人工智能技术学报(英文)最新文献

{"title":"Review on Offloading of Vehicle Edge Computing","authors":"M. Wang, Hualing Yi, Feng Jiang, Ling-En Lin, M. Gao","doi":"10.37965/jait.2022.0120","DOIUrl":"https://doi.org/10.37965/jait.2022.0120","url":null,"abstract":"Vehicle edge computing (VEC) is a new technology that can extend computing and storage functions to the edge of the Internet of Things (IoT) systems. For limited computing power and delay sensitive mobile applications on the Internet of Vehicles (IOV). It is important to offload computing tasks to the end of the VEC network. Still, High mobility data security and privacy resource management and the randomness of IOV brought about new problems to the offloading of VEC. To this end, this study focuses on the offloading of computing tasks in VEC. We survey principal offloading schemes and methods in the VEC field and classify the current offloading of computing tasks into different categories. We also discuss the prospect of VEC. This survey could give a reference for researchers to find and understand the important characteristics of VEC, which helps choose the optimal solutions for the offloading of computing tasks in VEC.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41257196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Datamining and Its Applications","authors":"J. Brieva","doi":"10.37965/jait.2022.0125","DOIUrl":"https://doi.org/10.37965/jait.2022.0125","url":null,"abstract":"\u0000 \u0000 \u0000Artificial intelligence and machine learning are widely applied in all domain applications today, including noncontact vital sign monitoring, data mining and denoising, data analysis, and application as traffic simulation and green finance. We briefly introduce the noncontact vital sign monitoring using video data and the solutions to this problem supplied by Artificial intelligence and machine learning. Then, we present the five papers selected in the related areas for this journal issue. \u0000 \u0000 \u0000","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42423988","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single Image Dehazing Based on Two-Stream Convolutional Neural Network","authors":"Jun Meng, Yuanyuan Li, Huahua Liang, You Ma","doi":"10.37965/jait.2022.0110","DOIUrl":"https://doi.org/10.37965/jait.2022.0110","url":null,"abstract":"Objective The haze weather environment leads to the deterioration of the visual effect of the image, and it is difficult to carry out the work of the advanced vision task. Therefore, dehazing the haze image is an important step before the execution of the advanced vision task. Traditional dehazing algorithms achieve image dehazing by improving image brightness and contrast or constructing artificial priors such as color attenuation priors and dark channel priors, but the effect is unstable when dealing with complex scenes. In the method based on convolutional neural network, the image dehazing network of the encoding and decoding structure does not consider the difference before and after the dehazing image, and the image spatial information is lost in the encoding stage. In order to overcome these problems, this paper proposes a novel end-to-end two-stream convolutional neural network for single image dehazing. Method The network model is composed of a spatial information feature stream and a high-level semantic feature stream. The spatial information feature stream retains the detailed information of the dehazing image, and the high-level semantic feature stream extracts the multi-scale structural features of the dehazing image. A spatial information auxiliary module is designed between the feature streams. This module uses the attention mechanism to construct a unified expression of different types of information, and realizes the gradual restoration of the clear image with the semantic information auxiliary spatial information in the dehazing network. A parallel residual twicing module is proposed, which performs dehazing on the difference information of features at different stages to improve the model’s ability to discriminate haze images. Result The peak signal-to-noise ratio and structural similarity are used to quantitatively evaluate the similarity between the dehazing results of each algorithm and the original image. The structure similarity and peak signal-to-noise ratio of the method in this paper reached 0.852 and 17.557dB on the Hazerd dataset, which were higher than all comparison algorithms. On the SOTS dataset, the indicators are 0.955 and 27.348dB, which are sub-optimal results. In experiments with real haze images, this method can also achieve excellent visual restoration effects.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47198190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Impact of Green Finance on the Ecologicalization of Urban Industrial Structure —— Based on GMM Model of Dynamic Panel System","authors":"K. Lin, Huawei Zhao","doi":"10.37965/jait.2022.0115","DOIUrl":"https://doi.org/10.37965/jait.2022.0115","url":null,"abstract":" Although a number of papers have been published in the general area of various factors affecting the ecologicalization of urban industrial structure, little work has been carried out for empirical studies quantitatively analyzing the relevance between green finance development and the ecologicalization of urban industrial structure. Therefore, based on a comprehensive index of green finance development, this research employs panel data of target cities for the period 2012–2020 to explore the influence of green finance on the ecologicalization of urban industrial structure. And the empirical results show that green finance development significantly improves the ecologicalization level of urban industrial structure. In addition, it’s found that green finance plays a stronger role in promoting the ecologicalization of industrial structure in economically developed regions than in economically underdeveloped regions. The research results could provide valuable policy implications for urban green financial market planning and green product innovation. \u0000  \u0000  \u0000 ","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46628759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Traffic Dataset for Dynamic Routing Algorithm in Traffic Simulation","authors":"Zhemin Zhang, Gennaro De Luca, Brian Archambault, J. Chavez, B. Rice","doi":"10.37965/jait.2022.0106","DOIUrl":"https://doi.org/10.37965/jait.2022.0106","url":null,"abstract":"The purpose of this research is to create a simulated environment for teaching algorithms, big data processing, and machine learning. The environment is similar to Google Maps, with the capacity of finding the fastest path between two points in dynamic traffic situations. However, the system is significantly simplified for educational purposes. Students can choose different traffic patterns and program a car to navigate through the traffic dynamically based on the changing traffic. The environments used in the project are VIPLE (Visual IoT/Robotics Programming Language Environment) and a traffic simulator developed in the Unity game engine. This paper focuses on creating realistic traffic data for the traffic simulator and implementing dynamic routing algorithms in VIPLE. The traffic data is generated from the recorded real traffic data published on the Arizona Maricopa County website. Based on the generated traffic data, VIPLE programs are developed to implement the traffic simulation with support for dynamic changing data.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44168632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Coherent Pattern Mining Algorithm Based on All Contiguous Column Bicluster","authors":"Xiaohui Hu, Qiuhua Kuang, Qianhua Cai, Yun Xue, Weixing Zhou, Ying Li","doi":"10.37965/jait.2022.0105","DOIUrl":"https://doi.org/10.37965/jait.2022.0105","url":null,"abstract":"Microarray contains a large matrix of information and has been widely used by biologists and bio data scientist for monitoring combinations of genes in different organisms. The coherent patterns in all continuous columns are mined in gene microarray data matrices. It is investigated, in this study, the coherent patterns in all continuous columns in gene microarray data matrix by developing the time series similarity measure for the coherent patterns in all continuous columns, as well as the evaluation function for verifying the proposed algorithm and the corresponding biclusters. The continuous time changes are taken into account in the coherent patterns in all continuous columns, and co-expression patterns in time series are searched. In order to use all the common information between sequences, a similarity measure for the coherent patterns in continuous columns is defined in this paper. To validate the efficiency of the similarity measure to mine biological information at continuous time points, an evaluation function is defined to measure biclusters and an effective algorithm is proposed to mine the biclusters. Simulation experiments are conducted to verify the biological significance of the biclusters, which include synthetic datasets and real gene microarray datasets. The performance of the algorithm is analyzed and the results show that the algorithm is highly efficient.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44862101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hybrid CNN for Image Denoising","authors":"Menghua Zheng, Keyan Zhi, Jiawen Zeng, Chunwei Tian, Lei You","doi":"10.37965/jait.2022.0101","DOIUrl":"https://doi.org/10.37965/jait.2022.0101","url":null,"abstract":"Deep convolutional neural networks (CNNs) with strong learning abilities have been used in the field of image super-resolution. However, some CNNs depends on a single deep network to training an image super-resolution model, which will have poor performance in complex screens. To address this problem, we propose a hybrid denoising CNN (HDCNN). HDCNN is composed of a dilated block (DB), RepVGG block (RVB) and feature refinement block (FB), a single convolution. DB combines a dilated convolution, batch normalization (BN), common convolutions, activation function of ReLU to obtain more context information. RVB uses parallel combination of convolution and BN, ReLU to extract complementary width features. FB is used to obtain more accurate information via refining obtained feature from the RVB. A single convolution collaborates a residual learning operation to construct a clean image. These key components make the HDCNN have good performance in image denoising. Experiment shows that the proposed HDCNN enjoys good denoising effect in public datasets.  ","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42714842","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Infused Semantic Model to Enrich and Expand Programming Question Generation","authors":"I-Han Hsiao, Cheng-Yu Chung","doi":"10.37965/jait.2022.0090","DOIUrl":"https://doi.org/10.37965/jait.2022.0090","url":null,"abstract":"Creating practice questions for programming learning is not easy. It requires the instructor to diligently organize heterogeneous learning resources, i.e., conceptual programming concepts and procedural programming rules. Today’s programming question generation (PQG) is still largely replying on the demanding creation task performed by the instructors without advanced technological support. In this work, we propose a semantic PQG model that aims to help the instructor generate new programming questions and expand the assessment items. The PQG model is designed to transform conceptual and procedural programming knowledge from textbooks into a semantic network by the Local Knowledge Graph (LKG) and Abstract Syntax Tree (AST). For any given question, the model queries the established network to find related code examples and generates a set of questions by the associated LKG/AST semantic structures. We conduct analysis to compare instructor-made questions from 9 undergraduate introductory programming courses and textbook questions. The results show that the instructor-made questions had much simpler complexity than the textbook ones. The disparity of topic distribution intrigued us to further research the breadth and depth of question quality and also to investigate the complexity of the questions in relations to the student performances. Finally, we report an user study results on the proposed AI-infused semantic PQG model in examining the machine-generated questions quality.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42635193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-World Data for the Drug Development in the Digital Era","authors":"Xianchen Liu","doi":"10.37965/jait.2022.0091","DOIUrl":"https://doi.org/10.37965/jait.2022.0091","url":null,"abstract":"Randomized clinical trials (RCTs) have long been recognized the gold standard for regulatory approval in the drug development. However, RCTs may not be feasible in some diseases and/or under certain situations and findings from RCTs may not be generalized to real-world patients in the routine clinical practice. Real-world evidence (RWE) generated from various real-world data has become more and more important for the drug development and clinical decision making in the digital era. This paper described real-world data, RWE, and RWE generation, followed by the characteristics and differences between RCTs and RWE studies. Furthermore, the challenges and limitations of real-world data and RWE studies were discussed.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42046468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Key Radar Signal Sorting and Recognition Method Based on Clustering Combined PRI transform Algorithm","authors":"Kaige Kang, Yixiao Zhang, Wenpu Guo, Luogeng Tian","doi":"10.37965/jait.2022.0076","DOIUrl":"https://doi.org/10.37965/jait.2022.0076","url":null,"abstract":"In this paper, we investigate the problem of key radar signal sorting and recognition in electronic intelligence (ELINT). Our major contribution is the development of a combined approach based on clustering and PRI transform algorithm, to solve the problem that the traditional methods based on Pulse Description Words (PDW) were not exclusively targeted at tiny particular signals and, less time-efficient. We achieve this in three steps: firstly, PDW presorting is carried out by the DBSCAN clustering algorithm, then, PRI estimates of each cluster are obtained by the PRI transformation algorithm, finally, by judging the matching between various PRI estimates and key targets, it is determined whether the current signal contains key target signals or not. Simulation results show that the proposed method should improve the time efficiency of key signal recognition and deal with the complex signal environment with noise interference and overlapping signals.","PeriodicalId":70996,"journal":{"name":"人工智能技术学报(英文)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43005146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}