Reviewerly/exHarmony huggingface dataset:
A two-stage model extraction attack on GANs with a small collected dataset

Due to their capacity for image generation, GAN models may be considered as a solution for the use of private data, which enhances their commercial value. However, unlike discriminative models such as CNNs, extraction attacks on GANs have not received significant attention, with only a few relevant works available. This paper proposes a novel two-stage extraction attack on GANs that does not require access to the victim GAN model. In the first stage, data augmentation is performed using GAN inversion techniques and vector arithmetic, tailored to scenarios with small query budgets. The second stage comprises two types of training: the innocent transfer training on unprocessed collected samples and the additional training on the augmented set. We provide three options for additional training: training of the discriminator alone (D), training of the generator alone (G), and training of both (D+G). We then conduct a quantity and quality evaluation of our extraction attacks compared to the baseline attack. Experiment results demonstrate that our proposed approach can successfully extract a GAN model, where the distribution of the extracted GAN closely resembles the distribution of the target GAN, even with a hundred generated images of the target GAN. Specifically, we achieve stable performance with better extraction, a more disentangled latent space for the extracted GAN, and significant semantic attribute editing performance using the two-stage extraction with additional training of the discriminator (D). By demonstrating that even a hundred leaked images may result in severe extraction, this study raises serious concerns regarding GAN model leakage.
Demodulation of interferometry parameters with multiple-harmonic superposition based on the DTFT and semi-blind spectral analysis

The core issue of multi-surface wavelength-shifting interferometry at free cavity lengths is mainly centered on adaptively matching the tuning step. To avoid harmonic aliasing and undersampling, the discrete-time Fourier transform method with global spectrum densification is used to comprehensively analyze the spectral characteristics under different sampling cases and cavity lengths, then a composite multi-surface wavelength-shifting interferometry algorithm is proposed accordingly. The proposed method includes a preprocessing algorithm and three spectral criteria to identify and avoid undesirable sampling problems for in-situ measurements. The algorithm performance is quantitatively evaluated from several aspects of the simulation analysis. A flat plate with an average thickness of 20 mm was repeatedly measured using a Fizeau wavelength-shifting interferometer in the experiments, and the PVQ and RMS errors at different measurement distances were less than 5 mm and 1 mm, respectively.
An statistical model for the short-term albedo estimation applied to PV bifacial modules

Albedo estimation is an essential input parameter for bifacial PV modules. However, there was no clear agreement on which albedo values should be used and how they should be measured, either with satellite measurements or using onsite measurements which can be obtained from nearby meteorological stations. Since long-term measurements are not always available for new PV system locations, short-term albedo measurements are also used as input parameters in PV system performance estimation models. Short-term albedo presents high variability due to factors such as weather, seasonality or changes in the surrounding surface among others. In addition, apparently random albedo variations of 60% can be observed, even during consecutive days or within the same day. Therefore, this study presents a two-parameter exponential model that modelates the albedo data statistical distribution with an error of less than 5% in all cases and from which it is possible to determine the reliability of the obtained data. This model has been evaluated at a set of locations, with different surface types and climates, and assessed using onsite and satellite data. The impact of the methodology used to estimate the albedo data uncertainty and its reliability has also been studied as a function of several parameters such as the global horizontal irradiance and measurement time, allowing the process optimisation and enhancing its reliability.
Multiple attributes based physical layer authentication through propagation scenario identification in the internet of vehicles

The Internet of Vehicles (IoV) systems improve road safety through coordination among vehicles, but they are vulnerable to impersonation attack due to their broadcast communication nature. Existing cryptographic authentication approaches are complex for resource-limited IoV devices, which motivated the development of less complex Physical Layer Authentication (PLA) approaches. However, the existing PLA approaches use all channel attributes for all communication scenarios and do not update reference attributes used to authenticate newly observed attributes during authentication, which leads to poor performance when the attributes become insensitive to communication scenarios. To address these limitations, we propose a multiple attributes-based PLA scheme that flexibly selects effective attributes according to current communication scenarios for improved authentication. First, the historical channel attributes including the ricean K-Factor (KF), the Delay Spread (DS), and the Azimuth Spread of Arrival (ASA) together with the communication scenarios (i.e., Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS)) information of transmitters are used to establish a relationship and train a Long Short-Term Memory (LSTM) neural network to identify current communication scenarios. The softmax function is then used to evaluate the scenario identification performance of each attribute and the attributes with maximum softmax values are dynamically selected for authentication. Second, to track and update the reference attributes, we use the historical attributes together with the past location and communication scenarios information of transmitters and train our model to predict future attributes for authentication. Finally, we use the Euclidean distance to measure the similarity between the selected and the predicted attributes to authenticate the transmitters. We validate the effectiveness of our approach and compare its performance with the existing methods through extensive simulations using realistic radio channel characteristics generated from the Quasi Deterministic Radio channel Generator (QuaDRiGa) platform. The results of the evaluation show that our system improves authentication performance and its false alarm and miss detection are respectively 19.19% and 52% lower than the existing approaches.
Let model keep evolving: Incremental learning for encrypted traffic classification

Encrypted Traffic Classification (ETC) is valuable for many network management and security solutions as it provides insights into applications active on the network. However, the network environment constantly evolves, and new applications emerge in an endless stream daily, which gradually makes well-trained ETC models ineffective. The conventional approach to adapting new applications is to re-train the models on a re-formed dataset with both pre-existing and new application samples. The major limitation is that requiring redundant computing resources and sufficient storage spaces. In this work, we propose an Incremental Learning (IL) framework based on multi-view sequences fusion, MISS, to keep ETC models evolving with new applications. The key novelty of MISS is three-fold: extract cross-view information from multi-view sequences to capture sufficient knowledge; propose an exemplar selection algorithm from communication patterns to reduce redundant consumption; design a pair of branches from the learnability of parameters to mitigate accuracy loss during evolution. MISS outperforms the existing IL methods of ETC, and the state-of-the-art ETC models using the classic IL framework, on the real-world network traffic datasets, which achieves satisfactory improvements of 11.37%↑ and 1.58%↑. Furthermore, we comprehensively perform incremental experiments to evaluate the evolution ability of MISS, which is able to select representative exemplars of old applications, counteract the adverse effects of homogeneous applications, and keep evolving with unknown applications.
Third-party software library migration at the method-level using multi-objective evolutionary search

Software developers commonly use third-party software libraries to reduce implementation efforts and mitigate errors in their source code while building high-quality and reliable software. To support software evolution, newer libraries are released periodically with added features, improvements in performance as well as critical updates such as bug fixes. Hence, older, existing (source) libraries need to be replaced with their newer, updated (target) counterparts, in a process known as library migration. Typically, library migration is a time-consuming, manual, and error-prone process that requires developers to analyze the source and target library code and its documentation. Specifically, developers examine Application Programming Interface (API) implementations and documentation to replace each source API with a target API without modifying the underlying software functionality. While recent works have used various techniques to recommend suitable target library replacements, these approaches do not generalize well, e.g., when a source library method needs to be replaced by one or more target library APIs or methods. To address this limitation, we propose the use of multi-objective evolutionary algorithms to identify suitable method replacements during library migration. In particular, we formulate library migration at the method-level as a multi-objective combinatorial optimization problem and examine the performance of 7 multi-objective evolutionary algorithms: UNSGAIII, RNSGAII, AGEMOEA, SMSEMOA, NSGAII, IBEA and MOEAD. We use method signature and documentation similarity, and co-occurrence probability to accurately recommend one or more target library methods. We evaluate our approach by conducting an empirical study on 9 popular Java library migrations mined from 57,447 open-source projects on GitHub. Our results demonstrate that UNSGAIII, RNSGAII, AGEMOEA, SMSEMOA, NSGAII, IBEA and MOEAD achieve 90%, 89%, 94%, 90%, 91%, 94%, and 71% precision on average, and 83%, 23%, 58%, 63%, 58%, 60% and 17% average recall respectively. In the interest of reproducibility, we make all code and results publicly available at: http://bit.ly/MOO-api-migration.
LiDAR-based Online Control Barrier Function Synthesis for Safe Navigation in Unknown Environments

This paper presents a novel extension of the Control Barrier Function (CBF) as the low-level safety controller for autonomous mobile robots navigating in unknown environments. The main challenges of implementing CBF in real-world situations arise from the absence of a model or the lack of an exact one for the environment. Additionally, online learning is needed for the robot to maneuver in an unknown environment which leads to dealing with the sampled data set size, memory, and computational complexity. We address these challenges by designing an online non-parametric Lidar-based safety function using the Gaussian process (GP). It is both efficient in data size and eliminates the requirement to store previous data. Then, a CBF is synthesized using the proposed safety function to rectify the safe control input. The effectiveness of the Lidar-based CBF synthesis for navigation in unknown environments was validated by conducting experiments on unicycle-type robots.
Generating and Transferring Priors for Causal Bayesian Network Parameter Estimation in Robotic Tasks

Robots acting in human environments will often face new situations and can benefit from transferring prior experience. Priors could enable robots to handle new tasks zero-shot and help prevent failures, which can be particularly costly in real robot applications. Due to their interpretable nature, causal Bayesian Networks (CBN) are popular for modeling cause-effect relations between semantically meaningful environment features and their effects on action success. While the CBN structure is often intuitively transferable to a new context, its probability distribution might change, requiring data-intensive relearning. In this work, we propose three strategies that utilize semantic similarity and relatedness between the variables of two CBNs to generate and transfer informed CBN distribution priors. We evaluate the parameter prior accuracy in five different transfer scenarios, including sim-2-real, transferring parameters to more complex tasks with a larger number of parameters and even between two different tasks, which is particularly challenging. We show that the priors lead to better distribution estimates, particularly under a limited amount of new experiments, and improve the robot's ability to predict and prevent action failures by up to 50%.
Event-Triggered Image Moments Predictive Control for Tracking Evolving Features Using UAVs

This paper presents a novel approach for tracking deformable contour targets using Unmanned Aerial Vehicles (UAVs). The proposed scheme combines image moments descriptor and Event-Triggered (ET) Nonlinear Model Predictive Control (NMPC) for efficient and accurate tracking. The deformable contour model allows adaptation to the evolving target's shape, while the proposed event-triggered scheme achieves improved computational efficiency and extended flight duration while generating new control sequences for the UAV. Real-world experimental validation as well as a comparative simulation performance analysis validate the scheme, showcasing its robustness in handling complex scenarios. This approach holds promise for various applications, such as surveillance and autonomous navigation.
Multidimensional Force Sensor Dynamic Compensation Based on Multistrategy Improved Sparrow Search Algorithm

Multi-dimensional force sensors are extensively utilized in industrial milling machining for measuring cutting force and in intelligent robot applications for measuring joint force. These sensors offer several advantages, including improved static characteristics, well-established static calibration techniques, and temperature compensation technology. However, with the increasing demand for measuring dynamic forces in various applications, force sensors need to possess enhanced dynamic characteristics. Unfortunately, strain force sensors typically exhibit low intrinsic frequency and damping ratio, resulting in slower dynamic response of the sensor.To address this issue and enhance the dynamic performance of multidimensional force sensors, this study proposes a dynamic compensation method based on an improved sparrow search algorithm.Chebyshev chaotic mapping was implemented to increase randomness and ergodicity in the initial population. An adaptive weight factor was incorporated to improve the position update formula of finders and the ratio of vigilantes. These changes enhanced the algorithm’s ability to conduct early global searches and late local depth mining. Subsequently, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> -distribution changes and Chebyshev chaotic perturbations were introduced to expand the local search capability.The enhanced sparrow search algorithm improves the optimization capabilities of the original algorithm. By utilizing dynamic calibration experimental data from three-dimensional force sensors, the algorithm’s effectiveness was verified. The results indicate that the method successfully reduces the overshooting amount in each channel of the sensors and shortens the regulation time. Consequently, the dynamic performance of the three-dimensional force sensors is improved, and the algorithm proves to be effective, practical, and robust in compensating for the sensors’ dynamic behavior.
Salient Feature Pyramid Network for Ship Detection in SAR Images

Recently, convolutional neural networks (CNNs) have achieved remarkable performance in SAR ship detection. However, intense background interference in SAR images and the inherent attributes of the ship targets severely hinder further improvements in detection accuracy. To address these problems, we propose a novel anchor-free detector with a salient feature pyramid network (SFPN) and an intersection-over-union (IoU) branch. First, SFPN aggregates multi-level semantic features and generates salient features for ship targets through a saliency extraction (SE) module. Then, a saliency fusion (SF) module merges the salient features and FPN features. In particular, we introduce a heatmap branch in SFPN as an extra supervision signal to direct the network to focus on salient regions containing ship targets rather than interference. Second, considering the shape characteristic that the length of a ship target is much larger than its width, we apply the IoU branch to the detection head to predict localization confidence for each detected bounding box. Extensive experiments conducted on High-Resolution SAR Images Dataset (HRSID) and SAR-Ship-Dataset demonstrate that the proposed method effectively improves detection accuracy and achieves state-of-the-art performance.
Toward identifying malicious encrypted traffic with a causality detection system

The main methods for protecting user privacy and addressing cybersecurity problems caused by encrypted traffic are non-decryption detection approaches. However, these methods face problems such as the small number of trainable features and imbalanced training datasets, which seriously affect the robustness of existing malicious encrypted traffic detection systems (TDSs). To address these issues, we identify malicious encrypted traffic with a causality explainable detection system (METCXDS). Our detection tasks increase the number of noncryptographic features and eliminate noise features to improve interpretability. First, we extract and recombine temporal and statistical features to increase the number of trainable features. Then, the influence of causal features on the results is enhanced by weighting causal reasoning features, and noise features with low weights are removed to improve the causal interpretability of the detection results. Furthermore, samples are generated through a Wasserstein generative adversarial network (WGAN) that learns the causal feature distribution to balance the training samples. Finally, the METCXDS performance is evaluated through the detection balance index and causal feature index. On the CICIDS2017 and DoHBrw2020 datasets, compared with previous TDSs, our system improves the overall F1 score by up to >10 %. And the causality of the detection results is verified by the causal effects, which identify the causes of anomalies in encrypted traffic and clarify the decision-making process of the detection system.
An accurate and robust strain field smoothing method based on polynomial fitting and anisotropic diffusion in digital image correlation

Digital Image Correlation (DIC) is a popular optical measurement method. However, the accuracy of strain values obtained by DIC may be adversely affected by random noise. This paper proposes an Anisotropic Nonlinear Diffusion Filtering (ADF) method, combining Pointwise Least Square (PLS) with the Perona-Malik (PM) model, extracts accurate and smooth strain fields from noisy displacement fields. The strain field is extracted point by point using PLS and then smoothed by the PM model. The ADF method was tested on simulated and real speckle deformation experiments. The results indicate that the ADF method significantly reduces the negative impact of inappropriate calculation parameters, such as the strain window size, compared to the PLS method. Furthermore, the PM model was found to be better suited for smoothing strain fields compared to other image denoising models. The ADF method demonstrates excellent noise suppression performance and accurate strain identification in both simulated and real experiments.
A region-based hierarchical image compression method with simulated visual perception

Most learned image compression methods focus on reducing the average code length of the image, which is unreasonable for the human visual system. Thus, appropriate bit allocation gains significance. This paper presents a novel end-to-end method for perceptual image compression, emphasizing content prioritization and a region-based hierarchical strategy. Firstly, our modified class activation mapping (CAM) can serve as the visual perception network for simulating human visual perception. On this basis, we develop an efficient compression system that leverages a discretized hybrid entropy model to allocate bits automatically according to the perceptual prioritization of image content. In our compression system, an image is compressed jointly and hierarchically with different standards, guided by the importance map extracted by the visual perception network. We introduce content-weighted perceptual metrics to evaluate visual quality more reasonably and objectively. Experiments on publicly available datasets demonstrate that our method outperforms traditional codecs and recent content-oriented learned methods in overall performance. Compared with other methods, the proposed method reconstructs images that are more friendly to the human visual system.
An efficient adaptive Masi entropy multilevel thresholding algorithm based on dynamic programming

Masi entropy multilevel thresholding can utilize additive and non-extensive information in images to effectively segment a complex image. However, the entropy index of Masi entropy cannot be selected automatically, and the time complexity of the multilevel algorithm by exhaustive searching grows exponentially with the increase of the threshold numbers. To address these two problems, an adaptive entropy index selection strategy based on image histogram information is proposed first. To improve the computation efficiency, an efficient solution for the adaptive Masi entropy multilevel thresholding algorithm based on dynamic programming (DP + AMasi) is also proposed. The DP + AMasi algorithm is compared with the Masi entropy multilevel thresholding algorithm by exhaustive search and state-of-the-art metaheuristic algorithms on three benchmark datasets. The effectiveness of the DP + AMasi is verified by fitness function values, Uniformity Measure, Davies Bouldin index, and CPU run time. In addition, the Wilcoxon test is used to analyze the differences between algorithms.
Probing teleported quantum correlations in a two-qubit system inside a coherent field

This paper investigates the impact of dipole interactions and the initial quantum state configurations within a quantum system on teleported quantum correlations involving a pair of qubits embedded in a single-mode field. The analysis encompasses an exploration of non-separability, steerability, non-locality, and steered coherence of the teleported state, considering different initial Bell states for the two-qubit channel. The initial atomic setting state primarily influences the teleported quantum correlations. The study highlights that dipole interaction exert a substantial effect on both the maximum and minimum values of quantum correlations. Furthermore, increasing this interaction demonstrates the potential to enhance control over the decay of correlations induced by the detuning parameter.