Reviewerly/exHarmony huggingface dataset:
Towards secure <scp>IoT</scp> system from a smart city perspective: An optimized algorithm and implementation

Abstract Securing Internet of Things (IoT) systems is crucial for maintaining the resilience and trustworthiness of smart cities. In this context, the routing protocol's efficiency is pivotal for large‐scale IoT applications. However, achieving dependable and energy‐efficient routing in wireless sensor networks (WSNs) poses significant challenges due to limited and non‐rechargeable node energy resources. Also, traditional approaches often fell short of ensuring robust security and efficient data transmission. Hence, the purpose of this research was to address these shortcomings by introducing the Blowfish Honey‐Improved Spotted Hyena Optimization (BONY‐ISHO) protocol aimed at bolstering the security and efficiency of clustering and data routing in WSNs. Firstly, it introduces blockchain technology to establish secure and authenticated clustering, ensuring that only legitimate cluster heads and member nodes can participate. This innovation significantly enhances network security. Secondly, our protocol incorporates a hybrid Blowfish‐Honey cryptographic technique for data protection within the Area of Interest (AOI). This dual‐layer security approach, comprising Blowfish encryption and Honey encryption for key management, guarantees robust privacy and security. Thirdly, we have optimized data routing through the Improved Spotted Hyena Optimization (ISHO) algorithm, extending the network's lifetime and conserving energy resources. Performance evaluations confirm the superiority of the BONY‐ISHO protocol and existing solutions. It achieves a higher packet delivery ratio (99.8%), increased throughput (45 000 kbps), reduced energy consumption (1.68 joules), and minimized latency (0.068 sec) when compared to traditional methods. These performance enhancements make the BONY‐ISHO protocol a compelling choice for securing and optimizing IoT‐based WSNs. Our study concludes that the BONY‐ISHO protocol is a highly effective solution for addressing security and efficiency challenges in IoT‐based WSNs. By combining blockchain‐based authentication, hybrid cryptography, and intelligent routing algorithms, we have achieved a comprehensive enhancement of network security and data transmission efficiency. The protocol's ability to thwart malicious nodes and optimize routing pathways ensures the integrity and reliability of IoT‐based WSNs.
On the Copyright of Content Generated by Artificial Intelligence

While generative artificial intelligence participates in human creative activities on a large scale, it cannot be a natural human subject or an anthropomorphic subject because of its lack of rationality. However, copyright law encourages the creation of works, and under certain conditions, artificial intelligence generated content is conducive to the realization of the legislative purpose of copyright law and satisfies the objective criteria of originality of works, and therefore, such content has copyrightability. Under this premise, the attribution of artificial intelligence generated content copyright needs to be discussed. At present, there are designer’s, user’s and owner’s views on its attribution. According to the current copyright law, in principle, the copyright of artificial intelligence generated content belongs to the user, but in special cases, it belongs to legal persons, employers, trustees and other subjects, unless otherwise agreed by the parties.
Influence and scope of mobile learning in higher education: A systematic literature review

Due to the soaring progression in portable innovations and mobile technology, the popularity of mobile phones has expanded dramatically, which has led to the widespread adoption of mobile gadgets by almost every age group, especially the youth. The apparent consequence is embracing mobile phones in teaching-learning processes. Researchers have been inclined to study the potential and impact of incorporating mobile devices into the education system in recent years. Many studies have been conducted to explore the viability of integrating mobile gadgets into the higher education system. However, only a few works have addressed the realm from the perspective of the Indian higher education system. The present work employs the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) framework and endeavours to contribute to the literature by exercising a systematic literature review of the open-access literary works carried out from the standpoint of India, especially the Uttarakhand province. The essential keywords, inclusion/exclusion criteria, and research questions addressing the cruxes of the desired investigation are contemplated and designed first. The articles are then populated from the leading academic databases, general web resources, and government reports. The PRISMA guidelines are followed to filter out articles satisfying the adopted criteria. The final shortlisted articles are then rigorously studied to develop insights and to answer the framed questions. The observations signalize the need to frame effective policies and address the hurdles to optimize the usage and impact of mobile learning in Indian higher education, including the education system of Uttarakhand.
Leukemia Blood Cells Detection using Neural Network Classifier

Image segmentation is an image processing operation performed on the image in order to partition the image into some images based on the information contained in the original image. Image segmentation plays an important role in many medical imaging applications, image segmentation facilitates the anatomy process in a particular body of human body. Classification and clustering are the methods used un data mining for analyzing the data sets and divide them on the basis of some particular classification rules. There are many image segmentation tools that used for medical purpose, so it is necessary to define and/or to improve the image segmentation methods in order to get the best method. In this study, the image of leukemia and red blood cells will be used as samples to determine the best algorithm in image segmentation. The procedure for doing segmentation itself is clustering image, edge detection on image, and image classification. The clustering is to extract important information from an image. The edge detection is to determine the existence of edges of lines in image in order to investigate and localize the desired edge features. Moreover, the classification analyzes the properties of some images and organizes the information into certain categories. In this study, the Neural Network and K-Nearest Neighbor are used for image classification by paired with Local Binary Pattern and Principal Component Analysis. The results revealed that the best method of proven in classifying images is from Local Binary Pattern feature extraction with the average accuracy of 94%.
An Efficient E-Voting System for Business Intelligence Innovation Based on Blockchain

Abstract Business intelligence (BI) is driven by data and provides valuable business insights and decision support through data analysis, mining, and visualization. The application of blockchain technology in electronic voting can make the voting process more fair and transparent. This is because the decentralized nature of blockchain technology ensures that voting data is not lost due to a single central server failure and also mitigates the risk of data tampering. However, electronic voting still faces security issues that are not easily resistant to quantum attacks. To address these challenges, we propose post-quantum cryptography and verifiable random functions for secure and efficient business intelligence electronic voting. Firstly, we propose a post-quantum verifiable random function algorithm that can resist quantum computer attacks. Secondly, we introduce a consensus algorithm based on random functions to achieve fast and efficient consensus. Thirdly, we propose a blockchain architecture based on the consensus algorithm to achieve secure and efficient blockchain applications. By integrating post-quantum verifiable random functions, consensus algorithms, and blockchain technology, we present an efficient business intelligence electronic voting system. Implementation and comparison with relevant designs demonstrate that this system provides efficient and secure electronic voting services for business intelligence users. Furthermore, the efficient consensus algorithm can be utilized to improve other blockchain applications or decentralized applications.
Interpretable Model-Agnostic Explanations Based on Feature Relationships for High-Performance Computing

In the explainable artificial intelligence (XAI) field, an algorithm or a tool can help people understand how a model makes a decision. And this can help to select important features to reduce computational costs to realize high-performance computing. But existing methods are usually used to visualize important features or highlight active neurons, and few of them show the importance of relationships between features. In recent years, some methods based on a white-box approach have taken relationships between features into account, but most of them can only work on some specific models. Although methods based on a black-box approach can solve the above problems, most of them can only be applied to tabular data or text data instead of image data. To solve these problems, we propose a local interpretable model-agnostic explanation approach based on feature relationships. This approach combines the relationships between features into the interpretation process and then visualizes the interpretation results. Finally, this paper conducts a lot of experiments to evaluate the correctness of relationships between features and evaluates this XAI method in terms of accuracy, fidelity, and consistency.
Transfer of higher education system and switching of education model based on multi-scale feature fusion network

Abstract In this paper, we use computer techniques to extract the features of each convolutional layer of CNN, analyze the feature variations of different depth convolutions, and propose a new network framework to improve the head pose estimation task performance by combining the task characteristics of head pose estimation. Multi-scale feature information fusion is the basis of the proposed head pose estimation method (IRHP-Net), which consists of a feature extraction module and a multi-scale feature information fusion module. In the smart classroom learning environment, the algorithm is used to identify students’ attention areas and construct the distraction index and threshold parameters to determine the inattentive state and provide relevant teaching measures. Smart classroom teaching resulted in a 39.69% increase in students’ attention, as shown in the results. Students in the traditional teaching mode showed a 16.7% lower level of learning engagement. A 17.24% increase in academic performance was also a result of the increase in attention and learning engagement.
Research on the optimal allocation of resources for innovation and entrepreneurial education in universities in the context of deep learning

Abstract Optimizing educational resource allocation will undoubtedly influence how universities construct their entrepreneurship and innovation curricula. In this study, we examine innovation and entrepreneurial education, allocate educational resources, and choose neural network methods related to deep learning. The convolutional neural network algorithm is studied in four stages: the convolutional layer, the pooling layer, the activation function, and the fully connected layer. The convolutional neural network model uses backpropagation to adjust the output parameters’ divergence from the ideal values, adjust and update the weight parameters, and confirm the computational layer data and hidden layer data from the propagation process. The DEA algorithm is reviewed to enhance and evaluate the resource allocation for innovation and entrepreneurial education at universities, and a DEA-BPNN efficiency assessment technique is created. The complete quality of students has a strong positive association with the effectiveness of allocating resources for entrepreneurship and innovation education. The efficiency of deploying resources for innovation and entrepreneurial education increases by 0.0512 per unit improvement in student quality.
Social media, academic libraries and their cultural functions

Purpose The purpose of this paper is to examine how social media can be an effective tool for academic libraries to support their communities in the digital environment and relay information about their cultural functions. Design/methodology/approach This paper involves a literature review to discuss the implementation of academic libraries’ cultural functions through social media account. This paper also observes some academic libraries’ accounts that use social media to carry out their cultural functions. Findings Social media provides opportunities for academic libraries in performing and developing their cultural functions. Academic libraries already use social media to deliver and communicate their cultural functions. However, some challenges might arise and necessitate a review of the library’s resources and the skills of library professionals. Originality/value This paper fills the lack of empirical studies analyzing the implementation of academic libraries’ cultural functions through social media accounts and contributes to the literature about the cultural functions of libraries in the digital environment. This paper can help those academic libraries wanting to leverage, bring innovation and increase their cultural functions in the digital environment through their social media accounts.
Visual Analysis on the Research of Color-based Image Retrieval

This study retrieved relevant literature on color-based image retrieval in the Web of Science core database, and used bibliometrics and CiteSpace 6.1.R2 software to visually analyze the authors, institutions, and keywords of literature published in this field from 2005 to 2023. This study summarized the current status and development trend of color-based image retrieval and combined information extraction to select and include 716 articles for analysis. The results showed that: (1) In the last 20 years, the number of publications in this field has gone through a slow budding phase, a fluctuating growth phase, and an explosive growth phase; research in this area has been highly active in recent three years. (2) The authors of this field have a relatively even distribution of publications and some authors have formed close collaborative groups. (3) The research institutions mainly include research institutes and comprehensive universities, which have close collaborations with each other. (4) Co-occurrence and clustering analysis of keywords showed that research in this field is mainly focused on the main paths, research methods, and technological tools of color-based image retrieval, as well as key objects and potential influencing factors in the technological process. (5) Emerging keywords analysis predicted that more precise color description and extraction technology will be the focus of future research. In conclusion, color-based image retrieval is still in a hot research and technology explosion phase, and cooperation between countries, institutions, and authors should be strengthened to conduct more in-depth research.
The Impact of Parent-Child Reading on Children's Language Development

Parent-child reading is a valuable activity that is widely recognized for its positive impact on children's language development. This paper aims to explore the impact of parent-child reading on children's language development and offers practical strategies for parents to help them promote their children's language skills.The research found that parent-child reading had a significant positive impact on children's language development, particularly in areas such as vocabulary, syntax, and communication skills. Reading with children from an early age can help them develop a strong foundation in language and literacy, which can have long-term benefits in their academic and social lives.The paper also provides several practical strategies for parents to promote their children's language development through reading. These strategies include involving children in the reading process by allowing them to select books and encouraging them to ask questions and share their thoughts about the story. Additionally, parents should select age-appropriate books that match their child's interests and reading level.Overall, this paper highlights the importance of parent-child reading in promoting children's language development and offers practical strategies for parents to help them support their children's language skills. By incorporating these strategies into their daily routine, parents can help their children develop strong language skills, setting them up for success in the future.
Research on the Mechanism of Dual Integration and Promotion of Confucian Moral Education Thought and College Students’ Civic Education Based on Data Fusion Model

Abstract This paper uses Data fusion technology to integrate Confucian moral education with Civic Education. Different information sources are detected, and their parameter states are estimated using a weighted estimation algorithm. Principal component analysis is used to linearly transform the original information and calculate the weight values of the main feature vectors. The trust function is established using the D-S combination theory to achieve the fusion of knowledge structures. The results show that the average access rate of the teaching platform constructed based on data fusion reached 89.2% per week, and the average number of platform hits was around 716 per week. It shows that the integration of Confucian moral education ideas has enriched the content of Civic Education for college students and realized the teaching mechanism of double integration and double promotion.
Online Education Platform for Chinese Language Abroad Based on Remote Recognition System and Machine Learning

Abstract The remote video transmission system and machine learning are this paper’s first areas of analysis. Secondly, the remote recognition system’s image processing and content-based image retrieval are analyzed algorithmically, including image filtering, morphological processing, and canny edge extraction. The Chinese Bon platform dataset is the platform chosen for this research. The analysis of learners’ learning behavior and grade prediction is conducted based on the Chinese Bon platform dataset’s four categories: basic course information, basic learner profile, learning behavior record and learning effect. A prediction model was established for grades based on the feature variables, and its model accuracy reached 60.83%, while the accuracy of the model obtained based on the remote recognition system and machine learning algorithm was 73.35%.
Cloud computing systems in modern business

Purpose This paper aims to review the latest management developments across the globe and pinpoint practical implications from cutting-edge research and case studies. Design/methodology/approach This briefing is prepared by an independent writer who adds their own impartial comments and places the articles in context. Findings The authors examine Cloud computing systems and their benefits and pitfalls, as well as the security priorities of consumers with these servers. It is essential for firms to have secure places to store information, as trust and transparency underlie a firm's reputation. Originality/value The briefing saves busy executives, strategists and researchers hours of reading time by selecting only the very best, most pertinent information and presenting it in a condensed and easy-to-digest format.
Technological innovation, prosperity and corruption outlook in Africa: a mediation analysis

Purpose Ranking as the lowest-scoring region for many years on the corruption perceptions index (CPI) with an average score of 32 (2019–2021), Sub-Saharan Africa’s performance gives a bleak impression of inaction against corruption. The objective of this study aims to examine the effect of technological innovation in curbing corruption in Africa through prosperity. Design/methodology/approach CPI, prosperity index and individuals’ access to internet in the presence of some covariates were employed using the Andrew Hayes’ mediation analysis and cross sectional data in estimating the relationship among the variables as it affects all 54 African countries in the years 2012, 2015 and 2018. Findings The coefficients of the direct, indirect and total effects showed that internet access is only significant in reducing corruption if it is engaged in activities that create national prosperity (jobs, profits, infrastructural development and good governance). Originality/value The uniqueness of this study is predicated on the fact that a realistic analysis of the effect of technological innovation on corruption should include the channels it goes through. Thus, this study evaluates the direct, indirect and total effects of innovation on corruption through prosperity enhancement. Another unique aspect of this study is the use of market-creating innovation.
Research on the Construction of Knowledge Graph of Traditional Medical Intangible Cultural Heritage

By constructing a knowledge graph based on traditional medical intangible cultural heritage, comprehensive organization and storage of traditional medical intangible cultural heritage can be achieved, thereby achieving the protection of traditional medical culture. Firstly, Python's web crawler technology was used to obtain relevant data on traditional medical intangible cultural heritage, and it was cleaned and organized. Subsequently, using knowledge graph technology, thematic extraction, fusion, and correlation analysis were performed on the data to construct a knowledge graph centered on traditional medicine. In the process of constructing the knowledge graph, ontology modeling and semantic network technology were used to model and represent traditional pharmaceutical related concepts and relationships, achieving structured storage of the knowledge graph. At the same time, using visualization technology, the knowledge graph is presented in a graphical form, enabling the intuitive presentation of the knowledge and value of traditional medical intangible cultural heritage. Ultimately, a knowledge graph based on traditional medical intangible cultural heritage is obtained, which covers a wealth of traditional medical knowledge and related resources, and can help users gain a more comprehensive understanding and learning of traditional medical culture. By storing and visualizing the knowledge of traditional medical intangible cultural heritage in a structured manner, it provides important support and foundation for the protection and inheritance of traditional medicine.
Research on Language Evolution and Language Diversity Based on Chinese Speech Pitch Deviation Features

Abstract This paper constructs a Chinese speech evaluation system based on the characteristics of the vocal mechanism of Chinese speech and uses the detection framework of statistical speech recognition to realize the automatic detection function of pronunciation bias. Assuming that the amplitude spectra of speech and noise obey the generalized Gamma distribution and Gaussian distribution, respectively, the MMSE estimator of the logarithmic spectrum of the speech signal is derived. The speech presence probability under the generalized Gamma speech model is derived as a correction to the MMSE estimation based on language evolution and diversity features. The correct recognition rate of the proposed algorithm reaches 64%, which indicates that the proposed algorithm can effectively suppress the interference of convolutional noise on the speech signal features and improve the subjective perceptual quality of diverse languages.