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arXiv 2025年

作者： Chen, Chen Guan, Peiyuan Chen, Ziru Taherkordi, Amir Hou, Fen Cai, Lin X. Department of Computer Science and Technology University of Cambridge United Kingdom Department of Informatics University of Oslo Norway Department of Electrical and Computer Engineering Illinois Institute of Technology United States Department of Electrical and Computer Engineering University of Macau China

Serverless computing adopts a pay-as-you-go billing model where applications are executed in stateless and short-lived containers triggered by events, resulting in a reduction of monetary costs and resource utilization. However, existing platforms do not provide an upper bound for the billing model which makes the overall cost unpredictable, precluding many organizations from managing their budgets. Due to the diverse ranges of serverless functions and the heterogeneous capacity of edge devices, it is challenging to receive near-optimal solutions for deployment cost in a polynomial time. In this paper, we investigated the function scheduling problem with a budget constraint for serverless computing in wireless networks. Users and IoT devices are sending requests to edge nodes, improving the latency perceived by users. We propose two online scheduling algorithms based on reinforcement learning, incorporating several important characteristics of serverless functions. Via extensive simulations, we justify the superiority of the proposed algorithm by comparing with an ILP solver (Midaco). Our results indicate that the proposed algorithms efficiently approximate the results of Midaco within a factor of 1.03 while our decision-making time is 5 orders of magnitude less than that of Midaco. © 2025, CC BY.

关键词： Budget control

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Exploring Children’s Strategies in Response to Robot’s Advice During a Group Task with iCub and Nao 16th

Exploring Children’s Strategies in Response to Robot’s A...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Pusceddu, Giulia Sangineto, Mariapia Cocchella, Francesca Bogliolo, Michela Belgiovine, Giulia Lastrico, Linda Casadio, Maura Rea, Francesco Gena, Cristina Sciutti, Alessandra Robotics Brain and Cognitive Science Department Italian Institute of Technology Genoa Italy Department of Informatics Bioengineering Robotics and Systems Engineering University of Genoa Genoa Italy Department of Computer Science University of Turin Turin Italy Cognitive Architecture for Collaborative Technologies Unit Italian Institute of Technology Genoa Italy Scuola di Robotica Genoa Italy

ISBN: (纸本)9789819635184

This study investigates children’s trust in two humanoid robots, Nao and iCub, through a cooperative game designed to elicit spontaneous behaviors and group dynamics. We investigate whether participants change their choices after hearing advice from a robot, considering rounds where the advice is correct or incorrect, with the robot apologizing for its mistakes. Results reveal that, in most cases, participants prefer not to change their moves after the robot’s advice. Additionally, after the robots made mistakes, participants are less likely to actively follow their advice and more likely to reject it. No significant differences in strategies among the iCub and Nao samples are evidenced. Furthermore, we examine the influence of group dynamics, identifying "Proactive Players" who consistently initiated moves. A significant association between the strategies of this player and their teammates is found, suggesting potential influence. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Adversarial machine learning

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"It’s like an explosion": Cyberwarfare harms for civilian population in Ukraine during the Russian invasion

"It’s like an explosion": Cyberwarfare harms for civilian p...

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2025 CHI Conference on Human Factors in Computing Systems, CHI EA 2025

作者： Kulyk, Oksana Kickbusch, Jari Mayer, Peter IT University of Copenhagen Copenhagen Denmark Department of Mathematics and Computer Science University of Southern Denmark Odense Denmark Institute of Applied Informatics and Formal Description Methods Karlsruhe Institute of Technology Karlsruhe Germany

ISBN: (纸本)9798400713958

Studies on civilian costs of cyberwarfare operations are crucial in understanding how to protect the population in large-scale cyberattacks conducted by state actors. In this preliminary study, we conduct interviews (N=5) focusing on the harms experienced by Ukrainian civilians due to Russian cyberwarfare operations since the start of the full-scale invasion in 2022 as well as mitigation measures for protection of the civil society. The findings include harms such as lack of information, lack of access to essential services, physical harms, anxiety and lack of trust in the government. The study shows that cyberwarfare operations have the potential of causing severe harm to civilians and that there is a need for future studies to be conducted and adopted by institutions in order to develop measures to mitigate, not only in Ukraine but in any countries in risk of such attacks. © 2025 Copyright held by the owner/author(s).

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Novel Noise Filter Techniques and Dynamic Ensemble Selection for Classification

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Recent Advances in computer Science and Communications 2022年第1期15卷 48-59页

作者： Nguyen, Thi Ngoc A. Nhu, Quynh P. Solanki, Vijender K. School of Applied Mathematics and Informatics Hanoi University of Science and Technology Hanoi Viet Nam CMC Institute of Science and Technology 11 Duy Tan Cau Giay Hanoi Viet Nam Department of Computer Science and Engineering CMR Institute of Technology TS Hyderabad India

Background: Ensemble selection is one of the most researched topics for ensemble learning. Researchers have been attracted to selecting a subset of base classifiers that may perform more helpful than the whole ensemble system classifiers. Dynamic Ensemble Selection (DES) is one of the most effective techniques in classification problems. DES systems select the most appropriate classifiers from the candidate classifier pool. Ensemble models that balance diversity and accuracy in the training process improve performance than the whole classifiers. Objective: In this paper, novel techniques are proposed by combining Noise Filter (NF) and Dynamic Ensemble System (DES) to have better predictive accuracy. In other words, a noise filter and DES make the data cleaner and DES improves the performance of classification. Methods: The proposed NF-DES model, which was demonstrated on twelve datasets, especially has three credit scoring datasets and a performance measure accuracy. Results: The results show that our proposed model is better than other models. Conclusion: The novel noise filer and dynamic ensemble learning with the aim to improve the classification ability are presented. To improve the performance of classification, noise filter with dynamic ensemble learning makes the noise data toward the correct class. Then, novel dynamic ensemble learning chooses the appropriate subset classifiers in the pool of base classifiers. © 2022 Bentham Science Publishers.

关键词： Support vector machines

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Grid Search Hyperparameters Tuning with Supervised Machine Learning for Awngi Language Named Entity Recognition 2

Grid Search Hyperparameters Tuning with Supervised Machine L...

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2nd International Conference Computational and Characterization Techniques in Engineering and Sciences, IC3TES 2024

作者： Ayalew, Amogne Andualem Bitew, Gizachew Salau, Ayodeji Olalekan Tegegne, Melaku Lake Gupta, Shashi Kant Israr, Mohammad Tegegne, Tesfa Assegie, Tsehay Admassu Mizan-Tepi University School of Computing and Informatics Department of Information Technology Tepi Ethiopia Mizan Tepi University School of Computing Computer Science Department Mizan-Tepi Ethiopia Afe Babalola University Department of Electrical/Electronics and Computer Engineering Ado-Ekiti Nigeria Injibara University College of Engineering and Technology Department of Information Technology Injibara Ethiopia Eudoxia Research University Department of Computer Science and Engineering United States Maryam Abacha American University of Nigeria Hotoro Gra Kano State Nigeria Bahir Dar University Faculty of Computing Bahir Dar Institute of Technology Bahir Dara Ethiopia Injibara University College of Engineering and Technology Department of Computer Science Injibara Ethiopia

ISBN: (纸本)9798350364699

Named entity recognition has emerged as a critical step in recognizing, classifying, and extracting the most significant information from unstructured text without human intervention. It is used in information retrieval, conversation systems, machine translation, data mining, and information extraction. However, the Awngi language lacks a NER system due to inadequate datasets. This study combines feature extraction techniques with supervised machine learning algorithms, including support vector machines, Naïve Bayes, and conditional random fields with a grid search algorithm parameter optimization. These supervised machine-learning models often have various hyperparameters that must be tuned to optimize the model's performance by automatically selecting the parameter. Furthermore, the dataset used for training and testing consists of 20,193 annotated Awngi tokens, collected from Amhara Media Corporation, Awngi Telegram, and Facebook pages. The proposed SVM with grid search algorithm approach provided a promising result in ANER with an accuracy of 92% compared with Naïve Bayes, conditional random field, and decision-tree supervised machine learning models. Finally, the experiment results show that the proposed parameter tuning-based model can detect and categorize Awingi-named entities and outperforms the performance accuracy without parameter adjustment. © 2024 IEEE.

关键词： Support vector machines

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Ultrasound Image Analysis in Breast Cancer: A Comparative Study of Decision Trees and Random Forests 16

Ultrasound Image Analysis in Breast Cancer: A Comparative St...

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16th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2024

作者： Sivagami, S. Sathya, A. Afsin, S. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Computer Science and Engineering India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Department of Bio Informatics Tamil Nadu Chennai India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505264

Breast cancer remains a leading cause of cancer-related mortality among women globally, underscoring the critical need for early detection and accurate diagnosis. Ultrasound imaging serves as a non-invasive diagnostic tool for identifying breast abnormalities, yet the complexity of these images presents significant interpretation challenges. This study explores the application of Machine Learning (ML) models, specifically Decision Tree (DT) and Random Forest (RF) classifiers, for the classification of breast cancer ultrasound images into benign, malignant, and normal categories. Data augmentation techniques are employed to address class imbalance and enhance model generalization using the Breast Cancer Ultrasonic Image dataset. Among preprocessing steps, pixel values were normalized to provide a consistent input for the models. RF model performed better than DT with an accuracy of 88.75% against an accuracy of 86.25% and those masks for ultrasound images contribute a lot to the overall classification accuracy. In summary, the results of our study can provide support for grand challenges in health and emphasize implications such as the potentiality of ML models to advance breast cancer diagnosis and it implies a necessity for an even further expansion that will allow complete patient outcomes optimization. © 2024 IEEE.

关键词： Radio frequency Accuracy Ultrasonic imaging Training data Predictive models Breast cancer Data models Decision trees Random forests Tumors

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A model of an intelligent clustering system with an external module for the architecture of RTOS with intensive changes of states regarding their flexibility and balancing 1

A model of an intelligent clustering system with an external...

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1st International Workshop on Advanced Applied Information Technologies, AdvAIT 2024

作者： Kozelskiy, Oleksandr Kashtalian, Antonina Stetsyuk, Vasyl Martiniuk, Dmytro Sachenko, Anatoliy Khmelnitsky National University Instytutska street 11 Khmelnitsky29016 Ukraine Kazimierz Pulaski University of Technology and Humanities Department of Informatics Radom Poland Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine

The article considers the creation of a model of a clustering system with an external module for the architecture of OSRCH with intensive changes of states, which concerns their flexibility and load. An overview of modern approaches to clustering and load balancing in real-time operating systems was performed, and their advantages and disadvantages were identified. The study is aimed at identifying the main problems that need to be solved in order to increase the efficiency of clustering and improve load balancing in the real conditions of the operation of real-time operating systems. The main challenges arising in the context of ensuring high performance of such systems are considered. The obtained results can be used to develop new methods that increase the efficiency of resource management and ensure the stability of real-time operating systems. © 2024 Copyright for this paper by its authors.

关键词： flexibility enhancement operating system resource management RTOS task scheduler

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Logics from rough sets

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Journal of Applied Non-Classical Logics 2024年第2-3期34卷 171-173页

作者： Banerjee, Mohua Chakraborty, Mihir K. Szałas, Andrzej Department of Mathematics and Statistics Indian Institute of Technology Kanpur Kanpur India School of Cognitive Science Jadavpur University Kolkata India Department of Mathematics Indraprastha Institute of Information Technology Delhi New Delhi India Department of Mathematics Informatics and Mechanics University of Warsaw Warsaw Poland Department of Computer and Information Science Linköping University Linköping Sweden

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Introducing Graph Learning over Polytopic Uncertain Graph

arXiv

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arXiv 2024年

作者： Kishida, Masako Ono, Shunsuke Principles of Informatics Research Division National Institute of Informatics Tokyo Japan The Department of Computer Science Tokyo Institute of Technology Tokyo Japan

This extended abstract introduces a class of graph learning applicable to cases where the underlying graph has polytopic uncertainty, i.e., the graph is not exactly known, but its parameters or properties vary within a known range. By incorporating this assumption that the graph lies in a polytopic set into two established graph learning frameworks, we find that our approach yields better results with less computation. © 2024, CC BY.

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Monotone Arithmetic Complexity of Graph Homomorphism Polynomials 49

Monotone Arithmetic Complexity of Graph Homomorphism Polynom...

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49th EATCS International Conference on Automata, Languages, and Programming, ICALP 2022

作者： Komarath, Balagopal Pandey, Anurag Rahul, Chengot Sankaramenon Indian Institute of Technology Gandhinagar India Department of Computer Science Universität des Saarlances Saarland Informatics Campus Saarbrücken Germany School of Mathematics and Computer Science Indian Institute of Technology Goa India

ISBN: (纸本)9783959772358

We study homomorphism polynomials, which are polynomials that enumerate all homomorphisms from a pattern graph H to n-vertex graphs. These polynomials have received a lot of attention recently for their crucial role in several new algorithms for counting and detecting graph patterns, and also for obtaining natural polynomial families which are complete for algebraic complexity classes VBP, VP, and VNP. We discover that, in the monotone setting, the formula complexity, the ABP complexity, and the circuit complexity of such polynomial families are exactly characterized by the treedepth, the pathwidth, and the treewidth of the pattern graph respectively. Furthermore, we establish a single, unified framework, using our characterization, to collect several known results that were obtained independently via different methods. For instance, we attain superpolynomial separations between circuits, ABPs, and formulas in the monotone setting, where the polynomial families separating the classes all correspond to well-studied combinatorial problems. Moreover, our proofs rediscover fine-grained separations between these models for constant-degree polynomials. © Balagopal Komarath, Anurag Pandey, and Chengot Sankaramenon Rahul;licensed under Creative Commons License CC-BY 4.0

关键词： Polynomials

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