In order to identify cyber-physical threats in additive manufacturing systems, this study suggests a sophisticated technique that uses data from side-channel monitoring. Strong attack detection capabilities are guaran...
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In the volatile landscape of financial markets, making well-informed decisions for intraday trading and long-term investment necessitates an integrated analysis of real-time stock data and the contextual information p...
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The maximal coding rate reduction (MCR2) objective for learning structured and compact deep representations is drawing increasing attention, especially after its recent usage in the derivation of fully explainable and...
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The maximal coding rate reduction (MCR2) objective for learning structured and compact deep representations is drawing increasing attention, especially after its recent usage in the derivation of fully explainable and highly effective deep network architectures. However, it lacks a complete theoretical justification: only the properties of its global optima are known, and its global landscape has not been studied. In this work, we give a complete characterization of the properties of all its local and global optima, as well as other types of critical points. Specifically, we show that each (local or global) maximizer of the MCR2 problem corresponds to a low-dimensional, discriminative, and diverse representation, and furthermore, each critical point of the objective is either a local maximizer or a strict saddle point. Such a favorable landscape makes MCR2 a natural choice of objective for learning diverse and discriminative representations via first-order optimization methods. To validate our theoretical findings, we conduct extensive experiments on both synthetic and real data sets. Copyright 2024 by the author(s)
This article presents a nonlinear dynamic inversion-based motion plan for a levitating robotic satellite emulation platform. The frictionless motion of such a levitating platform has dynamic equivalency with a satell...
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In this opinion piece, we question the efficacy of students conducting systematic reviews (SRs) at the very start of their PhDs, especially now that we are riding, or drowning in, the Generative AI wave. How would the...
computer vision, driven by artificial intelligence, has become pervasive in diverse applications such as self-driving cars and law enforcement. However, the susceptibility of these systems to attacks has raised signif...
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Timely and accurate credit card fraud detection (CCFD) is concerned by all financial institutions. Existing CCFD methods generally employ aggregated or raw features as their representations to train their detection mo...
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Nowadays,the cloud environment faces numerous issues like synchronizing information before the switch over the data *** requirement for a centralized internet of things(IoT)-based system has been restricted to some **...
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Nowadays,the cloud environment faces numerous issues like synchronizing information before the switch over the data *** requirement for a centralized internet of things(IoT)-based system has been restricted to some *** to low scalability on security considerations,the cloud seems *** healthcare networks demand computer operations on large amounts of data,the sensitivity of device latency evolved among health networks is a challenging *** comparison to cloud domains,the new paradigms of fog computing give fresh alternatives by bringing resources closer to users by providing low latency and energy-efficient data processing *** fog computing frameworks have various flaws,such as overvaluing response time or ignoring the accuracy of the result yet handling both at the same time compromises the network *** this proposed work,Health Fog is integrated with the Optimized Cascaded Convolution Neural Network framework for diagnosing heart ***,the data is collected,and then pre-processing is done by Linear Discriminant *** the features are extracted and optimized using Galactic Swarm *** optimized features are given into the Health Fog framework for diagnosing heart disease *** uses ensemble-based deep learning in edge computing devices,which automatically monitors real-life health networks such as heart disease ***,the classifiers such as bagging,boosting,XGBoost,Multi-Layer Perceptron(MLP),and Partitions(PART)are used for classifying the *** the majority voting classifier predicts the *** work uses FogBus architecture and evaluates the execution of power usage,bandwidth of the network,latency,execution time,and accuracy.
—Spatial optimization problems (SOPs) refer to a class of problems where the decision variables require spatial organization. Existing methods based on evolutionary algorithms (EAs) fit conventional evolutionary oper...
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Cooperative communication is an emerging method that allows devices with a single antenna to share their antennas and assist other nodes in transmitting signals. This leads to enhanced spatial diversity, lower power c...
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