Don't Lose Yourself! Empathetic Response Generation via Explicit Self-Other Awareness

As a critical step to achieve human-like chatbots, empathetic response generation has attained increasing interests. Previous attempts are incomplete and not sufficient enough to elicit empathy because they only focus on the initial aspect of empathy to automatically mimic the feelings and thoughts of the user via other-awareness. However, they ignore to maintain and take the own views of the system into account, which is a crucial process to achieve the empathy called self-other awareness. To this end, we propose to generate Empathetic response with explicit Self-Other Awareness (EmpSOA). Specifically, three stages, self-other differentiation, self-other modulation and self-other generation, are devised to clearly maintain, regulate and inject the self-other aware information into the process of empathetic response generation. Both automatic and human evaluations on the benchmark dataset demonstrate the superiority of EmpSOA to generate more empathetic responses

Is ChatGPT Equipped with Emotional Dialogue Capabilities?

This report presents a study on the emotional dialogue capability of ChatGPT, an advanced language model developed by OpenAI. The study evaluates the performance of ChatGPT on emotional dialogue understanding and generation through a series of experiments on several downstream tasks. Our findings indicate that while ChatGPT's performance on emotional dialogue understanding may still lag behind that of supervised models, it exhibits promising results in generating emotional responses. Furthermore, the study suggests potential avenues for future research directions

A lightweight temporal attention-based convolution neural network for driver's activity recognition in edge

Low inference latency and accurate response to environment changes play a crucial role in the automated driving system, especially in the current Level 3 automated driving. Achieving the rapid and reliable recognition of driver's non-driving related activities (NDRAs) is important for designing an intelligent takeover strategy that ensures a safe and quick control transition. This paper proposes a novel lightweight temporal attention-based convolutional neural network (LTA-CNN) module dedicated to edge computing platforms, specifically for NDRAs recognition. This module effectively learns spatial and temporal representations at a relatively low computational cost. Its superiority has been demonstrated in an NDRA recognition dataset, achieving 81.01% classification accuracy and an 8.37% increase compared to the best result of the efficient network (MobileNet V3) found in the literature. The inference latency has been evaluated to demonstrate its effectiveness in real applications. The latest NVIDIA Jetson AGX Orin could complete one inference in only 63 ms

The spatial resolution enhancement for a thermogram enabled by controlled sub-pixel movements

The measurement accuracy and reliability of thermography is largely limited by a relatively low spatial resolution of the thermal imager. Using a high-end camera to achieve high spatial resolution can be costly or infeasible due to a high sample rate required. Furthermore, the system miniaturisation becomes an inevitable trend with the continuous development of Internet of Things and their suitability to in-situ inspection scenarios. However, a miniaturised sensor usually suffers a low spatial resolution. Addressing this challenge, the paper reports a novel Spatial Resolution Enhancement for a Thermogram (SRE4T) system to significantly improve the spatial resolution without upgrading the sensor. A high-resolution thermal image is reconstructed by fusing a sequence of low-resolution images with sub-pixel movements. To achieve the best image quality, instead of benefiting from natural movements of existing studies, this paper proposes to use a high-resolution xy translation stage to produce a sequence of controlled sub-pixel movements. The performance of the proposed system was tested on both high-end and low-end thermal imagers. Both visual and quantitative results successfully demonstrated the considerable improvement of the quality of thermal images (up to 30.5% improvement of peak signal to noise ratio). This technique allows improving the measurement accuracy of thermography inspection without upgrading sensors. It also has the potential to be applied on other imaging systems

Gene regulatory networks elucidating huanglongbing disease mechanisms.

Next-generation sequencing was exploited to gain deeper insight into the response to infection by Candidatus liberibacter asiaticus (CaLas), especially the immune disregulation and metabolic dysfunction caused by source-sink disruption. Previous fruit transcriptome data were compared with additional RNA-Seq data in three tissues: immature fruit, and young and mature leaves. Four categories of orchard trees were studied: symptomatic, asymptomatic, apparently healthy, and healthy. Principal component analysis found distinct expression patterns between immature and mature fruits and leaf samples for all four categories of trees. A predicted protein - protein interaction network identified HLB-regulated genes for sugar transporters playing key roles in the overall plant responses. Gene set and pathway enrichment analyses highlight the role of sucrose and starch metabolism in disease symptom development in all tissues. HLB-regulated genes (glucose-phosphate-transporter, invertase, starch-related genes) would likely determine the source-sink relationship disruption. In infected leaves, transcriptomic changes were observed for light reactions genes (downregulation), sucrose metabolism (upregulation), and starch biosynthesis (upregulation). In parallel, symptomatic fruits over-expressed genes involved in photosynthesis, sucrose and raffinose metabolism, and downregulated starch biosynthesis. We visualized gene networks between tissues inducing a source-sink shift. CaLas alters the hormone crosstalk, resulting in weak and ineffective tissue-specific plant immune responses necessary for bacterial clearance. Accordingly, expression of WRKYs (including WRKY70) was higher in fruits than in leaves. Systemic acquired responses were inadequately activated in young leaves, generally considered the sites where most new infections occur

Synthetic blends of volatile, phytopathogen-induced odorants can be used to manipulate vector behavior

Volatile organic compounds (VOCs) are emitted from all plants and these VOCs are important means of communication between plants and insects. It has been documented that pathogen infections alter VOC profiles rendering infected plants more attractive to specific vectors transmitting these pathogens than uninfected plants, thus potentially aiding in pathogen propagation. Mimicking these chemical cues might enable insect attraction away from the plant or disruption of host finding behavior of the vector. However, the practical implications have not been fully explored. We used citrus, Diaphorina citri and huanglongbing (HLB) as a model host-vector-disease system because HLB threatens citrus production worldwide and is similar to other critical diseases of food crops, such as Zebra Chip affecting potato. We formulated a synthetic chemical blend using selected HLB-specific biomarker compounds, and tested the blend with the Attenu assay system for chemosensory proteins. The Attenu assay system is a procedure that identifies interactions between insect chemosensory proteins and their ligands. We found that an equimolar mixture of compounds mimicking the volatile profile of HLB-infected citrus bound chemosensory proteins. Further investigation of this blend in laboratory behavioral assays resulted in development of a synthetic lure that was more attractive to D. citri than natural citrus tree volatiles. This strategy could provide a new route to produce chemical lures for vector population control for a variety of plant and/or animal systems and it may result in the development of a practical lure for monitoring vectors of disease, such as D. citri

A miniaturised active thermography system to inspect composite laminates

With the rapid increase of the integration and complexity of industrial components, the inaccessibility and inapplicability of existing Non-destructive testing devices have become a bottleneck for in-situ inspection of these objects. This paper introduces a miniaturised active thermography system featured with a small size, low resolution and low-cost thermal sensor, where two optional excitation sources including flash and laser are integrated. Dedicated data analysis approaches to evaluate defects are proposed considering the degraded signal quality. Three carbon fibre reinforced polymer laminates with a variety of defects are evaluated quantitatively and qualitatively using the proposed system by comparing with two existing non-miniaturised inspection systems. The results show that the proposed system can work effectively for the degradation assessment of composite laminates. Even with the technical limitations that affect the detectability, for instance, the low pixel resolution, this technique will play an important role to inspect components featured with geometrically intricate spac

A miniaturised active thermography system for in-situ inspections

With the increase of the functionalisation, integration and complexity of industrial components and systems, deploying Non-Destructive Testing (NDT) devices for ‘in-situ’ inspection has become a major challenge for high-value assets. Due to the mismatching of size and volume between the existing inspection unit and the targeted complex object, inaccessibility and inapplicability have limited the applicability of NDT techniques. To address this challenge, this paper introduces a novel miniaturised active thermography system based on a commercial thermal imaging sensor featured with small size and low cost. Combining with different excitation sources, its detection performance on different types of defect of carbon fibre reinforced polymer (CFRP) is investigated and compared with an existing system. The results show that the proposed system can work with laser and flash effectively for degradation assessment although the detectability is compromised. Such a technique will play a unique role in the in-situ inspection where the space to deploy the device is limited