MailLeak: Obfuscation-Robust Character Extraction Using Transfer Learning

The following work presents a new algorithm for character recognition from obfuscated images. The presented method is an example of a potential threat to current postal services. This paper both analyzes the efficiency of the given algorithm and suggests countermeasures to prevent such threats from occurring.

A Note on Graph-Based Nearest Neighbor Search

Nearest neighbor search has found numerous applications in machine learning, data mining and massive data processing systems. The past few years have witnessed the popularity of the graph-based nearest neighbor search paradigm because of its superiority over the space-partitioning algorithms. While a lot of empirical studies demonstrate the efficiency of graph-based algorithms, not much attention has been paid to a more fundamental question: why graph-based algorithms work so well in practice? And which data property affects the efficiency and how? In this paper, we try to answer these questions. Our insight is that "the probability that the neighbors of a point o tends to be neighbors in the KNN graph" is a crucial data property for query efficiency. For a given dataset, such a property can be qualitatively measured by clustering coefficient of the KNN graph. To show how clustering coefficient affects the performance, we identify that, instead of the global connectivity, the local connectivity around some given query q has more direct impact on recall. Specifically, we observed that high clustering coefficient makes most of the k nearest neighbors of q sit in a maximum strongly connected component (SCC) in the graph. From the algorithmic point of view, we show that the search procedure is actually composed of two phases - the one outside the maximum SCC and the other one in it, which is different from the widely accepted single or multiple paths search models. We proved that the commonly used graph-based search algorithm is guaranteed to traverse the maximum SCC once visiting any point in it. Our analysis reveals that high clustering coefficient leads to large size of the maximum SCC, and thus provides good answer quality with the help of the two-phase search procedure. Extensive empirical results over a comprehensive collection of datasets validate our findings.

InSRL: A Multi-view Learning Framework Fusing Multiple Information Sources for Distantly-supervised Relation Extraction

Distant supervision makes it possible to automatically label bags of sentences for relation extraction by leveraging knowledge bases, but suffers from the sparse and noisy bag issues. Additional information sources are urgently needed to supplement the training data and overcome these issues. In this paper, we introduce two widely-existing sources in knowledge bases, namely entity descriptions, and multi-grained entity types to enrich the distantly supervised data. We see information sources as multiple views and fusing them to construct an intact space with sufficient information. An end-to-end multi-view learning framework is proposed for relation extraction via Intact Space Representation Learning (InSRL), and the representations of single views are jointly learned simultaneously. Moreover, inner-view and cross-view attention mechanisms are used to highlight important information on different levels on an entity-pair basis. The experimental results on a popular benchmark dataset demonstrate the necessity of additional information sources and the effectiveness of our framework. We will release the implementation of our model and dataset with multiple information sources after the anonymized review phase.

Clinical Temporal Relation Extraction with Probabilistic Soft Logic Regularization and Global Inference

There has been a steady need in the medical community to precisely extract the temporal relations between clinical events. In particular, temporal information can facilitate a variety of downstream applications such as case report retrieval and medical question answering. Existing methods either require expensive feature engineering or are incapable of modeling the global relational dependencies among the events. In this paper, we propose a novel method, Clinical Temporal ReLation Exaction with Probabilistic Soft Logic Regularization and Global Inference (CTRL-PG) to tackle the problem at the document level. Extensive experiments on two benchmark datasets, I2B2-2012 and TB-Dense, demonstrate that CTRL-PG significantly outperforms baseline methods for temporal relation extraction.

Long Term Motion Prediction Using Keyposes

Long term human motion prediction is an essential component in safety-critical applications, such as human-robot interaction and autonomous driving. We argue that, to achieve long term forecasting, predicting human pose at every time instant is unnecessary because human motion follows patterns that are well-represented by a few essential poses in the sequence. We call such poses "keyposes", and approximate complex motions by linearly interpolating between subsequent keyposes. We show that learning the sequence of such keyposes allows us to predict very long term motion, up to 5 seconds in the future. In particular, our predictions are much more realistic and better preserve the motion dynamics than those obtained by the state-of-the-art methods. Furthermore, our approach models the future keyposes probabilistically, which, during inference, lets us generate diverse future motions via sampling.

SAR-Net: A End-to-End Deep Speech Accent Recognition Network

This paper proposes a end-to-end deep network to recognize kinds of accents under the same language, where we develop and transfer the deep architecture in speaker-recognition area to accent classification task for learning utterance-level accent representation. Compared with the individual-level feature in speaker-recognition, accent recognition throws a more challenging issue in acquiring compact group-level features for the speakers with the same accent, hence a good discriminative accent feature space is desired. Our deep framework adopts multitask-learning mechanism and mainly consists of three modules: a shared CNNs and RNNs based front-end encoder, a core accent recognition branch, and an auxiliary speech recognition branch, where we take speech spectrogram as input. More specifically, with the sequential descriptors learned from a shared encoder, the accent recognition branch first condenses all descriptors into an embedding vector, and then explores different discriminative loss functions which are popular in face recognition domain to enhance embedding discrimination. Additionally, due to the accent is a speaking-related timbre, adding speech recognition branch effectively curbs the over-fitting phenomenon in accent recognition during training. We show that our network without any data-augment preproccessings is significantly ahead of the baseline system on the accent classification track in the Accented English Speech Recognition Challenge 2020 (AESRC2020), where the state-of-the-art loss function Circle-Loss achieves the best discriminative optimization for accent representation.

MUST-GAN: Multi-level Statistics Transfer for Self-driven Person Image Generation

Pose-guided person image generation usually involves using paired source-target images to supervise the training, which significantly increases the data preparation effort and limits the application of the models. To deal with this problem, we propose a novel multi-level statistics transfer model, which disentangles and transfers multi-level appearance features from person images and merges them with pose features to reconstruct the source person images themselves. So that the source images can be used as supervision for self-driven person image generation. Specifically, our model extracts multi-level features from the appearance encoder and learns the optimal appearance representation through attention mechanism and attributes statistics. Then we transfer them to a pose-guided generator for re-fusion of appearance and pose. Our approach allows for flexible manipulation of person appearance and pose properties to perform pose transfer and clothes style transfer tasks. Experimental results on the DeepFashion dataset demonstrate our method's superiority compared with state-of-the-art supervised and unsupervised methods. In addition, our approach also performs well in the wild.

Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations

Many real-world systems, such as moving planets, can be considered as multi-agent dynamic systems, where objects interact with each other and co-evolve along with the time. Such dynamics is usually difficult to capture, and understanding and predicting the dynamics based on observed trajectories of objects become a critical research problem in many domains. Most existing algorithms, however, assume the observations are regularly sampled and all the objects can be fully observed at each sampling time, which is impractical for many applications. In this paper, we propose to learn system dynamics from irregularly-sampled partial observations with underlying graph structure for the first time. To tackle the above challenge, we present LG-ODE, a latent ordinary differential equation generative model for modeling multi-agent dynamic system with known graph structure. It can simultaneously learn the embedding of high dimensional trajectories and infer continuous latent system dynamics. Our model employs a novel encoder parameterized by a graph neural network that can infer initial states in an unsupervised way from irregularly-sampled partial observations of structural objects and utilizes neuralODE to infer arbitrarily complex continuous-time latent dynamics. Experiments on motion capture, spring system, and charged particle datasets demonstrate the effectiveness of our approach.

VECO: Variable Encoder-decoder Pre-training for Cross-lingual Understanding and Generation

Recent studies about learning multilingual representations have achieved significant performance gains across a wide range of downstream cross-lingual tasks. They train either an encoder-only Transformer mainly for understanding tasks, or an encoder-decoder Transformer specifically for generation tasks, ignoring the correlation between the two tasks and frameworks. In contrast, this paper presents a variable encoder-decoder (VECO) pre-training approach to unify the two mainstreams in both model architectures and pre-training tasks. VECO splits the standard Transformer block into several sub-modules trained with both inner-sequence and cross-sequence masked language modeling, and correspondingly reorganizes certain sub-modules for understanding and generation tasks during inference. Such a workflow not only ensures to train the most streamlined parameters necessary for two kinds of tasks, but also enables them to boost each other via sharing common sub-modules. As a result, VECO delivers new state-of-the-art results on various cross-lingual understanding tasks of the XTREME benchmark covering text classification, sequence labeling, question answering, and sentence retrieval. For generation tasks, VECO also outperforms all existing cross-lingual models and state-of-the-art Transformer variants on WMT14 English-to-German and English-to-French translation datasets, with gains of up to 1$\sim$2 BLEU.