Reviewerly/exHarmony huggingface dataset:
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech

Automatic speaker verification (ASV) is one of the most natural and convenient means of biometric person recognition. Unfortunately, just like all other biometric systems, ASV is vulnerable to spoofing, also referred to as “presentation attacks.” These vulnerabilities are generally unacceptable and call for spoofing countermeasures or “presentation attack detection” systems. In addition to impersonation, ASV systems are vulnerable to replay, speech synthesis, and voice conversion attacks. The ASVspoof challenge initiative was created to foster research on anti-spoofing and to provide common platforms for the assessment and comparison of spoofing countermeasures. The first edition, ASVspoof 2015, focused upon the study of countermeasures for detecting of text-to-speech synthesis (TTS) and voice conversion (VC) attacks. The second edition, ASVspoof 2017, focused instead upon replay spoofing attacks and countermeasures. The ASVspoof 2019 edition is the first to consider all three spoofing attack types within a single challenge. While they originate from the same source database and same underlying protocol, they are explored in two specific use case scenarios. Spoofing attacks within a logical access (LA) scenario are generated with the latest speech synthesis and voice conversion technologies, including state-of-the-art neural acoustic and waveform model techniques. Replay spoofing attacks within a physical access (PA) scenario are generated through carefully controlled simulations that support much more revealing analysis than possible previously. Also new to the 2019 edition is the use of the tandem detection cost function metric, which reflects the impact of spoofing and countermeasures on the reliability of a fixed ASV system. This paper describes the database design, protocol, spoofing attack implementations, and baseline ASV and countermeasure results. It also describes a human assessment on spoofed data in logical access. It was demonstrated that the spoofing data in the ASVspoof 2019 database have varied degrees of perceived quality and similarity to the target speakers, including spoofed data that cannot be differentiated from bona fide utterances even by human subjects. It is expected that the ASVspoof 2019 database, with its varied coverage of different types of spoofing data, could further foster research on anti-spoofing.
A Proof-of-Trust Consensus Protocol for Enhancing Accountability in Crowdsourcing Services

Incorporating accountability mechanisms in online services requires effective trust management and immutable, traceable source of truth for transaction evidence. The emergence of the blockchain technology brings in high hopes for fulfilling most of those requirements. However, a major challenge is to find a proper consensus protocol that is applicable to the crowdsourcing services in particular and online services in general. Building upon the idea of using blockchain as the underlying technology to enable tracing transactions for service contracts and dispute arbitration, this paper proposes a novel consensus protocol that is suitable for the crowdsourcing as well as the general online service industry. The new consensus protocol is called “Proof-of-Trust” (PoT) consensus; it selects transaction validators based on the service participants' trust values while leveraging RAFT leader election and Shamir's secret sharing algorithms. The PoT protocol avoids the low throughput and resource intensive pitfalls associated with Bitcoin' s “Proof-of-Work” (PoW) mining, while addressing the scalability issue associated with the traditional Paxos-based and Byzantine Fault Tolerance (BFT)-based algorithms. In addition, it addresses the unfaithful behaviors that cannot be dealt with in the traditional BFT algorithms. The paper demonstrates that our approach can provide a viable accountability solution for the online service industry.
PRIMAL: Pathfinding via Reinforcement and Imitation Multi-Agent Learning

Multi-agent path finding (MAPF) is an essential component of many large-scale, real-world robot deployments, from aerial swarms to warehouse automation. However, despite the community's continued efforts, most state-of-the-art MAPF planners still rely on centralized planning and scale poorly past a few hundred agents. Such planning approaches are maladapted to real-world deployments, where noise and uncertainty often require paths be recomputed online, which is impossible when planning times are in seconds to minutes. We present PRIMAL, a novel framework for MAPF that combines reinforcement and imitation learning to teach fully-decentralized policies, where agents reactively plan paths online in a partially-observable world while exhibiting implicit coordination. This framework extends our previous work on distributed learning of collaborative policies by introducing demonstrations of an expert MAPF planner during training, as well as careful reward shaping and environment sampling. Once learned, the resulting policy can be copied onto any number of agents and naturally scales to different team sizes and world dimensions. We present results on randomized worlds with up to 1024 agents and compare success rates against state-of-the-art MAPF planners. Finally, we experimentally validate the learned policies in a hybrid simulation of a factory mockup, involving both real-world and simulated robots.
Crash Mitigation in Motion Planning for Autonomous Vehicles

A motion planning method for autonomous vehicles confronting emergency situations where collision is inevitable, generating a path to mitigate the crash as much as possible, is proposed in this paper. The Model predictive control (MPC) algorithm is adopted here for motion planning. If avoidance is impossible for the model predictive motion planning system, the potential crash severity, and artificial potential field are filled into the controller objective to achieve general obstacle avoidance and the lowest crash severity. Furthermore, the vehicle dynamic is also considered as an optimal control problem. Based on the analysis mentioned earlier, the model predictive controller can optimize the command following, obstacle avoidance, vehicle dynamics, road regulation, and mitigate the inevitable crash based on the predicted values. The proposed MPC algorithm has been proved by simulation to have the ability to avoid obstacles and mitigate the crash if collision is inevitable.
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective

Graph neural networks (GNNs) which apply the deep neural networks to graph data have achieved significant performance for the task of semi-supervised node classification. However, only few work has addressed the adversarial robustness of GNNs. In this paper, we first present a novel gradient-based attack method that facilitates the difficulty of tackling discrete graph data. When comparing to current adversarial attacks on GNNs, the results show that by only perturbing a small number of edge perturbations, including addition and deletion, our optimization-based attack can lead to a noticeable decrease in classification performance. Moreover, leveraging our gradient-based attack, we propose the first optimization-based adversarial training for GNNs. Our method yields higher robustness against both different gradient based and greedy attack methods without sacrifice classification accuracy on original graph.
Learning Modulated Loss for Rotated Object Detection

Popular rotated detection methods usually use five parameters (coordinates of the central point, width, height, and rotation angle) or eight parameters (coordinates of four vertices) to describe the rotated bounding box and l1 loss as the loss function. In this paper, we argue that the aforementioned integration can cause training instability and performance degeneration. The main reason is the discontinuity of loss which is caused by the contradiction between the definition of the rotated bounding box and the loss function. We refer to the above issues as rotation sensitivity error (RSE) and propose a modulated rotation loss to dismiss the discontinuity of loss. The modulated rotation loss can achieve consistent improvement on the five parameter methods and the eight parameter methods. Experimental results using one stage and two stages detectors demonstrate the effectiveness of our loss. The integrated network achieves competitive performances on several benchmarks including DOTA and UCAS AOD. The code is available at https://github.com/yangxue0827/RotationDetection.
Distribution Consistency Based Covariance Metric Networks for Few-Shot Learning

Few-shot learning aims to recognize new concepts from very few examples. However, most of the existing few-shot learning methods mainly concentrate on the first-order statistic of concept representation or a fixed metric on the relation between a sample and a concept. In this work, we propose a novel end-to-end deep architecture, named Covariance Metric Networks (CovaMNet). The CovaMNet is designed to exploit both the covariance representation and covariance metric based on the distribution consistency for the few-shot classification tasks. Specifically, we construct an embedded local covariance representation to extract the second-order statistic information of each concept and describe the underlying distribution of this concept. Upon the covariance representation, we further define a new deep covariance metric to measure the consistency of distributions between query samples and new concepts. Furthermore, we employ the episodic training mechanism to train the entire network in an end-to-end manner from scratch. Extensive experiments in two tasks, generic few-shot image classification and fine-grained fewshot image classification, demonstrate the superiority of the proposed CovaMNet. The source code can be available from https://github.com/WenbinLee/CovaMNet.git.
Robust Multi-Agent Reinforcement Learning via Minimax Deep Deterministic Policy Gradient

Despite the recent advances of deep reinforcement learning (DRL), agents trained by DRL tend to be brittle and sensitive to the training environment, especially in the multi-agent scenarios. In the multi-agent setting, a DRL agent’s policy can easily get stuck in a poor local optima w.r.t. its training partners – the learned policy may be only locally optimal to other agents’ current policies. In this paper, we focus on the problem of training robust DRL agents with continuous actions in the multi-agent learning setting so that the trained agents can still generalize when its opponents’ policies alter. To tackle this problem, we proposed a new algorithm, MiniMax Multi-agent Deep Deterministic Policy Gradient (M3DDPG) with the following contributions: (1) we introduce a minimax extension of the popular multi-agent deep deterministic policy gradient algorithm (MADDPG), for robust policy learning; (2) since the continuous action space leads to computational intractability in our minimax learning objective, we propose Multi-Agent Adversarial Learning (MAAL) to efficiently solve our proposed formulation. We empirically evaluate our M3DDPG algorithm in four mixed cooperative and competitive multi-agent environments and the agents trained by our method significantly outperforms existing baselines.
Adaptive Fusion and Category-Level Dictionary Learning Model for Multiview Human Action Recognition

Human actions are often captured by multiple cameras (or sensors) to overcome the significant variations in viewpoints, background clutter, object speed, and motion patterns in video surveillance, and action recognition systems often benefit from fusing multiple types of cameras (sensors). Therefore, adaptive fusion of the information from multiple domains is mandatory for multiview human action recognition. Two widely applied fusion schemes are feature-level fusion and score-level fusion. We point out that limitations still exist and there is tremendous room for improvement, including the separate computation of feature fusion and action recognition, or the fixed weights for each action and each camera. However, previous fusion methods cannot accomplish them. In this paper, inspired by nature, the above limitations are addressed for multiview action recognition by developing a novel adaptive fusion and category-level dictionary learning model (abbreviated to AFCDL). It can jointly learn the adaptive weight for each camera and optimize the reconstruction of samples toward the action recognition task. To induce the dictionary learning and the reconstruction of query set (or test samples), the induced set for each category is built, and the corresponding induced regularization term is designed for the objective function. Extensive experiments on four public multiview action benchmarks show that AFCDL can significantly outperforms the state-of-the-art methods with 3% to 10% improvement in recognition accuracy.
A Hierarchical Multi-Task Approach for Learning Embeddings from Semantic Tasks

Much effort has been devoted to evaluate whether multi-task learning can be leveraged to learn rich representations that can be used in various Natural Language Processing (NLP) down-stream applications. However, there is still a lack of understanding of the settings in which multi-task learning has a significant effect. In this work, we introduce a hierarchical model trained in a multi-task learning setup on a set of carefully selected semantic tasks. The model is trained in a hierarchical fashion to introduce an inductive bias by supervising a set of low level tasks at the bottom layers of the model and more complex tasks at the top layers of the model. This model achieves state-of-the-art results on a number of tasks, namely Named Entity Recognition, Entity Mention Detection and Relation Extraction without hand-engineered features or external NLP tools like syntactic parsers. The hierarchical training supervision induces a set of shared semantic representations at lower layers of the model. We show that as we move from the bottom to the top layers of the model, the hidden states of the layers tend to represent more complex semantic information.
Cross-Modal Attention With Semantic Consistence for Image–Text Matching

The task of image-text matching refers to measuring the visual-semantic similarity between an image and a sentence. Recently, the fine-grained matching methods that explore the local alignment between the image regions and the sentence words have shown advance in inferring the image-text correspondence by aggregating pairwise region-word similarity. However, the local alignment is hard to achieve as some important image regions may be inaccurately detected or even missing. Meanwhile, some words with high-level semantics cannot be strictly corresponding to a single-image region. To tackle these problems, we address the importance of exploiting the global semantic consistence between image regions and sentence words as complementary for the local alignment. In this article, we propose a novel hybrid matching approach named Cross-modal Attention with Semantic Consistency (CASC) for image-text matching. The proposed CASC is a joint framework that performs cross-modal attention for local alignment and multilabel prediction for global semantic consistence. It directly extracts semantic labels from available sentence corpus without additional labor cost, which further provides a global similarity constraint for the aggregated region-word similarity obtained by the local alignment. Extensive experiments on Flickr30k and Microsoft COCO (MSCOCO) data sets demonstrate the effectiveness of the proposed CASC on preserving global semantic consistence along with the local alignment and further show its superior image-text matching performance compared with more than 15 state-of-the-art methods.
"How Old Do You Think I Am?" A Study of Language and Age in Twitter

In this paper we focus on the connection between age and language use, exploring age prediction of Twitter users based on their tweets. We discuss the construction of a fine-grained annotation effort to assign ages and life stages to Twitter users. Using this dataset, we explore age prediction in three different ways: classifying users into age categories, by life stages, and predicting their exact age. We find that an automatic system achieves better performance than humans on these tasks and that both humans and the automatic systems have difficulties predicting the age of older people. Moreover, we present a detailed analysis of variables that change with age. We find strong patterns of change, and that most changes occur at young ages.
Abdominal multi-organ segmentation with organ-attention networks and statistical fusion

Accurate and robust segmentation of abdominal organs on CT is essential for many clinical applications such as computer-aided diagnosis and computer-aided surgery. But this task is challenging due to the weak boundaries of organs, the complexity of the background, and the variable sizes of different organs. To address these challenges, we introduce a novel framework for multi-organ segmentation by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity. OAN is a two-stage deep convolutional network, where deep network features from the first stage are combined with the original image, in a second stage, to reduce the complex background and enhance the discriminative information for the target organs. RCs are added to the first stage to give the lower layers semantic information thereby enabling them to adapt to the sizes of different organs. Our networks are trained on 2D views enabling us to use holistic information and allowing efficient computation. To compensate for the limited cross-sectional information of the original 3D volumetric CT, multi-sectional images are reconstructed from the three different 2D view directions. Then we combine the segmentation results from the different views using statistical fusion, with a novel term relating the structural similarity of the 2D views to the original 3D structure. To train the network and evaluate results, 13 structures were manually annotated by four human raters and confirmed by a senior expert on 236 normal cases. We tested our algorithm and computed Dice-Sorensen similarity coefficients and surface distances for evaluating our estimates of the 13 structures. Our experiments show that the proposed approach outperforms 2D- and 3D-patch based state-of-the-art methods.
A robust and fixed-time zeroing neural dynamics for computing time-variant nonlinear equation using a novel nonlinear activation function

Nonlinear activation functions play an important role in zeroing neural network (ZNN), and it has be proved that ZNN can achieve finite-time convergence when the sign-bi-power (SBP) activation function is explored. However, its upper bound depends on initial states of ZNN seriously, which will restrict some practical applications since the knowledge of initial conditions is generally unavailable in advance. Besides, SBP activation function does not make ZNN reject external disturbances simultaneously. To address the above two issues encountered by ZNN, by suggesting a new nonlinear activation function, a robust and fixed-time zeroing neural dynamics (RaFT-ZND) model is proposed and analyzed for time-variant nonlinear equation (TVNE). As compared to the previous ZNN model with SBP activation function, the RaFT-ZND model not only converges to the theoretical solution of TVNE within a fixed time, but also rejects external disturbances to show good robustness. In addition, the upper bound of the fixed-time convergence is theoretically computed in mathematics, which is independent of initial states of the RaFT-ZND model. At last, computer simulations are conducted under external disturbances, and comparative results demonstrate the effectiveness, robustness, and advantage of the RaFT-ZND model for solving TVNE.
Do NLP Models Know Numbers? Probing Numeracy in Embeddings

The ability to understand and work with numbers (numeracy) is critical for many complex reasoning tasks.Currently, most NLP models treat numbers in text in the same way as other tokens-they embed them as distributed vectors.Is this enough to capture numeracy?We begin by investigating the numerical reasoning capabilities of a state-of-the-art question answering model on the DROP dataset.We find this model excels on questions that require numerical reasoning, i.e., it already captures numeracy.To understand how this capability emerges, we probe token embedding methods (e.g., BERT, GloVe) on synthetic list maximum, number decoding, and addition tasks.A surprising degree of numeracy is naturally present in standard embeddings.For example, GloVe and word2vec accurately encode magnitude for numbers up to 1,000.Furthermore, character-level embeddings are even more precise-ELMo captures numeracy the best for all pre-trained methods-but BERT, which uses sub-word units, is less exact.
Every Document Owns Its Structure: Inductive Text Classification via Graph Neural Networks

Text classification is fundamental in natural language processing (NLP), and Graph Neural Networks (GNN) are recently applied in this task.However, the existing graph-based works can neither capture the contextual word relationships within each document nor fulfil the inductive learning of new words.In this work, to overcome such problems, we propose TextING 1 for inductive text classification via GNN.We first build individual graphs for each document and then use GNN to learn the finegrained word representations based on their local structures, which can also effectively produce embeddings for unseen words in the new document.Finally, the word nodes are incorporated as the document embedding.Extensive experiments on four benchmark datasets show that our method outperforms state-of-theart text classification methods.
An Embedding Cost Learning Framework Using GAN

Successful adaptive steganography has mainly focused on embedding the payload while minimizing an appropriately defined distortion function. The application of deep learning to steganalysis has greatly challenged present adaptive steganographic methods, but has also shown the potential for the improvement of steganography. This paper proposes a distortion function generating a framework for steganography. It has three modules: a generator with a U-Net architecture to translate a cover image into an embedding change probability map, a no-pre-training-required double-tanh function to approximate the optimal embedding simulator while preserving gradient norm during backpropagation in the adversarial training, and an enhanced steganalyzer based on a convolution neural network together with multiple high pass filters as the discriminator. Extensive experimental results on different datasets have shown that the proposed framework outperforms the current state-of-the-art steganographic schemes. Moreover, the adversarial training time is reduced dramatically compared with the GAN-based automatic steganographic distortion learning framework (ASDL-GAN).
A Lexicon-Based Graph Neural Network for Chinese NER

Recurrent neural networks (RNN) used for Chinese named entity recognition (NER) that sequentially track character and word information have achieved great success.However, the characteristic of chain structure and the lack of global semantics determine that RNN-based models are vulnerable to word ambiguities.In this work, we try to alleviate this problem by introducing a lexicon-based graph neural network with global semantics, in which lexicon knowledge is used to connect characters to capture the local composition, while a global relay node can capture global sentence semantics and longrange dependency.Based on the multiple graph-based interactions among characters, potential words, and the whole-sentence semantics, word ambiguities can be effectively tackled.Experiments on four NER datasets show that the proposed model achieves significant improvements against other baseline models.