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Intelligent Cyber Physical Systems and Internet of Things (ICoICI), International Conference on

作者： Neda Firoz Mohammad Sadman Tahsin Md. Zobayer Hossain Institute of Applied Mathematics and Computer Science Tomsk State University Tomsk Russia Department of Computer Science and Engineering BRAC Univeristy Dhaka Bangladesh Department of Electrical and Electronic Engineering University of Asia Pacific Dhaka Bangladesh

ISBN: (数字)9798331540661

ISBN: (纸本)9798331540678

Recently, the exponential rise of malicious software, particularly repackaged Android malware, has become a significant concern for mobile devices. Dealing with Android malware detection through dynamic analysis is helpful, but it is also very important to make a connection between an app's features and the parts that are needed to make it work. To tackle this challenge, we present an innovative approach for malware detection utilizing autoencoders. Our method uses a mixed deep-learning model that combines autoencoders (AE) for feature extraction and convolutional neural networks (CNN) for classifying malware. We used autoencoders to pull out attributes from noisy input. This creates groups of bad predictions through reconstruction error and effectively lowers the number of dimensions in the large feature set. The recently reconstructed input features are subsequently utilized in machine learning and deep learning models for predictions. To assess the effectiveness of our suggested model, we carried out experiments on three datasets, each containing two labels in the ‘class’ attribute: ‘0’ for Benign and ‘1’ for Malicious. We achieved 99.99% accuracy with an improved MAE value of 0.001 and RMSE value of 0.063 across three datasets. The results illustrate that our model attained exceptional accuracy and holds promise for delivering robust malware detection outcomes.

关键词： Visualization Accuracy Target recognition Operating systems Receivers Feature extraction Malware Convolutional neural networks Viruses (medical) Unsupervised learning

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Application of Deep Reinforcement Learning in Asset Liability Management

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SSRN 2023年

作者： Wekwete, Takura Asael Kufakunesu, Rodwell van Zyl, Gusti Department of Computer Science University of Pretoria Pretoria0002 South Africa Department of Mathematics and Applied Mathematics University of Pretoria Pretoria0002 South Africa

Asset Liability Management (ALM) is an essential risk management technique in Quantitative Finance and Actuarial science. It aims to maximise a risk-taker's ability to fulfil future liabilities. ALM is especially critical in environments of elevated interest rate changes, as has been experienced globally between 2021 and 2023. Traditional ALM implementation is still heavily dependent on the judgement of professionals such as Quants, Actuaries or Investment Managers. This over-reliance on human input critically limits ALM performance due to restricted automation, human irrationality and restricted scope for multi-objective optimisation. This paper addressed these limitations by applying Deep Reinforcement Learning (DRL), which optimises through trial, and error and continuous feedback from the environment. We defined the Reinforcement Learning (RL) components to optimise for ALM. These include the RL Agent, Environment, Actions, States and Reward Functions. The results demonstrate that implementing DRL provides a superior approach compared to traditional ALM. DRL allows for increased automation, flexibility, and multi-objective optimisation in ALM, reducing the negative impact of human limitations and improving risk management outcomes. The study shows that DRL can be used both at a given time point and dynamically on a high-frequency basis. DRL achieves similar levels of general duration matching and significantly better dynamic duration matching compared to traditional approaches, with fewer theoretical assumptions and restrictions. The findings and principles presented in this study apply to various institutional risk-takers, including insurers, banks, pension funds, and asset managers. Overall, the application of DRL in ALM provides a promising avenue for improving risk management outcomes. © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Inferring the Parameters of Ultrafast Lasers from Images with Surface Patterns 13

Inferring the Parameters of Ultrafast Lasers from Images wit...

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13th International Symposium on Image and Signal Processing and Analysis, ISPA 2023

作者： Litsas, Anastasis Fraggelakis, Fotis Tsibidis, George D. Stratakis, Emmanuel Pantazis, Yannis University of Crete Computer Science Department Heraklion Crete Greece Institute of Electronic Structure and Laser Foundation for Research and Technology - Hellas Heraklion Crete Greece Institute of Applied and Computational Mathematics Foundation for Research and Technology - Hellas Heraklion Crete Greece

ISBN: (纸本)9798350315363

In this paper, we explore the potential of deep learning techniques in the field of ultra-fast laser processing. More specifically, we trained convolutional neural networks on an in-house dataset with the aim of predicting the laser parameters from images which capture the morphological characteristics of the material surface after being laser fabricated. There are several challenges related with the collected dataset stemming from stochastic variations of the material and the laser, the sampling scheme and the overall low sample size. Therefore, to further test our models we additionally construct a synthetic dataset. The results on the synthetic dataset are almost perfect showing that the proposed predictive models have the capacity to learn the assigned tasks. As expected, the predictive performance decreases when the real dataset is utilized. Nevertheless, we show that both the accuracy in the classification task and the mean square error in the majority of the regression tasks are satisfactory (e.g., classification accuracy drops from 99.9% to 94.8%). © 2023 IEEE.

关键词： Convolutional neural networks

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Question Classification of University Admission using Named-Entity Recognition (NER)

Question Classification of University Admission using Named-...

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International Conference on Information Technology, computer and Electrical Engineering (ICITACEE)

作者： Emny Harna Yossy Derwin Suhartono Agung Trisetyarso Widodo Budiharto Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Mathematics Department School of Computer Science Bina Nusantara University Jakarta Indonesia

The university admission process can be overwhelming for applicants and admission officers, with many questions being asked and answered every year. The manual classification of these questions can be time-consuming and may lead to errors, making exploring automated solutions to this problem essential. In recent years, advances in natural language processing (NLP), including question classification. The research proposes an architecture for a question classification system using the Indonesian Language in closed-domain question answering. The purpose of this research is to find out the classification of questions. We created a system with the Named-Entity Recognition method to recognize the next word that will be used as a candidate type of question and candidate answer. The type of question used is a factoid question. Research makes patterns and rules for extracting important words as features to determine the taxonomy of question classification. Datasets are collected from one private university in Indonesia. Data sources from Binus Online Learning websites and data on questions frequently asked by users, especially prospective students. The results of the question classification are presented using descriptive statistics. The results show that the question classification with users’ most frequent question type is “What” by 63.3%, and the answer category is “tuition fees” by 22.4%. It can be concluded that the most frequent question type asked by users is “What”, and the most frequent topic of interest is “tuition fees”. Organizations can use this information to improve customer service by providing more relevant information and resources about tuition fees.

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Imaging atomic-scale chemistry from fused multi-modal electron microscopy

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npj Computational Materials 2022年第1期8卷 164-171页

作者： Jonathan Schwartz Zichao Wendy Di Yi Jiang Alyssa J.Fielitz Don-Hyung Ha Sanjaya D.Perera Ismail El Baggari Richard D.Robinson Jeffrey A.Fessler Colin Ophus Steve Rozeveld Robert Hovden Department of Materials Science and Engineering University of MichiganAnn ArborMIUSA Mathematics and Computer Science Division Argonne National LaboratoryLemontILUSA Advanced Photon Source Facility Argonne National LaboratoryLemontILUSA Dow Chemical Co. MidlandMIUSA Department of Material Science and Engineering Cornell UniversityIthacaNew YorkUSA School of Integrative Engineering Chung-Ang UniversitySeoulRepublic of Korea Department of Physics Cornell UniversityIthacaNYUSA The Rowland Institute at Harvard CambridgeMAUSA Department of Electrical Engineering and Computer Science University of MichiganAnn ArborMIUSA National Center for Electron Microscopy Lawrence Berkeley National LaboratoryMolecular FoundryBerkeleyCAUSA Applied Physics Program University of MIchiganAnn ArborMIUnited States

Efforts to map atomic-scale chemistry at low doses with minimal noise using electron microscopes are fundamentally limited by inelastic ***,fused multi-modal electron microscopy offers high signal-to-noise ratio(SNR)recovery of material chemistry at nano-and atomic-resolution by coupling correlated information encoded within both elastic scattering(high-angle annular dark-field(HAADF))and inelastic spectroscopic signals(electron energy loss(EELS)or energy-dispersive x-ray(EDX)).By linking these simultaneously acquired signals,or modalities,the chemical distribution within nanomaterials can be imaged at significantly lower doses with existing detector *** many cases,the dose requirements can be reduced by over one order of *** high SNR recovery of chemistry is tested against simulated and experimental atomic resolution data of heterogeneous nanomaterials.

关键词： materials chemistry fused

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Refining Blockchain-Powered Industrial Internet-of-Things Architecture for Improved Performance

Refining Blockchain-Powered Industrial Internet-of-Things Ar...

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Blockchain Computing and Applications (BCCA), International Conference on

作者： Ishaya Gambo Timilehin Ogundare Michael Magreola Temitope Ajayi Department of Computer Science and Engineering Obafemi Awolowo University Ile-Ife Nigeria Computer Science and Applied Mathematics University of the Witwatersrand Johannesburg South Africa Department of Computer Science University of Arkansas AR USA

This paper introduces a high-performance blockchain solution to enhance Industrial Internet of Things (IIoT) operations, effectively addressing the challenge of reconciling blockchain's computational demands with the limited capabilities of IIoT devices. Through quantitative research, we present an IIoT blockchain-powered deep reinforcement learning performance optimization protocol. Our evaluation covers key aspects such as latency, scalability, security, and decentralization, demonstrating enhanced scalability without compromising security or latency. We establish a scalable and robust blockchain architecture tailored for high-processing IIoT systems, fine-tuning its components using deep reinforcement learning. Our simulations illustrate the framework's capacity to significantly boost the performance of blockchain-powered IIoT systems while adapting to their diverse operational dynamics.

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High-dimensional inference for the average treatment effect under model misspecification using penalized bias-reduced double-robust estimation

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Biostatistics and Epidemiology 2022年第2期6卷 221-238页

作者： Avagyan, Vahe Vansteelandt, Stijn Biometris Wageningen University and Research Wageningen Wageningen Netherlands Department of Applied Mathematics Computer Science and Statistics Ghent University Gent Belgium Department of Medical Statistics London School of Hygiene and Tropical Medicine London United Kingdom

The presence of confounding by high-dimensional variables complicates the estimation of the average effect of a point treatment. On the one hand, it necessitates the use of variable selection strategies or more general high-dimensional statistical methods. On the other hand, the use of such techniques tends to result in biased estimators with a non-standard asymptotic behavior. Double-robust estimators are useful for offering a resolution because they possess a so-called small bias property. This property has been exploited to achieve valid (uniform) inference of the average causal effect when data-adaptive estimators of the propensity score and conditional outcome mean both converge to their respective truths at sufficiently fast rate. In this article, we extend this work in order to retain valid (uniform) inference when one of these estimators does not converge to the truth, regardless of which. This is done by generalizing prior work for low-dimensional settings by [Vermeulen K, Vansteelandt S. Bias-reduced doubly robust estimation. Am Stat Assoc. 2015;110(511):1024–1036.] to incorporate regularization. The proposed penalized bias-reduced double-robust estimation strategy exhibits promising performance in simulation studies and a data analysis, relative to competing proposals. © 2021 International Biometric Society–Chinese Region.

关键词： Confounding debiasing double robustness high-dimensional inference model misspecification penalized estimating equations

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Integration of aggregated data in causally interpretable meta-analysis by inverse weighting

arXiv

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arXiv 2025年

作者： Vo, Tat Thang Le, Tran Trong Khoi Afach, Sivem Vansteelandt, Stijn Research Group EPIDERME Faculty of Medicine University Paris Est Creteil France Department of Applied Mathematics Computer Science and Statistics Ghent University Belgium

Obtaining causally interpretable meta-analysis results is challenging when there are differences in the distribution of effect modifiers between eligible trials. To overcome this, recent work on transportability methods has considered standardizing results of individual studies over the case-mix of a target population, prior to pooling them as in a classical random-effect meta-analysis. One practical challenge, however, is that case-mix standardization often requires individual participant data (IPD) on outcome, treatments and case-mix characteristics to be fully accessible in every eligible study, along with IPD case-mix characteristics for a random sample from the target population. In this paper, we aim to develop novel strategies to integrate aggregated-level data from eligible trials with non-accessible IPD into a causal meta-analysis, by extending moment-based methods frequently used for population-adjusted indirect comparison in health technology assessment. Since valid inference for these moment-based methods by M-estimation theory requires additional aggregated data that are often unavailable in practice, computational methods to address this concern are also developed. We assess the finite-sample performance of the proposed approaches by simulated data, and then apply these on real-world clinical data to investigate the effectiveness of risankizumab versus ustekinumab among patients with moderate to severe psoriasis. © 2025, CC BY.

关键词： Inverse problems

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Application of Deep Reinforcement Learning in Asset Liability Management

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SSRN 2023年

关键词： Reinforcement learning

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Deep neural network for incongruent point clouds registration 46

Deep neural network for incongruent point clouds registratio...

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Applications of Digital Image Processing XLVI 2023

作者： Voronin, Sergei Vasilyev, Alexander Kober, Vitaly Makovetskii, Artyom Voronin, Aleksei Zhernov, Dmitrii Department of Mathematics Chelyabinsk State University 129 Br. Kashirinykh Chelyabinsk454001 Russia Department of Higher Mathematics Institute of Applied Mathematics and Mechanics Peter the Great St. Petersburg Polytechnical University 29 Politechnicheskaya Str. Saint-Petersburg195251 Russia Department of Computer Science CICESE Carretera Ensenada-Tijuana 3918 Zona Playitas B.C. 22860 Ensenada Mexico

ISBN: (纸本)9781510665620

Recently, there has been essential progress in the field of deep learning, which has led to compelling advances in most of the semantic tasks of computer vision, such as classification, detection, and segmentation. Point cloud registration is a task that aligns two or more different point clouds by evaluating the relative transformation between them. The Iterative Closest Points (ICP) algorithm and its variants have relatively good computational efficiency but are known to be subject to local minima, so rely on the quality of the initialization. In this paper, we propose a neural network based on the Deep Closest Points (DCP) neural network to solve the point cloud registration problem for incongruent point clouds. computer simulation results are provided to illustrate the performance of the proposed method. © 2023 SPIE.

关键词： Computational efficiency

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