Emotion recognition, among other natural language processing tasks, has greatly benefited from the use of large transformer models. Deploying these models on resource-constrained devices, however, is a major challenge due to their computational cost. In this paper, we show that the combination of large transformers, as high-quality feature extractors, and simple hardware-friendly classifiers based on linear separators can achieve competitive performance while allowing real-time inference and fast training. Various solutions including batch and Online Sequential Learning are analyzed. Additionally, our experiments show that latency and performance can be further improved via dimensionality reduction and pre-training, respectively. The resulting system is implemented on two types of edge device, namely an edge accelerator and two smartphones.

Neural point processes provide the flexibility needed to deal with time series of heterogeneous nature within the robust framework of point processes. This aspect is of particular relevance when dealing with real-world data, mixing generative processes characterized by radically different distributions and sampling. This brief discusses a neural point process approach for health and behavioral data, comprising both sparse events coming from user subjective declarations as well as fast-flowing time series from wearable sensors. We propose and empirically validate different neural architectures and we assess the effect of including input sources of different nature. The empirical analysis is built on the top of a challenging original dataset, never published before, and collected as part of a real-world experiment in an uncontrolled setting. Results show the potential of neural point processes both in terms of predicting the next event type as well as in predicting the time to next user interaction.

Dialogue systems are a popular natural language processing (NLP) task as it is promising in real-life applications. It is also a complicated task since many NLP tasks deserving study are involved. As a result, a multitude of novel works on this task are carried out, and most of them are deep learning based due to the outstanding performance. In this survey, we mainly focus on the deep learning based dialogue systems. We comprehensively review state-of-the-art research outcomes in dialogue systems and analyze them from two angles: model type and system type. Specifically, from the angle of model type, we discuss the principles, characteristics, and applications of different models that are widely used in dialogue systems. This will help researchers acquaint these models and see how they are applied in state-of-the-art frameworks, which is rather helpful when designing a new dialogue system. From the angle of system type, we discuss task-oriented and open-domain dialogue systems as two streams of research, providing insight into the hot topics related. Furthermore, we comprehensively review the evaluation methods and datasets for dialogue systems to pave the way for future research. Finally, some possible research trends are identified based on the recent research outcomes. To the best of our knowledge, this survey is the most comprehensive and up-to-date one at present for deep learning based dialogue systems, extensively covering the popular techniques. We speculate that this work is a good starting point for academics who are new to the dialogue systems or those who want to quickly grasp up-to-date techniques in this area.

In this paper, we target the problem of generating facial expressions from a piece of audio. This is challenging since both audio and video have inherent characteristics that are distinct from the other. Some words may have identical lip movements, and speech impediments may prevent lip-reading in some individuals. Previous approaches to generating such a talking head suffered from stiff expressions. This is because they focused only on lip movements and the facial landmarks did not contain the information flow from the audio. Hence, in this work, we employ spatio-temporal independent component analysis to accurately sync the audio with the corresponding face video. Proper word formation also requires control over the face muscles that can be captured using a barrier function. We first validated the approach on the diffusion of salt water in coastal areas using a synthetic finite element simulation. Next, we applied it to 3D facial expressions in toddlers for which training data is difficult to capture. Prior knowledge in the form of rules is specified using Fuzzy logic, and multi-objective optimization is used to collectively learn a set of rules. We observed significantly higher F-measure on three real-world problems.