Reviewerly/exHarmony huggingface dataset:
Lightweight Certificate-Based Public/Private Auditing Scheme Based on Bilinear Pairing for Cloud Storage

Ensuring the integrity of outsourced data is one of several functional requirements in cloud storage services, although designing an effective and secure public auditing scheme remains an ongoing research challenge. For example, a number of existing schemes are designed based on symmetric bilinear pairing, which are computationally expensive. While certificate-based auditing schemes can simplify certificate management and mitigate key escrow concern, such schemes are not popular in the literature. Therefore, we propose a lightweight certificate-based public/private auditing scheme based on asymmetric bilinear pairing for cloud storage. Specifically, we describe a new method of designing lightweight auditing schemes. Additionally, our proposed scheme is proven to be secure in the random oracle model. We then implement our scheme by using type D curve of the pairing-based library. The findings demonstrate that our auditing scheme significantly reduces client's computation cost in the tag-generation (TagGen) phase, particularly in comparison to several other competing schemes. For example, when the block size is 10 KB, our on-line computation cost in the TagGen phase takes only 0.45 seconds, while it requires at least 62.83 seconds in other schemes. Furthermore, the findings show that when the number of blocks is fixed, the on-line computation cost in our TagGen phase is constant despite varying file sizes.
Implicit Bias of Gradient Descent based Adversarial Training on Separable Data

Adversarial training is a principled approach for training robust neural networks. Despite of tremendous successes in practice, its theoretical properties still remain largely unexplored. In this paper, we provide new theoretical insights of gradient descent based adversarial training by studying its computational properties, specifically on its implicit bias. We take the binary classification task on linearly separable data as an illustrative example, where the loss asymptotically attains its infimum as the parameter diverges to infinity along certain directions. Specifically, we show that for any fixed iteration $T$, when the adversarial perturbation during training has proper bounded L2 norm, the classifier learned by gradient descent based adversarial training converges in direction to the maximum L2 norm margin classifier at the rate of $O(1/\sqrt{T})$, significantly faster than the rate $O(1/\log T}$ of training with clean data. In addition, when the adversarial perturbation during training has bounded Lq norm, the resulting classifier converges in direction to a maximum mixed-norm margin classifier, which has a natural interpretation of robustness, as being the maximum L2 norm margin classifier under worst-case bounded Lq norm perturbation to the data. Our findings provide theoretical backups for adversarial training that it indeed promotes robustness against adversarial perturbation.
Applying the big bang-big crunch metaheuristic to large-sized operational problems

In this study, we present an investigation of comparing the capability of a big bang-big crunch metaheuristic (BBBC) for managing operational problems including combinatorial optimization problems. The BBBC is a product of the evolution theory of the universe in physics and astronomy. Two main phases of BBBC are the big bang and the big crunch. The big bang phase involves the creation of a population of random initial solutions, while in the big crunch phase these solutions are shrunk into one elite solution exhibited by a mass center. This study looks into the BBBC’s effectiveness in assignment and scheduling problems. Where it was enhanced by incorporating an elite pool of diverse and high quality solutions; a simple descent heuristic as a local search method; implicit recombination; Euclidean distance; dynamic population size; and elitism strategies. Those strategies provide a balanced search of diverse and good quality population. The investigation is conducted by comparing the proposed BBBC with similar metaheuristics. The BBBC is tested on three different classes of combinatorial optimization problems; namely, quadratic assignment, bin packing, and job shop scheduling problems. Where the incorporated strategies have a greater impact on the BBBC's performance. Experiments showed that the BBBC maintains a good balance between diversity and quality which produces high-quality solutions, and outperforms other identical metaheuristics (e.g. swarm intelligence and evolutionary algorithms) reported in the literature.
Modality-Balanced Models for Visual Dialogue

The Visual Dialog task requires a model to exploit both image and conversational context information to generate the next response to the dialogue. However, via manual analysis, we find that a large number of conversational questions can be answered by only looking at the image without any access to the context history, while others still need the conversation context to predict the correct answers. We demonstrate that due to this reason, previous joint-modality (history and image) models over-rely on and are more prone to memorizing the dialogue history (e.g., by extracting certain keywords or patterns in the context information), whereas image-only models are more generalizable (because they cannot memorize or extract keywords from history) and perform substantially better at the primary normalized discounted cumulative gain (NDCG) task metric which allows multiple correct answers. Hence, this observation encourages us to explicitly maintain two models, i.e., an image-only model and an image-history joint model, and combine their complementary abilities for a more balanced multimodal model. We present multiple methods for this integration of the two models, via ensemble and consensus dropout fusion with shared parameters. Empirically, our models achieve strong results on the Visual Dialog challenge 2019 (rank 3 on NDCG and high balance across metrics), and substantially outperform the winner of the Visual Dialog challenge 2018 on most metrics.
Improving Domain-Adapted Sentiment Classification by Deep Adversarial Mutual Learning

Domain-adapted sentiment classification refers to training on a labeled source domain to well infer document-level sentiment on an unlabeled target domain. Most existing relevant models involve a feature extractor and a sentiment classifier, where the feature extractor works towards learning domain-invariant features from both domains, and the sentiment classifier is trained only on the source domain to guide the feature extractor. As such, they lack a mechanism to use sentiment polarity lying in the target domain. To improve domain-adapted sentiment classification by learning sentiment from the target domain as well, we devise a novel deep adversarial mutual learning approach involving two groups of feature extractors, domain discriminators, sentiment classifiers, and label probers. The domain discriminators enable the feature extractors to obtain domain-invariant features. Meanwhile, the label prober in each group explores document sentiment polarity of the target domain through the sentiment prediction generated by the classifier in the peer group, and guides the learning of the feature extractor in its own group. The proposed approach achieves the mutual learning of the two groups in an end-to-end manner. Experiments on multiple public datasets indicate our method obtains the state-of-the-art performance, validating the effectiveness of mutual learning through label probers.
Weighted Automata Extraction from Recurrent Neural Networks via Regression on State Spaces

We present a method to extract a weighted finite automaton (WFA) from a recurrent neural network (RNN). Our method is based on the WFA learning algorithm by Balle and Mohri, which is in turn an extension of Angluin's classic L* algorithm. Our technical novelty is in the use of regression methods for the so-called equivalence queries, thus exploiting the internal state space of an RNN to prioritize counterexample candidates. This way we achieve a quantitative/weighted extension of the recent work by Weiss, Goldberg and Yahav that extracts DFAs. We experimentally evaluate the accuracy, expressivity and efficiency of the extracted WFAs.
Ledgerdata Refiner: A Powerful Ledger Data Query Platform for Hyperledger Fabric

Blockchain is one of the most popular distributed ledger technologies. It can solve the trust issue among enterprises. Hyperledger Fabric is a permissioned blockchain aiming at enterprise-grade business applications. However, compared to traditional distributed database solutions, one issue of blockchain based application development is the limited data access. For Fabric, the ledger data can only be retrieved by limited interfaces provided by Fabric SDKs or chaincode. In order to meet the requirements of data query and provide flexible query functions for real applications built on Fabric, this paper proposed a ledger data query platform called Ledgerdata Refiner. With ledger data analysis middleware, we provide sufficient interfaces for users to retrieve block or transaction efficiently. It is also able to track historical operations for any specific state. In addition, schemas of ledger state have been analyzed and clustered, which enable users to perform rich queries against ledger data. Finally, we validate the effectiveness of our query platform on a real application.
Label Enhancement with Sample Correlations via Low-Rank Representation

Compared with single-label and multi-label annotations, label distribution describes the instance by multiple labels with different intensities and accommodates to more-general conditions. Nevertheless, label distribution learning is unavailable in many real-world applications because most existing datasets merely provide logical labels. To handle this problem, a novel label enhancement method, Label Enhancement with Sample Correlations via low-rank representation, is proposed in this paper. Unlike most existing methods, a low-rank representation method is employed so as to capture the global relationships of samples and predict implicit label correlation to achieve label enhancement. Extensive experiments on 14 datasets demonstrate that the algorithm accomplishes state-of-the-art results as compared to previous label enhancement baselines.
DPDnet: A robust people detector using deep learning with an overhead depth camera

This paper proposes a deep learning-based method to detect multiple people from a single overhead depth image with high precision. Our neural network, called DPDnet, is composed by two fully-convolutional encoder-decoder blocks built with residual layers. The main block takes a depth image as input and generates a pixel-wise confidence map, where each detected person in the image is represented by a Gaussian-like distribution, The refinement block combines the depth image and the output from the main block, to refine the confidence map. Both blocks are simultaneously trained end-to-end using depth images and ground truth head position labels. The paper provides a rigorous experimental comparison with some of the best methods of the state-of-the-art, being exhaustively evaluated in different publicly available datasets. DPDnet proves to outperform all the evaluated methods with statistically significant differences, and with accuracies that exceed 99%. The system was trained on one of the datasets (generated by the authors and available to the scientific community) and evaluated in the others without retraining, proving also to achieve high accuracy with varying datasets and experimental conditions. Additionally, we made a comparison of our proposal with other CNN-based alternatives that have been very recently proposed in the literature, obtaining again very high performance. Finally, the computational complexity of our proposal is shown to be independent of the number of users in the scene and runs in real time using conventional GPUs.
Adapting Language Models for Non-Parallel Author-Stylized Rewriting

Given the recent progress in language modeling using Transformer-based neural models and an active interest in generating stylized text, we present an approach to leverage the generalization capabilities of a language model to rewrite an input text in a target author's style. Our proposed approach adapts a pre-trained language model to generate author-stylized text by fine-tuning on the author-specific corpus using a denoising autoencoder (DAE) loss in a cascaded encoder-decoder framework. Optimizing over DAE loss allows our model to learn the nuances of an author's style without relying on parallel data, which has been a severe limitation of the previous related works in this space. To evaluate the efficacy of our approach, we propose a linguistically-motivated framework to quantify stylistic alignment of the generated text to the target author at lexical, syntactic and surface levels. The evaluation framework is both interpretable as it leads to several insights about the model, and self-contained as it does not rely on external classifiers, e.g. sentiment or formality classifiers. Qualitative and quantitative assessment indicates that the proposed approach rewrites the input text with better alignment to the target style while preserving the original content better than state-of-the-art baselines.
Efficient Automatic CASH via Rising Bandits

The Combined Algorithm Selection and Hyperparameter optimization (CASH) is one of the most fundamental problems in Automatic Machine Learning (AutoML). The existing Bayesian optimization (BO) based solutions turn the CASH problem into a Hyperparameter Optimization (HPO) problem by combining the hyperparameters of all machine learning (ML) algorithms, and use BO methods to solve it. As a result, these methods suffer from the low-efficiency problem due to the huge hyperparameter space in CASH. To alleviate this issue, we propose the alternating optimization framework, where the HPO problem for each ML algorithm and the algorithm selection problem are optimized alternately. In this framework, the BO methods are used to solve the HPO problem for each ML algorithm separately, incorporating a much smaller hyperparameter space for BO methods. Furthermore, we introduce Rising Bandits, a CASH-oriented Multi-Armed Bandits (MAB) variant, to model the algorithm selection in CASH. This framework can take the advantages of both BO in solving the HPO problem with a relatively small hyperparameter space and the MABs in accelerating the algorithm selection. Moreover, we further develop an efficient online algorithm to solve the Rising Bandits with provably theoretical guarantees. The extensive experiments on 30 OpenML datasets demonstrate the superiority of the proposed approach over the competitive baselines.
Joint Parsing and Generation for Abstractive Summarization

Sentences produced by abstractive summarization systems can be ungrammatical and fail to preserve the original meanings, despite being locally fluent. In this paper we propose to remedy this problem by jointly generating a sentence and its syntactic dependency parse while performing abstraction. If generating a word can introduce an erroneous relation to the summary, the behavior must be discouraged. The proposed method thus holds promise for producing grammatical sentences and encouraging the summary to stay true-to-original. Our contributions of this work are twofold. First, we present a novel neural architecture for abstractive summarization that combines a sequential decoder with a tree-based decoder in a synchronized manner to generate a summary sentence and its syntactic parse. Secondly, we describe a novel human evaluation protocol to assess if, and to what extent, a summary remains true to its original meanings. We evaluate our method on a number of summarization datasets and demonstrate competitive results against strong baselines.
Ultra Fine-Grained Image Semantic Embedding

"How to learn image embeddings that capture fine-grained semantics based on the instance of an image?" "Is it possible for such embeddings to further understand image semantics closer to humans' perception?" In this paper, we present, Graph-Regularized Image Semantic Embedding (Graph-RISE), a web-scale neural graph learning framework deployed at Google, which allows us to train image embeddings to discriminate an unprecedented O(40M) ultra-fine-grained semantic labels. The proposed Graph-RISE outperforms state-of-the-art image embedding algorithms on several evaluation tasks, including kNN search and triplet ranking: the accuracy is improved by approximately 2X on the ImageNet dataset and by more than 5X on the iNaturalist dataset. Qualitatively, image retrieval from one billion images based on the proposed Graph-RISE effectively captures semantics and, compared to the state-of-the-art, differentiates nuances at levels that are closer to human-perception.
Integrating Deep Learning with Logic Fusion for Information Extraction

Information extraction (IE) aims to produce structured information from an input text, e.g., Named Entity Recognition and Relation Extraction. Various attempts have been proposed for IE via feature engineering or deep learning. However, most of them fail to associate the complex relationships inherent in the task itself, which has proven to be especially crucial. For example, the relation between 2 entities is highly dependent on their entity types. These dependencies can be regarded as complex constraints that can be efficiently expressed as logical rules. To combine such logic reasoning capabilities with learning capabilities of deep neural networks, we propose to integrate logical knowledge in the form of first-order logic into a deep learning system, which can be trained jointly in an end-to-end manner. The integrated framework is able to enhance neural outputs with knowledge regularization via logic rules, and at the same time update the weights of logic rules to comply with the characteristics of the training data. We demonstrate the effectiveness and generalization of the proposed model on multiple IE tasks.
Visual Tactile Fusion Object Clustering

Object clustering, aiming at grouping similar objects into one cluster with an unsupervised strategy, has been extensively-studied among various data-driven applications. However, most existing state-of-the-art object clustering methods (e.g., single-view or multi-view clustering methods) only explore visual information, while ignoring one of most important sensing modalities, i.e., tactile information which can help capture different object properties and further boost the performance of object clustering task. To effectively benefit both visual and tactile modalities for object clustering, in this paper, we propose a deep Auto-Encoder-like Non-negative Matrix Factorization framework for visual-tactile fusion clustering. Specifically, deep matrix factorization constrained by an under-complete Auto-Encoder-like architecture is employed to jointly learn hierarchical expression of visual-tactile fusion data, and preserve the local structure of data generating distribution of visual and tactile modalities. Meanwhile, a graph regularizer is introduced to capture the intrinsic relations of data samples within each modality. Furthermore, we propose a modality-level consensus regularizer to effectively align the visual and tactile data in a common subspace in which the gap between visual and tactile data is mitigated. For the model optimization, we present an efficient alternating minimization strategy to solve our proposed model. Finally, we conduct extensive experiments on public datasets to verify the effectiveness of our framework.
Generative-Discriminative Complementary Learning

The majority of state-of-the-art deep learning methods are discriminative approaches, which model the conditional distribution of labels given inputs features. The success of such approaches heavily depends on high-quality labeled instances, which are not easy to obtain, especially as the number of candidate classes increases. In this paper, we study the complementary learning problem. Unlike ordinary labels, complementary labels are easy to obtain because an annotator only needs to provide a yes/no answer to a randomly chosen candidate class for each instance. We propose a generative-discriminative complementary learning method that estimates the ordinary labels by modeling both the conditional (discriminative) and instance (generative) distributions. Our method, we call Complementary Conditional GAN (CCGAN), improves the accuracy of predicting ordinary labels and is able to generate high-quality instances in spite of weak supervision. In addition to the extensive empirical studies, we also theoretically show that our model can retrieve the true conditional distribution from the complementarily-labeled data.
Coherent rotations of qubits within a surface ion-trap quantum computer

We describe, realize, and experimentally investigate a method to perform physical rotations of ion chains, trapped in a segmented surface Paul trap, as a building block for large-scale quantum computational sequences. Control of trapping potentials is achieved by parametrizing electrode voltages in terms of spherical harmonic potentials. Voltage sequences that enable crystal rotations are numerically obtained by optimizing time-dependent ion positions and motional frequencies, taking into account the effect of electrical filters in our setup. We minimize rotation-induced heating by expanding the sequences into Fourier components, and optimizing the resulting parameters with a machine-learning approach. Optimized sequences rotate Ca+40–Ca+40 crystals with axial heating rates of Δn¯com=0.6(−2)(+3) and Δn¯str=3.9(5) phonons per rotation for the common and stretch modes, at mode frequencies of 1.24 and 2.15 MHz. Qubit coherence loss is 0.2(2)% per rotation. We also investigate rotations of mixed species crystals (Ca+40–Sr+88) and achieve unity success rate.Received 20 February 2020Revised 16 July 2020Accepted 16 July 2020DOI:https://doi.org/10.1103/PhysRevA.102.022611©2020 American Physical SocietyPhysics Subject Headings (PhySH)Research AreasCoherent controlQuantum algorithmsPhysical SystemsTrapped ionsTechniquesAtom & ion trapping & guidingAtomic, Molecular & OpticalQuantum Information
Formal Verification of a Hybrid Machine Learning-Based Fault Prediction Model in Internet of Things Applications

By increasing the complexity of the Internet of Things (IoT) applications, fault prediction become an important challenge in interactions between human, and smart devices. Fault prediction is one of the key factors to achieve better arranging the IoT applications. Most of the current research studies evaluated the fault prediction methods using simulation environments. However, formal verification of the correctness of a fault prediction method has not been reported yet. This paper presents a behavioral modeling and formal verification of a hybrid machine learning-based fault prediction model with Multi-Layer Perceptron (MLP) and Particle Swarm Optimization (PSO) algorithms. In particular, the PSO is used for feature selection. Then, the fault prediction is considered as a behavior to be verified formally. The fault prediction behavior is divided into two types of behaviors: dimension reduction behavior and prediction behavior. For each of the behaviors, one formal model is designed. The behavioral models designed are mapped into the Labeled Transition System (LTS). The Process Analysis Toolkit (PAT) model checker is employed to evaluate the behavioral models. The accuracy of the fault prediction method is done by some existing specifications such as deadlock-free and reachability properties in terms of linear temporal logic formulas. Also, the verification of the fault prediction behaviors is used to detect the defect metrics of information-centric IoT applications. Experimental results showed that our proposed verification method has minimum verification time and memory usage for evaluating critical specification rules than other research studies.