With the raised privacy concerns and rigorous data regulations, federated learning has become a hot collaborative learning paradigm for the recommendation model without sharing the highly sensitive POI data. However, the time-sensitive, heterogeneous, and limited POI records seriously restrict the development of federated POI recommendation. To this end, in this paper, we design the fine-grained preference-aware personalized federated POI recommendation framework, namely PrefFedPOI, under extremely sparse historical trajectories to address the above challenges. In details, PrefFedPOI extracts the fine-grained preference of current time slot by combining historical recent preferences and periodic preferences within each local client. Due to the extreme lack of POI data in some time slots, a data amount aware selective strategy is designed for model parameters uploading. Moreover, a performance enhanced clustering mechanism with reinforcement learning is proposed to capture the preference relatedness among all clients to encourage the positive knowledge sharing. Furthermore, a clustering teacher network is designed for improving efficiency by clustering guidance. Extensive experiments are conducted on two diverse real-world datasets to demonstrate the effectiveness of proposed PrefFedPOI comparing with state-of-the-arts. In particular, personalized PrefFedPOI can achieve 7% accuracy improvement on average among data-sparsity clients.

The knowledge-enhanced BERT model solves the problem of lacking knowledge in downstream tasks by injecting external expertize, and achieves higher accuracy compared with BERT model. However, owning to large-scale external knowledge is utilized into knowledge-enhanced BERT, some shortcomings comes such as information noise, lower accuracy and weak generalization ability, and so on. To solve this problem, a knowledge enhancement method BERTBooster which combines incremental learning and gradient optimization is proposed. BERTBooster disassembles the input text corpus into entity noun sets through entity noun recognition, and uses the incremental learning task denoising entity auto-encoder to create an incremental task set of entity nouns and external knowledge triples. Furthermore, BERTBooster introduces a new gradient optimization algorithm ChildTuningF into BERT model to improve the generalization ability. BERTBooster can effectively improve the factual knowledge cognition ability of CAGBERT model and improve the accuracy of the model in downstream tasks. Experiments are carried out on six public data sets such as Book_Review, LCQMC, XNLI, Law_QA, Insureace_QA, and NLPCC-DBQA. The experimental results show that the accuracy rate in downstream tasks is increased by 0.65% on average after using BERTBooster on CAGBERT.

Terahertz (THz) wave is an electromagnetic wave with a frequency between far infrared ray and millimeter wave, which is widely used in hazardous material detection for its waveband fingerprint spectroscopy. THz time-domain spectroscopy technology based on deep learning can be used for nondestructive detection of various hazardous materials by recognizing the fingerprint spectrum of substances. However, due to the high cost of collecting spectral data, training samples are not easy to obtain and scarce for classification models, which leads to poor training effectiveness and low accuracy of classification. To address this problem, a fully connected layer-based auxiliary classifier generative adversarial network (FC-ACGAN) data augmentation method is proposed in this paper, we realized the generator and discriminator with fully connected layers to fit original data distribution better and generate data with higher quality. First, THz time-domain spectral data from seven flammable liquids were augmented using Mixup and FC-ACGAN, and then we fed the generated data set and expanded data set into Residual Network (ResNet), convolutional neural network, fully convolutional network, and multilayer perceptron for training. It is demonstrated that our method can solve the overfitting of models because of insufficient data. Compared with direct training on original data set, the accuracy of models using augmented data set improved by 5.1325% on average, which is 3.15% higher than that using Mixup. Furthermore, we experimented on expanded data set with ResNet long short-term memory for classification, the final accuracy reaches 99.42% on average, which is 1.09% higher than that using the original data set.

As a prevalent type of Web advertising, contextual advertising refers to the placement of the most relevant ads into a Web page, so as to increase the number of ad-clicks. However, some problems of homonymy and polysemy, low intersection of keywords etc., can lead to the selection of irrelevant ads for a page. In this paper, we present a new contextual advertising approach to overcome the problems, which uses Wikipedia concept and category information to enrich the content representation of an ad (or a page). First, we map each ad and page into a keyword vector, a concept vector and a category vector. Next, we select the relevant ads for a given page based on a similarity metric that combines the above three feature vectors together. Last, we evaluate our approach by using real ads, pages, as well as a great number of concepts and categories of Wikipedia. Experimental results show that our approach can improve the precision of ads-selection effectively.