Emotional cognitive capacity is crucial for AI applications. Existing methods generally rely on indirect evaluation approaches to measure AI’s emotional cognitive capacity, lacking effective means for comprehensive evaluation. Therefore, in this work, we introduce Emo-Lens, a more intuitive, accurate, and interpretable method for assessing AI’s emotional cognitive capacity. Emo-Lens calculates the relevant influence weights of entities for emotional judgment by using an XAI (Explainable Artificial Intelligence) algorithm, which are then used to calculate what we term ’significance redundancy.’ By calculating significance redundancy, Emo-Lens assesses the AI model’s ability to perform human-like emotional cognition, achieving a more intuitive, accurate, and interpretable assessment of AI’s emotional cognitive capacity.We also validate the accuracy and sensitivity of our Emo-Lens approach through several knowledge-enhancement experiments on the sentiment analysis task. We explore whether AI models with knowledge enhancement can identify correct emotional cues as humans do when making emotional judgments. Our results demonstrate that Emo-Lens is an accurate method for evaluating AI’s emotional cognitive capacity, as it reveals that AI’s emotional cognition can be improved through knowledge-enhancement. This enhancement process mirrors human behavior in terms of focusing attention and integrating background knowledge during emotional understanding. Emo-Lens has deepened our understanding of AI’s emotional cognitive capacity to some extent.

Empathetic dialogue systems can recognize users’ emotions and provide appropriate responses, which are crucial for enhancing the user experience. However, existing empathetic dialogue systems often fall short in understanding some complex implicit emotions. To address this problem, we propose a multi-level knowledge-enhanced prompting approach to achieve more effective empathetic dialogue generation effect. We first acquire topic words and emotional keywords as low-level emotional knowledge. Next, we retrieve dialogue samples that are most similar in topic and emotional attributes, forming mid-level emotional knowledge. Subsequently, we guide a large language model (LLM) to generate high-level comprehensive emotional knowledge based on the information from the previous two levels and the dialogue context. Finally, based on the emotional knowledge, we further guide LLM to generate empathetic responses. The research results indicate that our multi-level knowledge-enhanced prompting approach outperforms other baselines.

Large Language Models (LLMs) have showcased astounding capabilities across numerous tasks. Given that an LLM ingests vast amounts of data from human society, its behavior exhibits striking human-like characteristics. However, the ramifications of these anthropomorphic features on the model’s proficiency in specialized tasks remain an unresolved query. This paper delves into the impact of personality traits on model performance. Specifically, we employ the Big Five Personality Theory for model analysis, guiding questions in diverse datasets using prompts constructed around specific personality traits to study LLM outputs. In comparison to the baseline test questions, tasks aligned with the Big Five personality traits manifested superior performance, with enhancements ranging from 0.6% to 3.6%, mirroring human behavior closely. This underscores that for optimal execution of specific professional tasks, incorporating corresponding personality traits in prompts can be an effective strategy when querying the LLM.

Sentiment is complex feedback of human perception of the outside world, which contains important potential information. Traditional sentiment analysis methods often use unimodal processing, which cannot accurately recognize complex emotional expressions. The existing multimodal sentiment analysis (MSA) methods based on feature fusion and post-fusion paradigms do not pay attention to the accuracy of the semantic expression of unimodal features and do not mine the correlation relationship between heterogeneous features, which leads to serious deviation of the fused multimodal sentiment. In this paper, a novel MSA method based on feature alignment and reconstruction constraints is proposed. The multimodal feature alignment module utilizes the cross-attention mechanism to establish the intrinsic connection between multimodal features and reduce the differences between multimodal heterogeneous features with the same sentiment. The multimodal feature reconstruction module is used to retain the unique semantics of unimodal features and reduce the loss of key information during heterogeneous feature alignment. Experimental results on the CH-SIMS v2.0 dataset show that the proposed method can significantly improve the model’s ability to cope with the recognition of complex sentiments.

Empathetic dialogue systems are designed to capture emotions in conversations and provide appropriate emotional responses. Previous researches have indicated that integrating specific knowledge into empathetic dialogue systems can enhance the overall effectiveness of generating empathetic responses. Nevertheless, existing methods for knowledge-enhanced empathetic dialogue generation lack a focus on the precise selection of knowledge enhancement configurations for this specific task. To address this, we propose a Case-Based Reasoning (CBR) framework called CBR-KNOWLEDGE for autonomously select precise knowledge enhancement configurations tailored to specific empathetic dialogue contexts. Firstly, CBR-KNOWLEDGE establishes a case base that mirrors the overall quality of empathetic dialogues generated under various knowledge enhancement configurations. Subsequently, CBR-KNOWLEDGE employs an innovative text representation method, integrating an additional representation for words with noteworthy emotional impact. This approach facilitates the retrieval of analogous empathetic dialogues, enabling the reuse of their knowledge enhancement configurations to determine a new knowledge enhancement configuration. Ultimately, CBR-KNOWLEDGE employs this precise knowledge enhancement configuration for the purpose of empathetic dialogue generation. Experimental results demonstrate that CBR-KNOWLEDGE effectively enhances the performance of empathetic dialogue generation task.

Recent advances in 3D point cloud completion adopt unsupervised deep learning-based methods, which does not rely on labeled data and improves generalization ability. However, existing methods tend to focus more on the generation overall shape rather than detailed structure. To explore unsupervised 3D point cloud completion methods that give attention to both, we propose a multi resolution completion net (MRC-Net) which introduces a multi-scale degradation (KM- mask) and multi-discriminator into GAN inversion paradigm. First, degrade the point clouds completed by the generator under three different resolutions. Then, the losses of multi-stage reconstruction and feature matching by multi-scale discriminator are used to jointly optimize the generator. The experimental results demonstrate that MRC-Net outperforms existing unsupervised point cloud completion methods and has better completion performances on virtual scanning dataset.

Action recognition applications achieve impressive success in various fields, yet existing approaches cannot make good use of different information flows. To tackle such an issue, multimodal action recognition exhibit remarkable potential in strengthening the video representation. However, the direct fusion may not enable the model to learn appropriate knowledge. To leverage extra feature information flow to help improve model performance, this paper proposes MFE, a multi-feature enhanced multimodal action recognition model. MFE introduces a guiding tag to explicit guide different extra feature information flow and designs a feature fusion module to fuse different information flows, to enhance hidden representation through more semantic supervision. The experimental results show that MFE achieves better or comparable accuracies with some advanced video action recognition models on several action recognition datasets.

Empathetic conversation systems identify the users' emotions and give appropriate responses, which is crucial to improve users' experiences. However, existing empathetic dialogue models (especially to the dominant pre-trained language model-based systems) did not focus on modelling the holistic properties of implicit and explicit emotions. In this paper, we propose an Implicit and Explicit Emotion Enhanced (IEEE) empathetic dialogue generation model to handle such challenges. Specifically, we first propose a prompt tuning-based approach to mine emotional words as additional information to obtain the users' explicit emotion. A variational auto-encoder is then introduced to extract the topic words of the input sequence as additional priori knowledge to get the implicit emotion related information. Finally, a pre-trained language model is utilized as the auto-regressive decoder to generate empathetic responses related to the content of the topics and user emotions. To demonstrate the effectiveness of the proposed approach, IEEE has been tested on empathic dialogue dataset. The experimental results show that our method achieves better performance than some competitive models.

Cross-technology Communication (CTC) is a promising paradigm for efficient coordination and cooperation among heterogeneous wireless technologies. Recent advances in physical-layer CTC (PHY-CTC) approaches the standards' maximum transmission rate by exploring PHY-layer signal features. However, due to the lack of reliable feedback, current PHY-CTC technologies can hardly ensure transmission reliability. This paper presents RAP (Reliable Acknowledged PHY-CTC), a bidirectional CTC design with reliable PHY-CTC feedback. First, we present a novel PHY-CTC technique to efficiently establish a reliable feedback channel (e.g., ACKs or NACKs). Then, based on the feedback, we propose a joint intra-packet coding and inter-packet coding scheme to improve the reliability of CTC. Finally, we present an on-demand data (re)transmission scheme to support unicast, multicast and broadcast more efficiently. We implement and evaluate RAP on USRP N210 with IEEE 802.11g PHY (WiFi) and commodity ZigBee devices. The experiment results show RAP achieves reliable data transmission (>99% packet reception rate (PRR)) and high throughput (over 35kbps) under a wide range of scenarios.