Light propagation underwater is susceptible to wavelength attenuation and scattering, leading to degradation plagued by color distortion, contrast degradation, and reduced visibility in underwater imaging. To handle the degradations, the paper proposes an underwater image enhancement method via advantage feature weighted fusion, called AFWF. Specifically, we propose a three-channel contrast enhancement strategy that effectively reduces the color distortion of a raw input image via a three-channel adaptive color compensation strategy. Meanwhile, we employ a fast exposure fusion to integrate the image sequences obtained from the multi-scale gamma correction and adaptive contrast enhancement strategies to improve the global contrast of the above-mentioned image. Subsequently, a single-channel contrast enhancement is proposed to improve the local contrast and edge detail information by enhancing the multi-level details of the raw image. Finally, we adopt the advantage feature weighted fusion strategy to analyze and selectively fuse the advantage feature of different enhanced images layer by layer to reconstruct a high-quality result. Extensive experimental verification results highlight that our AFWF method is superior to the state-of-the-art (SOTA) methods in improving raw underwater images’ color, contrast, and detail. The code is publicly available at: https://www.researchgate.net/publication/393021384_2025-AFWF.

This paper presents our simulation of cyber-attacks and detection strategies on the traffic control system in Daytona Beach, FL. using Raspberry Pi virtual machines and the OPNSense firewall, along with traffic dynamics from SUMO and exploitation via the Metasploit framework. We try to answer the research questions: are we able to identify cyber attacks by only analyzing traffic flow patterns. In this research, the cyber attacks are focused particularly when lights are randomly turned all green or red at busy intersections by adversarial attackers. Despite challenges stemming from imbalanced data and overlapping traffic patterns, our best model shows 85% accuracy when detecting intrusions purely using traffic flow statistics. Key indicators for successful detection included occupancy, jam length, and halting durations. All implementation details and source code are publicly available on GitHub at: https://github.com/U1overground/Cybersummer

Big data analytics and mining have the potential to enable real-time decision making and control in a range of Internet of Things (IoT) application domains, such as the Internet of Vehicles, the Internet of Wings, and the Airport of Things. The prediction toward air mobility, which is essential to the studies of air traffic management, has been a challenging task due to the complex spatial and temporal dependencies in air traffic data with highly nonlinear and variational patterns. Existing works for air traffic prediction only focus on either modeling static traffic patterns of individual flight or temporal correlation, with no or limited addressing of the spatial impact, namely, the propagation of traffic perturbation among airports. In this article, we propose to leverage the concept of graph and model the airports as nodes with time-series features and conduct data mining on graph-structured data. To be specific, first, airline on-time performance (AOTP) data is preprocessed to generate a temporal graph data set, which includes three features: 1) the number; 2) average delay; and 3) average taxiing time of departure and arrival flights. Then, a spatial–temporal graph neural networks model is implemented to forecast the mobility level at each airport over time, where a combination of graph convolution and time-dimensional convolution is used to capture the spatial and temporal correlation simultaneously. Experiments on the data set demonstrate the advantage of the model on spatial–temporal air mobility prediction, together with the impact of different priors on adjacency matrices and the effectiveness of the temporal attention mechanism. Finally, we analyze the prediction performance and discuss the capability of our model. The prediction framework proposed in this work has the potential to be generalized to other spatial–temporal tasks in IoT.

The Internet of Things (IoT) is reshaping modern society by allowing a decent number of RF devices to connect and share information through RF channels. However, such an open nature also brings obstacles to surveillance. For alleviation, a surveillance oracle, or a cognitive communication entity needs to identify and confirm the appearance of known or unknown signal sources in real-time. In this paper, we provide a deep learning framework for RF signal surveillance. Specifically, we jointly integrate the Deep Neural Networks (DNNs) and Quickest Detection (QD) to form a sequential signal surveillance scheme. We first analyze the latent space characteristic of neural network classification models, and then we leverage the response characteristics of DNN classifiers and propose a novel method to transform existing DNN classifiers into performance-assured binary abnormality detectors. In this way, we seamless integrate the DNNs with parametric quickest detection. Finally, we propose an enhanced Elastic Weight Consolidation (EWC) algorithm with better numerical stability for DNNs in signal surveillance system to evolve incrementally, we demonstrate that the zero-bias DNN is superior than regular DNN models considering incremental learning and decision fairness. We evaluated the proposed framework using real signal datasets and we believe this framework is helpful in developing a trustworthy IoT ecosystem.

Anomaly detection has been a challenging task given high-dimensional multivariate time series data generated by networked sensors and actuators in Cyber-Physical Systems (CPS). Besides the highly nonlinear, complex, and dynamic nature of such time series, the lack of labeled data impedes data exploitation in a supervised manner and thus prevents an accurate detection of abnormal phenomenons. On the other hand, the collected data at the edge of the network is often privacy sensitive and large in quantity, which may hinder the centralized training at the main server. To tackle these issues, we propose an unsupervised time series anomaly detection framework in a federated fashion to continuously monitor the behaviors of interconnected devices within a network and alert for abnormal incidents so that countermeasures can be taken before undesired consequences occur. To be specific, we leave the training data distributed at the edge to learn a shared Variational Autoencoder (VAE) based on Convolutional Gated Recurrent Unit (ConvGRU) model, which jointly captures feature and temporal dependencies in the multivariate time series data for representation learning and downstream anomaly detection tasks. Experiments on three real-world networked sensor datasets illustrate the advantage of our approach over other state-of-the-art models. We also conduct extensive experiments to demonstrate the effectiveness of our detection framework under non-federated and federated settings in terms of overall performance and detection latency.

To accommodate the unprecedented increase of commercial airlines over the next ten years, the Next Generation Air Transportation System (NextGen) has been implemented in the USA that records large-scale Air Traffic Management (ATM) data to make air travel safer, more efficient, and more economical. A key role of collaborative decision making for air traffic scheduling and airspace resource management is the accurate prediction of flight delay. There has been a lot of attempts to apply data-driven methods such as machine learning to forecast flight delay situation using air traffic data of departures and arrivals. However, most of them omit en-route spatial information of airlines and temporal correlation between serial flights which results in inaccuracy prediction. In this paper, we present a novel aviation delay prediction system based on stacked Long Short-Term Memory (LSTM) networks for commercial flights. The system learns from historical trajectories from automatic dependent surveillance-broadcast (ADS-B) messages and uses the correlative geolocations to collect indispensable features such as climatic elements, air traffic, airspace, and human factors data along posterior routes. These features are integrated and then are fed into our proposed regression model. The latent spatio-temporal patterns of data are abstracted and learned in the LSTM architecture. Compared with previous schemes, our approach is demonstrated to be more robust and accurate for large hub airports.

We propose to model learning of optimal decision rules using a mathematically rigorous descriptive model given by a modified set of Cohen-Grossberg neural network equations with reaction-diffusion processes in an environment of uncertainty. Our theory, which we call Neurohydrodynamics, naturally arises within the framework of neural networks while utilizing the foundations of Decision Field Theory (DFT) for describing the cognitive processes of the mammalian brain in the decision-making processes. Human cognition and intelligence requires more than an algorithmic description by a formal set of rules for its operation; it must possess what Alan Turing called an uncomputable human “intuition” (oracle) as a guide for decision-making processes. We draw an analogy with an idea from Quantum Hydrodynamics, namely, that a “pilot wave” guides quantum mechanical particles along a deterministic path by stochastic “forces” naturally arising from Schrodinger's wave equation. This type of equation was also investigated by Turing, and the reaction-diffusion processes of real neurons have been shown to aid in pattern formation while exhibiting self-organization. Because searching over all courses of action is costly, in both resources and time, we seek to include a mechanism that shortcuts the decision-making processes as described by DFT. Some empirical research has determined that diffusion does occur in the cognitive processing of real human brains. We propose a model for high-level decision processes by combining diffusion with other mechanisms (e.g., adaptive resonance and neuromodulation) for the interactions between different brain regions. For dynamic decision-making tasks, the diffusion processes within certain parts of the frontal lobes and basal ganglia are assumed to interact in hierarchical networks that integrate emotion and cognition incorporating both heuristic and deliberative decision rules.