Reviewerly/exHarmony huggingface dataset:
Defence against the dark artefacts: Smart home cybercrimes and cybersecurity standards

This paper analyses the assumptions underpinning a range of emerging EU and UK smart home cybersecurity standards.We use internet of things (IoT) case studies (such as the Mirai Botnet affair) and the criminological concept of 'routine activity theory' to situate our critique.Our study shows that current cybersecurity standards mainly assume smart home environments are (and will continue to be) underpinned by cloud architectures.This is a shortcoming in the longevity of standards.This paper argues that edge computing approaches, such as personal information management systems, are emerging for the IoT and challenge the cloud focused assumptions of these standards.In edge computing, data can be stored in a decentralised manner, locally and analysed on the client using federated learning.This can have advantages for security, privacy and legal compliance, over centralised cloud-based approaches, particularly around cross border data flows and edge based security analytics.As a consequence, standards should start to reflect the increased interest in this trend to make them more aspirational and responsive for the long term; as ultimately, current IoT architectures are a choice, as opposed to inherent.Our paper unpacks the importance of the adoption of edge computing models which could enable better management of external cyber-criminality threats in smart homes.We also briefly discuss challenges of building smart homes that can accommodate the complex nature of everyday life in the home.In addition to technical aspects, the social and interactional complexities of the home mean internal threats can also emerge.As these human factors remain unresolved in current approaches to smart home cybersecurity, a user's security can be impacted by such technical design choices.
Case Study: Deontological Ethics in NLP

Recent work in natural language processing (NLP) has focused on ethical challenges such as understanding and mitigating bias in data and algorithms; identifying objectionable content like hate speech, stereotypes and offensive language; and building frameworks for better system design and data handling practices.However, there has been little discussion about the ethical foundations that underlie these efforts.In this work, we study one ethical theory, namely deontological ethics, from the perspective of NLP.In particular, we focus on the generalization principle and the respect for autonomy through informed consent.We provide four case studies to demonstrate how these principles can be used with NLP systems.We also recommend directions to avoid the ethical issues in these systems.
Too Much in Common: Shifting of Embeddings in Transformer Language Models and its Implications

The success of language models based on the Transformer architecture appears to be inconsistent with observed anisotropic properties of representations learned by such models.We resolve this by showing, contrary to previous studies, that the representations do not occupy a narrow cone, but rather drift in common directions.At any training step, all of the embeddings except for the ground-truth target embedding are updated with gradient in the same direction.Compounded over the training set, the embeddings drift and share common components, manifested in their shape in all the models we have empirically tested.Our experiments show that isotropy can be restored using a simple transformation. 1
Management of a higher education institution and ways to implement effectively its innovation policy

Introduction. The utilization of innovation activity mechanisms increases the competitiveness and quality of educational services. The necessity to develop an innovative environment, to intensify the innovative activity of higher education institutions determines the relevance of research in the field of innovative management. The purpose of the article is to identify ways to implement effectively the innovation policy of a higher educational institution. Materials and methods. A theoretical analysis of research materials (data of ratings of technological innovations in education, publications in periodicals, conference proceedings, and analytical materials) was used. Results. The main innovative educational factors and technologies were identified: educational virtual and media technologies, human resource management, balanced scorecard as an innovative technology, bimodal educational systems. Discussion. The main functions of the educational knowledge management system are in solving interrelated problems: in the formation of innovative and self-learning corporate human capital; in social conditions, within which this capital realizes itself in the creation of innovations demanded by the market and other consumers in the form of educational products.
Realization of a Universal Quantum Gate Set for Itinerant Microwave Photons

Deterministic photon-photon gates enable the controlled generation of entanglement between mobile carriers of quantum information. Such gates have thus far been exclusively realized in the optical domain and by relying on post-selection. Here, we present a non-post-selected, deterministic, photon-photon gate in the microwave frequency range realized using superconducting circuits. We emit photonic qubits from a source chip and route those qubits to a gate chip with which we realize a universal gate set by combining controlled absorption and re-emission with single-qubit gates and qubit-photon controlled-phase gates. We measure quantum process fidelities of $75\,\%$ for single- and of $57\,\%$ for two-qubit gates, limited mainly by radiation loss and decoherence. This universal gate set has a wide range of potential applications in superconducting quantum networks.
Exploring Factors Influencing Adoption of Blockchain in Accounting Applications using Technology–Organization–Environment Framework

Blockchain is one of the most promising technological innovations of recent times, with the potential to change the very way information systems are used by the accounting function.It is however expected to be disruptive and yet to see high adoption rates.Identification of factors influencing the adoption is required to empower the accounting fraternity to harness the full potential of blockchains.This study is one of the first to inductively explore and develop an adoption model for blockchains as well as for accounting applications with theoretical groundings in the Technology-Organization-Environment (TOE) framework, which has been extended with a variable for trust.Triangulation of methods and data sources used in this study contributed to the depth of research and understanding.A comprehensive literature review was first conducted.Its results were further enhanced using the encoding methodology, based on which influencing factors were identified and a model for adoption was developed.A qualitative exploratory study was undertaken next on twelve organizations at the cusp of adoption for accounting applications.Eight significant factors influencing the adoption thus identified are: relative advantage, uncertainty, top management support, technology readiness, industry, regulatory environment, competitive pressure and trust.The study contributes to revealing the relevance of blockchain to accounting while highlighting potential disruptions to enable better evaluation of the technology for adoption.The results may have limited generalizability, which may be overcome through a quantitative study in the future.
Differential Phase Shift Quantum Secret Sharing Using a Twin Field with Asymmetric Source Intensities

As an essential application of quantum mechanics in classical cryptography, quantum secret sharing has become an indispensable component of quantum internet. Recently, a differential phase shift quantum secret sharing protocol using a twin field has been proposed to break the linear rate-distance boundary. However, this original protocol has a poor performance over channels with asymmetric transmittances. To make it more practical, we present a differential phase shift quantum secret sharing protocol with asymmetric source intensities and give the security proof of our protocol against individual attacks. Taking finite-key effects into account, our asymmetric protocol can theoretically obtain the key rate two orders of magnitude higher than that of the original protocol when the difference in length between Alice’s channel and Bob’s is fixed at 14 km. Moreover, our protocol can provide a high key rate even when the difference is quite large and has great robustness against finite-key effects. Therefore, our work is meaningful for the real-life applications of quantum secret sharing.
How to Motivate Your Dragon: Teaching Goal-Driven Agents to Speak and Act in Fantasy Worlds

We seek to create agents that both act and communicate with other agents in pursuit of a goal.Towards this end, we extend LIGHT (Urbanek et al., 2019)-a large-scale crowd-sourced fantasy text-game-with a dataset of "quests". 1.These contain natural language motivations paired with in-game goals and human demonstrations; completing a quest might require dialogue or actions (or both).We introduce a reinforcement learning system that (1) incorporates large-scale language modeling-based and commonsense reasoning-based pre-training to imbue the agent with relevant priors; and (2) leverages a factorized action space of action commands and dialogue, balancing between the two.We conduct zero-shot evaluations using held-out human expert demonstrations, showing that our agents are able to act consistently and talk naturally with respect to their motivations.Insssssolent pessst!I should immolate you for this tresssspasss.And why is that, dragon?Ssstealing my preccciousss golden egg! I'll tell you what, I'll give you 10 sssseconds to amussse me with your sssstory and THEN I'll burn you alive!You said you wanted to attack me, dragon, did you not?Go ahead, I'm lisssssstening.get golden dragon egg Now now!I would have given you that had you asked!Assssssk for my own property back?What a riduculousss notion Look here, I told you to watch your mouth and you didn't, so leave or I'll make you leave.And now threatsss!Thisss is proving to be a mossst engaging conversssation.hit knight Give my regardsss to the valley floor below!
Template Filling with Generative Transformers

Template filling is generally tackled by a pipeline of two separate supervised systemsone for role-filler extraction and another for template/event recognition.Since pipelines consider events in isolation, they can suffer from error propagation.We introduce a framework based on end-to-end generative transformers for this task (i.e., GTT).It naturally models the dependence between entities both within a single event and across the multiple events described in a document.Experiments demonstrate that this framework substantially outperforms pipeline-based approaches, and other neural end-to-end baselines that do not model between-event dependencies.We further show that our framework specifically improves performance on documents containing multiple events.
Incremental Deep Neural Network Learning Using Classification Confidence Thresholding

Most modern neural networks for classification fail to take into account the concept of the unknown. Trained neural networks are usually tested in an unrealistic scenario with only examples from a closed set of known classes. In an attempt to develop a more realistic model, the concept of working in an open set environment has been introduced. This in turn leads to the concept of incremental learning where a model with its own architecture and initial trained set of data can identify unknown classes during the testing phase and autonomously update itself if evidence of a new class is detected. Some problems that arise in incremental learning are inefficient use of resources to retrain the classifier repeatedly and the decrease of classification accuracy as multiple classes are added over time. This process of instantiating new classes is repeated as many times as necessary, accruing errors. To address these problems, this article proposes the classification confidence threshold (CT) approach to prime neural networks for incremental learning to keep accuracies high by limiting forgetting. A lean method is also used to reduce resources used in the retraining of the neural network. The proposed method is based on the idea that a network is able to incrementally learn a new class even when exposed to a limited number samples associated with the new class. This method can be applied to most existing neural networks with minimal changes to network architecture.
Do Syntactic Probes Probe Syntax? Experiments with Jabberwocky Probing

Analysing whether neural language models encode linguistic information has become popular in NLP.One method of doing so, which is frequently cited to support the claim that models like BERT encode syntax, is called probing; probes are small supervised models trained to extract linguistic information from another model's output.If a probe is able to predict a particular structure, it is argued that the model whose output it is trained on must have implicitly learnt to encode it.However, drawing a generalisation about a model's linguistic knowledge about a specific phenomena based on what a probe is able to learn may be problematic: in this work, we show that semantic cues in training data means that syntactic probes do not properly isolate syntax.We generate a new corpus of semantically nonsensical but syntactically well-formed Jabberwocky sentences, which we use to evaluate two probes trained on normal data.We train the probes on several popular language models (BERT, GPT-2, and RoBERTa), and find that in all settings they perform worse when evaluated on these data, for one probe by an average of 15.4 UUAS points absolute.Although in most cases they still outperform the baselines, their lead is reduced substantially, e.g. by 53% in the case of BERT for one probe.This begs the question: what empirical scores constitute knowing syntax?
English to Bangla Machine Translation Using Recurrent Neural Network

The applications of recurrent neural networks in machine translation are increasing in natural language processing. Besides other languages, Bangla language contains a large amount of vocabulary. Improvement of English to Bangla machine translation would be a significant contribution to Bangla Language processing. This paper describes an architecture of English to Bangla machine translation system. The system has been implemented with the encoder-decoder recurrent neural network. The model uses a knowledge-based context vector for the mapping of English and Bangla words. Performances of the model based on activation functions are measured here. The best performance is achieved for the linear activation function in encoder layer and the tanh activation function in decoder layer. From the execution of GRU and LSTM layer, GRU performed better than LSTM. The attention layers are enacted with softmax and sigmoid activation function. The approach of the model outperforms the previous state-of-the-art systems in terms of cross-entropy loss metrics. The reader can easily find out the structure of the machine translation of English to Bangla and the efficient activation functions from the paper.
IndT5: A Text-to-Text Transformer for 10 Indigenous Languages

Transformer language models have become fundamental components of natural language processing based pipelines.Although several Transformer models have been introduced to serve many languages, there is a shortage of models pre-trained for low-resource and Indigenous languages.In this work, we introduce IndT5, the first Transformer language model for Indigenous languages.To train IndT5, we build IndCorpus-a new dataset for ten Indigenous languages and Spanish.We also present the application of IndT5 to machine translation by investigating different approaches to translate between Spanish and the Indigenous languages as part of our contribution to the AmericasNLP 2021 Shared Task on Open Machine Translation.IndT5 and IndCorpus are publicly available for research. 1
Comparing the Performance of AdaBoost, XGBoost, and Logistic Regression for Imbalanced Data

An imbalanced data problem occurs in the absence of a good class distribution between classes.Imbalanced data will cause the classifier to be biased to the majority class as the standard classification algorithms are based on the belief that the training set is balanced.Therefore, it is crucial to find a classifier that can deal with imbalanced data for any given classification task.The aim of this research is to find the best method among AdaBoost, XGBoost, and Logistic Regression to deal with imbalanced simulated datasets and real datasets.The performances of these three methods in both simulated and real imbalanced datasets are compared using five performance measures, namely sensitivity, specificity, precision, F1-score, and g-mean.The results of the simulated datasets show that logistic regression performs better than AdaBoost and XGBoost in highly imbalanced datasets, whereas in the real imbalanced datasets, AdaBoost and logistic regression demonstrated similarly good performance.All methods seem to perform well in datasets that are not severely imbalanced.Compared to AdaBoost and XGBoost, logistic regression is found to predict better for datasets with severe imbalanced ratios.However, all three methods perform poorly for data with a 5% minority, with a sample size of n = 100.In this study, it is found that different methods perform the best for data with different minority percentages.
RST Parsing from Scratch

We introduce a novel top-down end-to-end formulation of document level discourse parsing in the Rhetorical Structure Theory (RST) framework.In this formulation, we consider discourse parsing as a sequence of splitting decisions at token boundaries and use a seq2seq network to model the splitting decisions.Our framework facilitates discourse parsing from scratch without requiring discourse segmentation as a prerequisite; rather, it yields segmentation as part of the parsing process.Our unified parsing model adopts a beam search to decode the best tree structure by searching through a space of high scoring trees.With extensive experiments on the standard English RST discourse treebank, we demonstrate that our parser outperforms existing methods by a good margin in both end-to-end parsing and parsing with gold segmentation.More importantly, it does so without using any handcrafted features, making it faster and easily adaptable to new languages and domains.
ArQTiC: A Full-stack Software Package for Simulating Materials on Quantum Computers

ArQTiC is an open-source, full-stack software package built for the simulations of materials on quantum computers. It currently can simulate materials that can be modeled by any Hamiltonian derived from a generic, one-dimensional, time-dependent Heisenberg Hamiltonian. ArQTiC includes modules for generating quantum programs for real- and imaginary-time evolution, quantum circuit optimization, connection to various quantum backends via the cloud, and post-processing of quantum results. By enabling users to seamlessly design, execute, and analyze materials simulations on quantum computers, ArQTiC opens this field to a broader community of scientists from a wider range of scientific domains.
ROBUST FEATURE EXTRACTION BASED ON SPECTRAL AND PROSODIC FEATURES FOR CLASSICAL ARABIC ACCENTS RECOGNITION

The variability of speech patterns produced by individuals is unique. The uniqueness is due to the accent influenced by the individual’s native dialect. Modeling individual variation of spoken language is a challenge under the Automatic Speech Recognition (ASR) field. The individual differences concerning of accent revealed the critical issues in Classical Arabic (CA) recitation among Malay speakers. This problem is caused by the misarticulate phonemes, which affected by the Malay colloquial dialect and native language. Most of ASR researchers are unable to understand the behavior of phonemes and speech patterns in CA, thus degrading the ASR performance. This paper focuses on identifying the accent of Malay speakers on the recitation of Sūrah Al-Fātiḥah with 7 Quranic accents, using the proposed feature extraction technique. In this work, the technique presented is a combination of spectral and prosodic features, which are mainly designed for accent in ASR. Differed with current conventional method, where the spectral feature alone has been applied for feature extraction in many ASR research. The prosodic elements in CA such as pitch, energy and spectral-tilt need to be taken into consideration, thus a significant variety of features for each phoneme able to help in distinguishing one accent from another. Meanwhile, the spectral representation of Mel-Frequency Cepstral Coefficients (MFCC) is utilized for the decorrelating property of the cepstrum. At present, Gaussian Mixture Models (GMM) has been applied for the classification stage. From experimental results, the system performance is the best when the prosodic is integrated with MFCC, alongside the GMM with 81.7%-89.6% of accuracy. It was 5.5%-7.3% increment as compared to MFCC alone.
BERT-Defense: A Probabilistic Model Based on BERT to Combat Cognitively Inspired Orthographic Adversarial Attacks

Adversarial attacks expose important blind spots of deep learning systems.While wordand sentence-level attack scenarios mostly deal with finding semantic paraphrases of the input that fool NLP models, character-level attacks typically insert typos into the input stream.It is commonly thought that these are easier to defend via spelling correction modules.In this work, we show that both a standard spellchecker and the approach of Pruthi et al. ( 2019), which trains to defend against insertions, deletions and swaps, perform poorly on the character-level benchmark recently proposed in Eger and Benz (2020) which includes more challenging attacks such as visual and phonetic perturbations and missing word segmentations.In contrast, we show that an untrained iterative approach which combines context-independent character-level information with context-dependent information from BERT's masked language modeling can perform on par with human crowd-workers from Amazon Mechanical Turk (AMT) supervised via 3-shot learning.
ExplainaBoard: An Explainable Leaderboard for NLP

With the rapid development of NLP research, leaderboards have emerged as one tool to track the performance of various systems on various NLP tasks.They are effective in this goal to some extent, but generally present a rather simplistic one-dimensional view of the submitted systems, communicated only through holistic accuracy numbers.In this paper, we present a new conceptualization and implementation of NLP evaluation: the EXPLAIN-ABOARD, which in addition to inheriting the functionality of the standard leaderboard, also allows researchers to (i) diagnose strengths and weaknesses of a single system (e.g.what is the best-performing system bad at?) (ii) interpret relationships between multiple systems.(e.g.where does system A outperform system B? What if we combine systems A, B and C?) and (iii) examine prediction results closely (e.g.what are common errors made by multiple systems or in what contexts do particular errors occur?).So far, EXPLAINABOARD covers more than 400 systems, 50 datasets, 40 languages, and 12 tasks. 1We not only released an online platform at the website 2 but also make our evaluation tool an API with MIT Licence at Github 3 and PyPi 4 that allows users to conveniently assess their models offline.We additionally release all output files from systems that we have run or collected to motivate "output-driven" research in the future.