Reviewerly/exHarmony huggingface dataset:
Target Localization Method Based on Image Degradation Suppression and Multi-Similarity Fusion in Low-Illumination Environments

Frame buildings as important nodes of urban space. The include high-speed railway stations, airports, residences, and office buildings, which carry various activities and functions. Due to illumination irrationality and mutual occlusion between complex objects, low illumination situations frequently develop in these architectural environments. In this case, the location information of the target is difficult to determine. At the same time, the change in the indoor electromagnetic environment also affects the location information of the target. Therefore, this paper adopts the vision method to achieve target localization in low-illumination environments by feature matching of images collected in the offline state. However, the acquired images have serious quality degradation problems in low-illumination conditions, such as low brightness, low contrast, color distortion, and noise interference. These problems mean that the local features in the collected images are missing, meaning that they fail to achieve a match with the offline database images; as a result, the location information of the target cannot be determined. Therefore, a Visual Localization with Multiple-Similarity Fusions (VLMSF) is proposed based on the Nonlinear Enhancement And Local Mean Filtering (NEALMF) preprocessing enhancement. The NEALMF method solves the problem of missing local features by improving the quality of the acquired images, thus improving the robustness of the visual positioning system. The VLMSF method solves the problem of low matching accuracy in similarity retrieval methods by effectively extracting and matching feature information. Experiments show that the average localization error of the VLMSF method is only 8 cm, which is 33.33% lower than that of the Kears-based VGG-16 similarity retrieval method. Meanwhile, the localization error is reduced by 75.76% compared with the Perceptual hash (Phash) retrieval method. The results show that the method proposed in this paper greatly alleviates the influence of low illumination on visual methods, thus helping city managers accurately grasp the location information of targets under complex illumination conditions.
Performance Evaluation of a Quadcopter by an Optimized Proportional–Integral–Derivative Controller

This paper presents an analysis of a quadcopter’s stability and its sensors’ data reading using an IMU (inertial measurement unit). Firstly, the angular velocity and acceleration data are read by an Stm® development board embedded with an Stm32f401ccu6 microcontroller. The altitude of the measurement instrument platform was determined using an MS5611 barometric pressure sensor combined with a temperature sensor. The quadcopter’s control was achieved by connecting a brushless DC motor to the Stm® board, which received four PWM (pulse-width modulation) signals via the output port. An electronic governor was utilized to control the brushless DC motor, while a pre-existing remote control was designated as the transmitter. The quadcopter receiver received a 2.4 GHz signal from the transmitter using the BLE (Bluetooth low-energy) protocol, which was used to ensure the simultaneous operation of the four brushless DC motors. Finally, a PID (proportional–integral–derivative) controller was employed for parameter adjustment. The collected PID parameter program was developed in the Simulink software as a simulation platform, allowing for the execution of the Simulink model on the Stm® MCU. The Stm® module facilitates monitoring of the performance of the UAV (unmanned aerial vehicle) and enables immediate parameter adjustments to ensure flight stability. This research aims to reduce calculation errors in sensor and controller usage and improve the efficacy of the remote machine module for future industrial applications.
Amortized Bayesian Meta-Learning with Accelerated Gradient Descent Steps

Recent meta-learning models often learn priors from observed tasks using a network optimized via stochastic gradient descent (SGD), which usually takes more training steps to convergence. In this paper, we propose an accelerated Bayesian meta-learning structure with a stochastic inference network (ABML-SIN). The proposed model aims to solve the training procedure of Bayesian meta-learning to improve the training speed and efficiency. Current approaches of meta-learning hardly converge within a few descent steps, owing to the small number of training samples. Therefore, we introduce an accelerated gradient descent learning network based on teacher–student architecture to learn the meta-latent variable θt for task t. With this amortized fast inference network, the meta-learner is able to learn the task-specific latent θt within a few training steps; thus, it improves the learning speed of the meta-learner. To refine the latent variables generated from the transductive amortization network of the meta-learner, SIN—followed by a conventional SGD-optimized network—is introduced as the student–teacher network to online-update the parameters. SIN extracts the local latent variables and accelerates the convergence of the meta-learning network. Our experiments on simulation data demonstrate that the proposed method provides generalization and scalability on unseen samples, and produces competitive/superior uncertainty estimations on few-shot learning tasks on two widely adopted 2D datasets with fewer training epochs compared to the state-of-the-art meta-learning approaches. Furthermore, the parameters generated by SIN act as perturbations on latent weights, enhancing the probability of accelerating the training efficiency of the meta-learner. Extensive qualitative experiments show that our method performs well across different meta-learning tasks in both simulated and real-world circumstances.
Artificial Intelligence Model to Detect and Classify Arabic Dialects

The Arabic Dialect (AD) detection method involves analyzing the matching sound wave for various characteristics that identify the speaker's dialect.Among these features are accent, intonation, stress, vowel length, vowel type, and other acoustic characteristics.Data from different speakers of different dialects is usually used in training machine learning algorithms.Based on this data, an algorithm is created to accurately identify the speaker's dialect.Arabic dialects can be detected and classified using several models and techniques available in literature.Various models have been proposed from different perspectives.Therefore, this paper discussed different studies about AD for building an understanding of conceptual deep learning model to detect and classify Arabic dialects.The model captured the semantic, syntactic, and phonological characteristics of these dialects using Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs).The proposed model consists of six stages: Natural Language Processing (NLP) stage, feature engineering techniques, neural networks, language models, optimization techniques, and evaluation techniques.Each stage of the proposed model has several techniques that can be used to detect and classify AD.The accuracy and capability of the proposed model will be performed in the future work.
SUShe: simple unsupervised shadow removal

Abstract Shadow removal is an important problem in computer vision, since the presence of shadows complicates core computer vision tasks, including image segmentation and object recognition. Most state-of-the-art shadow removal methods are based on complex deep learning architectures, which require training on a large amount of data. In this paper a novel and efficient methodology is proposed aiming to provide a simple solution to shadow removal, both in terms of implementation and computational cost. The proposed methodology is fully unsupervised, based solely on color image features. Initially, the shadow region is automatically extracted by a segmentation algorithm based on Electromagnetic-Like Optimization. Superpixel-based segmentation is performed and pairs of shadowed and non-shadowed regions, which are nearest neighbors in terms of their color content, are identified as parts of the same object. The shadowed part of each pair is relighted by means of histogram matching, using the content of its non-shadowed counterpart. Quantitative and qualitative experiments on well-recognized publicly available benchmark datasets are conducted to evaluate the performance of proposed methodology in comparison to state-of-the-art methods. The results validate both its efficiency and effectiveness, making evident that solving the shadow removal problem does not necessarily require complex deep learning-based solutions.
Information Fusion for Radar Signal Sorting with the Distributed Reconnaissance Receivers

The conventional method of centralizing information fusion is commonly employed for sorting radar signals in reconnaissance receivers. However, challenges arise when the distance between reconnaissance receivers and the fusion center is distant, or when the fusion center is compromised by hostile forces. To address these issues, this paper proposes a novel distributed information fusion method. In this method, each reconnaissance receiver is restricted to accessing adjacent nodes within an undirected graph for information transmission and local computation. The distributed Dempster’s combination rule and the cautious conjunctive rule are implemented using weight functions and consensus algorithms. Furthermore, an innovative outlier detection algorithm is incorporated into the fusion process to enhance its robustness. Experimental results demonstrate that the proposed method effectively improves the accuracy of radar signal sorting. When the sorting accuracy of a single reconnaissance receiver is equal to or higher than 60%, both fusion rules achieve a sorting accuracy of 100%. Even when the sorting accuracy of a single reconnaissance receiver is as low as 50%, the fused result still maintains a sorting accuracy of over 97%.
Priorities for the development of the Chinese education management system in the era of digitalisation

The active process of digitalisation of all spheres of human activity, defined as the Industrial Revolution 4.0, has a direct impact on the transformation of the education system and learning management in the direction of meeting the need for lifelong learning and adapting to the dynamically changing requirements of the global labour market. This paper aims to determine the priorities of developing the education management system in the digitalisation era and study their peculiarities. The sample of respondents was formed by simple randomisation, consisted of two subject groups and included 327 students and 295 university professors. Respondents’ responses were evaluated using a 5-point Likert scale and subsequent statistical processing of the results obtained based on Student’s t-test to determine their statistical significance and significance. The conducted research revealed that the students considered education management’s most important strategic tasks to be those aimed directly at developing distance learning and its technological support. Shared goals and issues of digitalising scientific, methodological and organisational support of the educational process for lifelong learning are more critical for the teachers. The authors showed the paramount importance of teachers’ professional development and digital competence in ensuring the digitalisation of education management. They developed a hierarchical model of priorities for the strategic development of the higher education management system. The model makes it possible to optimise the development of the education management system in the era of digitalisation, manage pedagogical resources more effectively.
Adaptive 3D Reversible Data Hiding Technique Based on the Cumulative Peak Bins in the Histogram of Directional Prediction Error

Reversible data hiding (RDH) is crucial in modern data security, ensuring confidentiality and tamper-proofness in various industries like copyright protection, medical imaging, and digital forensics. As technology advances, RDH techniques become essential, but the trade-off between embedding capacity and visual quality must be heeded. In this paper, the relative correlation between the pixel’s local complexity and its directional prediction error is employed to enhance an efficient RDH without using a location map. An embedding process based on multiple cumulative peak region localization (MCPRL) is proposed to hide information in the 3D-directional prediction error histogram with a lower local complexity value and avoid the underflow/overflow problems. The carrier image is divided into three color channels, and then each channel is split into two non-overlapping sets: blank and shadow. Two half-directional prediction errors (the blank set and the shadow set) are constructed to generate a full-directional prediction error for each color channel belonging to the host image. The local complexity value and directional prediction error are critical metrics in the proposed embedding process to improve security and robustness. By utilizing these metrics to construct a 3D stego-Blank Set, the 3D stego-shadow Set will be subsequently constructed using the 3D blank set. The proposed technique outperforms other state-of-the-art techniques in terms of embedding capacity, image quality, and robustness against attacks without an extra location map. The experimental results illustrate the effectiveness of the proposed method for various 3D RDH techniques.
Sensor Virtualization for Anomaly Detection of Turbo-Machinery Sensors—An Industrial Application

We apply a Granger causality and auto-correlation analysis to train a recurrent neural network (RNN) that acts as a virtual sensor model. These models can be used to check the status of several hundreds of sensors during turbo-machinery units’ operation. Checking the health of each sensor is a time-consuming activity. Training a supervised algorithm is not feasible because we do not know all the failure modes that the sensors can undergo. We use a semi-supervised approach and train an RNN (LSTM) on non-anomalous data to build a virtual sensor using other sensors as regressors. We use the Granger causality test to identify the set of input sensors for a given target sensor. Moreover, we look at the auto-correlation function (ACF) to understand the temporal dependency in data. We then compare the predicted signal vs. the real one to raise (in case) an anomaly in real time. Results report 96% precision and 100% recall.
Personalized facial makeup transfer based on outline correspondence

Abstract Most existing makeup transfer techniques focus on light makeup styles, which limits the task of makeup transfer to color manipulation issues such as eye shadow and lip gloss. However, the makeup in real life is diverse and personalized, not only the most basic foundation, eye makeup, but also the painted patterns on the face, jewelry decoration and other personalized makeup. Inspired by the painting steps of drawing the outline first and then coloring, we propose a makeup transfer network for personalized makeup, which realizes face makeup transfer by learning outline correspondence. Specifically, we propose the outline feature extraction module and outline loss that can promote outline correspondence. Our network can not only transfer daily light makeup, but also complete transfer for complex facial painting patterns. Experiments show that our method can obtain visually more accurate makeup transfer results. Quantitative and qualitative experimental results show that the method proposed in this paper achieves superior results in extreme makeup transfer compared to the state‐of‐the‐art methods.
Research on <scp>3D</scp> advertising placement based on virtual reality simulation

Abstract Virtual reality based on computer 3D technology has brought revolutionary changes in advertising creativity. To enhance the economic value of advertising itself, it is necessary to provide personalized and precise advertising placement for users from a massive advertising database. A recommendation system based on deep graph convolutional networks is a personalized system that can recommend based on their attributes and historical records. However, complex models are required to learn higher‐order feature information, and the mining of user interaction behavior is insufficient. This article proposes a graph convolutional network with multi‐head attention embedding for 3D advertising placement. We construct advertising graph data based on user behavior and multi‐feature embedding mapping. To exploit higher‐order features, a multi‐head attention mechanism is embedded in the graph convolutional network. The experimental results show that the proposed model achieves better performance compared to other depth models on Criteo and Avazu datasets. We will deploy the entire model on the server to achieve precise advertising placement.
Optimal ranking and directional signature classification using the integral strategy of multi-objective optimization-based association rule mining of multi-omics data

Introduction: Association rule mining (ARM) is a powerful tool for exploring the informative relationships among multiple items (genes) in any dataset. The main problem of ARM is that it generates many rules containing different rule-informative values, which becomes a challenge for the user to choose the effective rules. In addition, few works have been performed on the integration of multiple biological datasets and variable cutoff values in ARM. Methods: To solve all these problems, in this article, we developed a novel framework MOOVARM (multi-objective optimized variable cutoff-based association rule mining) for multi-omics profiles. Results: In this regard, we identified the positive ideal solution (PIS), which maximized the profit and minimized the loss, and negative ideal solution (NIS), which minimized the profit and maximized the loss for all gene sets (item sets), belonging to each extracted rule. Thereafter, we computed the distance (d +) from PIS and distance (d -) from NIS for each gene set or product. These two distances played an important role in determining the optimized associations among various pairs of genes in the multi-omics dataset. We then globally estimated the relative closeness to PIS for ranking the gene sets. When the relative closeness score of the rule is greater than or equal to the pre-defined threshold value, the rule can be considered a final resultant rule. Moreover, MOOVARM evaluated the relative score of the rule based on the status of all genes instead of individual genes. Conclusions:MOOVARM produced the final rank of the extracted (multi-objective optimized) rules of correlated genes which had better disease classification than the state-of-the-art algorithms on gene signature identification.
Communication-Efficient and Privacy-Preserving Verifiable Aggregation for Federated Learning

Federated learning is a distributed machine learning framework, which allows users to save data locally for training without sharing data. Users send the trained local model to the server for aggregation. However, untrusted servers may infer users' private information from the provided data and mistakenly execute aggregation protocols to forge aggregation results. In order to ensure the reliability of the federated learning scheme, we must protect the privacy of users' information and ensure the integrity of the aggregation results. This paper proposes an effective secure aggregation verifiable federated learning scheme, which has both high communication efficiency and privacy protection function. The scheme encrypts the gradients with a single mask technology to securely aggregate gradients, thus ensuring that malicious servers cannot deduce users' private information from the provided data. Then the masked gradients are hashed to verify the aggregation results. The experimental results show that our protocol is more suited for bandwidth-constraint and offline-users scenarios.
Diagnosing of Malignant and Benign Breast Cancers: Using a Metasearch Neural Propagation Classifier

One of the world’s most significant health issues is breast cancer. There is a maximal probability of survival if anomalies in breast cancer are diagnosed early. For such an early prediction, we could choose mammography. Mammography is among the more effective approaches to screening and identification of breast cancer. The classification of medical data is an easy way to diagnose the disease using mammography images. Many authors have experimented with using different classifiers in the area of Mammography classification. But the machine learning classifiers cannot yield satisfactory accuracy. We have proposed a Meta Search Neural Propagation Classifier (MNPC) to detect breast cancer. The mammogram image is first segmented using the Otsu method, and then the features are extracted by utilising Discrete Wavelet Transform (DWT). The extracted features are sent to the Meta Search Neural Propagation Classifier, which categorises the mammogram’s image malignant or benign. The analysis of the images gives that for detecting the malignant, and benign; the proposed method is accurate and reliable and the specificity, sensitivity, F-score, accuracy, precision, and the classifier’s efficiency are enhanced.
Hand‐drawn anime line drawing colorization of faces with texture details

Abstract Automatic or semi‐automatic colorization can reduce the burden of illustrators in color illustration production, which is a research area with significant market demand. Texture details in eyes and hair influence the impression of character illustrations. Generally, these details are not expressed in line drawings. Many existing automatic or semi‐automatic colorization methods do not target hand‐drawn line drawings and it is difficult to paint texture details on such drawings. In this paper, we propose the semi‐automatic colorization of character line drawings around faces with texture details. Our method uses a reference image as a color hint and transfers the textures of the reference image to a line drawing. To achieve this, our method uses semantic segmentation masks to match parts of the line drawing with the same parts of the reference image. We create two types of segmentation datasets to train a segmentation network that creates segmentation masks. We transfer texture details to a hand‐drawn line drawing by mapping each part of the reference image to the corresponding part of the line drawing using segmentation masks. We show that our method is more effective for hand‐drawn line drawings than existing methods using qualitative and quantitative evaluations.