Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence最新文献

{"title":"Exploring Entity-level User Preference on the Knowledge Graph for Recommender System","authors":"Pengfei Chen, Qi Wang, Yuan Tian","doi":"10.1145/3579654.3579701","DOIUrl":"https://doi.org/10.1145/3579654.3579701","url":null,"abstract":"Knowledge graphs (KG) have attracted extensive attention in recommender systems since they contain rich external knowledge. The recent trend in KG-enhanced recommender systems is to employ graph neural networks (GNN) to learn the node representations of the involved graph structures in the recommender system to speculate user preferences. However, existing KG-enhanced recommendation models face two major issues: i) User preferences are mainly built at the relation level and fail to model preferences at the attribute entity level; ii) Implicit feedback lacks accurate user rating information and the data may contain noisy interactions. Such inaccurate preference modeling and imperfect interaction data hinder the capture of users’ actual preferences. To this end, we propose an Entity-level user Preference-aware model on Knowledge Graph (EPKG), which models user preferences at the attribute entity level. Specifically, we introduce the number of connections between attribute entities and user interaction items in the knowledge graph and establish a weight distribution on the number of connections to speculate user preferences for attribute entities. Furthermore, we devise user preference learning to model user preferences to the finer attribute entity level. Afterward, we design a preference-aware aggregation strategy that uses entity-level user preferences to guide the learning of item weights in user interaction history, which in turn alleviates the effects of lack of user rating information and noisy interactions. Experimental results on the three datasets show that EPKG achieves significant improvement compared to the state-of-the-art models. Especially for the Last-FM dataset, EPKG improves NDCG@20 and Recall@20 by 31.5% and 18.4%, respectively.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122045960","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":"Lip Reading Bengali Words","authors":"M. Rahman, Md Rashad Tanjim, S. Hasan, Sayeed Md. Shaiban, Mohammad Ashrafuzzaman Khan","doi":"10.1145/3579654.3579677","DOIUrl":"https://doi.org/10.1145/3579654.3579677","url":null,"abstract":"This work aims to lip-read Bengali words from talking faces without using audio. Lip reading for English words and sentences is well explored in literature. However, to our knowledge, we are the first to explore this for Bengali words, a language spoken by about 272 million people in south-east Asia [7]. We used a CNN to extract features from the video frames in sequence and provided the features to a bidirectional LSTM network followed by a classifier. We trained the entire network end-to-end. We investigated the effects of using different types of convolution operations during feature collection. We used convolution with filters of multiple scales in a single stage (Inception [24]), depthwise and pointwise convolution (MobileNet [25]), traditional CNN (VGG16 [26], ResNet [17], DenseNet [27], ResNeXt [28]), and a custom CNN. For Bengali word lip reading, MobileNet [25] (as CNN) followed by a bidirectional LSTM and classifier achieved the highest accuracy of 84.75%. Moreover, we found that longer words have better detection rates than shorter ones using any type of convolution.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"2011 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129886028","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":"IHUMN: an improved high-utility itemsets mining algorithm with negative utility items","authors":"Huijiao Wang, Jinghai Wei, Xin Wang, Xing Li, Hua Jiang","doi":"10.1145/3579654.3579766","DOIUrl":"https://doi.org/10.1145/3579654.3579766","url":null,"abstract":"High-utility itemset mining is to mine high profit itemsets from transaction databases. But if there are some itemsets with negative utility values in the transaction database, the high-utility itemsets with the negative values may be pruned incorrectly and the subset of the low-utility itemsets may be the high-utility itemsets. In this paper, an improved high-utility itemsets mining algorithm with negative utility items (IHUMN) is proposed. A novel utility-list buffer structure with negative unit profits is proposed to efficiently store and retrieve utility-list, and reduce the memory consumption during the mining process. Moreover, Transitive Extension with Negative utility formula is constructed to compute the upper bound of utility avoiding the overestimation of low-utility itemsets as high-utility itemsets. The performance of IHUMN is evaluated, and compared against the FHN and GHUM method. The results of the experiments confirm that IHUMN has a favorable improvement in terms of time costs, the memory utilization and the number of visited nodes. The IHUMN algorithm consumes 40% less memory than GHUM. Moreover, the algorithm has good performance on dense datasets.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127878456","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":"Application of UKF/KF in Single-axial Rotation Initial alignment","authors":"Jianguo Xu, Yuan Zhou, Shaolei Wang","doi":"10.1145/3579654.3579689","DOIUrl":"https://doi.org/10.1145/3579654.3579689","url":null,"abstract":"In order to improve the initial alignment precision on static base is restricted because the system is not completely observable, UKF used in large misalignment angle has more calculate amount, the scheme of single-axial rotation is put forward, and UKF/KF feedback algorithm is applied to reduce calculate amount. The continuous rotation nonlinear error model is deduced out when the azimuth misalignment angle is large, attitude matrix is updated by equivalent rotation vector attitude algorithm, and UKF/KF is used to complete the process of precise alignment. UKF is used to state estimate when model is nonlinear error model, when misalignment angle is reduced less than one static value by feedback, error model can be considered as linear model and KF is used to complete state estimate. The simulation result shows that the alignment estimation accuracy in single-axial rotation scheme is obviously higher than two-position alignment and static alignment, UKF/KF has the same alignment precision and speed compare with UKF, but the speed of compute is enhanced.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"266 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121328322","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":"Big Data Analysis and Potential Development Research of Regional Logistics Warehousing Based on Baidu Index and Warehouse in Cloud","authors":"Zhe Wang, Juan Huang, Lijuan Xu, Qiaomei Zeng, Renqiang Li, S. Li, Lin Zhou","doi":"10.1145/3579654.3579776","DOIUrl":"https://doi.org/10.1145/3579654.3579776","url":null,"abstract":"Based on the Baidu Index, taking \"warehousing\" and \"warehouse\" as the keywords, the Baidu search index of \"warehousing\" and \"warehouse\" nationwide is statistically analyzed. It is found that the Baidu search index with \"warehousing\" and \"warehouse\" as the keywords has increased significantly before and after the epidemic, indicating that the basic role of logistics warehousing in the national economic and social development is increasingly apparent, and the corresponding demand for logistics warehousing is growing. Based on the big data of Warehouse in Cloud, the incomplete statistical analysis of \"warehousing demand\" by province (municipality directly under the central government) of \"demand location\" is similar to the analysis of the differences of different search population sources (regions and provinces) through the \"population portrait\" of Baidu Index. For the incomplete statistical analysis of \"warehousing demand\" and \"warehousing supply\" of cities in Sichuan and Chongqing, the correlation analysis of warehousing rent and demand area is focused on the main cities in Sichuan and Chongqing (incomplete statistics), and on this basis, the potential analysis of warehousing rent and demand area is carried out for the main cities in Sichuan and Chongqing (incomplete statistics). It is found that Chengdu and Chongqing have obvious attraction effect on logistics and warehousing in Sichuan and Chongqing. Moreover, Mianyang, Yibin, Dazhou, Luzhou, Nanchong, Deyang, Zigong and Leshan, have certain economic foundation and comparative advantages in terms of the potential of logistics warehousing rent and the potential of logistics warehousing demand area. Optimizing regional industrial layout, accelerating economic development and transportation logistics construction could play a positive role in achieving regional coordinated development. Furthermore, cultivating and strengthening regional central cities can promote regional coordinated and sustainable development.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122742391","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":"High-quality rainy image generation method for autonomous driving based on few-shot learning","authors":"Haiyan Shao, Jihong Yang, Guancheng Chen, Yixiang Xie, Huabiao Qin, Linyi Huang","doi":"10.1145/3579654.3579673","DOIUrl":"https://doi.org/10.1145/3579654.3579673","url":null,"abstract":"Rainy image generation aims to transfer images from standard domains such as daytime into rainy domains. Related researches can be divided into unsupervised methods and supervised methods according to use of semantic label constraints. The generalization ability of unsupervised methods is highly related to the domain gap between rain-free images and rain-effect images, which is difficult to keep the layout consistency due to the lack of semantic constraints on the paired data of the target domain. In supervised generative models, the scarcity of paired datasets has a serious impact on the performance of generative results. Moreover, most of the existing rainy paired datasets are synthesized by simply merging and the rain simulated by noise, which can be very different from the images shot in natural condition. So, in order to improve the realism of rainy image generation, we proposed a realistic paired rainy dataset (PRD) in autonomous driving scenes to explore the real rain representations and fusion mechanism with clear images. Besides, aiming at lack of paired samples in autonomous driving scenarios, we are committed to the study of the few-shot learning in generative models. An incremental hybrid training strategy is proposed to make full use of a few datasets. Through extensive experiments, we verify the effectiveness of our proposed method, which achieves more realistic results on limited labeled data. In the future, the dataset can be applied in many other tasks of autonomous driving.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122988308","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 New Handwritten Essay Dataset for Automatic Essay Scoring with A New Benchmark","authors":"Shiyu Hu, Qichuan Yang, Yibing Yang","doi":"10.1145/3579654.3579684","DOIUrl":"https://doi.org/10.1145/3579654.3579684","url":null,"abstract":"The study of algorithms for Automatic Essay Scoring (AES) currently is motivated by textual essay-scoring datasets constructed by anonymous teachers from schools. We propose VisEssay, the first essay-scoring dataset containing handwriting images. VisEssay consists of over 13,000 visual essays originating from 25+ professional in-service teachers whose personal scoring accuracy are recorded by his/her scoring history, together with crowdsourced OCR result per handwriting image. VisEssay differs from the many existing AES datasets because 1) handwriting images are captured from non-native speakers with complementary essay types for existing datasets, 2) teachers scoring these essays are with personal profiles and score accuracy, and 3) corresponding text is checked to keep the consistency. Evaluation of modern algorithms for AES and text classification reveals that the proposed VisEssay is a challenging dataset. In the cause of encouraging a larger community to develop more generalized educational algorithms, we introduce three novel AES systems together with VisEssay and analysis the result as a new benchmark.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123004288","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":"Online Review System Using Relational Triple Extraction with Novel Data Augmentation Methods","authors":"Yufei Song, Junyang Mo, Zhongming Pan","doi":"10.1145/3579654.3579741","DOIUrl":"https://doi.org/10.1145/3579654.3579741","url":null,"abstract":"The online review system is a fundamental application system. However, the existing system not only can not automatically check the matching of comments and score ratings but also can not give a fair reference score according to the comments, In this work, we utilize a relational triple extraction method to solve the two problems for the first time. In addition, considering that the existing online review systems are generally characterized by a lack of high-quality labeled data, we present five novel data augmentation techniques for boosting performance specifically on relational triple extraction tasks. The five data augmentation techniques demonstrate particularly strong results for both datasets of the review system and the public datasets of relational triple extraction.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123147883","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":"Moving targets detection and parameters estimation based on DPCA and FrFT in dual-channel SAR","authors":"Changli Li","doi":"10.1145/3579654.3579699","DOIUrl":"https://doi.org/10.1145/3579654.3579699","url":null,"abstract":"Improvements based on DPCA–Fractional Fourier Transform(FrFT) are presented in this paper, so as to improve the performance of moving target detection and parameters’ estimation: firstly, aiming at the problem of acceleration in range, it is used as a parameter of moving target and estimated, this parameter mainly affects doppler rate; secondly, aiming at the problem of complicated system, this paper not only uses FrFT to estimate the doppler parameters of moving target, but also uses the maximum value of signal energy after FrFT. It can realize the detection and parameters’ estimation of moving target in dual-channel SAR and simplifies the structure of SAR's system; finally, due to the presence of noise in SAR's system, moving target's reflection coefficient is not an ideal value; The reflection coefficient is estimated by the amplitude averaging method to solve the problem, so as to improve the accuracy of parameters’ estimation of moving target.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"219 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115465984","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":"Preprocessing Genuine and Fake Fingerprint Images and Recognition Based on Neural Network","authors":"Ke Han","doi":"10.1145/3579654.3579697","DOIUrl":"https://doi.org/10.1145/3579654.3579697","url":null,"abstract":"Fingerprint feature information can be used for individual identification. The appearance of forged fingerprints has a negative impact on the authenticity of individual identification. In this paper, a method based on the neural network is proposed to identify the genuine and fake fingerprint images. The method preprocesses the fingerprint images. The resolution of each fingerprint image is set to 500 dpi. Then, the fingerprint images are cut. A moving window with the size of 360 × 256 is defined to move the window on the fingerprint image at certain intervals. The proportion of the effective fingerprint area in the moving window is calculated. When the proportion of the effective fingerprint area reaches or exceeds a threshold, the color subimage in the moving window is saved as a training sample or a test sample. It is necessary to normalize the mean and variance of the fingerprint image before the fingerprint image is inputted into the neural network. The neural network proposed in this paper is based on the residual neural modules. The neural network is composed of a convolutional layer, a max-pooling layer, four residual modules and three fully-connected layers. The cross-entropy loss function is used as the objective function of the neural network. Adam algorithm is employed to optimize the parameters of the neural network. The proposed method is evaluated in different training sample datasets and test sample datasets which include the genuine and fake fingerprint images. The neural network method is compared with the k-nearest neighbor method in identifying the genuine and fake fingerprint images. The experimental results show that the method is superior to the k-nearest neighbor method in the accuracy of identifying the genuine and fake fingerprint images.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116021020","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}