Reviewerly/exHarmony huggingface dataset:
Blockchain Adoption in Academia: Promises and Challenges

Blockchain has received considerable attention recently, due to its promises of verifiable, permanent, and decentralized data handling. In 2017&ndash;2020, blockchain (and associated technologies such as smart contracts) has progressed beyond cryptocurrencies, and has been hailed as a disruptive technology for a score of industries. This study adds to the growing body of research on blockchain adoption and blockchain-driven innovation in various fields, including transport, finance, and education. However, the impact of distributed ledger technologies (DLT) on the management of science has not been systematically studied so far. This paper aims to fill the gap by studying the experience of adoption of blockchain-based solutions in academia in 2017&ndash;2020. The research is based upon a critical review of projects, relevant literature, and qualitative research: interviews (N = 24) and focus groups (N = 4) with startup founders, scholars, university executives, librarians, and IT experts from the European Union (EU), the United States of America (USA), Russia, and Belarus. Key challenges and barriers to blockchain adoption in academia are delineated: usability and security issues, legal concerns, conflict of values, and a critique of political dimensions of blockchain governance.
Co-Embedding of Nodes and Edges With Graph Neural Networks

Graph, as an important data representation, is ubiquitous in many real world applications ranging from social network analysis to biology. How to correctly and effectively learn and extract information from graph is essential for a large number of machine learning tasks. Graph embedding is a way to transform and encode the data structure in high dimensional and non-euclidean feature space to a low dimensional and structural space, which is easily exploited by other machine learning algorithms. We have witnessed a huge surge of such embedding methods, from statistical approaches to recent deep learning methods such as the graph convolutional networks (GCN). Deep learning approaches usually outperform the traditional methods in most graph learning benchmarks by building an end-to-end learning framework to optimize the loss function directly. However, most of the existing GCN methods can only perform convolution operations with node features, while ignoring the handy information in edge features, such as relations in knowledge graphs. To address this problem, we present <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CensNet</i> , <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</b> onvolution with <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">E</b> dge- <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">N</b> ode <bold xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</b> witching graph neural network, for learning tasks in graph-structured data with both node and edge features. CensNet is a general graph embedding framework, which embeds both nodes and edges to a latent feature space. By using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">line graph</i> of the original undirected graph, the role of nodes and edges are switched, and two novel graph convolution operations are proposed for feature propagation. Experimental results on real-world academic citation networks and quantum chemistry graphs show that our approach achieves or matches the state-of-the-art performance in four graph learning tasks, including semi-supervised node classification, multi-task graph classification, graph regression, and link prediction.
E-learning development in improving students' critical thinking ability

The research paper presented to explore valid, practical, and effective e-learning media. Reflectig the purpose of paper, this study is to improve critical thinking skills, to obtain an overview at high levels and increasing students' critical thinking skills including ability. The hypothesis is as a rational decision-making process on what believed is, through the aspect of providing basic explanations, building basic skills, concluding, making further explanations, and strategies and tactics in the lesson planning course, at the Faculty of Tarbiyah and Teacher Training at UIN Maulana Malik Ibrahim Malang. The paper explained ADDIE development model which included the design, development, analysis, implementation and evaluation stages which reffered research and development (R &amp; D). The data for testing e-learning media in learning and tests of critical thinking skills carried out from fourth-semester students . The study used the One Group Pretest-Posttest Design for trial design. The results concluded the average percentage of feasibility value of the e-learning media from the three validators was 86.6% with very feasible criteria, the critical thinking ability instrument was 79.9% through feasible criteria. E-learning media used in a limited trial that obtained an average score of 82.4% for lecturers and students with practical criteria. E-learning media is improving critical thinking skills effectively which indicated changes in students and critical thinking test presented the results from 56.7 to 81.3. The findings showed that the e-learning media is valid, practical, and an effective criteria to improve students' critical thinking skills.&#x0D; Keywords: E-learning, learning strategies, critical thinking skills, tactics;&#x0D;
Quantum Sphere-Packing Bounds With Polynomial Prefactors

We study lower bounds on the optimal error probability in classical coding over classicalquantum channels at rates below the capacity, commonly termed quantum sphere-packing bounds.Winter and Dalai have derived such bounds for classical-quantum channels; however, the exponents in their bounds only coincide when the channel is classical.In this paper, we show that these two exponents admit a variational representation and are related by the Golden-Thompson inequality, reaffirming that Dalai's expression is stronger in general classical-quantum channels.Second, we establish a finite blocklength sphere-packing bound for classical-quantum channels, which significantly improves Dalai's prefactor from the order of subexponential to polynomial.Furthermore, the gap between the obtained error exponent for constant composition codes and the best known classical random coding exponent vanishes in the order of o(log n/n), indicating our sphere-packing bound is almost exact in the high rate regime.Finally, for a special class of symmetric classical-quantum channels, we can completely characterize its optimal error probability without the constant composition code assumption.The main technical contributions are two converse Hoeffding bounds for quantum hypothesis testing and the saddle-point properties of error exponent functions.
Fully Quantized Transformer for Machine Translation

State-of-the-art neural machine translation methods employ massive amounts of parameters.Drastically reducing computational costs of such methods without affecting performance has been up to this point unsuccessful.To this end, we propose FullyQT: an allinclusive quantization strategy for the Transformer.To the best of our knowledge, we are the first to show that it is possible to avoid any loss in translation quality with a fully quantized Transformer.Indeed, compared to fullprecision, our 8-bit models score greater or equal BLEU on most tasks.Comparing ourselves to all previously proposed methods, we achieve state-of-the-art quantization results.
Intrinsic Evaluation of Summarization Datasets

High quality data forms the bedrock for building meaningful statistical models in NLP.Consequently, data quality must be evaluated either during dataset construction or post hoc.Almost all popular summarization datasets are drawn from natural sources and do not come with inherent quality assurance guarantees.In spite of this, data quality has gone largely unquestioned for many recent summarization datasets.We perform the first large-scale evaluation of summarization datasets by introducing 5 intrinsic metrics and applying them to 10 popular datasets.We find that data usage in recent summarization research is sometimes inconsistent with the underlying properties of the datasets employed.Further, we discover that our metrics can serve the additional purpose of being inexpensive heuristics for detecting generically low quality examples.
Finite-key analysis for twin-field quantum key distribution with composable security

Abstract Long-distance quantum key distribution (QKD) has long time seriously relied on trusted relay or quantum repeater, which either has security threat or is far from practical implementation. Recently, a solution called twin-field (TF) QKD and its variants have been proposed to overcome this challenge. However, most security proofs are complicated, a majority of which could only ensure security against collective attacks. Until now, the full and simple security proof can only be provided with asymptotic resource assumption. Here, we provide a composable finite-key analysis for coherent-state-based TF-QKD with rigorous security proof against general attacks. Furthermore, we develop the optimal statistical fluctuation analysis method to significantly improve secret key rate in high-loss regime. The results show that coherent-state-based TF-QKD is practical and feasible, with the potential to apply over nearly one thousand kilometers.
Unsupervised Learning of Foreground Object Segmentation

Unsupervised learning represents one of the most interesting challenges in computer vision today. The task has an immense practical value with many applications in artificial intelligence and emerging technologies, as large quantities of unlabeled images and videos can be collected at low cost. In this paper, we address the unsupervised learning problem in the context of segmenting the main foreground objects in single images. We propose an unsupervised learning system, which has two pathways, the teacher and the student, respectively. The system is designed to learn over several generations of teachers and students. At every generation the teacher performs unsupervised object discovery in videos or collections of images and an automatic selection module picks up good frame segmentations and passes them to the student pathway for training. At every generation multiple students are trained, with different deep network architectures to ensure a better diversity. The students at one iteration help in training a better selection module, forming together a more powerful teacher pathway at the next iteration. In experiments, we show that the improvement in the selection power, the training of multiple students and the increase in unlabeled data significantly improve segmentation accuracy from one generation to the next. Our method achieves top results on three current datasets for object discovery in video, unsupervised image segmentation and saliency detection. At test time, the proposed system is fast, being one to two orders of magnitude faster than published unsupervised methods. We also test the strength of our unsupervised features within a well known transfer learning setup and achieve competitive performance, proving that our unsupervised approach can be reliably used in a variety of computer vision tasks.
Quantum computing with multidimensional continuous-variable cluster states in a scalable photonic platform

This paper investigates a scalable architecture for photonic one way quantum computing using large scale entangled states. The proposed architecture utilizes frequency and time multiplexing in an integrated quantum photonic platform to generate three dimensional continuous variable cluster states and to realize quantum logic gates, two key ingredients for fault-tolerant quantum computing.
Fast High-Fidelity Quantum Nondemolition Qubit Readout via a Nonperturbative Cross-Kerr Coupling

A new qubit readout scheme preserves quantum state probabilities while maximizing fidelity with a fast readout time, thus providing a robust measurement method for a new generation of superconducting quantum processors.
Low-Resource Domain Adaptation for Compositional Task-Oriented Semantic Parsing

Task-oriented semantic parsing is a critical component of virtual assistants, which is responsible for understanding the user's intents (set reminder, play music, etc.).Recent advances in deep learning have enabled several approaches to successfully parse more complex queries (Gupta et al., 2018;Rongali et al., 2020), but these models require a large amount of annotated training data to parse queries on new domains (e.g.reminder, music).In this paper, we focus on adapting taskoriented semantic parsers to low-resource domains, and propose a novel method that outperforms a supervised neural model at a 10-fold data reduction.In particular, we identify two fundamental factors for low-resource domain adaptation: better representation learning and better training techniques.Our representation learning uses BART (Lewis et al., 2020) to initialize our model which outperforms encoder-only pre-trained representations used in previous work.Furthermore, we train with optimization-based meta-learning (Finn et al., 2017) to improve generalization to lowresource domains.This approach significantly outperforms all baseline methods in the experiments on a newly collected multi-domain taskoriented semantic parsing dataset (TOPv2 1 ).
Ordering of Trotterization: Impact on Errors in Quantum Simulation of Electronic Structure

Trotter–Suzuki decompositions are frequently used in the quantum simulation of quantum chemistry. They transform the evolution operator into a form implementable on a quantum device, while incurring an error—the Trotter error. The Trotter error can be made arbitrarily small by increasing the Trotter number. However, this increases the length of the quantum circuits required, which may be impractical. It is therefore desirable to find methods of reducing the Trotter error through alternate means. The Trotter error is dependent on the order in which individual term unitaries are applied. Due to the factorial growth in the number of possible orderings with respect to the number of terms, finding an optimal strategy for ordering Trotter sequences is difficult. In this paper, we propose three ordering strategies, and assess their impact on the Trotter error incurred. Initially, we exhaustively examine the possible orderings for molecular hydrogen in a STO-3G basis. We demonstrate how the optimal ordering scheme depends on the compatibility graph of the Hamiltonian, and show how it varies with increasing bond length. We then use 44 molecular Hamiltonians to evaluate two strategies based on coloring their incompatibility graphs, while considering the properties of the obtained colorings. We find that the Trotter error for most systems involving heavy atoms, using a reference magnitude ordering, is less than 1 kcal/mol. Relative to this, the difference between ordering schemes can be substantial, being approximately on the order of millihartrees. The coloring-based ordering schemes are reasonably promising—particularly for systems involving heavy atoms—however further work is required to increase dependence on the magnitude of terms. Finally, we consider ordering strategies based on the norm of the Trotter error operator, including an iterative method for generating the new error operator terms added upon insertion of a term into an ordered Hamiltonian.
Deep Feature Space Trojan Attack of Neural Networks by Controlled Detoxification

Trojan (backdoor) attack is a form of adversarial attack on deep neural networks where the attacker provides victims with a model trained/retrained on malicious data. The backdoor can be activated when a normal input is stamped with a certain pattern called trigger, causing misclassification. Many existing trojan attacks have their triggers being input space patches/objects (e.g., a polygon with solid color) or simple input transformations such as Instagram filters. These simple triggers are susceptible to recent backdoor detection algorithms. We propose a novel deep feature space trojan attack with five characteristics: effectiveness, stealthiness, controllability, robustness and reliance on deep features. We conduct extensive experiments on 9 image classifiers on various datasets including ImageNet to demonstrate these properties and show that our attack can evade state-of-the-art defense.
A general quantum algorithm for open quantum dynamics demonstrated with the Fenna-Matthews-Olson complex

Using quantum algorithms to simulate complex physical processes and correlations in quantum matter has been a major direction of quantum computing research, towards the promise of a quantum advantage over classical approaches. In this work we develop a generalized quantum algorithm to simulate any dynamical process represented by either the operator sum representation or the Lindblad master equation. We then demonstrate the quantum algorithm by simulating the dynamics of the Fenna-Matthews-Olson (FMO) complex on the IBM QASM quantum simulator. This work represents a first demonstration of a quantum algorithm for open quantum dynamics with a moderately sophisticated dynamical process involving a realistic biological structure. We discuss the complexity of the quantum algorithm relative to the classical method for the same purpose, presenting a decisive query complexity advantage of the quantum approach based on the unique property of quantum measurement.
Current Perspectives on Augmented Reality in Medical Education: Applications, Affordances and Limitations

This systematic review has been developed against a background of rapid developments in augmented reality (AR) technology and its application in medical education.The objectives are to provide a critical synthesis of current trends in the field and to highlight areas for further research.The data sources used for the study were the PubMed, Web of Science and Discover databases.Sources included in the study comprised peer reviewed journal articles published between 2015 and 2020.Inclusion criteria included empirical research findings related to learning outcomes and the populations for the selected studies were medical students.Studies were appraised in terms of to what extent the use of AR contributed to learning gains in knowledge and/or skill.Twenty-one studies were included in the analysis, and the dates of these suggested an increasing trend of publications in this area.The uses of AR in each selected study were analyzed through a lens of affordance, to identify which specific affordances of AR appear to be most effective in this domain.Results of the study indicated that AR seems to be more effective in supporting skill development rather than knowledge gain when compared to other techniques.Some key affordances of AR in medical education are identified as developing practical skills in a spatial context, device portability across locations and situated learning in context.It is suggested that a focus on relevant affordances when designing AR systems for medical education may lead to better learning outcomes.It is noted that the majority of AR systems reported in the selected studies are concentrated in the areas of anatomy and surgery, but that are also other areas of practice being explored, and these may provide opportunities for new types of AR learning systems to be developed for medical education.
From Insult to Hate Speech: Mapping Offensive Language in German User Comments on Immigration

In recent debates on offensive language in participatory online spaces, the term ‘hate speech’ has become especially prominent. Originating from a legal context, the term usually refers to violent threats or expressions of prejudice against particular groups on the basis of race, religion, or sexual orientation. However, due to its explicit reference to the emotion of hate, it is also used more colloquially as a general label for any kind of negative expression. This ambiguity leads to misunderstandings in discussions about hate speech and challenges its identification. To meet this challenge, this article provides a modularized framework to differentiate various forms of hate speech and offensive language. On the basis of this framework, we present a text annotation study of 5,031 user comments on the topic of immigration and refuge posted in March 2019 on three German news sites, four Facebook pages, 13 YouTube channels, and one right-wing blog. An in-depth analysis of these comments identifies various types of hate speech and offensive language targeting immigrants and refugees. By exploring typical combinations of labeled attributes, we empirically map the variety of offensive language in the subject area ranging from insults to calls for hate crimes, going beyond the common ‘hate/no-hate’ dichotomy found in similar studies. The results are discussed with a focus on the grey area between hate speech and offensive language.
Microbiome Metadata Standards: Report of the National Microbiome Data Collaborative’s Workshop and Follow-On Activities

Microbiome samples are inherently defined by the environment in which they are found. Therefore, data that provide context and enable interpretation of measurements produced from biological samples, often referred to as metadata, are critical.
Characterizing network dynamics of online hate communities around the COVID-19 pandemic

Abstract Hate speech has long posed a serious problem for the integrity of digital platforms. Although significant progress has been made in identifying hate speech in its various forms, prevailing computational approaches have tended to consider it in isolation from the community-based contexts in which it spreads. In this paper, we propose a dynamic network framework to characterize hate communities, focusing on Twitter conversations related to COVID-19 in the United States and the Philippines. While average hate scores remain fairly consistent over time, hate communities grow increasingly organized in March, then slowly disperse in the succeeding months. This pattern is robust to fluctuations in the number of network clusters and average cluster size. Infodemiological analysis demonstrates that in both countries, the spread of hate speech around COVID-19 features similar reproduction rates as other COVID-19 information on Twitter, with spikes in hate speech generation at time points with highest community-level organization of hate speech. Identity analysis further reveals that hate in the US initially targets political figures, then grows predominantly racially charged; in the Philippines, targets of hate consistently remain political over time. Finally, we demonstrate that higher levels of community hate are consistently associated with smaller, more isolated, and highly hierarchical network clusters across both contexts. This suggests potentially shared structural conditions for the effective spread of hate speech in online communities even when functionally targeting distinct identity groups. Our findings bear theoretical and methodological implications for the scientific study of hate speech and understanding the pandemic’s broader societal impacts both online and offline.
OCTIS: Comparing and Optimizing Topic models is Simple!

In this paper, we present OCTIS, a framework for training, analyzing, and comparing Topic Models, whose optimal hyper-parameters are estimated using a Bayesian Optimization approach.The proposed solution integrates several state-of-the-art topic models and evaluation metrics.These metrics can be targeted as objective by the underlying optimization procedure to determine the best hyper-parameter configuration.OCTIS allows researchers and practitioners to have a fair comparison between topic models of interest, using several benchmark datasets and well-known evaluation metrics, to integrate novel algorithms, and to have an interactive visualization of the results for understanding the behavior of each model.