Deep learning Transformer architectures play a critical role in developing advanced code prediction models, which are essential in modern Integrated Development Environments (IDEs). Nevertheless, these architectures encounter a significant challenge in effectively capturing and utilizing the structural information present in Abstract Syntax Trees (ASTs). To tackle this challenge, we propose an innovative approach that leverages AST structural matrices to enhance Transformers for source code prediction. Specifically, we integrate three types of AST structural matrices - the R matrix, A&S matrix, and MVG matrix - into the attention module of the Transformer to effectively capture the structural information within ASTs. To ensure optimal utilization, we have devised a range of strategies and integration methods tailored specifically for the attention module. To assess the effectiveness of our proposal, we conduct empirical studies using a standard Python dataset. The results demonstrate that incorporating AST structural matrices significantly enhances the accuracy of code prediction models, leading to an overall improvement from 73.18% to 75.10%. Furthermore, we conduct an in-depth analysis of the impact of each matrix type, offering a comprehensive understanding of their application scenarios. This insightful analysis provides valuable guidance on how to effectively leverage each matrix type in various contexts.

Ensemble learning is a kind of machine learning method which can integrate multiple basic learners together and achieve higher accuracy. Recently, single machine learning methods have been established to predict survival for patients with cancer. However, it still lacked a robust ensemble learning model with high accuracy to pick out patients with high risks. To achieve this, we proposed a novel genetic algorithm-aided three-stage ensemble learning method (3S score) for survival prediction. During the process of constructing the 3S score, double training sets were used to avoid over-fitting; the gene-pairing method was applied to reduce batch effect; a genetic algorithm was employed to select the best basic learner combination. When used to predict the survival state of glioma patients, this model achieved the highest C-index (0.697) as well as area under the receiver operating characteristic curve (ROC-AUCs) (first year = 0.705, third year = 0.825 and fifth year = 0.839) in the combined test set (n = 1191), compared with 12 other baseline models. Furthermore, the 3S score can distinguish survival significantly in eight cohorts among the total of nine independent test cohorts (P < 0.05), achieving significant improvement of ROC-AUCs. Notably, ablation experiments demonstrated that the gene-pairing method, double training sets and genetic algorithm make sure the robustness and effectiveness of the 3S score. The performance exploration on pan-cancer showed that the 3S score has excellent ability on survival prediction in five kinds of cancers, which was verified by Cox regression, survival curves and ROC curves together. To enable its clinical adoption, we implemented the 3S score and other two clinical factors as an easy-to-use web tool for risk scoring and therapy stratification in glioma patients.

Email archives contain rich information about how we interact with different contacts and how events evolve throughout time. Making sense of the archived messages can be a good way to understand how things evolved and progressed in the past. Although much work has been devoted to email visualization, most work has focused on presenting one of the two aspects of email archives: discovering the evolution of emails and events, or the relationship between the email owner and his/her contacts over time. In this paper, we present Email Map, an email visualization which integrates the information of both events and contacts into a single view, enabling users to make sense of their email archives with complementary contextual information. Two visualization components are designed to portray complex information within the email archives: event flow and contact tracks. The event flow illustrates the evolution of past events, helping the users to grasp high-level pictures and patterns of their email archives. The contact tracks reveal the interaction between the email owner and his/her contacts.

To solve the problems of traditional reporting system, such as difficult manipulation, high levels of data coupling, this paper presents an ontology-based semantic reporting system. It builds a semantic layer between report designer and data source, where enterprise professional information, relevant properties and metadata about data source are described in ontology language. On the report designer interface, semantic information that saved in ontology library is shown and can be dragged to design the report. Ontology-based semantic layer greatly improves design efficiency and makes manipulation easy, because it seals the technical details of data source.