Emotion recognition in conversation (ERC) is a widely used technology in both affective dialogue bots and dialogue recommendation scenarios, where motivating a system to correctly recognize human emotions is crucial. Uncovering as much contextual information as possible with a limited amount of dialogue information is essential for eventually identifying the correct emotion of each sentence. The integration of contextual information using the existing approaches often results in inadequate access to information or information redundancy. Deeply integrating the different knowledge behind utterances is also difficult. Therefore, to address these problems, we propose a dynamic complementary graph convolutional network (DCGCN) for conversational emotion recognition. Our approach uses commonsense knowledge to complement the contextual information contained in utterances and enrich the extracted conversation information. We creatively propose the concept of utterance density to prevent redundancy and the loss of utterance information in context-dependent contextual information modeling cases. An utterance dependency structure is dynamically determined by the utterance density, and the contextual information is fully integrated into each sentence representation. We evaluate our proposed model in extensive experiments conducted on four public benchmark datasets that are commonly used for ERC. The results demonstrate the effectiveness of the DCGCN, which achieves competitive results in terms of well-known evaluation metrics. Our code is available at https://github.com/Tars-is-a-robot/Conversational-emotion-recognition.git .

Tele-immersive 3D multi-camera room environments are starting to emerge and with them new challenging research questions. One important question is how to organize the large amount of visual data, being captured, processed, transmitted and displayed, and their corresponding resources, over current COTS computing and networking infrastructures so that "everybody" would be able to install and use tele-immersive environments for conferencing and other activities. In this paper, we propose a novel cross-layer control and streaming framework over general purpose delivery infrastructure, called TEEVE (tele-immersive environments for everybody). TEEVE aims for effective and adaptive coordination, synchronization, and soft QoS-enabled delivery of tele-immersive visual streams to remote room(s). The TEEVE experiments between two tele-immersive rooms residing in different institutions more than 2000 miles apart show that we can sustain communication of up to 12 3D video streams with 4/spl sim/5 3D frames per second for each stream, yielding 4/spl sim/5 tele-immersive video rate.

Ubiquitin-Conjugating Enzyme E2 C (UBE2C) is a gene that encodes protein. Disorders associated with UBE2C include methotrexate-related lymphatic hyperplasia and complement component 7 deficiency. The encoded protein is necessary for the destruction of mitotic cell cyclins and cell cycle progression, and may be involved in cancer progression. In this paper, on the basis of public databases, we study the expression differential mechanism of gene expression of UBE2C in various tumors and the performance of prognosis, clinical features, immunity, methylation, etc.

Emotion recognition in conversation (ERC) aims at accurately identifying emotional states expressed in conversational content. Existing ERC methods, although relying on semantic understanding, often encounter challenges when confronted with incomplete or misleading semantic information. In addition, when dealing with the interaction between emotional and semantic information, existing methods are often difficult to effectively distinguish the complex relationship between the two, which affects the accuracy of emotion recognition. To address the problems of semantic misdirection and emotional cross-talk encountered by traditional models when confronted with complex conversational data, we propose a semantic and emotional dual channel (SEDC) strategy for emotion recognition in conversations to process emotional and semantic information independently. Under this strategy, emotion information provides an auxiliary recognition function when the semantics are unclear or lacking, enhancing the accuracy of the model. Our model consists of two modules: the emotion processing module accurately captures the emotional features of each utterance through contrastive learning, and then constructs a dialogue emotion propagation map to simulate the emotional information conveyed in the dialogue; the semantic processing module combines an external knowledge base to enhance the semantic expression of the dialogue through knowledge enhancement strategies. This divide-and-conquer approach allows us to more deeply analyze the emotional and semantic dimensions of complex dialogues. Experimental results on the IEMOCAP, EmoryNLP, MELD, and DailyDialog datasets show that our approach significantly outperforms existing techniques and effectively improves the accuracy of dialogue emotion recognition. Our code is available at https://anonymous.4open.science/r/SEDC-FCF1.

It is beneficial to use user review as a preference expression because they contain information that is not in the interaction record. However, most current research on recommendation systems only models the explicit records of users and items. It does not mine more personalized information from the review texts generated simultaneously with the interaction records. In this article, we proposed a heterogeneous fusion recommendation model for extracting fine-grained product attributes and user behavior from the review texts. The model we proposed is called TextOG. In the first half of the model, we used two blocks to learn user reviews and item reviews, one of which is dedicated to learning user behavior using reviews written by users, and the other block determines product attributes from reviews written for products. In the second half of the model, we connected the latent factors learned by users and items to perform spatial convolution on the graph. That way, implicit features can perform complex interactions in non-Euclidean spaces. We conducted experiments on a large review data set, and the results show that TextOG performs better than the baseline recommendation methods on various datasets.

Review text is a valuable source of information for recommendation systems and often contains rich semantics with user preferences and item attributes. Recently, mainstream recommendation approaches have been using deep learning techniques to utilize review text. These efforts employ an affinity matrix to model the correlations between users and items and in turn aggregate the contextual features of users and items to form latent representations with some interpretability. However, constructing this simple interaction does not capture the complex correlations between users and items. Furthermore, a single representation of latent features is not sufficient to express user preferences and item attributes in reviews with multiple granularities. To this end, we propose an aspect-based capsule network and mutual attention for recommendation systems, named Acapnet. We design a novel contextual mutual attention mechanism for modeling the fine-grained correlations in the contextual features of users and items. We employ reverse dynamic routing to aggregate user and item context features into aspect features (capsules) for rating predictions; the vector length of a capsule can be used as a basis for estimating aspect importance. Extensive experiments are conducted on five real-world datasets with different features. The results show that the proposed Acapnet is superior to recent state-of-the-art models in terms of performance. Further analysis reveals that our model effectively mitigates the homogeneity among various aspect capsules with strong interpretability. Impact Statement—On e-commerce platforms such as Amazon and Yelp, recommendation systems play a key role as a filtering technology that helps consumers find the best product or service among many choices. However, recent studies have shown that although it can provide relatively accurate services, it often fails to explain why that product is recommended to the user. The model we propose in this article, Acapnet, effectively overcomes the limitation. Compared to state-of-the-art recommendation methods, Acapnet not only outperforms in terms of performance, but also offers some superiority on interpretability.