Reviewerly/exHarmony huggingface dataset:
A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning

In this paper, we proposed a general framework for data poisoning attacks to graph-based semi-supervised learning (G-SSL). In this framework, we first unify different tasks, goals and constraints into a single formula for data poisoning attack in G-SSL, then we propose two specialized algorithms to efficiently solve two important cases --- poisoning regression tasks under $\ell_2$-norm constraint and classification tasks under $\ell_0$-norm constraint. In the former case, we transform it into a non-convex trust region problem and show that our gradient-based algorithm with delicate initialization and update scheme finds the (globally) optimal perturbation. For the latter case, although it is an NP-hard integer programming problem, we propose a probabilistic solver that works much better than the classical greedy method. Lastly, we test our framework on real datasets and evaluate the robustness of G-SSL algorithms. For instance, on the MNIST binary classification problem (50000 training data with 50 labeled), flipping two labeled data is enough to make the model perform like random guess (around 50\% error).
ABSApp: A Portable Weakly-Supervised Aspect-Based Sentiment Extraction System

We present ABSApp, a portable system for weakly-supervised aspect-based sentiment extraction 1 .The system is interpretable and user friendly and does not require labeled training data, hence can be rapidly and costeffectively used across different domains in applied setups.The system flow includes three stages: First, it generates domain-specific aspect and opinion lexicons based on an unlabeled dataset; second, it enables the user to view and edit those lexicons (weak supervision); and finally, it enables the user to select an unlabeled target dataset from the same domain, classify it, and generate an aspect-based sentiment report.ABSApp has been successfully used in a number of real-life use cases, among them movie review analysis and convention impact analysis.
Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic Data

This paper presents the systems submitted by the University of Groningen to the English-Kazakh language pair (both translation directions) for the WMT 2019 news translation task.We explore potential benefits from using (i) morphological segmentation (both unsupervised and rule-based), given the agglutinative nature of Kazakh, (ii) data from two additional languages (Turkish and Russian), given the scarcity of English-Kazakh data, and (iii) synthetic data, both for the source and for the target language.Our best submissions ranked second for Kazakh→English and third for English→Kazakh in terms of the BLEU automatic evaluation metric.
Intersubjective Management in Educational Economy

The modern economy, the management of its development, is one of the most actively developing aspects of the life of the community, closely related to others (cultural and ideological, political and historical, psychological and legal transformations.)Recently, researchers are seeking answers to questions of optimizing the development of the economy in theories and technologies not only monoagent and multi-agent control, but monoactor and multi-agent, quality and quantity management, logistics and production processes, management real-time management of strategic crisis management.All of them require the coordination of opinions and interests of various stakeholders in the context of specific internal and external conditions of their life.The successful implementation of innovation and non-destructive interaction with the community require economic and related structures and organizations to build partnerships.These relations should take into account the need to take care of the accumulation and deterioration of not only financial and material, but also social, human and cultural capital proper, work with cadres and consumers, and so on.This article presents a postnonclassical approach to create the science of management processes organization in a developing society, the focus of which is "the man of culture", i.e. the man, not just adhering to cultural norms, but also creating new concepts and products of culture.This science is proposed to be called Evergetics.This science was determine as the science of management processes organization in a developing and other industrial areas of society, each member of which is interested in augmenting his cultural heritage he is producing.It is entails a raise of cultural potential of the society as a whole and, as a consequence, an increase in the proportion of moral and ethical managerial decisions and corresponding to them benevolent actions in public life.Intersubjective management is a leading aspect of the development of the dynamic (cap)abilities of educational enterprises in general and managers of these enterprises in particular.The foresight for managing the educational economy should first of all be addressed to the capabilities of multi-actor management.Multi-actor management allows you to systematically, and not unilaterally, take into account all aspects of the educational institution and the entire network of institutions, including economic issues.
Enhancing Local Feature Extraction with Global Representation for Neural Text Classification

For text classification, traditional local feature driven models learn long dependency by deeply stacking or hybrid modeling.This paper proposes a novel Encoder1-Encoder2 architecture, where global information is incorporated into the procedure of local feature extraction from scratch.In particular, En-coder1 serves as a global information provider, while Encoder2 performs as a local feature extractor and is directly fed into the classifier.Meanwhile, two modes are also designed for their interactions.Thanks to the awareness of global information, our method is able to learn better instance specific local features and thus avoids complicated upper operations.Experiments conducted on eight benchmark datasets demonstrate that our proposed architecture promotes local feature driven models by a substantial margin and outperforms the previous best models in the fully-supervised setting.
Multi-headed Architecture Based on BERT for Grammatical Errors Correction

During last years we have seen tremendous progress in the development of NLPrelated solutions and area in general.It happened primarily due to emergence of pre-trained models based on the Transformer (Vaswani et al., 2017) architecture such as GPT (Radford et al., 2018) and BERT (Devlin et al., 2019).Fine-tuned models containing these representations can achieve stateof-the-art results in many NLP-related tasks.Given this, the use of pre-trained models in the Grammatical Error Correction (GEC) task seems reasonable.In this paper, we describe our approach to GEC using the BERT model for creation of encoded representation and some of our enhancements, namely, "Heads" are fullyconnected networks which are used for finding the errors and later receive recommendation from the networks on dealing with a highlighted part of the sentence only.Among the main advantages of our solution is increasing the system productivity and lowering the time of processing while keeping the high accuracy of GEC results.
A Progressive Model to Enable Continual Learning for Semantic Slot Filling

Semantic slot filling is one of the major tasks in spoken language understanding (SLU).After a slot filling model is trained on precollected data, it is crucial to continually improve the model after deployment to learn users' new expressions.As the data amount grows, it becomes infeasible to either store such huge data and repeatedly retrain the model on all data or fine tune the model only on new data without forgetting old expressions.In this paper, we introduce a novel progressive slot filling model, ProgModel.ProgModel consists of a novel context gate that transfers previously learned knowledge to a small size expanded component; and meanwhile enables this new component to be fast trained to learn from new data.As such, ProgModel learns the new knowledge by only using new data at each time and meanwhile preserves the previously learned expressions.Our experiments show that ProgModel needs much less training time and smaller model size to outperform various model fine tuning competitors by up to 4.24% and 3.03% on two benchmark datasets.
YouMakeup: A Large-Scale Domain-Specific Multimodal Dataset for Fine-Grained Semantic Comprehension

Multimodal semantic comprehension has attracted increasing research interests in recent years, such as visual question answering and caption generation.However, due to the data limitation, fine-grained semantic comprehension which requires to capture semantic details of multimodal contents has not been well investigated.In this work, we introduce "YouMakeup", a large-scale multimodal instructional video dataset to support finegrained semantic comprehension research in specific domain.YouMakeup contains 2,800 videos from YouTube, spanning more than 420 hours in total.Each video is annotated with a sequence of natural language descriptions for instructional steps, grounded in temporal video range and spatial facial areas.The annotated steps in a video involve subtle difference in actions, products and regions, which require fine-grained understanding and reasoning both temporally and spatially.In order to evaluate models' ability for fined-grained comprehension, we further propose two groups of tasks including generation tasks and visual question answering tasks from different aspects.We also establish a baseline of step caption generation for future comparison.The dataset will be publicly available at https:// github.com/AIM3-RUC/YouMakeup to support research investigation in fine-grained semantic comprehension.
Using Microsoft HoloLens to improve memory recall in anatomy and physiology: A pilot study to examine the efficacy of using augmented reality in education

This paper explores the use of augmented reality (AR) to improve memory recall and learning experiences in human anatomy and physiology.In particular, the opportunity to assess new computer technologies to improve learning environments may allow for an improvement in self-efficacy and a reduction in test anxiety.Twenty-two undergraduates participated in one of two study groups, the Microsoft HoloLens group (HLNS) or the traditional projector-based Microsoft Power Point group (PPT).Each group completed a five-minute, three-subject anatomy identification and brain physiology memory test.After a three-minute time delay, participants completed a fill-in-the-blank anatomy identification test and brain physiology question test, followed by questionnaires for systems self-efficacy and test anxiety.Results indicated the PPT group significantly outperformed the HLNS group in the brain lobe identification portion of the exam and had reported higher mean test anxiety scores (though not high enough to be considered unhealthy levels).However, the HLNS group showed a significantly higher preference to the learning experience compared to the PPT group based on the systems self-efficacy questionnaire.This pilot study demonstrates an opportunity for the HLNS to use used in learning environments to improve the psychological aspects of studying and test taking.
Generative adversarial network based on chaotic time series

Generative adversarial networks (GANs) are becoming increasingly important in the artificial construction of natural images and related functionalities, wherein two types of networks called generators and discriminators evolve through adversarial mechanisms. Using deep convolutional neural networks and related techniques, high-resolution and highly realistic scenes, human faces, etc. have been generated. GANs generally require large amounts of genuine training data sets, as well as vast amounts of pseudorandom numbers. In this study, we utilized chaotic time series generated experimentally by semiconductor lasers for the latent variables of a GAN, whereby the inherent nature of chaos could be reflected or transformed into the generated output data. We show that the similarity in proximity, which describes the robustness of the generated images with respect to minute changes in the input latent variables, is enhanced, while the versatility overall is not severely degraded. Furthermore, we demonstrate that the surrogate chaos time series eliminates the signature of the generated images that is originally observed corresponding to the negative autocorrelation inherent in the chaos sequence. We also address the effects of utilizing chaotic time series to retrieve images from the trained generator.
Augmenting Abstract Meaning Representation for Human-Robot Dialogue

We detail refinements made to Abstract Meaning Representation (AMR) that make the representation more suitable for supporting a situated dialogue system, where a human remotely controls a robot for purposes of search and rescue and reconnaissance.We propose 36 augmented AMRs that capture speech acts, tense and aspect, and spatial information.This linguistic information is vital for representing important distinctions, for example whether the robot has moved, is moving, or will move.We evaluate two existing AMR parsers for their performance on dialogue data.We also outline a model for graph-to-graph conversion, in which output from AMR parsers is converted into our refined AMRs.The design scheme presented here, though task-specific, is extendable for broad coverage of speech acts using AMR in future task-independent work.
HITSZ-ICRC: A Report for SMM4H Shared Task 2019-Automatic Classification and Extraction of Adverse Effect Mentions in Tweets

This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fourth Social Media Mining for Health Applications (SMM4H) shared task in 2019.The two subtasks are automatic classification and extraction of adverse effect mentions in tweets.The systems for the two subtasks are based on bidirectional encoder representations from transformers (BERT), and achieves promising results.Among the systems we developed for subtask1, the best F1-score was 0.6457, for subtask2, the best relaxed F1-score and the best strict F1-score were 0.614 and 0.407 respectively.Our system ranks first among all systems on subtask1.
Comparison of 3D prospectivity modeling methods for Fe-Cu skarn deposits: A case study of the Zhuchong Fe-Cu deposit in the Yueshan orefield (Anhui), eastern China

Three-dimensional (3D) metallogenic prospectivity modeling has become increasingly important in deep Fe-Cu skarn mineralization prospecting. The selection of prediction models and their comparison are of great significance. In this paper, we integrated the geological and geophysical data from the Zhuchong Fe-Cu deposit in the Yueshan orefield (Anhui, eastern China) to establish the local 3D geological framework. We extracted the 3D ore-controlling factors quantitatively by means of 3D spatial analysis methods, and compared the 3D mineralization locations predicted by the weight of evidence method and the artificial neural network method. The results show that both methods can locate most of the known orebodies, suggesting that both are relatively reliable. In addition, the weight of evidence method performed distinctly better than the artificial neural network method in terms of both prediction effectiveness and efficiency. Therefore, the weight of evidence method may be more applicable in 3D prospectivity modelling for Fe-Cu skarn deposits in the region. Finally, the composite area predicted by the two methods is taken as the final prediction result of the mineralization area of the deposit. And it is suggested that in the quantitative 3D prediction, multiple selection of different methods with good adaptability should be involved in the prediction at the same time, and compound prediction method should be used to delineate the target area. Our work has guiding significance for future deep prospecting and exploitation for other similar Fe-Cu skarn deposits.
Two-Dimensional Compact Variational Mode Decomposition-Based Low-Light Image Enhancement

In this paper, a novel methodology is proposed for low-light image enhancement. The proposed algorithm contains three stages: image reconstruction, image enhancement and color restoration. Two-dimensional compact variational mode decomposition (2D-TV-VMD) is employed to covert the RGB image into gray map through decomposing it on multiple gray eigenfunctions. A binary artifact indicator function is used to identify and eliminate potential artifact pixels in an image, and then low-light image enhancement via illumination map estimation (LIME) is used to enhance the reconstructed gray-scale map. Finally, color restoration is performed in RGB-color space to recover the color information. Subjective evaluation and objective evaluation of the proposed method, including no-reference image quality metric of contrast-distorted images based on information maximization (NIQMC), is conducted on different low-light images. Objective and subjective experimental performance demonstrate the competitive performance of the proposed algorithm compared with other state-of-art methods.
Extending Atomic Cross-Chain Swaps

Cryptocurrencies enable users to send and receive value in a trust-less manner. Unfortunately trading the associated assets usually happens on centralized exchange which becomes a trusted third party. This defeats the purpose of a trust-less system. Atomic cross-chain swaps solve this problem for exchanges of value without intermediaries. But they require both blockchains to support Hash locked and Time locked transactions. We propose an extension to atomic swap for blockchains that only support multi-signatures (multisig) transactions. This provides greater capabilities for cross-chain communications without adding any extra trust hypothesis.
Artificial neural network assisted prediction of dissolution spatial distribution in the volcanic weathered crust: A case study from Chepaizi Bulge of Junggar Basin, northwestern China

The reservoir rocks in the volcanic weathered crust are characterized by plentiful dissolution pores, caverns and fractures. However, as an essential control factor of the reservoir distribution, volcanic dissolution is often ignored in quantitative studies. In this paper, we propose a complete artificial intelligence workflow to model and predict the volcanic dissolution distribution by integrating the image logging semi-quantitative analysis with seismic prediction. A synthetic reservoir property named DCV (Dissolution Comprehensive Values) is generated as logging curves from XRMI resistivity pseudo-pictures to indicate the intensity of dissolution. These curves are then used as the target for the seismic-based prediction in an artificial intelligence framework. A multi-layer artificial neural network (ANN) model is constructed in order to map the seismic attributes into the DCV. The methodology is demonstrated on a real case study from the Junggar basin in northwestern China. The maps and profiles from the prediction represent the dissolution values in the volcanic weathered crust. The predictions are consistent with the geological volcanic dissolution model provided by geological knowledge. It is concluded that the spatial dissolution distribution in volcanic weathered crust can be reliably predicted by this integrated method.
Formal models for safety and performance analysis of a data center system

Data centers have evolved dramatically to meet the new technologies demands such as cloud computing, e-commerce and social networks. Ensuring a continuous service by avoiding downtime is becoming a competitive factor for companies. Several factors affect the availability of a data center system, among them the power energy production and temperature variations within the data center room. In this paper we investigate the different interactions between the data center sub-systems (electrical, thermal and network) and their impact on the data center reliability. We use Production Trees, a new modeling methodology for dealing with availability issues of production systems. Our results illustrate the applicability of the proposed approach and include an analysis of the system availability, reliability and performance.
Overview of the NLPCC 2019 Shared Task: Cross-Domain Dependency Parsing

This paper presents an overview of the NLPCC 2019 shared task on cross-domain dependency parsing, including (1) the data annotation process, (2) task settings, (3) methods, results, and analysis of submitted systems and our recent work (Li+19), (4) discussions on related works and future directions. Considering that unsupervised domain adaptation is very difficult and has made limited progress in the past decades, we for the first time setup semi-supervised subtasks that allow to use a few thousand target-domain labeled sentences for training. We provide about 17 K labeled sentences from a balanced corpus as the source domain (BC), and as three target domains 10 K sentences from product comments (PC), 8 K sentences from product blogs (PB), and 3 K sentences from the web fiction named “Zhuxian” (ZX). All information about this task can be found at http://hlt.suda.edu.cn/index.php/Nlpcc-2019-shared-task , including the data sharing agreement.
Right-sizing fluxonium against charge noise

Fluxonium qubits employ a chain of $N$ Josephson junctions in series. We investigate how the charge-noise induced coherence time ${T}_{2}$ of the qubit depends upon $N$, holding fixed the chain's effective capacitance and inductance. The pure dephasing rate decreases with $N$, but the relaxation rate increases. As a result, ${T}_{2}$ achieves a maximum. The optimal $N$ can be much smaller than the number typically chosen in experiments, implying that fluxonium qubits can be significantly simplified without compromising performance.
Dampak Pengembangan Buku Cerita Bergambar Untuk Anak Usia Dini

Development in the world of education is very necessary. Innovation and creativity are needed to update and develop existing products or those that want to be developed according to changing times. Picture books that emphasize character values in accordance with the vision and mission of one Kindergarten located in Central Jakarta are very urgent to develop because, at the school , there is no character education book with the value of servite et amate. This was revealed by the teacher through an interview. Based on observations, it was also found that there was indeed no character education book that was in accordance with the school's vision and mission. Picture storybooks are interrelated books between images and text which are the unity of a story. This study uses the research and development method, with the ADDIE model. The data collection techniques used were instrument validation sheets and feedback tests for children. Through the 4 stages of evaluation from experts, the results of the final validation from design experts, linguists and content experts/material get a value of 100% with a very feasible category. From the results of the trials conducted on 2 different classes of treatment, it was shown that this story book illustrated with Layanilah and Cintailah has benefits for kindergarten children. This is evidenced by the mean obtained from the class without the product, namely four questions that get the highest mean value. The other three questions get a low mean. The illustrated storybooks has a positive impact on the understanding of this school, especially in terms of knowing examples of actions that are consistent with the values of servite et amate.