Reviewerly/exHarmony huggingface dataset:
Quantum state preparation for coupled period tripling oscillators

An anharmonic oscillator driven at triple its eigenfrequency has three equivalent vibrational states in addition to a quiet state. In an array of coupled oscillators the degeneracy of the vibrational states of individual oscillators is broken. This article investigates the quantum phase transition to the broken-symmetry state. It also studies the steady state of a single quantum dissipative oscillator.
It’s not Greek to mBERT: Inducing Word-Level Translations from Multilingual BERT

Recent works have demonstrated that multilingual BERT (mBERT) learns rich cross-lingual representations, that allow for transfer across languages.We study the word-level translation information embedded in mBERT and present two simple methods that expose remarkable translation capabilities with no finetuning.The results suggest that most of this information is encoded in a non-linear way, while some of it can also be recovered with purely linear tools.As part of our analysis, we test the hypothesis that mBERT learns representations which contain both a languageencoding component and an abstract, crosslingual component, and explicitly identify an empirical language-identity subspace within mBERT representations.
Finding Cracks in Shields: On the Security of Control Flow Integrity Mechanisms

Control-flow integrity (CFI) is a promising technique to mitigate control-flow hijacking attacks. In the past decade, dozens of CFI mechanisms have been proposed by researchers. Despite the claims made by themselves, the security promises of these mechanisms have not been carefully evaluated, and thus are questionable.
Adaptive Video Highlight Detection by Learning from User History

Recently, there is an increasing interest in highlight detection research where the goal is to create a short duration video from a longer video by extracting its interesting moments. However, most existing methods ignore the fact that the definition of video highlight is highly subjective. Different users may have different preferences of highlight for the same input video. In this paper, we propose a simple yet effective framework that learns to adapt highlight detection to a user by exploiting the user's history in the form of highlights that the user has previously created. Our framework consists of two sub-networks: a fully temporal convolutional highlight detection network H that predicts highlight for an input video and a history encoder network M for user history. We introduce a newly designed temporal-adaptive instance normalization (T-AIN) layer to H where the two sub-networks interact with each other. T-AIN has affine parameters that are predicted from M based on the user history and is responsible for the user-adaptive signal to H. Extensive experiments on a large-scale dataset show that our framework can make more accurate and user-specific highlight predictions.
A stochastic approach to unitary coupled cluster

Unitary coupled cluster (UCC), originally developed as a variational alternative to the popular traditional coupled cluster method, has seen a resurgence as a functional form for use on quantum computers. However, the number of excitors present in the Ansatz often presents a barrier to implementation on quantum computers. Given the natural sparsity of wavefunctions obtained from quantum Monte Carlo methods, we consider here a stochastic solution to the UCC problem. Using the coupled cluster Monte Carlo framework, we develop cluster selection schemes that capture the structure of the UCC wavefunction, as well as its Trotterized approximation, and use these to solve the corresponding projected equations. Due to the fast convergence of the equations with order in the cluster expansion, this approach scales polynomially with the size of the system. Unlike traditional UCC implementations, our approach naturally produces a non-variational estimator for the energy in the form of the projected energy. For unitary coupled cluster singles and doubles (UCCSD) in small systems, we find that this agrees well with the expectation value of the energy and, in the case of two electrons, with full configuration interaction results. For the larger N2 system, the two estimators diverge, with the projected energy approaching the coupled cluster result, while the expectation value is close to results from traditional UCCSD.
Dynamic survival prediction in intensive care units from heterogeneous time series without the need for variable selection or curation

Abstract Extensive monitoring in intensive care units (ICUs) generates large quantities of data which contain numerous trends that are difficult for clinicians to systematically evaluate. Current approaches to such heterogeneity in electronic health records (EHRs) discard pertinent information. We present a deep learning pipeline that uses all uncurated chart, lab, and output events for prediction of in-hospital mortality without variable selection. Over 21,000 ICU patients and tens of thousands of variables derived from the MIMIC-III database were used to train and validate our model. Recordings in the first few hours of a patient’s stay were found to be strongly predictive of mortality, outperforming models using SAPS II and OASIS scores, AUROC 0.72 and 0.76 at 24 h respectively, within just 12 h of ICU admission. Our model achieves a very strong predictive performance of AUROC 0.85 (95% CI 0.83–0.86) after 48 h. Predictive performance increases over the first 48 h, but suffers from diminishing returns, providing rationale for time-limited trials of critical care and suggesting that the timing of decision making can be optimised and individualised.
Analysis and Improvement of a Certificateless Signature Scheme for Resource-Constrained Scenarios

Recently, Thumbur <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> introduced a paring-free certifcateless signature scheme to resolve security and efficiency issues in resource-constrained devices and claimed it was secure. However, we find that their scheme is vulnerable to signature forgery attack. To solve the above security challenges, we present a new pairing-free certificateless signature scheme and then formally prove its security under the ECDLP assumption, and finally we conduct a performance analysis and comparison. Security analysis and performance evaluation demonstrate that our new proposal can enjoy a higher level of security with a lower computation cost and communication cost, it is more practical in resource-constrained scenarios.
The indigenous languages of Sicily

This paper provides a brief synthesis of the evidence and principal points of discussion concerning the indigenous languages of ancient Sicily. Traditionally, three indigenous languages (Sikel, Sikan, and Elymian) are identified in use in Sicily in the period between the seventh and fourth centuries BCE. The evidence is extremely fragmentary, and its study is additionally complicated by the absence of up-to-date systematic collection of the material. The evidence is listed and the key points of linguistic and graphic discussion are presented. The traditional separation of Sikel and Sikan had already been challenged in existing scholarship; this paper suggests, in line with recent work, that the existing assumptions about the separation of Elymian also deserve to be challenged, and that the traditional assumptions about material and/or ethnic cultural boundaries on the island are potentially misleading.
DSPBench: A Suite of Benchmark Applications for Distributed Data Stream Processing Systems

Applications characterized by the continuous processing of large data streams have recently attracted the attention of the scientific community and industrial stakeholders.The need of high-level programming tools has led to the design of Data Stream Processing Systems (DSPSs) able to ease the development of streaming applications in distributed computing environments.Several systems of this kind have been released and currently maintained as open source projects, like Apache Storm and Spark Streaming.Although some benchmark applications are often used by the scientific community to test and evaluate new techniques to improve the performance and usability of DSPSs, the available benchmark suites are still lacking of representative workloads coming from the different areas of interest in the stream processing domain.The goal of this paper is to present a new benchmark suite composed of 15 applications coming from areas like Finance, Telecommunication, Sensor Networks, Social Networks and others.The paper describes in detail the nature of these applications, their full workload characterization in terms of selectivity, processing cost, input size and overall memory occupation, and provides a first assessment of the usefulness of our benchmark suite to compare real DSPSs by selecting Apache Storm and Spark Streaming for this analysis.
Variational Quantum Eigensolvers for Sparse Hamiltonians

Hybrid quantum-classical variational algorithms such as the variational quantum eigensolver (VQE) and the quantum approximate optimization algorithm (QAOA) are promising applications for noisy, intermediate-scale quantum computers. Both VQE and QAOA variationally extremize the expectation value of a Hamiltonian. All work to date on VQE and QAOA has been limited to Pauli representations of Hamiltonians. However, many cases exist in which a sparse representation of the Hamiltonian is known but there is no efficient Pauli representation. We extend VQE to general sparse Hamiltonians. We provide a decomposition of a fermionic second-quantized Hamiltonian into a number of one-sparse, self-inverse, Hermitian terms linear in the number of ladder operator monomials in the second-quantized representation. We provide a decomposition of a general $d$-sparse Hamiltonian into $O({d}^{2})$ such terms. In both cases, a single sample of any term can be obtained using two ansatz state preparations and at most six oracle queries. The number of samples required to estimate the expectation value to precision $\ensuremath{\epsilon}$ scales as ${\ensuremath{\epsilon}}^{\ensuremath{-}2}$ as for Pauli-based VQE. This widens the domain of applicability of VQE to systems whose Hamiltonian and other observables are most efficiently described in terms of sparse matrices.
AMI @ EVALITA2020: Automatic Misogyny Identification

Automatic Misogyny Identification (AMI) is a shared task proposed at the Evalita 2020 evaluation campaign. The AMI challenge, based on Italian tweets, is organized into two subtasks: (1) Subtask A about misogyny and aggressiveness identification and (2) Subtask B about the fairness of the model. At the end of the evaluation phase, we received a total of 20 runs for Subtask A and 11 runs for Subtask B, submitted by 8 teams. In this paper, we present an overview of the AMI shared task, the datasets, the evaluation methodology, the results obtained by the participants and a discussion about the methodology adopted by the teams. Finally, we draw some conclusions and discuss future work.
Biased TextRank: Unsupervised Graph-Based Content Extraction

We introduce Biased TextRank, a graph-based content extraction method inspired by the popular TextRank algorithm that ranks text spans according to their importance for language processing tasks and according to their relevance to an input "focus."Biased TextRank enables focused content extraction for text by modifying the random restarts in the execution of TextRank.The random restart probabilities are assigned based on the relevance of the graph nodes to the focus of the task.We present two applications of Biased TextRank: focused summarization and explanation extraction, and show that our algorithm leads to improved performance on two different datasets by significant ROUGE-N score margins.Much like its predecessor, Biased TextRank is unsupervised, easy to implement and orders of magnitude faster and lighter than current state-ofthe-art Natural Language Processing methods for similar tasks.
ExerSense: Physical Exercise Recognition and Counting Algorithm from Wearables Robust to Positioning

Wearable devices are currently popular for fitness tracking. However, these general usage devices only can track limited and prespecified exercises. In our previous work, we introduced ExerSense that segments, classifies, and counts multiple physical exercises in real-time based on a correlation method. It also can track user-specified exercises collected only one motion in advance. This paper is the extension of that work. We collected acceleration data for five types of regular exercises by four different wearable devices. To find the best accurate device and its position for multiple exercise recognition, we conducted 50 times random validations. Our result shows the robustness of ExerSense, working well with various devices. Among the four general usage devices, the chest-mounted sensor is the best for our target exercises, and the upper-arm-mounted smartphone is a close second. The wrist-mounted smartwatch is third, and the worst one is the ear-mounted sensor.
An Open System for Collection and Automatic Recognition of Pottery through Neural Network Algorithms

In the last ten years, artificial intelligence (AI) techniques have been applied in archaeology. The ArchAIDE project realised an AI-based application to recognise archaeological pottery. Pottery is of paramount importance for understanding archaeological contexts. However, recognition of ceramics is still a manual, time-consuming activity, reliant on analogue catalogues. The project developed two complementary machine-learning tools to propose identifications based on images captured on-site, for optimising and economising this process, while retaining key decision points necessary to create trusted results. One method relies on the shape of a potsherd; the other is based on decorative features. For the shape-based recognition, a novel deep-learning architecture was employed, integrating shape information from points along the inner and outer profile of a sherd. The decoration classifier is based on relatively standard architectures used in image recognition. In both cases, training the algorithms meant facing challenges related to real-world archaeological data: the scarcity of labelled data; extreme imbalance between instances of different categories; and the need to take note of minute differentiating features. Finally, the creation of a desktop and mobile application that integrates the AI classifiers provides an easy-to-use interface for pottery classification and storing pottery data.
Quantum illumination receiver using double homodyne detection

A quantum receiver is an essential element of quantum illumination (QI), which outperforms its classical counterpart, called classical illumination. However, there are only few proposals for realizable quantum receiver, which exploits nonlinear effects leading to increasing the complexity of receiver setups. To compensate this, in this paper, we design a quantum receiver with linear optical elements for Gaussian QI. Rather than exploiting nonlinear effect, our receiver consists of a 50:50 beam splitter and homodyne detection. Using double homodyne detection after the 50:50 beam splitter, we analyze the performance of the QI in different regimes of target reflectivity, source power, and noise level. We show that our receiver has better signal-to-noise ratio and is more robust against noise than the existing simple-structured receivers.Received 2 September 2020Accepted 1 December 2020DOI:https://doi.org/10.1103/PhysRevResearch.3.013006Published 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 AreasQuantum metrologyQuantum Information
A commonsense reasoning framework for explanatory emotion attribution, generation and re-classification

We present DEGARI (Dynamic Emotion Generator And ReclassIfier), an explainable system for emotion attribution and recommendation. This system relies on a recently introduced commonsense reasoning framework, the TCL logic, which is based on a human-like procedure for the automatic generation of novel concepts in a Description Logics knowledge base. Starting from an ontological formalization of emotions based on the Plutchik model, known as ArsEmotica, the system exploits the logic TCL to automatically generate novel commonsense semantic representations of compound emotions (e.g. Love as derived from the combination of Joy and Trust according to Plutchik). The generated emotions correspond to prototypes, i.e. commonsense representations of given concepts, and have been used to reclassify emotion-related contents in a variety of artistic domains, ranging from art datasets to the editorial contents available in RaiPlay, the online platform of RAI Radiotelevisione Italiana (the Italian public broadcasting company). We show how the reported results (evaluated in the light of the obtained reclassifications, the user ratings assigned to such reclassifications, and their explainability) are encouraging, and pave the way to many further research directions.
Unveiling the Importance and Evolution of Design Components Through the “Tree of Blockchain”

This study covers the evolutionary development of blockchain technologies over the last 11 years (2009–2019) and sheds lights on potential areas of innovation in heretofore unexplored sub-components. For this purpose, we collected and analyzed detailed data on 107 different blockchain technologies and studied their component-wise technological evolution. The diversity of their designs was captured by deconstructing the blockchains using the Tasca-Tessone taxonomy to build what we call the “ tree of blockchain ” composed of blockchain main and sub-components. With the support of information theory and phylogenetics, we found that most design explorations have been conducted within the components in the areas of consensus mechanisms and cryptographic primitives. We also show that some sub-components like Consensus Immutability and Failure Tolerance, Access and Control layer , and Access Supply Management have predictive power over other sub-components. We finally found that few dominant design models—the genetic driving clusters of Bitcoin, Ethereum, and XRP—influenced the evolutionary paths of most of the succeeding blockchains.
An Efficient Access Control Scheme With Outsourcing and Attribute Revocation for Fog-Enabled E-Health

Fog computing is increasingly popular partly due to its capability to minimize data transfer and latency requirements, for example by moving some of the computational operations away from the cloud servers and closer to the users.To achieve fine-grained access control in fog-enabled application scenarios to guarantee data security and user privacy, one could use ciphertext-policy attribute-based encryption (CP-ABE).However, the lack of an effective mechanism to carry out access right revocation in conventional CP-ABE schemes limits the deployment of such schemes in practice.Thus, we propose an efficient CP-ABE scheme with attribute revocation capability, designed to construct a fine-grained access control system in fogenabled E-health (referred to as AC-FEH).In our AC-FEH system, fog nodes undertake data encryption and decryption operations; thus, computational costs for data owners and users are minimized.In comparison to several other competing access control schemes based on CP-ABE, our AC-FEH system reduces the computational costs associated with encryption and decryption.We also prove the selective security of the underlying CP-ABE scheme under the intractability assumption of the q-parallel BDHE problem.
A Virtual Experiment for Learning the Principle of Daniell Cell Based on Augmented Reality

Chemistry is a science emphasizing both theory and experimentation. After learning the theoretical knowledge, experimental operation can help students understand chemical concepts and transform them into practical knowledge. Considering the safety issue and the lack of teaching time and experimental equipment, some teachers often choose to demonstrate an experiment instead of letting students conduct it by themselves. This may affect their learning motivation as well as the construction of chemical concepts and hands-on skills. This study combined the augmented reality (AR) technology with the operating principle of the Daniell cell to develop a virtual experiment for the application in high school chemistry courses. Students can conduct the virtual experiment using mobile devices by selecting the required equipment and materials from a deck of cards to set up the experimental environment. In the virtual experiment, students can use the galvanometer to measure the current after mounting the salt bridge on the beakers containing zinc sulfate and copper sulfate solutions. They can also see the change of molecular structures and movement of electrons and ions during the redox reactions to understand the important concepts and knowledge. An empirical research has been performed, and the analytical results show that both the virtual experiment and the real experiment could improve students’ learning achievement, but the former was more effective for the low-achievement students because they could explore autonomously to enhance cognition by observing the submicroscopic view of the redox reactions. The post-test results show that the average score of the low-achievement students in the experimental group (41.60) was significantly higher than that of the control group (27.67). Questionnaire results reveal that most students were satisfied with the learning contents, user interface, learning motivation, system reality, and practicality of the virtual experiment, and the average satisfaction score was 3.98 out of 5. The application of virtual experiments is not limited by time or space. Students only need to download the application (APP) software and print out the AR cards to practice at home, so it is suitable for large-scale promotion in rural areas.
Behavioral Intention of e-Learning: A Case Study of Distance Learning at a Junior High School in Indonesia due to the COVID-19 Pandemic

The spread of COVID-19 caused schools that previously used only a face-to-face system to deliver instruction online from home, a method commonly called e-learning.e-Learning is extremely beneficial in providing cost-effective education irrespective of time and geographical boundaries.Despite the benefit of e-learning, there are many problems associated with its use in Indonesia.Based on the results of research by Indonesian Child Protection Commission (KPAI), the application of e-learning in secondary schools in Indonesia is quite challenging.There is currently an education gap between the able and disadvantaged groups in Indonesia currently created by elearning.This study aims to assess the behavior intention of e-learning at junior high schools in Jakarta and Tangerang, Indonesia, due to the impact of COVID19.The research model is based on the unified theory of acceptance and use of a technology framework.The behavioral intention of performance expectation, effort expectancy, attitude, facilitating condition, social influence, and cost were examined.Data were collected by administering questionnaires to respondents who studied at the junior high schools in Jakarta and Tangerang.Data analysis was performed using structural equation modeling.The results showed that the social influence of e-learning has a strong positive relationship with behavioral intention.The findings of the study contribute to the understanding of behavioral beliefs about e-learning in Jakarta and Tangerang due to COVID-19.This contribution could be a reference for the government in improving the education system for elearning during COVID-19.