Reviewerly/exHarmony huggingface dataset:
Indicators sensitivity analysis for environmental engineering geological patterns caused by underground coal mining with integrating variable weight theory and improved matter-element extension model

This research utilized the variable weight theory (VWT) and improved the matter-element extension model (MEEM) to conduct an indicators sensitivity analysis of environmental engineering geological patterns (EEGPs) related to underground coal mining. First, four types of EEGPs, i.e., basically unaffected model, gradually restored model after destruction, gradually deteriorated model and disaster model, were defined. Subsequently, 13 indicators were selected from different spheres. Then, a sensitivity analysis for EEGPs was conducted by VWT and improved MEEM. On the basis of changes in indicator values by ±10–50%, indicators sensitivity was separately determined in four types of EEGPs. In conclusion, rainfall capacity, evaporation capacity and normalized difference vegetation index (NDVI) are sensitive indicators in a basically unaffected model, while the thicknesses of the coal seam and laterite are sensitive indicators in the other three EEGPs. For comparison, types of EEGPs were tested by both VWT and traditional MEEM, verifying the accuracy of indicators sensitivity results and reasonability by VWT and improved MEEM method. Finally, mining measures such as layered, stripe, filling mining and grouting reinforce method corresponding to thicknesses of coal seam and laterite were proposed. Therefore, specific mining methods under types of EEGPs can be provided to decision-makers in the mining industry and to environmental protection departments.
Critical Care, Critical Data

As big data, machine learning, and artificial intelligence continue to penetrate into and transform many facets of our lives, we are witnessing the emergence of these powerful technologies within health care. The use and growth of these technologies has been contingent on the availability of reliable and usable data, a particularly robust resource in critical care medicine where continuous monitoring forms a key component of the infrastructure of care. The response to this opportunity has included the development of open databases for research and other purposes; the development of a collaborative form of clinical data science intended to fully leverage these data resources, and the creation of data-driven applications for purposes such as clinical decision support. Most recently, data levels have reached the thresholds required for the development of robust artificial intelligence features for clinical purposes. The systematic capture and analysis of clinical data in both individuals and populations allows us to begin to move toward precision medicine in the intensive care unit (ICU). In this perspective review, we examine the fundamental role of data as we present the current progress that has been made toward an artificial intelligence (AI)-supported, data-driven precision critical care medicine.
Cross-Lingual Training for Automatic Question Generation

Automatic question generation (QG) is a challenging problem in natural language understanding.QG systems are typically built assuming access to a large number of training instances where each instance is a question and its corresponding answer.For a new language, such training instances are hard to obtain making the QG problem even more challenging.Using this as our motivation, we study the reuse of an available large QG dataset in a secondary language (e.g.English) to learn a QG model for a primary language (e.g.Hindi) of interest.For the primary language, we assume access to a large amount of monolingual text but only a small QG dataset.We propose a cross-lingual QG model which uses the following training regime: (i) Unsupervised pretraining of language models in both primary and secondary languages and (ii) joint supervised training for QG in both languages.We demonstrate the efficacy of our proposed approach using two different primary languages, Hindi and Chinese.We also create and release a new question answering dataset for Hindi consisting of 6555 sentences.
Drifted Twitter Spam Classification Using Multiscale Detection Test on K-L Divergence

Twitter spam classification is a tough challenge for social media platforms and cyber security companies. Twitter spam with illegal links may evolve over time in order to deceive filtering models, causing disastrous loss to both users and the whole network. We define this distributional evolution as a concept drift scenario. To build an effective model, we adopt K-L divergence to represent spam distribution and use a multiscale drift detection test (MDDT) to localize possible drifts therein. A base classifier is then retrained based on the detection result to gain performance improvement. Comprehensive experiments show that K-L divergence has highly consistent change patterns between features when a drift occurs. Also, the MDDT is proved to be effective in improving final classification result in both accuracy, recall, and f-measure.
Modeling Frames in Argumentation

In argumentation, framing is used to emphasize a specific aspect of a controversial topic while concealing others.When discussing the legalization of drugs, for instance, its economical aspect may be emphasized.In general, we call a set of arguments that focus on the same aspect a frame.An argumentative text has to serve the "right" frame(s) to convince the audience to adopt the author's stance (e.g., being pro or con legalizing drugs).More specifically, an author has to choose frames that fit the audience's interests and cultural background.This paper introduces frame identification, which is the task of splitting a set of arguments into a set of non-overlapping frames.We present a fully unsupervised approach to this task, which first removes topical information from the arguments and then identifies frames using clustering.For evaluation purposes, we provide a corpus with 12 326 debateportal arguments, organized along the frames of the debates' topics.On this corpus, our approach outperforms different strong baselines, achieving an F 1 -score of 0.28.
Layerwise Relevance Visualization in Convolutional Text Graph Classifiers

Representations in the hidden layers of Deep Neural Networks (DNN) are often hard to interpret since it is difficult to project them into an interpretable domain.Graph Convolutional Networks (GCN) allow this projection, but existing explainability methods do not exploit this fact, i.e. do not focus their explanations on intermediate states.In this work, we present a novel method that traces and visualizes features that contribute to a classification decision in the visible and hidden layers of a GCN.Our method exposes hidden cross-layer dynamics in the input graph structure.We experimentally demonstrate that it yields meaningful layerwise explanations for a GCN sentence classifier.
The Embodiment of Objects: Review, Analysis, and Future Directions

Here we offer a thorough review of the empirical literature on the conditions under which an object, such as a tool or a prosthetic (whether real or virtual), can be experienced as being in some sense a part or extension of one’s body. We discuss this literature both from the standpoint of the apparent malleability of our body representations, and also from within the framework of radical embodied cognition, which understands the phenomenon to result not from an alteration to a representation, but rather from the achievement of a certain kind of sensory/motor coupling. We highlight both the tensions between these frameworks, and also areas where they can productively complement one another for future research.
Purification of the residual entanglement

Entanglement purification is an indispensable ingredient in extended quantum communication networks and usually determines the efficiency and communication rate of quantum communication protocols. Different from all existing entanglement purification protocols (EPPs) where two or more copies of low quality mixed entangled states are selected from the same ensemble, here we describe a general and optimal EPP for arbitrary initial mixed states from different ensembles. We show that the successful operation of EPP may not obtain a higher fidelity mixed state, while the discarded source pair, which is usually regarded as a failure in existing EPPs, may have residual entanglement and can be reused to increase the yield of entanglement purification. We give the criterions of both the successful purification to obtain a higher fidelity mixed state and the existence of residual entanglement. Moreover, we reveal that entanglement purification procedure causes some entanglement loss. Finally, we provide an optimal approach to reduce the entanglement loss. This approach can also be used to increase the yield of entanglement purification. Our EPP may have potential application in long-distance quantum communications.
Banff Digital Pathology Working Group: Going digital in transplant pathology

The Banff Digital Pathology Working Group (DPWG) was formed in the time leading up to and during the joint American Society for Histocompatibility and Immunogenetics/Banff Meeting, September 23-27, 2019, held in Pittsburgh, Pennsylvania. At the meeting, the 14th Banff Conference, presentations directly and peripherally related to the topic of "digital pathology" were presented; and discussions before, during, and after the meeting have resulted in a list of issues to address for the DPWG. Included are practice standardization, integrative approaches for study classification, scoring of histologic parameters (eg, interstitial fibrosis and tubular atrophy and inflammation), algorithm classification, and precision diagnosis (eg, molecular pathways and therapeutics). Since the meeting, a survey with international participation of mostly pathologists (81%) was conducted, showing that whole slide imaging is available at the majority of centers (71%) but that artificial intelligence (AI)/machine learning was only used in ≈12% of centers, with a wide variety of programs/algorithms employed. Digitalization is not just an end in itself. It also is a necessary precondition for AI and other approaches. Discussions at the meeting and the survey highlight the unmet need for a Banff DPWG and point the way toward future contributions that can be made.
ReInceptionE: Relation-Aware Inception Network with Joint Local-Global Structural Information for Knowledge Graph Embedding

The goal of Knowledge graph embedding (KGE) is to learn how to represent the lowdimensional vectors for entities and relations based on the observed triples.The conventional shallow models are limited to their expressiveness.ConvE (Dettmers et al., 2018) takes advantage of CNN and improves the expressive power with parameter efficient operators by increasing the interactions between head and relation embeddings.However, there is no structural information in the embedding space of ConvE, and the performance is still limited by the number of interactions.The recent KBGAT (Nathani et al., 2019) provides another way to learn embeddings by adaptively utilizing structural information.In this paper, we take the benefits of ConvE and KBGAT together and propose a Relation-aware Inception network with joint local-global structural information for knowledge graph Embedding (ReInceptionE).Specifically, we first explore the Inception network to learn query embedding, which aims to further increase the interactions between head and relation embeddings.Then, we propose to use a relation-aware attention mechanism to enrich the query embedding with the local neighborhood and global entity information.Experimental results on both WN18RR and FB15k-237 datasets demonstrate that ReIncep-tionE achieves competitive performance compared with state-of-the-art methods.
Adaptive terahertz image super-resolution with adjustable convolutional neural network

During the real-aperture-scanning imaging process, terahertz (THz) images are often plagued with the problem of low spatial resolution. Therefore, an accommodative super-resolution framework for THz images is proposed. Specifically, the 3D degradation model for the imaging system is firstly proposed by incorporating the focused THz beam distribution, which determines the relationship between the imaging range and the corresponding image restoration level. Secondly, an adjustable CNN is introduced to cope with this range dependent super-resolution problem. By simply tuning an interpolation parameter, the network can be adjusted to produce arbitrary restoration levels between the trained fixed levels without extra training. Finally, by selecting the appropriate interpolation coefficient according to the measured imaging range, each THz image can be coped with its matched network and reach the outstanding super-resolution effect. Both the simulated and real tested data, acquired by a 160 ∼ 220 GHz imager, have been used to demonstrate the superiority of our method.
On the Influence of Bias-Correction on Distributed Stochastic Optimization

Various bias-correction methods such as EXTRA, gradient tracking methods, and exact diffusion have been proposed recently to solve distributed deterministic optimization problems. These methods employ constant step-sizes and converge linearly to the exact solution under proper conditions. However, their performance under stochastic and adaptive settings is less explored. It is still unknown whether, when and why these bias-correction methods can outperform their traditional counterparts with noisy gradient and constant step-sizes. This work studies the performance of exact diffusion under the stochastic and adaptive setting, and provides conditions under which exact diffusion has superior steady-state mean-square deviation (MSD) performance than traditional algorithms without bias-correction. In particular, it is proven that this superiority is more evident over sparsely-connected network topologies such as lines, cycles, or grids. Conditions are also provided under which exact diffusion method can or degrade the performance of traditional methods. Simulations are provided to validate the theoretical findings.
Efficient Clustering of Emails Into Spam and Ham: The Foundational Study of a Comprehensive Unsupervised Framework

The spread and adoption of spam emails in malicious activities like information and identity theft, malware propagation, monetary and reputational damage etc. are on the rise with increased effectiveness and diversification. Without doubt these criminal acts endanger the privacy of many users and businesses'. Several research initiatives have taken place to address the issue with no complete solution until now; and we believe an intelligent and automated methodology should be the way forward to tackle the challenges. However, till date limited studies have been conducted on the applications of purely unsupervised frameworks and algorithms in tackling the problem. To explore and investigate the possibilities, we intend to propose an anti-spam framework that fully relies on unsupervised methodologies through a multi-algorithm clustering approach. This article presents an in-depth analysis on the methodologies of the first component of the framework, examining only the domain and header related information found in email headers. A novel method of feature reduction using an ensemble of `unsupervised' feature selection algorithms has also been investigated in this study. In addition, a comprehensive novel dataset of 100,000 records of ham and spam emails has been developed and used as the data source. Key findings are summarized as follows: I) out of six different clustering algorithms used - Spectral and K-means demonstrated acceptable performance while OPTICS projected the optimum clustering with an average of 3.5% better efficiency than Spectral and K-means, validated through a range of validations processes II) The other three algorithms- BIRCH, HDBSCAN and K-modes, did not fare well enough. III) The average balanced accuracy for the optimum three algorithms has been found to be ≈94.91%, and IV) The proposed feature reduction framework achieved its goal with high confidence.
Integration of Internet-of-Things With Blockchain Technology to Enhance Humanitarian Logistics Performance

There has been incredible interest in Internet-of-Things (IoT) and blockchain technology (BCT) around the world and across sectors. Following great achievement in the other sectors, the implementation of IoT and BCT have gained great interest in Humanitarian Logistics (HL) at many levels despite remaining in an earlier stage. The profit and non-profit organizations both are under increasing worldwide pressure for transparency, with donors and governments calling for enhanced transparency and information exchange in the humanitarian sector. This study, which is based on transactive memory systems (TMS) theory perspectives, proposes a study framework to understand “how can the transparency, public trust, and coordination in HL be improved through the integration of IoT with BCT?”. We framed and tested six research hypotheses, using data collected from Humanitarian Organizations (HOs) employees. We have applied a Covariance-based structure equation model (CB-SEM) with confirmatory factor analysis (CFA). This study results confirm that our all hypotheses were supported. The research results show that the association between explanatory variables (i.e., IoT and BCT) and the response variables (i.e., public trust and coordination) is mediated by transparency. This study provides substantial and valid contributions to the literature on IoT, BCT, transparency, public trust and coordination. This study proves that transparency plays a crucial role in enhancing public trust, coordination, and ultimately HL performance through the integration of IoT with BCT. The study results could be helpful for all the stakeholders of disaster risk management since they are insistently looking for strategies to support afflicts. Our study is a good candidate solution to raise awareness of fast, fair, and safe HL to reveal research gaps and provide opportunities for future research. The study will provide an enormous understanding of IoT and BCT in HL, which has not been investigated empirically before.
XOR QA: Cross-lingual Open-Retrieval Question Answering

Multilingual question answering tasks typically assume that answers exist in the same language as the question.Yet in practice, many languages face both information scarcity-where languages have few reference articles-and information asymmetry-where questions reference concepts from other cultures.This work extends open-retrieval question answering to a cross-lingual setting enabling questions from one language to be answered via answer content from another language.We construct a large-scale dataset built on 40K information-seeking questions across 7 diverse non-English languages that TYDI QA could not find same-language answers for.Based on this dataset, we introduce a task framework, called Cross-lingual Open-Retrieval Question Answering (XOR QA), that consists of three new tasks involving crosslingual document retrieval from multilingual and English resources.We establish baselines with state-of-the-art machine translation systems and cross-lingual pretrained models.Experimental results suggest that XOR QA is a challenging task that will facilitate the development of novel techniques for multilingual question answering.Our data and code are available at https://nlp.cs.washington.edu/xorqa/.We provide baselines that extend state-of-theart open-retrieval QA systems (Asai et al., 2020;Karpukhin et al., 2020) to our multilingual retrieval setting.Our best baseline achieves an average of 18.7 F1 points on XOR-FULL.This result indicates that XOR-TYDI QA poses unique challenges to tackle toward building a real-world open-retrieval QA system for diverse languages.We expect that our dataset opens up new challenges to make progress in multilingual representation learning.
Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph

Node classification is an important problem on graphs. While recent advances in graph neural networks achieve promising performance, they require abundant labeled nodes for training. However, in many practical scenarios, there often exist novel classes in which only one or a few labeled nodes are available as supervision, known as few-shot node classification. Although meta-learning has been widely used in vision and language domains to address few-shot learning, its adoption on graphs has been limited. In particular, graph nodes in a few-shot task are not independent and relate to each other. To deal with this, we propose a novel model called Relative and Absolute Location Embedding (RALE) hinged on the concept of hub nodes. Specifically, RALE captures the task-level dependency by assigning each node a relative location within a task, as well as the graph-level dependency by assigning each node an absolute location on the graph to further align different tasks toward learning a transferable prior. Finally, extensive experiments on three public datasets demonstrate the state-of-the-art performance of RALE.
An Electric Fish-Based Arithmetic Optimization Algorithm for Feature Selection

With the widespread use of intelligent information systems, a massive amount of data with lots of irrelevant, noisy, and redundant features are collected; moreover, many features should be handled. Therefore, introducing an efficient feature selection (FS) approach becomes a challenging aim. In the recent decade, various artificial methods and swarm models inspired by biological and social systems have been proposed to solve different problems, including FS. Thus, in this paper, an innovative approach is proposed based on a hybrid integration between two intelligent algorithms, Electric fish optimization (EFO) and the arithmetic optimization algorithm (AOA), to boost the exploration stage of EFO to process the high dimensional FS problems with a remarkable convergence speed. The proposed EFOAOA is examined with eighteen datasets for different real-life applications. The EFOAOA results are compared with a set of recent state-of-the-art optimizers using a set of statistical metrics and the Friedman test. The comparisons show the positive impact of integrating the AOA operator in the EFO, as the proposed EFOAOA can identify the most important features with high accuracy and efficiency. Compared to the other FS methods whereas, it got the lowest features number and the highest accuracy in 50% and 67% of the datasets, respectively.