Reviewerly/exHarmony huggingface dataset:
Weighting Factor Selection of the Ensemble Model for Improving Forecast Accuracy of Photovoltaic Generating Resources

Among renewable energy sources, solar power is rapidly growing as a major power source for future power systems. However, solar power has uncertainty due to the effects of weather factors, and if the penetration rate of solar power in the future increases, it could reduce the reliability of the power system. A study of accurate solar power forecasting should be done to improve the stability of the power system operation. Using the empirical data from solar power plants in South Korea, the short-term forecasting of solar power outputs were carried out for 2016. We performed solar power forecasting with the support vector regression (SVR) model, the naïve Bayes classifier (NBC), and the hourly regression model. We proposed the ensemble method including the selection of weighting factors for each model to improve forecasting accuracy. The forecasted solar power generation error was indicated using normalized mean absolute error (NMAE) compared to the plant capacity. For the ensemble method, the results of each forecasting model were weighted with the reciprocal of the standard deviation of the forecast error, thus improving the solar power outputs forecast accuracy.
Extracting Possessions from Social Media: Images Complement Language

This paper describes a new dataset and experiments to determine whether authors of tweets possess the objects they tweet about.We work with 5,000 tweets and show that both humans and neural networks benefit from images in addition to text.We also introduce a simple yet effective strategy to incorporate visual information into any neural network beyond weights from pretrained networks.Specifically, we consider the tags identified in an image as an additional textual input, and leverage pretrained word embeddings as usually done with regular text.Experimental results show this novel strategy is beneficial.
Some remarks on a pair of seemingly unrelated regression models

Abstract Linear regression models are foundation of current statistical theory and have been a prominent object of study in statistical data analysis and inference. A special class of linear regression models is called the seemingly unrelated regression models (SURMs) which allow correlated observations between different regression equations. In this article, we present a general approach to SURMs under some general assumptions, including establishing closed-form expressions of the best linear unbiased predictors (BLUPs) and the best linear unbiased estimators (BLUEs) of all unknown parameters in the models, establishing necessary and sufficient conditions for a family of equalities of the predictors and estimators under the single models and the combined model to hold. Some fundamental and valuable properties of the BLUPs and BLUEs under the SURM are also presented.
Atrous Temporal Convolutional Network for Video Action Segmentation

Fine-grained temporal human action segmentation in untrimmed videos is receiving increasing attention due to its extensive applications in surveillance, robotics, and beyond. It is crucial for an action segmentation system to be robust to the temporal scale of different actions since in practical applications the duration of an action can vary from less than a second to tens of minutes. In this paper, we introduce a novel atrous temporal convolutional network (AT-Net), which explicitly generates multiscale video contextual representations by utilizing atrous temporal pyramid pooling (ATPP) and has an architecture of encoder-decoder fully convolutional network. In the decoding stage, AT-Net combines multiscale contextual features with low-level local features to generate high-quality action segmentation results. Experiments on the 50 Salads, GTEA and JIGSAWS benchmarks demonstrate that AT-Net achieves improvement over the state of the art.
Guiding the Flowing of Semantics: Interpretable Video Captioning via POS Tag

In the current video captioning models, the video frames are collected in one network and the semantics are mixed into one feature, which not only increase the difficulty of the caption decoding, but also decrease the interpretability of the captioning models.To address these problems, we propose an Adaptive Semantic Guidance Network (ASGN), which instantiates the whole video semantics to different POS-aware semantics with the supervision of part of speech (POS) tag.In the encoding process, the POS tag activates the related neurons and parses the whole semantic information into corresponding encoded video representations.Furthermore, the potential of the model is stimulated by the POS-aware video features.In the decoding process, the related video features of noun and verb are used as the supervision to construct a new adaptive attention model which can decide whether to attend to the video feature or not.With the explicit improving of the interpretability of the network, the learning process is more transparent and the results are more predictable.Extensive experiments demonstrate the effectiveness of our model when compared with stateof-the-art models.
Huawei’s NMT Systems for the WMT 2019 Biomedical Translation Task

This paper describes Huawei's neural machine translation systems for the WMT 2019 biomedical translation shared task.We trained and fine-tuned our systems on a combination of out-of-domain and in-domain parallel corpora for six translation directions covering English-Chinese, English-French and English-German language pairs.Our submitted systems achieve the best BLEU scores on English-French and English-German language pairs according to the official evaluation results.In the English-Chinese translation task, our systems are in the second place.The enhanced performance is attributed to more indomain training and more sophisticated models developed.Development of translation models and transfer learning (or domain adaptation) methods has significantly contributed to the progress of the task.
Solving Word Analogies: A Machine Learning Perspective

Analogical proportions are statements of the form ‘a is to b as c is to d’, formally denoted . This means that the way a and b (resp. b and a) differ is the same as c and d (resp. d and c) differ, as revealed by their logical modeling. The postulates supposed to govern such proportions entail that when holds, then seven permutations of a, b, c, d still constitute valid analogies. It can also be derived that does not hold except if $$a=b$$ . From a machine learning perspective, this provides guidelines to build training sets of positive and negative examples. We then suggest improved methods to classify word-analogies and also to solve analogical equations. Viewing words as vectors in a multi-dimensional space, we depart from the traditional parallelogram view of analogy to adopt a purely machine-learning approach. In some sense, we learn a functional definition of analogical proportions without assuming any pre-existing formulas. We mainly use the logical properties of proportions to define our training sets and to design proper neural networks, approximating the hidden relations. Using a GloVe embedding, the results we get show high accuracy and improve state of the art on words analogy-solving problems.
Fuzzy Expert System for Prediction of Prostate Cancer

A fuzzy expert system (FES) for the prediction of prostate cancer (PC) is prescribed in this paper. Age, prostate-specific antigen (PSA), prostate volume (PV) and [Formula: see text] Free PSA ([Formula: see text]FPSA) are fed as inputs into the FES and prostate cancer risk (PCR) is obtained as the output. Using knowledge-based rules in Mamdani type inference method the output is calculated. If PCR [Formula: see text], then the patient shall be advised to go for a biopsy test for confirmation. The efficacy of the designed FES is tested against a clinical dataset. The true prediction for all the patients turns out to be [Formula: see text] whereas only for positive biopsy cases it rises to [Formula: see text]. This simple yet effective FES can be used as supportive tool for decision-making in medical diagnosis.
Pretrained AI Models: Performativity, Mobility, and Change

The paradigm of pretrained deep learning models has recently emerged in artificial intelligence practice, allowing deployment in numerous societal settings with limited computational resources, but also embedding biases and enabling unintended negative uses. In this paper, we treat pretrained models as objects of study and discuss the ethical impacts of their sociological position. We discuss how pretrained models are developed and compared under the common task framework, but that this may make self-regulation inadequate. Further how pretrained models may have a performative effect on society that exacerbates biases. We then discuss how pretrained models move through actor networks as a kind of computationally immutable mobile, but that users also act as agents of technological change by reinterpreting them via fine-tuning and transfer. We further discuss how users may use pretrained models in malicious ways, drawing a novel connection between the responsible innovation and user-centered innovation literatures. We close by discussing how this sociological understanding of pretrained models can inform AI governance frameworks for fairness, accountability, and transparency.
Long story short: finding health advice with informative summaries on health social media

Purpose Whether automatically generated summaries of health social media can aid users in managing their diseases appropriately is an important question. The purpose of this paper is to introduce a novel text summarization approach for acquiring the most informative summaries from online patient posts accurately and effectively. Design/methodology/approach The data set regarding diabetes and HIV posts was, respectively, collected from two online disease forums. The proposed summarizer is based on the graph-based method to generate summaries by considering social network features, text sentiment and sentence features. Representative health-related summaries were identified and summarization performance as well as user judgments were analyzed. Findings The findings show that awarding sentences without using all the incorporating features decreases summarization performance compared with the classic summarization method and comparison approaches. The proposed summarizer significantly outperformed the comparison baseline. Originality/value This study contributes to the literature on health knowledge management by analyzing patients’ experiences and opinions through the health summarization model. The research additionally develops a new mindset to design abstractive summarization weighting schemes from the health user-generated content.
Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approach

Zero-shot text classification (0Shot-TC) is a challenging NLU problem to which little attention has been paid by the research community. 0Shot-TC aims to associate an appropriate label with a piece of text, irrespective of the text domain and the aspect (e.g., topic, emotion, event, etc.) described by the label. And there are only a few articles studying 0Shot-TC, all focusing only on topical categorization which, we argue, is just the tip of the iceberg in 0Shot-TC. In addition, the chaotic experiments in literature make no uniform comparison, which blurs the progress. This work benchmarks the 0Shot-TC problem by providing unified datasets, standardized evaluations, and state-of-the-art baselines. Our contributions include: i) The datasets we provide facilitate studying 0Shot-TC relative to conceptually different and diverse aspects: the ``topic'' aspect includes ``sports'' and ``politics'' as labels; the ``emotion'' aspect includes ``joy'' and ``anger''; the ``situation'' aspect includes ``medical assistance'' and ``water shortage''. ii) We extend the existing evaluation setup (label-partially-unseen) -- given a dataset, train on some labels, test on all labels -- to include a more challenging yet realistic evaluation label-fully-unseen 0Shot-TC (Chang et al., 2008), aiming at classifying text snippets without seeing task specific training data at all. iii) We unify the 0Shot-TC of diverse aspects within a textual entailment formulation and study it this way. Code & Data: https://github.com/yinwenpeng/BenchmarkingZeroShot
The Australian Research Data Commons

A research data commons can provide researchers with the data and resources necessary to conduct world class research. More than this, a research data commons can be transformational in facilitating change in the way research is conducted, in terms of both research culture and the availability of research data and analytical tools. This paper describes frameworks needed to build a transformational data commons, through examination of the development of the Australian Research Data Commons (ARDC) ARDC was formed in 2018 as part of a 20-year vision to transform Australia's research culture by enabling access to the digital data and eResearch platforms that can significantly enhance research capacity. ARDC is located within both national and international eResearch ecosystems, and its unique positioning must be understood, alongside the achievements of its three predecessor organisations, to understand the niche from which ARDC aims to provide maximum value and impact. Consideration is given to the challenges inherent in both the current Australian ecosystem and beyond, to articulate ARDC's focus going forward. The paper concludes with consideration of the international dimension, drawing on discussions around the development of a global data commons.
E-Bayesian Estimation for the Exponential Model Based on Record Statistics

This paper is concerned with using the E-Bayesian method for computing estimates for the parameter and reliability function of the Exponential distribution based on a set of upper record statistics values.The estimates are derived based on a conjugate prior for the scale parameter and squared error loss function.A comparisons between the new method and the corresponding Bayes technique are made using the Monte Carlo simulation.
Analysis of Multilingual Sequence-to-Sequence Speech Recognition Systems

This paper investigates the applications of various multilingual approaches developed in conventional hidden Markov model (HMM) systems to sequence-to-sequence (seq2seq) automatic speech recognition (ASR). On a set composed of Babel data, we first show the effectiveness of multi-lingual training with stacked bottle-neck (SBN) features. Then we explore various architectures and training strategies of multi-lingual seq2seq models based on CTC-attention networks including combinations of output layer, CTC and/or attention component re-training. We also investigate the effectiveness of language-transfer learning in a very low resource scenario when the target language is not included in the original multi-lingual training data. Interestingly, we found multilingual features superior to multilingual models, and this finding suggests that we can efficiently combine the benefits of the HMM system with the seq2seq system through these multilingual feature techniques.
Supporting successful data sharing practices in earthquake engineering

Purpose Prior studies identified a need for further comparison of data-sharing practices across different disciplines and communities. Toward addressing this need, the purpose of this paper is to examine the data-sharing practices of the earthquake engineering (EE) community, which could help inform data-sharing policies in EE and provide different stakeholders of the EE community with suggestions regarding data management and curation. Design/methodology/approach This study conducted qualitative semi-structured interviews with 16 EE researchers to gain an understanding of which data might be shared, with whom, under what conditions and why; and their perceptions of data ownership. Findings This study identified 29 data-sharing factors categorized into five groups. Requirements from funding agencies and academic genealogy were frequent impacts on EE researchers’ data-sharing practices. EE researchers were uncertain of data ownership and their perceptions varied. Originality/value Based on the findings, this study provides funding agencies, research institutions, data repositories and other stakeholders of the EE community with suggestions, such as allowing researchers to adjust the timeframe they can withhold data based on project size and the amount of experimental data generated; expanding the types and states of data required to share; defining data ownership in grant requirements; integrating data sharing and curation into curriculum; and collaborating with library and information schools for curriculum development.
Realization of controllable open system with NMR

Abstract An open quantum system is now attracting much attention because a quantum device such as quantum computers and quantum sensors is an emerging technology. Here, we present a model of the open system that shows either time-homogeneous Markovian relaxations or non-Markovian relaxations depending on its parameters that we can control. This model is fully described with the master equation that is analytically solvable. More importantly, this model can be easily realized with molecules in isotropic liquids and measured with NMR techniques.
Knowledge Graph Implementation of Archival Descriptions Through CIDOC-CRM

Archives have well-established description standards, namely the ISAD(G) and ISAAR(CPF) with a hierarchical structure adapted to the nature of archival assets. However, as archives connect to a growing diversity of data, they aim to make their representations more apt to the so-called linked data cloud. The corresponding move from hierarchical, ISAD-conforming descriptions to graph counterparts requires state-of-the-art technologies, data models and vocabularies. Our approach addresses this problem from two perspectives. The first concerns the data model and description vocabularies, as we adopt and build upon the CIDOC-CRM standard. The second is the choice of technologies to support a knowledge graph, including a graph database and an Object Graph Mapping library. The case study is the Portuguese National Archives, Torre do Tombo, and the overall goal is to build a CIDOC-CRM-compliant system for document description and retrieval, to be used by professionals and the public. The early stages described here include the design of the core data model for archival records represented as the ArchOnto ontology and its embodiment in the ArchGraph knowledge graph. The goal of a semantic archival information system will be pursued in the migration of existing records to the richer representation and the development of applications supported on the graph.
NamedKeys

A vast amount of biomedical literature is generated and digitized every year. As a result is a growing need to develop methods for discovering, accessing, and sharing knowledge from medical literature. Keyphrase extraction is the task of summarizing a text by identifying the key concepts. The keyphrases can be single-word or multi-word linguistic units which can concisely represent a document. Although a variety of models have been proposed for automated keyphrase extraction, the performance is poor in comparison with other natural language processing tasks. The problem is even more daunting for biomedical domain where the text is filled with highly domain-specific terminologies. We propose a new method, NamedKeys, to automatically identify meaningful and informative keyphrases from biomedical text. NamedKeys integrates named entity recognition, phrase embedding, phrase quality scoring, ranking, and clustering to extract author-assigned keywords from biomedical documents. Performance evaluation on PubMed abstracts demonstrates that NamedKeys achieves significant improvements over existing state-of-the-art keyphrase extraction models. Furthermore, we propose the first benchmark dataset for keyphrase extraction from biomedical text.
Hint-Based Training for Non-Autoregressive Machine Translation

Due to the unparallelizable nature of the autoregressive factorization, AutoRegressive Translation (ART) models have to generate tokens sequentially during decoding and thus suffer from high inference latency. Non-AutoRegressive Translation (NART) models were proposed to reduce the inference time, but could only achieve inferior translation accuracy. In this paper, we proposed a novel approach to leveraging the hints from hidden states and word alignments to help the training of NART models. The results achieve significant improvement over previous NART models for the WMT14 En-De and De-En datasets and are even comparable to a strong LSTM-based ART baseline but one order of magnitude faster in inference.
Hierarchical Posture Representation for Robust Action Recognition

By modeling an action as the evolution of postures, Posture-based recognition algorithms possess interpretative patterns. However, there are two limitations resulted from individual diversity. First, the same action can be performed by different organs. Such actions are denoted as ambiguous actions. Second, the postures of the same action can be badly influenced by personal characteristics (e.g., personal habits and the height). In order to tackle the problems above, we propose a hierarchical posture representation (HPR). A posture is composed of several skeletal points. Each skeletal point can perform a unique operation and provide a certain amount of information. Thus, an action can be recognized based on the relationships and the information contribution levels of all skeletal points. In HPR, each skeletal point is first represented by its interaction features with other skeletal points. The interaction features are independent of the type of posture. Then, the information contribution level of each skeletal point is estimated based on its interaction features. An end-to-end adaptive action recognition network (AARN) is proposed to accomplish the three processes (estimating the information contribution level, mining the relationships among skeletal points, and recognizing actions). The proposed algorithm shows promising accuracy on two databases. Our work does not only solve the problem above but also introduce a novel action modeling method.
Oil Tank Extraction Based on Joint-Spatial Saliency Analysis for Multiple SAR Images

The lack of true color and the presence of background clutter reduce the accuracy rate of the saliency analysis for oil tank extraction in the synthetic aperture radar (SAR) images. This letter proposes a specially designed unsupervised method to extract oil tanks using the joint-spatial saliency analysis (JSSA) for multiple SAR images. First, the intrasaliency analysis is established on a saliency driven iterative clustering. This considers the spatial intensity and texture feature within a single image and suppresses most backgrounds. Second, the cospatial residual and the local grayscale statistics are considered independently in the intersaliency analysis. The common salient parts among the input series are extracted and used to overcome the problem of the lack of true color. Third, to make the fusion of the two kinds of saliency maps, the low-rank matrix is introduced. The weights of different maps are calculated and the saliency cues are integrated efficiently. Finally, after the statistics of the highlight points within the candidates, the location of the oil tanks is refined. The experiments show the superiority of the proposed method in both the pixel level and the geometric segmentation. The result of the JSSA model appears to improve the accuracy with fewer missing objects compared with the competing algorithms.