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Graph Convolutional Networks (GCN) which typically follows a neural message passing framework to model dependencies among skeletal joints has achieved high success in skeleton-based human motion prediction task. Nevertheless, how to construct a graph from a skeleton sequence and how to perform message passing on the graph are still open problems, which severely affect the performance of GCN.

Website Fingerprinting (WF) is a network traffic mining technique for anonymous traffic identification, which enables a local adversary to identify the target website that an anonymous network user is browsing. WF attacks based on deep convolutional neural networks (CNN) get the state-of-the-art anonymous traffic classification performance. However, due to the locality restriction of CNN architecture for feature extraction on sequence data, these methods ignore the temporal feature extraction in the anonymous traffic analysis.

Compared to gait recognition, Gait Attribute Recognition (GAR) is a seldom-investigated problem. However, since gait attribute recognition can provide richer and finer semantic descriptions, it is an indispensable part of building intelligent gait analysis systems. Nonetheless, the types of attributes considered in the existing datasets are very limited.

We propose a differentiable imaging framework to address uncertainty in measurement coordinates such as sensor locations and projection angles. We formulate the problem as measurement interpolation at unknown nodes supervised through the forward operator. To solve it we apply implicit neural networks, also known as neural fields, which are naturally differentiable with respect to the input coordinates. We also develop differentiable spline interpolators which perform as well as neural networks, require less time to optimize and have well-understood properties.

Speech applications in far-field real world settings often deal with signals that are corrupted by reverberation. The task of dereverberation constitutes an important step to improve the audible quality and to reduce the error rates in applications like automatic speech recognition (ASR). We propose a unified framework of speech dereverberation for improving the speech quality and the ASR performance using the approach of envelope-carrier decomposition provided by an autoregressive (AR) model.

The speaker recognition evaluation is conducted in a framework in which three score distributions and two decision thresholds are employed, and the statistic of interest is an average of the two weighted sums of the probabilities of type I and type II errors at the two thresholds correspondingly. And data dependence caused by multiple use of the same subjects exists ubiquitously in order to generate more samples because of limited resources.

Vision Transformer (ViT)-based image super-resolution (SR) methods have achieved impressive performance and surpassed CNN-based SR methods by utilizing Multi-Head Self-Attention (MHSA) to model long-range dependencies. However, the quadratic complexity of MHSA and the inefficiency of non-parallelized window partition seriously affect the inference speed, hindering these SR methods from being applied to application scenarios requiring speed and quality.

Decoding silent reading Electroencephalography (EEG) signals is challenging because of its low signal-to-noise ratio. In addition, EEG signals are typically non-Euclidean structured, therefore merely using a two-dimensional matrix to represent the variation of sampling points of each channel in time cannot richly represent the spatial connection between channels.