Reviewerly/exHarmony huggingface dataset:
Reducing Unintended Identity Bias in Russian Hate Speech Detection

Toxicity has become a grave problem for many online communities and has been growing across many languages, including Russian.Hate speech creates an environment of intimidation, discrimination, and may even incite some real-world violence.Both researchers and social platforms have been focused on developing models to detect toxicity in online communication for a while now.A common problem of these models is the presence of bias towards some words (e.g.woman, black, jew or женщина, черный, еврей) that are not toxic, but serve as triggers for the classifier due to model caveats.In this paper, we describe our efforts towards classifying hate speech in Russian, and propose simple techniques of reducing unintended bias, such as generating training data with language models using terms and words related to protected identities as context and applying word dropout to such words.
Incorporating Multimodal Information in Open-Domain Web Keyphrase Extraction

Open-domain Keyphrase extraction (KPE) on the Web is a fundamental yet complex NLP task with a wide range of practical applications within the field of Information Retrieval.In contrast to other document types, web page designs are intended for easy navigation and information finding.Effective designs encode within the layout and formatting signals that point to where the important information can be found.In this work, we propose a modeling approach that leverages these multi-modal signals to aid in the KPE task.In particular, we leverage both lexical and visual features (e.g., size, font, position) at the micro-level to enable effective strategy induction, and metalevel features that describe pages at a macrolevel to aid in strategy selection.Our evaluation demonstrates that a combination of effective strategy induction and strategy selection within this approach for the KPE task outperforms state-of-the-art models.A qualitative post-hoc analysis illustrates how these features function within the model.
Class imbalance techniques for high energy physics

A common problem in a high energy physics experiment is extracting a signal from a much larger background. Posed as a classification task, there is said to be an imbalance in the number of samples belonging to the signal class versus the number of samples from the background class. In this work we provide a brief overview of class imbalance techniques in a high energy physics setting. Two case studies are presented: (1) the measurement of the longitudinal polarization fraction in same-sign $WW$ scattering, and (2) the decay of the Higgs boson to charm-quark pairs.
Benchmarks of nonclassicality for qubit arrays

Abstract We present a set of practical benchmarks for N -qubit arrays that economically test the fidelity of achieving multi-qubit nonclassicality. The benchmarks are measurable correlators similar to two-qubit Bell correlators, and are derived from a particular set of geometric structures from the N -qubit Pauli group. These structures prove the Greenberger–Horne–Zeilinger (GHZ) theorem, while the derived correlators witness genuine N -partite entanglement and establish a tight lower bound on the fidelity of particular stabilizer state preparations. The correlators need only M ≤ N + 1 distinct measurement settings, as opposed to the 2 2 N − 1 settings that would normally be required to tomographically verify their associated stabilizer states. We optimize the measurements of these correlators for a physical array of qubits that can be nearest-neighbor-coupled using a circuit of controlled- Z gates with constant gate depth to form N -qubit linear cluster states. We numerically simulate the provided circuits for a realistic scenario with N = 3, …, 9 qubits, using ranges of T 1 energy relaxation times, T 2 dephasing times, and controlled- Z gate-fidelities consistent with Google’s 9-qubit superconducting chip. The simulations verify the tightness of the fidelity bounds and witness nonclassicality for all nine qubits, while also showing ample room for improvement in chip performance.
An Empirical Study on Model-agnostic Debiasing Strategies for Robust Natural Language Inference

The prior work on natural language inference (NLI) debiasing mainly targets at one or few known biases while not necessarily making the models more robust.In this paper, we focus on the model-agnostic debiasing strategies and explore how to (or is it possible to) make the NLI models robust to multiple distinct adversarial attacks while keeping or even strengthening the models' generalization power.We firstly benchmark prevailing neural NLI models including pretrained ones on various adversarial datasets.We then try to combat distinct known biases by modifying a mixture of experts (MoE) ensemble method (Clark et al., 2019) and show that it's nontrivial to mitigate multiple NLI biases at the same time, and that model-level ensemble method outperforms MoE ensemble method.We also perform data augmentation including text swap, word substitution and paraphrase and prove its efficiency in combating various (though not all) adversarial attacks at the same time.Finally, we investigate several methods to merge heterogeneous training data (1.35M) and perform model ensembling, which are straightforward but effective to strengthen NLI models.
Fluctuations in the detection of the HOM effect

Hong-Ou-Mandel (HOM) effect is known to be one of the main phenomena in quantum optics. It is believed that the effect occurs when two identical single-photon waves enter a 1:1 beam splitter, one in each input port. When the photons are identical, they will extinguish each other. In this work, it is shown that these fundamental provisions of the HOM interference may not always be fulfilled. One of the main elements of the HOM interferometer is the beam splitter, which has its own coefficients of reflection [Formula: see text] and transmission [Formula: see text]. Here we consider the general mechanism of the interaction of two photons in a beam splitter, which shows that in the HOM theory of the effect it is necessary to know (including when planning the experiment) not only [Formula: see text] and [Formula: see text], but also their root-mean-square fluctuations [Formula: see text], which arise due to the dependence of [Formula: see text] and [Formula: see text] on the frequencies where [Formula: see text] are the frequencies of the first and second photons, respectively. Under certain conditions, specifically when the dependence of the fluctuations [Formula: see text] and [Formula: see text] can be neglected and [Formula: see text] is chosen, the developed theory coincides with previously known results.
Quantifying entanglement of parametric down-converted states in all degrees of freedom

The amount of entanglement that exists in a parametric down-converted state is investigated in terms of all the degrees of freedom of the state. We quantify the amount of entanglement by the Schmidt number of the state, represented as a pure bipartite state by tagging the down-converted photons in terms of orthogonal states of polarization with the aid of type II phase-matching. To facilitate our calculations, we use a Wigner functional approach, which allows the incorporation of the full infinite dimensional spatiotemporal degrees of freedom. A quantitative example with reasonably achievable experimental conditions is considered to demonstrate that extremely large Schmidt numbers are achievable.
Towards Metric Temporal Answer Set Programming

We elaborate upon the theoretical foundations of a metric temporal extension of Answer Set Programming. In analogy to previous extensions of ASP with constructs from Linear Temporal and Dynamic Logic, we accomplish this in the setting of the logic of Here-and-There and its non-monotonic extension, called Equilibrium Logic. More precisely, we develop our logic on the same semantic underpinnings as its predecessors and thus use a simple time domain of bounded time steps. This allows us to compare all variants in a uniform framework and ultimately combine them in a common implementation.
Fine-tuning for multi-domain and multi-label uncivil language detection

Incivility is a problem on social media, and it comes in many forms (name-calling, vulgarity, threats, etc.) and domains (microblog posts, online news comments, Wikipedia edits, etc.).Training machine learning models to detect such incivility must handle the multilabel and multi-domain nature of the problem.We present a BERT-based model for incivility detection and propose several approaches for training it for multi-label and multi-domain datasets.We find that individual binary classifiers outperform a joint multi-label classifier, and that simply combining multiple domains of training data outperforms other recentlyproposed fine-tuning strategies.We also establish new state-of-the-art performance on several incivility detection datasets.
Developing a New Classifier for Automated Identification of Incivility in Social Media

Incivility is not only prevalent on online social media platforms, but also has concrete effects on individual users, online groups, the platforms themselves, and the society at large.Given the prevalence and effects of online incivility, and the challenges involved in humanbased incivility detection, it is urgent to develop validated and versatile automatic approaches to identifying uncivil posts and comments.This project advances both a neural, BERT-based classifier as well as a logistic regression classifier to identify uncivil comments.The classifier is trained on a dataset of Reddit posts, which are annotated for incivility, and further expanded using a combination of labeled data from Reddit and Twitter.Our best performing model achieves an F 1 of 0.802 on our Reddit test set.The final model is not only applicable across social media platforms and their distinct data structures, but also computationally versatile, and -as such -ready to be used on vast volumes of online data.All trained models and annotated data are made available to the research community.
Learning VAE-LDA Models with Rounded Reparameterization Trick

The introduction of VAE provides an efficient framework for the learning of generative models, including generative topic models.However, when the topic model is a Latent Dirichlet Allocation (LDA) model, a central technique of VAE, the reparameterization trick, fails to be applicable.This is because no reparameterization form of Dirichlet distributions is known to date that allows the use of the reparameterization trick.In this work, we propose a new method, which we call Rounded Reparameterization Trick (RRT), to reparameterize Dirichlet distributions for the learning of VAE-LDA models.This method, when applied to a VAE-LDA model, is shown experimentally to outperform the existing neural topic models on several benchmark datasets and on a synthetic dataset.
Protection of a Qubit via Subradiance: A Josephson Quantum Filter

The coupling between a superconducting qubit and a control line inevitably results in radiative decay of the qubit into the line. We propose a Josephson quantum filter (JQF), which protects the data qubit (DQ) from radiative decay through the control line without reducing the gate speed on DQ. JQF consists of a qubit strongly coupled to the control line to DQ, and its working principle is a subradiance effect characteristic to waveguide quantum electrodynamics setups. JQF is a passive circuit element and is therefore suitable for integration in a scalable superconducting qubit system.
Deep Learning for Distinguishing Computer Generated Images and Natural Images: A Survey

With the development of computer graphics, realistic computer graphics (CG) have become more and more common in our field of vision.This rendered image is invisible to the naked eye.How to effectively identify CG and natural images (NI) has been become a new issue in the field of digital forensics.In recent years, a series of deep learning network frameworks have shown great advantages in the field of images, which provides a good choice for us to solve this problem.This paper aims to track the latest developments and applications of deep learning in the field of CG and NI forensics in a timely manner.Firstly, it introduces the background of deep learning and the knowledge of convolutional neural networks.The purpose is to understand the basic model structure of deep learning applications in the image field, and then outlines the mainstream framework; secondly, it briefly introduces the application of deep learning in CG and NI forensics, and finally points out the problems of deep learning in this field and the prospects for the future.
On the Detection of Shilling Attacks in Federated Collaborative Filtering

Federated collaborative filtering (Fed-CF) is a variant of federated learning (FL) models, which can protect user privacy in recommender systems. In Fed-CF, the recommendation model is collectively trained across multiple decentralized clients by exchanging gradients only. However, the decentralized nature of Fed-CF makes it vulnerable to shilling attacks, which can be realized by inserting fake ratings of target items to distort recommendation results. Unfortunately, previous detection algorithms cannot work well in the FL framework, as all original data samples are not disclosed at all. In this paper, we are the first to systematically study the problem of shilling attacks in the context of federated learning, and propose an effective detection method called Federated Shilling Attack Detector (FSAD) to detect shilling attackers in Fed-CF. We first show the feasibility of shilling attacks in Fed-CF. Next, we dedicatedly design four novel features based on exchanged gradients among clients. By incorporating these gradient-based features, we train a semi-supervised Bayes classifier to identify shilling attackers effectively. Finally, we conduct extensive experiments based on real-world datasets to evaluate the performance of our proposed FSAD method. The experimental results show that FSAD can detect shilling attackers in Fed-CF with high accuracy, with the F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> value as high as 0.90 on the Netflix dataset, which approaches the performance of the optimal detector that utilizes complete private user information for detection.
Two efficient measurement device independent quantum dialogue protocols

Quantum dialogue is a process of two way secure and simultaneous communication using a single channel. Recently, a Measurement Device Independent Quantum Dialogue (MDI-QD) protocol has been proposed (Quantum Information Processing 16.12 (2017): 305). To make the protocol secure against information leakage, the authors have discarded almost half of the qubits remaining after the error estimation phase. In this paper, we propose two modified versions of the MDI-QD protocol such that the number of discarded qubits is reduced to almost one-fourth of the remaining qubits after the error estimation phase. We use almost half of their discarded qubits along with their used qubits to make our protocol more efficient in qubits count. We show that both of our protocols are secure under the same adversarial model given in MDI-QD protocol.
Transition probabilities in generalized quantum search Hamiltonian evolutions

A relevant problem in quantum computing concerns how fast a source state can be driven into a target state according to Schrödinger’s quantum mechanical evolution specified by a suitable driving Hamiltonian. In this paper, we study in detail the computational aspects necessary to calculate the transition probability from a source state to a target state in a continuous time quantum search problem defined by a multiparameter generalized time-independent Hamiltonian. In particular, quantifying the performance of a quantum search in terms of speed (minimum search time) and fidelity (maximum success probability), we consider a variety of special cases that emerge from the generalized Hamiltonian. In the context of optimal quantum search, we find it is possible to outperform, in terms of minimum search time, the well-known Farhi–Gutmann analog quantum search algorithm. In the context of nearly optimal quantum search, instead, we show it is possible to identify sub-optimal search algorithms capable of outperforming optimal search algorithms if only a sufficiently high success probability is sought. Finally, we briefly discuss the relevance of a tradeoff between speed and fidelity with emphasis on issues of both theoretical and practical importance to quantum information processing.
Unsupervised Distillation of Syntactic Information from Contextualized Word Representations

Contextualized word representations, such as ELMo and BERT, were shown to perform well on various semantic and syntactic task.In this work, we tackle the task of unsupervised disentanglement between semantics and structure in neural language representations: we aim to learn a transformation of the contextualized vectors, that discards the lexical semantics, but keeps the structural information.To this end, we automatically generate groups of sentences which are structurally similar but semantically different, and use metric-learning approach to learn a transformation that emphasizes the structural component that is encoded in the vectors.We demonstrate that our transformation clusters vectors in space by structural properties, rather than by lexical semantics.Finally, we demonstrate the utility of our distilled representations by showing that they outperform the original contextualized representations in a few-shot parsing setting.
Image Retrieval Based on Deep Feature Extraction and Reduction with Improved CNN and PCA

With the rapid development of information technology, the speed and efficiency of image retrieval are increasingly required in many fields, and a compelling image retrieval method is critical for the development of information.Feature extraction based on deep learning has become dominant in image retrieval due to their discrimination more complete, information more complementary and higher precision.However, the high-dimension deep features extracted by CNNs (convolutional neural networks) limits the retrieval efficiency and makes it difficult to satisfy the requirements of existing image retrieval.To solving this problem, the high-dimension feature reduction technology is proposed with improved CNN and PCA quadratic dimensionality reduction.Firstly, in the last layer of the classical networks, this study makes a well-designed DR-Module (dimensionality reduction module) to compress the number of channels of the feature map as much as possible, and ensures the amount of information.Secondly, the deep features are compressed again with PCA (Principal Components Analysis), and the compression ratios of the two dimensionality reductions are reduced, respectively.Therefore, the retrieval efficiency is dramatically improved.Finally, it is proved on the Cifar100 and Caltech101 datasets that the novel method not only improves the retrieval accuracy but also enhances the retrieval efficiency.Experimental results strongly demonstrate that the proposed method performs well in small and medium-sized datasets.
Comparison of Four Classification Algorithms for Facial Expression Recognition

Facial expression recognition (FER) has achieved an extreme role in research area since the 1990s. This paper provides a comparison approach for FER based on three feature selection methods which are correlation, gain ration, and information gain for determining the most distinguished features of face images using multi-classification algorithms which are multilayer perceptron, Naïve Bayes, decision tree, and K-nearest neighbor (KNN). These classifiers are used for the mission of expression recognition and for comparing their proportional performance. The main aim of the provided approach is to determine the most effective classifier based on minimum acceptable number of features by analyzing and comparing their performance. The provided approach has been applied on CK+ dataset. The experimental results show that KNN is proven to be better classifier with 91% accuracy using only 30 features.
Compact Optimization Algorithms with Re-Sampled Inheritance

Compact optimization algorithms are a class of Estimation of Distribution Algorithms (EDAs) characterized by extremely limited memory requirements (hence they are called “compact”). As all EDAs, compact algorithms build and update a probabilistic model of the distribution of solutions within the search space, as opposed to population-based algorithms that instead make use of an explicit population of solutions. In addition to that, to keep their memory consumption low, compact algorithms purposely employ simple probabilistic models that can be described with a small number of parameters. Despite their simplicity, compact algorithms have shown good performances on a broad range of benchmark functions and real-world problems. However, compact algorithms also come with some drawbacks, i.e. they tend to premature convergence and show poorer performance on non-separable problems. To overcome these limitations, here we investigate a possible algorithmic scheme obtained by combining compact algorithms with a non-disruptive restart mechanism taken from the literature, named Re-Sampled Inheritance (RI). The resulting compact algorithms with RI are tested on the CEC 2014 benchmark functions. The numerical results show on the one hand that the use of RI consistently enhances the performances of compact algorithms, still keeping a limited usage of memory. On the other hand, our experiments show that among the tested algorithms, the best performance is obtained by compact Differential Evolution with RI.
Pushing the Limits of AMR Parsing with Self-Learning

Meaning Representation (AMR) parsing has experienced a notable growth in performance in the last two years, due both to the impact of transfer learning and the development of novel architectures specific to AMR.At the same time, self-learning techniques have helped push the performance boundaries of other natural language processing applications, such as machine translation or question answering.In this paper, we explore different ways in which trained models can be applied to improve AMR parsing performance, including generation of synthetic text and AMR annotations as well as refinement of actions oracle.We show that, without any additional human annotations, these techniques improve an already performant parser and achieve state-ofthe-art results on AMR 1.0 and AMR 2.0.