Reviewerly/exHarmony huggingface dataset:
Collision Avoidance Strategy for Unmanned Surface Vessel Considering Actuator Faults Using Kinodynamic Rapidly Exploring Random Tree-Smart and Radial Basis Function Neural Network-Based Model Predictive Control

Path planning and tracking are essential technologies for unmanned surface vessels (USVs). The kinodynamic constraints and actuator faults, however, bring difficulties in finding feasible paths and control efforts. This paper proposes a collision avoidance strategy for USV by developing the kinodynamic rapidly exploring random tree-smart (kinodynamic RRT*-smart) algorithm and the fault-tolerant control method. By utilizing the triangular inequality and the intelligent biased sampling strategy, the kinodynamic RRT*-smart shows its advantages in terms of path length, cost and running time. With consideration of kinodynamic constraints, a feasible and collision-free trajectory can be provided. Then, a radial basis function neural network-based model predictive control (RBF-MPC) method was designed that compensates for the model’s uncertainties by developing the radial basis function neural network (RBF-NN) approximator and by constructing a feedback-state training dataset in real time. Furthermore, two types of fault situation were analyzed considering the thruster failure. We established the faults’ mathematical models and investigated the fault-tolerant strategies for different fault types. The simulation studies were conducted to validate the effectiveness of the proposed strategy. The results show that the proposed planning and control methods can avoid obstacles in faulty conditions.
Vision-based underwater target real-time detection for autonomous underwater vehicle subsea exploration

Autonomous underwater vehicles (AUVs) equipped with online visual inspection systems can detect underwater targets during underwater operations, which is of great significance to subsea exploration. However, the undersea scene has some instinctive challenging problems, such as poor lighting conditions, sediment burial, and marine biofouling mimicry, which makes it difficult for traditional target detection algorithms to achieve online, reliable, and accurate detection of underwater targets. To solve the above issues, this paper proposes a real-time object detection algorithm for underwater targets based on a lightweight convolutional neural network model. To improve the imaging quality of underwater images, contrast limited adaptive histogram equalization with the fused multicolor space (FCLAHE) model is designed to enhance the image quality of underwater targets. Afterwards, a spindle-shaped backbone network is designed. The inverted residual block and group convolutions are used to extract depth features to ensure the target detection accuracy on one hand and to reduce the model parameter volume on the other hand under complex scenarios. Through extensive experiments, the precision, recall, and mAP of the proposed algorithm reached 91.2%, 90.1%, and 88.3%, respectively. It is also noticeable that the proposed method has been integrated into the embedded GPU platform and deployed in the AUV system in the practical scenarios. The average computational time is 0.053s, which satisfies the requirements of real-time object detection.
A joint sparse representation and low rank prior regularization model for image deblocking

When the compression quality factor (QF) is low, JPEG images are usually accompanied by very severe block artifacts. The conventional image deblocking models based on low-rank regularization focus on eliminating block artifacts, but lack the capacity of recovering texture details on image edges. In this paper, an image deblocking model which combines sparse representation and low-rank regularization based on fractional norm is proposed. To avoid the abrupt increase in computational complexity, multiple orthogonal dictionary learning is proposed to enhance the ability to sparsify diverse image structures and eliminate correlations among the atoms in the overcomplete dictionary. To further improve the image deblocking effect, a quantization constraint and fractional norm are used to more effectively enforce the low-rank property. Finally, an alternating minimization strategy is applied to solve the derived optimization problem. The model proposed in this paper preserves more texture details while effectively removing image block artifacts compared with the mainstream image deblocking model utilizing low-rank regularization alone. Experimental results show that the proposed method outperforms the current state-of-the-art image deblocking models in terms of both subjective metrics and objective perception.
Mammography Datasets for Neural Networks—Survey

Deep neural networks have gained popularity in the field of mammography. Data play an integral role in training these models, as training algorithms requires a large amount of data to capture the general relationship between the model’s input and output. Open-access databases are the most accessible source of mammography data for training neural networks. Our work focuses on conducting a comprehensive survey of mammography databases that contain images with defined abnormal areas of interest. The survey includes databases such as INbreast, the Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), the OPTIMAM Medical Image Database (OMI-DB), and The Mammographic Image Analysis Society Digital Mammogram Database (MIAS). Additionally, we surveyed recent studies that have utilized these databases in conjunction with neural networks and the results they have achieved. From these databases, it is possible to obtain at least 3801 unique images with 4125 described findings from approximately 1842 patients. The number of patients with important findings can be increased to approximately 14,474, depending on the type of agreement with the OPTIMAM team. Furthermore, we provide a description of the annotation process for mammography images to enhance the understanding of the information gained from these datasets.
Use of a Bayesian network as a decision support tool for watershed management: a case study in a highly managed river-dominated estuary

Decision making in water resource management has many dimensions including water supply, flood protection, and meeting ecological needs, therefore, is complex, full of uncertainties, and often contentious due to competing needs and distrust among stakeholders. It benefits from robust tools for supporting the decision-making process and for communicating with stakeholders. This paper presents a Bayesian network (BN) modeling framework for analyzing various management interventions regulating freshwater discharges to an estuary. This BN was constructed using empirical data from 98 months of monitoring the Caloosahatchee River Estuary in south Florida during the period 2008-2021 as a case study to illustrate the potential advantages of the BN approach. Results from three different management scenarios and their implications on down-estuary conditions as they affected eastern oysters (Crassostrea virginica) and seagrass (Halodule wrightii) are presented and discussed. Finally, the directions for future applications of the BN modeling framework to support management in similar systems are offered.
Multi-party simultaneous quantum identity authentication based on Greenberger-Horne-Zeilinger states

Abstract A successful implementation of any secure communication protocols requires the identity authentication as a prerequisite. Quantum identity authentication (QIA) can ensure the unconditional security of identity code. In this paper, we propose a multi-party simultaneous quantum identity authentication (MSQIA) protocol based on the Greenberger-Horne-Zeilinger (GHZ) state. In the protocol, N certified users can directly send their identity codes to the authenticator Alice simultaneously through quantum channels without classical communication, which enables Alice to authenticate their identity at the same time. Our protocol only requires Alice to perform the single-photon measurement. In theory, this protocol is unconditionally secure and one pair of ( N + 1)-photon GHZ state can transmit N bits of identity codes. Our MSQIA protocol has application potential in the quantum communication field in the near future.
Statistically Valid Variational Bayes Algorithm for Ising Model Parameter Estimation

AbstractAbstractIsing models originated in statistical physics and are widely used in modeling spatial data and computer vision problems. However, statistical inference of this model remains challenging due to intractable nature of the normalizing constant in the likelihood. Here, we use a pseudo-likelihood instead, to study the Bayesian estimation of two-parameter, inverse temperature and magnetization, Ising model with a fully specified coupling matrix. We develop a computationally efficient variational Bayes procedure for model estimation. Under the Gaussian mean-field variational family, we derive posterior contraction rates of the variational posterior obtained under the pseudo-likelihood. We also discuss the loss incurred due to variational posterior over true posterior for the pseudo-likelihood approach. Extensive simulation studies validate the efficacy of mean-field Gaussian and bivariate Gaussian families as the possible choices of the variational family for inference of Ising model parameters. Supplementary materials for this article are available online.Keywords: Black box variational inferenceCoupling matrixELBOKullback-Leibler distancePosterior contraction ratesPseudo-likelihoodStochastic Optimization AcknowledgmentsWe are thankful to the Associate Editor and the Reviewers for their comments which helped improve the manuscript significantly.Supplementary MaterialsThe supplementary file contains implementation details and theoretical details.Disclosure StatementThere is no conflict of interest to report.Additional informationFundingThe authors are grateful to P. Ghosal and S. Mukherjee for kindly sharing codes of their work. The research is partially supported by the National Science Foundation grants NSF DMS-1952856 and 1924724.
Ion trap long-range XY model for quantum state transfer and optimal spatial search

Linear ion trap chains are a promising platform for quantum computation and simulation. The XY model with long-range interactions can be implemented with a single side-band Molmer-Sorensen scheme, giving interactions that decay as $1/r^\alpha$, where $\alpha$ parameterises the interaction range. Lower $\alpha$ leads to longer range interactions, allowing faster long-range gate operations for quantum computing. However, decreasing $\alpha$ causes an increased generation of coherent phonons and appears to dephase the effective XY interaction model. We characterise and show how to correct for this effect completely, allowing lower $\alpha$ interactions to be coherently implemented. Ion trap chains are thus shown to be a viable platform for spatial quantum search in optimal $O(\sqrt{N})$ time, for $N$ ions. Finally, we introduce a $O(\sqrt{N})$ quantum state transfer protocol, with a qubit encoding that maintains a high fidelity.
Less is More: Deep Learning Framework for Digital Forensics in Resource-Constrained Environments

Machine learning (ML)-enabled tools and techniques are an unavoidable necessity for modern digital forensics. However, the performance of such tools often requires extensive training using powerful computing environments that may expend thousands of dollars and several training hours for a single model. Further, this process necessitates using high-volume datasets that are generally unavailable due to court-mandated restrictions. To overcome these problems, this paper proposes using a two-layered strategy that fuses several features of transfer learning and knowledge distillation-based techniques to build lightweight neural networks that can be trained using low-volume datasets to achieve high accuracy. Experimental results highlight we achieved an accuracy of 98.5% and 97.5% during training and testing of the heavier ML model, and an accuracy of 97.08% and 97.2% on the lightweight ML model (during training and testing respectively), therefore demonstrating the robustness and validity of the proposed technique.
Transformer-based Sentiment Analysis for Anomaly Detection on Drone Forensic Timeline

An IoT device such as a drone is constantly generating log records to store every event that happens to the drone during a flight. In case the drone encounters a problem or experiences an incident, the log can be analyzed to find the root cause. A drone flight log contains a number of parameters, including sensor, state, and message data. These data can be utilized to perform anomaly detection. A common approach to detecting anomalies in log data is measuring the deviation of the log sequence. As an initial attempt, this paper proposes sentiment analysis as an approach for anomaly detection on drone flight log data. We construct our dataset by collecting and annotating the human-readable messages extracted from public datasets. Several existing pre-trained LLMs are fine-tuned to find the best model with the highest evaluation score. The proposed approach can distinguish between anomalous and normal events with 92.527% accuracy.
Designer quantum states on a fractal substrate: Compact localization, flat bands and the edge modes

Compact localized single particle eigenstates on a deterministic fractal substrate, modelled by a triangular Sierpinski gasket of arbitrarily large size, are unravelled and examined analytically. We prescribe an exact real space renormalization group (RSRG) decimation scheme within a tight binding formalism to discern these states, and argue that the number of such states can be infinite if the fractal substrate is enlarged to its thermodynamic limit. Interestingly, these localized states turn out to populate the non-dispersive, flat bands in a periodic array of Sierpinski gasket motifs, however large they may be. Our results match and corroborate the recently observed compact localized, flat band states engineered on two dimensional photonic waveguide networks with a fractal geometry, and provide a whole subset of them, which, in principle, should be observable in fractal photonic lattice experiments.
FedShare: Secure Aggregation based on Additive Secret Sharing in Federated Learning

Federated learning is a machine learning technique where multiple clients with local data collaborate in training a machine learning model. In FedAvg, the main federated learning algorithm, clients train machine learning models locally and share the trained model with the server. While the sensitive data will never be sent to the server, a malicious server can construct the original training data by having access to the clients' models in each training round. Secure aggregation techniques such as cryptography, trusted execution environment, or differential privacy are used to solve this problem. However, these techniques incur computation and communication overhead or affect the model's accuracy. In this paper, we consider a secure multi-party computation setup where clients use additive secret sharing to send their models to multiple servers. Our solution provides secure aggregation as long as there are at least two non-colluding servers. Moreover, we provide mathematical proof to show that the securely aggregated model at the end of each training round is exactly equal to the one provided by FedAvg without affecting accuracy and with efficient communication and computation. In comparison with SCOTCH, the state-of-the-art secure aggregation solution, experimental results show that our approach is 557% faster compared to SCOTCH and at the same time it reduces the communication cost of clients by 25%. Additionally, the accuracy of the trained model is exactly as FedAvg under balanced, unbalanced, IID, and Non-IID data distributions while it is only 8% slower.
2MiCo: A Contrastive Semi-Supervised Method with Double Mixup for Smart Meter Modbus RS-485 Communication Security

Industrial control systems (ICSs) are getting integrated into cyber-physical systems (CPSs) for a smarter and more energy-efficient society. As they organize the infrastructure of our modern society, cyber-attacks on ICSs may lead to catastrophic disasters if there are no protection, detection, and mitigation schemes. Modbus / RS-485 networks work as the backbone at the edges of ICSs, which security monitoring data on the communication signals comes with tabular forms and only with a few ground truth labels in the practical scenario, which makes the detection of attacks challenging with legacy machine learning. This paper presents a new approach called 2MiCo (Double Mixup and Contrastive) for semi-supervised learning in tabular security data sets. These data sets pose unique challenges for machine learning due to the loss of contextual information and imbalanced data. We address these challenges with a triplet mixup data augmentation approach in the input layer and a common mixup in the hidden layer. Our approach achieves state-of-the-art performance on both binary and multi-class data sets in the Modbus Attack DataSet for AMI (MAMI) compared to other methods. While semi-supervised methods are well-studied in image and language domains, they have been underutilized in security domains, particularly in tabular data sets. 2MiCo demonstrates promise in addressing these challenges and improving the performance of machine learning in security domains.
Comparing YOLOV3, YOLOV5 &amp; YOLOV7 Architectures for Underwater Marine Creatures Detection

The improvement of the marine economy depends on the object recognition of marine species. It is crucial for the intelligence of marine equipment to swiftly and reliably identify marine organisms in a challenging maritime environment. This research report suggests an architecture for a YOLO convolutional neural network trained on aquatic marine life. The most crucial step in object recognition is image pre-processing, which comes before YOLO processing. The pictures produced by the underwater system exhibit symptoms of inadequate lighting, low brightness, and the production of sea fret due to light absorption and dispersion under the sea. The image is improved using the Weighted L1-norm-based Contextual Regularization dehazing technique. This study used a variety of YOLO series to identify seven distinct marine animals, including fish, jellyfish, penguins, puffins, sharks, starfish, and stingrays. When the Enhanced YOLO series is contrasted with the Regular YOLO series, it is found that the former outperforms the latter significantly. Our research also reveals that the YOLOv7 framework performs better in terms of detection accuracy than the YOLOv5 and YOLOv3 architecture while retaining a slower inference time. The enhanced YOLOv7 exceeds the other five frameworks when comparing the YOLO series with enhanced YOLO frameworks, with a mAP of 0.82.
Early Warning System from Threat of Wild Animals using Digital Image Processing

Wildlife infiltration in places with high human mobility has been proven to be dangerous for both humans and animals. If people fail to recognize an approaching wild animal, it may result in a direct attack. Due to their size and style of movement, monitoring and surveillance of wild animals is challenging. Additionally, it is a significant task to identify the species that were photographed. Elephants, tigers, and monkeys pose a serious threat to humans, and it will take a very long time for them to recover. Because interactions between humans and animals can be harmful to both species, successive frame differencing makes it possible to identify moving objects in videos. By utilizing the traits, the moving objects can be identified. Interactions between humans and animals can be hazardous. The proposed system is based on digital image processing, convolutional neural network and background subtraction method.
Enhancing Hate Speech Detection through Explainable AI

The potential of XAI in detecting hate speech using deep learning models is versatile and multifaceted. To better understand the decision-making process of complex AI models, this study applied XAI to the dataset and investigated the interpretability and explanation of their decisions. The data was preprocessed by cleaning, tokenizing, lemmatizing, and removing inconsistencies in tweets. Simplification of categorical variables was also performed during training. Exploratory data analysis was conducted to identify patterns and insights in the dataset. The study used a set of existing models, including LIME, SHAP, XGBoost, and KTrain, to analyze the accuracy. The KTrain model achieved the highest accuracy and lowest loss among the variants developed to increase explainability.
A Reversible Framework for Efficient and Secure Visual Privacy Protection

The number of images produced by people everyday is rapidly increasing in recent years and their local storage space may be not big enough for storing all these images. As a result, people are currently accustomed to uploading images to cloud platforms, which raises privacy concerns. Traditional image encryption is a way to protect image privacy without preserving visual usability, so that image owners fail to conveniently browse and manage their images stored in the cloud. Hence some visual privacy protection schemes were proposed to balance image privacy and usability, while many of them are irreversible. Recently, a novel reversible technology, called Thumbnail-Preserving Encryption (TPE), has been a hot topic. However, existing TPE schemes are either inefficient, or cannot perfectly restore the original image and meanwhile achieve Nonce-Respecting (NR) security. In view of this, we propose a reversible framework for efficient and secure visual privacy protection, which tunably preserves image visual usability for image owners with the idea of data hiding. In the framework, the original image is firstly divided into several regions by our proposed region division methods and one of the regions is vacated by data hiding. Then, the vacated region is utilized to preserve the original thumbnail by pixel adjustment after image encryption. Finally, pixels in each sub block are permuted for security. According to our theoretical analysis, the above processes are completely reversible and the processed image achieves NR security. Furthermore, we conduct extensive experiments, including recognition by various application programming interfaces, user surveys, and efficiency comparison, to demonstrate that our framework is efficient and strikes a good balance between privacy and usability.
A New Construction of Nonlinear Codes via Algebraic Function Fields

In coding theory, constructing codes with good parameters is one of the most important and fundamental problems. A great many good codes have been constructed over alphabets of sizes equal to prime powers, however, good block codes over other alphabet sizes are rare. In this paper, we provide a new explicit construction of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$(q+1)$ </tex-math></inline-formula> -ary nonlinear codes via algebraic function fields, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$q$ </tex-math></inline-formula> is a prime power. Our codes are constructed by evaluating rational functions at all rational places of an algebraic function field. Compared with algebraic geometry codes, the main difference is that we allow rational functions to be evaluated at pole places. After evaluating rational functions from a union of Riemann-Roch spaces, we obtain a family of nonlinear codes over the alphabet <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {F}_{q}\cup \{\infty \}$ </tex-math></inline-formula> . It turns out that our codes have better parameters than those obtained from MDS codes or good algebraic geometry codes via code alphabet extension and restriction.