Reviewerly/exHarmony huggingface dataset:
Underwater image enhancement method based on golden jackal optimization

Authors:
raw_author_name: Jie Yang
raw_affiliation_string: School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621000, China
raw_author_name: Jun Wang
raw_affiliation_string: School of Information Engineering, Southwest University of Science and Technology, Mianyang, 621000, China

Abstract: The propagation of light underwater is affected by water absorption and scattering, resulting in poor visibility of obtained underwater images, and exhibiting degradation phenomena such as color deviation and low contrast. To improve the quality and visibility of underwater images, a method of underwater image enhancement based on Golden Jackal optimization is proposed. The algorithm consists of two processing stages. The first stage is mainly processed in the RGB color space. Based on the attenuation characteristics of underwater color channels, a constructed color compensation and correction strategy is used to eliminate color deviation caused by water absorption. In second stage, it is mainly processed in the Lab color space, using the Golden Jackal optimization algorithm and fitness function to search the optimal parameters of transformation function to improve the image contrast by optimizing the grey intensity of L component and its histogram distribution. The feasibility and effectiveness of the proposed method have been verified through testing and analysis of much underwater degraded image data. In subjective and objective analysis with multiple state-of-the-art underwater image clarity methods, the proposed method performs excellent in color restoration and detail texture enhancement. Further experiments have shown that the proposed method has strong robustness and universality. It is not only suitable for enhancing various underwater scene images, but also for enhancing environmental images with fog, low lighting, and sandstorms. Meanwhile, the proposed method can effectively improve the performance of underwater image segmentation, keypoint detection, and saliency detection.
Leveraging Machine Learning for Generating and Utilizing Motion Primitives in Adversarial Environments

Authors:
raw_author_name: Zachary C. Goddard
raw_affiliation_string: Georgia Institute of Technology, Atlanta, Georgia 30332
raw_author_name: Kyle Williams
raw_affiliation_string: Sandia National Laboratories, Albuquerque, New Mexico 87123
raw_author_name: Anirban Mazumdar
raw_affiliation_string: Georgia Institute of Technology, Atlanta, Georgia 30332

Abstract: Motion primitives enable fast planning for complex and dynamic environments. Adversarial environments pose a particularly challenging and unpredictable scenario. Motion-primitive-based planners have the potential to provide benefit in these types of environments. The key challenge is to design a library of maneuvers that effectively capture the necessary capabilities of the vehicle. This work presents a primitive-based game tree search to solve adversarial games in continuous state and action spaces and applies a reinforcement learning framework to autonomously generate effective primitives for the given task. The results demonstrate the ability of the learning framework to produce maneuvers necessary for competing against adversaries. Furthermore, we propose a method for learning a model to estimate the state-dependent value of each motion primitives and demonstrate how to incorporate this model to increase planning performance under time constraints. Additionally, we compare our primitive-based algorithm against forward simulated methods from existing literature and highlight the benefits of motion primitives.
MFI-YOLO: Multi-fault insulator detection based on an improved YOLOv8

Authors:
raw_author_name: Liang Qin
raw_affiliation_string: Hubei Key Laboratory of Power Equipment &amp; System Security for Integrated Energy, Department of Electrical Engineering and Automation, Wuhan University, Wuhan, China
raw_author_name: Xinlan Deng
raw_affiliation_string: Hubei Key Laboratory of Power Equipment &amp; System Security for Integrated Energy, Department of Electrical Engineering and Automation, Wuhan University, Wuhan, China
raw_author_name: Kaipei Liu
raw_affiliation_string: Hubei Key Laboratory of Power Equipment &amp; System Security for Integrated Energy, Department of Electrical Engineering and Automation, Wuhan University, Wuhan, China

Abstract: Insulators are essential components in power transmission lines. Due to the harsh variations in bad environments, insulators may experience faults. Detecting these insulator faults promptly and effectively is an urgent issue. To rapidly and accurately locate insulators and their faulty regions in aerial images of insulators with complex backgrounds and varying fault sizes, this paper proposes an improved YOLOv8 algorithm for the detection of multiple insulator fault types (MFI-YOLO). This algorithm achieved target feature extraction in complex background images by replacing the C2F network constructed by fusing the GhostNet and multi-scale asymmetric convolution (MSA-GhostBlock). Furthermore, in the feature fusion stage, a multi-scale feature fusion structure called ResPANet, based on residual skip connections, was constructed to replace the PANet. This enhancement aims to improve the network detection accuracy in multi-target scenarios. Finally, to evaluate the algorithm's performance, this study constructed a target detection dataset containing four types of insulators: normal, self-explosive, damaged, and flashover. Experimental results indicate that, compared to the original model, the improved model has increased mean accuracy from 89.2% to 93.9%. The designed model exhibits high detection accuracy in the insulator and its three fault categories, especially for some hard-to-detect fitting.
Reading compound words in Finnish and Chinese: An eye-tracking study

Authors:
raw_author_name: Jukka Hyönä
raw_affiliation_string: University of Turku, Finland

Abstract: Two eye-tracking experiments in alphabetic Finnish and two in logographic Chinese examined the recognition of two-constituent compound words in reading. In Finnish, two-constituent compound words vary greatly in length, whereas in Chinese they are identical in length. According to the visual acuity principle (Bertram & Hyönä, 2003), short Finnish compound words and all two-character Chinese compound words that fit in foveal vision are recognized holistically, whereas long Finnish compound words are recognized via components. Experiment 1 in Finnish provided evidence consistent with the account, whereas the results for long compound words presented in condensed font in Experiment 2 were inconsistent with it. In Chinese, the first-character frequency effect was non-significant even when the compound words were presented in large font. The Finnish results suggest that componential processing is necessary when the compound word entails more than 10 letters. The Chinese results are compatible with the Chinese Reading Model (Li & Pollatsek, 2020) that assumes whole-word representations to overrule the activation of components during compound word recognition.
Deep learning-based software bug classification

Authors:
raw_author_name: Jyoti Prakash Meher
raw_affiliation_string: Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, 721302, West Bengal, India
raw_author_name: Rajib Mall
raw_affiliation_string: Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, 721302, West Bengal, India

Abstract: Accurate classification of bugs can help accelerate the bug triage process, code inspection, and repair activities. In this context, many machine learning techniques have been proposed to classify bugs. The expressive power of deep learning could be used to further improve classification. We propose a novel deep learning-based bug classification approach. We first build a bug taxonomy with eight bug classes, each characterized by a set of keywords. Subsequently, we heuristically annotate a moderately large set (∼1.36M) of software bug resolution reports using an earth-mover distance technique based on the keywords. Finally, we use four attention-based classification techniques to classify these curated bugs. Our experiments on a carefully collected dataset indicate that our proposed technique achieved a mean F1-Score of 84.78% and a mean macro-average ROC of 98.25%. Our proposed approach was observed to outperform the existing techniques by 16.88% on an average in terms of F1-Score for the considered dataset.
Complex amplitude extraction from a set of interferograms by simultaneous modulation of amplitude and phase steps

Authors:
raw_author_name: Carlos Augusto Flores-Meneses
raw_affiliation_string: Laboratorio de Luz Estructurada, Facultad de Ciencias Físico-Matemáticas, Benemérita Universidad Autónoma de Puebla, Av. San Claudio y 18 Sur, Col. San Manuel, Apdo. Postal 165, C.P. 72570 Puebla, Pue., Mexico
raw_author_name: Cruz Meneses-Fabian
raw_affiliation_string: Laboratorio de Luz Estructurada, Facultad de Ciencias Físico-Matemáticas, Benemérita Universidad Autónoma de Puebla, Av. San Claudio y 18 Sur, Col. San Manuel, Apdo. Postal 165, C.P. 72570 Puebla, Pue., Mexico

Abstract: The measurement of an optical field arises in various fields of science and engineering, including holography, crystallography, microscopy, and many others. Nowadays, there are several methods to estimate amplitude and phase including Shack-Hartman wavefront sensor, algorithms type Gerchberg-Saxton, algorithm-based on the transport of irradiance equation, holographic, interferometric, among others. This paper introduces an interferometric method for retrieving the complex amplitude from a set of interferograms modulated simultaneously in visibility and phase steps. Instead of phase only as the method of so-called phase-shifting interferometry. This approach is based on the modulation of amplitude and phase steps and is presented for the case of synchronous detection and coherence light. It is validated with a numerical noise study, turning out to be accurate and robust to detuning errors, with low distortion, and a very high signal-to-noise. The experiment is carried out on a Mach–Zehnder interferometer adapted to modulate the reference amplitude, and the phase steps by modulation of polarization, instead of phase steps only as in phase-shifting interferometry.
Unawareness detection: Discovering black-box malicious models and quantifying privacy leakage risks

Authors:
raw_author_name: Jiacheng Xu
raw_affiliation_string: School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China
raw_author_name: Chengxiang Tan
raw_affiliation_string: School of Electronic and Information Engineering, Tongji University, Shanghai 201804, China

Abstract: Because machine learning models, especially black-box malicious models vulnerable to attribute inference attacks, are capable of generating a great deal of privacy leakage, recent work has focused on assessing these models in an attempt to prevent unexpected attribute privacy leakage. While there has been some success at model privacy risk evaluations, these traditional solutions are almost brittle in practice because they not only require white-box access to obtain model feature layer outputs but also their evaluation results are heavily influenced by the training dataset and the model structure, leading to difficulty in generalization. In this paper, we propose a novel unawareness detection mechanism for discovering black-box malicious models and quantifying potential unawareness privacy leakage risk along with machine learning models to overcome the two limitations. A new method for quantifying the privacy risk caused by a specific loss function has been proposed to mitigate the impact of the training dataset and the model structure. A new evaluation model has also been proposed that uses Matthew's correlation coefficient score as a new metric and the final output of the target model as a new input. In addition, the theoretical upper bound of the model privacy risk has also been given a mathematical formula that is positively correlated to the mutual information between the sensitive attributes and the target model outputs. Compared with traditional detection methods, our evaluation model reduces the requirement for model access and minimizes evaluation errors caused by data imbalance, and our privacy risk assessment method and theoretical upper bounds on privacy risk can be applied to a broader range of datasets and target model structures. The experimental results show that the adversary's prediction capability is affected by the distribution of datasets and the level of malicious intent of the model, which is consistent with the theoretical prediction, and the detecting method can find potential model privacy leaks in public datasets UTKFace and FairFace.
Numerical model study on influences of photovoltaic plants on local microclimate

Authors:
raw_author_name: Biao Li
raw_affiliation_string: Harbin Institute of Technology, School of Energy Science and Engineering, 92 West Dazhi Rd, Harbin, China
raw_author_name: Yong Shuai
raw_affiliation_string: Harbin Institute of Technology, School of Energy Science and Engineering, 92 West Dazhi Rd, Harbin, China

Abstract: The construction of large-scale photovoltaic plants will inevitably change the local environment. We consider that the influences of such increasing large-scale photovoltaic installations on radiation, temperature, wind profile and other factors within the region can be described in a layer of photovoltaic canopy. This paper improves the UCRC-Solar model for photovoltaic plants, with reference to the calculation of sensible heat flux of the underlying forest surface. Model verification was done with measured data from the Red Rock photovoltaic plant, Arizona, USA. In this study, it indicates that photovoltaic plant has locally generated heat island effects in daytime. The calculation of local microclimate changes before and after the construction of the photovoltaic plants and, the impact of photovoltaic coverage finds an increase in the temperature daily range and a reduction in the ground temperature by about 5 °C. Furthermore, a 15 % increase in photovoltaic coverage increased the daily 3 m air temperature difference by 0.55 °C and reduced the ground temperature by 3.6 °C in the day and 1.1 °C at night, respectively. In the future, rain and snow processes would be considered to further explore the microclimate impact of photovoltaic plants.
Stealthy Attack on Remote Control System With Local Controller and Its Countermeasures

Authors:
raw_author_name: Nachuan Yang
raw_affiliation_string: Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Hong Kong, Hong Kong
raw_author_name: Jun Fu
raw_affiliation_string: State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China
raw_author_name: Hao Liu
raw_affiliation_string: School of Automation, Shenyang Aerospace University, Shenyang, China

Abstract: Cyber–physical systems (CPSs) driven by a local controller and a remote controller have been gaining significant research interest in recent years due to its application scenarios in practice, such as unmanned aerial vehicles (UAVs). In this article, we consider the security issue in the remote control system with a local controller under stealthy attacks. Under this framework, one controller is designed locally based on the limited measurements collected by a local sensor, and the other controller is designed remotely and is transmitted to the actuator through a wireless communication channel, which may suffer malicious attacks due to its openness. To defend attacks on remote control signal, the K–L divergence-based detector or <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\chi^{2}$</tex-math> </inline-formula> detector is often adopted. However, there may be attackers adopting stealthy attacks, which can bypass such detectors. Therefore, we analyze the existence of such attacks, and analytically characterize the worst-estimation performance degradation induced by the remote control signal attack. Further, we construct the optimal attack signal to achieve the upper bound on the estimation performance degradation. In addition, we also give countermeasures against such stealthy attacks. Simulations are provided to illustrate the proposed results.
Measurement Combination Estimator for Multisensor Extended Object Tracking Using Random Matrix

Authors:
raw_author_name: Jian Lan
raw_affiliation_string: Center for Information Engineering Science Research, Faculty of Electronic and Information Engineering, Xi&#x0027;an Jiaotong University, Xi&#x0027;an, China

Abstract: For extended object tracking (EOT), the random-matrix approach has received extensive attention. It is able to jointly estimate the kinematic state and extension of an extended object. Multiple sensors with different characteristics and perspectives can provide new information especially for EOT. However, how to fuse the measurements from multiple sensors to guarantee the improvement of the estimation accuracy of the state and extension still needs further research. This paper proposes a random-matrix approach to elliptical EOT using multiple sensors based on a centralized fusion architecture. In order to handle the different distortions of multiple sensors, a measurement combination (MC) approach is firstly proposed. As a result, a likelihood function with a proper form is obtained, and it can also pre-process the multi-sensor measurements and take the scattering matrix of measurement means into consideration. Based on the likelihood function, the measurement combination estimator (MCE) is derived for multi-sensor EOT in a Bayesian framework. It inherits the analytical and concise scheme of the single-sensor EOT approach. Based on the proposed MCE approach, it is theoretically proved that the estimation accuracy of the state and extension can be improved when the sensor number increases. Effectiveness of the MCE approach is demonstrated by simulation and experimental results compared with the existing multi-sensor EOT approaches using random matrix.
Up- and down-correlations in normal variance mixture models

Authors:
raw_author_name: Jonathan Ansari
raw_affiliation_string: Paris Lodron Universität Salzburg, Austria
raw_author_name: Tomer Shushi
raw_affiliation_string: Ben-Gurion University of the Negev, Israel
raw_author_name: Steven Vanduffel
raw_affiliation_string: Vrije Universiteit Brussels, Belgium

Abstract: We study conditional correlations in normal variance mixture models. For several families, we determine explicit formulas for up-(down-)correlations defined as the correlation between the sum of risks and an individual component, conditionally on the sum being large (small).
Adaptive phase measuring profilometry for robustly detecting saturated pixels

Authors:
raw_author_name: Geyou Zhang
raw_affiliation_string: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chendu, Sichuan, 611731, China
raw_author_name: Daniel L. Lau
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Kentucky, Lexington, Kentucky, 40506, USA
raw_author_name: Bo Zhang
raw_affiliation_string: School of Mechanical Engineering, Sichuan University, Chendu, Sichuan, 610064, China
raw_author_name: Bin Xu
raw_affiliation_string: School of Mechanical Engineering, Sichuan University, Chendu, Sichuan, 610064, China
raw_author_name: Kai Liu
raw_affiliation_string: College of Electrical Engineering, Sichuan University, Chendu, Sichuan, 610065, China

Abstract: In this paper, we propose an adaptive method for phase measuring profilometry to more accurately reconstruct 3D point clouds of high-reflective surfaces. First, we project high-frequency sinusoidal fringe patterns to robustly detect saturated pixels and then obtain their corresponding optimal projection intensity. Second, based on the coordinate mapping relationship between a camera-projector pair, we generate a set of fringe patterns with adaptive intensity. Finally, we project the adaptive fringes patterns to achieve more accurate 3D reconstruction. The experimental results show that our method can more accurately detect saturated pixels, and thus improve the quality of 3D reconstruction.
Improved ShuffleNetV2 for Action Recognition in BPPV Treatment

Authors:
raw_author_name: Xiao Jin
raw_affiliation_string: School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

Abstract: In clinical examination and repositioning for BPPV(benign paroxysmal positional vertigo), analyzing the head position trajectory at the algorithm level to obtain information on each examination and repositioning posture helps to provide guidance for subsequent treatment, which has important practical value in clinical applications. This paper uses the improved ShuffleNetV2 on the basic the data obtained by the swivel chair for action recognition, and locates the interval where the action occurs. Specific network improvements include: the introduction of the SE channel attention block, which improves the performance of the network with a small amount of parameters and computation; the Hardswish activation function is used instead of ReLU in the original network; by reducing the layers in the network, deleting the second 1*1 convolution in the right branch of the downsampling block, and changing the kernel size of the DW convolution, the network can be lightweight while maximizing the capability of action recognition. The network’s performance has been enhanced, resulting in an accuracy of 98.53% with only 71,444 parameters and 15.76M FLOPs, while maintaining a recognition speed of 97.1 images per second. It achieves a good balance between speed and accuracy, enabling it to effectively accomplish the task of action recognition.