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computing (ICOCO), IEEE International Conference on

作者： Teck Wai Chin Sian Lun Lau Richard T. K. Wong Luis Bravo Rahul Babu Koneru Prashant Khare Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Bandar Sunway Malaysia Dept. of Engineering School of Engineering and Technology Sunway University Bandar Sunway Malaysia DEVCOM Army Research Laboratory Army Research Directorate Aberdeen Proving Ground MD Department of Aerospace Engineering University of Maryland College Park Maryland USA Department of Aerospace Engineering University of Cincinnati Cincinnati OH

ISBN: (数字)9798331530303

ISBN: (纸本)9798331530310

Deep learning models, particularly U-Net, have been widely used for image segmentation tasks in various domains such as medical, biological, autonomous driving, and industrial applications. However, the black-box nature of these models raises public concerns about the lack of transparency and interpretability of their decision-making processes. Unlike typical static objects, the segmentation of dynamic objects from synthetic fluid datasets are more complicated as the properties like position and shape change over time. This paper investigates these issues by exploring the uses of saliency-based Explainable Artificial Intelligence (XAI) techniques such as Class Activation Mapping (CAM), Gradient-weighted Class Activation Mapping (Grad-CAM), Grad-CAM++ and Segmentation Grad-CAM (Seg-Grad-CAM) to generate heatmap visualizations for providing insights into the prediction of U-net semantic segmentation. Using a dataset of synthetic liquid spray images, we systematically apply these techniques to visualize the model’s attention and highlights the properties and regions of the image that are influential in the model’s predictions for different classes such as lobe, ligaments and droplets. Our results demonstrate that while all techniques provide interpretable visualizations, Seg-Grad-CAM and Grad-CAM++ offer better localization of salient features, providing more precise explanations for U-Net’s segmentation decisions. The comparison of the results using quantitative analysis such as Pointing Game Accuracy and Mean Localization Error also indicated that Seg-Grad-CAM and Grad-CAM++ are more effective for interpreting complex segmentation models in liquid spray analysis.

关键词： Location awareness Visualization Analytical models Liquids Accuracy Explainable AI Semantic segmentation Predictive models Brain modeling Ligaments

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Complex-Valued DNN for Broadband Dielectric Characterization of Dispersive Lossy Materials

Complex-Valued DNN for Broadband Dielectric Characterization...

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Antennas and Propagation (LACAP), IEEE Latin American Conference on

作者： Nuwan Bandara Martina Gugliermino Mauro Lumia Giuseppe Virone Francesca Vipiana David Orlando Rodriguez-Duarte School of Computing and Information Systems Singapore Management University Singapore Dept. of Electronics and Telecommunications Politecnico di Torino Torino Italy National Research Council Inst. of Electronics and Information and Telecommunications Engineering Torino Italy

ISBN: (数字)9798331527587

ISBN: (纸本)9798331527594

This paper presents a broadband dielectric characterization method based on a Complex-Valued Deep Neural Network (CVNN) that allows the retrieval of permittivity and conductivity of dispersive lossy materials using ad-hoc setups. To validate the method, we numerically tested it employing a partially filled custom-made double-ridge waveguide setup, working from 0.95 to 4.2GHz. Moreover, we include a feature importance analysis using agnostic explainable-AI (XAI) techniques. The results demonstrate the flexibility and the retrieval capabilities of the method, as well as the advantages and drawbacks in comparison with traditional techniques. We publicly release the dataset and codes to support further research.

关键词： Waveguide components Explainable AI Dielectric losses Artificial neural networks Conductivity Propagation losses Broadband communication Broadband antennas Permittivity Dispersion

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Approximate Novelty Search 31

Approximate Novelty Search

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31st International Conference on Automated Planning and Scheduling, ICAPS 2021

作者： Singh, Anubhav Lipovetzky, Nir Ramirez, Miquel Segovia-Aguas, Javier School of Computing and Information Systems University of Melbourne Australia Electrical and Electronic Engineering University of Melbourne Australia Dept. Information and Communication Technologies Universitat Pompeu Fabra Spain

ISBN: (纸本)9781713832317

Width-based search algorithms seek plans by prioritizing states according to a suitably defined measure of novelty, that maps states into a set of novelty categories. Space and time complexity to evaluate state novelty is known to be exponential on the cardinality of the set. We present novel methods to obtain polynomial approximations of novelty and width-based search. First, we approximate novelty computation via random sampling and Bloom filters, reducing the runtime and memory footprint. Second, we approximate the best-first search using an adaptive policy that decides whether to forgo the expansion of nodes in the open list. These two techniques are integrated into existing width-based algorithms, resulting in new planners that perform significantly better than other state-of-the-art planners over benchmarks from the International Planning Competitions. Copyright © 2021, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Polynomial approximation

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TPTO: A Transformer-PPO based Task Offloading Solution for Edge computing Environments

TPTO: A Transformer-PPO based Task Offloading Solution for E...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Niloofar Gholipour Marcos Dias de Assuncao Pranav Agarwal Julien Gascon-Samson Rajkumar Buyya Dept. of Software Engineering and IT École de Technologie Supérieure Univ. of Quebec Montreal Canada CLOUDS Lab School of Computing and Information Systems The Univ. of Melbourne Australia

Emerging applications in healthcare, autonomous vehicles, and wearable assistance require interactive and low-latency data analysis services. Unfortunately, cloud-centric architectures cannot fulfill the low-latency demands of these applications, as user devices are often distant from cloud data centers. Edge computing aims to reduce the latency by enabling processing tasks to be offloaded to resources located at the network’s edge. However, determining which tasks must be offloaded to edge servers to reduce the latency of application requests is not trivial, especially if the tasks present dependencies. This paper proposes a Deep Reinforcement Learning (DRL) approach called TPTO, which leverages Transformer Networks and Proximal Policy Optimization (PPO) to offload dependent tasks of IoT applications in edge computing. We consider users with various preferences, where devices can offload computation to an edge server via wireless channels. Performance evaluation results demonstrate that under fat application graphs, TPTO is more effective than state-of-the-art methods, such as Greedy, HEFT, and MRLCO, by reducing latency by 30.24%, 29.61%, and 12.41%, respectively. In addition, TPTO presents a training time approximately 2.5 times faster than an existing DRL approach.

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TPTO: A Transformer-PPO based Task Offloading Solution for Edge computing Environments

arXiv

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arXiv 2023年

作者： Gholipour, Niloofar de Assuncao, Marcos Dias Agarwal, Pranav Gascon-Samson, Julien Buyya, Rajkumar Dept. of Software Engineering and IT École de Technologie Supérieure Univ. of Quebec Montreal Canada CLOUDS Lab School of Computing and Information Systems The Univ. of Melbourne Australia

关键词： Reinforcement learning

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Learning Goal-based Movement via Motivational-based Models in Cognitive Mobile Robots

arXiv

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arXiv 2023年

作者： Berto, Letícia Costa, Paula Simões, Alexandre Gudwin, Ricardo Colombini, Esther Laboratory of Robotics and Cognitive Systems Institute of Computing University of Campinas Brazil Dept. of Computer Engineering and Industrial Automation School of Electrical and Computer Engineering University of Campinas Brazil Dept. of Control and Automation Engineering Institute of Science and Technology São Paulo State University Sorocaba Brazil University of Campinas Brazil

Humans have needs motivating their behavior according to intensity and context. However, we also create preferences associated with each action’s perceived pleasure, which is susceptible to changes over time. This makes decision-making more complex, requiring learning to balance needs and preferences according to the context. To understand how this process works and enable the development of robots with a motivational-based learning model, we computationally model a motivation theory proposed by Hull. In this model, the agent (an abstraction of a mobile robot) is motivated to keep itself in a state of homeostasis. We added hedonic dimensions to see how preferences affect decision-making, and we employed reinforcement learning to train our motivated-based agents. We run three agents with energy decay rates representing different metabolisms in two different environments to see the impact on their strategy, movement, and behavior. The results show that the agent learned better strategies in the environment that enables choices more adequate according to its metabolism. The use of pleasure in the motivational mechanism significantly impacted behavior learning, mainly for slow metabolism agents. When survival is at risk, the agent ignores pleasure and equilibrium, hinting at how to behave in harsh scenarios. Copyright © 2023, The Authors. All rights reserved.

关键词： Metabolism

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AI in Agriculture: Yield Optimization and Food Security

AI in Agriculture: Yield Optimization and Food Security

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Technological Advancements in Computational Sciences (ICTACS), International Conference on

作者： Lavanya Chavan Rishikesh Kumbhalkar Poorva Agrawal Nitin Rakesh R. Parvathi Mohammad Shahid Symbiosis Institute of Technology Nagpur Campus Symbiosis International (Deemed University) Pune India Dept. of Smart Computing School of Computer Science Engineering and Information Systems Vellore Institute of Technology (VIT) Vellore India Noida Institute of Engineering and Technology Greater Noida India

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Artificial Intelligence (AI) is remarkably automating the agricultural methods. In this process, AI is helping to optimize yield, and improving food security in a world with growing population and environmental concern(s). AI technology integration in agriculture is the focus of this paper which includes predictive analytics, machine learning and IoT. These applications led to precision farming and resource management which lead to efficient use of resources. The discussion on the benefits of AI in increasing productivity and sustainability is based on the summarization of the latest literature and empirical studies. Yet, these technologies bring along their own issues such as initial capital investment to implement the systems in emergence economies as well as the need for the rapid upgrading of the infrastructure and human capital. This research demonstrates that AI has the potential to change agriculture radically, while at the same time acknowledging the pending problems that hinder full-fledged implementation.

关键词： Training Food security Agriculture Resource management Artificial intelligence Sustainable development Socioeconomics Optimization Farming Investment

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Revolutionizing Public Transport: RFID-Enabled Occupancy Monitoring and Frequency Analysis System

Revolutionizing Public Transport: RFID-Enabled Occupancy Mon...

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International Conference on Communication, computing and Internet of Things (IC3IoT)

作者： J. Uma Mageswaran G. Indumathi A.V. Kalpana G. Elangovan S.D. Lalitha S. Nalini Mayank Kumar Sohanda Dept. of Computer Science Engineering Amrita School of Computing Vengal Campus Tiruvallur Dist Tamil Nadu SRM Institute of Science and Technology Ramapuram Department of Data Science and Business Systems SRM Institute of Science and Technology Kattankulathur Department of Data Science and Business Systems SRM Institute of Science and Technology Department of Computer Science and Engineering R.M.K Engineering College Department of Computing Technologies SRM Institute of Science and Technolog Kattankulathur

ISBN: (数字)9798350352689

ISBN: (纸本)9798350352696

Public bus stops in India are becoming more crowded due to the country's fast population expansion. People wait a long time for buses to come, then suddenly congregate around them when they do, packing the buses with people and pushing them up into the footboards, which causes accidents. An additional concern of overcrowding is theft. All of this is the result of numerous bus stops not having adequate information on when busses would arrive. The proposed work paves a very important role for the passengers. The passengers will be provided with the information of the arrival of next bus, seat occupancy and the total passengers inside the bus. The information about the seat occupancy, in-passengers and out-passengers is done with the help of Radio Frequency Identification (RFID) tags. The arrival of the next bus is determined by calculating the duration of the buses that are near using RFID readers placed in different bus stations. The count and the frequency will be interfaced in the web application. The passengers can check the application to know the occupancy and today's society where people can catch the right bus at the station and at the right time. This will solve the problems like waiting in bus stops and wasting time and going in a crowded bus.

关键词： Sociology Telecommunication computing Internet of Things Statistics RFID tags Monitoring Accidents

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD)

Blockchain Integration for Robust Intellectual Property Prot...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： P. Chinnasamy Subashini. B Nijanthan Nijanthan Madasamy Raja. G Devulapally Swetha Sreenivasulu Reddy L Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Data Science and Business Systems School of Computing SRMIST India Computer Science and Engineering Paavai Engineering College Namakkal Tamilnadu India Information Technology Paavai Engineering College Namakkal Tamilnadu India Dept. of Computer Science and Engineering Vaagdevi Engineering College Telangana India Department of Mathematics School of Advanced Sciences Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms.

关键词： Patents Technological innovation Costs Smart contracts Intellectual property Product development Blockchains Security Protection Resilience

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Privacy-Preserving Machine Learning for E-Health Applications: A Survey

Privacy-Preserving Machine Learning for E-Health Application...

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computing and Machine Intelligence (ICMI), International Conference on

作者： Jared Romeo Mahmoud Abbass Ahmed Sherif Mohammad M. R. Khan Mamun Mohamed Elsersy Kasem Khalil School of Computing Sciences and Computer Engineering University of Southern Mississippi MS USA Electrical and Computer Engineering Department Tennessee Technological University TN USA Computer and Information Systems Dept. Higher Colleges of Technology AlAin Abu Dhabi UAE Electrical and Computer Engineering Department University of Mississippi MS USA

ISBN: (数字)9798350372977

ISBN: (纸本)9798350372984

As more information about people's health is gath-ered and analyzed, privacy concerns have become increasingly essential. This paper summarizes how machine learning (ML) methods are presently used to safeguard privacy in e-health apps. Furthermore, we discuss how privacy-preserving approaches such as differential privacy, secure multi-party computing, and homomorphic cryptography may be utilized to protect privacy in ML. The study also conducts an overview of the literature on how these methods can be used in various e- health scenarios, such as predictive modeling, disease detection, and clinical decision support. Based on the privacy-preserving technique, we classified current privacy-preserving ML work into four categories (i.e., data encryption, data anonymization, model encryption, and model obfuscation). Finally, the article discusses the challenges of applying privacy-preserving machine learning (PPML) in the e-health domain and what types of research could be conducted as open future problems in this field.

关键词： Surveys Privacy Computational modeling Machine learning Medical services Predictive models Data models

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