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With the development of computer technology, automatic test paper generation systems have gradually become an effective tool for detecting and maintaining national machine security and protecting the rights and interests of workers. This article achieved multi-level oral scores for different types of questions through online scoring using artificial neural networks in recent years. Based on its specific situation and evaluation index requirements, an analysis module that is reasonable, efficient, and in line with the hierarchical structure and module requirements of national conditions has been designed to complete the research on automatic test paper generation technology, in order to help better manage and allocate human resources and improve production efficiency. Afterwards, this article conducted functional testing on the technical module. The test results showed that the scalability of the system was over 82%. [ABSTRACT FROM AUTHOR]

This paper explores the artificial intelligence (AI) concept for complex engineering design processes in the shipping industry. It is driven by the computer technologies advancement for fast and concurrent tasks processing, machine learnability, and data-centric approach. While AI has been adopted in many industries, it is still lacking the structured approaches for practical implementation. This is especially on the generality of the methodologies and explaining AI to the non-technical members and their preparedness. Therefore, this work proposed a conceptual framework to systematically extract, represent and visualize the ship design knowledge, to develop and deploy the machine learning (ML) models, and to demonstrate the AI-based ship design processes. Comparisons to the generic ship design model were made and discussed to highlight the improvements observed. It is found that while the conventional algorithmic approach procedures were faster in terms of execution time, the stepwise empirical models were often limited by the dataset and the design assumptions with restricted estimation capabilities for solving the nonlinear ship design problems. The findings presented the impact in improving the existing processes and effectively reducing its cycle. Additionally, the approach emphasised on the validated ship design data thus its generalization for fast and wide adoptions at scales. [ABSTRACT FROM AUTHOR]

After hundreds of years of changes, due to the development of chemistry, the development of human life has undergone tremendous changes. People have used chemical science to manufacture many supplies, such as various medicines and various types of cosmetics. In recent decades, electronic components and computer software technology have developed rapidly, and the fourth round of technological revolution is underway, which has a favorable impact on the development of various industries. Traditional paper-cut and cosmetic bag design methods should also take into account the advantages of wireless communication and artificial intelligence technology and combine other types of traditional industries to carry out technological reforms to help traditional craftsmanship pass down. In modern life, due to the vigorous promotion of cosmetics, there are more and more cosmetics on the market, and the traditional paper-cut art itself is an artistic design method similar to cosmetic design. Both industries will grow rapidly after the use of artificial intelligence and wireless communication technology for the update of both industries. Therefore, the purpose of this paper is to combine wireless communication technology with artificial intelligence technology to transform the traditional paper-cut art and cosmetic packaging design. After consulting the reasons for the decline of the traditional handicraft industry and the reasons for the turmoil caused by modern technology, this paper conducts a combined design of artificial intelligence technology, wireless communication technology, and cosmetic packaging and then performs wireless communication and artificial intelligence on the cutting of traditional paper design elements. The design of the technical matching system is also designed for traditional paper-cut art and cosmetic packaging. And find professional practitioners for research and discussion and multiple transformations and obtain experimental analysis results data. After many experiments, it can be seen that the combination of wireless communication and artificial intelligence technology can transform the traditional paper-cut art and cosmetic packaging design, improve their relevance, continue the inheritance of paper-cut art, and possibly improve the efficacy of cosmetics. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles in the issue on the topics including demonstrates exciting results based on self-imitation learning within a cross-modal setting; and providing advances in controllable generation and interpolation across attributes.

This document is a guest editorial from the journal IET Computer Vision, discussing the topic of advanced image restoration and enhancement. The editorial highlights the challenges faced in this field, such as the complexity of degradation models for real-world low-quality images and the difficulty of acquiring paired data. It also introduces a special issue of the journal that includes five accepted papers, which focus on video reconstruction and image super-resolution. The editorial concludes by providing brief summaries of each accepted paper. The guest editors of the special issue are also mentioned, along with their research interests and affiliations. [Extracted from the article]

In their paper 'RIS-Assisted Secure UAV Communications with Resource Allocation and Cooperative Jamming', Guo et al. combine an RIS with unmanned aerial vehicles (UAVs) to improve the physical layer security of a wireless network. Current 5G wireless systems depend heavily on the quality of the wireless propagation environment, which had conventionally been modelled as an exogenous entity that can only be adapted and not controlled. This new vision of wireless network design is enabled by an emerging technology of reconfigurable intelligent surfaces (RISs), also known as intelligent reflecting surfaces (IRSs). [Extracted from the article]

The present study uses bio-bibliometrics to examine the contributions made by Prof. K.R. Venugopal, the Vice-Chancellor of Bangalore University. He was the Principal of the University Visvesvaraya College of Engineering (UVCE) for 15 years and Chairman of the Department of Electronics and Communications, Computer Science Engineering, and Information Science Engineering at UVCE. He has an illustrious, distinguished, and brilliant academic career with eleven degrees, including two Ph.Ds. He was a post-doctoral research scholar and visiting professor at the University of Southern California, USA. It is essential to study his contributions to understand the magnitudes and contributions in a particular domain. Data has been collected based on his curriculum vitae, available on the website of Bangalore University. Further, the paper highlights Prof. K.R. Venugopal's author productivity, year-wise distribution, collaboration, communications channels, highly cited documents and research papers, and most downloaded books. [ABSTRACT FROM AUTHOR]

An introduction is presented in which the editor discusses articles articles in the issue on topics including such as advances in cellular automata technology, applications in Very Large-Scale Integration (VLSI) design, pattern recognition, and the synergy between theoreticians and engineers.

This study was aimed at developing an automatic exam system as an intelligent and high-quality higher math examination solution for Department of Computer Engineering at Pai Chai University (PCU) based on S/W Engineering. As a research and development (R&D) project, the study used the rational unified process (RUP) method for software development. RUP describes how to effectively use commercial and reliable methods to develop and deploy software system. It is a heavyweight process; therefore, it is particularly suitable for large teams to develop large projects. This paper describes the RUP process of this R&D project what we named it as Automatic Higher Mathematically Exam System (AHMES). AHMES provides a new way to automatically generate exam questions. In the study, our team and the requesting department collected the requirements and selected methods and tools. Then, our team designed and implemented the framework of the system, completed the development of some main functions, tested and summarized the system, and planned the future work. [ABSTRACT FROM AUTHOR]

With the development of the digital society, forensic analysis of signatures, in addition to the traditional analysis of signatures written with a writing medium on a common writing base, has also expanded to the analysis of signatures that are digitally scanned or digitally captured. Digitally scanned signatures represent some authentic signatures that have been scanned and implemented in the disputed document using computer technology and can in no way be considered authentic, especially if the scanning was performed by a person whose name is not signed in the disputed signature. Digitally captured signatures are gradually replacing the traditional way of signing, especially in banking transactions both within the Republic of Croatia and internationally. They become part of the modern forensic analysis of signatures and represent the authentic signatures of a person who made the writing with his own handwriting with a pen on the signature plate. The paper aims to present the latest trends in the development of technologies used for signing and also to present the latest trends in signature forgery. Additionally, in this paper, we present the possibilities of analysing such signatures in potential real situations. [ABSTRACT FROM AUTHOR]

This paper analyzes the performance requirements that need to be met by a clock generator applied to a low-temperature quantum computer and analyzes the negative effects on the clock generator circuit under low-temperature conditions. In order to meet the performance requirements proposed in this paper and suppress the negative effects brought about by the low temperature, a clock generator for ultra-low-temperature quantum computing is designed. This clock generator is designed by using F-CLASS Voltage Controlled Oscillator (VCO), power filter, tail resistor, differential charge pump, and other techniques. And the noise characteristics of the clock generator are analyzed by Impulse Sensitive Function (ISF) and simulation results. After simulation tests, the average power consumption of the clock generator designed in this paper is 7 mW, the phase noise is −121 dBc/Hz@1 MHz, and the jitter is 62 fs. The performance of the clock generator meets the performance requirements proposed in this paper, and the reduction in the corner frequency proves that the circuit will have better performance at low temperatures. [ABSTRACT FROM AUTHOR]

In order to improve the comprehensive ability evaluation effect of students majoring in preschool education, this paper combines computer technology to evaluate the comprehensive ability of students majoring in preschool education. Through linearization, this paper deduces the state space model of data load of students' comprehensive ability and the transfer function model of student's comprehensive ability data load. Moreover, this paper establishes an analytical expression between the model coefficients of the transfer function of student's comprehensive ability data load and the transfer parameters of student's comprehensive ability data load. In addition, this paper analyzes and selects the identification method of the parameters of students' comprehensive ability data load model. Since the parameters of the student comprehensive ability data load model have physical meanings, the parameter identification of the student comprehensive ability data load model is a gray box identification, and constraints are required when identifying the parameters of the student comprehensive ability data load model. The experimental study has verified that the comprehensive ability evaluation model of students majoring in preschool education based on computer technology can play an important role in the comprehensive ability evaluation of students majoring in preschool education. This article adopts the comprehensive load model in the Comprehensive Stability Calculation Program (PSASP) to eliminate the shortcomings of factor analysis in traditional teaching comprehensive evaluation, and has a certain reference value for the promotion of intelligent teaching in the future. [ABSTRACT FROM AUTHOR]

With the development of computer technology, landslide recognition based on machine learning methods has been widely applied in geological disaster management and research. However, in landslide identification, the problems of an insufficient number of samples and an imbalance of samples are often ignored; that is, landslide samples are much smaller than non-landslide samples. In order to solve this problem, taking the main urban area of Lanzhou City as an example, this paper proposes to construct a semi-supervised generated countermeasure network (SSGAN) model, which aims to achieve high performance with a limited number of labeled samples for precise landslide identification, and to help prevent and reduce the harm caused by disasters. In order to express the environmental characteristics of landslide development and the optical texture features of landslide occurrence, the study constructs three sets of samples to represent landslide features, including a landslide influencing factor sample set, a Sentinel-2A optical remote sensing sample set, a joint influencing factor and Sentinel-2A sample set. The three kinds of sample sets are transferred to SSGAN for training to form a comparative study. The results show that the joint sample set has excellent feature results in discriminator and generator. Through the experimental comparison, the model proposed in this paper is compared with the model without semi-supervised generated confrontation training. The experimental results show that the proposed method is better than the unsupervised adversarial learning model in terms of accuracy, F1 score, Kappa coefficient, and MIoU. A total of 160 landslides have been identified in the study area, with a total area of 10.328 km2, with an accuracy rate of 83%. Therefore, the generated results are accurate and reliable, and show that SSGAN can better distinguish landslides from non-landslides in an image, under the condition of obtaining a large number of unmarked environmental features; enhance the effect of landslide classification in complex geographical environment; and then put forward effective suggestions for the prevention and control of landslides and geological disasters in the main urban area of Lanzhou. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology, communication technology, and control technology, cyber-physical systems (CPSs) have been widely used and developed. However, there are massive information interactions in CPSs, which lead to an increase in the amount of data transmitted over the network. The data communication, once attacked by the network, will seriously affect the security and stability of the system. In this paper, for the data tampering attack existing in the linear system with multiple binary observations, in the case where the estimation algorithm of the defender is unknown, the optimization index is constructed based on information entropy from the attacker's point of view, and the problem is modeled. For the problem of the multi-parameter optimization with energy constraints, this paper uses particle swarm optimization (PSO) to obtain the optimal data tampering attack solution set, and gives the estimation method of unknown parameters in the case of unknown parameters. To implement the real-time improvement of online implementation, the BP neural network is designed. Finally, the validity of the conclusions is verified through numerical simulation. This means that the attacker can construct effective metrics based on information entropy without the knowledge of the defense's discrimination algorithm. In addition, the optimal attack strategy implementation based on PSO and BP is also effective. [ABSTRACT FROM AUTHOR]

Building facade completion is an important part of digitizing the structures of buildings using computer technology. Due to the intricate textures and structures in building facade images, existing image-completion algorithms cannot accurately restore the rich texture and detailed information. In response, this paper proposes a novel network to simultaneously recover the texture and semantic structural features of building facades. By incorporating dynamic convolutions into each layer of the feature encoder, the shallow layers of the completion network can create a global receptive field, thus enhancing the model's feature-extraction capability. Additionally, a spatial attention branch is integrated into the dynamic convolution module to boost the correlation between the completion area and its surrounding edge area, resulting in improved edge clarity and accuracy of the completed facade image. Experimental results on multiple public image datasets demonstrate that the proposed model in this paper achieves state-of-the-art results when applied to real-world datasets. [ABSTRACT FROM AUTHOR]

Warpage of the injection moulded parts is one of the most common defects after demoulding, which is intensively present in thin-walled moulded parts or parts with non-uniform wall thickness. The level of the warpage can be reduced by optimizing the mould tempering system in the phase of mould design, by optimizing the injection moulding parameters and recently by optimizing the shape of the mould cavity elements geometry – so-called inverse contouring. Computer simulation of injection moulding process can show the level of moulded part warpage after ejection from the mould, but it will not allow detailed measuring of critical moulded part measures deviations. Detailed analysis and prediction of the critical moulded part dimensions can be performed by aligning of the deformed moulded part design obtained with computer simulation, with moulded part reference CAD geometry. This paper shows the main steps and an example of the geometry aligning process for a specific thermoplastic part. [ABSTRACT FROM AUTHOR]

Honeypots are computer systems that deceive cyber attackers into believing they are ordinary computer systems designed for invasion, when in fact they are primarily designed to collect data about attack methods, resulting in better protection and defense against malicious actors. As a result, developing reliability metrics for measuring the performance, strength, and effectiveness of honeypot deception is advantageous. Despite extensive and mature research on honeynet system, reliability modeling, analysis and performance prediction and evaluation, based on copula techniques for accurately testing, estimating and optimizing the overall performance of honeynet systems remain lacking. To start, a copula approach for analyzing and optimizing the performance of honeynet systems was proposed. Any honeynet system’s performance can be classified based on its availability, dependability and profit generated. As a result, the current paper sought to investigate the performance of a multi-state honeynet system in terms of availability, dependability and expected profit. This paper examines two types of repairs. Type I repairs are known as general repairs and they are used to recover from a partial or non-lethal failure to a perfect state, whereas Type II repairs are known as copula repairs they are used to recover from a complete or lethal failure to a perfect state. For the sake of generality, the supplementary variable technique and Laplace transforms were used to develop the performance models that are essential to this research, such as availability, reliability, mean time to failure (MTTF), sensitivity and profit function. The models’ numerical validation was fully carried out. The results are shown in tables and figures, enabling us to draw the conclusion that Type II repair is a superior repair policy. Type II repair, according to the findings, can more accurately portray system structure and states while still allowing for efficient assessment. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI), machine learning (ML) and deep learning (DL) techniques have made a breakthrough in many different research fields, such as computer vision, natural language processing, network management and mobile communication. One of the primary goals in 5G network pursues high efficiency, higher spectrum and energy efficiency, higher system capacity and data rates, and lower latency with cost reduction. The papers on the topic of "AI-enabled intelligent networks for 5G and beyond" contain novel network architectures and mechanisms by using AI/ML/DL approaches for 5G/B5G networks. [Extracted from the article]

The innovative application of Crowd Intelligent Devices (CIDS) in edge networks has garnered attention due to the rapid development of artificial intelligence and computer technology. This application offers users more reliable and low-latency computing services through computation offloading technology. However, the dynamic nature of network terminals and the limited coverage of edge servers pose challenges, such as data loss and service interruption. Furthermore, the high-speed mobility of intelligent terminals in the dynamic edge network environment further complicates the design of computation offloading and service migration strategies. To address these challenges, this paper explores the computation offloading model of cluster intelligence collaboration in a heterogeneous network environment. This model involves multiple intelligences collaborating to provide computation offloading services for terminals. To accommodate various roles, a switching strategy of split-cluster group collaboration is introduced, assigning the cluster head, the alternate cluster head, and the ordinary user are assigned to a group with different functions. Additionally, the paper formulates the optimal offloading strategy for group smart terminals as a Markov decision process, taking into account factors such as user mobility, service delay, service accuracy, and migration cost. To implement this strategy, the paper utilizes the deep reinforcement learning-based CCSMS algorithm. Simulation results demonstrate that the proposed edge network service migration strategy, rooted in groupwise cluster collaboration, effectively mitigates interruption delay and enhances service migration efficiency. [ABSTRACT FROM AUTHOR]

In recent years, the proliferation of health data sources due to computer technologies has prompted the use of visualization techniques to tackle epidemiological challenges. However, existing reviews lack a specific focus on the spatial and temporal analysis of epidemiological data using visualization tools. This study aims to address this gap by conducting a scoping review following the PRISMA-ScR guidelines, examining the literature from 2000 to 2024 on spatial–temporal visualization techniques when applied to epidemics, across five databases: PubMed, IEEE Xplore, Scopus, Google Scholar, and ACM Digital Library until 24 January 2024. Among 1312 papers reviewed, 114 were selected, emphasizing aggregate measures, web platform tools, and geospatial data representation, particularly favoring choropleth maps and extended charts. Visualization techniques were predominantly utilized for real-time data presentation, trend analysis, and predictions. Evaluation methods, categorized into standard methodology, user experience, task efficiency, and accuracy, were observed. Although various open-access datasets were available, only a few were commonly used, mainly those related to COVID-19. This study sheds light on the current trends in visualizing epidemiological data over the past 24 years, highlighting the gaps in standardized evaluation methodologies and the limited exploration of individual epidemiological data and diseases acquired in hospitals during epidemics. [ABSTRACT FROM AUTHOR]

Improvements in and the assessment of land use efficiency are crucial pillars for achieving the Sustainable Development Goals (SDGs). This study reviews 208 representative papers, oral reports, and project reports to provide a systematic and comprehensive understanding of the current status and future trends of research on land use efficiency assessment. The findings reveal that (1) the number of papers on land use efficiency assessment is rapidly increasing, with research primarily focused on environmental science and ecology (n = 157, 75.48%). (2) Quantitative models are gaining popularity for land use efficiency assessment, with more than 46.63% of the studies adopting the data envelopment analysis (DEA) model. (3) The definition and analysis perspectives of land use efficiency are diverse, but research on relative land use efficiency and comprehensive analysis perspectives accounts for a significant proportion. (4) Constructing a large and complex model that incorporates geospatial effects, big data, and computer technology is a hot topic for future research methods. On the other hand, conducting land use efficiency research on a global scale is more conducive to achieving the SDGs. (5) The core to improving land use efficiency lies in the joint implementation of multi-pronged measures. [ABSTRACT FROM AUTHOR]

Human–computer interaction (HCI) research involves the design and use of computer technology, focusing in particular on the interfaces between people (users) and computers. HCI researchers observe the ways in which humans interact with computers and design technologies that allow them to interact in novel ways. As HCI evolves into HCI 2.0, user experiences and feedback become ever more relevant. This Special Issue, "Human Computer Interactions 2.0", presents 11 excellent papers about topics related to human–computer interactions. It aims to provide a broad international forum for world researchers, engineers and professionals in human–computer interaction research for the discussion and exchange of various scientific, technical and management discoveries across the world. [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]

Research Purposes. The performance characteristics of traditional digital media art have been gradually accepted by people, which is not innovative enough for people, so this paper mainly studies the design performance characteristics of a new digital media art design relying on computer technology. Research Method. This paper analyzes the concept and characteristics of digital media art based on the relevant theory of computer, combined with computer-aided technology and IRT theory. This paper finally designs the digital media art form based on computer technology. This paper also designs the USB trace feature extraction experiment, computer technology development investigation, and experimental test, and finally optimizes by analyzing the data obtained from the experiment. In order to verify the difference between the representation designed in this paper and the traditional representation, this paper also designs a set of control experiments. Research Result. The results show that the expression of artistic creation based on the digital media art expression designed in this paper is 14.12% higher than that of the traditional artistic expression. However, the innovative effect of the artistic creation based on the digital media art expression designed in this paper is 20.27% higher than that of the traditional expression. [ABSTRACT FROM AUTHOR]

Computational Visual Media is a journal that has been successful for nine years, and the editor-in-chief expresses gratitude to the authors, reviewers, and editorial board members for their contributions. In 2023, the journal published 48 papers in four issues, including regular submissions and papers recommended by the CVM Conference. Starting in 2024, the journal will change from quarterly to bimonthly issues to accommodate the increasing number of submissions. The journal has achieved a high impact factor and is included in important indexing and database services. The editor looks forward to receiving excellent papers in 2024. [Extracted from the article]

Taking the motivation of the knowledge aggregation of age-friendly design resources as the starting point, this paper compares the development history of age-friendly design for interactive interfaces, understands the research status, hotspots, and development trend of age-friendly design for interactive interfaces from 2003 to 2022 worldwide, and summarizes and analyses them. This paper explores five aspects of aging-friendly design in the field of interactive interface design extracted from the Web of Science core collection database, including keyword co-occurrence analysis of global research literature, country/region information analysis, fundamental research areas, major research institutions, and future research directions. It explores the design methods, design trends, and design laws of aging-friendly design for interactive interfaces from a multidisciplinary perspective. The results show that: (a) Smart Home, Ambient Assisted Living (AAL), User Experience, Internet of Things (IoT), etc., are the hot topics at the forefront of interface aging design. (b) USA is the world's leading research site for interface aging design and works closely with China, where the population aging is more serious. (c) The main research areas of interface aging are computer science and engineering ethics. (d) Udice French Research Universities, the primary publishing institution, is worth attention. (e) This review proposes three research hotspots as important development directions for interactive interface aging design, including "smart home product interaction interface aging design will become a hot topic,""fusion of multi-source data assisted interface aging design," and "interdisciplinary interface aging design." [ABSTRACT FROM AUTHOR]

Electric power operation, as one of the key fields in the world, faces particularly prominent safety issues. Ensuring the safety of operators has become the most fundamental requirement in power operation. However, there are some safety hazards in power construction. These hazards are mainly due to weak safety awareness among staff and the failure to standardize the wearing of safety helmets. In order to effectively address this situation, technical means such as video surveillance technology and computer vision technology can be utilized to monitor whether staff are wearing helmets and provide timely feedback. Such measures will greatly enhance the safety level of power operation. This paper proposes an improved lightweight helmet detection algorithm named YOLO-M3C. The algorithm first replaces the YOLOv5s backbone network with MobileNetV3, successfully reducing the model size from 13.7 MB to 10.2 MB, thereby increasing the model's detection speed from 42.0 frames per second to 55.6 frames per second. Then, the CA attention mechanism is introduced into the backbone network to enhance the feature extraction capability of the model. Finally, in order to further improve the detection recall rate and accuracy of the model, a knowledge distillation of the model was carried out. The experimental results show that, compared with the original YOLOv5s algorithm, the average accuracy of the improved YOLO-M3C algorithm is improved by 0.123, and the recall rate is the same. These results verify that the algorithm YOLO-M3C has excellent performance in target detection and recognition, which can improve accuracy and confidence, while reducing false detection and missing detection, and effectively meet the needs of helmet-wearing detection. [ABSTRACT FROM AUTHOR]

Massive teaching resources will cause serious teaching efficiency problems for online teaching, and traditional online teaching models are even inferior to traditional classroom teaching in terms of teaching effects. Based on this, this paper analyzes massive educational resources and builds a scalable computer interactive education system based on large-scale multimedia data analysis. Moreover, this paper sets the role of the system according to the actual teaching situation, and constructs the functional module of the system structure. In addition, this paper uses computer simulation technology to analyze interactive technology and make technical improvements to make interactive technology the core technology of the computer interactive education system, and get an extensible interactive education system based on the characteristics of network teaching. Then helps to monitor and access the performance of an interactive educational system. Furthermore, this paper designs an experiment to evaluate the performance of the computer interactive education system, which is mainly carried out from two aspects: interactive evaluation and teaching evaluation. From the experimental research results, we can see that this system can effectively improve the quality of teaching. [ABSTRACT FROM AUTHOR]

With the rapid development of computer technology, distributed systems have become an indispensable part of the field of information storage and management. In the process of large-scale data processing, it is an important issue to compress or replay files to ensure their integrity. In order to solve the problem of large-scale computing resources and data storage, the distributed file system emerged as a new system structure. The purpose of this paper to is study the key technologies of the distributed memory file system of high-performance computers is to improve the capability and efficiency of the distributed system. This paper mainly uses the experimental method and the comparative method to analyze the key technology of the distributed memory file system of the high-performance computer. Experimental results show that the maximum bandwidth value of DFMS in file memory processing can reach more than 2000, and the value becomes more stable as the file increases. [ABSTRACT FROM AUTHOR]

Purpose: This study examines artificial intelligence (AI) impact on real estate, highlighting its role in improving processes, enhancing customer experiences, and ensuring competitiveness while prioritising data protection and security. Methodology/Approach: A comprehensive literature review compared AI technologies in real estate, including drones, the Internet of Things (IoT), cloud computing, big data, 3D scanning, wearables, virtual and augmented realities, and robotics. It examines their impact on operations, focusing on AI in Heating, Ventilation and Air Conditioning (HVAC) systems for energy efficiency and comfort. Findings: AI enhances real estate development by optimising operations, transforming customer relationships, and improving energy efficiency and comfort with AI-controlled HVAC systems. Benefits include automated data processing, better decision-making, and optimised marketing strategies. Research Limitation/implication: AI has great potential, but its implementation is in the early stages, including in real estate. Challenges include data protection, security, costs, and new hardware for HVAC systems. Broader real-case testing is needed. Originality/Value of paper: This paper broadens the perspective on AI's impact on real estate, showing how to implement AI in various processes. It improves performance and transforms customer experiences, setting new standards for property management. [ABSTRACT FROM AUTHOR]

A novel approach is presented in this paper to address the limitations of virtual machine technology, active kernel technology, heuristic killing technology, and behaviour killing technology in computer network virus defence. The proposed method provides data mining technology, specifically Object-Oriented Analysis (OOA) mining, to detect deformed and unknown viruses by analyzing the sequence of Win API calls in PE files. Experimental results showcase the Data Mining-based Antivirus (DMAV) system's superiority over existing virus scanning software in multiple aspects: higher accuracy in deformed virus detection, enhanced active defence capabilities against unknown viruses (with a recognition rate of 92%), improved efficiency, and a reduced false alarm rate for non-virus file detection. Furthermore, the paper introduces an OOA rule generator to optimize feature extraction, enhancing the system's intelligence and robustness. This research provides a promising solution to support virus detection accuracy, active defence mechanisms, and overall efficiency while minimizing false positives in virus scanning, thus contributing significantly to the advancement of computer network security. [ABSTRACT FROM AUTHOR]

Generally, Least Squares (LS) Method treats only random errors of observation vector in adjustment function models. However, both observation vector and elements of coefficient matrix of adjustment function model contain random errors. Therefore, there is no guarantee that the result of adjustment by LS method is the global optimal solution. Total Least Square (TLS) method is a primary estimation method that treats random errors of observation vector and coefficient matrix in adjustment functional models. Since TLS method take into account both random errors of observation vector and coefficient matrix based on errors-in-variables model, it is possible to improve the accuracy compared with the result of LS method. So TLS method has been applied to different fields of science and technology including signal and image processing, computer vision,communication engineering and geodesy. However, weighted total least square (WTLS) method has been not applied in geodetic network adjustment problem compared with other fields widely. So the purpose of this paper is to summarize the algorithm of WTLS briefly and to propose an application method in adjustment of triangulation network. Key problem in application of WTLS to adjustment of geodetic network is to determine the weight matrix (or cofactor matrix) for elements of coefficient matrix in adjustment function model. In this paper proposed a method to determine cofactor matrix for errors of coefficient matrix in triangulation network, and verifies the effectiveness of suggested method through example applied to triangulation network. [ABSTRACT FROM AUTHOR]

Shrimp farming has traditionally served as a crucial source of seafood and revenue for coastal countries. However, with the rapid development of society, conventional small-scale manual shrimp farming can no longer meet the increasing demand for rapid growth. As a result, it is imperative to continuously develop automation technology for efficient large-scale shrimp farming. Smart shrimp farming represents an innovative application of advanced technologies and management practices in shrimp aquaculture to expand the scale of production. Nonetheless, the use of these new technologies is not without difficulties, including the scarcity of public datasets and the high cost of labeling. In this paper, we focus on the application of advanced computer vision techniques to shrimp farming. To achieve this objective, we first establish a high-quality shrimp dataset for training various deep learning models. Subsequently, we propose a method that combines unsupervised learning with downstream instance segmentation tasks to mitigate reliance on large training datasets. Our experiments demonstrate that the method involving contrastive learning outperforms the direct fine-tuning of an instance segmentation model for shrimp in instance segmentation tasks. Furthermore, the concepts presented in this paper can extend to other fields that utilize computer vision technologies. [ABSTRACT FROM AUTHOR]

The traditional Chinese language and literature distance teaching system has the problem of incomplete instruction recognition function, which leads to the slow connection speed of the system. This paper designs a computer multimedia Chinese language and literature distance teaching system. For the hardware part, strain gauge and sensor are combined, connected to VCC through 10 K ohm pull-up resistance, and connected to other components. The software part obtains the expression form of Chinese language and literature; formulates the training target, according to the antimonotone characteristics of the support degree; adopts the association rule algorithm to build the remote teaching system database, combined with the client to parse the signaling message; and designs the software instruction recognition function by using the computer multimedia technology. Experimental Results. The average connection speeds of the Chinese language and literature distance teaching system in this paper and the other two kinds of Chinese language and literature distance teaching system are 8.737 s, 13.140 s, and 13.156 s, respectively, which proves that the performance of the Chinese language and literature distance teaching system combined with computer multimedia technology is more perfect. [ABSTRACT FROM AUTHOR]

This paper proposes a new neural machine translation model of electrical engineering that combines a transformer with gated recurrent unit (GRU) networks. By fusing global information and memory information, the model effectively improves the performance of low-resource neural machine translation. Unlike traditional transformers, our proposed model includes two different encoders: one is the global information encoder, which focuses on contextual information, and the other is the memory encoder, which is responsible for capturing recurrent memory information. The model with these two types of attention can encode both global and memory information and learn richer semantic knowledge. Because transformers require global attention calculation for each word position, the time and space complexity are both squared with the length of the source language sequence. When the length of the source language sequence becomes too long, the performance of the transformer will sharply decline. Therefore, we propose a memory information encoder based on the GRU to improve this drawback. The model proposed in this paper has a maximum improvement of 2.04 BLEU points over the baseline model in the field of electrical engineering with low resources. [ABSTRACT FROM AUTHOR]

A large number of raw data collected by satellites are processed by the production chain to obtain a large number of product data, of which the secure exchange and storage is of interest to researchers in the field of remote sensing information science. Authentic, secure data provide a critical foundation for data analysis and decision-making. Traditional centralized cloud computing systems are vulnerable to attack and, once the central server is successfully attacked, all data will be lost. Distributed ledger technology (DLT) is an innovative computer technology that can ensure information security and traceability, is tamper-proof, and can be applied to the field of remote sensing. Although there are many advantages to using DLT in remote sensing applications, there are some obstacles and limitations to its application. Remote sensing data have the characteristics of a large data volume, a spatiotemporal nature, global scale, and so on, and it is difficult to store and interconnect remote sensing data in the blockchain. To address these issues, this paper proposes a trustworthy and decentralized system using blockchain technology. The novelty of this paper is the proposal of a multi-level blockchain architecture in which the system collects remote sensing data and stores them in the Interplanetary File System (IPFS) network; after generating the IPFS hash, the network rehashes the value again and uploads it on the Ethereum chain for public query. The distributed data storage improves data security, supports the secure exchange of information, and improves the efficiency of data management. [ABSTRACT FROM AUTHOR]

This paper aims to highlight the life of computer technologies to understand what kind of 'technological intentionality' is present in computers based upon the phenomenological elements constituting the objects in general. Such a study can better explain the effects of new digital technologies on our society and highlight the role of digital technologies by focusing on their activities. Even if Husserlian phenomenology rarely talks about technologies, some of its aspects can be used to address the actions performed by the digital technologies by focusing on the objects' inner 'life' thanks to the analysis of passive synthesis and phenomenological horizons in the objects. These elements can be used in computer technologies to show how digital objects are 'alive.' This paper focuses on programs developed through high-order languages like C++ and unsupervised learning techniques like 'Generative Adversarial Model.' The phenomenological analysis reveals the computer's autonomy within the programming stages. At the same time, the conceptual inquiry into the digital system's learning ability shows the alive and changeable nature of the technological object itself. [ABSTRACT FROM AUTHOR]

This article explores the concept of the circular economy and its connection to emerging technologies like the Internet of Things (IoT) and Industry 4.0. It emphasizes the significance of the circular economy in promoting sustainability and the reuse of materials. The article also discusses the potential benefits of IoT technologies in implementing circular economy practices. It presents various research papers that delve into the challenges, risks, and advantages of utilizing the IoT for the circular economy. The overall aim of the research is to contribute to the existing knowledge on the circular economy and inspire further progress in the field. [Extracted from the article]

Livestock's live body dimensions are a pivotal indicator of economic output. Manual measurement is labor-intensive and time-consuming, often eliciting stress responses in the livestock. With the advancement of computer technology, the techniques for livestock live body dimension measurement have progressed rapidly, yielding significant research achievements. This paper presents a comprehensive review of the recent advancements in livestock live body dimension measurement, emphasizing the crucial role of computer-vision-based sensors. The discussion covers three main aspects: sensing data acquisition, sensing data processing, and sensing data analysis. The common techniques and measurement procedures in, and the current research status of, live body dimension measurement are introduced, along with a comparative analysis of their respective merits and drawbacks. Livestock data acquisition is the initial phase of live body dimension measurement, where sensors are employed as data collection equipment to obtain information conducive to precise measurements. Subsequently, the acquired data undergo processing, leveraging techniques such as 3D vision technology, computer graphics, image processing, and deep learning to calculate the measurements accurately. Lastly, this paper addresses the existing challenges within the domain of livestock live body dimension measurement in the livestock industry, highlighting the potential contributions of computer-vision-based sensors. Moreover, it predicts the potential development trends in the realm of high-throughput live body dimension measurement techniques for livestock. [ABSTRACT FROM AUTHOR]

Nowadays, with the development of computer science and technology, computer intelligent algorithms are more and more widely used in various industries. Every calculation formula in the computer intelligent algorithm has systematic logic and singleness, in order to expound the dynamic algorithm of football training optimized by the computer intelligent algorithm in detail. In this paper, the monitoring system using the computer intelligent algorithm can dynamically observe people or objects and systematically analyze them. This paper mainly studies the research of a football training dynamic monitoring system based on the computer intelligent algorithm and the design and optimization of the computer intelligent dynamic monitoring system in football training. Finally, the overall composition of the computer intelligent dynamic monitoring system and the application of the optimized computer intelligent dynamic monitoring system to the analysis of sample data are studied. [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]

Recent applications like mmWave technologies require antenna characteristics such as high gain and wide bandwidth for smooth operation with high speed. Hence, this paper presents a novel pi‐shaped patch antenna framed within a square structure for mmWave applications at 28 GHz. In the process of designing a four‐element linear‐planar antenna array (LPAA), a thin single antenna element is designed on a 0.275 mm Rogers 5880 substrate with a dielectric constant of 2.2. The proposed single‐element antenna provides a gain of 3 dBi at 28 GHz and a wide impedance bandwidth ranging from 22.482 GHz to 40.511 GHz. Then, this proposed structure is transformed into a four‐element LPAA with a compact dimension of 0.263 mm × 18.55 mm × 23.99 mm. To enhance and predict the impedance bandwidth of the proposed LPAA, mathematical modeling using response surface methodology and constrained numerical optimization is applied. In the optimization process of the antenna, independent factors such as substrate height (Hs) and interelement spacing (d) that influence the antenna's responses such as impedance bandwidth (BW) and operating frequency (Fr) are considered. These factors are varied repeatedly and simulated using computer simulation technology (CST) suite 2019 for preparing the dataset, which is used as an input for mathematical modeling. Then, response surface methodology (RSM) is employed to relate the responses with independent factors. Models are validated using analysis of variance (ANOVA). The optimum parameters determined by applying constrained numerical optimization are substrate height (Hs) and interelement spacing (d) of values 0.263 mm and 5.61 mm, respectively. The optimized LPAA provides a wide bandwidth of 11 GHz and a peak gain of 9.25 dBi. The antenna also gives radiation efficiency of ≥98.5% and VSWR of less than 2 in its operating frequency range. The results included here are simulated using the CST EM solver and validated using the Ansys High‐Frequency Simulation Software (HFSS). The proposed antenna provides high gain and wide bandwidth, which makes it a good candidate for 5G wireless communication applications. [ABSTRACT FROM AUTHOR]

The slowdown of Moore's Law necessitates an exploration of novel computing methodologies, new materials, and advantages in chip design. Thus, carbon-based materials have promise for more energy-efficient computing systems in the future. Moreover, sustainability emerges as a new concern for the semiconductor industry. The production and recycling processes associated with current chips present huge environmental challenges. Electronic waste is a major problem, and sustainable solutions in computing must be found. In this review, we examine an alternative chip design based on nanocellulose, which also features semiconductor properties and transistors. Our review highlights that nanocellulose (NC) is a versatile material and a high-potential composite, as it can be fabricated to gain suitable electronic and semiconducting properties. NC provides ideal support for ink-printed transistors and electronics, including green paper electronics. Here, we summarise various processing procedures for nanocellulose and describe the structure of exclusively nanocellulose-based transistors. Furthermore, we survey the recent scientific efforts in organic chip design and show how fully automated production of such a full NC chip could be achieved, including a Process Design Kit (PDK), expected variation models, and a standard cell library at the logic-gate level, where multiple transistors are connected to perform basic logic operations—for instance, the NOT-AND (NAND) gate. Taking all these attractive nanocellulose features into account, we envision how chips based on nanocellulose can be fabricated using Electronic Design Automation (EDA) tool chains. [ABSTRACT FROM AUTHOR]

As technology advances, the field of electrical and computer engineering continuously demands the introduction of innovative new tools and methodologies to facilitate the effective learning and comprehension of fundamental concepts. This research addresses an identified gap in technology-augmented education capabilities and researches the integration of virtual reality (VR) technology with real-time electronic circuit simulation to enable and enhance the visualization of non-observable concepts such as voltage distribution and current flow within these circuits. In this paper, we describe the development of our immersive educational platform, which makes understanding these abstract concepts intuitive and engaging. This research also involves the design and development of a VR-based circuit simulation environment. By leveraging VR's immersive capabilities, our system enables users to physically interact with electronic components, observe the flow of electrical signals, and manipulate circuit parameters in real-time. Through this immersive experience, learners can gain a deeper understanding of fundamental electronic principles, transcending the limitations of traditional two-dimensional diagrams and equations. Furthermore, this research focuses on the implementation of advanced and novel visualization techniques within the VR environment for non-observable electrical and electromagnetic properties, providing users with a clearer and more intuitive understanding of electrical circuit concepts. Examples include color-coded pathways for current flow and dynamic voltage gradient visualization. Additionally, real-time data representation and graphical overlays are researched and integrated to offer users insights into the dynamic behavior of circuits, allowing for better analysis and troubleshooting. [ABSTRACT FROM AUTHOR]

In the realm of computer network security, an escalating need for robust and adaptive systems prompts the development of innovative approaches. This paper introduces a novel framework, termed "ALPSO AutoLSTM-PSO Security Optimization Framework," designed for the optimization of computer network security systems. The framework synergistically integrates advanced techniques, including Autoencoder (Auto), Long Short-Term Memory (LSTM), and Particle Swarm Optimization (PSO). The Autoencoder, trained on normal network traffic data, serves as a feature learning mechanism, capturing essential representations. The LSTM, adept at modeling temporal dependencies, complements this by recognizing sequential patterns in network behavior. Furthermore, the PSO algorithm is employed to finely tune the parameters of both the Autoencoder and LSTM networks, enhancing their collective performance. The integrated model, forged through this holistic approach, forms the cornerstone of an improved neural network algorithm. To demonstrate the efficacy of the proposed ALPSO, comprehensive experiments are conducted using the NSL-KDD dataset. This dataset provides a realistic and diverse set of network traffic scenarios, enabling a thorough evaluation of the framework's capabilities. The algorithm, enriched by the dynamic fusion of Autoencoder and LSTM outputs, is adept at anomaly detection and security threat identification. This framework, coupled with efficient data searching techniques, enables real-time analysis of network traffic, thereby fortifying the security infrastructure. The ALPSO Framework represents a comprehensive solution that amalgamates state-of-the-art technologies to address the evolving challenges in computer network security. [ABSTRACT FROM AUTHOR]

At the present stage, the field of detecting vegetable pests and diseases is in dire need of the integration of computer vision technologies. However, the deployment of efficient and lightweight object-detection models on edge devices in vegetable cultivation environments is a key issue. To address the limitations of current target-detection models, we propose a novel lightweight object-detection model based on YOLOv8n while maintaining high accuracy. In this paper, (1) we propose a new neck structure, Focus Multi-scale Feature Diffusion Interaction (FMDI), and inject it into the YOLOv8n architecture, which performs multi-scale fusion across hierarchical features and improves the accuracy of pest target detection. (2) We propose a new efficient Multi-core Focused Network (MFN) for extracting features of different scales and capturing local contextual information, which optimizes the processing power of feature information. (3) We incorporate the novel and efficient Universal Inverted Bottleneck (UIB) block to replace the original bottleneck block, which effectively simplifies the structure of the block and achieves the lightweight model. Finally, the performance of YOLO-FMDI is evaluated through a large number of ablation and comparison experiments. Notably, compared with the original YOLOv8n, our model reduces the parameters, GFLOPs, and model size by 18.2%, 6.1%, and 15.9%, respectively, improving the mean average precision (mAP50) by 1.2%. These findings emphasize the excellent performance of our proposed model for tomato pest and disease detection, which provides a lightweight and high-precision solution for vegetable cultivation applications. [ABSTRACT FROM AUTHOR]