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Detecting micro-blog user interest communities through the integration of explicit user relationship and implicit topic relations

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Science China(Information Sciences) 2017年第9期60卷 64-77页

作者： Yu QIN Zhengtao YU Yanbing WANG shengxiang gao Linbin SHI Institute of Information Engineering and Automation Kunming University of Science and Technology Key Laboratory of Intelligent Information Processing Kunming University of Science and Technology

In order to effectively utilize the explicit user relationship and implicit topic relations for the detection of micro-blog user interest communities, a micro-blog user interest community(MUIC) detection approach is proposed. First, through the analysis of the follow relationship between users, we have defined three types of such relationships to construct the user follow-ship network. Second, taking the semantic correlation between user tags into account, we construct the user interest feature vectors based on the concept of feature mapping to build a user tag based interest relationship network. Third, user behaviors, such as reposting, commenting, replying, and receiving comments from others, are able to provide certain guidance for the extraction of micro-blog topics. Hence, we propose to integrate the four mentioned user behaviors that are considered to provide guidance information for the traditional latent Dirichlet allocation(LDA) model. Thereby, in addition to the construction of a topic-based interest relationship network, a guided topic model can be built to extract the topics in which the user is interested. Finally, with the integration of the afore-mentioned three types of relationship network, a micro-blog user interest relationship network can be created. Meanwhile, we propose a MUIC detection algorithm based on the contribution of the neighboring nodes. The experiment result proves the effectiveness of our approach in detecting MUICs.

关键词： feature mapping implicit topic guided topic model contribution of the neighboring nodes

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Semantic-aware entity alignment for low resource language knowledge graph

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Frontiers of Computer Science 2024年第4期18卷 97-106页

作者： Junfei TANG Ran SONG Yuxin HUANG shengxiang gao Zhengtao YU Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500China

Entity alignment(EA)is an important technique aiming to find the same real entity between two different source knowledge graphs(KGs).Current methods typically learn the embedding of entities for EA from the structure of KGs for *** EA models are designed for rich-resource languages,requiring sufficient resources such as a parallel corpus and pre-trained language ***,low-resource language KGs have received less attention,and current models demonstrate poor performance on those low-resource ***,researchers have fused relation information and attributes for entity representations to enhance the entity alignment performance,but the relation semantics are often *** address these issues,we propose a novel Semantic-aware Graph Neural Network(SGNN)for entity ***,we generate pseudo sentences according to the relation triples and produce representations using pre-trained ***,our approach explores semantic information from the connected relations by a graph neural *** model captures expanded feature information from *** results using three low-resource languages demonstrate that our proposed SGNN approach out performs better than state-of-the-art alignment methods on three proposed datasets and three public datasets.

关键词： graph neural network knowledge graph entity alignment low-resource language

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Review Expert Collaborative Recommendation Algorithm Based on Topic Relationship

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IEEE/CAA Journal of Automatica Sinica 2015年第4期2卷 403-411页

作者： shengxiang gao Zhengtao Yu Linbin Shi Xin Yan Haixia Song School of Information Engineering and Automation and Key Laboratory of Intelligent Information Processing Kunming University of Science and Technology

The project review information plays an important role in the recommendation of review experts. In this paper, we aim to determine review expert's rating by using the historical rating records and the final decision results on the previous projects, and by means of some rules, we construct a rating matrix for projects and experts. For the data sparseness problem of the rating matrix and the 'cold start' problem of new expert recommendation, we assume that those projects/experts with similar topics have similar feature vectors and propose a review expert collaborative recommendation algorithm based on topic relationship. Firstly, we obtain topics of projects/experts based on latent Dirichlet allocation (LDA) model, and build the topic relationship network of projects/experts. Then, through the topic relationship between projects/experts, we find a neighbor collection which shares the largest similarity with target project/expert, and integrate the collection into the collaborative filtering recommendation algorithm based on matrix factorization. Finally, by learning the rating matrix to get feature vectors of the projects and experts, we can predict the ratings that a target project will give candidate review experts, and thus achieve the review expert recommendation. Experiments on real data set show that the proposed method could predict the review expert rating more effectively, and improve the recommendation effect of review experts. © 2014 Chinese Association of Automation.

关键词： Algorithms Collaborative filtering Factorization Rating Statistics

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LKMT:Linguistics Knowledge-Driven Multi-Task Neural Machine Translation for Urdu and English

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Computers, Materials & Continua 2024年第10期81卷 951-969页

作者： Muhammad Naeem Ul Hassan Zhengtao Yu Jian Wang Ying Li shengxiang gao Shuwan Yang Cunli Mao Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming650500China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming650500China

Thanks to the strong representation capability of pre-trained language models,supervised machine translation models have achieved outstanding ***,the performances of these models drop sharply when the scale of the parallel training corpus is *** the pre-trained language model has a strong ability for monolingual representation,it is the key challenge for machine translation to construct the in-depth relationship between the source and target language by injecting the lexical and syntactic information into pre-trained language *** alleviate the dependence on the parallel corpus,we propose a Linguistics Knowledge-Driven MultiTask(LKMT)approach to inject part-of-speech and syntactic knowledge into pre-trained models,thus enhancing the machine translation *** the one hand,we integrate part-of-speech and dependency labels into the embedding layer and exploit large-scale monolingual corpus to update all parameters of pre-trained language models,thus ensuring the updated language model contains potential lexical and syntactic *** the other hand,we leverage an extra self-attention layer to explicitly inject linguistic knowledge into the pre-trained language model-enhanced machine translation *** on the benchmark dataset show that our proposed LKMT approach improves the Urdu-English translation accuracy by 1.97 points and the English-Urdu translation accuracy by 2.42 points,highlighting the effectiveness of our LKMT *** ablation experiments confirm the positive impact of part-of-speech and dependency parsing on machine translation.

关键词： Urdu NMT(neural machine translation) Urdu natural language processing Urdu Linguistic features low resources language linguistic features pretrain model

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The Teaching Experiment of Speech Recognition based on HMM

The Teaching Experiment of Speech Recognition based on HMM

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第26届中国控制与决策会议

作者： Mingjia An Zhengtao Yu Jianyi Guo shengxiang gao Yantuan Xian School of Information Engineering and Automation Kunming University of Science and Technology Intelligent Information Processing Key Laboratory Kunming University of Science and Technology

ISBN: (纸本)9781479937097

Because the basis of students’ math and limited time,the voice recognition is facing a dilemma. This article contact teaching *** content of the article is divided into two types, one is voice recognition principle, the other is speech recognition practice, We propose two types of the weakening formula derivation and stressed that the process of practice teaching ideas. In order to improve the students ’interest in learning, deepen the students’ understanding of speech recognition, we present an example for the pilot teaching methods;The same time, we have given some useful speech recognition practices, from the cultivation of students’ practical ability and scientific interest angle,the teaching experiments in speech based on HTK platform（HTK is a kit of hidden Markov model, HMM can be used to simulate any time series）,It describes a hidden Markov model speech recognition principle and implementation process,design and realization of a speech recognition system, Describes the principle of speech recognition, speech signal pre-processing, speech training and recognition. Finally, the test results indicate the feasibility of the model.

关键词： Teaching experiment of speech recognition HTK platform Speech pre-processing Hidden markov model

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铜微合金化对QT600球墨铸铁组织和性能的影响

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金属热处理 2016年第4期41卷 38-41页

作者：邓丽霞高生祥浙江工业职业技术学院浙江绍兴312000

对QT600球墨铸铁进行了表面感应加热和控制冷却,分析不同Cu含量对马氏体转变的影响,利用光学显微镜分析了其显微组织,并对不同Cu含量的QT600球墨铸铁试样进行了拉伸和冲击试验,研究强度以及冲击性能的变化规律。结果表明,微合金元素Cu... 详细信息

对QT600球墨铸铁进行了表面感应加热和控制冷却,分析不同Cu含量对马氏体转变的影响,利用光学显微镜分析了其显微组织,并对不同Cu含量的QT600球墨铸铁试样进行了拉伸和冲击试验,研究强度以及冲击性能的变化规律。结果表明,微合金元素Cu含量会影响淬火组织、强度和冲击性能。在920~1000℃加热淬火后,Cu有助于马氏体转变,提高淬透性能。Cu含量控制在0.6%时,对马氏体转变的促进作用不显著,组织中保留大量残留奥氏体,马氏体组织的冲击性能较差。进一步提高Cu含量,发现Cu可以阻碍合金元素磷共晶偏析,细化马氏体组织,减少残留奥氏体。当Cu含量超过0.9%马氏体转变不均匀,容易产生淬火裂纹,冲击性能下降。合理控制Cu含量可以加快马氏体转变,得到力学性能优良的微观组织。

关键词：球墨铸铁微合金元素马氏体力学性能

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面向多模态情感分析的多通道时序卷积融合

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计算机科学与探索 2024年第11期18卷 3041-3050页

作者：孙杰车文刚高盛祥昆明理工大学信息工程与自动化学院昆明650500

多模态情感分析已成为情感计算领域中的热门研究方向,它将基于单模态的情感分析扩展到基于多模态信息交流的环境。词级表示融合是建模跨模态信息交互的关键技术之一,旨在建模不同模态元素之间的相互作用。该任务面临两大挑战:模态元素... 详细信息

多模态情感分析已成为情感计算领域中的热门研究方向,它将基于单模态的情感分析扩展到基于多模态信息交流的环境。词级表示融合是建模跨模态信息交互的关键技术之一,旨在建模不同模态元素之间的相互作用。该任务面临两大挑战:模态元素之间的局部交互和时间维度上的全局交互。现有方法在建模局部交互时,常采用注意力机制刻画模态元素整体特征间的相关性,但忽视了相邻元素及局部特征间的交互作用,计算成本也较高。为解决上述问题,提出一种多通道时序卷积融合(MCTCF)模型,该方法运用二维卷积网络获取多模态元素之间的局部交互。其中,局部连接可捕获相邻元素的关联,多通道卷积可学习多模态元素局部特征之间的融合,权重共享大幅降低了计算量。在得到局部交互后的序列上,时序LSTM网络可进一步建模时间维度上的全局关联。在MOSI和MOSEI数据集上的大量实验证明了MCTCF的有效性与高效性。仅用一个卷积核(三通道,28个权重参数),在许多指标上取得了最先进或具有竞争力的结果。消融研究表明,局部卷积融合和全局时序建模都是提高性能的关键。该研究强化了词级表示融合,降低了计算复杂度。

关键词：多模态情感分析词级表示融合二维卷积网络

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轻型多尺度黑色素瘤目标检测网络模型的建立:基于注意力机制调控

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南方医科大学学报 2022年第11期42卷 1662-1671页

作者：钟友闻车文刚高盛祥昆明理工大学信息工程与自动化学院云南昆明650500 昆明理工大学云南省计算机技术应用重点实验室云南昆明650500 昆明理工大学云南省人工智能重点实验室云南昆明650500

目的提出一种融入坐标注意力和高效通道注意力机制的深度学习目标检测模型AM-YOLO。方法运用Mosaic图像增强与MixUp混类增强对图像进行预处理,采用One-Stage结构的目标检测模型YOLOv5s,并对该模型的骨干网络与颈部网络进行改进。在该模... 详细信息

目的提出一种融入坐标注意力和高效通道注意力机制的深度学习目标检测模型AM-YOLO。方法运用Mosaic图像增强与MixUp混类增强对图像进行预处理,采用One-Stage结构的目标检测模型YOLOv5s,并对该模型的骨干网络与颈部网络进行改进。在该模型的骨干网络中把空间金字塔的最大池化层替换成二维最大池化层,接着将坐标注意力机制和高效通道注意力机制分别融入到YOLOv5s模型的C3模块与该模型的骨干网络中。将改进后的模型与未改进的YOLOv5s模型,YOLOv3模型,YOLOv3-SPP模型,YOLOv3-tiny模型进行相关算法指标的对比实验。结果融入了坐标注意力和高效通道注意力机制的AM-YOLO模型能够有效提升对黑色素瘤的识别率,同时也减少了模型权重的大小。AM-YOLO模型在准确率,召回率以及平均精度均值上都要明显优于其他模型,并且对于早期和晚期黑色素瘤的平均精度均值分别达92.8%和87.1%。结论本文采用的深度学习目标检测算法模型能够应用于黑色素瘤目标的识别中。

关键词：深度学习目标检测注意力机制 YOLOv5s 黑色素瘤

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镁合金电火花沉积改性层性能及组织结构的研究

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热加工工艺 2016年第4期45卷 137-139,142页

作者：高生祥邓丽霞浙江工业职业技术学院浙江绍兴312000

应用电火花沉积设备在AZ91D镁合金表面沉积纯铝涂层进行表面改性处理,研究了电火花沉积工艺参数以及前处理对沉积改性层性能和组织结构的影响;并结合扫描电镜、中性盐雾试验等研究手段对镁合金改性涂层的微观表面形貌、截面形貌、腐蚀... 详细信息

应用电火花沉积设备在AZ91D镁合金表面沉积纯铝涂层进行表面改性处理,研究了电火花沉积工艺参数以及前处理对沉积改性层性能和组织结构的影响;并结合扫描电镜、中性盐雾试验等研究手段对镁合金改性涂层的微观表面形貌、截面形貌、腐蚀情况以及显微硬度进行了分析和对比;同时,也对镁合金表面沉积纯铝涂层的沉积机理和沉积层改善基体耐腐蚀性、硬度进行分析与讨论。研究结果表明:电火花沉积工艺参数及预处理对AZ91D镁合金表面沉积纯铝涂层的组织结构、微观形貌特点有重要影响;电火花沉积工艺参数的降低以及沉积前对母材和电极的预热处理,涂层表面显微硬度较基材有了较大程度的提高;同时,强化层组织结构均匀致密、涂层厚度适中、涂层内部裂纹孔洞等缺陷明显降低,与镁合金基材相比,表现出较好的耐腐蚀性能。

关键词：电火花沉积镁合金铝涂层组织结构

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机械钢制垫片的表面改性工艺研究

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钢铁钒钛 2018年第4期39卷 164-167页

作者：高生祥邓丽霞浙江工业职业技术学院

采用不同的激光功率、光斑直径和扫描速度,对1Cr5Mo VSr机械钢制垫片进行了激光熔敷表面改性,并进行了耐磨损性能和冲击性能的测试与分析。结果表明:随激光功率从1 k W增至5 k W,光斑直径从1 mm增至3mm、扫描速度从2 mm/s增至8 mm/s,垫... 详细信息

采用不同的激光功率、光斑直径和扫描速度,对1Cr5Mo VSr机械钢制垫片进行了激光熔敷表面改性,并进行了耐磨损性能和冲击性能的测试与分析。结果表明:随激光功率从1 k W增至5 k W,光斑直径从1 mm增至3mm、扫描速度从2 mm/s增至8 mm/s,垫片试样的耐磨损性能和冲击性能均先提高后下降。机械钢制垫片的激光功率优选为3 k W、光斑直径优选为2 mm、扫描速度优选为6 mm/s。

关键词：机械钢制垫片表面改性耐磨损性能冲击性能

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