Reviewerly/exHarmony huggingface dataset:
Learning to Explain: Datasets and Models for Identifying Valid Reasoning Chains in Multihop Question-Answering

Despite the rapid progress in multihop question-answering (QA), models still have trouble explaining why an answer is correct, with limited explanation training data available to learn from.To address this, we introduce three explanation datasets in which explanations formed from corpus facts are annotated.Our first dataset, eQASC, contains over 98K explanation annotations for the multihop question answering dataset QASC, and is the first that annotates multiple candidate explanations for each answer.The second dataset eQASC-perturbed is constructed by crowd-sourcing perturbations (while preserving their validity) of a subset of explanations in QASC, to test consistency and generalization of explanation prediction models.The third dataset eOBQA is constructed by adding explanation annotations to the OBQA dataset to test generalization of models trained on eQASC.We show that this data can be used to significantly improve explanation quality (+14% absolute F1 over a strong retrieval baseline) using a BERT-based classifier, but still behind the upper bound, offering a new challenge for future research.We also explore a delexicalized chain representation in which repeated noun phrases are replaced by variables, thus turning them into generalized reasoning chains (for example: "X is a Y" AND "Y has Z" IMPLIES "X has Z").We find that generalized chains maintain performance while also being more robust to certain perturbations. 1
Impact of Politically Biased Data on Hate Speech Classification

One challenge that social media platforms are facing nowadays is hate speech.Hence, automatic hate speech detection has been increasingly researched in recent years -in particular with the rise of deep learning.A problem of these models is their vulnerability to undesirable bias in training data.We investigate the impact of political bias on hate speech classification by constructing three politicallybiased data sets (left-wing, right-wing, politically neutral) and compare the performance of classifiers trained on them.We show that (1) political bias negatively impairs the performance of hate speech classifiers and (2) an explainable machine learning model can help to visualize such bias within the training data.The results show that political bias in training data has an impact on hate speech classification and can become a serious issue.
UNION: An Unreferenced Metric for Evaluating Open-ended Story Generation

Despite the success of existing referenced metrics (e.g., BLEU and MoverScore), they correlate poorly with human judgments for openended text generation including story or dialog generation because of the notorious oneto-many issue: there are many plausible outputs for the same input, which may differ substantially in literal or semantics from the limited number of given references.To alleviate this issue, we propose UNION, a learnable UNreferenced metrIc for evaluating Open-eNded story generation, which measures the quality of a generated story without any reference.Built on top of BERT, UNION is trained to distinguish human-written stories from negative samples and recover the perturbation in negative stories.We propose an approach of constructing negative samples by mimicking the errors commonly observed in existing NLG models, including repeated plots, conflicting logic, and long-range incoherence.Experiments on two story datasets demonstrate that UNION is a reliable measure for evaluating the quality of generated stories, which correlates better with human judgments and is more generalizable than existing state-of-theart metrics.
CSP:Code-Switching Pre-training for Neural Machine Translation

This paper proposes a new pre-training method, called Code-Switching Pre-training (CSP for short) for Neural Machine Translation (NMT).Unlike traditional pre-training method which randomly masks some fragments of the input sentence, the proposed CSP randomly replaces some words in the source sentence with their translation words in the target language.Specifically, we firstly perform lexicon induction with unsupervised word embedding mapping between the source and target languages, and then randomly replace some words in the input sentence with their translation words according to the extracted translation lexicons.CSP adopts the encoderdecoder framework: its encoder takes the codemixed sentence as input, and its decoder predicts the replaced fragment of the input sentence.In this way, CSP is able to pre-train the NMT model by explicitly making the most of the cross-lingual alignment information extracted from the source and target monolingual corpus.Additionally, we relieve the pretrainfinetune discrepancy caused by the artificial symbols like [mask].To verify the effectiveness of the proposed method, we conduct extensive experiments on unsupervised and supervised NMT.Experimental results show that CSP achieves significant improvements over baselines without pre-training or with other pre-training methods.
Bootstrapping quantum process tomography via a perturbative ansatz

Quantum process tomography has become increasingly critical as the need grows for robust verification and validation of candidate quantum processors. Here, we present an approach for efficient quantum process tomography that uses a physically motivated ansatz for an unknown quantum process. Our ansatz bootstraps to an effective description for an unknown process on a multi-qubit processor from pairwise two-qubit tomographic data. Further, our approach can inherit insensitivity to system preparation and measurement error from the two-qubit tomography scheme. We benchmark our approach using numerical simulation of noisy three-qubit gates, and show that it produces highly accurate characterizations of quantum processes. Further, we demonstrate our approach experimentally, building three-qubit gate reconstructions from two-qubit tomographic data.
Blockchain for Peer-to-Peer Transactive Energy Trading in Networked Microgrids: Providing an Effective and Decentralized Strategy

Renewable energy resources are key components of the sustainable social development that has been rapidly deployed in recent years. The proliferation of renewable energy resources promotes socioeconomic development in various parts of the world, and islandable microgrids (MGs) play an increasingly important role in such development. MGs represent a viable alternative to conventional bulk power transmission for addressing the vulnerabilities of long-distance power delivery from centralized generation units to distributed customer sites. A controllable MG equipped with on-site distributed energy resources (DERs), which could include distributed generators, energy storage, and economic demand responses, cultivates local resources to enhance the reliability, resilience, sustainability, security, and economics of local power systems.
XHate-999: Analyzing and Detecting Abusive Language Across Domains and Languages

We present XHATE-999, a multi-domain and multilingual evaluation data set for abusive language detection.By aligning test instances across six typologically diverse languages, XHATE-999 for the first time allows for disentanglement of the domain transfer and language transfer effects in abusive language detection.We conduct a series of domain-and language-transfer experiments with state-of-the-art monolingual and multilingual transformer models, setting strong baseline results and profiling XHATE-999 as a comprehensive evaluation resource for abusive language detection.Finally, we show that domain-and language-adaptation, via intermediate masked language modeling on abusive corpora in the target language, can lead to substantially improved abusive language detection in the target language in the zero-shot transfer setups.
Specializing Unsupervised Pretraining Models for Word-Level Semantic Similarity

Unsupervised pretraining models have been shown to facilitate a wide range of downstream NLP applications.These models, however, retain some of the limitations of traditional static word embeddings.In particular, they encode only the distributional knowledge available in raw text corpora, incorporated through language modeling objectives.In this work, we complement such distributional knowledge with external lexical knowledge, that is, we integrate the discrete knowledge on word-level semantic similarity into pretraining.To this end, we generalize the standard BERT model to a multi-task learning setting where we couple BERT's masked language modeling and next sentence prediction objectives with an auxiliary task of binary word relation classification.Our experiments suggest that our "Lexically Informed" BERT (LIBERT), specialized for the word-level semantic similarity, yields better performance than the lexically blind "vanilla" BERT on several language understanding tasks.Concretely, LIBERT outperforms BERT in 9 out of 10 tasks of the GLUE benchmark and is on a par with BERT in the remaining one.Moreover, we show consistent gains on 3 benchmarks for lexical simplification, a task where knowledge about word-level semantic similarity is paramount, as well as large gains on lexical reasoning probes.
Deep Reinforcement Learning: A State-of-the-Art Walkthrough

Deep Reinforcement Learning is a topic that has gained a lot of attention recently, due to the unprecedented achievements and remarkable performance of such algorithms in various benchmark tests and environmental setups. The power of such methods comes from the combination of an already established and strong field of Deep Learning, with the unique nature of Reinforcement Learning methods. It is, however, deemed necessary to provide a compact, accurate and comparable view of these methods and their results for the means of gaining valuable technical and practical insights. In this work we gather the essential methods related to Deep Reinforcement Learning, extracting common property structures for three complementary core categories: a) Model-Free, b) Model-Based and c) Modular algorithms. For each category, we present, analyze and compare state-of-the-art Deep Reinforcement Learning algorithms that achieve high performance in various environments and tackle challenging problems in complex and demanding tasks. In order to give a compact and practical overview of their differences, we present comprehensive comparison figures and tables, produced by reported performances of the algorithms under two popular simulation platforms: the Atari Learning Environment and the MuJoCo physics simulation platform. We discuss the key differences of the various kinds of algorithms, indicate their potential and limitations, as well as provide insights to researchers regarding future directions of the field.
Application of hyperspectral remote sensing for supplementary investigation of polymetallic deposits in Huaniushan ore region, northwestern China

Abstract A gold–silver–lead–zinc polymetallic ore was selected in Huaniushan, Gansu Province as the study area. Hyperspectral aerial images as the primary information source, ground spectrum tests, and sampling analysis were used as auxiliary techniques. They were combined with large-scale mineral and geological maps and other high-resolution satellite remote sensing images. Hyperspectral remote sensing classification identification and quantitative analysis methods were used to study the main mineral resources and rock mass occurrence. Finally, deposit distribution information was extracted and validated. The results showed that the effective classification methods by hyperspectral images were spectral angle mapping, minimum noise fraction transform, and mixed tuned matched filtering. Based on the ground survey, combined with sampling analysis, the accuracy of classification was 80%. The recognition rate of the main ore body—the iron-manganese cap lead–zinc oxide ore—was as high as 81%. This research showed that hyperspectral remote sensing in this mining area has excellent demonstration effects and is worth completing and supplementing original mineral and geological maps. The targets are important areas for detailed follow-up on mineral resource exploration.
Predicting Individual Susceptibility to Visually Induced Motion Sickness by Questionnaire

Background: The introduction of new visual technologies increases the risk of visually induced motion sickness (VIMS). The aim was to evaluate the 6-item Visually Induced Motion Sickness Susceptibility Questionnaire (VIMSSQ; also known as the VIMSSQ-short) and other predictors for individual susceptibility to VIMS. Methods: Healthy participants (10M + 20F), mean age 22.9 (SD 5.0) years, viewed a 360° panoramic city scene projected in the visual equivalent to the situation of rotating about an axis tilted from the vertical. The scene rotated at 0.2 Hz (72° s −1 ), with a ‘wobble’ produced by superimposed 18° tilt on the rotational axis, with a field of view of 83.5°. Exposure was 10 min or until moderate nausea was reported. Simulator Sickness Questionnaire (SSQ) was the index of VIMS. Predictors/correlates were VIMSSQ, Motion Sickness Susceptibility Questionnaire (MSSQ), migraine (scale), syncope, Social &amp;amp; Work Impact of Dizziness (SWID), sleep quality/disturbance, personality (“Big Five” TIPI), a prior multisensory Stepping-Vection test, and vection during exposure. Results: The VIMSSQ had good scale reliability (Cronbach’s alpha = 0.84) and correlated significantly with the SSQ ( r = 0.58). Higher MSSQ, migraine, syncope, and SWID also correlated significantly with SSQ. Other variables had no significant relationships with SSQ. Regression models showed that the VIMSSQ predicted 34% of the individual variation of VIMS, increasing to 56% as MSSQ, migraine, syncope, and SWID were incorporated as additional predictors. Conclusion: The VIMSSQ is a useful adjunct to the MSSQ in predicting VIMS. Other predictors included migraine, syncope, and SWID. No significant relationship was observed between vection and VIMS.
Visualizing and Interacting with Geospatial Networks: A Survey and Design Space

Abstract This paper surveys visualization and interaction techniques for geospatial networks from a total of 95 papers. Geospatial networks are graphs where nodes and links can be associated with geographic locations. Examples can include social networks, trade and migration, as well as traffic and transport networks. Visualizing geospatial networks poses numerous challenges around the integration of both network and geographical information as well as additional information such as node and link attributes, time and uncertainty. Our overview analyses existing techniques along four dimensions: (i) the representation of geographical information, (ii) the representation of network information, (iii) the visual integration of both and (iv) the use of interaction. These four dimensions allow us to discuss techniques with respect to the trade‐offs they make between showing information across all these dimensions and how they solve the problem of showing as much information as necessary while maintaining readability of the visualization. https://geonetworks.github.io .
Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.
A Black Box Tool for Robustness Testing of REST Services

REST services are nowadays being used to support many businesses, with most major companies exposing their services via REST interfaces (e.g., Google, Amazon, Instagram, and Slack). In this type of scenarios, heterogeneity is prevalent and software is sometimes exposed to unexpected conditions that may activate residual bugs, leading service operations to fail. Such failures may lead to financial or reputation losses (e.g., information disclosure). Although techniques and tools for assessing robustness have been thoroughly studied and applied to a large diversity of domains, REST services still lack practical approaches that specialize in robustness evaluation. In this paper, we present a tool (named bBOXRT) for performing robustness tests over REST services, solely based on minimal information expressed in their interface descriptions. We used bBOXRT to evaluate an heterogeneous set of 52 REST services that comprise 1,351 operations and fit in distinct categories (e.g., public, private, in-house). We were able to disclose several different types of robustness problems, including issues in services with strong reliability requirements and also a few security vulnerabilities. The results show that REST services are being deployed preserving software defects that harm service integration, and also carrying security vulnerabilities that can be exploited by malicious users.
Forward and Backward Private Conjunctive Searchable Symmetric Encryption

Consider, for instance, a client that offloads an encrypted database of (potentially sensitive) emails to an untrusted server.At a later point of time, the client might want to issue a query of the form "retrieve all emails received from xyz@foobar.org or "retrieve all emails with the keyword "research" in the subject field".Ideally, the client should be able to perform this task without revealing any sensitive information to the server, such as the sources and contents of the emails, the keywords underlying a given query, the distribution of keywords across emails, etc.Unfortunately, techniques such as fully homomorphic encryption [19], that potentially allow achieving such an "ideal" notion of privacy, are unsuitable for practical deployment due to large performance overheads.
A Low-Cost Three-Dimensional DenseNet Neural Network for Alzheimer’s Disease Early Discovery

Alzheimer’s disease is the most prevalent dementia among the elderly population. Early detection is critical because it can help with future planning for those potentially affected. This paper uses a three-dimensional DenseNet architecture to detect Alzheimer’s disease in magnetic resonance imaging. Our work is restricted to the use of freely available tools. We constructed a deep neural network classifier with metrics of 0.86¯ mean accuracy, 0.86¯ mean sensitivity (micro-average), 0.86¯ mean specificity (micro-average), and 0.91¯ area under the receiver operating characteristic curve (micro-average) for the task of discriminating between five different disease stages or classes. The use of tools available for free ensures the reproducibility of the study and the applicability of the classification system in developing countries.
Preprocessing Arabic text on social media

<h2>Abstract</h2> <b>C</b>urrently, social media plays an important role in daily life and routine. Millions of people use social media for different purposes. Large amounts of data flow through online networks every second, and these data contain valuable information that can be extracted if the data are properly processed and analyzed. However, most of the processing results are affected by preprocessing difficulties. This paper presents an approach to extract information from social media Arabic text. It provides an integrated solution for the challenges in preprocessing Arabic text on social media in four stages: data collection, cleaning, enrichment, and availability. The preprocessed Arabic text is stored in structured database tables to provide a useful corpus to which, information extraction and data analysis algorithms can be applied. The experiment in this study reveals that the implementation of the proposed approach yields a useful and full-featured dataset and valuable information. The resultant dataset presented the Arabic text in three structured levels with more than 20 features. Additionally, the experiment provides valuable information and processed results such as topic classification and sentiment analysis.
Using Secure Multi-Party Computation to Protect Privacy on a Permissioned Blockchain

The development of information technology has brought great convenience to our lives, but at the same time, the unfairness and privacy issues brought about by traditional centralized systems cannot be ignored. Blockchain is a peer-to-peer and decentralized ledger technology that has the characteristics of transparency, consistency, traceability and fairness, but it reveals private information in some scenarios. Secure multi-party computation (MPC) guarantees enhanced privacy and correctness, so many researchers have been trying to combine secure MPC with blockchain to deal with privacy and trust issues. In this paper, we used homomorphic encryption, secret sharing and zero-knowledge proofs to construct a publicly verifiable secure MPC protocol consisting of two parts—an on-chain computation phase and an off-chain preprocessing phase—and we integrated the protocol as part of the chaincode in Hyperledger Fabric to protect the privacy of transaction data. Experiments showed that our solution performed well on a permissioned blockchain. Most of the time taken to complete the protocol was spent on communication, so the performance has a great deal of room to grow.
Cyberattacks and threats during COVID-19: A systematic literature review

Background: The novel Coronavirus Disease-2019 (COVID-19) pandemic, a deadly contagious disease has left the global village in disarray, driving people and firms, especially healthcare service providers to rely heavily on information communication technologies (ICTs) for administering telemedicine through digital tools. This study contributes to knowledge and information sharing and debates on cybersecurity. Objective: The objective was to analyse the impact of cybercrimes on the global economy at a time when the whole world is focused on fighting and minimising the spread of COVID-19. The study also analysed common cybersecurity threats, attacks and information systems security vulnerabilities during the period of the pandemic. Method: The study adopted a systematic literature review from December 2019 to June 2020. There are global research studies on cybersecurity issues brought about by the coronavirus pandemic, and therefore, literature survey was not limited to any geographic area. A mixed method research was adopted in this study. Results: The study revealed that there is an exponential growth of cyberattacks and threats because the global economy has been paying much attention to the COVID-19 pandemic. During the pandemic, large corporations, healthcare industry and government agencies have been targets for cyberattacks and threats. Conclusion: It has been demonstrated that cyberattacks and threats during the COVID-19 pandemic are rising exponentially, creating another wave of challenges for the global economy, which is already reeling under the novel coronavirus. Thus, exerting excessive pressure on financial and human resources that have to contend with the novel coronavirus, with the expectation that resources have to be mobilised to deal with cybercrimes. The study recommends that firms and individuals should devise cybersecurity interventions to protect their data and information systems infrastructure.