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检索条件"任意字段=7th International Workshop on Approximation and Online Algorithms"

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online Algorithm for Clustering with Capacity Constraints 24

Online Algorithm for Clustering with Capacity Constraints

引用

7th ACM India Joint international Conference on Data Science and Management of Data (CODS-COMAD) / 11th ACM IKDD CODS Conference / 29th COMAD Conference

作者： Gupta, Shivam Jain, Shweta Krishnan, Narayanan C. Ghalme, Ganesh Hemachandra, Nandyala IIT Ropar Rupnagar Punjab India IIT Hyderabad Hyderabad Andhra Pradesh India IIT Palakkad Palakkad Kerala India Indian Inst Technol Bombay Maharashtra India

ISBN: (纸本)9798400716348

Traditional clustering often results in imbalanced clusters, limiting its suitability for real-world problems. In response, capacitated clustering methods have emerged, aiming to achieve balanced clusters by limiting points in each cluster. In this paper, we introduce on-line algorithms with provable bounds on opened centers and cost approximation. We validate our methods experimentally.

关键词： Unsupervised Learning Capacitated Clustering online Algorithm

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3L-Person 2022 - Proceedings of the 7th international workshop on Professional Retraining and Life-Long Learning using ICT: Person-Oriented Approach

3L-Person 2022 - Proceedings of the 7th International Worksh...

引用

7th international workshop on Professional Retraining and Life-Long Learning using ICT: Person-Oriented Approach, 3L-Person 2022

the proceedings contain 14 papers. the topics discussed include: ICT for disaster-resilient education and training;online training of youth volunteers for projecting socially significant actions in the COVID-19 pandemic;CoCalc: an integrated environment for open science education in informatics and mathematics;internet resources for foreign language education in primary school: challenges and opportunities;digital resources for developing key competencies in Ukrainian education: teachers' experience and challenges;YouTube as an open resource for foreign language learning: a case study of German;developing digital learning aids for pre-service IT specialists using the functional approach in holistic vocational training;developing professional stability of future socionomic specialists using cloud technologies in blended learning;and inquiry-based learning for enhancing students' interest in mathematical research: a case study on approximation theory and Fourier series.

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A Dynamic Algorithm for Submodular Maximization with a Knapsack Constraint 23

A Dynamic Algorithm for Submodular Maximization with a Knaps...

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23rd IEEE international Conference on High Performance Computing and Communications, 7th IEEE international Conference on Data Science and Systems, 19th IEEE international Conference on Smart City and 7th IEEE international Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Zhu, Tianshuai Han, Kai Zhang, Enpei Wu, Benwei School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781665494571

Submodular maximization is a fundamental problem in computer science theory, as well as occupies a significant place in machine learning and data mining applications, including influence maximization in social networks, recommendation systems, exemplar-based clustering, viral marketing, and data summarization. In this paper, we study this classic problem under a knapsack constraint in a fully dynamic setting. Given a stream of arbitrary interleaved element insertions and deletions, the dynamic setting requires a solution quickly after each update. By adopting repeated sampling, threshold greedy and filtering technique, our algorithm achieves an approximation ratio of 1/3 for monotone submodular objective function, with polylogarithmic amortized update time. We extensively evaluate the performance of our algorithm against the existing work in two applications including maximum coverage and movie recommendation. the experimental results demonstrate the superiority of our algorithm. © 2021 IEEE.

关键词： Machine learning algorithms Social networking (online) Smart cities Filtering Heuristic algorithms Machine learning approximation algorithms

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Harnessing online services for creating augmented reality enhanced comics in primary education 7

Harnessing online services for creating augmented reality en...

引用

7th international workshop on Augmented Reality in Education, AREdu 2024

作者： Bodnenko, Dmytro M. Lokaziuk, Oleksandra V. Poryadchenko, Lesya A. Proshkin, Volodymyr V. Borys Grinchenko Kyiv Metropolitan University 18/2 Bulvarno-Kudriavska Str. Kyiv04053 Ukraine

this article explores the current state and practical solutions for addressing the readiness of primary school students and teachers to utilise cloud services for creating augmented reality (AR) enhanced comics. the didactic potential of comics as an educational tool is clarified, and a comparative analysis of modern cloud services, programs, and applications for comic creation is presented, highlighting their advantages and disadvantages. the functionality of AR programs for creating comic-based content (Vuforia, EasyAR, Maxst, ARCore, ***, 8th Wall) is analysed. A survey of teachers reveals the most popular cloud services, including Pixton, Marvel HD, and Comica. algorithms for developing educational materials using these cloud services to create AR-enhanced comics are presented. Readiness levels for using cloud services to create AR-enhanced comics (elementary, basic, and creative) are developed, and the current state of readiness among teachers and students is characterised. Educational and methodological support has been developed for implementation in university education and teacher professional development. Prospects for further research, including monitoring the readiness of students and teachers to use cloud services for creating AR-enhanced comics following the introduction of the authors’ educational and methodological support, are discussed. © 2025 Copyright for this paper by its authors.

关键词： Students

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Intelligent Methods and Models for Assessing Level of Student Adaptation to online Learning 7

Intelligent Methods and Models for Assessing Level of Studen...

引用

7th international Conference on Computational Linguistics and Intelligent Systems. Volume I: Machine Learning workshop, CoLInS 2023

作者： Teslyuk, Vasyl Doroshenko, Anastasiya Savchuk, Dmytro Lviv Polytechnic National University S. Bandera str. 12 Lviv79013 Ukraine

the problem of introducing online learning is becoming more and more popular in our society. Due to COVID-19 and the war in Ukraine, there is an urgent need for the transition of educational institutions to online learning, so this paper will help people not make mistakes in the process and afterward. the paper's primary purpose is to investigate the effectiveness of machine learning tools that can solve the problem of assessing student adaptation to online learning. these tools include intelligent methods and models, such as classification techniques and neural networks. this work uses data from an online survey of students at different levels: school, college, and university. the survey consists of questions such as gender, age, level of education, whether the student is in the city, class duration, quality of Internet connection, government/non-government educational institution, availability of virtual learning environment, whether the student is familiar with IT, financial conditions, type of Internet connection, a device used for studying, etc. To obtain the results on the effectiveness of online education were used the following machine learning algorithms and models: Random Forest (RF), Extra Trees (ET), Extreme, Light, and Simple Gradient Boosting (GB), Decision Trees (DT), K-neighbors (K-mean), Logistic Regression (LR), Support Vector Machine (SVM), Naїve Bayes (NB) classifier and others. An intelligent neural network model (NNM) was built to address the main issue. © 2023 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0). CEUR workshop Proceedings (***)

关键词： Students

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TERRAIN PREDICTION WIth A LOW-COST LIDAR SENSOR FOR MOBILE ROBOTS 7

TERRAIN PREDICTION WITH A LOW-COST LIDAR SENSOR FOR MOBILE R...

引用

7th international workshop on LowCost 3D - Sensors, algorithms, Applications

作者： Edlinger, R. Nuechter, A. Univ Appl Sci Upper Austria Vienna Austria Julius Maximilian Univ Wurzburg Robot & Telemat Wurzburg Germany

Terrain modelling influences various aspects of mobile robot navigation. the ability to explore in rough terrain and to recognise ground conditions are essential to perform different activities efficiently, safely and satisfactorily. For this reason, intelligent vehicles and robotic systems need cognitive capabilities to understand the terrain and derive information from it. the information is mostly acquired and processed by very high resolution 3D-cameras and LiDAR sensors which provide full 360-degree environmental view to deliver accurate 3D data. the aim of this paper is to find out whether a low-cost sensor variant can measure sufficient and significant data from the terrain in order to modify the navigation behaviour and provide the correct control commands. In this paper we describe a low-cost sensor with Infrared Time-of-Flight (ToF) technology and 64 pixel depth image. Furthermore, different experiments on the detection of the sensor were conducted and with appropriate filters and signal processing algorithms the environmental perception could be significantly improved. In summary, our results provide both evidence and guidelines for the use of the selected sensor in environmental perception to improve local obstacle detection and terrain modelling, which we believe will lead to a very cost-effective improvement in competence and situational awareness.

关键词： 3d scanning low-cost terrain prediction online terrain estimation ToF sensors

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Anomaly Intrusion Detection in online Learning Space Based on XGBoost 7

Anomaly Intrusion Detection in Online Learning Space Based o...

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7th international Conference on Image, Vision and Computing, ICIVC 2022

作者： Dong, Qian Sun, Tingting Li, Tingwei Lu, Xiaoqian Yan, Kai University of Electronic Science and Technology of China School of Continuing Education Chengdu China Library University of Electronic Science and Technology of China Chengdu China University of Electronic Science and Technology of China Information Center Chengdu China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

To ensure the network security of the online learning space, this paper proposes a network anomaly intrusion detection method based on XGBoost that can be applied to the online learning space. Considering about the high latitude and non-linear characteristics of abnormal network traffic data in real scenarios, this method combines multiple weak classifiers into a stronger classifier through an ensemble learning method to solve the dimensional disaster and low operation efficiency of traditional machine learning algorithms. Compared with the traditional ensemble learning methods, this method adds a regularization operator, and uses the second-order approximation of the loss function to select features at the intermediate nodes. these make the method more powerful in detection performance and efficiency. To examine the effect of the proposed method, this paper uses a public data set for validation. In the experiments, the AUC value is 0.9957, and the trend of the ROC curve points out that the method can effectively detect anomaly attacks. © 2022 IEEE.

关键词： Learning systems

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7th international Symposium on Combinatorial Optimization, ISCO 2022

7th International Symposium on Combinatorial Optimization, I...

引用

7th international Symposium on Combinatorial Optimization, ISCO 2022

ISBN: (纸本)9783031185298

the proceedings contain 24 papers. the special focus in this conference is on Combinatorial Optimization. the topics include: Mitigating Anomalies in Parallel Branch-and-Bound Based algorithms for Mixed-Integer Nonlinear Optimization;exact Price of Anarchy for Weighted Congestion Games with Two Players;nash Balanced Assignment Problem;on the thinness of Trees;generating Spanning-Tree Sequences of a Fan Graph in Lexicographic Order and Ranking/Unranking algorithms;high Multiplicity Strip Packing with three Rectangle Types;improved Bounds for Stochastic Extensible Bin Packing Under Distributional Assumptions;one Transfer per Patient Suffices: Structural Insights About Patient-to-Room Assignment;tool Switching Problems in the Context of Overlay Printing with Multiple Colours;branch-and-Cut for a 2-Commodity Flow Relocation Model with Time Constraints;optimal Vaccination Strategies for Multiple Dose Vaccinations;pervasive Domination;unified Greedy Approximability Beyond Submodular Maximization;neighborhood Persistency of the Linear Optimization Relaxation of Integer Linear Optimization;polynomial-Time approximation Schemes for a Class of Integrated Network Design and Scheduling Problems with Parallel Identical Machines;the Constrained-Routing and Spectrum Assignment Problem: Valid Inequalities and Branch-and-Cut Algorithm;top-k List Aggregation: Mathematical Formulations and Polyhedral Comparisons;bounded Variation in Binary Sequences;on Minimally Non-firm Binary Matrices;few Induced Disjoint Paths for H-Free Graphs;on Permuting Some Coordinates of Polytopes;piecewise Linearization of Bivariate Nonlinear Functions: Minimizing the Number of Pieces Under a Bounded approximation Error.

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Proceedings of the 7th ACM SIGSPATIAL international workshop Emergency Management using GIS, EM-GIS 2021

Proceedings of the 7th ACM SIGSPATIAL International Workshop...

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7th ACM SIGSPATIAL international workshop Emergency Management using GIS, EM-GIS 2021

the proceedings contain 3 papers. the topics discussed include: spatial scene similarity assessment based on deep learning;putting out wildfires in Victoria based on intelligent algorithms;and FedURR: a federated tran...

ISBN: (纸本)9781450390996

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Adaptive Dynamic Programing for Optimal Consensus of Multi-agent Systems Using ANFIS 7

Adaptive Dynamic Programing for Optimal Consensus of Multi-a...

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7th IEEE international Conference on Advanced Robotics and Mechatronics

作者： Yu, Yinsheng Xie, Songbo Zhang, Xianxia Shanghai Univ Sch Mechatron & Automat Shanghai Key Lab Power Stn Automat Technol Shanghai 200444 Peoples R China Shandong Laigang Yongfeng Steel Corp Dezhou Peoples R China

ISBN: (数字)9781665483063

ISBN: (纸本)9781665483063

A new adaptive dynamic programming approach is presented in this paper. It proposes ANFIS-based distributed consensus control of linear multi-agent systems. Firstly, the optimal tracking control problem is formed for a multi-agent system. Secondly, a policy iteration-based ADHDP approach is used to compute the performance index and control policy. Moreover, the convergence analysis of the optimal solutions is given. thirdly, the control policy is approximated using an ANFIS based actor network, while the iterative performance index function is approximated using an NN based critic network, and this algorithm implements the policy iteration approach online without using system dynamic knowledge. Finally, the suggested optimal tracking control approach is demonstrated accroding to simulation results.

关键词： Knowledge engineering Mechatronics System dynamics Heuristic algorithms Simulation approximation algorithms Iterative algorithms

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