DataSet

摘要： For the problems of complex model structure and too many training parameters in facial expression recognition algorithms,we proposed a residual network structure with a multi-headed channel attention(MCA)module.The migration learning algorithm is used to pre-train the convolutional layer parameters and mitigate the overfitting caused by the insufficient number of training samples.The designed MCA module is integrated into the ResNet18 backbone network.The attention mechanism highlights important information and suppresses irrelevant information by assigning different coefficients or weights,and the multi-head structure focuses more on the local features of the pictures,which improves the efficiency of facial expression recognition.Experimental results demonstrate that the model proposed in this paper achieves excellent recognition results in Fer2013,CK+and Jaffe datasets,with accuracy rates of 72.7%,98.8%and 93.33%,respectively.

摘要： Content Based Image Retrieval, CBIR, performed an automated classification task for a queried image. It could relieve a user from the laborious and time-consuming metadata assigning for an image while working on massive image collection. For an image, user’s definition or description is subjective where it could belong to different categories as defined by different users. Human based categorization and computer-based categorization might produce different results due to different categorization criteria that rely on dataset structure and the clustering techniques. This paper is aimed to exhibit an idea for planning the dataset structure and choosing the clustering algorithm for CBIR implementation. There are 5 sections arranged in this paper;CBIR and QBE concepts are introduced in Section 1, related image categorization research is listed in Section 2, the 5 type of image clustering are described in Section 3, comparative analysis in Section 4, and Section 5 conclude this study. Outcome of this paper will be benefiting CBIR developer for various applications.

摘要： Kentucky bluegrass (Poa pratensis L.) is the most common perennial turfgrass species grown on playgrounds, municipal and residential lawn areas, and golf tees, fairways and roughs. Fertilization is the most efficient way to improve and maintain turfgrass aesthetic quality. Tissue diagnosis can guide fertilization, but tissue concentration ranges are biased by not taking into consideration nutrient inter-relationships, carryover effects and other key features. The centered log-ratio transformation reflects nutrient interactions in plants and avoids statistical biases. Machine learning (ML) models relate the target variable to the key features ex ante, and can predict future events from prior knowledge. The objective of his study was to predict turfgrass quality from key features and rank nutrients in the order of their limitations. The experimental setup comprised four N, three P, and four K rates applied on permanent plots during three consecutive years. Soils were a loam and an USGA sand. Eleven elements (N, S, P, K, Ca, Mg, B, Cu, Zn, Mn, Fe) were quantified in clippings collected during spring, summer and autumn every year. Turfgrass quality was categorized as target variable by color rating. Concentrations were centered log-ratioed (clr) partitioned into four quadrants in the confusion matrix generated by the xgboost ML model. The area under curve (AUC) and model accuracy were high to predict turfgrass color from the nutrient analyses of clippings collected in the preceding season, facilitating the seasonal adjustment of the fertilization regime to sustain high turfgrass quality. We provide a computational example to run the ML model and rank nutrients in the order of their limitations.

摘要： 本文基于ASP.ADO编程中使用的Net软件,详细介绍了ado.net技术。Net系统结构和数据库访问模式。首先,创建连接对象,建立数据库连接,然后使用命令对象执行命令(例如SQL语句),提供的读取方法 datareader对象读取数据库数据(当读取的数据量非常大时),或使用DataAdapter对象填充读取数据集的数据(读取大量数据时),应该获取数据库的t对象。

摘要： The thermal issue is of great importance during the layout design of heat source components in systems engineering,especially for high functional-density products.Thermal analysis requires complex simulation,which leads to an unaffordable computational burden to layout optimization as it iteratively evaluates different schemes.Surrogate modeling is an effective method for alleviating computation complexity.However,the temperature field prediction(TFP)with complex heat source layout(HSL)input is an ultra-high dimensional nonlinear regression problem,which brings great difficulty to traditional regression models.The deep neural network(DNN)regression method is a feasible way for its good approximation performance.However,it faces great challenges in data preparation for sample diversity and uniformity in the layout space with physical constraints and proper DNN model selection and training for good generality,which necessitates the efforts of layout designers and DNN experts.To advance this cross-domain research,this paper proposes a DNN-based HSL-TFP surrogate modeling task benchmark.With consideration for engineering applicability,sample generation,dataset evaluation,DNN model,and surrogate performance metrics are thoroughly investigated.Experiments are conducted with ten representative state-of-the-art DNN models.A detailed discussion on baseline results is provided,and future prospects are analyzed for DNN-based HSL-TFP tasks.

摘要： :Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and services.The increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making process.To overcome this challenge,extracting suggestions from opinionated text is a possible solution.In this study,the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’reviews.A classification using a word-embedding approach is used via the XGBoost classifier.The two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews.F1,precision,recall,and accuracy scores are calculated.The results demonstrated that the XGBoost classifier outperforms—with an accuracy of more than 80%.Moreover,the results revealed that suggestion keywords and phrases are the predominant features for suggestion extraction.Thus,this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.

摘要： There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.

摘要： There are numerous application areas of computing similarity between process models.It includes finding similar models from a repository,controlling redundancy of process models,and finding corresponding activities between a pair of process models.The similarity between two process models is computed based on their similarity between labels,structures,and execution behaviors.Several attempts have been made to develop similarity techniques between activity labels,as well as their execution behavior.However,a notable problem with the process model similarity is that two process models can also be similar if there is a structural variation between them.However,neither a benchmark dataset exists for the structural similarity between process models nor there exist an effective technique to compute structural similarity.To that end,we have developed a large collection of process models in which structural changes are handcrafted while preserving the semantics of the models.Furthermore,we have used a machine learning-based approach to compute the similarity between a pair of process models having structural and label differences.Finally,we have evaluated the proposed approach using our generated collection of process models.

摘要： Deep neural networks have shown great success in stereo matching in recent years.On the KITTI datasets,most top performing methods are based on neural networks.However,on the Middlebury datasets,these methods usually do not perform well.The KITTI datasets are collected in outdoor scenes while the Middlebury datasets are collected in indoor scenes.It is commonly believed that the community still lacks a large labelled dataset for stereo matching in indoor scenes.In this paper,we introduce a new stereo dataset called InS tereo2K.It contains 2050 pairs of stereo images with highly accurate groundtruth disparity maps,including 2000 pairs for training and 50 pairs for test.Experimental results show that our dataset can significantly improve the performance of several latest networks(including StereoNet and PSMNet)on the Middlebury 2014 dataset.The large scale,high accuracy and rich diversity of the proposed InS tereo2K dataset provide new opportunities to researchers in the area of stereo matching and beyond.It also takes end-to-end stereo matching methods a step towards practical applications.

摘要： 本文介绍了DTO以及在企业应用构架中的使用.并着重讨论了基于微软.NET平台的DTO实现方案,以及使用DataSet作为DTO在实际项目中的应用.

摘要： 本文介绍了DTO以及在企业应用构架中的使用.并着重讨论了基于微软.NET平台的DTO实现方案,以及使用DataSet作为DTO在实际项目中的应用.

摘要： 本文介绍了DTO以及在企业应用构架中的使用.并着重讨论了基于微软.NET平台的DTO实现方案,以及使用DataSet作为DTO在实际项目中的应用.