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Mediterranean Conference on Control and Automation (MED)

作者： Angelos Zacharia Savvas Papaioannou Panayiotis Kolios Christos Panayiotou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence (KIOS CoE) University of Cyprus Nicosia Cyprus

Cooperative control of multi-UAV systems has attracted substantial research attention due to its significance in various application sectors such as emergency response, search and rescue missions, and critical infrastructure inspection. This paper proposes a distributed control algorithm to generate collision-free trajectories that drive the multi-UAV system to completely inspect a set of 3D points on the surface of an object of interest. The objective of the UAVs is to cooperatively inspect the object of interest in the minimum amount of time. Extensive numerical simulations for a team of quadrotor UAVs inspecting a real 3D structure illustrate the validity and effectiveness of the proposed approach.

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IoT Based Smart Systems using Machine Learning (ML) and Artificial Intelligence (AI): Vulnerabilities and Intelligent Solutions 13

IoT Based Smart Systems using Machine Learning (ML) and Arti...

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13th International Conference on Society and Information Technologies, ICSIT 2022

作者： Ahmed, Shaftab Ilyas, Mohammad Raja, M. Yasin Akhtar Software Engineering Department Bahria University Islamabad Pakistan College of Engineering and Computer Science Florida Atlantic University Boca RatonFL United States Center for Optoelectronics & Optical Communications University of North Carolina Charlotte NC United States

ISBN: (纸本)9781950492633

Internet of Things (IoT) has assumed great importance in technical and social domains due to desire of smart living and intelligent solutions for industrial operations, home automation and healthcare. The telecommunication networks provide all-time internet connectivity for the devices in physical systems and hand- held devices. The developments have made it easy to remain engaged on all time, anywhere basis, while users interact with one or more applications. Many smart devices may interact in the background resulting in event-driven intelligent activities raising alerts or recording status summary under a policy. The smart solutions are being shaped for the industry, transport, eHealthcare, eEducation and other daily life activities. IoT activities are autonomous and support dynamic Machine-to-Machine (M2M) communication. The challenges of hetrogeneity, dynamic variation in signal quality and large volume of data are being addressed through number of techniques. In this paper, we discuss IoT based smart system technologies, security, vulnerabilities and role of intelligent solutions using Machine Learning (ML) and Artificial Intelligence (AI). A crucial factor hindering the ongoing efforts for widespread IoT-adoption, is security. We propose the requirement of Standard Security Framework (SSF) for platform independent and interoperable hardware/software modules in distributed networks domains. Trends to use Fog- and Edge-computing along with cloud applications has been also reviewed in the context to security and efficiency. © 2022 by the International Institute of Informatics and Systemics. All rights reserved.

关键词： Internet of things

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Rank-Based Adaptive Brooding in a Mimetic Coral Reefs Search for Feature Selection

Rank-Based Adaptive Brooding in a Mimetic Coral Reefs Search...

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Iranian Conference on Electrical engineering (ICEE)

作者： Seyed Amirhossein Farjadi M.-R. Akbarzadeh-T. Department of Computer Engineering Center of Excellence on Soft Computing and Intelligent Information Processing Ferdowsi University of Mashhad Mashhad Iran

Feature selection (FS) is an essential preprocessing step in machine learning and data mining. FS algorithms eliminate the irrelevant and redundant features from a feature vector, thereby increasing the accuracy and robustness of the data mining process while offering higher transparency and lower computation. Here, we suggest using the coral reefs optimization algorithms combined with a betahill climbing algorithm as part of a mimetic search strategy. The Coral Reefs is a population-based global search technique that combines well with local search techniques, as suggested here. However, such a combination is also computationally expensive. They could also converge prematurely with few iterations or insufficient population diversity. Accordingly, we propose here a more efficient search in the problem space by transforming the brooding operator into a function of the ranks of coral larvae. In this way, the chance of disturbing a larva with a higher rank is reduced, and at the same time, the exploratory role of larvae with a lower rank is used best. The performance of the proposed method has been evaluated using three KNN, Decision Tree, and SVM classifiers on eight UCI standard datasets using Precision, Recall and f1 score criteria. Results show that the rank-based adaptive brooding approach is superior to the fixed brooding approach in the high-dimensional problem space.

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RescueAid: Smartphone-Aided Situational Awareness For Emergency Response

RescueAid: Smartphone-Aided Situational Awareness For Emerge...

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International Conference on Mobile Data Management

作者： Christos Laoudias Panayiotis Kolios Georgios Ellinas KIOS Research and Innovation Center of Excellence University of Cyprus Cyprus Department of Electrical and Computer Engineering University of Cyprus Cyprus

This demonstration paper presents the RescueAid platform, that leverages the collective intelligence of edge devices by using spatio-temporal data readily available on modern smartphones (i.e., camera, GPS, and inertial sensor measurements) to deliver situational awareness during emergency response operations. Within the RescueAid mobile application, popular machine learning (ML) frameworks are deployed with pretrained models for object detection and image segmentation and used to automatically annotate and tag collected images from the field. RescueAid will be demonstrated in two modes: (i) Interactive mode, where attendees will be able to walk around outside the conference venue carrying RescueAid-enabled smartphones to generate sample data that will be automatically shared and displayed on the web platform interface and (ii) Trace-driven mode, where attendees will have the opportunity to visualize on the platform’s user interface the location traces and associated images collected during a real field exercise.

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Implementing Mission-Critical UAV Swarm Coordination Through the Integration of LoRa and ROS Frameworks

Implementing Mission-Critical UAV Swarm Coordination Through...

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International Conference on Information and Communication Technologies for Disaster Management (ICT-DM)

作者： Maria Karatzia Panayiotis Kolios Georgios Ellinas Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence (KIOS CoE) University of Cyprus Nicosia Cyprus

The operation of unmanned aerial vehicles (UAVs) has emerged as a promising enabling technology in many mission-critical operations. The ability of UAVs to communicate is essential for receiving and transmitting commands, telemetry data, and other payload information. This work proposes a wireless communication system using an integrated LoRa-ROS (long range communication - robot operating system) implementation of the two popular protocols for communication and coordination, respectively. The system is designed for UAV-to-X (UAV/GCS - ground control station) applications. The LoRa protocol, with its long-range and low-power features, is well-suited for UAV-to-X communication, especially in remote or rural areas, or during disasters, where the traditional cellular/WiFi networks are unavailable or unreliable. At the same time, ROS provides the necessary software framework to orchestrate the behavior of the UAVs and ensure seamless multi-agent coordination. The developed prototype system is implemented and empirically tested on a number of UAVs and ground control stations, and the results demonstrate the feasibility and effectiveness of the proposed solution.

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An Efficient Distributed Framework for Secured Multimedia Content using Cloud Storage 2

An Efficient Distributed Framework for Secured Multimedia Co...

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2nd International Conference on computer Technologies, ICCTech 2023

作者： Faniband, Yunus Parvej Ishak, Iskandar Sidi, Fatimah Jabar, Marzanah A. Yap, Deloria Shaniska Razin C.H. Center for Communication & IT Research Research Institute King Fahd University of Petroleum & Minerals Dhahran31261 Saudi Arabia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia UPM Serdang Selangor 43400 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology Universiti Putra Malaysia UPM Serdang Selangor 43400 Malaysia

ISBN: (纸本)9781665455824

Applications that support multimedia data such as medical pictures and videos, video broadcasting, and secure video conferencing consume vast quantities of information that require large numbers of servers to provide services for the many client users who access them. Cloud-based sensitive data becomes increasingly important for the respective users to be kept confidential without infringing confidentiality, integrity, and availability. We propose a framework based on the OpenStack cloud-computing platform for multimedia data security. A prototype framework with a helper tool EncSwift is implemented, which allows stringent data access control and applying the data-at-rest Over-Encryption approach. The implementation is evaluated for computation of cryptography operations, cost of communications, and update policy consequences for the framework. With OpenStack Swift’s open source and modular design, our multimedia data management and management framework is well suited to protecting and managing clouds’ multimedia with low computational costs as shown in the experimental results. © 2023 IEEE.

关键词： Cryptography

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MURDOC: Transforming Pixels into Perception for Camouflage Detection 43

MURDOC: Transforming Pixels into Perception for Camouflage D...

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43rd AIAA DATC/IEEE Digital Avionics Systems Conference, DASC 2024

作者： Hogue, Debra Kastl, Zak Karch, Joseph Nguyen, Don Schley, Lacey Lewis, Justin Connelly, Shane Weaver, Chris School of Computer Science The University of Oklahoma United States Us Air Force's 76th Software Engineering Group United States Oklahoma Aerospace and Defense Innovation Institute The University of Oklahoma United States The University of Oklahoma Department of Psychology Center for Applied Social Research United States

ISBN: (纸本)9798350349610

The MURDOC project introduces an application to enhance trustworthiness and explainability in computer vision models, focusing on camouflage detection. It aims to address the need for transparent and interpretable AI systems in sensitive domains such as security and defense. MURDOC integrates advanced eXplainable AI techniques including off-ramps for collecting interpretable insights, attention mechanisms for high-lighting relevant features, and a user-centric image pre-processing tool within the visualization interface. This paper offers an assessment of MURDOC's potential impact on trustworthiness and explainability in camouflaged object detection tasks. It discusses and assesses the potential effectiveness of off-ramps in gathering XAI output at various model stages, such as feature maps, attention mechanisms, and activation maps. Additionally, the paper investigates how user-driven image pre-processing mechanisms may enhance the trustworthiness of the model's predictions and decisions, allowing users to modify the input and observe prediction changes. As a work in progress, the development of MURDOC shows promise in bridging the gap for transparency and interpretability in camouflage detection. The paper also discusses the challenges, future directions, and potential applications of MURDOC's capabilities in diverse domains requiring transparent and trustworthy AI systems. The source code for the version of MURDOC in this paper is available on GitHub at: https://***/department-70/DASC-2024. © 2024 IEEE.

关键词： Camouflage

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Efficient and High-quality Recommendations via Momentum-incorporated Parallel Stochastic Gradient Descent-Based Learning

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 402-411页

作者： Xin Luo Wen Qin Ani Dong Khaled Sedraoui MengChu Zhou the School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808 Hengrui(Chongqing)Artificial Intelligence Research Center Department of Big Data Analyses TechniquesCloudwalkChongqing 401331China the School of Computer Science and Technology Chongqing University of Posts and TelecommunicationsChongqing 400065 Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China the Department of Computer and Information Science City College of Dongguan University of TechnologyDongguan 523419China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia

A recommender system(RS)relying on latent factor analysis usually adopts stochastic gradient descent(SGD)as its learning ***,owing to its serial mechanism,an SGD algorithm suffers from low efficiency and scalability when handling large-scale industrial *** at addressing this issue,this study proposes a momentum-incorporated parallel stochastic gradient descent(MPSGD)algorithm,whose main idea is two-fold:a)implementing parallelization via a novel datasplitting strategy,and b)accelerating convergence rate by integrating momentum effects into its training *** it,an MPSGD-based latent factor(MLF)model is achieved,which is capable of performing efficient and high-quality *** results on four high-dimensional and sparse matrices generated by industrial RS indicate that owing to an MPSGD algorithm,an MLF model outperforms the existing state-of-the-art ones in both computational efficiency and scalability.

关键词： Big data industrial application industrial data latent factor analysis machine learning parallel algorithm recommender system(RS) stochastic gradient descent(SGD)

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Design and experimental evaluation of a model-free controller for autonomous intersection crossing under imperfect localization

Design and experimental evaluation of a model-free controlle...

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European Control Conference (ECC)

作者： Michail Angelos Pappas Stelios Timotheou Christos Panayiotou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence (KIOS CoE) University of Cyprus Nicosia Cyprus

Despite the extensive amount of research that exists regarding Connected and Autonomous Vehicles (CAVs), experimental results are few. Furthermore, most works assume an accurate vehicle model and either assume perfect localization or ensure that the localization accuracy is very high by conducting overly specific experiments or utilizing sensor setups that are difficult to be replicated in real scenarios. Motivated by these facts, we study an unsignalized intersection crossing problem with fully autonomous traffic and develop an online, safety-critical and model-free centralized controller for CAVs under imperfect localization. The centralized controller follows a First-Come-First-Served (FCFS) reservation policy and ensures no more than one vehicle is present inside the center of the intersection in order to avoid collisions. Realistic experiments are conducted with 1:10 scaled vehicles and the results are discussed.

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Interweaving Artificial Intelligence and Bio-Signals in Mental Fatigue: Unveiling Dynamics and Future Pathways 20

Interweaving Artificial Intelligence and Bio-Signals in Ment...

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20th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Parveen, Saba Bin Heyat, Md Belal Akhtar, Faijan Parveen, Saima Asrafali, Barkathulla Singh, Bandana Ali, Kazim Huang, Lie Li, Xiao Peng Jabbar, Ayesha Asad, Maimoona Li, Jian Ping College of Electronics and Information Engineering Shenzhen University Shenzhen China CenBRAIN Neurotech Center of Excellence School of Engineering Westlake University Zhejiang Hangzhou China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Shah Abdul Latif University Department of Mathematics Sindh Khairpur Pakistan College of Physics and Optoelectronics Engineering Shenzhen University Shenzhen China School of Media and Communication Shenzhen University Shenzhen China

ISBN: (纸本)9798350318982

This study conducts a thorough examination of machine learning models within Artificial Intelligence (AI)- integrated mental fatigue research, focusing on their identification and assessment. Investigating various biomedical signals linked to mental fatigue-such as Electroencephalogram (EEG), Electrocardiogram (ECG), and Galvanic Skin Response (GSR) this research sheds light on their roles in deciphering cognitive states and emotional responses. Through integration, these signals facilitate the creation of network visualizations, unveiling the intricate relationship between physiological markers and cognitive aspects during mental fatigue scenarios. Additionally, this study identifies gaps in current mental fatigue research with AI, emphasizing the need for an integrated approach across diverse domains and populations. It outlines future directions, suggesting avenues like multi-domain integration, personalized interventions, ethical considerations, longitudinal studies, and interdisciplinary collaborations to propel advancements in mental fatigue research, offering promising prospects for innovative AI-driven solutions in mental health and well-being. © 2023 IEEE.

关键词： Electrophysiology

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