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Advanced Research in Computing (ICARC), International Conference on

作者： Milinda Arambawela Achala Aponso Computer Science and Engineering University of Westminster London UK Department of Computing Informatics Institute of Technology Colombo Sri Lanka

The advent of the Internet has significantly stream-lined daily tasks through the rapid increase of online services. Everyday activities, such as purchasing goods and scheduling appointments with healthcare professionals, have become more speedy, efficient and user-friendly with the integration of the Internet. The continuous improvement of online services has led to many people moving towards digital activities. As a result, it has heightened the recording of personal and payment transaction data across various storage mediums, including databases and log files. The protection and regulation of this sensitive data are imperative, aligning with the guidelines outlined in GDPR and PCI-DSS compliances. Recognizing exposed personal data poses a considerable challenge. This research introduces a novel approach to identifying payment card industry data (PCI) and personally identifiable information (PII). The research project proposes a machine learning-based text classification model utilizing the Convolutional Neural Network (CNN) model to discern PII and PCI data within a given text. The CNN model has been constructed and compared against Naive Bayes, Gradient Boost, Random Forest, and Support Vector Machine (SVM) models. The CNN model achieved the highest accuracy at 0.96 (96%). Additionally, the F1 scores for each class were significant, with PII scoring 0.94, PCI scoring 0.95, and Normal scoring 0.99. Following the model's construction and training, it was employed with the saved tokenizer's word indexes and label encoders in the developed classification tool. This tool successfully delivered the promised results, identifying exposed PII and PCI data.

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Robustness challenges in Reinforcement Learning based time-critical cloud resource scheduling: A Meta-Learning based solution

Robustness challenges in Reinforcement Learning based time-c...

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作者： Liu, Hongyun Chen, Peng Ouyang, Xue Gao, Hui Yan, Bing Grosso, Paola Zhao, Zhiming Informatics Institute University of Amsterdam Amsterdam1098 XH Netherlands Graduate School of Informatics University of Amsterdam Amsterdam1098 XH Netherlands School of Computer and Software Engineering Xihua University Chengdu610039 China School of Computer Science National University of Defense Technology Changsha410073 China College of Electrical and Control Engineering Shaanxi University of Science and Technology Xi'an710021 China School of Electrical and Electronic Engineering University of Adelaide Adelaide South Australia5005 Australia

Cloud computing attracts increasing attention in processing dynamic computing tasks and automating the software development and operation pipeline. In many cases, the computing tasks have strict deadlines. The cloud resource manager (e.g., orchestrator) effectively manages the resources and provides tasks Quality of Service (QoS). Cloud task scheduling is tricky due to the dynamic nature of task workload and resource availability. Reinforcement Learning (RL) has attracted lots of research attention in scheduling. However, those RL-based approaches suffer from low scheduling performance robustness when the task workload and resource availability change, particularly when handling time-critical tasks. This paper focuses on both challenges of robustness and deadline guarantee among such RL, specifically Deep RL (DRL)-based scheduling approaches. We quantify the robustness measurements as the retraining time and investigate how to improve both robustness and deadline guarantee of DRL-based scheduling. We propose MLR-TC-DRLS, a practical, robust Meta Deep Reinforcement Learning-based scheduling solution to provide time-critical tasks deadline guarantee and fast adaptation under highly dynamic situations. We comprehensively evaluate MLR-TC-DRLS performance against RL-based and RL advanced variants-based scheduling approaches using real-world and synthetic data. The evaluations validate that our proposed approach improves the scheduling performance robustness of typical DRL variants scheduling approaches with 97%–98.5% deadline guarantees and 200%–500% faster adaptation. © 2023 The Author(s)

关键词： Reinforcement learning

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Optimization Algorithm for Estimating the Velocity Characteristics of Complex Medium

Optimization Algorithm for Estimating the Velocity Character...

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2022 IEEE International Multi-Conference on engineering, computer and Information Sciences, SIBIRCON 2022

作者： Pinigina, Daria L. Oksana, Kopylova A. Novosibirsk State Technical University Department of Computer Engineering Novosibirsk Russia Laboratory of Geophysical Informatics Department of Computer Engineering Institute of Computational Mathematics and Mathematical Geophysics SB RAS Novosibirsk State Technical University Novosibirsk Russia

ISBN: (纸本)9781665464802

An optimization computational grid algorithm and the results of its application for estimating the velocity characteristics of complex medium based on experimentally obtained seismic wave arrival times are presented. An increase in the resolution of the algorithm for detecting inhomogeneities is achieved by adaptively selecting the grid step in areas with a pronounced heterogeneity of the medium structure. The algorithm automatically identifies zones of inhomogeneity, taking into account non-linear variations in the hodograph of seismic waves. The restriction on the minimum allowable grid step is deter-mined taking into account the upper limiting frequency of sounding oscillations generated by the vibration source and the number of intersections of seismic wave fronts at the grid nodes. The proposed algorithm has been tested on experimental data obtained from vibration sounding of mud volcano zones and the Baikal Rift Zone (BRZ). The consistency of the reconstructed theoretical velocity hodograph with the geological structure of the heterogeneity of the structure of medium in the areas of field works is shown. © 2022 IEEE.

关键词： Seismic waves

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WildGaussians: 3D Gaussian Splatting in the Wild 38

WildGaussians: 3D Gaussian Splatting in the Wild

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Kulhanek, Jonas Peng, Songyou Kukelova, Zuzana Pollefeys, Marc Sattler, Torsten Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Czech Republic Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic Department of Computer Science ETH Zurich Switzerland Visual Recognition Group Faculty of Electrical Engineering Czech Technical University in Prague Czech Republic

While the field of 3D scene reconstruction is dominated by NeRFs due to their photorealistic quality, 3D Gaussian Splatting (3DGS) has recently emerged, offering similar quality with real-time rendering speeds. However, both methods primarily excel with well-controlled 3D scenes, while in-the-wild data - characterized by occlusions, dynamic objects, and varying illumination - remains challenging. NeRFs can adapt to such conditions easily through per-image embedding vectors, but 3DGS struggles due to its explicit representation and lack of shared parameters. To address this, we introduce WildGaussians, a novel approach to handle occlusions and appearance changes with 3DGS. By leveraging robust DINO features and integrating an appearance modeling module within 3DGS, our method achieves state-of-the-art results. We demonstrate that WildGaussians matches the real-time rendering speed of 3DGS while surpassing both 3DGS and NeRF baselines in handling in-the-wild data, all within a simple architectural framework. © 2024 Neural information processing systems foundation. All rights reserved.

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Reducing ADC Front-end Costs During Training of On-sensor Printed Multilayer Perceptrons

arXiv

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arXiv 2024年

作者： Afentaki, Florentia Duarte, Paula Carolina Lozano Zervakis, Georgios Tahoori, Mehdi B. Computer Engineering & Informatics Dept. University of Patras Patras26504 Greece Department of Computer Science Karlsruhe Institute of Technology Karlsruhe76131 Germany

Printed electronics technology offers a cost-effective and fully-customizable solution to computational needs beyond the capabilities of traditional silicon technologies, offering advantages such as on-demand manufacturing and conformal, low-cost hardware. However, the low-resolution fabrication of printed electronics, which results in large feature sizes, poses a challenge for integrating complex designs like those of machine learning (ML) classification systems. Current literature optimizes only the Multilayer Perceptron (MLP) circuit within the classification system, while the cost of analog-to-digital converters (ADCs) is overlooked. Printed applications frequently require on-sensor processing, yet while the digital classifier has been extensively optimized, the analog-to-digital interfacing, specifically the ADCs, dominates the total area and energy consumption. In this work, we target digital printed MLP classifiers and we propose the design of customized ADCs per MLP’s input which involves minimizing the distinct represented numbers for each input, simplifying thus the ADC’s circuitry. Incorporating this ADC optimization in the MLP training, enables eliminating ADC levels and the respective comparators, while still maintaining high classification accuracy. Our approach achieves 11.2x lower ADC area for less than 5% accuracy drop across varying MLPs. Copyright © 2024, The Authors. All rights reserved.

关键词： Comparator circuits

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Artificial Intelligence Enabled IOT System for Football Identification in a Football Match 14th

Artificial Intelligence Enabled IOT System for Football Iden...

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12th International Advanced Computing Conference, IACC 2022

作者： Singh, Mahesh Kumar Singh, Akhilesh Kumar Singh, Pushpendra Kalpana Rishi, Om Prkash Department of Computer Science ABES Engineering College Ghaziabad201009 India Department of Computer Science and Engineering Jaypee Institute of Information Technology Noida India Department of Information Technology GL Bajaj Institute of Technology and Management Uttar Pradesh Gr Noida201306 India Department of Information Technology Raj Kumar Goel Institute of Technology Uttar Pradesh Ghaziabad201003 India Department of Computer Science Informatics University of Kota Rajasthan Kota324010 India

ISBN: (纸本)9783031356438

Due to growth of AI enabled IOT devices, their use in sports feature identification can be beneficial in terms of recognition quality. computer Vision is a powerful tool for moving object recognition and tracking. Football identification and recognition problem can be implemented using OpenCV libraries with the help of a programming language like Java, Python etc. OpenCV has special modules that contain specific algorithms and features for moving object recognition and tracking. Internet of things provides a way to connect hardware components through software implementation. In the proposed model the automatic camera module has been implemented through IoT with the help of a servo motor and raspberry pi. The main aim of this paper is to automate the work of a cameraman in a football match by automatically tracking and recognizing the football and broadcasting it with the help of an automatic camera. The initial loss was about 1749, and after 1600+ epochs iterations, the loss reduced to 0.09 in the final model. © 2023, Springer Nature Switzerland AG.

关键词： Cameras

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Deep Learning Based License Plate Number Recognition for Smart Cities

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computers, Materials & Continua 2022年第1期70卷 2049-2064页

作者： T.Vetriselvi E.Laxmi Lydia Sachi Nandan Mohanty Eatedal Alabdulkreem Shaha Al-Otaibi Amal Al-Rasheed Romany F.Mansour Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTrichy620002India Computer Science and Engineering Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science&Engineering Vardhaman College of Engineering(Autonomous)Hyderabad501218India Department of Informatics and Computing Singidunum UniversitySerbia Computer Science Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Information Systems Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Mathematics Faculty of ScienceNew Valley UniversityEl-Kharga72511Egypt

Smart city-aspiring urban areas should have a number of necessary elements in place to achieve the intended *** controlling and management of traffic conditions,increased safety and surveillance,and enhanced incident avoidance and management should be top priorities in smart city *** the same time,Vehicle License Plate Number Recognition(VLPNR)has become a hot research topic,owing to several real-time applications like automated toll fee processing,traffic law enforcement,private space access control,and road traffic *** VLPNR is a computer vision-based technique which is employed in the recognition of automobiles based on vehicle number *** current research paper presents an effective Deep Learning(DL)-based VLPNR called DLVLPNR model to identify and recognize the alphanumeric characters present in license *** proposed model involves two main stages namely,license plate detection and Tesseract-based character *** detection of alphanumeric characters present in license plate takes place with the help of fast RCNN with Inception V2 ***,the characters in the detected number plate are extracted using Tesseract Optical Character Recognition(OCR)*** performance of DL-VLPNR model was tested in this paper using two benchmark databases,and the experimental outcome established the superior performance of the model compared to other methods.

关键词： Deep learning smart city tesseract computer vision vehicle license plate recognition

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Root-zone Soil Moisture Nowcasting using Context Aware Machine Learning

Root-zone Soil Moisture Nowcasting using Context Aware Machi...

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International Conference on Agro-Geoinformatics

作者： Ayda F. Aktas Burak Berk Üstündağ Informatics Institute Istanbul Technical University Istanbul Turkey Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey

Estimation of root-zone soil moisture (SM) is crucial for effective agricultural management and water resource planning. However, current methods for soil moisture estimation exhibit several limitations that hinder their practical application. This study introduces a novel nowcasting model, which integrates in-situ and remote sensing data through a Predictive Error Compensated wavelet Neural NETwork (PECNET), addressing the drawbacks of existing *** SM estimation techniques often suffer from limited accuracy, inadequate contextual information, and no real-time monitoring capabilities. Although remote sensing technologies offer promising advantages, such as wide spatial coverage and frequent data acquisition, they are not immune to limitations. Vegetation coverage and density present challenges in accurately estimating root-zone SM using remote sensing techniques. These factors can introduce uncertainties and errors in the estimation process, thereby impacting the reliability of the *** overcome these limitations and enhance the accuracy of root-zone SM estimation, this study proposes the integration of remote sensing data with in-situ measurements. Specifically, Normalized Difference Vegetation Index (NDVI) calculations from Landsat 7 and Landsat 8 satellites are fused with evapotranspiration and rainfall data obtained from agrometeorological stations. Combining these datasets generates an 8-day time series for the target parcels, leveraging the contextual information provided by NDVI and seasonality to improve the accuracy of root-zone soil moisture *** develop a robust and efficient model, we introduce PECNET, which ensures the orthogonality of input features and facilitates the learning of non-linear relationships between variables. Notably, PECNET addresses the challenge of limited labeled training data, minimizing the risk of overfitting and enabling accurate estimation with fewer labeled samples. In addition, this study employs

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Approximate EFX and Exact tEFX Allocations for Indivisible Chores: Improved Algorithms

arXiv

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arXiv 2024年

作者： Afshinmehr, Mahyar Ansaripour, Matin Danaei, Alireza Mehlhorn, Kurt Department of Computer Engineering Sharif University of Technology Iran Computer and Communications Sciences Department EPFL Switzerland Max Planck Institute for Informatics SIC Germany

We explore the fair distribution of a set of m indivisible chores among n agents, where each agent’s costs are evaluated using a monotone cost function. Our focus lies on two fairness criteria: envy-freeness up to any item (EFX) and a relaxed notion, namely envy-freeness up to the transfer of any item (tEFX). We demonstrate that a 2-approximate EFX allocation exists and is computable in_polynomial time for three agents with subadditive cost functions, improving upon the previous (2 + √6) approximation for additive cost functions. This result requires extensive case analysis. Christoforidis and Santorinaios (2024) independently claim the same approximation for additive cost functions;however, we provide a counter-example to their algorithm. We expand the number of agents to any number to get the same approximation guarantee with the assumption of partially identical ordering (IDO) for the cost functions. Additionally, we establish that a tEFX allocation is achievable for three agents if one has an additive 2-ratio bounded cost function, while the others may have general monotone cost functions. This is an improvement from the prior requirement of two agents with additive 2-ratio bounded cost functions. This allocation can also be extended to agent groups with identical valuations. Further, we show various analyses of EFX allocations for chores, such as the relaxations for additive α-ratio-bounded cost functions. © 2024, CC BY-NC-SA.

关键词： Cost functions

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Bottleneck-minimal indexing for generative document retrieval 24

Bottleneck-minimal indexing for generative document retrieva...

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Proceedings of the 41st International Conference on Machine Learning

作者： Xin Du Lixin Xiu Kumiko Tanaka-Ishii Waseda Research Institute for Science and Engineering Waseda University Department of Mathematical Informatics The University of Tokyo Department of Computer Science and Engineering Waseda University

We apply an information-theoretic perspective to reconsider generative document retrieval (GDR), in which a document x ∈ Χ is indexed by t ∈ Τ, and a neural autoregressive model is trained to map queries Q to Τ. GDR can be considered to involve information transmission from documents X to queries Q, with the requirement to transmit more bits via the indexes Τ. By applying Shannon's rate-distortion theory, the optimality of indexing can be analyzed in terms of the mutual information, and the design of the indexes Τ can then be regarded as a bottleneck in GDR. After reformulating GDR from this perspective, we empirically quantify the bottleneck underlying GDR. Finally, using the NQ320K and MARCO datasets, we evaluate our proposed bottleneck-minimal indexing method in comparison with various previous indexing methods, and we show that it outperforms those methods.

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