OpenRaiser/Intern-Atlas huggingface dataset:
High-order Refining for End-to-end Chinese Semantic Role Labeling
Venue: arXiv cs.CL 2020
Year: 2020

Current end-to-end semantic role labeling is mostly accomplished via graph-based neural models. However, these all are first-order models, where each decision for detecting any predicate-argument pair is made in isolation with local features. In this paper, we present a high-order refining mechanism to perform interaction between all predicate-argument pairs. Based on the baseline graph model, our high-order refining module learns higher-order features between all candidate pairs via attention calculation, which are later used to update the original token representations. After several iterations of refinement, the underlying token representations can be enriched with globally interacted features. Our high-order model achieves state-of-the-art results on Chinese SRL data, including CoNLL09 and Universal Proposition Bank, meanwhile relieving the long-range dependency issues.
Promoting Connectivity of Network-Like Structures by Enforcing Region Separation
Venue: arXiv cs.CV 2020
Year: 2020

We propose a novel, connectivity-oriented loss function for training deep convolutional networks to reconstruct network-like structures, like roads and irrigation canals, from aerial images. The main idea behind our loss is to express the connectivity of roads, or canals, in terms of disconnections that they create between background regions of the image. In simple terms, a gap in the predicted road causes two background regions, that lie on the opposite sides of a ground truth road, to touch in prediction. Our loss function is designed to prevent such unwanted connections between background regions, and therefore close the gaps in predicted roads. It also prevents predicting false positive roads and canals by penalizing unwarranted disconnections of background regions. In order to capture even short, dead-ending road segments, we evaluate the loss in small image crops. We show, in experiments on two standard road benchmarks and a new data set of irrigation canals, that convnets trained with our loss function recover road connectivity so well, that it suffices to skeletonize their output to produce state of the art maps. A distinct advantage of our approach is that the loss can be plugged in to any existing training setup without further modifications.
SA-Net: A deep spectral analysis network for image clustering
Venue: arXiv cs.CV 2020
Year: 2020

Although supervised deep representation learning has attracted enormous attentions across areas of pattern recognition and computer vision, little progress has been made towards unsupervised deep representation learning for image clustering. In this paper, we propose a deep spectral analysis network for unsupervised representation learning and image clustering. While spectral analysis is established with solid theoretical foundations and has been widely applied to unsupervised data mining, its essential weakness lies in the fact that it is difficult to construct a proper affinity matrix and determine the involving Laplacian matrix for a given dataset. In this paper, we propose a SA-Net to overcome these weaknesses and achieve improved image clustering by extending the spectral analysis procedure into a deep learning framework with multiple layers. The SA-Net has the capability to learn deep representations and reveal deep correlations among data samples. Compared with the existing spectral analysis, the SA-Net achieves two advantages: (i) Given the fact that one spectral analysis procedure can only deal with one subset of the given dataset, our proposed SA-Net elegantly integrates multiple parallel and consecutive spectral analysis procedures together to enable interactive learning across different units towards a coordinated clustering model; (ii) Our SA-Net can identify the local similarities among different images at patch level and hence achieves a higher level of robustness against occlusions. Extensive experiments on a number of popular datasets support that our proposed SA-Net outperforms 11 benchmarks across a number of image clustering applications.
Old Photo Restoration via Deep Latent Space Translation
Venue: arXiv cs.CV 2020
Year: 2020

We propose to restore old photos that suffer from severe degradation through a deep learning approach. Unlike conventional restoration tasks that can be solved through supervised learning, the degradation in real photos is complex and the domain gap between synthetic images and real old photos makes the network fail to generalize. Therefore, we propose a novel triplet domain translation network by leveraging real photos along with massive synthetic image pairs. Specifically, we train two variational autoencoders (VAEs) to respectively transform old photos and clean photos into two latent spaces. And the translation between these two latent spaces is learned with synthetic paired data. This translation generalizes well to real photos because the domain gap is closed in the compact latent space. Besides, to address multiple degradations mixed in one old photo, we design a global branch with apartial nonlocal block targeting to the structured defects, such as scratches and dust spots, and a local branch targeting to the unstructured defects, such as noises and blurriness. Two branches are fused in the latent space, leading to improved capability to restore old photos from multiple defects. Furthermore, we apply another face refinement network to recover fine details of faces in the old photos, thus ultimately generating photos with enhanced perceptual quality. With comprehensive experiments, the proposed pipeline demonstrates superior performance over state-of-the-art methods as well as existing commercial tools in terms of visual quality for old photos restoration.
MLMLM: Link Prediction with Mean Likelihood Masked Language Model
Venue: arXiv cs.CL 2020
Year: 2020

Knowledge Bases (KBs) are easy to query, verifiable, and interpretable. They however scale with man-hours and high-quality data. Masked Language Models (MLMs), such as BERT, scale with computing power as well as unstructured raw text data. The knowledge contained within those models is however not directly interpretable. We propose to perform link prediction with MLMs to address both the KBs scalability issues and the MLMs interpretability issues. To do that we introduce MLMLM, Mean Likelihood Masked Language Model, an approach comparing the mean likelihood of generating the different entities to perform link prediction in a tractable manner. We obtain State of the Art (SotA) results on the WN18RR dataset and the best non-entity-embedding based results on the FB15k-237 dataset. We also obtain convincing results on link prediction on previously unseen entities, making MLMLM a suitable approach to introducing new entities to a KB.
A Systematic Study of Lattice-based NIST PQC Algorithms: from Reference Implementations to Hardware Accelerators
Venue: arXiv cs.CR 2020
Year: 2020

Security of currently deployed public key cryptography algorithms is foreseen to be vulnerable against quantum computer attacks. Hence, a community effort exists to develop post-quantum cryptography (PQC) algorithms, i.e., algorithms that are resistant to quantum attacks. In this work, we have investigated how lattice-based candidate algorithms from the NIST PQC standardization competition fare when conceived as hardware accelerators. To achieve this, we have assessed the reference implementations of selected algorithms with the goal of identifying what are their basic building blocks. We assume the hardware accelerators will be implemented in application specific integrated circuit (ASIC) and the targeted technology in our experiments is a commercial 65nm node. In order to estimate the characteristics of each algorithm, we have assessed their memory requirements, use of multipliers, and how each algorithm employs hashing functions. Furthermore, for these building blocks, we have collected area and power figures for 12 candidate algorithms. For memories, we make use of a commercial memory compiler. For logic, we make use of a standard cell library. In order to compare the candidate algorithms fairly, we select a reference frequency of operation of 500MHz. Our results reveal that our area and power numbers are comparable to the state of the art, despite targeting a higher frequency of operation and a higher security level in our experiments. The comprehensive investigation of lattice-based NIST PQC algorithms performed in this paper can be used for guiding ASIC designers when selecting an appropriate algorithm while respecting requirements and design constraints.
Revealing the Weaknesses of File Sharing System on Cloud Storages
Venue: arXiv cs.CR 2020
Year: 2020

Cloud storage provides the simpler way to share the files privately and publicly. A good Cloud Storage Provider (SCP) is not only measured by the access speed or file size that can be shared to others, but also regarding the security issues in file sharing itself. In this paper, we analyze the security of file sharing in 3 Chinese CSPs, which are: Baidu, Weiyun and Kanbox. Those CSPs have their own vulnerabilities that successfully revealed. We also provide some suggestions to countermeasure the weaknesses so that they can maintain the quality while improving the security.
Contrastive and Generative Graph Convolutional Networks for Graph-based Semi-Supervised Learning
Venue: arXiv cs.LG 2020
Year: 2020

Graph-based Semi-Supervised Learning (SSL) aims to transfer the labels of a handful of labeled data to the remaining massive unlabeled data via a graph. As one of the most popular graph-based SSL approaches, the recently proposed Graph Convolutional Networks (GCNs) have gained remarkable progress by combining the sound expressiveness of neural networks with graph structure. Nevertheless, the existing graph-based methods do not directly address the core problem of SSL, i.e., the shortage of supervision, and thus their performances are still very limited. To accommodate this issue, a novel GCN-based SSL algorithm is presented in this paper to enrich the supervision signals by utilizing both data similarities and graph structure. Firstly, by designing a semi-supervised contrastive loss, improved node representations can be generated via maximizing the agreement between different views of the same data or the data from the same class. Therefore, the rich unlabeled data and the scarce yet valuable labeled data can jointly provide abundant supervision information for learning discriminative node representations, which helps improve the subsequent classification result. Secondly, the underlying determinative relationship between the data features and input graph topology is extracted as supplementary supervision signals for SSL via using a graph generative loss related to the input features. Intensive experimental results on a variety of real-world datasets firmly verify the effectiveness of our algorithm compared with other state-of-the-art methods.
Autonomous Learning of Features for Control: Experiments with Embodied and Situated Agents
Venue: arXiv cs.LG 2020
Year: 2020

As discussed in previous studies, the efficacy of evolutionary or reinforcement learning algorithms for continuous control optimization can be enhanced by including a neural module dedicated to feature extraction trained through self-supervised methods. In this paper we report additional experiments supporting this hypothesis and we demonstrate how the advantage provided by feature extraction is not limited to problems that benefit from dimensionality reduction or that involve agents operating on the basis of allocentric perception. We introduce a method that permits to continue the training of the feature-extraction module during the training of the policy network and that increases the efficacy of feature extraction. Finally, we compare alternative feature-extracting methods and we show that sequence-to-sequence learning yields better results than the methods considered in previous studies.
Generative models with kernel distance in data space
Venue: arXiv cs.LG 2020
Year: 2020

Generative models dealing with modeling a~joint data distribution are generally either autoencoder or GAN based. Both have their pros and cons, generating blurry images or being unstable in training or prone to mode collapse phenomenon, respectively. The objective of this paper is to construct a~model situated between above architectures, one that does not inherit their main weaknesses. The proposed LCW generator (Latent Cramer-Wold generator) resembles a classical GAN in transforming Gaussian noise into data space. What is of utmost importance, instead of a~discriminator, LCW generator uses kernel distance. No adversarial training is utilized, hence the name generator. It is trained in two phases. First, an autoencoder based architecture, using kernel measures, is built to model a manifold of data. We propose a Latent Trick mapping a Gaussian to latent in order to get the final model. This results in very competitive FID values.
Mimic and Conquer: Heterogeneous Tree Structure Distillation for Syntactic NLP
Venue: arXiv cs.CL 2020
Year: 2020

Syntax has been shown useful for various NLP tasks, while existing work mostly encodes singleton syntactic tree using one hierarchical neural network. In this paper, we investigate a simple and effective method, Knowledge Distillation, to integrate heterogeneous structure knowledge into a unified sequential LSTM encoder. Experimental results on four typical syntax-dependent tasks show that our method outperforms tree encoders by effectively integrating rich heterogeneous structure syntax, meanwhile reducing error propagation, and also outperforms ensemble methods, in terms of both the efficiency and accuracy.
Pooling Methods in Deep Neural Networks, a Review
Venue: arXiv cs.CV 2020
Year: 2020

Nowadays, Deep Neural Networks are among the main tools used in various sciences. Convolutional Neural Network is a special type of DNN consisting of several convolution layers, each followed by an activation function and a pooling layer. The pooling layer is an important layer that executes the down-sampling on the feature maps coming from the previous layer and produces new feature maps with a condensed resolution. This layer drastically reduces the spatial dimension of input. It serves two main purposes. The first is to reduce the number of parameters or weights, thus lessening the computational cost. The second is to control the overfitting of the network. An ideal pooling method is expected to extract only useful information and discard irrelevant details. There are a lot of methods for the implementation of pooling operation in Deep Neural Networks. In this paper, we reviewed some of the famous and useful pooling methods.
Robust Person Re-Identification through Contextual Mutual Boosting
Venue: arXiv cs.CV 2020
Year: 2020

Person Re-Identification (Re-ID) has witnessed great advance, driven by the development of deep learning. However, modern person Re-ID is still challenged by background clutter, occlusion and large posture variation which are common in practice. Previous methods tackle these challenges by localizing pedestrians through external cues (e.g., pose estimation, human parsing) or attention mechanism, suffering from high computation cost and increased model complexity. In this paper, we propose the Contextual Mutual Boosting Network (CMBN). It localizes pedestrians and recalibrates features by effectively exploiting contextual information and statistical inference. Firstly, we construct two branches with a shared convolutional frontend to learn the foreground and background features respectively. By enabling interaction between these two branches, they boost the accuracy of the spatial localization mutually. Secondly, starting from a statistical perspective, we propose the Mask Generator that exploits the activation distribution of the transformation matrix for generating the static channel mask to the representations. The mask recalibrates the features to amplify the valuable characteristics and diminish the noise. Finally, we propose the Contextual-Detachment Strategy to optimize the two branches jointly and independently, which further enhances the localization precision. Experiments on the benchmarks demonstrate the superiority of the architecture compared the state-of-the-art.
UXNet: Searching Multi-level Feature Aggregation for 3D Medical Image Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Aggregating multi-level feature representation plays a critical role in achieving robust volumetric medical image segmentation, which is important for the auxiliary diagnosis and treatment. Unlike the recent neural architecture search (NAS) methods that typically searched the optimal operators in each network layer, but missed a good strategy to search for feature aggregations, this paper proposes a novel NAS method for 3D medical image segmentation, named UXNet, which searches both the scale-wise feature aggregation strategies as well as the block-wise operators in the encoder-decoder network. UXNet has several appealing benefits. (1) It significantly improves flexibility of the classical UNet architecture, which only aggregates feature representations of encoder and decoder in equivalent resolution. (2) A continuous relaxation of UXNet is carefully designed, enabling its searching scheme performed in an efficient differentiable manner. (3) Extensive experiments demonstrate the effectiveness of UXNet compared with recent NAS methods for medical image segmentation. The architecture discovered by UXNet outperforms existing state-of-the-art models in terms of Dice on several public 3D medical image segmentation benchmarks, especially for the boundary locations and tiny tissues. The searching computational complexity of UXNet is cheap, enabling to search a network with the best performance less than 1.5 days on two TitanXP GPUs.
Exploring Speech Cues in Web-mined COVID-19 Conversational Vlogs
Venue: arXiv cs.MM 2020
Year: 2020

The COVID-19 pandemic caused by the novel SARS-Coronavirus-2 (n-SARS-CoV-2) has impacted people's lives in unprecedented ways. During the time of the pandemic, social vloggers have used social media to actively share their opinions or experiences in quarantine. This paper collected videos from YouTube to track emotional responses in conversational vlogs and their potential associations with events related to the pandemic. In particular, vlogs uploaded from locations in New York City were analyzed given that this was one of the first epicenters of the pandemic in the United States. We observed some common patterns in vloggers' acoustic and linguistic features across the time span of the quarantine, which is indicative of changes in emotional reactivity. Additionally, we investigated fluctuations of acoustic and linguistic patterns in relation to COVID-19 events in the New York area (e.g. the number of daily new cases, number of deaths, and extension of stay-at-home order and state of emergency). Our results indicate that acoustic features, such as zero-crossing-rate, jitter, and shimmer, can be valuable for analyzing emotional reactivity in social media videos. Our findings further indicate that some of the peaks of the acoustic and linguistic indices align with COVID-19 events, such as the peak in the number of deaths and emergency declaration.
Reconfigurable Intelligent Surface Assisted Massive MIMO with Antenna Selection
Venue: arXiv cs.IT 2020
Year: 2020

Antenna selection is capable of reducing the hardware complexity of massive multiple-input multiple-output (MIMO) networks at the cost of certain performance degradation. Reconfigurable intelligent surface (RIS) has emerged as a cost-effective technique that can enhance the spectrum-efficiency of wireless networks by reconfiguring the propagation environment. By employing RIS to compensate the performance loss due to antenna selection, in this paper we propose a new network architecture, i.e., RIS-assisted massive MIMO system with antenna selection, to enhance the system performance while enjoying a low hardware cost. This is achieved by maximizing the channel capacity via joint antenna selection and passive beamforming while taking into account the cardinality constraint of active antennas and the unit-modulus constraints of all RIS elements. However, the formulated problem turns out to be highly intractable due to the non-convex constraints and coupled optimization variables, for which an alternating optimization framework is provided, yielding antenna selection and passive beamforming subproblems. The computationally efficient submodular optimization algorithms are developed to solve the antenna selection subproblem under different channel state information assumptions. The iterative algorithms based on block coordinate descent are further proposed for the passive beamforming design by exploiting the unique problem structures. Experimental results will demonstrate the algorithmic advantages and desirable performance of the proposed algorithms for RIS- assisted massive MIMO systems with antenna selection.
Comment on "Open is not forever: a study of vanished open access journals"
Venue: arXiv cs.DL 2020
Year: 2020

We comment on a recent article by Laakso et al. (arXiv:2008.11933 [cs.DL]), in which the disappearance of 176 open access journals from the Internet is noted. We argue that one reason these journals may have vanished is that they were predatory journals. The de-listing of predators from the Directory of Open Access Journals in 2014 and the abundance of predatory journals and awareness thereof in North America parsimoniously explain the temporal and geographic patterns Laakso et al. observed.
Compressing Facial Makeup Transfer Networks by Collaborative Distillation and Kernel Decomposition
Venue: arXiv cs.CV 2020
Year: 2020

Although the facial makeup transfer network has achieved high-quality performance in generating perceptually pleasing makeup images, its capability is still restricted by the massive computation and storage of the network architecture. We address this issue by compressing facial makeup transfer networks with collaborative distillation and kernel decomposition. The main idea of collaborative distillation is underpinned by a finding that the encoder-decoder pairs construct an exclusive collaborative relationship, which is regarded as a new kind of knowledge for low-level vision tasks. For kernel decomposition, we apply the depth-wise separation of convolutional kernels to build a light-weighted Convolutional Neural Network (CNN) from the original network. Extensive experiments show the effectiveness of the compression method when applied to the state-of-the-art facial makeup transfer network -- BeautyGAN.
Neural Dialogue State Tracking with Temporally Expressive Networks
Venue: arXiv cs.CL 2020
Year: 2020

Dialogue state tracking (DST) is an important part of a spoken dialogue system. Existing DST models either ignore temporal feature dependencies across dialogue turns or fail to explicitly model temporal state dependencies in a dialogue. In this work, we propose Temporally Expressive Networks (TEN) to jointly model the two types of temporal dependencies in DST. The TEN model utilizes the power of recurrent networks and probabilistic graphical models. Evaluating on standard datasets, TEN is demonstrated to be effective in improving the accuracy of turn-level-state prediction and the state aggregation.