Reviewerly/exHarmony huggingface dataset:
A hybrid approach for high-dimensional optimization: Combining particle swarm optimization with mechanisms in neuro-endocrine-immune systems

Particle swarm optimization (PSO) tends to fall into local optimum during the high-dimensional optimization process To address this limitation, a hybrid optimization approach by combining PSO with mechanisms in neuro-endocrine-immune systems (NEI-PSO) is proposed. The NEI-PSO includes a nervous guidance unit, an endocrine regulation unit, and an immune orientation unit. The nervous guidance unit and the immune orientation unit are designed based on the nervous system guidance mechanism and the immune system orientation mechanism respectively. Through the joint effect of these two units, the update mode of particle movement is changed; as a result, the global search ability of the NEI-PSO can be improved. The endocrine regulation unit changes the learning factor based on the hormone regulation law of the endocrine system, and in turn improves the optimization convergence speed of the proposed approach. In this paper, the NEI-PSO is evaluated using eight high-dimensional benchmark functions and a real-world high-dimensional optimization application for a non-Pieper six-axis robot. The results demonstrate that the proposed NEI-PSO approach has prominent advantages in search accuracy, convergence ability, and stability, compared to some existing optimization approaches.
The Collection and Utilization of Web Resources for Teaching World History Based on Data Mining Technology

The foundation of research for academics involved in world history education and research is timely access to pertinent foreign information, comprehension of domestic and international academic developments, and access to fundamental historical materials and research outputs from our ancestors. As a result, it is essential to investigate the necessity and viability of using network resources on the basis of analysis in order to respond to the educational philosophy of the new curriculum reform and adapt to the development of modern teaching methods based on network technology. The creation and use of curriculum resources is an essential component of curriculum development and a crucial assurance for curriculum implementation. The growth and acceptance of online learning will undoubtedly influence how history education is practised around the world. The scarcity of online learning resources is currently a bottleneck impeding the growth of online education. On the other hand, data mining (DM) takes the massive amount of incomplete data and extracts the useful knowledge and information hidden within it. This paper explores the "DM" process of utilizing online resources and suggests a method for gathering and utilizing world history education online resources based on DM technology. The experimental results show that the test interval between MapReduce and DM gradually increases with the increase of data volume. The advantage of DM is more obvious, as the average test time of DM is 27.66 seconds shorter than that of MapReduce. Therefore, DM has high application value in the field of search engines and social network analysis.
Identification of orthotropic elastic properties of wood by digital image correlation and finite element model updating techniques

Computer-aided engineering systems rely on constitutive models and their parameters to describe the material behaviour. The identification process, however, grows in complexity for more elaborated material models, increasing the number of experimental tests needed, therefore time and cost consuming. Recently, with the development of digital image technology, there has been a growing interest in the use of full-field deformation measurements. This advancement has highlighted a new methodology on inverse identification techniques such as the finite element model updating method. This work aims to identify orthotropic linear elastic constitutive parameters of Pinus pinaster Ait. wood based on a single uniaxial compressive experimental test. Clear wood specimens oriented in the radial-tangential plane were used and tested under quasi-static loading condition. Full-field displacement and strain maps were obtained using digital image correlation and used to simultaneously identify the whole set of elastic properties through finite element model updating methodology.
TDA-Net: A Novel Transfer Deep Attention Network for Rapid Response to Building Damage Discovery

The rapid and accurate discovery of damage information of the affected buildings is of great significance for postdisaster emergency rescue. In some related studies, the models involved can detect damaged buildings relatively accurately, but their time cost is high. Models that can guarantee both detection accuracy and high efficiency are urgently needed. In this paper, we propose a new transfer-learning deep attention network (TDA-Net). It can achieve a balance of accuracy and efficiency. The benchmarking network for TDA-Net uses a pair of deep residual networks and is pretrained on a large-scale dataset of disaster-damaged buildings. The pretrained deep residual networks have strong sensing properties on the damage information, which ensures the effectiveness of the network in prefeature grasping. In order to make the network have a more robust perception of changing features, a set of deep attention bidirectional encoding and decoding modules is connected after the TDA-Net benchmark network. When performing a new task, only a small number of samples are needed to train the network, and the damage information of buildings in the whole area can be extracted. The bidirectional encoding and decoding structure of the network allows two images to be input into the model independently, which can effectively capture the features of a single image, thereby improving the detection accuracy. Our experiments on the xView2 dataset and three datasets of disaster regions achieve high detection accuracy, which demonstrates the feasibility of our method.
Exploring the geo virtual linguistic landscape of Dublin urban areas: before and during the COVID-19 outbreak

The COVID-19 pandemic has markedly reshaped people's language behaviour on social media and may beget varying interactive possibilities in the online environment. From a perspective of the virtual linguistic landscape, this enquiry looks into the dynamic and multilingual nature of the geotagged social media content by situating this issue in the context of the pandemic. An additional aim is to explore in what way the pandemic has influenced the form of this linguistic landscape. To achieve these goals, the enquiry collects and analyses over 90,000 posts on Instagram from three sites in Dublin, Ireland, between 2019 and 2021, using a set of established computational approaches from the field of natural language processing as well as diversity indices from ecology. The results picture a dynamic mechanism of the virtual linguistic landscape in Dublin over the COVID-19 pandemic, in which linguistic richness and diversity were revealed. This richness and diversity, according to our analysis, were beset by the pandemic associated with a decrease in online activities at the research sites.
Model‐guided boosting for image denoising

Boosting algorithms have demonstrated their effectiveness in improving the restoration quality of existing image denoising methods by extracting the residual signal or removing the noise leftover iteratively. Unlike existing boosting algorithms that focus on designing an ingenious recursive step by making use of the residual signal or the noise leftover, in this paper, we propose a novel model-guided boosting framework. Specifically, we derive the recursive step from an overall restoration model constructed with the technique of Regularization by Denoising (RED) towards an interpretable, extensible and flexible boosting mechanism. By using the RED, we can apply explicit regularization equipped with powerful image denoising engine to establish the global minimization problem, making the obtained model is clearly defined and well optimized. The framework enjoys the advantage of easily extending to the case of composite denoising via superadding a regularization term. As such, we develop a simultaneous model through the joint use of deep neural network and low-rank regularization to fully utilize both external and internal image properties. The resulting restoration models are capable of being flexibly solved with fixed-point strategy and steepest-descent method, leading to two types of denoising boosters. It is shown that the proposed schemes have promise results due to the improvement in signal-to-noise ratio of input signal, and are guaranteed to converge. Experiments verify the validity of the boosters for several denoising algorithms, and show that combining the power of internal and external denoising based on our framework achieves enhancement in denoising performance.
OGCNet: Overlapped group convolution for deep convolutional neural networks

The deployment of deep convolutional neural networks (CNNs) is heavily constrained by its high computational costs and parameter redundancy. For this reason, general group convolution (GGC) and depthwise convolution (DWC) were proposed, but they limited the information transfer in the channel dimension. In this paper, a novel and efficient overlapped group convolution (OGC) is proposed to improve the information transfer between channels. In OGC, the input feature maps can be overlapped between different groups. Compared with GGC, OGC has better information transfer in the channel dimension without additional parameters and computational cost. In theory, OGC unifies the standard convolution (SDC), GGC, and DWC. In other words, SDC, GGC, and DWC all belong to the special cases of OGC. In OGC, two flexible hyperparameters are defined, the number of input feature maps in each group (g) and the stride between adjacent groups (s), which make OGC more flexible and can make the trade-off between accuracy and parameters. The performance of OGC is analyzed in terms of parameters, computational cost, accuracy, run time, etc. The classification and object detection tasks are used to evaluate the performance of OGC. Experimental results show that the OGC has higher accuracy and is more efficient than the corresponding SDC, GGC, and DWC. The ratio of the two hyperparameters in OGC has a great impact on accuracy. When 23<sg<67, OGC has higher accuracy than others. The proposed OGC is more stable and robust than GGC.
Explaining and teaching acoustics through comics, interactive web pages, and video games

Comics and manga have long been seen as childish literature, even if sometimes referred to as an art. Webpages discussing matters like Mathematics or Science are usually considered popularized content and not as tools with which research can be built. Arcade video games are often not considered serious games that can be used for learning or team building purposes. In this work, we present examples of comics, interactive web pages, and arcade video games that can be used to learn the basics of vibro-acoustics or to focus on more specific topics such as the acoustics of porous media. The reader can experience most of these visual narrative examples following the provided links. An objective of this collection is to make them better known and to stimulate further initiatives on these topics in acoustics education. To this effect, recommendations from various use cases of these visual narratives for different levels of education are also given.
Stacked Gated Recurrent Unit Classifier with CT Images for Liver Cancer Classification

Liver cancer is one of the major diseases with increased mortality in recent years, across the globe.Manual detection of liver cancer is a tedious and laborious task due to which Computer Aided Diagnosis (CAD) models have been developed to detect the presence of liver cancer accurately and classify its stages.Besides, liver cancer segmentation outcome, using medical images, is employed in the assessment of tumor volume, further treatment plans, and response monitoring.Hence, there is a need exists to develop automated tools for liver cancer detection in a precise manner.With this motivation, the current study introduces an Intelligent Artificial Intelligence with Equilibrium Optimizer based Liver cancer Classification (IAIEO-LCC) model.The proposed IAIEO-LCC technique initially performs Median Filtering (MF)-based pre-processing and data augmentation process.Besides, Kapur's entropy-based segmentation technique is used to identify the affected regions in liver.Moreover, VGG-19 based feature extractor and Equilibrium Optimizer (EO)-based hyperparameter tuning processes are also involved to derive the feature vectors.At last, Stacked Gated Recurrent Unit (SGRU) classifier is exploited to detect and classify the liver cancer effectively.In order to demonstrate the superiority of the proposed IAIEO-LCC technique in terms of performance, a wide range of simulations was conducted and the results were inspected under different measures.The comparison study results infer that the proposed IAIEO-LCC technique achieved an improved accuracy of 98.52%.
The Effect of Role-Playing Techniques on the Speaking Skills of Students at University

Students must master the skill of speaking in order to be able to communicate effectively in a foreign language. The fear of making errors and a lack of motivation are just a few reasons for students' difficulty in speaking. Role-Playing is used to help students overcome speaking difficulties. This study aims to determine the efficacy of role-playing in teaching speaking to second-grade students at the University of Sumer's College of Basic Education and determine whether there is a significant difference in speaking achievement between students taught with and without role play. The data were collected in 2021 by applying the quasi-experimental method in which two groups were involved in pre-test and post-test. The research consisted of forty-six students from two classes. The study indicates that the student's speaking score is improving. As a result, role-playing benefits students by offering an opportunity to struggle with oral communication. The results showed a significant improvement in the speaking skill of the experimental group. The students taught through the role-play method performed better than those taught through the grammar-translation method. It can be concluded that role-play significantly affects students’ speaking skills. These findings have positive implications for implementing role-playing techniques at the University of Sumer.
Efficient Management and Application of Human Resources Based on Genetic Ant Colony Algorithm

With the increasing demand of human resources, the cost of staffing and management is increasing, and it is difficult to dynamically allocate and adjust personnel among different parts. It is the key of intelligent management technology to realize efficient application and mining in human resource management. In the aspect of human resource allocation and management, this paper puts forward the efficient management and application of human resource based on the genetic ant colony algorithm. Firstly, this paper describes the process management and parameter application of the genetic algorithm and ant colony algorithm and manages the resource allocation and management process under the two algorithms. Secondly, several current test functions are applied to the genetic algorithm and ant colony algorithm to test the efficiency of the algorithm, which has obvious advantages in convergence efficiency. Finally, the paper uses the efficient management configuration of human resources for comprehensive application and management and applies the system uploading and downloading services on human resumes, respectively. The genetic ant colony algorithm has obvious advantages in efficiency. In human resource data matching, the genetic algorithm is slightly better than the ant colony algorithm in the case of relatively few data in the early stage, and the accuracy of the ant colony algorithm is slightly better than the genetic algorithm in the later stage. The ACO-GA algorithm is more consistent with the actual value, which not only ensures the stability but also ensures the accuracy of prediction, which is more in line with the actual needs.
On the Benefit of Width for Neural Networks: Disappearance of Basins

Wide networks are often believed to have a nice optimization landscape, but what rigorous results can we prove? To understand the benefit of width, it is important to identify the difference between wide and narrow networks. In this work, we prove that from narrow to wide networks, there is a phase transition from having suboptimal basins to no suboptimal basins. Specifically, we prove two results: on the positive side, for any continuous activation functions, the loss surface of a class of wide networks has no suboptimal basin, where “basin” is defined as the setwise strict local minimum; on the negative side, for a large class of networks with width below a threshold, we construct strict local minima that are not global. These two results together show the phase transition from narrow to wide networks.
Hybrid particle swarm optimization with sequential one point flipping algorithm for feature selection

Summary Scientific and technological advancements lead to the continuous generation of a large amount of data. These datasets are analyzed computationally to reveal patterns and trends. While the presence of noisy and irrelevant features or attributes in these datasets is unavoidable, they negatively impact the performance of classification techniques. Feature selection is a method to pre‐process these datasets by selecting the most informative features while concurrently improving the classification accuracy. Recently, several metaheuristic algorithms were employed in this feature selection process, including particle swarm optimization (PSO). PSO is prominent in the field of feature selection due to its simplicity and global search abilities. However, it may get stuck in local optima. To solve this problem, a new update mechanism in PSO is proposed and the PSO is hybridized with a local search method. To evaluate the performance of the proposed algorithm, benchmark datasets from the University of California in Irvine (UCI) repository were utilized, the k‐nearest neighbor as the classifier. Results show that the proposed feature selection technique outperforms other optimization algorithms on these feature selection problems.
The Importance of Social Competence for 21st Century Citizens

This chapter presents the results of a systematic review that investigated the use of mixed reality (MR) technology to assist social competence learning in mainstream education. Social competence represents our ability to interact with others and, therefore, is a key determinant for many of the possibilities that are presented to us across the lifespan. In today's digital world, MR technology has been widely used in special and inclusive education for social competence training purposes but has not yet been generally applied to mainstream education. This systematic review bridges this research gap. Findings support (1) the potential for MR technology as a medium for social competence learning activities and (2) how the MR approach can contribute to a universally designed (UD) learning environment.
Prospects for the Impact of Blockchain on the Innovation and Development of the Tourism Industry

Blockchain technology has been initially used in the development of tourism. Based on the core characteristics of blockchain technology, summarizing its application status in the tourism industry, analysis found that this technology has made certain progress in smart tourism construction, encrypted currency payment and accommodation industry management. Combined with domestic and foreign literature studies, it is found that the main driving force for the application of tourism blockchain technology comes from the practical demand for innovation and development within the tourism industry; at the same time, it is also found that the main obstacles to the promotion of blockchain technology involve technology scalability, legal and regulatory and Safety hazards and other aspects. With the development of the sharing economy, the application of blockchain technology in tourism industry management, information management, corporate financial management, tourism sharing economy system construction, supply chain management, credit management and customer loyalty management will reshape the traditional tourism business model. Therefore, it is recommended to strengthen the training of tourism blockchain technology talents and innovative team building, introduce corresponding regulatory systems and laws and regulations, implement classified supervision on the application of tourism blockchain technology, and actively guide tourism companies to reshape their organizational structure and culture to enhance tourists Experience quality, conform to diversified tourism consumption needs, and actively respond to technological changes in the era of intelligent interconnection.
Nonparametric bagging clustering methods to identify latent structures from a sequence of dependent categorical data

Nonparametric bagging clustering methods are studied and compared to identify latent structures from a sequence of dependent categorical data observed along a one-dimensional (discrete) time domain. The frequency of the observed categories is assumed to be generated by a (slowly varying) latent signal, according to latent state-specific probability distributions. The bagging clustering methods use random tessellations (partitions) of the time domain and clustering of the category frequencies of the observed data in the tessellation cells to recover the latent signal, within a bagging framework. New and existing ways of generating the tessellations and clustering are discussed and combined into different bagging clustering methods. Edge tessellations and adaptive tessellations are the new proposed ways of forming partitions. Composite methods are also introduced, that are using (automated) decision rules based on entropy measures to choose among the proposed bagging clustering methods. The performance of all the methods is compared in a simulation study. From the simulation study it can be concluded that local and global entropy measures are powerful tools in improving the recovery of the latent signal, both via the adaptive tessellation strategies (local entropy) and in designing composite methods (global entropy). The composite methods are robust and overall improve performance, in particular the composite method using adaptive (edge) tessellations.