Reviewerly/exHarmony huggingface dataset:
Locate Globally, Segment Locally: A Progressive Architecture With Knowledge Review Network for Salient Object Detection

Salient object location and segmentation are two different tasks in salient object detection (SOD). The former aims to globally find the most attractive objects in an image, whereas the latter can be achieved only using local regions that contain salient objects. However, previous methods mainly accomplish the two tasks simultaneously in a simple end-to-end manner, which leads to the ignorance of the differences between them. We assume that the human vision system orderly locates and segments objects, so we propose a novel progressive architecture with knowledge review network (PA-KRN) for SOD. It consists of three parts. (1) A coarse locating module (CLM) that uses body-attention label locates rough areas containing salient objects without boundary details. (2) An attention-based sampler highlights salient object regions with high resolution based on body-attention maps. (3) A fine segmenting module (FSM) finely segments salient objects. The networks applied in CLM and FSM are mainly based on our proposed knowledge review network (KRN) that utilizes the finest feature maps to reintegrate all previous layers, which can make up for the important information that is continuously diluted in the top-down path. Experiments on five benchmarks demonstrate that our single KRN can outperform state-of-the-art methods. Furthermore, our PA-KRN performs better and substantially surpasses the aforementioned methods.
Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter

Lexicon information and pre-trained models, such as BERT, have been combined to explore Chinese sequence labelling tasks due to their respective strength.However, existing methods solely fuse lexicon features via a shallow and random initialized sequence layer and do not integrate them into the bottom layers of BERT.In this paper, we propose Lexicon Enhanced BERT (LEBERT) for Chinese sequence labelling, which integrates external lexicon knowledge into BERT layers directly by a Lexicon Adapter layer.Compared with the existing methods, our model facilitates deep lexicon knowledge fusion at the lower layers of BERT.Experiments on ten Chinese datasets of three tasks including Named Entity Recognition, Word Segmentation, and Part-of-Speech Tagging, show that LEBERT achieves the stateof-the-art results.
A Survey on Efficient Convolutional Neural Networks and Hardware Acceleration

Over the past decade, deep-learning-based representations have demonstrated remarkable performance in academia and industry. The learning capability of convolutional neural networks (CNNs) originates from a combination of various feature extraction layers that fully utilize a large amount of data. However, they often require substantial computation and memory resources while replacing traditional hand-engineered features in existing systems. In this review, to improve the efficiency of deep learning research, we focus on three aspects: quantized/binarized models, optimized architectures, and resource-constrained systems. Recent advances in light-weight deep learning models and network architecture search (NAS) algorithms are reviewed, starting with simplified layers and efficient convolution and including new architectural design and optimization. In addition, several practical applications of efficient CNNs have been investigated using various types of hardware architectures and platforms.
SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation

Graph contrastive learning (GCL) has emerged as a dominant technique for graph representation learning which maximizes the mutual information between paired graph augmentations that share the same semantics. Unfortunately, it is difficult to preserve semantics well during augmentations in view of the diverse nature of graph data. Currently, data augmentations in GCL broadly fall into three unsatisfactory ways. First, the augmentations can be manually picked per dataset by trial-and-errors. Second, the augmentations can be selected via cumbersome search. Third, the augmentations can be obtained with expensive domain knowledge as guidance. All of these limit the efficiency and more general applicability of existing GCL methods. To circumvent these crucial issues, we propose a Simple framework for GRAph Contrastive lEarning, SimGRACE for brevity, which does not require data augmentations. Specifically, we take original graph as input and GNN model with its perturbed version as two encoders to obtain two correlated views for contrast. SimGRACE is inspired by the observation that graph data can preserve their semantics well during encoder perturbations while not requiring manual trial-and-errors, cumbersome search or expensive domain knowledge for augmentations selection. Also, we explain why SimGRACE can succeed. Furthermore, we devise adversarial training scheme, dubbed AT-SimGRACE, to enhance the robustness of graph contrastive learning and theoretically explain the reasons. Albeit simple, we show that SimGRACE can yield competitive or better performance compared with state-of-the-art methods in terms of generalizability, transferability and robustness, while enjoying unprecedented degree of flexibility and efficiency. The code is available at: https://github.com/junxia97/SimGRACE.
StyleSwin: Transformer-based GAN for High-resolution Image Generation

Despite the tantalizing success in a broad of vision tasks, transformers have not yet demonstrated on-par ability as ConvNets in high-resolution image generative modeling. In this paper, we seek to explore using pure transformers to build a generative adversarial network for high-resolution image synthesis. To this end, we believe that local attention is crucial to strike the balance between computational efficiency and modeling capacity. Hence, the proposed generator adopts Swin transformer in a style-based architecture. To achieve a larger receptive field, we propose double attention which simultaneously leverages the context of the local and the shifted windows, leading to improved generation quality. Moreover, we show that offering the knowledge of the absolute position that has been lost in window-based transformers greatly benefits the generation quality. The proposed StyleSwin is scalable to high resolutions, with both the coarse geometry and fine structures benefit from the strong expressivity of transformers. However, blocking artifacts occur during high-resolution synthesis because performing the local attention in a block-wise manner may break the spatial coherency. To solve this, we empirically investigate various solutions, among which we find that employing a wavelet discriminator to examine the spectral discrepancy effectively suppresses the artifacts. Extensive experiments show the superiority over prior transformer-based GANs, especially on high resolutions, e.g., <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1024 \times$</tex> 1024. The StyleSwin, without complex training strategies, excels over StyleGAN on CelebA-HQ 1024, and achieves on-par performance on FFHQ-1024, proving the promise of using transformers for high-resolution image generation. The code and pretrained models are available at https://github.com/microsoft/StyleSwin.
SCPNet: Spatial-Channel Parallelism Network for Joint Holistic and Partial Person Re-identification

Holistic person re-identification (ReID) has received extensive study in the past few years and achieves impressive progress. However, persons are often occluded by obstacles or other persons in practical scenarios, which makes partial person re-identification non-trivial. In this paper, we propose a spatial-channel parallelism network (SCPNet) in which each channel in the ReID feature pays attention to a given spatial part of the body. The spatial-channel corresponding relationship supervises the network to learn discriminative feature for both holistic and partial person re-identification. The single model trained on four holistic ReID datasets achieves competitive accuracy on these four datasets, as well as outperforms the state-of-the-art methods on two partial ReID datasets without training.
A Single Attention-Based Combination of CNN and RNN for Relation Classification

As a vital task in natural language processing, relation classification aims to identify relation types between entities from texts. In this paper, we propose a novel Att-RCNN model to extract text features and classify relations by combining recurrent neural network (RNN) and convolutional neural network (CNN). This network structure utilizes RNN to extract higher level contextual representations of words and CNN to obtain sentence features for the relation classification task. In addition to this network structure, both word-level and sentence-level attention mechanisms are employed in Att-RCNN to strengthen critical words and features to promote the model performance. Moreover, we conduct experiments on four distinct datasets: SemEval-2010 task 8, SemEval-2018 task 7 (two subtask datasets), and KBP37 dataset. Compared with the previous public models, Att-RCNN has the overall best performance and achieves the highest $F_{1}$ score, especially on the KBP37 dataset.
Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting

Over the past decade, PV power plants have increasingly contributed to power generation. However, PV power generation widely varies due to environmental factors; thus, the accurate forecasting of PV generation becomes essential. Meanwhile, weather data for environmental factors include many missing values; for example, when we estimated the missing values in the precipitation data of the Korea Meteorological Agency, they amounted to ~16% from 2015–2016, and further, 19% of the weather data were missing for 2017. Such missing values deteriorate the PV power generation prediction performance, and they need to be eliminated by filling in other values. Here, we explore the impact of missing data imputation methods that can be used to replace these missing values. We apply four missing data imputation methods to the training data and test data of the prediction model based on support vector regression. When the k-nearest neighbors method is applied to the test data, the prediction performance yields results closest to those for the original data with no missing values, and the prediction model’s performance is stable even when the missing data rate increases. Therefore, we conclude that the most appropriate missing data imputation for application to PV forecasting is the KNN method.
An Improved Artificial Bee Colony Algorithm With its Application

The artificial bee colony is a popular evolutionary algorithm that exhibits strong exploration ability but slow convergence. This paper proposes two new updating equations to boost the performances of employed and onlooker bees, respectively. In the new updating equations, two intelligent learning strategies give bees a chance to learn from individuals with better performances. New control operators are also utilized to balance global and local searches. Second, we define a new search direction mechanism to overcome the oscillation phenomenon in employed bees. Finally, an intelligent learning mechanism is proposed to accelerate the convergence rate of the worst employed bee. To test the effectiveness of our algorithm, a series of benchmark functions and two industrial problems are utilized. Experimental results demonstrate that our proposed algorithm performs more favorably on both theoretical and practical problems.
Multiple Ant Colony Optimization Based on Pearson Correlation Coefficient

Ant Colony Optimization algorithms have been successfully applied to solve the Traveling Salesman Problem (TSP).However, they still have a tendency to fall into local optima, mainly resulting from poor diversity, especially in those TSPs with a lot of cities.To address this problem, and further obtain a better result in big-scale TSPs, we propose an algorithm called Multiple Colonies Ant Colony Optimization Based on Pearson Correlation Coefficient (PCCACO).To improve the diversity, first, we introduce a novel single colony termed Unit Distance-Pheromone Operator, which along with two other classic ant populations: Ant Colony System and Max-Min Ant System, make the final whole algorithm.A Pearson correlation coefficient is further employed to erect multi-colony communication with an adaptive frequency.Besides that, an initialization is applied when the algorithm is stagnant, which helps it to jump out of the local optima.Finally, we render a dropout approach to reduce the running time.The extensive simulations in TSP demonstrate that our algorithm can get a better solution with a reasonable variation.
Federated Forest

Most real-world data are scattered across different companies or government organizations, and cannot be easily integrated under data privacy and related regulations such as the European Union's General Data Protection Regulation (GDPR) and China' Cyber Security Law. Such data islands situation and data privacy & security are two major challenges for applications of artificial intelligence. In this paper, we tackle these challenges and propose a privacy-preserving machine learning model, called Federated Forest, which is a lossless learning model of the traditional random forest method, i.e., achieving the same level of accuracy as the non-privacy-preserving approach. Based on it, we developed a secure cross-regional machine learning system that allows a learning process to be jointly trained over different regions' clients with the same user samples but different attribute sets, processing the data stored in each of them without exchanging their raw data. A novel prediction algorithm was also proposed which could largely reduce the communication overhead. Experiments on both real-world and UCI data sets demonstrate the performance of the Federated Forest is as accurate as the non-federated version. The efficiency and robustness of our proposed system had been verified. Overall, our model is practical, scalable and extensible for real-life tasks.
Error-mitigated quantum gates exceeding physical fidelities in a trapped-ion system

Abstract Various quantum applications can be reduced to estimating expectation values, which are inevitably deviated by operational and environmental errors. Although errors can be tackled by quantum error correction, the overheads are far from being affordable for near-term technologies. To alleviate the detrimental effects of errors on the estimation of expectation values, quantum error mitigation techniques have been proposed, which require no additional qubit resources. Here we benchmark the performance of a quantum error mitigation technique based on probabilistic error cancellation in a trapped-ion system. Our results clearly show that effective gate fidelities exceed physical fidelities, i.e., we surpass the break-even point of eliminating gate errors, by programming quantum circuits. The error rates are effectively reduced from (1.10 ± 0.12) × 10 −3 to (1.44 ± 5.28) × 10 −5 and from (0.99 ± 0.06) × 10 −2 to (0.96 ± 0.10) × 10 −3 for single- and two-qubit gates, respectively. Our demonstration opens up the possibility of implementing high-fidelity computations on a near-term noisy quantum device.
Cross-domain and Cross-lingual Abusive Language Detection: A Hybrid Approach with Deep Learning and a Multilingual Lexicon

The development of computational methods to detect abusive language in social media within variable and multilingual contexts has recently gained significant traction.The growing interest is confirmed by the large number of benchmark corpora for different languages developed in the latest years.However, abusive language behaviour is multifaceted and available datasets are featured by different topical focuses.This makes abusive language detection a domain-dependent task, and building a robust system to detect general abusive content a first challenge.Moreover, most resources are available for English, which makes detecting abusive language in low-resource languages a further challenge.We address both challenges by considering ten publicly available datasets across different domains and languages.A hybrid approach with deep learning and a multilingual lexicon to cross-domain and cross-lingual detection of abusive content is proposed and compared with other simpler models.We show that training a system on general abusive language datasets will produce a cross-domain robust system, which can be used to detect other more specific types of abusive content.We also found that using the domain-independent lexicon HurtLex is useful to transfer knowledge between domains and languages.In the cross-lingual experiment, we demonstrate the effectiveness of our jointlearning model also in out-domain scenarios.
Learning to Score Figure Skating Sport Videos

This paper aims at learning to score the figure skating sports videos. To address this task, we propose a deep architecture that includes two complementary components, i.e., Self-Attentive LSTM and Multi-scale Convolutional Skip LSTM. These two components can efficiently learn the local and global sequential information in each video. Furthermore, we present a large-scale figure skating sports video dataset - FisV dataset. This dataset includes 500 figure skating videos with the average length of 2 minutes and 50 seconds. Each video is annotated by two scores of nine different referees, i.e., Total Element Score(TES) and Total Program Component Score (PCS). Our proposed model is validated on FisV and MIT-skate datasets. The experimental results show the effectiveness of our models in learning to score the figure skating videos. The codes and datasets would be downloaded from https://github.com/loadder/MS_LSTM.git.
A Comparative Study of Deep Learning-Based Vulnerability Detection System

Source code static analysis has been widely used to detect vulnerabilities in the development of software products. The vulnerability patterns purely based on human experts are laborious and error prone, which has motivated the use of machine learning for vulnerability detection. In order to relieve human experts of defining vulnerability rules or features, a recent study shows the feasibility of leveraging deep learning to detect vulnerabilities automatically. However, the impact of different factors on the effectiveness of vulnerability detection is unknown. In this paper, we collect two datasets from the programs involving 126 types of vulnerabilities, on which we conduct the first comparative study to quantitatively evaluate the impact of different factors on the effectiveness of vulnerability detection. The experimental results show that accommodating control dependency can increase the overall effectiveness of vulnerability detection F1-measure by 20.3%; the imbalanced data processing methods are not effective for the dataset we create; bidirectional recurrent neural networks (RNNs) are more effective than unidirectional RNNs and convolutional neural network, which in turn are more effective than multi-layer perception; using the last output corresponding to the time step for the bidirectional long short-term memory (BLSTM) can reduce the false negative rate by 2.0% at the price of increasing the false positive rate by 0.5%.
Context-Aware Self-Attention Networks

Self-attention model has shown its flexibility in parallel computation and the effectiveness on modeling both long- and short-term dependencies. However, it calculates the dependencies between representations without considering the contextual information, which has proven useful for modeling dependencies among neural representations in various natural language tasks. In this work, we focus on improving self-attention networks through capturing the richness of context. To maintain the simplicity and flexibility of the self-attention networks, we propose to contextualize the transformations of the query and key layers, which are used to calculate the relevance between elements. Specifically, we leverage the internal representations that embed both global and deep contexts, thus avoid relying on external resources. Experimental results on WMT14 English⇒German and WMT17 Chinese⇒English translation tasks demonstrate the effectiveness and universality of the proposed methods. Furthermore, we conducted extensive analyses to quantify how the context vectors participate in the self-attention model.
Remote Sensing Image Superresolution Using Deep Residual Channel Attention

The current trend in remote sensing image super-resolution (SR) is to use supervised deep learning models to effectively enhance the spatial resolution of airborne and satellite-based optical imagery.Nonetheless, the inherent complexity of these architectures/data often makes these methods very difficult to train.Despite these recent advances, the huge amount of network parameters that must be fine-tuned and the lack of suitable highresolution remotely sensed imagery in actual operational scenarios still raise some important challenges that may become relevant limitations in existent Earth observation data production environments.To address these problems, we propose a new remote sensing SR approach that integrates a visual attention mechanism within a residualbased network design in order to allow the SR process to focus on those features extracted from land-cover components that require more computations to be super-resolved.As a result, the network training process is significantly improved, because it aims at learning the most relevant high-frequency information while the proposed architec-
Meta-Learning for Low-resource Natural Language Generation in Task-oriented Dialogue Systems

Natural language generation (NLG) is an essential component of task-oriented dialogue systems. Despite the recent success of neural approaches for NLG, they are typically developed for particular domains with rich annotated training examples. In this paper, we study NLG in a low-resource setting to generate sentences in new scenarios with handful training examples. We formulate the problem from a meta-learning perspective, and propose a generalized optimization-based approach (Meta-NLG) based on the well-recognized model-agnostic meta-learning (MAML) algorithm. Meta-NLG defines a set of meta tasks, and directly incorporates the objective of adapting to new low-resource NLG tasks into the meta-learning optimization process. Extensive experiments are conducted on a large multi-domain dataset (MultiWoz) with diverse linguistic variations. We show that Meta-NLG significantly outperforms other training procedures in various low-resource configurations. We analyze the results, and demonstrate that Meta-NLG adapts extremely fast and well to low-resource situations.
Effective Dimensionality Reduction for Word Embeddings

Pre-trained word embeddings are used in several downstream applications as well as for constructing representations for sentences, paragraphs and documents.Recently, there has been an emphasis on improving the pretrained word vectors through post-processing algorithms.One improvement area is reducing the dimensionality of word embeddings.Reducing the size of word embeddings can improve their utility in memory constrained devices, benefiting several realworld applications.In this work, we present a novel technique that efficiently combines PCA based dimensionality reduction with a recently proposed post-processing algorithm (Mu and Viswanath, 2018), to construct effective word embeddings of lower dimensions.Empirical evaluations on several benchmarks show that our algorithm efficiently reduces the embedding size while achieving similar or (more often) better performance than original embeddings.To foster reproducibility, we have released the source code along with paper 1 .