Reviewerly/exHarmony huggingface dataset:
Distributed subgraph matching on timely dataflow

Recently there emerge many distributed algorithms that aim at solving subgraph matching at scale. Existing algorithm-level comparisons failed to provide a systematic view of distributed subgraph matching mainly due to the intertwining of strategy and optimization. In this paper, we identify four strategies and three general-purpose optimizations from representative state-of-the-art algorithms. We implement the four strategies with the optimizations based on the common Timely dataflow system for systematic strategy-level comparison. Our implementation covers all representative algorithms. We conduct extensive experiments for both unlabelled matching and labelled matching to analyze the performance of distributed subgraph matching under various settings, which is finally summarized as a practical guide.
Field survey and evacuation behaviour during the 2018 Sunda Strait tsunami

On the 22nd of December 2018 the shorelines of Sunda Strait, Indonesia, were hit by tsunami waves generated by the flank collapse of the Anak Krakatau volcano. The authors conducted a field survey of the affected areas in both Sumatra and Java islands to collect information on tsunami inundation and run-up heights, damage patterns at each coastal community, and the evacuation behaviour and tsunami awareness of the affected people. The survey results showed that in Sumatra island inundation heights of more than 4 m were measured along the coastline that was situated to the north-north-east of Anak Krakatau, while less than 4 m were measured along the north-western direction. Inundation heights of over 10 m were measured at Cipenyu Beach (Pandeglang Regency) in Java island (south-south-eastern direction from Anak Krakatau). A questionnaire survey conducted by the authors revealed residents’ perception of danger and evacuation patterns during the event. The results indicate the importance of having an operational tsunami warning system in Sunda Strait and the establishment of an appropriate evacuation plan so that residents can start evacuation immediately and reach a safe place without facing severe congestion along evacuation routes.
Attend to count: Crowd counting with adaptive capacity multi-scale CNNs

Crowd counting is a challenging task due to the large variations in crowd distributions. Previous methods tend to tackle the whole image with a single fixed structure, which is unable to handle diverse complicated scenes with different crowd densities. Hence, we propose the Adaptive Capacity Multi-scale convolutional neural networks (ACM-CNN), a novel crowd counting approach which can assign different capacities to different portions of the input. The intuition is that the model should focus on important regions of the input image and optimize its capacity allocation conditioning on the crowd intensive degree. ACM-CNN consists of three types of modules: A coarse network, a fine network, and a smooth network. The coarse network is used to explore the areas that need to be focused via count attention mechanism, and generate a rough feature map. Then the fine network processes the areas of interest into a fine feature map. To alleviate the sense of division caused by fusion, the smooth network is designed to combine two feature maps organically to produce high-quality density maps. Extensive experiments are conducted on five mainstream datasets. The results demonstrate the effectiveness of the proposed model for both density estimation and crowd counting tasks.
Nighttime Single Image Dehazing via Pixel-Wise Alpha Blending

In this paper, we propose a novel method to address the nighttime single image dehazing problem. Estimation of the ambient illumination map and transmission map are the key steps of modern dehazing approaches. For hazy scenes at night, ambient illumination is usually not globally isotropic as a nighttime scene typically contains multiple light sources. Frequently, Light source regions and non-light source regions exhibit distinct color features. However, existing nighttime dehazing methods have been attempting to process these two regions based on identical prior assumptions. Moreover, the commonly-used local maximum pixel method tends to over-estimate the ambient illumination. These two drawbacks result in color distortions and halo artifacts around the light source regions in the output images. In this work, we present a pixel-wise alpha blending method for estimating the transmission map, where the transmissions estimated from dark channel prior (non-light source region) and the proposed bright channel prior (light source region) are effectively blended into one transmission map guided by a brightness-aware weights map. Based on the Retinex theory, a channel difference guided filtering method is proposed to estimate the ambient illumination, which produces a spatially variant low-frequency passband that selectively retains the high-frequency edge details. Extensive experiments on the benchmarks demonstrate that our method outperforms the state-of-the-art methods for nighttime image dehazing, especially in terms of color consistency and halo artifacts reduction in the dehazed images.
Network structure effects in reservoir computers

A reservoir computer is a complex nonlinear dynamical system that has been shown to be useful for solving certain problems, such as prediction of chaotic signals, speech recognition, or control of robotic systems. Typically, a reservoir computer is constructed by connecting a large number of nonlinear nodes in a network, driving the nodes with an input signal and using the node outputs to fit a training signal. In this work, we set up reservoirs where the edges (or connections) between all the network nodes are either +1 or 0 and proceed to alter the network structure by flipping some of these edges from +1 to -1. We use this simple network because it turns out to be easy to characterize; we may use the fraction of edges flipped as a measure of how much we have altered the network. In some cases, the network can be rearranged in a finite number of ways without changing its structure; these rearrangements are symmetries of the network, and the number of symmetries is also useful for characterizing the network. We find that changing the number of edges flipped in the network changes the rank of the covariance of a matrix consisting of the time series from the different nodes in the network and speculate that this rank is important for understanding the reservoir computer performance.
Mixture Content Selection for Diverse Sequence Generation

Generating diverse sequences is important in many NLP applications such as question generation or summarization that exhibit semantically one-to-many relationships between source and the target sequences.We present a method to explicitly separate diversification from generation using a general plug-and-play module (called SELECTOR) that wraps around and guides an existing encoder-decoder model.The diversification stage uses a mixture of experts to sample different binary masks on the source sequence for diverse content selection.The generation stage uses a standard encoder-decoder model given each selected content from the source sequence.Due to the non-differentiable nature of discrete sampling and the lack of ground truth labels for binary mask, we leverage a proxy for ground-truth mask and adopt stochastic hard-EM for training.In question generation (SQuAD) and abstractive summarization (CNN-DM), our method demonstrates significant improvements in accuracy, diversity and training efficiency, including state-of-the-art top-1 accuracy in both datasets, 6% gain in top-5 accuracy, and 3.7 times faster training over a state-of-the-art model.Our code is publicly available at https://github.com/clovaai/FocusSeq2Seq.
Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis

In recent times, multi-modal analysis has been an emerging and highly sought-after field at the intersection of natural language processing, computer vision, and speech processing.The prime objective of such studies is to leverage the diversified information, (e.g., textual, acoustic and visual), for learning a model.The effective interaction among these modalities often leads to a better system in terms of performance.In this paper, we introduce a recurrent neural network based approach for the multi-modal sentiment and emotion analysis.The proposed model learns the inter-modal interaction among the participating modalities through an auto-encoder mechanism.We employ a context-aware attention module to exploit the correspondence among the neighboring utterances.We evaluate our proposed approach for five standard multi-modal affect analysis datasets.Experimental results suggest the efficacy of the proposed model for both sentiment and emotion analysis over various existing state-of-the-art systems.
Multi-interaction Network with Object Relation for Video Question Answering

Video question answering is an important task for testing machine's ability of video understanding. The existing methods normally focus on the combination of recurrent and convolutional neural networks to capture spatial and temporal information of the video. Recently, some work has also shown that using attention mechanism can achieve better performance. In this paper, we propose a new model called Multi-interaction network for video question answering. There are two types of interactions in our model. The first type is the multi-modal interaction between the visual and textual information. The second type is the multi-level interaction inside the multi-modal interaction. Specifically, instead of using original self-attention, we propose a new attention mechanism called multi-interaction, which can capture both element-wise and segment-wise sequence interactions, simultaneously. And in addition to the normal frame-level interaction, we also take the object relations into consideration, in order to obtain more fine-grained information, such as motions and other potential relations among these objects. We evaluate our method on TGIF-QA and other two video QA datasets. The qualitative and quantitative experimental results show the effectiveness of our model, which achieves the new state-of-the-art performance.
Learning to Discriminate Perturbations for Blocking Adversarial Attacks in Text Classification

Adversarial attacks against machine learning models have threatened various real-world applications such as spam filtering and sentiment analysis.In this paper, we propose a novel framework, learning to discriminate perturbations (DISP), to identify and adjust malicious perturbations, thereby blocking adversarial attacks for text classification models.To identify adversarial attacks, a perturbation discriminator validates how likely a token in the text is perturbed and provides a set of potential perturbations.For each potential perturbation, an embedding estimator learns to restore the embedding of the original word based on the context and a replacement token is chosen based on approximate kNN search.DISP can block adversarial attacks for any NLP model without modifying the model structure or training procedure.Extensive experiments on two benchmark datasets demonstrate that DISP significantly outperforms baseline methods in blocking adversarial attacks for text classification.In addition, in-depth analysis shows the robustness of DISP across different situations.
Reversing Unknown Quantum Transformations: Universal Quantum Circuit for Inverting General Unitary Operations

Given a quantum gate implementing a d-dimensional unitary operation U_{d}, without any specific description but d, and permitted to use k times, we present a universal probabilistic heralded quantum circuit that implements the exact inverse U_{d}^{-1}, whose failure probability decays exponentially in k. The protocol employs an adaptive strategy, proven necessary for the exponential performance. It requires that k≥d-1, proven necessary for the exact implementation of U_{d}^{-1} with quantum circuits. Moreover, even when quantum circuits with indefinite causal order are allowed, k≥d-1 uses are required. We then present a finite set of linear and positive semidefinite constraints characterizing universal unitary inversion protocols and formulate a convex optimization problem whose solution is the maximum success probability for given k and d. The optimal values are computed using semidefinite programing solvers for k≤3 when d=2 and k≤2 for d=3. With this numerical approach we show for the first time that indefinite causal order circuits provide an advantage over causally ordered ones in a task involving multiple uses of the same unitary operation.
New Mathematical Model to Analyze Security of Sharding-Based Blockchain Protocols

In recent years, the scalability issue of blockchain protocols has received huge attention.Sharding is one of the most promising solutions to scale blockchain.The basic idea behind sharding is to divide the blockchain network into multiple committees where each committee processes a separate set of transactions.In this paper, we propose a mathematical model to analyze the security of sharding-based blockchain protocols.Moreover, we analyze well-known sharding protocols including RapidChain, OmniLedger, and Zilliga to validate our model.The key contribution of our paper is to bound the failure probability for one committee and so for each epoch using probability bounds for sums of upper-bounded hypergeometric and binomial distributions.In addition, this paper contribution answers the following fundamental question: ''how to keep the failure probability, for a given sharding protocol, smaller than a predefined threshold?''.Three probability bounds are used: Chebyshev, Hoeffding, and Chvátal.To illustrate the effectiveness of our proposed model, we conduct a numerical and comparative analysis of the proposed bounds.
Limitations of information extraction methods and techniques for heterogeneous unstructured big data

During the recent era of big data, a huge volume of unstructured data are being produced in various forms of audio, video, images, text, and animation. Effective use of these unstructured big data is a laborious and tedious task. Information extraction (IE) systems help to extract useful information from this large variety of unstructured data. Several techniques and methods have been presented for IE from unstructured data. However, numerous studies conducted on IE from a variety of unstructured data are limited to single data types such as text, image, audio, or video. This article reviews the existing IE techniques along with its subtasks, limitations, and challenges for the variety of unstructured data highlighting the impact of unstructured big data on IE techniques. To the best of our knowledge, there is no comprehensive study conducted to investigate the limitations of existing IE techniques for the variety of unstructured big data. The objective of the structured review presented in this article is twofold. First, it presents the overview of IE techniques from a variety of unstructured data such as text, image, audio, and video at one platform. Second, it investigates the limitations of these existing IE techniques due to the heterogeneity, dimensionality, and volume of unstructured big data. The review finds that advanced techniques for IE, particularly for multifaceted unstructured big data sets, are the utmost requirement of the organizations to manage big data and derive strategic information. Further, potential solutions are also presented to improve the unstructured big data IE systems for future research. These solutions will help to increase the efficiency and effectiveness of the data analytics process in terms of context-aware analytics systems, data-driven decision-making, and knowledge management.
Can gradient clipping mitigate label noise

Gradient clipping is a widely-used technique in the training of deep networks, and is generally motivated from an optimisation lens: informally, it controls the dynamics of iterates, thus enhancing the rate of convergence to a local minimum. This intuition has been made precise in a line of recent works, which show that suitable clipping can yield significantly faster convergence than vanilla gradient descent. In this paper, we propose a new lens for studying gradient clipping, namely, robustness: informally, one expects clipping to provide robustness to noise, since one does not overly trust any single sample. Surprisingly, we prove that for the common problem of label noise in classification, standard gradient clipping does not in general provide robustness. On the other hand, we show that a simple variant of gradient clipping is provably robust, and corresponds to suitably modifying the underlying loss function. This yields a simple, noise-robust alternative to the standard cross-entropy loss which performs well empirically.
Attention-Informed Mixed-Language Training for Zero-Shot Cross-Lingual Task-Oriented Dialogue Systems

Recently, data-driven task-oriented dialogue systems have achieved promising performance in English. However, developing dialogue systems that support low-resource languages remains a long-standing challenge due to the absence of high-quality data. In order to circumvent the expensive and time-consuming data collection, we introduce Attention-Informed Mixed-Language Training (MLT), a novel zero-shot adaptation method for cross-lingual task-oriented dialogue systems. It leverages very few task-related parallel word pairs to generate code-switching sentences for learning the inter-lingual semantics across languages. Instead of manually selecting the word pairs, we propose to extract source words based on the scores computed by the attention layer of a trained English task-related model and then generate word pairs using existing bilingual dictionaries. Furthermore, intensive experiments with different cross-lingual embeddings demonstrate the effectiveness of our approach. Finally, with very few word pairs, our model achieves significant zero-shot adaptation performance improvements in both cross-lingual dialogue state tracking and natural language understanding (i.e., intent detection and slot filling) tasks compared to the current state-of-the-art approaches, which utilize a much larger amount of bilingual data.
Generating Adversarial Examples for Holding Robustness of Source Code Processing Models

Automated processing, analysis, and generation of source code are among the key activities in software and system lifecycle. To this end, while deep learning (DL) exhibits a certain level of capability in handling these tasks, the current state-of-the-art DL models still suffer from non-robust issues and can be easily fooled by adversarial attacks.Different from adversarial attacks for image, audio, and natural languages, the structured nature of programming languages brings new challenges. In this paper, we propose a Metropolis-Hastings sampling-based identifier renaming technique, named \fullmethod (\method), which generates adversarial examples for DL models specialized for source code processing. Our in-depth evaluation on a functionality classification benchmark demonstrates the effectiveness of \method in generating adversarial examples of source code. The higher robustness and performance enhanced through our adversarial training with \method further confirms the usefulness of DL models-based method for future fully automated source code processing.
Fully Homomorphic Encryption Encapsulated Difference Expansion for Reversible Data Hiding in Encrypted Domain

This paper proposes a fully homomorphic encryption encapsulated difference expansion (FHEE-DE) scheme for reversible data hiding in encrypted domain (RDH-ED). The homomorphic circuits and ciphertext operations are elaborated. Key-switching and bootstrapping techniques are introduced to control the ciphertext extension and decryption failure of homomorphic encryption. A key-switching based least-significant-bit (KS-LSB) data hiding method has been designed to realize data extraction directly from the encrypted domain without the private key. In application, the user first encrypts the plaintext and uploads ciphertext to the server. The server embeds additional data into the ciphertext by performing FHEE-DE data hiding and KS-LSB data hiding. Additional data can be extracted directly from the marked ciphertext by the server without the private key. The user owns the private key and can decrypt the marked ciphertext to obtain the marked plaintext. Then additional data or plaintext can be obtained from the marked plaintext by using the standard DE extraction or recovery. The server could also implement FHEE-DE recovery or extraction on the marked ciphertext to return the ciphertext of original plaintext or additional data to the user. Experimental results demonstrate that the embedding capacity and reversibility of the proposed scheme are superior to existing RDH-ED methods, and fully separability is achieved without reducing the security of encryption.
From Few to More: Large-Scale Dynamic Multiagent Curriculum Learning

A lot of efforts have been devoted to investigating how agents can learn effectively and achieve coordination in multiagent systems. However, it is still challenging in large-scale multiagent settings due to the complex dynamics between the environment and agents and the explosion of state-action space. In this paper, we design a novel Dynamic Multiagent Curriculum Learning (DyMA-CL) to solve large-scale problems by starting from learning on a multiagent scenario with a small size and progressively increasing the number of agents. We propose three transfer mechanisms across curricula to accelerate the learning process. Moreover, due to the fact that the state dimension varies across curricula, and existing network structures cannot be applied in such a transfer setting since their network input sizes are fixed. Therefore, we design a novel network structure called Dynamic Agent-number Network (DyAN) to handle the dynamic size of the network input. Experimental results show that DyMA-CL using DyAN greatly improves the performance of large-scale multiagent learning compared with state-of-the-art deep reinforcement learning approaches. We also investigate the influence of three transfer mechanisms across curricula through extensive simulations.
IPO: Interior-Point Policy Optimization under Constraints

In this paper, we study reinforcement learning (RL) algorithms to solve real-world decision problems with the objective of maximizing the long-term reward as well as satisfying cumulative constraints. We propose a novel first-order policy optimization method, Interior-point Policy Optimization (IPO), which augments the objective with logarithmic barrier functions, inspired by the interior-point method. Our proposed method is easy to implement with performance guarantees and can handle general types of cumulative multi-constraint settings. We conduct extensive evaluations to compare our approach with state-of-the-art baselines. Our algorithm outperforms the baseline algorithms, in terms of reward maximization and constraint satisfaction.