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8th International Conference on Information Technology, InCIT 2024

作者： Hnoohom, Narit Mekruksavanich, Sakorn Jitpattanakul, Anuchit Mahidol University Faculty of Engineering Image Information and Intelligence Laboratory Department of Computer Engineering Nakhon Pathom Thailand University of Phayao Department of Computer Engineering School of Information and Communication Technology Phayao Thailand King Mongkut's University of Technology Faculty of Applied Science Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics North Bangkok Thailand

ISBN: (纸本)9798350366303

Motorcyclist behavior recognition (MBR) is a significant factor in improving road safety and preventing accidents. Inertial sensors, such as accelerometers, gyroscopes, and magnetometers, have shown promising potential for capturing the dynamic behavior of motorcyclists. However, existing approaches often require complex models and significant computational resources, limiting their deployment in real-world scenarios. This study presents a MotoNeXt architecture, which is a light deep residual network for correctly identifying the behavior of motorcyclists using data from inertial sensors. Our approach leverages the power of deep learning while maintaining a compact model size, making it suitable for resource-constrained environments. The proposed network incorporates multi-kernel residual blocks to facilitate the learning of hierarchical features and improve gradient flow during training. We evaluate our method on a publicly available benchmark dataset, MB-IMU, collected from multiple riders under various riding conditions. Experimentation has shown that our lightweight model achieves cutting-edge performance, outperforming a variety of existing techniques. In Scenario I, using accelerometer, gyroscope, and magnetometer data, MotoNeXt achieved an accuracy result of 78.90088% along with an F1-score of 81.56738%. According to Scenario II, which included additional sensor data such as VelInc, OriInc, and Euler angles, MotoNeXt further improved its performance, achieving an accuracy result of 83.29735% along with an F1-score of 84.95352%. The proposed approach offers a practical solution for real-time MBR, enabling the development of intelligent transportation systems and rider assistance technologies. Our experiments highlight the effectiveness of lightweight deep learning models in extracting meaningful patterns from inertial sensor data. This opens up opportunities for further investigation in this field. © 2024 IEEE.

关键词： Accelerometers

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A Link-Quality Anomaly Detection Framework for Software-Defined Wireless Mesh Networks

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 495-510页

作者： Skaperas, Sotiris Mamatas, Lefteris Tsaoussidis, Vassilis University of Macedonia Department of Applied Informatics Thessaloniki546 36 Greece Athena Research and Innovation Center Marousi15125 Greece Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi67100 Greece

Software-defined wireless mesh networks are being increasingly deployed in diverse settings, such as smart cities and public Wi-Fi access infrastructures. The signal propagation and interference issues that typically characterize these environments can be handled by employing SDN controller mechanisms, effectively monitoring link quality and triggering appropriate mitigation strategies, such as adjusting link and/or routing protocols. In this paper, we propose an unsupervised machine learning (ML) online framework for link quality detection consisting of: 1) improved preprocessing clustering algorithm, based on elastic similarity measures, to efficiently characterize wireless links in terms of reliability, and 2) a novel change point (CP) detector for the real-time identification of anomalies in the quality of selected links, which minimizes the overestimation error through the incorporation of a rank-based test and a recursive max-type procedure. In this sense, considering the communication constraints of such environments, our approach minimizes the detection overhead and the inaccurate decisions caused by overestimation. The proposed detector is validated, both on its individual components and as an overall mechanism, against synthetic but also real data traces;the latter being extracted from real wireless mesh network deployments. © 2023 CCBY.

关键词： Real time systems

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On the convergence of projected alternating maximization for equitable and optimal transport

The Journal of Machine Learning Research

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The Journal of Machine Learning research 2024年第1期25卷 16456-16488页

作者： Minhui Huang Shiqian Ma Lifeng Lai Department of Electrical and Computer Engineering University of California Davis CA Department of Computational Applied Mathematics and Operations Research Rice University Houston TX

This paper studies the equitable and optimal transport (EOT) problem, which has many applications such as fair division problems and optimal transport with multiple agents etc. In the discrete distributions case, the EOT problem can be formulated as a linear program (LP). Since this LP is prohibitively large for general LP solvers, (Scetbon et al., 2021) suggests to perturb the problem by adding an entropy regularization. They proposed a projected alternating maximization algorithm (PAM) to solve the dual of the entropy regularized EOT. In this paper, we provide the first convergence analysis of PAM. A novel rounding procedure is proposed to help construct the primal solution for the original EOT problem. We also propose a variant of PAM by incorporating the extrapolation technique that can numerically improve the performance of PAM. Results in this paper may shed lights on block coordinate (gradient) descent methods for general optimization problems.

关键词： equitable and optimal transport fairness saddle point problem projected alternating maximization block coordinate descent acceleration rounding

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Intelligent Personality Assessment and Verification from Handwriting using Machine Learning

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International Journal of Information Technology (Singapore) 2024年第7期16卷 4541-4546页

作者： Mishra, Pradeep Kumar Mishra, Gouri Sankar Abidi, Ali Imam Kumar, Rajiv Sahoo, Ashok Kumar Maini, Tarun Department of Computer Science and Application SSET Sharda University Department of Computer Science and Engineering SSET Sharda University NVIDIA AI Learning & amp Research Center Department of Applied Computational Science and Engineering G L Bajaj Institute of Technology and Management Greater Noida Graphic Era Hill University School of Computer Science and Engineering Galgotias University

It is possible to tell a lot about a person just by looking at their handwriting. The way someone writes might tell you a lot about who, they are as a person. There has been a lot of study on the possibility of deducing aspects of a person’s character from their handwriting. In this piece, we’ll look at the different personality qualities and how they might be utilized to make an evaluation. The report includes a dataset showing a correlation between several traits of personality. The Big Five Personality Traits are mapped using handwritten personality data. It was suggested that a comparison table should be used to spot distinctive character quirks. The article includes a review of previous studies and an assessment of the current status of the topic. Handwriting embodies have both creativity and expressiveness. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Agreeableness Conscientiousness Extraversion Neuroticism Openness

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Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI)

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npj Computational Materials 2023年第1期9卷 1543-1555页

作者： Alexander E.Siemenn Zekun Ren Qianxiao Li Tonio Buonassisi Department of Mechanical Engineering Massachusetts Institute of TechnologyCambridgeMAUSA Department of Electrical and Computer Engineering Singapore-MIT Alliance for Research and TechnologySingaporeSingapore Xinterra SingaporeSingapore Department of Mathematics National University of SingaporeSingaporeSingapore Institute for Functional Intelligent Materials National University of SingaporeSingaporeSingapore

Needle-in-a-Haystack problems exist across a wide range of applications including rare disease prediction,ecological resource management,fraud detection,and material property optimization.A Needle-in-a-Haystack problem arises when there is an extreme imbalance of optimum conditions relative to the size of the ***,current state-of-the-art optimization algorithms are not designed with the capabilities to find solutions to these challenging multidimensional Needle-in-a-Haystack problems,resulting in slow convergence or pigeonholing into a local *** this paper,we present a Zooming Memory-Based Initialization algorithm,entitled ZoMBI,that builds on conventional Bayesian optimization principles to quickly and efficiently optimize Needle-in-a-Haystack problems in both less time and fewer *** ZoMBI algorithm demonstrates compute time speed-ups of 400×compared to traditional Bayesian optimization as well as efficiently discovering optima in under 100 experiments that are up to 3×more highly optimized than those discovered by similar methods.

关键词： optimization problems extreme

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Dimensional Homogeneity in Classifying Second-Order Differential Invariant Systems for Four-Dimensional Lie Algebras

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International Journal of applied and Computational mathematics 2024年第6期10卷 1-44页

作者： Ayub, Muhammad Sultan, Zahida Mahomed, F.M. Ijaz, Saima Department of Mathematics COMSATS University Islamabad Abbottabad Campus Abbottabad Pakistan Department of Mathematics Azad Jammu and Kashmir University Muzaffarabad Pakistan DSI-NRF Centre of Excellence in Mathematical and Statistical Sciences School of Computer Science and Applied Mathematics University of the Witwatersrand Wits 2050 Johannesburg South Africa Department of Mathematics University of Engineering and Technology Lahore 54000 Pakistan

This research investigates a specific mathematical structure called a nonsingular differential invariant structure, which plays a crucial role in describing physical phenomena through differential equations. Focusing on a four-dimensional Lie algebra in (1+3)-dimensional space, this study classifies these structures. Here, a method is developed to identify second-order differential invariants and showed how they translate into systems of three second-order ordinary differential equations for the specific case of a four-dimensional Lie algebra, and these invariants are presented in the form of list in Table 1. To further refine the analysis, these systems are classified based on their “dimensional homogeneity structure,” which reveals their behavior when scaling all variables. This sub-classification described in Table 2, goes beyond the initial broad categorization, potentially linking these structures to specific physical applications. Novelty: To best of our knowledge, the classification of nonsingular differential invariant structure in (1+3)-dimensional space of four-dimensional real Lie algebras and association of dimensional homogeneity structure in classification scheme are new contributions in algebraic classification problems in literature and never reported before this. Such type of classification scheme is fruitful in analysis of many physical systems. In addition, the algebraic properties of these peculiar invariant systems including integrability, are analysed in a detailed manner both mathematically and from a mechanics viewpoint. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2024.

关键词： Classification Dimensional homogeneity Integrability Lie algebra Nonsingular differential invariants Systems of second-order ODEs

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Approximation Schemes for Geometric Knapsack for Packing Spheres and Fat Objects 51

Approximation Schemes for Geometric Knapsack for Packing Sph...

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51st International Colloquium on Automata, Languages, and Programming, ICALP 2024

作者： Acharya, Pritam Bhore, Sujoy Gupta, Aaryan Khan, Arindam Mondal, Bratin Wiese, Andreas Department of Mathematics Indian Institute of Science Education and Research Pune India Department of Computer Science and Engineering Indian Institute of Technology Bombay India Department of Computer Science and Automation Indian Institute of Science Bengaluru India Department of Mathematics Technical University of Munich Germany

ISBN: (纸本)9783959773225

We study the geometric knapsack problem in which we are given a set of d-dimensional objects (each with associated profits) and the goal is to find the maximum profit subset that can be packed non-overlappingly into a given d-dimensional (unit hypercube) knapsack. Even if d = 2 and all input objects are disks, this problem is known to be NP-hard [Demaine, Fekete, Lang, 2010]. In this paper, we give polynomial time (1 + Ε)-approximation algorithms for the following types of input objects in any constant dimension d: disks and hyperspheres, a class of fat convex polygons that generalizes regular k-gons for k ≥ 5 (formally, polygons with a constant number of edges, whose lengths are in a bounded range, and in which each angle is strictly larger than π/2), arbitrary fat convex objects that are sufficiently small compared to the knapsack. We remark that in our PTAS for disks and hyperspheres, we output the computed set of objects, but for a OΕ(1) of them we determine their coordinates only up to an exponentially small error. However, it is not clear whether there always exists a (1 + Ε)-approximate solution that uses only rational coordinates for the disks’ centers. We leave this as an open problem which is related to well-studied geometric questions in the realm of circle packing. © Pritam Acharya, Sujoy Bhore, Aaryan Gupta, Arindam Khan, Bratin Mondal, and Andreas Wiese.

关键词： Approximation algorithms

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Novelty of Different Distance Approach for Multi-Criteria Decision-Making Challenges Using q-Rung Vague Sets

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computer Modeling in engineering & Sciences 2024年第6期139卷 3353-3385页

作者： Murugan Palanikumar Nasreen Kausar Dragan Pamucar Seifedine Kadry Chomyong Kim Yunyoung Nam Saveetha School of Engineering Saveetha Institute of Medical and Technical SciencesChennai602105India Department of Mathematics Faculty of Arts and ScienceYildiz Technical UniversityEsenlerIstanbul34220Turkey Department of Operations Research and Statistics Faculty of Organizational SciencesUniversity of BelgradeBelgrade11000Serbia College of Engineering Yuan Ze UniversityTaoyuan City320315TaiwanChina Department of Applied Data Science Noroff University CollegeKristiansand4612Norway Artificial Intelligence Research Center(AIRC) Ajman UniversityAjman346United Arab Emirates Department of Electrical and Computer Engineering Lebanese American UniversityByblos1102-2801Lebanon ICT Convergence Research Center Soonchunhyang UniversityAsan31538South Korea Department of Computer Science and Engineering Soonchunhyang UniversityAsan31538South Korea

In this article,multiple attribute decision-making problems are solved using the vague normal set(VNS).It is possible to generalize the vague set(VS)and q-rung fuzzy set(FS)into the q-rung vague set(VS).A log q-rung normal vague weighted averaging(log q-rung NVWA),a log q-rung normal vague weighted geometric(log q-rung NVWG),a log generalized q-rung normal vague weighted averaging(log Gq-rung NVWA),and a log generalized q-rungnormal vagueweightedgeometric(logGq-rungNVWG)operator are discussed in this *** is provided of the scoring function,accuracy function and operational laws of the log q-rung *** algorithms underlying these functions are also described.A numerical example is provided to extend the Euclidean distance and the Humming ***,idempotency,boundedness,commutativity,and monotonicity of the log q-rung VS are examined as they facilitate recognizing the optimal alternative more quickly and help clarify *** chose five anemia patients with four types of symptoms including seizures,emotional shock or hysteria,brain cause,and high fever,who had either retrograde amnesia,anterograde amnesia,transient global amnesia,post-traumatic amnesia,or infantile *** numbers q are used to express the results of the *** demonstrate the effectiveness and accuracy of the models we are investigating,we compare several existing models with those that have been developed.

关键词： Vague set aggregating operators euclidean distance hamming distance decision making

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Adaptive Neural Network-Based Improved Fabric Defect Detection Scheme 12th

Adaptive Neural Network-Based Improved Fabric Defect Detecti...

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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Prabha, P. Anantha Abirami, S. Varshini, N. Amirtha Garishma, K. Priya, M. Deva Karthick, S. Department of Computer Science and Engineering PSG Institute of Technology and Applied Research Tamil Nadu Coimbatore India Department of Computer Science and Engineering Sri Krishna College of Technology Tamil Nadu Coimbatore India Department of Computer Science and Engineering Karpagam College of Engineering Tamil Nadu Coimbatore India Department of Computer Science and Engineering Delhi - NCR Campus SRM Institute of Science and Technology Uttar Pradesh Ghaziabad India

ISBN: (纸本)9789819789450

Efficient detection of defects in fabric plays a dominant role in automating quality control. It significantly enhances efficiency as well as accuracy of quality assurance in textile manufacturing. Skilled humans who physically examine defective designs in numerous sectors use traditional defect detection techniques. The main drawbacks of manual methods include deficiency of concentration, inaccuracy, weariness and increased time consumption. computer Vision (CV) and Digital Image Processing (DIP) techniques may be efficient in overcoming these shortcomings. The proposed approach involves data acquisition to record images which are subsequently pre-processed using histogram equalization to improve the defective area. A distinctive Neural Network (NN) is used for defect detection. This method improves precision and efficiency in identifying defects. The proposed method is built using Adaptive NNs (ANNs) along with Radial Basis Functions (RBFs) and is aimed at revolutionary identification of fabric flaws. The networks are built using attributes derived from images segmented using Expectation Maximization (EM) depending on target’s region attributes. As a result, the type of defects is classified based on the extracted features, thus yielding increased detection accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Radial basis function networks

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High probability convergence bounds for non-convex stochastic gradient descent with sub-weibull noise

The Journal of Machine Learning Research

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The Journal of Machine Learning research 2024年第1期25卷 11648-11683页

作者： Liam Madden Stephen Becker Department of Electrical and Computer Engineering University of British Columbia Department of Applied Mathematics University of Colorado Boulder

Stochastic gradient descent is one of the most common iterative algorithms used in machine learning and its convergence analysis is a rich area of research. Understanding its convergence properties can help inform what modifications of it to use in different settings. However, most theoretical results either assume convexity or only provide convergence results in mean. This paper, on the other hand, proves convergence bounds in high probability without assuming convexity. Assuming strong smoothness, we prove high probability convergence bounds in two settings: (1) assuming the Polyak- Lojasiewicz inequality and norm sub-Gaussian gradient noise and (2) assuming norm sub-Weibull gradient noise. In the second setting, as an intermediate step to proving convergence, we prove a sub-Weibull martingale difference sequence self-normalized concentration inequality of independent interest. It extends Freedman-type concentration beyond the sub-exponential threshold to heavier-tailed martingale difference sequences. We also provide a post-processing method that picks a single iterate with a provable convergence guarantee as opposed to the usual bound for the unknown best iterate. Our convergence result for sub-Weibull noise extends the regime where stochastic gradient descent has equal or better convergence guarantees than stochastic gradient descent with modifications such as clipping, momentum, and normalization.

关键词： stochastic gradient descent convergence bounds sub-Weibull distributions Polyak-Lojasiewicz inequality freedman inequality

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