Reviewerly/exHarmony huggingface dataset:
Learning Markov Blankets From Multiple Interventional Data Sets

Learning Markov blankets (MBs) plays an important role in many machine learning tasks, such as causal Bayesian network structure learning, feature selection, and domain adaptation. Since variables included in the MB of a target variable of interest have causal relationships with the target, the MB can serve as the basis of learning the global structure of a causal Bayesian network or as a reliable and robust feature set for classification, both within the same domain or across domains. In this article, we study the problem of learning the MB of a target variable from multiple interventional data sets. Data sets attained from interventional experiments contain richer causal information than passively observed data (observational data) for MB discovery. However, almost all existing MB discovery methods are designed for learning MBs from a single observational data set. To learn MBs from multiple interventional data sets, we face two challenges: 1) unknown intervention variables and 2) nonidentical data distributions. To address these challenges, we theoretically analyze: 1) under what conditions we can find the correct MB of a target variable and 2) under what conditions we can identify the causes of the target variable via discovering its MB. Based on the theoretical analysis, we propose a new algorithm for learning MBs from multiple interventional data sets, and we present the conditions/assumptions that assure the correctness of the algorithm. To the best of our knowledge, this article is the first to present the theoretical analyses about the conditions for MB discovery in multiple interventional data sets and the algorithm to find the MBs in relation to the conditions. Using benchmark Bayesian networks and real-world data sets, the experiments have validated the effectiveness and efficiency of the proposed algorithm in this article.
Pun-GAN: Generative Adversarial Network for Pun Generation

In this paper, we focus on the task of generating a pun sentence given a pair of word senses.A major challenge for pun generation is the lack of largescale pun corpus to guide the supervised learning.To remedy this, we propose an adversarial generative network for pun generation (Pun-GAN), which does not require any pun corpus.It consists of a generator to produce pun sentences, and a discriminator to distinguish between the generated pun sentences and the real sentences with specific word senses.The output of the discriminator is then used as a reward to train the generator via reinforcement learning, encouraging it to produce pun sentences which can support two word senses simultaneously.Experiments show that the proposed Pun-GAN can generate sentences that are more ambiguous and diverse in both automatic and human evaluation. 1
Evaluating Pronominal Anaphora in Machine Translation: An Evaluation Measure and a Test Suite

The neural revolution in machine translation has made it easier to model larger contexts beyond the sentence-level, which can potentially help resolve some discourse-level ambiguities such as pronominal anaphora, thus enabling better translations.Unfortunately, even when the resulting improvements are seen as substantial by humans, they remain virtually unnoticed by traditional automatic evaluation measures such as BLEU, as only a few words end up being affected.Thus, specialized evaluation measures are needed.With this aim in mind, we contribute an extensive, targeted dataset that can be used as a test suite for pronoun translation, covering multiple source languages and different pronoun errors drawn from real system translations, for English.We further propose an evaluation measure to differentiate good and bad pronoun translations.Finally, we conduct a user study and we report correlation with human judgments.
Granularity of algorithmically constructed publication-level classifications of research publications: Identification of specialties

In this work, we build on and use the outcome of an earlier study on topic identification in an algorithmically constructed publication-level classification (ACPLC), and address the issue of how to algorithmically obtain a classification of topics (containing articles), where the classes of the classification correspond to specialties. The methodology we propose, which is similar to that used in the earlier study, uses journals and their articles to construct a baseline classification. The underlying assumption of our approach is that journals of a particular size and focus have a scope that corresponds to specialties. By measuring the similarity between (1) the baseline classification and (2) multiple classifications obtained by topic clustering and using different values of a resolution parameter, we have identified a best performing ACPLC. In two case studies, we could identify the subject foci of the specialties involved, and the subject foci of specialties were relatively easy to distinguish. Further, the class size variation regarding the best performing ACPLC is moderate, and only a small proportion of the articles belong to very small classes. For these reasons, we conclude that the proposed methodology is suitable for determining the specialty granularity level of an ACPLC.
SINS/CNS/GNSS Integrated Navigation Based on an Improved Federated Sage–Husa Adaptive Filter

Among the methods of the multi-source navigation filter, as a distributed method, the federated filter has a small calculation amount with Gaussian state noise, and it is easy to achieve global optimization. However, when the state noise is time-varying or its initial estimation is not accurate, there will be a big difference with the true value in the result of the federated filter. For the systems with time-varying noise, adaptive filter is widely used for its remarkable advantages. Therefore, this paper proposes a federated Sage-Husa adaptive filter for multi-source navigation systems with time-varying or mis-estimated state noise. Because both the federated and the adaptive principles are different in updating the covariance of the state noise, it is required to weight the two updating methods to obtain a combined method with stability and adaptability. In addition, according to the characteristics of the system, the weighting coefficient is formed by the exponential function. This federated adaptive filter is applied to the SINS/CNS/GNSS integrated navigation, and the simulation results show that this method is effective.
Two-Phase Cooperative Bargaining Game Approach for Shard-Based Blockchain Consensus Scheme

In the last few years, blockchain technologies have come to the forefront of the research and industrial communities as they bring potential benefits for many industries. Even though the blockchain is a safe, reliable, and innovative mechanism, current blockchain solutions have fatal drawbacks of non-supervision and huge computational overhead. Therefore, they cannot be directly applied for real world operations. To resolve this problem, a novel model is presented in this study where the key idea is to split the transactions among multiple shards while processing them in parallel. To achieve the maximum system efficiency through the shard mechanism, we focus on the cooperative game theory. Based on the egalitarian bargaining solution, total transactions per each time period are divided for each shard. According to the proportional bargaining solution, assigned transactions in each individual shard are validated by blockchain nodes in a distributed manner. The main advantage of our two-phase bargaining game model is to provide an axiom-based strategic solution for the shard-based consensus problem while dynamically responding to the current blockchain network conditions. The numerical simulation results show that the effectiveness and efficiency of our game based approach by comparing the existing state-of-the-art blockchain control schemes. In the conclusion, we present our conclusions and provide important future research directions based on the combination of blockchain with other technologies.
Deep Clustering via Weighted $k$-Subspace Network

Subspace clustering aims to separate the data into clusters under the hypothesis that the samples within the same cluster will lie in the same low-dimensional subspace. Due to the tough pairwise constraints, k-subspace clustering is sensitive to outliers and initialization. In this letter, we present a novel deep architecture for k-subspace clustering to address this issue, called as Deep Weighted k-Subspace Clustering (DWSC). Specifically, our framework consists of autoencoder and weighted k-subsapce network. We first use the autoencoder to non-linearly compress the samples into the low-dimensional latent space. In the weighted k-subspace network, we feed the latent representation into the assignment network to output soft assignments which represent the probability of data belonging to the according subspace. Subsequently, the optimal k subspaces are identified by minimizing the projection residuals of the latent representations to all subspaces, using the learned soft assignments as a weighting vector. Finally, we jointly optimize the representation learning and clustering in a unified framework. Experimental results show that our approach outperforms the state-of-the-art subspace clustering methods on two benchmark datasets.
Multilingual Speech Recognition with Corpus Relatedness Sampling

Multilingual acoustic models have been successfully applied to low-resource speech recognition. Most existing works have combined many small corpora together and pretrained a multilingual model by sampling from each corpus uniformly. The model is eventually fine-tuned on each target corpus. This approach, however, fails to exploit the relatedness and similarity among corpora in the training set. For example, the target corpus might benefit more from a corpus in the same domain or a corpus from a close language. In this work, we propose a simple but useful sampling strategy to take advantage of this relatedness. We first compute the corpus-level embeddings and estimate the similarity between each corpus. Next, we start training the multilingual model with uniform-sampling from each corpus at first, then we gradually increase the probability to sample from related corpora based on its similarity with the target corpus. Finally, the model would be fine-tuned automatically on the target corpus. Our sampling strategy outperforms the baseline multilingual model on 16 low-resource tasks. Additionally, we demonstrate that our corpus embeddings capture the language and domain information of each corpus.
Digital educational resources of the university: design, analysis and expertise

Introduction: the relevance of this topic is due to the fact that at the moment there is no uniform structured approach to the creation of digital educational resources (DER), at the state level there are no clear requirements for their structure and content. Accordingly, there is a need to systematize DER and develop unified requirements for their creation, structure and content for the purpose of application in educational institutions. The aim of the study is to identify a systematic approach to the structure and use of the DER in the educational process. The object of study is selected DER. The subject of the study is the DER in the educational activities of the modern university. The scientific novelty of the research is as follows: identified existing approaches to systematization of the DER; proposed systematization of the DER for educational and methodological functions, by appointment. indicated the feasibility and the need to ensure the educational and educational process of the DER. Materials and methods: the writing of the article was accompanied by the use of the following methods - analysis of literary sources, pedagogical experiment, comparative analysis, method of expert assessments. Results: the practical significance of the work lies in the refinement and updating of the information component of the existing electronic educational and methodological complexes (EEMC) on the discipline "Information security", "Computers and peripherals" and "Introduction to operating systems" developed on the Moodle platform. Discussion and conclusions: the problem of integrated assessment of the DER is relevant along with the formulation of requirements for the DER, since the issue of health protection in the conditions of working with a computer has priority.
Lombard Speech Synthesis Using Transfer Learning in a Tacotron Text-to-Speech System

CSMAPLR Constrained structured maximum a posterior linear regression CSS
Determining the Parameters of the Ångström‐Prescott Model for Estimating Solar Radiation in Different Regions of China: Calibration and Modeling

Abstract The Ångström‐Prescott model (referred to as the A‐P model) is one of the most accurate and widely used models for estimating global solar radiation (Rs). In the absence of Rs measurements and given the regional discrepancy of model parameters, it is crucial to increase the availability of these parameters and the applicability of parameter‐predicted models in different regions. In this study, we evaluated and compared the applicability and performance of the calibrated model and eight predictive models in terms of A‐P model parameters, using daily Rs and meteorological data from 105 radiation stations in seven natural geographic zones in China. These models were evaluated based on their coefficient of determination ( R 2 ), root mean square error, Nash‐Sutcliffe efficiency coefficients, percent bias, and global performance indexes. Results indicated that altitude was the main factor determining the Ångström‐Prescott parameters in most regions. All models performed well, with acceptable accuracy across the whole country; however, their performances varied among regions. The best performing predictive models for the northeast region (Zone 1), north China (Zone 2), central China (Zone 3), south China (Zone 4), Inner Mongolia (Zone 5), northwest region (Zone 6), and Qinghai‐Tibet region (Zone 7) were obtained: These were Models 6, 1, 7, 3, 6, 1, and 7, respectively. The present results support the application of these predictive models for the estimation of daily global Rs in the corresponding regions of China, where measured Rs data are not available, and possibly in other regions with a similar climate.
Guided Neural Language Generation for Automated Storytelling

Neural network based approaches to automated story plot generation attempt to learn how to generate novel plots from a corpus of natural language plot summaries. Prior work has shown that a semantic abstraction of sentences called events improves neural plot generation and and allows one to decompose the problem into: (1) the generation of a sequence of events (event-to-event) and (2) the transformation of these events into natural language sentences (event-to-sentence). However, typical neural language generation approaches to event-to-sentence can ignore the event details and produce grammatically-correct but semantically-unrelated sentences. We present an ensemble-based model that generates natural language guided by events. Our method outperforms the baseline sequence-to-sequence model. Additionally, we provide results for a full end-to-end automated story generation system, demonstrating how our model works with existing systems designed for the event-to-event problem.
WriterForcing: Generating more interesting story endings

We study the problem of generating interesting endings for stories.Neural generative models have shown promising results for various text generation problems.Sequence to Sequence (Seq2Seq) models are typically trained to generate a single output sequence for a given input sequence.However, in the context of a story, multiple endings are possible.Seq2Seq models tend to ignore the context and generate generic and dull responses.Very few works have studied generating diverse and interesting story endings for a given story context.In this paper, we propose models which generate more diverse and interesting outputs by 1) training models to focus attention on important keyphrases of the story, and 2) promoting generation of non-generic words.We show that the combination of the two leads to more diverse and interesting endings.
Untranscribed Web Audio for Low Resource Speech Recognition

Speech recognition models are highly susceptible to mismatch in the acoustic and language domains between the training and the evaluation data.For low resource languages, it is difficult to obtain transcribed speech for target domains, while untranscribed data can be collected with minimal effort.Recently, a method applying lattice-free maximum mutual information (LF-MMI) to untranscribed data has been found to be effective for semi-supervised training.However, weaker initial models and domain mismatch can result in high deletion rates for the semi-supervised model.Therefore, we propose a method to force the base model to overgenerate possible transcriptions, relying on the ability of LF-MMI to deal with uncertainty.On data from the IARPA MATERIAL programme, our new semi-supervised method outperforms the standard semisupervised method, yielding significant gains when adapting for mismatched bandwidth and domain.
Research on Dynamic Sign Language Algorithm Based on Sign Language Trajectory and Key Frame Extraction

Based on the depth information of Kinect, this paper studies the real-time dynamic sign language recognition algorithm and improves the dynamic time warping algorithm for the recognition of sign language trajectories. The traditional DTW algorithm is improved by using the path constraint of the relaxed endpoint, adding the lower bound function to cull part of the candidate sequence and terminate the match early. The key frames are extracted according to the sign language trajectory density, and the dynamic hand sign trajectory and key gesture type information are combined to obtain the final dynamic sign language recognition result. Experiments show that the average recognition rate of this method is more than 90%, which is better than the traditional DTW algorithm in recognition speed and recognition accuracy.
An adaptive clustering and classification algorithm for Twitter data streaming in Apache Spark

On-going big data from social networks sites alike Twitter or Facebook has been an entrancing hotspot for investigation by researchers in current decades as a result of various aspects including up-to-date-ness, accessibility and popularity; however anyway there may be a trade off in accuracy. Moreover, clustering of twitter data has caught the attention of researchers. As such, an algorithm which can cluster data within a lesser computational time, especially for data streaming is needed. The presented adaptive clustering and classification algorithm is used for data streaming in Apache spark to overcome the existing problems is processed in two phases. In the first phase, the input pre-processed twitter data is viably clustered utilizing an Improved Fuzzy C-means clustering and the proposed clustering is additionally improved by an Adaptive Particle swarm optimization (PSO) algorithm. Further the clustered data streaming is assessed utilizing spark engine. In the second phase, the input pre-processed Higgs data is classified utilizing the modified support vector machine (MSVM) classifier with grid search optimization. At long last the optimized information is assessed in spark engine and the assessed esteem is utilized to discover an accomplished confusion matrix. The proposed work is utilizing Twitter dataset and Higgs dataset for the data streaming in Apache Spark. The computational examinations exhibit the superiority of presented approach comparing with the existing methods in terms of precision, recall, F-score, convergence, ROC curve and accuracy.
Evolvable fuzzy systems from data streams with missing values: With application to temporal pattern recognition and cryptocurrency prediction

Data streams with missing values are common in real-world applications. This paper presents an evolving granular fuzzy-rule-based model for temporal pattern recognition and time series prediction in online nonstationary context, where values may be missing. The model has a modified rule structure that includes reduced-term consequent polynomials, and is supplied by an incremental learning algorithm that simultaneously impute missing data and update model parameters and structure. The evolving Fuzzy Granular Predictor (eFGP) handles single and multiple Missing At Random (MAR) and Missing Completely At Random (MCAR) values in nonstationary data streams. Experiments on cryptocurrency prediction show the usefulness, accuracy, processing speed, and eFGP robustness to missing values. Results were compared to those provided by fuzzy and neuro-fuzzy evolving modeling methods.
Design and Implementation of E-Campus Ontology with a Hybrid Software Engineering Methodology

Semantic Web according to the vision of the W3C is the future of WWW (or Web). It is an extension of the current Web through standards by the W3C. Data of the Semantic Web has well-defined meanings, can be understood by devices and allows machines and people to work in collaboration. Ontologies are vital components of the Semantic Web infrastructure and are more often recognized as the backbone of the Semantic Web. Although numerous developments occur in the field of developing ontologies along the lines with the Semantic Web implementation, but standardizing the process models, tools and methodologies need to be improved in the future. In literature, experts in ontology engineering have stated that setting a methodology for developing ontology applications with support of integrated tools is an essential task for ontology engineering to be succeeded. In this paper, an e-campus ontology for educational purposes is designed and implemented, and mainly focused on the learning hierarchy of C-sharp programming language. A hybrid methodology based on software engineering approaches for developing ontologies is presented. Finally, the developed methodology is applied on the implemented ontology.
Stance Detection of Microblog Text Based on Two-Channel CNN-GRU Fusion Network

In recent years, stance detection has become an important topic in the field of natural language processing. In earlier work, researchers have used feature engineering for stance detection but they need to define and extract appropriate features according to the particular application. This leads to poor generalization and a complex modeling process. Other researchers have applied deep learning methods. However, the popular convolutional neural network (CNN) method has the problem of information loss and a single-size CNN filter cannot accurately extract features that have different lengths from text, and so cannot deal with the diverse nature of features. In order to address these problems, we propose a two-channel CNN-GRU fusion network. First, a convolution layer with two filters with different window sizes is used to extract local features within the topic content and text content. Then, a gated recurrent unit (GRU) network is used to extract their timing characteristics. After that, the intermediate features are spliced and input to a classifier to complete the stance detection. Our method is validated using data from NLPCC 2016. The experimental results show that ACC and average F1 score of this method are 13.1% and 15.6% better than SVM method, 6.2% and 11.6% better than CNN method, 5.6% and 3.3% better than GRU method, and 1.1% and 2.2% better compared with hybrid model proposed by Nanyu, respectively, which is used as a baseline with no increase in run-time, and achieves the same accuracy with less run-time than another baseline of a semantic attention-based model proposed by Zhou. In addition, our method allows better classification than the single channel model. Finally, we find that the operation time of a multi-channel CNN-GRU increases gradually with increasing number of channels, but the classification accuracy does not improve, so a two-channel CNN-GRU is the most appropriate choice.