Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence最新文献_第4页

Research on rate adaptive control of power communication dispatching based on iterative weighted virtual force algorithm 基于迭代加权虚力算法的电力通信调度速率自适应控制研究

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579712

Xiaomeng He, Yingshu Wang, Shu Yuan, Xiaobing Xiao, Yue Li

{"title":"Research on rate adaptive control of power communication dispatching based on iterative weighted virtual force algorithm","authors":"Xiaomeng He, Yingshu Wang, Shu Yuan, Xiaobing Xiao, Yue Li","doi":"10.1145/3579654.3579712","DOIUrl":"https://doi.org/10.1145/3579654.3579712","url":null,"abstract":"The adaptive control of power communication scheduling rate based on iterative weighted virtual force algorithm is studied to expand the scope of power communication scheduling and improve the information transmission rate of power communication network. The iterative weighted virtual force algorithm is used to weight the grid cells iteratively, and the perceived nodes of the nodes are constantly changed through the initialization stage and the iteration stage, so as to expand the coverage of the power communication network; Based on OFDM allocation technology, a mathematical model of power minimization and transmission rate maximization in power communication is constructed, and a bit algorithm is proposed to adaptively optimize the mathematical model. According to the gain estimation value, the estimated value of signal-to-noise ratio of more subchannels is known, and additional bits are adaptively allocated to subchannels with lower bit error rate, so as to realize adaptive control of power communication scheduling rate. The experiment shows that:; This method can control a wider power communication range, faster information transmission rate and lower signal-to-noise ratio.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"33 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":"116115033","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}

引用次数: 0

Analyses of Software Data and Their Interpretations: A Framework of Information Granules 软件数据分析及其解释:一个信息颗粒的框架

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579675

Ayomide Bakare, Yegor Bugayenko, A. Kruglov, W. Pedrycz, G. Succi

{"title":"Analyses of Software Data and Their Interpretations: A Framework of Information Granules","authors":"Ayomide Bakare, Yegor Bugayenko, A. Kruglov, W. Pedrycz, G. Succi","doi":"10.1145/3579654.3579675","DOIUrl":"https://doi.org/10.1145/3579654.3579675","url":null,"abstract":"Data collected from software applications such as issue management systems or version control systems are abstract and require their thorough and comprehensive analysis. Automated issue generation is an understudied area in automated software development despite its effectiveness, safety, and satisfaction which increases developer productivity. Analysis of software data from automated issue generation provides information which could be used by relevant tools or monitored as any other feature in the development process. In this paper, we systematically apply a suite of methods, including clustering algorithms, cluster validity indexes, and information granularity, to generate explainable prototypes using software data from generated GitHub Issues. Among other approaches of data analytics, we employ the principle of justifiable granularity and a method of optimal information allocation. These methods are applied to two dimensional synthetic Gaussian data to illustrate the performance of the methods. The study provides the experimental results using the methods applied to real industrial data coming from the 0pdd software. The resultant groups provide some insights into structure for organising puzzles with similar characteristics.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"21 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":"116690403","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}

引用次数: 0

Research and application of matching network 匹配网络的研究与应用

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579775

Chao Jiang, Junyang Mo, Zhongming Pan

引用次数: 0

Multi-granularity Feature Fusion Algorithm for Short Chinese Texts Based on Hierarchical Attention Networks 基于层次关注网络的中文短文本多粒度特征融合算法

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579715

Zhifeng Lu, Hao-dong Xia, Wenxing Hong

引用次数: 0

A deep learning based scene text detector combining two strategies 结合两种策略的基于深度学习的场景文本检测器

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579676

Ting Jin, Zhaogong Zhang, Zhichao Zhang

{"title":"A deep learning based scene text detector combining two strategies","authors":"Ting Jin, Zhaogong Zhang, Zhichao Zhang","doi":"10.1145/3579654.3579676","DOIUrl":"https://doi.org/10.1145/3579654.3579676","url":null,"abstract":"Detecting scene text has been a challenging task due to the complex geometric layouts of texts. We can broadly classify the state-of-the-art scene text detection methods into two categories. The first category is the top-down methods, which view text as a whole and locate text by regression learning on the points of text bounding boxes or by learning the geometric properties of text, but most algorithms have difficulty in separating neighboring text. The second category is the bottom-up methods, which treat the text as composed of simple local components and obtain text instances by post-processings, but most algorithms rely on accurate segmentation results. In this paper, we propose a method that combines these two types of ideas while avoiding their drawbacks. Specifically, we use a top-down strategy to obtain text contours, and then use a contour scoring module to score the text contours to obtain more accurate results. In addition, we use a bottom-up strategy to obtain kernels and similarity vectors. Subsequently, pixel aggregation is used to combine the results of the two parts to obtain a more flexible representation of the text instances. Experiments on several benchmark datasets demonstrate the effectiveness of the proposed method.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"22 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":"129525053","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}

引用次数: 0

DTTrack: Target Tracking Algorithm Combining DaSiamRPN Tracker and Transformer Tracker DTTrack:结合DaSiamRPN跟踪器和变压器跟踪器的目标跟踪算法

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579734

Yingying Duan, Wencong Wu, Liwei Liu, Siyuan Liu, Peng Liang, Yungang Zhang

{"title":"DTTrack: Target Tracking Algorithm Combining DaSiamRPN Tracker and Transformer Tracker","authors":"Yingying Duan, Wencong Wu, Liwei Liu, Siyuan Liu, Peng Liang, Yungang Zhang","doi":"10.1145/3579654.3579734","DOIUrl":"https://doi.org/10.1145/3579654.3579734","url":null,"abstract":"At present, transformer-based target tracking algorithms mainly use transformers to fuse deep convolution features, their tracking accuracy is competitive, however compared with convolutional neural networks, their tracking speed is slow. Due to the long-distance dependence characteristics, it is difficult to obtain rich local information when extracting visual features, the tracking results may become worse, or even the tracking may fail in the later tracking procedures. The partial target tracking algorithm based on the Siamese network has great advantages in extracting local information, however its tracking accuracy cannot fully reach the transformer-based target tracking algorithm. According to the characteristics of the two trackers, combining the response scores and Hamming distance which is used to calculate the similarity, then a target tracking algorithm combining DaSiamRPN and Transformer is proposed. This structure can judge whether the tracking effect of the transformer tracker has deteriorated according to the response score and the Hamming distance between the resulting frame and the initial frame during transformer tracking, in order to replace another tracker in time. The proposed method can reduce the drift and obtain higher accuracy as well. Experiments show that our tracker achieves good results on three datasets. Our method achieved 72.0%, 69.1%, and 67.1% success rates on the GOT-10k, OTB2015, and UAV123 datasets, respectively.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"32 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":"116510228","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}

引用次数: 0

Chinese Medical Named Entity Recognition Based on Parameter Transfer Learning 基于参数迁移学习的中医命名实体识别

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579713

Menglin Zhou, Kecun Gong

引用次数: 0

Social Media Text Summarisation Techniques and Approaches: A Literature Review 社交媒体文本摘要技术和方法:文献综述

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579774

Afrodite Papagiannopoulou, C. Angeli

引用次数: 0

VLS: Vehicle Tail Light Signal Detection Benchmark 车辆尾灯信号检测基准

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579770

Ruili Lai, Chumei Wen, Jingmin Xu, Delu Zeng, Bo Wu

{"title":"VLS: Vehicle Tail Light Signal Detection Benchmark","authors":"Ruili Lai, Chumei Wen, Jingmin Xu, Delu Zeng, Bo Wu","doi":"10.1145/3579654.3579770","DOIUrl":"https://doi.org/10.1145/3579654.3579770","url":null,"abstract":"Many car accidents are caused by the driver’s failure to accurately and timely identify the driving state of the vehicle ahead. Therefore, it is very important to accurately and timely detect the driving state of the vehicle ahead in an automated manner. There are many factors that affect the recognition accuracy, such as the light condition, weather, and the angle of the tail lights of the vehicle. Few existing autonomous driving datasets can be used to train the deep learning models to accurately identify the driving state of the vehicle ahead and perfectly meet the needs above. The proposed VLS (vehicle tail light signal) Dataset consists of eight vehicle driving states namely normal driving, braking, left turn, and right turn during the day and night. The dataset could help us predict the future trajectory of the vehicle ahead and make appropriate decisions, by identifying the vehicle driving states in the real world scenarios based on the on-off states of the tail lights (on the left, right and top of the vehicle tail). The reasons why some hard samples are difficult to be detected are also analyzed. Six mainstream object detection algorithms are used to train and test our dataset with their detection accuracy. These algorithms are readily available to identify the vehicle driving states and achieves the best speed-accuracy trade-off on our VLS dataset. The dataset is proved to be productive and useful to the development of autonomous driving systems.","PeriodicalId":146783,"journal":{"name":"Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence","volume":"9 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":"133118245","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}

引用次数: 1

Visualization Research of WEB front-end development technology in Shandong Yellow River Union Cloud Exhibition Hall 山东黄河联合云展厅WEB前端开发技术可视化研究

Proceedings of the 2022 5th International Conference on Algorithms, Computing and Artificial Intelligence Pub Date : 2022-12-23 DOI: 10.1145/3579654.3579716

Tie-Cheng Shan, A. Zhang, Hua Jiang, Zhao-Xia Xu

引用次数: 0