Reviewerly/exHarmony huggingface dataset:
Generative machine learning with tensor networks: Benchmarks on near-term quantum computers

Noisy, intermediate-scale quantum (NISQ) computing devices have become an industrial reality in the last few years, and cloud-based interfaces to these devices are enabling the exploration of near-term quantum computing on a range of problems. As NISQ devices are too noisy for many of the algorithms with a known quantum advantage, discovering impactful applications for near-term devices is the subject of intense research interest. We explore a quantum-assisted machine learning (QAML) workflow using NISQ devices through the perspective of tensor networks (TNs), which offer a robust platform for designing resource-efficient and expressive machine learning models to be dispatched on quantum devices. In particular, we lay out a framework for designing and optimizing TN-based QAML models using classical techniques, and then compiling these models to be run on quantum hardware, with demonstrations for generative matrix product state (MPS) models. We put forth a generalized canonical form for MPS models that aids in compilation to quantum devices, and demonstrate greedy heuristics for compiling with a given topology and gate set that outperforms known generic methods in terms of the number of entangling gates, e.g., CNOTs, in some cases by an order of magnitude. We present an exactly solvable benchmark problem for assessing the performance of MPS QAML models and also present an application for the canonical MNIST handwritten digit dataset. The impacts of hardware topology and day-to-day experimental noise fluctuations on model performance are explored by analyzing both raw experimental counts and statistical divergences of inferred distributions. We also present parametric studies of depolarization and readout noise impacts on model performance using hardware simulators.61 MoreReceived 16 October 2020Revised 3 March 2021Accepted 7 March 2021DOI:https://doi.org/10.1103/PhysRevResearch.3.023010Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasMachine learningTechniquesTensor network methodsQuantum InformationStatistical Physics
Password policy characteristics and keystroke biometric authentication

Behavioural biometrics have the potential to provide an additional or alternative authentication mechanism to those involving a shared secret (i.e. a password). Keystroke timings are the focus of this study, where key press and release timings are acquired whilst monitoring a user typing a known phrase. Many studies exist in keystroke biometrics, but there is an absence of literature aiming to understand the relationship between characteristics of password policies and the potential of keystroke biometrics. Furthermore, benchmark datasets used in keystroke biometric research do not enable useful insights into the relationship between their capability and password policy. Herein, substitutions of uppercase, numeric, special characters, and their combination of passwords derived from English words are considered. Timings for 42 participants for the same 40 passwords are acquired. A matching system using the Manhattan distance measure with seven different feature sets is implemented, culminating in an Equal Error Rate of between 6% and 11% and accuracy values between 89% and 94%, demonstrating comparable accuracy to other threshold-based systems. Further analysis suggests that the best feature sets are those containing all timings and trigraph press to press. Evidence also suggests that phrases containing fewer characters have greater accuracy, except for those with special character substitutions.
Nonclassicality and entanglement criteria for bipartite optical fields characterized by quadratic detectors. II. Criteria based on probabilities

Numerous nonclassicality criteria based on the probabilities of experimental photocount or theoretical photon-number distributions are derived using several approaches. Relations among the derived criteria are revealed and the fundamental criteria are identified. They are grouped into parametric systems that allow the analysis of the nonclassicality from different points of view (``local'' nonclassicality, pairwise character of photon correlations, etc.). Considering their structure, the criteria may be divided into groups that differ in the power to resolve the nonclassicality. Quantification of the nonclassicality using the Lee nonclassicality depth and the nonclassicality counting parameter is discussed. The number of field modes used is identified as an important parameter that may cause unexpected results. An appropriate linear transformation of a photocount (photon-number) distribution into its $s$-ordered form, needed for the determination of the nonclassicality depth, is derived. For comparison, the derived criteria are applied both to an experimental photocount histogram of a twin beam and the reconstructed photon-number distributions with different levels of the noise.
Compositional splines for representation of density functions

In the context of functional data analysis, probability density functions as non-negative functions are characterized by specific properties of scale invariance and relative scale which enable to represent them with the unit integral constraint without loss of information. On the other hand, all these properties are a challenge when the densities need to be approximated with spline functions, including construction of the respective spline basis. The Bayes space methodology of density functions enables to express them as real functions in the standard $$L^2$$ space using the centered log-ratio transformation. The resulting functions satisfy the zero integral constraint. This is a key to propose a new spline basis, holding the same property, and consequently to build a new class of spline functions, called compositional splines, which can approximate probability density functions in a consistent way. The paper provides also construction of smoothing compositional splines and possible orthonormalization of the spline basis which might be useful in some applications. Finally, statistical processing of densities using the new approximation tool is demonstrated in case of simplicial functional principal component analysis with anthropometric data.
Interpreting plural predication: homogeneity and non-maximality

Plural definite descriptions across many languages display two well-known properties. First, they can give rise to so-called non-maximal readings, in the sense that they 'allow for exceptions' (Mary read the books on the reading list, in some contexts, can be judged true even if Mary didn't read all the books on the reading list). Second, while they tend to have a quasi-universal quantificational force in affirmative sentences ('quasi-universal' rather than simply 'universal' due to the possibility of exceptions we have just mentioned), they tend to be interpreted existentially in the scope of negation (a property often referred to as homogeneity, cf. Löbner in Linguist Philos 23:213-308, 2000). Building on previous works (in particular Krifka in Proceedings of SALT VI, Cornell University, pp 136-153, 1996 and Malamud in Semant Pragmat, 5:1-28, 2012), we offer a theory in which sentences containing plural definite expressions trigger a family of possible interpretations, and where general principles of language use account for their interpretation in various contexts and syntactic environments. Our theory solves a number of problems that these previous works encounter, and has broader empirical coverage in that it offers a precise analysis for sentences that display complex interactions between plural definites, quantifiers and bound variables, as well as for cases involving non-distributive predicates. The resulting proposal is briefly compared with an alternative proposal by Križ (Aspects of homogeneity in the semantics of natural language, University of Vienna, 2015), which has similar coverage but is based on a very different architecture and sometimes makes subtly different predictions.
Interpreting Predictions of NLP Models

Although neural NLP models are highly expressive and empirically successful, they also systematically fail in counterintuitive ways and are opaque in their decision-making process.This tutorial will provide a background on interpretation techniques, i.e., methods for explaining the predictions of NLP models.We will first situate example-specific interpretations in the context of other ways to understand models (e.g., probing, dataset analyses).Next, we will present a thorough study of example-specific interpretations, including saliency maps, input perturbations (e.g., LIME, input reduction), adversarial attacks, and influence functions.Alongside these descriptions, we will walk through source code that creates and visualizes interpretations for a diverse set of NLP tasks.Finally, we will discuss open problems in the field, e.g., evaluating, extending, and improving interpretation methods.The tutorial slides and the accompanying code is available online at https: //www.ericswallace.com/interpretability.
Quantum speed limit based on the bound of Bures angle

Abstract In this paper, we investigate the unified bound of quantum speed limit time in open systems based on the modified Bures angle. This bound is applied to the damped Jaynes-Cummings model and the dephasing model, and the analytical quantum speed limit time is obtained for both models. As an example, the maximum coherent qubit state with white noise is chosen as the initial states for the damped Jaynes-Cummings model. It is found that the quantum speed limit time in both the non-Markovian and the Markovian regimes can be decreased by the white noise compared with the pure state. In addition, for the dephasing model, we find that the quantum speed limit time is not only related to the coherence of initial state and non-Markovianity, but also dependent on the population of initial excited state.
Unsupervised Cross-Lingual Part-of-Speech Tagging for Truly Low-Resource Scenarios

We describe a fully unsupervised cross-lingual transfer approach for part-of-speech (POS) tagging under a truly low resource scenario.We assume access to parallel translations between the target language and one or more source languages for which POS taggers are available.We use the Bible as parallel data in our experiments: small size, out-of-domain and covering many diverse languages.Our approach innovates in three ways: 1) a robust approach of selecting training instances via cross-lingual annotation projection that exploits best practices of unsupervised type and token constraints, word-alignment confidence and density of projected POS, 2) a Bi-LSTM architecture that uses contextualized word embeddings, affix embeddings and hierarchical Brown clusters, and 3) an evaluation on 12 diverse languages in terms of language family and morphological typology.In spite of the use of limited and out-of-domain parallel data, our experiments demonstrate significant improvements in accuracy over previous work.In addition, we show that using multi-source information, either via projection or output combination, improves the performance for most target languages.
Classifier‐Guided Visual Correction of Noisy Labels for Image Classification Tasks

Abstract Training data plays an essential role in modern applications of machine learning. However, gathering labeled training data is time‐consuming. Therefore, labeling is often outsourced to less experienced users, or completely automated. This can introduce errors, which compromise valuable training data, and lead to suboptimal training results. We thus propose a novel approach that uses the power of pretrained classifiers to visually guide users to noisy labels, and let them interactively check error candidates, to iteratively improve the training data set. To systematically investigate training data, we propose a categorization of labeling errors into three different types, based on an analysis of potential pitfalls in label acquisition processes. For each of these types, we present approaches to detect, reason about, and resolve error candidates, as we propose measures and visual guidance techniques to support machine learning users. Our approach has been used to spot errors in well‐known machine learning benchmark data sets, and we tested its usability during a user evaluation. While initially developed for images, the techniques presented in this paper are independent of the classification algorithm, and can also be extended to many other types of training data.
An External Parameter Independent Novel Cost Function for Evolving Bijective Substitution-Boxes

The property of nonlinearity has high importance for the design of strong substitution boxes. Therefore, the development of new techniques to produce substitution boxes with high values of nonlinearity is essential. Many research papers have shown that optimization algorithms are an efficient technique to obtain good solutions. However, there is no reference in the public literature showing that a heuristic method obtains optimal nonlinearity unless seeded with optimal initial solutions. Moreover, the majority of papers with the best nonlinearity reported for pseudo-random seeding of the algorithm(s) often achieve their results with the help of some cost function(s) over the Walsh–Hadamard spectrum of the substitution. In the sense, we proposed to present, in this paper, a novel external parameter independent cost function for evolving bijective s-boxes of high nonlinearity, which is highly correlated to this property. Several heuristic approaches including GaT (genetic and tree), LSA (local search algorithm), and the Hill Climbing algorithm have been investigated to assess the performance of evolved s-boxes. A performance comparison has been done to show the advantages of our new cost function, with respect to cost functions for s-boxes like Clark’s and Picek’s cost functions.
Secure Outsourcing and Sharing of Cloud Data Using a User-Side Encrypted File System

Cloud computing is an emerging paradigm that aims to provide computing resources, massive data storage capacity and, flexible data sharing services. The explosive growth of data produced persuade business and users, driven by the cloud-top features, to outsource their data to the cloud storage systems. However, the confidentiality and integrity of outsourced sensitive data in remote cloud servers are becoming a major concern. Data must be encrypted prior to storing it in the, potentially untrustworthy cloud. Existing traditional encryption systems impose a heavy burden of managing files and encryption operations on data owners. They suffer from serious security, efficiency, and usability issues, and some schemes are inappropriate for protecting cloud data. In this paper, we introduce OutFS, a user-side encrypted file system, focused on providing a transparent encryption for stored and shared outsourced data. In OutFS, we utilize a hybrid encryption scheme structure based on symmetric and asymmetric methods. The key management is conveniently designed. In order to ensure robust data sharing security, the identity-based encryption scheme (IBE) is integrated with OutFS. OutFS is designed to preserve the integrity of outsourced file data and file system data structure. Analysis of performance and experimental results show that OutFS is efficient. It can achieve an average throughput of 8.8 MB/sec, and 10.5 MB/sec for writing and reading outsourced files. Security analysis indicates that OutFS is extremely secure and robust against attacks such as brute-force, eavesdropping, man-in-the-middle, and offline-dictionary attacks.
Photonic two-particle quantum walks in Su–Schrieffer–Heeger lattices

We report on the experimental demonstration of two-photon quantum walks at the edge of a photonic Su–Schrieffer–Heeger lattice and compare them to those observed when launching photons at the edge of a homogeneous lattice. Whereas at the topological edge, one of the photons primarily remains close to the edge, both photons penetrate freely from the trivial edge into the bulk. This behavior manifests also in the average inter-particle distance, which is significantly larger at the topological edge. Hence, for a given propagation length, the entangled two-photon state launched at the topological edge extends over a wider domain of the lattice.
Cold-Start and Interpretability: Turning Regular Expressions into Trainable Recurrent Neural Networks

Neural networks can achieve impressive performance on many natural language processing applications, but they typically need large labeled data for training and are not easily interpretable.On the other hand, symbolic rules such as regular expressions are interpretable, require no training, and often achieve decent accuracy; but rules cannot benefit from labeled data when available and hence underperform neural networks in rich-resource scenarios.In this paper, we propose a type of recurrent neural networks called FA-RNNs that combine the advantages of neural networks and regular expression rules.An FA-RNN can be converted from regular expressions and deployed in zero-shot and cold-start scenarios.It can also utilize labeled data for training to achieve improved prediction accuracy.After training, an FA-RNN often remains interpretable and can be converted back into regular expressions.We apply FA-RNNs to text classification and observe that FA-RNNs significantly outperform previous neural approaches in both zeroshot and low-resource settings and remain very competitive in rich-resource settings.
Data Security and Privacy in Bitcoin System: A Survey

To date, bitcoin has been the most successful application of blockchain technology and has received considerable attention from both industry and academia. Bitcoin is an electronic payment system based on cryptography rather than on credit. Regardless of whether people are in the same city or country, bitcoin can be sent by any one person to any other person when they reach an agreement. The market value of bitcoin has been rising since its advent in 2009, and its current market value is US160 billion. Since its development, bitcoin itself has exposed many problems and is facing challenges from all the sectors of society; therefore, adversaries may use bitcoin’s weakness to make considerable profits. This survey presents an overview and detailed investigation of data security and privacy in bitcoin system. We examine the studies in the literature/Web in two categories: 1) analyses of the attacks to the privacy, availability, and consistency of bitcoin data and 2) summaries of the countermeasures for bitcoin data security. Based on the literature/Web, we list and describe the research methods and results for the two categories. We compare the performance of these methods and illustrate the relationship between the performance and the methods. Moreover, we present several important open research directions to identify the follow-up studies in this area.
Learning to Learn to Disambiguate: Meta-Learning for Few-Shot Word Sense Disambiguation

The success of deep learning methods hinges on the availability of large training datasets annotated for the task of interest.In contrast to human intelligence, these methods lack versatility and struggle to learn and adapt quickly to new tasks, where labeled data is scarce.Metalearning aims to solve this problem by training a model on a large number of few-shot tasks, with an objective to learn new tasks quickly from a small number of examples.In this paper, we propose a meta-learning framework for few-shot word sense disambiguation (WSD), where the goal is to learn to disambiguate unseen words from only a few labeled instances.Meta-learning approaches have so far been typically tested in an N -way, K-shot classification setting where each task has N classes with K examples per class.Owing to its nature, WSD deviates from this controlled setup and requires the models to handle a large number of highly unbalanced classes.We extend several popular meta-learning approaches to this scenario, and analyze their strengths and weaknesses in this new challenging setting.
Using Transfer-based Language Models to Detect Hateful and Offensive Language Online

Distinguishing hate speech from non-hate offensive language is challenging, as hate speech not always includes offensive slurs and offensive language not always express hate.Here, four deep learners based on the Bidirectional Encoder Representations from Transformers (BERT), with either general or domain-specific language models, were tested against two datasets containing tweets labelled as either 'Hateful', 'Normal' or 'Offensive'.The results indicate that the attention-based models profoundly confuse hate speech with offensive and normal language.However, the pre-trained models outperform state-of-the-art results in terms of accurately predicting the hateful instances.
A Distributed Trust Framework for Privacy-Preserving Machine Learning

When training a machine learning model, it is standard procedure for the researcher to have full knowledge of both the data and model. However, this engenders a lack of trust between data owners and data scientists. Data owners are justifiably reluctant to relinquish control of private information to third parties. Privacy-preserving techniques distribute computation in order to ensure that data remains in the control of the owner while learning takes place. However, architectures distributed amongst multiple agents introduce an entirely new set of security and trust complications, including data poisoning and model theft. This paper outlines a distributed infrastructure which can be used to facilitate peer-to-peer trust between entities; collaboratively performing a privacy-preserving workflow. Our outlined prototype enables the initialisation of industry gatekeepers and governance bodies as credential issuers under a certain application domain. Before participating in the distributed learning workflow, malicious actors must first negotiate valid credentials from these gatekeepers. We detail a proof of concept using Hyperledger Aries, Decentralised Identifiers (DIDs) and Verifiable Credentials (VCs) to establish a distributed trust architecture during a privacy-preserving machine learning experiment. Specifically, we utilise secure and authenticated DID communication channels in order to facilitate a federated learning workflow related to mental health care data.
BiTeM at WNUT 2020 Shared Task-1: Named Entity Recognition over Wet Lab Protocols using an Ensemble of Contextual Language Models

Recent improvements in machine-reading technologies attracted much attention to automation problems and their possibilities.In this context, WNUT 2020 introduces a Name Entity Recognition (NER) task based on wet laboratory procedures.In this paper, we present a 3-step method based on deep neural language models that reported the best overall exact match F 1 -score (77.99%) of the competition.By fine-tuning 10 times, 10 different pretrained language models, this work shows the advantage of having more models in an ensemble based on a majority of votes strategy.On top of that, having 100 different models allowed us to analyse the combinations of ensemble that demonstrated the impact of having multiple pretrained models versus fine-tuning a pretrained model multiple times.