Reviewerly/exHarmony huggingface dataset:
Multidimensional Force Sensor Dynamic Compensation Based on Multistrategy Improved Sparrow Search Algorithm

Authors:
raw_author_name: Qi An
raw_affiliation_string: College of Automation and Electrical Engineering, Linyi University, Linyi, China
raw_author_name: Haochen Zhang
raw_affiliation_string: College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, China

Abstract: 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

Authors:
raw_author_name: Yu Tang
raw_affiliation_string: College of Communication Engineering, Jilin University, Changchun, China
raw_author_name: Jian Wei
raw_affiliation_string: College of Communication Engineering, Jilin University, Changchun, China
raw_author_name: Yan Zhao
raw_affiliation_string: College of Communication Engineering, Jilin University, Changchun, China
raw_author_name: Jiehua Lin
raw_affiliation_string: College of Communication Engineering, Jilin University, Changchun, China

Abstract: 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

Authors:
raw_author_name: ZengRi Zeng
raw_affiliation_string: College of Computers, National University of Defense Technology, Changsha, Hunan, China; School of Information, Hunan University of Humanities and Technology, Loudi, Hunan, China
raw_author_name: Peng Xun
raw_affiliation_string: College of Computers, National University of Defense Technology, Changsha, Hunan, China
raw_author_name: Wei Peng
raw_affiliation_string: College of Computers, National University of Defense Technology, Changsha, Hunan, China
raw_author_name: BaoKang Zhao
raw_affiliation_string: College of Computers, National University of Defense Technology, Changsha, Hunan, China

Abstract: 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

Authors:
raw_author_name: Zitong Wang
raw_affiliation_string: School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China
raw_author_name: Pan Zhu
raw_affiliation_string: Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
raw_author_name: Lu Liu
raw_affiliation_string: School of Machinery and Automation, Wuhan University of Science and Technology, Wuhan 430081, China
raw_author_name: Xinglin Zhou
raw_affiliation_string: Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China

Abstract: 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

Authors:
raw_author_name: Li Zeng
raw_affiliation_string: Engineering Research Center of Industrial Computed Tomography, Nondestructive Testing of the Education Ministry of China, Chongqing University, Chongqing 400044, China

Abstract: 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

Authors:
raw_author_name: Bo Lei
raw_affiliation_string: School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
raw_author_name: Jinming Li
raw_affiliation_string: School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
raw_author_name: Ningning Wang
raw_affiliation_string: School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
raw_author_name: Haiyan Yu
raw_affiliation_string: School of Communications and Information Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China

Abstract: 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

Authors:
raw_author_name: M.Y. Abd-Rabbou
raw_affiliation_string: Mathematics Department, Faculty of Science, Al-Azhar University, Nasr City 11884, Cairo, Egypt
raw_author_name: E.M. Khalil
raw_affiliation_string: Department of Mathematics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi Arabia
raw_author_name: Saud Al-Awfi
raw_affiliation_string: Department of Physics, Faculty of Science, Taibah University, Medina, Saudi Arabia

Abstract: 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.
A time series anomaly detection method based on series-parallel transformers with spatial and temporal association discrepancies

Authors:
raw_author_name: Shiyuan Fu
raw_affiliation_string: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Xin Gao
raw_affiliation_string: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Feng Zhai
raw_affiliation_string: China Electric Power Research Institute Company Limited, Beijing 100192, China; School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China
raw_author_name: Baofeng Li
raw_affiliation_string: China Electric Power Research Institute Company Limited, Beijing 100192, China
raw_author_name: Bing Xue
raw_affiliation_string: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Jiahao Yu
raw_affiliation_string: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Zhihang Meng
raw_affiliation_string: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China
raw_author_name: Guangyao Zhang
raw_affiliation_string: School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, China

Abstract: Multivariate time series anomaly detection methods can discover malfunctions in a complex system by detecting anomalies in the monitoring data. Multivariate time series have complex temporal and spatial correlations. Existing methods that explicitly model both correlations only extract semantic information sequentially in a single temporal-spatial or spatial-temporal order. However, different orders have a significant influence on the method's performance. In addition, a static graph structure trained on normal data has difficulties capturing the change of correlations between features when anomalies occur, limiting the method's performance. This paper proposes the Spatial-Temporal Anomaly Transformer (STAT), which extracts semantic information comprehensively by combining both spatial-temporal and temporal-spatial orders. STAT uses the Temporal Anomaly Transformer and the Spatial Anomaly Transformer (SAT) to explicitly compute temporal and spatial association discrepancies to conduct anomaly detection. Since the topological structure is usually unknown, SAT adopts a trainable hypersphere to learn the prior-association between features on normal data and computes the distance between the attention weights and the center of the hypersphere as the spatial association discrepancy. AUC and two recently proposed evaluation metrics are used to compare STAT with various typical methods on public data sets, proving that our method can achieve state-of-the-art performance.
Anthropomorphic chatbots' for future healthcare services: Effects of personality, gender, and roles on source credibility, user satisfaction, and intention to use

Authors:
raw_author_name: Gianluigi Guido
raw_affiliation_string: Dept of Economic Science, University of Salento, Italy

Abstract: In healthcare, chatbots are used to automate interactions between healthcare professionals and patients. To make these conversations realistic, designers anthropomorphized chatbots (e.g., attributed human characteristics to chatbot) by equipping them with personality (warmth/competence), attributable gender (male/female), and role (prevention/diagnosis/therapy). Notwithstanding, scholarly inquiry has predominantly disregarded these factors when ascertaining the proclivity of individuals to utilize chatbots in healthcare. Based on human-machine interactions and counter-stereotypes theories, the aim of this research is to evaluate the conditions under which chatbot anthropomorphism can increase users' satisfaction and intention of using related services. Analyses conducted on a sample of 1147 users show that chatbots' personality does not directly influence intention of being used in healthcare. Instead, this relationship is mediated by chatbots' perceived credibility and user satisfaction, and moderated by chatbots' anthropomorphism, gender, and role. Specifically, when there is a mismatch between chatbot's gender and its stereotypical descriptive property (i.e., competence for female chatbots; warmth for male chatbots), the chatbot is more credible, satisfying, and inviting to use. Moreover, chatbots with female anthropomorphic characteristics or with a low level of anthropomorphism are better suited for prevention roles such as counseling, while chatbots with male characteristics are more appropriate for therapeutic roles.
FeSAD ransomware detection framework with machine learning using adaption to concept drift

Authors:
raw_author_name: Nikos Komninos
raw_affiliation_string: Department of Computer Science, School of Mathematics, Computer Science and Engineering, City, University of London, UK

Abstract: This paper proposes FeSAD, a framework that will allow a machine learning classifier to detect evolutionary ransomware. Ransomware is a critical player in the malware space that causes hundreds of millions of dollars of damage globally and evolves quickly. The evolution of ransomware in machine learning classifiers is often calculated as concept drift. Concept drift is dangerous as changes in the behavior of ransomware can easily lead to misclassifications, and misclassification can harm individuals and businesses. Our proposed framework consists of a feature selection layer, drift calibration layer and drift decision layer that allows a machine learning classifier to detect and classify concept drift samples reliably. We evaluate the FeSAD framework in various concept drift scenarios and observe its ability to detect drifting samples effectively. The FeSAD framework is also evaluated on its ability to extend the lifespan of a classifier. The results obtained by this research show that FeSAD can successfully and reliably classify ransomware and benign samples while under concept drift and can extend the time between retraining.
Quantitative analysis of facial soft tissue using weighted cascade regression model applicable for facial plastic surgery

Authors:
raw_author_name: Shamsi Mousa
raw_affiliation_string: Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran

Abstract: Localization of facial landmarks plays an important role in the measurement of facial metrics applicable for beauty analysis and facial plastic surgery. The first step in detecting facial landmarks is to estimate the face bounding box. Clinical images of patients' faces usually show intensity non-uniformity. These conditions cause common face detection algorithms do not perform well in face detection under varying illumination. To solve this problem, a modified fuzzy c-means (MFCM) algorithm is used under varying illumination modeling. The cascade regression method (CRM) has an appropriate performance in face alignment. This algorithm has two main drawbacks. (1) In the training phase, increasing the real data without considering normal data can lead to over-fitting. To solve this problem, a weighted CRM (WCRM) is presented. (2) In the test phase, using a mean shape causes the initial shape to be either near to or far from the face shape. To overcome this problem, a Procrustes-based analysis is presented. One of the most important steps in facial landmark localization is feature extraction. In this study, to increase detection accuracy of the cephalometric landmarks, local phase quantization (LPQ) is used for feature extraction in all three channels of RGB color space. Finally, the proposed algorithm is used to measure facial anthropometric metrics. Experimental results show that the proposed algorithm has a better performance in facial landmark localization than other compared algorithms.
Personal CAPTCHA-based authentication protocol

Authors:
raw_author_name: Natalia Krzyworzeka
raw_affiliation_string: AGH University of Krakow, 30 Mickiewicza Ave, PL-30-059 Krakow, Poland
raw_author_name: Lidia Ogiela
raw_affiliation_string: AGH University of Krakow, 30 Mickiewicza Ave, PL-30-059 Krakow, Poland
raw_author_name: Marek R. Ogiela
raw_affiliation_string: AGH University of Krakow, 30 Mickiewicza Ave, PL-30-059 Krakow, Poland

Abstract: In recent years, the number of personal accounts assigned to a single user has considerably increased. According recent study, the average employee had above 190 individual login credentials. The most important problems users face with such a large number of passwords are the strength of passwords and the user's ability to remember them. Researchers have shown that, in general, users are aware of what a secure password should look like, but may forgot these security measures in favour of more convenient passwords, depending largely on the type of account (le Bras, 2015). Repeating the same password across multiple platforms or creating it using dictionary words has also proven to be a common practice among many people. In this article, a new password reminder scheme will be presented. The goal was for the user to create a CAPTCHA-like image with a hidden meaning that only the user would be able to reveal. The image must have some connection to the person's experience and memory or unique knowledge. With such an image, presented each time the user logs in, he is asked to associate a password consisting of two or more words and a number. If the image is appropriately selected and associated with a strong association with the person's visual memory, the chances of recalling the long password he created in this way should not be a problem.