Reviewerly/exHarmony huggingface dataset:
Game-theoretic Vocabulary Selection via the Shapley Value and Banzhaf Index

The input vocabulary and their learned representations are crucial to the performance of neural NLP models.Using the full vocabulary results in less explainable and more memory intensive models, with the embedding layer often constituting the majority of model parameters.It is thus common to use a smaller vocabulary to lower memory requirements and construct more interpertable models.We propose a vocabulary selection method that views words as members of a team trying to maximize the model's performance.We apply power indices from cooperative game theory, including the Shapley value and Banzhaf index, that measure the relative importance of individual team members in accomplishing a joint task.We approximately compute these indices to identify the most influential words.Our empirical evaluation examines multiple NLP tasks, including sentence and document classification, question answering and textual entailment.We compare to baselines that select words based on frequency, TF-IDF and regression coefficients under L1 regularization, and show that this game-theoretic vocabulary selection outperforms all baselines on a range of different tasks and datasets.
Exchange-mediated mutual correlations and dephasing in free-electrons and light interactions

Abstract The quantum world distinguishes itself from the classical world by being governed by probability amplitudes rather than probabilities. On a single-particle level, quantum phases can be manipulated leading to observable interference patterns that can be used as a probe e.g. in matter wave microscopy. But the quantum world bears even more fascinating effects when it comes to the interplay between more than one particle. Correlations between quantum particles such as entanglement can be exploited to speed up computational algorithms or enable secure cryptography. Here, we propose and numerically explore a thought experiment to address the question whether quantum correlations between particles can be used in matter wave microscopy. Specifically, we address the following questions: can information be transferred between two mutually spin-correlated free-electron wavepackets? Can Coulomb and exchange correlations be linked to the decoherence and dephasing mechanisms of matter waves? Using a time-dependent Hartree–Fock algorithm, we will show that the exchange term has a substantial role in transferring the information between two mutually spin-correlated electrons, whereas the Hartree potential (or mean-field Coulomb potential) dominates the dephasing on a single-particle level. Our findings might facilitate fermionic matter wave interferometry experiments designed to retrieve information about non-classical correlations and the mechanism of decoherence in open quantum systems.
KW-ATTN: Knowledge Infused Attention for Accurate and Interpretable Text Classification

Text classification has wide-ranging applications in various domains.While neural network approaches have drastically advanced performance in text classification, they tend to be powered by a large amount of training data, and interpretability is often an issue.As a step towards better accuracy and interpretability especially on small data, in this paper we present a new knowledge-infused attention mechanism, called KW-ATTN (KnoWledgeinfused ATTentioN) to incorporate high-level concepts from external knowledge bases into Neural Network models.We show that KW-ATTN outperforms baseline models using only words as well as other approaches using concepts by classification accuracy, which indicates that high-level concepts help model prediction.Furthermore, crowdsourced human evaluation suggests that additional concept information helps interpretability of the model.
NRC-CNRC Machine Translation Systems for the 2021 AmericasNLP Shared Task

We describe the NRC-CNRC systems submitted to the AmericasNLP shared task on machine translation.We submitted systems translating from Spanish into Wixárika, Nahuatl, Rarámuri, and Guaraní.Our best neural machine translation systems used multilingual pretraining, ensembling, finetuning, training on parts of the development data, and subword regularization.We also submitted translation memory systems as a strong baseline.
The Role of Unified Theory of Acceptance and Use of Technology in E-Learning Adoption in Higher Education Institutions in the UAE

Electronic learning is fast becoming a crucial aspect of learning in higher institutions of learning. Just as in any other technology, there are important factors that affect users’ behavioural intention to adopt e-learning. The unified theory of acceptance and use of technology (UTAUT) identified performance expectancy, effort expectancy, social influence and facilitating conditions as some of the factors that may influence the behavioural intention and actual use of a technology.
Is Incoherence Surprising? Targeted Evaluation of Coherence Prediction from Language Models

Coherent discourse is distinguished from a mere collection of utterances by the satisfaction of a diverse set of constraints, for example choice of expression, logical relation between denoted events, and implicit compatibility with world-knowledge.Do neural language models encode such constraints?We design an extendable set of test suites addressing different aspects of discourse and dialogue coherence.Unlike most previous coherence evaluation studies, we address specific linguistic devices beyond sentence order perturbations, allowing for a more fine-grained analysis of what constitutes coherence and what neural models trained on a language modelling objective do encode.Extending the targeted evaluation paradigm for neural language models (Marvin and Linzen, 2018) to phenomena beyond syntax, we show that this paradigm is equally suited to evaluate linguistic qualities that contribute to the notion of coherence.
Boosting Adversarial Attacks on Neural Networks with Better Optimizer

Convolutional neural networks have outperformed humans in image recognition tasks, but they remain vulnerable to attacks from adversarial examples. Since these data are crafted by adding imperceptible noise to normal images, their existence poses potential security threats to deep learning systems. Sophisticated adversarial examples with strong attack performance can also be used as a tool to evaluate the robustness of a model. However, the success rate of adversarial attacks can be further improved in black-box environments. Therefore, this study combines a modified Adam gradient descent algorithm with the iterative gradient-based attack method. The proposed Adam Iterative Fast Gradient Method is then used to improve the transferability of adversarial examples. Extensive experiments on ImageNet showed that the proposed method offers a higher attack success rate than existing iterative methods. By extending our method, we achieved a state-of-the-art attack success rate of 95.0% on defense models.
NAMER: A Node-Based Multitasking Framework for Multi-Hop Knowledge Base Question Answering

We present NAMER, an open-domain Chinese knowledge base question answering system based on a novel node-based framework that better grasps the structural mapping between questions and KB queries by aligning the nodes in a query with their corresponding mentions in question.Equipped with techniques including data augmentation and multitasking, we show that the proposed framework outperforms the previous SoTA on CCKS CKBQA dataset.Moreover, we develop a novel data annotation strategy that facilitates the node-tomention alignment, a dataset 1 with such strategy is also published to promote further research.An online demo of NAMER 2 is provided to visualize our framework and supply extra information for users, a video illustration 3 of NAMER is also available.
Quantum algorithm for matrix functions by Cauchy's integral formula

For matrix A, vector b and function f, the computation of vector f(A)b arises in many scientific computing applications. We consider the problem of obtaining quantum state |f&gt; corresponding to vector f(A)b. There is a quantum algorithm to compute state |f&gt; using eigenvalue estimation that uses phase estimation and Hamiltonian simulation e^{\im A t}. However, the algorithm based on eigenvalue estimation needs \poly(1/\epsilon) runtime, where \epsilon is the desired accuracy of the output state. Moreover, if matrix A is not Hermitian, \e^{\im A t} is not unitary and we cannot run eigenvalue estimation. In this paper, we propose a quantum algorithm that uses Cauchy's integral formula and the trapezoidal rule as an approach that avoids eigenvalue estimation. We show that the runtime of the algorithm is \poly(\log(1/\epsilon)) and the algorithm outputs state |f&gt; even if A is not Hermitian.
Stay Together: A System for Single and Split-antecedent Anaphora Resolution

The state-of-the-art on basic, singleantecedent anaphora has greatly improved in recent years.Researchers have therefore started to pay more attention to more complex cases of anaphora such as split-antecedent anaphora, as in Time-Warner is considering a legal challenge to Telecommunications Inc's plan to buy half of Showtime Networks Inc-a move that could lead to all-out war between the two powerful companies.Split-antecedent anaphora is rarer and more complex to resolve than single-antecedent anaphora; as a result, it is not annotated in many datasets designed to test coreference, and previous work on resolving this type of anaphora was carried out in unrealistic conditions that assume gold mentions and/or gold split-antecedent anaphors are available.These systems also focus on split-antecedent anaphors only.In this work, we introduce a system that resolves both single and split-antecedent anaphors, and evaluate it in a more realistic setting that uses predicted mentions.We also start addressing the question of how to evaluate single and split-antecedent anaphors together using standard coreference evaluation metrics. 1
Case Study of the Integration of Digital Competencies into Teacher Preparation

The requirements imposed on schools and the competencies of teachers change depending on the development of society, and currently their constant growth is considerable. These facts lead to the need to continuously innovate pre-service teacher training, especially with a focus on creating professional digital literacy. The creation of a proposal of an optimal model of pre-service teacher training in the field of teacher trainees’ professional didactic-technological competency development was the subject of the research, which is described in the article. The described research examined the importance of the integration of various kinds of digital didactic tools into pre-service teacher training curricula with regard to the successful performance of the teaching profession. The necessary research data were obtained on the basis of screening the opinions of teacher trainees in Slovakia and the Czech Republic (n = 280). The respondents of the research survey expressed, in terms of various aspects, their opinions on the importance of integrating the issue of working with specified kinds of the given digital means into the curricula of teacher trainees’ study programs. The obtained research data were analysed depending on three segmentation factors of the respondents, which were the nationality of the student (i.e., the COUNTRY of his/her study), the GENDER of the respondent, and the combination of these two factors, i.e., COUNTRY X GENDER. According to the achieved results, there is a need to include or strengthen the teaching of software applications such as ActivInspire, FreeMind, SMART Notebook, Google Docs and, if possible, Prezi and Mindomo, and also a need to emphasize the methodological aspects of the use of these technical means in teaching.
Bifurcated Signatures: Folding the Accountability vs. Anonymity Dilemma into a Single Private Signing Scheme

Over the development of modern cryptography, often, alternative cryptographic schemes are developed to achieve goals that in some important respect are orthogonal. Thus, we have to choose either a scheme which achieves the first goal and not the second, or vice versa. This results in two types of schemes that compete with each other. In the basic area of user privacy, specifically in anonymous (multi-use credentials) signing, such an orthogonality exists between anonymity and accountability. The conceptual contribution of this work is to reverse the above orthogonality by design, which essentially typifies the last 25 years or so, and to suggest an alternative methodology where the opposed properties are carefully folded into a single scheme. The schemes will support both opposing properties simultaneously in a bifurcated fashion, where: Specifically, we precisely define and give the first construction and security proof of a "Bifurcated Anonymous Signature" (BiAS): A scheme which supports either absolute anonymity or anonymity with accountability, based on a specific contextual predicate, while being branch-hiding. This novel signing scheme has numerous applications not easily implementable or not considered before, especially because: (i) the conditional traceability does not rely on a trusted authority as it is (non-interactively) encapsulated into signatures; and (ii) signers know the predicate value and can make a conscious choice at each signing time. Technically, we realize BiAS from homomorphic commitments for a general family of predicates that can be represented by bounded-depth circuits. Our construction is generic and can be instantiated in the standard model from lattices and, more efficiently, from bilinear maps. In particular, the signature length is independent of the circuit size when we use commitments with suitable efficiency properties.
Psychological factors of online–learning efficiency of students

М. В. Клименских, Ю. В
Determination of Student Subjects in Higher Education Using Hybrid Data Mining Method with the K-Means Algorithm and FP Growth

The large number of courses offered in an educational institution raises new problems related to the selection of specialization courses. Students experience difficulties and confusion in determining the course to be taken when compiling the study plan card. The purpose of this study was to cluster student value data. Then the values that have been grouped are seen in the pattern (pattern) of the appearance of the data based on the values they got previously so that students can later use the results of the patterning as a guideline for taking what skill courses in the next semester. The method used in this research is the K-Means and FP-Growth methods. The results of this rule can provide input to students or academic supervisors when compiling student study plan cards. Lecturers and students can analyze the right specialization subject by following the pattern given. This study produces a pattern that shows that the specialization course with the theme of business information systems is more followed by students than the other 2 themes
Masked Conditional Random Fields for Sequence Labeling

Tianwen Wei, Jianwei Qi, Shenghuan He, Songtao Sun. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.
Virtual or face to face classes: Ecuadorian university students’ perceptions during the pandemic

Due to the pandemic, classes have moved online for 2020-2021, and a return to face-to-face classes in the National University of Education (UNAE) in Ecuador is unknown. Students studying in the undergraduate program to be English language teachers are slowly adapting to this new form of E-learning, and some have found that they prefer online classes, while many are waiting for the day they return to classes. The objective of this study is to look at the pros and cons given by 69 students discussing why taking classes virtually or face to face is a better option for EFL and being an ELT professional in these turbulent times. Some prefer online learning for various reasons such as safety, comfort, economic reasons, and having more time in general. While some find E-learning difficult and want to return to synchronous classes because they feel there is little interaction between classmates and teachers, cannot practice English authentically, cannot get clarification from the teacher, have a hard time concentrating online, and there are constant internet connection issues. In conclusion, this information has great importance for teachers today who are struggling to teach in this new medium and teach to the needs of their students to aid in their planning and will contribute to future teachers who will teach online.
Extractive and Abstractive Explanations for Fact-Checking and Evaluation of News

In this paper, we explore the construction of natural language explanations for news claims, with the goal of assisting fact-checking and news evaluation applications.We experiment with two methods: (1) an extractive method based on Biased TextRank -a resource-effective unsupervised graph-based algorithm for content extraction; and (2) an abstractive method based on the GPT-2 language model.We perform comparative evaluations on two misinformation datasets in the political and health news domains, and find that the extractive method shows the most promise.
Optimizing operational costs and PV production at utility scale: An optical fiber network analogy for solar park clustering

Increasing the share of renewable energy sources in power systems is key to a successful energy transition. Optimal renewable site selection requires a holistic approach, involving land, resources, environmental and economical data and constraints. In this paper we consider the problem of solar PV penetration into the power network as a spatiotemporal analysis combined with decision support targeted for policy makers and investors. Our goal is to seek new models that maximize energy penetration and stability into the network, while minimizing the operational costs. We show how the selection of solar PV sites can be accomplished to satisfy such objectives by investigating the optimal clustering of multiple solar PV parks around a shared electrical substation. This is a combinatorial problem in terms of all the potential clusters given the set of PV site candidates. Our main contribution lies in identifying and proposing a modeling analogy of our problem with the so-called SONET problem, tackled in fiber network designs. We show how this new spatiotemporal PV park placement model minimizes operational costs, while increasing energy stability of the solutions produced. We also introduce a GIS preprocessing step to reduce the computational cost of the proposed approach. We compare our proposed SONET-based model to an existing GIS-optimization model on a real case study and data from the French Guiana's power system. This new approach aggregates multiple PV parks into clusters distributed across the territory. In the case of French Guiana, the same global nominal power (≈45MW) can, for instance, be distributed among 11 PV parks and 3 clusters, against 3 large-scale PV parks. Results show substantial gain in costs per kWh produced, up to 10 MW of extra installed power and 16 GWh of extra power generation when considering PV parks ⩽5MW. The new cluster configuration also ensures improved energy stability of the solutions, resulting in mitigation of the risks for both the network manager and the decision maker.
Exploring Cybersecurity Ecosystem in the Middle East: Towards an SME Recommender System

Cybersecurity is described as the protection of data resources by treating threats that jeopardize data. Enterprises must manage the cybersecurity risks so that the security and resilience of their assets may be improved. Cyber-attacks on Small and Medium Enterprises (SMEs) are rising. However, they often lack effective strategies to prevent threats such as malware, phishing, denial of service (DoS), and others. Their weak defense system is often an attractive avenue for hackers to explore loopholes. There is a lack of cybersecurity initiatives in SMEs, and several past attacks have exposed the weak systems. This paper first attempts to investigate the current scenario of cybersecurity in the context of Middle East SMEs. A survey of SMEs in the Middle East (cybersecurity space) helped understand the existing scenario, actual requirements, and challenges SMEs face. The research then explores the need for SMEs to choose the apt security solution to cater to their business needs. By reviewing the existing standards and pointers in different parts of the world, this research proposes a cybersecurity recommender system for SMEs in the Middle East. One of the survey findings reveals that most SMEs require adequate cybersecurity awareness, followed by evaluating the organization’s preventive capabilities. The dearth of information available online and the IT consultants’ conflicting guidance usually creates an information overload in deciding a neutral solution to address their needs. The recommender system attempts to structure the information available as a framework in deciding a cybersecurity solution for SMEs.
Biometrics-based Internet of Things and Big data design framework

<abstract> Application Specific Internet of Things (ASIoTs) has recently been proposed to address specific requirements for IoT. The objective of this paper is to serve as a framework for the design of ASIoTs using biometrics as the application. This paper provides comprehensive discussions for an ASIoT architecture considering the requirements for biometrics-based security, multimedia content and Big data applications. A comprehensive architecture for Biometrics-based IoT (BiometricIoT) and Big data applications needs to address three challenges: 1) IoT devices are hardware-constrained and cannot afford resource-demanding cryptographic protocols; 2) Biometrics devices introduce multimedia data content due to different biometric traits; and 3) The rapid growth of biometrics-based IoT devices and content creates large amounts of data for computational processing. The proposed BiometricIoT architecture consists of seven layers which have been designed to handle the challenges for biometrics applications and decision making. The latter part of the paper gives discussions for design factors for the BiometricIoT from four perspectives: 1) parallel divide-and-conquer (D&amp;C) computation; 2) computational complexity; 3) device security; and 4) algorithm efficacies. Experimental results are given to validate the effectiveness of the D&amp;C approach. The paper motivates the further research towards the research and development of ASIoTs for biometrics applications. </abstract>