Herding Effect based Attention for Personalized Time-Sync Video Recommendation

Time-sync comment (TSC) is a new form of user-interaction review associated with real-time video contents, which contains a user's preferences for videos and therefore well suited as the data source for video recommendations. However, existing review-based recommendation methods ignore the context-dependent (generated by user-interaction), real-time, and time-sensitive properties of TSC data. To bridge the above gaps, in this paper, we use video images and users' TSCs to design an Image-Text Fusion model with a novel Herding Effect Attention mechanism (called ITF-HEA), which can predict users' favorite videos with model-based collaborative filtering. Specifically, in the HEA mechanism, we weight the context information based on the semantic similarities and time intervals between each TSC and its context, thereby considering influences of the herding effect in the model. Experiments show that ITF-HEA is on average 3.78\% higher than the state-of-the-art method upon F1-score in baselines.Comment: ACCEPTED for ORAL presentation at IEEE ICME 201

A Scope Sensitive and Result Attentive Model for Multi-Intent Spoken Language Understanding

Multi-Intent Spoken Language Understanding (SLU), a novel and more complex scenario of SLU, is attracting increasing attention. Unlike traditional SLU, each intent in this scenario has its specific scope. Semantic information outside the scope even hinders the prediction, which tremendously increases the difficulty of intent detection. More seriously, guiding slot filling with these inaccurate intent labels suffers error propagation problems, resulting in unsatisfied overall performance. To solve these challenges, in this paper, we propose a novel Scope-Sensitive Result Attention Network (SSRAN) based on Transformer, which contains a Scope Recognizer (SR) and a Result Attention Network (RAN). Scope Recognizer assignments scope information to each token, reducing the distraction of out-of-scope tokens. Result Attention Network effectively utilizes the bidirectional interaction between results of slot filling and intent detection, mitigating the error propagation problem. Experiments on two public datasets indicate that our model significantly improves SLU performance (5.4\% and 2.1\% on Overall accuracy) over the state-of-the-art baseline

The prewarning value of Alexandrium tamarense PSP in an area with frequent outburst of red tide

The PSP toxicity of Alexandrium tamarense (Lebour) Balech on the blue mussel (Mytilus edulis) from the western waters of Xiamen in China was studied by following the standard method of PSP mouse bioassay developed by the Association of Official Analytical Chemists (AOAC). The results showed that the mice survived when the density of A. tamarense cells was lower than 1 x 10(5) cells per cubic decimetre and died when the density was higher than 1 X 10(6) cells per cubic decimetre. The past record of red tide events in the western waters of Xiamen showed a general trend of starting from the bloom of non-toxic planktonic diatoms in local waters and resulting in a harmful algal bloom due to the fade of planktonic diatoms which failed in the survival competition in the unfavorable and deteriorated eco-environment. On the basis of experimental results and natural environment of Xiamen waters and by making reference to the critical criteria of shellfish toxins in various states, a prewarning value 1 x 10(5) cells per cubic decimetre of A. tamarense PSP toxicity was proposed for the areas in South China where red tides frequently occur