edge2vec: Representation learning using edge semantics for biomedical knowledge discovery

Representation learning provides new and powerful graph analytical approaches and tools for the highly valued data science challenge of mining knowledge graphs. Since previous graph analytical methods have mostly focused on homogeneous graphs, an important current challenge is extending this methodology for richly heterogeneous graphs and knowledge domains. The biomedical sciences are such a domain, reflecting the complexity of biology, with entities such as genes, proteins, drugs, diseases, and phenotypes, and relationships such as gene co-expression, biochemical regulation, and biomolecular inhibition or activation. Therefore, the semantics of edges and nodes are critical for representation learning and knowledge discovery in real world biomedical problems. In this paper, we propose the edge2vec model, which represents graphs considering edge semantics. An edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph via the trained transition matrix. edge2vec is validated on three biomedical domain tasks: biomedical entity classification, compound-gene bioactivity prediction, and biomedical information retrieval. Results show that by considering edge-types into node embedding learning in heterogeneous graphs, \textbf{edge2vec}\ significantly outperforms state-of-the-art models on all three tasks. We propose this method for its added value relative to existing graph analytical methodology, and in the real world context of biomedical knowledge discovery applicability.Comment: 10 page

Does PAL work? An exploration of affect amongst First-year HE in FE students

The study evaluates a peer-assisted learning (PAL) scheme as an effective strategy in alleviating levels of negative emotions and, in the process, contributes to explorations of affect in first-year students in an HE in FE environment, with a particular focus on anxiety and related emotions. Various types of anxieties are defined in the context of a student’s experience in HE, followed by an explanation of the present interventional study in an HE in FE institution, including the survey method used to collect data analyzed through SPSS. Surprisingly, the main findings are that overall anxiety and worry increased for students belonging to most faculties with time, regardless of participation in the PAL scheme. A positive finding was nonetheless that anxiety levels increased more steadily for students who belonged to the control group. The PAL scheme may have thus influenced how less anxious PAL students felt, compared to those who did not participate in the PAL scheme who were left feeling more anxious at the end of the semester

Whole-Chain Recommendations

With the recent prevalence of Reinforcement Learning (RL), there have been tremendous interests in developing RL-based recommender systems. In practical recommendation sessions, users will sequentially access multiple scenarios, such as the entrance pages and the item detail pages, and each scenario has its specific characteristics. However, the majority of existing RL-based recommender systems focus on optimizing one strategy for all scenarios or separately optimizing each strategy, which could lead to sub-optimal overall performance. In this paper, we study the recommendation problem with multiple (consecutive) scenarios, i.e., whole-chain recommendations. We propose a multi-agent RL-based approach (DeepChain), which can capture the sequential correlation among different scenarios and jointly optimize multiple recommendation strategies. To be specific, all recommender agents (RAs) share the same memory of users' historical behaviors, and they work collaboratively to maximize the overall reward of a session. Note that optimizing multiple recommendation strategies jointly faces two challenges in the existing model-free RL model - (i) it requires huge amounts of user behavior data, and (ii) the distribution of reward (users' feedback) are extremely unbalanced. In this paper, we introduce model-based RL techniques to reduce the training data requirement and execute more accurate strategy updates. The experimental results based on a real e-commerce platform demonstrate the effectiveness of the proposed framework.Comment: 29th ACM International Conference on Information and Knowledge Managemen

Increasing the impact of mathematics support on aiding student transition in higher education

The ever growing gap between secondary and university level mathematics is a major concern to higher education institutions. The increase in diversity of students’ background in mathematics, with entry qualifications ranging from the more traditional A-level programmes to BTEC or international qualifications is compounded where institutions attempt to widen participation. For example, work-based learners may have been out of education for prolonged periods, and consequently, are often unprepared for the marked shift in levels, and catering for all abilities is difficult in the normal lecture, tutorial format. Lack of sufficient mathematical knowledge not only affects students’ achievement on courses but also leads to disengagement and higher drop-out rates during the first two years of study. Many universities now offer a maths support service in an attempt to overcome these issues, but their success is varied. This paper presents a novel approach to maths support designed and adopted by the University of Lincoln, School of Engineering, to bridge this transition gap for students, offer continued support through assessment for learning (AFL) and Individual Learning Plans (ILP’s), and ultimately increase student achievement, engagement and retention. The paper then extends this proven approach and discusses recently implemented enhancements through the use of on-line diagnostic testing and a ‘student expert’ system to harness mathematical knowledge held by those gifted and talented students (often overlooked by higher education institutions) and to promote peer-to-peer mentoring. The paper shows that with the proven system in place, there is a marked increase in student retention compared with national benchmark data, and an increase in student engagement and achievement measured through student feedback and assessments. Although the on-line enhancements are in the early stages of implementation it is expected, based on these results, that further improvements will be shown