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With the development of artificial intelligence, computer intelligent grouping, as a research hotspot of political ideology examination paper proposition, can greatly shorten the time of generating examination papers, reduce the human cost, reduce the human factor, and improve the quality of political ideology teaching evaluation. Aiming at the problem that the current political ideology examination paper-grouping strategy method easily falls into the local optimum, a kind of intelligent paper-grouping method for political ideology examination based on the improved stock market trading optimisation algorithm is proposed. Firstly, by analyzing the traditional steps of political test paper generation, according to the index genus of the grouping problem and the condition constraints, we construct the grouping model of political thought test questions; then, combining the segmented real number coding method and the fitness function, we use the securities market trading optimization algorithm based on the Circle chaotic mapping initialization strategy and adaptive tdistribution variability strategy to solve the grouping problem of the political thought test. The experimental results show that the method can effectively find the optimal strategy of political thought exam grouping, and the test questions have higher knowledge point coverage, moderate difficulty, and more stable performance. [ABSTRACT FROM AUTHOR]

These days, we receive most information through digital mediums such as emails and social networking applications. Investigating the characteristics of human-paper interactions can help us design more meaningful interactions and better understand why people use paper documents in the age of digitalization. In this study, the interaction of people with physical documents was studied through a mixed-method of conducting a literature review, gathering expert opinions, interviewing subjects, and analysing Instagram photos. By codifying the gathered information, the human-paper interaction framework was developed. This framework articulates the advantages of physical documents compared to electronic documents and serves researchers and practitioners by providing insightful human factors about human-document interaction. Finally, we propose six design themes as the solutions to the findings of this study. These implications can provide practical foundations for future design and research. [ABSTRACT FROM AUTHOR]

Development of mobile applications is becoming increasingly important as a field as a result of the proliferation of mobile devices. However, due to poor User Interface (UI) and User Experience (UX), many apps struggle to hold the interest of their intended users. As a result, achieving user expectations is difficult for developers. Numerous facets of mobile application design and UX/UI have been examined. Yet, they often overlook critical factors such as context, user behavior, emotions, control, usability, adaptability, language, and feedback, instead favoring isolated issue-centric approaches Taking into account all of the aforementioned factors, This review gathered preferences and perceptions. These preferences offer vital information for mobile application developers and act as a useful reference. Based on the pertinent context, the method helps designers and developers to address certain UI/UX difficulties in mobile applications. The UI and UX elements that affect the interface of mobile applications are compared in this essay. Developers and designers can better comprehend crucial elements during the development process by looking at these criteria. This helps to improve the UI and UX of mobile applications, which ultimately boosts usability and user satisfaction. [ABSTRACT FROM AUTHOR]

This study aims to determine the creative thinking patterns of students contained in scientific papers. The method used is a qualitative research method. Sources of data in research are students and documents in the form of papers. There are three data collection techniques, namely (1) reading papers, (2) interviews, and (3) observation. The results of this study show a pattern of lateral thinking that appears in several developments of ideas. Then, there are still traditional thinking patterns. Factors that cause traditional thinking are (1) experience, (2) sources of information, (3) lack of confidence, and (4) willingness to learn. [ABSTRACT FROM AUTHOR]

The performance of the existing student classroom behavior detection model is affected by various aspects such as dataset, algorithm and height as well as the differences between different classrooms, and there are problems such as a single dataset, low accuracy and low efficiency. In order to improve the accuracy of student classroom behavior detection algorithm, this paper proposes a student classroom behavior detection method based on improved convolutional neural network algorithm. Firstly, the student behavior detection dataset is constructed, and the student classroom behavior detection technology scheme is designed; secondly, in order to improve the detection accuracy, the features are extracted by using the new jumping bi-directional paths, and the attention mechanism module is added at different positions to improve the path aggregation network; weekly, the embedding positions of the attention mechanism strategy are determined by analyzing multiple sets of experiments, and the proposed student classroom behavior detection algorithm's effectiveness and superiority. [ABSTRACT FROM AUTHOR]

Knowledge transfer is a phenomenon faced by many organizations especially when dealing with the challenges of globalization. Organizations develop valuable knowledge from time to time and this knowledge has to be shared among the organizational members so that the organizations can leverage it to achieve the competitive edge. This paper is meant to discuss the factors that contribute to the phenomenon of interest including communication, management, transfer process, and also the working process in a virtual organization as a study setting. This issue is chosen because of the difficulties in dealing with knowledge transfer within the virtual organization and to identify the right factors involved in the knowledge transfer process. Perhaps from this study, the best approach of knowledge transfer process could be recognized and adapted by the virtual organization to strive in a business competitive world. Organizations that failed to adapt to the changing business environment especially with regard to the knowledge transfer process will be phased out from the business competition. The implications of the study will also be discussed in the paper. [ABSTRACT FROM AUTHOR]

This paper describes the types of MOOC considered by researchers, and highlights the latter's focus on Connectivist MOOC. In addition, it analyses MOOC methodologies, and learners' interest in MOOC based on the concepts of adaptability, connectivism, and socio-constructivism. This is to address the high dropout rate issue on MOOC platforms. The main objective of this work is to review the empirical results reported in these studies. To reach this goal, a Systematic Literature Review of 798 papers was carried out from 2013 until April 2021, where 446 papers were selected as primary studies. The results obtained from the classification and the analysis of the collected data confirmed the importance of continuing research in the field. Based on the concepts of socio-constructivism and adaptability, the objective is to provide an adaptive cMOOC for the profile and the needs of each learner; blending learning styles and pedagogical models with machine learning technologies. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Even while studying programming languages is essential for science and technology education, some students, especially novices, may find it challenging. One reason might be that these pupils are unable to comprehend programming basics, notably the usage of selective and repeated structures (loops), which are too complex and abstract for them to comprehend. OBJECTIVES: Programming structured applications requires understanding the relationship between variable-operators and declarations, so a more intuitive and practical visualization technique is needed. In view of this, this article presents an augmented reality (AR) learning system using a DF-RA mobile application that offers visual representation and interactivity to help college students in entry-level computer science-related majors learn to program structured applications using dynamic and interactive flowcharts. METHODS: In order to examine the influences of said Augmented Reality-enhanced system on student learning, an experiment will be carried out within the group with 34 university students. All students used both an augmented realityenhanced version and a conventional paper version (classic methodology with paper flowcharts). RESULTS: The expected results is that the augmented reality version through the DF-RA mobile application made students have a better learning efficiency than the traditional paper system. In addition, the system enhanced with Augmented Reality also made students have improved perceptions in terms of system usability, flow experience, and usage perception. CONCLUSION: Experimental findings were analyzed to demonstrate that the augmented reality learning system increases students' motivation to study structured programming fundamentals and their practical competence. [ABSTRACT FROM AUTHOR]

With the reform of China's education industry, more and more universities are using computers to conduct examinations. For the automatic correction of essays as subjective questions, existing automatic English text scoring systems suffer from insufficient extraction of coherence information and low accuracy when analysing text coherence. Therefore, this paper proposes an unsupervised semantic coherence analysis model for English texts based on sentence semantic graphs, taking Chinese students' English compositions as the research context. Guided by the semantic coherence theory, the English text is represented as a sentence semantic graph, and an improved VF2 subgraph matching algorithm is used to mine the frequently occurring subgraph patterns in the sentence semantic graph. After that, the set of frequent subgraphs is generated by filtering the subgraph patterns according to their frequencies, and the subgraph frequency of each frequent subgraph is calculated separately. Finally, the distribution characteristics of frequent subgraphs and the semantic values of subgraphs in the sentence semantic graphs are extracted to quantify the overall coherence quality of English texts. The experimental results show that the model proposed in this paper has higher accuracy and practical value compared with the current methods of coherence analysis. [ABSTRACT FROM AUTHOR]

The research paper represents a novel approach to the design and optimization of a compiler for a domain-specific language (DSL) focused on geometric shape creation and manipulation. The primary objective is to develop a compiler capable of generating efficient machine code while offering users a high level of abstraction. The paper begins with an overview of DSLs and compilers, emphasizing their importance in software development. Next, it outlines the specific requirements of the geometric shape DSL and proposes a compiler design that addresses them. This innovative approach considers DSL's unique features, such as shape creation and manipulation, and aims to generate high-quality machine code. The paper also discusses optimization techniques to enhance the generated code's quality and performance, including loop unrolling and instruction scheduling. These optimizations are particularly suited to the DSL, which focuses on geometric shape creation and manipulation and are integral to achieving efficient machine code generation. In conclusion, the paper emphasizes the novelty of this approach to DSL compiler design and anticipates exciting results from testing the compiler developed for the geometric shape DSL. [ABSTRACT FROM AUTHOR]

Cloud computing delivers computing resources like software and hardware as a service to the users through a network. Due to the scale of the modern datacentres and their dynamic resources provisioning nature, we need efficient scheduling techniques to manage these resources. The main objective of scheduling is to assign tasks to adequate resources in order to achieve one or more optimization criteria. Scheduling is a challenging issue in the cloud environment, therefore many researchers have attempted to explore an optimal solution for task scheduling in the cloud environment. They have shown that traditional scheduling is not efficient in solving this problem and produce an optimal solution with polynomial time in the cloud environment. However, they introduced sub-optimal solutions within a short period of time. Meta-heuristic techniques have provided near-optimal or optimal solutions within an acceptable time for such problems. In this work, we have introduced the major concepts of resource scheduling and provided a comparative analysis of many task scheduling techniques based on different optimization criteria. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Intelligent diagnosis of CNC machine tool faults can not only early detection and troubleshooting to improve the reliability of machine tool operation and work efficiency, but also in advance of the station short maintenance to extend the life of the machine tool to ensure that the production line of normal production. OBJECTIVES: For the current research on CNC machine tool fault diagnosis, there are problems such as poorly considered feature selection and insufficiently precise methods. METHODS: This paper proposes a CNC machine tool fault diagnosis method based on improving random forest by intelligent optimisation algorithm with integrated learning as the framework. Firstly, the CNC machine tool fault diagnosis process is analysed to extract the CNC machine tool fault features and construct the time domain, frequency domain and time-frequency domain feature system; then, the random forest is improved by the marine predator optimization algorithm with integrated learning as the framework to construct the CNC machine tool fault diagnosis model; finally, the validity and superiority of the proposed method is verified by simulation experiment analysis. RESULTS: The results show that the proposed method meets the real-time requirements while improving the diagnosis accuracy. [ABSTRACT FROM AUTHOR]

In order to improve the safety of college physical education resources recommendation and reduce the test overlap rate and resource exposure rate, a personalized recommendation method of college P.E. teaching resources based on cognitive diagnosis model is proposed. A cognitive diagnosis model based on multi-level attribute score is designed to model students' resource mastery level according to existing answers and the relevance of knowledge points. The knowledge mastery attribute model of the tested students is used for probability matrix decomposition to predict the students' answers, and make corresponding resource recommendations according to the score prediction and resource difficulty. Experiments show that resource exposure value of the method in this paper is lower than 1, and its security is high. Regarding the experiment of overlapping indicators, the value of the test overlap rate of the method in this paper is always lower than 0.01, and the recommended resources are more accurate. The F1 value of the method in this paper is up to 0.98, and the deviation of resource recommendation is small. And the real-time performance is high after the method is applied, and the phenomenon of cold start will not occur. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Timely and effective early warning of new media events not only provides academic value to the study of new media events, but also can play a positive role in promoting the resolution of public opinion. OBJECTIVES: Aiming at the current research on early warning of new media events, there are problems such as the theoretical research is not in-depth and the early warning model is not comprehensive. METHOD: In this paper, K-means and seasonal optimization algorithm are used to construct new media event early warning method. Firstly, by analyzing the construction process of new media event early warning system, extracting text feature vector and carrying out text feature dimensionality reduction; then, combining with the random forest algorithm, the new media event early warning method based on intelligent optimization algorithm optimizing K-means clustering algorithm is proposed; finally, the validity and superiority of the proposed method is verified through the analysis of simulation experiments. RESULTS: The method developed in this paper improves the accuracy, time performance of new media event warning techniques. CONCLUSION:Addresses the lack of comprehensiveness of current approaches to early warning of new media events. [ABSTRACT FROM AUTHOR]

INTRODUCTION: In present days, Automation of everything has become essential. Internet of things (IoT) play an important role among all medical advances of IT. In this paper, feasible solutions are discussed to compare and design better healthcare systems. A thorough investigation and survey of suitable approaches were done to select IoT based systems in hospitals consisting of various high precision sensors. OBJECTIVES: The challenge healthcare system face is to manage the real time patient’s data with high accuracy. Second challenge is at fog devices level to manage the load distribution to all sensors with limited availability of bandwidth. METHODS: This paper summarizes the selection criterions of suitable load balancing algorithms to reduce energy consumption and computational cost of fog devices and increase the network usage that are supposed to be used in IoT based healthcare systems. According to the survey BNBKnapack algorithm has been selected as best suitable approach to analyze the overall performance of fog devices and results are also verify the same. RESULTS: Comparative analysis of Overall performance of fog devices has been proposed with using SJF algorithm and Adaptive BNBKnapsack algorithm. It has been observed by analysing system performance, which is found as best among other load balancing algorithm Adaptive BNBKnapsack is successfully reduce the energy consumption by (99.29%), computational cost by (98.34%) and increase the network usage by (99.95%) of system CONCLUSION: It has been observed by analysing system performance, Adaptive BNBKnapsack Load balancing is successfully able to reduce the computational cost and energy consumption also increase the network usage of the fog network. The performance of the system is found best among other load balancing algorithm. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Objective, accurate and fair development of research and effective performance prediction methodology for the construction of the talent team is the current needs of the new era of innovation and reform and development of university management, as well as the need to improve the quality of scientific research and teaching level of the talent team. OBJCTIVES: To address the problems of irrational principle of indicator selection, incomplete system and imprecise methodology in the current research on performance prediction of talent team building. METHODS: This paper proposes a talent team construction performance prediction method based on intelligent optimization algorithm improving neural network with integrated learning as the framework. First of all, through the analysis of the current talent team construction performance prediction influencing factors selection principles, analyze the talent team construction performance management process, select the talent team construction performance prediction influencing factors, and construct the talent team construction performance analysis system; then, with the integrated learning as a framework, improve the neural network through the internal search optimization algorithm to construct the talent team construction performance prediction model; finally, through the simulation experiments to analyze and verify the effectiveness and superiority of the proposed method. The effective type and superiority of the proposed method are verified. RESULTS: The results show that the proposed method satisfies the real-time requirements while improving the prediction accuracy. CONCLUSION: This paper addresses the lack of precision in forecasting the performance of the talent pipeline and the lack of a sound analytical system. [ABSTRACT FROM AUTHOR]

INTRODUCTION: With the development of information technology in education, many educational institutions and companies have flocked to the emerging online education market. MOOC is a new method of e-learning that has changed the world and significantly impacted the educational community. OBJECTIVES: This paper aims to present typical features for developing an online music education platform using MOOC music, AI-based music learning software, and interactive real-time music learning. METHODS: This paper discusses different models of music teaching methods and pedagogy and summarizes the advantages and disadvantages of such models. RESULTS: Online music education is an internet technology that fully uses the Internet right to organize good music education. CONCLUSION: Based on the characteristics and content of music education, this paper presents possible suggestions for the sustainable and healthy development of new data and information models for music education on the Internet in the future. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The paper constructs a massive data mining model of distributed spatiotemporal databases for the Internet of Things. Then a homologous data fusion method based on information entropy is proposed. The storage space required by the tree structure is reduced by constructing the data schema tree of the merged data set. Secondly, the optimal dynamic support degree is obtained by using a neural network and genetic algorithm. Frequent items in the Internet of Things data are mined to achieve the normalization of the clustered feature data based on the threshold value. Experiments show that the F-measure of the data mining algorithm improves the efficiency by 15.64% and 18.25% compared with the kinds of other literatures respectively. RI increased by 21.17% and 26.07%, respectively. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Robotics is currently the most cutting-edge international science and technology, as well as a highvalue-added core technology. Robots are widely used in a variety of industrial fields, as a new direction in the development of robotics, and play an important role in solving the current employment problems in China. OBJECTIVES: This paper combines its research results, introduces the machine learning method in the robot control system, and establishes the employment index system in the robot working environment by combining the employment factors with the environmental relationship analysis. METHODS: This paper combines its research results, introduces the machine learning method in the robot control system, and establishes the employment index system in the robot working environment by combining the employment factors with the environmental relationship analysis. RESULTS: The study found that the willingness of university students to choose a job gradually increases as their education level rises; the lower the level of education, the weaker their willingness to look for a job; the higher the level of education the more sensitive they are to the quality of education and educational specialities, the higher their willingness to work. CONCLUSION: Based on the robot control system design the factors that have an impact on the environment in real economic activities (e.g., age, gender, occupation, education level, etc.) play a role in promoting the future application and development of robotics in China. [ABSTRACT FROM AUTHOR]

Ubiquitous learning refers to the advancement of online learning, adapted to the development of communication and informatics. The study aimed to carry out a bibliometric approach to ubiquitous learning worldwide indexed in Scopus from 2003 to 2023. Methodologically, it was a quantitative, bibliometric study. Scientific output indicators were generated from 2668 selected Scopus papers using English keywords ("ubiquitous" AND "learning"). From 2007 to 2023, the number of published documents increased (90.9%), indicating a growing interest in the subject of study. The United States has the highest scientific production (21%), and the CNRS Centre National de la Recherche Scientifique has the most publications (n=39). The journal IEEE Access received 2514 citations, with the author Cook, D.J. (n=532) being the most cited. It is concluded that the orientation on ubiquitous learning is dynamic and increasingly linked to the development of information and communication technologies (ICT), as this learning style uses technology to give students more independence over their learning process. [ABSTRACT FROM AUTHOR]

In complex scenes, the accuracy of dance movement recognition is not high. Therefore, this paper proposes a deep learning and deformable part model (DPM) for dance movement feature extraction. Firstly, the number of filters in DPM is increased, and the branch and bound algorithm is combined to improve the accuracy. Secondly, deep neural network model is used to sample points of interest according to human dance movements. The features extracted from the DPM and deep neural network are fused. It achieves a large reduction in the number of model parameters and avoids the network being too deep. Finally, dance movement recognition is performed on the input data through the full connection layer. Experimental results show that the proposed method in this paper can get the recognition result more quickly and accurately on the dance movement data set. [ABSTRACT FROM AUTHOR]

Cloud technology makes it possible for users to access information from anywhere, all the time, on any device, and that is the major cause of the many different types of assaults. In principle, multiple dangers, including data leakage, information leakage, and unauthorized information accessibility, are active in cloud environment layering. Modern technological advancements are made accessible on a daily basis through cloud technology. In the cloud, access control and encryption solutions are more complicated. Because of this greater level, security flaws in online applications and systems are more likely to occur. Somewhere at the ends of the end nodes, a malignant insider can carry out protection assaults. Nevertheless, problems with user privacy and data protection on cloud-based social networking sites continue to exist. Such problems are not known to users. On that social networking site, they post a variety of images, videos, and private information that endures even after eradication. However, some of the data that has been made public was intended to be kept private; as a result, online social information has significantly increased the risk of personally identifiable information leaking. The context of cloud technology depends on the customer capabilities such as quick storing and retrieving offered through cloud computing environments. Dependable cloud providers use a number of methodologies to deliver various digital services, creating a variety of security risks. In this paper, the study of determining intrusive cyber-attacks over the online applications using the cloud data security. Restricting access to shared resources is essential to prevent hackers from stealing vulnerabilities in cloud computing to get unauthorised access to a user's activities as well as information. Gaining access to customer information and obstructing the use of cloud computing are the primary objectives of intrusions on cloud services. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The article discusses the key steps in digital visual design reengineering, with a special emphasis on the importance of information decoding and feature extraction for flat cultural heritage. These processes not only minimize damage to the aesthetic heritage itself but also feature high quality, efficiency, and recyclability. OBJECTIVES: The aim of the article is to explore the issues of gene extraction methods in digital visual design reengineering, proposing a visual gene extraction method through an improved K-means clustering algorithm. METHODS: A visual gene extraction method based on an improved K-means clustering algorithm is proposed. Initially analyzing the digital visual design reengineering process, combined with a color extraction method using the improved JSO algorithm-based K-means clustering algorithm, a gene extraction and clustering method for digital visual design reengineering is proposed and validated through experiments. .ASA-RESULT: The results show that the proposed method improves the accuracy, robustness, and real-time performance of clustering. Through comparative analysis with Dunhuang murals, the effectiveness of the color extraction method based on the K-means-JSO algorithm in the application of digital visual design reengineering is verified. The method based on the K-means-GWO algorithm performs best in terms of average clustering time and standard deviation. The optimization curve of color extraction based on the K-means-JSO algorithm converges faster and with better accuracy compared to the K-means-ABC, K-means-GWO, K-means-DE, K-means-CMAES, and K-means-WWCD algorithms. CONCLUSION: The color extraction method of the K-means clustering algorithm improved by the JSO algorithm proposed in this paper solves the problems of insufficient standardization in feature selection, lack of generalization ability, and inefficiency in visual gene extraction methods. [ABSTRACT FROM AUTHOR]

This exploration of paper presents an investigation of the Forest Region Inclusion Dataset that gives data on the backwoods inclusion of different nations overall from 1990 to 2020. The dataset contains country-wise information on population, population density, population development rate, total population rate, and forest region inclusion. We examined this dataset to decide the patterns in woodland region inclusion across various nations and mainlands, as well as the connection among populace and backwoods region inclusion. Our discoveries show that while certain nations have essentially expanded their forest region inclusion, others have encountered a decline. Besides, we found that population density and development rate are adversely related with forest area coverage. Authors have implemented four machine learning algorithms that are Linear Regression, Decision Tree, Random Forest and Support Vector Machine on the dataset. [ABSTRACT FROM AUTHOR]

Data pipelines are crucial for processing and transforming data in various domains, including finance, healthcare, and ecommerce. Ensuring the reliability and accuracy of data pipelines is of utmost importance to maintain data integrity and make informed business decisions. In this paper, we explore the significance of continuous monitoring in data pipelines and its contribution to data observability. This work discusses the challenges associated with monitoring data pipelines in realtime, propose a framework for real-time monitoring, and highlight its benefits in enhancing data observability. The findings of this work emphasize the need for organizations to adopt continuous monitoring practices to ensure data quality, detect anomalies, and improve overall system performance. [ABSTRACT FROM AUTHOR]

Function inlining is a critical optimization technique used by compilers to improve program performance by replacing a function call with the body of the function and eliminating the overhead associated with function calls. However, the decision of when to inline functions and when not to is a nontrivial problem due to interactions with the rest of the compiler pipeline. Incorrect inlining decisions can cause runtime performance degradation, making this problem a crucial one to study. This paper reviews the different techniques used to optimize function inlining, including simple textual substitution, profile-guided inlining, interprocedural optimization, partial inlining, speculative inlining, and advanced techniques such as indirect call optimizations. Each technique has its strengths, weaknesses, and trade-offs, and ongoing research is exploring ways to overcome these challenges. Optimizing function inlining is a complex problem, and different techniques offer different tradeoffs. Further research to improve the performance of function inlining while minimizing any potential drawbacks could be pursued based on this paper. [ABSTRACT FROM AUTHOR]

In the era of the 5.0 industry, the use of the Internet of Things (IoT) has increased. The data generates from sensors through IoT industrial systems, any fault in those systems affects their performance and leads to real disaster. Protecting them from any possible attacks is an essential task. to secure any system, it needs to predict in the first place possible attacks and faults that could happen in the future. Predicting and initiating the attack type and the accuracy of these predictions can be done with machine learning models nowadays on the datasets produced with IoT networks. This paper classifies several attacks type based on several criteria and techniques to enhance the performance of machine learning (ML) models such as Voting techniques beside six ML models; Random Forest (RF), Decision Tree (DT), K-nearest neighbor (KNN), Support Vector Machine (SVM), Logistic regression (LR), and eXtreme Gradient Boosting (XGBoost) using Enriching IoT dataset [1]. The results showed that 100% accuracy was achieved in estimating process with the XGBoost model. [ABSTRACT FROM AUTHOR]

This research is to see an overview of the management control system (MCS) associated with the organization using a bibliometrix analysis approach. The research method used is bibliometrix analysis approach using Vosviewer and Publish or Perish software. The total sample data observed from Scopus sources is 200 sample data from 2010-2021 related to MCS and Organizations. Findings – MCS and the organization of a company complement each other in operational processes and from the metadata results it can be seen that the number of studies that are linked between the Organization and MCS is still being carried out and increasing from year to year. This proves that MCS and organizations have an important role for the sustainability of an organization. Limitations – only use research metadata mapping with Vos Viewer Software. [ABSTRACT FROM AUTHOR]

Blockchain innovation (BT) guarantees benefits in believe capacity, collaboration, organization, identifying proof, validity, and transparency. These solid suggestions for the long run of how to verify the authenticity of academic certificates because a paper-based certification is fallible to manipulation and susceptible to fraud. This literature review started by collecting a test of basically peer-reviewed sources, as well as an enlightening outline of articles from different other channels to analyse about approximately later approaches, techniques and later patterns on instruction which is being utilized in blockchain innovation on document verification from different sector such as banking, medical records and education system etc. This paper proposes a digital certificate verification blockchain based system employing anowner authentication scheme and time, space of the students are stored as blocks using blockchain technology. A distributed public record with tamper proof and immutable that preserves the state of the document, which creates security in the digital asset. This clearly stated that this technology is required to keep digital assets in secure and any one can access without loss of data and maintained it with minimum cost. [ABSTRACT FROM AUTHOR]

Smart storage is widely used for its efficient storage and applications. For making dynamic decisions when robots conflict and eliminating robot conflicts and improving efficiency from a global perspective, path-planning Algorithm will be analyzed and improved by integrating dynamic weighted and heat-map algorithm based on the scalable information of multi-robot in this paper. Firstly, a small storage grid model applicable to a variety of storage modes is established. Second, in order to solve the frontal collision problem of robots, an improved reservation table is established, which greatly reduces the storage space occupied by the reservation table while improving the operation efficiency; the A* algorithm is improved to achieve the purpose of avoiding vertex conflict and edge conflict at the same time; dynamic weighting table is added to solve the multi-robot driving strategy of intersection conflict and ensure that the most urgent goods are out of the warehouse firstly; the heat map algorithm is appended to reasonably allocate tasks, avoiding congested areas and realizing the dynamic assignment of tasks. Finally, the simulation was done by the proposed path planning method, the average transportation time was reduced by 14.97% comparing with the traditional path algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we propose an automatic scoring method for Chinese-English spoken translation based on attention LSTM. We select semantic keywords, sentence drift and spoken fluency as the main parameters of scoring. In order to improve the accuracy of keyword scoring, this paper uses synonym discrimination method to identify the synonyms in the examinees' answer keywords. At the sentence level, attention LSTM model is used to analyze examinees' translation of sentence general idea. Finally, spoken fluency is scored based on tempo/rate and speech distribution. The final translation quality score is obtained by combining the weighted scores of the three parameters. The experimental results show that the proposed method is in good agreement with the result of manual grading, and achieves the expected design goal compared with other methods. [ABSTRACT FROM AUTHOR]

INTRODUCTION: A method for diagnosing abnormal body temperature based on deep neural network is proposed. OBJECTIVES: To improve the diagnostic accuracy, reduce the false alarm rate, and improve the diagnostic level of abnormal body temperature. METHODS: According to the weight of the temperature sensor node itself and its neighbor nodes, the network trust relationship is established, and the node trust value is output through the combination of decision-making. Use trust value and double threshold to identify and remove malicious nodes, and optimize the network structure. The optimized temperature sensor network is used to collect human body temperature data. RESULTS: A deep neural network is used to construct a diagnosis model of abnormal body temperature, so as to realize the diagnosis of abnormal body temperature. CONCLUSION: The experimental results show that the method in this paper has high diagnostic accuracy, low false positive rate and high diagnostic efficiency, and can improve the diagnostic level of abnormal body temperature. [ABSTRACT FROM AUTHOR]

In relation extraction, a major challenge is the absence of annotated samples. Relation extraction aims to extract the relationships between entity pairs from a large amount of unstructured data. To solve the above problems, this paper presents a new method for English relation extraction based on correlation temporal feature extraction network via residual network. Firstly, the attention mechanism and recurrent neural network are used to obtain the temporal features of English word correlation. Secondly, a multi-branch feature sensing convolutional neural network is constructed to obtain global and local temporal correlation features respectively. Residual network can dynamically reduce the influence of noise data and better extract the deep information of English text. Finally, the relation extraction is realized with Softmax classifier. Experimental results show that the proposed method can extract English relation effectively than other methods. [ABSTRACT FROM AUTHOR]

Applying federated learning into the covert communication can not only ensure the communication reliability, but also reduce the probability of enemy detection. The use of airspace resources is an effective way to achieve covert communication. However, most of the existing works on the airspace covert communication represented by MIMO need to adapt to the channel state and cannot improve the channel, resulting in the performance bottleneck of covert communication. The intelligent reflective surface (IRS) provides a new perspective for the covert communication by flexibly adjusting the reflection phase shift of the incident signal and intelligently configuring the wireless channel. However, the potential of IRS in the covert communication is far from being fully exploited. To solve this issue, this paper firstly performs a comprehensive literature review on the secure communication with the aid of federated learning, and then gives some challenges on the secure communication in poor channel state. In further, this paper provides some solutions to the challenges on the secure communication, where some results are provided to show the advantages. The research results have important theoretical and practical significance for forming a new research paradigm of airspace intelligent and controllable covert communication and promoting the application and popularization of covert communication in various fields of security. [ABSTRACT FROM AUTHOR]

This paper employs deep learning technique to perform the research of text mining for power grid networks, focusing on fundamental elements such as loss and activation functions. Through some analysis and formulas, we explain how these functions contribute to deep learning. We also introduce major deep learning training models, including CNN and RNN, and provide visual aids to aid understanding. To demonstrate the impact of various factors on deep learning training, we employ control variable experiments to analyze the influence of factors such as learning rate, batch size, and data noise on model training trends. While the influence of hyperparameters and data noise are covered in this paper, other factors such as CPU and memory frequency, as well as GPU performance, also play a crucial role in deep learning training. Therefore, continuous adjustments to various factors are necessary to achieve optimal training results for deep learning models in power grid networks. [ABSTRACT FROM AUTHOR]

INTRODCTION: By analyzing the problem of self-monitoring in English online learning and constructing a strategyintegrated evaluation method, we can not only enrich the theoretical research results of self-monitoring in online learning, but also improve the independent learning ability and self-monitoring ability of students in English online learning. OBJECTIVES: To address the problem of poor optimization performance of current fusion optimization methods.METHODS:This paper proposes an online learning self-monitoring strategy fusion method based on improved nuclear reaction heuristic intelligent algorithm. First, the problems and enhancement strategies of online learning selfmonitoring are analyzed; then, the online learning self-monitoring strategy fusion model is constructed by improving the nuclear reaction heuristic intelligent algorithm; finally, the proposed method is verified to be effective and feasible through the analysis of simulation experiments. RESLUTS: The results show that the fusion method of learning self-monitoring strategies on the line at the 20th iteration number starts to converge to optimization with less than 0.1s optimization time, and the error of the statistical score value before and after weight optimization is controlled within 0.05. CONCLUSION:Addressing the Optimization of Convergence of Self-Monitoring Strategies for English Online Learning. [ABSTRACT FROM AUTHOR]

INTRODCTION: Immersive teaching and learning methods based on virtual reality-integrated remote platforms not only allow foreign language learners to learn in a vivid and intuitive learning environment, but also provide good conditions for multi-channel perceptual experiences of foreign language learners in terms of sight, sound and touch. OBJECTIVES: To address the problems of insufficiently systematic analysis and quantification, poor robustness and low accuracy of analysis methods in current effect analysis methods. METHODS: This paper proposes an effect analysis method of virtual reality fusion remote platform based on crawfish optimization algorithm to improve echo state network. First, the effect analysis system is constructed by analyzing the process of virtual reality fusion remote platform and extracting the effect analysis influencing elements; then, the echo state network is improved by the crayfish optimization algorithm and the effect analysis model is constructed; finally, the high accuracy of the proposed method is verified by the analysis of simulation experiments. RESLUTS: The proposed method improves the accuracy of the virtual reality fusion remote platform effect analysis model, the analysis time is 0.002s, which meets the real-time requirements, and the number of optimization convergence iterations is 16, which is better than other algorithms. CONCLUSION: The problems of insufficiently systematic analytical quantification of effect analysis methods, poor robustness of analytical methods, and low accuracy have been solved. [ABSTRACT FROM AUTHOR]

INTRODCTION: The study of automatic marking methods in the Department of Language Translation is conducive to the fairness and rationality of marking by examining the comprehensive level of the students' language, as well as sharing the objectivity and pressure of the marking teachers in marking the scripts. OBJECTIVES: Aiming at the current automatic scoring methods of translation systems, which have the problems of not considering the global nature of influence features and low precision. METHODS: This paper proposes an automatic scoring method for translation system based on intelligent optimization algorithm to improve the deep network. First, by analyzing the language translation scoring problem, selecting the key scoring influencing factors and analyzing the correlation and principal components; then, improving the long and shortterm memory network through the triangle search optimization algorithm and constructing the automatic scoring model of the translation system; finally, the high efficiency of the proposed method is verified through the analysis of simulation experiments. RESULTS: The proposed method is effective and improves the accuracy of the scoring model. CONCLUSION: solves the problem of inefficient scoring in the automatic scoring method of the translation system. [ABSTRACT FROM AUTHOR]

INTRODCTION: Listening strategy analysis and assessment not only need objective and fair sound listening strategy analysis, but also need high-precision and high real-time assessment model, and even more need in-depth analysis and feature extraction of the influencing factors of listening assessment. OBJECTIVES: To address the problems of current automatic assessment methods, such as non-specific application, poor generalization, low assessment accuracy, and poor real-time performance. METHODS: This paper proposes an automatic assessment method based on a deep confidence network based on crawfish optimization algorithm. First, the multi-dimensional listening strategy evaluation system is constructed by analyzing the listening improvement strategy; then, the depth confidence network is improved by the crayfish optimization algorithm to construct the automatic evaluation model; finally, through the analysis of simulation experiments. RESLUTS: The proposed method improves the evaluation accuracy, robustness, and real-time performance. The absolute value of the relative error of the automatic evaluation value of the proposed method is controlled in the range of 0.011, and the evaluation time is less than 0.005 s. The method is based on a deep confidence network based on the crayfish optimization algorithm. CONCLUSION: The problems of non-specific application of automated assessment methods, poor generalization, low assessment accuracy, and poor real-time performance are addressed. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The rise of social media platforms has brought about a concerning surge in the creation of fraudulent user profiles, with intentions ranging from spreading false information and perpetrating fraud to engaging in cyberbullying. The detection of these deceptive profiles has emerged as a critical imperative to safeguard the trustworthiness and security of online communities. OBJECTIVES: This paper focused on the detection and identification of fake social media profiles. METHODS: This paper introduces an innovative approach for discerning and categorizing counterfeit social media profiles by leveraging the majority voting approach. The proposed methodology integrates a range of machine learning algorithms, including Decision Trees, XGBoost, Random Forest, Extra Trees, Logistic Regression, AdaBoost and K-Nearest Neighbors each tailored to capture distinct facets of user behavior and profile attributes. This amalgamation of diverse methods results in an ensemble of classifiers, which are subsequently subjected to a majority voting mechanism to render a conclusive judgment regarding the legitimacy of a given social media profile. RESULTS: We conducted thorough experiments using a dataset containing both legitimate and fake social media profiles to determine the efficiency of our methodology. Our findings substantiate that the Majority Voting Technique surpasses individual classifiers, attaining an accuracy rate of 99.12%, a precision rate of 99.12%, a recall rate of 99.12%, and an F1-score of 99.12%. CONCLUSION: The results show that the majority vote method is reliable for detecting and recognising fake social media profiles. [ABSTRACT FROM AUTHOR]

INTRODUCTION: Sports video is an essential information resource. Classifying sports videos with high accuracy can effectively improve users' browsing and query effect. This project intends to study a motion video classification algorithm based on deep-learning particle filters to solve the problems of solid subjectivity and low accuracy of existing motion video classification algorithms. A critical box extraction method based on similarity is proposed. The moving video classification algorithm is studied based on a deep learning coding model. Examples of various types of sports videos are analyzed. The overall performance of the motion video classification algorithm proposed in this paper is much better than other existing motion video classification algorithms. This algorithm can significantly improve the classification performance of motion video. [ABSTRACT FROM AUTHOR]

The beyond 5G millimeter-wave cellular network system is expected to provide high-quality service in indoor areas. Due to the high density of obstacles, cooperative communication techniques are employed to improve the user's desired signal power by finding more than one appropriate station to serve that user. While the conventional system utilizes additional equipment such as Reconfigurable Intelligent Surfaces (RIS) and relays to enable cooperative features, the paper introduces a new network paradigm that utilizes the second nearest Base Station (BS) of the typical user as the Decode and Forward (DF) relay. Thus, depending on the success of decoding the message from the user's serving BS or the second nearest BS, the typical user can work with or without assistance from the relay, whose operation follows the discipline of the power-domain NOMA technique. In the case of relay assistance, the Maximum Ratio Combining technique is utilized by the typical user to combine the desired signals. To examine the performance of the proposed system, the Nakagamim and the newly developed path loss model, which considers the density of walls and their properties, are adopted to derive the coverage probability of the user with and without relay assistance. The closedform expressions of this performance metric are derived using Gauss quadrature and Welch-Satterthwaite approximation. Through analytical and simulation results, it is seen that the proposed system can improve the user coverage probability by up to 10%. [ABSTRACT FROM AUTHOR]

The role of information transmission in social life is irreplaceable, and language is a very important information carrier. Among all kinds of languages, English always occupies an important position. In the process of English learning, grammar error has become a difficult problem for most learners. In this paper, we propose an automatic grammar error correction model based on encoder-decoder structure. Different from traditional encoders, we design a dual-encoder structure to capture the information of source sentence and context sentence separately. The decoder is designed with a gated structure, it can effectively integrate output information of encoders. At the same time, the self-attention mechanism is combined to better solve the problem of long-distance information extraction. In addition, we propose a dynamic beam search algorithm to improve the accuracy of the word prediction process, and achieve dynamic extraction of the decoder output by combining kernel sampling techniques. We add a penalty factor to reduce the probability of generating repeated words, while suppressing the model's preference for generating shorter sentences. Finally, the proposed method is validated on the official English grammar error correction dataset. Experiments show that the dual encoder model in this paper has a good performance. [ABSTRACT FROM AUTHOR]

INTRODUCTION: The optimization of the teaching evaluation system, as an essential part of teaching reform in higher vocational colleges and universities, is conducive to the development of higher vocational colleges and universities' disciplines, making the existing teaching more standardized. OBJECTIVES: Aiming at the problems of inefficiency, incomplete index system, and low assessment accuracy in evaluation methods of higher vocational colleges and universities. METHODS: Proposes a teaching evaluation method for higher vocational colleges and universities with a big data mining algorithm and an intelligent optimization algorithm. Firstly, the teaching evaluation index system of higher vocational colleges and universities is downgraded and analyzed by using principal component analysis; then, the random forest hyperparameters are optimized by the grey wolf optimization algorithm, and the teaching evaluation model of higher vocational colleges and universities is constructed; finally, the validity and stability of the proposed method is verified by simulation experimental analysis. RESULTS: The results show that the proposed method improves the accuracy of the evaluation model. CONCLUSION: Solves the problems of low evaluation accuracy, incomplete system, and low efficiency of teaching evaluation methods in higher vocational colleges. [ABSTRACT FROM AUTHOR]

This article aims to explore the learning characteristics of online learners within the smart education framework, with a specific emphasis on how they might use Internet of Things (IoT) technologies to improve their educational experience. The term "online learning" refers to the process of acquiring knowledge via electronic means, most often the global web. Online education, e-learning, web-based learning, and computer-assisted learning all share this term. The challenging characteristics of such online learners for students are technical issues, lack of motivation, and slow loading times in online courses. Hence, in this research, the Internet of Things-empowered Smart Education (IoT-SE) Framework has been improved for online learners for students by leveraging IoT tech that tracks how learners interact with learning resources and their environment. This paper aims to revolutionize web-based education through tailored instructions targeting individuals' unique needs and fads as availed by the IoT-SE system. This paper offers evaluation parameters such as level of engagement among learners, retention rates on knowledge acquired while studying e-courses, and satisfaction from an online program. Besides overcoming limitations associated with conventional e-learning approaches, such systems like IoT-SE technology promise more effective pedagogy and student satisfaction for online learners. [ABSTRACT FROM AUTHOR]

Stress is a major problem in society. Prolonged stress can lead to ill-health and a decrease in self-confidence. It is necessary to detect stress at an early stage to prevent its adverse effects on our physical and psychological health. The paper presents a stress detection model using physiological signals. In this paper, WESAD (Wearable and Stress Affect Detection) dataset is used which consists of physiological data recorded from both the chest and wrist. Further, a Long Short-Term Memory (LSTM) based model is used to detect stress. The simulation results indicate that, indeed, Electrocardiograph (ECG), Electromyogram (EMG), and Respiration (RESP) signals may not be necessary for identifying stress. A three-way validation is carried out with an accuracy of 98%. The novelty of the paper is the way time-series data is handled to make it closer to real-time data captured from sensors. The work can be used widely in clinical practices to detect stress at an early stage. [ABSTRACT FROM AUTHOR]

Motivated by the progress in artificial intelligence such as deep learning and IoT networks, this paper presents an intelligent flink framework for real-time voltage computing systems in autonomous and controllable environments. The proposed framework employs machine learning algorithms to predict voltage values and adjust them in real-time to ensure the optimal performance of the power grid. The system is designed to be autonomous and controllable, enabling it to adapt to changing conditions and optimize its operation without human intervention. The paper also presents experimental results that demonstrate the effectiveness of the proposed framework in improving the accuracy and efficiency of voltage computing systems. Simulation results are provided to verify that the proposed intelligent flink framework can work well for real-time voltage computing systems in autonomous and controllable environments, compared with the conventional DRL and cross-entropy methods, in terms of convergence rate and estimation result. Overall, the intelligent flink framework presented in this paper has the potential to significantly improve the performance and reliability of power grids, leading to more efficient and sustainable energy systems. [ABSTRACT FROM AUTHOR]

Internet of Things is the interconnection of different components so that it should be able to communicate to each other to achieve a common goal. Therefore, for these components to communicate there will be a need of the communication Technologies. With the coming in of 5G wireless Network which provide more ability over the 2G,3G, 4G and other communication technologies. In this survey paper focuses much on what have been done before with 5G wireless Network in relation to Internet of Things, Application of 5G IoT, it also describes the various challenges which may arise when implementing it and the research questions. The next-generation protocol that will help to alleviate the current challenges which have been encountered. This paper will analyze the comprehensive survey on fifth-generation wireless network and Internet of Things from different papers and this will help to map the way forward on what best can apply 5G and other protocols in Internet of things. [ABSTRACT FROM AUTHOR]

In this paper, we study the relaying and cache aided digital grid data transmission, where the relaying may be equipped by caching or not, depending on specific applications. For both cases, we evaluate the impact of relaying and caching on the system performance of digital grid data transmission through theoretical derivation. To this end, an analytical expression on the outage probability is firstly derived for the data transmission. We then provide an asymptotic expression on the system outage probability. Finally, some simulation results are provided to verify the correctness of the derived analysis on the system performance, and show the impact of relaying and caching on the data transmission of digital grid system. In particular, the usage of caching at the relaying can help strengthen the data transmission performance of the considered system effectively. The results in this paper could provide some reference to the development of wireless transmission and scalable information systems. [ABSTRACT FROM AUTHOR]

Road scene understanding is one of the important modules in the field of autonomous driving. It can provide more information about roads and play an important role in building high-precision maps and real-time planning. Among them, semantic segmentation can assign category information to each pixel of image, which is the most commonly used method in automatic driving scene understanding. However, most commonly used semantic segmentation algorithms cannot achieve a good balance between speed and precision. In this paper, a road scene segmentation model based on dilated convolutional neural network is constructed. The model consists of a front-end module and a context module. The frontend module is an improved structure of VGG-16 fused dilated convolution, and the context module is a cascade of dilated convolution layers with different expansion coefficients, which is trained by a two-stage training method. The network proposed in this paper can run in real time and ensure the accuracy to meet the requirements of practical applications, and has been verified and analyzed on Cityscapes data set. [ABSTRACT FROM AUTHOR]