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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Senkerik, Roman Byrski, Aleksander Guzowski, Hubert Janku, Peter Kadavy, Tomas Oplatkova, Zuzana Kominkova Matusu, Radek Pluhacek, Michal Pekar, Libor Smolka, MacIej Viktorin, Adam Tomas Bata University A.I. Lab Faculty of Applied Informatics Department of Informatics and Artificial Intelligence Zlin Czech Republic Tomas Bata University Faculty of Applied Informatics Department of Automation and Control Engineering Zlin Czech Republic Institute of Computer Science Agh University of Science and Technology Krakow Poland

ISBN: (纸本)9798350337020

This paper presents an experimental study that compares the performance of four selected metaheuristic algorithms for optimizing a time delay system model. Time delay system models are complex and challenging to optimize due to their inherent characteristics, such as non-linearity, multi-modality, and constraints. The study includes an explanation of the choice and core functionality of the selected algorithms, which are both baseline and state-of-the-art variants of self-organizing migrating algorithm (SOMA), state-of-the-art variant from the Success-History-based Adaptive Differential Evolution family of algorithms, with emphasis on diverse search (DISH algorithm), and Covariance Matrix Adaptation Evolution Strategy (CMA-ES) algorithm. The hyperparameters of the metaheuristic algorithms were set using the iRace automatic algorithm configuration framework. The paper emphasizes the importance of metaheuristic algorithms in control engineering for time-delay systems to develop more effective and efficient control strategies and precise model identifications. The experimental results highlight the effectiveness of the state-of-the-art algorithms with specific adaptive mechanisms like population organization process, diverse search and adaptation mechanisms ensuring a gradual transition from exploration to exploitation. Overall, this study contributes to understanding the challenges and advantages of using metaheuristic algorithms in control engineering for time delay systems. The results provide valuable insights into the performance of modern metaheuristic algorithms and can help guide the selection of appropriate adaptive mechanisms of metaheuristics. © 2023 IEEE.

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POPD: Partial Occluded Pedestrian Detection Using A Multimodal Deep Learning Approach

POPD: Partial Occluded Pedestrian Detection Using A Multimod...

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International Joint Conference on Neural Networks (IJCNN)

作者： Deepanshu Garg Alok Kumar Sivaraman Eswaran Youcef Djenouri Gautam Srivastava Computer Science & Engineering Department Chandigarh University India Department of Electrial and Computer Science Curtin University Malaysia Norwegian Research Centre University of South-Eastern Norway Oslo Norway Department of Math and Computer Science Brandon University Canada Department of Computer Science and Math Lebanese American University Beirut Lebanon

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

With the enhancement in the growth of the automotive industry, pedestrian detection is one of the major issues to prevent collisions on roads. This also plays a significant role in various applications ranging from road safety to urban planning. Hence, in this paper, a multimodal deep learning scheme based Partial Occluded Pedestrian Detection (POPD) approach has been proposed. Primarily, data fusion has been performed by considering multimodal data from different sources for effective implementation of pedestrian detection. In the next step, a key-point detection algorithm was applied to this aggregated data, and Mask-RCNN was deployed to classify different occlusion profiles. To evaluate results, extensive simulations have been performed and exhibited results show that the proposed scheme has provided satisfactory results when compared with benchmark schemes. The findings of this study show a considerable increase in terms of detection accuracy, particularly in situations with a lot of occlusions.

关键词： Deep learning Pedestrians Accuracy Soft sensors Urban planning Data integration Robustness

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Private everlasting prediction 23

Private everlasting prediction

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Moni Naor Kobbi Nissim Uri Stemmer Chao Yan Department of Computer Science and Applied Math Weizmann Institute of Science Department of Computer Science Georgetown University Blavatnik School of Computer Science Tel Aviv University and Google Research

A private learner is trained on a sample of labeled points and generates a hypothesis that can be used for predicting the labels of newly sampled points while protecting the privacy of the training set [Kasiviswannathan et al., FOCS 2008]. Past research uncovered that private learners may need to exhibit significantly higher sample complexity than non-private learners as is the case of learning of one-dimensional threshold functions [Bun et al., FOCS 2015, Alon et al., STOC 2019].We explore prediction as an alternative to learning. A predictor answers a stream of classification queries instead of outputting a hypothesis. Earlier work has considered a private prediction model with a single classification query [Dwork and Feldman, COLT 2018]. We observe that when answering a stream of queries, a predictor must modify the hypothesis it uses over time, and in a manner that cannot rely solely on the training *** introduce private everlasting prediction taking into account the privacy of both the training set and the (adaptively chosen) queries made to the predictor. We then present a generic construction of private everlasting predictors in the PAC model. The sample complexity of the initial training sample in our construction is quadratic (up to polylog factors) in the VC dimension of the concept class. Our construction allows prediction for all concept classes with finite VC dimension, and in particular threshold functions over infinite domains, for which (traditional) private learning is known to be impossible.

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Virtual Network Embedding: Literature Assessment, Recent Advancements, Opportunities, and Challenges

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IEEE Communications Surveys and Tutorials 2025年

作者： Satpathy, Anurag Sahoo, Manmath Narayan Swain, Chittaranjan Bellavista, Paolo Guizani, Mohsen Muhammad, Khan Bakshi, Sambit Missouri University of Science and Technology Department of Computer Science RollaMO United States National Institute of Technology Visual Surveillance Laboratory Department of Computer Science and Engineering Odisha Rourkela769008 India Atal Bihari Vajpayee Indian Institute of Information Technology and Management Department of Computer Science and Engineering Gwalior India Department of Computer Science and Engineering Italy Machine Learning Department Abu Dhabi United Arab Emirates Department of Applied Artificial Intelligence School of Convergence College of Computing and Informatics Seoul03063 Korea Republic of

Network virtualization (NV) allows service providers (SPs) to instantiate logically isolated entities called virtual networks (VNs) on top of a substrate network (SN). Though VNs bring about multiple benefits, particularly in terms of economic costs and elasticity, they also force various technical challenges to be addressed. The primary one is the issue of optimally allocating resources to VNs, also termed virtual network embedding (VNE). This paper presents an exhaustive survey of VNE by extensively covering the state-of-the-art research field in this very active field and focusing on the emerging research trends in industry and academia over the last decade. In addition, this survey originally contributes to the literature by proposing a novel taxonomy for existing VNE solutions and providing a thorough comparative study of their strategies. © 1998-2012 IEEE.

关键词： Virtual machine

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Next-Generation Advanced Security systems for Communities Using Integrated IoT and Blockchain Over Cloud Computing

Next-Generation Advanced Security systems for Communities Us...

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2024 International Symposium on Securing Next Generation Systems Using Future Artificial Intelligence Technologies, SNSFAIT 2024

作者： Quraishi, Aadam Rusho, Maher Ali Alghayadh, Faisal Yousef Mahalakshmi, V. Soni, Mukesh Bhatt, Mohammed Wasim Intervention Treatment Institute HoustonTX United States Department of Lockheed Martin Engineering Management University of Colorado Boulder BoulderCO United States Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of Computer Science College of Engineering and computer science Jazan University Jazan45142 Saudi Arabia Dr. D. Y. Patil Vidyapeeth Pune Dr. D. Y. Patil School of Science & Technology Tathawade Pune India Model Institute of Engineering and Technology J&K Jammu India

According to human requirements, various structures are being constructed and require some protection from fire accidents, floods, earthquakes, any gas leaks, and any other concerns that may arise in the neighborhood. So far, their security goal is to propose a system integrated with blockchain and an IoT system. We structure the system in different steps in our proposal to process the alarm rung by IoT devices. This IoT gadget may be used in either a private or public setting. To do this, we must process the various settings for checking and responding to the devices. Data centers that interact between sender and recipient will verify blockchain data. Finally, we provide a security solution to avoid reply assaults, transmission interruptions, and data integrity. This entire system may work together to promote community safety and eliminate avoidable disputes. © 2024 Copyright for this paper by its authors.

关键词： Data integrity

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Fuzzy with Metaheuristics Based Routing for Clustered Wireless Sensor Networks

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Intelligent Automation & Soft Computing 2023年第1期35卷 367-380页

作者： Ashit Kumar Dutta Yasser Albagory Majed Alsanea Abdul Rahaman Wahab Sait Hazim Saleh AlRawashdeh Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Computing University Name:Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia Department of Cyber Security Onaizah Private CollegesUnaizah51911Kingdom of Saudi Arabia

Wireless sensor network(WSN)plays a vital part in real time tracking and data collection *** incorporates a set of numerous sensor nodes(SNs)commonly utilized to observe the target *** SNs operate using an inbuilt battery and it is not easier to replace or charge ***,proper utilization of available energy in the SNs is essential to prolong the lifetime of the *** this study,an effective Type-II Fuzzy Logic with Butterfly Optimization Based Route Selection(TFL-BOARS)has been developed for clustered *** TFL-BOARS technique intends to optimally select the cluster heads(CHs)and routes in the clustered ***,the TFL-BOARS technique incorporates Type-II Fuzzy Logic(T2FL)technique with distinct input parameters namely residual energy(RE),link quality(LKQ),trust level(TRL),inter-cluster distance(ICD)and node degree(NDE)to select CHs and construct ***,the butterfly optimization algorithm based route selection(BOARS)technique is derived to select optimal set of routes in the *** addition,the BOARS technique has computed afitness function using three parameters such as communication cost,distance and *** order to demonstrate the improved energy effectiveness and prolonged lifetime of the WSN,a wide-ranging simulation analysis was implemented and the experimental results reported the supremacy of the TFL-BOARS technique.

关键词： Type II-fuzzy logic uncertainty WSN energy lifetime route selection

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Remote Employee Monitoring and Management 7th

Remote Employee Monitoring and Management

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7th International Conference on Computing in engineering and Technology, ICCET 2022

作者： Shah, Chaitya Shah, Jinal Shah, Viraj Raul, Nataasha Naik, Meghana Department of Computer Engineering Sardar Patel Institute of Technology Mumbai India Department of Applied Science and Humanities Sardar Patel Institute of Technology Mumbai India

ISBN: (纸本)9789811927188

Organizations have moved towards tracking their employees’ devices and keeping track of their work. We intend to develop a system that not only respects the privacy of employees but also helps employers manage their employees and also get a reliable productivity report. We have been able to monitor the background activities and log in the application names and their active time. The employee authentication process is implemented via a face recognition system which helps in maintaining authenticity. A total of 135 desktop apps and websites were opened while using the application and out of that 125 were successfully logged and their time was recorded accurately and productivity was calculated. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Productivity

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Train in Austria, Race in Montecarlo: Generalized RL for Cross-Track F1tenthLIDAR-Based Races 19

Train in Austria, Race in Montecarlo: Generalized RL for Cro...

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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Bosello, Michael Tse, Rita Pau, Giovanni Università di Bologna DISI: Department of Computer Science and Engineering Cesena Italy School of Applied Sciences Macao Polytechnic Institute China Ucla Samueli Computer Science Department Los Angeles United States

ISBN: (纸本)9781665431620

Autonomous vehicles have received great attention in the last years, promising to impact a market worth billions. Nevertheless, the dream of fully autonomous cars has been delayed with current self-driving systems relying on complex processes coupled with supervised learning techniques. The deep reinforcement learning approach gives us newer possibilities to solve complex control tasks like the ones required by autonomous vehicles. It let the agent learn by interacting with the environment and from its mistakes. Unfortunately, RL is mainly applied in simulated environments, and transferring learning from simulations to the real world is a hard problem. In this paper, we use LIDAR data as input of a Deep Q-Network on a realistic 1/10 scale car prototype capable of performing training in real-time. The robot-driver learns how to run in race tracks by exploiting the experience gained through a mechanism of rewards that allow the agent to learn without human supervision. We provide a comparison of neural networks to find the best one for LIDAR data processing, two approaches to address the sim2real problem, and a detail of the performances of DQN in time-lap tasks for racing robots. © 2022 IEEE.

关键词： Real time systems

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Efficiently Optimizing Crane Scheduling: A Rule-Based Approach for Dynamic and Uncertain Environments

Efficiently Optimizing Crane Scheduling: A Rule-Based Approa...

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International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Philipp Bao-Phuc Pham Zu Hyun Lee Doina Logofătu Department of Computer Science and Engineering Frankfurt University of Applied Sciences Frankfurt am Main Germany

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

In logistics and industrial operations, optimizing crane scheduling is crucial for efficiency and resource allocation. Given the dynamic nature of crane scheduling environments, influenced by factors such as fluctuating order volumes and varying crane availability, robust scheduling solutions are imperative. This paper introduces a rule-based approach designed to optimize crane scheduling within a simulated dynamic and uncertain environment, focusing on real-time responsiveness, efficient task allocation, and continuous operational efficiency. Our approach was developed while participating in the “Crane Scheduling” track of the “Dynamic Stacking Optimization in Uncertain Environments” (DynStack) competition, held with the Genetic and Evolutionary Computation Conference (GECCO) 2024. Our experimental results underscore the efficacy of our approach in achieving zero delivery errors, maximizing the handling of blocks, and minimizing parking durations, hereby promoting cost-effectiveness and environmental sustainability in crane operations. As of July 17, 2024, our approach has secured first place in the “Crane Scheduling” competition.

关键词： Cranes Job shop scheduling Processor scheduling Scientific computing Heuristic algorithms Stacking Green products Dynamic scheduling Resource management Vehicle dynamics

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Hybrid Deep Learning Framework for Automated Classification of Wildlife Camera Trap Images

Hybrid Deep Learning Framework for Automated Classification ...

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International Conference on Computing and Networking Technology (ICCNT)

作者： P Sudeepa P D Seema Shubham Kumawat Mohan Sah Rahul C Choudhary Department of Computer Science and Engineering M S Ramaiah University of Applied Sciences Bangalore India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

Camera traps play a crucial role in wildlife research and conservation, as they provide invaluable insights into animal behavior and population dynamics. However, the manual analysis of the extensive image data generated by these traps presents a significant challenge. Deep-learning models offer a promising solution for automating image classification and facilitating efficient wildlife monitoring. In this paper, a novel hybrid object classification model for wildlife image classification in camera trap data has been proposed. This model combines the strengths of two well-established deep learning architectures, ResNet-50 and DenseNet-121, with an ensemble-learning approach using logistic regression. The data was acquired from camera traps deployed in wildlife habitats and pre-processed it to enhance image quality and facilitate model training. The pretrained ResNet-50 and DenseNet-121 models on the wildlife image dataset were fine-tuned and the ensemble approach combined the feature vectors extracted by both models and uses logistic regression for final classification. The performance of the model was evaluated using metrics such as $\log$ loss and accuracy. The results show that the proposed hybrid object classification model achieves high accuracy of $\mathbf{89\%}$ in classifying wildife species from camera trap images. Th advances automated wildlife image classification and supports conservation efforts by enabling efficient species identification and population monitoring

关键词： Deep learning Logistic regression Accuracy Computational modeling Wildlife Cameras Feature extraction Data models Monitoring Image classification

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