Reviewerly/exHarmony huggingface dataset:
Optimization of Classroom Teaching Strategies for College English Listening and Speaking Based on Random Matrix Theory

Online English teaching systems are more and more widely used in higher education teaching. Based on random matrix theory, this study constructs an optimization model of classroom teaching strategies for college English listening and speaking. By comparing and analyzing the universal properties of the research system and the random system matrix, the model can examine the random properties and special nonrandom properties within the system and solve the quantification problem of college English classroom education. First, based on the existing college English listening and speaking classroom teaching system, with the help of J2EE structure in information technology, the existing college English listening and speaking classroom teaching system is optimized, the customer layer, web layer, business logic layer, and student listening and speaking classroom learning effect management layer are designed, and the system is applied to college English listening and speaking classroom teaching. During the simulation process, a random matrix theoretical dynamics graph library and a subject library were written, so that combining the elements of each vector into a new matrix. Experiments show that the ensemble framework based on ranking learning can not only integrate multiple matrix factorization algorithm models to obtain better recommendation accuracy but also can more fully reflect the eigenvalues of each individual. The experimental results show that when the number of sampling points is 80 and the signal-to-noise ratio is −15 dB, the detection probability is close to 80%, which effectively improves the classroom random detection performance of English teaching and the effect of strategy optimization.
Novel Stacking Classification and Prediction Algorithm Based Ambient Assisted Living for Elderly

Recognition of human activity is a significant area of research with numerous uses. In developed countries, the rising age of citizens requires the improvement of the medical service structure, which raises the price of resources, both financial and human. In that sense, ambient assisted living (AAL) is a relatively novel information and communication technology (ICT) that presents services and recognizes various products that enable older people and the disabled to live autonomously and improve their quality of life. It further assists in reducing the cost of hospital services. In the AAL environment, various sensors and devices are fixed to gather a broad range of data. Moreover, AAL will be the motivating technology for the latest care models by acting as an adjunct. This will become thought-provoking research in a fast-growing world, but exploring different ADL and self-classification will become a major challenge. This paper proposed a Novel Stacking Classification and Prediction (NSCP) algorithm based AAL for the elderly with Multi-strategy Combination based Feature Selection (MCFS) and Novel Clustering Aggregation (NCA) algorithms. This paper’s main aim is to recognize the activity of older people, such as standing, walking, sitting, falling, cramps, and running. The dataset is derived from the Kaggle repository, which refers to data collection from wearable IoT devices. The experimental outcomes demonstrate that the MCFS, NCA, and NSCP algorithms work more efficiently than existing feature selection, clustering, and classification algorithms, respectively, regarding the accuracy, sensitivity, specificity, precision, recall, F-measure, and execution time dataset size and the number of features. Furthermore, the NSCP algorithm provided high accuracy, precision, recall, and F-measure are 98%, 0.96, 0.95, and 0.98, respectively.
Necessity and Feasibility of Outdoor Sports Teaching for Teenagers Integrating the Wireless Communication Network

With the rapid development of mobile communication technology, the research on wireless positioning technology is also deepening and improving. As one of the most popular services, positioning service has a very broad development prospect. With the popularity of outdoor sports among young people in recent years, colleges and universities have also paid more attention to outdoor sports teaching. But in outdoor sports, there will be many risks, so it is necessary to integrate positioning methods in wireless communication networks to ensure the safety of young people. The purpose of this paper is to study the necessity and feasibility of outdoor sports teaching for teenagers integrating wireless communication networks. This paper proposes a multireference point localization method, a signal fingerprint localization method, and a level-based localization method based on a converged wireless communication network. The experimental results of this paper show that 25 boys and 20 girls are very interested in outdoor sports, 20 boys and 13 girls are more interested, and 2 boys and 5 girls are not interested at all. Overall, far more people are interested in outdoor sports than those who are not. In terms of gender, boys are more interested in outdoor sports than girls, but the difference is not significant. It can be seen that today’s young people are still very interested in outdoor sports, and it is necessary to implement outdoor sports teaching.
Towards a Deeper Understanding of Data and Materiality

Data physicalization enables people to represent and interact with data physically rather than digitally. Physical representations afford visual analysis in comparable ways to traditional, desktop-based visualization by introducing new capabilities, such as facilitating tactile manipulation, accessible interactions, and immersion, that are beyond traditional 2D visualizations. However, physicalization has historically been a niche aspect of visualization research due to its unique challenges. In this paper, I discuss the current challenges of data physicalization and address three areas where data physicalization can aid other research thrusts: broadening participation, supporting analytics, and promoting creative expression. This paper exemplifies each approach through the lens of my work.
Identifying Engagement in Children’s Interaction whilst Composing Digital Music at Home

Identifying points of engagement from a person's interaction with computers could be used to assess their experience and to adapt user interfaces in real-time. However, it is difficult to identify points of engagement unobtrusively; HCI studies typically use retrospective protocols or rely on cumbersome sensors for real-time analysis. We present a case study on how children compose digital music at home in which we remotely identify points of engagement from patterns of interaction with a musical interface. A mixed-methods approach is contributed in which video recordings of children's interactions whilst composing are labelled for engagement and linked to i) interaction logs from the interface to identify indicators of engagement in interaction, and ii) interview data gathered using a remote video-cued recall technique to understand the experiential qualities of engaging interactions directly from users. We conclude by speculating on how the suggested indicators of engagement inform the design of adaptive music systems.
The Application of Internet of Things Data Analysis in the Development of International Trade

There are some problems in the application of current data analysis methods in international economy and trade, such as low service efficiency, low data utilization, and low degree of intelligence. Based on this, this paper studies the application of the Internet of things data analysis method in international trade development and economic and industrial growth. Firstly, the Internet of things economic data analysis model (IOT-EET model) based on simulated annealing early warning algorithm is established to store and analyze the data in the whole chain of international trade. Then, combined with the analysis methods of international trade economic data over the years, it is fed back to the IOT-EET model for error calibration. Finally, relevant experiments are designed to analyze the correlation between international trade development and national economic growth. The results show that compared with the traditional method based on module data analysis, this IOT-EET model can realize the correlation matching analysis of the data involved in the development of international trade in combination with the Internet of things technology and analyze the factors affecting international trade transactions. Therefore, it has the advantages of good reliability and strong pertinence.
Machine learning‐based gene alteration prediction model for primary lung cancer using cytologic images

Understanding the gene alteration status of primary lung cancers is important for determining treatment strategies, but gene testing is both time-consuming and costly, limiting its application in clinical practice. Here, potential therapeutic targets were selected by predicting gene alterations in cytologic specimens before conventional gene testing.This was a retrospective study to develop a cytologic image-based gene alteration prediction model for primary lung cancer. Photomicroscopic images of cytology samples were collected and image patches were generated for analyses. Cancer-positive (n = 106) and cancer-negative (n = 32) samples were used to develop a neural network model for selecting cancer-positive images. Cancer-positive cases were randomly assigned to training (n = 77) and validation (n = 26) data sets. Another neural network model was developed to classify cancer images of the training data set into 4 groups: anaplastic lymphoma kinase (ALK)-fusion, epidermal growth factor receptor (EGFR), or Kirsten rat sarcoma viral oncogene homologue (KRAS) mutated groups, and other (None group), and images of the validation data set were classified. A decision algorithm to predict gene alteration for cases with 3 probability ranks was developed.The accuracy and precision for selecting cancer-positive patches were 0.945 and 0.991, respectively. Predictive accuracy for the EGFR and KRAS groups in the validation data set was ~0.95, whereas that for the ALK and None groups was ~0.75 and ~ 0.80, respectively. Gene status was correctly predicted in the probability rank A cases. The model extracted characteristic conventional cytologic findings in images and a novel specific feature was discovered for the EGFR group.A gene alteration prediction model for lung cancers by machine learning based on cytologic images was successfully developed.
Insights from a cultural-historical HE library makerspace case study on the potential for academic libraries to lead on supporting ethical-making underpinned by ‘Critical Material Literacy’

This article explores the tensions and contradictions in the potential success of maker-learning in Higher Education (HE) as supported in academic library makerspaces. Insights are formed from an in-depth, Cultural-Historical Activity Theory framed case study on a well-established North American HE academic library-based makerspace service. Lessons are drawn from the organisational tensions that emerged as challenges in its development. Participants were from the library service, students and academics from different disciplines that make significant use of the library makerspace. The ‘relational agency’ and ‘common knowledge’ of academic librarians in bringing together academic and student perspectives on the utility of maker-learning is found to be key. Maker-learning is observed to be an intertwined embodied/haptic, social/dialogic and rational/critical expansive cross-disciplinary system in a Zone of Proximal Development. Evidence of attempts to address the themes of inclusivity, diversity and sustainability to achieve ethical-maker-learning outcomes are discussed and developed. The article then expands on Ratto’s Critical Maker pedagogy utilised by the case study library service. I conclude with the proposal of a potentially transformative new concept for supporting cross-disciplinary maker-learning systems, ‘Critical Material Literacy’ (CML), whereby technical and material awareness connects with progressive concerns for people and the planet. This new theoretical concept is designed to start proactively addressing the key case study themes, with academic librarians becoming critical agents in creating ethical-maker knowledge hubs.
An Improved Hybrid Model for Nonlinear Regression with Missing Values Using Deep <scp>Quasi‐Linear</scp> Kernel

Abstract Missing values are ubiquitous in the nonlinear regression research, and may lead to bias and a loss of efficiency. Even in a large dataset, values drop‐out can substantially reduce the available information for analysis. In this paper, we propose an improved hybrid model to solve the nonlinear regression problem under missing data scenarios, consisting of two parts: an overcomplete winner‐take‐all (WTA) autoencoder and a multilayer gated linear network. The WTA autoencoder is trained in an adversarial training process by taking advantage of gradually renewed teacher signals and the discrimination of missing values and observed values, and is designed to play two roles: (1) to impute missing components conditioned on observed samples; (2) to generate gate control sequences. On the other hand, the multilayer gated linear network with the generated gate control sequences implements a powerful piecewise linear regression model, whose parameters are optimized by formulating a support vector regression (SVR) with a deep quasi‐linear kernel. Experimental results based on different real‐world datasets demonstrate the effectiveness of our proposed hybrid model. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.
Motion-centric Tools to Reflect on Digital Creative Experiences and Created Outputs

Craft and motion are strongly connected, and reflection about this connection enhances our understanding of digital creative experiences. We summarize seven key steps for reflection in craft and create machine-assisted motion-centric tools to support reflection for each key step. These tools reveal different properties of the recorded movement sequence and the relationship between movement and created models. We tested the tools with a sample of novice users who were asked to create a wire-based jewelry model with a set of creation intentions. The resultant qualitative data and visual analysis charts show that introducing the motion information helps the user to meaningfully reflect on their creative experiences and ultimately enhance their decision-making in terms of selecting the models and creating subsequent digital craft-making movements. We therefore hope that our study will stimulate future discussion to bring motion-centric reflection to digital craft processes.
Contemporary Trends and Technologies in Research Libraries

This chapter discusses some of the contemporary trends and emerging technologies in research libraries. The chapter begins by explaining the concept of a research library and differentiating between the services offered in traditional versus digital research libraries. The contemporary trends and technologies discussed in this study include big data, blockchain technology, podcast, vodcast, data everywhere, drones, robotics and artificial intelligence (AI) technology, unplugged, makerspace, and the internet of things (IoT). The chapter is important to library users, librarians, and the general public interested in library practices.
A Digital Library for Researchers, Scientists, and Scholars

This chapter discusses the application of Mendeley desktop in academic and research libraries. The features of Mendeley were used to justify it as a digital library for researchers, scientists, and scholars. The importance of Mendeley desktop application as a digital library was also compared with a traditional library. This chapter should thus enable anyone without prior knowledge of Mendeley to effectively utilise it as a digital library as it provides an extensive guide on how to work with the Mendeley Application to perform various tasks.
A multi-stage spatio-temporal adaptive network for video super-resolution

Video super-resolution aims at restoring the spatial resolution of the reference frame based on consecutive input low-resolution (LR) frames. Existing implicit alignment-based video super-resolution methods commonly utilize convolutional LSTM (ConvLSTM) to handle sequential input frames. However, vanilla ConvLSTM processes input features and hidden states independently in operations and has limited ability to handle the inter-frame temporal redundancy in low-resolution fields. In this paper, we propose a multi-stage spatio-temporal adaptive network (MS-STAN). A spatio-temporal adaptive ConvLSTM (STAC) module is proposed to handle input features in low-resolution fields. The proposed STAC module utilizes the correlation between input features and hidden states in the ConvLSTM unit and modulates the hidden states adaptively conditioned on fused spatio-temporal features. A residual stacked bidirectional (RSB) architecture is further proposed to fully exploit the processing ability of the STAC unit. The proposed STAC and RSB architecture promote the vanilla ConvLSTM's ability to exploit the inter-frame correlations, thus improving the reconstruction quality. Furthermore, different from existing methods that only aggregate features from the temporal branch once at a specified stage of the network, the proposed network is organized in a multi-stage manner. The corresponding temporal correlation in features at different stages can be fully exploited. Experimental results on Vimeo-90K-T and UMD10 datasets show that the proposed method has comparable performance with current video super-resolution methods. The code is available at https://github.com/yhjoker/MS-STAN.
ShChain_3D-ResNet: Sharding Blockchain with 3D-Residual Network (3D-ResNet) Deep Learning Model for Classifying DDoS Attack in Software Defined Network

The distributed denial of service (DDoS) vulnerabilities have rapidly extended and have been given different possibilities for even more advanced assaults on specific targets in recent times, thanks to the growth of innovative technology such as the Internet of Things (IoT) and Software-Defined Networking (SDN). The attack patterns route comprises unprotected and susceptible IoT systems that are internet-connected, as well as denial of service weaknesses in the SDN controllers, such as southbound connection exhaustion. (1) Background: The review does not go into detail about the symmetry blockchain approaches used to mitigate DDoS attacks, nor does it classify them in IoT; (2) To overcome the privacy issues, a novel deep learning-based privacy preservation method was proposed named ShChain_3D-ResNet. This novel method combines Sharding, blockchain and Residual Network for securing the SDN. Under this network, the proposed efficient attention module jointly learns attention to enforce the symmetry on weights for various channels in spatial dimension as well as attention weights of multiple frames in temporal dimension assistance of pre-training, updating, and dense convolution process; (3) Results: the proposed ShChain_3D-ResNet achieves 95.6% of accuracy, 97.3% of precision, 95.2% of recall, 94.4% of F1-score, 32.5 ms of encryption time and 35.2 ms of decryption time for dataset-1. Further, it achieves 97.3% accuracy, 95.3% precision, 96.1% recall, 98.2% F1-score, 32.1 ms of encryption time, and 36.2 ms of decryption time for dataset-2; (4) Conclusions: The Sharding strategy can increase ShChain performance while simultaneously utilizing Multi User (MU) resources for SDN.
Blockchain-Based Banking

Blockchain has become the latest buzzword and hottest technology in academics and the IT industry. Blockchain is a decentralized model that maintains data growing at a rapid pace. In 2008, Satoshi Nakamoto has introduced the concept of blockchain. With the development of bitcoin, blockchain concept became popular and is now considered one of the most impactful inventions of the last decade. It has the potential applications in different areas of study such as banks, education, healthcare, internet of things, cryptography, transportation, and so on. It is considered the future of next digital transformation after mainframe computer, personal computer, smart mobiles, and social networking. The main purpose of this chapter is to introduce the concept of blockchain technology, its few important applications in banking industry, and how it can be used to transform financial institutions. Some of the most important applications of blockchain-based banking are payment systems, loans, KYC, and so on.
Exploring affordances through design-after-design: the re-purposing of an exhibition artefact by museum visitors

Museums have increasingly focused on digital technologies and play as means to provide personalized, engaging experiences for their audience. Balancing educational and playful values is often conflicting. To address that conflict, museums often employ participatory design strategies. However, those strategies usually end after the deployment of those experiences, thus they do not accommodate for what occurs during actual use. In this article, we follow Light House, a research-through-design experiment of an installation that was developed using an iterative design approach which expands on actual use by deploying undetermined artefacts to support the discovery of novel interactions by visitors. Through our findings, we explore the “failure” of Light House to support the discovery of such interactions in relation to its educational character, but rather it inspired people to incorporate it in the activities supported by the surrounding space. Finally, we discuss the implications those discovered interactions in terms of potential re-design directions.
Supporting Data Preservation Through Institutional Repositories of the Academic Libraries in South Africa

Institutional repositories (IRs) are open access platforms that could be viewed as ideal platforms for supporting the management of the scientific knowledge which enhances knowledge generation, preservation, use, and sharing and for increasing the scale of research performance in a research community. This chapter investigates the use of IRs in preserving data at selected academic libraries in KwaZulu-Natal province, South Africa, guided by the Digital Curation Centre (DCC) Lifecycle Model. The interpretivist research paradigm following a qualitative research approach through a case study was employed. The findings of the study reveal uniform IRs for data preservation in the participated academic libraries. The findings also show a strong need for training and workshops to equip the librarians and researchers with the necessary skills and knowledge for preserving data in the IRs. A lack of resources is the biggest threat to preserving data for most academic libraries.