A Dual-Stream Recurrence-Attention Network With Global-Local Awareness for Emotion Recognition in Textual Dialog

In real-world dialog systems, the ability to understand the user's emotions and interact anthropomorphically is of great significance. Emotion Recognition in Conversation (ERC) is one of the key ways to accomplish this goal and has attracted growing attention. How to model the context in a conversation is a central aspect and a major challenge of ERC tasks. Most existing approaches struggle to adequately incorporate both global and local contextual information, and their network structures are overly sophisticated. For this reason, we propose a simple and effective Dual-stream Recurrence-Attention Network (DualRAN), which is based on Recurrent Neural Network (RNN) and Multi-head ATtention network (MAT). DualRAN eschews the complex components of current methods and focuses on combining recurrence-based methods with attention-based ones. DualRAN is a dual-stream structure mainly consisting of local- and global-aware modules, modeling a conversation simultaneously from distinct perspectives. In addition, we develop two single-stream network variants for DualRAN, i.e., SingleRANv1 and SingleRANv2. According to the experimental findings, DualRAN boosts the weighted F1 scores by 1.43% and 0.64% on the IEMOCAP and MELD datasets, respectively, in comparison to the strongest baseline. On two other datasets (i.e., EmoryNLP and DailyDialog), our method also attains competitive results.Comment: Accepted by Engineering Applications of Artificial Intelligence (EAAI

An empirical study of 3-vertex connectivity algorithms

Graph connectivity is one of the most basic properties of graph. Owing to this reason, it is fundamental to the studies of many important applications such as network reliability, cluster analysis, graph optimization, quantum physics, bioinformatics and social networks. Triconnectivity is a topic in graph connectivity which has been used in graph drawing, graph decomposition in geometry constraint solver, and social network studies. Hopcroft and Tarjan (1973) proposed the first linear-time algorithm for this problem. Although elegant, this algorithm is very complex and contains many minor but crucial errors which make it very difficult to understand and implement correctly. Gutwenger and Mutzel (2001) published a list of errors, outlining how to fix them and implemented the corrected algorithm.Recently, Tsin (2012) proposed a new linear-time algorithm which is based on a new graph transformation technique. Tsin\u27s algorithm is conceptually very simple and performs one less pass over the given graph than Hopcroft et al. These make the algorithm much easier to implement. In this thesis , we implemented Tsin\u27s algorithm and compare its performance with Gutwenger and Mutzel\u27s implementation of the algorithm of Hopcroft and Tarjan by carrying out an empirical study

Probing terahertz surface plasmon waves in graphene structures

Epitaxial graphene mesas and ribbons are investigated using terahertz (THz) nearfield microscopy to probe surface plasmon excitation and THz transmission properties on the sub-wavelength scale. The THz near-field images show variation of graphene properties on a scale smaller than the wavelength, and excitation of THz surface waves occurring at graphene edges, similar to that observed at metallic edges. The Fresnel reflection at the substrate SiC/air interface is also found to be altered by the presence of graphene ribbon arrays, leading to either reduced or enhanced transmission of the THz wave depending on the wave polarization and the ribbon width.Comment: accepted for publication in Applied Physics Lette