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One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations. [ABSTRACT FROM AUTHOR]

In order to improve the effect of intelligent automatic test paper composition, this paper combines the hybrid fuzzy clustering algorithm to study the computer automatic test paper composition algorithm. In this paper, a computer automatic test paper composition system based on hybrid fuzzy clustering algorithm is constructed. Moreover, the hybrid fuzzy clustering method used in this paper is used as the basic algorithm of the system, and the algorithm is improved according to the actual needs of intelligent paper composition. In addition, this paper uses an intelligent algorithm to input the relevant constraint parameters and combines the original parameters to select the most suitable test questions from the database and combine them into test papers. Finally, this paper constructs the system structure based on the requirements of intelligent test paper composition. The experimental research shows that the computer automatic test paper composition system based on the hybrid fuzzy clustering algorithm proposed in this paper has a good test paper composition function, which can effectively promote the progress of the intelligent examination mode in colleges and universities. [ABSTRACT FROM AUTHOR]

In order to improve the teaching effect of sports dance, this paper analyzes the traditional dance teaching motion capture, uses sensor motion perception algorithms to capture sports dance motion perception, and designs an intelligent sensor system that can be used for sports dance motion capture. Moreover, this paper combines the digital filter algorithm to design the hardware system structure of the sports dance motion capture system and builds a motion capture system for sports dance teaching based on the digital filter algorithm according to actual needs. In addition, this paper combines the simulation test to evaluate the performance of the system designed in this paper. The research results show that the motion capture system for sports dance teaching based on the digital filtering algorithm proposed in this paper can play an important role in sports dance teaching and effectively improve the efficiency of sports dance teaching. [ABSTRACT FROM AUTHOR]

At present, the monitoring of concrete cracks is still mainly carried out by engineering personnel using simple mechanical monitoring instruments. The human inspection will undoubtedly be interfered by the individual's psychological, physical, and external conditions, and there may also be unobjective emotions, so it is impossible to ensure that the quality of the detection is up to standard and accurate. This paper combines digital image processing technology and multiscale feature analysis automatic detection algorithm to construct an intelligent building structure crack detection system. Moreover, this paper proposes an enrichment scheme for the unknown partially entangled states of building microparticles and utilizes the entanglement exchange process based on the Raman interaction of two building microparticles. The experimental results show that the automatic detection method of building cracks based on digital image processing technology and multiscale feature analysis has a good effect. [ABSTRACT FROM AUTHOR]

In order to improve the effect of 3D virtual product network display, this paper combines digital drive technology to analyze the virtual simulation algorithm and proposes a digital drive-based Hough transform clustering virtual image processing algorithm. Through the knowledge of clustering and generalized Hough transform, generalized Hough transform is applied to clustering. Moreover, this paper uses cluster analysis to determine the image characteristics of three-dimensional virtual products. In addition, this article combines the methods proposed in this article to construct a three-dimensional virtual product network display system. The research shows that the digital-driven 3D virtual product network display algorithm model proposed in this paper has a good 3D virtual display effect. [ABSTRACT FROM AUTHOR]

Cross-language communication puts forward higher requirements for information mining in English translation course. Aiming at the problem that the frequent patterns in the current digital mining algorithms produce a large number of patterns and rules, with a long execution time, this paper proposes a digital mining algorithm for English translation course information based on digital twin technology. According to the results of word segmentation and tagging, the feature words of English translation text are extracted, and the cross-language mapping of text is established by using digital twin technology. The estimated probability of text translation is maximized by corresponding relationship. The text information is transformed into text vector, the semantic similarity of text is calculated, and the degree of translation matching is judged. Based on this data dimension, the frequent sequence is constructed by transforming suffix sequence into prefix sequence, and the digital mining algorithm is designed. The results of example analysis show that the execution time of digital mining algorithm based on digital twin technology is significantly shorter than that based on Apriori and Map Reduce, and the mining accuracy rate reached more than 80%, which has good performance in processing massive data. [ABSTRACT FROM AUTHOR]

With the development of the times, figurative expressions no longer meet the creative needs of artists and the aesthetic demands of the people. In order to express art in a more profound way, the perfect use of abstract graphics plays a crucial role in the success of the work. In recent years, there has been a surge in the creation of digital art, but there is relatively little theoretical literature on the combination of abstract graphics as a visual language and digital art. In addition, research on the theoretical aspects of digital art design is also relatively weak, so it is essential to analyse the formal aesthetics and innovative applications. In fact, digital art design has a very important role to play in promoting the development of creative cultural industries. In other words, the healthy development of digital art design can influence the future prospects of a country's creative and cultural industries. Digital art design is an integrated and complex production process and labour outcome. In addition to its human, aesthetic, and social value, digital art also has an economic value. Digital art is a new art form that combines digital technology and artistic aesthetics. As such, digital art is characterised by high technology, diverse forms, popularised art, and the advantages of high communication, interactivity, and influence, which can provide more assistance for the innovation and application of abstract graphics. Digital art is multifaceted and has an artistic expression that cannot be matched by other forms of technology. Abstract graphics, driven by digital art, are full of novelty and interest and can greatly enrich people's emotions and senses. Abstract graphics bring the experience of digital art to its fullest potential. The combination of digital art and abstract graphics offers more innovation and possibilities for the development of art and will bring great prosperity to art communication. With the widespread use of computer and network technology, the Internet has developed rapidly. In this context, digital art, as art created in a digital way and concept, has gained widespread attention. As a result, how to integrate existing computer resources in the new environment to build a model of digital art design effectiveness will cause a direct influence on the quality of digital art design with digital content innovation as the core. At the same time, as digital art becomes more and more popular, the demand for digital talents becomes very urgent. As a result, the cultivation of high-quality digital talents has become a major concern for society. Therefore, in order to explore the success of digital art design and the cultivation of digital art talents, and to better serve the innovation of digital art, this paper proposes a digital art effectiveness model based on the K-medoids algorithm. This model can provide a deeper and more comprehensive understanding of digital art and abstract graphics and provide theoretical support for professional design creation. [ABSTRACT FROM AUTHOR]

On the network service platform for vocational education, there are currently over 10,000 online courses. Learners face a challenge in selecting interesting courses from the vast resources available. Learners' urgent need for personalized learning is becoming more apparent as educational informatization progresses. Personalized recommendation (PR) technology can aid personalized learning and increase learners' learning efficiency significantly. This paper constructs a smart classroom model based on AI (artificial intelligence) by studying the connotation and characteristics of smart classroom in light of the current research status and trend of smart classroom at home and abroad. The merits of the recommendation system are determined by the recommendation algorithm used by PR system. This paper primarily focuses on developing a personalized learning platform based on the CF (collaborative filtering) algorithm, as well as conducting system requirements analysis, database design, functional module design, implementation, and testing on this foundation. Experiments are carried out to see if the optimized PR algorithm in the network learning platform is effective. [ABSTRACT FROM AUTHOR]

Data anomaly detection in small hydropower stations is an important research area because it positively affects the reliability of optimal scheduling and subsequent analytical studies of small hydropower station clusters. Although many anomaly detection algorithms have been introduced in the data preprocessing stage in various research areas, there is still little research on effective and highly reliable anomaly detection systems for practical applications in small hydropower stations. Therefore, this paper proposes a real‐time data anomaly detection system for small hydropower clusters (RDADS‐SHC) considering multiple time series. It addresses the difficulties of timely detection, alerting, and management of real‐time data anomalies (errors, omissions, and so on) in existing small hydropower stations. It proposes a real‐time data anomaly detection algorithm for small hydropower stations integrated with the Z‐score and dynamic time warping, which can detect and process abnormal information more accurately and efficiently, thereby improving the stability and reliability of data sampling. The paper proposes a Keepalived‐based hot‐standby RDADS‐SHC deployment model with m (m ≥ 2) units. It can automatically remove and restart faulty services and switch to their standbys, which significantly improve the reliability of the proposed system, ensuring the safe and stable operation of related functional services. This paper can detect anomalous data more accurately, and the system is more stable and reliable in a cluster detection environment. The actual operation has shown that compared with existing anomaly detection systems, the architecture and algorithms proposed in this paper can detect anomalous data more accurately, and the system is more stable and reliable in the small hydropower cluster detection environment. It solves abnormal data management in small hydropower stations and provides reliable support for subsequent analysis and decision‐making. [ABSTRACT FROM AUTHOR]

Sensor-based agriculture monitoring systems have limited outcomes on the detection or counting of vegetables from agriculture fields due to the utilization of either conventional color transformations or machine learning-based methods. To overcome these limitations, this research is aimed at proposing an IoT-based smart agriculture monitoring system with multiple algorithms such as detection, quantification, ripeness checking, and detection of infected vegetables. This paper presents smart agriculture monitoring systems for Internet of Things (IoT) applications. The CHT has been applied to detect and quantify vegetables from the agriculture field. Using color thresholding and color segmentation techniques, defected vegetables have also been detected. A machine learning method-convolutional neural network (CNN) has been used for the development and implementation of all algorithms. A comparison between traditional methods and CNN has been simulated in MATLAB to find out the optimal method for its implementation in this agricultural monitoring system. Compared to the traditional methods, the CNN is the optimal method in this research work which performed better over the previously developed algorithms with an accuracy of more than 90%. As an example (case study), a tomato field in Chittagong, Bangladesh, was chosen where a camera-mounted mobile robot captured images from the agriculture field for which the proposed IoT-based smart monitoring system was developed. This system will benefit farmers through the digitally monitored output at an agriculture field in Bangladesh as well as in Malaysia. Since this proposed smart IoT-based system is still driven by bulky, costly, and limited powered sensors, in a future work, for the required power of sensors, this research work is aimed at the design and development of an energy harvester (hybrid) (HEH) based on ultralow power electronics circuits to generate the required power of sensors. Implementation of multiple algorithms using CNN, circular Hough transformation (CHT), color thresholding, and color segmentation methods for the detection, quantification, ripeness checking, and detection of infected crops. [ABSTRACT FROM AUTHOR]

Sensor-based agriculture monitoring systems have limited outcomes on the detection or counting of vegetables from agriculture fields due to the utilization of either conventional color transformations or machine learning-based methods. To overcome these limitations, this research is aimed at proposing an IoT-based smart agriculture monitoring system with multiple algorithms such as detection, quantification, ripeness checking, and detection of infected vegetables. This paper presents smart agriculture monitoring systems for Internet of Things (IoT) applications. The CHT has been applied to detect and quantify vegetables from the agriculture field. Using color thresholding and color segmentation techniques, defected vegetables have also been detected. A machine learning method-convolutional neural network (CNN) has been used for the development and implementation of all algorithms. A comparison between traditional methods and CNN has been simulated in MATLAB to find out the optimal method for its implementation in this agricultural monitoring system. Compared to the traditional methods, the CNN is the optimal method in this research work which performed better over the previously developed algorithms with an accuracy of more than 90%. As an example (case study), a tomato field in Chittagong, Bangladesh, was chosen where a camera-mounted mobile robot captured images from the agriculture field for which the proposed IoT-based smart monitoring system was developed. This system will benefit farmers through the digitally monitored output at an agriculture field in Bangladesh as well as in Malaysia. Since this proposed smart IoT-based system is still driven by bulky, costly, and limited powered sensors, in a future work, for the required power of sensors, this research work is aimed at the design and development of an energy harvester (hybrid) (HEH) based on ultralow power electronics circuits to generate the required power of sensors. Implementation of multiple algorithms using CNN, circular Hough transformation (CHT), color thresholding, and color segmentation methods for the detection, quantification, ripeness checking, and detection of infected crops. [ABSTRACT FROM AUTHOR]

Sensor-based agriculture monitoring systems have limited outcomes on the detection or counting of vegetables from agriculture fields due to the utilization of either conventional color transformations or machine learning-based methods. To overcome these limitations, this research is aimed at proposing an IoT-based smart agriculture monitoring system with multiple algorithms such as detection, quantification, ripeness checking, and detection of infected vegetables. This paper presents smart agriculture monitoring systems for Internet of Things (IoT) applications. The CHT has been applied to detect and quantify vegetables from the agriculture field. Using color thresholding and color segmentation techniques, defected vegetables have also been detected. A machine learning method-convolutional neural network (CNN) has been used for the development and implementation of all algorithms. A comparison between traditional methods and CNN has been simulated in MATLAB to find out the optimal method for its implementation in this agricultural monitoring system. Compared to the traditional methods, the CNN is the optimal method in this research work which performed better over the previously developed algorithms with an accuracy of more than 90%. As an example (case study), a tomato field in Chittagong, Bangladesh, was chosen where a camera-mounted mobile robot captured images from the agriculture field for which the proposed IoT-based smart monitoring system was developed. This system will benefit farmers through the digitally monitored output at an agriculture field in Bangladesh as well as in Malaysia. Since this proposed smart IoT-based system is still driven by bulky, costly, and limited powered sensors, in a future work, for the required power of sensors, this research work is aimed at the design and development of an energy harvester (hybrid) (HEH) based on ultralow power electronics circuits to generate the required power of sensors. Implementation of multiple algorithms using CNN, circular Hough transformation (CHT), color thresholding, and color segmentation methods for the detection, quantification, ripeness checking, and detection of infected crops. [ABSTRACT FROM AUTHOR]

This paper proposes a smart grid distributed security architecture based on blockchain technology and SDN cluster structure, referred to as ClusterBlock model, which combines the advantages of two emerging technologies, blockchain and SDN. The blockchain technology allows for distributed peer-to-peer networks, where the network can ensure the trusted interaction of untrusted nodes in the network. At the same time, this article adopts the design of an SDN controller distributed cluster to avoid single point of failure and balance the load between equipment and the controller. A cluster head was selected in each SDN cluster, and it was used as a blockchain node to construct an SDN cluster head blockchain. By combining blockchain technology, the security and privacy of the SDN communication network can be enhanced. At the same time, this paper designs a distributed control strategy and network attack detection algorithm based on blockchain consensus and introduces the Jaccard similarity coefficient to detect the network attacks. Finally, this paper evaluates the ClusterBlock model and the existing model based on the OpenFlow protocol through simulation experiments and compares the security performance. The evaluation results show that the ClusterBlock model has more stable bandwidth and stronger security performance in the face of DDoS attacks of the same scale. [ABSTRACT FROM AUTHOR]

Accurate and timely forecasting of critical components is pivotal in intelligent transportation systems and traffic management, crucially mitigating congestion and enhancing safety. This paper aims to comprehensively review deep learning algorithms and classical models employed in traffic forecasting. Spanning diverse traffic datasets, the study encompasses various scenarios, offering a nuanced understanding of traffic forecasting methods. Reviewing 111 seminal research works since the 1980s, encompassing both deep learning and classical models, the paper begins by detailing the data sources utilized in transportation systems. Subsequently, it delves into the theoretical underpinnings of prevalent deep learning algorithms and classical models prevalent in traffic forecasting. Furthermore, it investigates the application of these algorithms and models in forecasting key traffic characteristics, informed by their utility in transport and traffic analyses. Finally, the study elucidates the merits and drawbacks of proposed models through applied research in traffic forecasting. Findings indicate that while deep learning algorithms and classic models serve as valuable tools, their suitability varies across contexts, necessitating careful consideration in future studies. The study underscores research opportunities in road traffic forecasting, providing a comprehensive guide for future endeavors in this domain. [ABSTRACT FROM AUTHOR]

Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking. [ABSTRACT FROM AUTHOR]

A method with high detection rate, low false-alarm rate, and low computational cost is presented for removing stars and noise and detecting space debris with signal-to-noise ratio (SNR>3) in consecutive raw frames. The top-hat transformation is implemented firstly to remove background, then a masking technique is proposed to remove stars, and finally, a weighted algorithm is used to detect the pieces of space debris. The simulation samples are images taken by 600 mm ground-based telescope. And a series of simulation targets are added to the image in order to test the detection rate and false-alarm rate of different SNRs. The telescope in this paper is worked in "staring target mode." The experimental results show that the proposed method can detect space debris effectively with low false-alarm by only three frames. When the SNR is higher than 3, the detection probability can reach 94%, and the false-alarm rate is below 2%. The running time of this algorithm is within 1 second. Additionally, algorithm performance tests in different regions are also carried out. A large set of image sequences are tested, which proves the stableness and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Currently, regardless of the algorithm used, motion planners for dealing with dynamic obstructions need to rely on high‐precision sensors and high performance processors. The requirements for hardware increase as the density of dynamic obstructions in an area becomes higher. Additionally, motion planners are more prone to errors in complex environments. The Rapidly‐exploring Random Tree (RRT ∗) algorithm only considers static obstructions and cannot effectively avoid densely populated regions of dynamic obstructions. This paper develops an improved RRT ∗ algorithm that is capable of avoiding densely populated regions of dynamic obstructions. In this algorithm, the cost function of the traditional RRT ∗ algorithm is modified based on the density of dynamic obstructions, allowing the planned path to bypass densely populated regions. The algorithm also introduces reasonable penalty terms to penalize segments that pass through densely populated regions, while maintaining asymptotic optimality of the traditional RRT ∗ algorithm. Numerical experiments reveal that the improved RRT ∗ algorithm is able to successfully avoid densely populated regions of dynamic obstructions with minimal time cost and exhibits better robustness during the path search process in comparison to the traditional RRT ∗ algorithm. Thus, the improved RRT ∗ algorithm possesses the ability to adapt to more complex areas for path planning. [ABSTRACT FROM AUTHOR]

The rapid development of mobile Internet has promoted the rapid rise of cloud computing technology. Mobile terminal devices have greatly expanded the service capacity of mobile terminals by migrating complex computing tasks to run in the cloud. However, in the process of data exchange between mobile terminals and cloud computing centers, on the one hand, it consumes the limited power of mobile terminals, and on the other hand, it results in longer communication time, which negatively affects user QoE. Mobile cloud can effectively improve user QoE by shortening the data transmission distance, reducing the power consumption, and shortening the communication time at the same time. In this paper, we utilize the property that genetic algorithm can perform global search seeking the global optimal solution and construct a dynamic task scheduling model by combining the device-cloud link. The task scheduling model based on genetic algorithm and random scheduling algorithm is compared through comparison experiments, which show that the assignment time of the task scheduling model based on genetic algorithm is shortened by 11.82% to 48.51% and the energy consumption is reduced by 22.28% to 47.52% under different load conditions. [ABSTRACT FROM AUTHOR]

This paper proposes a new algorithm composition network from the perspective of machine learning, based on an in-depth study of related literature. At the same time, this paper examines the characteristics of music and develops a model for recognising musical emotions. Using the model's information entropy of pitch and intensity to extract the main melody track, note features are extracted from bar features. Finally, the cosine of the vector included angle is used to judge the similarity between feature vectors of several adjacent sections, allowing the music to be divided into several independent segments. The emotional model of music is used to analyze each segment's emotion. By quantifying music features, this paper classifies and quantifies music emotion based on the mapping relationship between music features and emotion. Music emotion can be accurately identified by the model. The model's emotion recognition accuracy is up to 93.78 percent, and the algorithm's recall rate is up to 96.3 percent, according to simulation results. The recognition method used in this paper has a higher recognition ability than other methods, and the emotion recognition result is more reliable. This paper can not only meet the composer's auxiliary creative needs, but it can also help intelligent music services. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology and electronic information technology, the sports training system no longer depends on the traditional algorithm for operation support, and various advanced posture algorithms are emerging. At the same time, it also further optimizes the intelligence and accuracy of the sports training algorithm. As an advanced algorithm combined with virtual reality technology, human posture estimation algorithm plays an obvious role in optimizing the effect of sports training. This paper will design a motion training system based on the optimized and improved human posture trajectory algorithm, use the depth image correlation theory to solve the problem of non-Gaussian noise crosstalk in the depth image of the traditional human posture algorithm in principle, improve the accurate feature extraction of the depth image by the algorithm, and solve the problem of human feature redundancy, so as to further improve the accuracy of the establishment of a single human model; on the problem of multi-person posture estimation algorithm, this paper proposes a high-resolution multi-person posture high-precision network model and adds the focus mechanism. Based on this, this paper realizes the high-precision and high-speed modeling of multi-person posture, so as to provide an accurate model for the multi-person function of sports training system and improve the efficiency of the algorithm. In the experimental part, this paper takes tennis as a typical case to design the sports training system and experiments based on the system designed in this paper. The experimental results show that the system under the proposed algorithm has obvious advantages in accuracy and training effect. [ABSTRACT FROM AUTHOR]

This paper takes the flying defense team as the operating unit and studies the route planning and tasks in the process of plant protection operations as system problems. In the route planning, the flight defense team's operating time is used as the optimization goal of route planning. Taking the UAV battery and drug load as constraints, the flight mission is divided into subtasks that can be completed by a single UAV in a single flight. On this basis, the particle swarm algorithm is used to optimize the task allocation, so as to achieve the goal of UAV plant protection route and task division with minimum time loss. In order to verify the effectiveness of the method in this paper, 3 and 4 UAVs flying defense teams were used as the experimental objects, and experiments were carried out in large and small field groups. In terms of time consumption, the method in this paper is compared with the unit operation area method and the single size method. The energy consumption advantage is weaker than time consumption. In terms of time consumption, the method in this paper can greatly improve the efficiency of large-area operation areas. Compared with the unit operation area method, 3 drones can increase the efficiency by 58.98%, which is increased by 10.22% compared with the unit size method. The efficiency of 4 units can be increased by 58.08% compared with the unit operation area method, and the efficiency is increased by 10.22% compared with the unit size method. [ABSTRACT FROM AUTHOR]

Ideological and political education plays an important role in university education and is an important way to realize the function of educating people. It is of great significance to establish a perfect automatic question answering system for ideological and political education. Traditional automatic question answering methods usually rely on predicates and other prior information to achieve knowledge base question answering, which requires a lot of manpower and poor generalization ability. In order to solve this problem, this paper designs a question answering system for ideological and political education based on BiLSTM-CRF algorithm model (BiLSTM: Bidirectional Long Short-Term Memory and CRF: Conditional Random Fields). For the knowledge base question answering method with weak-dependent information, this paper combines BERT (Bidirectional Encoder Representation from Transformers) and BiLSTM-CRF network to extract the named entity in the questions and locate the triplet information related to the entity in the knowledge base. Through the answer matching network, the similarity score is marked for the answers in the triplet set, and the threshold selection strategy is used to select the answers that meet the requirements. And according to the similarity score from high to paper, it is presented to the user. The experimental results show that the method weakens the dependence on prior information, reduces manual intervention and ensures the quality of question answering, and completes the validity verification of the question answering system of ideological and political education. [ABSTRACT FROM AUTHOR]

3D modeling is the most basic technology to realize VR (virtual reality). VOS (video object segmentation) is a pixel-level task, which aims to segment the moving objects in each frame of the video. Combining theory with practice, this paper studies the process of 3D virtual scene construction, and on this basis, researches the optimization methods of 3D modeling. In this paper, an unsupervised VOS algorithm is proposed, which initializes the target by combining the moving edge of the target image and the appearance edge of the target and assists the modeling of the VR 3D model, which has reference significance for the future construction of large-scale VR scenes. The results show that the segmentation accuracy of this algorithm can reach more than 94%, which is about 9% higher than that of the FASTSEG method. 3D modeling technology is the foundation of 3D virtual scene; so, it is of practical significance to study the application of 3D modeling technology. At the same time, it is of positive significance to use the unsupervised VOS algorithm to assist the VR 3D model modeling. [ABSTRACT FROM AUTHOR]

Blockchain technology is widely used in the field of digital right protection technology. The traditional digital right protection scheme is not only inefficient and highly centralized but also has the risk of being modified. Due to its own characteristics, blockchain cannot completely store all the original files of digital resources. In this paper, a convolutional neural network algorithm based on visual priority rule is proposed (CNNVP). This algorithm can recognize facial expressions in the original files of digital resources (for short video of face class). The algorithm extracts facial expression features accurately and makes these features form log files that can represent the original files of digital resources. Then, the paper proposes a short video copyright storage algorithm based on blockchain and facial expression recognition and stores the log file into the blockchain. The above methods not only improve the efficiency of short video copyright storage, reduce the degree of storage centralization, and eliminate the risk that copyright is easy to be modified. Moreover, the computing operation of deep learning technology on short video not only ensures the privacy of storage certificate information but also ensures the possibility of blockchain storage of video information. Experiments show that the algorithm proposed in this paper is more efficient than the traditional copyright storage method. Moreover, the algorithm proposed in this paper can provide technical support to the media resource management department. [ABSTRACT FROM AUTHOR]

The data acquisition of Internet of Things (IoT) is mostly brought out by wireless sensor networks (WSNs), and the efficiency of IoT is directly affected by the time delay in the process of data acquisition, which is researched mainly by the data aggregation of WSNs. Minimizing the delay in the data aggregation process is one of the most important operations. In order to reduce the data aggregation delay, a hybrid delay-minimization data aggregation scheduling (HDDAS) algorithm is proposed, which optimizes the delay from the two main aspects that affect the delay, namely, the transmission path and the collision rate. Accordingly, the algorithm is divided into two phases: the aggregation tree construction and the data transmission scheduling. In the aggregation tree construction phase, the fat tree (FT) is introduced to generate an optimal aggregation tree with the shortest path; in the data transmission scheduling phase, the maximum parallel transmission set (MPTS) is adopted to increase the amount of the collision-free data transmission. The simulation experiment results demonstrate that the HDDAS algorithm proposed in the paper has performed favorably in terms of the data aggregation delay. [ABSTRACT FROM AUTHOR]

This paper proposes a blockchain-based identity authentication (BA) scheme for IoT devices to solve the authentication security problem of IoT devices. The BA scheme uses hashing and digital signature algorithms to achieve integrity and nonrepudiation of authentication messages. Blockchain technology is used to achieve decentralised and distributed storage and management of authentication data. Besides, the BA scheme uses the idea of trust domains and trust credentials to establish a master-slave connection between IoT devices. The BA scheme is then compared with the existing four schemes and analysed from six perspectives to show that the BA scheme has better security. Also, the results show that the BA scheme has reasonable computational and storage overhead. Finally, the advantages of the BA scheme over traditional centralised and existing blockchain-based authentication schemes are compared and analysed. The results show that it can perfectly solve the problem of overreliance on trusted third parties in traditional authentication schemes. [ABSTRACT FROM AUTHOR]

Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. [ABSTRACT FROM AUTHOR]

In the field of computer vision, it is a very challenging task to use artificial intelligence deep learning method to realize the programming and creation of NFT artwork. With the continuous development and improvement of deep learning technology, this task has become a reality. The generative adversarial network model used in deep learning can generate new images based on the extraction and analysis of image data features and has become an important tool for NFT artwork image generation. In order to better realize the NFT artwork programming, this paper analyzes the working principle of the traditional adversarial generation method and then uses the StyleGAN model to edit the higher-level attributes of the image, which can effectively control the generated style and style of the NFT artwork image. Finally, in order to improve the quality of the generated images, this paper introduces a channel attention mechanism and a spatial attention mechanism to ensure that the generated images are more reasonable and realistic. Finally, through a large number of experiments, it is proved that the NFT artwork transmission programming algorithm based on artificial intelligence deep learning proposed in this paper can control the overall style of image generation according to the needs of the transmission, and the generated image features have good details and high visual quality. [ABSTRACT FROM AUTHOR]

In recent years, with the acceleration of urbanization and the implementation of compulsory education, the pressure on students' study and life has increased, and the phenomenon of psychological and behavioral problems has become increasingly prominent. Therefore, the school has regarded students' mental health education as the top priority in teaching work. Effective expression classification can assist psychology researchers to study psychology and other disciplines and analyze children's psychological activities and mental states by classifying expressions, thereby reducing the occurrence of psychological behavior problems. Most of the current mainstream methods focus on the exploration of text explicit features and the optimization of representation models, and few works pay attention to deeper language expressions. Metaphors, as language expressions often used in daily life, are closely related to an individual's emotion, cognition, and psychological state. This paper studies children's smiling face recognition based on deep neural network. In order to obtain a better identification effect of mental health problems of children, this paper attempts to use multisource data, including consumption data, access control data, network logs, and grade data, and proposes a multisource data-based mental health problem identification algorithm. The main research focus is feature extraction, trying to use one-dimensional convolutional neural network (1D-CNN) to mine students' online patterns from online behavior sequences, calculate abnormal scores based on students' consumption data in the cafeteria, and describe the dietary differences among students. At the same time, this paper uses the students' psychological state data provided by the psychological center as a label to improve the deficiencies caused by the questionnaire. This paper uses the training set to train five common classification algorithms, evaluates them through the validation set, and selects the best classifier as our algorithm and uses it to identify students with mental health problems in the test set. The experimental results show that precision reaches 0.68, recall reaches 0.56, and F 1 -measure reaches 0.67. [ABSTRACT FROM AUTHOR]

Due to the lack of accurate modeling information in environment modeling, the traditional path planning algorithm for robot obstacle avoidance is of low accuracy. Therefore, this paper designs an obstacle avoidance path planning algorithm for embedded robot based on machine vision. First, the method of target edge detection is optimized in this paper. The edge detection results are obtained by color space transformation, and the complete target is obtained by edge fusion combined with surrounding pixel attributes. Then, the distance of the obstacle is measured by binocular depth ranging, and the longitudinal positioning of the robot is obtained, and the position of the obstacle is further obtained. Finally, a fuzzy control method for obstacle avoidance path planning is designed to obtain a complete planning scheme. The performance test results of the obstacle avoidance path algorithm show that the obstacle avoidance path planning scheme obtained by the algorithm designed in this paper has better performance in different obstacle avoidance test environments and can successfully avoid obstacles when the robot runs at high speed. [ABSTRACT FROM AUTHOR]

In the field of computer vision, action recognition is a very difficult topic to study. This paper suggests a dance movement recognition method based on DL network in accordance with the characteristics of dance movements. The backbone network in this study is a thin network called Mobile Net. The two-dimensional convolution network, which can only extract spatial features, can extract and fuse time domain features and use them for dance movement recognition by combining the time domain modelling strategy of time domain feature transfer between convolution layers. It uses fewer network parameters and less computation than the original multitarget detection model. Using the clustering method to preset the prior frames of human detection with various sizes and numbers also enhances the model's performance. Finally, the experimental findings demonstrate that the algorithm suggested in this paper outperforms the Incision v3 algorithm in F1 by 9.87 percent and outperforms the traditional CNN algorithm in identification accuracy by 6.51 percent and 10.76 percent, respectively. It is evident that the algorithm used in this paper reduces running time and, to a certain extent, improves the accuracy of dance movement recognition. For related research, it offers some references. [ABSTRACT FROM AUTHOR]

As China becomes more and more international, the number of people traveling abroad is also increasing. The demand for English recognition is becoming more and more vigorous, and traditional translation software is time-consuming, laborious, and less accurate. This article optimizes the target detection model YOLOV3. Firstly, the image is divided into multiple model structures, and the K-means++ clustering algorithm is used to determine the target detection prior frame value and the high frame of the corresponding frame according to the characteristics of the English image. Then, by using K-means++ clustering algorithm to optimize the anchor parameters, the model structure is better adapted to the English identification dataset scene; finally, the feature information extracted by the DarkNet-53 model is spliced to improve the structure of the YOLOV3 convolutional layer, using 3090 graphics card GPU to perform multiscale training and testing. Experimental results show that the improved YOLOV3 algorithm in this paper has a mAP of 0.95 on the English identification dataset and a detection speed of 50fps, which is 0.11 higher than the mAP before optimization. Therefore, optimizing the YOLOV3 algorithm in this article has a good effect. In the future, English translation will become a necessary software program for Chinese people to go abroad. [ABSTRACT FROM AUTHOR]

In view of the issue that the features of the images in the shallow layer cannot be fully utilized when the image description is generated and the target association of the image cannot be sufficiently obtained, a generation method for the description of the acquisition of attention images is put forward in this paper. The proportions of the features of images at various depths are autonomously assigned based on the content data of the language model, and the images thus generated are all pictures with image features with attention. In this way, the effect of description generation of images has been improved. After the testing of the database, the results indicate that the calculation method of the algorithm put forward in this paper is more accurate than the top-down multimedia image algorithm generated by a single attention. [ABSTRACT FROM AUTHOR]

With the rapid development of the information age, the development of industrial robots is also advancing by leaps and bounds. In the scenes of automobile, medicine, aerospace, and public service, we have fully enjoyed the convenience brought by industrial robots. However, with the continuous development of industrial robot-related concepts and technologies, human-computer interaction and cooperation have become the development trend of industrial robot. In this paper, the human-machine cooperation and path optimization of industrial robot in a complex road environment are studied and analyzed. At the theoretical modeling level, firstly, the industrial robot is modeled and obstacle avoidance is analyzed based on the kinematics of industrial robot; thus, an efficient and concise collision detection model of industrial robot is proposed. At the algorithm level, in view of the complex road conditions faced by industrial robots, this paper will study and analyze the obstacle avoidance strategy of human-computer cooperation and real-time path optimization algorithm of industrial robots. Based on the virtual target point algorithm, this paper further improves the problem that the goal of the traditional path planning algorithm cannot be fully covered, so as to propose the corresponding improved path planning algorithm of industrial robots. In the experimental part, based on the existing industrial robot system, the human-machine cooperation and path planning system proposed in this paper are designed. The experimental results show that the algorithm proposed in this paper improves the accuracy of obstacle avoidance by about 10 points and the corresponding convergence speed by about 5% compared with the traditional algorithm and the experimental effect is remarkable. [ABSTRACT FROM AUTHOR]

The minimum spanning tree (MST) is widely used in planning the most economical network. The algorithm for finding the MST of a connected graph is essential. In this paper, a new algorithm for finding MST is proposed based on a deduced theorem of MST. The algorithm first sorts the edges in the graph according to their weight, from large to small. Next, on the premise of ensuring the connectivity of the graph, it deletes the edges in this order until the number of edges of the graph is equal to the number of vertexes minus 1. Finally, the remaining graph is an MST. This algorithm is equivalent to an inverse Kruskal algorithm. For sparse graphs, the proposed algorithm has higher efficiency than the Kruskal algorithm. Experiments and analysis verify the effectiveness of the algorithm proposed in this paper. The algorithm provides a better choice for finding the MST of the sparse graph. [ABSTRACT FROM AUTHOR]

Clustering analysis, as one of the important methods in data mining technology, merely provides a method for the research and analysis of large amounts of data. Starting with the most important nodes, this paper divides clustering based on data field characteristics. The sensitivity pruning algorithm is then used to further adjust and optimize the structure of the fuzzy neural network, allowing the network to automatically learn the structure and parameters of the system in different environments and obtain the optimal control rules. Finally, the clustering function brings the algorithm's output result to a close. The experimental results show that the adaptive clustering algorithm of complex networks presented in this paper can effectively improve network cluster division, reduce algorithm time complexity, and avoid the problem of providing the number of cluster structures in advance. This method's cluster structure efficiency can reach 97.6 percent, and its highest clustering accuracy can reach 96.8 percent. The adaptive clustering algorithm proposed in this paper not only overcomes the traditional algorithm's flaws, such as the need to predetermine the number of clusters and the clustering result being dependent on the initial clustering center selection, but also has ideal clustering accuracy. This study introduces a novel and more effective method for addressing the difficult problems of practical complex control systems. [ABSTRACT FROM AUTHOR]

Doctoral education is an important part of higher education and plays an important role in cultivating many higher talents for the country. In doctoral education, thesis writing is an indispensable teaching section. The quality of the doctoral thesis will directly affect the graduation of doctoral students. Therefore, the analysis of the quality of the doctoral thesis is a typical aspect of the evaluation of doctoral education work. This paper aims to study the quality clustering analysis of the doctoral dissertation based on the depth algorithm. This paper establishes a doctoral paper quality cluster analysis model based on the depth algorithm and carries out the doctoral paper quality cluster analysis experiment based on this model. After the experiment, the main factors affecting the quality of doctoral dissertation were also analyzed. The conclusion is the following: The accuracy rate of the doctoral paper quality cluster analysis model based on the depth algorithm has reached 88.5%. [ABSTRACT FROM AUTHOR]

With the rapid development of information age, various social groups and corresponding institutions are producing a large amount of information data every day. For such huge data storage and identification, in order to manage such data more efficiently and reasonably, traditional semantic similarity algorithm emerges. However, the accuracy of the traditional semantic similarity algorithm is relatively low, and the convergence of corresponding algorithm is poor. Based on this problem, this paper starts with the conceptual structure of language, analyzes the depth of language structure and the distance between nodes, and analyzes the two levels as the starting point. For the information of a specific data resource description frame type, the weight of interconnected edges is used for impact analysis so as to realize the semantic similarity impact analysis of all information data. Based on the above improvements, this paper also systematically establishes the data information modeling process based on language conceptual structure and establishes the corresponding model. In the experimental part, the improved algorithm is simulated and analyzed. The simulation results show that compared with the traditional algorithm, the algorithm has obvious accuracy improvement. [ABSTRACT FROM AUTHOR]

With the rapid development of 5G communication and wireless Internet of Things technology, the application of intelligent wearable devices based on wireless data transmission technology is becoming more and more popular. However, due to the bandwidth of wireless data transmission nodes and the power consumption of the device system, the use time and data storage of wearable devices are severely restricted. The compressed sensing (CS) technology has become an effective method to tackle this problem. CS technology includes data compressive sensing at the transmission end and data reconstruction at the receiving end. In this paper, we consider the reconstruction of nonnegative sparse vectors, an important problem in the area of CS for wireless data transmission. It is known that the interval-passing (IP) algorithm is a low-complexity message-passing type method for the problem. However, the reconstruction performance of the IP algorithm is inferior to that of the other state-of-the-art CS reconstruction algorithms such as the orthogonal matching pursuit (OMP) algorithm. In order to address the problem, we propose a two-stage reconstruction algorithm in this paper. The proposed algorithm applies the IP algorithm for the first stage of reconstruction. If the reconstruction fails, the OMP algorithm is then used on the basis of the results in the first stage. The proposed algorithm is evaluated and compared with other state-of-the-art algorithms by the probability of perfect reconstruction under the given sparsity order value. Simulation results suggest that the proposed two-stage algorithm can greatly improve the reconstruction performance of the IP algorithm and can even outperform the OMP algorithm. In addition, the low-complexity advantages of the IP algorithm are maintained in the proposed algorithm. [ABSTRACT FROM AUTHOR]

To establish a suitable pure electric bus arrival time prediction model, this paper takes pure electric bus as the research object. Based on the analysis of the influencing factors of the arrival time of the pure electric bus, the BP neural network arrival time prediction model optimized by the firefly algorithm (FA-BP prediction model) is established by selecting vehicle type, SOC value, battery age, and time as input conditions. The model is trained and tested by using bus operation data. The root mean square error of the Kalman filter model is 0.351, of the BP neural network model is 0.059, and of the FA-BP prediction model is 0.04. The results show that the model in this paper effectively improves the prediction accuracy and has good reliability and feasibility. It can provide some theoretical references for pure electric bus operation and managers and provide some basis for improving bus reliability. [ABSTRACT FROM AUTHOR]

For the characteristics of channel instability in wireless sensor networks, this paper proposes an intrusion detection algorithm based on FedAvg (federated averaging) and XGBoost (extreme gradient boosting) wireless sensor networks using fog computing architecture. First, the network edge is extended by introducing fog computing nodes to reduce the communication delay. It reduces the transmission bandwidth and privacy leakage risk while improving the accuracy of jointly learned global and local models. Then, the histogram-based approximation calculation method is improved to adapt to the unbalanced data characteristics of wireless sensor networks. Finally, by introducing TOP-K gradient selection, the number of model parameter uploads is minimized, and the efficiency of model parameter interaction is improved. The experimental results show that this algorithm has superior detection performance and low energy consumption. It is also compared with other algorithms to demonstrate the high detection rate and low computational complexity of this algorithm. [ABSTRACT FROM AUTHOR]

In this paper, the reentry phase of the Aerospaceplane is taken as the research object, and the performance parameters of the reusable rocket of a private company are analyzed. Aiming at the guidance and control scheme of the spacecraft returning to the reentry trajectory in the real environment, the original natural algorithm is optimized by considering various reentry flight constraints, and the improved original natural algorithm is used to optimize the reentry trajectory of the Aerospaceplane. We obtained two types of reentry trajectories in the presence of large flight-restricted areas, the "S-type" trajectory and the "spiral-type" trajectory, and obtained data on various influencing factors. The results showed that the basic state parameters of the spiral trajectory optimized using the improved original natural algorithm after adding constraints met the constraint conditions. The aerodynamic heating rate and overload of the spiral reentry trajectory were to some extent greater than those of the S-type trajectory. Under the increasingly stringent requirements of the aerospace environment, new requirements were put forward for the thermal protection system to meet the wider environmental situation. This paper uses the improved original natural algorithm for the first time and applies it to the field of aerospace reentry and entry and adds more constraints to this algorithm for computation. Besides, for the first time, the macroscopic nature of trajectory types is used as a comparative element for parameter comparison, providing a reference basis for selecting trajectory optimization directions from the macroscopic perspective of trajectory types. [ABSTRACT FROM AUTHOR]

In this paper, a single English text is taken as the research object, and the automatic extraction method of text summary is studied using data-driven method. This paper takes a single text as the research object, establishes the connection relationship between article sentences, and proposes a method of automatic extraction of text summary based on graph model and topic model. The method combines the text graph model, complex network theory, and LDA topic model to construct a sentence synthesis scoring function to calculate the text single-sentence weights and output the sentences within the text threshold in descending order as text summaries. The algorithm improves the readability of the text summary while providing enough information for the text summary. In this paper, we propose a BERT-based topic-aware text summarization model based on a neural topic model. The approach uses the potential topic embedding representation encoded by the neural topic model to match with the embedding representation of BERT to guide topic generation to meet the requirements of semantic representation of text and explores topic inference and summary generation jointly in an end-to-end manner through the transformer architecture to capture semantic features while modelling long-range dependencies by a self-attentive mechanism. In this paper, we propose improvements based on pretrained models on both extractive and generative algorithms, making them enhanced for global information memory. Combining the advantages of both algorithms, a new joint model is proposed, which makes it possible to generate summaries that are more consistent with the original topic and have a reduced repetition rate for evenly distributed article information. Comparative experiments were conducted on several datasets and small uniformly distributed private datasets were constructed. In several comparative experiments, the evaluation metrics were improved by up to 2.5 percentage points, proving the effectiveness of the method, and a prototype system for an automatic abstract generation was built to demonstrate the results. [ABSTRACT FROM AUTHOR]

In recent years, as the research objects of phonetics have expanded to accent and colloquial natural speech, the construction of the dialect accent Mandarin voice database has become another important research direction in the field of computer technology. Among them, voice segmentation is a time-consuming and laborious link in the construction of the voice database. The application of artificial intelligence technology helps to improve the construction efficiency of the Mandarin dialect voice database. Based on this, this article mainly researches the application of the artificial intelligence algorithm in the automatic segmentation of dialect accent Mandarin. This paper constructs a voice corpus of dialect accents and Mandarin Chinese and specifically describes the construction process of the voice corpus. This paper uses artificial intelligence algorithms, combined with the HMM (hidden Markov model), and Viterbi algorithm to propose a new method of automatic speech segmentation. This paper studies the automatic speech segmentation model, extracts the general parameters of the training data in the Mandarin corpus, and conducts HMM training. This paper conducts tests based on the voice of the test set to verify the accuracy of the method proposed in this paper. The experimental results show that, in the speech data of 60 people, the error range of each sentence time period is less than 5 ms accounting for 79.16%, less than 10 ms accounting for 82.96%, less than 20 ms accounting for 83.14%, and less than 50 ms accounting for 86.92%. It can be seen that the algorithm proposed in this paper can meet practical applications in automatic speech segmentation. [ABSTRACT FROM AUTHOR]

This research aims to improve the rationality and intelligence of AUTOMATICALLY HIGHER MATHEMATICALLY EXAM SYSTEM (AHMES) through some AI algorithms. AHMES is an intelligent and high-quality higher math examination solution for the Department of Computer Engineering at Pai Chai University. This research redesigned the difficulty system of AHMES and used some AI algorithms for initialization and continuous adjustment. This paper describes the multiple linear regression algorithm involved in this research and the AHMES learning (AL) algorithm improved by the Q-learning algorithm. The simulation test results of the upgraded AHMES show the effectiveness of these algorithms. [ABSTRACT FROM AUTHOR]

With the continuous improvement of the network hardware environment, people turn the demand target to the network application environment and the construction of information resources. How to build a network teaching platform for general undergraduate teaching to ensure the stability of the system and high-quality services during operation especially large-scale concurrent access will inevitably lead to the increase in the business volume of each core part of the network, the number of visits, and data traffic. With the growth of the network, the corresponding processing power and computing intensity also increase rapidly, which causes problems such as overloading of core network equipment, network bottlenecks, and network congestion. Simply pursuing high-performance hardware to solve problems will undoubtedly result in high cost investment; moreover, equipment with excellent performance cannot meet the needs of the current rapidly growing business volume. According to the design goal of the dance online teaching platform, to meet the online teaching load requirements of many people at the same time, the pressure of the web server cluster must be great. Because many people in online at the same time put too much pressure on the web server, this part of the network cannot be processed in time, which leads to the phenomenon that the performance of this part and even the whole network is degraded. In severe cases, it will even cause network communication services to come to a standstill, that is, the so-called deadlock phenomenon. If the protocol software cannot detect congestion and reduce the packet sending rate, the network will be paralyzed due to congestion. This situation will cause the problem of movement delay for online dance teaching, which will seriously affect the quality of teaching. Therefore, the dance online course system should be continuously improved, the quality of online courses should be continuously improved, and the study of the practical application of load balancing technology in the network teaching environment has become an important means to solve the relationship between supply and demand of network teaching. According to the experimental analysis, when the number of Worker' actuators is fixed, the execution time span of MakeSpan increases with the increase of tasks, while the time required by the optimized load balancing algorithm proposed in this paper increases by 1.32 s on average with the increase of tasks, and the time required by heuristic algorithm and bee colony algorithm increases by 3.68 s and 3.45 s on average with the increase of tasks. On the whole, the optimized load balancing algorithm proposed in this paper has obvious advantages. [ABSTRACT FROM AUTHOR]

This paper is concerned with two generalized inverse eigenvalue problems for a kind of pseudo‐Jacobi matrix. From a non‐Hermite matrix, an r × r Jacobi matrix, two distinct real eigenvalues, and part of the corresponding eigenvectors, an n × n pseudo‐Jacobi matrix is constructed. Furthermore, an n × n pseudo‐Jacobi matrix can be made by two different eigenpairs and a positive definite diagonal matrix. It is shown that a unique pseudo‐Jacobi matrix can be recovered from partial eigenpairs and certain special mixed eigendata. Two algorithms are provided for the reconstruction of such a pseudo‐Jacobi matrix, and illustrative numerical examples are presented to verify the proposed algorithms. [ABSTRACT FROM AUTHOR]

Splicing the shredded paper means the technology that, according the paper, which has been shredded to design a particular Algorithm to splice and recover the original paper. This paper introduced the algorithm of splicing the shredded paper based on the matching to texture feature. By means of this algorithm, we modeled for the problem of splicing the shredded paper and solved it. And, we used the algorithm to splice both pieces of English shredded paper and Chinese shredded paper. The recovered paper had proved the accuracy of the algorithm. [ABSTRACT FROM AUTHOR]

Traditional insurance policy settlement is a manual process that is never hassle-free. There are many issues, such as hidden conditions from the insurer or fraud claims by the insured, making the settlement process rough. This process also consumes a significant amount of time that makes the process very inefficient. This whole scenario can be disrupted by the implementation of blockchain and smart contracts in insurance. Blockchain and innovative contract technology can provide immutable data storage, security, transparency, authenticity, and security while any transaction process is triggered. With the implementation of blockchain, the whole insurance process, from authentication to claim settlement, can be done with more transparency and security. A blockchain is a virtual chain of data blocks that is a decentralized technology. Any transaction or change in the blocks is done after the decentralized validator entity, not a single person. The smart contract is a unique facility stored on the blockchain that gets executed when the predetermined conditions are met. This paper presents a framework where smart contracts are used for insurance contracts and stored on blockchain. In the case of a claim, if all the predetermined conditions are met, the transaction happens; otherwise, it is discarded. The conditions are immutable. That means there is scope for alteration from either side. This blockchain and intelligent contract-based framework are hosted on a private Ethereum network. The Solidity programming language is used to create smart contracts. The framework uses the Proof of Authority (PoA) consensus algorithm to validate the transactions. In the case of any faulty transaction request, the consensus algorithm acts according to and cancels the claim. With blockchain and smart contract implementation, this framework can solve all the trust and security issues that rely on a standard insurance policy. [ABSTRACT FROM AUTHOR]

In this paper, a new human resource scheduling algorithm is proposed based on the optimization simulation of the human resource scheduling algorithm to find the most suitable human resource allocation scheme for different regions. A simulation system for human resource allocation is proposed, which integrates the scheduling algorithm of this paper and conducts simulation experiments using the historical data of enterprise problems in each region collected in a smart city. The simulation experiment proves that the dispatching algorithm in this paper is more reasonable than the current dispatching algorithm, and the relationship between enterprise problems and the number of employees is also found, and finally, the simulation system in this paper is proved to be stable through large-scale simulation experiment. [ABSTRACT FROM AUTHOR]