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

作者： Shanmugam, Raju Babu, M. Tamilselvan, S. Rajasekar, P. Kalaiselvi, K. Unitedworld School of Computational Intelligence Karnavati University Gujarat India Department of Information Technology Rajalakshmi Engineering Çollege Thandalam Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Saveetha University Tamil Nadu Chennai India Department of ECE Sri Krishna College of Technology Kovaipudur Tamil Nadu Coimbatore India Department of Electronics and Communication Engineering Hindusthan College of Engineering and Technology Tamil Nadu Coimbatore India

ISBN: (纸本)9798350383867

The quantum annealing technique is such a paradigm that is next generation that can be used in solving optimization problem across different scientific domains like finance and material science and is capable of breaking through past obstacles to generate solutions for problems waiting to be solved. The purpose of this paper is the detailed depiction of the application of the quantum annealing in the optimization that includes the principles, applications, methodologies and outcomes and future trends all of them. Through a multidisciplinary approach, we examine the use of quantum annealing, as the method that exploits the specific features of the quantum annealing through the quantum mechanics, to explore the multitude of solution spaces faster and better than the classical methods, leading to the superior solution quality and the faster computing times. Next, we give examples for its implementation in Portfolio optimization, Machine learning, chemical optimization, Traffic management and so on depicting its role in delivering tangible benefits such as higher financial returns, precision improvement, and time savings contrast to the classical methods. Therefore, we also emphasize the current impediments, such as the scaling down the size and the ethical aspect, through which we give the way forward for the subsequent scientific researches and studies. In this abstract, we aim to depict the helix-like nature of quantum annealing in the revolution of optimization, bringing in innovations, and thus, shaping the future in which it is the quantum computing technologies that hold the central stage in addressing the complex interconnecting real-world issues. © 2024 IEEE.

关键词： Investments

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Threat Eye: Behavior Analytics for Cloud Security using ML

Threat Eye: Behavior Analytics for Cloud Security using ML

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International Conference on ent Computing and Control Systems (ICICCS)

作者： J. R. V. Jeny S. Shivaspandana K. Pavan N. Muthukumaran K. Akash Department of Computer Science and Engineering (AI & ML) Vignan Institute of Technology and Science Department of ECE Centre for Computational Imaging and Machine Vision Sri Eshwar College of Engineering Coimbatore India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Cloud computing has developed for its storage and accessibility of data, providing flexibility, scalability, and affordability. But its extensive use has also resulted in cyberattacks and unauthorized access. The traditional intrusion detection mechanisms become ineffective for different attack patterns, insider attacks and zero-day exploits. This paper provides an efficient way to address this issue by providing a mechanism to identify the threat based on the behaviour of the user. Unlike traditional approaches this paper provides a system that monitors the user behaviour patterns considering the features such as login history, authentication attempts, session duration, file access activities, password change frequency and IP location modifications. By considering these features the attack can be detected, and the data access is restricted to prevent from unauthorized access. This paper provides handling of attack with a combination of machine learning algorithms including SVM, Decision tree and XBoost, these help in distinguishing between the attacker and the authorized user. This helps in identifying complex attack behaviours and detect insider threats. Additionally, RSA encryption is integrated to secure data transmissions, preventing unauthorized access. By combining behavioural analysis, attacks prediction, and encryption, this provides a robust cybersecurity framework.

关键词： Support vector machines Logistic regression Machine learning algorithms Cloud computing security Scalability Intrusion detection Passwords Threat assessment Encryption Monitoring

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IMPROVING THE ADAPTIVE MOMENT ESTIMATION (ADAM) STOCHASTIC OPTIMIZER THROUGH AN IMPLICIT-EXPLICIT (IMEX) TIME-STEPPING APPROACH

arXiv

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arXiv 2024年

作者： Bhattacharjee, Abhinab Popov, Andrey A. Sarshar, Arash Sandu, Adrian Computational Science Lab Department of Computer Science Virginia Tech BlacksburgVA24060 United States The Oden Institute for Computational Engineering and Sciences The University of Texas AustinTX78712 United States Department of Computer Engineering and Computer Science California State University Long BeachCA90840 United States

The ADAM optimizer, often used in Machine Learning for neural network training, corresponds to an underlying ordinary differential equation (ODE) in the limit of very small learning rates. This work shows that the classical ADAM algorithm is a first-order implicit-explicit (IMEX) Euler discretization of the underlying ODE. Employing the time discretization point of view, we propose new extensions of the ADAM scheme obtained by using higher-order IMEX methods to solve the ODE. Based on this approach, we derive a new optimization algorithm for neural network training that performs better than classical ADAM on several regression and classification problems. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic systems

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Optimal eye surgeon: finding image priors through sparse generators at initialization 24

Optimal eye surgeon: finding image priors through sparse gen...

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Proceedings of the 41st International Conference on Machine Learning

作者： Avrajit Ghosh Xitong Zhang Kenneth Sun Qing Qu Saiprasad Ravishankar Rongrong Wang Department of Computational Mathematics Science and Engineering (CMSE) Michigan State University MI Department of Electrical Engineering and Computer Science (EECS) University of Michigan Ann Arbor MI Department of Computational Mathematics Science and Engineering (CMSE) Michigan State University MI and Department of Biomedical Engineering Michigan State University MI

We introduce Optimal Eye Surgeon (OES), a framework for pruning and training deep image generator networks. Typically, untrained deep convolutional networks, which include image sampling operations, serve as effective image priors (Ulyanov et al., 2018). However, they tend to overfit to noise in image restoration tasks due to being overparameterized. OES addresses this by adaptively pruning networks at random initialization to a level of underparameterization. This process effectively captures low-frequency image components even without training, by just masking. When trained to fit noisy image, these pruned subnetworks, which we term Sparse-DIP, resist overfitting to noise. This benefit arises from underparameterization and the regularization effect of masking, constraining them in the manifold of image priors (Figure-3). We demonstrate that subnetworks pruned through OES surpass other leading pruning methods, such as the Lottery Ticket Hypothesis, which is known to be suboptimal for image recovery tasks (Wu et al., 2023). Our extensive experiments demonstrate the transferability of OES-masks and the characteristics of sparse-subnetworks for image generation. Code is available at https://***/Avra98/***.

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Some new type inequalities for weighted maximal operators of partial sums of Walsh–Fourier series

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Positivity 2023年第3期27卷 1-14页

作者： Baramidze, Davit Persson, Lars-Erik Tephnadze, George School of Science and Technology The University of Georgia Tbilisi Georgia Department of Computer Science and Computational Engineering UiT The Arctic University of Norway Narvik Norway Department of Mathematics and Computer Science Karlstad University Karlstad Sweden

In this paper we introduce some new weighted maximal operators of the partial sums of the Walsh–Fourier series. We prove that for some “optimal” weights these new operators indeed are bounded from the martingale Hardy space $$H_{p}(G)$$ to the Lebesgue space $$L_{p}(G),$$ for $$0<1.$$ Moreover, we also prove sharpness of this result. As a consequence we obtain some new and well-known results.

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-Type inequalities for subsequences of Nörlund means of Walsh–Fourier series

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Journal of Inequalities and Applications 2023年第1期2023卷 1-13页

作者： Baramidze, David Persson, Lars-Erik Tangrand, Kristoffer Tephnadze, George School of Science and Technology The University of Georgia Tbilisi Georgia Department of Computer Science and Computational Engineering UiT The Arctic University of Norway Narvik Norway Department of Mathematics and Computer Science Karlstad University Karlstad Sweden

We investigate the subsequence $\{t_{2^{n}}f \}$ of Nörlund means with respect to the Walsh system generated by nonincreasing and convex sequences. In particular, we prove that a large class of such summability methods are not bounded from the martingale Hardy spaces $H_{p}$ to the space $\mathit{weak-}L_{p} $ for $0< p<1/(1+\alpha ) $ , where $0<\alpha <1$ . Moreover, some new related inequalities are derived. As applications, some well-known and new results are pointed out for well-known summability methods, especially for Nörlund logarithmic means and Cesàro means.

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Automatic Diagnosis of Polycystic Ovarian Syndrome Using Wrapper Methodology with Deep Learning Techniques

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computer Systems science & engineering 2023年第10期47卷 239-253页

作者： Mohamed Abouhawwash S.Sridevi Suma Christal Mary Sundararajan Rohit Pachlor Faten Khalid Karim Doaa Sami Khafaga Department of Mathematics Faculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational Mathematics Scienceand Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI48824USA Sri Ramachandra Faculty of Engineering&Technology Sri Ramachandra Institute of Higher Education and ResearchPorurChennai600116India Department of Information Technology Panimalar Institute of TechnologyChennai600123India Department of Computer Science and Engineering School of EngineeringMIT ArtDesign and Technology UniversityPune412201India Department of Computer Sciences College of Computer and Information SciencesPrincess Nourahbint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

One of the significant health issues affecting women that impacts their fertility and results in serious health concerns is Polycystic ovarian syndrome(PCOS).Consequently,timely screening of polycystic ovarian syndrome can help in the process of *** a method to aid doctors in this procedure was crucial due to the difficulties in detecting this *** research aimed to determine whether it is possible to optimize the detection of PCOS utilizing Deep Learning algorithms and ***,feature selection methods that produce the most important subset of features can speed up calculation and enhance the effectiveness of *** this research,the tri-stage wrapper method is used because it reduces the computation *** proposed study for the Automatic diagnosis of PCOS contains preprocessing,data normalization,feature selection,and classification.A dataset with 39 characteristics,including metabolism,neuroimaging,hormones,and biochemical information for 541 subjects,was employed in this *** start,this research pre-processed the *** for feature selection,a tri-stage wrapper method such as Mutual Information,ReliefF,Chi-Square,and Xvariance is ***,various classification methods are tested and *** learning techniques including convolutional neural network(CNN),multi-layer perceptron(MLP),Recurrent neural network(RNN),and Bi long short-term memory(Bi-LSTM)are utilized for *** experimental finding demonstrates that with effective feature extraction process using tri stage wrapper method+CNN delivers the highest precision(97%),high accuracy(98.67%),and recall(89%)when compared with other machine learning algorithms.

关键词： Deep learning automatic detection polycystic ovarian syndrome tri-stage wrapper method mutual information relief Chi-Square

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Towards Automatic Dataset Discovery From Scientific Publications

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IEEE Access 2025年

作者： Kumar, Sandeep Ghosal, Tirthankar Ekbal, Asif Indian Institute of Technology Department of Computer Science and Engineering Patna India Oak Ridge National Laboratory National Center for Computational Sciences United States

Datasets are a crucial artifact in research, and there is always a high demand for good datasets. Due to rapid scientific progress and exponential growth in the scientific literature, there has been a proportionate increase in the number of datasets. However, many datasets and research studies are left unexplored and under-utilized as these are not discoverable easily, leading to duplicate efforts. Building good datasets is costly in terms of time, money, and human effort. Hence, automated tools to facilitate the search and discovery of datasets are crucial to the scientific community. In this work, we investigate a deep neural network-based architecture to automate the dataset discovery from scientific publications. In this paper, we perform two tasks namely dataset mention extraction and entity linking. Our method outperforms the earlier ones and achieves an F1 score of 56.24 in extracting dataset mentions from research papers on a popular corpus of social science publications. Our approach also outperforms the prior research and achieves a precision score of 88.63 in linking research papers to a dataset knowledge base for another popular corpus of social science publications. We hope that this system will further promote data sharing, offset the researchers' workload in identifying the right dataset and increase the reusability of datasets. © 2013 IEEE.

关键词： Discovering Datasets Extracting Dataset References Mining Publications Natural Language Processing

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Sclera Vessel Analysis for Biometric System on Real Time Security Application

Sclera Vessel Analysis for Biometric System on Real Time Sec...

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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Divya Anantharajan M. Saravanakumar B. Bharathi Kannan S. Ponmaniraj Department of Computer Science and Engineering Vandayar Engineering College Thanjavur Department of computer science and Engineering Hindusthan College of Engineering and Technology Coimbatore Department of computational Intelligence SIMATS Saveetha school of Engineering Thandalam

Biometrics has an unavoidable space in the era of security in terms of identifying the Individuals. There are many biometrics which are providing more prominent result, but all these biometrics needs the user cooperation in acquisition. The sclera, white portion of the eye, vessel patterns are holding the uniqueness to identify the individuals. This becomes the root of many researchers to recognize the individual with non-co-operative user images. Sclera recognitions are beginning of the new era which is to leads the recognition on the surveillance camera. Since the Image quality is less in the surveillance camera images and the segmentation of sclera is more challenging which has more impact on the accuracy of the recognition. To improve the accuracy of the segmentation Convolutional Neural Network has been used which has increase the time complexity of the system. Since the accuracy of the segmentation has increased and to optimize the time integro Differential Operator is utilized which reduces the searching area of the iris so the time taken will be reduced. The combination of CNN and DIO will increase the accuracy and complexity of the Segmentation Process. The experiments are carried out on the UBIRIS Dataset V2.

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A Scalable Real-Time Data Assimilation Framework for Predicting Turbulent Atmosphere Dynamics

A Scalable Real-Time Data Assimilation Framework for Predict...

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

作者： Yin, Junqi Liang, Siming Liu, Siyan Bao, Feng Chipilski, Hristo G. Lu, Dan Zhang, Guannan Oak Ridge National Laboratory National Center for Computational Science Oak RidgeTN United States Florida State University Department of Mathematics TallahasseeFL United States Oak Ridge National Laboratory Computational Science and Engineering Division Oak RidgeTN United States Florida State University Department of Scientific Computing TallahasseeFL United States Oak Ridge National Laboratory Computer Science and Mathematics Division Oak RidgeTN United States

ISBN: (纸本)9798350355543

AI-based foundation models like FourCastNet, GraphCast, ClimaX, and Pangu-Weather are revolutionizing weather and climate predictions but are not yet ready for operational use. Their limitation lies in the absence of a data assimilation system to incorporate real-time Earth system observations, crucial for accurately forecasting events like tropical cyclones and atmospheric rivers. To overcome these obstacles, we introduce a generic real-time data assimilation framework and demonstrate its end-to-end performance on the Frontier supercomputer. This framework comprises two primary modules: an ensemble score filter (EnSF), which significantly outperforms the state-of-the-art data assimilation method, e.g., the Local Ensemble Transform Kalman Filter (LETKF);and a vision transformer-based surrogate capable of real-time adaptation through the integration of observational data. We demonstrate both the strong and weak scaling of our framework up to 1024 GPUs on the Exascale supercomputer, Frontier. Our results not only illustrate the framework's exceptional scalability on high-performance computing systems, but also demonstrate the importance of supercomputers in real-time data assimilation for weather and climate predictions. Even though the proposed framework is tested only on a benchmark surface quasi-geostrophic (SQG) turbulence system, it has the potential to be combined with existing AI-based foundation models, making it suitable for future operational implementations. © 2024 IEEE.

关键词： data assimilation Diffusion models foundation models generative AI high-performance computing uncertainty quantification

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