<abstract xmlns="http://www.w3.org/1999/xhtml">

Modeling the automated testing of mobile applications is a crucial aspect of mobile application automation testing. Due to the varied styles and complex interactions of mobile applications, automated modeling methods are urgently required, particularly in the context of their short development cycles, large numbers, and fast version iterations and updates. In this paper, we address the challenge of exploring mobile application behavior and state based on robotic testing environment without invading the application interior, and propose a method for automated exploration of GUI components and GUI events of applications combined with application domain knowledge to generate mobile application GUI semantic test models. Our results show that the proposed semantic model achieves 70.6% and 82.4% defect detection rate in the robot vision environment and simulation environment, respectively. Compared with the comparative testing method that can only find application crash defects, our method can explore both crash defects and functional anomalies with the application semantic understanding and domain knowledge, thereby extending the automated mobile application functional testing capability of mobile applications. In response to the limitations of mobile application automated testing modeling mentioned above, this paper introduces an automated testing method based on semantic models. It uses the proposed semantic testing model to guide the purposeful exploration of the tested application’s states. Subsequently, it generates positive and negative test cases based on the domain knowledge associated with the semantic model. This modeling approach leverages domain models in the mobile application field to conduct automated modeling tests imbued with functional significance, guided by domain knowledge. This optimization aims to address the shortcomings of current automated testing, particularly in terms of model reuse and test expansion.
</abstract>

A Model-Based Approach to Mobile Application Testing

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With the rapid development of the Internet of Things science and technology, people′s living standards are gradually improving, and the requirements for the living environment are also getting higher and higher, which makes smart homes gradually enter thousands of households. The purpose of this project is to design a system that integrates hardware and software and can measure and transmit various data. Among them, the hardware part includes data measurement and data display. The data measurement module consists of DHT11 temperature and humidity sensor, DSM501 particle number sensor and MQ3 alcohol concentration sensor. The experimental data will be displayed on the TFTLCD screen. The system software is partly run on the Windows operating system, using the Python language development. This system takes ESP8266 module as the transfer station, realizes the communication between STM32 development board and computer. The experiment shows that the system has the advantages of high measurement data accuracy, fast data refresh speed, complete data transmission, simple design, high reliability, easy installation, economical and practical, and has certain practical value in life, production, industry and other fields.
</abstract>

Design and Implementation of Smart Home System Based on STM32 Microcomputer

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The successful application of cutting-edge computer vision technology to automatic insect classification has long been a focus of research in insect taxonomy. The results of this research have a wide range of applications in areas such as environmental monitoring, pest diagnosis and epidemiology. However, there is still a gap between the current techniques used in automatic insect classification and the latest computer vision techniques. The research in this paper is conducted on Lepidoptera, a class of insects that are widely infested, including butterflies and moths. The study focuses on the application of deep learning algorithms in image processing of Lepidoptera insects. In order to improve the recognition rate for Lepidoptera insect recognition, this paper uses a detection model based on deep neural networks to realize the recognition of Lepidoptera insects in complex environments. Specifically, the yolov7 algorithm is adopted as the basic model for this experiment, and the reasons for using this model are explained in terms of the splicing of network modules, loss function, positive sample allocation strategy, and the merging of convolution and normalization, respectively. Through experiments, it is proved that the algorithm can effectively improve the gesture recognition rate, the recognition accuracy reaches 79.5%, and the recognition speed is as high as 33.08it/s.
</abstract>

Deep Learning Based Recognition of Lepidoptera Insects

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With the continuous improvement of various radio system performance indicators, the research work on antenna has become particularly important. According to different scenarios and requirements, practical projects also need the corresponding antennas to produce different radiation patterns. By reasonably setting the parameters of the array antenna, the target radiation pattern can be obtained to meet real life applications. When the array antenna has a large number of basic units and the expected far-field pattern is complicated, the design of the array antenna becomes a complicated optimization problem. To solve this problem, Whale Optimization Algorithm (WOA) is proposed. WOA is not only simple and fast, but can also get the global optimal solution. Therefore, WOA has developed rapidly in recent years. However, the application of this algorithm in the field of antenna design is still relatively rare, thus using WOA to solve the optimization problem of array antenna design is very valuable.
</abstract>

The Application of Whale Optimization Algorithm in Array Antennas

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Human pose estimation is a hot topic of computer research in recent years, which promotes the progress of society and brings many conveniences to people’s lives. Fom traditional methods to the mainstream deep learning-based methods, the primary approach in deep learning involves the use of convolutional neural networks to reduce computational complexity and improve network accuracy, but because the network structure is too deep to improve the accuracy, the trained model parameters are also very large, and it is very dependent on the input of hardware equipment. At this time, the lightweight human pose estimation can solve this problem very well. This paper mainly describes the knowledge of convolutional neural network in detail and compares it with traditional image algorithms. The OpenPose model is a classic model based on convolutional neural network that can well achieve single-person and multi-person human pose estimation model, but because the convolution kernel in its network structure is too large to increase the amount of calculation, this paper proposes three improvements to the network structure of the conventional OpenPose model. Finally, the precision of the model is improved by about 40%, which verifies the feasibility of lightweight human body posture estimation research.
</abstract>

Research on Multi-Person Pose Estimation Technology

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Object detection is the use of computer to find out all the objects of interest in the image, determine their categories and locations, is one of the core problems in the field of computer vision. Traditional animal image target detection usually adopts the sliding window method, but due to the different sizes of the input images, this method has some problems such as insufficient training samples, low detection accuracy and slow speed. In order to solve such problems, based on the development of deep learning in recent years, this paper proposes an object detection algorithm based on convolutional neural network. YOLOv5 was used to effectively distinguish, identify and mark animal categories, which accelerated the training of the model and greatly improved the accuracy of target detection. Through the analysis of experimental data, it was concluded that the algorithm studied in this paper had good performance and good target detection results. Finally, the key problems of object detection research are summarized, and the future development trend of this field is prospected. When the number of training rounds is 30, the accuracy rate has reached about 70%, and after 50 rounds of training, some accuracy can reach 90%, which is excellent and better than other traditional algorithms.
</abstract>

Research on Object Detection in Animal Images Based on Convolutional Neural Networks

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When the target localization algorithm based on reinforcement learning is trained on few-sample data sets, the accuracy of target localization is low due to the low degree of fitting. Therefore, on the basis of deep reinforcement learning target localization algorithm, this paper proposes a target localization algorithm based on meta-reinforcement learning. Firstly, during the initial training of the model, the meta-parameters were classified and stored according to the similarity of the training tasks. Then, for the new target location task, the task feature extraction was carried out and the meta parameters with the highest similarity were matched as the initial parameters of the model training. The model dynamically updated the meta parameter pool to ensure that the optimal meta parameters of multiple different types of features were saved in the meta parameter pool, so as to improve the generalization ability and recognition accuracy of multiple types of target location tasks. Experimental results show that in a variety of single target localization tasks, compared with the original reinforcement learning target localization algorithm, under the same data set size, the model converges under a small number of training steps with the meta-parameters in the matching meta-parameter pool as the initial training parameters. Moreover, the training speed of the meta-reinforcement learning method based on MAML-RL is increased by 28.2% for random initial parameters, and that of the meta-reinforcement learning method based on this paper is increased by 34.9%, indicating that the proposed algorithm effectively improves the training speed, generalization performance and localization accuracy of object detection.
</abstract>

Object Localization Algorithm Based on Meta-Reinforcement Learning

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GreatFree is a generic distributed programming language to develop various distributed systems over the Internet-oriented computing environment. The fundamental characters of GreatFree are shaped by three essential techniques, including the message-passing, the physical-machine-visible, and the thread-visible. More important, GreatFree is equipped with three additional distinguished mechanisms, i.e., the distributed primitives, the distributed common patterns, and the distributed threads on the application level, which are sufficient to turn GreatFree into a generic distributed programming technology. To the best of our knowledge, compared with any others, GreatFree is the first one to achieve the goal. Thereafter, GreatFree is capable of exploiting distributed computing resources flexibly to adapt to any heterogeneous environments with a uniform solution. It indicates that GreatFree represents the common principles existed in various complicated computing circumstances over the Internet. That inspires that GreatFree is a proper technology to build a new concept of cloud computing environment, i.e., the cloud-side operating system, which dominates diverse distributed computing resources upon the common principles of GreatFree. Such a system is a generic development and running environment for any distributed systems. Without doubt, within the environment, GreatFree is the unique choice to program any distributed systems in a scalable manner.
</abstract>

GreatFree as a Generic Distributed Programming Language and the Foundation of the Cloud-Side Operating System

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The increasing application of medical robots in the healthcare sector underscores the critical importance of intent recognition in enhancing the interaction and assistance capabilities of these robots. Traditional intent recognition methods utilize convolutional neural networks (CNNs) for text analysis but often fall short in capturing global features, resulting in incomplete information. To address this challenge, this paper introduces an innovative approach by combining an enhanced CNN with bidirectional gated recurrent units (BiGRU) to construct a dual-channel short-text intent recognition model. This model effectively leverages both local and global features to more accurately comprehend user needs and intentions. Experimental results demonstrate that this model excels, achieving an accuracy rate of 96.68% and an F1 score of 96.67% on the THUCNews_Title dataset. In comparison to conventional intent recognition models, it exhibits significantly improved performance, thereby providing substantial support for medical robots in patient care and assisting healthcare professionals.
</abstract>

Research on Improved Dual Channel Medical Short Text Intention Recognition Algorithm

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To overcome low efficiency and accuracy of existing forest fire detection algorithms, this paper proposes a network model to enhance the real-time and robustness of detection. This structure is based on the YOLOv5 target detection algorithm and combines the backbone network with The feature extraction module combines the attention module dsCBAM improved by depth-separable convolution, and replaces the loss function CIoU of the original model with a VariFocal loss function that is more suitable for the imbalanced characteristics of positive and negative samples in the forest fire data set. Experiments were conducted on a self-made and public forest fire data set. The accuracy and recall rate of the model can reach 87.1% and 81.6%, which are 7.40% and 3.20% higher than the original model, and the number of images processed per second reaches 64 frames, a growth rate of 8.47%. At the same time, this model was compared horizontally with other improved methods. The accuracy, recall rate and processing speed were all improved in the range of 3% to 10%. The effectiveness of the improved method in this article was verified, and the external perception level of the forest fire scene was deeper.
</abstract>

Research and Implementation of Forest Fire Detection Algorithm Improvement

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International Journal of Advanced Network, Monitoring and Controls

The International Journal of Advanced Network, Monitoring and Controls (ISSN:2470-8038）is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills, especially in the fields of advanced network, monitoring, sensors and controls. It is also the Journal's objective to promote engineering and technology education.  All papers submitted to the Journal will be blind peer-reviewed. Every paper is reviewed by two Professors and blind. All reviewers are independent of the editorial board. Only original articles will be published.  Open Access Policy  This journal provides immediate open access to its content under the Creative Commons CC BY-NC-ND 4.0 license on the principle that making research freely available to the public supports a greater global exchange of knowledge. Under the CC BY-NC-ND 4.0 license users are free to share the work (copy and redistribute the material in any medium or format), if the contribution is properly attributed and used for non-commercial purposes. The material published in the journal may not be altered or build upon.  Rejection rate: 50%  All submitted papers will be reviewed by more than two expert referees in their relevancy to the call for paper. After refereeing process, all accepted papers will be published and the full text is online available.   The acceptance process is as follows:   First of all, check the paper format and theme, and then entering the technical review process according to the quality standards.   The paper will be submitted to two reviewers for review according to the content of the paper; two blind reviewers will make comments and revisions to the paper, and then give an acceptance or rejection.   ABOUT SOCIETY  Xi'an Technological University has over 20,000 students and is authorized by Chinese government to award Ph.D. degrees. The State and Provincial Joint Engineering Laboratory of Advanced Network and Monitoring Control is located within the School of Computer Science and Engineering, Xi'an Technological University. The Lab.is mainly focused on researches in theoretical researches and practical applications of computing technologies, advanced networks, system simulations, artificial intelligence, automated monitoring and controls.  Archiving  Sciendo archives the contents of this journal in Portico - digital long-term preservation service of scholarly books, journals and collections.  Plagiarism Policy  The editorial board is participating in a growing community of Similarity Check System's users in order to ensure that the content published is original and trustworthy. Similarity Check is a medium that allows for comprehensive manuscripts screening, aimed to eliminate plagiarism and provide a high standard and quality peer-review process. 