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14th International Symposium on Parameterized and Exact Computation, IPEC 2019

作者： Novotná, Jana Okrasa, Karolina Pilipczuk, Michal Rzążewski, Pawel Van Leeuwen, Erik Jan Walczak, Bartosz Department of Applied Mathematics Faculty of Mathematics and Physics Charles University Prague Czech Republic Faculty of Mathematics and Information Science Warsaw University of Technology Poland Institute of Informatics Faculty of Mathematics Informatics and Mechanics University of Warsaw Poland Department of Information and Computing Sciences Utrecht University Netherlands Department of Theoretical Computer Science Faculty of Mathematics and Computer Science Jagiellonian University Kraków Poland

ISBN: (纸本)9783959771290

Let C and D be hereditary graph classes. Consider the following problem: given a graph G ∈ D, find a largest, in terms of the number of vertices, induced subgraph of G that belongs to C. We prove that it can be solved in 2o(n) time, where n is the number of vertices of G, if the following conditions are satisfied: the graphs in C are sparse, i.e., they have linearly many edges in terms of the number of vertices;the graphs in D admit balanced separators of size governed by their density, e.g., O(∆) or O(√m), where ∆ and m denote the maximum degree and the number of edges, respectively;and the considered problem admits a single-exponential fixed-parameter algorithm when parameterized by the treewidth of the input graph. This leads, for example, to the following corollaries for specific classes C and D: a largest induced forest in a Pt-free graph can be found in 2Õ(n2/3) time, for every fixed t;and a largest induced planar graph in a string graph can be found in 2Õ(n3/4) time. © Jana Novotná, Karolina Okrasa, Michal Pilipczuk, Pawel Rzążewski, Erik Jan van Leeuwen, and Bartosz Walczak;licensed under Creative Commons License CC-BY.

关键词： Graphic methods

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Configuring the stochastic Helmholtz machine for subcortical emotional learning

Configuring the stochastic Helmholtz machine for subcortical...

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International Joint Conference on Neural Networks (IJCNN)

作者： Chi-Yung Yau Kevin Burn Stefan Wermter Faculty of Applied Sciences Department of Computing Engineering and Technology University of Sunderland UK Institute for Knowledge Technology Department of Informatics University of Hamburg Germany

Emotional learning involves two stages. The first is to acquire reinforcers from stimuli and the second is to associate such reinforcers with emotional responses. Both stages can be found occurring in the amygdala. LeDoux's fear circuit model suggests two routes, a subcortical route and a cortical route, for emotional information entering the amygdala for associative learning. It can be used to explain how the actual recognition of emotions from facial expressions can be processed in the brain. Based on the model, a neural architecture is proposed using the stochastic Helmholtz machine (SHM) with the wake-sleep algorithm. In this paper, the results of three experiments about the subcortical emotional learning are reported, where different configurations of SHMs are involved. The first two experiments are to identify a suitable way to allow behavioural responses entering the central nucleus of the amygdala for association. However, both experiments show symptoms of overfitting, where some weights and biases of neurons are observed that will unusually increase during training. Therefore, the final experiment is designed to maintain the range of weights between -1 and +1 in order to solve the overfitting problem. The last experiment shows that the neural architecture with the new weight policy holds a lot of potential for modelling subcortical learning.

关键词： Biological system modeling Feature extraction Neurons Mathematical model Visualization Training Data models

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Encryption with Image Steganography Based Data Hiding Technique in IIoT Environment

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Computers, Materials & Continua 2022年第7期72卷 1323-1338页

作者： Mahmoud Ragab Samah Alshehri Hani A.Alhadrami Faris Kateb Ehab Bahaudien Ashary SAbdel-khalek Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Centre of Artificial Intelligence for Precision Medicines King Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceAl-Azhar UniversityNaser City11884CairoEgypt Department of Pharmacy Practice Faculty of PharmacyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Medical Laboratory Technology Faculty of Applied Medical SciencesKing Abdulaziz UniversityJeddah21589Saudi Arabia Molecular Diagnostic Lab King Abdulaziz University HospitalKing Abdulaziz UniversityJeddah21589Saudi Arabia Special Infectious Agent Unit King Fahd Medical Research CenterKing Abdulaziz UniversityJeddah21589Saudi Arabia Electrical and Computer Engineering Department Faculty of EngineeringKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Mathematics Faculty of ScienceTaif UniversityTaif21944Saudi Arabia Department of Mathematics Faculty of ScienceSohag UniversitySohag82524Egypt

Rapid advancements of the Industrial Internet of Things(IIoT)and artificial intelligence(AI)pose serious security issues by revealing secret ***,security data becomes a crucial issue in IIoT communication where secrecy needs to be guaranteed in real ***,AI techniques can be utilized to design image steganographic techniques in *** addition,encryption techniques act as an important role to save the actual information generated from the IIoT devices to avoid unauthorized *** order to accomplish secure data transmission in IIoT environment,this study presents novel encryption with image steganography based data hiding technique(EISDHT)for IIoT *** proposed EIS-DHT technique involves a new quantum black widow optimization(QBWO)to competently choose the pixel values for hiding secrete data in the cover *** addition,the multi-level discrete wavelet transform(DWT)based transformation process takes ***,the secret image is divided into three R,G,and B bands which are then individually encrypted using Blowfish,Twofish,and Lorenz Hyperchaotic *** last,the stego image gets generated by placing the encrypted images into the optimum pixel locations of the cover *** order to validate the enhanced data hiding performance of the EIS-DHT technique,a set of simulation analyses take place and the results are inspected interms of different *** experimental outcomes stated the supremacy of the EIS-DHT technique over the other existing techniques and ensure maximum security.

关键词： IIoT security data hiding technique image steganography encryption secure communication

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Optimizing Routing Paths in Mobile Wireless Sensor Networks: A Sub-Flow Adaptive Multipath Approach for Energy Efficiency and Delay Sensitivity 8

Optimizing Routing Paths in Mobile Wireless Sensor Networks:...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Raja, R. Vinston Siddiqui, Shavej Ali Avilasha, B.G. Kannan, L. Mohana Srm Institute of Science and Technology Faculty of Engineering and Technology School of Computing Department of Computational Intelligence Chennai Tamil Nadu Kattankulathur India Khwaja Moinuddin Chishti Language University Faculty of Engineering and Technology Deparment of Applied Science and Humanities Lucknow India Dayanandasagar College of Engineering Deparment of Mechanical Engineering Bangalore India Erode Sengunthar Engineering College Department of Biomedical Engineering Tamil Nadu Perundurai India

ISBN: (纸本)9798350376425

Wireless Sensor Networks (WSNs) have become crucial in various domains such as social applications, healthcare, and the military. However, their diversity makes them susceptible to a wide range of attacks, leading to challenges like poor throughput, high network latency, and excessive energy consumption. This study proposes a novel approach that combines residual Riemannian neural networks with the Sub-Flow Adaptive Multipath Routing algorithm for enhanced routing and attack detection in WSNs. By leveraging node position data, the Sub-Flow Adaptive Multipath Routing algorithm optimizes energy consumption and extends network lifespan. Simultaneously, residual Riemannian neural networks analyze residual patterns in network traffic data to accurately detect rogue nodes, thereby enhancing security. Blockchain technology is then employed to securely store classified data, ensuring data integrity and security. This integrated approach significantly improves network performance, maintains robust security against potential threats, and enhances the reliability of WSNs in demanding environments. © 2024 IEEE.

关键词： Energy utilization

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Intelligent Data Engineering and Analytics 1

Intelligent Data Engineering and Analytics

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丛书名： Smart Innovation, Systems and Technologies

1000年

作者： Vikrant Bhateja Preeti Patel Milan Simic

ISBN: (数字)9789819601394

ISBN: (纸本)9789819601387;9789819601417

The book presents the proceedings of the 12th International Conference on Frontiers of Intelligent computing: Theory and Applications (FICTA 2024), held at Intelligent Systems Research Group (ISRG), London Metropolitan University, London, United Kingdom, during June 6–7, 2024. Researchers, scientists, engineers and practitioners exchange new ideas and experiences in the domain of intelligent computing theories with prospective applications in various engineering disciplines in the book. This book is divided into four volumes. It covers broad areas of information and decision sciences, with papers exploring both the theoretical and practical aspects of data-intensive computing, data mining, evolutionary computation, knowledge management and networks, sensor networks, signal processing, wireless networks, protocols and architectures. This book is a valuable resource for postgraduate students in various engineering disciplines.

关键词： Computational Intelligence Artificial Intelligence Mobile and Network Security Signal, Image and Speech Processing

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Detecting emotional stress during typing task with time pressure

Detecting emotional stress during typing task with time pres...

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Science and Information Conference (SAI)

作者： Yee Mei Lim Aladdin Ayesh Martin Stacey Faculty of Applied Sciences and Computing Tunku Abdul Rahman University Kuala Lumpur Malaysia Faculty of Technology De Montfort University Leicester United Kingdom

Automated stress detection is important in developing adaptive e-learning systems. Empirical evidence suggests that mouse dynamics and keyboard dynamics analyses can be both effective in user behaviour modelling as well as emotion detection compared to physiological measures and facial expression recognitions, and yet they are far less expensive and considered non-intrusive. This paper investigates how mouse dynamics and keyboard dynamics can be affected by emotional stress, particularly stress induced by time pressure, text length and language familiarity. Our research findings show that longer text and unfamiliar language raise users' stress perceptions. Demanding job such as long typing task could result in anomalous behaviours once the users have lost motivation. Language familiarity mainly affects keyboard behaviour but text length change mouse behaviour. This shows that there is a good potential of developing an adaptive e-learning system by detecting learners' emotional stress from keyboard and mouse input.

关键词： Mice Stress Keyboards Timing Electronic learning Educational institutions Stress measurement

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Rear-End Collision Avoidance Using Multi-Agent Deep Reinforcement Learning

Rear-End Collision Avoidance Using Multi-Agent Deep Reinforc...

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Advancements in computing (ICAC), International Conference on

作者： Ranuja Perera Surith Arawwala Shalika Harshamali Yohan Perera Samadhi Rathnayake Nelum Chathuranga Amarasena Sasadara Adikari Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Faculty of Applied Sciences University of Sri Jayawardenapura Nugegoda Sri Lanka

ISBN: (数字)9798331517878

ISBN: (纸本)9798331517885

This research paper offers a novel approach to detect rear-end collisions and increase the acceleration of the victim car to possibly avoid or minimize the impact of the collision. A Deep Q-Learning model is utilized to predict the collision, the optimum acceleration of the victim car and the optimum braking of the committer car. The study uses a multi-agent reinforcement learning framework in a simulated environment, focusing on synchronized responses between lead and following vehicles. A comprehensive reward function encourages safe driving behaviors, optimized braking, and successful obstacle detection and avoidance. The continuous action space allows for subtle vehicle control, enhancing the system's adaptability to various collision scenarios. Experimental results demonstrate an overall improvement in collision avoidance and impact reduction over 5000 training episodes. This approach addresses a critical gap in existing safety systems by specifically targeting rear-end collisions, offering the potential for a significant reduction in accident rates and severity. Future work includes real-world testing and expansion to more complex traffic scenarios.

关键词： Training Predictive models Lead Deep reinforcement learning Control systems Safety Automobiles Synchronization Collision avoidance Testing

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Identification of integrating processes with time delay

Identification of integrating processes with time delay

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10th IFAC Symposium on Dynamics and Control of Process Systems, DYCOPS 2013

作者： Ahmed, Salim Cox, Chris Imtiaz, Syed A. Faculty of Engineering and Applied Science Memorial University St. John's NL A1B 3X5 Canada Department of Computing Engineering and Technology University of Sunderland SR6 0DD United Kingdom

ISBN: (纸本)9783902823595

A set of methods for identification of continuous-time transfer function models for integrating processes with time delay is proposed. The step, piecewise constant and piecewise linear inputs are considered which indeed cover most of the input signals commonly used in industries. For all of the three types of input signals, estimation equations to simultaneously obtain model parameters and the time delay are derived. The final parameter estimation equations are in a form suitable for the least-squares solution. Mathematical formulation of the methods is presented using the example of an integrating process with a first order lag dynamics and a zero which can be extended for other structures. An instrumental variable method to deal with the bias issue in least-squares solutions is used. Simulation results are presented to demonstrate the efficacy of the algorithms and their relative performance. © IFAC.

关键词： Time delay

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Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering Scheme for Cognitive Radio Wireless Sensor Networks

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Computer Systems Science & Engineering 2023年第10期47卷 105-119页

作者： Sami Saeed Binyamin Mahmoud Ragab Computer and Information Technology Department The Applied CollegeKing Abdulaziz UniversityJeddah21589Saudi Arabia Information Technology Department Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia

Cognitive radio wireless sensor networks(CRWSN)can be defined as a promising technology for developing bandwidth-limited *** is widely utilized by future Internet of Things(IoT)*** a promising technology,Cognitive Radio(CR)can be modelled to alleviate the spectrum scarcity ***,CRWSN has cognitive radioenabled sensor nodes(SNs),which are energy *** clusterrelated techniques for overall network management can be suitable for the scalability and stability of the *** paper focuses on designing the Modified Dwarf Mongoose Optimization Enabled Energy Aware Clustering(MDMO-EAC)Scheme for *** MDMO-EAC technique mainly intends to group the nodes into clusters in the ***,theMDMOEAC algorithm is based on the dwarf mongoose optimization(DMO)algorithm design with oppositional-based learning(OBL)concept for the clustering process,showing the novelty of the *** addition,the presented MDMO-EAC algorithm computed a multi-objective function for improved network *** presented model is validated using a comprehensive range of experiments,and the outcomes were scrutinized in varying *** comparison study stated the improvements of the MDMO-EAC method over other recent approaches.

关键词： Cognitive radio wireless sensor networks clustering dwarf mongoose optimization algorithm fitness function

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A Survey on Usage of Multimedia Databases for Emotion Elicitation: A Quantitative Report on How Content Diversity Can Improve Performance

A Survey on Usage of Multimedia Databases for Emotion Elicit...

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Proceedings of the International Convention MIPRO

作者： M. Horvat P. Jerčić Department of Applied Computing Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Graz University of Technology Institute of Interactive Systems and Data Science Graz Austria

Affective picture databases provide a standardized set of images to elicit controlled and consistent emotional responses in research participants. They are a valuable tool for studying various emotion-related phenomena across several research domains. These domains include emotion perception, emotion regulation, and the neural basis of emotion. However, affective picture databases have diverse schemas, structures, and content, making them difficult to use. Searching and retrieving optimal pictures relevant to affective stimulation may be challenging and time-consuming. In this context, we surveyed domain experts about their practices and experiences working with affective multimedia databases such as IAPS, NAPS, OASIS, GAPED, and others. The survey identified a need for novel data observatory software. This finding motivates the authors’ intention to develop and validate such software platform that relies on AI. Such a platform would describe better, retrieve, and integrate various semi-structured affective multimedia datasets. The results prominently indicate the overwhelming dissatisfaction regarding stimuli content diversity and cultural bias, specifically regarding emotional and semantic context. The main driver of satisfaction from users of existing automated retrieval software is the quality of semantic descriptors available. This points to the direction AI should take in novel data observatory software. This survey follows up on a similar survey conducted ten years ago and explores the differences in researchers’ opinions and experiences during that time. The complete aggregated results are publicly available at https://***/mhorvat/stimdbsurvey.

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