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In view of the problems of data sparsity and the diversity in academic paper recom-mender systems,based on CONVNCF, this paper proposed an algorithm of convolution with word and heterogeneous information network for academic paper recommendation ( WN -APR) . Firstly, WN -APR algorithm learned user and paper' s diverse features from different semantics to alleviate the sparsity problem. Then it designed an outer product fusing way to seamlessly combine user features with paper features. Replacing of 2D CNN, this algorithm applied 3 D convolution to mine the influence of different features on the performance. Finally, it modified the BPR loss function to enhance diversity in recommendations. Experimental results on CiteULike-a and CiteULike-t datasets show that WN-APR improves the performance of accuracy and diversity over the baseline models. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of China Pulp & Paper is the property of China Pulp & Paper Magazines Publisher and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to reduce the repeated work intensity of drilling personnel and the risks faced by high-risk environments during oilfield exploitation, an intelligent manipulator is introduced into the drilling platform, and a dynamic target tracking control method of the manipulator based on deep reinforcement learning is proposed in this paper. In this paper The digital twin of the manipulator is constructed in the virtual drilling simulation environment, and multi-agent parallel training is carried out using the machine learning agents(ML-Agents) framework and proximal policy optimization(PPO) algorithm. The ideal training algorithm model is deployed to the virtual manipulator on the virtual drilling platform, and the virtual and real manipulator can track the dynamic target synchronously through serial communication. The experimental results indicate that synchronous tracking of dynamic targets with virtual and real robotic arms is feasible and accurate, providing a new approach for the digital twin application in the oil and gas industry. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Human action recognition has significant application prospects in fields such as video surveillance, human-computer interaction, medical care, and sports event analysis. In recent years, with the rapid development of sensor technology and human pose estimation algorithms, skeleton-based human action recognition has gained increasing attention from researchers. Compared to traditional video or image data, skeleton data have the characteristics of being centered on the human subject, highly abstract motion information, and low data dimensions, providing a new perspective for modeling behavior information. This paper focuses on skeleton-based human action recognition and provides a comprehensive systematic review and analysis of relevant work. Firstly, through a literature citation analysis, it systematically summarizes the development trajectory of skeleton-based action recognition. Based on this, the paper reviews traditional recognition methods based on manual features and deep learning-based methods, focusing on the basic principles, improvement strategies, and representative works of convolutional neural networks, recurrent neural networks, graph convolutional neural networks, and Transformer methods, and briefly discusses the research status of network model learning algorithms. Secondly, it summarizes three types of publicly available datasets based on motion capture systems, Kinect camera, and RGB images, and discusses their characteristics and applications in detail. Finally, combined with the current research status and thinking analysis at home and abroad, the paper summarizes the key challenges and difficulties of skeleton-based human action recognition, and looks forward to future development directions, aiming to establish a comprehensive domain research perspective for researchers and provide a reference and inspiration for work in related fields. [ABSTRACT FROM AUTHOR]

Abnormal network traffic detection is a key cybersecurity technology that assists in identifying and preventing malicious network attacks. Existing methods for detecting abnormal network traffic typically rely on complex machine learning models and a large amount of labeled data. Consequently, these methods are challenging to apply to different scenarios without retraining the model and cannot effectively handle large-scale, ongoing network attacks in real-time. To address these issues, this paper proposes a classification method based on a network flow energy model. It utilizes a reverse statistical physics model to learn target traffic features in the network, allowing it to be based on macroscopic real observations or real data without the need for manual labeling. Subsequently, the paper combines the concept of the energy model to construct a network traffic recognition model. This model judges whether a sample conforms to the main statistical distribution. Specifically, the method describes individual behavior characteristics and interaction features between traffic packets through the local field and coupling field in the energy model. By combining these two features, the method calculates the sample' s energy. If the energy is below a threshold, the sample aligns with the main distribution, indicating normal data; otherwise, it is considered abnormal data. As this method does not rely on manual labeling, it can adapt to various network environments without the need for repetitive training. This addresses current issues in traffic abnormality detection methods, which struggle to adapt to different scenarios and require extensive labeling. To evaluate the effectiveness of this method, the paper validates it using the Kitsune-2018 and CTU-13 datasets. Experimental results demonstrate that the proposed method achieves good classification performance and overall effectiveness in network traffic classification tasks. This further indicates its accuracy in performing network flow classification tasks and its adaptability to changing scenarios. [ABSTRACT FROM AUTHOR]

With the development of Internet of things technology, intelligent terminals of the Internet of things have gained popularity. At present, there are many security vulnerabilities in the firmware of the Internet of things terminal, and it is very inconvenient to use manual methods to detect the vulnerabilities of the Internet of things terminal equipment. The intelligent fuzzing technology based on genetic algorithms is mainly used, and the firmware to be tested is automatically tested using random variation data. Aiming at the low efficiency of the existing fuzzing technology based on genetic algorithms, this paper proposes an intelligent fuzzing model based on multiple reinforcement learning algorithms. In this model, reinforcement learning algorithms are used to optimize the mutation operator selection strategy of fuzzing and the code coverage of fuzzing is improved by intelligently selecting different mutation operators for different test cases. This paper compares the performance of DDQN, DDPG, TRPO, and PPO algorithms in the model through comparative experiments on LAVA datasets and traditional fuzzing methods. The results show that in the fuzzing environment, there are significant differences in the performance of different algorithms for different target programs and the fuzzing method based on reinforcement learning is obviously superior to the traditional fuzzing method, proving the proposed model' s availability and effectiveness. [ABSTRACT FROM AUTHOR]

Copyright of China Mining Magazine is the property of China Mining Magazine Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Chinese Journal of Tissue Engineering Research / Zhongguo Zuzhi Gongcheng Yanjiu is the property of Chinese Journal of Tissue Engineering Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of South China University of Technology (Natural Science Edition) is the property of South China University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Hydrogeology & Engineering Geology / Shuiwendizhi Gongchengdizhi is the property of Hydrogeology & Engineering Geology Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The unique flavor properties and rich nutrients of traditional fermented food are closely related to its complex and variable microbial structure, which also makes it difficult to control the quality of final fermented product. In order to explore the changes of microbial structure and sensory property and nutritional property in the process of food fermentation and the internal relationship between them, the data analysis process is a key step. Therefore, it is necessary to establish a fast and accurate data analysis method for quality control of fermented food. Machine learning has the advantages of high- dimensional simplification rate, large data throughput and high prediction accuracy, showing great application potential in the field of quality control of fermented food. Hence, machine learning has become one of the research hotspots. This paper reviews the application of machine learning in the quality control of fermented food. On the basis of an overview of common models of machine learning, this paper systematically summarizes the application of machine learning in the prediction of microbial structure evolution, flavor compound composition analysis and customization of personalized consumption in the process of food fermentation. The problems and developmental trends in the application of machine learning to quality control of traditional fermented food are summarized and prospected. Although the application of machine learning in fermented food is still confined by the problems such as insufficient general applicability of the model, limited quality indicators, and limited personalized consumption scenario, etc., with the iterative update of the technical model, the adaptation for multi-factors and whole process, and the application expansion in the background of personalized consumption, machine learning will show a greater value for practical application in the field of fermented food. The purpose of this study is to provide guidance for the further application of machine learning in the standardized and controllable production of traditional fermented food. [ABSTRACT FROM AUTHOR]

In the ever-evolving landscape of cyber threats, tactics, techniques and procedures (TTPs) play a crucial role in understanding malicious activities, providing a fine-grained perspective on the status of cybersecurity, and comprehensively illustrating cyber attack behaviors. Despite significant research efforts in the field of automated identification and extraction of TTPs, a comprehensive systematic review is currently lacking. This paper presents an in-depth analysis of the progress in this area by employing three principal approaches : traditional natural language processing, machine learning, and large language models. The study categorizes the tasks into information extraction, text classification, and text generation, and presents a summary of the general framework for identification and extraction processes. It offers a clear scope of unstructured text and TTPs, while refining the processing and analysis procedures, as well as innovative directions for each approaches. Moreover, building upon existing research, the paper identifies current challenges and proposes future research directions and development opportunities. This comprehensive survey serves as a valuable literature review to support readers in applying advanced technologies and methods for advancing research in this field. [ABSTRACT FROM AUTHOR]

Domain adaptation, the important branch of transfer learning, aims to solve the problem that the performance of traditional machine learning algorithms drops sharply when the training and test samples obey different data distributions. Transformer is a deep learning framework based on a self-attention mechanism, which has strong global feature extraction ability and modeling ability. In recent years, the combination of Transformer and domain adaptation has also become a research hotspot. Although many relative methods have been published, the review of Transformer application in domain adaptation has not been reported. In order to fill the gap in this field and provide reference for relevant research, this paper summarizes and analyzes some typical domain adaptation methods based on Transformer in recent years. This paper summarizes the concepts related to domain adaptation and the basic structure of the Transformer, sorts out various domain adaptation methods based on Transformer from four applications, i.e. image classification, image semantic segmentation, object detection and medical image analysis and compares the domain adaptation methods in image classification. Finally, the challenges of the current domain adaptation Transformer model are summarized, and the feasible research directions in the future are discussed. [ABSTRACT FROM AUTHOR]

Population based health data collection and analysis are important in epidemiological research. In recent years, with the rapid development of big data, Internet and cloud computing, artificial intelligence has gradually attracted attention of epidemiological researchers. More and more researchers are trying to use artificial intelligence algorithms for genome sequencing and medical image data mining, and for disease diagnosis, risk prediction and others. In recent years, machine learning, a branch of artificial intelligence, has been widely used in epidemiological research. This paper summarizes the key fields and progress in the application of machine learning in epidemiology, reviews the development history of machine learning, analyzes the classic cases and current challenges in its application in epidemiological research, and introduces the current application scenarios and future development trends of machine learning and artificial intelligence algorithms for the better exploration of the epidemiological research value of massive medical health data in China.

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Smart Rail Transit is the property of Smart Rail Transit Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Academic Library & Information Science is the property of Anhui University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Unmanned aerial vehicles (UAVs) assisted Internet of things (IoT) data collection is an efficient and promising approach. The optimization of resource allocation in path planning is addressed in this paper by refining the energy consumption model and considering three metrics: the amount of collected data, time efficiency, and energy efficiency. The problem is formulated as a distributed partially observable Markov decision process (POMDP) and a novel deep reinforcement learning algorithm called RISE (Rényi state entropy)-D3QN (dueling double deep Q network) is proposed. It combines intrinsic rewards, prioritized experience replay, and soft-max exploration strategy, enabling path planning for UAV swarms while adapting to changes in UAV battery capacity, IoT device locations, data volume, and quantity. Simulation results demonstrate that compared to traditional D3QN and DQN algorithms, the proposed approach significantly increases. the amount of collected data from IoT devices while reducing UAV flight time and energy consumption, all while ensuring UAV safety during flight. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In recent years, the rapid development of artificial intelligence has enhanced the efficiency of medical services, accuracy of disease prediction, and innovation in the healthcare industry. Among the many advances, machine learning has become a focal point of development in various fields. Although its use in nursing research and clinical care has been limited, technological progress promises broader applications of machine learning in these areas in the future. In this paper, the authors discuss the application of machine learning in nursing research and care. First, the types and classifications of machine learning are introduced. Next, common neural machine learning models, including recurrent neural networks, transformers, and natural language processing, are described and analyzed. Subsequently, the principles and steps of machine learning are explored and compared to traditional statistical methods, highlighting the quality-monitoring strategies used by machine learning models and the potential limitations and challenges of using machine learning. Finally, interdisciplinary collaboration is encouraged to share knowledge between information technology and nursing disciplines, analyze the advantages and disadvantages of various analytical models, continuously review the research process, and reflect on methodological limitations. Following this course, can help maximize the potential of artificial-intelligence-based technologies to drive innovation and progress in nursing research. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Food Safety & Quality is the property of Journal of Food Safety & Quality Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Atomic Energy Science & Technology is the property of Editorial Board of Atomic Energy Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Science & Technology of Cereals, Oils & Foods is the property of Science & Technology of Cereals, Oils & Foods Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The double block concrete sleeper is the most used prefabricated components in Chinese high-speed railway construction. At present, it is mainly produced by assembly line prefabrication. The traditional manual inspection method cannot fully satisfy the requirements of the double block sleeper. In order to solve the problem of high accuracy requirements and heavy inspection tasks, this paper proposed a double block sleeper shape and appearance quality automatic inspection system based on high-precision 3D laser scanner and highresolution industrial camera. The inspection system was integrated with production line, so that the inspection system was automated operated online with inspection function. This paper researched the automatic acquisition technology of 3D point cloud and high-definition image of double block sleeper, 3D point cloud analysis and processing technology of double block sleeper, images analysis and processing technology of double block sleeper. Through point cloud segmentation and model reconstruction algorithm, the key components model of double block sleeper was obtained, automatic detection of the shape dimensions was realized. Through image template matching and image deep learning algorithm, intelligent recognition of sleeper numbers and cracks was realized. The inspect accuracy of the system can reach 0. 1 mm, the detection efficiency is better than 3 minutes/ group, the crack recognition accuracy is better than 95%, and the detection items and accuracy meet the relevant specifications. The system has the characteristics of automation, real-time, high precision, and high efficiency, can effectively solve the quality control problems of double block sleeper prefabricated factory [ABSTRACT FROM AUTHOR]

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Natural Gas Industry is the property of Natural Gas Industry Journal Agency and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of World Nuclear Geoscience is the property of World Nuclear Geoscience Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Modern Educational Technology is the property of Editorial Board of Modern Educational Technology, Tsinghua University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Cryptologic Research (2097-4116) is the property of Editorial Board of Journal of Cryptologic Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Operations Research Transactions / Yunchouxue Xuebao is the property of Editorial office of Operations Research Transactions and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The North Mountain landslide in Lijie Town has been in a long-term creeping deformation state and has experienced multiple landslide and debris flow disasters. Monitoring the surface deformation of landslide to grasp the surface deformation pattern of disaster body is a reliable basis for realizing early warning prediction of geological disaster. In this paper, a machine learning model is introduced to predict the relevant data, and a long and short-term memory network is used to predict the landslide deformation by monitoring the displacement data of North Mountain in Lijie, and the prediction results are compared with the actual data and analyzed. In this paper, root mean square error, mean absolute error, coefficient of determination and explainable variance are used to evaluate the prediction results, among which the coefficient of determination and explainable variance reach 0.99. It shows that the long short-term memory network used in this paper achieves good prediction performance in the prediction of landslide deformation in the North Mountain of Lijie. [ABSTRACT FROM AUTHOR]

In the study of age estimation in living individuals, a lot of data needs to be analyzed by mathematical statistics, and reasonable medical statistical methods play an important role in data design and analysis. The selection of accurate and appropriate statistical methods is one of the key factors affecting the quality of research results. This paper reviews the principles and applicable principles of the commonly used medical statistical methods such as descriptive statistics, difference analysis, consistency test and multivariate statistical analysis, as well as machine learning methods such as shallow learning and deep learning in the age estimation research of living individuals, and summarizes the relevance and application prospects between medical statistical methods and machine learning methods. This paper aims to provide technical guidance for the age estimation research of living individuals to obtain more scientific and accurate results.

Most of the existing MAPF methods based on communication learning have poor scalability or aggregate too much redundant information, resulting in inefficient communication. To solve these problems, this paper proposed disruptor identifiable communication (DIC), which learned concise communication excluding non-disruptors by judging whether the agent in the center of the field of view would change its decision-making due to the presence of neighbors, and successfully filtered out redundant information. At the same time, this paper further instantiated DIC and developed a new highly scalable distributed MAPF solver: disruptor identifiable communication based on reinforcement and imitation learning algorithm (DICRIA). Firstly, the disruptor discriminator and the policy output layer of DICRIA identified the disruptor. Secondly, the algorithm updated the information of the disruptor and the communication wish sender in two rounds of communication respectively. Finally, DICRIA output the final policy according to the coding results of each module. Experimental results show that DICRIA S performance is better than other similar solvers in almost all environment settings, and the algorithm increases the success rate by 5.2% on average compared to the baseline solver. Especially in dense problem instances with large-size maps, the algorithm even increases the success rate of DICRIA by 44.5% compared to the baseline solver. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Frontiers of Computer Science & Technology is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The key to driverless technology is that the decision-making level makes accurate instructions based on the input information of the perception link. Reinforcement learning and imitation learning are better suited for complex scenarios than traditional rules. However, the imitation learning represented by behavioral cloning has the problem of composite error, and this paper uses the priority empirical playback algorithm to improve the behavioral cloning to improve the fitting ability of the model to the demo dataset. The original DDPG (deep deterministic policy gradient) algorithm has the problem of low exploration efficiency, and the experience pool separation and random network distillation (RND) technology are used to improve the DDPG algorithm and improve the training efficiency of DDPG algorithm. The improved algorithm is used for joint training to reduce the useless exploration in the early stage of DDPG training. Verified by TORC (the open racing car simulator) simulation platform, the experimental results show that the proposed method can explore more stable road maintenance, speed maintenance and obstacle avoidance ability within the same number of training times. [ABSTRACT FROM AUTHOR]

Copyright of Atomic Energy Science & Technology is the property of Editorial Board of Atomic Energy Science & Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the dramatic increase in the scales of e-commerce livestreaming the number of e-commerce livestreaming users has reached 48. 8% of the overall Internet users in China. The huge supply demand requires live e-commerce stores to improve their dispatching efficiency and reduce inventory. Therefore in order to avoid retailers' profit loss it is necessary to find a more accurate method to predict livestreaming sales. The sales prediction methods mainly include traditional statistical methods and machine learning algorithms. Due to the instability of livestreaming sales and the large number of influencing factors traditional statistical methods often fail to predict the sales accurately. To complete the index system of livestreaming sales prediction and improve the accuracy of livestreaming sales prediction this paper adopted a variety of machine learning algorithms BP neural network decision tree DT random forest RF K-nearest neighbor KNN and support vector machine SVM analyzed the influencing factors of apparel livestreaming sales predicted apparel livestreaming sales and selected the best performing algorithms. The detailed research process is as follows. Firstly 17 influencing factors of livestreaming sales were selected through literature review and nine most important influencing factors were selected by using Spearman' s correlation coefficient combined with significance. Secondly different machine learning algorithms were used to establish clothing sales prediction models and the method of 5-fold cross-validation was adopted to initially screen out three algorithms RF KNN and SVM with high and stable model fit with R² as an indicator. Finally the parameters of the three algorithms were optimized and then three prediction models were constructed. R² MAE RMSE and MAPE were used as evaluation indexes and the optimal algorithms were selected by using the method of 5-fold cross-validation to test the performance of each model. The results of the study show that the multicollinearity between the nine factors number of fans of the anchor average number of viewers of the anchor in the last 30 days average pit output of the anchor in the last 30 days product price duration of product explanation historical sales of the product in the last 30 days number of fans of the brand historical sales of the brand in the last 30 days and discounts is weak and their correlation with the livestreaming sales is significant. Therefore these nine factors can be used as influencing factors in the prediction model. Among the influencing factors the correlation among product sales in the last 30 days the duration of product explanation and livestreaming sales is the highest. In the meanwhile the prediction algorithms KNN and RF perform better with R² being greater than 0. 98 and MAPE within 30. 5% . Compared with the KNN algorithm the RF algorithm is more stable and its R² RMSE and MAE perform better than those of the KNN algorithm. But the MAPE of the KNN algorithm is smaller than that of the RF algorithm for which the possible reason is that the KNN algorithm is more accurate in predicting low sales items and the relative error is smaller. According to the result of 5-fold cross-validation the RF algorithm is more stable compared with the KNN algorithm and the possible reason is that the KNN algorithm is more suitable for the dataset with more similar data features. Therefore RF can be used as the main prediction algorithm in practical applications to ensure the stability of the overall sales trend prediction. In predicting the sales of the same brand or the same category the similarity between the data is higher and then the KNN algorithm can be considered for prediction. This paper compares the performance of various prediction algorithms on livestreaming sales prediction optimizes the parameters and improves the accuracy of livestreaming sales prediction. The prediction results can help retailers make inventory planning adjust production schedules develop marketing strategies and provide data support for product purchasing pricing and promotion. Due to the fact that only some of the easily quantifiable influencing factors are explored in this paper and the sample distribution is limited future research can expand the scope of sample selection and further improve the predictive indicator system to achieve more accurate predictions. [ABSTRACT FROM AUTHOR]

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The development of large-scale models has led to the widespread application of model inference services. Constructing a stable and reliable architectural support for model inference services has become a focus for cloud service providers. Serverless computing is a cloud service computing paradigm with fine-grained resource granularity and high abstraction level. It offers advantages such as on-demand billing and elastic scalability, which can effectively improve the computational efficiency of model inference services. However, the multi-stage nature of model inference service workflows makes it challenging for independent serverless computing frameworks to ensure optimal execution of each stage. Therefore, the key problem to be addressed is how to leverage the performance characteristics of different serverless computing frameworks to achieve online switching of model inference service workflows and reduce the overall execution time. This paper discusses the switching problem of model inference services on different serverless computing frameworks. Firstly, a pre-trained model is used to construct model inference service functions and derive the performance characteristics of heterogeneous serverless computing frameworks. Secondly, a machine learning technique is employed to build a binary classifica- tion model that combines the performance characteristics of heterogeneous serverless computing frameworks, enabling online switching of the model inference service framework. Finally, a testing platform is established to generate model inference service workflows and evaluate the performance of the online switching framework prototype. Preliminary experimental results indicate that compared with the independent serverless computing framework, the online switching framework prototype can reduce the execution time of model inference service workflows by up to 57%. [ABSTRACT FROM AUTHOR]

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