Reviewerly/exHarmony huggingface dataset:
High-Accuracy 3D Contour Measurement by Using the Quaternion Wavelet Transform Image Denoising Technique

In this paper, we propose an image denoising algorithm based on the quaternion wavelet transform (QWT) to address sinusoidal fringe images under strong noise in structured light 3D profilometry. The analysis of a quaternion wavelet shows that the amplitude image of the quaternion wavelet is easily affected by noise. However, the three phase images, which mainly reflect edge and texture information, are randomly and disorderly distributed with respect to noise. The QWT denoising algorithm is suitable for processing sinusoidal fringe images of complex structures in a high-accuracy 3D measurement system. Sinusoidal fringe images are collected and denoised by using the QWT algorithm and classical Gaussian smoothing (GS) denoising algorithm, and GS is used as a reference for the QWT algorithm. The results indicate that the standard deviation is reduced from 0.1448 for raw sinusoidal fringe images to 0.0192, and the signal-to-noise ratio is improved from 4.6213 dB to 13.3463 dB by using the QWT algorithm. The two algorithms have the same denoising effect for a surface with less information. For a surface with rich information, the details of the 3D contour are lost because of the image “blurring” caused by using the GS algorithm, while all edge details of the 3D contour are reconstructed by using the QWT denoising algorithm because of its characteristic of information and noise being separated from the source. For the measured face mask, the error is less than ±0.02 mm. In addition, it takes less than 20 s to run the QWT algorithm to process eight sinusoidal fringe images, which meets the requirements of high-precision measurements.
A novel robotic 6DOF pose measurement strategy for large-size casts based on stereo vision

Purpose This paper aims to study six degrees-of-freedom (6DOF) pose measurement of reflective metal casts by machine vision, analyze the problems existing in the positioning of metal casts by stereo vision sensor in unstructured environment and put forward the visual positioning and grasping strategy that can be used in industrial robot cell. Design/methodology/approach A multikeypoints detection network Binocular Attention Hourglass Net is constructed, which can complete the two-dimensional positioning of the left and right cameras of the stereo vision system at the same time and provide reconstruction information for three-dimensional pose measurement. Generate adversarial networks is introduced to enhance the image of local feature area of object surface, and the three-dimensional pose measurement of object is completed by combining RANSAC ellipse fitting algorithm and triangulation method. Findings The proposed method realizes the high-precision 6DOF positioning and grasping of reflective metal casts by industrial robots; it has been applied in many fields and solves the problem of difficult visual measurement of reflective casts. The experimental results show that the system exhibits superior recognition performance, which meets the requirements of the grasping task. Research limitations/implications Because of the chosen research approach, the research results may lack generalizability. The proposed method is more suitable for objects with plane positioning features. Originality/value This paper realizes the 6DOF pose measurement of reflective casts by vision system, and solves the problem of positioning and grasping such objects by industrial robot.
Intelligent Modeling of the Incineration Process in Waste Incineration Power Plant Based on Deep Learning

The incineration process in waste-to-energy plants is characterized by high levels of inertia, large delays, strong coupling, and nonlinearity, which makes accurate modeling difficult. Therefore, an intelligent modeling method for the incineration process in waste-to-energy plants based on deep learning is proposed. First, the output variables were selected from the three aspects of safety, stability and economy. The initial variables related to the output variables were determined by mechanism analysis and the input variables were finally determined by removing invalid and redundant variables through the Lasso algorithm. Secondly, each delay time was calculated, and a multi-input and multi-output model was established on the basis of deep learning. Finally, the deep learning model was compared and verified with traditional models, including LSSVM, CNN, and LSTM. The simulation results show that the intelligent model of the incineration process in the waste-to-energy plant based on deep learning is more accurate and effective than the traditional LSSVM, CNN and LSTM models.
ISM-based analysis of VR-AEC adoption barriers and their inner mechanisms

Purpose As an emerging visualization technology, virtual reality (VR) falls into the dilemma of having great potential but a low adoption degree in the architectural, engineering and construction (AEC) industry. However, few studies paid attention to studying barriers affecting VR’s adoption and their inner mechanisms. This makes AEC users hard to catch the key points for VR’s implementations. This study aims to get a clear structure of these barriers and provide insights for the improvement. Design/methodology/approach First, 12 major VR-AEC adoption barriers were identified by a systematic literature review and expert interviews (EI). Second, EI and similarity aggregation method were conducted to achieve reliable barrier relationships. Third, interpretive structural modeling was used to establish a multi-level model for barriers. Finally, ten crucial barriers were targeted with a comprehensive strategy framework. Findings The findings help AEC stakeholders get a thorough understanding of the VR-AEC adoption barriers. Besides, the inner mechanism among barriers is revealed and analyzed, followed by a systematic strategy framework. It is anticipated that users could conduct more effective VR-AEC promotions in the future. Originality/value This paper is the first to propose a comprehensive literature review on the VR-AEC adoption barriers. In addition, this paper is novel in building a hierarchy model that explores barriers’ inner mechanism, where structural strategies are proposed.
SREFBN: Enhanced feature block network for single‐image super‐resolution

Deep learning has assisted the field of single-image super-resolution (SR) in achieving new heights. However, the task of restoring a high-resolution (HR) image from a highly degraded low-resolution (LR) image is sophisticated due to poor image restoration quality. A novel and effective lightweight SR method is presented as super-resolution via an enhanced feature block network (SREFBN) that successfully reconstructs an HR image using a corresponding LR image with a purposed deep residual block. In addition, a novel shared parameters approach in the top-down pathway among low-level feature maps is introduced. The experimental results prove that SREFBN achieves remarkable performance. The presented framework requires lower computational cost and outperforms many state-of-the-art methods. It is also highly adaptable with low-end devices, requiring lower multiplication and adding operations. A trade-off comparison between the number of parameters, execution time, and accuracies is given while also showing different variations of our approach to prove the effectiveness and reliability of the shared parameters. Most importantly, the results indicate that our framework has gained state-of-the-art performance on larger scales 3 and 4. Code is available at https://github.com/curzii23/SREFBN.
Sequential Detection of Microgrid Bad Data via a Data-Driven Approach Combining Online Machine Learning With Statistical Analysis

Bad data is required to be detected and removed from the microgrid data stream because it misleads the decision-making of the Energy Management Systems (EMS) and puts the microgrid at risk of instability. In this paper, the authors propose a sequential detection method that combines three data mining algorithms, that is the Online Sequential Extreme Learning Machine (OSELM), statistical analysis within a sliding time window, and the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). After sequential data training, OSELM is used to construct an online updated error-filtering map to extract the electrical feature of the microgrid data sequence. Meanwhile, the statistical features, i.e. the surge of the variance and the corresponding correlation coefficients under a sliding time window are first proposed as another two complementary feature dimensions. The three-dimensional features are finally analyzed by DBSCAN to discriminate the bad data. The detection performance of this approach is verified by the data sequence collected from a four-terminal ring-shaped DC microgrid prototype. Compared with bad data detection using a single electrical feature or only statistical features, this approach shows the best performance. Moreover, it can be further applied to the online detection of microgrid bad data in the future.
Low‐light image haze removal with light segmentation and nonlinear image depth estimation

Hazy image obtained in the low-light environment has the characteristics of low contrast, non-uniform illumination, color cast and much noise. In this paper, a method is put forward which can be properly applied to recover low-light hazy images. The original image is first decomposed into glow layer and haze layer with a modified color channel transformation for glow artifacts and color balanced. A new light segmentation function is proposed next by using gamma correction of channel difference and setting threshold levels to determine if the pixel belongs to light source regions. Then the ambient illuminance map is estimated using maximum reflectance prior to computing the atmosphere light in the light and non-light regions. Finally, a novel nonlinear image depth estimation model is established to build the relationship between the image depth map and three image features including luminance, saturation and gradient map for the light areas. The experimental results prove that the dehazing algorithm is reliable for removing haze and glow artifacts of active light sources, reducing much noise and improving the visibility.
Shrieking, Shrugging, and the Australian Plan

We observe that Jc Beall’s shrieking and shrugging strategy gives us an opportunity to reflect on the Australian plan for negation in FDE, a basic subclassical logic that is used in Beall’s argument for subclassical logics. An implication of our observation is applied to a recent defense of the Australian plan for negation by Francesco Berto and Greg Restall.
Psychological Difficulties of Adopting Distance Education Technologies in Higher Education During the COVID-19

The subject of the study is the psychological difficulties of adopting distance education technologies by university students. The materials of exploratory research obtained on a sample of students from several universities of the Sakha Republic (Yakutia) are presented. The study involved respondents aged 19 to 22 (N = 86), 39% of whom were males. In the research, the unstructured interview method was used, during which we asked students to share in detail about their user experience, describing not only the learning process itself but the whole learning context in general. Considering the results using the TAM model, it can be assumed that students are not satisfied with any of the components. Distance learning technologies are not perceived by students as easy to use, and there is a low assessment of the perceived usefulness of these technologies. The attitude towards technology is also rather negative. From the point of view of student acceptance of technology, it can be said that distance learning causes some difficulties associated with cognitive, and emotional aspects, as well as interaction in a virtual environment, and the learning process. Learning creates anxiety and dissatisfaction with the learning process itself.
An Efficient Detection Approach for Unmanned Aerial Vehicle (UAV) Small Targets Based on Group Convolution

To solve the problem that small drones in the sky are easily confused with background objects and difficult to detect, according to the characteristics of irregular movement, small size, and changeable shape of drones, using a regional target recognition algorithm, the structure characteristics of Group Convolution (GC) in Resnext50 are absorbed. The optimized GC-faster-RCNN is obtained by improving the Fast-RCNN algorithm and the following methods are performed. First, a clustering method is used to analyze the dataset, and appropriate prior bounding box types are obtained. Second, the Resnext50 is used to replace the original feature extraction network, and the improved channel attention mechanism is integrated into its network output to enhance its feature map information. Then, we calculate its effective receptive field according to the Feature Pyramid Network (FPN) structure and redesign the prior bounding box of the corresponding size to construct a multi-scale detection network for small targets. Experiments show that the algorithm has a recognition accuracy of up to 94.8% under 1080 P image quality, and a recognition speed of 8FPS, which can effectively detect the positions of 1–5 small UAVs in a picture. This method provides an effective positioning detection for low-altitude UAVs.
An efficient algorithm for data parallelism based on stochastic optimization

Deep neural network models can achieve greater performance in numerous machine learning tasks by raising the depth of the model and the amount of training data samples. However, these essential procedures will proportionally raise the cost of training deep neural network models. Accelerating the training process of deep neural network models in a distributed computing environment has become the most often utilized strategy for developers in order to better cope with a huge quantity of training overhead. The current deep neural network model is the stochastic gradient descent (SGD) technique. It is one of the most widely used training techniques in network models, although it is prone to gradient obsolescence during parallelization, which impacts the overall convergence. The majority of present solutions are geared at high-performance nodes with minor performance changes. Few studies have taken into account the cluster environment in high-performance computing (HPC), where the performance of each node varies substantially. A dynamic batch size stochastic gradient descent approach based on performance-aware technology is suggested to address the aforesaid difficulties (DBS-SGD). By assessing the processing capacity of each node, this method dynamically allocates the minibatch of each node, guaranteeing that the update time of each iteration between nodes is essentially the same, lowering the average gradient of the node. The suggested approach may successfully solve the asynchronous update strategy’s gradient outdated problem. The Mnist and cifar10 are two widely used image classification benchmarks, that are employed as training data sets, and the approach is compared with the asynchronous stochastic gradient descent (ASGD) technique. The experimental findings demonstrate that the proposed algorithm has better performance as compared with existing algorithms.
SIAKAD machine learning for correcting errors in speaking Arabic

Information technology provides a lot of convenience for humans in completing their tasks and getting results according to targets. In line with that, language teachers have a duty to find out the level of language skills and forms of language errors in students. Machine Learning as part of technology can be maximized to detect forms of Arabic speaking error in students. This study was conducted with a qualitative approach. Data were collected via SIAKAD machine learning containing Arabic videos. Based on the results, the SIAKAD machine learning uncovered several Arabic speaking errors such as grammar, pronunciation, shifat al-huruf, vowels, word expression, and concatenated sentences. Therefore, machine learning with various types can be maximized in Arabic learning which ultimately leads to technological developments that must be accompanied by the ability of teachers to be skilled in operationalizing technology. &#x0D; Keywords: Arabic Language Education; Machine Learning; Speaking Errors
Collaborative filtering recommendation using fusing criteria against shilling attacks

The collaborative filtering recommendation technique (CFR) is one of the techniques used in recommended systems, in which the most proximal neighbours to a target user are selected. Their profiles are used to predict rating for items as yet unrated by that target user. However, malicious users inject fake user profiles to destroy the security and reliability of the recommender systems, which is called shilling attacks. Therefore, it is crucial to improve the recommendation technique against shilling attacks. Malicious users use a single method to perform shilling attacks. Intuitively, fusing multiple criteria to construct CFR can effectively resist shilling attacks. A novel CFR is proposed against shilling attacks (called CFR-F). In our approach, a similar interest users’ resource set is obtained first by integrating users’ dynamic interest model and social tags. Then, a similar interest user resource set is selected according to a strategy that selects preference influence weight based on user background. Our experimental results show that our approach can recommend accurate information resources and has a lower Mean Absolute Error (MAE) and Average Prediction Shift (APS) than traditional techniques by 50% and 20%, respectively.
An Improved Gumowski–Mira Map with Symmetric Lyapunov Exponents

The symmetric Lyapunov exponents (LEs) are known to be an inherent property of continuous-time conservative systems. However, the research on this interesting phenomenon in a discrete-time chaotic map has not been reported. Thus, this paper presents an improved 2D chaotic map based on Gumowski–Mira (GM) transformation, which has a stable fixed point or an unstable fixed point depending on its control parameters. Furthermore, it can display symmetric LEs and an infinite number of coexisting attractors with different amplitudes and different shapes. To demonstrate the complex dynamics of the 2D chaotic map, this paper studies its control parameters related to dynamical behaviors employing numerical analysis methods. Then, the hardware implementation based on STM32 platform is established for illustrating the numerical simulation results. Next, the random performance of the 2D chaotic map is tested by NIST FIPS140-2 suite. Finally, an image encryption algorithm based on the 2D chaotic map is designed, and the results obtained reveal that the proposed chaotic map has excellent randomness and is more suitable for many chaos-based image encryptions.
A Decade of <i>GigaScience</i>: The Challenges of Gigapixel Pathology Images

Abstract In the last decade, the field of computational pathology has advanced at a rapid pace because of the availability of deep neural networks, which achieved their first successes in computer vision tasks in 2012. An important driver for the progress of the field were public competitions, so called ‘Grand Challenges’, in which increasingly large data sets were offered to the public to solve clinically relevant tasks. Going from the first Pathology challenges, which had data obtained from 23 patients, to current challenges sharing data of thousands of patients, performance of developed deep learning solutions has reached (and sometimes surpassed) the level of experienced pathologists for specific tasks. We expect future challenges to broaden the horizon, for instance by combining data from radiology, pathology and tumor genetics, and to extract prognostic and predictive information independent of currently used grading schemes.
Formation of software design skills among software engineering students

The study focuses on one of the mobile-oriented environment competence components for software engineering (SE) students. It has been demonstrated that the implementation of the higher education standard for SE bachelors has generated a number of issues in terms of ensuring training quality, principally due to a lack of specification for both skills and learning outcomes. Designing a precise framework of professional competencies for SE bachelors is one method to overcome these issues. The research examines methods for developing K14 (the ability to participate in software design, including modeling (formal description) of its structure, behavior, and working processes), a critical particular professional competency for future software engineers. Recommendations for software design teaching techniques, learning content, modeling and design tools, and assessment of the level of formation of relevant competence are developed based on a historical and genetic review of software design training among SE students in the UK, USA, Canada, Australia, New Zealand, and Singapore. The industrial-style software design training (studio training) is used as an example. The transition from architectural to detailed design, as well as project implementation, are discussed. The study's future prospects include substantiating the third engineering component of SE – software construction (after requirements engineering and design engineering).
Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion

Recent years have witnessed the extraordinary development of automatic speaker verification (ASV). However, previous works show that state-of-the-art ASV models are seriously vulnerable to voice spoofing attacks, and the recently proposed high-performance spoofing countermeasure (CM) models only focus solely on the standalone anti-spoofing tasks, and ignore the subsequent speaker verification process. How to integrate the CM and ASV together remains an open question. A spoofing aware speaker verification (SASV) challenge has recently taken place with the argument that better performance can be delivered when both CM and ASV subsystems are optimized jointly. Under the challenge's scenario, the integrated systems proposed by the participants are required to reject both impostor speakers and spoofing attacks from target speakers, which intuitively and effectively matches the expectation of a reliable, spoofing-robust ASV system. This work focuses on fusion-based SASV solutions and proposes a multi-model fusion framework to leverage the power of multiple state-of-the-art ASV and CM models. The proposed framework vastly improves the SASV-EER from 8.75% to 1.17\%, which is 86% relative improvement compared to the best baseline system in the SASV challenge.
Learning to Limit Data Collection via Scaling Laws: A Computational Interpretation for the Legal Principle of Data Minimization

Modern machine learning systems are increasingly characterized by extensive personal data collection, despite the diminishing returns and increasing societal costs of such practices. Yet, data minimisation is one of the core data protection principles enshrined in the European Union's General Data Protection Regulation ('GDPR') and requires that only personal data that is adequate, relevant and limited to what is necessary is processed. However, the principle has seen limited adoption due to the lack of technical interpretation.