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Improving the Resource Allocation in IoT Systems Based on the Integration of Reinforcement Learning and Rule-Based Approaches in Multi-agent Systems 8

Improving the Resource Allocation in IoT Systems Based on th...

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8th International Conference on Smart Cities, Internet of Things and Applications, SCIoT 2024

作者： Yarahmadi, Hossein Navidi, Hamidreza Challenger, Moharram University of Antwerp Department of Computer Science Belgium Islamic Azad University Department of Computer Engineering Borujerd Branch Borujerd Iran Shahed University Department of Mathematics and Computer Science Tehran Iran University of Antwerp Flanders Make Strategic Research Center Department of Computer Science Flanders Belgium

ISBN: (纸本)9798350374674

In recent years, the spread of Multi-agent Systems (MAS) and Reinforcement Learning (RL) as a subdomain of Machine Learning (ML) has affected many fields, such as Cyber-Physical Systems (CPS) and the Internet of Things (IoT). There are many Multi-agent Reinforcement Learning (MARL) applications in CPS and IoT, including energy management, smart transportation systems, job scheduling, etc. Due to the fact that the RL and MARL are slow processes, it is necessary to improve and accelerate them. In this study, we review some applications of MAS in the CPS and IoT. To improve the decision-making in MAS, we apply a combination of learning and reasoning (rule-based). We tested the performance of our proposed method in smart homes for 4 distinct tasks, including Face Detection and Window Blind Control, Finger Touch and Gate Control, Weather Check and Thermostat Control, and Temperature Check and Start Fan to assess its efficacy. We applied our method to the job scheduling problem using two parameters: process unit and bandwidth. The results reveal that the proposed approach performed better than the bankruptcy methods based on these criteria. © 2024 IEEE.

关键词： Multi agent systems

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Personality-based and trust-aware products recommendation in social networks

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Applied Intelligence 2023年第1期53卷 879-903页

作者： Vatani, Nasim Rahmani, Amir Masoud Javadi, Hamid Haj Seyyed Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Department of Mathematics and Computer Science Shahed University Tehran Iran

In recent years, with the development of technology, the shopping approach of people has moved towards pervasive online social shopping. As a result, how to create a recommendation algorithm that offers products based on the personal and different needs and tastes of people on social networks is a significant research issue. This article proposes a personality-based and trust-aware probabilistic product recommendation algorithm in social networks. We present a dynamic method for determining how similar people in social networks are. For this purpose, we consider the personality-based features of recommendation attributes of products in social networks. Then, the level of trust of products and types of correlations among the products is considered to create a probabilistic matrix of product recommendation. Moreover, for solving the cold start problem of products, we consider qualitative aspects of products while exploiting personality-based user behavior regarding their purchases. At last, the empirical experiments are conducted to analyze the impact of the algorithm’s different influence factors using the Amazon dataset. Moreover, the results of comprehensive experiments adopted to verify the proposed personalized recommendation algorithm’s effectiveness show that the proposed algorithm has the appropriate effectiveness and the higher accuracy. © 2022, The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature.

关键词： Behavioral research

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DUAL STUDENT NETWORKS FOR DATA-FREE MODEL STEALING 11

DUAL STUDENT NETWORKS FOR DATA-FREE MODEL STEALING

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11th International Conference on Learning Representations, ICLR 2023

作者： Beetham, James Kardan, Navid Mian, Ajmal Shah, Mubarak Center for Research in Computer Vision University of Central Florida OrlandoFL32816 United States Department of Computer Science University of Western Australia CrawleyWA6009 Australia

Data-free model stealing aims to replicate a target model without direct access to either the training data or the target model. To accomplish this, existing methods use a generator to produce samples in order to train a student model to match the target model outputs. To this end, the two main challenges are estimating gradients of the target model without access to its parameters, and generating a diverse set of training samples that thoroughly explores the input space. We propose a Dual Student method where two students are symmetrically trained in order to provide the generator a criterion to generate samples that the two students disagree on. On one hand, disagreement on a sample implies at least one student has classified the sample incorrectly when compared to the target model. This incentive towards disagreement implicitly encourages the generator to explore more diverse regions of the input space. On the other hand, our method utilizes gradients of student models to indirectly estimate gradients of the target model. We show that this novel training objective for the generator network is equivalent to optimizing a lower bound on the generator's loss if we had access to the target model gradients. In other words, our method alters the standard data-free model stealing paradigm by substituting the target model with a separate student model, thereby creating a lower bound which can be directly optimized without additional target model queries or separate synthetic datasets. We show that our new optimization framework provides more accurate gradient estimation of the target model and better accuracies on benchmark classification datasets. Additionally, our approach balances improved query efficiency with training computation cost. Finally, we demonstrate that our method serves as a better proxy model for transfer-based adversarial attacks than existing data-free model stealing methods. © 2023 11th International Conference on Learning Representations, ICLR 2023.

关键词： Students

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Recombination Operators for the Multi-Objective Team Formation Problem in Social Networks 13

Recombination Operators for the Multi-Objective Team Formati...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Juárez, Julio Brizuela, Carlos A. Terashima-Marín, Hugo Coello Coello, Carlos A. School of Engineering and Sciences Tecnologico de Monterrey Monterrey Mexico Cicese Research Center Department of Computer Science Ensenada Mexico CINVESTAV-IPN Computer Science Department Mexico City Mexico

ISBN: (纸本)9798350308365

The Team Formation Problem in Social Networks (TFP-SN) describes the process of finding an effective group of people, drawn from a network of experts, to perform a particular task. For a team to be considered as effective, it requires to comply with a task-specific skills set while also showing a high degree of cohesiveness. Although team effectiveness is subject to multiple criteria, the study of the problem from a multi-objective (MO) perspective is still scarce. In this paper, we focus on an MO TFP-SN whose objective is to maximize the team's level of expertise and the team's density, simultaneously. To solve this problem, we introduce two novel recombination operators to be used within the framework of the well-known NSGA-II. Our proposed crossover operators act as heuristics that compute the parents' unique and shared information, which is then combined for generating potentially improved offspring. Our experiments show that each of the two proposed crossover operators lead to significantly better results when compared to a naive crossover operator taken from the specialized literature. Particularly, the results consistently show higher hypervolume values when compared to the use of an adaptation of a simple recombination operator commonly used for this problem. The good performance of our proposed operators may be attributed to the incorporation of knowledge that exploits the structure of the problem. © 2024 IEEE.

关键词： Combinatorial optimization Multi-objective optimization Recombination operators Team formation problem

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Segmented-Based and Segmented-Free Approach for COVID-19 Detection 5th

Segmented-Based and Segmented-Free Approach for COVID-19 D...

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5th International Conference on Computational Intelligence in Communications and Business Analytics, CICBA 2023

作者： Lasker, Asifuzzaman Ghosh, Mridul Das, Sahana Obaidullah, Sk Md Chakraborty, Chandan Goncalves, Teresa Roy, Kaushik Department of Computer Science and Engineering Aliah University Kolkata700160 India Department of Computer Science Shyampur Siddheswari Mahavidyalaya Howrah711312 India School of Computer Science Swami Vivekananda University Kolkata700121 India Department of Computer Science and Engineering National Institute of Technical Teachers’ Training and Research Kolkata700106 India Computer Science Department and ALGORITMI Center University of Évora Évora Portugal Department of Computer Science West Bengal State University Barasat700126 India

ISBN: (纸本)9783031488788

According to WHO, lung infection is one of the most serious problems across the world, especially for children under five years old and older people over sixteen years old. In this study, we designed a deep learning-based model to aid medical practitioners in their diagnostic process. Here, U-Net based segmentation framework is considered to get the region of interest (ROI) of the lung area from the chest x-ray images. Two standard deep learning models and a developed CNN model comprise this framework. A deep ensemble framework method is presented to detect COVID-19 disease from a collection of chest X-ray images of disparate cases in both segment-free and segmented-based lung images. Different public datasets were used for segmentation and classification to test the system’s robustness. The performance of segmentation and classification approaches returns promising outcomes compared to the state-of-the-art. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： COVID-19

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A Reputation-Based AODV Protocol for Blackhole and Malfunction Nodes Detection and Avoidance

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computers, Materials & Continua 2024年第8期80卷 1867-1888页

作者： Qussai M.Yaseen Monther Aldwairi Ahmad Manasrah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjman346United Arab Emirates Department of Computer Information Systems Faculty of Computer and Information TechnologyJordan University of Science and TechnologyIrbid22110Jordan College of Technological Innovation Zayed UniversityAbu Dhabi144534United Arab Emirates Faculty of Information Technology and Computer Science Yarmouk UniversityIrbid21163Jordan Computer Information Systems Higher Colleges of TechnologySharjah341214United Arab Emirates

Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their ***,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN *** paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering *** proposed version employs a network-based reputation system to select the best and most secure path to a *** achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation *** minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation ***,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast *** proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other *** the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing *** paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different ***,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.

关键词： AODV blackhole malfunction nodes pathrater reputation system wireless sensor networks(WSNs) watchdogs

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Leveraging OpenStack-based Private Cloud for Intrusion Detection Using Deep Learning Techniques 1

Leveraging OpenStack-based Private Cloud for Intrusion Detec...

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1st International Conference for Women in Computing, InCoWoCo 2024

作者： Roshan, Rahul G. Salanke, Vishal S Nagasundari, S. Research Center for Information Security Forensics and Cyber Resilience PES University Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798331518943

OpenStack is a cloud platform that offers infrastructure services, providing access to computing, storage, and network resources. However, due to its open-source nature, the cloud environment can be vulnerable to security threats. OpenStack security measures are generally applied to specific services, rather than being integrated comprehensively across the entire cloud environment. Despite offering flexibility and scalability, the widespread use of OpenStack calls for robust security solutions. Current intrusion detection systems often struggle to adapt to constantly evolving traffic patterns and attack vectors in the OpenStack environment. This study introduces a two-stage deep learning framework to create a robust Intrusion Detection System (IDS) for OpenStack-based private clouds. The proposed framework efficiently analyzes network traffic data in these clouds by implementing dimensionality reduction and anomaly detection techniques. It utilizes a network-based IDS, trained on two publicly available datasets, which enables a broader detection range of known attack patterns. The results indicate that the OpenStack IDS significantly enhances OpenStack's security, addressing the limitations of generic IDS solutions. © 2024 IEEE.

关键词： Network intrusion

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A Semiautomatic Image Processing-Based Method for Binary Segmentation of Lungs in Computed Tomography Images

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SN computer science 2024年第6期5卷 689页

作者： Ramos, Leo Pineda, Israel School of Mathematical and Computational Sciences Yachay Tech University Deep Learning for Autonomous Driving Robotics and Computer Vision (DeepARC) Research Group Yachay Tech University Kauel Inc. Computer Vision Center Universitat Autònoma de Barcelona College of Sciences and Engineering Universidad San Francisco de Quito

Precise biomedical image segmentation is pivotal in medical diagnosis and treatment. Among various methodologies, image processing-based techniques are useful due to their swift and consistent results, free from the dependency on extensive training data required by deep learning approaches. This study introduces a tri-phase, semi-automated pipeline for lung computed tomography image segmentation, beginning with a preprocessing phase that includes contrast enhancement, noise reduction, and edge detection to improve image quality. This is followed by the application of the region growing method for segmentation and is completed with morphological closing to refine the final output. Comparative tests reveal that our technique surpasses prevalent methods like split and merge, Otsu’s technique, and basic region growing in terms of the dice coefficient (DC) and intersection over union (IoU). Specifically, our approach achieved an average DC of 0.9633, contrasting with 0.4668, 0.005, and 0.4067 recorded by Otsu’s, split and merge, and basic region growing techniques respectively. Similarly, the average IoU for our method was 0.9341, in comparison to 0.3105, 0.0660, and 0.2605 for the aforementioned methods. Additionally, comparative analysis with contemporary segmentation models demonstrates that our approach is competitive, even without relying on large training datasets. These results highlight our method’s efficiency and practicality, distinguishing it as a compelling option in medical scenarios where access to extensive training data is limited. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Image analysis Image processing Image segmentation Medical image Pattern recognition Region growing

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A Comprehensive Dataset of Real-scene Images for Text Detection and Recognition in Persian 14

A Comprehensive Dataset of Real-scene Images for Text Detect...

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14th International Conference on computer and Knowledge engineering, ICCKE 2024

作者： Souzanchi, Iman Rahimi, Ramin Anvari, Mohammad Ali Majidi Baniasadi, Atefeh Sadeghi, Ashkan Mohammadi, Mohammad Reza PART AI Research Center Tehran Iran School of Computer Engineering Iran University of Science and Technology Tehran Iran

ISBN: (纸本)9798331511272

— Extracting text from scene images is a widely utilized field owing to the abundance of information available in scene images and their potential utilization in computer vision applications such as self-driving cars, text translation, information extraction from invoices, shopfronts, license plate retrieval, etc. Nonetheless, this field presents challenges because of the varying fonts, styles, sizes, and other characteristics of the text. Despite the existence of numerous studies on scene text recognition for languages such as English that employ deep learning models, a major barrier to implementing these models in Persian is the lack of an appropriate and sufficient dataset both in terms of quantity and quality. This paper aims to introduce a comprehensive collection of Persian scene images obtained from diverse sources, including newspapers, magazines, books, business cards, road signs, advertising billboards, shopfronts, invoices, and scanned documents. This dataset comprises over 250k annotated text lines from 5000 images, including various lengths, fonts, and sizes that have been prepared under different conditions, including varying brightness and viewing angles. Additionally, more than 2,500,000 images of meaningful sentences have been synthesized since the annotation of real data is so expensive. In order to assess the efficacy of our dataset, a scene text recognition model was trained from existing models, and a word accuracy of 83.9% was achieved on challenging test images. ©2024 IEEE.

关键词： Image annotation

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Machine Unlearning: An Overview of the Paradigm Shift in the Evolution of AI 21

Machine Unlearning: An Overview of the Paradigm Shift in the...

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21st International Learning and Technology Conference, L and T 2024

作者： Jaman, Layan Alsharabi, Reem Elkafrawy, Passent M. Effat University Effat College of Engineering Energy and Technology Research Center Computer Science Department Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

The rapid advancements in artificial intelligence (AI) have primarily focused on the process of learning from data to acquire knowledge for smart systems. However, the concept of machine unlearning has emerged as a transformative paradigm shift in the field of AI, due to the amount of false information that have been learned over the past. Machine unlearning refers to the ability of AI systems to reverse or discard previously acquired knowledge or patterns, enabling them to adapt and refine their understanding in response to changing circumstances or new insights. This paper explores the concept of machine unlearning, its implications, methods, challenges, and potential applications. The paper begins by providing an overview of the traditional learning-based approaches in AI and the limitations they impose on system adaptability and agility. It then delves into the concept of machine unlearning, discussing various techniques and algorithms employed to remove or modify learned knowledge from AI models or datasets. © 2024 IEEE.

关键词： Adaptive algorithms

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