Enhanced Semantic Representation Learning for Sarcasm Detection by Integrating Context-Aware Attention and Fusion Network.

Sarcasm is a sophisticated figurative language that is prevalent on social media platforms. Automatic sarcasm detection is significant for understanding the real sentiment tendencies of users. Traditional approaches mostly focus on content features by using lexicon, n-gram, and pragmatic feature-based models. However, these methods ignore the diverse contextual clues that could provide more evidence of the sarcastic nature of sentences. In this work, we propose a Contextual Sarcasm Detection Model (CSDM) by modeling enhanced semantic representations with user profiling and forum topic information, where context-aware attention and a user-forum fusion network are used to obtain diverse representations from distinct aspects. In particular, we employ a Bi-LSTM encoder with context-aware attention to obtain a refined comment representation by capturing sentence composition information and the corresponding context situations. Then, we employ a user-forum fusion network to obtain the comprehensive context representation by capturing the corresponding sarcastic tendencies of the user and the background knowledge about the comments. Our proposed method achieves values of 0.69, 0.70, and 0.83 in terms of accuracy on the Main balanced, Pol balanced and Pol imbalanced datasets, respectively. The experimental results on a large Reddit corpus, SARC, demonstrate that our proposed method achieves a significant performance improvement over state-of-art textual sarcasm detection methods.

Elusive workers are more likely to differentiate into replacement reproductives than aggressive workers in a lower termite.

One of the most intriguing questions in eusocial insects is to understand how the overt reproductive conflict in the colony appears limited when queens or kings are senescent or lost because the morphologically similar individuals in the colony are reproductively totipotent. Whether there are some individuals who preferentially differentiate into replacement reproductives or not has received little attention. The consistent individual behavioral differences (also termed "animal personality") of individuals from the colony can shape cunningly their task and consequently affect the colony fitness but have been rarely investigated in eusocial insects. Here, we used the termite Reticulitermes labralis to investigate if variations in individual personalities (elusiveness and aggressiveness) may predict which individuals will perform reproductive differentiation within colonies. We observed that when we separately reared elusive and aggressive workers, elusive workers differentiate into reproductives significantly earlier than aggressive workers. When we reared them together in the proportions 12:3, 10:5, and 8:7 (aggressive workers: elusive workers), the first reproductives mostly differentiated from the elusive workers, and the reproductives differentiated from the elusive workers significantly earlier than from aggressive workers. Furthermore, we found that the number of workers participating in reproductive differentiation was significantly lower in the groups of both types of workers than in groups containing only elusive workers. Our results demonstrate that the elusiveness trait was a strong predictor of workers' differentiation into replacement reproductives in R. labralis. Moreover, our results suggest that individual personalities within the insect society could play a key role in resolving the overt reproductive conflict.

Rumor Detection With Hierarchical Representation on Bipartite Ad Hoc Event Trees.

The rapid growth of social media has caused tremendous effects on information propagation, raising extreme challenges in detecting rumors. Existing rumor detection methods typically exploit the reposting propagation of a rumor candidate for detection by regarding all reposts to a rumor candidate as a temporal sequence and learning semantics representations of the repost sequence. However, extracting informative support from the topological structure of propagation and the influence of reposting authors for debunking rumors is crucial, which generally has not been well addressed by existing methods. In this article, we organize a claim post in circulation as an ad hoc event tree, extract event elements, and convert it into bipartite ad hoc event trees in terms of both posts and authors, i.e., author tree and post tree. Accordingly, we propose a novel rumor detection model with hierarchical representation on the bipartite ad hoc event trees called BAET. Specifically, we introduce word embedding and feature encoder for the author and post tree, respectively, and design a root-aware attention module to perform node representation. Then we adopt the tree-like RNN model to capture the structural correlations and propose a tree-aware attention module to learn tree representation for the author tree and post tree, respectively. Extensive experimental results on two public Twitter datasets demonstrate the effectiveness of BAET in exploring and exploiting the rumor propagation structure and the superior detection performance of BAET over state-of-the-art baseline methods.

Construction of Spirolactams by Unexpected Oxidative Cyclization of Alkenyl Diynes via Copper Catalysis.

A copper-catalyzed oxidative cyclization of alkenyl N-propargyl ynamides is described. This protocol enables the practical synthesis of diverse spirocyclic γ-lactams bearing an exocyclic double bond with generally high Z/E selectivity in moderate to good yields. Importantly, this copper-catalyzed oxidative cyclization demonstrates a distinctive selectivity in comparison with the related gold catalysis.

Deep Mobile Linguistic Therapy for Patients with ASD.

Autistic spectrum disorder (ASD) is one of the most complex groups of neurobehavioral and developmental conditions. The reason is the presence of three different impaired domains, such as social interaction, communication, and restricted repetitive behaviors. Some children with ASD may not be able to communicate using language or speech. Many experts propose that continued therapy in the form of software training in this area might help to bring improvement. In this work, we propose a design of software speech therapy system for ASD. We combined different devices, technologies, and features with techniques of home rehabilitation. We used TensorFlow for Image Classification, ArKit for Text-to-Speech, Cloud Database, Binary Search, Natural Language Processing, Dataset of Sentences, and Dataset of Images with two different Operating Systems designed for Smart Mobile devices in daily life. This software is a combination of different Deep Learning Technologies and makes Human-Computer Interaction Therapy very easy to conduct. In addition, we explain the way these were connected and put to work together. Additionally, we explain in detail the architecture of software and how each component works together as an integrated Therapy System. Finally, it allows the patient with ASD to perform the therapy anytime and everywhere, as well as transmitting information to a medical specialist.

Deep Neural Network Framework Based on Word Embedding for Protein Glutarylation Sites Prediction.

In recent years, much research has found that dysregulation of glutarylation is associated with many human diseases, such as diabetes, cancer, and glutaric aciduria type I. Therefore, glutarylation identification and characterization are essential tasks for determining modification-specific proteomics. This study aims to propose a novel deep neural network framework based on word embedding techniques for glutarylation sites prediction. Multiple deep neural network models are implemented to evaluate the performance of glutarylation sites prediction. Furthermore, an extensive experimental comparison of word embedding techniques is conducted to utilize the most efficient method for improving protein sequence data representation. The results suggest that the proposed deep neural networks not only improve protein sequence representation but also work effectively in glutarylation sites prediction by obtaining a higher accuracy and confidence rate compared to the previous work. Moreover, embedding techniques were proven to be more productive than the pre-trained word embedding techniques for glutarylation sequence representation. Our proposed method has significantly outperformed all traditional performance metrics compared to the advanced integrated vector support, with accuracy, specificity, sensitivity, and correlation coefficient of 0.79, 0.89, 0.59, and 0.51, respectively. It shows the potential to detect new glutarylation sites and uncover the relationships between glutarylation and well-known lysine modification.

Neural Time-Aware Sequential Recommendation by Jointly Modeling Preference Dynamics and Explicit Feature Couplings.

In recommendation, both stationary and dynamic user preferences on items are embedded in the interactions between users and items (e.g., rating or clicking) within their contexts. Sequential recommender systems (SRSs) need to jointly involve such context-aware user-item interactions in terms of the couplings between the user and item features and sequential user actions on items over time. However, such joint modeling is non-trivial and significantly challenges the existing work on preference modeling, which either only models user-item interactions by latent factorization models but ignores user preference dynamics or only captures sequential user action patterns without involving user/item features and context factors and their coupling and influence on user actions. We propose a neural time-aware recommendation network (TARN) with a temporal context to jointly model 1) stationary user preferences by a feature interaction network and 2) user preference dynamics by a tailored convolutional network. The feature interaction network factorizes the pairwise couplings between non-zero features of users, items, and temporal context by the inner product of their feature embeddings while alleviating data sparsity issues. In the convolutional network, we introduce a convolutional layer with multiple filter widths to capture multi-fold sequential patterns, where an attentive average pooling (AAP) obtains significant and large-span feature combinations. To learn the preference dynamics, a novel temporal action embedding represents user actions by incorporating the embeddings of items and temporal context as the inputs of the convolutional network. The experiments on typical public data sets demonstrate that TARN outperforms state-of-the-art methods and show the necessity and contribution of involving time-aware preference dynamics and explicit user/item feature couplings in modeling and interpreting evolving user preferences.

Copper-Catalyzed Asymmetric Diyne Cyclization via [1,2]-Stevens-Type Rearrangement for the Synthesis of Chiral Chromeno[3,4-c]pyrroles.

Here, we report a copper-catalyzed asymmetric cascade cyclization/[1,2]-Stevens-type rearrangement via a non-diazo approach, leading to the practical and atom-economic assembly of various valuable chiral chromeno[3,4-c]pyrroles bearing a quaternary carbon stereocenter in generally moderate to good yields with wide substrate scope and excellent enantioselectivities (up to 99 % ee). Importantly, this protocol not only represents the first example of catalytic asymmetric [1,2]-Stevens-type rearrangement based on alkynes but also constitutes the first asymmetric formal carbene insertion into the Si-O bond.

Copper-Catalyzed Cyclization of N-Propargyl Ynamides with Borane Adducts through B-H Bond Insertion.

An efficient copper-catalyzed cyclization of N-propargyl ynamides with borane adducts through B-H bond insertion has been developed. A series of valuable organoboron compounds are constructed in generally good yields with a wide substrate scope and good functional group tolerance under mild reaction conditions. Importantly, this protocol via vinyl cation intermediates constitutes a novel way of B-H bond insertion.