With the popularity of Internet of Things (IoT) and large-scale deployment of sensors, data has exploded. Big data is utilized to extract useful knowledge and information, and served as data services to consumers. While most of the current researches in the field of big data services focuses on developing and improving algorithms for data mining and information extraction, and rarely studies "big data" from an economic perspective. This paper thus studies the pricing and profit maximization problems in big data markets from an economic perspective. First, a method of quantifying the value of data is proposed to study the utility of raw data. Then we build an economic model of data market, which consists of three parties: data vendor, service provider and service users. The data vendor gathers various raw data and sells them to service provider. The raw data is further processed into data services by service provider, who provides service subscriptions to users to obtain profits. The interactions among them are formulated as a Stackelberg game to maximize the profits of all participators. The existence and uniqueness of equilibria pricing strategies are proved. Finally, numerical results show that the participators of data market can achieve the maximum profit through the proposed pricing mechanism and economic model.

Unsupervised domain adaptation has attracted widespread attention as a promising method to solve the labeling difficulties of semantic segmentation tasks. It trains a segmentation network for unlabeled real target images using easily available labeled virtual source images. To improve performance, clustering is used to obtain domain-invariant feature representations. However, most clustering-based methods indiscriminately cluster all features mapped by category from both domains, causing the centroid shift and affecting the generation of discriminative features. We propose a novel clustering-based method that uses an adaptive refining-aggregation-separation framework, which learns the discriminative features by designing different adaptive schemes for different domains and features. The clustering does not require any tunable thresholds. To estimate more accurate domain-invariant centroids, we design different ways to guide the adaptive refinement of different domain features. A critic is proposed to directly evaluate the confidence of target features to solve the absence of target labels. We introduce a domain-balanced aggregation loss and two adaptive separation losses for distance and similarity respectively, which can discriminate clustering features by combining the refinement strategy to improve segmentation performance. Experimental results on GTA5→Cityscapes and SYNTHIA→Cityscapes benchmarks show that our method outperforms existing state-of-the-art methods.

In recent years, many sentiments classification models, such as deep learning models and traditional machine learning models, claim that they can achieve state-of-the-art performance in sentiment analysis problems. Admittedly, this is based on the premise that the training samples are class balanced. However, in the real world, the training data sets we can get are often imbalanced, which will cause the trained classifier to tend to predict the test samples into a majority, making the recall of minority very low. In order to minimize the influence of the imbalanced data class on the model performance, a transfer learning method based on a convolution neural network is proposed in this paper. First, we use a CNN-based model for pre-training in the class-balanced source domain data set, before transferring the model to the target domain for fine-tuning to improve the recall of minority class; furthermore, we propose a transfer learning-based under-sampling technique, which can under-sample the majority class in the target domain. In the data set after under-sampling, we again fine-tune the pre-trained model, so that the recall and precision of the minority class have been greatly improved. The experiments on real-world data sets show that our proposed under-sampling method has obvious advantages compared with others.

Domain adaptive semantic segmentation enables robust pixel- wise understanding in real-world driving scenes. Source-free domain adaptation, as a more practical technique, addresses the concerns of data privacy and storage limitations in typical unsupervised domain adaptation methods, making it especially relevant in the context of intelligent vehicles. It utilizes a well-trained source model and unlabeled target data to achieve adaptation in the target domain. However, in the absence of source data and target labels, current solutions cannot sufficiently reduce the impact of domain shift and fully leverage the information from the target data. In this paper, we propose an end-to-end source-free domain adaptation semantic segmentation method via Importance-Aware and Prototype-Contrast (IAPC) learning. The proposed IAPC framework effectively extracts domain-invariant knowledge from the well-trained source model and learns domain-specific knowledge from the unlabeled target domain. Specifically, considering the problem of domain shift in the prediction of the target domain by the source model, we put forward an importance-aware mechanism for the biased target prediction probability distribution to extract domain-invariant knowledge from the source model. We further introduce a prototype-contrast strategy, which includes a prototype-symmetric cross-entropy loss and a prototype-enhanced cross-entropy loss, to learn target intra-domain knowledge without relying on labels. A comprehensive variety of experiments on two domain adaptive semantic segmentation benchmarks demonstrates that the proposed end-to-end IAPC solution outperforms existing state-of-the-art methods. The source code is publicly available at https://github.com/yihong-97/Source-free-IAPC .