Reviewerly/exHarmony huggingface dataset:
K-RBBSO Algorithm: A Result-Based Stochastic Search Algorithm in Big Data

Clustering is widely used in client-facing businesses to categorize their customer base and deliver personalized services. This study proposes an algorithm to stochastically search for an optimum solution based on the outcomes of a data clustering process. Fundamentally, the aforementioned goal is achieved using a result-based stochastic search algorithm. Hence, shortcomings of existing stochastic search algorithms are identified, and the k-means-initiated rapid biogeography-based silhouette optimization (K-RBBSO) algorithm is proposed to overcome them. The proposed algorithm is validated by creating a data clustering engine and comparing the performance of the K-RBBSO algorithm with those of currently used stochastic search techniques, such as simulated annealing and artificial bee colony, on a validation dataset. The results indicate that K-RBBSO is more effective with larger volumes of data compared to the other algorithms. Finally, we describe some prospective beneficial uses of a data clustering algorithm in unsupervised learning based on the findings of this study.
DYNAMIC FACIAL ANALYSIS BY COMMON SPACE VARIATIONAL RECURRENT DEEP EMBEDDING

In human–machine interaction, non-verbal communication through the recognition of facial expressions is a process that operates on a time scale of the order of milliseconds, bringing a new dimension to how machines can affect modern social life. The ambiguity at this time scale is significant, necessitating that humans and machines rely on rich cognitive abilities rather than slow symbolic conclusions, making it necessary for humans and machines to rely on rich perceptual skills rather than sluggish figurative conclusions, especially in critical applications such as elderly care and supportive procedures for people with mobility or communication problems. This research field includes interdisciplinary contributions and specialized support from cognitive areas of psychology, sociology, linguistics, industrial design, and informatics. This paper presents an innovative emotion recognition system through dynamic facial analysis for optimal decision-making in non-verbal communication. It is an innovative model of artificial vision, for the recognition of emotions in the human–machine interaction, combining the look and the basic facial expressions of a person. This approach implements and proposes for the first time in the literature the application of a Common Space Variational Recurrent Deep Embedding (CSVRdE) intelligent learning system. The suggested technique simplifies the process of training customized extraction functions for appropriate image transformations in complicated neural network architectures, resulting in increased learning consistency, superior prediction reliability, and outstanding classification efficiency. Specifically, the suggested technique produces extremely accurate findings without reoccurring difficulties of unknown origin since all characteristics in the dataset are effectively managed.
Diversity Preference-Aware Link Recommendation for Online Social Networks

Link recommendation, such as “People You May Know” on LinkedIn, recommends links to connect unlinked online social network users. Existing link recommendation methods tend to recommend similar friends to a user but overlook the fact that different users have different diversity preferences when making friends in a social network. That is, some users prefer to connect with friends of similar profiles while some others prefer to befriend those of different profiles. For example, Jane prefers to connect with those primarily majoring in mathematics, whereas Jack prefers to befriend those in many different majors. To address this research gap, we define and operationalize the concept of diversity preference and propose a new link recommendation problem: the diversity preference-aware link recommendation problem. We then develop a novel link recommendation method that recommends friends to cater each user’s diversity preference. Our study informs researchers and practitioners about a new perspective on link recommendation – diversity preference-aware link recommendation. Our study also suggests that recommender systems need to be designed to meet each user’s diversity preference rather than indiscriminately increase the diversity of recommended items for every user.
The Simulation-Based Approach for Random Speckle Pattern Representation in Synthetically Generated Video Sequences of Dynamic Phenomena

Structural health monitoring systems that employ vision data are under constant development. Generating synthetic vision data is an actual issue. It allows, for example, for obtention of additional data for machine learning techniques or predicting the result of observations using a vision system with a reduced number of experiments. A random speckle pattern (RSP) fixed on the surface of the observed structure is usually used in measurements. The determination of displacements of its areas using digital image correlation (DIC) methods allows for extracting the structure’s deformation in both static and dynamic cases. An RSP modeling methodology for synthetic image generation is developed within this paper. The proposed approach combines the finite element modeling technique and simulation results with the Blender graphics environment to generate video sequences of the mechanical structure with deformable RSP attached to it. The comparative analysis showed high compliance of the displacement between the synthetic images processed with the DIC method and numerical data.
SecMDGM: Federated Learning Security Mechanism Based on Multi−Dimensional Auctions

As a newly emerging distributed machine learning technology, federated learning has unique advantages in the era of big data. We explore how to motivate participants to experience auctions more actively and safely. It is also essential to ensure that the final participant who wins the right to participate can guarantee relatively high-quality data or computational performance. Therefore, a secure, necessary and effective mechanism is needed through strict theoretical proof and experimental verification. The traditional auction theory is mainly oriented to price, not giving quality issues as much consideration. Hence, it is challenging to discover the optimal mechanism and solve the privacy problem when considering multi-dimensional auctions. Therefore, we (1) propose a multi-dimensional information security mechanism, (2) propose an optimal mechanism that satisfies the Pareto optimality and incentive compatibility named the SecMDGM and (3) verify that for the aggregation model based on vertical data, this mechanism can improve the performance by 2.73 times compared to that of random selection. These are all important, and they complement each other instead of being independent or in tandem. Due to security issues, it can be ensured that the optimal multi-dimensional auction has practical significance and can be used in verification experiments.
One-Sided Unsupervised Image Dehazing Network Based on Feature Fusion and Multi-Scale Skip Connection

Haze and mist caused by air quality, weather, and other factors can reduce the clarity and contrast of images captured by cameras, which limits the applications of automatic driving, satellite remote sensing, traffic monitoring, etc. Therefore, the study of image dehazing is of great significance. Most existing unsupervised image-dehazing algorithms rely on a priori knowledge and simplified atmospheric scattering models, but the physical causes of haze in the real world are complex, resulting in inaccurate atmospheric scattering models that affect the dehazing effect. Unsupervised generative adversarial networks can be used for image-dehazing algorithm research; however, due to the information inequality between haze and haze-free images, the existing bi-directional mapping domain translation model often used in unsupervised generative adversarial networks is not suitable for image-dehazing tasks, and it also does not make good use of extracted features, which results in distortion, loss of image details, and poor retention of image features in the haze-free images. To address these problems, this paper proposes an end-to-end one-sided unsupervised image-dehazing network based on a generative adversarial network that directly learns the mapping between haze and haze-free images. The proposed feature-fusion module and multi-scale skip connection based on residual network consider the loss of feature information caused by convolution operation and the fusion of different scale features, and achieve adaptive fusion between low-level features and high-level features, to better preserve the features of the original image. Meanwhile, multiple loss functions are used to train the network, where the adversarial loss ensures that the network generates more realistic images and the contrastive loss ensures a meaningful one-sided mapping from the haze image to the haze-free image, resulting in haze-free images with good quantitative metrics and visual effects. The experiments demonstrate that, compared with existing dehazing algorithms, our method achieved better quantitative metrics and better visual effects on both synthetic haze image datasets and real-world haze image datasets.
How do cohesion features inform science instruction for English learners?: A comparison study of digital and printed science texts

Cohesion plays an important role in science texts as their comprehension depends much on structural and textual signalling, which demonstrates the organisation of the text, the logical links of ideas, and the intended purpose of the writer. Particularly, research has shown that English learners (ELs) rely more on cohesive markers than their English-speaking peers during the reading process. To promote students’ reading comprehension in science, understanding how cohesion features are used to construct texts and providing pertinent instructions are important for practitioners. This study investigated a series of cohesion features commonly adopted in middle school science texts in the U.S. – in digital and printed texts respectively, through Coh-Metrix – and compared their frequency using Hotelling’s T-Squared test. This study is one of the pioneer studies that incorporated digital texts for cohesion analysis. Inclusion of the digital texts is meaningful as the pandemic facilitated technology uses in the U.S public school and elsewhere. Results showed that (1) commonly used cohesion features included additive connectives, 2nd person pronouns, and local referential cohesion, and (2) compared with the printed version of science texts, the digital version presented higher cohesion as indicated by index scores. The instructional implications for practitioners are discussed.
Research on Multi-Tag Propagation Clustering Method for Microblog Hot Topic Discovery

In the context of the information explosion, the improvement of people’s living standards has enabled computers and mobile phones to enter the homes of thousands of ordinary people. While promoting the popularization of the Internet, it has also enabled Weibo, a platform for expressing their opinions. Has been rapidly developed. With the continuous development of Weibo, the current mainstream Weibo platforms include Sina Weibo, Tencent Weibo, NetEase Weibo and Sohu Weibo. Based on the idea of multi-label propagation, this paper comprehensively considers node similarity and node life value and proposes the TCMLPA algorithm for hot word clustering, which can effectively improve the accuracy of public opinion recommendation.
Distributed square root fuzzy cubature information filter for object tracking on the possibilistic framework

Abstract In this study, a distributed fuzzy filter is proposed for a non‐linear state estimation problem on the possibilistic framework. Firstly, instead of Gaussian distribution on the probability framework, the process and observation noises are modelled as fuzzy random variables with trapezoidal possibility distributions. Secondly, a novel square root fuzzy cubature information filtering (SRFCIF) algorithm is proposed to deal with non‐linear state estimation with fuzzy noise; a fuzzy variable fusion (FVF) algorithm is used for fuzzy random variables fusion. Consequently, a distributed square root fuzzy cubature information filter (DSRFCIF) is proposed by embedding SRFCF and FVF into the consensus frame. Finally, consistency analysis and simulation demonstration are executed for the proposed filter.
On a special type of permutation rational functions

Let p be a prime and n be a positive integer. We consider rational functions $$f_b(X)=X+1/(X^p-X+b)$$ over $${\mathbb {F}}_{p^n}$$ with $$\textrm{Tr}(b)\ne 0$$ . In Hou and Sze (Finite Fields Appl 68, Paper No. 10175, 2020), it is shown that $$f_b(X)$$ is not a permutation for $$p>3$$ and $$n\ge 5$$ , while it is for $$p=2,3$$ and $$n\ge 1$$ . It is conjectured that $$f_b(X)$$ is also not a permutation for $$p>3$$ and $$n=3,4$$ , which was recently proved sufficiently large primes in Bartoli and Hou (Finite Fields Appl 76, Paper No. 101904, 2021). In this note, we give a new proof for the fact that $$f_b(X)$$ is not a permutation for $$p>3$$ and $$n\ge 5$$ . With this proof, we also show the existence of many elements $$b\in {\mathbb {F}}_{p^n}$$ for which $$f_b(X)$$ is not a permutation for $$n=3,4$$ .
DP-CSM: Efficient Differentially Private Synthesis for Human Mobility Trajectory with Coresets and Staircase Mechanism

Generating differentially private synthetic human mobility trajectories from real trajectories is a commonly used approach for privacy-preserving trajectory publishing. However, existing synthetic trajectory generation methods suffer from the drawbacks of poor scalability and suboptimal privacy–utility trade-off, due to continuous spatial space, high dimentionality of trajectory data and the suboptimal noise addition mechanism. To overcome the drawbacks, we propose DP-CSM, a novel differentially private trajectory generation method using coreset clustering and the staircase mechanism, to generate differentially private synthetic trajectories in two main steps. Firstly, it generates generalized locations for each timestamp, and utilizes coreset-based clustering to improve scalability. Secondly, it reconstructs synthetic trajectories with the generalized locations, and uses the staircase mechanism to avoid the over-perturbation of noises and maintain utility of synthetic trajectories. We choose three state-of-the-art clustering-based generation methods as the comparative baselines, and conduct comprehensive experiments on three real-world datasets to evaluate the performance of DP-CSM. Experimental results show that DP-CSM achieves better privacy–utility trade-off than the three baselines, and significantly outperforms the three baselines in terms of efficiency.
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A filtering scheme for phase map of active tissue interface strain based on adaptive variational mode decomposition

In this paper, a phase denoised digital speckle pattern interferometry (DSPI) method based on improved variational mode decomposition (IVMD) is proposed to reduce the noise interference in DSPI phase maps. The first step is to decompose the DSPI phase map using the IVMD method and obtain the optimal mode components. Then, an adaptively mode threshold method is proposed for processing the mode components based on the characteristics of the mode components. To obtain the denoised DSPI phase map, the denoised mode components are reconstructed. The results demonstrate that the proposed method is effective in filtering out noise interference and the peak signal-to-noise ratio (PSNR) is higher than that of other denoised methods.
An anti-vibration phase demodulation method applied to phase-shifting interferometric

As a high-precision detection technique of surface topography based on the interference theory of light, Phase-Shifting Interferometric is susceptible to vibration from the surrounding environment, which reduces the detection accuracy. In order to reduce the influence of vibration errors and improve the phase demodulation accuracy of measured wavefront, an anti-vibration phase demodulation method applied to Phase-Shifting Interferometric is proposed in this paper. This method first acquires two sequences of interference images with phase shifts of 0 and 𝜋𝜋/2 respectively, uses a digital image stabilization algorithm to suppress the vibration error of each frame in the sequence, and synthesizes two frames of interference images with phase shifts of 0 and 𝜋𝜋/2, respectively. Then, based on these two frames of synthesized images with determined phase shifts, the gradient algorithm and the Schmidt Orthogonal Transform are used for wavefront phase demodulation. The experimental results show that the percentage of pixels demodulated effectively at the truncated interface is more than 90%. Compared with the traditional demodulation method, the number of pixels demodulated effectively (demodulation accuracy) is improved.
Lost + Found: The Lost Angel Investigator

Each year, a large number of youngsters are found missing in India. Among them, a large number of cases are never solved due to various difficulties faced by the police ranging from heavy paperwork to lacking technology. Therefore, one of this work’s key goals is to provide an application that may assist people whose children have been missing and rescued by the public. This will also reduce the time required to find the missing child to reunite the child with their loved ones as soon as possible. The pictures of child victims can be uploaded by the citizens along with landmarks, to our web app. The photographs will be matched to the missing child’s registered photographs if existing in the database. A deep neural network model is trained to locate the lost youngster using a facial picture uploaded by the citizens. Multi-Tasking CNN (MTCNN), the most efficient DNN technique for image-based apps, is used for facial Identification. The images were passed through an augmentation layer to get images of different orientations, brightness, and contrast, which were used ahead to train the EfficientNetB0 model. This model is then used to recognize faces in photographs. Using the MTCNN model for facial recognition with EfficientNetB0 and developing it yields a deep learning model that is free from all types of distortion. The model’s training accuracy is 96.66 percent and its testing accuracy is 76.81 percent, implying that there is approximately 77 percent possibility of finding a match for the missing kid. It was evaluated using 25 Child classes. Each Child class has around 15 to 20 images. These images are taken with different backgrounds and real-time settings so that model will work even when noise is present in the image.
Automatic Adaptive Filtering Technique for Removal of Impulse Noise Using Gabor Filter

Tremendous development in Internet of Things (IoT) and mobile devices lead to several images pooled on social media websites and communicated through networking channels. These images are mostly corrupted with impulse noises due to hot pixels generated in the camera sensors and communication channels. Adaptive mean filter technique removes impulse noise at low density but is unsuccessful as noise density increases and computationally expensive. In this paper, automatic adaptive filtering technique for removal of impulse (salt and pepper) noise is demonstrated. The proposed algorithm consists of impulse noise detection and noise removal modules. An automatic impulse noise detection module is based on mean and variance technique that selects the noisy pixels among the entire image. The noise removal module is based on replacement of noisy pixel through mean and edge direction using Gabor filter. The proposed technique demonstrated better robustness compared with existing techniques.
Gaussian mixture probability hypothesis density filter with dynamic probabilities: Application to road traffic surveillance

With the development of autonomous driving system, accurate tracking of the number of vehicles and their states is critical to ensure traffic safety. Since the numbers and states of the vehicles are unknown and time-varying, the road traffic surveillance can be considered as a multiple-target tracking (MTT) problem. The Gaussian mixture probability hypothesis density (GM-PHD) filter is a popular approach in solving MTT problem and has been successfully applied to a range of applications including ground vehicle tracking. However, due to the road constraint, the maneuverability of a ground vehicle is limited to the road lane. Thus, the target detection probability, survival probability, and birth rate, which are assumed to be constant in the original GM-PHD filter, vary according to the surveillance area as well as its own state. To address this, we propose an algorithm that dynamically updates the probability of detection and survival, and birth rate simultaneously in a mathematically rigorous way. This is done by integrating the semantic information inferred from the environment with the state-dependent GM-PHD filter. The performance and effectiveness of the proposed algorithm are demonstrated via illustrative road traffic scenarios.
Mobile game props recommendation for machine learning

Mobile game providers benefit by selling virtual items in the game. Each event is described as an example in the player log data, and the player indicates the purchase status of the various game props as a plurality of tags, the game props recommendation question is abstractd into a multi-instance multi-label learning problem. On this basis, the fast multi-instance multi-label learning algorithm is designed for recommendation of mobile online game props, and semi-supervised learning is used to improve the recommendation performance. Off-line data sets and the online game experimental results of the actual online mobile phone show that the game props based on multi-instance multi-tagging learning technology brings a significant increase in game revenue.
Features of the Formation of Information Support for Space Simulators

One of the approaches to improving the quality of control of the “Operator - Simulator - Cosmonaut” complex is proposed during the training process of preparation, practical training and development by astronauts of skills and abilities to control systems of manned spacecraft. The formalization of the problem of research when the situation changes during the training of astronauts is made.
Computationally Efficient Learning of Gaussian Linear Structural Equation Models with Equal Error Variances

This study develops a computationally efficient and statistically consistent learning algorithm for large-scale Gaussian linear structural equation models with equal error variances. The novelty of the proposed algorithm is that it applies a hypothesis test rather than a score when recovering the directions of the edges. Specifically, the proposed algorithm estimates the topological layers using a Z-test. Subsequently, it estimates the parents of each node using a consistent conditional independence test. This study proves that the proposed topological layer estimation approach is asymptotically consistent. It further shows that the proposed method is computationally more efficient than existing score-based algorithms for Gaussian linear SEMs by comparing the computational complexities. It is confirmed through synthetic and real-world data that the proposed algorithm is consistent and computationally much faster than the state-of-the-art large-scale Gaussian linear SEM learning algorithms, such as the USF, USB, TD, and HGSM algorithms. Supplementary materials for this article are available online.