Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can...
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Semantic segmentation is an important sub-task for many ***,pixel-level ground-truth labeling is costly,and there is a tendency to overfit to training data,thereby limiting the generalization *** domain adaptation can potentially address these problems by allowing systems trained on labelled datasets from the source domain(including less expensive synthetic domain)to be adapted to a novel target *** conventional approach involves automatic extraction and alignment of the representations of source and target domains *** limitation of this approach is that it tends to neglect the differences between classes:representations of certain classes can be more easily extracted and aligned between the source and target domains than others,limiting the adaptation over all ***,we address:this problem by introducing a Class-Conditional Domain Adaptation(CCDA)*** incorporates a class-conditional multi-scale discriminator and class-conditional losses for both segmentation and ***,they measure the segmentation,shift the domain in a classconditional manner,and equalize the loss over *** results demonstrate that the performance of our CCDA method matches,and in some cases,surpasses that of state-of-the-art methods.
Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injec...
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Identifying cyberattacks that attempt to compromise digital systems is a critical function of intrusion detection systems(IDS).Data labeling difficulties,incorrect conclusions,and vulnerability to malicious data injections are only a few drawbacks of using machine learning algorithms for *** overcome these obstacles,researchers have created several network IDS models,such as the Hidden Naive Bayes Multiclass Classifier and supervised/unsupervised machine learning *** study provides an updated learning strategy for artificial neural network(ANN)to address data categorization problems caused by unbalanced *** to traditional approaches,the augmented ANN’s 92%accuracy is a significant improvement owing to the network’s increased resilience to disturbances and computational complexity,brought about by the addition of a random weight and standard *** the ever-evolving nature of cybersecurity threats,this study introduces a revolutionary intrusion detection method.
The static nature of cyber defense systems gives attackers a sufficient amount of time to explore and further exploit the vulnerabilities of information technology *** this paper,we investigate a problem where multiag...
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The static nature of cyber defense systems gives attackers a sufficient amount of time to explore and further exploit the vulnerabilities of information technology *** this paper,we investigate a problem where multiagent sys-tems sensing and acting in an environment contribute to adaptive cyber *** present a learning strategy that enables multiple agents to learn optimal poli-cies using multiagent reinforcement learning(MARL).Our proposed approach is inspired by the multiarmed bandits(MAB)learning technique for multiple agents to cooperate in decision making or to work *** study a MAB approach in which defenders visit a system multiple times in an alternating fash-ion to maximize their rewards and protect their *** find that this game can be modeled from an individual player’s perspective as a restless MAB *** discover further results when the MAB takes the form of a pure birth process,such as a myopic optimal policy,as well as providing environments that offer the necessary incentives required for cooperation in multiplayer projects.
Significant health hazards have been associated with the COVID-19 pandemic, especially for women, whose mental and physical health have suffered greatly. In order to predict the risk of COVID-19 in women, this study i...
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The rapid advancements in artificial intelligence (AI) and machine learning (ML) have significantly enhanced progress in computer vision, opening doors to innovative technological possibilities and enabling a range of...
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The ephemeris and timing errors of low Earth orbit (LEO) satellites are modeled, leading to an approach to disambiguate these errors from pseudorange-type measurements. First, a model is derived describing the ephemer...
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The ephemeris and timing errors of low Earth orbit (LEO) satellites are modeled, leading to an approach to disambiguate these errors from pseudorange-type measurements. First, a model is derived describing the ephemeris error's impact on ranging measurements from LEO space vehicles (SVs) with imprecise ephemerides. A simulation study is presented comparing the impact of ephemeris error on ranging error for 5 LEO constellations (Statlink, OneWeb, Orbcomm, Iridium, and Globalstar) and 5 medium Earth orbit (MEO) constellations (GPS, GLONASS, Galileo, BeiDou-3, and O3B). Second, it is shown that for a particular SV position, the ephemeris error has no effect on range measurements. Next, the ephemeris and timing errors are parametrized by the three-dimensional (3-D) ephemeris error magnitude and its direction angle from the in-track axis. This parametrization is exploited in a proposed algorithm to disambiguate the ephemeris and timing error from the LEO SVs' pseudorange measurements at a reference receiver. The two parameters can be communicated to any unknown receiver listening to the same LEO SVs to correct for ephemerides ranging error, leading to improved positioning, navigation, and timing (PNT) precision. Monte Carlo simulation results are presented demonstrating the efficacy of the proposed algorithm. The simulations considered a reference receiver tracking via pseudorange measurements 22 Starlink and 4 OneWeb LEO SVs with poorly known ephemerides (obtained from two-line element (TLE) files, propagated with SGP4). The proposed algorithm reduced the 3-D position error of all SVs from a few kilometers to less than 120 m. The parameters were communicated to an unknown receiver to correct the LEO ephemerides, after which the receiver estimated its position by fusing its LEO pseudoranges via an extended Kalman filter (EKF), resulting in a horizontal position error of 0.91 m, as compared to 213 m utilizing TLE+SGP4 ephemerides. Two sets of experimental results are pres
Fog computing offers a compelling paradigm for real-time healthcare data processing by minimizing latency and bringing computation closer to its source. However, efficient service placement remains a critical challeng...
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Blood cancer cell diagnosis is crucial in medical diagnostics. It demands accurate classification of blood cell images. Proper classification of blood cancer cells is fundamental for accurately diagnosing the specific...
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Automated speaker identification is an important research topic in recent advanced technologies. This process helps to analyze the speakers in their emergencies. Various existing approaches are used for speaker identi...
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Advances in Internet-of-Things (IoT) wearable sensors and Edge-Artificial Intelligence (Edge-AI) have enabled practical realizations of Machine Learning (ML)-enabled mobile sensing applications like Human Activity Rec...
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