analysis

SSRN

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SSRN 2023年

作者： Kravetc, Tatiana Department of Computer Science and Computational Engineering Faculty of Engineering Science and Technology UiT The Arctic University of Norway Norway

Finite elements are allowed to be of a shape suitable for the specific problem. This choice defines thereafter the accuracy of the approximated solution. Moreover, flexible element shapes allow for the construction of an arbitrary domain topology. Polygon meshes are a common representation of the domain that cover any choice of the finite element *** an alternative tool for modeling and analysis, blending spline surfaces support representation on polygon grids. The blending splines have a hierarchical structure, which is obtained by generating local surfaces that cover each node support and then blended with a special type of basis functions. This type of splines in their tensor product form is suitable for application to isogeometric analysis problems. A more general representation constructed on polygonal elements can be used on a wider range of domain topology in comparison with tensor product *** this paper we introduce a novel approach to constructing curvilinear polygon meshes in the blending spline representation in application to the isogeometric analysis context. © 2023, The Authors. All rights reserved.

关键词： Blending

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Innovations in Natural Language Processing through Enhanced Linguistic Model Accuracy and Efficiency using Advanced Reinforcement Learning Techniques 2

Innovations in Natural Language Processing through Enhanced ...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Kalaiselvi, K. Shanmugam, Raju Tamilselvan, S. Manikandan, S.P. Rajasekar, P. Department of Electronics and Communication Engineering Hindusthan College of Engineering and Technology Tamil Nadu Coimbatore India Unitedworld School of Computational Intelligence Karnavati University Gujarat India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Saveetha University Tamil Nadu Chennai India School of Engineering and Technology CMR UNIVERSITY Karnataka Bengaluru India Department of ECE Sri Krishna College of Technology Kovaipudur Coimbatore India

ISBN: (纸本)9798350383867

The paper will be focusing on combining sophisticated reinforcement learning approaches with natural language processing (NLP) platforms to step up the accuracy, efficiency, and adaptability of the linguistic models. The methodology approach is based on stepwise creation of efficient basic model architectures which is combined with formulation of tasks as RL problems and careful selection of appropriate algorithms and training processes. In our study on the development of an advanced AI model which took varying evaluation metrics, it is now apparent through the comprehensive evaluation of the same, that our model's performance on different evaluation metrics was significantly better than that of the baseline approaches. For instance, this technique leads to better precision and operation speed, allowing NLP systems to undertake more complex linguistic tasks with higher rate of precision and faster inferential speeds. Furthermore, this approach facilitates the model adaptation process which helps in accommodating a hybrid intelligence environment where models are able to evolve in changing language contexts and domains. Interpretability assessment is focused on producing not only the transparency of the described method but also the ability for the user to understand the reasoning of the model and the decision-making processes. Smart cities also undertake a set of ethical considerations that encompass the significance of regards to bias mitigation, fairness awareness and transparency to build equitable and trustworthy outcomes in the real-world implementation. In total, research done shows the possibility for advanced reinforcement learning techniques to have a significant impact to NLP, thus creating innovative NLP models capable of ensuring that they are diverse, adaptable and have necessary measures of ethics as the needs are changing. © 2024 IEEE.

关键词： Adversarial machine learning

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A Keyphrase-Centric Search Engine for Scientific Papers 23

A Keyphrase-Centric Search Engine for Scientific Papers

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Proceedings of the 15th Annual Meeting of the Forum for Information Retrieval Evaluation

作者： Avishek Lahiri Debarshi Kumar Sanyal Imon Mukherjee School of Mathematical and Computational Sciences Indian Association for the Cultivation of Science India Department of Computer Science and Engineering IIIT Kalyani India

ISBN: (纸本)9798400716324

The ever increasing interest and participation in scientific research and development has been a major contributor towards the exponential increase of textual and multi-modal scientific data on the Web over the past few years. It is imperative for scientists to keep track of the latest development in their respective area of research, but the ever-expanding landscape of research publications makes it quite challenging. Automating the parsing, retrieval, and understanding of this literature through state-of-the-art NLP and IR tools is the need of the hour. The goal of this doctoral work will be to develop new tools singularly for the low-resource scientific domain. We propose to contribute to this field by not only developing deep learning solutions for these tasks, but also providing the NLP and IR community with unique challenging datasets wherever necessary.

关键词： Citation intent classification Entity linking Keyphrase extraction Knowledge graph Named entity recognition

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Enhancing Object Detection Robustness for Cross-Depiction Through Neural Style Transfer 8

Enhancing Object Detection Robustness for Cross-Depiction Th...

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8th International Conference of Yearly Reports on Informatics, Mathematics, and engineering, ICYRIME 2023

作者： Fiani, Francesca Puglisi, Adriano Napoli, Christian Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Computational Intelligence Czestochowa University of Technology Czestochowa42-201 Poland

Modern neural networks models for computer vision are trained on millions of images. The idea is that models are able to increase generalization when the dataset contains well diversified images, e.g. with varied illumination and environmental conditions of the same objects. Generalization is particularly relevant in object detection, especially for what concerns the cross-depiction problem. In this work we explore the use of Neural Style Transfer as a novel technique to morph the original data, with the aim to enhance model generalization. To verify the effect on performances for object detection models, we selected the Faster R-CNN model to be applied on the Pascal VOC 2012 dataset. A number of tests were performed through style variations on images and by tuning Neural Style Transfer parameters to maintain the content of the original images. The experiments showed promising results, which effectively provide a foundation for future studies on cross-depiction via Neural Style Transfer. © 2023 Copyright for this paper by its authors.

关键词： Object detection

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Can microplastic contamination affect the wing morphology and wingbeat frequency of Aedes aegypti (Diptera: Culicidae) mosquitoes?

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Environmental science and Pollution Research 2024年第49期31卷 59289-59301页

作者： de Nadai, Barbara Lepretti Moura, Lidia Castro, Gleyson B. Silva, Kamila Jessie Sammarro Maletzke, André Gustavo Corbi, Juliano José Batista, Gustavo E. A. P. A. Machado, Renato Bobsin Computational Entomology Laboratory Department of Engineering and Exact Sciences Western Paraná State University Foz Do Iguaçu Brazil Environmental Engineering and Sanitation Leonardo da Vinci University Center Indaial Brazil Laboratory of Ecotoxicology and Applied Ecology Department of Hydraulic Engineering and Sanitation São Carlos School of Engineering University of Sao Paulo Sao Carlos Brazil Environmental Biophotonics Laboratory São Carlos Institute of Physics University of Sao Paulo Sao Carlos Brazil School of Computer Science and Engineering University of New South Wales Sydney Australia

Microplastics (MPs) are increasingly widespread in the environment, which raises questions about their potential effects at different biological levels. It is essential to assess the impacts on biodiversity, and it is also crucial to understand whether the presence of MPs can interfere with the biological traits of species of relevance in public health. Considering that the life-history traits of mosquitoes, such as size and the wingbeat frequency (WBF), are related to its vector competence, here, we study the effects of 106 particles L−1 (as expected concentration of MPs on the environment, using the polyethylene type) on WBF, as well as wing morphology, testing the Culicidae species found across all continents, Aedes aegypti, as an indicator. Results show that larvae survival and development were not affected by the tested concentration of MP. Geometric morphometrics showed some asymmetry in female mosquito wings, which were also smaller for individuals reared in MP suspension. As for WBF, results did not indicate any significant differences between females. Male mosquitoes, however, showed alterations in WBF and wing morphology, suggesting possible sex-specific reactions to microplastic exposure. Also, the combination of morphological parameters analyzed as covariates (wing centroid size and body weight) did not significantly affect WBF for both female and male mosquitoes. Overall, this study shows an inaugural investigation of the effects of MP on wing size and WBF on Ae. aegypti, shedding light on these parameters tested for a current pollution issue and its impact on a virus vector. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Invertebrates

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ADAPTIVE RANK ALLOCATION: SPEEDING UP MODERN TRANSFORMERS WITH RANA ADAPTERS

arXiv

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arXiv 2025年

作者： Garcia, Roberto Liu, Jerry Sorvisto, Daniel Eyuboglu, Sabri Institute of Computational and Mathematical Engineering Stanford University United States Department of Computer Science Stanford University United States

Large Language Models (LLMs) are computationally intensive, particularly during inference. Neuron-adaptive techniques, which selectively activate neurons in Multi-Layer Perceptron (MLP) layers, offer some speedups but suffer from limitations in modern Transformers. These include reliance on sparse activations, incompatibility with attention layers, and the use of costly neuron masking techniques. To address these issues, we propose the Adaptive Rank Allocation framework and introduce the Rank and Neuron Allocator (RaNA) adapter. RaNA adapters leverage rank adapters, which operate on linear layers by applying both low-rank matrix decompositions and adaptive masking to efficiently allocate compute without depending on activation sparsity. This enables RaNA to be generally applied to MLPs and linear components of attention modules, while eliminating the need for expensive maskers found in neuron-adaptive methods. Notably, when compared to neuron adapters, RaNA improves perplexity by up to 7 points and increases accuracy by up to 8 percentage-points when reducing FLOPs by ∼44% in state-of-the-art Transformer architectures. These results position RaNA as a robust solution for improving inference efficiency in modern Transformer architectures. © 2025, CC BY.

关键词： Neurons

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Graph Neural Networks for Optimizing Reliability in Vehicle-to-Infrastructure Communication in Autonomous Systems

Graph Neural Networks for Optimizing Reliability in Vehicle-...

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International Conference on Communication and Electronics Systems (ICCES)

作者： G Neelamegam J. Revathy M. Kannukkiniyal M. Devendran J. Akshya Mani Deepak Choudhry Department of Computer Science and Engineering VSB College of Engineering and Technology Coimbatore Department of Artificial Intelligence and Data Science Christ the King Engineering College Coimbatore Department of Computer Science and Engineering Kongu Engineering College Erode Department of Computer Science and Engineering Hindustan institute of technology Coimbatore Department of Computational Intelligence SRM Institute of Science and Technology Chennai Department of Computing Technologies SRM Institute of Science and Technology Chennai

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

High reliance on autonomous systems necessitates efficient and reliable data exchange through Vehicle-to-Infrastructure (V2I) communication to have appropriate and stable network performance in dynamic vehicular environments. With heuristic approaches, Dijkstra's algorithm, and Deep Q-Networks (DQN), several methods are proposed for optimizing latency reduction, packet delivery rate improvement, and power efficiency in the direction of V2I communications. However, these approaches suffer from important challenges: low adaptability of the network to real-time dynamics, suboptimal resource usage, and difficulties in scalability towards complex large-scale networks. In this scenario, we propose a framework that incorporates a GNN inside autonomous systems for V2I communication to optimize performance. The GNN will process real-time graph features, including signal strength, packet loss, and mobility speed, as it dynamically changes in adaptation to the evolution of network behaviour and other functions. This work shows the superiority of the proposed GNN model for the efficiency of experimentally conducted simulations compared to the current models; it achieved latency reduced by up to 28%, 5-10% PDR, and 20% energy efficiency across the various scenarios with urban, rural, highway, and suburban networks. The results validate robustness and scalability for practical implementations by the proposed approach in real-world autonomous systems. Further development is possible with this framework: for example, the use of multi-agent reinforcement learning techniques within cooperative decision-making and the enhancement through blockchain-based security mechanisms to ensure the integrity of the data.

关键词： Road transportation Adaptation models Vehicle-to-infrastructure Scalability Heuristic algorithms Reinforcement learning Graph neural networks Real-time systems Energy efficiency Vehicle dynamics

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Mosquito Type Identification using Convolution Neural Network 3

Mosquito Type Identification using Convolution Neural Networ...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Kumar, V Senthil Prasad, Veduri Veera Kunaisa, Khallik Swain, Mahendra Pratap Anandaram, Harishchander Kumar, Ashok Malla Reddy Institute of Technology and Science Department of Computer Science and Engineering Telangana Secunderabad500100 India Aditya College of Engineering Department of Computer Science and Engineering Andhra Pradesh Surampalem India Hkbk College of Engineering Department of Computer Science and Engineering Karnataka Bengaluru560045 India Department of Pharmaceutical Sciences and Technology Jharkhand Mesra Ranchi835215 India Amrita Vishwa Vidyapeetham Centre for Excellence in Computational Engineering and Networking Tamil Nadu Coimbatore641112 India Banasthali Vidyapith Department of Computer Science Rajasthan Banasthali304022 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Mosquitoes are insects which are most commonly found in places with high water content. There are various types of mosquitos and each type has its own destructive characteristics. In order to completely eradicate mosquitoes from a particular place, people must know what type of mosquito it is. But this is not a task that can be done with naked eyes. To help this situation, this study develops a Deep Learning (DL) model which can predict the type of mosquito with great accuracy. For this purpose, a dataset of images of the Aedes and culex species of mosquito is collected from Kaggle. Following that, this dataset is split into three parts: training, validation, and testing. Then, pixel and image scaling is used to preprocess this dataset. A DL model is produced using the Convolutional Neural Network (CNN) technique. The model is then trained using the preprocessed dataset. The results of the training are also calculated and documented. The results are then put into a graph for easier examination. The highest accuracy of the model is produced during training and it is 99.2%. As for the loss values, the lowest loss value is 0.8% which is so small for a classification algorithm. Next, tests are conducted on the trained and validated models. A confusion matrix is then used to examine the model performance. The model is then assessed using the metrics such as accuracy, True Positive Rate (TPR), True Negative Rate (TNR), False Positive Rate (FPR), False Negative Rate (FNR), precision, and F1 score. These parameters' final values were also determined to be satisfactory. © 2022 IEEE.

关键词： Convolutional neural networks

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A 5G NB-IoT Infrastructure for Secured Demand-Side Information Transmission and Predictive Analyses in Smarter Grids 3

A 5G NB-IoT Infrastructure for Secured Demand-Side Informati...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Ramana, K Seshadri Priyadarshini, Y. Indira Jambukesh, H.J. Singh, Rajesh Kandasamy, Manivel Kumar, Bura Vijay Ravindra College of Engineering for Women Department of Computer Science and Engineering Kurnool India Ravindra College of Engineering for Women Department of CSE Kurnool India Government Engineering College Electronics and Communication Haveri India Uttaranchal University Uttaranchal Institute of Technology India Karnavati University Unitedworld School of Computational Intelligence Gujarat Gandhinagar382422 India SR University School of Computer Science and Artificial Intelligence Telangana Warangal India

ISBN: (纸本)9798350399264

In sequence to start making utilization of Dedicated Short - spectrum of devices (NB-IoT) just on buyer edge of the energy grid, improve the security of transferring details, and utilize market records, we focus on the dependable interaction, trustworthy assistance, and interesting analyzation of 5G + Smart Grid as well as we construct a conclusive remedy that includes encrypting data stations, managed service providers, and clever analytical strategies. The proposed 5GNBIoT design from the research can aid in grid control and market responses in contrast to increasing the degree of connectivity among Five G and the nationwide grid. This study will therefore help bring a new wave of electronic innovations to the modern grid, develop a deeper integrated services or software for the 5G + Modern Wire, improve the networks' cognitive, and advance the dual carbon goal of the energy network. © 2023 IEEE.

关键词： Commerce

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