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Deformation Adjustment with Single Real Signature Image for Biometric Verification Using CNN

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Computational Intelligence and Neuroscience 2022年 2022卷 4406101页

作者： Kumar, Rakesh Saraswat, Mala Ather, Danish Mumtaz Bhutta, Muhammad Nasir Basheer, Shakila Thakur, R.N. Department of Computer Engineering & Applications GLA University Mathura Mathura281406 India Department of Computer Science and Engineering ABES Engineering College Ghaziabad India Department of Computer Science & Engineering School of Engineering & Technology Sharda University Grater Noida India College of Engineering Abu Dhabi University P.O. Box 5991 Abu Dhabi United Arab Emirates Department of Information Systems College of Computer and Information Science Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia LBEF Campus Kathmandu Nepal

Signature verification is the widely used biometric verification method for maintaining individual privacy. It is generally used in legal documents and in financial transactions. A vast range of research has been done so far to tackle different system issues, but there are various hot issues that remain unaddressed. The scale and orientation of the signatures are some issues to address, and the deformation of the signature within the genuine examples is the most critical for the verification system. The extent of this deformation is the basis for verifying a given sample as a genuine or forgery signature, but in the case of only a single signature sample for a class, the intra-class variation is not available for decision-making, making the task difficult. Besides this, most real-world signature verification repositories have only one genuine sample, and the verification system is abiding to verify the query signature with a single target sample. In this work, we utilize a two-phase system requiring only one target signature image to verify a query signature image. It takes care of the target signature's scaling, orientation, and spatial translation in the first phase. It creates a transformed signature image utilizing the affine transformation matrix predicted by a deep neural network. The second phase uses this transformed sample image and verifies the given sample as the target signature with the help of another deep neural network. The GPDS synthetic and MCYT datasets are used for the experimental analysis. The performance analysis of the proposed method is carried out on FAR, FRR, and AER measures. The proposed method obtained leading performance with 3.56 average error rate (AER) on GPDS synthetic, 4.15 AER on CEDAR, and 3.51 AER on MCYT-75 datasets. © 2022 Rakesh Kumar et al.

关键词： Deformation

The Use of Covariate Adjustment in Randomized Controlled Trials: An Overview

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arXiv 2023年

作者： Van Lancker, Kelly Bretz, Frank Dukes, Oliver Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Novartis Pharma AG Basel Switzerland Section for Medical Statistics Center for Medical Statistics Informatics and Intelligent Systems Medical University of Vienna Vienna Austria

There has been a growing interest in covariate adjustment in the analysis of randomized controlled trials in past years. For instance, the U.S. Food and Drug Administration recently issued guidance that emphasizes the importance of distinguishing between conditional and marginal treatment effects. Although these effects coincide in linear models, this is not typically the case in other settings, and this distinction is often overlooked in clinical trial practice. Considering these developments, this paper provides a review of when and how to utilize covariate adjustment to enhance precision in randomized controlled trials. We describe the differences between conditional and marginal estimands and stress the necessity of aligning statistical analysis methods with the chosen estimand. Additionally, we highlight the potential misalignment of current practices in estimating marginal treatment effects. Instead, we advocate for the utilization of standardization, which can improve efficiency by leveraging the information contained in baseline covariates while remaining robust to model misspecification. Finally, we present practical considerations that have arisen in our respective consultations to further clarify the advantages and limitations of covariate adjustment. © 2023, CC BY.

关键词： Efficiency

Correction: Harnessing AI for sustainable higher education: ethical considerations, operational efficiency, and future directions

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Discover Sustainability 2025年第1期6卷 1-2页

作者： Khan, Sunawar Mazhar, Tehseen Shahzad, Tariq Khan, Muhammad Amir Rehman, Ateeq Ur Saeed, Mamoon M. Hamam, Habib Department of Software Engineering Islamia University of Bahawalpur Bahawalnagar Pakistan School of Computer Science National College of Business Administration and Economics Lahore Pakistan Department of Computer Science School Education Department Government of Punjab Layyah Pakistan Department of Computer Engineering COMSATS University Islamabad Sahiwal Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Malaysia School of Computing Gachon University Seongnam Republic of Korea Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Sana’a Yemen Faculty of Engineering Uni de Moncton Moncton Canada School of Electrical Engineering University of Johannesburg Johannesburg South Africa International Institute of Technology and Management (IITG) Av. Grandes Ecoles Libreville BP 1989 Gabon Bridges for Academic Excellence-Spectrum Tunis Tunisia

Advancing SQL Injection Detection for High-Speed Data Centers: A Novel Approach Using Cascaded NLP

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arXiv 2023年

作者： Tasdemir, Kasim Khan, Rafiullah Siddiqui, Fahad Sezer, Sakir Kurugollu, Fatih Yengec-Tasdemir, Sena Busra Bolat, Alperen Centre for Secure Information Technologies Queen's University Belfast Belfast United Kingdom NVIDIA Corporation Belfast United Kingdom Department of Computer Science College of Computing and Informatics University of Sharjah United Arab Emirates

Detecting SQL Injection (SQLi) attacks is crucial for web-based data center security, but it's challenging to balance accuracy and computational efficiency, especially in high-speed networks. Traditional methods struggle with this balance, while NLP-based approaches, although accurate, are computationally intensive. We introduce a novel cascade SQLi detection method, blending classical and transformer-based NLP models, achieving a 99.86% detection accuracy with significantly lower computational demands-20 times faster than using transformer-based models alone. Our approach is tested in a realistic setting and compared with 35 other methods, including Machine Learning-based and transformer models like BERT, on a dataset of over 30,000 SQL sentences. Our results show that this hybrid method effectively detects SQLi in high-traffic environments, offering efficient and accurate protection against SQLi vulnerabilities with computational efficiency. The code is available at GitHub. Copyright © 2023, The Authors. All rights reserved.

关键词： Machine learning

ON THE GCD GRAPHS OVER POLYNOMIAL RINGS AND RELATED TOPICS

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arXiv 2024年

作者： Mináč, Ján Nguyen, Tung T. Tân, Nguyên Duy Department of Mathematics Western University LondonONN6A 5B7 Canada Department of Mathematics and Computer Science Lake Forest College Lake ForestIL United States Faculty Mathematics and Informatics Hanoi University of Science and Technology 1 Dai Co Viet Road Hanoi Viet Nam

Gcd-graphs over the ring of integers modulo n are a natural generalization of unitary Cayley graphs. The study of these graphs has foundations in various mathematical fields, including number theory, ring theory, and representation theory. Using the theory of Ramanujan sums, it is known that these gcd-graphs have integral spectra;i.e., all their eigenvalues are integers. In this work, inspired by the analogy between number fields and function fields, we define and study gcd-graphs over polynomial rings with coefficients in finite fields. We establish some fundamental properties of these graphs, emphasizing their analogy to their counterparts over Z . © 2024, CC BY.

关键词： Choquet integral

Risk Management in Robotic Process Automation

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Risk Management in Robotic Process Automation

Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Dileep Kumar Yendluri Ramya Thatikonda Reshmi Thatikonda Jainath Ponnala M. Kempanna A. Bhuvanesh Department of Computer Science University of the Cumberlands Williamsburg USA Department of Information Technology University of the Cumberlands Williamsburg USA Department of Computer Information Systems University of Central Missouri Williamsburg USA Department of Electronics and Communication Engineering Jawaharlal Technological University Hyderbad India Department of Artificial Intelligence and Machine Learning Bangalore Institute of Technology Bangalore India Department of Electrical and Electronics Engineering PSN College of Engineering and Technology Tirunelveli India

The paper investigates the nuances of RPA risk management, detailing the difficulties, tendencies, and approaches organizations must adopt to integrate RPA into their operations successfully. From automation to optimization, RPA’s impact on industry workflow is revolutionary. However, RPA introduces a spectrum of technical, operational, business, and compliance risks alongside its benefits. Amidst the intricate landscape of RPA, this paper sheds light on key risks. It offers practical strategies for tackling them, enabling organizations to confidently unlock automation’s transformative power. Trends like advanced analytics fusion, AI-driven risk analysis, and blockchain utilization will likely influence future RPA risk management. In addition, the paper discusses evolving challenges, including ethical issues, breakneck technology evolution, and digital security dangers. Using an in-depth examination of RPA risk management, this paper prepares organizations for the ever-changing automation environment with informed decisions and proactive measures.

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Online Scheduling in Virtualized TSN Networks via Joint Admission Control and VNF Embedding

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Online Scheduling in Virtualized TSN Networks via Joint Admi...

IEEE International Conference on Communications in China (ICCC)

作者： Yajing Zhang Cailian Chen Chaoqun You Mingyan Li Xinping Guan Tony Q. S. Quek Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai P. R. China School of Computer Science Fudan University College of Computer Science Chongqing University Chongqing P. R. China Information Systems Technology and Design Singapore University of Technology and Design Singapore

ISBN: (数字)9798350378412

ISBN: (纸本)9798350378429

Introducing virtualization enhances the flexibility of time-sensitive networking (TSN), wherein applications manifest as service function chains comprising a series of virtual network functions (VNFs). However, such virtualized TSN realizes determinacy through global configuration, being too complicated to serve dynamic applications in time. To address this issue, we innovatively propose to achieve TSN scheduling by distributively executing admission control (AC), whereby the scheduling complexity is radically reduced. Specifically, we first build a two-way AC model that captures TSN multi-queue characteristics. Then, we define admissible regions of nodes and links, working as metrics for AC decision-making and enabling feasible TSN scheduling. Built upon this, we propose a joint AC and VNF embedding mechanism, Rapid Admission Control (RapidAC), which consists of two algorithms. The first algorithm responds to dynamic applications rapidly and derives node-mapping solutions by judging nodes’ admissible regions. Based on this, the second algorithm augments the detailed VNF embedding solution according to admissible regions of links. Simulation results show that RapidAC reduces runtime by 90% compared with existing TSN scheduling algorithms.

关键词： Measurement Runtime Scheduling algorithms Service function chaining Heuristic algorithms Simulation Admission control

Selecting Machine Learning Models for Student Performance Prediction Aligned with Pedagogical Objectives

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Selecting Machine Learning Models for Student Performance Pr...

Computing (ICOCO), IEEE International Conference on

作者： Ijaz Khan Mohammed Hazli Mohamed Zabil Abdul Rahim Ahmad Nafaa Jabeur Department of Computing College of Computing and Informatics Univsiti Tenaga Nasional Kajang Malaysia Information Technology Dept. Buraimi University College Al-Buraimi Oman Deparment of Computing and Informatics' Universiti Tenaga Nasional (UniTen) Kajang Malaysia Computer Science Dept. German University of Technology Muscat Oman

Machine learning classifiers emerge as productive tools to develop prediction models which forecast the final outcome of the students, in a course, and provide an opportunity to the instructor to take appropriate measures. A single prediction model may not be enough to achieve all the objectives of an instructor. The selection of appropriate prediction models is yet a challenging task. This paper proposes a novel framework that applies a set of machine learning classifiers over the training dataset of a course. Along with the training dataset, the instructor clarifies the prime of objective of the binary model whether it must focus over the identification of fail, pass, or both students. The framework recommends a convenient model to achieve the specific objectives of the instructor. This research concludes that models inclined to reduce misclassification of minority class are suitable if the primary objective of the institution is the correct identification of students who are struggling to achieve the minimum course requirements. Further, a model specialized solely in amplifying the correct classification of majority class instance is appropriate if the aim is to focus on the correct identification of excellent students. The empirical analysis in this research leads towards the fact that accuracy alone is not an adequate metric to assess the performance of a model and that systematic selection of evaluation metrics is required to develop constructive models.

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Three-Filters-to-Normal+: Revisiting Discontinuity Discrimination in Depth-to-Normal Translation

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arXiv 2023年

作者： Yang, Jingwei Xue, Bohuan Feng, Yi Wang, Deming Fan, Rui Chen, Qijun The College of Electronics & Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems The State Key Laboratory of Intelligent Autonomous Systems Frontiers Science Center for Intelligent Autonomous Systems Tongji University Shanghai201804 China The Department of Computer Science & Engineering The Hong Kong University of Science and Technology Hong Kong SAR Hong Kong

This article introduces three-filters-to-normal+ (3F2N+), an extension of our previous work three-filters-to-normal (3F2N), with a specific focus on incorporating discontinuity discrimination capability into surface normal estimators (SNEs). 3F2N+ achieves this capability by utilizing a novel discontinuity discrimination module (DDM), which combines depth curvature minimization and correlation coefficient maximization through conditional random fields (CRFs). To evaluate the robustness of SNEs on noisy data, we create a large-scale synthetic surface normal (SSN) dataset containing 20 scenarios (ten indoor scenarios and ten outdoor scenarios with and without random Gaussian noise added to depth images). Extensive experiments demonstrate that 3F2N+ achieves greater performance than all other geometry-based surface normal estimators, with average angular errors of 7.85◦, 8.95◦, 9.25◦, and 11.98◦ on the clean-indoor, clean-outdoor, noisy-indoor, and noisy-outdoor datasets, respectively. We conduct three additional experiments to demonstrate the effectiveness of incorporating our proposed 3F2N+ into downstream robot perception tasks, including freespace detection, 6D object pose estimation, and point cloud completion. Our source code and datasets are publicly available at https://***/3F2Nplus. Note to Practitioners—The primary motivation behind this work arises from the need to develop a high-performing surface normal estimator for practical robotics and computer vision applications. While geometry-based surface normal estimators have been widely used in these domains, the existing solutions focus merely on discontinuity discrimination. To tackle this problem, this article introduces a plug-and-play module that leverages both depth curvature and correlation coefficient to quantify discontinuity levels, thereby optimizing surface normal estimation, particularly near or on discontinuous regions. Moreover, this article also introduces a large-scale public dataset wit

关键词： Gaussian noise (electronic)