Vertical federated learning is a distributed machine learning framework that participants share the joint users' ID space but differ in feature space. Logistic regression is one of the companies most widely used classification algorithms for it's simplicity, computationally efficient and interpretability. Many works focus on vertical federated logistic regression to train high-quality models collaboratively. Most existing works need a third-party coordinator to assist with intermediate data computing. However, finding an authoritative third party in the real world is nearly impossible. Some works do not need a trusted third-party coordinator but risk leaking original data from plaintext intermediate data. To solve these problems, we propose an efficient and secure vertical federated logistic regression framework without a third-party coordinator (ESVFLR) using homomorphic encryption. Our method can prevent leaking privacy information in the both training and inference phase. We evaluate the performance of ESVFLR on public datasets, and the experimental results show that it has a low computation and communication cost while maintaining a high level of accuracy.

Attention based methods for image-text generation often focus on visual features individually, while ignoring relationship information among image features that provides important guidance for generating sentences. To alleviate this issue, in this work we propose the Joint Relationship Attention Network (JRAN) that novelly explores the relationships among the features. Specifically, different from the previous relationship based approaches that only explore the single relationship in the image, our JRAN can effectively learn two relationships, the visual relationships among region features and the visual-semantic relationships between region features and semantic features, and further make a dynamic trade-off between them during outputting the relationship representation. Moreover, we devise a new relationship based attention, which can adaptively focus on the output relationship representation when predicting different words. Extensive experiments on large-scale MSCOCO and small-scale Flickr30k datasets show that JRAN achieves state-of-the-art performance. More remarkably, JRAN achieves new 28.3% and 58.2% performance in terms of BLEU4 and CIDEr metric on Flickr30k dataset.

The system call sequences of processes are important for host-based anomaly detection. However, the detection accuracy can be seriously degenerated by the subsequences which simultaneously appeared in the call sequences of both normal and abnormal processes. Furthermore, the detection may be obstructed especially when the normal/abnormal distributions of subsequences are extremely imbalanced along with many ambiguous samples. In the paper, the system call sequences are divided into weighted subsequences with fixed-length. Secondly, a suffix tree of each system call sequence is constructed to automatically extract the variable-length subsequence from the longest repeated substring of the tree. The frequencies of the fixed-and variable-length subsequences that appeared in each system call sequence constitute its feature vector. Finally, vectors are input into a cost-sensitive and relaxed support vector machine, in which the penalty-free slack of the relaxed SVM is split independently between the two classes with different weights. The experimental results on two public datasets ADFA-LD and UNM showed that the AUC of the proposed method can reach 99%, while the false alarm rate is only 2.4%.

In recent years, as an active topic in the field of vision and language, image captioning has made great progress. Previous approaches have demonstrated the superiority of spatial and channel attentions in image captioning task. However, such attention-based approaches ignore the difference between function words (e.g., “to”, “for” and “out”) and notional words (e.g., “girl”, “teddy” and “bear”). To address above issue, in this paper we propose a dynamical balancing attention model (BAM) based on attention variation for image captioning, which uses attention variation to fuse channel attention and region attention. Generating function and notional words, it effectively balances the contribution of image channel feature and that of image region feature. Further, the proposed approach dynamically focuses on the most relevant attention features in word prediction. Extensive experimental results on typical datasets show our approach outperforms the attention based approaches and achieves competitive performance over existing end-to-end leading approaches.

Pose variation has been one of the challenges of face recognition. To solve this challenge, the authors propose a classification algorithm using supervised subspace learning and non‐local representation (SSLNR). In SSLNR, they first propose a supervised subspace learning algorithm (SSLA). SSLA includes three different terms. The first term is the difference term, which can reduce the intra‐class differences. The second term is the block‐diagonal regularisation term, which promotes the samples to be represented by intra‐class samples. The last one is the noise robust term. Then, the original samples are mapped to the learned subspace by using SSLA. Thus, the intra‐class differences of the samples mapped to the learned subspace are reduced. Finally, those mapped samples are classified by proposed non‐local constraint‐based extended sparse representation classifier. SSLNR is extensively evaluated using four databases, namely Georgia Tech, Label faces in the wild, FEI and CVL. Experimental results show that SSLNR achieves better performance than some state‐of‐the‐art algorithms, such as DARG and RRNN.

Application Programming Interfaces (APIs) represent key tools for software developers to build complex software systems. However, several studies have revealed that even major API providers tend to have incomplete or inconsistent API documentation. This can severely hamper the API comprehension and, as a consequence, the quality of the software built on them. In this paper, we propose DRONE (Detect and Repair of dOcumentatioN dEfects), a framework to automatically detect and repair defects from API documents by leveraging techniques from program analysis, natural language processing, and constraint solving. Specifically, we target at the directives of API documents, which are related to parameter constraints and exception handling declarations. Furthermore, in presence of defects, we also provide a prototypical repair recommendation system. We evaluate our approach on parts of the well-documented APIs of JDK 1.8 APIs (including javaFX) and Android 7.0 (level 24). Across the two empirical studies, our approach can detect API defects with an average F-measure of 79.9, 71.7, and 81.4 percent, respectively. The API repairing capability has also been evaluated on the generated recommendations in a further experiment. User judgments indicate that the constraint information is addressed correctly and concisely in the rendered directives.