The following topics are dealt with: computationalintelligence; security of data; defense application; military computing; image processing and evolutionary computation
The following topics are dealt with: computationalintelligence; security of data; defense application; military computing; image processing and evolutionary computation
The proceedings contain 15 papers. The topics discussed include: comparing heuristic search methods for finding effective real-time strategy game plans;evolving team tactics using potential fields;positioning of milit...
ISBN:
(纸本)9781467359115
The proceedings contain 15 papers. The topics discussed include: comparing heuristic search methods for finding effective real-time strategy game plans;evolving team tactics using potential fields;positioning of military combat units through weight-based terrain analysis using NASA world wind;testing a distributed denial of service defense mechanism using red teaming;multiple UAV area decomposition and coverage;cooperative path planning for UAVs with UAV loss considerations;neural adaptive flight controller for ducted-fan UAV performing nonlinear maneuver;a novel obstacle avoidance control algorithm in a dynamic environment;design of a nonlinear dynamic inversion controller for trajectory following and maneuvering for fixed wing aircraft;and investigating application behavior in network traffic traces.
The proceedings contain 17 papers. The topics discussed include: optimising multistatic sensor locations using path planning and game theory;rapid prototyping of high performance fuzzy computing applications using hig...
ISBN:
(纸本)9781424499410
The proceedings contain 17 papers. The topics discussed include: optimising multistatic sensor locations using path planning and game theory;rapid prototyping of high performance fuzzy computing applications using high level GPU programming for maritime operations support;clustering of tracklets for on-line multi-target tracking in networked camera systems;modelling search and rescue systems with dynamical networks;sensor network management using multiobjective evolutionary optimization;object set matching with an evolutionary algorithm;computation of most threatening radar trajectories areas and corridors based on fast-marching & level sets;accelerating common operational pictures through network consensus;complex decision making experimental platform (CODEM): a counter-insurgency scenario;high-dimensional objective-based data farming;and multi-objective evolutionary optimization of a military air transportation fleet mix with the flexibility objective.
The proceedings contain 29 papers. The topics discussed include: toward open-set text-independent speaker identification in tactical communications;the PSO-based adaptive window for people tracking;ATR applications in...
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ISBN:
(纸本)1424407001
The proceedings contain 29 papers. The topics discussed include: toward open-set text-independent speaker identification in tactical communications;the PSO-based adaptive window for people tracking;ATR applications in military missions;solving multicommodity capacitated network design problems using a multiodjective evolutionary algorithm;computational verification of system architectures;next generation end-to-end logistics decision support tools. Evolutionary logistics planning;face recognition system using ant colony optimization-based selected features;improved missile route planning and targeting using game-based computationalintelligence;a review of intelligent systems software for autonomous vehicles;computationalintelligence support for smart queries and adaptive data;and a template-based method for force group classification in situation assessment.
The proceedings contain 48 papers. The topics discussed include: a framework for the design of a military operational supply network;local feature analysis for robust face recognition;minimizing risk on a fleet mix pr...
ISBN:
(纸本)9781424437641
The proceedings contain 48 papers. The topics discussed include: a framework for the design of a military operational supply network;local feature analysis for robust face recognition;minimizing risk on a fleet mix problem with a multiobjective evolutionary algorithm;dynamical networks as a tool for system analysis and exploration;a detailed analysis of the KDD cup 99 data set;BlastSim - multi agent simulation of suicide bombing;evolution and evaluation of biometric systems;information assurances and threat identification in networked organizations;neural-based iterative approach for iris detection in iris recognition systems;machine learning based encrypted traffic classification: identifying SSH and Skype;multiple UAV teams for multiple tasks;passive multitarget tracking using transmitters of opportunity;application of voiced-speech variability descriptors to emotion recognition;and integrating reasoning with personality effects in simulated operators.
The proceedings contain 28 papers. The topics discussed include: using computational swarm intelligence for real-time asset allocation;rigorous sensor resource management: methodology and evolutionary optimization;dyn...
ISBN:
(纸本)9781467375573
The proceedings contain 28 papers. The topics discussed include: using computational swarm intelligence for real-time asset allocation;rigorous sensor resource management: methodology and evolutionary optimization;dynamic asset allocation for counter-smuggling operations under disconnected, intermittent and low-bandwidth environment;design and analysis of neuromemristive echo state networks with limited-precision synapses;memristive computational architecture of an echo state network for real-time speech-emotion recognition;spiking-based matrix computation by leveraging memristor crossbar array;design of experiments based empirical models to support cognitive radio decision making;detecting push to talk radios using two tone intermodulation distortion;and evolving spiking neural networks: a novel growth algorithm corrects the teacher.
The proceedings contain 24 papers. The topics discussed include: effects of the cookie cutter function shapes on Monte Carlo simulations of weapon effectiveness;malware detection using genetic programming;intrusion de...
ISBN:
(纸本)9781479954315
The proceedings contain 24 papers. The topics discussed include: effects of the cookie cutter function shapes on Monte Carlo simulations of weapon effectiveness;malware detection using genetic programming;intrusion detection system using discrete Fourier transform;a simple braking model for detecting incidents locations by smartphones;a hybrid framework for enhancing correlation to solve cold-start problem in recommender systems;an improved memetic algorithm to enhance the sustainability and reliability of transport in container terminals;automated generation of ham rules for Vietnamese spam filtering;design of takagi-sugeno fuzzy controller for automatic stabilization system of missiles with blended aerodynamic and lateral impulsive reaction-jet;and behavior-driven video analytics system for critical infrastructure protection.
The 2011 ieeesymposium on computationalintelligence for security and defenseapplications will present a wide range of applications to very challenging problems in the security and defense domains. ieee CISDA 2011, ...
The 2011 ieeesymposium on computationalintelligence for security and defenseapplications will present a wide range of applications to very challenging problems in the security and defense domains. ieee CISDA 2011, the fourth such forum (the first was held in 2007), aims to present the most recent results on computationalintelligence technologies and their applications to security, defense and military problems. The following topics will be discussed at the symposium: mine detection, complex adaptive systems, radar systems, modeling and simulation of military operations, network security, and maritime applications.
Among these applications, emotion AI has emerged as a transformative tool in the educational sector, enabling personalized learning experiences by adapting content and teaching methods based on students' emotional...
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Among these applications, emotion AI has emerged as a transformative tool in the educational sector, enabling personalized learning experiences by adapting content and teaching methods based on students' emotional states. This often involves integrating multimodal data, including text, speech, and facial expressions, to accurately interpret and respond to students' needs. However, despite its potential, deep learning models are highly vulnerable to adversarial example attacks (AE). These malicious inputs cause incorrect decisions while remaining imperceptible to humans. The risks posed by AEs are particularly concerning in education, where misinterpreting emotional cues may disrupt adaptive learning processes and hinder student engagement. Effectively addressing these challenges is essential. Existing research detects attacks by comparing normal and AEs, but this method struggles with the variability of normal data and the complexity of high-dimensional features, resulting in low detection accuracy and high computational costs. Furthermore, AEs often exhibit unique morphological characteristics, making it difficult to develop a universal detection mechanism. This article proposes a novel method using Poisson distribution to analyze neuron output differences, enhancing model robustness against adversarial attacks and ensuring cross-model generality. By improving adversarial robustness, this method aims to protect emotion AI applications in education, ensuring accurate multimodal data interpretation and supporting adaptive learning systems. Experimental results show that our method performs robustly in multimodal data processing, encompassing text, speech, and facial expressions. Additionally, its strong performance across various attack scenarios highlights the propose method's generalization capabilities.
Deep learning has achieved remarkable performance in computer vision applications, and gradually becomes one of the mainstream technologies. Image scaling, as an indispensable data pre-processing procedure for most of...
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Deep learning has achieved remarkable performance in computer vision applications, and gradually becomes one of the mainstream technologies. Image scaling, as an indispensable data pre-processing procedure for most of the computer vision applications, is implemented to resize the unmatched data to fit the input sizes of deep learning models. However, such kind of data pre-processing is vulnerable to be exploited to launch an attack called image-scaling attack, which can generate attack images presenting entirely different appearances after scaling. In this paper, we develop the attack model based on generative adversarial network (GAN) to fuse the source and target images, called Attack-GAN, which guarantees the generated attack images are imperceptible as well as being scaled to the target images. Experiments show that the superiority of Attack-GAN over the optimization method is that it can generate attack images with better camouflage and achieve higher attack success rates as well as accelerate the generation of attack images. Furthermore, we propose the defense-GAN to learn and approximate the distribution of unperturbed images from attack images, which aims to eliminate and compensate the adversarial pixels. Based on different capabilities, customized defense strategies for different defenders are developed to resist the image-scaling attack. Experimental results manifest the proposed defense strategies are effective against the image-scaling attack, i.e., the generator of defense-GAN can recover the attack images and retain the original 'semantics'.
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