Reviewerly/exHarmony huggingface dataset:
Dependency-Based Self-Attention for Transformer NMT

In this paper, we propose a new Transformer neural machine translation (NMT) model that incorporates dependency relations into self-attention on both source and target sides, dependency-based selfattention.The dependency-based selfattention is trained to attend to the modifiee for each token under constraints based on the dependency relations, inspired by linguistically-informed self-attention (LISA).While LISA was originally designed for Transformer encoder for semantic role labeling, this paper extends LISA to Transformer NMT by masking future information on words in the decoderside dependency-based self-attention.Additionally, our dependency-based selfattention operates at subword units created by byte pair encoding.Experiments demonstrate that our model achieved a 1.0 point gain in BLEU over the baseline model on the WAT'18 Asian Scientific Paper Excerpt Corpus Japanese-to-English translation task.
Automatic Question Answering for Medical MCQs: Can It Go Further than Information Retrieval?

We present a novel approach to automatic question answering that does not depend on the performance of an information retrieval (IR) system and does not require training data.We evaluate the system performance on a challenging set of university-level medical science multiple-choice questions.Best performance is achieved when combining a neural approach with an IR approach, both of which work independently.Unlike previous approaches, the system achieves statistically significant improvement over the random guess baseline even for questions that are labeled as challenging based on the performance of baseline solvers.
Variational Semi-Supervised Aspect-Term Sentiment Analysis via Transformer

Aspect-term sentiment analysis (ATSA) is a long-standing challenge in natural language processing.It requires fine-grained semantical reasoning about a target entity appeared in the text.As manual annotation over the aspects is laborious and time-consuming, the amount of labeled data is limited for supervised learning.This paper proposes a semisupervised method for the ATSA problem by using the Variational Autoencoder based on Transformer.The model learns the latent distribution via variational inference.By disentangling the latent representation into the aspect-specific sentiment and the lexical context, our method induces the underlying sentiment prediction for the unlabeled data, which then benefits the ATSA classifier.Our method is classifier-agnostic, i.e., the classifier is an independent module and various supervised models can be integrated.Experimental results are obtained on the SemEval 2014 task 4 and show that our method is effective with different five specific classifiers and outperforms these models by a significant margin.
Minimally-Augmented Grammatical Error Correction

There has been an increased interest in lowresource approaches to automatic grammatical error correction.We introduce Minimally-Augmented Grammatical Error Correction (MAGEC) that does not require any errorlabelled data.Our unsupervised approach is based on a simple but effective synthetic error generation method based on confusion sets from inverted spell-checkers.In low-resource settings, we outperform the current state-ofthe-art results for German and Russian GEC tasks by a large margin without using any real error-annotated training data.When combined with labelled data, our method can serve as an efficient pre-training technique.
Neural Poetry: Learning to Generate Poems Using Syllables

Motivated by the recent progresses on machine learning-based models that learn artistic styles, in this paper we focus on the problem of poem generation. This is a challenging task in which the machine has to capture the linguistic features that strongly characterize a certain poet, as well as the semantics of the poet's production, that are influenced by his personal experiences and by his literary background. Since poetry is constructed using syllables, that regulate the form and structure of poems, we propose a syllable-based neural language model, and we describe a poem generation mechanism that is designed around the poet style, automatically selecting the most representative generations. The poetic work of a target author is usually not enough to successfully train modern deep neural networks, so we propose a multi-stage procedure that exploits non-poetic works of the same author, and also other publicly available huge corpora to learn syntax and grammar of the target language. We focus on the Italian poet Dante Alighieri, widely famous for his Divine Comedy. A quantitative and qualitative experimental analysis of the generated tercets is reported, where we included expert judges with strong background in humanistic studies. The generated tercets are frequently considered to be real by a generic population of judges, with relative difference of 56.25% with respect to the ones really authored by Dante, and expert judges perceived Dante's style and rhymes in the generated text.
Compositional Hyponymy with Positive Operators

Language is used to describe concepts, and many of these concepts are hierarchical.Moreover, this hierarchy should be compatible with forming phrases and sentences.We use linear-algebraic methods that allow us to encode words as collections of vectors.The representations we use have an ordering, related to subspace inclusion, which we interpret as modelling hierarchical information.The word representations built can be understood within a compositional distributional semantic framework, providing methods for composing words to form phrase and sentence level representations.The resulting representations give competitive results on simple sentencelevel entailment datasets.
Open Innovation with Fuzzy Cognitive Mapping for Modeling the Barriers of University Technology Transfer: A Philippine Scenario

The University technology transfer (UTT) process is hindered by various barriers to achieving a successful translation of innovative technologies from universities to industries and other partners. Identifying these various barriers and understanding their interrelationships would provide a better understanding of the complex nature of the UTT process, which may be considered as inputs to crucial decision-making initiatives. This paper addresses this gap by holistically determining UTT barriers and their intertwined relationships. Using the Delphi method and fuzzy cognitive mapping, a case study in a state university in the Philippines was conducted to carry out this objective. The Delphi process extracts 24 relevant barriers of UTT, out of 46 barriers obtained from a comprehensive review of the extant literature. The results show that misalignment between research and commercialization objectives is the barrier that was influenced most by the other barriers. In contrast, high costs of managing joint research projects in terms of time and money and institutional bureaucracy have the highest out-degree measures or are the barriers that influence other barriers the most. These findings provide guidelines to various stakeholders and decision-makers in understanding the existence of barriers in the formulation of strategies and initiatives for a successful UTT process.
Actions to be taken in Mexico towards education 4.0 and society 5.0

&lt;span&gt;Education in Mexico has not evolved in the way it is taught and evaluated. In the historical moment that postmodernism enters, it has not been possible to adapt to&lt;/span&gt;&lt;span lang="IN"&gt; Information and Communication Technologies&lt;/span&gt;&lt;span lang="IN"&gt;(&lt;/span&gt;&lt;span&gt;ICT&lt;/span&gt;&lt;span lang="IN"&gt;)&lt;/span&gt;&lt;span&gt;, or rapid technological innovation, or changes and trends in the industry, to the sharing economy or the rise of distance races. In its analysis, an essential deficiency is detected in the current education models, and it is detected that educational freedom is nil or scarce, which has led to poor student performance, high youth unemployment, which cause severe social problems and the economy of the economy. The current government is working closely with the private sector to connect education and promote employment. It is necessary to grant public schools more autonomy, freedom and responsibility. This paper explains how to act so that Mexico can adapt education 4.0 with solid steps to reach society 5.0.&lt;/span&gt;
Generating Abstractive Summaries with Finetuned Language Models

Neural abstractive document summarization is commonly approached by models that exhibit a mostly extractive behavior.This behavior is facilitated by a copy-attention which allows models to copy words from a source document.While models in the mostly extractive news summarization domain benefit from this inductive bias, they commonly fail to paraphrase or compress information from the source document.Recent advances in transferlearning from large pretrained language models give rise to alternative approaches that do not rely on copy-attention and instead learn to generate concise and abstractive summaries.In this paper, as part of the TL;DR challenge, we compare the abstractiveness of summaries from different summarization approaches and show that transfer-learning can be efficiently utilized without any changes to the model architecture.We demonstrate that the approach leads to a higher level of abstraction for a similar performance on the TL;DR challenge tasks, enabling true natural language compression.
Generative Localization With Uncertainty Estimation Through Video-CT Data for Bronchoscopic Biopsy

Robot-assisted endobronchial intervention requires accurate localization based on both intra- and pre-operative data. Most existing methods achieve this by registering 2D videos with 3D CT models according to a defined similarity metric with local features. Instead, we formulate the bronchoscopic localization as a learning-based global localisation using deep neural networks. The proposed network consists of two generative architectures and one auxiliary learning component. The cycle generative architecture bridges the domain variance between the real bronchoscopic videos and virtual views derived from pre-operative CT data so that the proposed approach can be trained through a large number of generated virtual images but deployed through real images. The auxiliary learning architecture leverages complementary relative pose regression to constrain the search space, ensuring consistent global pose predictions. Most importantly, the uncertainty of each global pose is obtained through variational inference by sampling within the learned underlying probability distribution. Detailed validation results demonstrate the localization accuracy with reasonable uncertainty achieved and its potential clinical value. A demonstration video demo can be found on the website <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://youtu.be/ci9LMY49aF8</uri> .
A Visual Residual Perception Optimized Network for Blind Image Quality Assessment

Blind image quality assessment (BIQA) is a fundamental yet challenging problem in the image processing system. Existing BIQA models have the following problems: 1) Due to the lack of available quality-label images, most of the methods have poor generalization ability in different distortion categories; 2) The impact of human visual characteristics on the content of images is not taken into account. In this paper, we proposed a visual residual perception optimized network (VRPON) that can effectively solve these problems. The proposed method separates the training of BIQA into two stages: 1) a distortion degree identification network and 2) an image quality prediction network. In the first stage, the spatial and temporal features of image sequences are extracted by CNN and LSTM respectively, which are used to evaluate the degree of image distortion. And then the proposed model is learned to predict image patches’ scores in the second stage with the outputs of the first stage. Finally, a pooling strategy that follows the human visual saliency is designed to evaluate the quality score of the whole image. Experimental results show that the proposed VRPON not only has better performance than state-of-the-art methods on synthetic distorted images (LIVE, TID2013, CSIQ), but also has better robustness for different authentic distortions (LIVE challenge).
A Gesture-Based Teleoperation System for Compliant Robot Motion

Currently, the gesture-based teleoperation system cannot generate precise and compliant robot motions because human motions have the characteristics of uncertainty and low-resolution. In this paper, a novel, gesture-based teleoperation system for compliant robot motion is proposed. By using the left hand as the commander and the right hand as a positioner, different operation modes and scaling ratios can be tuned on-the-fly to meet the accuracy and efficiency requirements. Moreover, a vibration-based force feedback system was developed to provide the operator with a telepresence capability. The pick-and-place and peg-in-hole tasks were used to test the effectiveness of the teleoperation system we developed. The experiment results prove that the gesture-based teleoperation system is effective at handling compliant robot motions.
AAGAN: Enhanced Single Image Dehazing With Attention-to-Attention Generative Adversarial Network

Due to the atmospheric scattering and absorption, hazy weather often occurs in our everyday life, thus reducing the visibility of scenes. Single image dehazing is considered as an ill-posed and challenging problem in computer vision. To restore visibility in inclement weather, we propose an attention-to-attention generative adversarial network (AAGAN) whose motivation is the human visual perceptual mechanism. More specifically, a dense channel attention model is embedded into the encoder. Moreover, its output is projected forward to the corresponding multiscale spatial attention model in the decoder. Both attention models form an attention-to-attention mechanism to implement attention projection, thus capturing global feature dependencies of the whole network. Besides, we analyze the dehazing mechanism based on the atmospheric scattering model, and then utilize an improved RaLSGAN to recover more realistic texture information and enhance visual contrast for different hazy scenes. Finally, in order to improve the visual performance of image restoration, we remove all the instance normalization layers to avoid unnecessary artifacts, and then introduce spectral normalization for all the convolution layers to stabilize the entire training process. Qualitative assessments and analyses demonstrate that our proposed approach can achieve remarkable dehazing performance on both synthetic and real-world scenes against previous state-of-the-art methods.
ADDMC: Weighted Model Counting with Algebraic Decision Diagrams

We present an algorithm to compute exact literal-weighted model counts of Boolean formulas in Conjunctive Normal Form. Our algorithm employs dynamic programming and uses Algebraic Decision Diagrams as the main data structure. We implement this technique in ADDMC, a new model counter. We empirically evaluate various heuristics that can be used with ADDMC. We then compare ADDMC to four state-of-the-art weighted model counters (Cachet, c2d, d4, and miniC2D) on 1914 standard model counting benchmarks and show that ADDMC significantly improves the virtual best solver.
Multi-security-level cloud storage system based on improved proxy re-encryption

Abstract Based on the characteristics and data security requirements of the cloud environment, we present a scheme for a multi-security-level cloud storage system that is combined with AES symmetric encryption and an improved identity-based proxy re-encryption (PRE) algorithm. Our optimization includes support for fine-grained control and performance optimization. Through a combination of attribute-based encryption methods, we add a fine-grained control factor to our algorithm in which each authorization operation is only valid for a single factor. By reducing the number of bilinear mappings, which are the most time-consuming processes, we achieve our aim of optimizing performance. Last but not least, we implement secure data sharing among heterogeneous cloud systems. As shown in experiment, our proposed multi-security-level cloud storage system implements services such as the direct storage of data, transparent AES encryption, PRE protection that supports fine-grained and ciphertext heterogeneous transformation, and other functions such as authentication and data management. In terms of performance, we achieve time-cost reductions of 29.8% for the entire process, 48.3% for delegation and 47.2% for decryption.
Optimal phase sensitivity by quantum squeezing based on a Mach–Zehnder interferometer

Abstract A novel scheme for the enhancement of phase sensitivity based on a Mach–Zehnder interferometer (MZI) and intensity detection is proposed. With the input of bright entangled twin beams from four wave mixing (FWM), the phase sensitivity can beat shot noise limit (SNL) and approach Heisenberg limit. This scheme is special due to that only one of bright entangled twin beams enters into the MZI and the other one is employed for measurement. In addition, by altering the parametric strength of FWM and the implementation of maximum quantum squeezing, the optimal phase sensitivity can reach sub-SNL. Optical intensity depletion of photon detectors and internal intensity depletion of the MZI are also discussed. The scheme displays that by employing external resources, while one input of the MZI is an vacuum beam, the phase sensitivity still can beat SNL.
Designing a Theoretical Integration Framework for Mobile Learning

&lt;br /&gt;&lt;table class="data" width="100%"&gt;&lt;tbody&gt;&lt;tr valign="top"&gt;&lt;td class="value"&gt;&lt;p&gt;New technologies are rapidly changing mobile learning and making it difficult to control. In addition to educational factors and learning content, a modern mobile learning system must take into account the technical and personal aspects of learning, the devices and aspects related to its evolution and interoperability. Teaching on the other hand has also evolved involving more flexibility in tasks and learning stages, thus using modern technologies that offer more alternatives now. In addition, such tasks may be specific to the learning content as well as the learning context or furthermore the learner's environment.&lt;/p&gt;&lt;p&gt;Traditionally, mobile platform design relies on the skills of a mobile developer whose knowledge allows him to design mobile applications that are useful to users. But with mobile learning, the design phase involves more than just mobile development skills. For example, if you are designing a platform for practical work, the instructors responsible for the training should be involved. However, the empirical results show that educators do not integrate technology effectively into their curricula. To enable these instructors to develop mobile learning platforms, it is important to facilitate their integration through a theoretical model that will take into account all the ingredients necessary to complete this learning and to balance them in order to ensure its efficiency. In this study authors used a thematic synthesis methodology to present a framework for mobile devices integration in learning. They focused on three models that they think are the most cited in the field of ICT (information and communication technologies) integration in learning.&lt;/p&gt;&lt;p&gt;The five-axis framework consists of enriching the TPACK framework(Technological Pedagogical Content Knowledge model in order to more precisely address mobile learning by covering the following parts: pedagogy, content, mobile technology, learning environment and learner’s profile. It describes relatively in depth the various factors involved as well as the effective interconnection to be ensured to achieve an optimal and efficient integration of m-learning. Balancing those five parts will be a matter of plural reflection when designing or consulting on a mobile learning platform.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;
An interobserver reproducibility analysis of size-set semiautomatic counting for Ki67 assessment in breast cancer

<h2>Abstract</h2><h3>Purpose</h3> The proliferation marker Ki67 has prognostic and predictive values in breast cancer, and the cutoff of the Ki67 label index (LI) is a key index for chemotherapy. However, poor interobserver consistency in Ki67 assessment has limited the clinical use of Ki67, especially in luminal cancers. Here, we reported a modified Ki67 assessment method, size-set semiautomatic counting (SSSAC) and investigated its interobserver reproducibility. <h3>Methods</h3> One hundred invasive breast cancer tissues were set immunostained for Ki67 in one laboratory, scanned as digital slides, and sent to 41 pathologists at the laboratories of 16 hospitals for Ki67 LI assessment using size-set semiautomatic counting (SSSAC), size-set visual assessment (SSVA) and size-set digital image analysis (SSDIA) with a specific image viewing software (Aperio Image Scope, Leica, Germany). The intraclass correlation coefficient (ICC) and Bland-Altman plot were used to evaluate interobserver reproducibility. The Wilcoxon signed-rank test was used to analyze the difference in the Ki67 values assessed by SSSAC and SSDIA. <h3>Results</h3> SSSAC demonstrated better interobserver reproducibility (ICC = 0.942) than SSVA (ICC = 0.802). The interobserver reproducibility was better in Ki67 homogeneously stained slides and centralized hot-spot slides than in scattered hot-spot slides. The Ki67 value assessed with SSSAC was obviously higher than that assessed with SSDIA (negative ranks (SSDIA < SSSAC): N = 80, sum of ranks = 4274.50; positive ranks (SSDIA > SSSAC): N = 17, sum of ranks = 478.50; Z = −6.837; P < 0.001). <h3>Conclusion</h3> SSSAC shows satisfactory interobserver reproducibility in the Ki67 assessment of breast cancer and may be a candidate standard method for Ki67 LI assessment in breast cancer and other malignancies.
Co-Training Semi-Supervised Deep Learning for Sentiment Classification of MOOC Forum Posts

Sentiment classification of forum posts of massive open online courses is essential for educators to make interventions and for instructors to improve learning performance. Lacking monitoring on learners’ sentiments may lead to high dropout rates of courses. Recently, deep learning has emerged as an outstanding machine learning technique for sentiment classification, which extracts complex features automatically with rich representation capabilities. However, deep neural networks always rely on a large amount of labeled data for supervised training. Constructing large-scale labeled training datasets for sentiment classification is very laborious and time consuming. To address this problem, this paper proposes a co-training, semi-supervised deep learning model for sentiment classification, leveraging limited labeled data and massive unlabeled data simultaneously to achieve performance comparable to those methods trained on massive labeled data. To satisfy the condition of two views of co-training, we encoded texts into vectors from views of word embedding and character-based embedding independently, considering words’ external and internal information. To promote the classification performance with limited data, we propose a double-check strategy sample selection method to select samples with high confidence to augment the training set iteratively. In addition, we propose a mixed loss function both considering the labeled data with asymmetric and unlabeled data. Our proposed method achieved a 89.73% average accuracy and an 93.55% average F1-score, about 2.77% and 3.2% higher than baseline methods. Experimental results demonstrate the effectiveness of the proposed model trained on limited labeled data, which performs much better than those trained on massive labeled data.