QDEE: Question Difficulty and Expertise Estimation in Community Question Answering Sites

In this paper, we present a framework for Question Difficulty and Expertise Estimation (QDEE) in Community Question Answering sites (CQAs) such as Yahoo! Answers and Stack Overflow, which tackles a fundamental challenge in crowdsourcing: how to appropriately route and assign questions to users with the suitable expertise. This problem domain has been the subject of much research and includes both language-agnostic as well as language conscious solutions. We bring to bear a key language-agnostic insight: that users gain expertise and therefore tend to ask as well as answer more difficult questions over time. We use this insight within the popular competition (directed) graph model to estimate question difficulty and user expertise by identifying key hierarchical structure within said model. An important and novel contribution here is the application of "social agony" to this problem domain. Difficulty levels of newly posted questions (the cold-start problem) are estimated by using our QDEE framework and additional textual features. We also propose a model to route newly posted questions to appropriate users based on the difficulty level of the question and the expertise of the user. Extensive experiments on real world CQAs such as Yahoo! Answers and Stack Overflow data demonstrate the improved efficacy of our approach over contemporary state-of-the-art models. The QDEE framework also allows us to characterize user expertise in novel ways by identifying interesting patterns and roles played by different users in such CQAs.Comment: Accepted in the Proceedings of the 12th International AAAI Conference on Web and Social Media (ICWSM 2018). June 2018. Stanford, CA, US

Semi-supervised Embedding in Attributed Networks with Outliers

In this paper, we propose a novel framework, called Semi-supervised Embedding in Attributed Networks with Outliers (SEANO), to learn a low-dimensional vector representation that systematically captures the topological proximity, attribute affinity and label similarity of vertices in a partially labeled attributed network (PLAN). Our method is designed to work in both transductive and inductive settings while explicitly alleviating noise effects from outliers. Experimental results on various datasets drawn from the web, text and image domains demonstrate the advantages of SEANO over state-of-the-art methods in semi-supervised classification under transductive as well as inductive settings. We also show that a subset of parameters in SEANO is interpretable as outlier score and can significantly outperform baseline methods when applied for detecting network outliers. Finally, we present the use of SEANO in a challenging real-world setting -- flood mapping of satellite images and show that it is able to outperform modern remote sensing algorithms for this task.Comment: in Proceedings of SIAM International Conference on Data Mining (SDM'18

Symmetrization for Embedding Directed Graphs

Recently, one has seen a surge of interest in developing such methods including ones for learning such representations for (undirected) graphs (while preserving important properties). However, most of the work to date on embedding graphs has targeted undirected networks and very little has focused on the thorny issue of embedding directed networks. In this paper, we instead propose to solve the directed graph embedding problem via a two-stage approach: in the first stage, the graph is symmetrized in one of several possible ways, and in the second stage, the so-obtained symmetrized graph is embedded using any state-of-the-art (undirected) graph embedding algorithm. Note that it is not the objective of this paper to propose a new (undirected) graph embedding algorithm or discuss the strengths and weaknesses of existing ones; all we are saying is that whichever be the suitable graph embedding algorithm, it will fit in the above proposed symmetrization framework.Comment: has been accepted to The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI 2019) Student Abstract and Poster Progra

Simplified control strategies for modular multilevel matrix converter for offshore low frequency AC transmission system

PhD ThesisThe Low frequency AC (LFAC) transmission system is considered as the most cost-saving choice for the short and intermediate distance. It not only improves the transmission capacity and distance but also has higher reliability which makes it more advantageous than the HVDC transmission system. Modular Multilevel Matrix Converter (M3C) is recognized as the most suitable frequency converter for the LFAC transmission system which is responsible for connecting 16.7 Hz and 50 Hz ac systems. In such applications, the ‘double αβ0 transform’ control method is most popular technique that realizes the decoupled control of the input current, output current and circulating current. However, the derivation process of the mathematical model is so complicated that it gives too much burden on the controller of the M3C system. Therefore, this thesis is focusing on simplifying the M3C control strategies when used in LFAC systems and the primary contribution to the knowledge is outlined as follows: (1) A simplified hierarchical energy balance control method which employs an independent control for each of three sub-converters in M3C is proposed in Chapter 5. The output frequency circulating current is injected and utilized to balance the energy between the three arms of the sub-converter. The proposed method achieves a reduced execution time and a simplified control structure, with which a low-cost processor is applicable and the control bandwidth of the system is improved. (2) An improved energy balance control method with injecting both input and output frequency circulating currents is proposed in Chapter 6. The magnitudes of the circulating current responsible for the energy balance control in either frequency are half reduced as compared to the single frequency injection method in Chapter 5. This arrangement alleviates the negative impact of the injected circulating current on the external grid and allows the M3C systems work through larger grid unbalance situations. Finally, the effectiveness of the proposed control strategy is demonstrated by extensive simulation results and validated experimentally using a scaled-down laboratory prototype