Proceedings of the 4th International Conference on Advanced Information Science and System最新文献_第3页

Research on An Intelligent Fault Diagnosis Method for Rolling Bearing Based on Wavelet Packet and PSO-BP Network Algorithm 基于小波包和PSO-BP网络算法的滚动轴承故障智能诊断方法研究

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574520

Shengzhi Yuan, Jiayu Zhang, J. Lai, Haidi Dong

引用次数: 0

Research on Construction and Operation of Rural E-commerce Supply chain 农村电子商务供应链建设与运营研究

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574488

Jing Zhang

{"title":"Research on Construction and Operation of Rural E-commerce Supply chain","authors":"Jing Zhang","doi":"10.1145/3573834.3574488","DOIUrl":"https://doi.org/10.1145/3573834.3574488","url":null,"abstract":"Abstract: in the development of information environment, rural areas of e-commerce to achieve urban and rural development of integration, information, supply chain logistics needs to develop. In the era of e- commerce development, supply chain management makes the speed and scope of information sharing, the efficiency of information processing, the mass storage of information, the value of information has been significantly enhanced, especially in rural areas, the circulation system is less developed, it is more necessary to study the local circulation supply chain system and put forward feasible optimization measures. At the same time, supply chain management, as one of the integrated management methods, has been paid more and more attention, especially in the era of e-commerce development, the research on the construction of rural circulation supply chain system is not only for the realization of rural circulation modernization, but also for the promotion of rural economic development, it is of great significance to solve the problems of agriculture, rural areas and farmers in our country, and it also provides an important theoretical basis for the development of rural circulation. Based on the study of rural e-commerce ecological chain and value chain, this paper constructs the rural e-commerce supply chain system, it is of theoretical and practical value to analyze how the rural e-commerce supply chain system with e-commerce service center works more effectively.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129485530","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

Improve GAN-based Neural Vocoder using Truncated Pointwise Relativistic Least Square GAN 利用截断点相对最小二乘GAN改进基于GAN的神经声码器

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574506

Yanli Li, Congyi Wang

{"title":"Improve GAN-based Neural Vocoder using Truncated Pointwise Relativistic Least Square GAN","authors":"Yanli Li, Congyi Wang","doi":"10.1145/3573834.3574506","DOIUrl":"https://doi.org/10.1145/3573834.3574506","url":null,"abstract":"Neural vocoders are widely utilized in modern text-to-speech (TTS) and voice conversion (VC) systems due to their high generation quality and fast inference speed. Recently, GAN-based neural vocoders have attracted great interest due to their lightweight and parallel structures, which enable them to generate a high-fidelity waveform in a real-time manner. Most existing GAN-based vocoders adopt the Least Square GAN (LSGAN) training framework. In this paper, we analyze the weaknesses of the LSGAN waveform synthesis framework and, inspired by Relativistic GAN, propose a simple yet effective variant of the LSGAN framework, named Truncated Pointwise Relativistic LSGAN (T-PRLSGAN). In this method, we consider the pointwise truism score distribution of real and fake wave segments and combine the Mean Squared error (MSE) loss with the proposed truncated pointwise relative discrepancy loss to increase the difficulty of the generator to fool the discriminator, leading to improved audio generation quality and stability. To demonstrate the effectiveness and generalization ability of our method, subjective and objective experiments have been conducted based on Avocodo, UnivNet, HiFiGAN, ParallelWaveGAN, and MelGAN vocoders, which show a consistent performance boost over those typical LSGAN-based vocoders. Moreover, our T-PRLSGAN can support multiple types of discriminators, i.e. multi-scale wave discriminator(MSD), multi-period discriminator(MPD), and multi-resolution spectrogram discriminator(MRD), without modifying their architecture or inference speed.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127138341","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: 2

Event Recommendation Based on Heterogeneous Social Network Information and Time Information 基于异构社会网络信息和时间信息的事件推荐

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574514

Xiaofan Zhao, Wenming Ma

{"title":"Event Recommendation Based on Heterogeneous Social Network Information and Time Information","authors":"Xiaofan Zhao, Wenming Ma","doi":"10.1145/3573834.3574514","DOIUrl":"https://doi.org/10.1145/3573834.3574514","url":null,"abstract":"In recent years, event-based social networks have developed rapidly, and event recommendation has attracted more and more attention. At present, for event recommendation, it is centered on the event, and aims to help users get the events which they are interested in from a large number of events. However, compared with traditional recommendation problems, event recommendation has many challenges. First of all, there is no obvious explicit rating of users’ response to events, but implicit feedback. Secondly, the recommendation of events has heterogeneous social network relations. At the same time, most users participate in few events, which leads to a very serious data sparsity problem. In order to address these challenges and improve the effectiveness of event recommendation, this paper proposes an event recommendation model that integrates users’ online and offline heterogeneous social network information and time information. The model uses Bayesian personalized ranking as the framework to process the implicit feedback information of users and events, and simultaneously combines online and offline social network information and time information to model together to improve the accuracy of recommendation. Experimental results based on real data sets show that the performance of the proposed model is better than other methods.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125317573","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

TRAPPER:Learning with Weak Supervision for Threat Intelligence Entity Recognition 基于弱监督的威胁情报实体识别学习

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574526

Yaru Yang, Zhi Liu, Jiaxing Song

{"title":"TRAPPER:Learning with Weak Supervision for Threat Intelligence Entity Recognition","authors":"Yaru Yang, Zhi Liu, Jiaxing Song","doi":"10.1145/3573834.3574526","DOIUrl":"https://doi.org/10.1145/3573834.3574526","url":null,"abstract":"The emergence of threat intelligence provides more foundation for tracing the source of network attacks, but it also necessitates a significant amount of manual analysis. Although data-driven automatic information extraction can effectively reduce labor consumption, it is limited by a lack of labeled data in the field of threat intelligence. To overcome this limitation, we propose TRAPPER, a threat entity recognition framework that can infer real threat entities from unlabeled threat sentences, avoiding the difficult labeling work. TRAPPER relies on label functions and three components, label aggregator, label predictor, and label expander, which guides the model with weak supervision and uses transfer knowledge as an aid. The label functions permit us to inject expert knowledge into the label aggregator to generate the inputs needed by the label predictor. It enables the label predictor to learn to recognize threat entities. The label expander combines the multi-source noisy label information with the transferred entity recognition semantic knowledge to further expand the entities. Throughout the process, the components promote each other by learning from each other. Comparative experiments on three threat intelligence-related datasets show that our method can effectively identify threat entities and achieve a maximum F1 score improvement of 6.3% over the best baseline.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129196807","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

Rearch on AI industry prediction based on Markov model 基于马尔可夫模型的人工智能产业预测研究

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574473

Lin Lu, Jian Zhang

{"title":"Rearch on AI industry prediction based on Markov model","authors":"Lin Lu, Jian Zhang","doi":"10.1145/3573834.3574473","DOIUrl":"https://doi.org/10.1145/3573834.3574473","url":null,"abstract":"Artificial intelligence has become the core driving force of a new round of industrial transformation, and scientific and accurate artificial intelligence industry development prediction is of great strategic significance for improving the quality of industrial development and future industrial chain development planning. Taking the development of artificial intelligence industry in Tianjin as an example, the future development trend of artificial intelligence industry is predicted and analyzed by the quantitative comparison and analysis of Markov linear programming mathematical model. Using variable substitution method, the quadratic programming model is transformed into a linear programming model, which not only has mature solution software, but also can be solved analytically, which is more convenient and reliable. The Markov property programming model reduces the time, simplifies the complexity, reduces the difficulty of model solving, and the prediction accuracy value is high, with the average error value accounting for 2.96%, 2.07%, and 5.03%. Finally, combined with the artificial intelligence refinement industry, the countermeasures to cultivate and accelerate the development of artificial intelligence industry in Tianjin are discussed from three levels: basic industry support, technology industry innovation and application industry expansion.","PeriodicalId":345434,"journal":{"name":"Proceedings of the 4th International Conference on Advanced Information Science and System","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117175049","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

Trip Itinerary Generation with 2-Opt Algorithm 基于2-Opt算法的行程生成

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3573907

Omar Challaf, Sook-Ling Chua, Lee Kien Foo

引用次数: 1

HIL-DDoS: A DDoS Attack Simulation System Based on Hardware-In-the-Loop 基于硬件在环的DDoS攻击仿真系统

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574530

Leigang Sun, Jin Zhang

引用次数: 0

Capsule Network with Inception Structure for Image Classification 基于初始结构的胶囊网络图像分类

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574507

Chen Zhao

引用次数: 0

Gushing Resolvers: Measuring Open Resolvers’ Recursive Behavior 喷涌解析器:测量开放解析器的递归行为

Proceedings of the 4th International Conference on Advanced Information Science and System Pub Date : 2022-11-25 DOI: 10.1145/3573834.3574533

Chengxi Xu, Yunyi Zhang, Fan Shi, Huimin Ma, Wanmeng Ding, Hong Shan

引用次数: 0